diff --git a/.gitmodules b/.gitmodules index 5520c9b9188f0484ddc34eef630a86bca933a7a3..8179b9caa5701f7a94ed6c1dbda3ad75db1b866e 100644 --- a/.gitmodules +++ b/.gitmodules @@ -6,13 +6,13 @@ url = https://github.com/taosdata/hivemq-tdengine-extension.git [submodule "deps/jemalloc"] path = deps/jemalloc - url = https://github.com/jemalloc/jemalloc + url = https://github.com/jemalloc/jemalloc.git [submodule "src/kit/taos-tools"] path = src/kit/taos-tools - url = https://github.com/taosdata/taos-tools + url = https://github.com/taosdata/taos-tools.git [submodule "src/plugins/taosadapter"] path = src/plugins/taosadapter - url = https://github.com/taosdata/taosadapter + url = https://github.com/taosdata/taosadapter.git [submodule "examples/rust"] path = examples/rust url = https://github.com/songtianyi/tdengine-rust-bindings.git diff --git a/.mailmap b/.mailmap new file mode 100644 index 0000000000000000000000000000000000000000..9e5fb9468dbc65cd2bacb51b78a055998fad5bf6 --- /dev/null +++ b/.mailmap @@ -0,0 +1,16 @@ +# +# This list is used by git-shortlog to fix a few botched name translations +# in the git archive, either because the author's full name was messed up +# and/or not always written the same way, making contributions from the +# same person appearing not to be so or badly displayed. Also allows for +# old email addresses to map to new email addresses. +# +# For format details, see "MAPPING AUTHORS" in "man git-shortlog". +# +# Please keep this list dictionary sorted. +# + +Jeff Tao +Wade Zhang +Shuduo Sang +Pan Yang \ No newline at end of file diff --git a/Jenkinsfile2 b/Jenkinsfile2 index fceaa6554faa5bde7ae028ec9204627d505e8b95..f213afe3b9bd707c8488ae0f4431e56c01d30a4e 100644 --- a/Jenkinsfile2 +++ b/Jenkinsfile2 @@ -87,7 +87,9 @@ def sync_source() { cd ${WKC} git fetch origin +refs/pull/${CHANGE_ID}/merge git checkout -qf FETCH_HEAD - + ''' + sh ''' + cd ${WKC} if [ ! -d src/connector/python/.github ]; then rm -rf src/connector/python/* || : rm -rf src/connector/python/.* || : @@ -95,7 +97,6 @@ def sync_source() { else cd src/connector/python || echo "src/connector/python not exist" git pull || : - cd ${WKC} fi ''' } else if (env.CHANGE_URL =~ /\/TDinternal\//) { @@ -104,7 +105,9 @@ def sync_source() { cd ${WK} git fetch origin +refs/pull/${CHANGE_ID}/merge git checkout -qf FETCH_HEAD - + ''' + sh ''' + cd ${WKC} if [ ! -d community/src/connector/python/.github ]; then rm -rf community/src/connector/python/* || : rm -rf community/src/connector/python/.* || : @@ -112,7 +115,6 @@ def sync_source() { else cd community/src/connector/python || echo "community/src/connector/python not exist" git pull || : - cd ${WK} fi ''' } else { diff --git a/README.md b/README.md index 6e6b13821892839216fad1e3e5839fa623eb59e1..aa944ef75dab53bcfec068a59285b561d064b429 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ We are hiring, check [here](https://www.taosdata.com/en/careers/) # What is TDengine？ -TDengine is a high-performance, scalable time-series database with SQL support. Its code including cluster feature is open source under [GNU AGPL v3.0](http://www.gnu.org/licenses/agpl-3.0.html). Besides the database, it provides caching, stream processing, data data subscription and other functionalities to reduce the complexity and cost of development and operation. TDengine differentiates itself from other TSDBs with the following advantages. +TDengine is a high-performance, scalable time-series database with SQL support. Its code including cluster feature is open source under [GNU AGPL v3.0](http://www.gnu.org/licenses/agpl-3.0.html). Besides the database, it provides caching, stream processing, data subscription and other functionalities to reduce the complexity and cost of development and operation. TDengine differentiates itself from other TSDBs with the following advantages. - **High Performance**: TDengine outperforms other time series databases in data ingestion and querying while significantly reducing storage cost and compute costs, with an innovatively designed and purpose-built storage engine. diff --git a/cmake/version.inc b/cmake/version.inc index ae16262748653f7955a54cec0474f55611a7fd6d..dbd2277f9513d1698803e7316a51af588c3efda8 100755 --- a/cmake/version.inc +++ b/cmake/version.inc @@ -4,7 +4,7 @@ PROJECT(TDengine) IF (DEFINED VERNUMBER) SET(TD_VER_NUMBER ${VERNUMBER}) ELSE () - SET(TD_VER_NUMBER "2.4.0.0") + SET(TD_VER_NUMBER "2.7.0.0") ENDIF () IF (DEFINED VERCOMPATIBLE) diff --git a/docs-cn/02-intro.md b/docs-cn/02-intro.md index 2a56c5e9e667b511003b1ee08801ddcb54ff2ec4..673c2e96b65814fc1cd572d54f948793ed6fa521 100644 --- a/docs-cn/02-intro.md +++ b/docs-cn/02-intro.md @@ -62,7 +62,7 @@ TDengine的主要功能如下：
-![TDengine技术生态图](eco_system.png) +![TDengine Database 技术生态图](eco_system.webp)
图 1. TDengine技术生态图
@@ -119,7 +119,6 @@ TDengine的主要功能如下： - [用 InfluxDB 开源的性能测试工具对比 InfluxDB 和 TDengine](https://www.taosdata.com/blog/2020/01/13/1105.html) - [TDengine 与 OpenTSDB 对比测试](https://www.taosdata.com/blog/2019/08/21/621.html) - [TDengine 与 Cassandra 对比测试](https://www.taosdata.com/blog/2019/08/14/573.html) -- [TDengine 与 InfluxDB 对比测试](https://www.taosdata.com/blog/2019/07/19/419.html) - [TDengine VS InfluxDB ，写入性能大 PK ！](https://www.taosdata.com/2021/11/05/3248.html) - [TDengine 和 InfluxDB 查询性能对比测试报告](https://www.taosdata.com/2022/02/22/5969.html) - [TDengine 与 InfluxDB、OpenTSDB、Cassandra、MySQL、ClickHouse 等数据库的对比测试报告](https://www.taosdata.com/downloads/TDengine_Testing_Report_cn.pdf) diff --git a/docs-cn/04-concept/index.md b/docs-cn/04-concept/index.md index ca25595260953f8d941ccaf367bdc45a8325488f..8e97d4a2f43537c1229c8e8ea092ddfc1257dde7 100644 --- a/docs-cn/04-concept/index.md +++ b/docs-cn/04-concept/index.md @@ -29,7 +29,7 @@ title: 数据模型和基本概念 10.3 219 0.31 -Beijing.Chaoyang +California.SanFrancisco 2 @@ -38,7 +38,7 @@ title: 数据模型和基本概念 10.2 220 0.23 -Beijing.Chaoyang +California.SanFrancisco 3 @@ -47,7 +47,7 @@ title: 数据模型和基本概念 11.5 221 0.35 -Beijing.Haidian +California.LosAngeles 3 @@ -56,7 +56,7 @@ title: 数据模型和基本概念 13.4 223 0.29 -Beijing.Haidian +California.LosAngeles 2 @@ -65,7 +65,7 @@ title: 数据模型和基本概念 12.6 218 0.33 -Beijing.Chaoyang +California.SanFrancisco 2 @@ -74,7 +74,7 @@ title: 数据模型和基本概念 11.8 221 0.28 -Beijing.Haidian +California.LosAngeles 2 @@ -83,7 +83,7 @@ title: 数据模型和基本概念 10.3 218 0.25 -Beijing.Chaoyang +California.SanFrancisco 3 @@ -92,7 +92,7 @@ title: 数据模型和基本概念 12.3 221 0.31 -Beijing.Chaoyang +California.SanFrancisco 2 diff --git a/docs-cn/05-get-started/index.md b/docs-cn/05-get-started/index.md index 458df909166b9769af2052ba654699e869d2081c..878d7f020245fbff383308c281fbc3fa28ba5f6c 100644 --- a/docs-cn/05-get-started/index.md +++ b/docs-cn/05-get-started/index.md @@ -132,7 +132,7 @@ Query OK, 2 row(s) in set (0.003128s) taosBenchmark ``` -该命令将在数据库 test 下面自动创建一张超级表 meters，该超级表下有 1 万张表，表名为 "d0" 到 "d9999"，每张表有 1 万条记录，每条记录有 (ts, current, voltage, phase) 四个字段，时间戳从 "2017-07-14 10:40:00 000" 到 "2017-07-14 10:40:09 999"，每张表带有标签 location 和 groupId，groupId 被设置为 1 到 10， location 被设置为 "beijing" 或者 "shanghai"。 +该命令将在数据库 test 下面自动创建一张超级表 meters，该超级表下有 1 万张表，表名为 "d0" 到 "d9999"，每张表有 1 万条记录，每条记录有 (ts, current, voltage, phase) 四个字段，时间戳从 "2017-07-14 10:40:00 000" 到 "2017-07-14 10:40:09 999"，每张表带有标签 location 和 groupId，groupId 被设置为 1 到 10， location 被设置为 "California.SanFrancisco" 或者 "California.LosAngeles"。 这条命令很快完成 1 亿条记录的插入。具体时间取决于硬件性能，即使在一台普通的 PC 服务器往往也仅需十几秒。 @@ -154,10 +154,10 @@ taos> select count(*) from test.meters; taos> select avg(current), max(voltage), min(phase) from test.meters; ``` -查询 location="beijing" 的记录总条数： +查询 location="California.SanFrancisco" 的记录总条数： ```sql -taos> select count(*) from test.meters where location="beijing"; +taos> select count(*) from test.meters where location="California.SanFrancisco"; ``` 查询 groupId=10 的所有记录的平均值、最大值、最小值等： diff --git a/docs-cn/07-develop/01-connect/index.md b/docs-cn/07-develop/01-connect/index.md index 3a15d03f93cee7dd064f29b4911019cae3632b9a..b1857b973932b4f9cfd1564b709dd79f26701951 100644 --- a/docs-cn/07-develop/01-connect/index.md +++ b/docs-cn/07-develop/01-connect/index.md @@ -212,7 +212,7 @@ curl -L -o php-tdengine.tar.gz https://github.com/Yurunsoft/php-tdengine/archive && tar -xzf php-tdengine.tar.gz -C php-tdengine --strip-components=1 ``` -> 版本 `v1.0.0` 可替换为任意更新的版本，可在 Release 中查看最新版本。 +> 版本 `v1.0.2` 只是示例，可替换为任意更新的版本，可在 [TDengine PHP Connector 发布历史](https://github.com/Yurunsoft/php-tdengine/releases) 中查看可用版本。 **非 Swoole 环境：** diff --git a/docs-cn/07-develop/02-model/index.mdx b/docs-cn/07-develop/02-model/index.mdx index a060e3c84b8c5b8e25714ce15fb2bc7afc7d49d2..7e2762b6e78393493c2c5b61959e9a6ff57a7b13 100644 --- a/docs-cn/07-develop/02-model/index.mdx +++ b/docs-cn/07-develop/02-model/index.mdx @@ -55,10 +55,10 @@ CREATE STABLE meters (ts timestamp, current float, voltage int, phase float) TAG TDengine 对每个数据采集点需要独立建表。与标准的关系型数据库一样，一张表有表名，Schema，但除此之外，还可以带有一到多个标签。创建时，需要使用超级表做模板，同时指定标签的具体值。以[表 1](/tdinternal/arch#model_table1)中的智能电表为例，可以使用如下的 SQL 命令建表： ```sql -CREATE TABLE d1001 USING meters TAGS ("Beijing.Chaoyang", 2); +CREATE TABLE d1001 USING meters TAGS ("California.SanFrancisco", 2); ``` -其中 d1001 是表名，meters 是超级表的表名，后面紧跟标签 Location 的具体标签值 ”Beijing.Chaoyang"，标签 groupId 的具体标签值 2。虽然在创建表时，需要指定标签值，但可以事后修改。详细细则请见 [TAOS SQL 的表管理](/taos-sql/table) 章节。 +其中 d1001 是表名，meters 是超级表的表名，后面紧跟标签 Location 的具体标签值 "California.SanFrancisco"，标签 groupId 的具体标签值 2。虽然在创建表时，需要指定标签值，但可以事后修改。详细细则请见 [TAOS SQL 的表管理](/taos-sql/table) 章节。 :::warning 目前 TDengine 没有从技术层面限制使用一个 database (db1) 的超级表作为模板建立另一个 database (db2) 的子表，后续会禁止这种用法，不建议使用这种方法建表。 @@ -72,10 +72,10 @@ TDengine 建议将数据采集点的全局唯一 ID 作为表名(比如设备序 在某些特殊场景中，用户在写数据时并不确定某个数据采集点的表是否存在，此时可在写入数据时使用自动建表语法来创建不存在的表，若该表已存在则不会建立新表且后面的 USING 语句被忽略。比如： ```sql -INSERT INTO d1001 USING meters TAGS ("Beijng.Chaoyang", 2) VALUES (now, 10.2, 219, 0.32); +INSERT INTO d1001 USING meters TAGS ("California.SanFrancisco", 2) VALUES (now, 10.2, 219, 0.32); ``` -上述 SQL 语句将记录`(now, 10.2, 219, 0.32)`插入表 d1001。如果表 d1001 还未创建，则使用超级表 meters 做模板自动创建，同时打上标签值 `"Beijing.Chaoyang", 2`。 +上述 SQL 语句将记录`(now, 10.2, 219, 0.32)`插入表 d1001。如果表 d1001 还未创建，则使用超级表 meters 做模板自动创建，同时打上标签值 `"California.SanFrancisco", 2`。 关于自动建表的详细语法请参见 [插入记录时自动建表](/taos-sql/insert#插入记录时自动建表) 章节。 diff --git a/docs-cn/07-develop/03-insert-data/01-sql-writing.mdx b/docs-cn/07-develop/03-insert-data/01-sql-writing.mdx index e63ffce6dd07366da99fe1f41d0a2a8d7a623f31..99a92573c87d0f90f699a8d1352619f4df4aef39 100644 --- a/docs-cn/07-develop/03-insert-data/01-sql-writing.mdx +++ b/docs-cn/07-develop/03-insert-data/01-sql-writing.mdx @@ -52,7 +52,7 @@ INSERT INTO d1001 VALUES (1538548685000, 10.3, 219, 0.31) (1538548695000, 12.6, :::info -- 要提高写入效率，需要批量写入。一批写入的记录条数越多，插入效率就越高。但一条记录不能超过 16K，一条 SQL 语句总长度不能超过 1M 。 +- 要提高写入效率，需要批量写入。一批写入的记录条数越多，插入效率就越高。但一条记录不能超过 48K，一条 SQL 语句总长度不能超过 1M 。 - TDengine 支持多线程同时写入，要进一步提高写入速度，一个客户端需要打开 20 个以上的线程同时写。但线程数达到一定数量后，无法再提高，甚至还会下降，因为线程频繁切换，带来额外开销。 ::: diff --git a/docs-cn/07-develop/03-insert-data/02-influxdb-line.mdx b/docs-cn/07-develop/03-insert-data/02-influxdb-line.mdx index dedd7f0e70834e21257bda78dd184f5ddc520160..54f02c91475bb5524e259a0aa890363603a86fba 100644 --- a/docs-cn/07-develop/03-insert-data/02-influxdb-line.mdx +++ b/docs-cn/07-develop/03-insert-data/02-influxdb-line.mdx @@ -29,7 +29,7 @@ measurement,tag_set field_set timestamp 例如： ``` -meters,location=Beijing.Haidian,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611249500 +meters,location=California.LosAngeles,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611249500 ``` :::note @@ -42,7 +42,6 @@ meters,location=Beijing.Haidian,groupid=2 current=13.4,voltage=223,phase=0.29 16 要了解更多可参考：[InfluxDB Line 协议官方文档](https://docs.influxdata.com/influxdb/v2.0/reference/syntax/line-protocol/) 和 [TDengine 无模式写入参考指南](/reference/schemaless/#无模式写入行协议) - ## 示例代码 diff --git a/docs-cn/07-develop/03-insert-data/03-opentsdb-telnet.mdx b/docs-cn/07-develop/03-insert-data/03-opentsdb-telnet.mdx index dfbe6efda67b6928999287900637e0a251b86562..2b397e1bdc7a4c76686cd4b6d457a25dbcc2c950 100644 --- a/docs-cn/07-develop/03-insert-data/03-opentsdb-telnet.mdx +++ b/docs-cn/07-develop/03-insert-data/03-opentsdb-telnet.mdx @@ -29,10 +29,10 @@ OpenTSDB 行协议同样采用一行字符串来表示一行数据。OpenTSDB 例如： ```txt -meters.current 1648432611250 11.3 location=Beijing.Haidian groupid=3 +meters.current 1648432611250 11.3 location=California.LosAngeles groupid=3 ``` -参考[OpenTSDB Telnet API文档](http://opentsdb.net/docs/build/html/api_telnet/put.html)。 +参考[OpenTSDB Telnet API 文档](http://opentsdb.net/docs/build/html/api_telnet/put.html)。 ## 示例代码 @@ -76,9 +76,9 @@ Query OK, 2 row(s) in set (0.002544s) taos> select tbname, * from `meters.current`; tbname | ts | value | groupid | location | ================================================================================================================================== - t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.249 | 10.800000000 | 3 | Beijing.Haidian | - t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.250 | 11.300000000 | 3 | Beijing.Haidian | - t_7e7b26dd860280242c6492a16... | 2022-03-28 09:56:51.249 | 10.300000000 | 2 | Beijing.Chaoyang | - t_7e7b26dd860280242c6492a16... | 2022-03-28 09:56:51.250 | 12.600000000 | 2 | Beijing.Chaoyang | + t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.249 | 10.800000000 | 3 | California.LosAngeles | + t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.250 | 11.300000000 | 3 | California.LosAngeles | + t_7e7b26dd860280242c6492a16... | 2022-03-28 09:56:51.249 | 10.300000000 | 2 | California.SanFrancisco | + t_7e7b26dd860280242c6492a16... | 2022-03-28 09:56:51.250 | 12.600000000 | 2 | California.SanFrancisco | Query OK, 4 row(s) in set (0.005399s) ``` diff --git a/docs-cn/07-develop/03-insert-data/04-opentsdb-json.mdx b/docs-cn/07-develop/03-insert-data/04-opentsdb-json.mdx index 5d445997d061ca052e4f3673b8e881ea4acf0ade..a15f80a5851ad29605e871f16aed60b68109038a 100644 --- a/docs-cn/07-develop/03-insert-data/04-opentsdb-json.mdx +++ b/docs-cn/07-develop/03-insert-data/04-opentsdb-json.mdx @@ -19,33 +19,33 @@ OpenTSDB JSON 格式协议采用一个 JSON 字符串表示一行或多行数据 ```json [ - { - "metric": "sys.cpu.nice", - "timestamp": 1346846400, - "value": 18, - "tags": { - "host": "web01", - "dc": "lga" - } - }, - { - "metric": "sys.cpu.nice", - "timestamp": 1346846400, - "value": 9, - "tags": { - "host": "web02", - "dc": "lga" - } + { + "metric": "sys.cpu.nice", + "timestamp": 1346846400, + "value": 18, + "tags": { + "host": "web01", + "dc": "lga" } + }, + { + "metric": "sys.cpu.nice", + "timestamp": 1346846400, + "value": 9, + "tags": { + "host": "web02", + "dc": "lga" + } + } ] ``` 与 OpenTSDB 行协议类似， metric 将作为超级表名， timestamp 表示时间戳，value 表示度量值， tags 表示标签集。 - -参考[OpenTSDB HTTP API文档](http://opentsdb.net/docs/build/html/api_http/put.html)。 +参考[OpenTSDB HTTP API 文档](http://opentsdb.net/docs/build/html/api_http/put.html)。 :::note + - 对于 JSON 格式协议，TDengine 并不会自动把所有标签转成 nchar 类型， 字符串将将转为 nchar 类型， 数值将同样转换为 double 类型。 - TDengine 只接收 JSON **数组格式**的字符串，即使一行数据也需要转换成数组形式。 @@ -93,7 +93,7 @@ Query OK, 2 row(s) in set (0.001954s) taos> select * from `meters.current`; ts | value | groupid | location | =================================================================================================================== - 2022-03-28 09:56:51.249 | 10.300000000 | 2.000000000 | Beijing.Chaoyang | - 2022-03-28 09:56:51.250 | 12.600000000 | 2.000000000 | Beijing.Chaoyang | + 2022-03-28 09:56:51.249 | 10.300000000 | 2.000000000 | California.SanFrancisco | + 2022-03-28 09:56:51.250 | 12.600000000 | 2.000000000 | California.SanFrancisco | Query OK, 2 row(s) in set (0.004076s) ``` diff --git a/docs-cn/07-develop/04-query-data/index.mdx b/docs-cn/07-develop/04-query-data/index.mdx index b0a6bad3eaad174a97d8dce4e1ba0125cbf5dc03..824f36ef2f98aac227bdcaf2016d7be0a2e59328 100644 --- a/docs-cn/07-develop/04-query-data/index.mdx +++ b/docs-cn/07-develop/04-query-data/index.mdx @@ -50,14 +50,14 @@ Query OK, 2 row(s) in set (0.001100s) ### 示例一 -在 TAOS Shell，查找北京所有智能电表采集的电压平均值，并按照 location 分组。 +在 TAOS Shell，查找加利福尼亚州所有智能电表采集的电压平均值，并按照 location 分组。 ``` taos> SELECT AVG(voltage) FROM meters GROUP BY location; avg(voltage) | location | ============================================================= - 222.000000000 | Beijing.Haidian | - 219.200000000 | Beijing.Chaoyang | + 222.000000000 | California.LosAngeles | + 219.200000000 | California.SanFrancisco | Query OK, 2 row(s) in set (0.002136s) ``` @@ -88,10 +88,10 @@ taos> SELECT sum(current) FROM d1001 INTERVAL(10s); Query OK, 2 row(s) in set (0.000883s) ``` -降采样操作也适用于超级表，比如：将北京所有智能电表采集的电流值每秒钟求和 +降采样操作也适用于超级表，比如：将加利福尼亚州所有智能电表采集的电流值每秒钟求和 ``` -taos> SELECT SUM(current) FROM meters where location like "Beijing%" INTERVAL(1s); +taos> SELECT SUM(current) FROM meters where location like "California%" INTERVAL(1s); ts | sum(current) | ====================================================== 2018-10-03 14:38:04.000 | 10.199999809 | diff --git a/docs-cn/07-develop/05-delete-data.mdx b/docs-cn/07-develop/05-delete-data.mdx new file mode 100644 index 0000000000000000000000000000000000000000..eafe8cff264b271384096adc2a0be9a7d01e579a --- /dev/null +++ b/docs-cn/07-develop/05-delete-data.mdx @@ -0,0 +1,43 @@ +--- +sidebar_label: 删除数据 +description: "删除指定表或超级表中的数据记录" +title: "删除数据" +--- + +删除数据是 TDengine 提供的根据指定时间段删除指定表或超级表中数据记录的功能，方便用户清理由于设备故障等原因产生的异常数据。 +注意：本功能只在企业版 2.6.0.0 及以后的版本中提供，如需此功能请点击下面的链接访问[企业版产品](https://www.taosdata.com/products#enterprise-edition-link) + + +**语法：** + +```sql +DELETE FROM [ db_name. ] tb_name [WHERE condition]; +``` + +**功能：** 删除指定表或超级表中的数据记录 + +**参数：** + +- `db_name` ： 可选参数，指定要删除表所在的数据库名，不填写则在当前数据库中 +- `tb_name` ： 必填参数，指定要删除数据的表名，可以是普通表、子表，也可以是超级表。 +- `condition`： 可选参数，指定删除数据的过滤条件，不指定过滤条件则为表中所有数据，请慎重使用。特别说明，这里的where 条件中只支持对第一列时间列的过滤，如果是超级表，支持对 tag 列过滤。 + +**特别说明：** + +数据删除后不可恢复，请慎重使用。为了确保删除的数据确实是自己要删除的，建议可以先使用 `select` 语句加 `where` 后的删除条件查看要删除的数据内容，确认无误后再执行 `delete` 命令。 + +**示例：** + +`meters` 是一个超级表，`groupid` 是 int 类型的 tag 列，现在要删除 `meters` 表中时间小于 2021-10-01 10:40:00.100 且 tag 列 `groupid` 值为 1 的所有数据，sql 如下： + +```sql +delete from meters where ts < '2021-10-01 10:40:00.100' and groupid=1 ; +``` + +执行后显示结果为： + +``` +Deleted 102000 row(s) from 1020 table(s) (0.421950s) +``` + +表示从 1020 个子表中共删除了 102000 行数据 diff --git a/docs-cn/07-develop/05-continuous-query.mdx b/docs-cn/07-develop/06-continuous-query.mdx similarity index 97% rename from docs-cn/07-develop/05-continuous-query.mdx rename to docs-cn/07-develop/06-continuous-query.mdx index 2fd1b3cc755188f513fe511541a84efa3558d3ea..b2223d15e33114d263b9833df51e4201bc01c772 100644 --- a/docs-cn/07-develop/05-continuous-query.mdx +++ b/docs-cn/07-develop/06-continuous-query.mdx @@ -34,8 +34,8 @@ SLIDING: 连续查询的时间窗口向前滑动的时间间隔 ```sql create table meters (ts timestamp, current float, voltage int, phase float) tags (location binary(64), groupId int); -create table D1001 using meters tags ("Beijing.Chaoyang", 2); -create table D1002 using meters tags ("Beijing.Haidian", 2); +create table D1001 using meters tags ("California.SanFrancisco", 2); +create table D1002 using meters tags ("California.LosAngeles", 2); ... ``` diff --git a/docs-cn/07-develop/06-subscribe.mdx b/docs-cn/07-develop/07-subscribe.md similarity index 91% rename from docs-cn/07-develop/06-subscribe.mdx rename to docs-cn/07-develop/07-subscribe.md index d471c114e827d7c4b40195c2c1b3c8f6a9d26ed4..0f531e07c9dce7dbb03bacebf8e5cbefae82671f 100644 --- a/docs-cn/07-develop/06-subscribe.mdx +++ b/docs-cn/07-develop/07-subscribe.md @@ -145,7 +145,7 @@ void subscribe_callback(TAOS_SUB* tsub, TAOS_RES *res, void* param, int code) { taos_unsubscribe(tsub, keep); ``` -其第二个参数，用于决定是否在客户端保留订阅的进度信息。如果这个参数是**false**（**0**），那无论下次调用 `taos_subscribe` 时的 `restart` 参数是什么，订阅都只能重新开始。另外，进度信息的保存位置是 _{DataDir}/subscribe/_ 这个目录下，每个订阅有一个与其 `topic` 同名的文件，删掉某个文件，同样会导致下次创建其对应的订阅时只能重新开始。 +其第二个参数，用于决定是否在客户端保留订阅的进度信息。如果这个参数是**false**（**0**），那无论下次调用 `taos_subscribe` 时的 `restart` 参数是什么，订阅都只能重新开始。另外，进度信息的保存位置是 _{DataDir}/subscribe/_ 这个目录下（注：`taos.cfg` 配置文件中 `DataDir` 参数值默认为 **/var/lib/taos/**,但是 Windows 服务器上本身不存在该目录，所以需要在 Windows 的配置文件中修改 `DataDir` 参数值为相应的已存在目录"），每个订阅有一个与其 `topic` 同名的文件，删掉某个文件，同样会导致下次创建其对应的订阅时只能重新开始。 代码介绍完毕，我们来看一下实际的运行效果。假设： @@ -184,8 +184,8 @@ taos> use power; # create super table "meters" taos> create table meters(ts timestamp, current float, voltage int, phase int) tags(location binary(64), groupId int); # create tabes using the schema defined by super table "meters" -taos> create table d1001 using meters tags ("Beijing.Chaoyang", 2); -taos> create table d1002 using meters tags ("Beijing.Haidian", 2); +taos> create table d1001 using meters tags ("California.SanFrancisco", 2); +taos> create table d1002 using meters tags ("California.LosAngeles", 2); # insert some rows taos> insert into d1001 values("2020-08-15 12:00:00.000", 12, 220, 1),("2020-08-15 12:10:00.000", 12.3, 220, 2),("2020-08-15 12:20:00.000", 12.2, 220, 1); taos> insert into d1002 values("2020-08-15 12:00:00.000", 9.9, 220, 1),("2020-08-15 12:10:00.000", 10.3, 220, 1),("2020-08-15 12:20:00.000", 11.2, 220, 1); @@ -193,27 +193,28 @@ taos> insert into d1002 values("2020-08-15 12:00:00.000", 9.9, 220, 1),("2020-08 taos> select * from meters where current > 10; ts | current | voltage | phase | location | groupid | =========================================================================================================== - 2020-08-15 12:10:00.000 | 10.30000 | 220 | 1 | Beijing.Haidian | 2 | - 2020-08-15 12:20:00.000 | 11.20000 | 220 | 1 | Beijing.Haidian | 2 | - 2020-08-15 12:00:00.000 | 12.00000 | 220 | 1 | Beijing.Chaoyang | 2 | - 2020-08-15 12:10:00.000 | 12.30000 | 220 | 2 | Beijing.Chaoyang | 2 | - 2020-08-15 12:20:00.000 | 12.20000 | 220 | 1 | Beijing.Chaoyang | 2 | + 2020-08-15 12:10:00.000 | 10.30000 | 220 | 1 | California.LosAngeles | 2 | + 2020-08-15 12:20:00.000 | 11.20000 | 220 | 1 | California.LosAngeles | 2 | + 2020-08-15 12:00:00.000 | 12.00000 | 220 | 1 | California.SanFrancisco | 2 | + 2020-08-15 12:10:00.000 | 12.30000 | 220 | 2 | California.SanFrancisco | 2 | + 2020-08-15 12:20:00.000 | 12.20000 | 220 | 1 | California.SanFrancisco | 2 | Query OK, 5 row(s) in set (0.004896s) ``` + ### 示例代码 - + - + {/* */} - + {/* @@ -222,20 +223,20 @@ Query OK, 5 row(s) in set (0.004896s) */} - - + + ### 运行示例程序 - + 示例程序会先消费符合查询条件的所有历史数据： ```bash -ts: 1597464000000 current: 12.0 voltage: 220 phase: 1 location: Beijing.Chaoyang groupid : 2 -ts: 1597464600000 current: 12.3 voltage: 220 phase: 2 location: Beijing.Chaoyang groupid : 2 -ts: 1597465200000 current: 12.2 voltage: 220 phase: 1 location: Beijing.Chaoyang groupid : 2 -ts: 1597464600000 current: 10.3 voltage: 220 phase: 1 location: Beijing.Haidian groupid : 2 -ts: 1597465200000 current: 11.2 voltage: 220 phase: 1 location: Beijing.Haidian groupid : 2 +ts: 1597464000000 current: 12.0 voltage: 220 phase: 1 location: California.SanFrancisco groupid : 2 +ts: 1597464600000 current: 12.3 voltage: 220 phase: 2 location: California.SanFrancisco groupid : 2 +ts: 1597465200000 current: 12.2 voltage: 220 phase: 1 location: California.SanFrancisco groupid : 2 +ts: 1597464600000 current: 10.3 voltage: 220 phase: 1 location: California.LosAngeles groupid : 2 +ts: 1597465200000 current: 11.2 voltage: 220 phase: 1 location: California.LosAngeles groupid : 2 ``` 接着，使用 TDengine CLI 向表中新增一条数据： @@ -249,5 +250,5 @@ taos> insert into d1001 values(now, 12.4, 220, 1); 因为这条数据的电流大于 10A，示例程序会将其消费： ``` -ts: 1651146662805 current: 12.4 voltage: 220 phase: 1 location: Beijing.Chaoyang groupid: 2 +ts: 1651146662805 current: 12.4 voltage: 220 phase: 1 location: California.SanFrancisco groupid: 2 ``` diff --git a/docs-cn/07-develop/07-cache.md b/docs-cn/07-develop/08-cache.md similarity index 93% rename from docs-cn/07-develop/07-cache.md rename to docs-cn/07-develop/08-cache.md index fd31335310d62d792e5173e38a9aa778ee6c6c60..cc59c0353c0d12fb7a8f0f20254087d741361031 100644 --- a/docs-cn/07-develop/07-cache.md +++ b/docs-cn/07-develop/08-cache.md @@ -1,6 +1,6 @@ --- sidebar_label: 缓存 -title: 缓存 +title: 缓存 description: "提供写驱动的缓存管理机制，将每个表最近写入的一条记录持续保存在缓存中，可以提供高性能的最近状态查询。" --- @@ -15,7 +15,7 @@ TDengine 将内存池按块划分进行管理，数据在内存块里是以行 你可以通过函数 last_row() 快速获取一张表或一张超级表的最后一条记录，这样很便于在大屏显示各设备的实时状态或采集值。例如： ```sql -select last_row(voltage) from meters where location='Beijing.Chaoyang'; +select last_row(voltage) from meters where location='California.SanFrancisco'; ``` -该 SQL 语句将获取所有位于北京朝阳区的电表最后记录的电压值。 +该 SQL 语句将获取所有位于加利福尼亚州旧金山市的电表最后记录的电压值。 diff --git a/docs-cn/07-develop/08-udf.md b/docs-cn/07-develop/09-udf.md similarity index 100% rename from docs-cn/07-develop/08-udf.md rename to docs-cn/07-develop/09-udf.md diff --git a/docs-cn/10-cluster/01-deploy.md b/docs-cn/10-cluster/01-deploy.md index cee140c0ec13bc9c8052a599a2147acc1aa15a8d..b44d2942f2e4672ef6060aa9d084db1d3342e1c8 100644 --- a/docs-cn/10-cluster/01-deploy.md +++ b/docs-cn/10-cluster/01-deploy.md @@ -22,7 +22,7 @@ title: 集群部署 ### 第二步 -建议关闭所有物理节点的防火墙，至少保证端口：6030 - 6042 的 TCP 和 UDP 端口都是开放的。强烈建议先关闭防火墙，集群搭建完毕之后，再来配置端口； +确保集群中所有主机在端口 6030-6042 上的 TCP/UDP 协议能够互通。 ### 第三步 diff --git a/docs-cn/12-taos-sql/03-table.md b/docs-cn/12-taos-sql/03-table.md index 675c157b3def0d670f771f55b767f3ca4f2a28af..d7235f312933ec46ed427d5da7e2c5a229fa2926 100644 --- a/docs-cn/12-taos-sql/03-table.md +++ b/docs-cn/12-taos-sql/03-table.md @@ -12,7 +12,7 @@ CREATE TABLE [IF NOT EXISTS] tb_name (timestamp_field_name TIMESTAMP, field1_nam 1. 表的第一个字段必须是 TIMESTAMP，并且系统自动将其设为主键； 2. 表名最大长度为 192； -3. 表的每行长度不能超过 16k 个字符;（注意：每个 BINARY/NCHAR 类型的列还会额外占用 2 个字节的存储位置） +3. 表的每行长度不能超过 48KB;（注意：每个 BINARY/NCHAR 类型的列还会额外占用 2 个字节的存储位置） 4. 子表名只能由字母、数字和下划线组成，且不能以数字开头，不区分大小写 5. 使用数据类型 binary 或 nchar，需指定其最长的字节数，如 binary(20)，表示 20 字节； 6. 为了兼容支持更多形式的表名，TDengine 引入新的转义符 "\`"，可以让表名与关键词不冲突，同时不受限于上述表名称合法性约束检查。但是同样具有长度限制要求。使用转义字符以后，不再对转义字符中的内容进行大小写统一。 diff --git a/docs-cn/12-taos-sql/04-stable.md b/docs-cn/12-taos-sql/04-stable.md index a3c227317c85917b64b2477994d335710610ec70..3901427736e80bc8dd0dd87b454947af6e586561 100644 --- a/docs-cn/12-taos-sql/04-stable.md +++ b/docs-cn/12-taos-sql/04-stable.md @@ -86,7 +86,7 @@ ALTER STABLE stb_name MODIFY COLUMN field_name data_type(length); ALTER STABLE stb_name ADD TAG new_tag_name tag_type; ``` -为 STable 增加一个新的标签，并指定新标签的类型。标签总数不能超过 128 个，总长度不超过 16k 个字符。 +为 STable 增加一个新的标签，并指定新标签的类型。标签总数不能超过 128 个，总长度不超过 16KB 。 ### 删除标签 diff --git a/docs-cn/12-taos-sql/05-insert.md b/docs-cn/12-taos-sql/05-insert.md index e542e442b78c9033ae37196f4913a7c67fb19d8b..04118303f3f6517d65d8ecbbe9fdeb774a3177b7 100644 --- a/docs-cn/12-taos-sql/05-insert.md +++ b/docs-cn/12-taos-sql/05-insert.md @@ -67,7 +67,7 @@ INSERT INTO d1001 VALUES ('2021-07-13 14:06:34.630', 10.2, 219, 0.32) ('2021-07- 如果用户在写数据时并不确定某个表是否存在，此时可以在写入数据时使用自动建表语法来创建不存在的表，若该表已存在则不会建立新表。自动建表时，要求必须以超级表为模板，并写明数据表的 TAGS 取值。例如： ``` -INSERT INTO d21001 USING meters TAGS ('Beijing.Chaoyang', 2) VALUES ('2021-07-13 14:06:32.272', 10.2, 219, 0.32); +INSERT INTO d21001 USING meters TAGS ('California.SanFrancisco', 2) VALUES ('2021-07-13 14:06:32.272', 10.2, 219, 0.32); ``` 也可以在自动建表时，只是指定部分 TAGS 列的取值，未被指定的 TAGS 列将置为 NULL。例如： @@ -79,7 +79,7 @@ INSERT INTO d21001 USING meters (groupId) TAGS (2) VALUES ('2021-07-13 14:06:33. 自动建表语法也支持在一条语句中向多个表插入记录。例如： ``` -INSERT INTO d21001 USING meters TAGS ('Beijing.Chaoyang', 2) VALUES ('2021-07-13 14:06:34.630', 10.2, 219, 0.32) ('2021-07-13 14:06:35.779', 10.15, 217, 0.33) +INSERT INTO d21001 USING meters TAGS ('California.SanFrancisco', 2) VALUES ('2021-07-13 14:06:34.630', 10.2, 219, 0.32) ('2021-07-13 14:06:35.779', 10.15, 217, 0.33) d21002 USING meters (groupId) TAGS (2) VALUES ('2021-07-13 14:06:34.255', 10.15, 217, 0.33) d21003 USING meters (groupId) TAGS (2) (ts, current, phase) VALUES ('2021-07-13 14:06:34.255', 10.27, 0.31); ``` @@ -108,13 +108,13 @@ INSERT INTO d1001 FILE '/tmp/csvfile.csv'; 从 2.1.5.0 版本开始，支持在插入来自 CSV 文件的数据时，以超级表为模板来自动创建不存在的数据表。例如： ``` -INSERT INTO d21001 USING meters TAGS ('Beijing.Chaoyang', 2) FILE '/tmp/csvfile.csv'; +INSERT INTO d21001 USING meters TAGS ('California.SanFrancisco', 2) FILE '/tmp/csvfile.csv'; ``` 也可以在一条语句中向多个表以自动建表的方式插入记录。例如： ``` -INSERT INTO d21001 USING meters TAGS ('Beijing.Chaoyang', 2) FILE '/tmp/csvfile_21001.csv' +INSERT INTO d21001 USING meters TAGS ('California.SanFrancisco', 2) FILE '/tmp/csvfile_21001.csv' d21002 USING meters (groupId) TAGS (2) FILE '/tmp/csvfile_21002.csv'; ``` @@ -137,7 +137,7 @@ Query OK, 1 row(s) in set (0.001029s) taos> SHOW TABLES; Query OK, 0 row(s) in set (0.000946s) -taos> INSERT INTO d1001 USING meters TAGS('Beijing.Chaoyang', 2) VALUES('a'); +taos> INSERT INTO d1001 USING meters TAGS('California.SanFrancisco', 2) VALUES('a'); DB error: invalid SQL: 'a' (invalid timestamp) (0.039494s) diff --git a/docs-cn/12-taos-sql/06-select.md b/docs-cn/12-taos-sql/06-select.md index 3a860119cfe664f9ac3b0ebd046b5f4f0a612118..92abc4344b7562842fae71a84fe0cb9a168596ed 100644 --- a/docs-cn/12-taos-sql/06-select.md +++ b/docs-cn/12-taos-sql/06-select.md @@ -40,15 +40,15 @@ Query OK, 3 row(s) in set (0.001165s) taos> SELECT * FROM meters; ts | current | voltage | phase | location | groupid | ===================================================================================================================================== - 2018-10-03 14:38:05.500 | 11.80000 | 221 | 0.28000 | Beijing.Haidian | 2 | - 2018-10-03 14:38:16.600 | 13.40000 | 223 | 0.29000 | Beijing.Haidian | 2 | - 2018-10-03 14:38:05.000 | 10.80000 | 223 | 0.29000 | Beijing.Haidian | 3 | - 2018-10-03 14:38:06.500 | 11.50000 | 221 | 0.35000 | Beijing.Haidian | 3 | - 2018-10-03 14:38:04.000 | 10.20000 | 220 | 0.23000 | Beijing.Chaoyang | 3 | - 2018-10-03 14:38:16.650 | 10.30000 | 218 | 0.25000 | Beijing.Chaoyang | 3 | - 2018-10-03 14:38:05.000 | 10.30000 | 219 | 0.31000 | Beijing.Chaoyang | 2 | - 2018-10-03 14:38:15.000 | 12.60000 | 218 | 0.33000 | Beijing.Chaoyang | 2 | - 2018-10-03 14:38:16.800 | 12.30000 | 221 | 0.31000 | Beijing.Chaoyang | 2 | + 2018-10-03 14:38:05.500 | 11.80000 | 221 | 0.28000 | California.LosAngeles | 2 | + 2018-10-03 14:38:16.600 | 13.40000 | 223 | 0.29000 | California.LosAngeles | 2 | + 2018-10-03 14:38:05.000 | 10.80000 | 223 | 0.29000 | California.LosAngeles | 3 | + 2018-10-03 14:38:06.500 | 11.50000 | 221 | 0.35000 | California.LosAngeles | 3 | + 2018-10-03 14:38:04.000 | 10.20000 | 220 | 0.23000 | California.SanFrancisco | 3 | + 2018-10-03 14:38:16.650 | 10.30000 | 218 | 0.25000 | California.SanFrancisco | 3 | + 2018-10-03 14:38:05.000 | 10.30000 | 219 | 0.31000 | California.SanFrancisco | 2 | + 2018-10-03 14:38:15.000 | 12.60000 | 218 | 0.33000 | California.SanFrancisco | 2 | + 2018-10-03 14:38:16.800 | 12.30000 | 221 | 0.31000 | California.SanFrancisco | 2 | Query OK, 9 row(s) in set (0.002022s) ``` @@ -104,8 +104,8 @@ Query OK, 1 row(s) in set (0.000849s) taos> SELECT location, groupid, current FROM d1001 LIMIT 2; location | groupid | current | ====================================================================== - Beijing.Chaoyang | 2 | 10.30000 | - Beijing.Chaoyang | 2 | 12.60000 | + California.SanFrancisco | 2 | 10.30000 | + California.SanFrancisco | 2 | 12.60000 | Query OK, 2 row(s) in set (0.003112s) ``` @@ -284,10 +284,10 @@ SELECT COUNT(TBNAME) FROM meters; taos> SELECT TBNAME, location FROM meters; tbname | location | ================================================================== - d1004 | Beijing.Haidian | - d1003 | Beijing.Haidian | - d1002 | Beijing.Chaoyang | - d1001 | Beijing.Chaoyang | + d1004 | California.LosAngeles | + d1003 | California.LosAngeles | + d1002 | California.SanFrancisco | + d1001 | California.SanFrancisco | Query OK, 4 row(s) in set (0.000881s) taos> SELECT COUNT(tbname) FROM meters WHERE groupId > 2; @@ -327,15 +327,15 @@ Query OK, 1 row(s) in set (0.001091s) - <\> 算子也可以写为 != ，请注意，这个算子不能用于数据表第一列的 timestamp 字段。 - like 算子使用通配符字符串进行匹配检查。 - - 在通配符字符串中：'%'（百分号）匹配 0 到任意个字符；'\_'（下划线）匹配单个任意 ASCII 字符。 - - 如果希望匹配字符串中原本就带有的 \_（下划线）字符，那么可以在通配符字符串中写作 `\_`，也即加一个反斜线来进行转义。（从 2.2.0.0 版本开始支持） - - 通配符字符串最长不能超过 20 字节。（从 2.1.6.1 版本开始，通配符字符串的长度放宽到了 100 字节，并可以通过 taos.cfg 中的 maxWildCardsLength 参数来配置这一长度限制。但不建议使用太长的通配符字符串，将有可能严重影响 LIKE 操作的执行性能。） + - 在通配符字符串中：'%'（百分号）匹配 0 到任意个字符；'\_'（下划线）匹配单个任意 ASCII 字符。 + - 如果希望匹配字符串中原本就带有的 \_（下划线）字符，那么可以在通配符字符串中写作 `\_`，也即加一个反斜线来进行转义。（从 2.2.0.0 版本开始支持） + - 通配符字符串最长不能超过 20 字节。（从 2.1.6.1 版本开始，通配符字符串的长度放宽到了 100 字节，并可以通过 taos.cfg 中的 maxWildCardsLength 参数来配置这一长度限制。但不建议使用太长的通配符字符串，将有可能严重影响 LIKE 操作的执行性能。） - 同时进行多个字段的范围过滤，需要使用关键词 AND 来连接不同的查询条件，暂不支持 OR 连接的不同列之间的查询过滤条件。 - - 从 2.3.0.0 版本开始，已支持完整的同一列和/或不同列间的 AND/OR 运算。 + - 从 2.3.0.0 版本开始，已支持完整的同一列和/或不同列间的 AND/OR 运算。 - 针对单一字段的过滤，如果是时间过滤条件，则一条语句中只支持设定一个；但针对其他的（普通）列或标签列，则可以使用 `OR` 关键字进行组合条件的查询过滤。例如： `((value > 20 AND value < 30) OR (value < 12))`。 - - 从 2.3.0.0 版本开始，允许使用多个时间过滤条件，但首列时间戳的过滤运算结果只能包含一个区间。 + - 从 2.3.0.0 版本开始，允许使用多个时间过滤条件，但首列时间戳的过滤运算结果只能包含一个区间。 - 从 2.0.17.0 版本开始，条件过滤开始支持 BETWEEN AND 语法，例如 `WHERE col2 BETWEEN 1.5 AND 3.25` 表示查询条件为“1.5 ≤ col2 ≤ 3.25”。 -- 从 2.1.4.0 版本开始，条件过滤开始支持 IN 算子，例如 `WHERE city IN ('Beijing', 'Shanghai')`。说明：BOOL 类型写作 `{true, false}` 或 `{0, 1}` 均可，但不能写作 0、1 之外的整数；FLOAT 和 DOUBLE 类型会受到浮点数精度影响，集合内的值在精度范围内认为和数据行的值完全相等才能匹配成功；TIMESTAMP 类型支持非主键的列。 +- 从 2.1.4.0 版本开始，条件过滤开始支持 IN 算子，例如 `WHERE city IN ('California.SanFrancisco', 'California.SanDieo')`。说明：BOOL 类型写作 `{true, false}` 或 `{0, 1}` 均可，但不能写作 0、1 之外的整数；FLOAT 和 DOUBLE 类型会受到浮点数精度影响，集合内的值在精度范围内认为和数据行的值完全相等才能匹配成功；TIMESTAMP 类型支持非主键的列。 - 从 2.3.0.0 版本开始，条件过滤开始支持正则表达式，关键字 match/nmatch，不区分大小写。 ## 正则表达式过滤 @@ -380,7 +380,7 @@ WHERE t1.ts = t2.ts AND t1.deviceid = t2.deviceid AND t1.status=0; :::note -JOIN语句存在如下限制要求： +JOIN 语句存在如下限制要求： - 参与一条语句中 JOIN 操作的表/超级表最多可以有 10 个。 - 在包含 JOIN 操作的查询语句中不支持 FILL。 @@ -409,13 +409,13 @@ SELECT ... FROM (SELECT ... FROM ...) ...; - 在内层和外层查询中，都支持普通的表间/超级表间 JOIN。内层查询的计算结果也可以再参与数据子表的 JOIN 操作。 - 目前内层查询、外层查询均不支持 UNION 操作。 - 内层查询支持的功能特性与非嵌套的查询语句能力是一致的。 - - 内层查询的 ORDER BY 子句一般没有意义，建议避免这样的写法以免无谓的资源消耗。 + - 内层查询的 ORDER BY 子句一般没有意义，建议避免这样的写法以免无谓的资源消耗。 - 与非嵌套的查询语句相比，外层查询所能支持的功能特性存在如下限制： - - 计算函数部分： - - 如果内层查询的结果数据未提供时间戳，那么计算过程依赖时间戳的函数在外层会无法正常工作。例如：TOP, BOTTOM, FIRST, LAST, DIFF。 - - 计算过程需要两遍扫描的函数，在外层查询中无法正常工作。例如：此类函数包括：STDDEV, PERCENTILE。 - - 外层查询中不支持 IN 算子，但在内层中可以使用。 - - 外层查询不支持 GROUP BY。 + - 计算函数部分： + - 如果内层查询的结果数据未提供时间戳，那么计算过程依赖时间戳的函数在外层会无法正常工作。例如：TOP, BOTTOM, FIRST, LAST, DIFF。 + - 计算过程需要两遍扫描的函数，在外层查询中无法正常工作。例如：此类函数包括：STDDEV, PERCENTILE。 + - 外层查询中不支持 IN 算子，但在内层中可以使用。 + - 外层查询不支持 GROUP BY。 ::: diff --git a/docs-cn/12-taos-sql/07-function.md b/docs-cn/12-taos-sql/07-function.md index f6e564419ddaa18931b0f0e0e4e7b5b3219a92f6..2349e6aa3c02eb62fba1fc7e4eef15e08e3924d1 100644 --- a/docs-cn/12-taos-sql/07-function.md +++ b/docs-cn/12-taos-sql/07-function.md @@ -261,6 +261,92 @@ taos> select hyperloglog(dbig) from shll; Query OK, 1 row(s) in set (0.008388s) ``` +### HISTOGRAM + +``` +SELECT HISTOGRAM(field_name，bin_type, bin_description, normalized) FROM tb_name [WHERE clause]; +``` + +**功能说明**：统计数据按照用户指定区间的分布。 + +**返回结果类型**：如归一化参数 normalized 设置为 1，返回结果为双精度浮点类型 DOUBLE，否则为长整形 INT64。 + +**应用字段**：数值型字段。 + +**支持的版本**：2.6.0.0 及以后的版本。 + +**适用于**: 表和超级表。 + +**说明**： +1. bin_type 用户指定的分桶类型, 有效输入类型为"user_input“, ”linear_bin", "log_bin"。 +2. bin_description 描述如何生成分桶区间，针对三种桶类型，分别为以下描述格式(均为 JSON 格式字符串)： + - "user_input": "[1, 3, 5, 7]" + 用户指定 bin 的具体数值。 + + - "linear_bin": "{"start": 0.0, "width": 5.0, "count": 5, "infinity": true}" + "start" 表示数据起始点，"width" 表示每次 bin 偏移量, "count" 为 bin 的总数，"infinity" 表示是否添加（-inf, inf）作为区间起点跟终点， + 生成区间为[-inf, 0.0, 5.0, 10.0, 15.0, 20.0, +inf]。 + + - "log_bin": "{"start":1.0, "factor": 2.0, "count": 5, "infinity": true}" + "start" 表示数据起始点，"factor" 表示按指数递增的因子，"count" 为 bin 的总数，"infinity" 表示是否添加（-inf, inf）作为区间起点跟终点， + 生成区间为[-inf, 1.0, 2.0, 4.0, 8.0, 16.0, +inf]。 +3. normalized 是否将返回结果归一化到 0~1 之间 。有效输入为 0 和 1。 + +**示例**： + +```mysql +taos> SELECT HISTOGRAM(voltage, "user_input", "[1,3,5,7]", 1) FROM meters; + histogram(voltage, "user_input", "[1,3,5,7]", 1) | + ======================================================= + {"lower_bin":1, "upper_bin":3, "count":0.333333} | + {"lower_bin":3, "upper_bin":5, "count":0.333333} | + {"lower_bin":5, "upper_bin":7, "count":0.333333} | + Query OK, 3 row(s) in set (0.004273s) + +taos> SELECT HISTOGRAM(voltage, 'linear_bin', '{"start": 1, "width": 3, "count": 3, "infinity": false}', 0) FROM meters; + histogram(voltage, 'linear_bin', '{"start": 1, "width": 3, " | + =================================================================== + {"lower_bin":1, "upper_bin":4, "count":3} | + {"lower_bin":4, "upper_bin":7, "count":3} | + {"lower_bin":7, "upper_bin":10, "count":3} | + Query OK, 3 row(s) in set (0.004887s) + +taos> SELECT HISTOGRAM(voltage, 'log_bin', '{"start": 1, "factor": 3, "count": 3, "infinity": true}', 0) FROM meters; + histogram(voltage, 'log_bin', '{"start": 1, "factor": 3, "count" | + =================================================================== + {"lower_bin":-inf, "upper_bin":1, "count":3} | + {"lower_bin":1, "upper_bin":3, "count":2} | + {"lower_bin":3, "upper_bin":9, "count":6} | + {"lower_bin":9, "upper_bin":27, "count":3} | + {"lower_bin":27, "upper_bin":inf, "count":1} | +``` + +### ELAPSED + +```mysql +SELECT ELAPSED(field_name[, time_unit]) FROM { tb_name | stb_name } [WHERE clause] [INTERVAL(interval [, offset]) [SLIDING sliding]]; +``` + +**功能说明**：elapsed函数表达了统计周期内连续的时间长度，和twa函数配合使用可以计算统计曲线下的面积。在通过INTERVAL子句指定窗口的情况下，统计在给定时间范围内的每个窗口内有数据覆盖的时间范围；如果没有INTERVAL子句，则返回整个给定时间范围内的有数据覆盖的时间范围。注意，ELAPSED返回的并不是时间范围的绝对值，而是绝对值除以time_unit所得到的单位个数。 + +**返回结果类型**：Double + +**应用字段**：Timestamp类型 + +**支持的版本**：2.6.0.0 及以后的版本。 + +**适用于**: 表，超级表，嵌套查询的外层查询 + +**说明**： +- field_name参数只能是表的第一列，即timestamp主键列。 +- 按time_unit参数指定的时间单位返回，最小是数据库的时间分辨率。time_unit参数未指定时，以数据库的时间分辨率为时间单位。 +- 可以和interval组合使用，返回每个时间窗口的时间戳差值。需要特别注意的是，除第一个时间窗口和最后一个时间窗口外，中间窗口的时间戳差值均为窗口长度。 +- order by asc/desc不影响差值的计算结果。 +- 对于超级表，需要和group by tbname子句组合使用，不可以直接使用。 +- 对于普通表，不支持和group by子句组合使用。 +- 对于嵌套查询，仅当内层查询会输出隐式时间戳列时有效。例如select elapsed(ts) from (select diff(value) from sub1)语句，diff函数会让内层查询输出隐式时间戳列，此为主键列，可以用于elapsed函数的第一个参数。相反，例如select elapsed(ts) from (select * from sub1) 语句，ts列输出到外层时已经没有了主键列的含义，无法使用elapsed函数。此外，elapsed函数作为一个与时间线强依赖的函数，形如select elapsed(ts) from (select diff(value) from st group by tbname)尽管会返回一条计算结果，但并无实际意义，这种用法后续也将被限制。 +- 不支持与leastsquares、diff、derivative、top、bottom、last_row、interp等函数混合使用。 + ## 选择函数 在使用所有的选择函数的时候，可以同时指定输出 ts 列或标签列（包括 tbname），这样就可以方便地知道被选出的值是源于哪个数据行的。 @@ -698,7 +784,7 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } WHERE ts='timestamp' [FILL SELECT TAIL(field_name, k, offset_val) FROM {tb_name | stb_name} [WHERE clause]; ``` -**功能说明**：返回跳过最后 offset_value 个，然后取连续 k 个记录，不忽略 NULL 值。offset_val 可以不输入。此时返回最后的 k 个记录。当有 offset_val 输入的情况下，该函数功能等效于 `order by ts desc LIMIT k OFFSET offset_val`。 +**功能说明**：返回跳过最后 offset_val 个，然后取连续 k 个记录，不忽略 NULL 值。offset_val 可以不输入。此时返回最后的 k 个记录。当有 offset_val 输入的情况下，该函数功能等效于 `order by ts desc LIMIT k OFFSET offset_val`。 **参数范围**：k: [1,100] offset_val: [0,100]。 @@ -1766,6 +1852,8 @@ SELECT TIMEDIFF(ts_val1 | datetime_string1 | ts_col1, ts_val2 | datetime_string2 1u(微秒)，1a(毫秒)，1s(秒)，1m(分)，1h(小时)，1d(天)。 - 如果时间单位 time_unit 未指定， 返回的时间差值精度与当前 DATABASE 设置的时间精度一致。 +**支持的版本**：2.6.0.0 及以后的版本。 + **示例**： ```sql diff --git a/docs-cn/12-taos-sql/08-interval.md b/docs-cn/12-taos-sql/08-interval.md index d62e11b0dbd0ba49ceedb3807e05361f060969b3..b0619ea5ce3759e9bca1234b76e2a16176511547 100644 --- a/docs-cn/12-taos-sql/08-interval.md +++ b/docs-cn/12-taos-sql/08-interval.md @@ -11,7 +11,7 @@ TDengine 支持按时间段窗口切分方式进行聚合结果查询，比如 INTERVAL 子句用于产生相等时间周期的窗口，SLIDING 用以指定窗口向前滑动的时间。每次执行的查询是一个时间窗口，时间窗口随着时间流动向前滑动。在定义连续查询的时候需要指定时间窗口（time window ）大小和每次前向增量时间（forward sliding times）。如图，[t0s, t0e] ，[t1s , t1e]， [t2s, t2e] 是分别是执行三次连续查询的时间窗口范围，窗口的前向滑动的时间范围 sliding time 标识 。查询过滤、聚合等操作按照每个时间窗口为独立的单位执行。当 SLIDING 与 INTERVAL 相等的时候，滑动窗口即为翻转窗口。 -![时间窗口示意图](/img/sql/timewindow-1.png) +![TDengine Database 时间窗口示意图](./timewindow-1.webp) INTERVAL 和 SLIDING 子句需要配合聚合和选择函数来使用。以下 SQL 语句非法： @@ -33,7 +33,7 @@ _ 从 2.1.5.0 版本开始，INTERVAL 语句允许的最短时间间隔调整为 使用整数（布尔值）或字符串来标识产生记录时候设备的状态量。产生的记录如果具有相同的状态量数值则归属于同一个状态窗口，数值改变后该窗口关闭。如下图所示，根据状态量确定的状态窗口分别是[2019-04-28 14:22:07，2019-04-28 14:22:10]和[2019-04-28 14:22:11，2019-04-28 14:22:12]两个。（状态窗口暂不支持对超级表使用） -![时间窗口示意图](/img/sql/timewindow-3.png) +![TDengine Database 时间窗口示意图](./timewindow-3.webp) 使用 STATE_WINDOW 来确定状态窗口划分的列。例如： @@ -45,7 +45,7 @@ SELECT COUNT(*), FIRST(ts), status FROM temp_tb_1 STATE_WINDOW(status); 会话窗口根据记录的时间戳主键的值来确定是否属于同一个会话。如下图所示，如果设置时间戳的连续的间隔小于等于 12 秒，则以下 6 条记录构成 2 个会话窗口，分别是：[2019-04-28 14:22:10，2019-04-28 14:22:30]和[2019-04-28 14:23:10，2019-04-28 14:23:30]。因为 2019-04-28 14:22:30 与 2019-04-28 14:23:10 之间的时间间隔是 40 秒，超过了连续时间间隔（12 秒）。 -![时间窗口示意图](/img/sql/timewindow-2.png) +![TDengine Database 时间窗口示意图](./timewindow-2.webp) 在 tol_value 时间间隔范围内的结果都认为归属于同一个窗口，如果连续的两条记录的时间超过 tol_val，则自动开启下一个窗口。（会话窗口暂不支持对超级表使用） diff --git a/docs-cn/12-taos-sql/09-limit.md b/docs-cn/12-taos-sql/09-limit.md index 3c86a3862174377e6a00d046fb69627c773fe76e..7673e24a83cc1ba5335b11f29803cf9f3eae26e5 100644 --- a/docs-cn/12-taos-sql/09-limit.md +++ b/docs-cn/12-taos-sql/09-limit.md @@ -7,9 +7,9 @@ title: 边界限制 - 数据库名最大长度为 32。 - 表名最大长度为 192，不包括数据库名前缀和分隔符 -- 每行数据最大长度 16k 个字符, 从 2.1.7.0 版本开始，每行数据最大长度 48k 个字符（注意：数据行内每个 BINARY/NCHAR 类型的列还会额外占用 2 个字节的存储位置）。 +- 每行数据最大长度 48KB （注意：数据行内每个 BINARY/NCHAR 类型的列还会额外占用 2 个字节的存储位置）。 - 列名最大长度为 64，最多允许 4096 列，最少需要 2 列，第一列必须是时间戳。注：从 2.1.7.0 版本（不含)以前最多允许 4096 列 -- 标签名最大长度为 64，最多允许 128 个，至少要有 1 个标签，一个表中标签值的总长度不超过 16k 个字符。 +- 标签名最大长度为 64，最多允许 128 个，至少要有 1 个标签，一个表中标签值的总长度不超过 16KB 。 - SQL 语句最大长度 1048576 个字符，也可通过客户端配置参数 maxSQLLength 修改，取值范围 65480 ~ 1048576。 - SELECT 语句的查询结果，最多允许返回 4096 列（语句中的函数调用可能也会占用一些列空间），超限时需要显式指定较少的返回数据列，以避免语句执行报错。注： 2.1.7.0 版本（不含）之前为最多允许 1024 列 - 库的数目，超级表的数目、表的数目，系统不做限制，仅受系统资源限制。 diff --git a/docs-cn/12-taos-sql/12-keywords/index.md b/docs-cn/12-taos-sql/12-keywords/index.md index 608d4e080967cfd97072706cf0963ae669960be6..0b9ec4de862fc6b6ade11e733a0f7b169a79a324 100644 --- a/docs-cn/12-taos-sql/12-keywords/index.md +++ b/docs-cn/12-taos-sql/12-keywords/index.md @@ -23,17 +23,17 @@ title: TDengine 参数限制与保留关键字 去掉了 `` ‘“`\ `` (单双引号、撇号、反斜杠、空格) - 数据库名：不能包含“.”以及特殊字符，不能超过 32 个字符 -- 表名：不能包含“.”以及特殊字符，与所属数据库名一起，不能超过 192 个字符，每行数据最大长度 16k 个字符 -- 表的列名：不能包含特殊字符，不能超过 64 个字符 +- 表名：不能包含“.”以及特殊字符，与所属数据库名一起，不能超过 192 个字节 ，每行数据最大长度 48KB +- 表的列名：不能包含特殊字符，不能超过 64 个字节 - 数据库名、表名、列名，都不能以数字开头，合法的可用字符集是“英文字符、数字和下划线” - 表的列数：不能超过 1024 列，最少需要 2 列，第一列必须是时间戳（从 2.1.7.0 版本开始，改为最多支持 4096 列） -- 记录的最大长度：包括时间戳 8 byte，不能超过 16KB（每个 BINARY/NCHAR 类型的列还会额外占用 2 个 byte 的存储位置） -- 单条 SQL 语句默认最大字符串长度：1048576 byte，但可通过系统配置参数 maxSQLLength 修改，取值范围 65480 ~ 1048576 byte +- 记录的最大长度：包括时间戳 8 字节，不能超过 48KB（每个 BINARY/NCHAR 类型的列还会额外占用 2 个 字节 的存储位置） +- 单条 SQL 语句默认最大字符串长度：1048576 字节，但可通过系统配置参数 maxSQLLength 修改，取值范围 65480 ~ 1048576 字节 - 数据库副本数：不能超过 3 -- 用户名：不能超过 23 个 byte -- 用户密码：不能超过 15 个 byte +- 用户名：不能超过 23 个 字节 +- 用户密码：不能超过 15 个 字节 - 标签(Tags)数量：不能超过 128 个，可以 0 个 -- 标签的总长度：不能超过 16K byte +- 标签的总长度：不能超过 16KB - 记录条数：仅受存储空间限制 - 表的个数：仅受节点个数限制 - 库的个数：仅受节点个数限制 @@ -85,3 +85,44 @@ title: TDengine 参数限制与保留关键字 | CONNECTIONS | HAVING | NOT | SOFFSET | VNODES | | CONNS | ID | NOTNULL | STABLE | WAL | | COPY | IF | NOW | STABLES | WHERE | +| _C0 | _QSTART | _QSTOP | _QDURATION | _WSTART | +| _WSTOP | _WDURATION | + +## 特殊说明 +### TBNAME +`TBNAME` 可以视为超级表中一个特殊的标签，代表子表的表名。 + +获取一个超级表所有的子表名及相关的标签信息： +```mysql +SELECT TBNAME, location FROM meters; + +统计超级表下辖子表数量： +```mysql +SELECT COUNT(TBNAME) FROM meters; +``` + +以上两个查询均只支持在WHERE条件子句中添加针对标签（TAGS）的过滤条件。例如： +```mysql +taos> SELECT TBNAME, location FROM meters; + tbname | location | +================================================================== + d1004 | California.SanFrancisco | + d1003 | California.SanFrancisco | + d1002 | California.LosAngeles | + d1001 | California.LosAngeles | +Query OK, 4 row(s) in set (0.000881s) + +taos> SELECT COUNT(tbname) FROM meters WHERE groupId > 2; + count(tbname) | +======================== + 2 | +Query OK, 1 row(s) in set (0.001091s) +``` +### _QSTART/_QSTOP/_QDURATION +表示查询过滤窗口的起始，结束以及持续时间 (从2.6.0.0版本开始支持) + +### _WSTART/_WSTOP/_WDURATION +窗口切分聚合查询（例如 interval/session window/state window）中表示每个切分窗口的起始，结束以及持续时间（从 2.6.0.0 版本开始支持） + +### _c0 +表示表或超级表的第一列 \ No newline at end of file diff --git a/docs-cn/12-taos-sql/index.md b/docs-cn/12-taos-sql/index.md index 269bc1d2b5ddfa25c42652d8f639bfe2fb1d42e5..cb01b3a918778abc6c7891c1ff185f1db32d3d36 100644 --- a/docs-cn/12-taos-sql/index.md +++ b/docs-cn/12-taos-sql/index.md @@ -7,8 +7,6 @@ description: "TAOS SQL 支持的语法规则、主要查询功能、支持的 SQ TAOS SQL 是用户对 TDengine 进行数据写入和查询的主要工具。TAOS SQL 为了便于用户快速上手，在一定程度上提供与标准 SQL 类似的风格和模式。严格意义上，TAOS SQL 并不是也不试图提供标准的 SQL 语法。此外，由于 TDengine 针对的时序性结构化数据不提供删除功能，因此在 TAO SQL 中不提供数据删除的相关功能。 -TAOS SQL 不支持关键字的缩写，例如 DESCRIBE 不能缩写为 DESC。 - 本章节 SQL 语法遵循如下约定： - <\> 里的内容是用户需要输入的，但不要输入 <\> 本身 @@ -37,4 +35,4 @@ import DocCardList from '@theme/DocCardList'; import {useCurrentSidebarCategory} from '@docusaurus/theme-common'; -``` \ No newline at end of file +``` diff --git a/docs-cn/12-taos-sql/timewindow-1.webp b/docs-cn/12-taos-sql/timewindow-1.webp new file mode 100644 index 0000000000000000000000000000000000000000..82747558e96df752a0010d85be79a4af07e4a1df Binary files /dev/null and b/docs-cn/12-taos-sql/timewindow-1.webp differ diff --git a/docs-cn/12-taos-sql/timewindow-2.webp b/docs-cn/12-taos-sql/timewindow-2.webp new file mode 100644 index 0000000000000000000000000000000000000000..8f1314ae34f7f5c5cca1d3cb80455f555fad38c3 Binary files /dev/null and b/docs-cn/12-taos-sql/timewindow-2.webp differ diff --git a/docs-cn/12-taos-sql/timewindow-3.webp b/docs-cn/12-taos-sql/timewindow-3.webp new file mode 100644 index 0000000000000000000000000000000000000000..5bd16e68e7fd5da6805551e9765975277cd5d4d9 Binary files /dev/null and b/docs-cn/12-taos-sql/timewindow-3.webp differ diff --git a/docs-cn/13-operation/11-optimize.md b/docs-cn/13-operation/11-optimize.md index 1ca9e8c44492a5882613a0b55d959d7abca8b5f6..d06c3cb8f5601a241fd63d73ef1a5a6165eb1617 100644 --- a/docs-cn/13-operation/11-optimize.md +++ b/docs-cn/13-operation/11-optimize.md @@ -74,7 +74,7 @@ TDengine 集群中加入一个新的 dnode 时，涉及集群相关的一些参 - offlineThreshold: dnode 离线阈值，超过该时间将导致该 dnode 从集群中删除。单位为秒，默认值：86400\*10（即 10 天）。 - statusInterval: dnode 向 mnode 报告状态时长。单位为秒，默认值：1。 - maxTablesPerVnode: 每个 vnode 中能够创建的最大表个数。默认值：1000000。 -- maxVgroupsPerDb: 每个数据库中能够使用的最大 vgroup 个数。 +- maxVgroupsPerDb: 每个数据库中能够使用的最大 vgroup 个数。0：自动配置为 CPU 的核数。默认值：0。 - arbitrator: 系统中裁决器的 endpoint，缺省为空。 - timezone、locale、charset 的配置见客户端配置。（2.0.20.0 及以上的版本里，集群中加入新节点已不要求 locale 和 charset 参数取值一致） - balance：是否启用负载均衡。0：否，1：是。默认值：1。 diff --git a/docs-cn/14-reference/02-rest-api/02-rest-api.mdx b/docs-cn/14-reference/02-rest-api/02-rest-api.mdx index c7680ab3e9e109dbb328711f62881283241444fb..43099319b9c5bb1420c199cfa9f7def0b2c44d3d 100644 --- a/docs-cn/14-reference/02-rest-api/02-rest-api.mdx +++ b/docs-cn/14-reference/02-rest-api/02-rest-api.mdx @@ -16,7 +16,7 @@ RESTful 接口不依赖于任何 TDengine 的库，因此客户端不需要安 在已经安装 TDengine 服务器端的情况下，可以按照如下方式进行验证。 -下面以 Ubuntu 环境中使用 curl 工具（确认已经安装）来验证 RESTful 接口的正常。 +下面以 Ubuntu 环境中使用 curl 工具（确认已经安装）来验证 RESTful 接口的正常，验证前请确认 taosAdapter 服务已开启，在 Linux 系统上此服务默认由 systemd 管理，使用命令 `systemctl start taosadapter` 启动。 下面示例是列出所有的数据库，请把 h1.taosdata.com 和 6041（缺省值）替换为实际运行的 TDengine 服务 FQDN 和端口号： diff --git a/docs-cn/14-reference/03-connector/03-connector.mdx b/docs-cn/14-reference/03-connector/03-connector.mdx index c0e714f148a7821e070be38a5484484fdd747e9a..7a4a85276ef4bb4ab829250fcf67076962dbb871 100644 --- a/docs-cn/14-reference/03-connector/03-connector.mdx +++ b/docs-cn/14-reference/03-connector/03-connector.mdx @@ -4,7 +4,7 @@ title: 连接器 TDengine 提供了丰富的应用程序开发接口，为了便于用户快速开发自己的应用，TDengine 支持了多种编程语言的连接器，其中官方连接器包括支持 C/C++、Java、Python、Go、Node.js、C# 和 Rust 的连接器。这些连接器支持使用原生接口（taosc）和 REST 接口（部分语言暂不支持）连接 TDengine 集群。社区开发者也贡献了多个非官方连接器，例如 ADO.NET 连接器、Lua 连接器和 PHP 连接器。 -![image-connector](/img/connector.png) +![TDengine Database connector architecture](./connector.webp) ## 支持的平台 diff --git a/docs-cn/14-reference/03-connector/connector.webp b/docs-cn/14-reference/03-connector/connector.webp new file mode 100644 index 0000000000000000000000000000000000000000..040cf5c26c726b345b2e0e5363dd3c677bec61be Binary files /dev/null and b/docs-cn/14-reference/03-connector/connector.webp differ diff --git a/docs-cn/14-reference/03-connector/java.mdx b/docs-cn/14-reference/03-connector/java.mdx index 55abf84fd50fe1c4b5b6a07b28731a00d4534a05..ddab9e5f24c64e51e82cad6e299f3ea0d741b349 100644 --- a/docs-cn/14-reference/03-connector/java.mdx +++ b/docs-cn/14-reference/03-connector/java.mdx @@ -11,7 +11,7 @@ import TabItem from '@theme/TabItem'; `taos-jdbcdriver` 是 TDengine 的官方 Java 语言连接器，Java 开发人员可以通过它开发存取 TDengine 数据库的应用软件。`taos-jdbcdriver` 实现了 JDBC driver 标准的接口，并提供两种形式的连接器。一种是通过 TDengine 客户端驱动程序（taosc）原生连接 TDengine 实例，支持数据写入、查询、订阅、schemaless 接口和参数绑定接口等功能，一种是通过 taosAdapter 提供的 REST 接口连接 TDengine 实例（2.4.0.0 及更高版本）。REST 连接实现的功能集合和原生连接有少量不同。 -![tdengine-connector](tdengine-jdbc-connector.png) +![TDengine Database Connector Java](tdengine-jdbc-connector.webp) 上图显示了两种 Java 应用使用连接器访问 TDengine 的两种方式： @@ -93,8 +93,8 @@ Maven 项目中，在 pom.xml 中添加以下依赖： 可以通过下载 TDengine 的源码，自己编译最新版本的 Java connector ```shell -git clone https://github.com/taosdata/TDengine.git -cd TDengine/src/connector/jdbc +git clone https://github.com/taosdata/taos-connector-jdbc.git +cd taos-connector-jdbc mvn clean install -Dmaven.test.skip=true ``` @@ -199,6 +199,7 @@ url 中的配置参数如下： - user：登录 TDengine 用户名，默认值 'root'。 - password：用户登录密码，默认值 'taosdata'。 - batchfetch: true：在执行查询时批量拉取结果集；false：逐行拉取结果集。默认值为：false。逐行拉取结果集使用 HTTP 方式进行数据传输。从 taos-jdbcdriver-2.0.38 和 TDengine 2.4.0.12 版本开始，JDBC REST 连接增加批量拉取数据功能。taos-jdbcdriver 与 TDengine 之间通过 WebSocket 连接进行数据传输。相较于 HTTP，WebSocket 可以使 JDBC REST 连接支持大数据量查询，并提升查询性能。 +- charset: 当开启批量拉取数据时，指定解析字符串数据的字符集。 - batchErrorIgnore：true：在执行 Statement 的 executeBatch 时，如果中间有一条 SQL 执行失败，继续执行下面的 SQL 了。false：不再执行失败 SQL 后的任何语句。默认值为：false。 **注意**：部分配置项（比如：locale、timezone）在 REST 连接中不生效。 @@ -208,10 +209,10 @@ url 中的配置参数如下： - 与原生连接方式不同，REST 接口是无状态的。在使用 JDBC REST 连接时，需要在 SQL 中指定表、超级表的数据库名称。例如： ```sql -INSERT INTO test.t1 USING test.weather (ts, temperature) TAGS('beijing') VALUES(now, 24.6); +INSERT INTO test.t1 USING test.weather (ts, temperature) TAGS('California.SanFrancisco') VALUES(now, 24.6); ``` -- 从 taos-jdbcdriver-2.0.36 和 TDengine 2.2.0.0 版本开始，如果在 url 中指定了 dbname，那么，JDBC REST 连接会默认使用/rest/sql/dbname 作为 restful 请求的 url，在 SQL 中不需要指定 dbname。例如：url 为 jdbc:TAOS-RS://127.0.0.1:6041/test，那么，可以执行 sql：insert into t1 using weather(ts, temperature) tags('beijing') values(now, 24.6); +- 从 taos-jdbcdriver-2.0.36 和 TDengine 2.2.0.0 版本开始，如果在 url 中指定了 dbname，那么，JDBC REST 连接会默认使用/rest/sql/dbname 作为 restful 请求的 url，在 SQL 中不需要指定 dbname。例如：url 为 jdbc:TAOS-RS://127.0.0.1:6041/test，那么，可以执行 sql：insert into t1 using weather(ts, temperature) tags('California.SanFrancisco') values(now, 24.6); ::: @@ -260,7 +261,7 @@ properties 中的配置参数如下： - TSDBDriver.PROPERTY_KEY_BATCH_LOAD: true：在执行查询时批量拉取结果集；false：逐行拉取结果集。默认值为：false。 - TSDBDriver.PROPERTY_KEY_BATCH_ERROR_IGNORE：true：在执行 Statement 的 executeBatch 时，如果中间有一条 SQL 执行失败，继续执行下面的 sq 了。false：不再执行失败 SQL 后的任何语句。默认值为：false。 - TSDBDriver.PROPERTY_KEY_CONFIG_DIR：仅在使用 JDBC 原生连接时生效。客户端配置文件目录路径，Linux OS 上默认值 `/etc/taos`，Windows OS 上默认值 `C:/TDengine/cfg`。 -- TSDBDriver.PROPERTY_KEY_CHARSET：仅在使用 JDBC 原生连接时生效。 客户端使用的字符集，默认值为系统字符集。 +- TSDBDriver.PROPERTY_KEY_CHARSET：客户端使用的字符集，默认值为系统字符集。 - TSDBDriver.PROPERTY_KEY_LOCALE：仅在使用 JDBC 原生连接时生效。 客户端语言环境，默认值系统当前 locale。 - TSDBDriver.PROPERTY_KEY_TIME_ZONE：仅在使用 JDBC 原生连接时生效。 客户端使用的时区，默认值为系统当前时区。 - 此外对 JDBC 原生连接，通过指定 URL 和 Properties 还可以指定其他参数，比如日志级别、SQL 长度等。更多详细配置请参考[客户端配置](/reference/config/#仅客户端适用)。 @@ -348,7 +349,7 @@ JDBC 连接器可能报错的错误码包括 3 种：JDBC driver 本身的报错 具体的错误码请参考： -- [TDengine Java Connector](https://github.com/taosdata/TDengine/blob/develop/src/connector/jdbc/src/main/java/com/taosdata/jdbc/TSDBErrorNumbers.java) +- [TDengine Java Connector](https://github.com/taosdata/taos-connector-jdbc/blob/main/src/main/java/com/taosdata/jdbc/TSDBErrorNumbers.java) - [TDengine_ERROR_CODE](https://github.com/taosdata/TDengine/blob/develop/src/inc/taoserror.h) ### 通过参数绑定写入数据 @@ -563,7 +564,7 @@ public class ParameterBindingDemo { // set table name pstmt.setTableName("t5_" + i); // set tags - pstmt.setTagNString(0, "北京-abc"); + pstmt.setTagNString(0, "California.SanFrancisco"); // set columns ArrayList tsList = new ArrayList<>(); @@ -574,7 +575,7 @@ public class ParameterBindingDemo { ArrayList f1List = new ArrayList<>(); for (int j = 0; j < numOfRow; j++) { - f1List.add("北京-abc"); + f1List.add("California.LosAngeles"); } pstmt.setNString(1, f1List, BINARY_COLUMN_SIZE); @@ -633,7 +634,7 @@ public class SchemalessInsertTest { private static final String host = "127.0.0.1"; private static final String lineDemo = "st,t1=3i64,t2=4f64,t3=\"t3\" c1=3i64,c3=L\"passit\",c2=false,c4=4f64 1626006833639000000"; private static final String telnetDemo = "stb0_0 1626006833 4 host=host0 interface=eth0"; - private static final String jsonDemo = "{\"metric\": \"meter_current\",\"timestamp\": 1346846400,\"value\": 10.3, \"tags\": {\"groupid\": 2, \"location\": \"Beijing\", \"id\": \"d1001\"}}"; + private static final String jsonDemo = "{\"metric\": \"meter_current\",\"timestamp\": 1346846400,\"value\": 10.3, \"tags\": {\"groupid\": 2, \"location\": \"California.SanFrancisco\", \"id\": \"d1001\"}}"; public static void main(String[] args) throws SQLException { final String url = "jdbc:TAOS://" + host + ":6030/?user=root&password=taosdata"; diff --git a/docs-cn/14-reference/03-connector/node.mdx b/docs-cn/14-reference/03-connector/node.mdx index 12345fa9fe995c41828df07703f0efb61a2e029d..9f2bed9e97cb33aeabfce3d69dc3774931b426c0 100644 --- a/docs-cn/14-reference/03-connector/node.mdx +++ b/docs-cn/14-reference/03-connector/node.mdx @@ -14,7 +14,6 @@ import NodeInfluxLine from "../../07-develop/03-insert-data/_js_line.mdx"; import NodeOpenTSDBTelnet from "../../07-develop/03-insert-data/_js_opts_telnet.mdx"; import NodeOpenTSDBJson from "../../07-develop/03-insert-data/_js_opts_json.mdx"; import NodeQuery from "../../07-develop/04-query-data/_js.mdx"; -import NodeAsyncQuery from "../../07-develop/04-query-data/_js_async.mdx"; `td2.0-connector` 和 `td2.0-rest-connector` 是 TDengine 的官方 Node.js 语言连接器。Node.js 开发人员可以通过它开发可以存取 TDengine 集群数据的应用软件。 @@ -189,14 +188,8 @@ let cursor = conn.cursor(); ### 查询数据 -#### 同步查询 - -#### 异步查询 - - - ## 更多示例程序 | 示例程序 | 示例程序描述 | diff --git a/docs-cn/14-reference/03-connector/php.mdx b/docs-cn/14-reference/03-connector/php.mdx new file mode 100644 index 0000000000000000000000000000000000000000..f150aed4c8a6ba855d5e830a2944a6d6f88ab0f5 --- /dev/null +++ b/docs-cn/14-reference/03-connector/php.mdx @@ -0,0 +1,150 @@ +--- +sidebar_position: 1 +sidebar_label: PHP +title: PHP Connector +--- + +`php-tdengine` 是由社区贡献的 PHP 连接器扩展，还特别支持了 Swoole 协程化。 + +PHP 连接器依赖 TDengine 客户端驱动。 + +项目地址： + +TDengine 服务端或客户端安装后，`taos.h` 位于： + +- Linux：`/usr/local/taos/include` +- Windows：`C:\TDengine\include` + +TDengine 客户端驱动的动态库位于： + +- Linux: `/usr/local/taos/driver/libtaos.so` +- Windows: `C:\TDengine\taos.dll` + +## 支持的平台 + +* Windows、Linux、MacOS + +* PHP >= 7.4 + +* TDengine >= 2.0 + +* Swoole >= 4.8 (可选) + +## 支持的版本 + +TDengine 客户端驱动的版本号与 TDengine 服务端的版本号是一一对应的强对应关系，建议使用与 TDengine 服务端完全相同的客户端驱动。虽然低版本的客户端驱动在前三段版本号一致（即仅第四段版本号不同）的情况下也能够与高版本的服务端相兼容，但这并非推荐用法。强烈不建议使用高版本的客户端驱动访问低版本的服务端。 + +## 安装步骤 + +### 安装 TDengine 客户端驱动 + +TDengine 客户端驱动的安装请参考 [安装指南](/reference/connector#安装步骤) + +### 编译安装 php-tdengine + +**下载代码并解压：** + +```shell +curl -L -o php-tdengine.tar.gz https://github.com/Yurunsoft/php-tdengine/archive/refs/tags/v1.0.2.tar.gz \ +&& mkdir php-tdengine \ +&& tar -xzf php-tdengine.tar.gz -C php-tdengine --strip-components=1 +``` + +> 版本 `v1.0.2` 可替换为任意更新的版本，可在 [TDengine PHP Connector 发布历史](https://github.com/Yurunsoft/php-tdengine/releases)。 + +**非 Swoole 环境：** + +```shell +phpize && ./configure && make -j && make install +``` + +**手动指定 tdengine 目录：** + +```shell +phpize && ./configure --with-tdengine-dir=/usr/local/Cellar/tdengine/2.4.0.0 && make -j && make install +``` + +> `--with-tdengine-dir=` 后跟上 tdengine 目录。 +> 适用于默认找不到的情况，或者 MacOS 系统用户。 + +**Swoole 环境：** + +```shell +phpize && ./configure --enable-swoole && make -j && make install +``` + +**启用扩展：** + +方法一：在 `php.ini` 中加入 `extension=tdengine` + +方法二：运行带参数 `php -dextension=tdengine test.php` + +## 示例程序 + +本节展示了使用客户端驱动访问 TDengine 集群的常见访问方式的示例代码。 + +> 所有错误都会抛出异常: `TDengine\Exception\TDengineException` + +### 建立连接 + +
+建立连接 + +```c +{{#include docs-examples/php/connect.php}} +``` + +
+ +### 插入数据 + +
+插入数据 + +```c +{{#include docs-examples/php/insert.php}} +``` + +
+ +### 同步查询 + +
+同步查询 + +```c +{{#include docs-examples/php/query.php}} +``` + +
+ +### 参数绑定 + +
+参数绑定 + +```c +{{#include docs-examples/php/insert_stmt.php}} +``` + +
+ +## 常量 + +| 常量 | 说明 | +| ------------ | ------------ +| `TDengine\TSDB_DATA_TYPE_NULL` | null | +| `TDengine\TSDB_DATA_TYPE_BOOL` | bool | +| `TDengine\TSDB_DATA_TYPE_TINYINT` | tinyint | +| `TDengine\TSDB_DATA_TYPE_SMALLINT` | smallint | +| `TDengine\TSDB_DATA_TYPE_INT` | int | +| `TDengine\TSDB_DATA_TYPE_BIGINT` | bigint | +| `TDengine\TSDB_DATA_TYPE_FLOAT` | float | +| `TDengine\TSDB_DATA_TYPE_DOUBLE` | double | +| `TDengine\TSDB_DATA_TYPE_BINARY` | binary | +| `TDengine\TSDB_DATA_TYPE_TIMESTAMP` | timestamp | +| `TDengine\TSDB_DATA_TYPE_NCHAR` | nchar | +| `TDengine\TSDB_DATA_TYPE_UTINYINT` | utinyint | +| `TDengine\TSDB_DATA_TYPE_USMALLINT` | usmallint | +| `TDengine\TSDB_DATA_TYPE_UINT` | uint | +| `TDengine\TSDB_DATA_TYPE_UBIGINT` | ubigint | diff --git a/docs-cn/14-reference/03-connector/python.mdx b/docs-cn/14-reference/03-connector/python.mdx index 6608fb7bd21ab586216d82c1b137830576a1e432..828e0a4abb758a72c3a127be13dd89c4d86186f4 100644 --- a/docs-cn/14-reference/03-connector/python.mdx +++ b/docs-cn/14-reference/03-connector/python.mdx @@ -199,10 +199,9 @@ curl -u root:taosdata http://:/rest/sql -d "select server_version()" `connect()` 函数的所有参数都是可选的关键字参数。下面是连接参数的具体说明： -- `host`： 要连接的主机。默认是 localhost。 +- `url`： taosAdapter REST 服务的 URL。默认是 。 - `user`： TDenigne 用户名。默认是 root。 - `password`： TDeingine 用户密码。默认是 taosdata。 -- `port`: taosAdapter REST 服务监听端口。默认是 6041. - `timeout`: HTTP 请求超时时间。单位为秒。默认为 `socket._GLOBAL_DEFAULT_TIMEOUT`。 一般无需配置。 diff --git a/docs-cn/14-reference/03-connector/tdengine-jdbc-connector.png b/docs-cn/14-reference/03-connector/tdengine-jdbc-connector.png deleted file mode 100644 index 1cb8401ea30b01d8db652ed4ea70ecc511de7461..0000000000000000000000000000000000000000 Binary files a/docs-cn/14-reference/03-connector/tdengine-jdbc-connector.png and /dev/null differ diff --git a/docs-cn/14-reference/03-connector/tdengine-jdbc-connector.webp b/docs-cn/14-reference/03-connector/tdengine-jdbc-connector.webp new file mode 100644 index 0000000000000000000000000000000000000000..0956d6005ffc5e90727d49d7566158affdda09c2 Binary files /dev/null and b/docs-cn/14-reference/03-connector/tdengine-jdbc-connector.webp differ diff --git a/docs-cn/14-reference/04-taosadapter.md b/docs-cn/14-reference/04-taosadapter.md index 90a31ec94c94559311e2c91cd34f75af7e87e9a0..6e259391d40acfd48d8db8db3246ad2196ce0520 100644 --- a/docs-cn/14-reference/04-taosadapter.md +++ b/docs-cn/14-reference/04-taosadapter.md @@ -24,7 +24,7 @@ taosAdapter 提供以下功能： ## taosAdapter 架构图 -![taosAdapter Architecture](taosAdapter-architecture.png) +![TDengine Database taosAdapter Architecture](taosAdapter-architecture.webp) ## taosAdapter 部署方法 diff --git a/docs-cn/14-reference/05-taosbenchmark.md b/docs-cn/14-reference/05-taosbenchmark.md index f34d12a5462a7c078b9c237ee8be19a86ed250a7..6b694543b1db435f507b5e2fb325cebe76261b48 100644 --- a/docs-cn/14-reference/05-taosbenchmark.md +++ b/docs-cn/14-reference/05-taosbenchmark.md @@ -21,7 +21,7 @@ taosBenchmark 有两种安装方式: ### 配置和运行方式 -taosBenchmark 支持两种配置方式：[命令行参数](#命令行参数详解) 和 [JSON 配置文件](#配置文件参数详解)。这两种方式是互斥的，在使用配置文件时只能使用一个命令行参数 `-f ` 指定配置文件。在使用命令行参数运行 taosBenchmark 并控制其行为时则不能使用 `-f` 参数而要用其它参数来进行配置。除此之外，taosBenchmark 还提供了一种特殊的运行方式，即无参数运行。 +taosBenchmark 需要在操作系统的终端执行,该工具支持两种配置方式：[命令行参数](#命令行参数详解) 和 [JSON 配置文件](#配置文件参数详解)。这两种方式是互斥的，在使用配置文件时只能使用一个命令行参数 `-f ` 指定配置文件。在使用命令行参数运行 taosBenchmark 并控制其行为时则不能使用 `-f` 参数而要用其它参数来进行配置。除此之外，taosBenchmark 还提供了一种特殊的运行方式，即无参数运行。 taosBenchmark 支持对 TDengine 做完备的性能测试，其所支持的 TDengine 功能分为三大类：写入、查询和订阅。这三种功能之间是互斥的，每次运行 taosBenchmark 只能选择其中之一。值得注意的是，所要测试的功能类型在使用命令行配置方式时是不可配置的，命令行配置方式只能测试写入性能。若要测试 TDengine 的查询和订阅性能，必须使用配置文件的方式，通过配置文件中的参数 `filetype` 指定所要测试的功能类型。 diff --git a/docs-cn/14-reference/06-taosdump.md b/docs-cn/14-reference/06-taosdump.md index 7131493ec9439225d8047288ed86026c887f0aac..3a9f2e9acd215be102991a1d91fba285ef6315bb 100644 --- a/docs-cn/14-reference/06-taosdump.md +++ b/docs-cn/14-reference/06-taosdump.md @@ -38,7 +38,7 @@ taosdump 有两种安装方式: :::tip - taosdump 1.4.1 之后的版本提供 `-I` 参数，用于解析 avro 文件 schema 和数据，如果指定 `-s` 参数将只解析 schema。 -- taosdump 1.4.2 之后的备份使用 `-B` 参数指定的批次数，默认值为 16384，如果在某些环境下由于网络速度或磁盘性能不足导致 "Error actual dump .. batch .." 可以通过 `-B` 参数挑战为更小的值进行尝试。 +- taosdump 1.4.2 之后的备份使用 `-B` 参数指定的批次数，默认值为 16384，如果在某些环境下由于网络速度或磁盘性能不足导致 "Error actual dump .. batch .." 可以通过 `-B` 参数调整为更小的值进行尝试。 ::: diff --git a/docs-cn/14-reference/07-tdinsight/assets/TDinsight-1-cluster-status.png b/docs-cn/14-reference/07-tdinsight/assets/TDinsight-1-cluster-status.png deleted file mode 100644 index 4708f836feb21980f2db7fed4a55f799b23a6ec1..0000000000000000000000000000000000000000 Binary files a/docs-cn/14-reference/07-tdinsight/assets/TDinsight-1-cluster-status.png and /dev/null differ diff --git a/docs-cn/14-reference/07-tdinsight/assets/TDinsight-1-cluster-status.webp b/docs-cn/14-reference/07-tdinsight/assets/TDinsight-1-cluster-status.webp new file mode 100644 index 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0000000000000000000000000000000000000000..49f1d88f4ad93286cd8582536e82b4dcc4ff271b Binary files /dev/null and b/docs-cn/14-reference/07-tdinsight/assets/tdengine_dashboard.webp differ diff --git a/docs-cn/14-reference/07-tdinsight/index.md b/docs-cn/14-reference/07-tdinsight/index.md index a554d7ee6b36797940282fa8401df2f22c4cf579..5990a831b8bc1788deaddfb38f717f2723969362 100644 --- a/docs-cn/14-reference/07-tdinsight/index.md +++ b/docs-cn/14-reference/07-tdinsight/index.md @@ -233,33 +233,33 @@ sudo systemctl enable grafana-server 指向 **Configurations** -> **Data Sources** 菜单，然后点击 **Add data source** 按钮。 -![添加数据源按钮](./assets/howto-add-datasource-button.png) +![TDengine Database TDinsight 添加数据源按钮](./assets/howto-add-datasource-button.webp) 搜索并选择**TDengine**。 -![添加数据源](./assets/howto-add-datasource-tdengine.png) +![TDengine Database TDinsight 添加数据源](./assets/howto-add-datasource-tdengine.webp) 配置 TDengine 数据源。 -![数据源配置](./assets/howto-add-datasource.png) +![TDengine Database TDinsight 数据源配置](./assets/howto-add-datasource.webp) 保存并测试，正常情况下会报告 'TDengine Data source is working'。 -![数据源测试](./assets/howto-add-datasource-test.png) +![TDengine Database TDinsight 数据源测试](./assets/howto-add-datasource-test.webp) ### 导入仪表盘 指向 **+** / **Create** - **import**（或 `/dashboard/import` url）。 -![导入仪表盘和配置](./assets/import_dashboard.png) +![TDengine Database TDinsight 导入仪表盘和配置](./assets/import_dashboard.webp) 在 **Import via grafana.com** 位置键入仪表盘 ID `15167` 并 **Load**。 -![通过 grafana.com 导入](./assets/import-dashboard-15167.png) +![通过 grafana.com 导入](./assets/import-dashboard-15167.webp) 导入完成后，TDinsight 的完整页面视图如下所示。 -![显示](./assets/TDinsight-full.png) +![TDengine Database TDinsight 显示](./assets/TDinsight-full.webp) ## TDinsight 仪表盘详细信息 @@ -269,7 +269,7 @@ TDinsight 仪表盘旨在提供 TDengine 相关资源使用情况[dnodes, mnodes ### 集群状态 -![tdinsight-mnodes-overview](./assets/TDinsight-1-cluster-status.png) +![TDengine Database TDinsight mnodes overview](./assets/TDinsight-1-cluster-status.webp) 这部分包括集群当前信息和状态，告警信息也在此处（从左到右，从上到下）。 @@ -289,7 +289,7 @@ TDinsight 仪表盘旨在提供 TDengine 相关资源使用情况[dnodes, mnodes ### DNodes 状态 -![tdinsight-mnodes-overview](./assets/TDinsight-2-dnodes.png) +![TDengine Database TDinsight mnodes overview](./assets/TDinsight-2-dnodes.webp) - **DNodes Status**：`show dnodes` 的简单表格视图。 - **DNodes Lifetime**：从创建 dnode 开始经过的时间。 @@ -298,14 +298,14 @@ TDinsight 仪表盘旨在提供 TDengine 相关资源使用情况[dnodes, mnodes ### MNode 概述 -![tdinsight-mnodes-overview](./assets/TDinsight-3-mnodes.png) +![TDengine Database TDinsight mnodes overview](./assets/TDinsight-3-mnodes.webp) 1. **MNodes Status**：`show mnodes` 的简单表格视图。 2. **MNodes Number**：类似于`DNodes Number`，MNodes 数量变化。 ### 请求 -![tdinsight-requests](./assets/TDinsight-4-requests.png) +![TDengine Database TDinsight requests](./assets/TDinsight-4-requests.webp) 1. **Requests Rate(Inserts per Second)**：平均每秒插入次数。 2. **Requests (Selects)**：查询请求数及变化率（count of second）。 @@ -313,7 +313,7 @@ TDinsight 仪表盘旨在提供 TDengine 相关资源使用情况[dnodes, mnodes ### 数据库 -![tdinsight-database](./assets/TDinsight-5-database.png) +![TDengine Database TDinsight database](./assets/TDinsight-5-database.webp) 数据库使用情况，对变量 `$database` 的每个值即每个数据库进行重复多行展示。 @@ -325,7 +325,7 @@ TDinsight 仪表盘旨在提供 TDengine 相关资源使用情况[dnodes, mnodes ### DNode 资源使用情况 -![dnode-usage](./assets/TDinsight-6-dnode-usage.png) +![TDengine Database TDinsight dnode-usage](./assets/TDinsight-6-dnode-usage.webp) 数据节点资源使用情况展示，对变量 `$fqdn` 即每个数据节点进行重复多行展示。包括： @@ -346,13 +346,13 @@ TDinsight 仪表盘旨在提供 TDengine 相关资源使用情况[dnodes, mnodes ### 登录历史 -![登录历史](./assets/TDinsight-7-login-history.png) +![TDengine Database TDinsight 登录历史](./assets/TDinsight-7-login-history.webp) 目前只报告每分钟登录次数。 ### 监控 taosAdapter -![taosadapter](./assets/TDinsight-8-taosadapter.png) +![TDengine Database TDinsight monitor taosadapter](./assets/TDinsight-8-taosadapter.webp) 支持监控 taosAdapter 请求统计和状态详情。包括： diff --git a/docs-cn/14-reference/08-taos-shell.md b/docs-cn/14-reference/08-taos-shell.md index d4a516d2d2c30c7ae7122a04006b646aa1103635..5778df7233e6997f9c0f71e4aa5b81d462746c19 100644 --- a/docs-cn/14-reference/08-taos-shell.md +++ b/docs-cn/14-reference/08-taos-shell.md @@ -8,7 +8,7 @@ TDengine 命令行程序（以下简称 TDengine CLI）是用户操作 TDengine ## 安装 -如果在 TDengine 服务器端执行，无需任何安装，已经自动安装好 TDengine CLI。如果要在非 TDengine 服务器端运行，需要安装 TDengine 客户端驱动安装包，具体安装，请参考 [连接器](/reference/connector/)。 +如果在 TDengine 服务器端执行，无需任何安装，已经自动安装好 TDengine CLI。如果要在非 TDengine 服务器端运行，需要安装 TDengine 客户端驱动安装包，具体安装，请参考 [安装客户端驱动](/reference/connector/#install-client-driver/#安装客户端驱动)。 ## 执行 diff --git a/docs-cn/14-reference/12-config/index.md b/docs-cn/14-reference/12-config/index.md index cbb3833b5bb170720c2aa7bea6687a50feeae7d5..2d1866d5dd1874164d03ffdfb382010c8345ad63 100644 --- a/docs-cn/14-reference/12-config/index.md +++ b/docs-cn/14-reference/12-config/index.md @@ -80,7 +80,7 @@ taos --dump-config | 补充说明 | RESTful 服务在 2.4.0.0 之前（不含）由 taosd 提供，默认端口为 6041; 在 2.4.0.0 及后续版本由 taosAdapter，默认端口为 6041 | :::note -对于端口，TDengine 会使用从 serverPort 起 13 个连续的 TCP 和 UDP 端口号，请务必在防火墙打开。因此如果是缺省配置，需要打开从 6030 到 6042 共 13 个端口，而且必须 TCP 和 UDP 都打开。（详细的端口情况请参见下表） +确保集群中所有主机在端口 6030-6042 上的 TCP/UDP 协议能够互通。（详细的端口情况请参见下表） ::: | 协议 | 默认端口 | 用途说明 | 修改方法 | | :--- | :-------- | :---------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------- | @@ -134,7 +134,7 @@ taos --dump-config | 适用范围 | 仅服务端适用 | | 含义 | 服务器内部的系统监控开关。监控主要负责收集物理节点的负载状况，包括 CPU、内存、硬盘、网络带宽、HTTP 请求量的监控记录，记录信息存储在`LOG`库中。 | | 取值范围 | 0：关闭监控服务， 1：激活监控服务。 | -| 缺省值 | 0 | +| 缺省值 | 1 | ### monitorInterval @@ -590,7 +590,7 @@ charset 的有效值是 UTF-8。 | 适用范围 | 仅服务端适用 | | 含义 | 每个 DB 中 能够使用的最大 vnode 个数 | | 取值范围 | 0-8192 | -| 缺省值 | | +| 缺省值 | 0 | ### maxTablesPerVnode diff --git a/docs-cn/14-reference/13-schemaless/13-schemaless.md b/docs-cn/14-reference/13-schemaless/13-schemaless.md index 4de310c248d7763690acef80cdca1c50f609d63b..f2712f2814593bddd65401cb129c8c58ee55a316 100644 --- a/docs-cn/14-reference/13-schemaless/13-schemaless.md +++ b/docs-cn/14-reference/13-schemaless/13-schemaless.md @@ -82,7 +82,7 @@ st,t1=3,t2=4,t3=t3 c1=3i64,c3="passit",c2=false,c4=4f64 1626006833639000000 :::tip 无模式所有的处理逻辑，仍会遵循 TDengine 对数据结构的底层限制，例如每行数据的总长度不能超过 -16k 字节。这方面的具体限制约束请参见 [TAOS SQL 边界限制](/taos-sql/limit) +48KB。这方面的具体限制约束请参见 [TAOS SQL 边界限制](/taos-sql/limit) ::: diff --git a/docs-cn/14-reference/taosAdapter-architecture.png b/docs-cn/14-reference/taosAdapter-architecture.png deleted file mode 100644 index 08a9018553aae6f86b42d127b372d0cecfa9bdf8..0000000000000000000000000000000000000000 Binary files a/docs-cn/14-reference/taosAdapter-architecture.png and /dev/null differ diff --git a/docs-cn/14-reference/taosAdapter-architecture.webp b/docs-cn/14-reference/taosAdapter-architecture.webp new file mode 100644 index 0000000000000000000000000000000000000000..a4162b0a037c06d34191784716c51080b9f8a570 Binary files /dev/null and b/docs-cn/14-reference/taosAdapter-architecture.webp differ diff --git a/docs-cn/20-third-party/01-grafana.mdx b/docs-cn/20-third-party/01-grafana.mdx index 9a4c33d8aceb086ff8ba8dca0f38b1bcbf762005..b54989f0115bc07bef81ca363b5909ffa970c6ad 100644 --- a/docs-cn/20-third-party/01-grafana.mdx +++ b/docs-cn/20-third-party/01-grafana.mdx @@ -3,6 +3,9 @@ sidebar_label: Grafana title: Grafana --- +import Tabs from "@theme/Tabs"; +import TabItem from "@theme/TabItem"; + TDengine 能够与开源数据可视化系统 [Grafana](https://www.grafana.com/) 快速集成搭建数据监测报警系统，整个过程无需任何代码开发，TDengine 中数据表的内容可以在仪表盘(DashBoard)上进行可视化展现。关于 TDengine 插件的使用您可以在[GitHub](https://github.com/taosdata/grafanaplugin/blob/master/README.md)中了解更多。 ## 前置条件 @@ -12,81 +15,112 @@ TDengine 能够与开源数据可视化系统 [Grafana](https://www.grafana.com/ - TDengine 集群已经部署并正常运行 - taosAdapter 已经安装并正常运行。具体细节请参考 [taosAdapter 的使用手册](/reference/taosadapter) +记录以下信息： + +- TDengine 集群 REST API 地址，如：`http://tdengine.local:6041`。 +- TDengine 集群认证信息，可使用用户名及密码。 + ## 安装 Grafana -目前 TDengine 支持 Grafana 7.0 以上的版本。用户可以根据当前的操作系统，到 Grafana 官网下载安装包，并执行安装。下载地址如下：。 +目前 TDengine 支持 Grafana 7.5 以上的版本。用户可以根据当前的操作系统，到 Grafana 官网下载安装包，并执行安装。下载地址如下：。 ## 配置 Grafana -TDengine 的 Grafana 插件托管在 GitHub，可从 下载，当前最新版本为 3.1.4。 +### 安装 Grafana Plugin 并配置数据源 -推荐使用 [`grafana-cli` 命令行工具](https://grafana.com/docs/grafana/latest/administration/cli/) 进行插件安装。 + + -```bash -sudo -u grafana grafana-cli \ - --pluginUrl https://github.com/taosdata/grafanaplugin/releases/download/v3.1.7/tdengine-datasource-3.1.7.zip \ - plugins install tdengine-datasource +将集群信息设置为环境变量；也可以使用 `.env` 文件，请参考 [dotenv](https://hexdocs.pm/dotenvy/dotenv-file-format.html)： + +```sh +export TDENGINE_API=http://tdengine.local:6041 +# user + password +export TDENGINE_USER=user +export TDENGINE_PASSWORD=password + +# 其他环境变量： +# - 是否安装数据源，默认为 true，表示安装 +export TDENGINE_DS_ENABLED=false +# - 数据源名称，默认为 TDengine +export TDENGINE_DS_NAME=TDengine +# - 数据源所属组织 ID，默认为 1 +export GF_ORG_ID=1 +# - 数据源是否可通过管理面板编辑，默认为 0，表示不可编辑 +export TDENGINE_EDITABLE=1 ``` -或者下载到本地并解压到 Grafana 插件目录。 +运行安装脚本： -```bash -GF_VERSION=3.1.7 -wget https://github.com/taosdata/grafanaplugin/releases/download/v$GF_VERSION/tdengine-datasource-$GF_VERSION.zip +```sh +bash -c "$(curl -fsSL https://raw.githubusercontent.com/taosdata/grafanaplugin/master/install.sh)" ``` -以 CentOS 7.2 操作系统为例，将插件包解压到 /var/lib/grafana/plugins 目录下，重新启动 grafana 即可。 +该脚本将自动安装 Grafana 插件并配置数据源。安装完毕后，需要重启 Grafana 服务后生效。 + +保存该脚本并执行 `./install.sh --help` 可查看详细帮助文档。 + + + + +使用 [`grafana-cli` 命令行工具](https://grafana.com/docs/grafana/latest/administration/cli/) 进行插件[安装](https://grafana.com/grafana/plugins/tdengine-datasource/?tab=installation)。 ```bash -sudo unzip tdengine-datasource-$GF_VERSION.zip -d /var/lib/grafana/plugins/ +grafana-cli plugins install tdengine-datasource +# with sudo +sudo -u grafana grafana-cli plugins install tdengine-datasource ``` -:::note -3.1.6 和更早版本未签名，会在 Grafana 7.3+ / 8.x 版本签名检查时失败导致无法加载插件，需要在 grafana.ini 文件中修改配置如下： +或者从 [GitHub](https://github.com/taosdata/grafanaplugin/tags) 或 [Grafana](https://grafana.com/grafana/plugins/tdengine-datasource/?tab=installation) 下载 .zip 文件到本地并解压到 Grafana 插件目录。命令行下载示例如下： -```ini -[plugins] -allow_loading_unsigned_plugins = tdengine-datasource +```bash +GF_VERSION=3.2.2 +# from GitHub +wget https://github.com/taosdata/grafanaplugin/releases/download/v$GF_VERSION/tdengine-datasource-$GF_VERSION.zip +# from Grafana +wget -O tdengine-datasource-$GF_VERSION.zip https://grafana.com/api/plugins/tdengine-datasource/versions/$GF_VERSION/download ``` -::: - -在 Docker 环境下，可以使用如下的环境变量设置自动安装并设置 TDengine 插件： +以 CentOS 7.2 操作系统为例，将插件包解压到 /var/lib/grafana/plugins 目录下，重新启动 grafana 即可。 ```bash -GF_INSTALL_PLUGINS=https://github.com/taosdata/grafanaplugin/releases/download/v3.1.4/tdengine-datasource-3.1.4.zip;tdengine-datasource -GF_PLUGINS_ALLOW_LOADING_UNSIGNED_PLUGINS=tdengine-datasource +sudo unzip tdengine-datasource-$GF_VERSION.zip -d /var/lib/grafana/plugins/ ``` -## 使用 Grafana +如果 Grafana 在 Docker 环境下运行，可以使用如下的环境变量设置自动安装 TDengine 数据源插件： -### 配置数据源 +```bash +GF_INSTALL_PLUGINS=tdengine-datasource +``` -用户可以直接通过 http://localhost:3000 的网址，登录 Grafana 服务器（用户名/密码：admin/admin），通过左侧 `Configuration -> Data Sources` 可以添加数据源，如下图所示： +之后，用户可以直接通过 的网址，登录 Grafana 服务器（用户名/密码：admin/admin），通过左侧 `Configuration -> Data Sources` 可以添加数据源，如下图所示： -![img](/img/connections/add_datasource1.jpg) +![TDengine Database Grafana plugin add data source](./add_datasource1.webp) 点击 `Add data source` 可进入新增数据源页面，在查询框中输入 TDengine 可选择添加，如下图所示： -![img](/img/connections/add_datasource2.jpg) +![TDengine Database Grafana plugin add data source](./add_datasource2.webp) 进入数据源配置页面，按照默认提示修改相应配置即可： -![img](/img/connections/add_datasource3.jpg) +![TDengine Database Grafana plugin add data source](./add_datasource3.webp) -- Host： TDengine 集群中提供 REST 服务 （在 2.4 之前由 taosd 提供， 从 2.4 开始由 taosAdapter 提供）的组件所在服务器的 IP 地址与 TDengine REST 服务的端口号(6041)，默认 http://localhost:6041。 +- Host： TDengine 集群中提供 REST 服务 （在 2.4 之前由 taosd 提供， 从 2.4 开始由 taosAdapter 提供）的组件所在服务器的 IP 地址与 TDengine REST 服务的端口号(6041)，默认 。 - User：TDengine 用户名。 - Password：TDengine 用户密码。 点击 `Save & Test` 进行测试，成功会有如下提示： -![img](/img/connections/add_datasource4.jpg) +![TDengine Database Grafana plugin add data source](./add_datasource4.webp) + + + ### 创建 Dashboard 回到主界面创建 Dashboard，点击 Add Query 进入面板查询页面： -![img](/img/connections/create_dashboard1.jpg) +![TDengine Database Grafana plugin create dashboard](./create_dashboard1.webp) 如上图所示，在 Query 中选中 `TDengine` 数据源，在下方查询框可输入相应 SQL 进行查询，具体说明如下： @@ -96,10 +130,17 @@ GF_PLUGINS_ALLOW_LOADING_UNSIGNED_PLUGINS=tdengine-datasource 按照默认提示查询当前 TDengine 部署所在服务器指定间隔系统内存平均使用量如下： -![img](/img/connections/create_dashboard2.jpg) +![TDengine Database Grafana plugin create dashboard](./create_dashboard2.webp) > 关于如何使用 Grafana 创建相应的监测界面以及更多有关使用 Grafana 的信息，请参考 Grafana 官方的[文档](https://grafana.com/docs/)。 ### 导入 Dashboard -在 2.3.3.0 及以上版本，您可以导入 TDinsight Dashboard (Grafana Dashboard ID： [15167](https://grafana.com/grafana/dashboards/15167)) 作为 TDengine 集群的监控可视化工具。安装和使用说明请见 [TDinsight 用户手册](/reference/tdinsight/)。 +在数据源配置页面，您可以为该数据源导入 TDinsight 面板，作为 TDengine 集群的监控可视化工具。该 Dashboard 已发布在 Grafana：[Dashboard 15167 - TDinsight](https://grafana.com/grafana/dashboards/15167)) 。其他安装方式和相关使用说明请见 [TDinsight 用户手册](/reference/tdinsight/)。 + +使用 TDengine 作为数据源的其他面板，可以[在此搜索](https://grafana.com/grafana/dashboards/?dataSource=tdengine-datasource)。以下是一份不完全列表： + +- [15146](https://grafana.com/grafana/dashboards/15146): 监控多个 TDengine 集群 +- [15155](https://grafana.com/grafana/dashboards/15155): TDengine 告警示例 +- [15167](https://grafana.com/grafana/dashboards/15167): TDinsight +- [16388](https://grafana.com/grafana/dashboards/16388): Telegraf 采集节点信息的数据展示 diff --git a/docs-cn/20-third-party/09-emq-broker.md b/docs-cn/20-third-party/09-emq-broker.md index f57ccb20e6517c51b55093d11fa767bef7d0c9a8..2125545f393819d74fc2c5df1c37784823e33343 100644 --- a/docs-cn/20-third-party/09-emq-broker.md +++ b/docs-cn/20-third-party/09-emq-broker.md @@ -8,31 +8,24 @@ MQTT 是流行的物联网数据传输协议，[EMQX](https://github.com/emqx/em ## 前置条件 要让 EMQX 能正常添加 TDengine 数据源，需要以下几方面的准备工作。 + - TDengine 集群已经部署并正常运行 - taosAdapter 已经安装并正常运行。具体细节请参考 [taosAdapter 的使用手册](/reference/taosadapter) -- 如果使用后文介绍的模拟写入程序，需要安装合适版本的 Node.js，推荐安装 v12。 +- 如果使用后文介绍的模拟写入程序，需要安装合适版本的 Node.js，推荐安装 v12 ## 安装并启动 EMQX 用户可以根据当前的操作系统，到 EMQX 官网下载安装包，并执行安装。下载地址如下：。安装后使用 `sudo emqx start` 或 `sudo systemctl start emqx` 启动 EMQX 服务。 -## 在 TDengine 中为接收 MQTT 数据创建相应数据库和表结构 - -### 以 Docker 安装 TDengine 为例 -```bash - docker exec -it tdengine bash - taos -``` +## 创建数据库和表 -### 创建数据库和表 +在 TDengine 中为接收 MQTT 数据创建相应数据库和表结构。进入 TDengine CLI 复制并执行以下 SQL 语句： ```sql - create database test; - use test; - create table: - - CREATE TABLE sensor_data (ts timestamp, temperature float, humidity float, volume float, PM10 float, pm25 float, SO2 float, NO2 float, CO float, sensor_id NCHAR(255), area TINYINT, coll_time timestamp); +CREATE DATABASE test; +USE test; +CREATE TABLE sensor_data (ts TIMESTAMP, temperature FLOAT, humidity FLOAT, volume FLOAT, pm10 FLOAT, pm25 FLOAT, so2 FLOAT, no2 FLOAT, co FLOAT, sensor_id NCHAR(255), area TINYINT, coll_time TIMESTAMP); ``` 注：表结构以博客[数据传输、存储、展现，EMQX + TDengine 搭建 MQTT 物联网数据可视化平台](https://www.taosdata.com/blog/2020/08/04/1722.html)为例。后续操作均以此博客场景为例进行，请你根据实际应用场景进行修改。 @@ -43,128 +36,92 @@ MQTT 是流行的物联网数据传输协议，[EMQX](https://github.com/emqx/em ### 登录 EMQX Dashboard -使用浏览器打开网址 http://IP:18083 并登录 EMQX Dashboard。初次安装用户名为 `admin` 密码为：`public` +使用浏览器打开网址 http://IP:18083 并登录 EMQX Dashboard。初次安装用户名为 `admin` 密码为：`public`。 -![img](./emqx/login-dashboard.png) +![TDengine Database EMQX login dashboard](./emqx/login-dashboard.webp) ### 创建规则（Rule） 选择左侧“规则引擎（Rule Engine）”中的“规则（Rule）”并点击“创建（Create）”按钮： -![img](./emqx/rule-engine.png) +![TDengine Database EMQX rule engine](./emqx/rule-engine.webp) ### 编辑 SQL 字段 -![img](./emqx/create-rule.png) +复制以下内容输入到 SQL 编辑框： + +```sql +SELECT + payload +FROM + "sensor/data" +``` + +其中 `payload` 代表整个消息体， `sensor/data` 为本规则选取的消息主题。 + +![TDengine Database EMQX create rule](./emqx/create-rule.webp) ### 新增“动作（action handler）” -![img](./emqx/add-action-handler.png) +![TDengine Database EMQX](./emqx/add-action-handler.webp) ### 新增“资源（Resource）” -![img](./emqx/create-resource.png) +![TDengine Database EMQX create resource](./emqx/create-resource.webp) -选择“发送数据到 Web 服务“并点击“新建资源”按钮： +选择“发送数据到 Web 服务”并点击“新建资源”按钮： ### 编辑“资源（Resource）” -选择“发送数据到 Web 服务“并填写 请求 URL 为 运行 taosAdapter 的服务器地址和端口（默认为 6041）。其他属性请保持默认值。 +选择“WebHook”并填写“请求 URL”为 taosAdapter 提供 REST 服务的地址，如果是本地启动的 taosadapter， 那么默认地址为： + +``` +http://127.0.0.1:6041/rest/sql +``` + +其他属性请保持默认值。 -![img](./emqx/edit-resource.png) +![TDengine Database EMQX edit resource](./emqx/edit-resource.webp) ### 编辑“动作（action）” -编辑资源配置，增加 Authorization 认证的键/值配对项，相关文档请参考[ TDengine REST API 文档](https://docs.taosdata.com/reference/rest-api/)。在消息体中输入规则引擎替换模板。 +编辑资源配置，增加 Authorization 认证的键/值配对项。默认用户名和密码对应的 Authorization 值为： +``` +Basic cm9vdDp0YW9zZGF0YQ== +``` +相关文档请参考[ TDengine REST API 文档](/reference/rest-api/)。 + +在消息体中输入规则引擎替换模板: -![img](./emqx/edit-action.png) +```sql +INSERT INTO test.sensor_data VALUES( + now, + ${payload.temperature}, + ${payload.humidity}, + ${payload.volume}, + ${payload.PM10}, + ${payload.pm25}, + ${payload.SO2}, + ${payload.NO2}, + ${payload.CO}, + '${payload.id}', + ${payload.area}, + ${payload.ts} +) +``` +![TDengine Database EMQX edit action](./emqx/edit-action.webp) + +最后点击左下方的 “Create” 按钮，保存规则。 ## 编写模拟测试程序 ```javascript - // mock.js - const mqtt = require('mqtt') - const Mock = require('mockjs') - const EMQX_SERVER = 'mqtt://localhost:1883' - const CLIENT_NUM = 10 - const STEP = 5000 // 模拟采集时间间隔 ms - const AWAIT = 5000 // 每次发送完后休眠时间，防止消息速率过快 ms - const CLIENT_POOL = [] - startMock() - function sleep(timer = 100) { - return new Promise(resolve => { - setTimeout(resolve, timer) - }) - } - async function startMock() { - const now = Date.now() - for (let i = 0; i < CLIENT_NUM; i++) { - const client = await createClient(`mock_client_${i}`) - CLIENT_POOL.push(client) - } - // last 24h every 5s - const last = 24 * 3600 * 1000 - for (let ts = now - last; ts <= now; ts += STEP) { - for (const client of CLIENT_POOL) { - const mockData = generateMockData() - const data = { - ...mockData, - id: client.clientId, - area: 0, - ts, - } - client.publish('sensor/data', JSON.stringify(data)) - } - const dateStr = new Date(ts).toLocaleTimeString() - console.log(`${dateStr} send success.`) - await sleep(AWAIT) - } - console.log(`Done, use ${(Date.now() - now) / 1000}s`) - } - /** - * Init a virtual mqtt client - * @param {string} clientId ClientID - */ - function createClient(clientId) { - return new Promise((resolve, reject) => { - const client = mqtt.connect(EMQX_SERVER, { - clientId, - }) - client.on('connect', () => { - console.log(`client ${clientId} connected`) - resolve(client) - }) - client.on('reconnect', () => { - console.log('reconnect') - }) - client.on('error', (e) => { - console.error(e) - reject(e) - }) - }) - } - /** - * Generate mock data - */ - function generateMockData() { - return { - "temperature": parseFloat(Mock.Random.float(22, 100).toFixed(2)), - "humidity": parseFloat(Mock.Random.float(12, 86).toFixed(2)), - "volume": parseFloat(Mock.Random.float(20, 200).toFixed(2)), - "PM10": parseFloat(Mock.Random.float(0, 300).toFixed(2)), - "pm25": parseFloat(Mock.Random.float(0, 300).toFixed(2)), - "SO2": parseFloat(Mock.Random.float(0, 50).toFixed(2)), - "NO2": parseFloat(Mock.Random.float(0, 50).toFixed(2)), - "CO": parseFloat(Mock.Random.float(0, 50).toFixed(2)), - "area": Mock.Random.integer(0, 20), - "ts": 1596157444170, - } - } +{{#include docs-examples/other/mock.js}} ``` 注意：代码中 CLIENT_NUM 在开始测试中可以先设置一个较小的值，避免硬件性能不能完全处理较大并发客户端数量。 -![img](./emqx/client-num.png) +![TDengine Database EMQX client num](./emqx/client-num.webp) ## 执行测试模拟发送 MQTT 数据 @@ -173,20 +130,19 @@ npm install mqtt mockjs --save --registry=https://registry.npm.taobao.org node mock.js ``` -![img](./emqx/run-mock.png) +![TDengine Database EMQX run-mock](./emqx/run-mock.webp) ## 验证 EMQX 接收到数据 在 EMQX Dashboard 规则引擎界面进行刷新，可以看到有多少条记录被正确接收到： -![img](./emqx/check-rule-matched.png) +![TDengine Database EMQX rule matched](./emqx/check-rule-matched.webp) ## 验证数据写入到 TDengine 使用 TDengine CLI 程序登录并查询相应数据库和表，验证数据是否被正确写入到 TDengine 中： -![img](./emqx/check-result-in-taos.png) +![TDengine Database EMQX result in taos](./emqx/check-result-in-taos.webp) TDengine 详细使用方法请参考 [TDengine 官方文档](https://docs.taosdata.com/)。 EMQX 详细使用方法请参考 [EMQX 官方文档](https://www.emqx.io/docs/zh/v4.4/rule/rule-engine.html)。 - diff --git a/docs-cn/20-third-party/11-kafka.md b/docs-cn/20-third-party/11-kafka.md index d12d5fab75671d8a1e7356e766d0e8979c6519c2..8369806adcfe1b195348e7d60160609cde9150e8 100644 --- a/docs-cn/20-third-party/11-kafka.md +++ b/docs-cn/20-third-party/11-kafka.md @@ -7,17 +7,17 @@ TDengine Kafka Connector 包含两个插件: TDengine Source Connector 和 TDeng ## 什么是 Kafka Connect？ -Kafka Connect 是 Apache Kafka 的一个组件，用于使其它系统，比如数据库、云服务、文件系统等能方便地连接到 Kafka。数据既可以通过 Kafka Connect 从其它系统流向 Kafka, 也可以通过 Kafka Connect 从 Kafka 流向其它系统。从其它系统读数据的插件称为 Source Connector, 写数据到其它系统的插件称为 Sink Connector。Source Connector 和 Sink Connector 都不会直接连接 Kafka Broker，Source Connector 把数据转交给 Kafka Connect。Sink Connector 从 Kafka Connect 接收数据。 +Kafka Connect 是 [Apache Kafka](https://kafka.apache.org/) 的一个组件，用于使其它系统，比如数据库、云服务、文件系统等能方便地连接到 Kafka。数据既可以通过 Kafka Connect 从其它系统流向 Kafka, 也可以通过 Kafka Connect 从 Kafka 流向其它系统。从其它系统读数据的插件称为 Source Connector, 写数据到其它系统的插件称为 Sink Connector。Source Connector 和 Sink Connector 都不会直接连接 Kafka Broker，Source Connector 把数据转交给 Kafka Connect。Sink Connector 从 Kafka Connect 接收数据。 -![](kafka/Kafka_Connect.png) +![TDengine Database Kafka Connector -- Kafka Connect structure](kafka/Kafka_Connect.webp) TDengine Source Connector 用于把数据实时地从 TDengine 读出来发送给 Kafka Connect。TDengine Sink Connector 用于 从 Kafka Connect 接收数据并写入 TDengine。 -![](kafka/streaming-integration-with-kafka-connect.png) +![TDengine Database Kafka Connector -- streaming integration with kafka connect](kafka/streaming-integration-with-kafka-connect.webp) ## 什么是 Confluent？ -Confluent 在 Kafka 的基础上增加很多扩展功能。包括： +[Confluent](https://www.confluent.io/) 在 Kafka 的基础上增加很多扩展功能。包括： 1. Schema Registry 2. REST 代理 @@ -26,7 +26,7 @@ Confluent 在 Kafka 的基础上增加很多扩展功能。包括： 5. 管理和监控 Kafka 的 GUI —— Confluent 控制中心 这些扩展功能有的包含在社区版本的 Confluent 中，有的只有企业版能用。 -![](kafka/confluentPlatform.png) +![TDengine Database Kafka Connector -- Confluent introduction](kafka/confluentPlatform.webp) Confluent 企业版提供了 `confluent` 命令行工具管理各个组件。 @@ -81,10 +81,10 @@ Development: false git clone https://github.com:taosdata/kafka-connect-tdengine.git cd kafka-connect-tdengine mvn clean package -unzip -d $CONFLUENT_HOME/share/confluent-hub-components/ target/components/packages/taosdata-kafka-connect-tdengine-0.1.0.zip +unzip -d $CONFLUENT_HOME/share/java/ target/components/packages/taosdata-kafka-connect-tdengine-*.zip ``` -以上脚本先 clone 项目源码，然后用 Maven 编译打包。打包完成后在 `target/components/packages/` 目录生成了插件的 zip 包。把这个 zip 包解压到安装插件的路径即可。安装插件的路径在配置文件 `$CONFLUENT_HOME/etc/kafka/connect-standalone.properties` 中。默认的路径为 `$CONFLUENT_HOME/share/confluent-hub-components/`。 +以上脚本先 clone 项目源码，然后用 Maven 编译打包。打包完成后在 `target/components/packages/` 目录生成了插件的 zip 包。把这个 zip 包解压到安装插件的路径即可。上面的示例中使用了内置的插件安装路径： `$CONFLUENT_HOME/share/java/`。 ### 用 confluent-hub 安装 @@ -98,7 +98,7 @@ confluent local services start ``` :::note -一定要先安装插件再启动 Confluent, 否则会出现找不到类的错误。Kafka Connect 的日志（默认路径: /tmp/confluent.xxxx/connect/logs/connect.log）中会输出成功安装的插件，据此可判断插件是否安装成功。 +一定要先安装插件再启动 Confluent, 否则加载插件会失败。 ::: :::tip @@ -125,6 +125,61 @@ Control Center is [UP] 清空数据可执行 `rm -rf /tmp/confluent.106668`。 ::: +### 验证各个组件是否启动成功 + +输入命令： + +``` +confluent local services status +``` + +如果各组件都启动成功，会得到如下输出： + +``` +Connect is [UP] +Control Center is [UP] +Kafka is [UP] +Kafka REST is [UP] +ksqlDB Server is [UP] +Schema Registry is [UP] +ZooKeeper is [UP] +``` + +### 验证插件是否安装成功 + +在 Kafka Connect 组件完全启动后，可用以下命令列出成功加载的插件： + +``` +confluent local services connect plugin list +``` + +如果成功安装，会输出如下： + +```txt {4,9} +Available Connect Plugins: +[ + { + "class": "com.taosdata.kafka.connect.sink.TDengineSinkConnector", + "type": "sink", + "version": "1.0.0" + }, + { + "class": "com.taosdata.kafka.connect.source.TDengineSourceConnector", + "type": "source", + "version": "1.0.0" + }, +...... +``` + +如果插件安装失败，请检查 Kafka Connect 的启动日志是否有异常信息，用以下命令输出日志路径： +``` +echo `cat /tmp/confluent.current`/connect/connect.stdout +``` +该命令的输出类似： `/tmp/confluent.104086/connect/connect.stdout`。 + +与日志文件 `connect.stdout` 同一目录，还有一个文件名为： `connect.properties`。在这个文件的末尾，可以看到最终生效的 `plugin.path`， 它是一系列用逗号分割的路径。如果插件安装失败，很可能是因为实际的安装路径不包含在 `plugin.path` 中。 + + ## TDengine Sink Connector 的使用 TDengine Sink Connector 的作用是同步指定 topic 的数据到 TDengine。用户无需提前创建数据库和超级表。可手动指定目标数据库的名字（见配置参数 connection.database）， 也可按一定规则生成(见配置参数 connection.database.prefix)。 @@ -144,7 +199,7 @@ vi sink-demo.properties sink-demo.properties 内容如下： ```ini title="sink-demo.properties" -name=tdengine-sink-demo +name=TDengineSinkConnector connector.class=com.taosdata.kafka.connect.sink.TDengineSinkConnector tasks.max=1 topics=meters @@ -153,6 +208,7 @@ connection.user=root connection.password=taosdata connection.database=power db.schemaless=line +data.precision=ns key.converter=org.apache.kafka.connect.storage.StringConverter value.converter=org.apache.kafka.connect.storage.StringConverter ``` @@ -179,6 +235,7 @@ confluent local services connect connector load TDengineSinkConnector --config . "connection.url": "jdbc:TAOS://127.0.0.1:6030", "connection.user": "root", "connector.class": "com.taosdata.kafka.connect.sink.TDengineSinkConnector", + "data.precision": "ns", "db.schemaless": "line", "key.converter": "org.apache.kafka.connect.storage.StringConverter", "tasks.max": "1", @@ -196,10 +253,10 @@ confluent local services connect connector load TDengineSinkConnector --config . 准备测试数据的文本文件，内容如下： ```txt title="test-data.txt" -meters,location=Beijing.Haidian,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249000000 -meters,location=Beijing.Haidian,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250000000 -meters,location=Beijing.Haidian,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249000000 -meters,location=Beijing.Haidian,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250000000 +meters,location=California.LosAngeles,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249000000 +meters,location=California.LosAngeles,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250000000 +meters,location=California.LosAngeles,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249000000 +meters,location=California.LosAngeles,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250000000 ``` 使用 kafka-console-producer 向主题 meters 添加测试数据。 @@ -223,10 +280,10 @@ Database changed. taos> select * from meters; ts | current | voltage | phase | groupid | location | =============================================================================================================================================================== - 2022-03-28 09:56:51.249000000 | 11.800000000 | 221.000000000 | 0.280000000 | 2 | Beijing.Haidian | - 2022-03-28 09:56:51.250000000 | 13.400000000 | 223.000000000 | 0.290000000 | 2 | Beijing.Haidian | - 2022-03-28 09:56:51.249000000 | 10.800000000 | 223.000000000 | 0.290000000 | 3 | Beijing.Haidian | - 2022-03-28 09:56:51.250000000 | 11.300000000 | 221.000000000 | 0.350000000 | 3 | Beijing.Haidian | + 2022-03-28 09:56:51.249000000 | 11.800000000 | 221.000000000 | 0.280000000 | 2 | California.LosAngeles | + 2022-03-28 09:56:51.250000000 | 13.400000000 | 223.000000000 | 0.290000000 | 2 | California.LosAngeles | + 2022-03-28 09:56:51.249000000 | 10.800000000 | 223.000000000 | 0.290000000 | 3 | California.LosAngeles | + 2022-03-28 09:56:51.250000000 | 11.300000000 | 221.000000000 | 0.350000000 | 3 | California.LosAngeles | Query OK, 4 row(s) in set (0.004208s) ``` @@ -275,7 +332,7 @@ DROP DATABASE IF EXISTS test; CREATE DATABASE test; USE test; CREATE STABLE meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT); -INSERT INTO d1001 USING meters TAGS(Beijing.Chaoyang, 2) VALUES('2018-10-03 14:38:05.000',10.30000,219,0.31000) d1001 USING meters TAGS(Beijing.Chaoyang, 2) VALUES('2018-10-03 14:38:15.000',12.60000,218,0.33000) d1001 USING meters TAGS(Beijing.Chaoyang, 2) VALUES('2018-10-03 14:38:16.800',12.30000,221,0.31000) d1002 USING meters TAGS(Beijing.Chaoyang, 3) VALUES('2018-10-03 14:38:16.650',10.30000,218,0.25000) d1003 USING meters TAGS(Beijing.Haidian, 2) VALUES('2018-10-03 14:38:05.500',11.80000,221,0.28000) d1003 USING meters TAGS(Beijing.Haidian, 2) VALUES('2018-10-03 14:38:16.600',13.40000,223,0.29000) d1004 USING meters TAGS(Beijing.Haidian, 3) VALUES('2018-10-03 14:38:05.000',10.80000,223,0.29000) d1004 USING meters TAGS(Beijing.Haidian, 3) VALUES('2018-10-03 14:38:06.500',11.50000,221,0.35000); +INSERT INTO d1001 USING meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 14:38:05.000',10.30000,219,0.31000) d1001 USING meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 14:38:15.000',12.60000,218,0.33000) d1001 USING meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 14:38:16.800',12.30000,221,0.31000) d1002 USING meters TAGS(California.SanFrancisco, 3) VALUES('2018-10-03 14:38:16.650',10.30000,218,0.25000) d1003 USING meters TAGS(California.LosAngeles, 2) VALUES('2018-10-03 14:38:05.500',11.80000,221,0.28000) d1003 USING meters TAGS(California.LosAngeles, 2) VALUES('2018-10-03 14:38:16.600',13.40000,223,0.29000) d1004 USING meters TAGS(California.LosAngeles, 3) VALUES('2018-10-03 14:38:05.000',10.80000,223,0.29000) d1004 USING meters TAGS(California.LosAngeles, 3) VALUES('2018-10-03 14:38:06.500',11.50000,221,0.35000); ``` 使用 TDengine CLI, 执行 SQL 文件。 @@ -302,8 +359,8 @@ kafka-console-consumer --bootstrap-server localhost:9092 --from-beginning --topi ``` ...... -meters,location="beijing.chaoyang",groupid=2i32 current=10.3f32,voltage=219i32,phase=0.31f32 1538548685000000000 -meters,location="beijing.chaoyang",groupid=2i32 current=12.6f32,voltage=218i32,phase=0.33f32 1538548695000000000 +meters,location="California.SanFrancisco",groupid=2i32 current=10.3f32,voltage=219i32,phase=0.31f32 1538548685000000000 +meters,location="California.SanFrancisco",groupid=2i32 current=12.6f32,voltage=218i32,phase=0.33f32 1538548695000000000 ...... ``` @@ -356,21 +413,33 @@ confluent local services connect connector unload TDengineSourceConnector 2. `connection.database.prefix`： 当 connection.database 为 null 时, 目标数据库的前缀。可以包含占位符 '${topic}'。 比如 kafka_${topic}, 对于主题 'orders' 将写入数据库 'kafka_orders'。 默认 null。当为 null 时，目标数据库的名字和主题的名字是一致的。 3. `batch.size`: 分批写入每批记录数。当 Sink Connector 一次接收到的数据大于这个值时将分批写入。 4. `max.retries`: 发生错误时的最大重试次数。默认为 1。 -5. `retry.backoff.ms`: 发送错误时重试的时间间隔。单位毫秒，默认 3000。 -6. `db.schemaless`: 数据格式，必须指定为： line、json、telnet 中的一个。分别代表 InfluxDB 行协议格式、 OpenTSDB JSON 格式、 OpenTSDB Telnet 行协议格式。 +5. `retry.backoff.ms`: 发送错误时重试的时间间隔。单位毫秒，默认为 3000。 +6. `db.schemaless`: 数据格式，可选值为： + 1. line ：代表 InfluxDB 行协议格式 + 2. json : 代表 OpenTSDB JSON 格式 + 3. telnet ：代表 OpenTSDB Telnet 行协议格式 +7. `data.precision`: 使用 InfluxDB 行协议格式时，时间戳的精度。可选值为： + 1. ms ： 表示毫秒 + 2. us ： 表示微秒 + 3. ns ： 表示纳秒。默认为纳秒。 ### TDengine Source Connector 特有的配置 1. `connection.database`: 源数据库名称，无缺省值。 2. `topic.prefix`： 数据导入 kafka 后 topic 名称前缀。 使用 `topic.prefix` + `connection.database` 名称作为完整 topic 名。默认为空字符串 ""。 -3. `timestamp.initial`: 数据同步起始时间。格式为'yyyy-MM-dd HH:mm:ss'。默认 "1970-01-01 00:00:00"。 -4. `poll.interval.ms`: 拉取数据间隔，单位为 ms。默认 1000。 +3. `timestamp.initial`: 数据同步起始时间。格式为'yyyy-MM-dd HH:mm:ss'。默认为 "1970-01-01 00:00:00"。 +4. `poll.interval.ms`: 拉取数据间隔，单位为 ms。默认为 1000。 5. `fetch.max.rows` : 检索数据库时最大检索条数。 默认为 100。 -6. `out.format`: 数据格式。取值 line 或 json。line 表示 InfluxDB Line 协议格式， json 表示 OpenTSDB JSON 格式。默认 line。 +6. `out.format`: 数据格式。取值 line 或 json。line 表示 InfluxDB Line 协议格式， json 表示 OpenTSDB JSON 格式。默认为 line。 + +## 其他说明 + +1. 插件的安装位置可以自定义，请参考官方文档：https://docs.confluent.io/home/connect/self-managed/install.html#install-connector-manually。 +2. 本教程的示例程序使用了 Confluent 平台，但是 TDengine Kafka Connector 本身同样适用于独立安装的 Kafka, 且配置方法相同。关于如何在独立安装的 Kafka 环境使用 Kafka Connect 插件， 请参考官方文档： https://kafka.apache.org/documentation/#connect。 ## 问题反馈 -https://github.com/taosdata/kafka-connect-tdengine/issues +无论遇到任何问题，都欢迎在本项目的 Github 仓库反馈： https://github.com/taosdata/kafka-connect-tdengine/issues。 ## 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b/docs-cn/20-third-party/kafka/streaming-integration-with-kafka-connect.webp new file mode 100644 index 0000000000000000000000000000000000000000..120d534ec132cea2ccef6cf87a3ce680a5ac6e9c Binary files /dev/null and b/docs-cn/20-third-party/kafka/streaming-integration-with-kafka-connect.webp differ diff --git a/docs-cn/21-tdinternal/01-arch.md b/docs-cn/21-tdinternal/01-arch.md index 6f479efc1ad13e27899e7819d194a2df59ed3ad1..433cb4808b60ce73c639a23beef45fb8e1afb7dd 100644 --- a/docs-cn/21-tdinternal/01-arch.md +++ b/docs-cn/21-tdinternal/01-arch.md @@ -11,7 +11,7 @@ TDengine 的设计是基于单个硬件、软件系统不可靠，基于任何 TDengine 分布式架构的逻辑结构图如下： -![TDengine架构示意图](/img/architecture/structure.png) +![TDengine Database 架构示意图](./structure.webp)
图 1 TDengine架构示意图
@@ -41,7 +41,7 @@ TDengine 分布式架构的逻辑结构图如下： - 集群数据节点对外提供 RESTful 服务占用一个 TCP 端口，是 serverPort+11。 - 集群内数据节点与 Arbitrator 节点之间通讯占用一个 TCP 端口，是 serverPort+12。 -因此一个数据节点总的端口范围为 serverPort 到 serverPort+12，总共 13 个 TCP/UDP 端口。使用时，需要确保防火墙将这些端口打开。每个数据节点可以配置不同的 serverPort。详细的端口情况请参见 [TDengine 2.0 端口说明](/train-faq/faq#port) +因此一个数据节点总的端口范围为 serverPort 到 serverPort+12，总共 13 个 TCP/UDP 端口。确保集群中所有主机在端口 6030-6042 上的 TCP/UDP 协议能够互通。详细的端口情况请参见 [TDengine 2.0 端口说明](/train-faq/faq#port) **集群对外连接：**TDengine 集群可以容纳单个、多个甚至几千个数据节点。应用只需要向集群中任何一个数据节点发起连接即可，连接需要提供的网络参数是一数据节点的 End Point（FQDN 加配置的端口号）。通过命令行 CLI 启动应用 taos 时，可以通过选项-h 来指定数据节点的 FQDN，-P 来指定其配置的端口号，如果端口不配置，将采用 TDengine 的系统配置参数 serverPort。 @@ -63,7 +63,7 @@ TDengine 分布式架构的逻辑结构图如下： 为解释 vnode、mnode、taosc 和应用之间的关系以及各自扮演的角色，下面对写入数据这个典型操作的流程进行剖析。 -![TDengine典型的操作流程](/img/architecture/message.png) +![TDengine Database 典型的操作流程](./message.webp)
图 2 TDengine 典型的操作流程
@@ -135,7 +135,7 @@ TDengine 除 vnode 分片之外，还对时序数据按照时间段进行分区 Master Vnode 遵循下面的写入流程： -![TDengine Master写入流程](/img/architecture/write_master.png) +![TDengine Database Master写入流程](./write_master.webp)
图 3 TDengine Master 写入流程
@@ -150,7 +150,7 @@ Master Vnode 遵循下面的写入流程： 对于 slave vnode，写入流程是： -![TDengine Slave 写入流程](/img/architecture/write_slave.png) +![TDengine Database Slave 写入流程](./write_slave.webp)
图 4 TDengine Slave 写入流程
@@ -284,7 +284,7 @@ SELECT COUNT(*) FROM d1001 WHERE ts >= '2017-7-14 00:00:00' AND ts < '2017-7-14 TDengine 对每个数据采集点单独建表，但在实际应用中经常需要对不同的采集点数据进行聚合。为高效的进行聚合操作，TDengine 引入超级表（STable）的概念。超级表用来代表一特定类型的数据采集点，它是包含多张表的表集合，集合里每张表的模式（schema）完全一致，但每张表都带有自己的静态标签，标签可以有多个，可以随时增加、删除和修改。应用可通过指定标签的过滤条件，对一个 STable 下的全部或部分表进行聚合或统计操作，这样大大简化应用的开发。其具体流程如下图所示： -![多表聚合查询原理图](/img/architecture/multi_tables.png) +![TDengine Database 多表聚合查询原理图](./multi_tables.webp)
图 5 多表聚合查询原理图
diff --git a/docs-cn/21-tdinternal/02-replica.md b/docs-cn/21-tdinternal/02-replica.md index 6a384b982d22956dd514d8df05dc827ca6f8b729..25d1edab6e9b97be13c8675491cc90ed54520865 100644 --- a/docs-cn/21-tdinternal/02-replica.md +++ b/docs-cn/21-tdinternal/02-replica.md @@ -93,7 +93,7 @@ TDengine采取的是Master-Slave模式进行同步，与流行的RAFT一致性 具体的流程图如下： -![replica-master.png](/img/architecture/replica-master.png) +![TDengine Database replica master](./replica-master.webp) 选择Master的具体规则如下： @@ -108,7 +108,7 @@ TDengine采取的是Master-Slave模式进行同步，与流行的RAFT一致性 如果vnode A是master, vnode B是slave, vnode A能接受客户端的写请求，而vnode B不能。当vnode A收到写的请求后，遵循下面的流程： -![replica-forward.png](/img/architecture/replica-forward.png) +![TDengine Database replica forward](./replica-forward.webp) 1. 应用对写请求做基本的合法性检查，通过，则给该请求包打上一个版本号(version, 单调递增） 2. 应用将打上版本号的写请求封装一个WAL Head, 写入WAL(Write Ahead Log) @@ -143,7 +143,7 @@ TDengine采取的是Master-Slave模式进行同步，与流行的RAFT一致性 整个数据恢复流程分为两大步骤，第一步，先恢复archived data(file), 然后恢复wal。具体流程如下： -![replica-restore.png](/img/architecture/replica-restore.png) +![TDengine Database replica restore](./replica-restore.webp) 1. 通过已经建立的TCP连接，发送sync req给master节点 2. master收到sync req后，以client的身份，向vnode B主动建立一新的专用于同步的TCP连接（syncFd) diff --git a/docs-cn/21-tdinternal/03-taosd.md b/docs-cn/21-tdinternal/03-taosd.md index 6a5734102c85db291339ce93a2231cb8196053f6..0cf0a1aaa222e82f7ca6cc4f0314aa5a50442924 100644 --- a/docs-cn/21-tdinternal/03-taosd.md +++ b/docs-cn/21-tdinternal/03-taosd.md @@ -9,7 +9,7 @@ title: taosd的设计 taosd 包含 rpc，dnode，vnode，tsdb，query，cq，sync，wal，mnode，http，monitor 等模块，具体如下图： -![modules.png](/img/architecture/modules.png) +![TDengine Database module](./modules.webp) taosd 的启动入口是 dnode 模块，dnode 然后启动其他模块，包括可选配置的 http，monitor 模块。taosc 或 dnode 之间交互的消息都是通过 rpc 模块进行，dnode 模块根据接收到的消息类型，将消息分发到 vnode 或 mnode 的消息队列，或由 dnode 模块自己消费。dnode 的工作线程（worker）消费消息队列里的消息，交给 mnode 或 vnode 进行处理。下面对各个模块做简要说明。 @@ -44,13 +44,13 @@ RPC 模块还提供数据压缩功能，如果数据包的字节数超过系统 taosd 的消息消费由 dnode 通过读写线程池进行控制，是系统的中枢。该模块内的结构体图如下： -![dnode.png](/img/architecture/dnode.png) +![TDengine Database dnode](./dnode.webp) ## VNODE 模块 vnode 是一独立的数据存储查询逻辑单元，但因为一个 vnode 只能容许一个 DB ，因此 vnode 内部没有 account，DB，user 等概念。为实现更好的模块化、封装以及未来的扩展，它有很多子模块，包括负责存储的 TSDB，负责查询的 query，负责数据复制的 sync，负责数据库日志的的 WAL，负责连续查询的 cq（continuous query），负责事件触发的流计算的 event 等模块，这些子模块只与 vnode 模块发生关系，与其他模块没有任何调用关系。模块图如下： -![vnode.png](/img/architecture/vnode.png) +![TDengine Database vnode](./vnode.webp) vnode 模块向下，与 dnodeVRead，dnodeVWrite 发生互动，向上，与子模块发生互动。它主要的功能有： diff --git a/docs-cn/21-tdinternal/dnode.webp b/docs-cn/21-tdinternal/dnode.webp new file mode 100644 index 0000000000000000000000000000000000000000..a56c7e4594df00a721cb48381d68ca3bc813cdc8 Binary files /dev/null and b/docs-cn/21-tdinternal/dnode.webp differ diff --git a/docs-cn/21-tdinternal/message.webp b/docs-cn/21-tdinternal/message.webp new file mode 100644 index 0000000000000000000000000000000000000000..a2a42abff3d6e932b41a3abe9feae4a5cc13c9e5 Binary files /dev/null and b/docs-cn/21-tdinternal/message.webp differ diff --git a/docs-cn/21-tdinternal/modules.webp b/docs-cn/21-tdinternal/modules.webp new file mode 100644 index 0000000000000000000000000000000000000000..718a6abccdbe40d4a0df5e3812fe0ab943a7c523 Binary files /dev/null and b/docs-cn/21-tdinternal/modules.webp differ diff --git a/docs-cn/21-tdinternal/multi_tables.webp b/docs-cn/21-tdinternal/multi_tables.webp new file mode 100644 index 0000000000000000000000000000000000000000..8f649e34a3a62d1b11b4403b2e743ff6b5e47be2 Binary files /dev/null and b/docs-cn/21-tdinternal/multi_tables.webp differ diff --git a/docs-cn/21-tdinternal/replica-forward.webp b/docs-cn/21-tdinternal/replica-forward.webp new file mode 100644 index 0000000000000000000000000000000000000000..512efd4eba8f23ad0f8607eaaf5525f51ecdcf0e Binary files /dev/null and b/docs-cn/21-tdinternal/replica-forward.webp differ diff --git a/docs-cn/21-tdinternal/replica-master.webp b/docs-cn/21-tdinternal/replica-master.webp new file mode 100644 index 0000000000000000000000000000000000000000..57030a11f563af2689dbcfd206183f410b121aee Binary files /dev/null and b/docs-cn/21-tdinternal/replica-master.webp differ diff --git a/docs-cn/21-tdinternal/replica-restore.webp b/docs-cn/21-tdinternal/replica-restore.webp new file mode 100644 index 0000000000000000000000000000000000000000..f282c2d4d23f517e3ef08e906cea7e9c5edc0b2a Binary files /dev/null and b/docs-cn/21-tdinternal/replica-restore.webp differ diff --git a/docs-cn/21-tdinternal/structure.webp b/docs-cn/21-tdinternal/structure.webp new file mode 100644 index 0000000000000000000000000000000000000000..b77a42c074b15302b5c3ab889fb550a46dd549b3 Binary files /dev/null and b/docs-cn/21-tdinternal/structure.webp differ diff --git a/docs-cn/21-tdinternal/vnode.webp b/docs-cn/21-tdinternal/vnode.webp new file mode 100644 index 0000000000000000000000000000000000000000..fae3104c89c542c26790b509d12ad56661082c32 Binary files /dev/null and b/docs-cn/21-tdinternal/vnode.webp differ diff --git a/docs-cn/21-tdinternal/write_master.webp b/docs-cn/21-tdinternal/write_master.webp new file mode 100644 index 0000000000000000000000000000000000000000..9624036ed3d46ed60924ead9ce5c61acee0f4652 Binary files /dev/null and b/docs-cn/21-tdinternal/write_master.webp differ diff --git a/docs-cn/21-tdinternal/write_slave.webp b/docs-cn/21-tdinternal/write_slave.webp new file mode 100644 index 0000000000000000000000000000000000000000..7c45dec11b00e6a738de458f9e1bedacfad75a96 Binary files /dev/null and b/docs-cn/21-tdinternal/write_slave.webp differ diff --git a/docs-cn/25-application/01-telegraf.md b/docs-cn/25-application/01-telegraf.md index f63a6701eed2b4c5b98f577d5b2867ae6dada387..95df8699ef85b02d6e9dba398c787644fc9089b2 100644 --- a/docs-cn/25-application/01-telegraf.md +++ b/docs-cn/25-application/01-telegraf.md @@ -16,7 +16,7 @@ IT 运维监测数据通常都是对时间特性比较敏感的数据，例如 本文介绍不需要写一行代码，通过简单修改几行配置文件，就可以快速搭建一个基于 TDengine + Telegraf + Grafana 的 IT 运维系统。架构如下图： -![IT-DevOps-Solutions-Telegraf.png](/img/IT-DevOps-Solutions-Telegraf.png) +![TDengine Database IT-DevOps-Solutions-Telegraf](./IT-DevOps-Solutions-Telegraf.webp) ## 安装步骤 @@ -75,7 +75,7 @@ sudo systemctl start telegraf 点击左侧齿轮图标并选择 `Plugins`，应该可以找到 TDengine data source 插件图标。 点击左侧加号图标并选择 `Import`，从 `https://github.com/taosdata/grafanaplugin/blob/master/examples/telegraf/grafana/dashboards/telegraf-dashboard-v0.1.0.json` 下载 dashboard JSON 文件后导入。之后可以看到如下界面的仪表盘： -![IT-DevOps-Solutions-telegraf-dashboard.png](/img/IT-DevOps-Solutions-telegraf-dashboard.png) +![TDengine Database IT-DevOps-Solutions-telegraf-dashboard](./IT-DevOps-Solutions-telegraf-dashboard.webp) ## 总结 diff --git a/docs-cn/25-application/02-collectd.md b/docs-cn/25-application/02-collectd.md index 5e6bc6577b2f4c8564e4533ced745d0b214ec748..78c61bb969092d7040ddcb3d02ce7bd29a784858 100644 --- a/docs-cn/25-application/02-collectd.md +++ b/docs-cn/25-application/02-collectd.md @@ -16,7 +16,7 @@ IT 运维监测数据通常都是对时间特性比较敏感的数据，例如 本文介绍不需要写一行代码，通过简单修改几行配置文件，就可以快速搭建一个基于 TDengine + collectd / statsD + Grafana 的 IT 运维系统。架构如下图： -![IT-DevOps-Solutions-Collectd-StatsD.png](/img/IT-DevOps-Solutions-Collectd-StatsD.png) +![TDengine Database IT-DevOps-Solutions-Collectd-StatsD](./IT-DevOps-Solutions-Collectd-StatsD.webp) ## 安装步骤 @@ -81,12 +81,12 @@ repeater 部分添加 { host:'', port: -### 18. go 语言编写组件编译失败怎样解决？ +### 19. go 语言编写组件编译失败怎样解决？ TDengine 2.3.0.0 及之后的版本包含一个使用 go 语言开发的 taosAdapter 独立组件，需要单独运行，取代之前 taosd 内置的 httpd ，提供包含原 httpd 功能以及支持多种其他软件（Prometheus、Telegraf、collectd、StatsD 等）的数据接入功能。 使用最新 develop 分支代码编译需要先 `git submodule update --init --recursive` 下载 taosAdapter 仓库代码后再编译。 @@ -184,7 +195,7 @@ go env -w GOPROXY=https://goproxy.cn,direct 如果希望继续使用之前的内置 httpd，可以关闭 taosAdapter 编译，使用 `cmake .. -DBUILD_HTTP=true` 使用原来内置的 httpd。 -### 19. 如何查询数据占用的存储空间大小？ +### 20. 如何查询数据占用的存储空间大小？ 默认情况下，TDengine 的数据文件存储在 /var/lib/taos ，日志文件存储在 /var/log/taos 。 @@ -193,3 +204,33 @@ go env -w GOPROXY=https://goproxy.cn,direct 若想查看单个数据库占用的大小，可在命令行程序 taos 内指定要查看的数据库后执行 `show vgroups;` ，通过得到的 VGroup id 去 /var/lib/taos/vnode 下查看包含的文件夹大小。 若仅仅想查看指定（超级）表的数据块分布及大小，可查看[_block_dist 函数](https://docs.taosdata.com/taos-sql/select/#_block_dist-%E5%87%BD%E6%95%B0) + +### 21. 客户端连接串如何保证高可用？ + +请看为此问题撰写的 [技术博客](https://www.taosdata.com/blog/2021/04/16/2287.html) + +### 22. 时间戳的时区信息是怎样处理的？ + +TDengine 中时间戳的时区总是由客户端进行处理，而与服务端无关。具体来说，客户端会对 SQL 语句中的时间戳进行时区转换，转为 UTC 时区（即 Unix 时间戳——Unix Timestamp）再交由服务端进行写入和查询；在读取数据时，服务端也是采用 UTC 时区提供原始数据，客户端收到后再根据本地设置，把时间戳转换为本地系统所要求的时区进行显示。 + +客户端在处理时间戳字符串时，会采取如下逻辑： + +1. 在未做特殊设置的情况下，客户端默认使用所在操作系统的时区设置。 +2. 如果在 taos.cfg 中设置了 timezone 参数，则客户端会以这个配置文件中的设置为准。 +3. 如果在 C/C++/Java/Python 等各种编程语言的 Connector Driver 中，在建立数据库连接时显式指定了 timezone，那么会以这个指定的时区设置为准。例如 Java Connector 的 JDBC URL 中就有 timezone 参数。 +4. 在书写 SQL 语句时，也可以直接使用 Unix 时间戳（例如 `1554984068000`）或带有时区的时间戳字符串，也即以 RFC 3339 格式（例如 `2013-04-12T15:52:01.123+08:00`）或 ISO-8601 格式（例如 `2013-04-12T15:52:01.123+0800`）来书写时间戳，此时这些时间戳的取值将不再受其他时区设置的影响。 + +### 23. TDengine 2.0 都会用到哪些网络端口？ + +使用到的网络端口请看文档：[serverport](/reference/config/#serverport) + +需要注意，文档上列举的端口号都是以默认端口 6030 为前提进行说明，如果修改了配置文件中的设置，那么列举的端口都会随之出现变化，管理员可以参考上述的信息调整防火墙设置。 + +### 24. 为什么 RESTful 接口无响应、Grafana 无法添加 TDengine 为数据源、TDengineGUI 选了 6041 端口还是无法连接成功？？ + +taosAdapter 从 TDengine 2.4.0.0 版本开始成为 TDengine 服务端软件的组成部分，是 TDengine 集群和应用程序之间的桥梁和适配器。在此之前 RESTful 接口等功能是由 taosd 内置的 HTTP 服务提供的，而如今要实现上述功能需要执行：```systemctl start taosadapter``` 命令来启动 taosAdapter 服务。 + +需要说明的是，taosAdapter 的日志路径 path 需要单独配置，默认路径是 /var/log/taos ；日志等级 logLevel 有 8 个等级，默认等级是 info ，配置成 panic 可关闭日志输出。请注意操作系统 / 目录的空间大小，可通过命令行参数、环境变量或配置文件来修改配置，默认配置文件是 /etc/taos/taosadapter.toml 。 + +有关 taosAdapter 组件的详细介绍请看文档：[taosAdapter](https://docs.taosdata.com/reference/taosadapter/) + diff --git a/docs-cn/27-train-faq/03-docker.md b/docs-cn/27-train-faq/03-docker.md index 845a8751846c0995a43fb1c01e6ace3080176838..7791569b25e102b4634f0fb899fc0973cacc0aa1 100644 --- a/docs-cn/27-train-faq/03-docker.md +++ b/docs-cn/27-train-faq/03-docker.md @@ -209,7 +209,7 @@ curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'show databases;' 127.0.0 Press enter key to continue or Ctrl-C to stop ``` - 回车后，该命令将在数据库 test 下面自动创建一张超级表 meters，该超级表下有 1 万张表，表名为 "d0" 到 "d9999"，每张表有 1 万条记录，每条记录有 (ts, current, voltage, phase) 四个字段，时间戳从 "2017-07-14 10:40:00 000" 到 "2017-07-14 10:40:09 999"，每张表带有标签 location 和 groupId，groupId 被设置为 1 到 10， location 被设置为 "beijing" 或者 "shanghai"。 + 回车后，该命令将在数据库 test 下面自动创建一张超级表 meters，该超级表下有 1 万张表，表名为 "d0" 到 "d9999"，每张表有 1 万条记录，每条记录有 (ts, current, voltage, phase) 四个字段，时间戳从 "2017-07-14 10:40:00 000" 到 "2017-07-14 10:40:09 999"，每张表带有标签 location 和 groupId，groupId 被设置为 1 到 10， location 被设置为 "California.SanFrancisco" 或者 "California.SanDieo"。 最后共插入 1 亿条记录。 @@ -279,7 +279,7 @@ curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'show databases;' 127.0.0 $ taos> select groupid, location from test.d0; groupid | location | ================================= - 0 | shanghai | + 0 | California.SanDieo | Query OK, 1 row(s) in set (0.003490s) ``` diff --git a/docs-cn/30-release/02-2.6.md b/docs-cn/30-release/02-2.6.md new file mode 100644 index 0000000000000000000000000000000000000000..85b76d9999e211336b5859beab3fdfc7988f4fda --- /dev/null +++ b/docs-cn/30-release/02-2.6.md @@ -0,0 +1,9 @@ +--- +title: 2.6 +--- + +[2.6.0.4](https://github.com/taosdata/TDengine/releases/tag/ver-2.6.0.4) + +[2.6.0.1](https://github.com/taosdata/TDengine/releases/tag/ver-2.6.0.1) + +[2.6.0.0](https://github.com/taosdata/TDengine/releases/tag/ver-2.6.0.0) diff --git a/docs-cn/30-release/03-2.4.md b/docs-cn/30-release/03-2.4.md new file mode 100644 index 0000000000000000000000000000000000000000..62580b327a3bd5098e1b7f1162a1c398ac2a5eff --- /dev/null +++ b/docs-cn/30-release/03-2.4.md @@ -0,0 +1,29 @@ +--- +title: 2.4 +--- + +[2.4.0.26](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.26) + +[2.4.0.25](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.25) + +[2.4.0.24](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.24) + +[2.4.0.20](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.20) + +[2.4.0.18](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.18) + +[2.4.0.16](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.16) + +[2.4.0.14](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.14) + +[2.4.0.12](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.12) + +[2.4.0.10](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.10) + +[2.4.0.7](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.7) + +[2.4.0.5](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.5) + +[2.4.0.4](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.4) + +[2.4.0.0](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.0) diff --git a/docs-cn/30-release/_category_.yml b/docs-cn/30-release/_category_.yml new file mode 100644 index 0000000000000000000000000000000000000000..745709c561e56de77d3fc0a4da7fe77d96c0482b --- /dev/null +++ b/docs-cn/30-release/_category_.yml @@ -0,0 +1 @@ +label: 发布历史 diff --git a/docs-cn/30-release/index.md b/docs-cn/30-release/index.md new file mode 100644 index 0000000000000000000000000000000000000000..65fcf70acdf3747acfef13599a62db75e6b8758f --- /dev/null +++ b/docs-cn/30-release/index.md @@ -0,0 +1,10 @@ +--- +title: 发布历史 +--- + +```mdx-code-block +import DocCardList from '@theme/DocCardList'; +import {useCurrentSidebarCategory} from '@docusaurus/theme-common'; + + +``` \ No newline at end of file diff --git a/docs-cn/eco_system.png b/docs-cn/eco_system.png deleted file mode 100644 index bf8bf8f1e0a2311fc12202d712a8a2f9b8ce419b..0000000000000000000000000000000000000000 Binary files a/docs-cn/eco_system.png and /dev/null differ diff --git a/docs-cn/eco_system.webp b/docs-cn/eco_system.webp new file mode 100644 index 0000000000000000000000000000000000000000..d60c38e97c67fa7b2acc703b2ba777d19ae5be13 Binary files /dev/null and b/docs-cn/eco_system.webp differ diff --git a/docs-en/02-intro/eco_system.png b/docs-en/02-intro/eco_system.png deleted file mode 100644 index bf8bf8f1e0a2311fc12202d712a8a2f9b8ce419b..0000000000000000000000000000000000000000 Binary files a/docs-en/02-intro/eco_system.png and /dev/null differ diff --git a/docs-en/02-intro/eco_system.webp b/docs-en/02-intro/eco_system.webp new file mode 100644 index 0000000000000000000000000000000000000000..d60c38e97c67fa7b2acc703b2ba777d19ae5be13 Binary files /dev/null and b/docs-en/02-intro/eco_system.webp differ diff --git a/docs-en/02-intro/index.md b/docs-en/02-intro/index.md index e2309943f3983dcbf7957ef6d478aefa64d7a902..f6766f910f4d7560b782bf02ffa97922523e6167 100644 --- a/docs-en/02-intro/index.md +++ b/docs-en/02-intro/index.md @@ -5,39 +5,39 @@ toc_max_heading_level: 2 TDengine is a high-performance, scalable time-series database with SQL support. Its code, including its cluster feature is open source under GNU AGPL v3.0. Besides the database engine, it provides [caching](/develop/cache), [stream processing](/develop/continuous-query), [data subscription](/develop/subscribe) and other functionalities to reduce the complexity and cost of development and operation. -This section introduces the major features, competitive advantages, suited scenarios and benchmarks to help you get a high level picture for TDengine. +This section introduces the major features, competitive advantages, typical use-cases and benchmarks to help you get a high level overview of TDengine. ## Major Features The major features are listed below: -1. Besides [using SQL to insert](/develop/insert-data/sql-writing)，it supports [Schemaless writing](/reference/schemaless/)，and it supports [InfluxDB LINE](/develop/insert-data/influxdb-line)，[OpenTSDB Telnet](/develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](/develop/insert-data/opentsdb-json) and other protocols. -2. Support for seamless integration with third-party data collection agents like [Telegraf](/third-party/telegraf)，[Prometheus](/third-party/prometheus)，[StatsD](/third-party/statsd)，[collectd](/third-party/collectd)，[icinga2](/third-party/icinga2), [TCollector](/third-party/tcollector), [EMQX](/third-party/emq-broker), [HiveMQ](/third-party/hive-mq-broker). Without a line of code, those agents can write data points into TDengine just by configuration. -3. Support for [all kinds of queries](/develop/query-data), including aggregation, nested query, downsampling, interpolation, etc. -4. Support for [user defined functions](/develop/udf) +1. While TDengine supports [using SQL to insert](/develop/insert-data/sql-writing), it also supports [Schemaless writing](/reference/schemaless/) just like NoSQL databases. TDengine also supports standard protocols like [InfluxDB LINE](/develop/insert-data/influxdb-line)，[OpenTSDB Telnet](/develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](/develop/insert-data/opentsdb-json) among others. +2. TDengine supports seamless integration with third-party data collection agents like [Telegraf](/third-party/telegraf)，[Prometheus](/third-party/prometheus)，[StatsD](/third-party/statsd)，[collectd](/third-party/collectd)，[icinga2](/third-party/icinga2), [TCollector](/third-party/tcollector), [EMQX](/third-party/emq-broker), [HiveMQ](/third-party/hive-mq-broker). These agents can write data into TDengine with simple configuration and without a single line of code. +3. Support for [all kinds of queries](/develop/query-data), including aggregation, nested query, downsampling, interpolation and others. +4. Support for [user defined functions](/develop/udf). 5. Support for [caching](/develop/cache). TDengine always saves the last data point in cache, so Redis is not needed in some scenarios. 6. Support for [continuous query](/develop/continuous-query). 7. Support for [data subscription](/develop/subscribe) with the capability to specify filter conditions. 8. Support for [cluster](/cluster/), with the capability of increasing processing power by adding more nodes. High availability is supported by replication. -9. Provides interactive [command-line interface](/reference/taos-shell) for management, maintenance and ad-hoc query. +9. Provides an interactive [command-line interface](/reference/taos-shell) for management, maintenance and ad-hoc queries. 10. Provides many ways to [import](/operation/import) and [export](/operation/export) data. -11. Provides [monitoring](/operation/monitor) on TDengine running instances. +11. Provides [monitoring](/operation/monitor) on running instances of TDengine. 12. Provides [connectors](/reference/connector/) for [C/C++](/reference/connector/cpp), [Java](/reference/connector/java), [Python](/reference/connector/python), [Go](/reference/connector/go), [Rust](/reference/connector/rust), [Node.js](/reference/connector/node) and other programming languages. 13. Provides a [REST API](/reference/rest-api/). -14. Supports the seamless integration with [Grafana](/third-party/grafana) for visualization. +14. Supports seamless integration with [Grafana](/third-party/grafana) for visualization. 15. Supports seamless integration with Google Data Studio. -For more detail on features, please read through the whole documentation. +For more details on features, please read through the entire documentation. ## Competitive Advantages -TDengine makes full use of [the characteristics of time series data](https://tdengine.com/2019/07/09/86.html), such as structured, no transaction, rarely delete or update, etc., and builds its own innovative storage engine and computing engine to differentiate itself from other time series databases with the following advantages. +Time-series data is structured, not transactional, and is rarely deleted or updated. TDengine makes full use of [these characteristics of time series data](https://tdengine.com/2019/07/09/86.html) to build its own innovative storage engine and computing engine to differentiate itself from other time series databases, with the following advantages. -- **[High Performance](https://tdengine.com/fast)**: TDengine outperforms other time series databases in data ingestion and querying while significantly reducing storage cost and compute costs, with an innovatively designed and purpose-built storage engine. +- **[High Performance](https://tdengine.com/fast)**: With an innovatively designed and purpose-built storage engine, TDengine outperforms other time series databases in data ingestion and querying while significantly reducing storage costs and compute costs. - **[Scalable](https://tdengine.com/scalable)**: TDengine provides out-of-box scalability and high-availability through its native distributed design. Nodes can be added through simple configuration to achieve greater data processing power. In addition, this feature is open source. -- **[SQL Support](https://tdengine.com/sql-support)**: TDengine uses SQL as the query language, thereby reducing learning and migration costs, while adding SQL extensions to handle time-series data better, and supporting convenient and flexible schemaless data ingestion. +- **[SQL Support](https://tdengine.com/sql-support)**: TDengine uses SQL as the query language, thereby reducing learning and migration costs, while adding SQL extensions to better handle time-series. Keeping NoSQL developers in mind, TDengine also supports convenient and flexible, schemaless data ingestion. - **All in One**: TDengine has built-in caching, stream processing and data subscription functions. It is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software in some scenarios. It makes the system architecture much simpler, cost-effective and easier to maintain. @@ -45,24 +45,24 @@ TDengine makes full use of [the characteristics of time series data](https://tde - **Zero Management**: Installation and cluster setup can be done in seconds. Data partitioning and sharding are executed automatically. TDengine’s running status can be monitored via Grafana or other DevOps tools. -- **Zero Learning Costs**: With SQL as the query language and support for ubiquitous tools like Python, Java, C/C++, Go, Rust, and Node.js connectors, there are zero learning costs. +- **Zero Learning Costs**: With SQL as the query language and support for ubiquitous tools like Python, Java, C/C++, Go, Rust, and Node.js connectors, and a REST API, there are zero learning costs. -- **Interactive Console**: TDengine provides convenient console access to the database to run ad hoc queries, maintain the database, or manage the cluster without any programming. +- **Interactive Console**: TDengine provides convenient console access to the database, through a CLI, to run ad hoc queries, maintain the database, or manage the cluster, without any programming. -With TDengine, the total cost of ownership of time-series data platform can be greatly reduced. Because 1: with its superior performance, the computing and storage resources are reduced significantly; 2：with SQL support, it can be seamlessly integrated with many third party tools, and learning costs/migration costs are reduced significantly; 3: with its simple architecture and zero management, the operation and maintenance costs are reduced. +With TDengine, the total cost of ownership of your time-series data platform can be greatly reduced. 1: With its superior performance, the computing and storage resources are reduced significantly 2: With SQL support, it can be seamlessly integrated with many third party tools, and learning costs/migration costs are reduced significantly 3: With its simple architecture and zero management, the operation and maintenance costs are reduced. ## Technical Ecosystem -In the time-series data processing platform, TDengine stands in a role like this diagram below: +This is how TDengine would be situated, in a typical time-series data processing platform: -![TDengine Technical Ecosystem ](eco_system.png) +![TDengine Database Technical Ecosystem ](eco_system.webp)
Figure 1. TDengine Technical Ecosystem
-On the left side, there are data collection agents like OPC-UA, MQTT, Telegraf and Kafka. On the right side, visualization/BI tools, HMI, Python/R, and IoT Apps can be connected. TDengine itself provides interactive command-line interface and web interface for management and maintenance. +On the left-hand side, there are data collection agents like OPC-UA, MQTT, Telegraf and Kafka. On the right-hand side, visualization/BI tools, HMI, Python/R, and IoT Apps can be connected. TDengine itself provides an interactive command-line interface and a web interface for management and maintenance. -## Suited Scenarios +## Typical Use Cases -As a high-performance, scalable and SQL supported time-series database, TDengine's typical application scenarios include but are not limited to IoT, Industrial Internet, Connected Vehicles, IT operation and maintenance, energy, financial markets and other fields. TDengine is a purpose-built database optimized for the characteristics of time series data, it cannot be used to process data from web crawlers, social media, e-commerce, ERP, CRM, etc. This section makes a more detailed analysis of the applicable scenarios. +As a high-performance, scalable and SQL supported time-series database, TDengine's typical use case include but are not limited to IoT, Industrial Internet, Connected Vehicles, IT operation and maintenance, energy, financial markets and other fields. TDengine is a purpose-built database optimized for the characteristics of time series data. As such, it cannot be used to process data from web crawlers, social media, e-commerce, ERP, CRM and so on. More generally TDengine is not a suitable storage engine for non-time-series data. This section makes a more detailed analysis of the applicable scenarios. ### Characteristics and Requirements of Data Sources diff --git a/docs-en/04-concept/index.md b/docs-en/04-concept/index.md index abc553ab6d90042cb2389ba0b71d3b5395dcebfd..850f705146c4829db579f14be1a686ef9052f678 100644 --- a/docs-en/04-concept/index.md +++ b/docs-en/04-concept/index.md @@ -2,7 +2,7 @@ title: Concepts --- -In order to explain the basic concepts and provide some sample code, the TDengine documentation takes smart meters as a typical time series data scenario. Assuming that each smart meter collects three metrics of current, voltage, and phase, there are multiple smart meters, and each meter has static attributes like location and group ID, the collected data will be similar to the following table: +In order to explain the basic concepts and provide some sample code, the TDengine documentation smart meters as a typical time series use case. We assume the following: 1. Each smart meter collects three metrics i.e. current, voltage, and phase 2. There are multiple smart meters, and 3. Each meter has static attributes like location and group ID. Based on this, collected data will look similar to the following table:
@@ -29,7 +29,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin - + @@ -38,7 +38,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin - + @@ -47,7 +47,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin - + @@ -56,7 +56,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin - + @@ -65,7 +65,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin - + @@ -74,7 +74,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin - + @@ -83,7 +83,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin - + @@ -92,7 +92,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin - + @@ -112,7 +112,7 @@ Label/Tag refers to the static properties of sensors, equipment or other types o ## Data Collection Point -Data Collection Point (DCP) refers to hardware or software that collects metrics based on preset time periods or triggered by events. A data collection point can collect one or multiple metrics, but these metrics are collected at the same time and have the same time stamp. For some complex equipments, there are often multiple data collection points, and the sampling rate of each collection point may be different, and fully independent. For example, for a car, there could be a data collection point to collect GPS position metrics, a data collection point to collect engine status metrics, and a data collection point to collect the environment metrics inside the car, so in this example the car would have three data collection points. +Data Collection Point (DCP) refers to hardware or software that collects metrics based on preset time periods or triggered by events. A data collection point can collect one or multiple metrics, but these metrics are collected at the same time and have the same time stamp. For some complex equipment, there are often multiple data collection points, and the sampling rate of each collection point may be different, and fully independent. For example, for a car, there could be a data collection point to collect GPS position metrics, a data collection point to collect engine status metrics, and a data collection point to collect the environment metrics inside the car. So in this example the car would have three data collection points. ## Table @@ -122,10 +122,10 @@ To make full use of time-series data characteristics, TDengine adopts a strategy 1. Since the metric data from different DCP are fully independent, the data source of each DCP is unique, and a table has only one writer. In this way, data points can be written in a lock-free manner, and the writing speed can be greatly improved. 2. For a DCP, the metric data generated by DCP is ordered by timestamp, so the write operation can be implemented by simple appending, which further greatly improves the data writing speed. -3. The metric data from a DCP is continuously stored in block by block. If you read data for a period of time, it can greatly reduce random read operations and improve read and query performance by orders of magnitude. -4. Inside a data block for a DCP, columnar storage is used, and different compression algorithms are used for different data types. Metrics generally don't vary as significantly between themselves over a time range as compared to other metrics, this allows for a higher compression rate. +3. The metric data from a DCP is continuously stored, block by block. If you read data for a period of time, it can greatly reduce random read operations and improve read and query performance by orders of magnitude. +4. Inside a data block for a DCP, columnar storage is used, and different compression algorithms are used for different data types. Metrics generally don't vary as significantly between themselves over a time range as compared to other metrics, which allows for a higher compression rate. -If the metric data of multiple DCPs are traditionally written into a single table, due to the uncontrollable network delay, the timing of the data from different DCPs arriving at the server cannot be guaranteed, the writing operation must be protected by locks, and the metric data from one DCP cannot be guaranteed to be continuously stored together. **One table for one data collection point can ensure the best performance of insert and query of a single data collection point to the greatest extent.** +If the metric data of multiple DCPs are traditionally written into a single table, due to uncontrollable network delays, the timing of the data from different DCPs arriving at the server cannot be guaranteed, write operations must be protected by locks, and metric data from one DCP cannot be guaranteed to be continuously stored together. **One table for one data collection point can ensure the best performance of insert and query of a single data collection point to the greatest possible extent.** TDengine suggests using DCP ID as the table name (like D1001 in the above table). Each DCP may collect one or multiple metrics (like the current, voltage, phase as above). Each metric has a corresponding column in the table. The data type for a column can be int, float, string and others. In addition, the first column in the table must be a timestamp. TDengine uses the time stamp as the index, and won’t build the index on any metrics stored. Column wise storage is used. @@ -139,7 +139,7 @@ In the design of TDengine, **a table is used to represent a specific data collec ## Subtable -When creating a table for a specific data collection point, the user can use a STable as a template and specifies the tag values of this specific DCP to create it. **The table created by using a STable as the template is called subtable** in TDengine. The difference between regular table and subtable is: +When creating a table for a specific data collection point, the user can use a STable as a template and specify the tag values of this specific DCP to create it. **The table created by using a STable as the template is called subtable** in TDengine. The difference between regular table and subtable is: 1. Subtable is a table, all SQL commands applied on a regular table can be applied on subtable. 2. Subtable is a table with extensions, it has static tags (labels), and these tags can be added, deleted, and updated after it is created. But a regular table does not have tags. 3. A subtable belongs to only one STable, but a STable may have many subtables. Regular tables do not belong to a STable. @@ -151,7 +151,7 @@ The relationship between a STable and the subtables created based on this STable 2. The schema of metrics or labels cannot be adjusted through subtables, and it can only be changed via STable. Changes to the schema of a STable takes effect immediately for all associated subtables. 3. STable defines only one template and does not store any data or label information by itself. Therefore, data cannot be written to a STable, only to subtables. -Queries can be executed on both a table (subtable) and a STable. For a query on a STable, TDengine will treat the data in all its subtables as a whole data set for processing. TDengine will first find the subtables that meet the tag filter conditions, then scan the time-series data of these subtables to perform aggregation operation, which can greatly reduce the data sets to be scanned, thus greatly improving the performance of data aggregation across multiple DCPs. +Queries can be executed on both a table (subtable) and a STable. For a query on a STable, TDengine will treat the data in all its subtables as a whole data set for processing. TDengine will first find the subtables that meet the tag filter conditions, then scan the time-series data of these subtables to perform aggregation operation, which reduces the number of data sets to be scanned which in turn greatly improves the performance of data aggregation across multiple DCPs. In TDengine, it is recommended to use a subtable instead of a regular table for a DCP. @@ -167,4 +167,4 @@ FQDN (Fully Qualified Domain Name) is the full domain name of a specific compute Each node of a TDengine cluster is uniquely identified by an End Point, which consists of an FQDN and a Port, such as h1.tdengine.com:6030. In this way, when the IP changes, we can still use the FQDN to dynamically find the node without changing any configuration of the cluster. In addition, FQDN is used to facilitate unified access to the same cluster from the Intranet and the Internet. -TDengine does not recommend using an IP address to access the cluster, FQDN is recommended for cluster management. +TDengine does not recommend using an IP address to access the cluster. FQDN is recommended for cluster management. diff --git a/docs-en/05-get-started/_pkg_install.mdx b/docs-en/05-get-started/_pkg_install.mdx index af04d2b70bda7575e57cc49a5aa60f19689113e6..cf10497c96ba1d777e45340b0312d97c127b6fcb 100644 --- a/docs-en/05-get-started/_pkg_install.mdx +++ b/docs-en/05-get-started/_pkg_install.mdx @@ -12,6 +12,6 @@ Between two major release versions, some beta versions may be delivered for user For the details please refer to [Install and Uninstall](/operation/pkg-install)。 -To see the details of versions, please refer to [Download List](https://www.taosdata.com/all-downloads) and [Release Notes](https://github.com/taosdata/TDengine/releases). +To see the details of versions, please refer to [Download List](https://tdengine.com/all-downloads) and [Release Notes](https://github.com/taosdata/TDengine/releases). diff --git a/docs-en/05-get-started/index.md b/docs-en/05-get-started/index.md index 39b2d02eca3c15aebd5715ee64e455781c8236e5..56958ef3ec1c206ee0cff45c67fd3c3a6fa6753a 100644 --- a/docs-en/05-get-started/index.md +++ b/docs-en/05-get-started/index.md @@ -10,7 +10,7 @@ import AptGetInstall from "./\_apt_get_install.mdx"; ## Quick Install -The full package of TDengine includes the server(taosd), taosAdapter for connecting with third-party systems and providing a RESTful interface, client driver(taosc), command-line program(CLI, taos) and some tools. For the current version, the server taosd and taosAdapter can only be installed and run on Linux systems. In the future taosd and taosAdapter will also be supported on Windows, macOS and other systems. The client driver taosc and TDengine CLI can be installed and run on Windows or Linux. In addition to the connectors of multiple languages, [RESTful interface](/reference/rest-api) is also provided by [taosAdapter](/reference/taosadapter) in TDengine. Prior to version 2.4.0.0, however, there is no taosAdapter, the RESTful interface is provided by the built-in HTTP service of taosd. +The full package of TDengine includes the server(taosd), taosAdapter for connecting with third-party systems and providing a RESTful interface, client driver(taosc), command-line program(CLI, taos) and some tools. For the current version, the server taosd and taosAdapter can only be installed and run on Linux systems. In the future taosd and taosAdapter will also be supported on Windows, macOS and other systems. The client driver taosc and TDengine CLI can be installed and run on Windows or Linux. In addition to connectors for multiple languages, TDengine also provides a [RESTful interface](/reference/rest-api) through [taosAdapter](/reference/taosadapter). Prior to version 2.4.0.0, taosAdapter did not exist and the RESTful interface was provided by the built-in HTTP service of taosd. TDengine supports X64/ARM64/MIPS64/Alpha64 hardware platforms, and will support ARM32, RISC-V and other CPU architectures in the future. @@ -130,7 +130,7 @@ After TDengine server is running，execute `taosBenchmark` (previously named tao taosBenchmark ``` -This command will create a super table "meters" under database "test". Under "meters", 10000 tables are created with names from "d0" to "d9999". Each table has 10000 rows and each row has four columns (ts, current, voltage, phase). Time stamp is starting from "2017-07-14 10:40:00 000" to "2017-07-14 10:40:09 999". Each table has tags "location" and "groupId". groupId is set 1 to 10 randomly, and location is set to "beijing" or "shanghai". +This command will create a super table "meters" under database "test". Under "meters", 10000 tables are created with names from "d0" to "d9999". Each table has 10000 rows and each row has four columns (ts, current, voltage, phase). Time stamp is starting from "2017-07-14 10:40:00 000" to "2017-07-14 10:40:09 999". Each table has tags "location" and "groupId". groupId is set 1 to 10 randomly, and location is set to "California.SanFrancisco" or "California.SanDiego". This command will insert 100 million rows into the database quickly. Time to insert depends on the hardware configuration, it only takes a dozen seconds for a regular PC server. @@ -152,10 +152,10 @@ query the average, maximum, minimum of 100 million rows: taos> select avg(current), max(voltage), min(phase) from test.meters; ``` -query the total number of rows with location="beijing": +query the total number of rows with location="California.SanFrancisco": ```sql -taos> select count(*) from test.meters where location="beijing"; +taos> select count(*) from test.meters where location="California.SanFrancisco"; ``` query the average, maximum, minimum of all rows with groupId=10: diff --git a/docs-en/07-develop/01-connect/index.md b/docs-en/07-develop/01-connect/index.md index ee11c8f5445233abe44e3bc006e1f15846b54ada..720f8e2384c565d5494ce7d84d531188dae96fe0 100644 --- a/docs-en/07-develop/01-connect/index.md +++ b/docs-en/07-develop/01-connect/index.md @@ -1,7 +1,7 @@ --- -sidebar_label: Connection -title: Connect to TDengine -description: "This document explains how to establish connection to TDengine, and briefly introduce how to install and use TDengine connectors." +sidebar_label: Connect +title: Connect +description: "This document explains how to establish connections to TDengine, and briefly introduces how to install and use TDengine connectors." --- import Tabs from "@theme/Tabs"; @@ -19,25 +19,24 @@ import InstallOnLinux from "../../14-reference/03-connector/\_windows_install.md import VerifyLinux from "../../14-reference/03-connector/\_verify_linux.mdx"; import VerifyWindows from "../../14-reference/03-connector/\_verify_windows.mdx"; -Any application programs running on any kind of platforms can access TDengine through the REST API provided by TDengine. For the details, please refer to [REST API](/reference/rest-api/). Besides, application programs can use the connectors of multiple programming languages to access TDengine, including C/C++, Java, Python, Go, Node.js, C#, and Rust. This chapter describes how to establish connection to TDengine and briefly introduce how to install and use connectors. For details about the connectors, please refer to [Connectors](/reference/connector/) +Any application programs running on any kind of platform can access TDengine through the REST API provided by TDengine. For details, please refer to [REST API](/reference/rest-api/). Additionally, application programs can use the connectors of multiple programming languages including C/C++, Java, Python, Go, Node.js, C#, Rust to access TDengine. This chapter describes how to establish a connection to TDengine and briefly introduces how to install and use connectors. TDengine community also provides connectors in LUA and PHP languages. For details about the connectors, please refer to [Connectors](/reference/connector/). ## Establish Connection There are two ways for a connector to establish connections to TDengine: -1. Connection through the REST API provided by taosAdapter component, this way is called "REST connection" hereinafter. +1. Connection through the REST API provided by the taosAdapter component, this way is called "REST connection" hereinafter. 2. Connection through the TDengine client driver (taosc), this way is called "Native connection" hereinafter. -Either way, same or similar APIs are provided by connectors to access database or execute SQL statements, no obvious difference can be observed. - Key differences： -1. With REST connection, it's not necessary to install TDengine client driver (taosc), it's more friendly for cross-platform with the cost of 30% performance downgrade. When taosc has an upgrade, application does not need to make changes. -2. With native connection, full compatibility of TDengine can be utilized, like [Parameter Binding](/reference/connector/cpp#parameter-binding-api), [Subscription](/reference/connector/cpp#subscription-and-consumption-api), etc. But taosc has to be installed, some platforms may not be supported. +1. The TDengine client driver (taosc) has the highest performance with all the features of TDengine like [Parameter Binding](/reference/connector/cpp#parameter-binding-api), [Subscription](/reference/connector/cpp#subscription-and-consumption-api), etc. +2. The TDengine client driver (taosc) is not supported across all platforms, and applications built on taosc may need to be modified when updating taosc to newer versions. +3. The REST connection is more accessible with cross-platform support, however it results in a 30% performance downgrade. ## Install Client Driver taosc -If choosing to use native connection and the application is not on the same host as TDengine server, TDengine client driver taosc needs to be installed on the host where the application is. If choosing to use REST connection or the application is on the same host as server side, this step can be skipped. It's better to use same version of taosc as the server. +If you are choosing to use the native connection and the the application is not on the same host as TDengine server, the TDengine client driver taosc needs to be installed on the application host. If choosing to use the REST connection or the application is on the same host as TDengine server, this step can be skipped. It's better to use same version of taosc as the TDengine server. ### Install @@ -201,6 +200,46 @@ install.packages("RJDBC") If the client driver (taosc) is already installed, then the C connector is already available.
+ + + +**Download Source Code Package and Unzip：** + +```shell +curl -L -o php-tdengine.tar.gz https://github.com/Yurunsoft/php-tdengine/archive/refs/tags/v1.0.2.tar.gz \ +&& mkdir php-tdengine \ +&& tar -xzf php-tdengine.tar.gz -C php-tdengine --strip-components=1 +``` + +> Version number `v1.0.2` is only for example, it can be replaced to any newer version, please check available version from [TDengine PHP Connector Releases](https://github.com/Yurunsoft/php-tdengine/releases). + +**Non-Swoole Environment：** + +```shell +phpize && ./configure && make -j && make install +``` + +**Specify TDengine Location：** + +```shell +phpize && ./configure --with-tdengine-dir=/usr/local/Cellar/tdengine/2.4.0.0 && make -j && make install +``` + +> `--with-tdengine-dir=` is followed by the TDengine installation location. +> This way is useful in case TDengine location can't be found automatically or macOS. + +**Swoole Environment：** + +```shell +phpize && ./configure --enable-swoole && make -j && make install +``` + +**Enable The Extension:** + +Option One: Add `extension=tdengine` in `php.ini` + +Option Two: Specify the extension on CLI `php -d extension=tdengine test.php` + diff --git a/docs-en/07-develop/02-model/index.mdx b/docs-en/07-develop/02-model/index.mdx index 962a75338f0384ee8facb4682342e25e536e4ecb..86853aaaa3f7285fe042a892e2ec903d57894111 100644 --- a/docs-en/07-develop/02-model/index.mdx +++ b/docs-en/07-develop/02-model/index.mdx @@ -2,19 +2,26 @@ title: Data Model --- -The data model employed by TDengine is similar to relational database, you need to create databases and tables. For a specific application, the design of databases, STables (abbreviated for super table), and tables need to be considered. This chapter will explain the big picture without syntax details. +The data model employed by TDengine is similar to that of a relational database. You have to create databases and tables. You must design the data model based on your own business and application requirements. You should design the STable (an abbreviation for super table) schema to fit your data. This chapter will explain the big picture without getting into syntactical details. ## Create Database -The characteristics of data from different data collection points may be different, such as collection frequency, days to keep, number of replicas, data block size, whether it's allowed to update data, etc. For TDengine to operate with the best performance, it's strongly suggested to put the data with different characteristics into different databases because different storage policy can be set for each database. When creating a database, there are a lot of parameters that can be configured, such as the days to keep data, the number of replicas, the number of memory blocks, time precision, the minimum and maximum number of rows in each data block, compress or not, the time range of the data in single data file, etc. Below is an example of the SQL statement for creating a database. +The [characteristics of time-series data](https://www.taosdata.com/blog/2019/07/09/86.html) from different data collection points may be different. Characteristics include collection frequency, retention policy and others which determine how you create and configure the database. For e.g. days to keep, number of replicas, data block size, whether data updates are allowed and other configurable parameters would be determined by the characteristics of your data and your business requirements. For TDengine to operate with the best performance, we strongly recommend that you create and configure different databases for data with different characteristics. This allows you, for example, to set up different storage and retention policies. When creating a database, there are a lot of parameters that can be configured such as, the days to keep data, the number of replicas, the number of memory blocks, time precision, the minimum and maximum number of rows in each data block, whether compression is enabled, the time range of the data in single data file and so on. Below is an example of the SQL statement to create a database. ```sql CREATE DATABASE power KEEP 365 DAYS 10 BLOCKS 6 UPDATE 1; ``` -In the above SQL statement, a database named "power" will be created, the data in it will be kept for 365 days, which means the data older than 365 days will be deleted automatically, a new data file will be created every 10 days, the number of memory blocks is 6, data is allowed to be updated. For more details please refer to [Database](/taos-sql/database). +In the above SQL statement: +- a database named "power" will be created +- the data in it will be kept for 365 days, which means that data older than 365 days will be deleted automatically +- a new data file will be created every 10 days +- the number of memory blocks is 6 +- data is allowed to be updated -After creating a database, the current database in use can be switched using SQL command `USE`, for example below SQL statement switches the current database to `power`. Without current database specified, table name must be preceded with the corresponding database name. +For more details please refer to [Database](/taos-sql/database). + +After creating a database, the current database in use can be switched using SQL command `USE`. For example the SQL statement below switches the current database to `power`. Without the current database specified, table name must be preceded with the corresponding database name. ```sql USE power; @@ -23,14 +30,14 @@ USE power; :::note - Any table or STable must belong to a database. To create a table or STable, the database it belongs to must be ready. -- JOIN operation can't be performed tables from two different databases. +- JOIN operations can't be performed on tables from two different databases. - Timestamp needs to be specified when inserting rows or querying historical rows. ::: ## Create STable -In a time-series application, there may be multiple kinds of data collection points. For example, in the electrical power system there are meters, transformers, bus bars, switches, etc. For easy and efficient aggregation of multiple tables, one STable needs to be created for each kind of data collection point. For example, for the meters in [table 1](/tdinternal/arch#model_table1), below SQL statement can be used to create the super table. +In a time-series application, there may be multiple kinds of data collection points. For example, in the electrical power system there are meters, transformers, bus bars, switches, etc. For easy and efficient aggregation of multiple tables, one STable needs to be created for each kind of data collection point. For example, for the meters in [table 1](/tdinternal/arch#model_table1), the SQL statement below can be used to create the super table. ```sql CREATE STable meters (ts timestamp, current float, voltage int, phase float) TAGS (location binary(64), groupId int); @@ -41,44 +48,46 @@ If you are using versions prior to 2.0.15, the `STable` keyword needs to be repl ::: -Similar to creating a regular table, when creating a STable, name and schema need to be provided too. In the STable schema, the first column must be timestamp (like ts in the example), and other columns (like current, voltage and phase in the example) are the data collected. The type of a column can be integer, float, double, string ,etc. Besides, the schema for tags need to be provided, like location and groupId in the example. The type of a tag can be integer, float, string, etc. The static properties of a data collection point can be defined as tags, like the location, device type, device group ID, manager ID, etc. Tags in the schema can be added, removed or updated. Please refer to [STable](/taos-sql/stable) for more details. +Similar to creating a regular table, when creating a STable, the name and schema need to be provided. In the STable schema, the first column must always be a timestamp (like ts in the example), and the other columns (like current, voltage and phase in the example) are the data collected. The remaining columns can [contain data of type](/taos-sql/data-type/) integer, float, double, string etc. In addition, the schema for tags, like location and groupId in the example, must be provided. The tag type can be integer, float, string, etc. Tags are essentially the static properties of a data collection point. For example, properties like the location, device type, device group ID, manager ID are tags. Tags in the schema can be added, removed or updated. Please refer to [STable](/taos-sql/stable) for more details. -For each kind of data collection points, a corresponding STable must be created. There may be many STables in an application. For electrical power system, we need to create a STable respectively for meters, transformers, busbars, switches. There may be multiple kinds of data collection points on a single device, for example there may be one data collection point for electrical data like current and voltage and another point for environmental data like temperature, humidity and wind direction, multiple STables are required for such kind of device. +For each kind of data collection point, a corresponding STable must be created. There may be many STables in an application. For electrical power system, we need to create a STable respectively for meters, transformers, busbars, switches. There may be multiple kinds of data collection points on a single device, for example there may be one data collection point for electrical data like current and voltage and another data collection point for environmental data like temperature, humidity and wind direction. Multiple STables are required for these kinds of devices. -At most 4096 (or 1024 prior to version 2.1.7.0) columns are allowed in a STable. If there are more than 4096 of metrics to bo collected for a data collection point, multiple STables are required for such kind of data collection point. There can be multiple databases in system, while one or more STables can exist in a database. +At most 4096 (or 1024 prior to version 2.1.7.0) columns are allowed in a STable. If there are more than 4096 of metrics to be collected for a data collection point, multiple STables are required. There can be multiple databases in a system, while one or more STables can exist in a database. ## Create Table -A specific table needs to be created for each data collection point. Similar to RDBMS, table name and schema are required to create a table. Beside, one or more tags can be created for each table. To create a table, a STable needs to be used as template and the values need to be specified for the tags. For example, for the meters in [Table 1](/tdinternal/arch#model_table1), the table can be created using below SQL statement. +A specific table needs to be created for each data collection point. Similar to RDBMS, table name and schema are required to create a table. Additionally, one or more tags can be created for each table. To create a table, a STable needs to be used as template and the values need to be specified for the tags. For example, for the meters in [Table 1](/tdinternal/arch#model_table1), the table can be created using below SQL statement. ```sql -CREATE TABLE d1001 USING meters TAGS ("Beijing.Chaoyang", 2); +CREATE TABLE d1001 USING meters TAGS ("California.SanFrancisco", 2); ``` -In the above SQL statement, "d1001" is the table name, "meters" is the STable name, followed by the value of tag "Location" and the value of tag "groupId", which are "Beijing.Chaoyang" and "2" respectively in the example. The tag values can be updated after the table is created. Please refer to [Tables](/taos-sql/table) for details. +In the above SQL statement, "d1001" is the table name, "meters" is the STable name, followed by the value of tag "Location" and the value of tag "groupId", which are "California.SanFrancisco" and "2" respectively in the example. The tag values can be updated after the table is created. Please refer to [Tables](/taos-sql/table) for details. -In TDengine system, it's recommended to create a table for a data collection point via STable. Table created via STable is called subtable in some parts of TDengine document. All SQL commands applied on regular table can be applied on subtable. +In the TDengine system, it's recommended to create a table for a data collection point via STable. A table created via STable is called subtable in some parts of the TDengine documentation. All SQL commands applied on regular tables can be applied on subtables. :::warning It's not recommended to create a table in a database while using a STable from another database as template. :::tip -It's suggested to use the global unique ID of a data collection point as the table name, for example the device serial number. If there isn't such a unique ID, multiple IDs that are not global unique can be combined to form a global unique ID. It's not recommended to use a global unique ID as tag value. +It's suggested to use the globally unique ID of a data collection point as the table name. For example the device serial number could be used as a unique ID. If a unique ID doesn't exist, multiple IDs that are not globally unique can be combined to form a globally unique ID. It's not recommended to use a globally unique ID as tag value. ## Create Table Automatically -In some circumstances, it's not sure whether the table already exists when inserting rows. The table can be created automatically using the SQL statement below, and nothing will happen if the table already exist. +In some circumstances, it's unknown whether the table already exists when inserting rows. The table can be created automatically using the SQL statement below, and nothing will happen if the table already exists. ```sql -INSERT INTO d1001 USING meters TAGS ("Beijng.Chaoyang", 2) VALUES (now, 10.2, 219, 0.32); +INSERT INTO d1001 USING meters TAGS ("California.SanFrancisco", 2) VALUES (now, 10.2, 219, 0.32); ``` -In the above SQL statement, a row with value `(now, 10.2, 219, 0.32)` will be inserted into table "d1001". If table "d1001" doesn't exist, it will be created automatically using STable "meters" as template with tag value `"Beijing.Chaoyang", 2`. +In the above SQL statement, a row with value `(now, 10.2, 219, 0.32)` will be inserted into table "d1001". If table "d1001" doesn't exist, it will be created automatically using STable "meters" as template with tag value `"California.SanFrancisco", 2`. For more details please refer to [Create Table Automatically](/taos-sql/insert#automatically-create-table-when-inserting). ## Single Column vs Multiple Column -Multiple columns data model is supported in TDengine. As long as multiple metrics are collected by same data collection point at same time, i.e. the timestamp are identical, these metrics can be put in single stable as columns. However, there is another kind of design, i.e. single column data model, a table is created for each metric, which means a STable is required for each kind of metric. For example, 3 STables are required for current, voltage and phase. +A multiple columns data model is supported in TDengine. As long as multiple metrics are collected by the same data collection point at the same time, i.e. the timestamps are identical, these metrics can be put in a single STable as columns. + +However, there is another kind of design, i.e. single column data model in which a table is created for each metric. This means that a STable is required for each kind of metric. For example in a single column model, 3 STables would be required for current, voltage and phase. -It's recommended to use multiple column data model as much as possible because it's better in the performance of inserting or querying rows. In some cases, however, the metrics to be collected vary frequently and correspondingly the STable schema needs to be changed frequently too. In such case, it's more convenient to use single column data model. +It's recommended to use a multiple column data model as much as possible because insert and query performance is higher. In some cases, however, the collected metrics may vary frequently and so the corresponding STable schema needs to be changed frequently too. In such cases, it's more convenient to use single column data model. diff --git a/docs-en/07-develop/03-insert-data/01-sql-writing.mdx b/docs-en/07-develop/03-insert-data/01-sql-writing.mdx index 9f66992d3de755389c3a0722ebb09097177742f1..397b1a14fd76c1372c79eb88575f2bf21cb62050 100644 --- a/docs-en/07-develop/03-insert-data/01-sql-writing.mdx +++ b/docs-en/07-develop/03-insert-data/01-sql-writing.mdx @@ -1,5 +1,5 @@ --- -sidebar_label: SQL +sidebar_label: Insert Using SQL title: Insert Using SQL --- @@ -22,11 +22,11 @@ import CStmt from "./_c_stmt.mdx"; ## Introduction -Application program can execute `INSERT` statement through connectors to insert rows. TAOS CLI can be launched manually to insert data too. +Application programs can execute `INSERT` statement through connectors to insert rows. The TAOS CLI can also be used to manually insert data. ### Insert Single Row -Below SQL statement is used to insert one row into table "d1001". +The below SQL statement is used to insert one row into table "d1001". ```sql INSERT INTO d1001 VALUES (1538548685000, 10.3, 219, 0.31); @@ -34,7 +34,7 @@ INSERT INTO d1001 VALUES (1538548685000, 10.3, 219, 0.31); ### Insert Multiple Rows -Multiple rows can be inserted in single SQL statement. Below example inserts 2 rows into table "d1001". +Multiple rows can be inserted in a single SQL statement. The example below inserts 2 rows into table "d1001". ```sql INSERT INTO d1001 VALUES (1538548684000, 10.2, 220, 0.23) (1538548696650, 10.3, 218, 0.25); @@ -42,7 +42,7 @@ INSERT INTO d1001 VALUES (1538548684000, 10.2, 220, 0.23) (1538548696650, 10.3, ### Insert into Multiple Tables -Data can be inserted into multiple tables in same SQL statement. Below example inserts 2 rows into table "d1001" and 1 row into table "d1002". +Data can be inserted into multiple tables in the same SQL statement. The example below inserts 2 rows into table "d1001" and 1 row into table "d1002". ```sql INSERT INTO d1001 VALUES (1538548685000, 10.3, 219, 0.31) (1538548695000, 12.6, 218, 0.33) d1002 VALUES (1538548696800, 12.3, 221, 0.31); @@ -52,14 +52,14 @@ For more details about `INSERT` please refer to [INSERT](/taos-sql/insert). :::info -- Inserting in batch can gain better performance. Normally, the higher the batch size, the better the performance. Please be noted each single row can't exceed 16K bytes and each single SQL statement can't exceed 1M bytes. -- Inserting with multiple threads can gain better performance too. However, depending on the system resources on the application side and the server side, with the number of inserting threads grows to a specific point, the performance may drop instead of growing. The proper number of threads need to be tested in a specific environment to find the best number. +- Inserting in batches can improve performance. Normally, the higher the batch size, the better the performance. Please note that a single row can't exceed 48K bytes and each SQL statement can't exceed 1MB. +- Inserting with multiple threads can also improve performance. However, depending on the system resources on the application side and the server side, when the number of inserting threads grows beyond a specific point the performance may drop instead of improving. The proper number of threads needs to be tested in a specific environment to find the best number. ::: :::warning -- If the timestamp for the row to be inserted already exists in the table, the behavior depends on the value of parameter `UPDATE`. If it's set to 0 (also the default value), the row will be discarded. If it's set to 1, the new values will override the old values for the same row. +- If the timestamp for the row to be inserted already exists in the table, the behavior depends on the value of parameter `UPDATE`. If it's set to 0 (the default value), the row will be discarded. If it's set to 1, the new values will override the old values for the same row. - The timestamp to be inserted must be newer than the timestamp of subtracting current time by the parameter `KEEP`. If `KEEP` is set to 3650 days, then the data older than 3650 days ago can't be inserted. The timestamp to be inserted can't be newer than the timestamp of current time plus parameter `DAYS`. If `DAYS` is set to 2, the data newer than 2 days later can't be inserted. ::: @@ -95,13 +95,13 @@ For more details about `INSERT` please refer to [INSERT](/taos-sql/insert). :::note 1. With either native connection or REST connection, the above samples can work well. -2. Please be noted that `use db` can't be used with REST connection because REST connection is stateless, so in the samples `dbName.tbName` is used to specify the table name. +2. Please note that `use db` can't be used with a REST connection because REST connections are stateless, so in the samples `dbName.tbName` is used to specify the table name. ::: ### Insert with Parameter Binding -TDengine also provides Prepare API that support parameter binding. Similar to MySQL, only `?` can be used in these APIs to represent the parameters to bind. From version 2.1.1.0 and 2.1.2.0, parameter binding support for inserting data has been improved significantly to improve the insert performance by avoiding the cost of parsing SQL statements. +TDengine also provides API support for parameter binding. Similar to MySQL, only `?` can be used in these APIs to represent the parameters to bind. From version 2.1.1.0 and 2.1.2.0, parameter binding support for inserting data has improved significantly to improve the insert performance by avoiding the cost of parsing SQL statements. Parameter binding is available only with native connection. diff --git a/docs-en/07-develop/03-insert-data/02-influxdb-line.mdx b/docs-en/07-develop/03-insert-data/02-influxdb-line.mdx index 172003d203fa309ce51b3ecae9a7490a59f513d7..be46ebf0c97a29b57c1b57eb8ea5c9394f85b93a 100644 --- a/docs-en/07-develop/03-insert-data/02-influxdb-line.mdx +++ b/docs-en/07-develop/03-insert-data/02-influxdb-line.mdx @@ -15,13 +15,13 @@ import CLine from "./_c_line.mdx"; ## Introduction -A single line of text is used in InfluxDB Line protocol format represents one row of data, each line contains 4 parts as shown below. +In the InfluxDB Line protocol format, a single line of text is used to represent one row of data. Each line contains 4 parts as shown below. ``` measurement,tag_set field_set timestamp ``` -- `measurement` will be used as the STable name +- `measurement` will be used as the name of the STable - `tag_set` will be used as tags, with format like `=,=` - `field_set`will be used as data columns, with format like `=,=` - `timestamp` is the primary key timestamp corresponding to this row of data @@ -29,13 +29,13 @@ measurement,tag_set field_set timestamp For example: ``` -meters,location=Beijing.Haidian,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611249500 +meters,location=California.LoSangeles,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611249500 ``` :::note -- All the data in `tag_set` will be converted to ncahr type automatically . -- Each data in `field_set` must be self-description for its data type. For example 1.2f32 means a value 1.2 of float type, it will be treated as double without the "f" type suffix. +- All the data in `tag_set` will be converted to nchar type automatically . +- Each data in `field_set` must be self-descriptive for its data type. For example 1.2f32 means a value 1.2 of float type. Without the "f" type suffix, it will be treated as type double. - Multiple kinds of precision can be used for the `timestamp` field. Time precision can be from nanosecond (ns) to hour (h). ::: diff --git a/docs-en/07-develop/03-insert-data/03-opentsdb-telnet.mdx b/docs-en/07-develop/03-insert-data/03-opentsdb-telnet.mdx index 66bb67c25669b906183526377f60b969ea3d1e85..18a695cda8efbef075451ff53e542d9e69c58e0b 100644 --- a/docs-en/07-develop/03-insert-data/03-opentsdb-telnet.mdx +++ b/docs-en/07-develop/03-insert-data/03-opentsdb-telnet.mdx @@ -15,21 +15,21 @@ import CTelnet from "./_c_opts_telnet.mdx"; ## Introduction -A single line of text is used in OpenTSDB line protocol to represent one row of data. OpenTSDB employs single column data model, so one line can only contains single data column. There can be multiple tags. Each line contains 4 parts as below: +A single line of text is used in OpenTSDB line protocol to represent one row of data. OpenTSDB employs a single column data model, so each line can only contain a single data column. There can be multiple tags. Each line contains 4 parts as below: ``` =[ =] ``` -- `metric` will be used as STable name. -- `timestamp` is the timestamp of current row of data. The time precision will be determined automatically based on the length of the timestamp. second and millisecond time precision are supported.\ +- `metric` will be used as the STable name. +- `timestamp` is the timestamp of current row of data. The time precision will be determined automatically based on the length of the timestamp. Second and millisecond time precision are supported. - `value` is a metric which must be a numeric value, the corresponding column name is "value". -- The last part is tag sets separated by space, all tags will be converted to nchar type automatically. +- The last part is the tag set separated by spaces, all tags will be converted to nchar type automatically. For example: ```txt -meters.current 1648432611250 11.3 location=Beijing.Haidian groupid=3 +meters.current 1648432611250 11.3 location=California.LoSangeles groupid=3 ``` Please refer to [OpenTSDB Telnet API](http://opentsdb.net/docs/build/html/api_telnet/put.html) for more details. @@ -60,7 +60,7 @@ Please refer to [OpenTSDB Telnet API](http://opentsdb.net/docs/build/html/api_te -2 STables will be crated automatically while each STable has 4 rows of data in the above sample code. +2 STables will be created automatically and each STable has 4 rows of data in the above sample code. ```cmd taos> use test; @@ -76,9 +76,9 @@ Query OK, 2 row(s) in set (0.002544s) taos> select tbname, * from `meters.current`; tbname | ts | value | groupid | location | ================================================================================================================================== - t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.249 | 10.800000000 | 3 | Beijing.Haidian | - t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.250 | 11.300000000 | 3 | Beijing.Haidian | - t_7e7b26dd860280242c6492a16... | 2022-03-28 09:56:51.249 | 10.300000000 | 2 | Beijing.Chaoyang | - t_7e7b26dd860280242c6492a16... | 2022-03-28 09:56:51.250 | 12.600000000 | 2 | Beijing.Chaoyang | + t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.249 | 10.800000000 | 3 | California.LoSangeles | + t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.250 | 11.300000000 | 3 | California.LoSangeles | + t_7e7b26dd860280242c6492a16... | 2022-03-28 09:56:51.249 | 10.300000000 | 2 | California.SanFrancisco | + t_7e7b26dd860280242c6492a16... | 2022-03-28 09:56:51.250 | 12.600000000 | 2 | California.SanFrancisco | Query OK, 4 row(s) in set (0.005399s) ``` diff --git a/docs-en/07-develop/03-insert-data/04-opentsdb-json.mdx b/docs-en/07-develop/03-insert-data/04-opentsdb-json.mdx index d4f723dcdeb78c54ba31fd4f6aa2528a90376c5f..3a239440311c736159d6060db5e730c5e5665bcb 100644 --- a/docs-en/07-develop/03-insert-data/04-opentsdb-json.mdx +++ b/docs-en/07-develop/03-insert-data/04-opentsdb-json.mdx @@ -47,7 +47,7 @@ Please refer to [OpenTSDB HTTP API](http://opentsdb.net/docs/build/html/api_http :::note - In JSON protocol, strings will be converted to nchar type and numeric values will be converted to double type. -- Only data in array format is accepted, array must be used even there is only one row. +- Only data in array format is accepted and so an array must be used even if there is only one row. ::: @@ -93,7 +93,7 @@ Query OK, 2 row(s) in set (0.001954s) taos> select * from `meters.current`; ts | value | groupid | location | =================================================================================================================== - 2022-03-28 09:56:51.249 | 10.300000000 | 2.000000000 | Beijing.Chaoyang | - 2022-03-28 09:56:51.250 | 12.600000000 | 2.000000000 | Beijing.Chaoyang | + 2022-03-28 09:56:51.249 | 10.300000000 | 2.000000000 | California.SanFrancisco | + 2022-03-28 09:56:51.250 | 12.600000000 | 2.000000000 | California.SanFrancisco | Query OK, 2 row(s) in set (0.004076s) ``` diff --git a/docs-en/07-develop/03-insert-data/index.md b/docs-en/07-develop/03-insert-data/index.md index ee80d436f11f19b422df261845f1c209620251f2..1a71e719a56448e4b535632e570ce8a04d2282bb 100644 --- a/docs-en/07-develop/03-insert-data/index.md +++ b/docs-en/07-develop/03-insert-data/index.md @@ -1,12 +1,12 @@ --- -title: Insert +title: Insert Data --- -TDengine supports multiple protocols of inserting data, including SQL, InfluxDB Line protocol, OpenTSDB Telnet protocol, OpenTSDB JSON protocol. Data can be inserted row by row, or in batch. Data from one or more collecting points can be inserted simultaneously. In the meantime, data can be inserted with multiple threads, out of order data and historical data can be inserted too. InfluxDB Line protocol, OpenTSDB Telnet protocol and OpenTSDB JSON protocol are the 3 kinds of schemaless insert protocols supported by TDengine. It's not necessary to create stable and table in advance if using schemaless protocols, and the schemas can be adjusted automatically according to the data to be inserted. +TDengine supports multiple protocols of inserting data, including SQL, InfluxDB Line protocol, OpenTSDB Telnet protocol, and OpenTSDB JSON protocol. Data can be inserted row by row, or in batches. Data from one or more collection points can be inserted simultaneously. Data can be inserted with multiple threads, and out of order data and historical data can be inserted as well. InfluxDB Line protocol, OpenTSDB Telnet protocol and OpenTSDB JSON protocol are the 3 kinds of schemaless insert protocols supported by TDengine. It's not necessary to create STables and tables in advance if using schemaless protocols, and the schemas can be adjusted automatically based on the data being inserted. ```mdx-code-block import DocCardList from '@theme/DocCardList'; import {useCurrentSidebarCategory} from '@docusaurus/theme-common'; -``` \ No newline at end of file +``` diff --git a/docs-en/07-develop/04-query-data/_category_.yml b/docs-en/07-develop/04-query-data/_category_.yml index 5912a48fc31ed36235c0d34d8b0909bf3b518aaa..809db34621a63505ceace7ba182e07c698bdbddb 100644 --- a/docs-en/07-develop/04-query-data/_category_.yml +++ b/docs-en/07-develop/04-query-data/_category_.yml @@ -1 +1 @@ -label: Select Data +label: Query Data diff --git a/docs-en/07-develop/04-query-data/index.mdx b/docs-en/07-develop/04-query-data/index.mdx index 4016f8453ba9e0679a2798b92cd40efcb926343b..a212fa9529215fc24c55c95a166cfc1a407359b2 100644 --- a/docs-en/07-develop/04-query-data/index.mdx +++ b/docs-en/07-develop/04-query-data/index.mdx @@ -1,6 +1,6 @@ --- -Sidebar_label: Select -title: Select +Sidebar_label: Query data +title: Query data description: "This chapter introduces major query functionalities and how to perform sync and async query using connectors." --- @@ -20,7 +20,7 @@ import CAsync from "./_c_async.mdx"; ## Introduction -SQL is used by TDengine as the query language. Application programs can send SQL statements to TDengine through REST API or connectors. TDengine CLI `taos` can also be used to execute SQL Ad-Hoc query. Here is the list of major query functionalities supported by TDengine： +SQL is used by TDengine as its query language. Application programs can send SQL statements to TDengine through REST API or connectors. TDengine's CLI `taos` can also be used to execute ad hoc SQL queries. Here is the list of major query functionalities supported by TDengine： - Query on single column or multiple columns - Filter on tags or data columns：>, <, =, <\>, like @@ -31,7 +31,7 @@ SQL is used by TDengine as the query language. Application programs can send SQL - Join query with timestamp alignment - Aggregate functions: count, max, min, avg, sum, twa, stddev, leastsquares, top, bottom, first, last, percentile, apercentile, last_row, spread, diff -For example, below SQL statement can be executed in TDengine CLI `taos` to select the rows whose voltage column is bigger than 215 and limit the output to only 2 rows. +For example, the SQL statement below can be executed in TDengine CLI `taos` to select records with voltage greater than 215 and limit the output to only 2 rows. ```sql select * from d1001 where voltage > 215 order by ts desc limit 2; @@ -46,46 +46,46 @@ taos> select * from d1001 where voltage > 215 order by ts desc limit 2; Query OK, 2 row(s) in set (0.001100s) ``` -To meet the requirements in many use cases, some special functions have been added in TDengine, for example `twa` (Time Weighted Average), `spared` (The difference between the maximum and the minimum), `last_row` (the last row), more and more functions will be added to better perform in many use cases. Furthermore, continuous query is also supported in TDengine. +To meet the requirements of varied use cases, some special functions have been added in TDengine. Some examples are `twa` (Time Weighted Average), `spread` (The difference between the maximum and the minimum), and `last_row` (the last row). Furthermore, continuous query is also supported in TDengine. For detailed query syntax please refer to [Select](/taos-sql/select). ## Aggregation among Tables -In many use cases, there are always multiple kinds of data collection points. A new concept, called STable (abbreviated for super table), is used in TDengine to represent a kind of data collection points, and a table is used to represent a specific data collection point. Tags are used by TDengine to represent the static properties of data collection points. A specific data collection point has its own values for static properties. By specifying filter conditions on tags, aggregation can be performed efficiently among all the subtables created via the same STable, i.e. same kind of data collection points, can be. Aggregate functions applicable for tables can be used directly on STables, syntax is exactly same. +In most use cases, there are always multiple kinds of data collection points. A new concept, called STable (abbreviation for super table), is used in TDengine to represent one type of data collection point, and a subtable is used to represent a specific data collection point of that type. Tags are used by TDengine to represent the static properties of data collection points. A specific data collection point has its own values for static properties. By specifying filter conditions on tags, aggregation can be performed efficiently among all the subtables created via the same STable, i.e. same type of data collection points. Aggregate functions applicable for tables can be used directly on STables; the syntax is exactly the same. -In summary, for a STable, its subtables can be aggregated by a simple query on STable, it's kind of join operation. But tables belong to different STables could not be aggregated. +In summary, records across subtables can be aggregated by a simple query on their STable. It is like a join operation. However, tables belonging to different STables can not be aggregated. ### Example 1 -In TDengine CLI `taos`, use below SQL to get the average voltage of all the meters in BeiJing grouped by location. +In TDengine CLI `taos`, use the SQL below to get the average voltage of all the meters in California grouped by location. ``` taos> SELECT AVG(voltage) FROM meters GROUP BY location; avg(voltage) | location | ============================================================= - 222.000000000 | Beijing.Haidian | - 219.200000000 | Beijing.Chaoyang | + 222.000000000 | California.LosAngeles | + 219.200000000 | California.SanFrancisco | Query OK, 2 row(s) in set (0.002136s) ``` ### Example 2 -In TDengine CLI `taos`, use below SQL to get the number of rows and the maximum current in the past 24 hours from meters whose groupId is 2. +In TDengine CLI `taos`, use the SQL below to get the number of rows and the maximum current in the past 24 hours from meters whose groupId is 2. ``` taos> SELECT count(*), max(current) FROM meters where groupId = 2 and ts > now - 24h; - cunt(*) | max(current) | + count(*) | max(current) | ================================== 5 | 13.4 | Query OK, 1 row(s) in set (0.002136s) ``` -Join query is allowed between only the tables of same STable. In [Select](/taos-sql/select), all query operations are marked as whether it supports STable or not. +Join queries are only allowed between subtables of the same STable. In [Select](/taos-sql/select), all query operations are marked as to whether they support STables or not. ## Down Sampling and Interpolation -In IoT use cases, down sampling is widely used to aggregate the data by time range. `INTERVAL` keyword in TDengine can be used to simplify the query by time window. For example, below SQL statement can be used to get the sum of current every 10 seconds from meters table d1001. +In IoT use cases, down sampling is widely used to aggregate data by time range. The `INTERVAL` keyword in TDengine can be used to simplify the query by time window. For example, the SQL statement below can be used to get the sum of current every 10 seconds from meters table d1001. ``` taos> SELECT sum(current) FROM d1001 INTERVAL(10s); @@ -96,10 +96,10 @@ taos> SELECT sum(current) FROM d1001 INTERVAL(10s); Query OK, 2 row(s) in set (0.000883s) ``` -Down sampling can also be used for STable. For example, below SQL statement can be used to get the sum of current from all meters in BeiJing. +Down sampling can also be used for STable. For example, the below SQL statement can be used to get the sum of current from all meters in California. ``` -taos> SELECT SUM(current) FROM meters where location like "Beijing%" INTERVAL(1s); +taos> SELECT SUM(current) FROM meters where location like "California%" INTERVAL(1s); ts | sum(current) | ====================================================== 2018-10-03 14:38:04.000 | 10.199999809 | @@ -110,7 +110,7 @@ taos> SELECT SUM(current) FROM meters where location like "Beijing%" INTERVAL(1s Query OK, 5 row(s) in set (0.001538s) ``` -Down sampling also supports time offset. For example, below SQL statement can be used to get the sum of current from all meters but each time window must start at the boundary of 500 milliseconds. +Down sampling also supports time offset. For example, the below SQL statement can be used to get the sum of current from all meters but each time window must start at the boundary of 500 milliseconds. ``` taos> SELECT SUM(current) FROM meters INTERVAL(1s, 500a); @@ -124,7 +124,7 @@ taos> SELECT SUM(current) FROM meters INTERVAL(1s, 500a); Query OK, 5 row(s) in set (0.001521s) ``` -In many use cases, it's hard to align the timestamp of the data collected by each collection point. However, a lot of algorithms like FFT require the data to be aligned with same time interval and application programs have to handle by themselves in many systems. In TDengine, it's easy to achieve the alignment using down sampling. +In many use cases, it's hard to align the timestamp of the data collected by each collection point. However, a lot of algorithms like FFT require the data to be aligned with same time interval and application programs have to handle this by themselves. In TDengine, it's easy to achieve the alignment using down sampling. Interpolation can be performed in TDengine if there is no data in a time range. @@ -162,16 +162,16 @@ In the section describing [Insert](/develop/insert-data/sql-writing), a database :::note -1. With either REST connection or native connection, the above sample code work well. -2. Please be noted that `use db` can't be used in case of REST connection because it's stateless. +1. With either REST connection or native connection, the above sample code works well. +2. Please note that `use db` can't be used in case of REST connection because it's stateless. ::: ### Asynchronous Query -Besides synchronous query, asynchronous query API is also provided by TDengine to insert or query data more efficiently. With similar hardware and software environment, async API is 2~4 times faster than sync APIs. Async API works in non-blocking mode, which means an operation can be returned without finishing so that the calling thread can switch to other works to improve the performance of the whole application system. Async APIs perform especially better in case of poor network. +Besides synchronous queries, an asynchronous query API is also provided by TDengine to insert or query data more efficiently. With a similar hardware and software environment, the async API is 2~4 times faster than sync APIs. Async API works in non-blocking mode, which means an operation can be returned without finishing so that the calling thread can switch to other work to improve the performance of the whole application system. Async APIs perform especially better in the case of poor networks. -Please be noted that async query can only be used with native connection. +Please note that async query can only be used with a native connection. diff --git a/docs-en/07-develop/05-continuous-query.mdx b/docs-en/07-develop/05-continuous-query.mdx deleted file mode 100644 index 97e32a17ff325a9f67ac0a732be3dd72ccca8888..0000000000000000000000000000000000000000 --- a/docs-en/07-develop/05-continuous-query.mdx +++ /dev/null @@ -1,83 +0,0 @@ ---- -sidebar_label: Continuous Query -description: "Continuous query is a query that's executed automatically according to predefined frequency to provide aggregate query capability by time window, it's actually a simplified time driven stream computing." -title: "Continuous Query" ---- - -Continuous query is a query that's executed automatically according to predefined frequency to provide aggregate query capability by time window, it's actually a simplified time driven stream computing. Continuous query can be performed on a table or STable in TDengine. The result of continuous query can be pushed to client or written back to TDengine. Each query is executed on a time window, which moves forward with time. The size of time window and the forward sliding time need to be specified with parameter `INTERVAL` and `SLIDING` respectively. - -Continuous query in TDengine is time driven, and can be defined using TAOS SQL directly without any extra operations. With continuous query, the result can be generated according to time window to achieve down sampling of original data. Once a continuous query is defined using TAOS SQL, the query is automatically executed at the end of each time window and the result is pushed back to client or written to TDengine. - -There are some differences between continuous query in TDengine and time window computation in stream computing： - -- The computation is performed and the result is returned in real time in stream computing, but the computation in continuous query is only started when a time window closes. For example, if the time window is 1 day, then the result will only be generated at 23:59:59. -- If a historical data row is written in to a time widow for which the computation has been finished, the computation will not be performed again and the result will not be pushed to client again either. If the result has been written into TDengine, there will be no update for the result. -- In continuous query, if the result is pushed to client, the client status is not cached on the server side and Exactly-once is not guaranteed by the server either. If the client program crashes, a new time window will be generated from the time where the continuous query is restarted. If the result is written into TDengine, the data written into TDengine can be guaranteed as valid and continuous. - -## Syntax - -```sql -[CREATE TABLE AS] SELECT select_expr [, select_expr ...] - FROM {tb_name_list} - [WHERE where_condition] - [INTERVAL(interval_val [, interval_offset]) [SLIDING sliding_val]] - -``` - -INTERVAL: The time window for which continuous query is performed - -SLIDING: The time step for which the time window moves forward each time - -## How to Use - -In this section the use case of meters will be used to introduce how to use continuous query. Assume the STable and sub tables have been created using below SQL statement. - -```sql -create table meters (ts timestamp, current float, voltage int, phase float) tags (location binary(64), groupId int); -create table D1001 using meters tags ("Beijing.Chaoyang", 2); -create table D1002 using meters tags ("Beijing.Haidian", 2); -``` - -The average voltage for each time window of one minute with 30 seconds as the length of moving forward can be retrieved using below SQL statement. - -```sql -select avg(voltage) from meters interval(1m) sliding(30s); -``` - -Whenever the above SQL statement is executed, all the existing data will be computed again. If the computation needs to be performed every 30 seconds automatically to compute on the data in the past one minute, the above SQL statement needs to be revised as below, in which `{startTime}` stands for the beginning timestamp in the latest time window. - -```sql -select avg(voltage) from meters where ts > {startTime} interval(1m) sliding(30s); -``` - -Another easier way for same purpose is prepend `create table {tableName} as` before the `select`. - -```sql -create table avg_vol as select avg(voltage) from meters interval(1m) sliding(30s); -``` - -A table named as `avg_vol` will be created automatically, then every 30 seconds the `select` statement will be executed automatically on the data in the past 1 minutes, i.e. the latest time window, and the result is written into table `avg_vol`. The client program just needs to query from table `avg_vol`. For example: - -```sql -taos> select * from avg_vol; - ts | avg_voltage_ | -=================================================== - 2020-07-29 13:37:30.000 | 222.0000000 | - 2020-07-29 13:38:00.000 | 221.3500000 | - 2020-07-29 13:38:30.000 | 220.1700000 | - 2020-07-29 13:39:00.000 | 223.0800000 | -``` - -Please be noted that the minimum allowed time window is 10 milliseconds, and no upper limit. - -Besides, it's allowed to specify the start and end time of continuous query. If the start time is not specified, the timestamp of the first original row will be considered as the start time; if the end time is not specified, the continuous will be performed infinitely, otherwise it will be terminated once the end time is reached. For example, the continuous query in below SQL statement will be started from now and terminated one hour later. - -```sql -create table avg_vol as select avg(voltage) from meters where ts > now and ts <= now + 1h interval(1m) sliding(30s); -``` - -`now` in above SQL statement stands for the time when the continuous query is created, not the time when the computation is actually performed. Besides, to avoid the trouble caused by the delay of original data as much as possible, the actual computation in continuous query is also started with a little delay. That means, once a time window closes, the computation is not started immediately. Normally, the result can only be available a little time later, normally within one minute, after the time window closes. - -## How to Manage - -`show streams` command can be used in TDengine CLI `taos` to show all the continuous queries in the system, and `kill stream` can be used to terminate a continuous query. diff --git a/docs-en/07-develop/05-delete-data.mdx b/docs-en/07-develop/05-delete-data.mdx new file mode 100644 index 0000000000000000000000000000000000000000..86443dca53b08f5f5c489d40f4a2ea8afc8081fb --- /dev/null +++ b/docs-en/07-develop/05-delete-data.mdx @@ -0,0 +1,42 @@ +--- +sidebar_label: Delete Data +description: "Delete data from table or Stable" +title: Delete Data +--- + +TDengine provides the functionality of deleting data from a table or STable according to specified time range, it can be used to cleanup abnormal data generated due to device failure. Please be noted that this functionality is only available in Enterprise version, please refer to [TDengine Enterprise Edition](https://tdengine.com/products#enterprise-edition-link) + + +**Syntax:** + +```sql +DELETE FROM [ db_name. ] tb_name [WHERE condition]; +``` + +**Description:** Delete data from a table or STable + +**Parameters:** + +- `db_name`: Optional parameter, specifies the database in which the table exists; if not specified, the current database will be used. +- `tb_name`: Mandatory parameter, specifies the table name from which data will be deleted, it can be normal table, subtable or STable. +- `condition`: Optional parameter, specifies the data filter condition. If no condition is specified all data will be deleted, so please be cautions to delete data without any condition. The condition used here is only applicable to the first column, i.e. the timestamp column. If the table is a STable, the condition is also applicable to tag columns. + +**More Explanations:** + +The data can't be recovered once deleted, so please be cautious to use the functionality of deleting data. It's better to firstly make sure the data to be deleted using `select` then execute `delete`. + +**Example:** + +`meters` is a STable, in which `groupid` is a tag column of int type. Now we want to delete the data older than 2021-10-01 10:40:00.100 and `groupid` is 1. The SQL for this purpose is like below: + +```sql +delete from meters where ts < '2021-10-01 10:40:00.100' and groupid=1 ; +``` + +The output is: + +``` +Deleted 102000 row(s) from 1020 table(s) (0.421950s) +``` + +It means totally 102,000 rows of data have been deleted from 1,020 sub tables. diff --git a/docs-en/07-develop/06-continuous-query.mdx b/docs-en/07-develop/06-continuous-query.mdx new file mode 100644 index 0000000000000000000000000000000000000000..1aea5783fc8116a4e02a4b5345d341707cd399ea --- /dev/null +++ b/docs-en/07-develop/06-continuous-query.mdx @@ -0,0 +1,83 @@ +--- +sidebar_label: Continuous Query +description: "Continuous query is a query that's executed automatically at a predefined frequency to provide aggregate query capability by time window. It is essentially simplified, time driven, stream computing." +title: "Continuous Query" +--- + +A continuous query is a query that's executed automatically at a predefined frequency to provide aggregate query capability by time window. It is essentially simplified, time driven, stream computing. A continuous query can be performed on a table or STable in TDengine. The results of a continuous query can be pushed to clients or written back to TDengine. Each query is executed on a time window, which moves forward with time. The size of time window and the forward sliding time need to be specified with parameter `INTERVAL` and `SLIDING` respectively. + +A continuous query in TDengine is time driven, and can be defined using TAOS SQL directly without any extra operations. With a continuous query, the result can be generated based on a time window to achieve down sampling of the original data. Once a continuous query is defined using TAOS SQL, the query is automatically executed at the end of each time window and the result is pushed back to clients or written to TDengine. + +There are some differences between continuous query in TDengine and time window computation in stream computing： + +- The computation is performed and the result is returned in real time in stream computing, but the computation in continuous query is only started when a time window closes. For example, if the time window is 1 day, then the result will only be generated at 23:59:59. +- If a historical data row is written in to a time window for which the computation has already finished, the computation will not be performed again and the result will not be pushed to client applications again. If the results have already been written into TDengine, they will not be updated. +- In continuous query, if the result is pushed to a client, the client status is not cached on the server side and Exactly-once is not guaranteed by the server. If the client program crashes, a new time window will be generated from the time where the continuous query is restarted. If the result is written into TDengine, the data written into TDengine can be guaranteed as valid and continuous. + +## Syntax + +```sql +[CREATE TABLE AS] SELECT select_expr [, select_expr ...] + FROM {tb_name_list} + [WHERE where_condition] + [INTERVAL(interval_val [, interval_offset]) [SLIDING sliding_val]] + +``` + +INTERVAL: The time window for which continuous query is performed + +SLIDING: The time step for which the time window moves forward each time + +## How to Use + +In this section the use case of meters will be used to introduce how to use continuous query. Assume the STable and subtables have been created using the SQL statements below. + +```sql +create table meters (ts timestamp, current float, voltage int, phase float) tags (location binary(64), groupId int); +create table D1001 using meters tags ("California.SanFrancisco", 2); +create table D1002 using meters tags ("California.LosAngeles", 2); +``` + +The SQL statement below retrieves the average voltage for a one minute time window, with each time window moving forward by 30 seconds. + +```sql +select avg(voltage) from meters interval(1m) sliding(30s); +``` + +Whenever the above SQL statement is executed, all the existing data will be computed again. If the computation needs to be performed every 30 seconds automatically to compute on the data in the past one minute, the above SQL statement needs to be revised as below, in which `{startTime}` stands for the beginning timestamp in the latest time window. + +```sql +select avg(voltage) from meters where ts > {startTime} interval(1m) sliding(30s); +``` + +An easier way to achieve this is to prepend `create table {tableName} as` before the `select`. + +```sql +create table avg_vol as select avg(voltage) from meters interval(1m) sliding(30s); +``` + +A table named as `avg_vol` will be created automatically, then every 30 seconds the `select` statement will be executed automatically on the data in the past 1 minute, i.e. the latest time window, and the result is written into table `avg_vol`. The client program just needs to query from table `avg_vol`. For example: + +```sql +taos> select * from avg_vol; + ts | avg_voltage_ | +=================================================== + 2020-07-29 13:37:30.000 | 222.0000000 | + 2020-07-29 13:38:00.000 | 221.3500000 | + 2020-07-29 13:38:30.000 | 220.1700000 | + 2020-07-29 13:39:00.000 | 223.0800000 | +``` + +Please note that the minimum allowed time window is 10 milliseconds, and there is no upper limit. + +It's possible to specify the start and end time of a continuous query. If the start time is not specified, the timestamp of the first row will be considered as the start time; if the end time is not specified, the continuous query will be performed indefinitely, otherwise it will be terminated once the end time is reached. For example, the continuous query in the SQL statement below will be started from now and terminated one hour later. + +```sql +create table avg_vol as select avg(voltage) from meters where ts > now and ts <= now + 1h interval(1m) sliding(30s); +``` + +`now` in the above SQL statement stands for the time when the continuous query is created, not the time when the computation is actually performed. To avoid the trouble caused by a delay in receiving data as much as possible, the actual computation in a continuous query is started after a little delay. That means, once a time window closes, the computation is not started immediately. Normally, the result are available after a little time, normally within one minute, after the time window closes. + +## How to Manage + +`show streams` command can be used in the TDengine CLI `taos` to show all the continuous queries in the system, and `kill stream` can be used to terminate a continuous query. diff --git a/docs-en/07-develop/06-subscribe.mdx b/docs-en/07-develop/06-subscribe.mdx deleted file mode 100644 index 56f4ed83d8ebc6f21afbdd2eca2e01f11b313883..0000000000000000000000000000000000000000 --- a/docs-en/07-develop/06-subscribe.mdx +++ /dev/null @@ -1,257 +0,0 @@ ---- -sidebar_label: Subscription -description: "Lightweight service for data subscription and pushing, the time series data inserted into TDengine continuously can be pushed automatically to the subscribing clients." -title: Data Subscription ---- - -import Tabs from "@theme/Tabs"; -import TabItem from "@theme/TabItem"; -import Java from "./_sub_java.mdx"; -import Python from "./_sub_python.mdx"; -import Go from "./_sub_go.mdx"; -import Rust from "./_sub_rust.mdx"; -import Node from "./_sub_node.mdx"; -import CSharp from "./_sub_cs.mdx"; -import CDemo from "./_sub_c.mdx"; - -## Introduction - -According to the time series nature of the data, data inserting in TDengine is similar to data publishing in message queues, they both can be considered as a new data record with timestamp is inserted into the system. Data is stored in ascending order of timestamp inside TDengine, so essentially each table in TDengine can be considered as a message queue. - -Lightweight service for data subscription and pushing is built in TDengine. With the API provided by TDengine, client programs can used `select` statement to subscribe the data from one or more tables. The subscription and and state maintenance is performed on the client side, the client programs polls the server to check whether there is new data, and if so the new data will be pushed back to the client side. If the client program is restarted, where to start for retrieving new data is up to the client side. - -There are 3 major APIs related to subscription provided in the TDengine client driver. - -```c -taos_subscribe -taos_consume -taos_unsubscribe -``` - -For more details about these API please refer to [C/C++ Connector](/reference/connector/cpp). Their usage will be introduced below using the use case of meters, in which the schema of STable and sub tables please refer to the previous section "continuous query". Full sample code can be found [here](https://github.com/taosdata/TDengine/blob/master/examples/c/subscribe.c). - -If we want to get notification and take some actions if the current exceeds a threshold, like 10A, from some meters, there are two ways: - -The first way is to query on each sub table and record the last timestamp matching the criteria, then after some time query on the data later than recorded timestamp and repeat this process. The SQL statements for this way are as below. - -```sql -select * from D1001 where ts > {last_timestamp1} and current > 10; -select * from D1002 where ts > {last_timestamp2} and current > 10; -... -``` - -The above way works, but the problem is that the number of `select` statements increases with the number of meters grows. Finally the performance of both client side and server side will be unacceptable once the number of meters grows to a big enough number. - -A better way is to query on the STable, only one `select` is enough regardless of the number of meters, like below: - -```sql -select * from meters where ts > {last_timestamp} and current > 10; -``` - -However, how to choose `last_timestamp` becomes a new problem if using this way. Firstly, the timestamp when the data is generated is different from the timestamp when the data is inserted into the database, sometimes the difference between them may be very big. Secondly, the time when the data from different meters may arrives at the database may be different too. If the timestamp of the "slowest" meter is used as `last_timestamp` in the query, the data from other meters may be selected repeatedly; but if the timestamp of the "fasted" meters is used as `last_timestamp`, some data from other meters may be missed. - -All the problems mentioned above can be resolved thoroughly using subscription provided by TDengine. - -The first step is to create subscription using `taos_subscribe`. - -```c -TAOS_SUB* tsub = NULL; -if (async) { -　　// create an asynchronous subscription, the callback function will be called every 1s -　　tsub = taos_subscribe(taos, restart, topic, sql, subscribe_callback, &blockFetch, 1000); -} else { -　　// create an synchronous subscription, need to call 'taos_consume' manually -　　tsub = taos_subscribe(taos, restart, topic, sql, NULL, NULL, 0); -} -``` - -The subscription in TDengine can be either synchronous or asynchronous. In the above sample code, the value of variable `async` is determined from the CLI input, then it's used to create either an async or sync subscription. Sync subscription means the client program needs to invoke `taos_consume` to retrieve data, and async subscription means another thread created by `taos_subscribe` internally invokes `taos_consume` to retrieve data and pass the data to `subscribe_callback` for processing, `subscribe_callback` is a call back function provided by the client program and it's suggested not to do time consuming operation in the call back function. - -The parameter `taos` is an established connection. There is nothing special in sync subscription mode. In async subscription, it should be exclusively by current thread, otherwise unpredictable error may occur. - -The parameter `sql` is a `select` statement in which `where` clause can be used to specify filter conditions. In our example, the data whose current exceeds 10A needs to be subscribed like below SQL statement: - -```sql -select * from meters where current > 10; -``` - -Please be noted that, all the data will be processed because no start time is specified. If only the data from one day ago needs to be processed, a time related condition can be added: - -```sql -select * from meters where ts > now - 1d and current > 10; -``` - -The parameter `topic` is the name of the subscription, it needs to be guaranteed unique in the client program, but it's not necessary to be globally unique because subscription is implemented in the APIs on client side. - -If the subscription named as `topic` doesn't exist, parameter `restart` would be ignored. If the subscription named as `topic` has been created before by the client program which then exited, when the client program is restarted to use this `topic`, parameter `restart` is used to determine retrieving data from beginning or from the last point where the subscription was broken. If the value of `restart` is **true** (i.e. a non-zero value), the data will be retrieved from beginning, or if it is **false** (i.e. zero), the data already consumed before will not be processed again. - -The last parameter of `taos_subscribe` is the polling interval in unit of millisecond. In sync mode, if the time difference between two continuous invocations to `taos_consume` is smaller than the interval specified by `taos_subscribe`, `taos_consume` would be blocked until the interval is reached. In async mode, this interval is the minimum interval between two invocations to the call back function. - -The last second parameter of `taos_subscribe` is used to pass arguments to the call back function. `taos_subscribe` doesn't process this parameter and simply passes it to the call back function. This parameter is simply ignored in sync mode. - -After a subscription is created, its data can be consumed and processed, below is the sample code of how to consume data in sync mode, in the else part if `if (async)`. - -```c -if (async) { -　　getchar(); -} else while(1) { -　　TAOS_RES* res = taos_consume(tsub); -　　if (res == NULL) { -　　　　printf("failed to consume data."); -　　　　break; -　　} else { -　　　　print_result(res, blockFetch); -　　　　getchar(); -　　} -} -``` - -In the above sample code, there is an infinite loop, each time carriage return is entered `taos_consume` is invoked, the return value of `taos_consume` is the selected result set, exactly as the input of `taos_use_result`, in the above sample `print_result` is used instead to simplify the sample. Below is the implementation of `print_result`. - -```c -void print_result(TAOS_RES* res, int blockFetch) { -　　TAOS_ROW row = NULL; -　　int num_fields = taos_num_fields(res); -　　TAOS_FIELD* fields = taos_fetch_fields(res); -　　int nRows = 0; -　　if (blockFetch) { -　　　　nRows = taos_fetch_block(res, &row); -　　　　for (int i = 0; i < nRows; i++) { -　　　　　　char temp[256]; -　　　　　　taos_print_row(temp, row + i, fields, num_fields); -　　　　　　puts(temp); -　　　　} -　　} else { -　　　　while ((row = taos_fetch_row(res))) { -　　　　　　char temp[256]; -　　　　　　taos_print_row(temp, row, fields, num_fields); -　　　　　　puts(temp); -　　　　　　nRows++; -　　　　} -　　} -　　printf("%d rows consumed.\n", nRows); -} -``` - -In the above code `taos_print_row` is used to process the data consumed. All the matching rows will be printed. - -In async mode, the data consuming is simpler as below. - -```c -void subscribe_callback(TAOS_SUB* tsub, TAOS_RES *res, void* param, int code) { -　　print_result(res, *(int*)param); -} -``` - -`taos_unsubscribe` can be invoked to terminate a subscription. - -```c -taos_unsubscribe(tsub, keep); -``` - -The second parameter `keep` is used to specify whether to keep the subscription progress on the client sde. If it is **false**, i.e. **0**, then subscription will be restarted from beginning regardless of the `restart` parameter's value in when `taos_subscribe` is invoked again. The subscription progress information is stored in _{DataDir}/subscribe/_ , under which there is a file with same name as `topic` for each subscription, the subscription will be restarted from beginning if the corresponding progress file is removed. - -Now let's see the effect of the above sample code, assuming below prerequisites have been done. - -- The sample code has been downloaded to local system -- TDengine has been installed and launched properly on same system -- The database, STable, sub tables required in the sample code have been ready - -It's ready to launch below command in the directory where the sample code resides to compile and start the program. - -```bash -make -./subscribe -sql='select * from meters where current > 10;' -``` - -After the program is started, open another terminal and launch TDengine CLI `taos`, then use below SQL commands to insert a row whose current is 12A into table **D1001**. - -```sql -use test; -insert into D1001 values(now, 12, 220, 1); -``` - -Then, this row of data will be shown by the example program on the first terminal because its current exceeds 10A. More data can be inserted for you to observe the output of the example program. - -## Examples - -Below example program demonstrates how to subscribe the data rows whose current exceeds 10A using connectors. - -### Prepare Data - -```bash -# create database "power" -taos> create database power; -# use "power" as the database in following operations -taos> use power; -# create super table "meters" -taos> create table meters(ts timestamp, current float, voltage int, phase int) tags(location binary(64), groupId int); -# create tabes using the schema defined by super table "meters" -taos> create table d1001 using meters tags ("Beijing.Chaoyang", 2); -taos> create table d1002 using meters tags ("Beijing.Haidian", 2); -# insert some rows -taos> insert into d1001 values("2020-08-15 12:00:00.000", 12, 220, 1),("2020-08-15 12:10:00.000", 12.3, 220, 2),("2020-08-15 12:20:00.000", 12.2, 220, 1); -taos> insert into d1002 values("2020-08-15 12:00:00.000", 9.9, 220, 1),("2020-08-15 12:10:00.000", 10.3, 220, 1),("2020-08-15 12:20:00.000", 11.2, 220, 1); -# filter out the rows in which current is bigger than 10A -taos> select * from meters where current > 10; - ts | current | voltage | phase | location | groupid | -=========================================================================================================== - 2020-08-15 12:10:00.000 | 10.30000 | 220 | 1 | Beijing.Haidian | 2 | - 2020-08-15 12:20:00.000 | 11.20000 | 220 | 1 | Beijing.Haidian | 2 | - 2020-08-15 12:00:00.000 | 12.00000 | 220 | 1 | Beijing.Chaoyang | 2 | - 2020-08-15 12:10:00.000 | 12.30000 | 220 | 2 | Beijing.Chaoyang | 2 | - 2020-08-15 12:20:00.000 | 12.20000 | 220 | 1 | Beijing.Chaoyang | 2 | -Query OK, 5 row(s) in set (0.004896s) -``` - -### Example Programs - - - - - - - - - {/* - - */} - - - - {/* - - - - - */} - - - - - -### Run the Examples - -The example programs firstly consume all historical data matching the criteria. - -```bash -ts: 1597464000000 current: 12.0 voltage: 220 phase: 1 location: Beijing.Chaoyang groupid : 2 -ts: 1597464600000 current: 12.3 voltage: 220 phase: 2 location: Beijing.Chaoyang groupid : 2 -ts: 1597465200000 current: 12.2 voltage: 220 phase: 1 location: Beijing.Chaoyang groupid : 2 -ts: 1597464600000 current: 10.3 voltage: 220 phase: 1 location: Beijing.Haidian groupid : 2 -ts: 1597465200000 current: 11.2 voltage: 220 phase: 1 location: Beijing.Haidian groupid : 2 -``` - -Next, use TDengine CLI to insert a new row. - -``` -# taos -taos> use power; -taos> insert into d1001 values(now, 12.4, 220, 1); -``` - -Because the current in inserted row exceeds 10A, it will be consumed by the example program. - -``` -ts: 1651146662805 current: 12.4 voltage: 220 phase: 1 location: Beijing.Chaoyang groupid: 2 -``` diff --git a/docs-en/07-develop/07-cache.md b/docs-en/07-develop/07-cache.md deleted file mode 100644 index 13db6c363802abed290cfc4d4466d40e48852f3d..0000000000000000000000000000000000000000 --- a/docs-en/07-develop/07-cache.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -sidebar_label: Cache -title: Cache -description: "The latest row of each table is kept in cache to provide high performance query of latest state." ---- - -The cache management policy in TDengine is First-In-First-Out (FIFO), which is also known as insert driven cache management policy and different from read driven cache management, i.e. Least-Recent-Used (LRU). It simply stores the latest data in cache and flushes the oldest data in cache to disk when the cache usage reaches a threshold. In IoT use cases, the most cared about data is the latest data, i.e. current state. The cache policy in TDengine is based the nature of IoT data. - -Caching the latest data provides the capability of retrieving data in milliseconds. With this capability, TDengine can be configured properly to be used as caching system without deploying another separate caching system to simplify the system architecture and minimize the operation cost. The cache will be emptied after TDengine is restarted, TDengine doesn't reload data from disk into cache like a real key-value caching system. - -The memory space used by TDengine cache is fixed in size, according to the configuration based on application requirement and system resources. Independent memory pool is allocated for and managed by each vnode (virtual node) in TDengine, there is no sharing of memory pools between vnodes. All the tables belonging to a vnode share all the cache memory of the vnode. - -Memory pool is divided into blocks and data is stored in row format in memory and each block follows FIFO policy. The size of each block is determined by configuration parameter `cache`, the number of blocks for each vnode is determined by `blocks`. For each vnode, the total cache size is `cache * blocks`. It's better to set the size of each block to hold at least tends of rows. - -`last_row` function can be used to retrieve the last row of a table or a STable to quickly show the current state of devices on monitoring screen. For example below SQL statement retrieves the latest voltage of all meters in Chaoyang district of Beijing. - -```sql -select last_row(voltage) from meters where location='Beijing.Chaoyang'; -``` diff --git a/docs-en/07-develop/07-subscribe.mdx b/docs-en/07-develop/07-subscribe.mdx new file mode 100644 index 0000000000000000000000000000000000000000..782fcdbaf221419dd231bd10958e26b8f4f856e5 --- /dev/null +++ b/docs-en/07-develop/07-subscribe.mdx @@ -0,0 +1,259 @@ +--- +sidebar_label: Data Subscription +description: "Lightweight service for data subscription and publishing. Time series data inserted into TDengine continuously can be pushed automatically to subscribing clients." +title: Data Subscription +--- + +import Tabs from "@theme/Tabs"; +import TabItem from "@theme/TabItem"; +import Java from "./_sub_java.mdx"; +import Python from "./_sub_python.mdx"; +import Go from "./_sub_go.mdx"; +import Rust from "./_sub_rust.mdx"; +import Node from "./_sub_node.mdx"; +import CSharp from "./_sub_cs.mdx"; +import CDemo from "./_sub_c.mdx"; + +## Introduction + +Due to the nature of time series data, data insertion into TDengine is similar to data publishing in message queues. Data is stored in ascending order of timestamp inside TDengine, and so each table in TDengine can essentially be considered as a message queue. + +A lightweight service for data subscription and publishing is built into TDengine. With the API provided by TDengine, client programs can use `select` statements to subscribe to data from one or more tables. The subscription and state maintenance is performed on the client side. The client programs poll the server to check whether there is new data, and if so the new data will be pushed back to the client side. If the client program is restarted, where to start retrieving new data is up to the client side. + +There are 3 major APIs related to subscription provided in the TDengine client driver. + +```c +taos_subscribe +taos_consume +taos_unsubscribe +``` + +For more details about these APIs please refer to [C/C++ Connector](/reference/connector/cpp). Their usage will be introduced below using the use case of meters, in which the schema of STable and subtables from the previous section [Continuous Query](/develop/continuous-query) are used. Full sample code can be found [here](https://github.com/taosdata/TDengine/blob/master/examples/c/subscribe.c). + +If we want to get a notification and take some actions if the current exceeds a threshold, like 10A, from some meters, there are two ways: + +The first way is to query each sub table and record the last timestamp matching the criteria. Then after some time, query the data later than the recorded timestamp, and repeat this process. The SQL statements for this way are as below. + +```sql +select * from D1001 where ts > {last_timestamp1} and current > 10; +select * from D1002 where ts > {last_timestamp2} and current > 10; +... +``` + +The above way works, but the problem is that the number of `select` statements increases with the number of meters. Additionally, the performance of both client side and server side will be unacceptable once the number of meters grows to a big enough number. + +A better way is to query on the STable, only one `select` is enough regardless of the number of meters, like below: + +```sql +select * from meters where ts > {last_timestamp} and current > 10; +``` + +However, this presents a new problem in how to choose `last_timestamp`. First, the timestamp when the data is generated is different from the timestamp when the data is inserted into the database, sometimes the difference between them may be very big. Second, the time when the data from different meters arrives at the database may be different too. If the timestamp of the "slowest" meter is used as `last_timestamp` in the query, the data from other meters may be selected repeatedly; but if the timestamp of the "fastest" meter is used as `last_timestamp`, some data from other meters may be missed. + +All the problems mentioned above can be resolved easily using the subscription functionality provided by TDengine. + +The first step is to create subscription using `taos_subscribe`. + +```c +TAOS_SUB* tsub = NULL; +if (async) { +　　// create an asynchronous subscription, the callback function will be called every 1s +　　tsub = taos_subscribe(taos, restart, topic, sql, subscribe_callback, &blockFetch, 1000); +} else { +　　// create an synchronous subscription, need to call 'taos_consume' manually +　　tsub = taos_subscribe(taos, restart, topic, sql, NULL, NULL, 0); +} +``` + +The subscription in TDengine can be either synchronous or asynchronous. In the above sample code, the value of variable `async` is determined from the CLI input, then it's used to create either an async or sync subscription. Sync subscription means the client program needs to invoke `taos_consume` to retrieve data, and async subscription means another thread created by `taos_subscribe` internally invokes `taos_consume` to retrieve data and pass the data to `subscribe_callback` for processing. `subscribe_callback` is a callback function provided by the client program. You should not perform time consuming operations in the callback function. + +The parameter `taos` is an established connection. Nothing special needs to be done for thread safety for synchronous subscription. For asynchronous subscription, the taos_subscribe function should be called exclusively by the current thread, to avoid unpredictable errors. + +The parameter `sql` is a `select` statement in which the `where` clause can be used to specify filter conditions. In our example, we can subscribe to the records in which the current exceeds 10A, with the following SQL statement: + +```sql +select * from meters where current > 10; +``` + +Please note that, all the data will be processed because no start time is specified. If we only want to process data for the past day, a time related condition can be added: + +```sql +select * from meters where ts > now - 1d and current > 10; +``` + +The parameter `topic` is the name of the subscription. The client application must guarantee that the name is unique. However, it doesn't have to be globally unique because subscription is implemented in the APIs on the client side. + +If the subscription named as `topic` doesn't exist, the parameter `restart` will be ignored. If the subscription named as `topic` has been created before by the client program, when the client program is restarted with the subscription named `topic`, parameter `restart` is used to determine whether to retrieve data from the beginning or from the last point where the subscription was broken. + +If the value of `restart` is **true** (i.e. a non-zero value), data will be retrieved from the beginning. If it is **false** (i.e. zero), the data already consumed before will not be processed again. + +The last parameter of `taos_subscribe` is the polling interval in units of millisecond. In sync mode, if the time difference between two continuous invocations to `taos_consume` is smaller than the interval specified by `taos_subscribe`, `taos_consume` will be blocked until the interval is reached. In async mode, this interval is the minimum interval between two invocations to the call back function. + +The second to last parameter of `taos_subscribe` is used to pass arguments to the call back function. `taos_subscribe` doesn't process this parameter and simply passes it to the call back function. This parameter is simply ignored in sync mode. + +After a subscription is created, its data can be consumed and processed. Shown below is the sample code to consume data in sync mode, in the else condition of `if (async)`. + +```c +if (async) { +　　getchar(); +} else while(1) { +　　TAOS_RES* res = taos_consume(tsub); +　　if (res == NULL) { +　　　　printf("failed to consume data."); +　　　　break; +　　} else { +　　　　print_result(res, blockFetch); +　　　　getchar(); +　　} +} +``` + +In the above sample code in the else condition, there is an infinite loop. Each time carriage return is entered `taos_consume` is invoked. The return value of `taos_consume` is the selected result set. In the above sample, `print_result` is used to simplify the printing of the result set. It is similar to `taos_use_result`. Below is the implementation of `print_result`. + +```c +void print_result(TAOS_RES* res, int blockFetch) { +　　TAOS_ROW row = NULL; +　　int num_fields = taos_num_fields(res); +　　TAOS_FIELD* fields = taos_fetch_fields(res); +　　int nRows = 0; +　　if (blockFetch) { +　　　　nRows = taos_fetch_block(res, &row); +　　　　for (int i = 0; i < nRows; i++) { +　　　　　　char temp[256]; +　　　　　　taos_print_row(temp, row + i, fields, num_fields); +　　　　　　puts(temp); +　　　　} +　　} else { +　　　　while ((row = taos_fetch_row(res))) { +　　　　　　char temp[256]; +　　　　　　taos_print_row(temp, row, fields, num_fields); +　　　　　　puts(temp); +　　　　　　nRows++; +　　　　} +　　} +　　printf("%d rows consumed.\n", nRows); +} +``` + +In the above code `taos_print_row` is used to process the data consumed. All matching rows are printed. + +In async mode, consuming data is simpler as shown below. + +```c +void subscribe_callback(TAOS_SUB* tsub, TAOS_RES *res, void* param, int code) { +　　print_result(res, *(int*)param); +} +``` + +`taos_unsubscribe` can be invoked to terminate a subscription. + +```c +taos_unsubscribe(tsub, keep); +``` + +The second parameter `keep` is used to specify whether to keep the subscription progress on the client sde. If it is **false**, i.e. **0**, then subscription will be restarted from beginning regardless of the `restart` parameter's value when `taos_subscribe` is invoked again. The subscription progress information is stored in _{DataDir}/subscribe/_ , under which there is a file with the same name as `topic` for each subscription(Note: The default value of `DataDir` in the `taos.cfg` file is **/var/lib/taos/**. However, **/var/lib/taos/** does not exist on the Windows server. So you need to change the `DataDir` value to the corresponding existing directory."), the subscription will be restarted from the beginning if the corresponding progress file is removed. + +Now let's see the effect of the above sample code, assuming below prerequisites have been done. + +- The sample code has been downloaded to local system +- TDengine has been installed and launched properly on same system +- The database, STable, and subtables required in the sample code are ready + +Launch the command below in the directory where the sample code resides to compile and start the program. + +```bash +make +./subscribe -sql='select * from meters where current > 10;' +``` + +After the program is started, open another terminal and launch TDengine CLI `taos`, then use the below SQL commands to insert a row whose current is 12A into table **D1001**. + +```sql +use test; +insert into D1001 values(now, 12, 220, 1); +``` + +Then, this row of data will be shown by the example program on the first terminal because its current exceeds 10A. More data can be inserted for you to observe the output of the example program. + +## Examples + +The example program below demonstrates how to subscribe, using connectors, to data rows in which current exceeds 10A. + +### Prepare Data + +```bash +# create database "power" +taos> create database power; +# use "power" as the database in following operations +taos> use power; +# create super table "meters" +taos> create table meters(ts timestamp, current float, voltage int, phase int) tags(location binary(64), groupId int); +# create tabes using the schema defined by super table "meters" +taos> create table d1001 using meters tags ("California.SanFrancisco", 2); +taos> create table d1002 using meters tags ("California.LoSangeles", 2); +# insert some rows +taos> insert into d1001 values("2020-08-15 12:00:00.000", 12, 220, 1),("2020-08-15 12:10:00.000", 12.3, 220, 2),("2020-08-15 12:20:00.000", 12.2, 220, 1); +taos> insert into d1002 values("2020-08-15 12:00:00.000", 9.9, 220, 1),("2020-08-15 12:10:00.000", 10.3, 220, 1),("2020-08-15 12:20:00.000", 11.2, 220, 1); +# filter out the rows in which current is bigger than 10A +taos> select * from meters where current > 10; + ts | current | voltage | phase | location | groupid | +=========================================================================================================== + 2020-08-15 12:10:00.000 | 10.30000 | 220 | 1 | California.LoSangeles | 2 | + 2020-08-15 12:20:00.000 | 11.20000 | 220 | 1 | California.LoSangeles | 2 | + 2020-08-15 12:00:00.000 | 12.00000 | 220 | 1 | California.SanFrancisco | 2 | + 2020-08-15 12:10:00.000 | 12.30000 | 220 | 2 | California.SanFrancisco | 2 | + 2020-08-15 12:20:00.000 | 12.20000 | 220 | 1 | California.SanFrancisco | 2 | +Query OK, 5 row(s) in set (0.004896s) +``` + +### Example Programs + + + + + + + + + {/* + + */} + + + + {/* + + + + + */} + + + + + +### Run the Examples + +The example programs first consume all historical data matching the criteria. + +```bash +ts: 1597464000000 current: 12.0 voltage: 220 phase: 1 location: California.SanFrancisco groupid : 2 +ts: 1597464600000 current: 12.3 voltage: 220 phase: 2 location: California.SanFrancisco groupid : 2 +ts: 1597465200000 current: 12.2 voltage: 220 phase: 1 location: California.SanFrancisco groupid : 2 +ts: 1597464600000 current: 10.3 voltage: 220 phase: 1 location: California.LoSangeles groupid : 2 +ts: 1597465200000 current: 11.2 voltage: 220 phase: 1 location: California.LoSangeles groupid : 2 +``` + +Next, use TDengine CLI to insert a new row. + +``` +# taos +taos> use power; +taos> insert into d1001 values(now, 12.4, 220, 1); +``` + +Because the current in the inserted row exceeds 10A, it will be consumed by the example program. + +``` +ts: 1651146662805 current: 12.4 voltage: 220 phase: 1 location: California.SanFrancisco groupid: 2 +``` diff --git a/docs-en/07-develop/08-cache.md b/docs-en/07-develop/08-cache.md new file mode 100644 index 0000000000000000000000000000000000000000..743452faff6a2be8466318a7dab61a44e33c3664 --- /dev/null +++ b/docs-en/07-develop/08-cache.md @@ -0,0 +1,19 @@ +--- +sidebar_label: Cache +title: Cache +description: "The latest row of each table is kept in cache to provide high performance query of latest state." +--- + +The cache management policy in TDengine is First-In-First-Out (FIFO). FIFO is also known as insert driven cache management policy and it is different from read driven cache management, which is more commonly known as Least-Recently-Used (LRU). FIFO simply stores the latest data in cache and flushes the oldest data in cache to disk, when the cache usage reaches a threshold. In IoT use cases, it is the current state i.e. the latest or most recent data that is important. The cache policy in TDengine, like much of the design and architecture of TDengine, is based on the nature of IoT data. + +Caching the latest data provides the capability of retrieving data in milliseconds. With this capability, TDengine can be configured properly to be used as a caching system without deploying another separate caching system. This simplifies the system architecture and minimizes operational costs. The cache is emptied after TDengine is restarted. TDengine does not reload data from disk into cache, like a key-value caching system. + +The memory space used by the TDengine cache is fixed in size and configurable. It should be allocated based on application requirements and system resources. An independent memory pool is allocated for and managed by each vnode (virtual node) in TDengine. There is no sharing of memory pools between vnodes. All the tables belonging to a vnode share all the cache memory of the vnode. + +The memory pool is divided into blocks and data is stored in row format in memory and each block follows FIFO policy. The size of each block is determined by configuration parameter `cache` and the number of blocks for each vnode is determined by the parameter `blocks`. For each vnode, the total cache size is `cache * blocks`. A cache block needs to ensure that each table can store at least dozens of records, to be efficient. + +`last_row` function can be used to retrieve the last row of a table or a STable to quickly show the current state of devices on monitoring screen. For example the below SQL statement retrieves the latest voltage of all meters in San Francisco, California. + +```sql +select last_row(voltage) from meters where location='California.SanFrancisco'; +``` diff --git a/docs-en/07-develop/08-udf.md b/docs-en/07-develop/08-udf.md deleted file mode 100644 index 61639e34404477d3bb5785da129a1d922a4d020e..0000000000000000000000000000000000000000 --- a/docs-en/07-develop/08-udf.md +++ /dev/null @@ -1,218 +0,0 @@ ---- -sidebar_label: UDF -title: User Defined Functions -description: "Scalar functions and aggregate functions developed by users can be utilized by the query framework to expand the query capability" ---- - -In some use cases, the query capability required by application programs can't be achieved directly by builtin functions. With UDF, the functions developed by users can be utilized by query framework to meet some special requirements. UDF normally takes one column of data as input, but can also support the result of sub query as input. - -From version 2.2.0.0, UDF programmed in C/C++ language can be supported by TDengine. - -Two kinds of functions can be implemented by UDF: scalar function and aggregate function. - -## Define UDF - -### Scalar Function - -Below function template can be used to define your own scalar function. - -`void udfNormalFunc(char* data, short itype, short ibytes, int numOfRows, long long* ts, char* dataOutput, char* interBuf, char* tsOutput, int* numOfOutput, short otype, short obytes, SUdfInit* buf)` - -`udfNormalFunc` is the place holder of function name, a function implemented based on the above template can be used to perform scalar computation on data rows. The parameters are fixed to control the data exchange between UDF and TDengine. - -- Definitions of the parameters: - - - data：input data - - itype：the type of input data, for details please refer to [type definition in column_meta](/reference/rest-api/), for example 4 represents INT - - iBytes：the number of bytes consumed by each value in the input data - - oType：the type of output data, similar to iType - - oBytes：the number of bytes consumed by each value in the output data - - numOfRows：the number of rows in the input data - - ts: the column of timestamp corresponding to the input data - - dataOutput：the buffer for output data, total size is `oBytes * numberOfRows` - - interBuf：the buffer for intermediate result, its size is specified by `BUFSIZE` parameter when creating a UDF. It's normally used when the intermediate result is not same as the final result, it's allocated and freed by TDengine. - - tsOutput：the column of timestamps corresponding to the output data; it can be used to output timestamp together with the output data if it's not NULL - - numOfOutput：the number of rows in output data - - buf：for the state exchange between UDF and TDengine - - [add_one.c](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/add_one.c) is one example of the simplest UDF implementations, i.e. one instance of the above `udfNormalFunc` template. It adds one to each value of a column passed in which can be filtered using `where` clause and outputs the result. - -### Aggregate Function - -Below function template can be used to define your own aggregate function. - -`void abs_max_merge(char* data, int32_t numOfRows, char* dataOutput, int32_t* numOfOutput, SUdfInit* buf)` - -`udfMergeFunc` is the place holder of function name, the function implemented with the above template is used to aggregate the intermediate result, only can be used in the aggregate query for STable. - -Definitions of the parameters: - -- data：array of output data, if interBuf is used it's an array of interBuf -- numOfRows：number of rows in `data` -- dataOutput：the buffer for output data, the size is same as that of the final result; If the result is not final, it can be put in the interBuf, i.e. `data`. -- numOfOutput：number of rows in the output data -- buf：for the state exchange between UDF and TDengine - -[abs_max.c](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/abs_max.c) is an user defined aggregate function to get the maximum from the absolute value of a column. - -The internal processing is that the data affected by the select statement will be divided into multiple row blocks and `udfNormalFunc`, i.e. `abs_max` in this case, is performed on each row block to generate the intermediate of each sub table, then `udfMergeFunc`, i.e. `abs_max_merge` in this case, is performed on the intermediate result of sub tables to aggregate to generate the final or intermediate result of STable. The intermediate result of STable is finally processed by `udfFinalizeFunc` to generate the final result, which contain either 0 or 1 row. - -Other typical scenarios, like covariance, can also be achieved by aggregate UDF. - -### Finalize - -Below function template can be used to finalize the result of your own UDF, normally used when interBuf is used. - -`void abs_max_finalize(char* dataOutput, char* interBuf, int* numOfOutput, SUdfInit* buf)` - -`udfFinalizeFunc` is the place holder of function name, definitions of the parameter are as below: - -- dataOutput：buffer for output data -- interBuf：buffer for intermediate result, can be used as input for next processing step -- numOfOutput：number of output data, can only be 0 or 1 for aggregate function -- buf：for state exchange between UDF and TDengine - -## UDF Conventions - -The naming of 3 kinds of UDF, i.e. udfNormalFunc, udfMergeFunc, and udfFinalizeFunc is required to have same prefix, i.e. the actual name of udfNormalFunc, which means udfNormalFunc doesn't need a suffix following the function name. While udfMergeFunc should be udfNormalFunc followed by `_merge`, udfFinalizeFunc should be udfNormalFunc followed by `_finalize`. The naming convention is part of UDF framework, TDengine follows this convention to invoke corresponding actual functions.\ - -According to the kind of UDF to implement, the functions that need to be implemented are different. - -- Scalar function：udfNormalFunc is required -- Aggregate function：udfNormalFunc, udfMergeFunc (if query on STable) and udfFinalizeFunc are required - -To be more accurate, assuming we want to implement a UDF named "foo". If the function is a scalar function, what we really need to implement is `foo`; if the function is aggregate function, we need to implement `foo`, `foo_merge`, and `foo_finalize`. For aggregate UDF, even though one of the three functions is not necessary, there must be an empty implementation. - -## Compile UDF - -The source code of UDF in C can't be utilized by TDengine directly. UDF can only be loaded into TDengine after compiling to dynamically linked library. - -For example, the example UDF `add_one.c` mentioned in previous sections need to be compiled into DLL using below command on Linux Shell. - -```bash -gcc -g -O0 -fPIC -shared add_one.c -o add_one.so -``` - -The generated DLL file `dd_one.so` can be used later when creating UDF. It's recommended to use GCC not older than 7.5. - -## Create and Use UDF - -### Create UDF - -SQL command can be executed on the same hos where the generated UDF DLL resides to load the UDF DLL into TDengine, this operation can't be done through REST interface or web console. Once created, all the clients of the current TDengine can use these UDF functions in their SQL commands. UDF are stored in the management node of TDengine. The UDFs loaded in TDengine would be still available after TDengine is restarted. - -When creating UDF, it needs to be clarified as either scalar function or aggregate function. If the specified type is wrong, the SQL statements using the function would fail with error. Besides, the input type and output type don't need to be same in UDF, but the input data type and output data type need to be consistent with the UDF definition. - -- Create Scalar Function - -```sql -CREATE FUNCTION ids(X) AS ids(Y) OUTPUTTYPE typename(Z) [ BUFSIZE B ]; -``` - -- ids(X)：the function name to be sued in SQL statement, must be consistent with the function name defined by `udfNormalFunc` -- ids(Y)：the absolute path of the DLL file including the implementation of the UDF, the path needs to be quoted by single or double quotes -- typename(Z)：the output data type, the value is the literal string of the type -- B：the size of intermediate buffer, in bytes; it's an optional parameter and the range is [0,512] - -For example, below SQL statement can be used to create a UDF from `add_one.so`. - -```sql -CREATE FUNCTION add_one AS "/home/taos/udf_example/add_one.so" OUTPUTTYPE INT; -``` - -- Create Aggregate Function - -```sql -CREATE AGGREGATE FUNCTION ids(X) AS ids(Y) OUTPUTTYPE typename(Z) [ BUFSIZE B ]; -``` - -- ids(X)：the function name to be sued in SQL statement, must be consistent with the function name defined by `udfNormalFunc` -- ids(Y)：the absolute path of the DLL file including the implementation of the UDF, the path needs to be quoted by single or double quotes -- typename(Z)：the output data type, the value is the literal string of the type -- B：the size of intermediate buffer, in bytes; it's an optional parameter and the range is [0,512] - -For details about how to use intermediate result, please refer to example program [demo.c](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/demo.c). - -For example, below SQL statement can be used to create a UDF rom `demo.so`. - -```sql -CREATE AGGREGATE FUNCTION demo AS "/home/taos/udf_example/demo.so" OUTPUTTYPE DOUBLE bufsize 14; -``` - -### Manage UDF - -- Delete UDF - -``` -DROP FUNCTION ids(X); -``` - -- ids(X)：same as that in `CREATE FUNCTION` statement - -```sql -DROP FUNCTION add_one; -``` - -- Show Available UDF - -```sql -SHOW FUNCTIONS; -``` - -### Use UDF - -The function name specified when creating UDF can be used directly in SQL statements, just like builtin functions. - -```sql -SELECT X(c) FROM table/STable; -``` - -The above SQL statement invokes function X for column c. - -## Restrictions for UDF - -In current version there are some restrictions for UDF - -1. Only Linux is supported when creating and invoking UDF for both client side and server side -2. UDF can't be mixed with builtin functions -3. Only one UDF can be used in a SQL statement -4. Single column is supported as input for UDF -5. Once created successfully, UDF is persisted in MNode of TDengineUDF -6. UDF can't be created through REST interface -7. The function name used when creating UDF in SQL must be consistent with the function name defined in the DLL, i.e. the name defined by `udfNormalFunc` -8. The name name of UDF name should not conflict with any of builtin functions - -## Examples - -### Scalar function example [add_one](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/add_one.c) - -
-add_one.c - -```c -{{#include tests/script/sh/add_one.c}} -``` - -
- -### Aggregate function example [abs_max](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/abs_max.c) - -
-abs_max.c - -```c -{{#include tests/script/sh/abs_max.c}} -``` - -
- -### Example for using intermediate result [demo](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/demo.c) - -
-demo.c - -```c -{{#include tests/script/sh/demo.c}} -``` - -
diff --git a/docs-en/07-develop/09-udf.md b/docs-en/07-develop/09-udf.md new file mode 100644 index 0000000000000000000000000000000000000000..49bc95bd91a4c31d42d2b21ef05d69225f1bd963 --- /dev/null +++ b/docs-en/07-develop/09-udf.md @@ -0,0 +1,240 @@ +--- +sidebar_label: UDF +title: User Defined Functions(UDF) +description: "Scalar functions and aggregate functions developed by users can be utilized by the query framework to expand query capability" +--- + +In some use cases, built-in functions are not adequate for the query capability required by application programs. With UDF, the functions developed by users can be utilized by the query framework to meet business and application requirements. UDF normally takes one column of data as input, but can also support the result of a sub-query as input. + +From version 2.2.0.0, UDF written in C/C++ are supported by TDengine. + + +## Types of UDF + +Two kinds of functions can be implemented by UDF: scalar functions and aggregate functions. + +Scalar functions return multiple rows and aggregate functions return either 0 or 1 row. + +In the case of a scalar function you only have to implement the "normal" function template. + +In the case of an aggregate function, in addition to the "normal" function, you also need to implement the "merge" and "finalize" function templates even if the implementation is empty. This will become clear in the sections below. + +### Scalar Function + +As mentioned earlier, a scalar UDF only has to implement the "normal" function template. The function template below can be used to define your own scalar function. + +`void udfNormalFunc(char* data, short itype, short ibytes, int numOfRows, long long* ts, char* dataOutput, char* interBuf, char* tsOutput, int* numOfOutput, short otype, short obytes, SUdfInit* buf)` + +`udfNormalFunc` is the place holder for a function name. A function implemented based on the above template can be used to perform scalar computation on data rows. The parameters are fixed to control the data exchange between UDF and TDengine. + +- Definitions of the parameters: + + - data：input data + - itype：the type of input data, for details please refer to [type definition in column_meta](/reference/rest-api/), for example 4 represents INT + - iBytes：the number of bytes consumed by each value in the input data + - oType：the type of output data, similar to iType + - oBytes：the number of bytes consumed by each value in the output data + - numOfRows：the number of rows in the input data + - ts: the column of timestamp corresponding to the input data + - dataOutput：the buffer for output data, total size is `oBytes * numberOfRows` + - interBuf：the buffer for an intermediate result. Its size is specified by the `BUFSIZE` parameter when creating a UDF. It's normally used when the intermediate result is not same as the final result. This buffer is allocated and freed by TDengine. + - tsOutput：the column of timestamps corresponding to the output data; it can be used to output timestamp together with the output data if it's not NULL + - numOfOutput：the number of rows in output data + - buf：for the state exchange between UDF and TDengine + + [add_one.c](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/add_one.c) is one example of a very simple UDF implementation, i.e. one instance of the above `udfNormalFunc` template. It adds one to each value of a passed in column, which can be filtered using the `where` clause, and outputs the result. + +### Aggregate Function + +For aggregate UDF, as mentioned earlier you must implement a "normal" function template (described above) and also implement the "merge" and "finalize" templates. + +#### Merge Function Template + +The function template below can be used to define your own merge function for an aggregate UDF. + +`void udfMergeFunc(char* data, int32_t numOfRows, char* dataOutput, int32_t* numOfOutput, SUdfInit* buf)` + +`udfMergeFunc` is the place holder for a function name. The function implemented with the above template is used to aggregate intermediate results and can only be used in the aggregate query for STable. + +Definitions of the parameters: + +- data：array of output data, if interBuf is used it's an array of interBuf +- numOfRows：number of rows in `data` +- dataOutput：the buffer for output data, the size is same as that of the final result; If the result is not final, it can be put in the interBuf, i.e. `data`. +- numOfOutput：number of rows in the output data +- buf：for the state exchange between UDF and TDengine + +#### Finalize Function Template + +The function template below can be used to finalize the result of your own UDF, normally used when interBuf is used. + +`void udfFinalizeFunc(char* dataOutput, char* interBuf, int* numOfOutput, SUdfInit* buf)` + +`udfFinalizeFunc` is the place holder of function name, definitions of the parameter are as below: + +- dataOutput：buffer for output data +- interBuf：buffer for intermediate result, can be used as input for next processing step +- numOfOutput：number of output data, can only be 0 or 1 for aggregate function +- buf：for state exchange between UDF and TDengine + +### Example abs_max.c + +[abs_max.c](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/abs_max.c) is an example of a user defined aggregate function to get the maximum from the absolute values of a column. + +The internal processing happens as follows. The results of the select statement are divided into multiple row blocks and `udfNormalFunc`, i.e. `abs_max` in this case, is performed on each row block to generate the intermediate results for each sub table. Then `udfMergeFunc`, i.e. `abs_max_merge` in this case, is performed on the intermediate result of sub tables to aggregate and generate the final or intermediate result of STable. The intermediate result of STable is finally processed by `udfFinalizeFunc`, i.e. `abs_max_finalize` in this example, to generate the final result, which contains either 0 or 1 row. + +Other typical aggregation functions such as covariance, can also be implemented using aggregate UDF. + +## UDF Naming Conventions + +The naming convention for the 3 kinds of function templates required by UDF is as follows: + - udfNormalFunc, udfMergeFunc, and udfFinalizeFunc are required to have same prefix, i.e. the actual name of udfNormalFunc. The udfNormalFunc doesn't need a suffix following the function name. + - udfMergeFunc should be udfNormalFunc followed by `_merge` + - udfFinalizeFunc should be udfNormalFunc followed by `_finalize`. + +The naming convention is part of TDengine's UDF framework. TDengine follows this convention to invoke the corresponding actual functions. + +Depending on whether you are creating a scalar UDF or aggregate UDF, the functions that you need to implement are different. + +- Scalar function：udfNormalFunc is required. +- Aggregate function：udfNormalFunc, udfMergeFunc (if query on STable) and udfFinalizeFunc are required. + +For clarity, assuming we want to implement a UDF named "foo": +- If the function is a scalar function, we only need to implement the "normal" function template and it should be named simply `foo`. +- If the function is an aggregate function, we need to implement `foo`, `foo_merge`, and `foo_finalize`. Note that for aggregate UDF, even though one of the three functions is not necessary, there must be an empty implementation. + +## Compile UDF + +The source code of UDF in C can't be utilized by TDengine directly. UDF can only be loaded into TDengine after compiling to dynamically linked library (DLL). + +For example, the example UDF `add_one.c` mentioned earlier, can be compiled into DLL using the command below, in a Linux Shell. + +```bash +gcc -g -O0 -fPIC -shared add_one.c -o add_one.so +``` + +The generated DLL file `add_one.so` can be used later when creating a UDF. It's recommended to use GCC not older than 7.5. + +## Create and Use UDF + +When a UDF is created in a TDengine instance, it is available across the databases in that instance. + +### Create UDF + +SQL command can be executed on the host where the generated UDF DLL resides to load the UDF DLL into TDengine. This operation cannot be done through REST interface or web console. Once created, any client of the current TDengine can use these UDF functions in their SQL commands. UDF are stored in the management node of TDengine. The UDFs loaded in TDengine would be still available after TDengine is restarted. + +When creating UDF, the type of UDF, i.e. a scalar function or aggregate function must be specified. If the specified type is wrong, the SQL statements using the function would fail with errors. The input type and output type don't need to be the same in UDF, but the input data type and output data type must be consistent with the UDF definition. + +- Create Scalar Function + +```sql +CREATE FUNCTION userDefinedFunctionName AS "/absolute/path/to/userDefinedFunctionName.so" OUTPUTTYPE [BUFSIZE B]; +``` + +- userDefinedFunctionName：The function name to be used in SQL statement which must be consistent with the function name defined by `udfNormalFunc` and is also the name of the compiled DLL (.so file). +- path：The absolute path of the DLL file including the name of the shared object file (.so). The path must be quoted with single or double quotes. +- outputtype：The output data type, the value is the literal string of the supported TDengine data type. +- B：the size of intermediate buffer, in bytes; it is an optional parameter and the range is [0,512]. + +For example, below SQL statement can be used to create a UDF from `add_one.so`. + +```sql +CREATE FUNCTION add_one AS "/home/taos/udf_example/add_one.so" OUTPUTTYPE INT; +``` + +- Create Aggregate Function + +```sql +CREATE AGGREGATE FUNCTION userDefinedFunctionName AS "/absolute/path/to/userDefinedFunctionName.so" OUTPUTTYPE [ BUFSIZE B ]; +``` + +- userDefinedFunctionName：the function name to be used in SQL statement which must be consistent with the function name defined by `udfNormalFunc` and is also the name of the compiled DLL (.so file). +- path：the absolute path of the DLL file including the name of the shared object file (.so). The path needs to be quoted by single or double quotes. +- OUTPUTTYPE：the output data type, the value is the literal string of the type +- B：the size of intermediate buffer, in bytes; it's an optional parameter and the range is [0,512] + +For details about how to use intermediate result, please refer to example program [demo.c](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/demo.c). + +For example, below SQL statement can be used to create a UDF from `demo.so`. + +```sql +CREATE AGGREGATE FUNCTION demo AS "/home/taos/udf_example/demo.so" OUTPUTTYPE DOUBLE bufsize 14; +``` + +### Manage UDF + +- Delete UDF + +``` +DROP FUNCTION ids(X); +``` + +- ids(X)：same as that in `CREATE FUNCTION` statement + +```sql +DROP FUNCTION add_one; +``` + +- Show Available UDF + +```sql +SHOW FUNCTIONS; +``` + +### Use UDF + +The function name specified when creating UDF can be used directly in SQL statements, just like builtin functions. + +```sql +SELECT X(c) FROM table/STable; +``` + +The above SQL statement invokes function X for column c. + +## Restrictions for UDF + +In current version there are some restrictions for UDF + +1. Only Linux is supported when creating and invoking UDF for both client side and server side +2. UDF can't be mixed with builtin functions +3. Only one UDF can be used in a SQL statement +4. Only a single column is supported as input for UDF +5. Once created successfully, UDF is persisted in MNode of TDengineUDF +6. UDF can't be created through REST interface +7. The function name used when creating UDF in SQL must be consistent with the function name defined in the DLL, i.e. the name defined by `udfNormalFunc` +8. The name of a UDF should not conflict with any of TDengine's built-in functions + +## Examples + +### Scalar function example [add_one](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/add_one.c) + +
+add_one.c + +```c +{{#include tests/script/sh/add_one.c}} +``` + +
+ +### Aggregate function example [abs_max](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/abs_max.c) + +
+abs_max.c + +```c +{{#include tests/script/sh/abs_max.c}} +``` + +
+ +### Example for using intermediate result [demo](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/demo.c) + +
+demo.c + +```c +{{#include tests/script/sh/demo.c}} +``` + +
diff --git a/docs-en/07-develop/index.md b/docs-en/07-develop/index.md index 122dd0d870ac42b62c4f9e694cf79eec3ca122a5..e3f55f290753f79ac1708337082ce90bb050b21f 100644 --- a/docs-en/07-develop/index.md +++ b/docs-en/07-develop/index.md @@ -2,15 +2,15 @@ title: Developer Guide --- -To develop an application using TDengine to process time-series data, we recommend taking the following steps: +To develop an application to process time-series data using TDengine, we recommend taking the following steps: -1. Choose the way for connection to TDengine. No matter what programming language you use, you can always use the REST interface to access TDengine, but you can also use connectors unique to each programming language. -2. Design the data model based on your own application scenarios. Learn the [concepts](/concept/) of TDengine including "one table for one data collection point" and the "super table" concept; learn about static labels, collected metrics, and subtables. According to the data characteristics, you may decide to create one or more databases, and you should design the STable schema to fit your data. -3. Decide how to insert data. TDengine supports writing using standard SQL, but also supports schemaless writing, so that data can be written directly without creating tables manually. -4. Based on business requirements, find out what SQL query statements need to be written. +1. Choose the method to connect to TDengine. No matter what programming language you use, you can always use the REST interface to access TDengine, but you can also use connectors unique to each programming language. +2. Design the data model based on your own use cases. Learn the [concepts](/concept/) of TDengine including "one table for one data collection point" and the "super table" (STable) concept; learn about static labels, collected metrics, and subtables. Depending on the characteristics of your data and your requirements, you may decide to create one or more databases, and you should design the STable schema to fit your data. +3. Decide how you will insert data. TDengine supports writing using standard SQL, but also supports schemaless writing, so that data can be written directly without creating tables manually. +4. Based on business requirements, find out what SQL query statements need to be written. You may be able to repurpose any existing SQL. 5. If you want to run real-time analysis based on time series data, including various dashboards, it is recommended that you use the TDengine continuous query feature instead of deploying complex streaming processing systems such as Spark or Flink. 6. If your application has modules that need to consume inserted data, and they need to be notified when new data is inserted, it is recommended that you use the data subscription function provided by TDengine without the need to deploy Kafka. -7. In many scenarios (such as fleet management), the application needs to obtain the latest status of each data collection point. It is recommended that you use the cache function of TDengine instead of deploying Redis separately. +7. In many use cases (such as fleet management), the application needs to obtain the latest status of each data collection point. It is recommended that you use the cache function of TDengine instead of deploying Redis separately. 8. If you find that the SQL functions of TDengine cannot meet your requirements, then you can use user-defined functions to solve the problem. This section is organized in the order described above. For ease of understanding, TDengine provides sample code for each supported programming language for each function. If you want to learn more about the use of SQL, please read the [SQL manual](/taos-sql/). For a more in-depth understanding of the use of each connector, please read the [Connector Reference Guide](/reference/connector/). If you also want to integrate TDengine with third-party systems, such as Grafana, please refer to the [third-party tools](/third-party/). diff --git a/docs-en/10-cluster/01-deploy.md b/docs-en/10-cluster/01-deploy.md index 8c921797ec038fb8afbf382a980b8f7a197fa898..200da1be3f8185818bd21dd3fcdc78c124a36831 100644 --- a/docs-en/10-cluster/01-deploy.md +++ b/docs-en/10-cluster/01-deploy.md @@ -6,29 +6,35 @@ title: Deployment ### Step 1 -The FQDN of all hosts need to be setup properly, all the FQDNs need to be configured in the /etc/hosts of each host. It must be guaranteed that each FQDN can be accessed (by ping, for example) from any other hosts. +The FQDN of all hosts must be setup properly. For e.g. FQDNs may have to be configured in the /etc/hosts file on each host. You must confirm that each FQDN can be accessed from any other host. For e.g. you can do this by using the `ping` command. -On each host command `hostname -f` can be executed to get the hostname. `ping` command can be executed on each host to check whether any other host is accessible from it. If any host is not accessible, the network configuration, like /etc/hosts or DNS configuration, need to be checked and revised to make any two hosts accessible to each other. +To get the hostname on any host, the command `hostname -f` can be executed. `ping ` command can be executed on each host to check whether any other host is accessible from it. If any host is not accessible, the network configuration, like /etc/hosts or DNS configuration, needs to be checked and revised, to make any two hosts accessible to each other. :::note -- The host where the client program runs also needs to configured properly for FQDN, to make sure all hosts for client or server can be accessed from any other. In other words, the hosts where the client is running are also considered as a part of the cluster. +- The host where the client program runs also needs to be configured properly for FQDN, to make sure all hosts for client or server can be accessed from any other. In other words, the hosts where the client is running are also considered as a part of the cluster. -- It's suggested to disable the firewall for all hosts in the cluster. At least TCP/UDP for port 6030~6042 need to be open if firewall is enabled. +- Please ensure that your firewall rules do not block TCP/UDP on ports 6030-6042 on all hosts in the cluster. ::: ### Step 2 -If any previous version of TDengine has been installed and configured on any host, the installation needs to be removed and the data needs to be cleaned up. For details about uninstalling please refer to [Install and Uninstall](/operation/pkg-install). To clean up the data, please use `rm -rf /var/lib/taos/\*` assuming the `dataDir` is configured as `/var/lib/taos`. +If any previous version of TDengine has been installed and configured on any host, the installation needs to be removed and the data needs to be cleaned up. For details about uninstalling please refer to [Install and Uninstall](/operation/pkg-install). To clean up the data, please use `rm -rf /var/lib/taos/\*` assuming the `dataDir` is configured as `/var/lib/taos`. + +:::note + +As a best practice, before cleaning up any data files or directories, please ensure that your data has been backed up correctly, if required by your data integrity, backup, security, or other standard operating protocols (SOP). + +::: ### Step 3 -Now it's time to install TDengine on all hosts without starting `taosd`, the versions on all hosts should be same. If it's prompted to input the existing TDengine cluster, simply press carriage return to ignore it. `install.sh -e no` can also be used to disable this prompt. For details please refer to [Install and Uninstall](/operation/pkg-install). +Now it's time to install TDengine on all hosts but without starting `taosd`. Note that the versions on all hosts should be same. If you are prompted to input the existing TDengine cluster, simply press carriage return to ignore the prompt. `install.sh -e no` can also be used to disable this prompt. For details please refer to [Install and Uninstall](/operation/pkg-install). ### Step 4 -Now each physical node (referred to as `dnode` hereinafter, it's abbreviation for "data node") of TDengine need to be configured properly. Please be noted that one dnode doesn't stand for one host, multiple TDengine nodes can be started on single host as long as they are configured properly without conflicting. More specifically each instance of the configuration file `taos.cfg` stands for a dnode. Assuming the first dnode of TDengine cluster is "h1.taosdata.com:6030", its `taos.cfg` is configured as following. +Now each physical node (referred to, hereinafter, as `dnode` which is an abbreviation for "data node") of TDengine needs to be configured properly. Please note that one dnode doesn't stand for one host. Multiple TDengine dnodes can be started on a single host as long as they are configured properly without conflicting. More specifically each instance of the configuration file `taos.cfg` stands for a dnode. Assuming the first dnode of TDengine cluster is "h1.taosdata.com:6030", its `taos.cfg` is configured as following. ```c // firstEp is the end point to connect to when any dnode starts @@ -44,9 +50,9 @@ serverPort 6030 #arbitrator ha.taosdata.com:6042 ``` -`firstEp` and `fqdn` must be configured properly. In `taos.cfg` of all dnodes in TDengine cluster, `firstEp` must be configured to point to same address, i.e. the first dnode of the cluster. `fqdn` and `serverPort` compose the address of each node itself. If you want to start multiple TDengine dnodes on a single host, please also make sure all other configurations like `dataDir`, `logDir`, and other resources related parameters are not conflicting. +`firstEp` and `fqdn` must be configured properly. In `taos.cfg` of all dnodes in TDengine cluster, `firstEp` must be configured to point to same address, i.e. the first dnode of the cluster. `fqdn` and `serverPort` compose the address of each node itself. If you want to start multiple TDengine dnodes on a single host, please make sure all other configurations like `dataDir`, `logDir`, and other resources related parameters are not conflicting. -For all the dnodes in a TDengine cluster, below parameters must be configured as exactly same, any node whose configuration is different from dnodes already in the cluster can't join the cluster. +For all the dnodes in a TDengine cluster, the below parameters must be configured exactly the same, any node whose configuration is different from dnodes already in the cluster can't join the cluster. | **#** | **Parameter** | **Definition** | | ----- | ------------------ | --------------------------------------------------------------------------------- | @@ -61,15 +67,17 @@ For all the dnodes in a TDengine cluster, below parameters must be configured as | 9 | maxVgroupsPerDb | Maximum number vgroups that can be used by each DB | :::note -Prior to version 2.0.19.0, besides the above parameters, `locale` and `charset` must be configured as same too for each dnode. +Prior to version 2.0.19.0, besides the above parameters, `locale` and `charset` must also be configured the same for each dnode. ::: ## Start Cluster +In the following example we assume that first dnode has FQDN h1.taosdata.com and the second dnode has FQDN h2.taosdata.com. + ### Start The First DNODE -The first dnode can be started following the instructions in [Get Started](/get-started/), for example h1.taosdata.com. Then TDengine CLI `taos` can be launched to execute command `show dnodes`, the output is as following for example: +The first dnode can be started following the instructions in [Get Started](/get-started/). Then TDengine CLI `taos` can be launched to execute command `show dnodes`, the output is as following for example: ``` Welcome to the TDengine shell from Linux, Client Version:2.0.0.0 @@ -80,27 +88,41 @@ Copyright (c) 2017 by TAOS Data, Inc. All rights reserved. taos> show dnodes; id | end_point | vnodes | cores | status | role | create_time | ===================================================================================== - 1 | h1.taos.com:6030 | 0 | 2 | ready | any | 2020-07-31 03:49:29.202 | + 1 | h1.taosdata.com:6030 | 0 | 2 | ready | any | 2020-07-31 03:49:29.202 | Query OK, 1 row(s) in set (0.006385s) taos> ``` -From the above output, it is shown that the end point of the started dnode is "h1.taos.com:6030", which is the `firstEp` of the cluster. +From the above output, it is shown that the end point of the started dnode is "h1.taosdata.com:6030", which is the `firstEp` of the cluster. ### Start Other DNODEs There are a few steps necessary to add other dnodes in the cluster. -Firstly, start `taosd` as instructed in [Get Started](/get-started/), assuming it's for the second dnode. Before starting `taosd`, please making sure the configuration is correct, especially `firstEp`, `FQDN` and `serverPort`, `firstEp` must be same as the dnode shown in the section "Start First DNODE", i.e. "h1.taosdata.com" in this example. +Let's assume we are starting the second dnode with FQDN, h2.taosdata.com. First we make sure the configuration is correct. + +```c +// firstEp is the end point to connect to when any dnode starts +firstEp h1.taosdata.com:6030 + +// must be configured to the FQDN of the host where the dnode is launched +fqdn h2.taosdata.com + +// the port used by the dnode, default is 6030 +serverPort 6030 + +``` + +Second, we can start `taosd` as instructed in [Get Started](/get-started/). -Then, on the first dnode, use TDengine CLI `taos` to execute below command to add the end point of the dnode in the cluster. In the command "fqdn:port" should be quoted using double quotes. +Then, on the first dnode i.e. h1.taosdata.com in our example, use TDengine CLI `taos` to execute the following command to add the end point of the dnode in the cluster. In the command "fqdn:port" should be quoted using double quotes. ```sql CREATE DNODE "h2.taos.com:6030"; ``` -Then on the first dnode, execute `show dnodes` in `taos` to show whether the second dnode has been added in the cluster successfully or not. +Then on the first dnode h1.taosdata.com, execute `show dnodes` in `taos` to show whether the second dnode has been added in the cluster successfully or not. ```sql SHOW DNODES; @@ -109,6 +131,6 @@ SHOW DNODES; If the status of the newly added dnode is offline, please check: - Whether the `taosd` process is running properly or not -- In the log file `taosdlog.0` to see whether the fqdn and port are correct or not +- In the log file `taosdlog.0` to see whether the fqdn and port are correct The above process can be repeated to add more dnodes in the cluster. diff --git a/docs-en/10-cluster/02-cluster-mgmt.md b/docs-en/10-cluster/02-cluster-mgmt.md index 3fcd68b29ce08519af9a0cde11d5361c6b4cd312..674c92e2766a4eb304079140af19c8efea72d55e 100644 --- a/docs-en/10-cluster/02-cluster-mgmt.md +++ b/docs-en/10-cluster/02-cluster-mgmt.md @@ -3,16 +3,16 @@ sidebar_label: Operation title: Manage DNODEs --- -It has been introduced that how to deploy and start a cluster from scratch. Once a cluster is ready, the dnode status in the cluster can be shown at any time, new dnode can be added to scale out the cluster, an existing dnode can be removed, even load balance can be performed manually.\ +The previous section, [Deployment],(/cluster/deploy) showed you how to deploy and start a cluster from scratch. Once a cluster is ready, the status of dnode(s) in the cluster can be shown at any time. Dnodes can be managed from the TDengine CLI. New dnode(s) can be added to scale out the cluster, an existing dnode can be removed and you can even perform load balancing manually, if necessary. :::note -All the commands to be introduced in this chapter need to be run through TDengine CLI, sometimes it's necessary to use root privilege. +All the commands introduced in this chapter must be run in the TDengine CLI - `taos`. Note that sometimes it is necessary to use root privilege. ::: ## Show DNODEs -below command can be executed in TDengine CLI `taos` to list all dnodes in the cluster, including ID, end point (fqdn:port), status (ready, offline), number of vnodes, number of free vnodes, etc. It's suggested to execute this command to check after adding or removing a dnode. +The below command can be executed in TDengine CLI `taos` to list all dnodes in the cluster, including ID, end point (fqdn:port), status (ready, offline), number of vnodes, number of free vnodes and so on. We recommend executing this command after adding or removing a dnode. ```sql SHOW DNODES; @@ -30,7 +30,7 @@ Query OK, 1 row(s) in set (0.008298s) ## Show VGROUPs -To utilize system resources efficiently and provide scalability, data sharding is required. The data of each database is divided into multiple shards and stored in multiple vnodes. These vnodes may be located in different dnodes, scaling out can be achieved by adding more vnodes from more dnodes. Each vnode can only be used for a single DB, but one DB can have multiple vnodes. The allocation of vnode is scheduled automatically by mnode according to system resources of the dnodes. +To utilize system resources efficiently and provide scalability, data sharding is required. The data of each database is divided into multiple shards and stored in multiple vnodes. These vnodes may be located on different dnodes. One way of scaling out is to add more vnodes on dnodes. Each vnode can only be used for a single DB, but one DB can have multiple vnodes. The allocation of vnode is scheduled automatically by mnode based on system resources of the dnodes. Launch TDengine CLI `taos` and execute below command: @@ -39,7 +39,7 @@ USE SOME_DATABASE; SHOW VGROUPS; ``` -The example output is as below: +The example output is below: ``` taos> show dnodes; @@ -87,7 +87,7 @@ taos> show dnodes; Query OK, 2 row(s) in set (0.001017s) ``` -It can be seen that the status of the new dnode is "offline", once the dnode is started and connects the firstEp of the cluster, execute the command again and get below example output, from which it can be seen that two dnodes are both in "ready" status. +It can be seen that the status of the new dnode is "offline". Once the dnode is started and connects to the firstEp of the cluster, you can execute the command again and get the example output below. As can be seen, both dnodes are in "ready" status. ``` taos> show dnodes; @@ -100,7 +100,7 @@ Query OK, 2 row(s) in set (0.001316s) ## Drop DNODE -Launch TDengine CLI `taos` and execute the command below to drop or remove a dnode from the cluster. In the command, `dnodeId` can be gotten from `show dnodes`. +Launch TDengine CLI `taos` and execute the command below to drop or remove a dnode from the cluster. In the command, you can get `dnodeId` from `show dnodes`. ```sql DROP DNODE "fqdn:port"; @@ -112,7 +112,7 @@ or DROP DNODE dnodeId; ``` -The example output is as below： +The example output is below： ``` taos> show dnodes; @@ -132,14 +132,14 @@ taos> show dnodes; Query OK, 1 row(s) in set (0.001137s) ``` -In the above example, when `show dnodes` is executed the first time, two dnodes are shown. Then `drop dnode 2` is executed, after that from the output of executing `show dnodes` again it can be seen that only the dnode with ID 1 is still in the cluster. +In the above example, when `show dnodes` is executed the first time, two dnodes are shown. After `drop dnode 2` is executed, you can execute `show dnodes` again and it can be seen that only the dnode with ID 1 is still in the cluster. :::note -- Once a dnode is dropped, it can't rejoin the cluster. To rejoin, the dnode needs to deployed again after cleaning up the data directory. Normally, before dropping a dnode, the data belonging to the dnode needs to be migrated to other place. -- Please be noted that `drop dnode` is different from stopping `taosd` process. `drop dnode` just removes the dnode out of TDengine cluster. Only after a dnode is dropped, can the corresponding `taosd` process be stopped. +- Once a dnode is dropped, it can't rejoin the cluster. To rejoin, the dnode needs to deployed again after cleaning up the data directory. Before dropping a dnode, the data belonging to the dnode MUST be migrated/backed up according to your data retention, data security or other SOPs. +- Please note that `drop dnode` is different from stopping `taosd` process. `drop dnode` just removes the dnode out of TDengine cluster. Only after a dnode is dropped, can the corresponding `taosd` process be stopped. - Once a dnode is dropped, other dnodes in the cluster will be notified of the drop and will not accept the request from the dropped dnode. -- dnodeID is allocated automatically and can't be interfered manually. dnodeID is generated in ascending order without duplication. +- dnodeID is allocated automatically and can't be manually modified. dnodeID is generated in ascending order without duplication. ::: @@ -155,7 +155,7 @@ ALTER DNODE BALANCE "VNODE:-DNODE:"; In the above command, `source-dnodeId` is the original dnodeId where the vnode resides, `dest-dnodeId` specifies the target dnode. vgId (vgroup ID) can be shown by `SHOW VGROUPS `. -Firstly `show vgroups` is executed to show the vgroup distribution. +First `show vgroups` is executed to show the vgroup distribution. ``` taos> show vgroups; @@ -172,7 +172,7 @@ taos> show vgroups; Query OK, 8 row(s) in set (0.001314s) ``` -It can be seen that there are 5 vgroups in dnode 3 and 3 vgroups in node 1, now we want to move vgId 18 from dnode 3 to dnode 1. Execute below command in `taos` +It can be seen that there are 5 vgroups in dnode 3 and 3 vgroups in node 1, now we want to move vgId 18 from dnode 3 to dnode 1. Execute the below command in `taos` ``` taos> alter dnode 3 balance "vnode:18-dnode:1"; @@ -207,7 +207,7 @@ It can be seen from above output that vgId 18 has been moved from dnode 3 to dno :::note - Manual load balancing can only be performed when the automatic load balancing is disabled, i.e. `balance` is set to 0. -- Only vnode in normal state, i.e. master or slave, can be moved. vnode can't moved when its in status offline, unsynced or syncing. +- Only a vnode in normal state, i.e. master or slave, can be moved. vnode can't be moved when its in status offline, unsynced or syncing. - Before moving a vnode, it's necessary to make sure the target dnode has enough resources: CPU, memory and disk. ::: diff --git a/docs-en/10-cluster/03-ha-and-lb.md b/docs-en/10-cluster/03-ha-and-lb.md index 53c95be9e995a728b2b4053e4f204df58271716e..bd718eef9f8dc181628132de831dbca2af59d158 100644 --- a/docs-en/10-cluster/03-ha-and-lb.md +++ b/docs-en/10-cluster/03-ha-and-lb.md @@ -7,44 +7,45 @@ title: High Availability and Load Balancing High availability of vnode and mnode can be achieved through replicas in TDengine. -The number of vnodes is associated with each DB, there can be multiple DBs in a TDengine cluster. For the purpose of operation, different number of replicas can be configured properly for each DB. When creating a database, the parameter `replica` is used to specify the number of replicas, the default value is 1. With single replica, the high availability of the system can't be guaranteed. Whenever one node is down, data service would be unavailable. The number of dnodes in the cluster must NOT be lower than the number of replicas set for any DB, otherwise the `create table` operation would fail with error "more dnodes are needed". Below SQL statement is used to create a database named as "demo" with 3 replicas. +A TDengine cluster can have multiple databases. Each database has a number of vnodes associated with it. A different number of replicas can be configured for each DB. When creating a database, the parameter `replica` is used to specify the number of replicas. The default value for `replica` is 1. Naturally, a single replica cannot guarantee high availability since if one node is down, the data service is unavailable. Note that the number of dnodes in the cluster must NOT be lower than the number of replicas set for any DB, otherwise the `create table` operation will fail with error "more dnodes are needed". The SQL statement below is used to create a database named "demo" with 3 replicas. ```sql CREATE DATABASE demo replica 3; ``` -The data in a DB is divided into multiple shards and stored in multiple vgroups. The number of vnodes in each group is determined by the number of replicas set for the DB. The vnodes in each vgroups store exactly same data. For the purpose of high availability, the vnodes in a vgroup must be located in different dnodes on different hosts. As long as over half of the vnodes in a vgroup are in online state, the vgroup is able to serve data access. Otherwise the vgroup can't handle any data access for reading or inserting data. +The data in a DB is divided into multiple shards and stored in multiple vgroups. The number of vnodes in each vgroup is determined by the number of replicas set for the DB. The vnodes in each vgroup store exactly the same data. For the purpose of high availability, the vnodes in a vgroup must be located in different dnodes on different hosts. As long as over half of the vnodes in a vgroup are in an online state, the vgroup is able to provide data access. Otherwise the vgroup can't provide data access for reading or inserting data. -There may be data for multiple DBs in a dnode. Once a dnode is down, multiple DBs may be affected. However, it's hard to say the cluster is guaranteed to work properly as long as over half of dnodes are online because vnodes are introduced and there may be complex mapping between vnodes and dnodes. +There may be data for multiple DBs in a dnode. When a dnode is down, multiple DBs may be affected. While in theory, the cluster will provide data access for reading or inserting data if over half the vnodes in vgroups are online, because of the possibly complex mapping between vnodes and dnodes, it is difficult to guarantee that the cluster will work properly if over half of the dnodes are online. ## High Availability of Mnode -Each TDengine cluster is managed by `mnode`, which is a module of `taosd`. For the high availability of mnode, multiple mnodes can be configured using system parameter `numOfMNodes`, the valid time range is [1,3]. To make sure the data consistency between mnodes, the data replication between mnodes is performed in synchronous way. +Each TDengine cluster is managed by `mnode`, which is a module of `taosd`. For the high availability of mnode, multiple mnodes can be configured using system parameter `numOfMNodes`. The valid range for `numOfMnodes` is [1,3]. To ensure data consistency between mnodes, data replication between mnodes is performed synchronously. -There may be multiple dnodes in a cluster, but only one mnode can be started in each dnode. Which one or ones of the dnodes will be designated as mnodes is automatically determined by TDengine according to the cluster configuration and system resources. Command `show mnodes` can be executed in TDengine `taos` to show the mnodes in the cluster. +There may be multiple dnodes in a cluster, but only one mnode can be started in each dnode. Which one or ones of the dnodes will be designated as mnodes is automatically determined by TDengine according to the cluster configuration and system resources. The command `show mnodes` can be executed in TDengine `taos` to show the mnodes in the cluster. ```sql SHOW MNODES; ``` -The end point and role/status (master, slave, unsynced, or offline) of all mnodes can be shown by the above command. When the first dnode is started in a cluster, there must be one mnode in this dnode, because there must be at least one mnode otherwise the cluster doesn't work. If `numOfMNodes` is configured to 2, another mnode will be started when the second dnode is launched. +The end point and role/status (master, slave, unsynced, or offline) of all mnodes can be shown by the above command. When the first dnode is started in a cluster, there must be one mnode in this dnode. Without at least one mnode, the cluster cannot work. If `numOfMNodes` is configured to 2, another mnode will be started when the second dnode is launched. For the high availability of mnode, `numOfMnodes` needs to be configured to 2 or a higher value. Because the data consistency between mnodes must be guaranteed, the replica confirmation parameter `quorum` is set to 2 automatically if `numOfMNodes` is set to 2 or higher. :::note -If high availability is important for your system, both vnode and mnode must be configured to have multiple replicas. How to configure for them are different and have been described. +If high availability is important for your system, both vnode and mnode must be configured to have multiple replicas. ::: -## Load Balance +## Load Balancing -Load balance will be triggered in 3 cades without manual intervention. +Load balancing will be triggered in 3 cases without manual intervention. -- When a new dnode is joined in the cluster, automatic load balancing may be triggered, some data from some dnodes may be transferred to the new dnode automatically. +- When a new dnode joins the cluster, automatic load balancing may be triggered. Some data from other dnodes may be transferred to the new dnode automatically. - When a dnode is removed from the cluster, the data from this dnode will be transferred to other dnodes automatically. - When a dnode is too hot, i.e. too much data has been stored in it, automatic load balancing may be triggered to migrate some vnodes from this dnode to other dnodes. -- :::tip - Automatic load balancing is controlled by parameter `balance`, 0 means disabled and 1 means enabled. + +:::tip +Automatic load balancing is controlled by the parameter `balance`, 0 means disabled and 1 means enabled. This is set in the file [taos.cfg](https://docs.tdengine.com/reference/config/#balance). ::: @@ -52,26 +53,26 @@ Load balance will be triggered in 3 cades without manual intervention. When a dnode is offline, it can be detected by the TDengine cluster. There are two cases: -- The dnode becomes online again before the threshold configured in `offlineThreshold` is reached, it is still in the cluster and data replication is started automatically. The dnode can work properly after the data syncup is finished. +- The dnode comes online before the threshold configured in `offlineThreshold` is reached. The dnode is still in the cluster and data replication is started automatically. The dnode can work properly after the data sync is finished. -- If the dnode has been offline over the threshold configured in `offlineThreshold` in `taos.cfg`, the dnode will be removed from the cluster automatically. System alert will be generated and automatic load balancing will be triggered too if `balance` is set to 1. When the removed dnode is restarted and becomes online, it will not be joined in the cluster automatically, it can only be joined manually by the system operator. +- If the dnode has been offline over the threshold configured in `offlineThreshold` in `taos.cfg`, the dnode will be removed from the cluster automatically. A system alert will be generated and automatic load balancing will be triggered if `balance` is set to 1. When the removed dnode is restarted and becomes online, it will not join the cluster automatically. The system administrator has to manually join the dnode to the cluster. :::note -If all the vnodes in a vgroup (or mnodes in mnode group) are in offline or unsynced status, the master node can only be voted after all the vnodes or mnodes in the group become online and can exchange status, then the vgroup (or mnode group) is able to provide service. +If all the vnodes in a vgroup (or mnodes in mnode group) are in offline or unsynced status, the master node can only be voted on, after all the vnodes or mnodes in the group become online and can exchange status. Following this, the vgroup (or mnode group) is able to provide service. ::: ## Arbitrator -If the number of replicas is set to an even number like 2, when half of the vnodes in a vgroup don't work master node can't be voted. Similar case is also applicable to mnode if the number of mnodes is set to an even number like 2. +The "arbitrator" component is used to address the special case when the number of replicas is set to an even number like 2,4 etc. If half of the vnodes in a vgroup don't work, it is impossible to vote and select a master node. This situation also applies to mnodes if the number of mnodes is set to an even number like 2,4 etc. -To resolve this problem, a new arbitrator component named `tarbitrator`, abbreviated for TDengine Arbitrator, was introduced. Arbitrator simulates a vnode or mnode but it's only responsible for network communication and doesn't handle any actual data access. With Arbitrator, any vgroup or mnode group can be considered as having number of member nodes and master node can be selected. +To resolve this problem, a new arbitrator component named `tarbitrator`, an abbreviation of TDengine Arbitrator, was introduced. The `tarbitrator` simulates a vnode or mnode but it's only responsible for network communication and doesn't handle any actual data access. As long as more than half of the vnode or mnode, including Arbitrator, are available the vnode group or mnode group can provide data insertion or query services normally. -Normally, it's suggested to configure replica number of each DB or system parameter `numOfMNodes` to an odd number. However, if a user is very sensitive to storage space, replica number of 2 plus arbitrator component can be used to achieve both lower cost of storage space and high availability. +Normally, it's prudent to configure the replica number for each DB or system parameter `numOfMNodes` to be an odd number. However, if a user is very sensitive to storage space, a replica number of 2 plus arbitrator component can be used to achieve both lower cost of storage space and high availability. Arbitrator component is installed with the server package. For details about how to install, please refer to [Install](/operation/pkg-install). The `-p` parameter of `tarbitrator` can be used to specify the port on which it provides service. -In the configuration file `taos.cfg` of each dnode, parameter `arbitrator` needs to be configured to the end point of the `tarbitrator` process. arbitrator component will be used automatically if the replica is configured to an even number and will be ignored if the replica is configured to an odd number. +In the configuration file `taos.cfg` of each dnode, parameter `arbitrator` needs to be configured to the end point of the `tarbitrator` process. Arbitrator component will be used automatically if the replica is configured to an even number and will be ignored if the replica is configured to an odd number. Arbitrator can be shown by executing command in TDengine CLI `taos` with its role shown as "arb". diff --git a/docs-en/10-cluster/index.md b/docs-en/10-cluster/index.md index a19a54e01d5a6429e95958c2544072961b0cb66a..5a45a2ce7b08c67322265cf1bbd54ef66cbfc027 100644 --- a/docs-en/10-cluster/index.md +++ b/docs-en/10-cluster/index.md @@ -3,7 +3,7 @@ title: Cluster keywords: ["cluster", "high availability", "load balance", "scale out"] --- -TDengine has a native distributed design and provides the ability to scale out. A few of nodes can form a TDengine cluster. If you need to get higher processing power, you just need to add more nodes into the cluster. TDengine uses virtual node technology to virtualize a node into multiple virtual nodes to achieve load balancing. At the same time, TDengine can group virtual nodes on different nodes into virtual node groups, and use the replication mechanism to ensure the high availability of the system. The cluster feature of TDengine is completely open source. +TDengine has a native distributed design and provides the ability to scale out. A few nodes can form a TDengine cluster. If you need higher processing power, you just need to add more nodes into the cluster. TDengine uses virtual node technology to virtualize a node into multiple virtual nodes to achieve load balancing. At the same time, TDengine can group virtual nodes on different nodes into virtual node groups, and use the replication mechanism to ensure the high availability of the system. The cluster feature of TDengine is completely open source. This chapter mainly introduces cluster deployment, maintenance, and how to achieve high availability and load balancing. diff --git a/docs-en/12-taos-sql/01-data-type.md b/docs-en/12-taos-sql/01-data-type.md index 931e3bbac7f0601a9de79d0dfa04ffc94ecced96..3f5a49e3135771c6c1e62bcf158a99ee30f1ed9d 100644 --- a/docs-en/12-taos-sql/01-data-type.md +++ b/docs-en/12-taos-sql/01-data-type.md @@ -1,23 +1,23 @@ --- title: Data Types -description: "The data types supported by TDengine include timestamp, float, JSON, etc" +description: "TDengine supports a variety of data types including timestamp, float, JSON and many others." --- -When using TDengine to store and query data, the most important part of the data is timestamp. Timestamp must be specified when creating and inserting data rows or querying data, timestamp must follow below rules: +When using TDengine to store and query data, the most important part of the data is timestamp. Timestamp must be specified when creating and inserting data rows. Timestamp must follow the rules below: -- the format must be `YYYY-MM-DD HH:mm:ss.MS`, the default time precision is millisecond (ms), for example `2017-08-12 18:25:58.128` -- internal function `now` can be used to get the current timestamp of the client side -- the current timestamp of the client side is applied when `now` is used to insert data +- The format must be `YYYY-MM-DD HH:mm:ss.MS`, the default time precision is millisecond (ms), for example `2017-08-12 18:25:58.128` +- Internal function `now` can be used to get the current timestamp on the client side +- The current timestamp of the client side is applied when `now` is used to insert data - Epoch Time：timestamp can also be a long integer number, which means the number of seconds, milliseconds or nanoseconds, depending on the time precision, from 1970-01-01 00:00:00.000 (UTC/GMT) -- timestamp can be applied with add/subtract operation, for example `now-2h` means 2 hours back from the time at which query is executed，the unit can be b(nanosecond), u(microsecond), a(millisecond), s(second), m(minute), h(hour), d(day), w(week.。 So `select * from t1 where ts > now-2w and ts <= now-1w` means the data between two weeks ago and one week ago. The time unit can also be n (calendar month) or y (calendar year) when specifying the time window for down sampling operation. +- Add/subtract operations can be carried out on timestamps. For example `now-2h` means 2 hours prior to the time at which query is executed. The units of time in operations can be b(nanosecond), u(microsecond), a(millisecond), s(second), m(minute), h(hour), d(day), or w(week). So `select * from t1 where ts > now-2w and ts <= now-1w` means the data between two weeks ago and one week ago. The time unit can also be n (calendar month) or y (calendar year) when specifying the time window for down sampling operations. -Time precision in TDengine can be set by the `PRECISION` parameter when executing `CREATE DATABASE`, like below, the default time precision is millisecond. +Time precision in TDengine can be set by the `PRECISION` parameter when executing `CREATE DATABASE`. The default time precision is millisecond. In the statement below, the precision is set to nanonseconds. ```sql CREATE DATABASE db_name PRECISION 'ns'; ``` -In TDengine, below data types can be used when specifying a column or tag. +In TDengine, the data types below can be used when specifying a column or tag. | # | **type** | **Bytes** | **Description** | | --- | :-------: | --------- | ------------------------- | @@ -25,13 +25,13 @@ In TDengine, below data types can be used when specifying a column or tag. | 2 | INT | 4 | Integer, the value range is [-2^31+1, 2^31-1], while -2^31 is treated as NULL | | 3 | BIGINT | 8 | Long integer, the value range is [-2^63+1, 2^63-1], while -2^63 is treated as NULL | | 4 | FLOAT | 4 | Floating point number, the effective number of digits is 6-7, the value range is [-3.4E38, 3.4E38] | -| 5 | DOUBLE | 8 | double precision floating point number, the effective number of digits is 15-16, the value range is [-1.7E308, 1.7E308] | +| 5 | DOUBLE | 8 | Double precision floating point number, the effective number of digits is 15-16, the value range is [-1.7E308, 1.7E308] | | 6 | BINARY | User Defined | Single-byte string for ASCII visible characters. Length must be specified when defining a column or tag of binary type. The string length can be up to 16374 bytes. The string value must be quoted with single quotes. The literal single quote inside the string must be preceded with back slash like `\'` | | 7 | SMALLINT | 2 | Short integer, the value range is [-32767, 32767], while -32768 is treated as NULL | | 8 | TINYINT | 1 | Single-byte integer, the value range is [-127, 127], while -128 is treated as NULL | | 9 | BOOL | 1 | Bool, the value range is {true, false} | -| 10 | NCHAR | User Defined| Multiple-Byte string that can include like Chinese characters. Each character of NCHAR type consumes 4 bytes storage. The string value should be quoted with single quotes. Literal single quote inside the string must be preceded with backslash, like `\’`. The length must be specified when defining a column or tag of NCHAR type, for example nchar(10) means it can store at most 10 characters of nchar type and will consume fixed storage of 40 bytes. Error will be reported the string value exceeds the length defined. | -| 11 | JSON | | json type can only be used on tag, a tag of json type is excluded with any other tags of any other type | +| 10 | NCHAR | User Defined| Multi-Byte string that can include multi byte characters like Chinese characters. Each character of NCHAR type consumes 4 bytes storage. The string value should be quoted with single quotes. Literal single quote inside the string must be preceded with backslash, like `\’`. The length must be specified when defining a column or tag of NCHAR type, for example nchar(10) means it can store at most 10 characters of nchar type and will consume fixed storage of 40 bytes. An error will be reported if the string value exceeds the length defined. | +| 11 | JSON | | JSON type can only be used on tags. A tag of json type is excluded with any other tags of any other type | :::tip TDengine is case insensitive and treats any characters in the sql command as lower case by default, case sensitive strings must be quoted with single quotes. @@ -39,7 +39,7 @@ TDengine is case insensitive and treats any characters in the sql command as low ::: :::note -Only ASCII visible characters are suggested to be used in a column or tag of BINARY type. Multiple-byte characters must be stored in NCHAR type. +Only ASCII visible characters are suggested to be used in a column or tag of BINARY type. Multi-byte characters must be stored in NCHAR type. ::: diff --git a/docs-en/12-taos-sql/02-database.md b/docs-en/12-taos-sql/02-database.md index 85b71bbde727ea1ff84080d3770e641d59b88c7b..80581b2f1bc7ce9cd046c18873d3f22b6804d8cf 100644 --- a/docs-en/12-taos-sql/02-database.md +++ b/docs-en/12-taos-sql/02-database.md @@ -4,7 +4,7 @@ title: Database description: "create and drop database, show or change database parameters" --- -## Create Datable +## Create Database ``` CREATE DATABASE [IF NOT EXISTS] db_name [KEEP keep] [DAYS days] [UPDATE 1]; @@ -12,11 +12,11 @@ CREATE DATABASE [IF NOT EXISTS] db_name [KEEP keep] [DAYS days] [UPDATE 1]; :::info -1. KEEP specifies the number of days for which the data in the database to be created will be kept, the default value is 3650 days, i.e. 10 years. The data will be deleted automatically once its age exceeds this threshold. +1. KEEP specifies the number of days for which the data in the database will be retained. The default value is 3650 days, i.e. 10 years. The data will be deleted automatically once its age exceeds this threshold. 2. UPDATE specifies whether the data can be updated and how the data can be updated. - 1. UPDATE set to 0 means update operation is not allowed, the data with an existing timestamp will be dropped silently. - 2. UPDATE set to 1 means the whole row will be updated, the columns for which no value is specified will be set to NULL - 3. UPDATE set to 2 means updating a part of columns for a row is allowed, the columns for which no value is specified will be kept as no change + 1. UPDATE set to 0 means update operation is not allowed. The update for data with an existing timestamp will be discarded silently and the original record in the database will be preserved as is. + 2. UPDATE set to 1 means the whole row will be updated. The columns for which no value is specified will be set to NULL. + 3. UPDATE set to 2 means updating a subset of columns for a row is allowed. The columns for which no value is specified will be kept unchanged. 3. The maximum length of database name is 33 bytes. 4. The maximum length of a SQL statement is 65,480 bytes. 5. Below are the parameters that can be used when creating a database @@ -35,7 +35,7 @@ CREATE DATABASE [IF NOT EXISTS] db_name [KEEP keep] [DAYS days] [UPDATE 1]; - maxVgroupsPerDb: [Description](/reference/config/#maxvgroupsperdb) - comp: [Description](/reference/config/#comp) - precision: [Description](/reference/config/#precision) -6. Please be noted that all of the parameters mentioned in this section can be configured in configuration file `taosd.cfg` at server side and used by default, can be override if they are specified in `create database` statement. +6. Please note that all of the parameters mentioned in this section are configured in configuration file `taos.cfg` on the TDengine server. If not specified in the `create database` statement, the values from taos.cfg are used by default. To override default parameters, they must be specified in the `create database` statement. ::: @@ -52,7 +52,7 @@ USE db_name; ``` :::note -This way is not applicable when using a REST connection +This way is not applicable when using a REST connection. In a REST connection the database name must be specified before a table or stable name. For e.g. to query the stable "meters" in database "test" the query would be "SELECT count(*) from test.meters" ::: @@ -63,13 +63,13 @@ DROP DATABASE [IF EXISTS] db_name; ``` :::note -All data in the database will be deleted too. This command must be used with caution. +All data in the database will be deleted too. This command must be used with extreme caution. Please follow your organization's data integrity, data backup, data security or any other applicable SOPs before using this command. ::: ## Change Database Configuration -Some examples are shown below to demonstrate how to change the configuration of a database. Please be noted that some configuration parameters can be changed after the database is created, but some others can't, for details of the configuration parameters of database please refer to [Configuration Parameters](/reference/config/). +Some examples are shown below to demonstrate how to change the configuration of a database. Please note that some configuration parameters can be changed after the database is created, but some cannot. For details of the configuration parameters of database please refer to [Configuration Parameters](/reference/config/). ``` ALTER DATABASE db_name COMP 2; @@ -81,7 +81,7 @@ COMP parameter specifies whether the data is compressed and how the data is comp ALTER DATABASE db_name REPLICA 2; ``` -REPLICA parameter specifies the number of replications of the database. +REPLICA parameter specifies the number of replicas of the database. ``` ALTER DATABASE db_name KEEP 365; @@ -124,4 +124,4 @@ SHOW DATABASES; SHOW CREATE DATABASE db_name; ``` -This command is useful when migrating the data from one TDengine cluster to another one. Firstly this command can be used to get the CREATE statement, which in turn can be used in another TDengine to create an exactly same database. +This command is useful when migrating the data from one TDengine cluster to another. This command can be used to get the CREATE statement, which can be used in another TDengine instance to create the exact same database. diff --git a/docs-en/12-taos-sql/03-table.md b/docs-en/12-taos-sql/03-table.md index a1524f45f98e8435425a9a937b7f6dc4431b6e06..f065a8e2396583bb7a512446b513ed60056ad55e 100644 --- a/docs-en/12-taos-sql/03-table.md +++ b/docs-en/12-taos-sql/03-table.md @@ -12,12 +12,12 @@ CREATE TABLE [IF NOT EXISTS] tb_name (timestamp_field_name TIMESTAMP, field1_nam :::info -1. The first column of a table must be in TIMESTAMP type, and it will be set as primary key automatically -2. The maximum length of table name is 192 bytes. -3. The maximum length of each row is 16k bytes, please be notes that the extra 2 bytes used by each BINARY/NCHAR column are also counted in. -4. The name of sub-table can only be consisted of English characters, digits and underscore, and can't be started with digit. Table names are case insensitive. -5. The maximum length in bytes must be specified when using BINARY or NCHAR type. -6. Escape character "\`" can be used to avoid the conflict between table names and reserved keywords, above rules will be bypassed when using escape character on table names, but the upper limit for name length is still valid. The table names specified using escape character are case sensitive. Only ASCII visible characters can be used with escape character. +1. The first column of a table MUST be of type TIMESTAMP. It is automatically set as the primary key. +2. The maximum length of the table name is 192 bytes. +3. The maximum length of each row is 48k bytes, please note that the extra 2 bytes used by each BINARY/NCHAR column are also counted. +4. The name of the subtable can only consist of characters from the English alphabet, digits and underscore. Table names can't start with a digit. Table names are case insensitive. +5. The maximum length in bytes must be specified when using BINARY or NCHAR types. +6. Escape character "\`" can be used to avoid the conflict between table names and reserved keywords, above rules will be bypassed when using escape character on table names, but the upper limit for the name length is still valid. The table names specified using escape character are case sensitive. Only ASCII visible characters can be used with escape character. For example \`aBc\` and \`abc\` are different table names but `abc` and `aBc` are same table names because they are both converted to `abc` internally. ::: @@ -28,9 +28,9 @@ CREATE TABLE [IF NOT EXISTS] tb_name (timestamp_field_name TIMESTAMP, field1_nam CREATE TABLE [IF NOT EXISTS] tb_name USING stb_name TAGS (tag_value1, ...); ``` -The above command creates a subtable using the specified super table as template and the specified tab values. +The above command creates a subtable using the specified super table as a template and the specified tag values. -### Create Subtable Using STable As Template With A Part of Tags +### Create Subtable Using STable As Template With A Subset of Tags ``` CREATE TABLE [IF NOT EXISTS] tb_name USING stb_name (tag_name1, ...) TAGS (tag_value1, ...); @@ -44,11 +44,11 @@ The tags for which no value is specified will be set to NULL. CREATE TABLE [IF NOT EXISTS] tb_name1 USING stb_name TAGS (tag_value1, ...) [IF NOT EXISTS] tb_name2 USING stb_name TAGS (tag_value2, ...) ...; ``` -This way can be used to create a lot of tables in a single SQL statement to accelerate the speed of the creating tables. +This can be used to create a lot of tables in a single SQL statement while making table creation much faster. :::info -- Creating tables in batch must use super table as template. +- Creating tables in batch must use a super table as a template. - The length of single statement is suggested to be between 1,000 and 3,000 bytes for best performance. ::: @@ -71,7 +71,7 @@ SHOW TABLES [LIKE tb_name_wildcard]; SHOW CREATE TABLE tb_name; ``` -This way is useful when migrating the data in one TDengine cluster to another one because it can be used to create exactly same tables in the target database. +This is useful when migrating the data in one TDengine cluster to another one because it can be used to create the exact same tables in the target database. ## Show Table Definition @@ -90,7 +90,7 @@ ALTER TABLE tb_name ADD COLUMN field_name data_type; :::info 1. The maximum number of columns is 4096, the minimum number of columns is 2. -2. The maximum length of column name is 64 bytes. +2. The maximum length of a column name is 64 bytes. ::: @@ -101,7 +101,7 @@ ALTER TABLE tb_name DROP COLUMN field_name; ``` :::note -If a table is created using a super table as template, the table definition can only be changed on the corresponding super table, but the change will be automatically applied to all the sub tables created using this super table as template. For tables created in normal way, the table definition can be changed directly on the table. +If a table is created using a super table as template, the table definition can only be changed on the corresponding super table, and the change will be automatically applied to all the subtables created using this super table as template. For tables created in the normal way, the table definition can be changed directly on the table. ::: @@ -111,10 +111,10 @@ If a table is created using a super table as template, the table definition can ALTER TABLE tb_name MODIFY COLUMN field_name data_type(length); ``` -The the type of a column is variable length, like BINARY or NCHAR, this way can be used to change (or increase) the length of the column. +If the type of a column is variable length, like BINARY or NCHAR, this command can be used to change the length of the column. :::note -If a table is created using a super table as template, the table definition can only be changed on the corresponding super table, but the change will be automatically applied to all the sub tables created using this super table as template. For tables created in normal way, the table definition can be changed directly on the table. +If a table is created using a super table as template, the table definition can only be changed on the corresponding super table, and the change will be automatically applied to all the subtables created using this super table as template. For tables created in the normal way, the table definition can be changed directly on the table. ::: diff --git a/docs-en/12-taos-sql/04-stable.md b/docs-en/12-taos-sql/04-stable.md index b7817f90287a6415bee020fb5adc8e6239cc6da4..b8a608792ab327a81129d29ddd0ff44d7af6e6c5 100644 --- a/docs-en/12-taos-sql/04-stable.md +++ b/docs-en/12-taos-sql/04-stable.md @@ -9,20 +9,20 @@ Keyword `STable`, abbreviated for super table, is supported since version 2.0.15 ::: -## Crate STable +## Create STable ``` CREATE STable [IF NOT EXISTS] stb_name (timestamp_field_name TIMESTAMP, field1_name data_type1 [, field2_name data_type2 ...]) TAGS (tag1_name tag_type1, tag2_name tag_type2 [, tag3_name tag_type3]); ``` -The SQL statement of creating STable is similar to that of creating table, but a special column named as `TAGS` must be specified with the names and types of the tags. +The SQL statement of creating a STable is similar to that of creating a table, but a special column set named `TAGS` must be specified with the names and types of the tags. :::info -1. The tag types specified in TAGS should NOT be timestamp. Since 2.1.3.0 timestamp type can be used in TAGS column, but its value must be fixed and arithmetic operation can't be applied on it. -2. The tag names specified in TAGS should NOT be same as other columns. -3. The tag names specified in TAGS should NOT be same as any reserved keywords.(Please refer to [keywords](/taos-sql/keywords/) -4. The maximum number of tags specified in TAGS is 128, but there must be at least one tag, and the total length of all tag columns should NOT exceed 16KB. +1. A tag can be of type timestamp, since version 2.1.3.0, but its value must be fixed and arithmetic operations cannot be performed on it. Prior to version 2.1.3.0, tag types specified in TAGS could not be of type timestamp. +2. The tag names specified in TAGS should NOT be the same as other columns. +3. The tag names specified in TAGS should NOT be the same as any reserved keywords.(Please refer to [keywords](/taos-sql/keywords/) +4. The maximum number of tags specified in TAGS is 128, there must be at least one tag, and the total length of all tag columns should NOT exceed 16KB. ::: @@ -32,7 +32,7 @@ The SQL statement of creating STable is similar to that of creating table, but a DROP STable [IF EXISTS] stb_name; ``` -All the sub-tables created using the deleted STable will be deleted automatically. +All the subtables created using the deleted STable will be deleted automatically. ## Show All STables @@ -40,7 +40,7 @@ All the sub-tables created using the deleted STable will be deleted automaticall SHOW STableS [LIKE tb_name_wildcard]; ``` -This command can be used to display the information of all STables in the current database, including name, creation time, number of columns, number of tags, number of tables created using this STable. +This command can be used to display the information of all STables in the current database, including name, creation time, number of columns, number of tags, and number of tables created using this STable. ## Show The Create Statement of A STable @@ -48,7 +48,7 @@ This command can be used to display the information of all STables in the curren SHOW CREATE STable stb_name; ``` -This command is useful in migrating data from one TDengine cluster to another one because it can be used to create an exactly same STable in the target database. +This command is useful in migrating data from one TDengine cluster to another because it can be used to create the exact same STable in the target database. ## Get STable Definition @@ -76,7 +76,7 @@ ALTER STable stb_name DROP COLUMN field_name; ALTER STable stb_name MODIFY COLUMN field_name data_type(length); ``` -This command can be used to change (or increase, more specifically) the length of a column of variable length types, like BINARY or NCHAR. +This command can be used to change (or more specifically, increase) the length of a column of variable length types, like BINARY or NCHAR. ## Change Tags of A STable @@ -94,7 +94,7 @@ This command is used to add a new tag for a STable and specify the tag type. ALTER STable stb_name DROP TAG tag_name; ``` -The tag will be removed automatically from all the sub tables crated using the super table as template once a tag is removed from a super table. +The tag will be removed automatically from all the subtables, created using the super table as template, once a tag is removed from a super table. ### Change A Tag @@ -102,7 +102,7 @@ The tag will be removed automatically from all the sub tables crated using the s ALTER STable stb_name CHANGE TAG old_tag_name new_tag_name; ``` -The tag name will be changed automatically from all the sub tables crated using the super table as template once a tag name is changed for a super table. +The tag name will be changed automatically for all the subtables, created using the super table as template, once a tag name is changed for a super table. ### Change Tag Length @@ -110,9 +110,9 @@ The tag name will be changed automatically from all the sub tables crated using ALTER STable stb_name MODIFY TAG tag_name data_type(length); ``` -This command can be used to change (or increase, more specifically) the length of a tag of variable length types, like BINARY or NCHAR. +This command can be used to change (or more specifically, increase) the length of a tag of variable length types, like BINARY or NCHAR. :::note -Changing tag value can be applied to only sub tables. All other tag operations, like add tag, remove tag, however, can be applied to only STable. If a new tag is added for a STable, the tag will be added with NULL value for all its sub tables. +Changing tag values can be applied to only subtables. All other tag operations, like add tag, remove tag, however, can be applied to only STable. If a new tag is added for a STable, the tag will be added with NULL value for all its subtables. ::: diff --git a/docs-en/12-taos-sql/05-insert.md b/docs-en/12-taos-sql/05-insert.md index 96e6a08ee17e0c72b15a35efc487a78ae4673017..1336cd7238a19190583ea9d268a64df242ffd3c9 100644 --- a/docs-en/12-taos-sql/05-insert.md +++ b/docs-en/12-taos-sql/05-insert.md @@ -19,15 +19,15 @@ INSERT INTO ## Insert Single or Multiple Rows -Single row or multiple rows specified with VALUES can be inserted into a specific table. For example +Single row or multiple rows specified with VALUES can be inserted into a specific table. For example: -Single row is inserted using below statement. +A single row is inserted using the below statement. ```sq; INSERT INTO d1001 VALUES (NOW, 10.2, 219, 0.32); ``` -Double rows can be inserted using below statement. +Double rows are inserted using the below statement. ```sql INSERT INTO d1001 VALUES ('2021-07-13 14:06:32.272', 10.2, 219, 0.32) (1626164208000, 10.15, 217, 0.33); @@ -36,7 +36,7 @@ INSERT INTO d1001 VALUES ('2021-07-13 14:06:32.272', 10.2, 219, 0.32) (162616420 :::note 1. In the second example above, different formats are used in the two rows to be inserted. In the first row, the timestamp format is a date and time string, which is interpreted from the string value only. In the second row, the timestamp format is a long integer, which will be interpreted based on the database time precision. -2. When trying to insert multiple rows in single statement, only the timestamp of one row can be set as NOW, otherwise there will be duplicate timestamps among the rows and the result may be out of expectation because NOW will be interpreted as the time when the statement is executed. +2. When trying to insert multiple rows in a single statement, only the timestamp of one row can be set as NOW, otherwise there will be duplicate timestamps among the rows and the result may be out of expectation because NOW will be interpreted as the time when the statement is executed. 3. The oldest timestamp that is allowed is subtracting the KEEP parameter from current time. 4. The newest timestamp that is allowed is adding the DAYS parameter to current time. @@ -51,13 +51,13 @@ INSERT INTO d1001 (ts, current, phase) VALUES ('2021-07-13 14:06:33.196', 10.27, ``` :::info -If no columns are explicitly specified, all the columns must be provided with values, this is called "all column mode". The insert performance of all column mode is much better than specifying a part of columns, so it's encouraged to use "all column mode" while providing NULL value explicitly for the columns for which no actual value can be provided. +If no columns are explicitly specified, all the columns must be provided with values, this is called "all column mode". The insert performance of all column mode is much better than specifying a subset of columns, so it's encouraged to use "all column mode" while providing NULL value explicitly for the columns for which no actual value can be provided. ::: ## Insert Into Multiple Tables -One or multiple rows can be inserted into multiple tables in single SQL statement, with or without specifying specific columns. +One or multiple rows can be inserted into multiple tables in a single SQL statement, with or without specifying specific columns. ```sql INSERT INTO d1001 VALUES ('2021-07-13 14:06:34.630', 10.2, 219, 0.32) ('2021-07-13 14:06:35.779', 10.15, 217, 0.33) @@ -66,40 +66,40 @@ INSERT INTO d1001 VALUES ('2021-07-13 14:06:34.630', 10.2, 219, 0.32) ('2021-07- ## Automatically Create Table When Inserting -If it's not sure whether the table already exists, the table can be created automatically while inserting using below SQL statement. To use this functionality, a STable must be used as template and tag values must be provided. +If it's unknown whether the table already exists, the table can be created automatically while inserting using the SQL statement below. To use this functionality, a STable must be used as template and tag values must be provided. ```sql -INSERT INTO d21001 USING meters TAGS ('Beijing.Chaoyang', 2) VALUES ('2021-07-13 14:06:32.272', 10.2, 219, 0.32); +INSERT INTO d21001 USING meters TAGS ('California.SanFrancisco', 2) VALUES ('2021-07-13 14:06:32.272', 10.2, 219, 0.32); ``` -It's not necessary to provide values for all tag when creating tables automatically, the tags without values provided will be set to NULL. +It's not necessary to provide values for all tags when creating tables automatically, the tags without values provided will be set to NULL. ```sql INSERT INTO d21001 USING meters (groupId) TAGS (2) VALUES ('2021-07-13 14:06:33.196', 10.15, 217, 0.33); ``` -Multiple rows can also be inserted into same table in single SQL statement using this way. +Multiple rows can also be inserted into the same table in a single SQL statement. ```sql -INSERT INTO d21001 USING meters TAGS ('Beijing.Chaoyang', 2) VALUES ('2021-07-13 14:06:34.630', 10.2, 219, 0.32) ('2021-07-13 14:06:35.779', 10.15, 217, 0.33) +INSERT INTO d21001 USING meters TAGS ('California.SanFrancisco', 2) VALUES ('2021-07-13 14:06:34.630', 10.2, 219, 0.32) ('2021-07-13 14:06:35.779', 10.15, 217, 0.33) d21002 USING meters (groupId) TAGS (2) VALUES ('2021-07-13 14:06:34.255', 10.15, 217, 0.33) d21003 USING meters (groupId) TAGS (2) (ts, current, phase) VALUES ('2021-07-13 14:06:34.255', 10.27, 0.31); ``` :::info -Prior to version 2.0.20.5, when using `INSERT` to create table automatically and specify the columns, the column names must follow the table name immediately. From version 2.0.20.5, the column names can follow the table name immediately, also can be put between `TAGS` and `VALUES`. In same SQL statement, however, these two ways of specifying column names can't be mixed. +Prior to version 2.0.20.5, when using `INSERT` to create tables automatically and specifying the columns, the column names must follow the table name immediately. From version 2.0.20.5, the column names can follow the table name immediately, also can be put between `TAGS` and `VALUES`. In the same SQL statement, however, these two ways of specifying column names can't be mixed. ::: ## Insert Rows From A File -Besides using `VALUES` to insert one or multiple rows, the data to be inserted can also be prepared in a CSV file with comma as separator and each field value quoted by single quotes. Table definition is not required in the CSV file. For example, if file "/tmp/csvfile.csv" contains below data: +Besides using `VALUES` to insert one or multiple rows, the data to be inserted can also be prepared in a CSV file with comma as separator and each field value quoted by single quotes. Table definition is not required in the CSV file. For example, if file "/tmp/csvfile.csv" contains the below data: ``` '2021-07-13 14:07:34.630', '10.2', '219', '0.32' '2021-07-13 14:07:35.779', '10.15', '217', '0.33' ``` -Then data in this file can be inserted by below SQL statement: +Then data in this file can be inserted by the SQL statement below: ```sql INSERT INTO d1001 FILE '/tmp/csvfile.csv'; @@ -107,30 +107,30 @@ INSERT INTO d1001 FILE '/tmp/csvfile.csv'; ## Create Tables Automatically and Insert Rows From File -From version 2.1.5.0, tables can be automatically created using a super table as template when inserting data from a CSV file, Like below: +From version 2.1.5.0, tables can be automatically created using a super table as template when inserting data from a CSV file, like below: ```sql -INSERT INTO d21001 USING meters TAGS ('Beijing.Chaoyang', 2) FILE '/tmp/csvfile.csv'; +INSERT INTO d21001 USING meters TAGS ('California.SanFrancisco', 2) FILE '/tmp/csvfile.csv'; ``` -Multiple tables can be automatically created and inserted in single SQL statement, like below: +Multiple tables can be automatically created and inserted in a single SQL statement, like below: ```sql -INSERT INTO d21001 USING meters TAGS ('Beijing.Chaoyang', 2) FILE '/tmp/csvfile_21001.csv' +INSERT INTO d21001 USING meters TAGS ('California.SanFrancisco', 2) FILE '/tmp/csvfile_21001.csv' d21002 USING meters (groupId) TAGS (2) FILE '/tmp/csvfile_21002.csv'; ``` ## More About Insert -For SQL statement like `insert`, stream parsing strategy is applied. That means before an error is found and the execution is aborted, the part prior to the error point has already been executed. Below is an experiment to help understand the behavior. +For SQL statement like `insert`, a stream parsing strategy is applied. That means before an error is found and the execution is aborted, the part prior to the error point has already been executed. Below is an experiment to help understand the behavior. -Firstly, a super table is created. +First, a super table is created. ```sql CREATE TABLE meters(ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS(location BINARY(30), groupId INT); ``` -It can be proved that the super table has been created by `SHOW STableS`, but no table exists by `SHOW TABLES`. +It can be proven that the super table has been created by `SHOW STableS`, but no table exists using `SHOW TABLES`. ``` taos> SHOW STableS; @@ -146,7 +146,7 @@ Query OK, 0 row(s) in set (0.000946s) Then, try to create table d1001 automatically when inserting data into it. ```sql -INSERT INTO d1001 USING meters TAGS('Beijing.Chaoyang', 2) VALUES('a'); +INSERT INTO d1001 USING meters TAGS('California.SanFrancisco', 2) VALUES('a'); ``` The output shows the value to be inserted is invalid. But `SHOW TABLES` proves that the table has been created automatically by the `INSERT` statement. @@ -161,4 +161,4 @@ taos> SHOW TABLES; Query OK, 1 row(s) in set (0.001091s) ``` -From the above experiment, we can see that even though the value to be inserted is invalid but the table is still created. +From the above experiment, we can see that while the value to be inserted is invalid the table is still created. diff --git a/docs-en/12-taos-sql/06-select.md b/docs-en/12-taos-sql/06-select.md index 11b181f65d4e7e0e7d47d04986b144ff362c879f..8a017cf92e40aa4a854dcd531b7df291a9243515 100644 --- a/docs-en/12-taos-sql/06-select.md +++ b/docs-en/12-taos-sql/06-select.md @@ -21,7 +21,7 @@ SELECT select_expr [, select_expr ...] ## Wildcard -Wilcard \* can be used to specify all columns. The result includes only data columns for normal tables. +Wildcard \* can be used to specify all columns. The result includes only data columns for normal tables. ``` taos> SELECT * FROM d1001; @@ -39,26 +39,26 @@ The result includes both data columns and tag columns for super table. taos> SELECT * FROM meters; ts | current | voltage | phase | location | groupid | ===================================================================================================================================== - 2018-10-03 14:38:05.500 | 11.80000 | 221 | 0.28000 | Beijing.Haidian | 2 | - 2018-10-03 14:38:16.600 | 13.40000 | 223 | 0.29000 | Beijing.Haidian | 2 | - 2018-10-03 14:38:05.000 | 10.80000 | 223 | 0.29000 | Beijing.Haidian | 3 | - 2018-10-03 14:38:06.500 | 11.50000 | 221 | 0.35000 | Beijing.Haidian | 3 | - 2018-10-03 14:38:04.000 | 10.20000 | 220 | 0.23000 | Beijing.Chaoyang | 3 | - 2018-10-03 14:38:16.650 | 10.30000 | 218 | 0.25000 | Beijing.Chaoyang | 3 | - 2018-10-03 14:38:05.000 | 10.30000 | 219 | 0.31000 | Beijing.Chaoyang | 2 | - 2018-10-03 14:38:15.000 | 12.60000 | 218 | 0.33000 | Beijing.Chaoyang | 2 | - 2018-10-03 14:38:16.800 | 12.30000 | 221 | 0.31000 | Beijing.Chaoyang | 2 | + 2018-10-03 14:38:05.500 | 11.80000 | 221 | 0.28000 | California.LoSangeles | 2 | + 2018-10-03 14:38:16.600 | 13.40000 | 223 | 0.29000 | California.LoSangeles | 2 | + 2018-10-03 14:38:05.000 | 10.80000 | 223 | 0.29000 | California.LoSangeles | 3 | + 2018-10-03 14:38:06.500 | 11.50000 | 221 | 0.35000 | California.LoSangeles | 3 | + 2018-10-03 14:38:04.000 | 10.20000 | 220 | 0.23000 | California.SanFrancisco | 3 | + 2018-10-03 14:38:16.650 | 10.30000 | 218 | 0.25000 | California.SanFrancisco | 3 | + 2018-10-03 14:38:05.000 | 10.30000 | 219 | 0.31000 | California.SanFrancisco | 2 | + 2018-10-03 14:38:15.000 | 12.60000 | 218 | 0.33000 | California.SanFrancisco | 2 | + 2018-10-03 14:38:16.800 | 12.30000 | 221 | 0.31000 | California.SanFrancisco | 2 | Query OK, 9 row(s) in set (0.002022s) ``` -Wildcard can be used with table name as prefix, both below SQL statements have same effects and return all columns. +Wildcard can be used with table name as prefix. Both SQL statements below have the same effect and return all columns. ```SQL SELECT * FROM d1001; SELECT d1001.* FROM d1001; ``` -In JOIN query, however, with or without table name prefix will return different results. \* without table prefix will return all the columns of both tables, but \* with table name as prefix will return only the columns of that table. +In a JOIN query, however, the results are different with or without a table name prefix. \* without table prefix will return all the columns of both tables, but \* with table name as prefix will return only the columns of that table. ``` taos> SELECT * FROM d1001, d1003 WHERE d1001.ts=d1003.ts; @@ -76,7 +76,7 @@ taos> SELECT d1001.* FROM d1001,d1003 WHERE d1001.ts = d1003.ts; Query OK, 1 row(s) in set (0.020443s) ``` -Wilcard \* can be used with some functions, but the result may be different depending on the function being used. For example, `count(*)` returns only one column, i.e. the number of rows; `first`, `last` and `last_row` return all columns of the selected row. +Wildcard \* can be used with some functions, but the result may be different depending on the function being used. For example, `count(*)` returns only one column, i.e. the number of rows; `first`, `last` and `last_row` return all columns of the selected row. ``` taos> SELECT COUNT(*) FROM d1001; @@ -96,20 +96,20 @@ Query OK, 1 row(s) in set (0.000849s) ## Tags -Starting from version 2.0.14, tag columns can be selected together with data columns when querying sub tables. Please be noted that, however, wildcard \* doesn't represent any tag column, that means tag columns must be specified explicitly like below example. +Starting from version 2.0.14, tag columns can be selected together with data columns when querying sub tables. Please note however, that, wildcard \* cannot be used to represent any tag column. This means that tag columns must be specified explicitly like the example below. ``` taos> SELECT location, groupid, current FROM d1001 LIMIT 2; location | groupid | current | ====================================================================== - Beijing.Chaoyang | 2 | 10.30000 | - Beijing.Chaoyang | 2 | 12.60000 | + California.SanFrancisco | 2 | 10.30000 | + California.SanFrancisco | 2 | 12.60000 | Query OK, 2 row(s) in set (0.003112s) ``` ## Get distinct values -`DISTINCT` keyword can be used to get all the unique values of tag columns from a super table, it can also be used to get all the unique values of data columns from a table or sub table. +`DISTINCT` keyword can be used to get all the unique values of tag columns from a super table. It can also be used to get all the unique values of data columns from a table or subtable. ```sql SELECT DISTINCT tag_name [, tag_name ...] FROM stb_name; @@ -118,15 +118,15 @@ SELECT DISTINCT col_name [, col_name ...] FROM tb_name; :::info -1. Configuration parameter `maxNumOfDistinctRes` in `taos.cfg` is used to control the number of rows to output. The minimum configurable value is 100,000, the maximum configurable value is 100,000,000, the default value is 1000,000. If the actual number of rows exceeds the value of this parameter, only the number of rows specified by this parameter will be output. -2. It can't be guaranteed that the results selected by using `DISTINCT` on columns of `FLOAT` or `DOUBLE` are exactly unique because of the precision nature of floating numbers. -3. `DISTINCT` can't be used in the sub-query of a nested query statement, and can't be used together with aggregate functions, `GROUP BY` or `JOIN` in same SQL statement. +1. Configuration parameter `maxNumOfDistinctRes` in `taos.cfg` is used to control the number of rows to output. The minimum configurable value is 100,000, the maximum configurable value is 100,000,000, the default value is 1,000,000. If the actual number of rows exceeds the value of this parameter, only the number of rows specified by this parameter will be output. +2. It can't be guaranteed that the results selected by using `DISTINCT` on columns of `FLOAT` or `DOUBLE` are exactly unique because of the precision errors in floating point numbers. +3. `DISTINCT` can't be used in the sub-query of a nested query statement, and can't be used together with aggregate functions, `GROUP BY` or `JOIN` in the same SQL statement. ::: ## Columns Names of Result Set -When using `SELECT`, the column names in the result set will be same as that in the select clause if `AS` is not used. `AS` can be used to rename the column names in the result set. For example +When using `SELECT`, the column names in the result set will be the same as that in the select clause if `AS` is not used. `AS` can be used to rename the column names in the result set. For example ``` taos> SELECT ts, ts AS primary_key_ts FROM d1001; @@ -161,7 +161,7 @@ SELECT * FROM d1001; ## Special Query -Some special query functionalities can be performed without `FORM` sub-clause. For example, below statement can be used to get the current database in use. +Some special query functions can be invoked without `FROM` sub-clause. For example, the statement below can be used to get the current database in use. ``` taos> SELECT DATABASE(); @@ -181,7 +181,7 @@ taos> SELECT DATABASE(); Query OK, 1 row(s) in set (0.000184s) ``` -Below statement can be used to get the version of client or server. +The statement below can be used to get the version of client or server. ``` taos> SELECT CLIENT_VERSION(); @@ -197,7 +197,7 @@ taos> SELECT SERVER_VERSION(); Query OK, 1 row(s) in set (0.000077s) ``` -Below statement is used to check the server status. One integer, like `1`, is returned if the server status is OK, otherwise an error code is returned. This way is compatible with the status check for TDengine from connection pool or 3rd party tools, and can avoid the problem of losing connection from connection pool when using wrong heartbeat checking SQL statement. +The statement below is used to check the server status. An integer, like `1`, is returned if the server status is OK, otherwise an error code is returned. This is compatible with the status check for TDengine from connection pool or 3rd party tools, and can avoid the problem of losing the connection from a connection pool when using the wrong heartbeat checking SQL statement. ``` taos> SELECT SERVER_STATUS(); @@ -248,12 +248,12 @@ summary: ## Special Keywords in TAOS SQL -- `TBNAME`： it is treated as a special tag when selecting on a super table, representing the name of sub-tables in that super table. +- `TBNAME`： it is treated as a special tag when selecting on a super table, representing the name of subtables in that super table. - `_c0`: represents the first column of a table or super table. ## Tips -To get all the sub tables and corresponding tag values from a super table: +To get all the subtables and corresponding tag values from a super table: ```SQL SELECT TBNAME, location FROM meters; @@ -271,10 +271,10 @@ Only filter on `TAGS` are allowed in the `where` clause for above two query stat taos> SELECT TBNAME, location FROM meters; tbname | location | ================================================================== - d1004 | Beijing.Haidian | - d1003 | Beijing.Haidian | - d1002 | Beijing.Chaoyang | - d1001 | Beijing.Chaoyang | + d1004 | California.LosAngeles | + d1003 | California.LosAngeles | + d1002 | California.SanFrancisco | + d1001 | California.SanFrancisco | Query OK, 4 row(s) in set (0.000881s) taos> SELECT COUNT(tbname) FROM meters WHERE groupId > 2; @@ -284,11 +284,11 @@ taos> SELECT COUNT(tbname) FROM meters WHERE groupId > 2; Query OK, 1 row(s) in set (0.001091s) ``` -- Wildcard \* can be used to get all columns, or specific column names can be specified. Arithmetic operation can be performed on columns of number types, columns can be renamed in the result set. -- Arithmetic operation on columns can't be used in where clause. For example, `where a*2>6;` is not allowed but `where a>6/2;` can be used instead for same purpose. +- Wildcard \* can be used to get all columns, or specific column names can be specified. Arithmetic operation can be performed on columns of numerical types, columns can be renamed in the result set. +- Arithmetic operation on columns can't be used in where clause. For example, `where a*2>6;` is not allowed but `where a>6/2;` can be used instead for the same purpose. - Arithmetic operation on columns can't be used as the objectives of select statement. For example, `select min(2*a) from t;` is not allowed but `select 2*min(a) from t;` can be used instead. - Logical operation can be used in `WHERE` clause to filter numeric values, wildcard can be used to filter string values. -- Result set are arranged in ascending order of the first column, i.e. timestamp, but it can be controlled to output as descending order of timestamp. If `order by` is used on other columns, the result may be not as expected. By the way, \_c0 is used to represent the first column, i.e. timestamp. +- Result sets are arranged in ascending order of the first column, i.e. timestamp, but it can be controlled to output as descending order of timestamp. If `order by` is used on other columns, the result may not be as expected. By the way, \_c0 is used to represent the first column, i.e. timestamp. - `LIMIT` parameter is used to control the number of rows to output. `OFFSET` parameter is used to specify from which row to output. `LIMIT` and `OFFSET` are executed after `ORDER BY` in the query execution. A simple tip is that `LIMIT 5 OFFSET 2` can be abbreviated as `LIMIT 2, 5`. - What is controlled by `LIMIT` is the number of rows in each group when `GROUP BY` is used. - `SLIMIT` parameter is used to control the number of groups when `GROUP BY` is used. Similar to `LIMIT`, `SLIMIT 5 OFFSET 2` can be abbreviated as `SLIMIT 2, 5`. @@ -296,7 +296,7 @@ Query OK, 1 row(s) in set (0.001091s) ## Where -Logical operations in below table can be used in `where` clause to filter the resulting rows. +Logical operations in below table can be used in the `where` clause to filter the resulting rows. | **Operation** | **Note** | **Applicable Data Types** | | ------------- | ------------------------ | ----------------------------------------- | @@ -314,17 +314,17 @@ Logical operations in below table can be used in `where` clause to filter the re **Explanations**: -- Operator `<\>` is equal to `!=`, please be noted that this operator can't be used on the first column of any table, i.e.timestamp column. +- Operator `<\>` is equal to `!=`, please note that this operator can't be used on the first column of any table, i.e.timestamp column. - Operator `like` is used together with wildcards to match strings - '%' matches 0 or any number of characters, '\_' matches any single ASCII character. - `\_` is used to match the \_ in the string. - - The maximum length of wildcard string is 100 bytes from version 2.1.6.1 (before that the maximum length is 20 bytes). `maxWildCardsLength` in `taos.cfg` can be used to control this threshold. Too long wildcard string may slowdown the execution performance of `LIKE` operator. + - The maximum length of wildcard string is 100 bytes from version 2.1.6.1 (before that the maximum length is 20 bytes). `maxWildCardsLength` in `taos.cfg` can be used to control this threshold. A very long wildcard string may slowdown the execution performance of `LIKE` operator. - `AND` keyword can be used to filter multiple columns simultaneously. AND/OR operation can be performed on single or multiple columns from version 2.3.0.0. However, before 2.3.0.0 `OR` can't be used on multiple columns. - For timestamp column, only one condition can be used; for other columns or tags, `OR` keyword can be used to combine multiple logical operators. For example, `((value > 20 AND value < 30) OR (value < 12))`. - From version 2.3.0.0, multiple conditions can be used on timestamp column, but the result set can only contain single time range. - From version 2.0.17.0, operator `BETWEEN AND` can be used in where clause, for example `WHERE col2 BETWEEN 1.5 AND 3.25` means the filter condition is equal to "1.5 ≤ col2 ≤ 3.25". -- From version 2.1.4.0, operator `IN` can be used in where clause. For example, `WHERE city IN ('Beijing', 'Shanghai')`. For bool type, both `{true, false}` and `{0, 1}` are allowed, but integers other than 0 or 1 are not allowed. FLOAT and DOUBLE types are impacted by floating precision, only values that match the condition within the tolerance will be selected. Non-primary key column of timestamp type can be used with `IN`. -- From version 2.3.0.0, regular expression is supported in where clause with keyword `match` or `nmatch`, the regular expression is case insensitive. +- From version 2.1.4.0, operator `IN` can be used in the where clause. For example, `WHERE city IN ('California.SanFrancisco', 'California.SanDiego')`. For bool type, both `{true, false}` and `{0, 1}` are allowed, but integers other than 0 or 1 are not allowed. FLOAT and DOUBLE types are impacted by floating point precision errors. Only values that match the condition within the tolerance will be selected. Non-primary key column of timestamp type can be used with `IN`. +- From version 2.3.0.0, regular expression is supported in the where clause with keyword `match` or `nmatch`. The regular expression is case insensitive. ## Regular Expression @@ -342,11 +342,11 @@ The regular expression being used must be compliant with POSIX specification, pl Regular expression can be used against only table names, i.e. `tbname`, and tags of binary/nchar types, but can't be used against data columns. -The maximum length of regular expression string is 128 bytes. Configuration parameter `maxRegexStringLen` can be used to set the maximum allowed regular expression. It's a configuration parameter on client side, and will take in effect after restarting the client. +The maximum length of regular expression string is 128 bytes. Configuration parameter `maxRegexStringLen` can be used to set the maximum allowed regular expression. It's a configuration parameter on the client side, and will take effect after restarting the client. ## JOIN -From version 2.2.0.0, inner join is fully supported in TDengine. More specifically, the inner join between table and table, that between STable and STable, and that between sub query and sub query are supported. +From version 2.2.0.0, inner join is fully supported in TDengine. More specifically, the inner join between table and table, between STable and STable, and between sub query and sub query are supported. Only primary key, i.e. timestamp, can be used in the join operation between table and table. For example: @@ -364,12 +364,12 @@ FROM temp_STable t1, temp_STable t2 WHERE t1.ts = t2.ts AND t1.deviceid = t2.deviceid AND t1.status=0; ``` -Similary, join operation can be performed on the result set of multiple sub queries. +Similarly, join operations can be performed on the result set of multiple sub queries. :::note Restrictions on join operation: -- The number of tables or STables in single join operation can't exceed 10. +- The number of tables or STables in a single join operation can't exceed 10. - `FILL` is not allowed in the query statement that includes JOIN operation. - Arithmetic operation is not allowed on the result set of join operation. - `GROUP BY` is not allowed on a part of tables that participate in join operation. @@ -380,9 +380,9 @@ Restrictions on join operation: ## Nested Query -Nested query is also called sub query, that means in a single SQL statement the result of inner query can be used as the data source of the outer query. +Nested query is also called sub query. This means that in a single SQL statement the result of inner query can be used as the data source of the outer query. -From 2.2.0.0, unassociated sub query can be used in the `FROM` clause. unassociated means the sub query doesn't use the parameters in the parent query. More specifically, in the `tb_name_list` of `SELECT` statement, an independent SELECT statement can be used. So a complete nested query looks like: +From 2.2.0.0, unassociated sub query can be used in the `FROM` clause. Unassociated means the sub query doesn't use the parameters in the parent query. More specifically, in the `tb_name_list` of `SELECT` statement, an independent SELECT statement can be used. So a complete nested query looks like: ```SQL SELECT ... FROM (SELECT ... FROM ...) ...; @@ -390,14 +390,14 @@ SELECT ... FROM (SELECT ... FROM ...) ...; :::info -- Only one layer of nesting is allowed, that means no sub query is allowed in a sub query -- The result set returned by the inner query will be used as a "virtual table" by the outer query, the "virtual table" can be renamed using `AS` keyword for easy reference in the outer query. +- Only one layer of nesting is allowed, that means no sub query is allowed within a sub query +- The result set returned by the inner query will be used as a "virtual table" by the outer query. The "virtual table" can be renamed using `AS` keyword for easy reference in the outer query. - Sub query is not allowed in continuous query. - JOIN operation is allowed between tables/STables inside both inner and outer queries. Join operation can be performed on the result set of the inner query. - UNION operation is not allowed in either inner query or outer query. -- The functionalities that can be used in the inner query is same as non-nested query. - - `ORDER BY` inside the inner query doesn't make any sense but will slow down the query performance significantly, so please avoid such usage. -- Compared to the non-nested query, the functionalities that can be used in the outer query have such restrictions as: +- The functions that can be used in the inner query are the same as those that can be used in a non-nested query. + - `ORDER BY` inside the inner query is unnecessary and will slow down the query performance significantly. It is best to avoid the use of `ORDER BY` inside the inner query. +- Compared to the non-nested query, the functionality that can be used in the outer query has the following restrictions: - Functions - If the result set returned by the inner query doesn't contain timestamp column, then functions relying on timestamp can't be used in the outer query, like `TOP`, `BOTTOM`, `FIRST`, `LAST`, `DIFF`. - Functions that need to scan the data twice can't be used in the outer query, like `STDDEV`, `PERCENTILE`. @@ -414,7 +414,7 @@ UNION ALL SELECT ... [UNION ALL SELECT ...] ``` -`UNION ALL` operator can be used to combine the result set from multiple select statements as long as the result set of these select statements have exactly same columns. `UNION ALL` doesn't remove redundant rows from multiple result sets. In single SQL statement, at most 100 `UNION ALL` can be supported. +`UNION ALL` operator can be used to combine the result set from multiple select statements as long as the result set of these select statements have exactly the same columns. `UNION ALL` doesn't remove redundant rows from multiple result sets. In a single SQL statement, at most 100 `UNION ALL` can be supported. ### Examples @@ -442,8 +442,8 @@ The sum of col1 and col2 for rows later than 2018-06-01 08:00:00.000 and whose c SELECT (col1 + col2) AS 'complex' FROM tb1 WHERE ts > '2018-06-01 08:00:00.000' AND col2 > 1.2 LIMIT 10 OFFSET 5; ``` -The rows in the past 10 minutes and whose col2 is bigger than 3.14 are selected and output to the result file `/home/testoutpu.csv` with below SQL statement: +The rows in the past 10 minutes and whose col2 is bigger than 3.14 are selected and output to the result file `/home/testoutput.csv` with below SQL statement: ```SQL -SELECT COUNT(*) FROM tb1 WHERE ts >= NOW - 10m AND col2 > 3.14 >> /home/testoutpu.csv; +SELECT COUNT(*) FROM tb1 WHERE ts >= NOW - 10m AND col2 > 3.14 >> /home/testoutput.csv; ``` diff --git a/docs-en/12-taos-sql/07-function.md b/docs-en/12-taos-sql/07-function.md index 9db5f36f92735c659a3bfae84c67089c62d577a6..86ff5a58ce31a357d6e247294ffdac791cb0c032 100644 --- a/docs-en/12-taos-sql/07-function.md +++ b/docs-en/12-taos-sql/07-function.md @@ -4,7 +4,7 @@ title: Functions ## Aggregate Functions -Aggregate query is supported in TDengine by following aggregate functions and selection functions. +Aggregate queries are supported in TDengine by the following aggregate functions and selection functions. ### COUNT @@ -12,18 +12,18 @@ Aggregate query is supported in TDengine by following aggregate functions and se SELECT COUNT([*|field_name]) FROM tb_name [WHERE clause]; ``` -**Description**:Get the number of rows or the number of non-null values in a table or a super table. +**Description**: Get the number of rows or the number of non-null values in a table or a super table. -**Return value type**:Long integer INT64 +**Return value type**: Long integer INT64 -**Applicable column types**:All +**Applicable column types**: All **Applicable table types**: table, super table, sub table **More explanation**: -- Wildcard (\*) can be used to represent all columns, it's used to get the number of all rows -- The number of non-NULL values will be returned if this function is used on a specific column +- Wildcard (\*) is used to represent all columns. The `COUNT` function is used to get the total number of all rows. +- The number of non-NULL values will be returned if this function is used on a specific column. **Examples**: @@ -47,13 +47,13 @@ Query OK, 1 row(s) in set (0.001075s) SELECT AVG(field_name) FROM tb_name [WHERE clause]; ``` -**Description**:Get the average value of a column in a table or STable +**Description**: Get the average value of a column in a table or STable -**Return value type**:Double precision floating number +**Return value type**: Double precision floating number -**Applicable column types**:Data types except for timestamp, binary, nchar and bool +**Applicable column types**: Data types except for timestamp, binary, nchar and bool -**Applicable table types**:table, STable +**Applicable table types**: table, STable **Examples**: @@ -77,17 +77,17 @@ Query OK, 1 row(s) in set (0.000943s) SELECT TWA(field_name) FROM tb_name WHERE clause; ``` -**Description**:Time weighted average on a specific column within a time range +**Description**: Time weighted average on a specific column within a time range -**Return value type**:Double precision floating number +**Return value type**: Double precision floating number -**Applicable column types**:Data types except for timestamp, binary, nchar and bool +**Applicable column types**: Data types except for timestamp, binary, nchar and bool -**Applicable table types**:table, STable +**Applicable table types**: table, STable **More explanations**: -- From version 2.1.3.0, function TWA can be used on stable with `GROUP BY`, i.e. timelines generated by `GROUP BY tbname` on a STable. +- Since version 2.1.3.0, function TWA can be used on stable with `GROUP BY`, i.e. timelines generated by `GROUP BY tbname` on a STable. ### IRATE @@ -95,17 +95,17 @@ SELECT TWA(field_name) FROM tb_name WHERE clause; SELECT IRATE(field_name) FROM tb_name WHERE clause; ``` -**Description**:instantaneous rate on a specific column. The last two samples in the specified time range are used to calculate instantaneous rate. If the last sample value is smaller, then only the last sample value is used instead of the difference between the last two sample values. +**Description**: instantaneous rate on a specific column. The last two samples in the specified time range are used to calculate instantaneous rate. If the last sample value is smaller, then only the last sample value is used instead of the difference between the last two sample values. -**Return value type**:Double precision floating number +**Return value type**: Double precision floating number -**Applicable column types**:Data types except for timestamp, binary, nchar and bool +**Applicable column types**: Data types except for timestamp, binary, nchar and bool -**Applicable table types**:table, STable +**Applicable table types**: table, STable **More explanations**: -- From version 2.1.3.0, function IRATE can be used on stble with `GROUP BY`, i.e. timelines generated by `GROUP BY tbname` on a STable. +- Since version 2.1.3.0, function IRATE can be used on stble with `GROUP BY`, i.e. timelines generated by `GROUP BY tbname` on a STable. ### SUM @@ -113,13 +113,13 @@ SELECT IRATE(field_name) FROM tb_name WHERE clause; SELECT SUM(field_name) FROM tb_name [WHERE clause]; ``` -**Description**:The sum of a specific column in a table or STable +**Description**: The sum of a specific column in a table or STable -**Return value type**:Double precision floating number or long integer +**Return value type**: Double precision floating number or long integer -**Applicable column types**:Data types except for timestamp, binary, nchar and bool +**Applicable column types**: Data types except for timestamp, binary, nchar and bool -**Applicable table types**:table, STable +**Applicable table types**: table, STable **Examples**: @@ -143,13 +143,13 @@ Query OK, 1 row(s) in set (0.000980s) SELECT STDDEV(field_name) FROM tb_name [WHERE clause]; ``` -**Description**:Standard deviation of a specific column in a table or STable +**Description**: Standard deviation of a specific column in a table or STable -**Return value type**:Double precision floating number +**Return value type**: Double precision floating number -**Applicable column types**:Data types except for timestamp, binary, nchar and bool +**Applicable column types**: Data types except for timestamp, binary, nchar and bool -**Applicable table types**:table, STable (starting from version 2.0.15.1) +**Applicable table types**: table, STable (since version 2.0.15.1) **Examples**: @@ -193,13 +193,13 @@ SELECT MODE(field_name) FROM tb_name [WHERE clause]; **Description**:The value which has the highest frequency of occurrence. NULL is returned if there are multiple values which have highest frequency of occurrence. It can't be used on timestamp column or tags. -**Return value type**:Same as the data type of the column being operated +**Return value type**:Same as the data type of the column being operated upon **Applicable column types**:Data types except for timestamp **More explanations**:Considering the number of returned result set is unpredictable, it's suggested to limit the number of unique values to 100,000, otherwise error will be returned. -**Applicable version**:From version 2.6.0.0 +**Applicable version**:Since version 2.6.0.0 **Examples**: @@ -234,7 +234,7 @@ SELECT HYPERLOGLOG(field_name) FROM { tb_name | stb_name } [WHERE clause]; **More explanations**: The benefit of using hyperloglog algorithm is that the memory usage is under control when the data volume is huge. However, when the data volume is very small, the result may be not accurate, it's recommented to use `select count(data) from (select unique(col) as data from table)` in this case. -**Applicable versions**:From version 2.6.0.0 +**Applicable versions**:Since version 2.6.0.0 **Examples**: @@ -259,9 +259,103 @@ taos> select hyperloglog(dbig) from shll; Query OK, 1 row(s) in set (0.008388s) ``` +### HISTOGRAM + +``` +SELECT HISTOGRAM(field_name，bin_type, bin_description, normalized) FROM tb_name [WHERE clause]; +``` + +**Description**：Returns count of data points in user-specified ranges. + +**Return value type**：Double or INT64, depends on normalized parameter settings. + +**Applicable column type**：Numerical types. + +**Applicable versions**：Since version 2.6.0.0. + +**Applicable table types**: table, STable + +**Explanations**： + +1. bin_type: parameter to indicate the bucket type, valid inputs are: "user_input", "linear_bin", "log_bin"。 +2. bin_description: parameter to describe how to generate buckets，can be in the following JSON formats for each bin_type respectively: + + - "user_input": "[1, 3, 5, 7]": User specified bin values. + + - "linear_bin": "{"start": 0.0, "width": 5.0, "count": 5, "infinity": true}" + "start" - bin starting point. + "width" - bin offset. + "count" - number of bins generated. + "infinity" - whether to add（-inf, inf）as start/end point in generated set of bins. + The above "linear_bin" descriptor generates a set of bins: [-inf, 0.0, 5.0, 10.0, 15.0, 20.0, +inf]. + + - "log_bin": "{"start":1.0, "factor": 2.0, "count": 5, "infinity": true}" + "start" - bin starting point. + "factor" - exponential factor of bin offset. + "count" - number of bins generated. + "infinity" - whether to add（-inf, inf）as start/end point in generated range of bins. + The above "log_bin" descriptor generates a set of bins:[-inf, 1.0, 2.0, 4.0, 8.0, 16.0, +inf]. + +3. normalized: setting to 1/0 to turn on/off result normalization. + +**Example**： + +```mysql +taos> SELECT HISTOGRAM(voltage, "user_input", "[1,3,5,7]", 1) FROM meters; + histogram(voltage, "user_input", "[1,3,5,7]", 1) | + ======================================================= + {"lower_bin":1, "upper_bin":3, "count":0.333333} | + {"lower_bin":3, "upper_bin":5, "count":0.333333} | + {"lower_bin":5, "upper_bin":7, "count":0.333333} | + Query OK, 3 row(s) in set (0.004273s) + +taos> SELECT HISTOGRAM(voltage, 'linear_bin', '{"start": 1, "width": 3, "count": 3, "infinity": false}', 0) FROM meters; + histogram(voltage, 'linear_bin', '{"start": 1, "width": 3, " | + =================================================================== + {"lower_bin":1, "upper_bin":4, "count":3} | + {"lower_bin":4, "upper_bin":7, "count":3} | + {"lower_bin":7, "upper_bin":10, "count":3} | + Query OK, 3 row(s) in set (0.004887s) + +taos> SELECT HISTOGRAM(voltage, 'log_bin', '{"start": 1, "factor": 3, "count": 3, "infinity": true}', 0) FROM meters; + histogram(voltage, 'log_bin', '{"start": 1, "factor": 3, "count" | + =================================================================== + {"lower_bin":-inf, "upper_bin":1, "count":3} | + {"lower_bin":1, "upper_bin":3, "count":2} | + {"lower_bin":3, "upper_bin":9, "count":6} | + {"lower_bin":9, "upper_bin":27, "count":3} | + {"lower_bin":27, "upper_bin":inf, "count":1} | +``` + +### ELAPSED + +```mysql +SELECT ELAPSED(field_name[, time_unit]) FROM { tb_name | stb_name } [WHERE clause] [INTERVAL(interval [, offset]) [SLIDING sliding]]; +``` + +**Description**：`elapsed` function can be used to calculate the continuous time length in which there is valid data. If it's used with `INTERVAL` clause, the returned result is the calcualted time length within each time window. If it's used without `INTERVAL` caluse, the returned result is the calculated time length within the specified time range. Please be noted that the return value of `elapsed` is the number of `time_unit` in the calculated time length. + +**Return value type**：Double + +**Applicable Column type**：Timestamp + +**Applicable versions**：Sicne version 2.6.0.0 + +**Applicable tables**: table, STable, outter in nested query + +**Explanations**： +- `field_name` parameter can only be the first column of a table, i.e. timestamp primary key. +- The minimum value of `time_unit` is the time precision of the database. If `time_unit` is not specified, the time precision of the database is used as the default ime unit. +- It can be used with `INTERVAL` to get the time valid time length of each time window. Please be noted that the return value is same as the time window for all time windows except for the first and the last time window. +- `order by asc/desc` has no effect on the result. +- `group by tbname` must be used together when `elapsed` is used against a STable. +- `group by` must NOT be used together when `elapsed` is used against a table or sub table. +- When used in nested query, it's only applicable when the inner query outputs an implicit timestamp column as the primary key. For example, `select elapsed(ts) from (select diff(value) from sub1)` is legal usage while `select elapsed(ts) from (select * from sub1)` is not. +- It can't be used with `leastsquares`, `diff`, `derivative`, `top`, `bottom`, `last_row`, `interp`. + ## Selection Functions -When any selective function is used, timestamp column or tag columns including `tbname` can be specified to show that the selected value are from which rows. +When any select function is used, timestamp column or tag columns including `tbname` can be specified to show that the selected value are from which rows. ### MIN @@ -269,13 +363,13 @@ When any selective function is used, timestamp column or tag columns including ` SELECT MIN(field_name) FROM {tb_name | stb_name} [WHERE clause]; ``` -**Description**:The minimum value of a specific column in a table or STable +**Description**: The minimum value of a specific column in a table or STable -**Return value type**:Same as the data type of the column being operated +**Return value type**: Same as the data type of the column being operated upon -**Applicable column types**:Data types except for timestamp, binary, nchar and bool +**Applicable column types**: Data types except for timestamp, binary, nchar and bool -**Applicable table types**:table, STable +**Applicable table types**: table, STable **Examples**: @@ -299,13 +393,13 @@ Query OK, 1 row(s) in set (0.000950s) SELECT MAX(field_name) FROM { tb_name | stb_name } [WHERE clause]; ``` -**Description**:The maximum value of a specific column of a table or STable +**Description**: The maximum value of a specific column of a table or STable -**Return value type**:Same as the data type of the column being operated +**Return value type**: Same as the data type of the column being operated upon -**Applicable column types**:Data types except for timestamp, binary, nchar and bool +**Applicable column types**: Data types except for timestamp, binary, nchar and bool -**Applicable table types**:table, STable +**Applicable table types**: table, STable **Examples**: @@ -329,19 +423,19 @@ Query OK, 1 row(s) in set (0.000987s) SELECT FIRST(field_name) FROM { tb_name | stb_name } [WHERE clause]; ``` -**Description**:The first non-null value of a specific column in a table or STable +**Description**: The first non-null value of a specific column in a table or STable -**Return value type**:Same as the column being operated +**Return value type**: Same as the column being operated upon -**Applicable column types**:Any data type +**Applicable column types**: Any data type -**Applicable table types**:table, STable +**Applicable table types**: table, STable **More explanations**: - FIRST(\*) can be used to get the first non-null value of all columns - NULL will be returned if all the values of the specified column are all NULL -- No result will NOT be returned if all the columns in the result set are all NULL +- A result will NOT be returned if all the columns in the result set are all NULL **Examples**: @@ -365,13 +459,13 @@ Query OK, 1 row(s) in set (0.001023s) SELECT LAST(field_name) FROM { tb_name | stb_name } [WHERE clause]; ``` -**Description**:The last non-NULL value of a specific column in a table or STable +**Description**: The last non-NULL value of a specific column in a table or STable -**Return value type**:Same as the column being operated +**Return value type**: Same as the column being operated upon -**Applicable column types**:Any data type +**Applicable column types**: Any data type -**Applicable table types**:table, STable +**Applicable table types**: table, STable **More explanations**: @@ -403,11 +497,11 @@ SELECT TOP(field_name, K) FROM { tb_name | stb_name } [WHERE clause]; **Description**: The greatest _k_ values of a specific column in a table or STable. If a value has multiple occurrences in the column but counting all of them in will exceed the upper limit _k_, then a part of them will be returned randomly. -**Return value type**:Same as the column being operated +**Return value type**: Same as the column being operated upon -**Applicable column types**:Data types except for timestamp, binary, nchar and bool +**Applicable column types**: Data types except for timestamp, binary, nchar and bool -**Applicable table types**:table, STable +**Applicable table types**: table, STable **More explanations**: @@ -440,9 +534,9 @@ Query OK, 2 row(s) in set (0.000810s) SELECT BOTTOM(field_name, K) FROM { tb_name | stb_name } [WHERE clause]; ``` -**Description**:The least _k_ values of a specific column in a table or STable. If a value has multiple occurrences in the column but counting all of them in will exceed the upper limit _k_, then a part of them will be returned randomly. +**Description**: The least _k_ values of a specific column in a table or STable. If a value has multiple occurrences in the column but counting all of them in will exceed the upper limit _k_, then a part of them will be returned randomly. -**Return value type**:Same as the column being operated +**Return value type**: Same as the column being operated upon **Applicable column types**: Data types except for timestamp, binary, nchar and bool @@ -549,7 +643,7 @@ SELECT LAST_ROW(field_name) FROM { tb_name | stb_name }; **Description**: The last row of a table or STable -**Return value type**: Same as the column being operated +**Return value type**: Same as the column being operated upon **Applicable column types**: Any data type @@ -576,7 +670,7 @@ SELECT LAST_ROW(field_name) FROM { tb_name | stb_name }; Query OK, 1 row(s) in set (0.001042s) ``` -### INTERP [From version 2.3.1] +### INTERP [Since version 2.3.1] ``` SELECT INTERP(field_name) FROM { tb_name | stb_name } [WHERE where_condition] [ RANGE(timestamp1,timestamp2) ] [EVERY(interval)] [FILL ({ VALUE | PREV | NULL | LINEAR | NEXT})]; @@ -584,7 +678,7 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } [WHERE where_condition] [ **Description**: The value that matches the specified timestamp range is returned, if existing; or an interpolation value is returned. -**Return value type**: same as the column being operated +**Return value type**: Same as the column being operated upon **Applicable column types**: Numeric data types @@ -593,7 +687,7 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } [WHERE where_condition] [ **More explanations** - `INTERP` is used to get the value that matches the specified time slice from a column. If no such value exists an interpolation value will be returned based on `FILL` parameter. -- The input data of `INTERP` is the value of the specified column, `where` can be used to filter the original data. If no `where` condition is specified then all original data is the input. +- The input data of `INTERP` is the value of the specified column and a `where` clause can be used to filter the original data. If no `where` condition is specified then all original data is the input. - The output time range of `INTERP` is specified by `RANGE(timestamp1,timestamp2)` parameter, with timestamp1<=timestamp2. timestamp1 is the starting point of the output time range and must be specified. timestamp2 is the ending point of the output time range and must be specified. If `RANGE` is not specified, then the timestamp of the first row that matches the filter condition is treated as timestamp1, the timestamp of the last row that matches the filter condition is treated as timestamp2. - The number of rows in the result set of `INTERP` is determined by the parameter `EVERY`. Starting from timestamp1, one interpolation is performed for every time interval specified `EVERY` parameter. If `EVERY` parameter is not used, the time windows will be considered as no ending timestamp, i.e. there is only one time window from timestamp1. - Interpolation is performed based on `FILL` parameter. No interpolation is performed if `FILL` is not used, that means either the original data that matches is returned or nothing is returned. @@ -608,7 +702,7 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } [WHERE where_condition] [ taos> SELECT INTERP(current) FROM t1 RANGE('2017-7-14 18:40:00','2017-7-14 18:40:00') FILL(LINEAR); ``` -- Get an original data every 5 seconds, no interpolation, between "2017-07-14 18:00:00" and "2017-07-14 19:00:00: +- Get original data every 5 seconds, no interpolation, between "2017-07-14 18:00:00" and "2017-07-14 19:00:00: ``` taos> SELECT INTERP(current) FROM t1 RANGE('2017-7-14 18:00:00','2017-7-14 19:00:00') EVERY(5s); @@ -632,7 +726,7 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } [WHERE where_condition] [ taos> SELECT INTERP(current) FROM t1 where ts >= '2017-07-14 17:00:00' and ts <= '2017-07-14 20:00:00' RANGE('2017-7-14 18:00:00','2017-7-14 19:00:00') EVERY(5s) FILL(LINEAR); ``` -### INTERP [Prior to version 2.3.1] +### INTERP [Since version 2.0.15.0] ``` SELECT INTERP(field_name) FROM { tb_name | stb_name } WHERE ts='timestamp' [FILL ({ VALUE | PREV | NULL | LINEAR | NEXT})]; @@ -640,7 +734,7 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } WHERE ts='timestamp' [FILL **Description**: The value of a specific column that matches the specified time slice -**Return value type**: Same as the column being operated +**Return value type**: Same as the column being operated upon **Applicable column types**: Numeric data type @@ -648,7 +742,6 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } WHERE ts='timestamp' [FILL **More explanations**: -- It can be used from version 2.0.15.0 - Time slice must be specified. If there is no data matching the specified time slice, interpolation is performed based on `FILL` parameter. Conditions such as tags or `tbname` can be used `Where` clause can be used to filter data. - The timestamp specified must be within the time range of the data rows of the table or STable. If it is beyond the valid time range, nothing is returned even with `FILL` parameter. - `INTERP` can be used to query only single time point once. `INTERP` can be used with `EVERY` to get the interpolation value every time interval. @@ -662,7 +755,7 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } WHERE ts='timestamp' [FILL Query OK, 1 row(s) in set (0.002652s) ``` -If there is not any data corresponding to the specified timestamp, an interpolation value is returned if interpolation policy is specified by `FILL` parameter; or nothing is returned\ +If there is no data corresponding to the specified timestamp, an interpolation value is returned if interpolation policy is specified by `FILL` parameter; or nothing is returned. ``` taos> SELECT INTERP(*) FROM meters WHERE tbname IN ('d636') AND ts='2017-7-14 18:40:00.005'; @@ -696,11 +789,11 @@ SELECT TAIL(field_name, k, offset_val) FROM {tb_name | stb_name} [WHERE clause]; **Parameter value range**: k: [1,100] offset_val: [0,100] -**Return value type**: Same as the column being operated +**Return value type**: Same as the column being operated upon **Applicable column types**: Any data type except form timestamp, i.e. the primary key -**Applicable versions**: From version 2.6.0.0 +**Applicable versions**: Since version 2.6.0.0 **Examples**: @@ -732,11 +825,11 @@ SELECT UNIQUE(field_name) FROM {tb_name | stb_name} [WHERE clause]; **Description**: The values that occur the first time in the specified column. The effect is similar to `distinct` keyword, but it can also be used to match tags or timestamp. -**Return value type**: Same as the column or tag being operated +**Return value type**: Same as the column or tag being operated upon **Applicable column types**: Any data types except for timestamp -**Applicable versions**: From version 2.6.0.0 +**Applicable versions**: Since version 2.6.0.0 **More explanations**: @@ -780,7 +873,7 @@ SELECT {DIFF(field_name, ignore_negative) | DIFF(field_name)} FROM tb_name [WHER **Description**: The different of each row with its previous row for a specific column. `ignore_negative` can be specified as 0 or 1, the default value is 1 if it's not specified. `1` means negative values are ignored. -**Return value type**: Same as the column being operated +**Return value type**: Same as the column being operated upon **Applicable column types**: Data types except for timestamp, binary, nchar and bool @@ -789,8 +882,8 @@ SELECT {DIFF(field_name, ignore_negative) | DIFF(field_name)} FROM tb_name [WHER **More explanations**: - The number of result rows is the number of rows subtracted by one, no output for the first row -- From version 2.1.30, `DIFF` can be used on STable with `GROUP by tbname` -- From version 2.6.0, `ignore_negative` parameter is supported +- Since version 2.1.30, `DIFF` can be used on STable with `GROUP by tbname` +- Since version 2.6.0, `ignore_negative` parameter is supported **Examples**: @@ -819,7 +912,7 @@ SELECT DERIVATIVE(field_name, time_interval, ignore_negative) FROM tb_name [WHER **More explanations**: -- It is available from version 2.1.3.0, the number of result rows is the number of total rows in the time range subtracted by one, no output for the first row.\ +- It is available from version 2.1.3.0, the number of result rows is the number of total rows in the time range subtracted by one, no output for the first row. - It can be used together with `GROUP BY tbname` against a STable. **Examples**: @@ -874,7 +967,7 @@ Query OK, 1 row(s) in set (0.000836s) SELECT CEIL(field_name) FROM { tb_name | stb_name } [WHERE clause]; ``` -**Description**: The round up value of a specific column +**Description**: The rounded up value of a specific column **Return value type**: Same as the column being used @@ -882,7 +975,7 @@ SELECT CEIL(field_name) FROM { tb_name | stb_name } [WHERE clause]; **Applicable table types**: table, STable -**Applicable nested query**: inner query and outer query +**Applicable nested query**: Inner query and outer query **More explanations**: @@ -896,9 +989,9 @@ SELECT CEIL(field_name) FROM { tb_name | stb_name } [WHERE clause]; SELECT FLOOR(field_name) FROM { tb_name | stb_name } [WHERE clause]; ``` -**Description**: The round down value of a specific column +**Description**: The rounded down value of a specific column -**More explanations**: The restrictions are same as `CEIL` function. +**More explanations**: The restrictions are same as those of the `CEIL` function. ### ROUND @@ -906,7 +999,7 @@ SELECT FLOOR(field_name) FROM { tb_name | stb_name } [WHERE clause]; SELECT ROUND(field_name) FROM { tb_name | stb_name } [WHERE clause]; ``` -**Description**: The round value of a specific column. +**Description**: The rounded value of a specific column. **More explanations**: The restrictions are same as `CEIL` function. @@ -933,7 +1026,7 @@ SELECT ROUND(field_name) FROM { tb_name | stb_name } [WHERE clause]; - Can only be used with aggregate functions - `Group by tbname` must be used together on a STable to force the result on a single timeline -**Applicable versions**: From 2.3.0.x +**Applicable versions**: Since 2.3.0.x ### MAVG @@ -941,7 +1034,7 @@ SELECT ROUND(field_name) FROM { tb_name | stb_name } [WHERE clause]; SELECT MAVG(field_name, K) FROM { tb_name | stb_name } [WHERE clause] ``` -**Description**: The moving average of continuous _k_ values of a specific column. If the number of input rows is less than _k_, nothing is returned. The applicable range is _k_ is [1,1000]. +**Description**: The moving average of continuous _k_ values of a specific column. If the number of input rows is less than _k_, nothing is returned. The applicable range of _k_ is [1,1000]. **Return value type**: Double precision floating point @@ -958,7 +1051,7 @@ SELECT ROUND(field_name) FROM { tb_name | stb_name } [WHERE clause]; - Can't be used with aggregate functions. - Must be used with `GROUP BY tbname` when it's used on a STable to force the result on each single timeline. -**Applicable versions**: From 2.3.0.x +**Applicable versions**: Since 2.3.0.x ### SAMPLE @@ -981,7 +1074,7 @@ SELECT ROUND(field_name) FROM { tb_name | stb_name } [WHERE clause]; - Arithmetic operation can't be operated on the result of `SAMPLE` function - Must be used with `Group by tbname` when it's used on a STable to force the result on each single timeline -**Applicable versions**: From 2.3.0.x +**Applicable versions**: Since 2.3.0.x ### ASIN @@ -1460,8 +1553,8 @@ SELECT field_name [+|-|*|/|%][Value|field_name] FROM { tb_name | stb_name } [WH **More explanations**: -- Arithmetic operations can be performed on two or more columns, `()` can be used to control the precedence -- NULL doesn't participate the operation, if one of the operands is NULL then result is NULL +- Arithmetic operations can be performed on two or more columns, Parentheses `()` can be used to control the order of precedence. +- NULL doesn't participate in the operation i.e. if one of the operands is NULL then result is NULL. **Examples**: @@ -1586,7 +1679,7 @@ Query OK, 6 row(s) in set (0.002613s) ## Time Functions -From version 2.6.0.0, below time related functions can be used in TDengine. +Since version 2.6.0.0, below time related functions can be used in TDengine. ### NOW @@ -1782,9 +1875,9 @@ taos> SELECT TO_UNIXTIMESTAMP(col_binary) FROM meters; SELECT TIMETRUNCATE(ts_val | datetime_string | ts_col, time_unit) FROM { tb_name | stb_name } [WHERE clause]; ``` -**Description**: Truncate the input timestamp with unit specified by `time_unit`\ +**Description**: Truncate the input timestamp with unit specified by `time_unit` -**Return value type**: TIMESTAMP\ +**Return value type**: TIMESTAMP **Applicable column types**: UNIX timestamp constant, string constant of date/time format, or a column of timestamp @@ -1840,6 +1933,8 @@ SELECT TIMEDIFF(ts_val1 | datetime_string1 | ts_col1, ts_val2 | datetime_string2 1u(microsecond),1a(millisecond),1s(second),1m(minute),1h(hour),1d(day). - The precision of the returned timestamp is same as the precision set for the current data base in use +**Applicable versions**:Since version 2.6.0.0 + **Examples**: ```sql diff --git a/docs-en/12-taos-sql/08-interval.md b/docs-en/12-taos-sql/08-interval.md index 5cc3fa8cb43749fd40b808699f82a8761525cc6a..acfb0de0e1521fd8c6a068497a3df7a17941524c 100644 --- a/docs-en/12-taos-sql/08-interval.md +++ b/docs-en/12-taos-sql/08-interval.md @@ -3,36 +3,36 @@ sidebar_label: Interval title: Aggregate by Time Window --- -Aggregate by time window is supported in TDengine. For example, each temperature sensor reports the temperature every second, the average temperature every 10 minutes can be retrieved by query with time window. -Window related clauses are used to divide the data set to be queried into subsets and then aggregate. There are three kinds of windows, time window, status window, and session window. There are two kinds of time windows, sliding window and flip time window. +Aggregation by time window is supported in TDengine. For example, in the case where temperature sensors report the temperature every seconds, the average temperature for every 10 minutes can be retrieved by performing a query with a time window. +Window related clauses are used to divide the data set to be queried into subsets and then aggregation is performed across the subsets. There are three kinds of windows: time window, status window, and session window. There are two kinds of time windows: sliding window and flip time/tumbling window. ## Time Window -`INTERVAL` clause is used to generate time windows of same time interval, `SLIDING` is used to specify the time step for which the time window moves forward. The query is performed on one time window each time, and the time window moves forward with time. When defining continuous query both the size of time window and the step of forward sliding time need to be specified. As shown in the figure blow, [t0s, t0e] ，[t1s , t1e]， [t2s, t2e] are respectively the time range of three time windows on which continuous queries are executed. The time step for which time window moves forward is marked by `sliding time`. Query, filter and aggregate operations are executed on each time window respectively. When the time step specified by `SLIDING` is same as the time interval specified by `INTERVAL`, the sliding time window is actually a flip time window. +The `INTERVAL` clause is used to generate time windows of the same time interval. The `SLIDING` parameter is used to specify the time step for which the time window moves forward. The query is performed on one time window each time, and the time window moves forward with time. When defining a continuous query, both the size of the time window and the step of forward sliding time need to be specified. As shown in the figure blow, [t0s, t0e] ，[t1s , t1e]， [t2s, t2e] are respectively the time ranges of three time windows on which continuous queries are executed. The time step for which time window moves forward is marked by `sliding time`. Query, filter and aggregate operations are executed on each time window respectively. When the time step specified by `SLIDING` is same as the time interval specified by `INTERVAL`, the sliding time window is actually a flip time/tumbling window. -![Time Window](/img/sql/timewindow-1.png) +![TDengine Database Time Window](./timewindow-1.webp) -`INTERVAL` and `SLIDING` should be used with aggregate functions and selection functions. Below SQL statement is illegal because no aggregate or selection function is used with `INTERVAL`. +`INTERVAL` and `SLIDING` should be used with aggregate functions and select functions. The SQL statement below is illegal because no aggregate or selection function is used with `INTERVAL`. ``` SELECT * FROM temp_tb_1 INTERVAL(1m); ``` -The time step specified by `SLIDING` can't exceed the time interval specified by `INTERVAL`. Below SQL statement is illegal because the time length specified by `SLIDING` exceeds that specified by `INTERVAL`. +The time step specified by `SLIDING` cannot exceed the time interval specified by `INTERVAL`. The SQL statement below is illegal because the time length specified by `SLIDING` exceeds that specified by `INTERVAL`. ``` SELECT COUNT(*) FROM temp_tb_1 INTERVAL(1m) SLIDING(2m); ``` -When the time length specified by `SLIDING` is same as that specified by `INTERVAL`, sliding window is actually flip window. The minimum time range specified by `INTERVAL` is 10 milliseconds (10a) prior to version 2.1.5.0. From version 2.1.5.0, the minimum time range by `INTERVAL` can be 1 microsecond (1u). However, if the DB precision is millisecond, the minimum time range is 1 millisecond (1a). Please be noted that the `timezone` parameter should be configured to same value in the `taos.cfg` configuration file on client side and server side. +When the time length specified by `SLIDING` is the same as that specified by `INTERVAL`, the sliding window is actually a flip/tumbling window. The minimum time range specified by `INTERVAL` is 10 milliseconds (10a) prior to version 2.1.5.0. Since version 2.1.5.0, the minimum time range by `INTERVAL` can be 1 microsecond (1u). However, if the DB precision is millisecond, the minimum time range is 1 millisecond (1a). Please note that the `timezone` parameter should be configured to be the same value in the `taos.cfg` configuration file on client side and server side. ## Status Window -In case of using integer, bool, or string to represent the device status at a moment, the continuous rows with same status belong to same status window. Once the status changes, the status window closes. As shown in the following figure，there are two status windows according to status, [2019-04-28 14:22:07，2019-04-28 14:22:10] and [2019-04-28 14:22:11，2019-04-28 14:22:12]. Status window is not applicable to STable for now. +In case of using integer, bool, or string to represent the status of a device at any given moment, continuous rows with the same status belong to a status window. Once the status changes, the status window closes. As shown in the following figure, there are two status windows according to status, [2019-04-28 14:22:07，2019-04-28 14:22:10] and [2019-04-28 14:22:11，2019-04-28 14:22:12]. Status window is not applicable to STable for now. -![Status Window](/img/sql/timewindow-3.png) +![TDengine Database Status Window](./timewindow-3.webp) -`STATE_WINDOW` is used to specify the column based on which to define status window, for example： +`STATE_WINDOW` is used to specify the column on which the status window will be based. For example： ``` SELECT COUNT(*), FIRST(ts), status FROM temp_tb_1 STATE_WINDOW(status); @@ -44,9 +44,9 @@ SELECT COUNT(*), FIRST(ts), status FROM temp_tb_1 STATE_WINDOW(status); SELECT COUNT(*), FIRST(ts) FROM temp_tb_1 SESSION(ts, tol_val); ``` -The primary key, i.e. timestamp, is used to determine which session window the row belongs to. If the time interval between two adjacent rows is within the time range specified by `tol_val`, they belong to same session window; otherwise they belong to two different time windows. As shown in the figure below, if the limit of time interval for session window is specified as 12 seconds, then the 6 rows in the figure constitutes 2 time windows, [2019-04-28 14:22:10，2019-04-28 14:22:30] and [2019-04-28 14:23:10，2019-04-28 14:23:30], because the time difference between 2019-04-28 14:22:30 and 2019-04-28 14:23:10 is 40 seconds, which exceeds the time interval limit of 12 seconds. +The primary key, i.e. timestamp, is used to determine which session window a row belongs to. If the time interval between two adjacent rows is within the time range specified by `tol_val`, they belong to the same session window; otherwise they belong to two different session windows. As shown in the figure below, if the limit of time interval for the session window is specified as 12 seconds, then the 6 rows in the figure constitutes 2 time windows, [2019-04-28 14:22:10，2019-04-28 14:22:30] and [2019-04-28 14:23:10，2019-04-28 14:23:30], because the time difference between 2019-04-28 14:22:30 and 2019-04-28 14:23:10 is 40 seconds, which exceeds the time interval limit of 12 seconds. -![Session Window](/img/sql/timewindow-2.png) +![TDengine Database Session Window](./timewindow-2.webp) If the time interval between two continuous rows are within the time interval specified by `tol_value` they belong to the same session window; otherwise a new session window is started automatically. Session window is not supported on STable for now. @@ -54,7 +54,7 @@ If the time interval between two continuous rows are within the time interval sp ### Syntax -The full syntax of aggregate by window is as following: +The full syntax of aggregate by window is as follows: ```sql SELECT function_list FROM tb_name @@ -73,11 +73,11 @@ SELECT function_list FROM stb_name ### Restrictions -- Aggregate functions and selection functions can be used in `function_list`, with each function having only one output, for example COUNT, AVG, SUM, STDDEV, LEASTSQUARES, PERCENTILE, MIN, MAX, FIRST, LAST. Functions having multiple output can't be used, for example DIFF or arithmetic operations. +- Aggregate functions and select functions can be used in `function_list`, with each function having only one output. For example COUNT, AVG, SUM, STDDEV, LEASTSQUARES, PERCENTILE, MIN, MAX, FIRST, LAST. Functions having multiple outputs, such as DIFF or arithmetic operations can't be used. - `LAST_ROW` can't be used together with window aggregate. - Scalar functions, like CEIL/FLOOR, can't be used with window aggregate. - `WHERE` clause can be used to specify the starting and ending time and other filter conditions -- `FILL` clause is used to specify how to fill when there is data missing in any window, including: \ +- `FILL` clause is used to specify how to fill when there is data missing in any window, including: 1. NONE: No fill (the default fill mode) 2. VALUE：Fill with a fixed value, which should be specified together, for example `FILL(VALUE, 1.23)` 3. PREV：Fill with the previous non-NULL value, `FILL(PREV)` @@ -87,22 +87,23 @@ SELECT function_list FROM stb_name :::info -1. Huge volume of interpolation output may be returned using `FILL`, so it's recommended to specify the time range when using `FILL`. The maximum interpolation values that can be returned in single query is 10,000,000. -2. The result set is in the ascending order of timestamp in aggregate by time window aggregate. +1. A huge volume of interpolation output may be returned using `FILL`, so it's recommended to specify the time range when using `FILL`. The maximum number of interpolation values that can be returned in a single query is 10,000,000. +2. The result set is in ascending order of timestamp when you aggregate by time window. 3. If aggregate by window is used on STable, the aggregate function is performed on all the rows matching the filter conditions. If `GROUP BY` is not used in the query, the result set will be returned in ascending order of timestamp; otherwise the result set is not exactly in the order of ascending timestamp in each group. - ::: + +::: Aggregate by time window is also used in continuous query, please refer to [Continuous Query](/develop/continuous-query). ## Examples -The table of intelligent meters can be created like below SQL statement： +A table of intelligent meters can be created by the SQL statement below： ```sql CREATE TABLE meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT); ``` -The average current, maximum current and median of current in every 10 minutes of the past 24 hours can be calculated using below SQL statement, with missing value filled with the previous non-NULL value. +The average current, maximum current and median of current in every 10 minutes for the past 24 hours can be calculated using the SQL statement below, with missing values filled with the previous non-NULL values. ``` SELECT AVG(current), MAX(current), APERCENTILE(current, 50) FROM meters diff --git a/docs-en/12-taos-sql/09-limit.md b/docs-en/12-taos-sql/09-limit.md index 873e484fbb4731294d00df323f8e0d2cbc6b1d30..db55cdd69e7bd29ca66ee15b61f28991568d9556 100644 --- a/docs-en/12-taos-sql/09-limit.md +++ b/docs-en/12-taos-sql/09-limit.md @@ -4,9 +4,9 @@ title: Limits & Restrictions ## Naming Rules -1. Only English characters, digits and underscore are allowed -2. Can't be started with digits -3. Case Insensitive without escape character "\`" +1. Only characters from the English alphabet, digits and underscore are allowed +2. Names cannot start with a digit +3. Case insensitive without escape character "\`" 4. Identifier with escape character "\`" To support more flexible table or column names, a new escape character "\`" is introduced. For more details please refer to [escape](/taos-sql/escape). @@ -16,38 +16,38 @@ The legal character set is `[a-zA-Z0-9!?$%^&*()_–+={[}]:;@~#|<,>.?/]`. ## General Limits -- Maximum length of database name is 32 bytes -- Maximum length of table name is 192 bytes, excluding the database name prefix and the separator -- Maximum length of each data row is 48K bytes from version 2.1.7.0 , before which the limit is 16K bytes. Please be noted that the upper limit includes the extra 2 bytes consumed by each column of BINARY/NCHAR type. -- Maximum of column name is 64. +- Maximum length of database name is 32 bytes. +- Maximum length of table name is 192 bytes, excluding the database name prefix and the separator. +- Maximum length of each data row is 48K bytes since version 2.1.7.0 , before which the limit was 16K bytes. Please note that the upper limit includes the extra 2 bytes consumed by each column of BINARY/NCHAR type. +- Maximum length of column name is 64. - Maximum number of columns is 4096. There must be at least 2 columns, and the first column must be timestamp. - Maximum length of tag name is 64. - Maximum number of tags is 128. There must be at least 1 tag. The total length of tag values should not exceed 16K bytes. -- Maximum length of singe SQL statement is 1048576, i.e. 1 MB bytes. It can be configured in the parameter `maxSQLLength` in the client side, the applicable range is [65480, 1048576]. -- At most 4096 columns (or 1024 prior to 2.1.7.0) can be returned by `SELECT`, functions in the query statement may constitute columns. Error will be returned if the limit is exceeded. -- Maximum numbers of databases, STables, tables are only depending on the system resources. -- Maximum of database name is 32 bytes, can't include "." and special characters. -- Maximum replica number of database is 3 -- Maximum length of user name is 23 bytes -- Maximum length of password is 15 bytes -- Maximum number of rows depends on the storage space only. -- Maximum number of tables depends on the number of nodes only. -- Maximum number of databases depends on the number of nodes only. -- Maximum number of vnodes for single database is 64. +- Maximum length of singe SQL statement is 1048576, i.e. 1 MB. It can be configured in the parameter `maxSQLLength` in the client side, the applicable range is [65480, 1048576]. +- At most 4096 columns (or 1024 prior to 2.1.7.0) can be returned by `SELECT`. Functions in the query statement constitute columns. An error is returned if the limit is exceeded. +- Maximum numbers of databases, STables, tables are dependent only on the system resources. +- Maximum of database name is 32 bytes, and it can't include "." or special characters. +- Maximum number of replicas for a database is 3. +- Maximum length of user name is 23 bytes. +- Maximum length of password is 15 bytes. +- Maximum number of rows depends only on the storage space. +- Maximum number of tables depends only on the number of nodes. +- Maximum number of databases depends only on the number of nodes. +- Maximum number of vnodes for a single database is 64. ## Restrictions of `GROUP BY` -`GROUP BY` can be performed on tags and `TBNAME`. It can be performed on data columns too, with one restriction that only one column and the number of unique values on that column is lower than 100,000. Please be noted that `GROUP BY` can't be performed on float or double type. +`GROUP BY` can be performed on tags and `TBNAME`. It can be performed on data columns too, with the only restriction being it can only be performed on one data column and the number of unique values in that column is lower than 100,000. Please note that `GROUP BY` cannot be performed on float or double types. ## Restrictions of `IS NOT NULL` -`IS NOT NULL` can be used on any data type of columns. The non-empty string evaluation expression, i.e. `<\>""` can only be used on non-numeric data types. +`IS NOT NULL` can be used on any data type of columns. The non-empty string evaluation expression, i.e. `< > ""` can only be used on non-numeric data types. ## Restrictions of `ORDER BY` -- Only one `order by` is allowed for normal table and sub table. +- Only one `order by` is allowed for normal table and subtable. - At most two `order by` are allowed for STable, and the second one must be `ts`. -- `order by tag` must be used with `group by tag` on same tag, this rule is also applicable to `tbname`. +- `order by tag` must be used with `group by tag` on same tag. This rule is also applicable to `tbname`. - `order by column` must be used with `group by column` or `top/bottom` on same column. This rule is applicable to table and STable. - `order by ts` is applicable to table and STable. - If `order by ts` is used with `group by`, the result set is sorted using `ts` in each group. @@ -56,11 +56,11 @@ The legal character set is `[a-zA-Z0-9!?$%^&*()_–+={[}]:;@~#|<,>.?/]`. ### Name Restrictions of Table/Column -The name of a table or column can only be composed of ASCII characters, digits and underscore, while digit can't be used as the beginning. The maximum length is 192 bytes. Names are case insensitive. The name mentioned in this rule doesn't include the database name prefix and the separator. +The name of a table or column can only be composed of ASCII characters, digits and underscore and it cannot start with a digit. The maximum length is 192 bytes. Names are case insensitive. The name mentioned in this rule doesn't include the database name prefix and the separator. ### Name Restrictions After Escaping -To support more flexible table or column names, new escape character "`" is introduced in TDengine to avoid the conflict between table name and keywords and break the above restrictions for table name. The escape character is not counted in the length of table name. +To support more flexible table or column names, new escape character "\`" is introduced in TDengine to avoid the conflict between table name and keywords and break the above restrictions for table names. The escape character is not counted in the length of table name. With escaping, the string inside escape characters are case sensitive, i.e. will not be converted to lower case internally. diff --git a/docs-en/12-taos-sql/10-json.md b/docs-en/12-taos-sql/10-json.md index 60468f1e0fd75cc04cae8a91b0a1a22b9bd3600b..7460a5e0ba3ce78ee7744569cda460c477cac19c 100644 --- a/docs-en/12-taos-sql/10-json.md +++ b/docs-en/12-taos-sql/10-json.md @@ -4,7 +4,7 @@ title: JSON Type ## Syntax -1. Tag of JSON type +1. Tag of type JSON ```sql create STable s1 (ts timestamp, v1 int) tags (info json); @@ -12,7 +12,7 @@ title: JSON Type create table s1_1 using s1 tags ('{"k1": "v1"}'); ``` -2. -> Operator of JSON +2. "->" Operator of JSON ```sql select * from s1 where info->'k1' = 'v1'; @@ -20,7 +20,7 @@ title: JSON Type select info->'k1' from s1; ``` -3. contains Operator of JSON +3. "contains" Operator of JSON ```sql select * from s1 where info contains 'k2'; @@ -30,7 +30,7 @@ title: JSON Type ## Applicable Operations -1. When JSON data type is used in `where`, `match/nmatch/between and/like/and/or/is null/is no null` can be used but `in` can't be used. +1. When a JSON data type is used in `where`, `match/nmatch/between and/like/and/or/is null/is no null` can be used but `in` can't be used. ```sql select * from s1 where info->'k1' match 'v*'; @@ -42,9 +42,9 @@ title: JSON Type select * from s1 where info->'k1' is not null; ``` -2. Tag of JSON type can be used in `group by`, `order by`, `join`, `union all` and sub query, for example `group by json->'key'` +2. A tag of JSON type can be used in `group by`, `order by`, `join`, `union all` and sub query; for example `group by json->'key'` -3. `Distinct` can be used with tag of JSON type +3. `Distinct` can be used with a tag of type JSON ```sql select distinct info->'k1' from s1; @@ -52,29 +52,29 @@ title: JSON Type 4. Tag Operations - The value of JSON tag can be altered. Please be noted that the full JSON will be override when doing this. + The value of a JSON tag can be altered. Please note that the full JSON will be overriden when doing this. - The name of JSON tag can be altered. A tag of JSON type can't be added or removed. The column length of a JSON tag can't be changed. + The name of a JSON tag can be altered. A tag of JSON type can't be added or removed. The column length of a JSON tag can't be changed. ## Other Restrictions -- JSON type can only be used for tag. There can be only one tag of JSON type, and it's exclusive to any other types of tag. +- JSON type can only be used for a tag. There can be only one tag of JSON type, and it's exclusive to any other types of tags. - The maximum length of keys in JSON is 256 bytes, and key must be printable ASCII characters. The maximum total length of a JSON is 4,096 bytes. - JSON format： - - The input string for JSON can be empty, i.e. "", "\t", or NULL, but can't be non-NULL string, bool or array. - - object can be {}, and the whole JSON is empty if so. Key can be "", and it's ignored if so. - - value can be int, double, string, boll or NULL, can't be array. Nesting is not allowed, that means value can't be another JSON. + - The input string for JSON can be empty, i.e. "", "\t", or NULL, but it can't be non-NULL string, bool or array. + - object can be {}, and the entire JSON is empty if so. Key can be "", and it's ignored if so. + - value can be int, double, string, bool or NULL, and it can't be an array. Nesting is not allowed which means that the value of a key can't be JSON. - If one key occurs twice in JSON, only the first one is valid. - Escape characters are not allowed in JSON. -- NULL is returned if querying a key that doesn't exist in JSON. +- NULL is returned when querying a key that doesn't exist in JSON. - If a tag of JSON is the result of inner query, it can't be parsed and queried in the outer query. -For example, below SQL statements are not supported. +For example, the SQL statements below are not supported. ```sql; select jtag->'key' from (select jtag from STable); diff --git a/docs-en/12-taos-sql/12-keywords.md b/docs-en/12-taos-sql/12-keywords.md index fa750300b71251e1172dba13f91d05822f9ac1f4..56a82a02a1fada712141f3572b761e0cd18576c6 100644 --- a/docs-en/12-taos-sql/12-keywords.md +++ b/docs-en/12-taos-sql/12-keywords.md @@ -46,3 +46,44 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam | CONNECTIONS | HAVING | NOT | SOFFSET | VNODES | | CONNS | ID | NOTNULL | STable | WAL | | COPY | IF | NOW | STableS | WHERE | +| _C0 | _QSTART | _QSTOP | _QDURATION | _WSTART | +| _WSTOP | _WDURATION | + +## Explanations +### TBNAME +`TBNAME` can be considered as a special tag, which represents the name of the subtable, in STable. + +Get the table name and tag values of all subtables in a STable. +```mysql +SELECT TBNAME, location FROM meters; + +Count the number of subtables in a STable. +```mysql +SELECT COUNT(TBNAME) FROM meters; +``` + +Only filter on TAGS can be used in WHERE clause in the above two query statements. +```mysql +taos> SELECT TBNAME, location FROM meters; + tbname | location | +================================================================== + d1004 | California.SanFrancisco | + d1003 | California.SanFrancisco | + d1002 | California.LosAngeles | + d1001 | California.LosAngeles | +Query OK, 4 row(s) in set (0.000881s) + +taos> SELECT COUNT(tbname) FROM meters WHERE groupId > 2; + count(tbname) | +======================== + 2 | +Query OK, 1 row(s) in set (0.001091s) +``` +### _QSTART/_QSTOP/_QDURATION +The start, stop and duration of a query time window (Since version 2.6.0.0). + +### _WSTART/_WSTOP/_WDURATION +The start, stop and duration of aggegate query by time window, like interval, session window, state window (Since version 2.6.0.0). + +### _c0 +The first column of a table or STable. \ No newline at end of file diff --git a/docs-en/12-taos-sql/index.md b/docs-en/12-taos-sql/index.md index 611f2bf75eb2a234ae139ce65f2e78d356483bb7..33656338a7bba38dc55cf536bdba8e95309c5acf 100644 --- a/docs-en/12-taos-sql/index.md +++ b/docs-en/12-taos-sql/index.md @@ -3,11 +3,9 @@ title: TDengine SQL description: "The syntax supported by TDengine SQL " --- -This section explains the syntax about operating database, table, STable, inserting data, selecting data, functions and some tips that can be used in TDengine SQL. It would be easier to understand with some fundamental knowledge of SQL. +This section explains the syntax of SQL to perform operations on databases, tables and STables, insert data, select data and use functions. We also provide some tips that can be used in TDengine SQL. If you have previous experience with SQL this section will be fairly easy to understand. If you do not have previous experience with SQL, you'll come to appreciate the simplicity and power of SQL. -TDengine SQL is the major interface for users to write data into or query from TDengine. For users to easily use, syntax similar to standard SQL is provided. However, please be noted that TDengine SQL is not standard SQL. Besides, because TDengine doesn't provide the functionality of deleting time series data, corresponding statements are not provided in TDengine SQL. - -TDengine SQL doesn't support abbreviation for keywords, for example `DESCRIBE` can't be abbreviated as `DESC`. +TDengine SQL is the major interface for users to write data into or query from TDengine. For ease of use, the syntax is similar to that of standard SQL. However, please note that TDengine SQL is not standard SQL. For instance, TDengine doesn't provide a delete function for time series data and so corresponding statements are not provided in TDengine SQL. Syntax Specifications used in this chapter: @@ -16,7 +14,7 @@ Syntax Specifications used in this chapter: - | means one of a few options, excluding | itself. - … means the item prior to it can be repeated multiple times. -To better demonstrate the syntax, usage and rules of TAOS SQL, hereinafter it's assumed that there is a data set of meters. Assuming each meter collects 3 data: current, voltage, phase. The data model is as below: +To better demonstrate the syntax, usage and rules of TAOS SQL, hereinafter it's assumed that there is a data set of data from electric meters. Each meter collects 3 data measurements: current, voltage, phase. The data model is shown below: ```sql taos> DESCRIBE meters; @@ -30,4 +28,4 @@ taos> DESCRIBE meters; groupid | INT | 4 | TAG | ``` -The data set includes the data collected by 4 meters, the corresponding table name is d1001, d1002, d1003, d1004 respectively based on the data model of TDengine. +The data set includes the data collected by 4 meters, the corresponding table name is d1001, d1002, d1003 and d1004 based on the data model of TDengine. diff --git a/docs-en/12-taos-sql/timewindow-1.webp b/docs-en/12-taos-sql/timewindow-1.webp new file mode 100644 index 0000000000000000000000000000000000000000..82747558e96df752a0010d85be79a4af07e4a1df Binary files /dev/null and b/docs-en/12-taos-sql/timewindow-1.webp differ diff --git a/docs-en/12-taos-sql/timewindow-2.webp b/docs-en/12-taos-sql/timewindow-2.webp new file mode 100644 index 0000000000000000000000000000000000000000..8f1314ae34f7f5c5cca1d3cb80455f555fad38c3 Binary files /dev/null and b/docs-en/12-taos-sql/timewindow-2.webp differ diff --git a/docs-en/12-taos-sql/timewindow-3.webp b/docs-en/12-taos-sql/timewindow-3.webp new file mode 100644 index 0000000000000000000000000000000000000000..5bd16e68e7fd5da6805551e9765975277cd5d4d9 Binary files /dev/null and b/docs-en/12-taos-sql/timewindow-3.webp differ diff --git a/docs-en/13-operation/01-pkg-install.md b/docs-en/13-operation/01-pkg-install.md index a1aad1c3c96c52689e9f68509c27ccce574d2082..c098002962d62aa0acc7a94462c052303cb2ed90 100644 --- a/docs-en/13-operation/01-pkg-install.md +++ b/docs-en/13-operation/01-pkg-install.md @@ -6,7 +6,7 @@ description: Install, Uninstall, Start, Stop and Upgrade import Tabs from "@theme/Tabs"; import TabItem from "@theme/TabItem"; -TDengine community version provides dev and rpm package for users to choose based on the system environment. deb supports Debian, Ubuntu and systems derived from them. rpm supports CentOS, RHEL, SUSE and systems derived from them. Furthermore, tar.gz package is provided for enterprise customers. +TDengine community version provides deb and rpm packages for users to choose from, based on their system environment. The deb package supports Debian, Ubuntu and derivative systems. The rpm package supports CentOS, RHEL, SUSE and derivative systems. Furthermore, a tar.gz package is provided for TDengine Enterprise customers. ## Install @@ -14,7 +14,7 @@ TDengine community version provides dev and rpm package for users to choose base 1. Download deb package from official website, for example TDengine-server-2.4.0.7-Linux-x64.deb -2. In the directory where the package is located, execute below command +2. In the directory where the package is located, execute the command below ```bash $ sudo dpkg -i TDengine-server-2.4.0.7-Linux-x64.deb @@ -46,7 +46,7 @@ TDengine is installed successfully! 1. Download rpm package from official website, for example TDengine-server-2.4.0.7-Linux-x64.rpm； -2. In the directory where the package is located, execute below command +2. In the directory where the package is located, execute the command below ``` $ sudo rpm -ivh TDengine-server-2.4.0.7-Linux-x64.rpm @@ -77,7 +77,7 @@ TDengine is installed successfully! 1. Download the tar.gz package, for example TDengine-server-2.4.0.7-Linux-x64.tar.gz； - 2、In the directory where the package is located, firstly decompress the file, then switch to the sub-directory generated in decompressing, i.e. "TDengine-enterprise-server-2.4.0.7/" in this example, and execute the `install.sh` script. +2. In the directory where the package is located, first decompress the file, then switch to the sub-directory generated in decompressing, i.e. "TDengine-enterprise-server-2.4.0.7/" in this example, and execute the `install.sh` script. ```bash $ tar xvzf TDengine-enterprise-server-2.4.0.7-Linux-x64.tar.gz @@ -124,7 +124,7 @@ taoskeeper is installed, enable it by `systemctl enable taoskeeper` ``` :::info -Some configuration will be prompted for users to provide when install.sh is executing, the interactive mode can be disabled by executing `./install.sh -e no`. `./install -h` can show all parameters and detailed explanation. +Users will be prompted to enter some configuration information when install.sh is executing. The interactive mode can be disabled by executing `./install.sh -e no`. `./install.sh -h` can show all parameters with detailed explanation. ::: @@ -132,7 +132,7 @@ Some configuration will be prompted for users to provide when install.sh is exec :::note -When installing on the first node in the cluster, when "Enter FQDN:" is prompted, nothing needs to be provided. When installing on following nodes, when "Enter FQDN:" is prompted, the end point of the first dnode in the cluster can be input if it has been already up; or just ignore it and configure later after installation is done. +When installing on the first node in the cluster, at the "Enter FQDN:" prompt, nothing needs to be provided. When installing on subsequent nodes, at the "Enter FQDN:" prompt, you must enter the end point of the first dnode in the cluster if it is already up. You can also just ignore it and configure it later after installation is finished. ::: @@ -181,14 +181,14 @@ taosKeeper is removed successfully! :::note -- It's strongly suggested not to use multiple kinds of installation packages on single host TDengine -- After deb package is installed, if the installation directory is removed manually so that uninstall or reinstall can't succeed, it can be resolved by cleaning up TDengine package information as below command and then reinstalling. +- We strongly recommend not to use multiple kinds of installation packages on a single host TDengine. +- After deb package is installed, if the installation directory is removed manually, uninstall or reinstall will not work. This issue can be resolved by using the command below which cleans up TDengine package information. You can then reinstall if needed. ```bash $ sudo rm -f /var/lib/dpkg/info/tdengine* ``` -- After rpm package is installed, if the installation directory is removed manually so that uninstall or reinstall can't succeed, it can be resolved by cleaning up TDengine package information as below command and then reinstalling. +- After rpm package is installed, if the installation directory is removed manually, uninstall or reinstall will not work. This issue can be resolved by using the command below which cleans up TDengine package information. You can then reinstall if needed. ```bash $ sudo rpm -e --noscripts tdengine @@ -219,7 +219,7 @@ lrwxrwxrwx 1 root root 13 Feb 22 09:34 log -> /var/log/taos/ During the installation process: - Configuration directory, data directory, and log directory are created automatically if they don't exist -- The default configuration file is located at /etc/taos/taos.cfg, which is a copy of /usr/local/taos/cfg/taos.cfg if not existing +- The default configuration file is located at /etc/taos/taos.cfg, which is a copy of /usr/local/taos/cfg/taos.cfg - The default data directory is /var/lib/taos, which is a soft link to /usr/local/taos/data - The default log directory is /var/log/taos, which is a soft link to /usr/local/taos/log - The executables at /usr/local/taos/bin are linked to /usr/bin @@ -228,14 +228,14 @@ During the installation process: :::note -- When TDengine is uninstalled, the configuration /etc/taos/taos.cfg, data directory /var/lib/taos, log directory /var/log/taos are kept. They can be deleted manually with caution because data can't be recovered once +- When TDengine is uninstalled, the configuration /etc/taos/taos.cfg, data directory /var/lib/taos, log directory /var/log/taos are kept. They can be deleted manually with caution, because data can't be recovered. Please follow data integrity, security, backup or relevant SOPs before deleting any data. - When reinstalling TDengine, if the default configuration file /etc/taos/taos.cfg exists, it will be kept and the configuration file in the installation package will be renamed to taos.cfg.orig and stored at /usr/local/taos/cfg to be used as configuration sample. Otherwise the configuration file in the installation package will be installed to /etc/taos/taos.cfg and used. ## Start and Stop -Linux system services `systemd`, `systemctl` or `service` is used to start, stop and restart TDengine. The server process of TDengine is `taosd`, which is started automatically after the Linux system is started. System operator can use `systemd`, `systemctl` or `service` to start, stop or restart TDengine server. +Linux system services `systemd`, `systemctl` or `service` are used to start, stop and restart TDengine. The server process of TDengine is `taosd`, which is started automatically after the Linux system is started. System operators can use `systemd`, `systemctl` or `service` to start, stop or restart TDengine server. -For example, if using `systemctl` , the commands to start, stop, restart and check TDengine server are as below: +For example, if using `systemctl` , the commands to start, stop, restart and check TDengine server are below: - Start server：`systemctl start taosd` @@ -263,20 +263,22 @@ Active: inactive (dead) There are two aspects in upgrade operation: upgrade installation package and upgrade a running server. -Upgrading package should follow the steps mentioned previously to firstly uninstall old version then install new version. +To upgrade a package, follow the steps mentioned previously to first uninstall the old version then install the new version. -Upgrading a running server is much more complex. Firstly please check the version number of old version and new version. The version number of TDengine consists of 4 sections, only the first 3 section match can the old version be upgraded to the new version. The steps of upgrading a running server are as below: +Upgrading a running server is much more complex. First please check the version number of the old version and the new version. The version number of TDengine consists of 4 sections, only if the first 3 sections match can the old version be upgraded to the new version. The steps of upgrading a running server are as below: - Stop inserting data -- Make sure all data persisted into disk +- Make sure all data is persisted to disk +- Make some simple queries (Such as total rows in stables, tables and so on. Note down the values. Follow best practices and relevant SOPs.) - Stop the cluster of TDengine - Uninstall old version and install new version - Start the cluster of TDengine -- Make some simple queries to make sure no data loss -- Make some simple data insertion to make sure the cluster works well -- Restore business data +- Execute simple queries, such as the ones executed prior to installing the new package, to make sure there is no data loss +- Run some simple data insertion statements to make sure the cluster works well +- Restore business services :::warning + TDengine doesn't guarantee any lower version is compatible with the data generated by a higher version, so it's never recommended to downgrade the version. ::: diff --git a/docs-en/13-operation/02-planning.mdx b/docs-en/13-operation/02-planning.mdx index 35a34aebc088c233ed9fc39723e8890ebc56e124..c1baf92dbfa8d93f83174c05c2ea631d1a469739 100644 --- a/docs-en/13-operation/02-planning.mdx +++ b/docs-en/13-operation/02-planning.mdx @@ -2,19 +2,19 @@ title: Resource Planning --- -The computing and storage resources need to be planned if using TDengine to build an IoT platform. How to plan the CPU, memory and disk required will be described in this chapter. +It is important to plan computing and storage resources if using TDengine to build an IoT, time-series or Big Data platform. How to plan the CPU, memory and disk resources required, will be described in this chapter. ## Memory Requirement of Server Side -The number of vgroups created for each database is same as the number of CPU cores by default and can be configured by parameter `maxVgroupsPerDb`, each vnode in a vgroup stores one replica. Each vnode consumes fixed size of memory, i.e. `blocks` \* `cache`. Besides, some memory is required for tag values associated with each table. A fixed amount of memory is required for each cluster. So, the memory required for each DB can be calculated using below formula: +By default, the number of vgroups created for each database is the same as the number of CPU cores. This can be configured by the parameter `maxVgroupsPerDb`. Each vnode in a vgroup stores one replica. Each vnode consumes a fixed amount of memory, i.e. `blocks` \* `cache`. In addition, some memory is required for tag values associated with each table. A fixed amount of memory is required for each cluster. So, the memory required for each DB can be calculated using the formula below: ``` Database Memory Size = maxVgroupsPerDb * replica * (blocks * cache + 10MB) + numOfTables * (tagSizePerTable + 0.5KB) ``` -For example, assuming the default value of `maxVgroupPerDB` is 64, the default value of `cache` 16M, the default value of `blocks` is 6, there are 100,000 tables in a DB, the replica number is 1, total length of tag values is 256 bytes, the total memory required for this DB is: 64 \* 1 \* (16 \* 6 + 10) + 100000 \* (0.25 + 0.5) / 1000 = 6792M. +For example, assuming the default value of `maxVgroupPerDB` is 64, the default value of `cache` is 16M, the default value of `blocks` is 6, there are 100,000 tables in a DB, the replica number is 1, total length of tag values is 256 bytes, the total memory required for this DB is: 64 \* 1 \* (16 \* 6 + 10) + 100000 \* (0.25 + 0.5) / 1000 = 6792M. -In real operation of TDengine, we are more concerned about the memory used by each TDengine server process `taosd`. +In the real operation of TDengine, we are more concerned about the memory used by each TDengine server process `taosd`. ``` taosd_memory = vnode_memory + mnode_memory + query_memory @@ -22,29 +22,29 @@ In real operation of TDengine, we are more concerned about the memory used by ea In the above formula: -1. "vnode_memory" of a `taosd` process is the memory used by all vnodes hosted by this `taosd` process. It can be roughly calculated by firstly adding up the total memory of all DBs whose memory usage can be derived according to the formula mentioned previously then dividing by number of dnodes and multiplying the number of replicas. +1. "vnode_memory" of a `taosd` process is the memory used by all vnodes hosted by this `taosd` process. It can be roughly calculated by firstly adding up the total memory of all DBs whose memory usage can be derived according to the formula for Database Memory Size, mentioned above, then dividing by number of dnodes and multiplying the number of replicas. ``` - vnode_memory = sum(Database memory) / number_of_dnodes \* replica + vnode_memory = (sum(Database Memory Size) / number_of_dnodes) * replica ``` 2. "mnode_memory" of a `taosd` process is the memory consumed by a mnode. If there is one (and only one) mnode hosted in a `taosd` process, the memory consumed by "mnode" is "0.2KB \* the total number of tables in the cluster". 3. "query_memory" is the memory used when processing query requests. Each ongoing query consumes at least "0.2 KB \* total number of involved tables". -Please be noted that the above formulas can only be used to estimate the minimum memory requirement, instead of maximum memory usage. In a real production environment, it's better to preserve some redundance beyond the estimated minimum memory requirement. If memory is abundant, it's suggested to increase the value of parameter `blocks` to speed up data insertion and data query. +Please note that the above formulas can only be used to estimate the minimum memory requirement, instead of maximum memory usage. In a real production environment, it's better to reserve some redundance beyond the estimated minimum memory requirement. If memory is abundant, it's suggested to increase the value of parameter `blocks` to speed up data insertion and data query. ## Memory Requirement of Client Side -The client programs use TDengine client driver `taosc` to connect to the server side, there is also memory requirement for a client program. +For the client programs using TDengine client driver `taosc` to connect to the server side there is a memory requirement as well. -The memory consumed by a client program is mainly about the SQL statements for data insertion, caching of table metadata, and some internal use. Assuming maximum number of tables is N (the memory consumed by the metadata of each table is 256 bytes), maximum number of threads for parallel insertion is T, maximum length of a SQL statement is S (normally 1 MB), the memory required by a client program can be estimated using below formula: +The memory consumed by a client program is mainly about the SQL statements for data insertion, caching of table metadata, and some internal use. Assuming maximum number of tables is N (the memory consumed by the metadata of each table is 256 bytes), maximum number of threads for parallel insertion is T, maximum length of a SQL statement is S (normally 1 MB), the memory required by a client program can be estimated using the below formula: ``` M = (T * S * 3 + (N / 4096) + 100) ``` -For example, if the number of parallel data insertion threads is 100, total number of tables is 10,000,000, then minimum memory requirement of a client program is: +For example, if the number of parallel data insertion threads is 100, total number of tables is 10,000,000, then the minimum memory requirement of a client program is: ``` 100 * 3 + (10000000 / 4096) + 100 = 2741 (MBytes) @@ -56,10 +56,10 @@ So, at least 3GB needs to be reserved for such a client. The CPU resources required depend on two aspects: -- **Data Insertion** Each dnode of TDengine can process at least 10,000 insertion requests in one second, while each insertion request can have multiple rows. The computing resource consumed between inserting 1 row one time and inserting 10 rows one time is very small. So, the more the rows to insert one time, the higher the efficiency. Inserting in bach also exposes requirement for the client side which needs to cache rows and insert in batch once the cached rows reaches a threshold. -- **Data Query** High efficiency query is provided in TDengine, but it's hard to estimate the CPU resource required because the queries used in different use cases and the frequency of queries vary significantly. It can only be verified with the query statements, query frequency, data size to be queried, etc provided by user. +- **Data Insertion** Each dnode of TDengine can process at least 10,000 insertion requests in one second, while each insertion request can have multiple rows. The difference in computing resource consumed, between inserting 1 row at a time, and inserting 10 rows at a time is very small. So, the more the number of rows that can be inserted one time, the higher the efficiency. Inserting in batch also imposes requirements on the client side which needs to cache rows to insert in batch once the number of cached rows reaches a threshold. +- **Data Query** High efficiency query is provided in TDengine, but it's hard to estimate the CPU resource required because the queries used in different use cases and the frequency of queries vary significantly. It can only be verified with the query statements, query frequency, data size to be queried, and other requirements provided by users. -In short words, the CPU resource required for data insertion can be estimated but it's hard to do so for query use cases. In real operation, it's suggested to control CPU usage below 50%. If this threshold is exceeded, it's a reminder for system operator to add more nodes in the cluster to expand resources. +In short, the CPU resource required for data insertion can be estimated but it's hard to do so for query use cases. In real operation, it's suggested to control CPU usage below 50%. If this threshold is exceeded, it's a reminder for system operator to add more nodes in the cluster to expand resources. ## Disk Requirement @@ -69,14 +69,14 @@ The compression ratio in TDengine is much higher than that in RDBMS. In most cas Raw DataSize = numOfTables * rowSizePerTable * rowsPerTable ``` -For example, there are 10,000,000 meters, while each meter collects data every 15 minutes and the data size of each collection si 128 bytes, so the raw data size of one year is: 10000000 \* 128 \* 24 \* 60 / 15 \* 365 = 44.8512(TB). Assuming compression ratio is 5, the actual disk size is: 44.851 / 5 = 8.97024(TB). +For example, there are 10,000,000 meters, while each meter collects data every 15 minutes and the data size of each collection is 128 bytes, so the raw data size of one year is: 10000000 \* 128 \* 24 \* 60 / 15 \* 365 = 44.8512(TB). Assuming compression ratio is 5, the actual disk size is: 44.851 / 5 = 8.97024(TB). -Parameter `keep` can be used to set how long the data will be kept on disk. To further reduce storage cost, multiple storage levels can be enabled in TDengine, with the coldest data stored on the cheapest storage device, and this is transparent to application programs. +Parameter `keep` can be used to set how long the data will be kept on disk. To further reduce storage cost, multiple storage levels can be enabled in TDengine, with the coldest data stored on the cheapest storage device. This is completely transparent to application programs. -To increase the performance, multiple disks can be setup for parallel data reading or data inserting. Please be noted that expensive disk array is not necessary because replications are used in TDengine to provide high availability. +To increase performance, multiple disks can be setup for parallel data reading or data inserting. Please note that an expensive disk array is not necessary because replications are used in TDengine to provide high availability. ## Number of Hosts -A host can be either physical or virtual. The total memory, total CPU, total disk required can be estimated according to the formulas mentioned previously. Then, according to the system resources that a single host can provide, assuming all hosts are same in resources, the number of hosts can be derived easily. +A host can be either physical or virtual. The total memory, total CPU, total disk required can be estimated according to the formulae mentioned previously. Then, according to the system resources that a single host can provide, assuming all hosts have the same resources, the number of hosts can be derived easily. **Quick Estimation for CPU, Memory and Disk** Please refer to [Resource Estimate](https://www.taosdata.com/config/config.html). diff --git a/docs-en/13-operation/03-tolerance.md b/docs-en/13-operation/03-tolerance.md index 367474cddb7395ea84a4a33623d1643e487f9d09..d4d48d7fcdc2c990b6ea0821e2347c70a809ed79 100644 --- a/docs-en/13-operation/03-tolerance.md +++ b/docs-en/13-operation/03-tolerance.md @@ -7,23 +7,26 @@ title: Fault Tolerance & Disaster Recovery TDengine uses **WAL**, i.e. Write Ahead Log, to achieve fault tolerance and high reliability. -When a data block is received by TDengine, the original data block is firstly written into WAL. The log in WAL will be deleted only after the data has been written into data files in the database. Data can be recovered from WAL in case the server is stopped abnormally due to any reason and then restarted. +When a data block is received by TDengine, the original data block is first written into WAL. The log in WAL will be deleted only after the data has been written into data files in the database. Data can be recovered from WAL in case the server is stopped abnormally for any reason and then restarted. There are 2 configuration parameters related to WAL: -- walLevel：0：wal is disabled; 1：wal is enabled without fsync; 2：wal is enabled with fsync. -- fsync：only valid when walLevel is set to 2, it specified the interval of invoking fsync. If set to 0, it means fsync is invoked immediately once WAL is written. +- walLevel： + - 0：wal is disabled + - 1：wal is enabled without fsync + - 2：wal is enabled with fsync +- fsync：This parameter is only valid when walLevel is set to 2. It specifies the interval, in milliseconds, of invoking fsync. If set to 0, it means fsync is invoked immediately once WAL is written. -To achieve absolutely no data loss, walLevel needs to be set to 2 and fsync needs to be set to 1. The penalty is the performance of data ingestion downgrades. However, if the concurrent threads of data insertion on the client side can reach a big enough number, for example 50, the data ingestion performance would be still good enough, our verification shows that the drop is only 30% compared to fsync is set to 3,000 milliseconds. +To achieve absolutely no data loss, walLevel should be set to 2 and fsync should be set to 1. There is a performance penalty to the data ingestion rate. However, if the concurrent data insertion threads on the client side can reach a big enough number, for example 50, the data ingestion performance will be still good enough. Our verification shows that the drop is only 30% when fsync is set to 3,000 milliseconds. ## Disaster Recovery -TDengine uses replications to provide high availability and disaster recovery capability. +TDengine uses replication to provide high availability and disaster recovery capability. -TDengine cluster is managed by mnode. To make sure the high availability of mnode, multiple replicas can be configured by system parameter `numOfMnodes`. The data replication between mnode replicas is in synchronous way to guarantee the metadata consistency. +A TDengine cluster is managed by mnode. To ensure the high availability of mnode, multiple replicas can be configured by the system parameter `numOfMnodes`. The data replication between mnode replicas is performed in a synchronous way to guarantee metadata consistency. -The number of replicas for time series data in TDengine is associated with each database, there can be a lot of databases in a cluster while each database can be configured with a different number of replicas. When creating a database, parameter `replica` is used to configure the number of replications. To achieve high availability, `replica` needs to be higher than 1. +The number of replicas for time series data in TDengine is associated with each database. There can be many databases in a cluster and each database can be configured with a different number of replicas. When creating a database, parameter `replica` is used to configure the number of replications. To achieve high availability, `replica` needs to be higher than 1. -The number of dnodes in a TDengine cluster must NOT be lower than the number of replicas for any database, otherwise it would fail when trying to create table. +The number of dnodes in a TDengine cluster must NOT be lower than the number of replicas for any database, otherwise it would fail when trying to create a table. -As long as the dnodes of a TDengine cluster are deployed on different physical machines and replica number is set to bigger than 1, high availability can be achieved without any other assistance. If dnodes of TDengine cluster are deployed in geographically different data centers, disaster recovery can be achieved too. +As long as the dnodes of a TDengine cluster are deployed on different physical machines and the replica number is higher than 1, high availability can be achieved without any other assistance. For disaster recovery, dnodes of a TDengine cluster should be deployed in geographically different data centers. diff --git a/docs-en/13-operation/06-admin.md b/docs-en/13-operation/06-admin.md index 1ca0dfeaf4a4b0b4c597e1a5ec6ece20224e2dba..458a91b88c6d8319fe8b84c2b34d8ff968957910 100644 --- a/docs-en/13-operation/06-admin.md +++ b/docs-en/13-operation/06-admin.md @@ -2,7 +2,7 @@ title: User Management --- -System operator can use TDengine CLI `taos` to create or remove user or change password. The SQL command is as low: +A system operator can use TDengine CLI `taos` to create or remove users or change passwords. The SQL commands are documented below: ## Create User @@ -10,7 +10,7 @@ System operator can use TDengine CLI `taos` to create or remove user or change p CREATE USER PASS <'password'>; ``` -When creating a user and specifying the user name and password, password needs to be quoted using single quotes. +When creating a user and specifying the user name and password, the password needs to be quoted using single quotes. ## Drop User @@ -18,7 +18,7 @@ When creating a user and specifying the user name and password, password needs t DROP USER ; ``` -Drop a user can only be performed by root. +Dropping a user can only be performed by root. ## Change Password @@ -26,7 +26,7 @@ Drop a user can only be performed by root. ALTER USER PASS <'password'>; ``` -To keep the case of the password when changing password, password needs to be quoted using single quotes. +To keep the case of the password when changing password, the password needs to be quoted using single quotes. ## Change Privilege @@ -36,7 +36,7 @@ ALTER USER PRIVILEGE ; The privileges that can be changed to are `read` or `write` without single quotes. -Note：there is another privilege `super`, which not allowed to be authorized to any user. +Note：there is another privilege `super`, which is not allowed to be authorized to any user. ## Show Users @@ -45,6 +45,6 @@ SHOW USERS; ``` :::note -In SQL syntax, `< >` means the part that needs to be input by user, excluding the `< >` itself. +In SQL syntax, `< >` means the part that needs to be input by the user, excluding the `< >` itself. ::: diff --git a/docs-en/13-operation/07-import.md b/docs-en/13-operation/07-import.md index befca38652abadca60b62721754de7ab718f65ea..8362cec1ab3072866018678b42a679d0c19b49de 100644 --- a/docs-en/13-operation/07-import.md +++ b/docs-en/13-operation/07-import.md @@ -2,26 +2,26 @@ title: Data Import --- -There are multiple ways of importing data provided byTDengine: import with script, import from data file, import using `taosdump`. +There are multiple ways of importing data provided by TDengine: import with script, import from data file, import using `taosdump`. ## Import Using Script -TDengine CLI `taos` supports `source ` command for executing the SQL statements in the file in batch. The SQL statements for creating databases, creating tables, and inserting rows can be written in single file with one statement on each line, then the file can be executed using `source` command in TDengine CLI `taos` to execute the SQL statements in order and in batch. In the script file, any line beginning with "#" is treated as comments and ignored silently. +TDengine CLI `taos` supports `source ` command for executing the SQL statements in the file in batch. The SQL statements for creating databases, creating tables, and inserting rows can be written in a single file with one statement on each line, then the file can be executed using the `source` command in TDengine CLI `taos` to execute the SQL statements in order and in batch. In the script file, any line beginning with "#" is treated as comments and ignored silently. ## Import from Data File -In TDengine CLI, data can be imported from a CSV file into an existing table. The data in single CSV must belong to same table and must be consistent with the schema of that table. The SQL statement is as below: +In TDengine CLI, data can be imported from a CSV file into an existing table. The data in a single CSV must belong to the same table and must be consistent with the schema of that table. The SQL statement is as below: ```sql insert into tb1 file 'path/data.csv'; ``` :::note -If there is description in the first line of a CSV file, please remove it before importing. If there is no value for a column, please use `NULL` without quotes. +If there is a description in the first line of the CSV file, please remove it before importing. If there is no value for a column, please use `NULL` without quotes. ::: -For example, there is a sub table d1001 whose schema is as below: +For example, there is a subtable d1001 whose schema is as below: ```sql taos> DESCRIBE d1001 @@ -49,7 +49,7 @@ The format of the CSV file to be imported, data.csv, is as below: '2018-10-12 06:38:05.000',18.30000,219,0.31000 ``` -Then, below SQL statement can be used to import data from file "data.csv", assuming the file is located under the home directory of current Linux user. +Then, the below SQL statement can be used to import data from file "data.csv", assuming the file is located under the home directory of the current Linux user. ```sql taos> insert into d1001 file '~/data.csv'; @@ -58,4 +58,4 @@ Query OK, 9 row(s) affected (0.004763s) ## Import using taosdump -A convenient tool for importing and exporting data is provided by TDengine, `taosdump`, which can used to export data from one TDengine cluster and import into another one. For the details of using `taosdump` please refer to [Tool for exporting and importing data: taosdump](/reference/taosdump). +A convenient tool for importing and exporting data is provided by TDengine, `taosdump`, which can be used to export data from one TDengine cluster and import into another one. For the details of using `taosdump` please refer to [Tool for exporting and importing data: taosdump](/reference/taosdump). diff --git a/docs-en/13-operation/08-export.md b/docs-en/13-operation/08-export.md index fa9625a7c5f6b0e6706d726bff410cee647286bb..5780de42faeaedbc1c985ad2aa2f52fe56c76971 100644 --- a/docs-en/13-operation/08-export.md +++ b/docs-en/13-operation/08-export.md @@ -2,11 +2,13 @@ title: Data Export --- -There are two ways of exporting data from a TDengine cluster, one is SQL statement in TDengine CLI, the other one is `taosdump`. +There are two ways of exporting data from a TDengine cluster: +- Using a SQL statement in TDengine CLI +- Using the `taosdump` tool ## Export Using SQL -If you want to export the data of a table or a STable, please execute below SQL statement in TDengine CLI. +If you want to export the data of a table or a STable, please execute the SQL statement below, in the TDengine CLI. ```sql select * from >> data.csv; @@ -16,4 +18,4 @@ The data of table or STable specified by `tb_name` will be exported into a file ## Export Using taosdump -With `taosdump`, you can choose to export the data of all databases, a database, a table or a STable, you can also choose export the data within a time range, or even only export the schema definition of a table. For the details of using `taosdump` please refer to [Tool for exporting and importing data: taosdump](/reference/taosdump). +With `taosdump`, you can choose to export the data of all databases, a database, a table or a STable, you can also choose to export the data within a time range, or even only export the schema definition of a table. For the details of using `taosdump` please refer to [Tool for exporting and importing data: taosdump](/reference/taosdump). diff --git a/docs-en/13-operation/09-status.md b/docs-en/13-operation/09-status.md index 3f3c6c9f1e86f9f33bafc7edfd79bebb175871cc..51396524ea281ae665c9fdf61d2e6e6202995537 100644 --- a/docs-en/13-operation/09-status.md +++ b/docs-en/13-operation/09-status.md @@ -3,7 +3,7 @@ sidebar_label: Connections & Tasks title: Manage Connections and Query Tasks --- -System operator can use TDengine CLI to show the connections, ongoing queries, stream computing, and can close connection or stop ongoing query task or stream computing. +A system operator can use the TDengine CLI to show connections, ongoing queries, stream computing, and can close connections or stop ongoing query tasks or stream computing. ## Show Connections @@ -13,7 +13,7 @@ SHOW CONNECTIONS; One column of the output of the above SQL command is "ip:port", which is the end point of the client. -## Close Connections Forcedly +## Force Close Connections ```sql KILL CONNECTION ; @@ -27,9 +27,9 @@ In the above SQL command, `connection-id` is from the first column of the output SHOW QUERIES; ``` -The first column of the output is query ID, which is composed of the corresponding connection ID and the sequence number of the current query task started on this connection, in format of "connection-id:query-no". +The first column of the output is query ID, which is composed of the corresponding connection ID and the sequence number of the current query task started on this connection. The format is "connection-id:query-no". -## Close Queries Forcedly +## Force Close Queries ```sql KILL QUERY ; @@ -43,12 +43,12 @@ In the above SQL command, `query-id` is from the first column of the output of ` SHOW STREAMS; ``` -The first column of the output is stream ID, which is composed of the connection ID and the sequence number of the current stream started on this connection, in the format of "connection-id:stream-no". +The first column of the output is stream ID, which is composed of the connection ID and the sequence number of the current stream started on this connection. The format is "connection-id:stream-no". -## Close Continuous Query Forcedly +## Force Close Continuous Query ```sql KILL STREAM ; ``` -The the above SQL command, `stream-id` is from the first column of the output of `SHOW STREAMS`. +The above SQL command, `stream-id` is from the first column of the output of `SHOW STREAMS`. diff --git a/docs-en/13-operation/10-monitor.md b/docs-en/13-operation/10-monitor.md index 019cf4f2948141fac79587429f1fdc3b06623945..a4679983f2bc77bb4e438f5d43fa1b8beb39b120 100644 --- a/docs-en/13-operation/10-monitor.md +++ b/docs-en/13-operation/10-monitor.md @@ -2,19 +2,19 @@ title: TDengine Monitoring --- -After TDengine is started, a database named `log` for monitoring is created automatically. The information about CPU, memory, disk, bandwidth, number of requests, disk I/O speed, slow query is written into `log` database on the basis of a predefined interval. Besides, some important system operations, like logon, create user, drop database, and alerts and warnings generated in TDengine are written into `log` database too. System operator can view the data in `log` database from TDengine CLI or from a web console. +After TDengine is started, a database named `log` is created automatically to help with monitoring. Information that includes CPU, memory and disk usage, bandwidth, number of requests, disk I/O speed, slow queries, is written into the `log` database at a predefined interval. Additionally, some important system operations, like logon, create user, drop database, and alerts and warnings generated in TDengine are written into the `log` database too. A system operator can view the data in `log` database from TDengine CLI or from a web console. -Collection of the monitoring information is enabled by default, but can be disabled by parameter `monitor` in configuration file. +The collection of the monitoring information is enabled by default, but can be disabled by parameter `monitor` in the configuration file. ## TDinsight -TDinsight is a total solution which uses the monitor database `log` mentioned previously and Grafana to monitor a TDengine cluster. +TDinsight is a complete solution which uses the monitoring database `log` mentioned previously, and Grafana, to monitor a TDengine cluster. From version 2.3.3.0, more monitoring data has been added in the `log` database. Please refer to [TDinsight Grafana Dashboard](https://grafana.com/grafana/dashboards/15167) to learn more details about using TDinsight to monitor TDengine. -A script `TDinsight.sh` is provided to deploy TDinsight in automatic way. +A script `TDinsight.sh` is provided to deploy TDinsight automatically. -Download `TDinsight.sh` with below command: +Download `TDinsight.sh` with the below command: ```bash wget https://github.com/taosdata/grafanaplugin/raw/master/dashboards/TDinsight.sh @@ -38,7 +38,7 @@ There are two ways to setup Grafana alert notification. sudo ./TDinsight.sh -a http://localhost:6041 -u root -p taosdata -E ``` -- The AliCloud SMS alert built in TDengine data source plugin can be enabled with parameter `-s`, the parameters of this way are as follows: +- The AliCloud SMS alert built in TDengine data source plugin can be enabled with parameter `-s`, the parameters of enabling this plugin are listed below: - `-I`: AliCloud SMS Key ID - `-K`: AliCloud SMS Key Secret @@ -47,7 +47,7 @@ There are two ways to setup Grafana alert notification. - `-T`: Input parameters in JSON format for the SMS notification template, for example`{"alarm_level":"%s","time":"%s","name":"%s","content":"%s"}` - `-B`: List of mobile numbers to be notified - Below is an example of the full command using this way. + Below is an example of the full command using the AliCloud SMS alert. ```bash sudo ./TDinsight.sh -a http://localhost:6041 -u root -p taosdata -s \ @@ -55,6 +55,6 @@ There are two ways to setup Grafana alert notification. -T '{"alarm_level":"%s","time":"%s","name":"%s","content":"%s"}' ``` -Launch `TDinsight.sh` as above command and restart Grafana, then open Dashboard `http://localhost:3000/d/tdinsight`. +Launch `TDinsight.sh` with the command above and restart Grafana, then open Dashboard `http://localhost:3000/d/tdinsight`. For more use cases and restrictions please refer to [TDinsight](/reference/tdinsight/). diff --git a/docs-en/13-operation/11-optimize.md b/docs-en/13-operation/11-optimize.md index 7cccfc8b0d51a4bfda9ae4827130a3747f10e649..69f8a49e4cf950fdf1e363e7a51aa9d888e22e04 100644 --- a/docs-en/13-operation/11-optimize.md +++ b/docs-en/13-operation/11-optimize.md @@ -2,19 +2,19 @@ title: Performance Optimization --- -After a TDengine cluster has been running for long enough time, because of updating data, deleting tables and deleting expired data, there may be fragments in data files and query performance may be impacted. To resolve the problem of fragments, from version 2.1.3.0 a new SQL command `COMPACT` can be used to defragment the data files. +After a TDengine cluster has been running for a long enough time, because of data insertion, table deletion and deletion of expired data, there may be fragments in data files and query performance may be impacted. To resolve the problem of fragments, since version 2.1.3.0 a new SQL command `COMPACT` can be used to defragment data files. ```sql COMPACT VNODES IN (vg_id1, vg_id2, ...) ``` -`COMPACT` can be used to defragment one or more vgroups. The defragmentation work will be put in task queue for scheduling execution by TDengine. `SHOW VGROUPS` command can be used to get the vgroup ids to be used in `COMPACT` command. There is a column `compacting` in the output of `SHOW GROUPS` to indicate the compacting status of the vgroup: 2 means the vgroup is waiting in task queue for compacting, 1 means compacting is in progress, and 0 means the vgroup has nothing to do with compacting. +`COMPACT` can be used to defragment one or more vgroups. The defragmentation work will be scheduled in the task queue for execution by TDengine. `SHOW VGROUPS` command can be used to get the vgroup ids to be used in `COMPACT` command. There is a column `compacting` in the output of `SHOW GROUPS` to indicate the compaction status of the vgroup: 2 means the vgroup is waiting in task queue for compaction, 1 means compaction is in progress, and 0 means the vgroup has not been scheduled for compaction. -Please be noted that a lot of disk I/O is required for defragementation operation, during which the performance may be impacted significantly for data insertion and query, data insertion may be blocked shortly in extreme cases. +Please note that a lot of disk I/O is required for defragementation operations. During defragmentation the performance may be impacted significantly for data insertion and query. Data insertion may even be blocked for very short periods, in extreme cases. ## Optimize Storage Parameters -The data in different use cases may have different characteristics, such as the days to keep, number of replicas, collection interval, record size, number of collection points, compression or not, etc. To achieve best efficiency in storage, the parameters in below table can be used, all of them can be either configured in `taos.cfg` as default configuration or in the command `create database`. For detailed definition of these parameters please refer to [Configuration Parameters](/reference/config/). +The data in different use cases may have different characteristics, such as the days to keep, number of replicas, collection interval, record size, number of collection points, compression or not, etc. To achieve best efficiency in storage, the parameters in the table below can be used. All of them can either be configured in `taos.cfg`, as default parameters, or can be set in the command `create database`. For detailed definition of these parameters please refer to [Configuration Parameters](/reference/config/). | # | Parameter | Unit | Definition | **Value Range** | **Default Value** | | --- | --------- | ---- | ------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------- | ----------------- | @@ -22,24 +22,24 @@ The data in different use cases may have different characteristics, such as the | 2 | keep | Day | The number of days the data is kept in the database | 1-36500 | 3650 | | 3 | cache | MB | The size of each memory block | 1-128 | 16 | | 4 | blocks | None | The number of memory blocks used by each vnode | 3-10000 | 6 | -| 5 | quorum | None | The number of required confirmation in case of multiple replicas | 1-2 | 1 | +| 5 | quorum | None | The number of required confirmations in case of multiple replicas | 1-2 | 1 | | 6 | minRows | None | The minimum number of rows in a data file | 10-1000 | 100 | -| 7 | maxRows | None | The maximum number of rows in a daa file | 200-10000 | 4096 | +| 7 | maxRows | None | The maximum number of rows in a data file | 200-10000 | 4096 | | 8 | comp | None | Whether to compress the data | 0：uncompressed; 1: One Phase compression; 2: Two Phase compression | 2 | | 9 | walLevel | None | wal sync level (named as "wal" in create database ) | 1：wal enabled without fsync; 2：wal enabled with fsync | 1 | -| 10 | fsync | ms | The time to wait for invoking fsync when walLevel is set to 2; 0 means no wait | 3000 | +| 10 | fsync | ms | The time to wait for invoking fsync when walLevel is set to 2; 0 means no wait | 0-3000 | | 11 | replica | none | The number of replications | 1-3 | 1 | | 12 | precision | none | Time precision | ms: millisecond; us: microsecond;ns: nanosecond | ms | -| 13 | update | none | Whether to allow updating data | 0: not allowed; 1: a row must be updated as whole; 2: a part of columns in a row can be updated | 0 | +| 13 | update | none | Whether to allow updating data | 0: not allowed; 1: a whole row must be updated; 2: a portion of columns in a row can be updated | 0 | | 14 | cacheLast | none | Whether the latest data of a table is cached in memory | 0: not cached; 1: the last row is cached; 2: the latest non-NULL value of each column is cached | 0 | -For a specific use case, there may be multiple kinds of data with different characteristics, it's best to put data with same characteristics in same database. So there may be multiple databases in a system while each database can be configured with different storage parameters to achieve best performance. The above parameters can be used when creating a database to override the default setting in configuration file. +Even for a specific use case, there may be multiple kinds of data with different characteristics. In this case it's best to put data with the same characteristics in the same database. There may be multiple databases in a system and each database can be configured with different storage parameters to achieve the best performance. The above parameters can be used when creating a database to override the default setting in the configuration file. ```sql CREATE DATABASE demo DAYS 10 CACHE 32 BLOCKS 8 REPLICA 3 UPDATE 1; ``` -The above SQL statement creates a database named as `demo`, in which each data file stores data across 10 days, the size of each memory block is 32 MB and each vnode is allocated with 8 blocks, the replica is set to 3, update operation is allowed, and all other parameters not specified in the command follow the default configuration in `taos.cfg`. +The above SQL statement creates a database named `demo`, in which each data file stores 10 days of data, the size of each memory block is 32 MB and 8 blocks are allocated to each vnode, there are 3 replicas and update operations are allowed. All other parameters not specified in the command, will default to the values in the configuration file `taos.cfg`. Once a database is created, only some parameters can be changed and be effective immediately while others are can't. @@ -67,7 +67,7 @@ Once a database is created, only some parameters can be changed and be effective **Explanation：** Prior to version 2.1.3.0, `taosd` server process needs to be restarted for these parameters to take in effect if they are changed using `ALTER DATABASE`. -When trying to join a new dnode into a running TDengine cluster, all the parameters related to cluster in the new dnode configuration must be consistent with the cluster, otherwise it can't join the cluster. The parameters that are checked when joining a dnode are as below. For detailed definition of these parameters please refer to [Configuration Parameters](/reference/config/). +When trying to join a new dnode into a running TDengine cluster, all the parameters related to the cluster in the new dnode configuration must be consistent with the cluster, otherwise it can't join the cluster. The parameters that are checked when joining a dnode are listed below. For detailed definition of these parameters please refer to [Configuration Parameters](/reference/config/). - numOfMnodes - mnodeEqualVnodeNum @@ -90,10 +90,10 @@ ALTER DNODE - dnode_id: from output of "SHOW DNODES" - config: the parameter to be changed, as below - - resetlog: close the old log file and create the new on + - resetlog: close the old log file and create the new one - debugFlag: 131 (INFO/ERROR/WARNING), 135 (DEBUG), 143 (TRACE) -For example +For example: ``` alter dnode 1 debugFlag 135; diff --git a/docs-en/13-operation/17-diagnose.md b/docs-en/13-operation/17-diagnose.md index b140d925c07386f93c82d492bb8bcf4d95349f12..2b474fddba4af5ba0c29103cd8ab1249d10d055b 100644 --- a/docs-en/13-operation/17-diagnose.md +++ b/docs-en/13-operation/17-diagnose.md @@ -4,19 +4,19 @@ title: Problem Diagnostics ## Network Connection Diagnostics -When the client is unable to access the server, the network connection between the client side and the server side needs to be checked to find out the root cause and resolve problems. +When a TDengine client is unable to access a TDengine server, the network connection between the client side and the server side must be checked to find the root cause and resolve problems. -The diagnostic for network connection can be executed between Linux and Linux or between Linux and Windows. +Diagnostics for network connections can be executed between Linux and Linux or between Linux and Windows. Diagnostic steps： -1. If the port range to be diagnosed are being occupied by a `taosd` server process, please firstly stop `taosd. -2. On the server side, execute command `taos -n server -P -l ` to monitor the port range starting from the port specified by `-P` parameter with the role of "server. -3. On the client side, execute command `taos -n client -h -P -l ` to send testing package to the specified server and port. +1. If the port range to be diagnosed is being occupied by a `taosd` server process, please first stop `taosd. +2. On the server side, execute command `taos -n server -P -l ` to monitor the port range starting from the port specified by `-P` parameter with the role of "server". +3. On the client side, execute command `taos -n client -h -P -l ` to send a testing package to the specified server and port. --l ： The size of the testing package, in bytes. The value range is [11, 64,000] and default value is 1,000. Please be noted that the package length must be same in the above 2 commands executed on server side and client side respectively. +-l ： The size of the testing package, in bytes. The value range is [11, 64,000] and default value is 1,000. Please note that the package length must be same in the above 2 commands executed on server side and client side respectively. -Output of the server side is as below for example: +Output of the server side for the example is below: ```bash # taos -n server -P 6000 @@ -47,7 +47,7 @@ Output of the server side is as below for example: 12/21 14:50:22.721261 0x7f53427ec700 UTL UDP: send:1000 bytes to 172.27.0.8 at 6011 ``` -Output of the client side is as below for example: +Output of the client side for the example is below: ```bash # taos -n client -h 172.27.0.7 -P 6000 @@ -65,13 +65,13 @@ Output of the client side is as below for example: 12/21 14:50:22.721274 0x7fc95d859200 UTL successed to test UDP port:6011 ``` -The output needs to be checked carefully for the system operator to find out root cause and solve the problem. +The output needs to be checked carefully for the system operator to find the root cause and resolve the problem. ## Startup Status and RPC Diagnostic -`taos -n startup -h ` can be used to check the startup status of a `taosd` process. This is a comman task for a system operator to do to determine whether `taosd` has been started successfully, especially in case of cluster. +`taos -n startup -h ` can be used to check the startup status of a `taosd` process. This is a common task which should be performed by a system operator, especially in the case of a cluster, to determine whether `taosd` has been started successfully. -`taos -n rpc -h ` can be used to check whether the port of a started `taosd` can be accessed or not. If `taosd` process doesn't respond or work abnormally, this command can be used to initiate a rpc communication with the specified fqdn to determine whether it's network problem or `taosd` is abnormal. +`taos -n rpc -h ` can be used to check whether the port of a started `taosd` can be accessed or not. If `taosd` process doesn't respond or is working abnormally, this command can be used to initiate a rpc communication with the specified fqdn to determine whether it's a network problem or whether `taosd` is abnormal. ## Sync and Arbitrator Diagnostic @@ -80,43 +80,43 @@ taos -n sync -P 6040 -h taos -n sync -P 6042 -h ``` -The above commands can be executed on Linux Shell to check whether the port for sync works well and whether the sync module of the server side works well. Besides, `-P 6042` is used to check whether the arbitrator is configured properly and works well. +The above commands can be executed in a Linux shell to check whether the port for sync is working well and whether the sync module on the server side is working well. Additionally, `-P 6042` is used to check whether the arbitrator is configured properly and is working well. ## Network Speed Diagnostic `taos -n speed -h -P 6030 -N 10 -l 10000000 -S TCP` -From version 2.2.0.0, the above command can be executed on Linux Shell to test the network speed, it sends uncompressed package to a running `taosd` server process or a simulated server process started by `taos -n server` to test the network speed. Parameters can be used when testing network speed are as below: +From version 2.2.0.0 onwards, the above command can be executed in a Linux shell to test network speed. The command sends uncompressed packages to a running `taosd` server process or a simulated server process started by `taos -n server` to test the network speed. Parameters can be used when testing network speed are as below: --n：When set to "speed", it means testing network speed --h：The FQDN or IP of the server process to be connected to; if not set, the FQDN configured in `taos.cfg` is used --P：The port of the server process to connect to, the default value is 6030 --N：The number of packages that will be sent in the test, range is [1,10000], default value is 100 --l：The size of each package in bytes, range is [1024, 1024 \* 1024 \* 1024], default value is 1024 --S：The type of network packages to send, can be either TCP or UDP, default value is +-n：When set to "speed", it means testing network speed. +-h：The FQDN or IP of the server process to be connected to; if not set, the FQDN configured in `taos.cfg` is used. +-P：The port of the server process to connect to, the default value is 6030. +-N：The number of packages that will be sent in the test, range is [1,10000], default value is 100. +-l：The size of each package in bytes, range is [1024, 1024 \* 1024 \* 1024], default value is 1024. +-S：The type of network packages to send, can be either TCP or UDP, default value is TCP. ## FQDN Resolution Diagnostic `taos -n fqdn -h ` -From version 2.2.0.0, the above command can be executed on Linux Shell to test the resolution speed of FQDN. It can be used to try to resolve a FQDN to an IP address and record the time spent in this process. The parameters that can be used for this purpose are as below: +From version 2.2.0.0 onward, the above command can be executed in a Linux shell to test the resolution speed of FQDN. It can be used to try to resolve a FQDN to an IP address and record the time spent in this process. The parameters that can be used for this purpose are as below: --n：When set to "fqdn", it means testing the speed of resolving FQDN --h：The FQDN to be resolved. If not set, the `FQDN` parameter in `taos.cfg` is used by default. +-n：When set to "fqdn", it means testing the speed of resolving FQDN. +-h：The FQDN to be resolved. If not set, the `FQDN` parameter in `taos.cfg` is used by default. ## Server Log -The parameter `debugFlag` is used to control the log level of `taosd` server process. The default value is 131, for debug purpose it needs to be escalated to 135 or 143. +The parameter `debugFlag` is used to control the log level of the `taosd` server process. The default value is 131. For debugging and tracing, it needs to be set to either 135 or 143 respectively. -Once this parameter is set to 135 or 143, the log file grows very quickly especially when there is huge volume of data insertion and data query requests. If all the logs are stored together, some important information may be missed very easily, so on server side important information is stored at different place from other logs. +Once this parameter is set to 135 or 143, the log file grows very quickly especially when there is a huge volume of data insertion and data query requests. If all the logs are stored together, some important information may be missed very easily and so on the server side, important information is stored in a different place from other logs. - The log at level of INFO, WARNING and ERROR is stored in `taosinfo` so that it is easy to find important information - The log at level of DEBUG (135) and TRACE (143) and other information not handled by `taosinfo` are stored in `taosdlog` ## Client Log -An independent log file, named as "taoslog+" is generated for each client program, i.e. a client process. The default value of `debugFlag` is also 131 and only log at level of INFO/ERROR/WARNING is recorded, it and needs to be changed to 135 or 143 so that log at DEBUG or TRACE level can be recorded for debugging purpose. +An independent log file, named as "taoslog+" is generated for each client program, i.e. a client process. The default value of `debugFlag` is also 131 and only logs at level of INFO/ERROR/WARNING are recorded. As stated above, for debugging and tracing, it needs to be changed to 135 or 143 respectively, so that logs at DEBUG or TRACE level can be recorded. The maximum length of a single log file is controlled by parameter `numOfLogLines` and only 2 log files are kept for each `taosd` server process. -log file is written in async way to minimize the workload on disk, bu the penalty is that a few log lines may be lost in some extreme conditions. +Log files are written in an async way to minimize the workload on disk, but the trade off for performance is that a few log lines may be lost in some extreme conditions. diff --git a/docs-en/13-operation/index.md b/docs-en/13-operation/index.md index a9801c0390f294d6b39b1219cc4055149871ef9c..c64749c40e26f091e4a25e0238827ebceff4b069 100644 --- a/docs-en/13-operation/index.md +++ b/docs-en/13-operation/index.md @@ -2,7 +2,7 @@ title: Administration --- -This chapter is mainly written for system administrators, covering download, install/uninstall, data import/export, system monitoring, user management, connection management, etc. Capacity planning and system optimization are also covered. +This chapter is mainly written for system administrators. It covers download, install/uninstall, data import/export, system monitoring, user management, connection management, capacity planning and system optimization. ```mdx-code-block import DocCardList from '@theme/DocCardList'; diff --git a/docs-en/14-reference/02-rest-api/02-rest-api.mdx b/docs-en/14-reference/02-rest-api/02-rest-api.mdx index f405d551e530a37a5221e71a824f605fba0c0db9..990af861961e9daf4ac775462e21d6d9852d17c1 100644 --- a/docs-en/14-reference/02-rest-api/02-rest-api.mdx +++ b/docs-en/14-reference/02-rest-api/02-rest-api.mdx @@ -2,23 +2,23 @@ title: REST API --- -To support the development of various types of platforms, TDengine provides an API that conforms to the REST principle, namely REST API. To minimize the learning cost, different from the other database REST APIs, TDengine directly requests the SQL command contained in the request BODY through HTTP POST to operate the database and only requires a URL. +To support the development of various types of applications and platforms, TDengine provides an API that conforms to REST principles; namely REST API. To minimize the learning cost, unlike REST APIs for other database engines, TDengine allows insertion of SQL commands in the BODY of an HTTP POST request, to operate the database. :::note -One difference from the native connector is that the REST interface is stateless, so the `USE db_name` command has no effect. All references to table names and super table names need to specify the database name prefix. (Since version 2.2.0.0, it is supported to specify db_name in RESTful URL. If the database name prefix is not specified in the SQL command, the `db_name` specified in the URL will be used. Since version 2.4.0.0, REST service is provided by taosAdapter by default. And it requires that the `db_name` must be specified in the URL.) +One difference from the native connector is that the REST interface is stateless and so the `USE db_name` command has no effect. All references to table names and super table names need to specify the database name in the prefix. (Since version 2.2.0.0, TDengine supports specification of the db_name in RESTful URL. If the database name prefix is not specified in the SQL command, the `db_name` specified in the URL will be used. Since version 2.4.0.0, REST service is provided by taosAdapter by default and it requires that the `db_name` must be specified in the URL.) ::: ## Installation -The REST interface does not rely on any TDengine native library, so the client application does not need to install any TDengine libraries. The client application's development language supports the HTTP protocol is enough. +The REST interface does not rely on any TDengine native library, so the client application does not need to install any TDengine libraries. The client application's development language only needs to support the HTTP protocol. ## Verification If the TDengine server is already installed, it can be verified as follows: -The following is an Ubuntu environment using the `curl` tool (to confirm that it is installed) to verify that the REST interface is working. +The following example is in an Ubuntu environment and uses the `curl` tool to verify that the REST interface is working. Note that the `curl` tool may need to be installed in your environment. -The following example lists all databases, replacing `h1.taosdata.com` and `6041` (the default port) with the actual running TDengine service FQDN and port number. +The following example lists all databases on the host h1.taosdata.com. To use it in your environment, replace `h1.taosdata.com` and `6041` (the default port) with the actual running TDengine service FQDN and port number. ```html curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'show databases;' h1.taosdata.com:6041/rest/sql @@ -89,7 +89,7 @@ For example, `http://h1.taos.com:6041/rest/sql/test` is a URL to `h1.taos.com:60 TDengine supports both Basic authentication and custom authentication mechanisms, and subsequent versions will provide a standard secure digital signature mechanism for authentication. -- The custom authentication information is as follows (Let's introduce token later) +- The custom authentication information is as follows. More details about "token" later. ``` Authorization: Taosd @@ -136,7 +136,7 @@ The return result is in JSON format, as follows: Description: -- status: tell if the operation result is success or failure. +- status: tells you whethre the operation result is success or failure. - head: the definition of the table, or just one column "affected_rows" if no result set is returned. (As of version 2.0.17.0, it is recommended not to rely on the head return value to determine the data column type but rather use column_meta. In later versions, the head item may be removed from the return value.) - column_meta: this item is added to the return value to indicate the data type of each column in the data with version 2.0.17.0 and later versions. Each column is described by three values: column name, column type, and type length. For example, `["current",6,4]` means that the column name is "current", the column type is 6, which is the float type, and the type length is 4, which is the float type with 4 bytes. If the column type is binary or nchar, the type length indicates the maximum length of content stored in the column, not the length of the specific data in this return value. When the column type is nchar, the type length indicates the number of Unicode characters that can be saved, not bytes. - data: The exact data returned, presented row by row, or just [[affected_rows]] if no result set is returned. The order of the data columns in each row of data is the same as that of the data columns described in column_meta. diff --git a/docs-en/14-reference/03-connector/03-connector.mdx b/docs-en/14-reference/03-connector/03-connector.mdx index 6be914bdb4b701f478b6b8b27366d6ebb5a39ec8..44685579005c2cebd5e0194a10d457cd1199051e 100644 --- a/docs-en/14-reference/03-connector/03-connector.mdx +++ b/docs-en/14-reference/03-connector/03-connector.mdx @@ -4,7 +4,7 @@ title: Connector TDengine provides a rich set of APIs (application development interface). To facilitate users to develop their applications quickly, TDengine supports connectors for multiple programming languages, including official connectors for C/C++, Java, Python, Go, Node.js, C#, and Rust. These connectors support connecting to TDengine clusters using both native interfaces (taosc) and REST interfaces (not supported in a few languages yet). Community developers have also contributed several unofficial connectors, such as the ADO.NET connector, the Lua connector, and the PHP connector. -![image-connector](/img/connector.png) +![TDengine Database image-connector](./connector.webp) ## Supported platforms diff --git a/docs-en/14-reference/03-connector/connector.webp b/docs-en/14-reference/03-connector/connector.webp new file mode 100644 index 0000000000000000000000000000000000000000..040cf5c26c726b345b2e0e5363dd3c677bec61be Binary files /dev/null and b/docs-en/14-reference/03-connector/connector.webp differ diff --git a/docs-en/14-reference/03-connector/cpp.mdx b/docs-en/14-reference/03-connector/cpp.mdx index 4b388d32a9050645e268bb267d16e9a5b8aa4bda..17d864dd7268537a21b4d77fd2d4805fee14d063 100644 --- a/docs-en/14-reference/03-connector/cpp.mdx +++ b/docs-en/14-reference/03-connector/cpp.mdx @@ -4,7 +4,7 @@ sidebar_label: C/C++ title: C/C++ Connector --- -C/C++ developers can use TDengine's client driver and the C/C++ connector, to develop their applications to connect to TDengine clusters for data writing, querying, and other functions. To use it, you need to include the TDengine header file _taos.h_, which lists the function prototypes of the provided APIs; the application also needs to link to the corresponding dynamic libraries on the platform where it is located. +C/C++ developers can use TDengine's client driver and the C/C++ connector, to develop their applications to connect to TDengine clusters for data writing, querying, and other functions. To use the C/C++ connector you must include the TDengine header file _taos.h_, which lists the function prototypes of the provided APIs. The application also needs to link to the corresponding dynamic libraries on the platform where it is located. ```c #include @@ -26,7 +26,7 @@ Please refer to [list of supported platforms](/reference/connector#supported-pla ## Supported versions -The version number of the TDengine client driver and the version number of the TDengine server require one-to-one correspondence and recommend using the same version of client driver as what the TDengine server version is. Although a lower version of the client driver is compatible to work with a higher version of the server, if the first three version numbers are the same (i.e., only the fourth version number is different), but it is not recommended. It is strongly discouraged to use a higher version of the client driver to access a lower version of the TDengine server. +The version number of the TDengine client driver and the version number of the TDengine server should be same. A lower version of the client driver is compatible with a higher version of the server, if the first three version numbers are the same (i.e., only the fourth version number is different). For e.g. if the client version is x.y.z.1 and the server version is x.y.z.2 the client and server are compatible. But in general we do not recommend using a lower client version with a newer server version. It is also strongly discouraged to use a higher version of the client driver to access a lower version of the TDengine server. ## Installation steps @@ -55,7 +55,7 @@ In the above example code, `taos_connect()` establishes a connection to port 603 :::note -- If not specified, when the return value of the API is an integer, _0_ means success, the others are error codes representing the reason for failure, and when the return value is a pointer, _NULL_ means failure. +- If not specified, when the return value of the API is an integer, _0_ means success. All others are error codes representing the reason for failure. When the return value is a pointer, _NULL_ means failure. - All error codes and their corresponding causes are described in the `taoserror.h` file. ::: @@ -140,13 +140,12 @@ The base API is used to do things like create database connections and provide a - `void taos_cleanup()` - Clean up the runtime environment and should be called before the application exits. + Cleans up the runtime environment and should be called before the application exits. - ` int taos_options(TSDB_OPTION option, const void * arg, ...) ` Set client options, currently supports region setting (`TSDB_OPTION_LOCALE`), character set -(`TSDB_OPTION_CHARSET`), time zone -(`TSDB_OPTION_TIMEZONE`), configuration file path (`TSDB_OPTION_CONFIGDIR`) . The region setting, character set, and time zone default to the current settings of the operating system. +(`TSDB_OPTION_CHARSET`), time zone (`TSDB_OPTION_TIMEZONE`), configuration file path (`TSDB_OPTION_CONFIGDIR`). The region setting, character set, and time zone default to the current settings of the operating system. - `char *taos_get_client_info()` @@ -159,7 +158,7 @@ The base API is used to do things like create database connections and provide a - host: FQDN of any node in the TDengine cluster - user: user name - pass: password - - db: database name, if the user does not provide, it can also be connected correctly, the user can create a new database through this connection, if the user provides the database name, it means that the database user has already created, the default use of the database + - db: the database name. Even if the user does not provide this, the connection will still work correctly. The user can create a new database through this connection. If the user provides the database name, it means that the database has already been created and the connection can be used for regular operations on the database. - port: the port the taosd program is listening on NULL indicates a failure. The application needs to save the returned parameters for subsequent use. @@ -187,7 +186,7 @@ The APIs described in this subsection are all synchronous interfaces. After bein - `TAOS_RES* taos_query(TAOS *taos, const char *sql)` - Executes an SQL command, either a DQL, DML, or DDL statement. The `taos` parameter is a handle obtained with `taos_connect()`. You can't tell if the result failed by whether the return value is `NULL`, but by parsing the error code in the result set with the `taos_errno()` function. + Executes an SQL command, either a DQL, DML, or DDL statement. The `taos` parameter is a handle obtained with `taos_connect()`. If the return value is `NULL` this does not necessarily indicate a failure. You can get the error code, if any, by parsing the error code in the result set with the `taos_errno()` function. - `int taos_result_precision(TAOS_RES *res)` @@ -231,7 +230,7 @@ typedef struct taosField { - ` void taos_free_result(TAOS_RES *res)` - Frees the query result set and the associated resources. Be sure to call this API to free the resources after the query is completed. Otherwise, it may lead to a memory leak in the application. However, note that the application will crash if you call a function like `taos_consume()` to get the query results after freeing the resources. + Frees the query result set and the associated resources. Be sure to call this API to free the resources after the query is completed. Failing to call this, may lead to a memory leak in the application. However, note that the application will crash if you call a function like `taos_consume()` to get the query results after freeing the resources. - `char *taos_errstr(TAOS_RES *res)` @@ -242,7 +241,7 @@ typedef struct taosField { Get the reason for the last API call failure. The return value is the error code. :::note -TDengine version 2.0 and above recommends that each thread of a database application create a separate connection or a connection pool based on threads. It is not recommended to pass the connection (TAOS\*) structure to different threads for shared use in the application. Queries, writes, etc., issued based on TAOS structures are multi-thread safe, but state quantities such as "USE statement" may interfere between threads. In addition, the C connector can dynamically create new database-oriented connections on demand (this procedure is not visible to the user), and it is recommended that `taos_close()` be called only at the final exit of the program to close the connection. +TDengine version 2.0 and above recommends that each thread of a database application create a separate connection or a connection pool based on threads. It is not recommended to pass the connection (TAOS\*) structure to different threads for shared use in the application. Queries, writes, and other operations issued that are based on TAOS structures are multi-thread safe, but state quantities such as the "USE statement" may interfere between threads. In addition, the C connector can dynamically create new database-oriented connections on demand (this procedure is not visible to the user), and it is recommended that `taos_close()` be called only at the final exit of the program to close the connection. ::: @@ -274,12 +273,12 @@ All TDengine's asynchronous APIs use a non-blocking call pattern. Applications c ### Parameter Binding API -In addition to direct calls to `taos_query()` to perform queries, TDengine also provides a set of `bind` APIs that supports parameter binding, similar in style to MySQL, and currently only supports using a question mark `? ` to represent the parameter to be bound. +In addition to direct calls to `taos_query()` to perform queries, TDengine also provides a set of `bind` APIs that supports parameter binding, similar in style to MySQL. TDengine currently only supports using a question mark `? ` to represent the parameter to be bound. -Starting with versions 2.1.1.0 and 2.1.2.0, TDengine has significantly improved the bind APIs to support for data writing (INSERT) scenarios. This avoids the resource consumption of SQL syntax parsing when writing data through the parameter binding interface, thus significantly improving write performance in most cases. A typical operation, in this case, is as follows. +Starting with versions 2.1.1.0 and 2.1.2.0, TDengine has significantly improved the bind APIs to support data writing (INSERT) scenarios. This avoids the resource consumption of SQL syntax parsing when writing data through the parameter binding interface, thus significantly improving write performance in most cases. A typical operation, in this case, is as follows. 1. call `taos_stmt_init()` to create the parameter binding object. -2. call `taos_stmt_prepare()` to parse the INSERT statement. 3. +2. call `taos_stmt_prepare()` to parse the INSERT statement. 3. call `taos_stmt_set_tbname()` to set the table name if it is reserved in the INSERT statement but not the TAGS. 4. call `taos_stmt_set_tbname_tags()` to set the table name and TAGS values if the table name and TAGS are reserved in the INSERT statement (for example, if the INSERT statement takes an automatic table build). 5. call `taos_stmt_bind_param_batch()` to set the value of VALUES in multiple columns, or call `taos_stmt_bind_param()` to set the value of VALUES in a single row. @@ -383,7 +382,7 @@ In addition to writing data using the SQL method or the parameter binding API, w **return value** TAOS_RES structure, application can get error message by using `taos_errstr()` and also error code by using `taos_errno()`. In some cases, the returned TAOS_RES is `NULL`, and it is still possible to call `taos_errno()` to safely get the error code information. - The returned TAOS_RES needs to be freed by the caller. Otherwise, a memory leak will occur. + The returned TAOS_RES needs to be freed by the caller in order to avoid memory leaks. **Description** The protocol type is enumerated and contains the following three formats. @@ -416,13 +415,13 @@ The Subscription API currently supports subscribing to one or more tables and co This function is responsible for starting the subscription service, returning the subscription object on success and `NULL` on failure, with the following parameters. - - taos: the database connection that has been established - - restart: if the subscription already exists, whether to restart or continue the previous subscription - - topic: the topic of the subscription (i.e., the name). This parameter is the unique identifier of the subscription - - sql: the query statement of the subscription, this statement can only be _select_ statement, only the original data should be queried, only the data can be queried in time order - - fp: the callback function when the query result is received (the function prototype will be introduced later), only used when called asynchronously. This parameter should be passed `NULL` when called synchronously - - param: additional parameter when calling the callback function, the system API will pass it to the callback function as it is, without any processing - - interval: polling period in milliseconds. The callback function will be called periodically according to this parameter when called asynchronously. not recommended to set this parameter too small To avoid impact on system performance when called synchronously. If the interval between two calls to `taos_consume()` is less than this period, the API will block until the interval exceeds this period. + - taos: the database connection that has been established. + - restart: if the subscription already exists, whether to restart or continue the previous subscription. + - topic: the topic of the subscription (i.e., the name). This parameter is the unique identifier of the subscription. + - sql: the query statement of the subscription which can only be a _select_ statement. Only the original data should be queried, and data can only be queried in temporal order. + - fp: the callback function when the query result is received only used when called asynchronously. This parameter should be passed `NULL` when called synchronously. The function prototype is described below. + - param: additional parameter when calling the callback function. The system API will pass it to the callback function as is, without any processing. + - interval: polling period in milliseconds. The callback function will be called periodically according to this parameter when called asynchronously. The interval should not be too small to avoid impact on system performance when called synchronously. If the interval between two calls to `taos_consume()` is less than this period, the API will block until the interval exceeds this period. - ` typedef void (*TAOS_SUBSCRIBE_CALLBACK)(TAOS_SUB* tsub, TAOS_RES *res, void* param, int code)` diff --git a/docs-en/14-reference/03-connector/csharp.mdx b/docs-en/14-reference/03-connector/csharp.mdx index 2969392a0594ff0705e88bede5be90fb9dfd646d..2d1b62fe89c542280c4264dd478538fa00634c79 100644 --- a/docs-en/14-reference/03-connector/csharp.mdx +++ b/docs-en/14-reference/03-connector/csharp.mdx @@ -48,7 +48,7 @@ Please refer to [version support list](/reference/connector#version-support) * Install the [.NET SDK](https://dotnet.microsoft.com/download) * [Nuget Client](https://docs.microsoft.com/en-us/nuget/install-nuget-client-tools) (optional installation) -* Install TDengine client driver, please refer to [Install client driver](/reference/connector#Install client driver) for details +* Install TDengine client driver, please refer to [Install client driver](/reference/connector/#install-client-driver) for details ### Install via dotnet CLI @@ -57,7 +57,7 @@ Please refer to [version support list](/reference/connector#version-support) You can reference the `TDengine.Connector` published in Nuget to the current project via the `dotnet` command under the path of the existing .NET project. -``` bash +``` dotnet add package TDengine.Connector ``` @@ -66,7 +66,7 @@ dotnet add package TDengine.Connector You can download TDengine's source code and directly reference the latest version of the TDengine.Connector library -```bash +``` git clone https://github.com/taosdata/TDengine.git cd TDengine/src/connector/C#/src/ cp -r TDengineDriver/ myProject @@ -79,7 +79,7 @@ dotnet add TDengineDriver/TDengineDriver.csproj ## Create a connection -``` C# +```csharp using TDengineDriver; namespace TDengineExample @@ -179,9 +179,9 @@ namespace TDengineExample 1. "Unable to establish connection", "Unable to resolve FQDN" - Usually, it cause by the FQDN configuration is incorrect, you can refer to [How to understand TDengine's FQDN (Chinese)](https://www.taosdata.com/blog/2021/07/29/2741.html) to solve it. 2. + Usually, it's caused by an incorrect FQDN configuration. Please refer to this section in the [FAQ](https://docs.tdengine.com/2.4/train-faq/faq/#2-how-to-handle-unable-to-establish-connection) to troubleshoot. -Unhandled exception. System.DllNotFoundException: Unable to load DLL 'taos' or one of its dependencies: The specified module cannot be found. +2. Unhandled exception. System.DllNotFoundException: Unable to load DLL 'taos' or one of its dependencies: The specified module cannot be found. This is usually because the program did not find the dependent client driver. The solution is to copy `C:\TDengine\driver\taos.dll` to the `C:\Windows\System32\` directory on Windows, and create the following soft link on Linux `ln -s /usr/local/taos/driver/libtaos.so.x.x .x.x /usr/lib/libtaos.so` will work. diff --git a/docs-en/14-reference/03-connector/go.mdx b/docs-en/14-reference/03-connector/go.mdx index fd5930f07ff7184bd8dd5ff19cd3860f9718eaf9..8a05f2d841bbcdbab2bdb7471691ca0ae49a4f6b 100644 --- a/docs-en/14-reference/03-connector/go.mdx +++ b/docs-en/14-reference/03-connector/go.mdx @@ -15,9 +15,9 @@ import GoOpenTSDBTelnet from "../../07-develop/03-insert-data/_go_opts_telnet.md import GoOpenTSDBJson from "../../07-develop/03-insert-data/_go_opts_json.mdx" import GoQuery from "../../07-develop/04-query-data/_go.mdx" -`driver-go` is the official Go language connector for TDengine, which implements the interface to the Go language [database/sql](https://golang.org/pkg/database/sql/) package. Go developers can use it to develop applications that access TDengine cluster data. +`driver-go` is the official Go language connector for TDengine. It implements the [database/sql](https://golang.org/pkg/database/sql/) package, the generic Go language interface to SQL databases. Go developers can use it to develop applications that access TDengine cluster data. -`driver-go` provides two ways to establish connections. One is **native connection**, which connects to TDengine instances natively through the TDengine client driver (taosc), supporting data writing, querying, subscriptions, schemaless writing, and bind interface. The other is the **REST connection**, which connects to TDengine instances via the REST interface provided by taosAdapter. The set of features implemented by the REST connection differs slightly from the native connection. +`driver-go` provides two ways to establish connections. One is **native connection**, which connects to TDengine instances natively through the TDengine client driver (taosc), supporting data writing, querying, subscriptions, schemaless writing, and bind interface. The other is the **REST connection**, which connects to TDengine instances via the REST interface provided by taosAdapter. The set of features implemented by the REST connection differs slightly from those implemented by the native connection. This article describes how to install `driver-go` and connect to TDengine clusters and perform basic operations such as data query and data writing through `driver-go`. @@ -55,25 +55,27 @@ A "REST connection" is a connection between the application and the TDengine ins ### Pre-installation -* Install Go development environment (Go 1.14 and above, GCC 4.8.5 and above) -* If you use the native connector, please install the TDengine client driver. Please refer to [Install Client Driver](/reference/connector#Install Client Driver) for specific steps +- Install Go development environment (Go 1.14 and above, GCC 4.8.5 and above) +- If you use the native connector, please install the TDengine client driver. Please refer to [Install Client Driver](/reference/connector/#install-client-driver) for specific steps Configure the environment variables and check the command. -* ```go env`` -* ```gcc -v`` +* `go env` +* `gcc -v` ### Use go get to install -``go get -u github.com/taosdata/driver-go/v2@develop`` +``` +go get -u github.com/taosdata/driver-go/v2@develop +``` ### Manage with go mod 1. Initialize the project with the `go mod` command. - ``text + ```text go mod init taos-demo - ``` text + ``` 2. Introduce taosSql @@ -88,7 +90,7 @@ Configure the environment variables and check the command. ```text go mod tidy - ``` 4. + ``` 4. Run the program with `go run taos-demo` or compile the binary with the `go build` command. @@ -213,7 +215,7 @@ func main() { Since the REST interface is stateless, the `use db` syntax will not work. You need to put the db name into the SQL command, e.g. `create table if not exists tb1 (ts timestamp, a int)` to `create table if not exists test.tb1 (ts timestamp, a int)` otherwise it will report the error `[0x217] Database not specified or available`. -You can also put the db name in the DSN by changing `root:taosdata@http(localhost:6041)/` to `root:taosdata@http(localhost:6041)/test`. This method is supported by taosAdapter in TDengine 2.4.0.5. is supported since TDengine 2.4.0.5. Executing the `create database` statement when the specified db does not exist will not report an error while executing other queries or writing against that db will report an error. +You can also put the db name in the DSN by changing `root:taosdata@http(localhost:6041)/` to `root:taosdata@http(localhost:6041)/test`. This method is supported by taosAdapter since TDengine 2.4.0.5. Executing the `create database` statement when the specified db does not exist will not report an error while executing other queries or writing against that db will report an error. The complete example is as follows. @@ -289,7 +291,7 @@ func main() { 6. `readBufferSize` parameter has no significant effect after being increased - If you increase `readBufferSize` will reduce the number of `syscall` calls when fetching results. If the query result is smaller, modifying this parameter will not improve significantly. If you increase the parameter value too much, the bottleneck will be parsing JSON data. If you need to optimize the query speed, you must adjust the value according to the actual situation to achieve the best query result. + Increasing `readBufferSize` will reduce the number of `syscall` calls when fetching results. If the query result is smaller, modifying this parameter will not improve performance significantly. If you increase the parameter value too much, the bottleneck will be parsing JSON data. If you need to optimize the query speed, you must adjust the value based on the actual situation to achieve the best query performance. 7. `disableCompression` parameter is set to `false` when the query efficiency is reduced @@ -309,6 +311,7 @@ func main() { :::info This API is created successfully without checking permissions, but only when you execute a Query or Exec, and check if user/password/host/port is legal. + ::: * `func (db *DB) Exec(query string, args . .interface{}) (Result, error)` diff --git a/docs-en/14-reference/03-connector/java.mdx b/docs-en/14-reference/03-connector/java.mdx index 328907c4d781bdea8d30623e01d431cedbf8d0fa..ff15acf1a9c5dbfd74e6f3101459cfc7bdeda515 100644 --- a/docs-en/14-reference/03-connector/java.mdx +++ b/docs-en/14-reference/03-connector/java.mdx @@ -9,19 +9,19 @@ description: TDengine Java based on JDBC API and provide both native and REST co import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -'taos-jdbcdriver' is TDengine's official Java language connector, which allows Java developers to develop applications that access the TDengine database. 'taos-jdbcdriver' implements the interface of the JDBC driver standard and provides two forms of connectors. One is to connect to a TDengine instance natively through the TDengine client driver (taosc), which supports functions including data writing, querying, subscription, schemaless writing, and bind interface. And the other is to connect to a TDengine instance through the REST interface provided by taosAdapter (2.4.0.0 and later). REST connections implement has a slight differences to compare the set of features implemented and native connections. +'taos-jdbcdriver' is TDengine's official Java language connector, which allows Java developers to develop applications that access the TDengine database. 'taos-jdbcdriver' implements the interface of the JDBC driver standard and provides two forms of connectors. One is to connect to a TDengine instance natively through the TDengine client driver (taosc), which supports functions including data writing, querying, subscription, schemaless writing, and bind interface. And the other is to connect to a TDengine instance through the REST interface provided by taosAdapter (2.4.0.0 and later). The implementation of the REST connection and those of the native connections have slight differences in features. -![tdengine-connector](tdengine-jdbc-connector.png) +![TDengine Database tdengine-connector](tdengine-jdbc-connector.webp) The preceding diagram shows two ways for a Java app to access TDengine via connector: - JDBC native connection: Java applications use TSDBDriver on physical node 1 (pnode1) to call client-driven directly (`libtaos.so` or `taos.dll`) APIs to send writing and query requests to taosd instances located on physical node 2 (pnode2). -- JDBC REST connection: The Java application encapsulates the SQL as a REST request via RestfulDriver, sends it to the REST server of physical node 2 (taosAdapter), requests TDengine server through the REST server, and returns the result. +- JDBC REST connection: The Java application encapsulates the SQL as a REST request via RestfulDriver, sends it to the REST server (taosAdapter) on physical node 2. taosAdapter forwards the request to TDengine server and returns the result. -Using REST connection, which does not rely on TDengine client drivers.It can be cross-platform more convenient and flexible but introduce about 30% lower performance than native connection. +The REST connection, which does not rely on TDengine client drivers, is more convenient and flexible, in addition to being cross-platform. However the performance is about 30% lower than that of the native connection. :::info -TDengine's JDBC driver implementation is as consistent as possible with the relational database driver. Still, there are differences in the use scenarios and technical characteristics of TDengine and relational object databases, so 'taos-jdbcdriver' also has some differences from traditional JDBC drivers. You need to pay attention to the following points when using: +TDengine's JDBC driver implementation is as consistent as possible with the relational database driver. Still, there are differences in the use scenarios and technical characteristics of TDengine and relational object databases. So 'taos-jdbcdriver' also has some differences from traditional JDBC drivers. It is important to keep the following points in mind: - TDengine does not currently support delete operations for individual data records. - Transactional operations are not currently supported. @@ -42,18 +42,18 @@ Please refer to [Version Support List](/reference/connector#version-support). TDengine currently supports timestamp, number, character, Boolean type, and the corresponding type conversion with Java is as follows: | TDengine DataType | JDBCType (driver version < 2.0.24) | JDBCType (driver version > = 2.0.24) | -| ----------------- | --------------------------------- | ---------------------------------- | -| TIMESTAMP | java.lang.Long | java.sql.Timestamp | -| INT | java.lang.Integer | java.lang.Integer | -| BIGINT | java.lang.Long | java.lang.Long | -| FLOAT | java.lang.Float | java.lang.Float | -| DOUBLE | java.lang.Double | java.lang.Double | -| SMALLINT | java.lang.Short | java.lang.Short | -| TINYINT | java.lang.Byte | java.lang.Byte | -| BOOL | java.lang.Boolean | java.lang.Boolean | -| BINARY | java.lang.String | byte array | -| NCHAR | java.lang.String | java.lang.String | -| JSON | - | java.lang.String | +| ----------------- | ---------------------------------- | ------------------------------------ | +| TIMESTAMP | java.lang.Long | java.sql.Timestamp | +| INT | java.lang.Integer | java.lang.Integer | +| BIGINT | java.lang.Long | java.lang.Long | +| FLOAT | java.lang.Float | java.lang.Float | +| DOUBLE | java.lang.Double | java.lang.Double | +| SMALLINT | java.lang.Short | java.lang.Short | +| TINYINT | java.lang.Byte | java.lang.Byte | +| BOOL | java.lang.Boolean | java.lang.Boolean | +| BINARY | java.lang.String | byte array | +| NCHAR | java.lang.String | java.lang.String | +| JSON | - | java.lang.String | **Note**: Only TAG supports JSON types @@ -69,7 +69,7 @@ Before using Java Connector to connect to the database, the following conditions ### Install the connectors - + - [sonatype](https://search.maven.org/artifact/com.taosdata.jdbc/taos-jdbcdriver) - [mvnrepository](https://mvnrepository.com/artifact/com.taosdata.jdbc/taos-jdbcdriver) @@ -77,7 +77,7 @@ Before using Java Connector to connect to the database, the following conditions Add following dependency in the `pom.xml` file of your Maven project: -```xml-dtd +```xml com.taosdata.jdbc taos-jdbcdriver @@ -88,15 +88,15 @@ Add following dependency in the `pom.xml` file of your Maven project: -You can build Java connector from source code after clone TDengine project: +You can build Java connector from source code after cloning the TDengine project: -```shell -git clone https://github.com/taosdata/TDengine.git -cd TDengine/src/connector/jdbc +``` +git clone https://github.com/taosdata/taos-connector-jdbc.git +cd taos-connector-jdbc mvn clean install -Dmaven.test.skip=true ``` -After compilation, a jar package of taos-jdbcdriver-2.0.XX-dist .jar is generated in the target directory, and the compiled jar file is automatically placed in the local Maven repository. +After compilation, a jar package named taos-jdbcdriver-2.0.XX-dist.jar is generated in the target directory, and the compiled jar file is automatically placed in the local Maven repository. @@ -140,40 +140,43 @@ When you use a JDBC native connection to connect to a TDengine cluster, you can 1. Do not specify hostname and port in Java applications. -```java -public Connection getConn() throws Exception{ - Class.forName("com.taosdata.jdbc.TSDBDriver"); - String jdbcUrl = "jdbc:TAOS://:/test?user=root&password=taosdata"; - Properties connProps = new Properties(); - connProps.setProperty(TSDBDriver.PROPERTY_KEY_CHARSET, "UTF-8"); - connProps.setProperty(TSDBDriver.PROPERTY_KEY_LOCALE, "en_US.UTF-8"); - connProps.setProperty(TSDBDriver.PROPERTY_KEY_TIME_ZONE, "UTC-8"); - Connection conn = DriverManager.getConnection(jdbcUrl, connProps); - return conn; -} -``` + ```java + public Connection getConn() throws Exception{ + Class.forName("com.taosdata.jdbc.TSDBDriver"); + String jdbcUrl = "jdbc:TAOS://:/test?user=root&password=taosdata"; + Properties connProps = new Properties(); + connProps.setProperty(TSDBDriver.PROPERTY_KEY_CHARSET, "UTF-8"); + connProps.setProperty(TSDBDriver.PROPERTY_KEY_LOCALE, "en_US.UTF-8"); + connProps.setProperty(TSDBDriver.PROPERTY_KEY_TIME_ZONE, "UTC-8"); + Connection conn = DriverManager.getConnection(jdbcUrl, connProps); + return conn; + } + ``` 2. specify the firstEp and the secondEp in the configuration file taos.cfg -```shell -# first fully qualified domain name (FQDN) for TDengine system -firstEp cluster_node1:6030 + ```shell + # first fully qualified domain name (FQDN) for TDengine system + firstEp cluster_node1:6030 -# second fully qualified domain name (FQDN) for TDengine system, for cluster only -secondEp cluster_node2:6030 + # second fully qualified domain name (FQDN) for TDengine system, for cluster only + secondEp cluster_node2:6030 -# default system charset -# charset UTF-8 + # default system charset + # charset UTF-8 -# system locale -# locale en_US.UTF-8 -``` + # system locale + # locale en_US.UTF-8 + ``` In the above example, JDBC uses the client's configuration file to establish a connection to a hostname `cluster_node1`, port 6030, and a database named `test`. When the firstEp node in the cluster fails, JDBC attempts to connect to the cluster using secondEp. In TDengine, as long as one node in firstEp and secondEp is valid, the connection to the cluster can be established normally. -> **Note**: The configuration file here refers to the configuration file on the machine where the application that calls the JDBC Connector is located, the default path is `/etc/taos/taos.cfg` on Linux, and the default path is `C://TDengine/cfg/taos.cfg` on Windows. +:::note +The configuration file here refers to the configuration file on the machine where the application that calls the JDBC Connector is located, the default path is `/etc/taos/taos.cfg` on Linux, and the default path is `C://TDengine/cfg/taos.cfg` on Windows. + +::: @@ -186,7 +189,7 @@ Connection conn = DriverManager.getConnection(jdbcUrl); In the above example, a RestfulDriver with a JDBC REST connection is used to establish a connection to a database named `test` with hostname `taosdemo.com` on port `6041`. The URL specifies the user name as `root` and the password as `taosdata`. -There is no dependency on the client driver when Using a JDBC REST connection. Compared to a JDBC native connection, only the following are required: 1. +There is no dependency on the client driver when Using a JDBC REST connection. Compared to a JDBC native connection, only the following are required: 1. driverClass specified as "com.taosdata.jdbc.rs.RestfulDriver". 2. jdbcUrl starting with "jdbc:TAOS-RS://". @@ -197,6 +200,7 @@ The configuration parameters in the URL are as follows. - user: Login TDengine user name, default value 'root'. - password: user login password, default value 'taosdata'. - batchfetch: true: pull the result set in batch when executing the query; false: pull the result set row by row. The default value is false. batchfetch uses HTTP for data transfer. The JDBC REST connection supports bulk data pulling function in taos-jdbcdriver-2.0.38 and TDengine 2.4.0.12 and later versions. taos-jdbcdriver and TDengine transfer data via WebSocket connection. Compared with HTTP, WebSocket enables JDBC REST connection to support large data volume querying and improve query performance. +- charset: specify the charset to parse the string, this parameter is valid only when set batchfetch to true. - batchErrorIgnore: true: when executing executeBatch of Statement, if one SQL execution fails in the middle, continue to execute the following SQL. false: no longer execute any statement after the failed SQL. The default value is: false. **Note**: Some configuration items (e.g., locale, timezone) do not work in the REST connection. @@ -206,10 +210,10 @@ The configuration parameters in the URL are as follows. - Unlike the native connection method, the REST interface is stateless. When using the JDBC REST connection, you need to specify the database name of the table and super table in SQL. For example. ```sql -INSERT INTO test.t1 USING test.weather (ts, temperature) TAGS('beijing') VALUES(now, 24.6); +INSERT INTO test.t1 USING test.weather (ts, temperature) TAGS('California.SanFrancisco') VALUES(now, 24.6); ``` -- Starting from taos-jdbcdriver-2.0.36 and TDengine 2.2.0.0, if dbname is specified in the URL, JDBC REST connections will use `/rest/sql/dbname` as the URL for REST requests by default, and there is no need to specify dbname in SQL. For example, if the URL is `jdbc:TAOS-RS://127.0.0.1:6041/test`, then the SQL can be executed: insert into t1 using weather(ts, temperature) tags('beijing') values(now, 24.6); +- Starting from taos-jdbcdriver-2.0.36 and TDengine 2.2.0.0, if dbname is specified in the URL, JDBC REST connections will use `/rest/sql/dbname` as the URL for REST requests by default, and there is no need to specify dbname in SQL. For example, if the URL is `jdbc:TAOS-RS://127.0.0.1:6041/test`, then the SQL can be executed: insert into test using weather(ts, temperature) tags('California.SanFrancisco') values(now, 24.6); ::: @@ -258,10 +262,10 @@ The configuration parameters in properties are as follows. - TSDBDriver.PROPERTY_KEY_BATCH_LOAD: true: pull the result set in batch when executing query; false: pull the result set row by row. The default value is: false. - TSDBDriver.PROPERTY_KEY_BATCH_ERROR_IGNORE: true: when executing executeBatch of Statement, if there is a SQL execution failure in the middle, continue to execute the following sq. false: no longer execute any statement after the failed SQL. The default value is: false. - TSDBDriver.PROPERTY_KEY_CONFIG_DIR: Only works when using JDBC native connection. Client configuration file directory path, default value `/etc/taos` on Linux OS, default value `C:/TDengine/cfg` on Windows OS. -- TSDBDriver.PROPERTY_KEY_CHARSET: takes effect only when using JDBC native connection. In the character set used by the client, the default value is the system character set. +- TSDBDriver.PROPERTY_KEY_CHARSET: In the character set used by the client, the default value is the system character set. - TSDBDriver.PROPERTY_KEY_LOCALE: this only takes effect when using JDBC native connection. Client language environment, the default value is system current locale. - TSDBDriver.PROPERTY_KEY_TIME_ZONE: only takes effect when using JDBC native connection. In the time zone used by the client, the default value is the system's current time zone. -For JDBC native connections, you can specify other parameters, such as log level, SQL length, etc., by specifying URL and Properties. For more detailed configuration, please refer to [Client Configuration](/reference/config/#Client-Only). + For JDBC native connections, you can specify other parameters, such as log level, SQL length, etc., by specifying URL and Properties. For more detailed configuration, please refer to [Client Configuration](/reference/config/#Client-Only). ### Priority of configuration parameters @@ -271,7 +275,7 @@ If the configuration parameters are duplicated in the URL, Properties, or client 2. Properties connProps 3. the configuration file taos.cfg of the TDengine client driver when using a native connection -For example, if you specify the password as `taosdata` in the URL and specify the password as `taosdemo` in the Properties simultaneously. In this case, JDBC will use the password in the URL to establish the connection. +For example, if you specify the password as `taosdata` in the URL and specify the password as `taosdemo` in the Properties simultaneously, JDBC will use the password in the URL to establish the connection. ## Usage examples @@ -323,7 +327,7 @@ while(resultSet.next()){ } ``` -> The query is consistent with operating a relational database. When using subscripts to get the contents of the returned fields, starting from 1, it is recommended to use the field names to get them. +> The query is consistent with operating a relational database. When using subscripts to get the contents of the returned fields, you have to start from 1. However, we recommend using the field names to get the values of the fields in the result set. ### Handling exceptions @@ -350,7 +354,7 @@ There are three types of error codes that the JDBC connector can report: For specific error codes, please refer to. -- [TDengine Java Connector](https://github.com/taosdata/TDengine/blob/develop/src/connector/jdbc/src/main/java/com/taosdata/jdbc/TSDBErrorNumbers.java) +- [TDengine Java Connector](https://github.com/taosdata/taos-connector-jdbc/blob/main/src/main/java/com/taosdata/jdbc/TSDBErrorNumbers.java) - [TDengine_ERROR_CODE](https://github.com/taosdata/TDengine/blob/develop/src/inc/taoserror.h) ### Writing data via parameter binding @@ -565,7 +569,7 @@ public class ParameterBindingDemo { // set table name pstmt.setTableName("t5_" + i); // set tags - pstmt.setTagNString(0, "Beijing-abc"); + pstmt.setTagNString(0, "California-abc"); // set columns ArrayList tsList = new ArrayList<>(); @@ -576,7 +580,7 @@ public class ParameterBindingDemo { ArrayList f1List = new ArrayList<>(); for (int j = 0; j < numOfRow; j++) { - f1List.add("Beijing-abc"); + f1List.add("California-abc"); } pstmt.setNString(1, f1List, BINARY_COLUMN_SIZE); @@ -623,7 +627,7 @@ public void setNString(int columnIndex, ArrayList list, int size) throws ### Schemaless Writing -Starting with version 2.2.0.0, TDengine has added the ability to schemaless writing. It is compatible with InfluxDB's Line Protocol, OpenTSDB's telnet line protocol, and OpenTSDB's JSON format protocol. See [schemaless writing](/reference/schemaless/) for details. +Starting with version 2.2.0.0, TDengine has added the ability to perform schemaless writing. It is compatible with InfluxDB's Line Protocol, OpenTSDB's telnet line protocol, and OpenTSDB's JSON format protocol. See [schemaless writing](/reference/schemaless/) for details. **Note**. @@ -635,7 +639,7 @@ public class SchemalessInsertTest { private static final String host = "127.0.0.1"; private static final String lineDemo = "st,t1=3i64,t2=4f64,t3=\"t3\" c1=3i64,c3=L\"passit\",c2=false,c4=4f64 1626006833639000000"; private static final String telnetDemo = "stb0_0 1626006833 4 host=host0 interface=eth0"; - private static final String jsonDemo = "{\"metric\": \"meter_current\",\"timestamp\": 1346846400,\"value\": 10.3, \"tags\": {\"groupid\": 2, \"location\": \"Beijing\", \"id\": \"d1001\"}}"; + private static final String jsonDemo = "{\"metric\": \"meter_current\",\"timestamp\": 1346846400,\"value\": 10.3, \"tags\": {\"groupid\": 2, \"location\": \"California.SanFrancisco\", \"id\": \"d1001\"}}"; public static void main(String[] args) throws SQLException { final String url = "jdbc:TAOS://" + host + ":6030/?user=root&password=taosdata"; @@ -666,16 +670,16 @@ The TDengine Java Connector supports subscription functionality with the followi #### Create subscriptions ```java -TSDBSubscribe sub = ((TSDBConnection)conn).subscribe("topic", "select * from meters", false); +TSDBSubscribe sub = ((TSDBConnection)conn).subscribe("topicname", "select * from meters", false); ``` The three parameters of the `subscribe()` method have the following meanings. -- topic: the subscribed topic (i.e., name). This parameter is the unique identifier of the subscription -- sql: the query statement of the subscription, this statement can only be `select` statement, only the original data should be queried, and you can query only the data in the positive time order +- topicname: the name of the subscribed topic. This parameter is the unique identifier of the subscription. +- sql: the query statement of the subscription. This statement can only be a `select` statement. Only original data can be queried, and you can query the data only temporal order. - restart: if the subscription already exists, whether to restart or continue the previous subscription -The above example will use the SQL command `select * from meters` to create a subscription named `topic`. If the subscription exists, it will continue the progress of the previous query instead of consuming all the data from the beginning. +The above example will use the SQL command `select * from meters` to create a subscription named `topicname`. If the subscription exists, it will continue the progress of the previous query instead of consuming all the data from the beginning. #### Subscribe to consume data @@ -808,11 +812,11 @@ Please refer to: [JDBC example](https://github.com/taosdata/TDengine/tree/develo ## Recent update logs -| taos-jdbcdriver version | major changes | -| :------------------: | :----------------------------: | -| 2.0.38 | JDBC REST connections add bulk pull function | -| 2.0.37 | Added support for json tags | -| 2.0.36 | Add support for schemaless writing | +| taos-jdbcdriver version | major changes | +| :---------------------: | :------------------------------------------: | +| 2.0.38 | JDBC REST connections add bulk pull function | +| 2.0.37 | Added support for json tags | +| 2.0.36 | Add support for schemaless writing | ## Frequently Asked Questions diff --git a/docs-en/14-reference/03-connector/node.mdx b/docs-en/14-reference/03-connector/node.mdx index 3d30148e8ed9d8f98d135fa0fa72809f1115231a..8f586acde4848af71efcb23358be1f8486cedb8e 100644 --- a/docs-en/14-reference/03-connector/node.mdx +++ b/docs-en/14-reference/03-connector/node.mdx @@ -14,7 +14,6 @@ import NodeInfluxLine from "../../07-develop/03-insert-data/_js_line.mdx"; import NodeOpenTSDBTelnet from "../../07-develop/03-insert-data/_js_opts_telnet.mdx"; import NodeOpenTSDBJson from "../../07-develop/03-insert-data/_js_opts_json.mdx"; import NodeQuery from "../../07-develop/04-query-data/_js.mdx"; -import NodeAsyncQuery from "../../07-develop/04-query-data/_js_async.mdx"; `td2.0-connector` and `td2.0-rest-connector` are the official Node.js language connectors for TDengine. Node.js developers can develop applications to access TDengine instance data. @@ -189,14 +188,8 @@ let cursor = conn.cursor(); ### Query data -#### Synchronous queries - -#### asynchronous query - - - ## More Sample Programs | Sample Programs | Sample Program Description | @@ -232,7 +225,7 @@ See [video tutorial](https://www.taosdata.com/blog/2020/11/11/1957.html) for the 2. "Unable to establish connection", "Unable to resolve FQDN" - Usually, root cause is the FQDN is not configured correctly. You can refer to [How to understand TDengine's FQDN (In Chinese)](https://www.taosdata.com/blog/2021/07/29/2741.html). + Usually, the root cause is an incorrect FQDN configuration. You can refer to this section in the [FAQ](https://docs.tdengine.com/2.4/train-faq/faq/#2-how-to-handle-unable-to-establish-connection) to troubleshoot. ## Important Updates diff --git a/docs-en/14-reference/03-connector/php.mdx b/docs-en/14-reference/03-connector/php.mdx new file mode 100644 index 0000000000000000000000000000000000000000..839a5c8c3cd27f39b234b51aab4d41ad05e93fbc --- /dev/null +++ b/docs-en/14-reference/03-connector/php.mdx @@ -0,0 +1,150 @@ +--- +sidebar_position: 1 +sidebar_label: PHP +title: PHP Connector +--- + +`php-tdengine` is the TDengine PHP connector provided by TDengine community. In particular, it supports Swoole coroutine. + +PHP Connector relies on TDengine client driver. + +Project Repository： + +After TDengine client or server is installed, `taos.h` is located at: + +- Linux：`/usr/local/taos/include` +- Windows：`C:\TDengine\include` + +TDengine client driver is located at: + +- Linux: `/usr/local/taos/driver/libtaos.so` +- Windows: `C:\TDengine\taos.dll` + +## Supported Platforms + +- Windows、Linux、MacOS + +- PHP >= 7.4 + +- TDengine >= 2.0 + +- Swoole >= 4.8 (Optional) + +## Supported Versions + +Because the version of TDengine client driver is tightly associated with that of TDengine server, it's strongly suggested to use the client driver of same version as TDengine server, even though the client driver can work with TDengine server if the first 3 sections of the versions are same. + +## Installation + +### Install TDengine Client Driver + +Regarding how to install TDengine client driver please refer to [Install Client Driver](/reference/connector#installation-steps) + +### Install php-tdengine + +**Download Source Code Package and Unzip：** + +```shell +curl -L -o php-tdengine.tar.gz https://github.com/Yurunsoft/php-tdengine/archive/refs/tags/v1.0.2.tar.gz \ +&& mkdir php-tdengine \ +&& tar -xzf php-tdengine.tar.gz -C php-tdengine --strip-components=1 +``` + +> Version number `v1.0.2` is only for example, it can be replaced to any newer version, please find available versions in [TDengine PHP Connector Releases](https://github.com/Yurunsoft/php-tdengine/releases). + +**Non-Swoole Environment：** + +```shell +phpize && ./configure && make -j && make install +``` + +**Specify TDengine location：** + +```shell +phpize && ./configure --with-tdengine-dir=/usr/local/Cellar/tdengine/2.4.0.0 && make -j && make install +``` + +> `--with-tdengine-dir=` is followed by TDengine location. +> It's useful in case TDengine installatio location can't be found automatically or MacOS. + +**Swoole Environment：** + +```shell +phpize && ./configure --enable-swoole && make -j && make install +``` + +**Enable Extension:** + +Option One: Add `extension=tdengine` in `php.ini`. + +Option Two: Use CLI `php -dextension=tdengine test.php`. + +## Sample Programs + +In this section a few sample programs which use TDengine PHP connector to access TDengine cluster are demonstrated. + +> Any error would throw exception: `TDengine\Exception\TDengineException` + +### Establish Conection + +
+Establish Connection + +```c +{{#include docs-examples/php/connect.php}} +``` + +
+ +### Insert Data + +
+Insert Data + +```c +{{#include docs-examples/php/insert.php}} +``` + +
+ +### Synchronous Query + +
+Synchronous Query + +```c +{{#include docs-examples/php/query.php}} +``` + +
+ +### Parameter Binding + +
+Parameter Binding + +```c +{{#include docs-examples/php/insert_stmt.php}} +``` + +
+ +## Constants + +| Constant | Description | +| ----------------------------------- | ----------- | +| `TDengine\TSDB_DATA_TYPE_NULL` | null | +| `TDengine\TSDB_DATA_TYPE_BOOL` | bool | +| `TDengine\TSDB_DATA_TYPE_TINYINT` | tinyint | +| `TDengine\TSDB_DATA_TYPE_SMALLINT` | smallint | +| `TDengine\TSDB_DATA_TYPE_INT` | int | +| `TDengine\TSDB_DATA_TYPE_BIGINT` | bigint | +| `TDengine\TSDB_DATA_TYPE_FLOAT` | float | +| `TDengine\TSDB_DATA_TYPE_DOUBLE` | double | +| `TDengine\TSDB_DATA_TYPE_BINARY` | binary | +| `TDengine\TSDB_DATA_TYPE_TIMESTAMP` | timestamp | +| `TDengine\TSDB_DATA_TYPE_NCHAR` | nchar | +| `TDengine\TSDB_DATA_TYPE_UTINYINT` | utinyint | +| `TDengine\TSDB_DATA_TYPE_USMALLINT` | usmallint | +| `TDengine\TSDB_DATA_TYPE_UINT` | uint | +| `TDengine\TSDB_DATA_TYPE_UBIGINT` | ubigint | diff --git a/docs-en/14-reference/03-connector/python.mdx b/docs-en/14-reference/03-connector/python.mdx index 2b238173e04e3e13de36b5ac4d91d0cda290ca72..58b94f13ae0f08404cef328834ef1c925c307816 100644 --- a/docs-en/14-reference/03-connector/python.mdx +++ b/docs-en/14-reference/03-connector/python.mdx @@ -11,18 +11,18 @@ import TabItem from "@theme/TabItem"; `taospy` is the official Python connector for TDengine. `taospy` provides a rich set of APIs that makes it easy for Python applications to access TDengine. `taospy` wraps both the [native interface](/reference/connector/cpp) and [REST interface](/reference/rest-api) of TDengine, which correspond to the `taos` and `taosrest` modules of the `taospy` package, respectively. In addition to wrapping the native and REST interfaces, `taospy` also provides a set of programming interfaces that conforms to the [Python Data Access Specification (PEP 249)](https://peps.python.org/pep-0249/). It is easy to integrate `taospy` with many third-party tools, such as [SQLAlchemy](https://www.sqlalchemy.org/) and [pandas](https://pandas.pydata.org/). -The connection to the server directly using the native interface provided by the client driver is referred to hereinafter as a "native connection"; the connection to the server using the REST interface provided by taosAdapter is referred to hereinafter as a "REST connection". +The direct connection to the server using the native interface provided by the client driver is referred to hereinafter as a "native connection"; the connection to the server using the REST interface provided by taosAdapter is referred to hereinafter as a "REST connection". The source code for the Python connector is hosted on [GitHub](https://github.com/taosdata/taos-connector-python). ## Supported Platforms -- The native connection [supported platforms](/reference/connector/#supported-platforms) is the same as the one supported by the TDengine client. +- The [supported platforms](/reference/connector/#supported-platforms) for the native connection are the same as the ones supported by the TDengine client. - REST connections are supported on all platforms that can run Python. ## Version selection -We recommend using the latest version of `taospy`, regardless what the version of TDengine is. +We recommend using the latest version of `taospy`, regardless of the version of TDengine. ## Supported features @@ -53,7 +53,7 @@ Earlier TDengine client software includes the Python connector. If the Python co ::: -#### to install `taospy` +#### To install `taospy` @@ -139,7 +139,7 @@ The FQDN above can be the FQDN of any dnode in the cluster, and the PORT is the -For REST connections and making sure the cluster is up, make sure the taosAdapter component is up. This can be tested using the following `curl ` command. +For REST connections, make sure the cluster and taosAdapter component, are running. This can be tested using the following `curl ` command. ``` curl -u root:taosdata http://:/rest/sql -d "select server_version()" @@ -199,10 +199,9 @@ The `connect()` function returns a `taos.TaosConnection` instance. In client-sid All arguments to the `connect()` function are optional keyword arguments. The following are the connection parameters specified. -- `host`: The host to connect to. The default is localhost. +- `url`： The URL of taosAdapter REST service. The default is . - `user`: TDengine user name. The default is `root`. - `password`: TDengine user password. The default is `taosdata`. -- `port`: The port on which the taosAdapter REST service listens. Default is 6041. - `timeout`: HTTP request timeout in seconds. The default is `socket._GLOBAL_DEFAULT_TIMEOUT`. Usually, no configuration is needed. @@ -312,7 +311,7 @@ For a more detailed description of the `sql()` method, please refer to [RestClie ### Exception handling -All database operations will be thrown directly if an exception occurs. The application is responsible for exception handling. For example: +All errors from database operations are thrown directly as exceptions and the error message from the database is passed up the exception stack. The application is responsible for exception handling. For example: ```python {{#include docs-examples/python/handle_exception.py}} @@ -320,7 +319,7 @@ All database operations will be thrown directly if an exception occurs. The appl ### About nanoseconds -Due to the current imperfection of Python's nanosecond support (see link below), the current implementation returns integers at nanosecond precision instead of the `datetime` type produced by `ms and `us`, which application developers will need to handle on their own. And it is recommended to use pandas' to_datetime(). The Python Connector may modify the interface in the future if Python officially supports nanoseconds in full. +Due to the current imperfection of Python's nanosecond support (see link below), the current implementation returns integers at nanosecond precision instead of the `datetime` type produced by `ms` and `us`, which application developers will need to handle on their own. And it is recommended to use pandas' to_datetime(). The Python Connector may modify the interface in the future if Python officially supports nanoseconds in full. 1. https://stackoverflow.com/questions/10611328/parsing-datetime-strings-containing-nanoseconds 2. https://www.python.org/dev/peps/pep-0564/ @@ -328,7 +327,7 @@ Due to the current imperfection of Python's nanosecond support (see link below), ## Frequently Asked Questions -Welcome to [ask questions or report questions] (https://github.com/taosdata/taos-connector-python/issues). +Welcome to [ask questions or report questions](https://github.com/taosdata/taos-connector-python/issues). ## Important Update diff --git a/docs-en/14-reference/03-connector/rust.mdx b/docs-en/14-reference/03-connector/rust.mdx index 2c8fe68c1ca8b091b8d685d8e20942a02ab2c5e8..a5cbaeac8077cda42690d9cc232062a685a51f41 100644 --- a/docs-en/14-reference/03-connector/rust.mdx +++ b/docs-en/14-reference/03-connector/rust.mdx @@ -30,7 +30,7 @@ REST connections are supported on all platforms that can run Rust. Please refer to [version support list](/reference/connector#version-support). -The Rust Connector is still under rapid development and is not guaranteed to be backward compatible before 1.0. Recommend to use TDengine version 2.4 or higher to avoid known issues. +The Rust Connector is still under rapid development and is not guaranteed to be backward compatible before 1.0. We recommend using TDengine version 2.4 or higher to avoid known issues. ## Installation @@ -45,15 +45,15 @@ Add the [libtaos][libtaos] dependency to the [Rust](https://rust-lang.org) proje -Add [libtaos][libtaos] to the ``Cargo.toml`'' file. +Add [libtaos][libtaos] to the `Cargo.toml` file. -``toml +```toml [dependencies] # use default feature libtaos = "*" ``` - Add [libtaos][libtaos] to the `Cargo.toml` file and enable the `rest` feature. @@ -206,7 +206,7 @@ let conn: Taos = cfg.connect(); ### Connection pooling -In complex applications, recommand to enable connection pool. Connection pool for [libtaos] is implemented using [r2d2]. +In complex applications, we recommend enabling connection pools. Connection pool for [libtaos] is implemented using [r2d2]. As follows, a connection pool with default parameters can be generated. @@ -269,7 +269,7 @@ The [Taos] structure is the connection manager in [libtaos] and provides two mai Note that Rust asynchronous functions and an asynchronous runtime are required. -[Taos] provides partial Rust methodization of SQL to reduce the frequency of `format!` code blocks. +[Taos] provides a few Rust methods that encapsulate SQL to reduce the frequency of `format!` code blocks. - `.describe(table: &str)`: Executes `DESCRIBE` and returns a Rust data structure. - `.create_database(database: &str)`: Executes the `CREATE DATABASE` statement. @@ -279,7 +279,7 @@ In addition, this structure is also the entry point for [Parameter Binding](#Par ### Bind Interface -Similar to the C interface, Rust provides the bind interface's wraping. First, create a bind object [Stmt] for a SQL command from the [Taos] object. +Similar to the C interface, Rust provides the bind interface's wrapping. First, create a bind object [Stmt] for a SQL command from the [Taos] object. ```rust let mut stmt: Stmt = taos.stmt("insert into ? values(? ,?)") ? ; diff --git a/docs-en/14-reference/03-connector/tdengine-jdbc-connector.png b/docs-en/14-reference/03-connector/tdengine-jdbc-connector.png deleted file mode 100644 index 7541aaf98ad73cbddac44c34bd775b32ab3a735e..0000000000000000000000000000000000000000 Binary files a/docs-en/14-reference/03-connector/tdengine-jdbc-connector.png and /dev/null differ diff --git a/docs-en/14-reference/03-connector/tdengine-jdbc-connector.webp b/docs-en/14-reference/03-connector/tdengine-jdbc-connector.webp new file mode 100644 index 0000000000000000000000000000000000000000..37cf6d90a528e320d5cb7d6da502d3a5b10aa4ee Binary files /dev/null and b/docs-en/14-reference/03-connector/tdengine-jdbc-connector.webp differ diff --git a/docs-en/14-reference/04-taosadapter.md b/docs-en/14-reference/04-taosadapter.md index 85fd2923b02189d6f3cfd73efff784d12c3bb69a..3264124655e7040e1d94b43500a0b582d95cb5a1 100644 --- a/docs-en/14-reference/04-taosadapter.md +++ b/docs-en/14-reference/04-taosadapter.md @@ -24,21 +24,21 @@ taosAdapter provides the following features. ## taosAdapter architecture diagram -![taosAdapter Architecture](taosAdapter-architecture.png) +![TDengine Database taosAdapter Architecture](taosAdapter-architecture.webp) ## taosAdapter Deployment Method ### Install taosAdapter -taosAdapter has been part of TDengine server software since TDengine v2.4.0.0. If you use the TDengine server, you don't need additional steps to install taosAdapter. You can download taosAdapter from [TAOSData official website](https://taosdata.com/en/all-downloads/) to download the TDengine server installation package (taosAdapter is included in v2.4.0.0 and later version). If you need to deploy taosAdapter separately on another server other than the TDengine server, you should install the full TDengine on that server to install taosAdapter. If you need to build taosAdapter from source code, you can refer to the [Building taosAdapter]( https://github.com/taosdata/taosadapter/blob/develop/BUILD.md) documentation. +taosAdapter has been part of TDengine server software since TDengine v2.4.0.0. If you use the TDengine server, you don't need additional steps to install taosAdapter. You can download taosAdapter from [TDengine official website](https://tdengine.com/all-downloads/) to download the TDengine server installation package (taosAdapter is included in v2.4.0.0 and later version). If you need to deploy taosAdapter separately on another server other than the TDengine server, you should install the full TDengine server package on that server to install taosAdapter. If you need to build taosAdapter from source code, you can refer to the [Building taosAdapter]( https://github.com/taosdata/taosadapter/blob/develop/BUILD.md) documentation. -### start/stop taosAdapter +### Start/Stop taosAdapter On Linux systems, the taosAdapter service is managed by `systemd` by default. You can use the command `systemctl start taosadapter` to start the taosAdapter service and use the command `systemctl stop taosadapter` to stop the taosAdapter service. ### Remove taosAdapter -Use the command `rmtaos` to remove the TDengine server software if you use tar.gz package or use package management command like rpm or apt to remove the TDengine server, including taosAdapter. +Use the command `rmtaos` to remove the TDengine server software if you use tar.gz package. If you installed using a .deb or .rpm package, use the corresponding command, for your package manager, like apt or rpm to remove the TDengine server, including taosAdapter. ### Upgrade taosAdapter @@ -153,8 +153,7 @@ See [example/config/taosadapter.toml](https://github.com/taosdata/taosadapter/bl ## Feature List -- Compatible with RESTful interfaces - [https://www.taosdata.com/cn/documentation/connector#restful](https://www.taosdata.com/cn/documentation/connector#restful) +- Compatible with RESTful interfaces [REST API](/reference/rest-api/) - Compatible with InfluxDB v1 write interface [https://docs.influxdata.com/influxdb/v2.0/reference/api/influxdb-1x/write/](https://docs.influxdata.com/influxdb/v2.0/reference/api/influxdb-1x/write/) - Compatible with OpenTSDB JSON and telnet format writes @@ -187,7 +186,7 @@ You can use any client that supports the http protocol to write data to or query ### InfluxDB -You can use any client that supports the http protocol to access the Restful interface address `http://:6041/` to write data in InfluxDB compatible format to TDengine. The EndPoint is as follows: +You can use any client that supports the http protocol to access the RESTful interface address `http://:6041/` to write data in InfluxDB compatible format to TDengine. The EndPoint is as follows: ```text /influxdb/v1/write @@ -204,7 +203,7 @@ Note: InfluxDB token authorization is not supported at present. Only Basic autho ### OpenTSDB -You can use any client that supports the http protocol to access the Restful interface address `http://:6041/` to write data in OpenTSDB compatible format to TDengine. +You can use any client that supports the http protocol to access the RESTful interface address `http://:6041/` to write data in OpenTSDB compatible format to TDengine. ```text /opentsdb/v1/put/json/:db @@ -241,7 +240,7 @@ node_export is an exporter of hardware and OS metrics exposed by the \*NIX kerne ## Memory usage optimization methods -taosAdapter will monitor its memory usage during operation and adjust it with two thresholds. Valid values range from -1 to 100 integers in percent of the system's physical memory. +taosAdapter will monitor its memory usage during operation and adjust it with two thresholds. Valid values are integers between 1 to 100, and represent a percentage of the system's physical memory. - pauseQueryMemoryThreshold - pauseAllMemoryThreshold @@ -277,7 +276,7 @@ Corresponding configuration parameter monitor.pauseQueryMemoryThreshold memory threshold for no more queries Environment variable `TAOS_MONITOR_PAUSE_QUERY_MEMORY_THRESHOLD` (default 70) ``` -You can adjust it according to the specific application scenario and operation strategy, and it is recommended to use operation monitoring software to monitor system memory status timely. The load balancer can also check the taosAdapter running status through this interface. +You should adjust this parameter based on your specific application scenario and operation strategy. We recommend using monitoring software to monitor system memory status. The load balancer can also check the taosAdapter running status through this interface. ## taosAdapter Monitoring Metrics @@ -326,7 +325,7 @@ You can also adjust the level of the taosAdapter log output by setting the `--lo ## How to migrate from older TDengine versions to taosAdapter -In TDengine server 2.2.x.x or earlier, the TDengine server process (taosd) contains an embedded HTTP service. As mentioned earlier, taosAdapter is a standalone software managed using `systemd` and has its process ID. And there are some configuration parameters and behaviors that are different between the two. See the following table for details. +In TDengine server 2.2.x.x or earlier, the TDengine server process (taosd) contains an embedded HTTP service. As mentioned earlier, taosAdapter is a standalone software managed using `systemd` and has its own process ID. There are some configuration parameters and behaviors that are different between the two. See the following table for details. | **#** | **embedded httpd** | **taosAdapter** | **comment** | | ----- | ------------------- | ------------------------------------ | ------------------------------------------------------------------ ------------------------------------------------------------------------ | diff --git a/docs-en/14-reference/05-taosbenchmark.md b/docs-en/14-reference/05-taosbenchmark.md index 1e2b0b99f652bca0d775bebe28378600470f8661..0f250f0767601a1825f9eda2eac75a5f9b532fdc 100644 --- a/docs-en/14-reference/05-taosbenchmark.md +++ b/docs-en/14-reference/05-taosbenchmark.md @@ -7,7 +7,7 @@ description: "taosBenchmark (once called taosdemo ) is a tool for testing the pe ## Introduction -taosBenchmark (formerly taosdemo ) is a tool for testing the performance of TDengine products. taosBenchmark can test the performance of TDengine's insert, query, and subscription functions and simulate large amounts of data generated by many devices. taosBenchmark can flexibly control the number and type of databases, supertables, tag columns, number and type of data columns, and sub-tables, and types of databases, super tables, the number and types of data columns, the number of sub-tables, the amount of data per sub-table, the time interval for inserting data, the number of working threads, whether and how to insert disordered data, and so on. The installer provides taosdemo as a soft link to taosBenchmark for compatibility with past users. +taosBenchmark (formerly taosdemo ) is a tool for testing the performance of TDengine products. taosBenchmark can test the performance of TDengine's insert, query, and subscription functions and simulate large amounts of data generated by many devices. taosBenchmark can be configured to generate user defined databases, supertables, subtables, and the time series data to populate these for performance benchmarking. taosBenchark is highly configurable and some of the configurations include the time interval for inserting data, the number of working threads and the capability to insert disordered data. The installer provides taosdemo as a soft link to taosBenchmark for compatibility with past users. ## Installation @@ -21,9 +21,9 @@ There are two ways to install taosBenchmark: ### Configuration and running methods -taosBenchmark supports two configuration methods: [Command-line arguments](#Command-line arguments in detailed) and [JSON configuration file](#Configuration file arguments in detailed). These two methods are mutually exclusive, and with only one command-line parameter, users can use `-f ` to specify a configuration file when using a configuration file. When running taosBenchmark with command-line arguments and controlling its behavior, users should use other parameters for configuration rather than `-f` parameter. In addition, taosBenchmark offers a special way of running without parameters. +TaosBenchmark needs to be executed on the terminal of the operating system, it supports two configuration methods: [Command-line arguments](#Command-line arguments in detailed) and [JSON configuration file](#Configuration file arguments in detailed). These two methods are mutually exclusive. Users can use `-f ` to specify a configuration file. When running taosBenchmark with command-line arguments to control its behavior, users should use other parameters for configuration, but not the `-f` parameter. In addition, taosBenchmark offers a special way of running without parameters. -taosBenchmark supports complete performance testing of TDengine. taosBenchmark supports the TDengine functions in three categories: write, query, and subscribe. These three functions are mutually exclusive, and users can select only one of them each time taosBenchmark runs. It is important to note that the type of functionality to be tested is not configurable when using the command-line configuration method, which can only test writing performance. To test the query and subscription performance of the TDengine, you must use the configuration file method and specify the function type to test via the parameter `filetype` in the configuration file. +taosBenchmark supports the complete performance testing of TDengine by providing functionaly to write, query, and subscribe. These three functions are mutually exclusive, users can only select one of them each time taosBenchmark runs. The query and subscribe functionalities are only configurable using a json configuration file by specifying the parameter `filetype`, while write can be performed through both the command-line and a configuration file. **Make sure that the TDengine cluster is running correctly before running taosBenchmark. ** @@ -35,17 +35,17 @@ Execute the following commands to quickly experience taosBenchmark's default con taosBenchmark ``` -When run without parameters, taosBenchmark connects to the TDengine cluster specified in `/etc/taos` by default and creates a database named test in TDengine, a super table named `meters` under the test database, and 10,000 tables under the super table with 10,000 records written to each table. Note that if there is already a test database, this table is not used. Note that if there is already a test database, this command will delete it first and create a new test database. +When run without parameters, taosBenchmark connects to the TDengine cluster specified in `/etc/taos` by default and creates a database named test in TDengine, a super table named `meters` under the test database, and 10,000 tables under the super table with 10,000 records written to each table. Note that if there is already a test database, this command will delete it first and create a new test database. ### Run with command-line configuration parameters -The `-f ` argument cannot be used when running taosBenchmark with command-line parameters and controlling its behavior. Users must specify all configuration parameters from the command-line. The following is an example of testing taosBenchmark writing performance using the command-line approach. +The `-f ` argument cannot be used when running taosBenchmark with command-line parameters. Users must specify all configuration parameters from the command-line. The following is an example of testing taosBenchmark writing performance using the command-line approach. ```bash taosBenchmark -I stmt -n 200 -t 100 ``` -The above command, `taosBenchmark` will create a database named `test`, create a super table `meters` in it, create 100 sub-tables in the super table and insert 200 records for each sub-table using parameter binding. +In the above command, `taosBenchmark` will create the default database named `test`, create the default super table named `meters`, create 100 subtables in the super table and insert 200 records for each subtable using parameter binding. ### Run with the configuration file @@ -92,70 +92,70 @@ taosBenchmark -f -## Command-line argument in detailed +## Command-line arguments in detail - **-f/--file ** : - specify the configuration file to use. This file includes All parameters. And users should not use this parameter with other parameters on the command-line. There is no default value. + specify the configuration file to use. This file includes All parameters. Users should not use this parameter with other parameters on the command-line. There is no default value. - **-c/--config-dir ** : - specify the directory where the TDengine cluster configuration file. the default path is `/etc/taos`. + specify the directory of the TDengine cluster configuration file. the default path is `/etc/taos`. - **-h/--host ** : - Specify the FQDN of the TDengine server to connect to. The default value is localhost. + specify the FQDN of the TDengine server to connect to. The default value is localhost. - **-P/--port ** : - The port number of the TDengine server to connect to, the default value is 6030. + specify the port number of the TDengine server to connect to, the default value is 6030. - **-I/--interface ** : - Insert mode. Options are taosc, rest, stmt, sml, sml-rest, corresponding to normal write, restful interface writing, parameter binding interface writing, schemaless interface writing, RESTful schemaless interface writing (provided by taosAdapter). The default value is taosc. + specify the insert mode. Options are taosc, rest, stmt, sml, sml-rest, corresponding to normal write, restful interface writing, parameter binding interface writing, schemaless interface writing, RESTful schemaless interface writing (provided by taosAdapter). The default value is taosc. - **-u/--user ** : - User name to connect to the TDengine server. Default is root. + specify the user name to connect to the TDengine server, the default is root. - **-p/--password ** : - The default password to connect to the TDengine server is `taosdata`. + specify the password to connect to the TDengine server, the default is `taosdata`. - **-o/--output ** : specify the path of the result output file, the default value is `. /output.txt`. - **-T/--thread ** : - The number of threads to insert data. Default is 8. + specify the number of threads to insert data, the default value is 8. - **-B/--interlace-rows ** : - Enables interleaved insertion mode and specifies the number of rows of data to be inserted into each child table. Interleaved insertion mode means inserting the number of rows specified by this parameter into each sub-table and repeating the process until all sub-tables have been inserted. The default value is 0, i.e., data is inserted into one sub-table before the next sub-table is inserted. + enables interleaved insertion mode and specifies the number of rows of data to be inserted into each child table. Interleaved insertion mode means inserting the number of rows specified by this parameter into each sub-table and repeating the process until all sub-tables have been inserted. The default value is 0, i.e., data is inserted into one sub-table before the next sub-table is inserted. - **-i/--insert-interval ** : - Specify the insert interval in `ms` for interleaved insert mode. The default value is 0. It only works if `-B/--interlace-rows` is greater than 0. That means that after inserting interlaced rows for each child table, the data insertion with multiple threads will wait for the interval specified by this value before proceeding to the next round of writes. + specify the insert interval in `ms` for interleaved insert mode. The default value is 0. It only works if `-B/--interlace-rows` is greater than 0. That means that after inserting interlaced rows for each child table, the data insertion with multiple threads will wait for the interval specified by this value before proceeding to the next round of writes. - **-r/--rec-per-req ** : - Writing the number of rows of records per request to TDengine, the default value is 30000. + specify the number of rows to write per request, the default value is 30000. - **-t/--tables ** : - Specify the number of sub-tables. The default is 10000. + specify the number of subtables to create, the default value is 10000. - **-S/--timestampstep ** : - Timestamp step for inserting data in each child table in ms, default is 1. + specify the timestamp step between records when inserting data in each child table in ms, the default value is 1. - **-n/--records ** : - The default value of the number of records inserted in each sub-table is 10000. + specify the number of records inserted into each sub-table, the default value is 10000. - **-d/--database ** : - The name of the database used, the default value is `test`. + specify the name of the database used, the default value is `test`. - **-b/--data-type ** : - specify the type of the data columns of the super table. It defaults to three columns of type FLOAT, INT, and FLOAT if not used. + specify the data column types of the super table. The default values are three columns of type FLOAT, INT, and FLOAT. - **-l/--columns ** : - specify the number of columns in the super table. If both this parameter and `-b/--data-type` is set, the final result number of columns is the greater of the two. If the number specified by this parameter is greater than the number of columns specified by `-b/--data-type`, the unspecified column type defaults to INT, for example: `-l 5 -b float,double`, then the final column is `FLOAT,DOUBLE,INT,INT,INT`. If the number of columns specified is less than or equal to the number of columns specified by `-b/--data-type`, then the result is the column and type specified by `-b/--data-type`, e.g.: `-l 3 -b float,double,float,bigint`. The last column is `FLOAT,DOUBLE, FLOAT,BIGINT`. + specify the number of columns in the super table. If both this parameter and `-b/--data-type` are set, the resulting number of columns is the greater of the two. If the number specified by this parameter is greater than the number of columns specified by `-b/--data-type`, the unspecified column types default to INT, for example: `-l 5 -b float,double`, then the column types are `FLOAT,DOUBLE,INT,INT,INT`. If the number of columns specified is less than or equal to the number of columns specified by `-b/--data-type`, then the columns specified by `-b/--data-type` will be used. e.g.: `-l 3 -b float,double,float,bigint` will result in the column types `FLOAT,DOUBLE,FLOAT,BIGINT`. - **-A/--tag-type ** : - The tag column type of the super table. nchar and binary types can both set the length, for example: + specify the tag column types of the super table. nchar and binary types can both set the length, for example: ``` taosBenchmark -A INT,DOUBLE,NCHAR,BINARY(16) ``` -If users did not set tag type, the default is two tags, whose types are INT and BINARY(16). +If the user does not set the tag type, the default is two tags, whose types are INT and BINARY(16). Note: In some shells, such as bash, "()" needs to be escaped, so the above command should be ``` @@ -163,48 +163,48 @@ taosBenchmark -A INT,DOUBLE,NCHAR,BINARY\(16\) ``` - **-w/--binwidth **: - specify the default length for nchar and binary types. The default value is 64. + specify the default length for nchar and binary types, the default value is 64. - **-m/--table-prefix ** : - The prefix of the sub-table name, the default value is "d". + specify the prefix of the sub-table names, the default value is "d". - **-E/--escape-character** : - Switch parameter specifying whether to use escape characters in the super table and sub-table names. By default is not used. + specify whether to use escape characters in the super table and sub-table names, the default is no. - **-C/--chinese** : - Switch specifying whether to use Unicode Chinese characters in nchar and binary. By default is not used. + specify whether to use Unicode Chinese characters in nchar and binary, the deault is no. - **-N/--normal-table** : - This parameter indicates that taosBenchmark will create only normal tables instead of super tables. The default value is false. It can be used if the insert mode is taosc, stmt, and rest. + specify whether taosBenchmark will create only normal tables instead of super tables. The default value is false. It can be used if the insert mode is taosc, stmt, and rest. - **-M/--random** : - This parameter indicates writing data with random values. The default is false. If users use this parameter, taosBenchmark will generate the random values. For tag/data columns of numeric type, the value is a random value within the range of values of that type. For NCHAR and BINARY type tag columns/data columns, the value is the random string within the specified length range. + specify whether taosBenchmark will generate random values. The default is false. When true, for tag/data columns of numeric type, the value is a random value within the range of values of that type. For NCHAR and BINARY type tag/data columns, the value is a random string within the specified length range. - **-x/--aggr-func** : - Switch parameter to indicate query aggregation function after insertion. The default value is false. + specify whether to query aggregation function after insertion. The default value is false. - **-y/--answer-yes** : - Switch parameter that requires the user to confirm at the prompt to continue. The default value is false. + specify whether to require the user to confirm at the prompt to continue. The default value is false. - **-O/--disorder ** : - Specify the percentage probability of disordered data, with a value range of [0,50]. The default is 0, i.e., there is no disordered data. + specify the percentage probability of disordered data, with a value range of [0,50]. The default value is 0, i.e., there is no disordered data. - **-R/--disorder-range ** : - Specify the timestamp range for the disordered data. It leads the resulting disorder timestamp as the ordered timestamp minus a random value in this range. Valid only if the percentage of disordered data specified by `-O/--disorder` is greater than 0. + specify the timestamp range for the disordered data. The disordered timestamp data will be out of order by the ordered timestamp minus a random value in this range. Valid only if the percentage of disordered data specified by `-O/--disorder` is greater than 0. - **-F/--prepare_rand ** : - Specify the number of unique values in the generated random data. A value of 1 means that all data are equal. The default value is 10000. + specify the number of unique values in the generated random data. A value of 1 means that all data are equal. The default value is 10000. - **-a/--replica ** : - Specify the number of replicas when creating the database. The default value is 1. + specify the number of replicas when creating the database. The default value is 1. - **-V/--version** : - Show version information only. Users should not use it with other parameters. + Show version information only. Users should not use this with other parameters. - **-? /--help** : Show help information and exit. Users should not use it with other parameters. -## Configuration file parameters in detailed +## Configuration file parameters in detail ### General configuration parameters @@ -213,17 +213,17 @@ The parameters listed in this section apply to all function modes. - **filetype** : The function to be tested, with optional values `insert`, `query` and `subscribe`. These correspond to the insert, query, and subscribe functions, respectively. Users can specify only one of these in each configuration file. **cfgdir**: specify the TDengine cluster configuration file's directory. The default path is /etc/taos. -- **host**: Specify the FQDN of the TDengine server to connect. The default value is `localhost`. +- **host**: specify the FQDN of the TDengine server to connect to. The default value is `localhost`. -- **port**: The port number of the TDengine server to connect to, the default value is `6030`. +- **port**: specify the port number of the TDengine server to connect to, the default value is `6030`. -- **user**: The user name of the TDengine server to connect to, the default is `root`. +- **user**: specify the user name to connect to the TDengine server, the default is `root`. -- **password**: The password to connect to the TDengine server, the default value is `taosdata`. +- **password**: specify the password to connect to the TDengine server, the default value is `taosdata`. ### Insert scenario configuration parameters -`filetype` must be set to `insert` in the insertion scenario. See [General Configuration Parameters](#General Configuration Parameters) +`filetype` must be set to `insert` in the insertion scenario. See [General Configuration Parameters](#general-configuration-parameters) #### Database related configuration parameters @@ -259,30 +259,30 @@ The parameters related to database creation are configured in `dbinfo` in the js - **fsync**: specify the interval of fsync in ms when users set WAL to 2. The default value is 3000. -- **update** : indicate whether to support data update, default value is 0, optional values are 0, 1, 2. +- **update** : indicate whether to support data update, default value is 0, values can be 0, 1, 2. #### Super table related configuration parameters The parameters for creating super tables are configured in `super_tables` in the json configuration file, as shown below. - **name**: Super table name, mandatory, no default value. -- **child_table_exists** : whether the child table already exists, default value is "no", optional value is "yes" or "no". +- **child_table_exists** : whether the child table already exists, default value is "no", values can be "yes" or "no". - **child_table_count** : The number of child tables, the default value is 10. - **child_table_prefix** : The prefix of the child table name, mandatory configuration item, no default value. -- **escape_character**: specify the super table and child table names containing escape characters. By default is "no". The value can be "yes" or "no". +- **escape_character**: specify whether the super table and child table names containing escape characters. By default is "no". The value can be "yes" or "no". - **auto_create_table**: only when insert_mode is taosc, rest, stmt, and childtable_exists is "no". "yes" means taosBenchmark will automatically create non-existent tables when inserting data; "no" means that taosBenchmark will create all tables before inserting. -- **batch_create_tbl_num** : the number of tables per batch when creating sub-tables, default is 10. Note: the actual number of batches may not be the same as this value when the executed SQL statement is larger than the maximum length supported, it will be automatically truncated and re-executed to continue creating. +- **batch_create_tbl_num** : the number of tables per batch when creating sub-tables, default is 10. Note: the actual number of batches may not be the same as this value. If the executed SQL statement is larger than the maximum length supported, it will be automatically truncated and re-executed to continue creating. -- **data_source**: specify the source of data-generating. Default is taosBenchmark randomly generated. Users can configure it as "rand" and "sample". When "sample" is used, taosBenchmark will use the data in the file specified by the `sample_file` parameter. +- **data_source**: specify the source of data-generation. Default is taosBenchmark randomly generated. Users can configure it as "rand" and "sample". When "sample" is used, taosBenchmark will use the data in the file specified by the `sample_file` parameter. - **insert_mode**: insertion mode with options taosc, rest, stmt, sml, sml-rest, corresponding to normal write, restful interface write, parameter binding interface write, schemaless interface write, restful schemaless interface write (provided by taosAdapter). The default value is taosc. -- **non_stop_mode**: Specify whether to keep writing. If "yes", insert_rows will be disabled, and writing will not stop until Ctrl + C stops the program. The default value is "no", i.e., taosBenchmark will stop the writing after the specified number of rows are written. Note: insert_rows must be configured as a non-zero positive integer even if it fails in continuous write mode. +- **non_stop_mode**: Specify whether to keep writing. If "yes", insert_rows will be disabled, and writing will not stop until Ctrl + C stops the program. The default value is "no", i.e., taosBenchmark will stop the writing after the specified number of rows are written. Note: insert_rows must be configured as a non-zero positive integer even if it is disabled in continuous write mode. - **line_protocol**: Insert data using line protocol. Only works when insert_mode is sml or sml-rest. The value can be `line`, `telnet`, or `json`. @@ -300,15 +300,15 @@ The parameters for creating super tables are configured in `super_tables` in the - **partial_col_num**: If this value is a positive number n, only the first n columns are written to, only if insert_mode is taosc and rest, or all columns if n is 0. -- **disorder_ratio** : Specifies the percentage probability of disordered data in the value range [0,50]. The default is 0, which means there is no disorder data. +- **disorder_ratio** : Specifies the percentage probability of disordered (i.e. out-of-order) data in the value range [0,50]. The default is 0, which means there is no disorder data. -- **disorder_range** : Specifies the timestamp fallback range for the disordered data. The generated disorder timestamp is the timestamp that should be used in the non-disorder case minus a random value in this range. Valid only if the percentage of disordered data specified by `-O/--disorder` is greater than 0. +- **disorder_range** : Specifies the timestamp fallback range for the disordered data. The disordered timestamp is generated by subtracting a random value in this range, from the timestamp that would be used in the non-disorder case. Valid only if the percentage of disordered data specified by `-O/--disorder` is greater than 0. -- **timestamp_step**: The timestamp step for inserting data in each child table, in units consistent with the `precision` of the database, the default value is 1. +- **timestamp_step**: The timestamp step for inserting data in each child table, in units consistent with the `precision` of the database. For e.g. if the `precision` is milliseconds, the timestamp step will be in milliseconds. The default value is 1. - **start_timestamp** : The timestamp start value of each sub-table, the default value is now. -- **sample_format**: The type of the sample data file, now only "csv" is supported. +- **sample_format**: The type of the sample data file; for now only "csv" is supported. - **sample_file**: Specify a CSV format file as the data source. It only works when data_source is a sample. If the number of rows in the CSV file is less than or equal to prepared_rand, then taosBenchmark will read the CSV file data cyclically until it is the same as prepared_rand; otherwise, taosBenchmark will read only the rows with the number of prepared_rand. The final number of rows of data generated is the smaller of the two. @@ -341,7 +341,7 @@ The configuration parameters for specifying super table tag columns and data col - **create_table_thread_count** : The number of threads to build the table, default is 8. -- **connection_pool_size** : The number of pre-established connections to the TDengine server. If not configured, it is the same number of threads specified. +- **connection_pool_size** : The number of pre-established connections to the TDengine server. If not configured, it is the same as number of threads specified. - **result_file** : The path to the result output file, the default value is . /output.txt. @@ -361,7 +361,7 @@ The configuration parameters for specifying super table tag columns and data col ### Query scenario configuration parameters -`filetype` must be set to `query` in the query scenario. See [General Configuration Parameters](#General Configuration Parameters) for details of this parameter and other general parameters +`filetype` must be set to `query` in the query scenario. See [General Configuration Parameters](#general-configuration-parameters) for details of this parameter and other general parameters #### Configuration parameters for executing the specified query statement @@ -392,7 +392,7 @@ The configuration parameters of the super table query are set in `super_table_qu ### Subscription scenario configuration parameters -`filetype` must be set to `subscribe` in the subscription scenario. See [General Configuration Parameters](#General Configuration Parameters) for details of this and other general parameters +`filetype` must be set to `subscribe` in the subscription scenario. See [General Configuration Parameters](#genera-configuration-parameters) for details of this and other general parameters #### Configuration parameters for executing the specified subscription statement diff --git a/docs-en/14-reference/06-taosdump.md b/docs-en/14-reference/06-taosdump.md index 973999704b595ea9b742f1ef759f973aa1f05649..5403e40925f633ce62795cc6037fc8c8f7aad07a 100644 --- a/docs-en/14-reference/06-taosdump.md +++ b/docs-en/14-reference/06-taosdump.md @@ -1,25 +1,25 @@ --- title: taosdump -description: "taosdump is a tool application that supports backing up data from a running TDengine cluster and restoring the backed up data to the same or another running TDengine cluster." +description: "taosdump is a tool that supports backing up data from a running TDengine cluster and restoring the backed up data to the same, or another running TDengine cluster." --- ## Introduction -taosdump is a tool application that supports backing up data from a running TDengine cluster and restoring the backed up data to the same or another running TDengine cluster. +taosdump is a tool that supports backing up data from a running TDengine cluster and restoring the backed up data to the same, or another running TDengine cluster. taosdump can back up a database, a super table, or a normal table as a logical data unit or backup data records in the database, super tables, and normal tables. When using taosdump, you can specify the directory path for data backup. If you do not specify a directory, taosdump will back up the data to the current directory by default. -Suppose the specified location already has data files. In that case, taosdump will prompt the user and exit immediately to avoid data overwriting which means that the same path can only be used for one backup. -Please be careful if you see a prompt for this. +If the specified location already has data files, taosdump will prompt the user and exit immediately to avoid data overwriting. This means that the same path can only be used for one backup. + +Please be careful if you see a prompt for this and please ensure that you follow best practices and relevant SOPs for data integrity, backup and data security. -taosdump is a logical backup tool and should not be used to back up any raw data, environment settings, Users should not use taosdump to back up raw data, environment settings, hardware information, server configuration, or cluster topology. taosdump uses [Apache AVRO](https://avro.apache.org/) as the data file format to store backup data. ## Installation There are two ways to install taosdump: -- Install the taosTools official installer. Please find taosTools from [All download links](https://www.taosdata.com/all-downloads) page and download and install it. +- Install the taosTools official installer. Please find taosTools from [All download links](https://www.tdengine.com/all-downloads) page and download and install it. - Compile taos-tools separately and install it. Please refer to the [taos-tools](https://github.com/taosdata/taos-tools) repository for details. @@ -28,14 +28,14 @@ There are two ways to install taosdump: ### taosdump backup data 1. backing up all databases: specify `-A` or `-all-databases` parameter. -2. backup multiple specified databases: use `-D db1,db2,... ` parameters; 3. +2. backup multiple specified databases: use `-D db1,db2,... ` parameters; 3. back up some super or normal tables in the specified database: use `-dbname stbname1 stbname2 tbname1 tbname2 ... ` parameters. Note that the first parameter of this input sequence is the database name, and only one database is supported. The second and subsequent parameters are the names of super or normal tables in that database, separated by spaces. 4. back up the system log database: TDengine clusters usually contain a system database named `log`. The data in this database is the data that TDengine runs itself, and the taosdump will not back up the log database by default. If users need to back up the log database, users can use the `-a` or `-allow-sys` command-line parameter. -5. Loose mode backup: taosdump version 1.4.1 onwards provides `-n` and `-L` parameters for backing up data without using escape characters and "loose" mode, which can reduce the number of backups if table names, column names, tag names do not use This can reduce the backup data time and backup data footprint if table names, column names, and tag names do not use `escape character`. If you are unsure about using `-n` and `-L` conditions, please use the default parameters for "strict" mode backup. See the [official documentation](/taos-sql/escape) for a description of escaped characters. +5. Loose mode backup: taosdump version 1.4.1 onwards provides `-n` and `-L` parameters for backing up data without using escape characters and "loose" mode, which can reduce the number of backups if table names, column names, tag names do not use escape characters. This can also reduce the backup data time and backup data footprint. If you are unsure about using `-n` and `-L` conditions, please use the default parameters for "strict" mode backup. See the [official documentation](/taos-sql/escape) for a description of escaped characters. :::tip - taosdump versions after 1.4.1 provide the `-I` argument for parsing Avro file schema and data. If users specify `-s` then only taosdump will parse schema. -- Backups after taosdump 1.4.2 use the batch count specified by the `-B` parameter. The default value is 16384. If, in some environments, low network speed or disk performance causes "Error actual dump ... batch ..." can be tried by challenging the `-B` parameter to a smaller value. +- Backups after taosdump 1.4.2 use the batch count specified by the `-B` parameter. The default value is 16384. If, in some environments, low network speed or disk performance causes "Error actual dump ... batch ...", then try changing the `-B` parameter to a smaller value. ::: @@ -44,7 +44,7 @@ There are two ways to install taosdump: Restore the data file in the specified path: use the `-i` parameter plus the path to the data file. You should not use the same directory to backup different data sets, and you should not backup the same data set multiple times in the same path. Otherwise, the backup data will cause overwriting or multiple backups. :::tip -taosdump internally uses TDengine stmt binding API for writing recovery data and currently uses 16384 as one write batch for better data recovery performance. If there are more columns in the backup data, it may cause a "WAL size exceeds limit" error. You can try to adjust to a smaller value by using the `-B` parameter. +taosdump internally uses TDengine stmt binding API for writing recovery data with a default batch size of 16384 for better data recovery performance. If there are more columns in the backup data, it may cause a "WAL size exceeds limit" error. You can try to adjust the batch size to a smaller value by using the `-B` parameter. ::: @@ -59,7 +59,7 @@ Usage: taosdump [OPTION...] dbname [tbname ...] or: taosdump [OPTION...] -i inpath or: taosdump [OPTION...] -o outpath - -h, --host=HOST Server host dumping data from. Default is + -h, --host=HOST Server host from which to dump data. Default is localhost. -p, --password User password to connect to server. Default is taosdata. @@ -72,10 +72,10 @@ Usage: taosdump [OPTION...] dbname [tbname ...] -r, --resultFile=RESULTFILE DumpOut/In Result file path and name. -a, --allow-sys Allow to dump system database -A, --all-databases Dump all databases. - -D, --databases=DATABASES Dump inputted databases. Use comma to separate - databases' name. + -D, --databases=DATABASES Dump listed databases. Use comma to separate + database names. -N, --without-property Dump database without its properties. - -s, --schemaonly Only dump tables' schema. + -s, --schemaonly Only dump table schemas. -y, --answer-yes Input yes for prompt. It will skip data file checking! -d, --avro-codec=snappy Choose an avro codec among null, deflate, snappy, @@ -98,7 +98,7 @@ Usage: taosdump [OPTION...] dbname [tbname ...] and try. The workable value is related to the length of the row and type of table schema. -I, --inspect inspect avro file content and print on screen - -L, --loose-mode Using loose mode if the table name and column name + -L, --loose-mode Use loose mode if the table name and column name use letter and number only. Default is NOT. -n, --no-escape No escape char '`'. Default is using it. -T, --thread-num=THREAD_NUM Number of thread for dump in file. 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a/docs-en/14-reference/07-tdinsight/index.md +++ b/docs-en/14-reference/07-tdinsight/index.md @@ -5,11 +5,11 @@ sidebar_label: TDinsight TDinsight is a solution for monitoring TDengine using the builtin native monitoring database and [Grafana]. -After TDengine starts, it will automatically create a monitoring database `log`. TDengine will automatically write many metrics in specific intervals into the `log` database. The metrics may include the server's CPU, memory, hard disk space, network bandwidth, number of requests, disk read/write speed, slow queries, other information like important system operations (user login, database creation, database deletion, etc.), and error alarms. With [Grafana] and [TDengine Data Source Plugin](https://github.com/taosdata/grafanaplugin/releases), TDinsight can visualize cluster status, node information, insertion and query requests, resource usage, etc., and also vnode, dnode, and mnode status, and exception alerts. Developers monitoring TDengine cluster operation status in real-time can be very convinient. This article will guide users to install the Grafana server, automatically install the TDengine data source plug-in, and deploy the TDinsight visualization panel through `TDinsight.sh` installation script. +After TDengine starts, it will automatically create a monitoring database `log`. TDengine will automatically write many metrics in specific intervals into the `log` database. The metrics may include the server's CPU, memory, hard disk space, network bandwidth, number of requests, disk read/write speed, slow queries, other information like important system operations (user login, database creation, database deletion, etc.), and error alarms. With [Grafana] and [TDengine Data Source Plugin](https://github.com/taosdata/grafanaplugin/releases), TDinsight can visualize cluster status, node information, insertion and query requests, resource usage, vnode, dnode, and mnode status, exception alerts and many other metrics. This is very convenient for developers who want to monitor TDengine cluster status in real-time. This article will guide users to install the Grafana server, automatically install the TDengine data source plug-in, and deploy the TDinsight visualization panel using the `TDinsight.sh` installation script. ## System Requirements -To deploy TDinsight, a single-node TDengine server or a multi-nodes TDengine cluster and a [Grafana] server are required. This dashboard requires TDengine 2.3.3.0 and above, with the `log` database enabled (`monitor = 1`). +To deploy TDinsight, a single-node TDengine server or a multi-node TDengine cluster and a [Grafana] server are required. This dashboard requires TDengine 2.3.3.0 and above, with the `log` database enabled (`monitor = 1`). ## Installing Grafana @@ -17,7 +17,7 @@ We recommend using the latest [Grafana] version 7 or 8 here. You can install Gra ### Installing Grafana on Debian or Ubuntu -For Debian or Ubuntu operating systems, we recommend the Grafana image repository and Use the following command to install from scratch. +For Debian or Ubuntu operating systems, we recommend the Grafana image repository and using the following command to install from scratch. ```bash sudo apt-get install -y apt-transport-https @@ -61,7 +61,7 @@ sudo yum install \ ## Automated deployment of TDinsight -We provide an installation script [`TDinsight.sh`](https://github.com/taosdata/grafanaplugin/releases/latest/download/TDinsight.sh) script to allow users to configure the installation automatically and quickly. +We provide an installation script [`TDinsight.sh`](https://github.com/taosdata/grafanaplugin/releases/latest/download/TDinsight.sh) to allow users to configure the installation automatically and quickly. You can download the script via `wget` or other tools: @@ -71,7 +71,7 @@ chmod +x TDinsight.sh ./TDinsight.sh ``` -This script will automatically download the latest [Grafana TDengine data source plugin](https://github.com/taosdata/grafanaplugin/releases/latest) and [TDinsight dashboard](https://grafana.com/grafana/dashboards/15167) with configurable parameters from the command-line options to the [Grafana Provisioning](https://grafana.com/docs/grafana/latest/administration/provisioning/) configuration file to automate deployment and updates, etc. With the alert setting options provided by this script, you can also get built-in support for AliCloud SMS alert notifications. +This script will automatically download the latest [Grafana TDengine data source plugin](https://github.com/taosdata/grafanaplugin/releases/latest) and [TDinsight dashboard](https://grafana.com/grafana/dashboards/15167) with configurable parameters for command-line options to the [Grafana Provisioning](https://grafana.com/docs/grafana/latest/administration/provisioning/) configuration file to automate deployment and updates, etc. With the alert setting options provided by this script, you can also get built-in support for AliCloud SMS alert notifications. Assume you use TDengine and Grafana's default services on the same host. Run `. /TDinsight.sh` and open the Grafana browser window to see the TDinsight dashboard. @@ -233,33 +233,33 @@ The default username/password is `admin`. Grafana will require a password change Point to the **Configurations** -> **Data Sources** menu, and click the **Add data source** button. -![Add data source button](./assets/howto-add-datasource-button.png) +![TDengine Database TDinsight Add data source button](./assets/howto-add-datasource-button.webp) Search for and select **TDengine**. -![Add datasource](./assets/howto-add-datasource-tdengine.png) +![TDengine Database TDinsight Add datasource](./assets/howto-add-datasource-tdengine.webp) Configure the TDengine datasource. -![Datasource Configuration](./assets/howto-add-datasource.png) +![TDengine Database TDinsight Datasource Configuration](./assets/howto-add-datasource.webp) Save and test. It will report 'TDengine Data source is working' under normal circumstances. -![datasource test](./assets/howto-add-datasource-test.png) +![TDengine Database TDinsight datasource test](./assets/howto-add-datasource-test.webp) ### Importing dashboards Point to **+** / **Create** - **import** (or `/dashboard/import` url). -![Import Dashboard and Configuration](./assets/import_dashboard.png) +![TDengine Database TDinsight Import Dashboard and Configuration](./assets/import_dashboard.webp) Type the dashboard ID `15167` in the **Import via grafana.com** location and **Load**. -![Import via grafana.com](./assets/import-dashboard-15167.png) +![TDengine Database TDinsight Import via grafana.com](./assets/import-dashboard-15167.webp) Once the import is complete, the full page view of TDinsight is shown below. -![show](./assets/TDinsight-full.png) +![TDengine Database TDinsight show](./assets/TDinsight-full.webp) ## TDinsight dashboard details @@ -269,7 +269,7 @@ Details of the metrics are as follows. ### Cluster Status -![tdinsight-mnodes-overview](./assets/TDinsight-1-cluster-status.png) +![TDengine Database TDinsight mnodes overview](./assets/TDinsight-1-cluster-status.webp) This section contains the current information and status of the cluster, the alert information is also here (from left to right, top to bottom). @@ -289,7 +289,7 @@ This section contains the current information and status of the cluster, the ale ### DNodes Status -![tdinsight-mnodes-overview](./assets/TDinsight-2-dnodes.png) +![TDengine Database TDinsight mnodes overview](./assets/TDinsight-2-dnodes.webp) - **DNodes Status**: simple table view of `show dnodes`. - **DNodes Lifetime**: the time elapsed since the dnode was created. @@ -298,14 +298,14 @@ This section contains the current information and status of the cluster, the ale ### MNode Overview -![tdinsight-mnodes-overview](./assets/TDinsight-3-mnodes.png) +![TDengine Database TDinsight mnodes overview](./assets/TDinsight-3-mnodes.webp) -1. **MNodes Status**: a simple table view of `show mnodes`. 2. +1. **MNodes Status**: a simple table view of `show mnodes`. 2. **MNodes Number**: similar to `DNodes Number`, the number of MNodes changes. ### Request -![tdinsight-requests](./assets/TDinsight-4-requests.png) +![TDengine Database TDinsight tdinsight requests](./assets/TDinsight-4-requests.webp) 1. **Requests Rate(Inserts per Second)**: average number of inserts per second. 2. **Requests (Selects)**: number of query requests and change rate (count of second). @@ -313,46 +313,46 @@ This section contains the current information and status of the cluster, the ale ### Database -![tdinsight-database](./assets/TDinsight-5-database.png) +![TDengine Database TDinsight database](./assets/TDinsight-5-database.webp) Database usage, repeated for each value of the variable `$database` i.e. multiple rows per database. -1. **STables**: number of super tables. 2. -2. **Total Tables**: number of all tables. 3. -3. **Sub Tables**: the number of all super table sub-tables. 4. +1. **STables**: number of super tables. +2. **Total Tables**: number of all tables. +3. **Sub Tables**: the number of all super table subtables. 4. **Tables**: graph of all normal table numbers over time. 5. **Tables Number Foreach VGroups**: The number of tables contained in each VGroups. ### DNode Resource Usage -![dnode-usage](./assets/TDinsight-6-dnode-usage.png) +![TDengine Database TDinsight dnode usage](./assets/TDinsight-6-dnode-usage.webp) Data node resource usage display with repeated multiple rows for the variable `$fqdn` i.e., each data node. Includes. 1. **Uptime**: the time elapsed since the dnode was created. -2. **Has MNodes?**: whether the current dnode is a mnode. 3. -3. **CPU Cores**: the number of CPU cores. 4. -4. **VNodes Number**: the number of VNodes in the current dnode. 5. -5. **VNodes Masters**: the number of vnodes in the master role. 6. +2. **Has MNodes?**: whether the current dnode is a mnode. +3. **CPU Cores**: the number of CPU cores. +4. **VNodes Number**: the number of VNodes in the current dnode. +5. **VNodes Masters**: the number of vnodes in the master role. 6. **Current CPU Usage of taosd**: CPU usage rate of taosd processes. 7. **Current Memory Usage of taosd**: memory usage of taosd processes. 8. **Disk Used**: The total disk usage percentage of the taosd data directory. -9. **CPU Usage**: Process and system CPU usage. 10. +9. **CPU Usage**: Process and system CPU usage. 10. **RAM Usage**: Time series view of RAM usage metrics. 11. **Disk Used**: Disks used at each level of multi-level storage (default is level0). 12. **Disk Increasing Rate per Minute**: Percentage increase or decrease in disk usage per minute. -13. **Disk IO**: Disk IO rate. 14. +13. **Disk IO**: Disk IO rate. 14. **Net IO**: Network IO, the aggregate network IO rate in addition to the local network. ### Login History -![Login History](./assets/TDinsight-7-login-history.png) +![TDengine Database TDinsight Login History](./assets/TDinsight-7-login-history.webp) Currently, only the number of logins per minute is reported. ### Monitoring taosAdapter -![taosadapter](./assets/TDinsight-8-taosadapter.png) +![TDengine Database TDinsight monitor taosadapter](./assets/TDinsight-8-taosadapter.webp) Support monitoring taosAdapter request statistics and status details. Includes. @@ -376,7 +376,7 @@ TDinsight installed via the `TDinsight.sh` script can be cleaned up using the co To completely uninstall TDinsight during a manual installation, you need to clean up the following. 1. the TDinsight Dashboard in Grafana. -2. the Data Source in Grafana. 3. +2. the Data Source in Grafana. 3. remove the `tdengine-datasource` plugin from the plugin installation directory. ## Integrated Docker Example diff --git a/docs-en/14-reference/08-taos-shell.md b/docs-en/14-reference/08-taos-shell.md index fe5e5f2bc29509a4b96646253732076c7a6ee7ea..9e077a3b11f2c373cd409f54401cbdb6350df6a8 100644 --- a/docs-en/14-reference/08-taos-shell.md +++ b/docs-en/14-reference/08-taos-shell.md @@ -1,14 +1,14 @@ --- -title: TDengine Command Line (CLI) -sidebar_label: TDengine CLI +title: TDengine Command Line Interface (CLI) +sidebar_label: Command Line Interface description: Instructions and tips for using the TDengine CLI --- -The TDengine command-line application (hereafter referred to as `TDengine CLI`) is the most simplest way for users to manipulate and interact with TDengine instances. +The TDengine command-line interface (hereafter referred to as `TDengine CLI`) is the simplest way for users to manipulate and interact with TDengine instances. ## Installation -If executed on the TDengine server-side, there is no need for additional installation steps to install TDengine CLI as it is already included and installed automatically. To run TDengine CLI on the environment which no TDengine server running, the TDengine client installation package needs to be installed first. For details, please refer to [connector](/reference/connector/). +If executed on the TDengine server-side, there is no need for additional installation steps to install TDengine CLI as it is already included and installed automatically. To run TDengine CLI in an environment where no TDengine server is running, the TDengine client installation package needs to be installed first. For details, please refer to [Install Client Driver](/reference/connector/#install-client-driver). ## Execution diff --git a/docs-en/14-reference/11-docker/index.md b/docs-en/14-reference/11-docker/index.md index 4ca84be369e14b3223e8609e06c9ebc4e35eaa2d..b7e60ab3e7f04a6078950977a563382a3524ebaa 100644 --- a/docs-en/14-reference/11-docker/index.md +++ b/docs-en/14-reference/11-docker/index.md @@ -13,7 +13,7 @@ The TDengine image starts with the HTTP service activated by default, using the docker run -d --name tdengine -p 6041:6041 tdengine/tdengine ``` -The above command starts a container named "tdengine" and maps the HTTP service end 6041 to the host port 6041. You can verify that the HTTP service provided in this container is available using the following command. +The above command starts a container named "tdengine" and maps the HTTP service port 6041 to the host port 6041. You can verify that the HTTP service provided in this container is available using the following command. ```shell curl -u root:taosdata -d "show databases" localhost:6041/rest/sql @@ -34,7 +34,7 @@ taos> show databases; Query OK, 1 row(s) in set (0.002843s) ``` -The TDengine server running in the container uses the container's hostname to establish a connection. Using TDengine CLI or various connectors (such as JDBC-JNI) to access the TDengine inside the container from outside the container is more complicated. So the above is the simplest way to access the TDengine service in the container and is suitable for some simple scenarios. Please refer to the next section if you want to access the TDengine service in the container from containerized using TDengine CLI or various connectors in some complex scenarios. +The TDengine server running in the container uses the container's hostname to establish a connection. Using TDengine CLI or various connectors (such as JDBC-JNI) to access the TDengine inside the container from outside the container is more complicated. So the above is the simplest way to access the TDengine service in the container and is suitable for some simple scenarios. Please refer to the next section if you want to access the TDengine service in the container from outside the container using TDengine CLI or various connectors for complex scenarios. ## Start TDengine on the host network @@ -42,7 +42,7 @@ The TDengine server running in the container uses the container's hostname to es docker run -d --name tdengine --network host tdengine/tdengine ``` -The above command starts TDengine on the host network and uses the host's FQDN to establish a connection instead of the container's hostname. It works too, like using `systemctl` to start TDengine on the host. If the TDengine client is already installed on the host, you can access it directly with the following command. +The above command starts TDengine on the host network and uses the host's FQDN to establish a connection instead of the container's hostname. It is the equivalent of using `systemctl` to start TDengine on the host. If the TDengine client is already installed on the host, you can access it directly with the following command. ```shell $ taos @@ -315,13 +315,13 @@ password: taosdata taoslog-td2: ``` - :::note +:::note - The `VERSION` environment variable is used to set the tdengine image tag - `TAOS_FIRST_EP` must be set on the newly created instance so that it can join the TDengine cluster; if there is a high availability requirement, `TAOS_SECOND_EP` needs to be used at the same time - `TAOS_REPLICA` is used to set the default number of database replicas. Its value range is [1,3] - We recommend setting with `TAOS_ARBITRATOR` to use arbitrator in a two-nodes environment. - ::: - + We recommend setting it with `TAOS_ARBITRATOR` to use arbitrator in a two-nodes environment. + + ::: 2. Start the cluster @@ -382,7 +382,7 @@ password: taosdata Suppose you want to deploy multiple taosAdapters to improve throughput and provide high availability. In that case, the recommended configuration method uses a reverse proxy such as Nginx to offer a unified access entry. For specific configuration methods, please refer to the official documentation of Nginx. Here is an example: ```docker - ersion: "3" + version: "3" networks: inter: diff --git a/docs-en/14-reference/12-config/index.md b/docs-en/14-reference/12-config/index.md index c4e7cc523c400ea5be6610b64f1561246b1bfa24..b6b535429b00796b5d2636c467153415a4281e59 100644 --- a/docs-en/14-reference/12-config/index.md +++ b/docs-en/14-reference/12-config/index.md @@ -65,7 +65,7 @@ taos --dump-config | ------------- | ------------------------------------------------------------------------ | | Applicable | Server Only | | Meaning | The FQDN of the host where `taosd` will be started. It can be IP address | -| Default Value | The first hostname configured for the hos | +| Default Value | The first hostname configured for the host | | Note | It should be within 96 bytes | ### serverPort @@ -78,7 +78,7 @@ taos --dump-config | Note | REST service is provided by `taosd` before 2.4.0.0 but by `taosAdapter` after 2.4.0.0, the default port of REST service is 6041 | :::note -TDengine uses continuous 13 ports, both TCP and TCP, from the port specified by `serverPort`. These ports need to be kept as open if firewall is enabled. Below table describes the ports used by TDengine in details. +TDengine uses 13 continuous ports, both TCP and UDP, starting with the port specified by `serverPort`. You should ensure, in your firewall rules, that these ports are kept open. Below table describes the ports used by TDengine in details. ::: @@ -134,7 +134,7 @@ TDengine uses continuous 13 ports, both TCP and TCP, from the port specified by | Applicable | Server Only | | Meaning | The switch for monitoring inside server. The workload of the hosts, including CPU, memory, disk, network, TTP requests, are collected and stored in a system builtin database `LOG` | | Value Range | 0: monitoring disabled, 1: monitoring enabled | -| Default Value | 0 | +| Default Value | 1 | ### monitorInterval @@ -182,8 +182,8 @@ TDengine uses continuous 13 ports, both TCP and TCP, from the port specified by | ------------- | -------------------------------------------- | | Applicable | Server Only | | Meaning | The maximum number of distinct rows returned | -| Value Range | [100,000 - 100, 000, 000] | -| Default Value | 100, 000 | +| Value Range | [100,000 - 100,000,000] | +| Default Value | 100,000 | | Note | After version 2.3.0.0 | ## Locale Parameters @@ -197,19 +197,19 @@ TDengine uses continuous 13 ports, both TCP and TCP, from the port specified by | Default Value | TimeZone configured in the host | :::info -To handle the data insertion and data query from multiple timezones, Unix Timestamp is used and stored TDengine. The timestamp generated from any timezones at same time is same in Unix timestamp. To make sure the time on client side can be converted to Unix timestamp correctly, the timezone must be set properly. +To handle the data insertion and data query from multiple timezones, Unix Timestamp is used and stored in TDengine. The timestamp generated from any timezones at same time is same in Unix timestamp. To make sure the time on client side can be converted to Unix timestamp correctly, the timezone must be set properly. On Linux system, TDengine clients automatically obtain timezone from the host. Alternatively, the timezone can be configured explicitly in configuration file `taos.cfg` like below. ``` -timezone UTC-8 +timezone UTC-7 timezone GMT-8 timezone Asia/Shanghai ``` The above examples are all proper configuration for the timezone of UTC+8. On Windows system, however, `timezone Asia/Shanghai` is not supported, it must be set as `timezone UTC-8`. -The setting for timezone impacts the strings not in Unix timestamp, keywords or functions related to date/time, for example +The setting for timezone impacts strings that are not in Unix timestamp format and keywords or functions related to date/time. For example: ```sql SELECT count(*) FROM table_name WHERE TS<'2019-04-11 12:01:08'; @@ -227,7 +227,7 @@ If the timezone is UTC, it's equal to SELECT count(*) FROM table_name WHERE TS<1554984068000; ``` -To avoid the problems of using time strings, Unix timestamp can be used directly. Furthermore, time strings with timezone can be used in SQL statement, for example "2013-04-12T15:52:01.123+08:00" in RFC3339 format or "2013-04-12T15:52:01.123+0800" in ISO-8601 format, they are not influenced by timezone setting when converted to Unix timestamp. +To avoid the problems of using time strings, Unix timestamp can be used directly. Furthermore, time strings with timezone can be used in SQL statements. For example "2013-04-12T15:52:01.123+08:00" in RFC3339 format or "2013-04-12T15:52:01.123+0800" in ISO-8601 format are not influenced by timezone setting when converted to Unix timestamp. ::: @@ -240,11 +240,11 @@ To avoid the problems of using time strings, Unix timestamp can be used directly | Default Value | Locale configured in host | :::info -A specific type "nchar" is provided in TDengine to store non-ASCII characters such as Chinese, Japanese, Korean. The characters to be stored in nchar type are firstly encoded in UCS4-LE before sending to server side. To store non-ASCII characters correctly, the encoding format of the client side needs to be set properly. +A specific type "nchar" is provided in TDengine to store non-ASCII characters such as Chinese, Japanese, and Korean. The characters to be stored in nchar type are firstly encoded in UCS4-LE before sending to server side. To store non-ASCII characters correctly, the encoding format of the client side needs to be set properly. The characters input on the client side are encoded using the default system encoding, which is UTF-8 on Linux, or GB18030 or GBK on some systems in Chinese, POSIX in docker, CP936 on Windows in Chinese. The encoding of the operating system in use must be set correctly so that the characters in nchar type can be converted to UCS4-LE. -The locale definition standard on Linux is: \_., for example, in "zh_CN.UTF-8", "zh" means Chinese, "CN" means China mainland, "UTF-8" means charset. On Linux andMac OSX, the charset can be set by locale in the system. On Windows system another configuration parameter `charset` must be used to configure charset because the locale used on Windows is not POSIX standard. Of course, `charset` can also be used on Linux to specify the charset. +The locale definition standard on Linux is: \_., for example, in "zh_CN.UTF-8", "zh" means Chinese, "CN" means China mainland, "UTF-8" means charset. On Linux and Mac OSX, the charset can be set by locale in the system. On Windows system another configuration parameter `charset` must be used to configure charset because the locale used on Windows is not POSIX standard. Of course, `charset` can also be used on Linux to specify the charset. ::: @@ -263,7 +263,7 @@ On Linux, if `charset` is not set in `taos.cfg`, when `taos` is started, the cha locale zh_CN.UTF-8 ``` -Besides, on Linux system, if the charset contained in `locale` is not consistent with that set by `charset`, the one who comes later in the configuration file is used. +On a Linux system, if the charset contained in `locale` is not consistent with that set by `charset`, the later setting in the configuration file takes precedence. ```title="Effective charset is GBK" locale zh_CN.UTF-8 @@ -778,8 +778,8 @@ To prevent system resource from being exhausted by multiple concurrent streams, ## HTTP Parameters :::note -HTTP server had been provided by `taosd` prior to version 2.4.0.0, now is provided by `taosAdapter` after version 2.4.0.0. -The parameters described in this section are only application in versions prior to 2.4.0.0. If you are using any version from 2.4.0.0, please refer to [taosAdapter]](/reference/taosadapter/). +HTTP service was provided by `taosd` prior to version 2.4.0.0 and is provided by `taosAdapter` after version 2.4.0.0. +The parameters described in this section are only application in versions prior to 2.4.0.0. If you are using any version from 2.4.0.0, please refer to [taosAdapter](/reference/taosadapter/). ::: diff --git a/docs-en/14-reference/12-directory.md b/docs-en/14-reference/12-directory.md index dbdba2b715bb41baf9b70dce91a3065e585d0434..304e3bcb434ee9a6ba338577a4d1ba546b548e3f 100644 --- a/docs-en/14-reference/12-directory.md +++ b/docs-en/14-reference/12-directory.md @@ -32,7 +32,7 @@ All executable files of TDengine are in the _/usr/local/taos/bin_ directory by d - _taosd-dump-cfg.gdb_: script to facilitate debugging of taosd's gdb execution. :::note -taosdump after version 2.4.0.0 require taosTools as a standalone installation. A few version taosBenchmark is include in taosTools too. +taosdump after version 2.4.0.0 require taosTools as a standalone installation. A new version of taosBenchmark is include in taosTools too. ::: :::tip diff --git a/docs-en/14-reference/13-schemaless/13-schemaless.md b/docs-en/14-reference/13-schemaless/13-schemaless.md index d9ce9b434dd14a89d243b2ed629f3fde64e6aba0..acbbb1cd3c5a7c50e226644f2de9e0e77274c6dd 100644 --- a/docs-en/14-reference/13-schemaless/13-schemaless.md +++ b/docs-en/14-reference/13-schemaless/13-schemaless.md @@ -1,19 +1,19 @@ --- title: Schemaless Writing -description: "The Schemaless write method eliminates the need to create super tables/sub tables in advance and automatically creates the storage structure corresponding to the data as it is written to the interface." +description: "The Schemaless write method eliminates the need to create super tables/sub tables in advance and automatically creates the storage structure corresponding to the data, as it is written to the interface." --- -In IoT applications, many data items are often collected for intelligent control, business analysis, device monitoring, etc. Due to the version upgrade of the application logic, or the hardware adjustment of the device itself, the data collection items may change more frequently. To facilitate the data logging work in such cases, TDengine starting from version 2.2.0.0, it provides a series of interfaces to the schemaless writing method, which eliminates the need to create super tables/sub tables in advance and automatically creates the storage structure corresponding to the data as the data is written to the interface. And when necessary, Schemaless writing will automatically add the required columns to ensure that the data written by the user is stored correctly. +In IoT applications, data is collected for many purposes such as intelligent control, business analysis, device monitoring and so on. Due to changes in business or functional requirements or changes in device hardware, the application logic and even the data collected may change. To provide the flexibility needed in such cases and in a rapidly changing IoT landscape, TDengine starting from version 2.2.0.0, provides a series of interfaces for the schemaless writing method. These interfaces eliminate the need to create super tables and subtables in advance by automatically creating the storage structure corresponding to the data as the data is written to the interface. When necessary, schemaless writing will automatically add the required columns to ensure that the data written by the user is stored correctly. -The schemaless writing method creates super tables and their corresponding sub-tables completely indistinguishable from the super tables and sub-tables created directly via SQL. You can write data directly to them via SQL statements. Note that the names of tables created by schemaless writing are based on fixed mapping rules for tag values, so they are not explicitly ideographic and lack readability. +The schemaless writing method creates super tables and their corresponding subtables. These are completely indistinguishable from the super tables and subtables created directly via SQL. You can write data directly to them via SQL statements. Note that the names of tables created by schemaless writing are based on fixed mapping rules for tag values, so they are not explicitly ideographic and they lack readability. ## Schemaless Writing Line Protocol -TDengine's schemaless writing line protocol supports to be compatible with InfluxDB's Line Protocol, OpenTSDB's telnet line protocol, and OpenTSDB's JSON format protocol. However, when using these three protocols, you need to specify in the API the standard of the parsing protocol to be used for the input content. +TDengine's schemaless writing line protocol supports InfluxDB's Line Protocol, OpenTSDB's telnet line protocol, and OpenTSDB's JSON format protocol. However, when using these three protocols, you need to specify in the API the standard of the parsing protocol to be used for the input content. For the standard writing protocols of InfluxDB and OpenTSDB, please refer to the documentation of each protocol. The following is a description of TDengine's extended protocol, based on InfluxDB's line protocol first. They allow users to control the (super table) schema more granularly. -With the following formatting conventions, Schemaless writing uses a single string to express a data row (multiple rows can be passed into the writing API at once to enable bulk writing). +With the following formatting conventions, schemaless writing uses a single string to express a data row (multiple rows can be passed into the writing API at once to enable bulk writing). ```json measurement,tag_set field_set timestamp @@ -23,7 +23,7 @@ where : - measurement will be used as the data table name. It will be separated from tag_set by a comma. - tag_set will be used as tag data in the format `=,=`, i.e. multiple tags' data can be separated by a comma. It is separated from field_set by space. -- field_set will be used as normal column data in the format of `=,=`, again using a comma to separate multiple normal columns of data. It is separated from the timestamp by space. +- field_set will be used as normal column data in the format of `=,=`, again using a comma to separate multiple normal columns of data. It is separated from the timestamp by a space. - The timestamp is the primary key corresponding to the data in this row. All data in tag_set is automatically converted to the NCHAR data type and does not require double quotes ("). @@ -32,7 +32,7 @@ In the schemaless writing data line protocol, each data item in the field_set ne - If there are English double quotes on both sides, it indicates the BINARY(32) type. For example, `"abc"`. - If there are double quotes on both sides and an L prefix, it means NCHAR(32) type. For example, `L"error message"`. -- Spaces, equal signs (=), commas (,), and double quotes (") need to be escaped with a backslash (\) in front. (All refer to the ASCII character) +- Spaces, equal signs (=), commas (,), and double quotes (") need to be escaped with a backslash (\\) in front. (All refer to the ASCII character) - Numeric types will be distinguished from data types by the suffix. | **Serial number** | **Postfix** | **Mapping type** | **Size (bytes)** | @@ -58,26 +58,25 @@ Note that if the wrong case is used when describing the data type suffix, or if Schemaless writes process row data according to the following principles. -1. You can use the following rules to generate the sub-table names: first, combine the measurement name and the key and value of the label into the next string: +1. You can use the following rules to generate the subtable names: first, combine the measurement name and the key and value of the label into the next string: ```json "measurement,tag_key1=tag_value1,tag_key2=tag_value2" ``` Note that tag_key1, tag_key2 are not the original order of the tags entered by the user but the result of using the tag names in ascending order of the strings. Therefore, tag_key1 is not the first tag entered in the line protocol. -The string's MD5 hash value "md5_val" is calculated after the ranking is completed. The calculation result is then combined with the string to generate the table name: "t_md5_val". "t*" is a fixed prefix that every table generated by this mapping relationship has. 2. +The string's MD5 hash value "md5_val" is calculated after the ranking is completed. The calculation result is then combined with the string to generate the table name: "t_md5_val". "t*" is a fixed prefix that every table generated by this mapping relationship has. 2. If the super table obtained by parsing the line protocol does not exist, this super table is created. -If the sub-table obtained by the parse line protocol does not exist, Schemaless creates the sub-table according to the sub-table name determined in steps 1 or 2. 4. +If the subtable obtained by the parse line protocol does not exist, Schemaless creates the sub-table according to the subtable name determined in steps 1 or 2. 4. If the specified tag or regular column in the data row does not exist, the corresponding tag or regular column is added to the super table (only incremental). 5. If there are some tag columns or regular columns in the super table that are not specified to take values in a data row, then the values of these columns are set to NULL. 6. For BINARY or NCHAR columns, if the length of the value provided in a data row exceeds the column type limit, the maximum length of characters allowed to be stored in the column is automatically increased (only incremented and not decremented) to ensure complete preservation of the data. -7. If the specified data sub-table already exists, and the specified tag column takes a value different from the saved value this time, the value in the latest data row overwrites the old tag column take value. +7. If the specified data subtable already exists, and the specified tag column takes a value different from the saved value this time, the value in the latest data row overwrites the old tag column take value. 8. Errors encountered throughout the processing will interrupt the writing process and return an error code. :::tip -All processing logic of schemaless will still follow TDengine's underlying restrictions on data structures, such as the total length of each row of data cannot exceed -16k bytes. See [TAOS SQL Boundary Limits](/taos-sql/limit) for specific constraints in this area. +All processing logic of schemaless will still follow TDengine's underlying restrictions on data structures, such as the total length of each row of data cannot exceed 48k bytes. See [TAOS SQL Boundary Limits](/taos-sql/limit) for specific constraints in this area. ::: ## Time resolution recognition @@ -87,7 +86,7 @@ Three specified modes are supported in the schemaless writing process, as follow | **Serial** | **Value** | **Description** | | -------- | ------------------- | ------------------------------- | | 1 | SML_LINE_PROTOCOL | InfluxDB Line Protocol | -| 2 | SML_TELNET_PROTOCOL | OpenTSDB Text Line Protocol | | 2 | SML_TELNET_PROTOCOL | OpenTSDB Text Line Protocol +| 2 | SML_TELNET_PROTOCOL | OpenTSDB Text Line Protocol | | 3 | SML_JSON_PROTOCOL | JSON protocol format | In the SML_LINE_PROTOCOL parsing mode, the user is required to specify the time resolution of the input timestamp. The available time resolutions are shown in the following table. @@ -106,8 +105,11 @@ In SML_TELNET_PROTOCOL and SML_JSON_PROTOCOL modes, the time precision is determ ## Data schema mapping rules -This section describes how data for line protocols are mapped to data with a schema. The data measurement in each line protocol is mapped to -The tag name in tag_set is the name of the tag in the data schema, and the name in field_set is the column's name. The following data is used as an example to illustrate the mapping rules. +This section describes how data for line protocols are mapped to data with a schema. The data measurement in each line protocol is mapped as follows: +- The tag name in tag_set is the name of the tag in the data schema +- The name in field_set is the column's name. + +The following data is used as an example to illustrate the mapping rules. ```json st,t1=3,t2=4,t3=t3 c1=3i64,c3="passit",c2=false,c4=4f64 1626006833639000000 @@ -139,7 +141,7 @@ st,t1=3,t2=4,t3=t3 c1=3i64,c5="pass" 1626006833639000000 st,t1=3,t2=4,t3=t3 c1=3i64,c5="passit" 1626006833640000000 ``` -The first line of the line protocol parsing will declare column c5 is a BINARY(4) field, the second line data write will extract column c5 is still a BINARY column. Still, its width is 6, then you need to increase the width of the BINARY field to be able to accommodate the new string. +The first line of the line protocol parsing will declare column c5 is a BINARY(4) field. The second line data write will parse column c5 as a BINARY column. But in the second line, c5's width is 6 so you need to increase the width of the BINARY field to be able to accommodate the new string. ```json st,t1=3,t2=4,t3=t3 c1=3i64 1626006833639000000 diff --git a/docs-en/14-reference/_collectd.mdx b/docs-en/14-reference/_collectd.mdx index 1f57d883eec9feadc3cc460bf968b0dd43fedfe8..ce88328098a181de48dcaa080ef45f228b20bf1c 100644 --- a/docs-en/14-reference/_collectd.mdx +++ b/docs-en/14-reference/_collectd.mdx @@ -25,7 +25,7 @@ The default database name written by taosAdapter is `collectd`. You can also mod #collectd collectd uses a plugin mechanism to write the collected monitoring data to different data storage software in various forms. tdengine supports both direct collection plugins and write_tsdb plugins. -#### is configured to receive data from the direct collection plugin +#### Configure the direct collection plugin Modify the relevant configuration items in the collectd configuration file (default location /etc/collectd/collectd.conf). @@ -62,7 +62,7 @@ LoadPlugin write_tsdb ``` -Where fills in the server's domain name or IP address running taosAdapter. Fill in the data that taosAdapter uses to receive the collectd write_tsdb plugin (default is 6047). +Where is the domain name or IP address of the server running taosAdapter. Fill in the data that taosAdapter uses to receive the collectd write_tsdb plugin (default is 6047). ```text LoadPlugin write_tsdb diff --git a/docs-en/14-reference/_tcollector.mdx b/docs-en/14-reference/_tcollector.mdx index 85794d54007b70acf205b1bbc897cec1d0c4f824..42b021410e3862c4fa328d8dae40dcac1456e929 100644 --- a/docs-en/14-reference/_tcollector.mdx +++ b/docs-en/14-reference/_tcollector.mdx @@ -17,7 +17,7 @@ password = "taosdata" ... ``` -The taosAdapter writes to the database with the default name `tcollector`. You can also modify the taosAdapter configuration file dbs entry to specify a different name. user and password fill in the actual TDengine configuration values. After changing the configuration file, you need to restart the taosAdapter. +The taosAdapter writes to the database with the default name `tcollector`. You can also modify the taosAdapter configuration file dbs entry to specify a different name. Fill in the actual user and password for TDengine. After changing the configuration file, you need to restart the taosAdapter. - You can also enable taosAdapter to receive tcollector data by using the taosAdapter command-line parameters or setting environment variables. @@ -25,7 +25,7 @@ The taosAdapter writes to the database with the default name `tcollector`. You c To use TCollector, you need to download its [source code](https://github.com/OpenTSDB/tcollector). Its configuration items are in its source code. Note: TCollector differs significantly from version to version, so here is an example of the latest code for the current master branch (git commit: 37ae920). -Modify the contents of the `collectors/etc/config.py` and `tcollector.py` files. Change the address of the OpenTSDB host to the domain name or IP address of the server where taosAdapter is deployed, and change the port to the port that taosAdapter supports TCollector on (default is 6049). +Modify the contents of the `collectors/etc/config.py` and `tcollector.py` files. Change the address of the OpenTSDB host to the domain name or IP address of the server where taosAdapter is deployed, and change the port to the port on which taosAdapter supports TCollector (default is 6049). Example of git diff output of source code changes. diff --git a/docs-en/14-reference/index.md b/docs-en/14-reference/index.md index 89f675902d01ba2d2c1b322408c372429d6bda1c..f350eebfc1a1ca2feaedc18c4b4fa798742e31b4 100644 --- a/docs-en/14-reference/index.md +++ b/docs-en/14-reference/index.md @@ -2,11 +2,11 @@ title: Reference --- -The reference guide is the detailed introduction to TDengine, various TDengine's connectors in different languages, and the tools that come with it. +The reference guide is a detailed introduction to TDengine including various TDengine connectors in different languages, and the tools that come with TDengine. ```mdx-code-block import DocCardList from '@theme/DocCardList'; import {useCurrentSidebarCategory} from '@docusaurus/theme-common'; -``` \ No newline at end of file +``` diff --git a/docs-en/14-reference/taosAdapter-architecture.png b/docs-en/14-reference/taosAdapter-architecture.png deleted file mode 100644 index 08a9018553aae6f86b42d127b372d0cecfa9bdf8..0000000000000000000000000000000000000000 Binary files a/docs-en/14-reference/taosAdapter-architecture.png and /dev/null differ diff --git a/docs-en/14-reference/taosAdapter-architecture.webp b/docs-en/14-reference/taosAdapter-architecture.webp new file mode 100644 index 0000000000000000000000000000000000000000..a4162b0a037c06d34191784716c51080b9f8a570 Binary files /dev/null and b/docs-en/14-reference/taosAdapter-architecture.webp differ diff --git a/docs-en/20-third-party/01-grafana.mdx b/docs-en/20-third-party/01-grafana.mdx index c1bfd4a96a4576df8570d8b480d5c2afe47e20b8..b51d5a8d904601802efec0db5847203b72fa2668 100644 --- a/docs-en/20-third-party/01-grafana.mdx +++ b/docs-en/20-third-party/01-grafana.mdx @@ -3,101 +3,146 @@ sidebar_label: Grafana title: Grafana --- -TDengine can be quickly integrated with the open-source data visualization system [Grafana](https://www.grafana.com/) to build a data monitoring and alerting system. The whole process does not require any code development. And you can visualize the contents of the data tables in TDengine on a DashBoard. +import Tabs from "@theme/Tabs"; +import TabItem from "@theme/TabItem"; + +TDengine can be quickly integrated with the open-source data visualization system [Grafana](https://www.grafana.com/) to build a data monitoring and alerting system. The whole process does not require any code development. And you can visualize the contents of the data tables in TDengine on a dashboard. You can learn more about using the TDengine plugin on [GitHub](https://github.com/taosdata/grafanaplugin/blob/master/README.md). ## Prerequisites In order for Grafana to add the TDengine data source successfully, the following preparations are required: + 1. The TDengine cluster is deployed and functioning properly 2. taosAdapter is installed and running properly. Please refer to the taosAdapter manual for details. +Record these values: + +- TDengine REST API url: `http://tdengine.local:6041`. +- TDengine cluster authorization, with user + password. + ## Installing Grafana -TDengine currently supports Grafana versions 7.0 and above. Users can go to the Grafana official website to download the installation package and execute the installation according to the current operating system. The download address is as follows: . +TDengine currently supports Grafana versions 7.5 and above. Users can go to the Grafana official website to download the installation package and execute the installation according to the current operating system. The download address is as follows: . ## Configuring Grafana -You can download The Grafana plugin for TDengine from . The current latest version is 3.1.4. +### Install Grafana Plugin and Configure Data Source + + + + +Set the url and authorization environment variables by `export` or a [`.env`(dotenv) file](https://hexdocs.pm/dotenvy/dotenv-file-format.html): -Recommend using the [``grafana-cli`` command-line tool](https://grafana.com/docs/grafana/latest/administration/cli/) for plugin installation. +```sh +export TDENGINE_API=http://tdengine.local:6041 +# user + password +export TDENGINE_USER=user +export TDENGINE_PASSWORD=password -``bash -sudo -u grafana grafana-cli \ - --pluginUrl https://github.com/taosdata/grafanaplugin/releases/download/v3.1.4/tdengine-datasource-3.1.4.zip \ - plugins install tdengine-datasource +# Other useful variables +# - If to install TDengine data source, default is true +export TDENGINE_DS_ENABLED=false +# - Data source name to be created, default is TDengine +export TDENGINE_DS_NAME=TDengine +# - Data source organization id, default is 1 +export GF_ORG_ID=1 +# - Data source is editable in admin ui or not, default is 0 (false) +export TDENGINE_EDITABLE=1 ``` -Or download it locally and extract it to the Grafana plugin directory. +Run `install.sh`: -```bash -GF_VERSION=3.1.4 -wget https://github.com/taosdata/grafanaplugin/releases/download/v$GF_VERSION/tdengine-datasource-$GF_VERSION.zip +```sh +bash -c "$(curl -fsSL https://raw.githubusercontent.com/taosdata/grafanaplugin/master/install.sh)" ``` -Take CentOS 7.2 for example, extract the plugin package to /var/lib/grafana/plugins directory, and restart grafana. +With this script, TDengine data source plugin and the Grafana data source will be installed and created automatically with Grafana provisioning configurations. Save the script and type `./install.sh --help` for the full usage of the script. + +And then, restart Grafana service and open Grafana in web-browser, usually . + + + + +Follow the installation steps in [Grafana](https://grafana.com/grafana/plugins/tdengine-datasource/?tab=installation) with the [``grafana-cli`` command-line tool](https://grafana.com/docs/grafana/latest/administration/cli/) for plugin installation. ```bash -sudo unzip tdengine-datasource-$GF_VERSION.zip -d /var/lib/grafana/plugins/ +grafana-cli plugins install tdengine-datasource +# with sudo +sudo -u grafana grafana-cli plugins install tdengine-datasource ``` -Grafana versions 7.3+ / 8.x do signature checks on plugins, so you also need to add the following line to the grafana.ini file to use the plugin correctly. +Alternatively, you can manually download the .zip file from [GitHub](https://github.com/taosdata/grafanaplugin/tags) or [Grafana](https://grafana.com/grafana/plugins/tdengine-datasource/?tab=installation) and unpack it into your grafana plugins directory. -```ini -[plugins] -allow_loading_unsigned_plugins = tdengine-datasource +```bash +GF_VERSION=3.2.2 +# from GitHub +wget https://github.com/taosdata/grafanaplugin/releases/download/v$GF_VERSION/tdengine-datasource-$GF_VERSION.zip +# from Grafana +wget -O tdengine-datasource-$GF_VERSION.zip https://grafana.com/api/plugins/tdengine-datasource/versions/$GF_VERSION/download ``` -The TDengine plugin can be automatically installed and set up using the following environment variable settings in a Docker environment. +Take CentOS 7.2 for example, extract the plugin package to /var/lib/grafana/plugins directory, and restart grafana. ```bash -GF_INSTALL_PLUGINS=https://github.com/taosdata/grafanaplugin/releases/download/v3.1.4/tdengine-datasource-3.1.4.zip;tdengine- datasource -GF_PLUGINS_ALLOW_LOADING_UNSIGNED_PLUGINS=tdengine-datasource +sudo unzip tdengine-datasource-$GF_VERSION.zip -d /var/lib/grafana/plugins/ ``` -## Using Grafana +If Grafana is running in a Docker environment, the TDengine plugin can be automatically installed and set up using the following environment variable settings: -### Configuring Data Sources +```bash +GF_INSTALL_PLUGINS=tdengine-datasource +``` -Users can log in to the Grafana server (username/password: admin/admin) directly through the URL `http://localhost:3000` and add a datasource through `Configuration -> Data Sources` on the left side, as shown in the following figure. +Now users can log in to the Grafana server (username/password: admin/admin) directly through the URL `http://localhost:3000` and add a datasource through `Configuration -> Data Sources` on the left side, as shown in the following figure. -![img](./grafana/add_datasource1.jpg) +![TDengine Database TDinsight plugin add datasource 1](./grafana/add_datasource1.webp) Click `Add data source` to enter the Add data source page, and enter TDengine in the query box to add it, as shown in the following figure. -![img](./grafana/add_datasource2.jpg) +![TDengine Database TDinsight plugin add datasource 2](./grafana/add_datasource2.webp) Enter the datasource configuration page, and follow the default prompts to modify the corresponding configuration. -![img](./grafana/add_datasource3.jpg) +![TDengine Database TDinsight plugin add database 3](./grafana/add_datasource3.webp) - Host: IP address of the server where the components of the TDengine cluster provide REST service (offered by taosd before 2.4 and by taosAdapter since 2.4) and the port number of the TDengine REST service (6041), by default use `http://localhost:6041`. - User: TDengine user name. - Password: TDengine user password. -Click `Save & Test` to test. Follows are a success. +Click `Save & Test` to test. You should see a success message if the test worked. + +![TDengine Database TDinsight plugin add database 4](./grafana/add_datasource4.webp) -![img](./grafana/add_datasource4.jpg) + + ### Create Dashboard -Go back to the main interface to create the Dashboard, click Add Query to enter the panel query page: +Go back to the main interface to create a dashboard and click Add Query to enter the panel query page: -![img](./grafana/create_dashboard1.jpg) +![TDengine Database TDinsight plugin create dashboard 1](./grafana/create_dashboard1.webp) As shown above, select the `TDengine` data source in the `Query` and enter the corresponding SQL in the query box below for query. -- INPUT SQL: enter the statement to be queried (the result set of the SQL statement should be two columns and multiple rows), for example: `select avg(mem_system) from log.dn where ts >= $from and ts < $to interval($interval)`, where, from, to and interval are built-in variables of the TDengine plugin, indicating the range and time interval of queries fetched from the Grafana plugin panel. In addition to the built-in variables, ` custom template variables are also supported. +- INPUT SQL: enter the statement to be queried (the result set of the SQL statement should be two columns and multiple rows), for example: `select avg(mem_system) from log.dn where ts >= $from and ts < $to interval($interval)`, where, from, to and interval are built-in variables of the TDengine plugin, indicating the range and time interval of queries fetched from the Grafana plugin panel. In addition to the built-in variables, custom template variables are also supported. - ALIAS BY: This allows you to set the current query alias. - GENERATE SQL: Clicking this button will automatically replace the corresponding variables and generate the final executed statement. Follow the default prompt to query the average system memory usage for the specified interval on the server where the current TDengine deployment is located as follows. -![img](./grafana/create_dashboard2.jpg) +![TDengine Database TDinsight plugin create dashboard 2](./grafana/create_dashboard2.webp) > For more information on how to use Grafana to create the appropriate monitoring interface and for more details on using Grafana, refer to the official Grafana [documentation](https://grafana.com/docs/). ### Importing the Dashboard -In version 2.3.3.0 and above, you can import the TDinsight Dashboard (Grafana Dashboard ID: [15168](https://grafana.com/grafana/dashboards/15167)) as a monitoring visualization tool for TDengine clusters. You can find installation and usage instructions in the TDinsight User Manual (/reference/tdinsight/). +You can install TDinsight dashboard in data source configuration page (like `http://localhost:3000/datasources/edit/1/dashboards`) as a monitoring visualization tool for TDengine cluster. The dashboard is published in Grafana as [Dashboard 15167 - TDinsight](https://grafana.com/grafana/dashboards/15167). Check the [TDinsight User Manual](/reference/tdinsight/) for the details. + +For more dashboards using TDengine data source, [search here in Grafana](https://grafana.com/grafana/dashboards/?dataSource=tdengine-datasource). Here is a sub list: + +- [15146](https://grafana.com/grafana/dashboards/15146): Monitor multiple TDengine clusters. +- [15155](https://grafana.com/grafana/dashboards/15155): TDengine alert demo. +- [15167](https://grafana.com/grafana/dashboards/15167): TDinsight. +- [16388](https://grafana.com/grafana/dashboards/16388): Telegraf node metrics dashboard using TDengine data source. diff --git a/docs-en/20-third-party/03-telegraf.md b/docs-en/20-third-party/03-telegraf.md index 0d563c9ff36268ac27e18e21fefed789789dc1a7..6a7aac322f9def880f58d7ed0adcc4a8f3687ed1 100644 --- a/docs-en/20-third-party/03-telegraf.md +++ b/docs-en/20-third-party/03-telegraf.md @@ -5,7 +5,7 @@ title: Telegraf writing import Telegraf from "../14-reference/_telegraf.mdx" -Telegraf is a viral metrics collection open-source software. Telegraf can collect the operation information of various components without writing any scripts to collect regularly, reducing the difficulty of data acquisition. +Telegraf is a viral, open-source, metrics collection software. Telegraf can collect the operation information of various components without having to write any scripts to collect regularly, reducing the difficulty of data acquisition. Telegraf's data can be written to TDengine by simply adding the output configuration of Telegraf to the URL corresponding to taosAdapter and modifying several configuration items. The presence of Telegraf data in TDengine can take advantage of TDengine's efficient storage query performance and clustering capabilities for time-series data. diff --git a/docs-en/20-third-party/05-collectd.md b/docs-en/20-third-party/05-collectd.md index 609e55842ab35cdc2d394663f5450f908e49f7f7..db62f2ecd1afb4936466ca0243a7e14ff294f8b6 100644 --- a/docs-en/20-third-party/05-collectd.md +++ b/docs-en/20-third-party/05-collectd.md @@ -6,7 +6,7 @@ title: collectd writing import CollectD from "../14-reference/_collectd.mdx" -collectd is a daemon used to collect system performance metric data. collectd provides various storage mechanisms to store different values. It periodically counts system performance statistics number while the system is running and storing information. You can use this information to help identify current system performance bottlenecks and predict future system load. +collectd is a daemon used to collect system performance metric data. collectd provides various storage mechanisms to store different values. It periodically counts system performance statistics while the system is running and storing information. You can use this information to help identify current system performance bottlenecks and predict future system load. You can write the data collected by collectd to TDengine by simply modifying the configuration of collectd to the domain name (or IP address) and corresponding port of the server running taosAdapter. It can take full advantage of TDengine's efficient storage query performance and clustering capability for time-series data. diff --git a/docs-en/20-third-party/06-statsd.md b/docs-en/20-third-party/06-statsd.md index bf4b6c7ab5dac4114cad0d650b2aeb026a67581c..40e927b9fd1d2eca9d454a987ac51d533eb75005 100644 --- a/docs-en/20-third-party/06-statsd.md +++ b/docs-en/20-third-party/06-statsd.md @@ -7,7 +7,7 @@ import StatsD from "../14-reference/_statsd.mdx" StatsD is a simple daemon for aggregating application metrics, which has evolved rapidly in recent years into a unified protocol for collecting application performance metrics. -You can write StatsD data to TDengine by simply modifying in the configuration file of StatsD with the domain name (or IP address) of the server running taosAdapter and the corresponding port. It can take full advantage of TDengine's efficient storage query performance and clustering capabilities for time-series data. +You can write StatsD data to TDengine by simply modifying the configuration file of StatsD with the domain name (or IP address) of the server running taosAdapter and the corresponding port. It can take full advantage of TDengine's efficient storage query performance and clustering capabilities for time-series data. ## Prerequisites diff --git a/docs-en/20-third-party/07-icinga2.md b/docs-en/20-third-party/07-icinga2.md index ba9cde8cea7504ac9df871d5f6aa42cc5c94d895..b27196dfe313b468eeb73ff4b114d9d955618c3e 100644 --- a/docs-en/20-third-party/07-icinga2.md +++ b/docs-en/20-third-party/07-icinga2.md @@ -5,7 +5,7 @@ title: icinga2 writing import Icinga2 from "../14-reference/_icinga2.mdx" -icinga2 is an open-source software monitoring host and network initially developed from the Nagios network monitoring application. Currently, icinga2 is distributed under the GNU GPL v2 license. +icinga2 is an open-source, host and network monitoring software initially developed from the Nagios network monitoring application. Currently, icinga2 is distributed under the GNU GPL v2 license. You can write the data collected by icinga2 to TDengine by simply modifying the icinga2 configuration to point to the taosAdapter server and the corresponding port, taking advantage of TDengine's efficient storage and query performance and clustering capabilities for time-series data. diff --git a/docs-en/20-third-party/09-emq-broker.md b/docs-en/20-third-party/09-emq-broker.md index 13562ba7f720499c23771437c5c6ba0f61819456..7c6b83cf99dd733f9e9a86435e079a2daee00ad9 100644 --- a/docs-en/20-third-party/09-emq-broker.md +++ b/docs-en/20-third-party/09-emq-broker.md @@ -3,7 +3,7 @@ sidebar_label: EMQX Broker title: EMQX Broker writing --- -MQTT is a popular IoT data transfer protocol, [EMQX](https://github.com/emqx/emqx) is an open-source MQTT Broker software, without any code, only need to use "rules" in EMQX Dashboard to do simple configuration. You can write MQTT data directly to TDengine. EMQX supports saving data to TDengine by sending it to web services and provides a native TDengine driver for direct saving in the Enterprise Edition. Please refer to the [EMQX official documentation](https://www.emqx.io/docs/en/v4.4/rule/rule-engine.html) for details on how to use it. tdengine). +MQTT is a popular IoT data transfer protocol. [EMQX](https://github.com/emqx/emqx) is an open-source MQTT Broker software. You can write MQTT data directly to TDengine without any code. You only need to setup "rules" in EMQX Dashboard to create a simple configuration. EMQX supports saving data to TDengine by sending data to a web service and provides a native TDengine driver for direct saving in the Enterprise Edition. Please refer to the [EMQX official documentation](https://www.emqx.io/docs/en/v4.4/rule/rule-engine.html) for details on how to use it.). ## Prerequisites @@ -16,22 +16,15 @@ The following preparations are required for EMQX to add TDengine data sources co Depending on the current operating system, users can download the installation package from the [EMQX official website](https://www.emqx.io/downloads) and execute the installation. After installation, use `sudo emqx start` or `sudo systemctl start emqx` to start the EMQX service. -## Create the appropriate database and table schema in TDengine for receiving MQTT data -### Take the Docker installation of TDengine as an example +## Create Database and Table -```bash - docker exec -it tdengine bash - taos -``` - -### Create Database and Table +In this step we create the appropriate database and table schema in TDengine for receiving MQTT data. Open TDengine CLI and execute SQL bellow: ```sql - CREATE DATABASE test; - USE test; - - CREATE TABLE sensor_data (ts timestamp, temperature float, humidity float, volume float, PM10 float, pm25 float, SO2 float, NO2 float, CO float, sensor_id NCHAR(255), area TINYINT, coll_time timestamp); +CREATE DATABASE test; +USE test; +CREATE TABLE sensor_data (ts TIMESTAMP, temperature FLOAT, humidity FLOAT, volume FLOAT, pm10 FLOAT, pm25 FLOAT, so2 FLOAT, no2 FLOAT, co FLOAT, sensor_id NCHAR(255), area TINYINT, coll_time TIMESTAMP); ``` Note: The table schema is based on the blog [(In Chinese) Data Transfer, Storage, Presentation, EMQX + TDengine Build MQTT IoT Data Visualization Platform](https://www.taosdata.com/blog/2020/08/04/1722.html) as an example. Subsequent operations are carried out with this blog scenario too. Please modify it according to your actual application scenario. @@ -44,126 +37,83 @@ Since the configuration interface of EMQX differs from version to version, here Use your browser to open the URL `http://IP:18083` and log in to EMQX Dashboard. The initial installation username is `admin` and the password is: `public`. -![img](./emqx/login-dashboard.png) +![TDengine Database EMQX login dashboard](./emqx/login-dashboard.webp) ### Creating Rule Select "Rule" in the "Rule Engine" on the left and click the "Create" button: ! -![img](./emqx/rule-engine.png) +![TDengine Database EMQX rule engine](./emqx/rule-engine.webp) ### Edit SQL fields -![img](./emqx/create-rule.png) +Copy SQL bellow and paste it to the SQL edit area： + +```sql +SELECT + payload +FROM + "sensor/data" +``` + +![TDengine Database EMQX create rule](./emqx/create-rule.webp) ### Add "action handler" -![img](./emqx/add-action-handler.png) +![TDengine Database EMQX add action handler](./emqx/add-action-handler.webp) ### Add "Resource" -![img](./emqx/create-resource.png) +![TDengine Database EMQX create resource](./emqx/create-resource.webp) Select "Data to Web Service" and click the "New Resource" button. ### Edit "Resource" -Select "Data to Web Service" and fill in the request URL as the address and port of the server running taosAdapter (default is 6041). Leave the other properties at their default values. +Select "WebHook" and fill in the request URL as the address and port of the server running taosAdapter (default is 6041). Leave the other properties at their default values. -![img](./emqx/edit-resource.png) +![TDengine Database EMQX edit resource](./emqx/edit-resource.webp) ### Edit "action" -Edit the resource configuration to add the key/value pairing for Authorization. Please refer to the [ TDengine REST API documentation ](https://docs.taosdata.com/reference/rest-api/) for the authorization in details. Enter the rule engine replacement template in the message body. +Edit the resource configuration to add the key/value pairing for Authorization. If you use the default TDengine username and password then the value of key Authorization is： +``` +Basic cm9vdDp0YW9zZGF0YQ== +``` + +Please refer to the [ TDengine REST API documentation ](/reference/rest-api/) for the authorization in details. + +Enter the rule engine replacement template in the message body: + +```sql +INSERT INTO test.sensor_data VALUES( + now, + ${payload.temperature}, + ${payload.humidity}, + ${payload.volume}, + ${payload.PM10}, + ${payload.pm25}, + ${payload.SO2}, + ${payload.NO2}, + ${payload.CO}, + '${payload.id}', + ${payload.area}, + ${payload.ts} +) +``` -![img](./emqx/edit-action.png) +![TDengine Database EMQX edit action](./emqx/edit-action.webp) +Finally, click the "Create" button at bottom left corner saving the rule. ## Compose program to mock data ```javascript - // mock.js - const mqtt = require('mqtt') - const Mock = require('mockjs') - const EMQX_SERVER = 'mqtt://localhost:1883' - const CLIENT_NUM = 10 - const STEP = 5000 // Data interval in ms - const AWAIT = 5000 // Sleep time after data be written once to avoid data writing too fast - const CLIENT_POOL = [] - startMock() - function sleep(timer = 100) { - return new Promise(resolve => { - setTimeout(resolve, timer) - }) - } - async function startMock() { - const now = Date.now() - for (let i = 0; i < CLIENT_NUM; i++) { - const client = await createClient(`mock_client_${i}`) - CLIENT_POOL.push(client) - } - // last 24h every 5s - const last = 24 * 3600 * 1000 - for (let ts = now - last; ts <= now; ts += STEP) { - for (const client of CLIENT_POOL) { - const mockData = generateMockData() - const data = { - ...mockData, - id: client.clientId, - area: 0, - ts, - } - client.publish('sensor/data', JSON.stringify(data)) - } - const dateStr = new Date(ts).toLocaleTimeString() - console.log(`${dateStr} send success.`) - await sleep(AWAIT) - } - console.log(`Done, use ${(Date.now() - now) / 1000}s`) - } - /** - * Init a virtual mqtt client - * @param {string} clientId ClientID - */ - function createClient(clientId) { - return new Promise((resolve, reject) => { - const client = mqtt.connect(EMQX_SERVER, { - clientId, - }) - client.on('connect', () => { - console.log(`client ${clientId} connected`) - resolve(client) - }) - client.on('reconnect', () => { - console.log('reconnect') - }) - client.on('error', (e) => { - console.error(e) - reject(e) - }) - }) - } - /** - * Generate mock data - */ - function generateMockData() { - return { - "temperature": parseFloat(Mock.Random.float(22, 100).toFixed(2)), - "humidity": parseFloat(Mock.Random.float(12, 86).toFixed(2)), - "volume": parseFloat(Mock.Random.float(20, 200).toFixed(2)), - "PM10": parseFloat(Mock.Random.float(0, 300).toFixed(2)), - "pm25": parseFloat(Mock.Random.float(0, 300).toFixed(2)), - "SO2": parseFloat(Mock.Random.float(0, 50).toFixed(2)), - "NO2": parseFloat(Mock.Random.float(0, 50).toFixed(2)), - "CO": parseFloat(Mock.Random.float(0, 50).toFixed(2)), - "area": Mock.Random.integer(0, 20), - "ts": 1596157444170, - } - } +{{#include docs-examples/other/mock.js}} ``` Note: `CLIENT_NUM` in the code can be set to a smaller value at the beginning of the test to avoid hardware performance be not capable to handle a more significant number of concurrent clients. -![img](./emqx/client-num.png) +![TDengine Database EMQX client num](./emqx/client-num.webp) ## Execute tests to simulate sending MQTT data @@ -172,19 +122,19 @@ npm install mqtt mockjs --save ---registry=https://registry.npm.taobao.org node mock.js ``` -![img](./emqx/run-mock.png) +![TDengine Database EMQX run mock](./emqx/run-mock.webp) ## Verify that EMQX is receiving data Refresh the EMQX Dashboard rules engine interface to see how many records were received correctly: -![img](./emqx/check-rule-matched.png) +![TDengine Database EMQX rule matched](./emqx/check-rule-matched.webp) ## Verify that data writing to TDengine Use the TDengine CLI program to log in and query the appropriate databases and tables to verify that the data is being written to TDengine correctly: -![img](./emqx/check-result-in-taos.png) +![TDengine Database EMQX result in taos](./emqx/check-result-in-taos.webp) Please refer to the [TDengine official documentation](https://docs.taosdata.com/) for more details on how to use TDengine. EMQX Please refer to the [EMQX official documentation](https://www.emqx.io/docs/en/v4.4/rule/rule-engine.html) for details on how to use EMQX. diff --git a/docs-en/20-third-party/11-kafka.md b/docs-en/20-third-party/11-kafka.md index b9c7a3814a75a066b498438b6e632690697ae7ca..6720af8bf81ea2f4fce415a54847453f578ababf 100644 --- a/docs-en/20-third-party/11-kafka.md +++ b/docs-en/20-third-party/11-kafka.md @@ -7,17 +7,17 @@ TDengine Kafka Connector contains two plugins: TDengine Source Connector and TDe ## What is Kafka Connect? -Kafka Connect is a component of Apache Kafka that enables other systems, such as databases, cloud services, file systems, etc., to connect to Kafka easily. Data can flow from other software to Kafka via Kafka Connect and Kafka to other systems via Kafka Connect. Plugins that read data from other software are called Source Connectors, and plugins that write data to other software are called Sink Connectors. Neither Source Connector nor Sink Connector will directly connect to Kafka Broker, and Source Connector transfers data to Kafka Connect. Sink Connector receives data from Kafka Connect. +Kafka Connect is a component of [Apache Kafka](https://kafka.apache.org/) that enables other systems, such as databases, cloud services, file systems, etc., to connect to Kafka easily. Data can flow from other software to Kafka via Kafka Connect and Kafka to other systems via Kafka Connect. Plugins that read data from other software are called Source Connectors, and plugins that write data to other software are called Sink Connectors. Neither Source Connector nor Sink Connector will directly connect to Kafka Broker, and Source Connector transfers data to Kafka Connect. Sink Connector receives data from Kafka Connect. -![](kafka/Kafka_Connect.png) +![TDengine Database Kafka Connector -- Kafka Connect](kafka/Kafka_Connect.webp) TDengine Source Connector is used to read data from TDengine in real-time and send it to Kafka Connect. Users can use The TDengine Sink Connector to receive data from Kafka Connect and write it to TDengine. -![](kafka/streaming-integration-with-kafka-connect.png) +![TDengine Database Kafka Connector -- streaming integration with kafka connect](kafka/streaming-integration-with-kafka-connect.webp) ## What is Confluent? -Confluent adds many extensions to Kafka. include: +[Confluent](https://www.confluent.io/) adds many extensions to Kafka. include: 1. Schema Registry 2. REST Proxy @@ -26,7 +26,7 @@ Confluent adds many extensions to Kafka. include: 5. GUI for managing and monitoring Kafka - Confluent Control Center Some of these extensions are available in the community version of Confluent. Some are only available in the enterprise version. -![](kafka/confluentPlatform.png) +![TDengine Database Kafka Connector -- Confluent platform](kafka/confluentPlatform.webp) Confluent Enterprise Edition provides the `confluent` command-line tool to manage various components. @@ -79,10 +79,10 @@ Development: false git clone https://github.com:taosdata/kafka-connect-tdengine.git cd kafka-connect-tdengine mvn clean package -unzip -d $CONFLUENT_HOME/share/confluent-hub-components/ target/components/packages/taosdata-kafka-connect-tdengine-0.1.0.zip +unzip -d $CONFLUENT_HOME/share/java/ target/components/packages/taosdata-kafka-connect-tdengine-*.zip ``` -The above script first clones the project source code and then compiles and packages it with Maven. After the package is complete, the zip package of the plugin is generated in the `target/components/packages/` directory. Unzip this zip package to the path where the plugin is installed. The path to install the plugin is in the configuration file `$CONFLUENT_HOME/etc/kafka/connect-standalone.properties`. The default path is `$CONFLUENT_HOME/share/confluent-hub-components/`. +The above script first clones the project source code and then compiles and packages it with Maven. After the package is complete, the zip package of the plugin is generated in the `target/components/packages/` directory. Unzip this zip package to plugin path. We used `$CONFLUENT_HOME/share/java/` above because it's a build in plugin path. ### Install with confluent-hub @@ -96,7 +96,7 @@ confluent local services start ``` :::note -Be sure to install the plugin before starting Confluent. Otherwise, there will be a class not found error. The log of Kafka Connect (default path: /tmp/confluent.xxxx/connect/logs/connect.log) will output the successfully installed plugin, which users can use to determine whether the plugin is installed successfully. +Be sure to install the plugin before starting Confluent. Otherwise, Kafka Connect will fail to discover the plugins. ::: :::tip @@ -123,6 +123,59 @@ Control Center is [UP] To clear data, execute `rm -rf /tmp/confluent.106668`. ::: +### Check Confluent Services Status + +Use command bellow to check the status of all service: + +``` +confluent local services status +``` + +The expected output is: +``` +Connect is [UP] +Control Center is [UP] +Kafka is [UP] +Kafka REST is [UP] +ksqlDB Server is [UP] +Schema Registry is [UP] +ZooKeeper is [UP] +``` + +### Check Successfully Loaded Plugin + +After Kafka Connect was completely started, you can use bellow command to check if our plugins are installed successfully: +``` +confluent local services connect plugin list +``` + +The output should contains `TDengineSinkConnector` and `TDengineSourceConnector` as bellow: + +``` +Available Connect Plugins: +[ + { + "class": "com.taosdata.kafka.connect.sink.TDengineSinkConnector", + "type": "sink", + "version": "1.0.0" + }, + { + "class": "com.taosdata.kafka.connect.source.TDengineSourceConnector", + "type": "source", + "version": "1.0.0" + }, +...... +``` + +If not, please check the log file of Kafka Connect. To view the log file path, please execute: + +``` +echo `cat /tmp/confluent.current`/connect/connect.stdout +``` +It should produce a path like:`/tmp/confluent.104086/connect/connect.stdout` + +Besides log file `connect.stdout` there is a file named `connect.properties`. At the end of this file you can see the effective `plugin.path` which is a series of paths joined by comma. If Kafka Connect not found our plugins, it's probably because the installed path is not included in `plugin.path`. + ## The use of TDengine Sink Connector The role of the TDengine Sink Connector is to synchronize the data of the specified topic to TDengine. Users do not need to create databases and super tables in advance. The name of the target database can be specified manually (see the configuration parameter connection.database), or it can be generated according to specific rules (see the configuration parameter connection.database.prefix). @@ -142,7 +195,7 @@ vi sink-demo.properties sink-demo.properties' content is following: ```ini title="sink-demo.properties" -name=tdengine-sink-demo +name=TDengineSinkConnector connector.class=com.taosdata.kafka.connect.sink.TDengineSinkConnector tasks.max=1 topics=meters @@ -151,6 +204,7 @@ connection.user=root connection.password=taosdata connection.database=power db.schemaless=line +data.precision=ns key.converter=org.apache.kafka.connect.storage.StringConverter value.converter=org.apache.kafka.connect.storage.StringConverter ``` @@ -177,6 +231,7 @@ If the above command is executed successfully, the output is as follows: "connection.url": "jdbc:TAOS://127.0.0.1:6030", "connection.user": "root", "connector.class": "com.taosdata.kafka.connect.sink.TDengineSinkConnector", + "data.precision": "ns", "db.schemaless": "line", "key.converter": "org.apache.kafka.connect.storage.StringConverter", "tasks.max": "1", @@ -194,10 +249,10 @@ If the above command is executed successfully, the output is as follows: Prepare text file as test data, its content is following: ```txt title="test-data.txt" -meters,location=Beijing.Haidian,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249000000 -meters,location=Beijing.Haidian,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250000000 -meters,location=Beijing.Haidian,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249000000 -meters,location=Beijing.Haidian,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250000000 +meters,location=California.LoSangeles,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249000000 +meters,location=California.LoSangeles,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250000000 +meters,location=California.LoSangeles,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249000000 +meters,location=California.LoSangeles,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250000000 ``` Use kafka-console-producer to write test data to the topic `meters`. @@ -221,14 +276,14 @@ Database changed. taos> select * from meters; ts | current | voltage | phase | groupid | location | =============================================================================================================================================================== - 2022-03-28 09:56:51.249000000 | 11.800000000 | 221.000000000 | 0.280000000 | 2 | Beijing.Haidian | - 2022-03-28 09:56:51.250000000 | 13.400000000 | 223.000000000 | 0.290000000 | 2 | Beijing.Haidian | - 2022-03-28 09:56:51.249000000 | 10.800000000 | 223.000000000 | 0.290000000 | 3 | Beijing.Haidian | - 2022-03-28 09:56:51.250000000 | 11.300000000 | 221.000000000 | 0.350000000 | 3 | Beijing.Haidian | + 2022-03-28 09:56:51.249000000 | 11.800000000 | 221.000000000 | 0.280000000 | 2 | California.LosAngeles | + 2022-03-28 09:56:51.250000000 | 13.400000000 | 223.000000000 | 0.290000000 | 2 | California.LosAngeles | + 2022-03-28 09:56:51.249000000 | 10.800000000 | 223.000000000 | 0.290000000 | 3 | California.LosAngeles | + 2022-03-28 09:56:51.250000000 | 11.300000000 | 221.000000000 | 0.350000000 | 3 | California.LosAngeles | Query OK, 4 row(s) in set (0.004208s) ``` -If you see the above data, the synchronization is successful. If not, check the logs of Kafka Connect. For detailed description of configuration parameters, see [Configuration Reference](#Configuration Reference). +If you see the above data, the synchronization is successful. If not, check the logs of Kafka Connect. For detailed description of configuration parameters, see [Configuration Reference](#configuration-reference). ## The use of TDengine Source Connector @@ -273,7 +328,7 @@ DROP DATABASE IF EXISTS test; CREATE DATABASE test; USE test; CREATE STABLE meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT); -INSERT INTO d1001 USING meters TAGS(Beijing.Chaoyang, 2) VALUES('2018-10-03 14:38:05.000',10.30000,219,0.31000) d1001 USING meters TAGS(Beijing.Chaoyang, 2) VALUES('2018-10-03 14:38:15.000',12.60000,218,0.33000) d1001 USING meters TAGS(Beijing.Chaoyang, 2) VALUES('2018-10-03 14:38:16.800',12.30000,221,0.31000) d1002 USING meters TAGS(Beijing.Chaoyang, 3) VALUES('2018-10-03 14:38:16.650',10.30000,218,0.25000) d1003 USING meters TAGS(Beijing.Haidian, 2) VALUES('2018-10-03 14:38:05.500',11.80000,221,0.28000) d1003 USING meters TAGS(Beijing.Haidian, 2) VALUES('2018-10-03 14:38:16.600',13.40000,223,0.29000) d1004 USING meters TAGS(Beijing.Haidian, 3) VALUES('2018-10-03 14:38:05.000',10.80000,223,0.29000) d1004 USING meters TAGS(Beijing.Haidian, 3) VALUES('2018-10-03 14:38:06.500',11.50000,221,0.35000); +INSERT INTO d1001 USING meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 14:38:05.000',10.30000,219,0.31000) d1001 USING meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 14:38:15.000',12.60000,218,0.33000) d1001 USING meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 14:38:16.800',12.30000,221,0.31000) d1002 USING meters TAGS(California.SanFrancisco, 3) VALUES('2018-10-03 14:38:16.650',10.30000,218,0.25000) d1003 USING meters TAGS(California.LoSangeles, 2) VALUES('2018-10-03 14:38:05.500',11.80000,221,0.28000) d1003 USING meters TAGS(California.LoSangeles, 2) VALUES('2018-10-03 14:38:16.600',13.40000,223,0.29000) d1004 USING meters TAGS(California.LoSangeles, 3) VALUES('2018-10-03 14:38:05.000',10.80000,223,0.29000) d1004 USING meters TAGS(California.LoSangeles, 3) VALUES('2018-10-03 14:38:06.500',11.50000,221,0.35000); ``` Use TDengine CLI to execute SQL script @@ -300,8 +355,8 @@ output: ```` ...... -meters,location="beijing.chaoyang",groupid=2i32 current=10.3f32,voltage=219i32,phase=0.31f32 1538548685000000000 -meters,location="beijing.chaoyang",groupid=2i32 current=12.6f32,voltage=218i32,phase=0.33f32 1538548695000000000 +meters,location="California.SanFrancisco",groupid=2i32 current=10.3f32,voltage=219i32,phase=0.31f32 1538548685000000000 +meters,location="California.SanFrancisco",groupid=2i32 current=12.6f32,voltage=218i32,phase=0.33f32 1538548695000000000 ...... ```` @@ -356,6 +411,7 @@ The following configuration items apply to TDengine Sink Connector and TDengine 4. `max.retries`: The maximum number of retries when an error occurs. Defaults to 1. 5. `retry.backoff.ms`: The time interval for retry when sending an error. The unit is milliseconds. The default is 3000. 6. `db.schemaless`: Data format, could be one of `line`, `json`, and `telnet`. Represent InfluxDB line protocol format, OpenTSDB JSON format, and OpenTSDB Telnet line protocol format. +7. `data.precision`: The time precision when use InfluxDB line protocol format data, could be one of `ms`, `us` and `ns`. The default is `ns`. ### TDengine Source Connector specific configuration @@ -366,7 +422,13 @@ The following configuration items apply to TDengine Sink Connector and TDengine 5. `fetch.max.rows`: The maximum number of rows retrieved when retrieving the database. Default is 100. 6. `out.format`: The data format. The value could be line or json. The line represents the InfluxDB Line protocol format, and json represents the OpenTSDB JSON format. Default is `line`. -## feedback + +## Other notes + +1. To install plugin to a customized location, refer to https://docs.confluent.io/home/connect/self-managed/install.html#install-connector-manually. +2. 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b/docs-en/21-tdinternal/01-arch.md @@ -5,38 +5,38 @@ title: Architecture ## Cluster and Primary Logic Unit -The design of TDengine is based on the assumption that any hardware or software system is not 100% reliable and that no single node can provide sufficient computing and storage resources to process massive data. Therefore, TDengine has been designed in a distributed and high-reliability architecture since day one of the development, so that hardware failure or software failure of any single even multiple servers will not affect the availability and reliability of the system. At the same time, through node virtualization and automatic load-balancing technology, TDengine can make the most efficient use of computing and storage resources in heterogeneous clusters to reduce hardware resources significantly. +The design of TDengine is based on the assumption that any hardware or software system is not 100% reliable and that no single node can provide sufficient computing and storage resources to process massive data. Therefore, since day one, TDengine has been designed as a natively distributed system, with high-reliability architecture. Hardware failure or software failure of a single, or even multiple servers will not affect the availability and reliability of the system. At the same time, through node virtualization and automatic load-balancing technology, TDengine can make the most efficient use of computing and storage resources in heterogeneous clusters to reduce hardware resource needs, significantly. ### Primary Logic Unit -Logical structure diagram of TDengine distributed architecture as following: +Logical structure diagram of TDengine's distributed architecture is as follows: -![TDengine architecture diagram](structure.png) +![TDengine Database architecture diagram](structure.webp)
Figure 1: TDengine architecture diagram
A complete TDengine system runs on one or more physical nodes. Logically, it includes data node (dnode), TDengine client driver (TAOSC) and application (app). There are one or more data nodes in the system, which form a cluster. The application interacts with the TDengine cluster through TAOSC's API. The following is a brief introduction to each logical unit. **Physical node (pnode)**: A pnode is a computer that runs independently and has its own computing, storage and network capabilities. It can be a physical machine, virtual machine, or Docker container installed with OS. The physical node is identified by its configured FQDN (Fully Qualified Domain Name). TDengine relies entirely on FQDN for network communication. If you don't know about FQDN, please check [wikipedia](https://en.wikipedia.org/wiki/Fully_qualified_domain_name). -**Data node (dnode):** A dnode is a running instance of the TDengine server-side execution code taosd on a physical node. A working system must have at least one data node. A dnode contains zero to multiple logical virtual nodes (VNODE), zero or at most one logical management node (mnode). The unique identification of a dnode in the system is determined by the instance's End Point (EP). EP is a combination of FQDN (Fully Qualified Domain Name) of the physical node where the dnode is located and the network port number (Port) configured by the system. By configuring different ports, a physical node (a physical machine, virtual machine or container) can run multiple instances or have multiple data nodes. +**Data node (dnode):** A dnode is a running instance of the TDengine server-side execution code taosd on a physical node (pnode). A working system must have at least one data node. A dnode contains zero to multiple logical virtual nodes (VNODE) and zero or at most one logical management node (mnode). The unique identification of a dnode in the system is determined by the instance's End Point (EP). EP is a combination of FQDN (Fully Qualified Domain Name) of the physical node where the dnode is located and the network port number (Port) configured by the system. By configuring different ports, a physical node (a physical machine, virtual machine or container) can run multiple instances or have multiple data nodes. -**Virtual node (vnode)**: To better support data sharding, load balancing and prevent data from overheating or skewing, data nodes are virtualized into multiple virtual nodes (vnode, V2, V3, V4, etc. in the figure). Each vnode is a relatively independent work unit, which is the basic unit of time-series data storage and has independent running threads, memory space and persistent storage path. A vnode contains a certain number of tables (data collection points). When a new table is created, the system checks whether a new vnode needs to be created. The number of vnodes that can be created on a data node depends on the hardware capacities of the physical node where the data node is located. A vnode belongs to only one DB, but a DB can have multiple vnodes. In addition to the stored time-series data, a vnode also stores the schema and tag values of the included tables. A virtual node is uniquely identified in the system by the EP of the data node and the VGroup ID to which it belongs and is created and managed by the management node. +**Virtual node (vnode)**: To better support data sharding, load balancing and prevent data from overheating or skewing, data nodes are virtualized into multiple virtual nodes (vnode, V2, V3, V4, etc. in the figure). Each vnode is a relatively independent work unit, which is the basic unit of time-series data storage and has independent running threads, memory space and persistent storage path. A vnode contains a certain number of tables (data collection points). When a new table is created, the system checks whether a new vnode needs to be created. The number of vnodes that can be created on a data node depends on the capacity of the hardware of the physical node where the data node is located. A vnode belongs to only one DB, but a DB can have multiple vnodes. In addition to the stored time-series data, a vnode also stores the schema and tag values of the included tables. A virtual node is uniquely identified in the system by the EP of the data node and the VGroup ID to which it belongs and is created and managed by the management node. -**Management node (mnode)**: A virtual logical unit responsible for monitoring and maintaining the running status of all data nodes and load balancing among nodes (M in the figure). At the same time, the management node is also responsible for the storage and management of metadata (including users, databases, tables, static tags, etc.), so it is also called Meta Node. Multiple (up to 5) mnodes can be configured in a TDengine cluster, and they are automatically constructed into a virtual management node group (M0, M1, M2 in the figure). The master/slave mechanism is adopted for the mnode group and the data synchronization is carried out in a strongly consistent way. Any data update operation can only be executed on the master. The creation of mnode cluster is completed automatically by the system without manual intervention. There is at most one mnode on each dnode, which is uniquely identified by the EP of the data node to which it belongs. Each dnode automatically obtains the EP of the dnode where all mnodes in the whole cluster are located through internal messaging interaction. +**Management node (mnode)**: A virtual logical unit responsible for monitoring and maintaining the running status of all data nodes and load balancing among nodes (M in the figure). At the same time, the management node is also responsible for the storage and management of metadata (including users, databases, tables, static tags, etc.), so it is also called Meta Node. Multiple (up to 5) mnodes can be configured in a TDengine cluster, and they are automatically constructed into a virtual management node group (M0, M1, M2 in the figure). The master/slave mechanism is adopted for the mnode group and the data synchronization is carried out in a strongly consistent way. Any data update operation can only be executed on the master. The creation of mnode cluster is completed automatically by the system without manual intervention. There is at most one mnode on each dnode, which is uniquely identified by the EP of the data node to which it belongs. Each dnode automatically obtains the EP of the dnode where all mnodes in the whole cluster are located, through internal messaging interaction. -**Virtual node group (VGroup)**: Vnodes on different data nodes can form a virtual node group to ensure the high availability of the system. The virtual node group is managed in a master/slave mechanism. Write operations can only be performed on the master vnode, and then replicated to slave vnodes, thus ensuring that one single replica of data is copied on multiple physical nodes. The number of virtual nodes in a vgroup equals the number of data replicas. If the number of replicas of a DB is N, the system must have at least N data nodes. The number of replicas can be specified by the parameter `“replica”` when creating DB, and the default is 1. Using the multi-replication feature of TDengine, the same high data reliability can be achieved without the need for expensive storage devices such as disk arrays. Virtual node group is created and managed by the management node, and the management node assigns a system unique ID, aka VGroup ID. If two virtual nodes have the same vnode group ID, means that they belong to the same group and the data is backed up to each other. The number of virtual nodes in a virtual node group can be dynamically changed, allowing only one, that is, no data replication. VGroup ID is never changed. Even if a virtual node group is deleted, its ID will not be reused. +**Virtual node group (VGroup)**: Vnodes on different data nodes can form a virtual node group to ensure the high availability of the system. The virtual node group is managed in a master/slave mechanism. Write operations can only be performed on the master vnode, and then replicated to slave vnodes, thus ensuring that one single replica of data is copied on multiple physical nodes. The number of virtual nodes in a vgroup equals the number of data replicas. If the number of replicas of a DB is N, the system must have at least N data nodes. The number of replicas can be specified by the parameter `“replica”` when creating a DB, and the default is 1. Using the multi-replication feature of TDengine, the same high data reliability can be achieved without the need for expensive storage devices such as disk arrays. Virtual node groups are created and managed by the management node, and the management node assigns a system unique ID, aka VGroup ID. If two virtual nodes have the same vnode group ID, it means that they belong to the same group and the data is backed up to each other. The number of virtual nodes in a virtual node group can be dynamically changed, allowing only one, that is, no data replication. VGroup ID is never changed. Even if a virtual node group is deleted, its ID will not be reused. -**TAOSC**: TAOSC is the driver provided by TDengine to applications, which is responsible for dealing with the interaction between application and cluster, and provides the native interface of C/C++ language, which is embedded in JDBC, C #, Python, Go, Node.js language connection libraries. Applications interact with the whole cluster through TAOSC instead of directly connecting to data nodes in the cluster. This module is responsible for obtaining and caching metadata; forwarding requests for insertion, query, etc. to the correct data node; when returning the results to the application, TAOSC also needs to be responsible for the final level of aggregation, sorting, filtering and other operations. For JDBC, C/C++/C #/Python/Go/Node.js interfaces, this module runs on the physical node where the application is located. At the same time, in order to support the fully distributed RESTful interface, TAOSC has a running instance on each dnode of TDengine cluster. +**TAOSC**: TAOSC is the driver provided by TDengine to applications. It is responsible for dealing with the interaction between application and cluster, and provides the native interface for the C/C++ language. It is also embedded in the JDBC, C #, Python, Go, Node.js language connection libraries. Applications interact with the whole cluster through TAOSC instead of directly connecting to data nodes in the cluster. This module is responsible for obtaining and caching metadata; forwarding requests for insertion, query, etc. to the correct data node; when returning the results to the application, TAOSC also needs to be responsible for the final level of aggregation, sorting, filtering and other operations. For JDBC, C/C++/C#/Python/Go/Node.js interfaces, this module runs on the physical node where the application is located. At the same time, in order to support the fully distributed RESTful interface, TAOSC has a running instance on each dnode of TDengine cluster. ### Node Communication -**Communication mode**: The communication among each data node of TDengine system, and among the client driver and each data node is carried out through TCP/UDP. Considering an IoT scenario, the data writing packets are generally not large, so TDengine uses UDP in addition to TCP for transmission, because UDP is more efficient and is not limited by the number of connections. TDengine implements its own timeout, retransmission, confirmation and other mechanisms to ensure reliable transmission of UDP. For packets with a data volume of less than 15K, UDP is adopted for transmission, and TCP is automatically adopted for transmission of packets with a data volume of more than 15K or query operations. At the same time, TDengine will automatically compress/decompress the data, digital sign/authenticate the data according to the configuration and data packet. For data replication among data nodes, only TCP is used for data transportation. +**Communication mode**: The communication among each data node of TDengine system, and among the client driver and each data node is carried out through TCP/UDP. Considering an IoT scenario, the data writing packets are generally not large, so TDengine uses UDP in addition to TCP for transmission, because UDP is more efficient and is not limited by the number of connections. TDengine implements its own timeout, retransmission, confirmation and other mechanisms to ensure reliable transmission of UDP. For packets with a data volume of less than 15K, UDP is adopted for transmission, and TCP is automatically adopted for transmission of packets with a data volume of more than 15K or query operations. At the same time, TDengine will automatically compress/decompress the data, digitally sign/authenticate the data according to the configuration and data packet. For data replication among data nodes, only TCP is used for data transportation. **FQDN configuration:** A data node has one or more FQDNs, which can be specified in the system configuration file taos.cfg with the parameter “fqdn”. If it is not specified, the system will automatically use the hostname of the computer as its FQDN. If the node is not configured with FQDN, you can directly set the configuration parameter “fqdn” of the node to its IP address. However, IP is not recommended because IP address may be changed, and once it changes, the cluster will not work properly. The EP (End Point) of a data node consists of FQDN + Port. With FQDN, it is necessary to ensure the DNS service is running, or hosts files on nodes are configured properly. **Port configuration**: The external port of a data node is determined by the system configuration parameter “serverPort” in TDengine, and the port for internal communication of cluster is serverPort+5. The data replication operation among data nodes in the cluster also occupies a TCP port, which is serverPort+10. In order to support multithreading and efficient processing of UDP data, each internal and external UDP connection needs to occupy 5 consecutive ports. Therefore, the total port range of a data node will be serverPort to serverPort + 10, for a total of 11 TCP/UDP ports. To run the system, make sure that the firewall keeps these ports open. Each data node can be configured with a different serverPort. -**Cluster external connection**: TDengine cluster can accommodate one single, multiple or even thousands of data nodes. The application only needs to initiate a connection to any data node in the cluster. The network parameter required for connection is the End Point (FQDN plus configured port number) of a data node. When starting the application taos through CLI, the FQDN of the data node can be specified through the option `-h`, and the configured port number can be specified through `-p`. If the port is not configured, the system configuration parameter “serverPort” of TDengine will be adopted. +**Cluster external connection**: TDengine cluster can accommodate a single, multiple or even thousands of data nodes. The application only needs to initiate a connection to any data node in the cluster. The network parameter required for connection is the End Point (FQDN plus configured port number) of a data node. When starting the application taos through CLI, the FQDN of the data node can be specified through the option `-h`, and the configured port number can be specified through `-p`. If the port is not configured, the system configuration parameter “serverPort” of TDengine will be adopted. **Inter-cluster communication**: Data nodes connect with each other through TCP/UDP. When a data node starts, it will obtain the EP information of the dnode where the mnode is located, and then establish a connection with the mnode in the system to exchange information. There are three steps to obtain EP information of the mnode: @@ -44,31 +44,33 @@ A complete TDengine system runs on one or more physical nodes. Logically, it inc 2. Check the system configuration file taos.cfg to obtain node configuration parameters “firstEp” and “secondEp” (the node specified by these two parameters can be a normal node without mnode, in this case, the node will try to redirect to the mnode node when connected). If these two configuration parameters do not exist or do not exist in taos.cfg, or are invalid, skip to the third step; 3. Set your own EP as a mnode EP and run it independently. After obtaining the mnode EP list, the data node initiates the connection. It will successfully join the working cluster after connection. If not successful, it will try the next item in the mnode EP list. If all attempts are made, but the connection still fails, sleep for a few seconds before trying again. -**The choice of MNODE**: TDengine logically has a management node, but there is no separated execution code. The server-side only has a set of execution code taosd. So which data node will be the management node? This is determined automatically by the system without any manual intervention. The principle is as follows: when a data node starts, it will check its End Point and compare it with the obtained mnode EP List. If its EP exists in it, the data node shall start the mnode module and become a mnode. If your own EP is not in the mnode EP List, the mnode module will not start. During the system operation, due to load balancing, downtime and other reasons, mnode may migrate to the new dnode, while totally transparent without manual intervention. The modification of configuration parameters is the decision made by mnode itself according to resources usage. +**The choice of MNODE**: TDengine logically has a management node, but there is no separate execution code. The server-side only has one set of execution code, taosd. So which data node will be the management node? This is determined automatically by the system without any manual intervention. The principle is as follows: when a data node starts, it will check its End Point and compare it with the obtained mnode EP List. If its EP exists in it, the data node shall start the mnode module and become a mnode. If your own EP is not in the mnode EP List, the mnode module will not start. During the system operation, due to load balancing, downtime and other reasons, mnode may migrate to the new dnode, totally transparently and without manual intervention. The modification of configuration parameters is the decision made by mnode itself according to resources usage. -**Add new data nodes:** After the system has a data node, it has become a working system. There are two steps to add a new node into the cluster. Step1: Connect to the existing working data node using TDengine CLI, and then add the End Point of the new data node with the command "create dnode"; Step 2: In the system configuration parameter file taos.cfg of the new data node, set the “firstEp” and “secondEp” parameters to the EP of any two data nodes in the existing cluster. Please refer to the detailed user tutorial for detailed steps. In this way, the cluster will be established step by step. +**Add new data nodes:** After the system has a data node, it has become a working system. There are two steps to add a new node into the cluster. +- Step1: Connect to the existing working data node using TDengine CLI, and then add the End Point of the new data node with the command "create dnode" +- Step 2: In the system configuration parameter file taos.cfg of the new data node, set the “firstEp” and “secondEp” parameters to the EP of any two data nodes in the existing cluster. Please refer to the user tutorial for detailed steps. In this way, the cluster will be established step by step. -**Redirection**: No matter about dnode or TAOSC, the connection to the mnode shall be initiated first, but the mnode is automatically created and maintained by the system, so the user does not know which dnode is running the mnode. TDengine only requires a connection to any working dnode in the system. Because any running dnode maintains the currently running mnode EP List, when receiving a connecting request from the newly started dnode or TAOSC, if it’s not a mnode by self, it will reply to the mnode EP List back. After receiving this list, TAOSC or the newly started dnode will try to establish the connection again. When the mnode EP List changes, each data node quickly obtains the latest list and notifies TAOSC through messaging interaction among nodes. +**Redirection**: Regardless of dnode or TAOSC, the connection to the mnode is initiated first. The mnode is automatically created and maintained by the system, so the user does not know which dnode is running the mnode. TDengine only requires a connection to any working dnode in the system. Because any running dnode maintains the currently running mnode EP List, when receiving a connecting request from the newly started dnode or TAOSC, if it’s not an mnode itself, it will reply to the mnode with the EP List. After receiving this list, TAOSC or the newly started dnode will try to establish the connection again. When the mnode EP List changes, each data node quickly obtains the latest list and notifies TAOSC through messaging interaction among nodes. ### A Typical Data Writing Process To explain the relationship between vnode, mnode, TAOSC and application and their respective roles, the following is an analysis of a typical data writing process. -![typical process of TDengine](message.png) +![typical process of TDengine Database](message.webp)
Figure 2: Typical process of TDengine
1. Application initiates a request to insert data through JDBC, ODBC, or other APIs. -2. TAOSC checks if meta data existing for the table in the cache. If so, go straight to Step 4. If not, TAOSC sends a get meta-data request to mnode. +2. TAOSC checks the cache to see if meta data exists for the table. If it does, it goes straight to Step 4. If not, TAOSC sends a get meta-data request to mnode. 3. Mnode returns the meta-data of the table to TAOSC. Meta-data contains the schema of the table, and also the vgroup information to which the table belongs (the vnode ID and the End Point of the dnode where the table belongs. If the number of replicas is N, there will be N groups of End Points). If TAOSC does not receive a response from the mnode for a long time, and there are multiple mnodes, TAOSC will send a request to the next mnode. 4. TAOSC initiates an insert request to master vnode. 5. After vnode inserts the data, it gives a reply to TAOSC, indicating that the insertion is successful. If TAOSC doesn't get a response from vnode for a long time, TAOSC will treat this node as offline. In this case, if there are multiple replicas of the inserted database, TAOSC will issue an insert request to the next vnode in vgroup. 6. TAOSC notifies APP that writing is successful. -For Step 2 and 3, when TAOSC starts, it does not know the End Point of mnode, so it will directly initiate a request to the configured serving End Point of the cluster. If the dnode that receives the request does not have a mnode configured, it will inform the mnode EP list in a reply message, so that TAOSC will re-issue a request to obtain meta-data to the EP of another new mnode. +For Step 2 and 3, when TAOSC starts, it does not know the End Point of mnode, so it will directly initiate a request to the configured serving End Point of the cluster. If the dnode that receives the request does not have a mnode configured, it will reply with the mnode EP list, so that TAOSC will re-issue a request to obtain meta-data to the EP of another mnode. -For Step 4 and 5, without caching, TAOSC can't recognize the master in the virtual node group, so assumes that the first vnode is the master and sends a request to it. If this vnode is not the master, it will reply to the actual master as a new target where TAOSC shall send a request to. Once the reply of successful insertion is obtained, TAOSC will cache the information of master node. +For Step 4 and 5, without caching, TAOSC can't recognize the master in the virtual node group, so assumes that the first vnode is the master and sends a request to it. If this vnode is not the master, it will reply to the actual master as a new target to which TAOSC shall send a request. Once a response of successful insertion is obtained, TAOSC will cache the information of master node. -The above is the process of inserting data, and the processes of querying and computing are the same. TAOSC encapsulates and hides all these complicated processes, and it is transparent to applications. +The above describes the process of inserting data. The processes of querying and computing are the same. TAOSC encapsulates and hides all these complicated processes, and it is transparent to applications. Through TAOSC caching mechanism, mnode needs to be accessed only when a table is accessed for the first time, so mnode will not become a system bottleneck. However, because schema and vgroup may change (such as load balancing), TAOSC will interact with mnode regularly to automatically update the cache. @@ -76,24 +78,24 @@ Through TAOSC caching mechanism, mnode needs to be accessed only when a table is ### Storage Model -The data stored by TDengine include collected time-series data, metadata related to database and tables, tag data, etc. These data are specifically divided into three parts: +The data stored by TDengine includes collected time-series data, metadata related to database and tables, tag data, etc. All of the data is specifically divided into three parts: -- Time-series data: stored in vnode and composed of data, head and last files. The amount of data is large and query amount depends on the application scenario. Out-of-order writing is allowed, but delete operation is not supported for the time being, and update operation is only allowed when database “update” parameter is set to 1. By adopting the model with **one table for each data collection point**, the data of a given time period is continuously stored, and the writing against one single table is a simple appending operation. Multiple records can be read at one time, thus ensuring the insert and query operation of a single data collection point with the best performance. -- Tag data: meta files stored in vnode. Four standard operations of create, read, update and delete are supported. The amount of data is not large. If there are N tables, there are N records, so all can be stored in memory. To make tag filtering efficient, TDengine supports multi-core and multi-threaded concurrent queries. As long as the computing resources are sufficient, even in face of millions of tables, the tag filtering results will return in milliseconds. -- Metadata: stored in mnode, including system node, user, DB, Table Schema and other information. Four standard operations of create, delete, update and read are supported. The amount of these data are not large and can be stored in memory, moreover, the query amount is not large because of the client cache. Therefore, TDengine uses centralized storage management, however, there will be no performance bottleneck. +- Time-series data: stored in vnode and composed of data, head and last files. The amount of data is large and query amount depends on the application scenario. Out-of-order writing is allowed, but delete operation is not supported for the time being, and update operation is only allowed when database “update” parameter is set to 1. By adopting the model with **one table for each data collection point**, the data of a given time period is continuously stored, and the writing against one single table is a simple appending operation. Multiple records can be read at one time, thus ensuring the best performance for both insert and query operations of a single data collection point. +- Tag data: meta files stored in vnode. Four standard operations of create, read, update and delete are supported. The amount of data is not large. If there are N tables, there are N records, so all can be stored in memory. To make tag filtering efficient, TDengine supports multi-core and multi-threaded concurrent queries. As long as the computing resources are sufficient, even with millions of tables, the tag filtering results will return in milliseconds. +- Metadata: stored in mnode and includes system node, user, DB, table schema and other information. Four standard operations of create, delete, update and read are supported. The amount of this data is not large and can be stored in memory. Moreover, the number of queries is not large because of client cache. Even though TDengine uses centralized storage management, because of the architecture, there is no performance bottleneck. -Compared with the typical NoSQL storage model, TDengine stores tag data and time-series data completely separately, which has two major advantages: +Compared with the typical NoSQL storage model, TDengine stores tag data and time-series data completely separately. This has two major advantages: -- Reduce the redundancy of tag data storage significantly: general NoSQL database or time-series database adopts K-V storage, in which Key includes a timestamp, a device ID and various tags. Each record carries these duplicated tags, so storage space is wasted. Moreover, if the application needs to add, modify or delete tags on historical data, it has to traverse the data and rewrite them again, which is extremely expensive to operate. -- Aggregate data efficiently between multiple tables: when aggregating data between multiple tables, it first finds out the tables which satisfy the filtering conditions, and then find out the corresponding data blocks of these tables to greatly reduce the data sets to be scanned, thus greatly improving the aggregation efficiency. Moreover, tag data is managed and maintained in a full-memory structure, and tag data queries in tens of millions can return in milliseconds. +- Reduces the redundancy of tag data storage significantly. General NoSQL database or time-series database adopts K-V (key-value) storage, in which the key includes a timestamp, a device ID and various tags. Each record carries these duplicated tags, so storage space is wasted. Moreover, if the application needs to add, modify or delete tags on historical data, it has to traverse the data and rewrite them again, which is an extremely expensive operation. +- Aggregate data efficiently between multiple tables: when aggregating data between multiple tables, it first finds the tables which satisfy the filtering conditions, and then finds the corresponding data blocks of these tables. This greatly reduces the data sets to be scanned which in turn improves the aggregation efficiency. Moreover, tag data is managed and maintained in a full-memory structure, and tag data queries in tens of millions can return in milliseconds. ### Data Sharding -For large-scale data management, to achieve scale-out, it is generally necessary to adopt the Partitioning or Sharding strategy. TDengine implements data sharding via vnode, and time-series data partitioning via one data file for a time range. +For large-scale data management, to achieve scale-out, it is generally necessary to adopt a Partitioning or Sharding strategy. TDengine implements data sharding via vnode, and time-series data partitioning via one data file for a time range. VNode (Virtual Data Node) is responsible for providing writing, query and computing functions for collected time-series data. To facilitate load balancing, data recovery and support heterogeneous environments, TDengine splits a data node into multiple vnodes according to its computing and storage resources. The management of these vnodes is done automatically by TDengine and is completely transparent to the application. -For a single data collection point, regardless of the amount of data, a vnode (or vnode group, if the number of replicas is greater than 1) has enough computing resource and storage resource to process (if a 16-byte record is generated per second, the original data generated in one year will be less than 0.5 G), so TDengine stores all the data of a table (a data collection point) in one vnode instead of distributing the data to two or more dnodes. Moreover, a vnode can store data from multiple data collection points (tables), and the upper limit of the tables’ quantity for a vnode is one million. By design, all tables in a vnode belong to the same DB. On a data node, unless specially configured, the number of vnodes owned by a DB will not exceed the number of system cores. +For a single data collection point, regardless of the amount of data, a vnode (or vnode group, if the number of replicas is greater than 1) has enough computing resource and storage resource to process (if a 16-byte record is generated per second, the original data generated in one year will be less than 0.5 G). So TDengine stores all the data of a table (a data collection point) in one vnode instead of distributing the data to two or more dnodes. Moreover, a vnode can store data from multiple data collection points (tables), and the upper limit of the tables’ quantity for a vnode is one million. By design, all tables in a vnode belong to the same DB. On a data node, unless specially configured, the number of vnodes owned by a DB will not exceed the number of system cores. When creating a DB, the system does not allocate resources immediately. However, when creating a table, the system will check if there is an allocated vnode with free tablespace. If so, the table will be created in the vacant vnode immediately. If not, the system will create a new vnode on a dnode from the cluster according to the current workload, and then a table. If there are multiple replicas of a DB, the system does not create only one vnode, but a vgroup (virtual data node group). The system has no limit on the number of vnodes, which is just limited by the computing and storage resources of physical nodes. @@ -101,43 +103,43 @@ The meta data of each table (including schema, tags, etc.) is also stored in vno ### Data Partitioning -In addition to vnode sharding, TDengine partitions the time-series data by time range. Each data file contains only one time range of time-series data, and the length of the time range is determined by DB's configuration parameter `“days”`. This method of partitioning by time rang is also convenient to efficiently implement the data retention policy. As long as the data file exceeds the specified number of days (system configuration parameter `“keep”`), it will be automatically deleted. Moreover, different time ranges can be stored in different paths and storage media, so as to facilitate the tiered-storage. Cold/hot data can be stored in different storage media to reduce the storage cost. +In addition to vnode sharding, TDengine partitions the time-series data by time range. Each data file contains only one time range of time-series data, and the length of the time range is determined by the database configuration parameter `“days”`. This method of partitioning by time range is also convenient to efficiently implement data retention policies. As long as the data file exceeds the specified number of days (system configuration parameter `“keep”`), it will be automatically deleted. Moreover, different time ranges can be stored in different paths and storage media, so as to facilitate tiered-storage. Cold/hot data can be stored in different storage media to significantly reduce storage costs. In general, **TDengine splits big data by vnode and time range in two dimensions** to manage the data efficiently with horizontal scalability. ### Load Balancing -Each dnode regularly reports its status (including hard disk space, memory size, CPU, network, number of virtual nodes, etc.) to the mnode (virtual management node), so mnode knows the status of the entire cluster. Based on the overall status, when the mnode finds a dnode is overloaded, it will migrate one or more vnodes to other dnodes. During the process, TDengine services keep running and the data insertion, query and computing operations are not affected. +Each dnode regularly reports its status (including hard disk space, memory size, CPU, network, number of virtual nodes, etc.) to the mnode (virtual management node) so that the mnode knows the status of the entire cluster. Based on the overall status, when the mnode finds a dnode is overloaded, it will migrate one or more vnodes to other dnodes. During the process, TDengine services keep running and the data insertion, query and computing operations are not affected. -If the mnode has not received the dnode status for a period of time, the dnode will be treated as offline. When offline lasts a certain period of time (configured by parameter `“offlineThreshold”`), the dnode will be forcibly removed from the cluster by mnode. If the number of replicas of vnodes on this dnode is greater than one, the system will automatically create new replicas on other dnodes to ensure the replica number. If there are other mnodes on this dnode and the number of mnodes replicas is greater than one, the system will automatically create new mnodes on other dnodes to ensure the replica number. +If the mnode has not received the dnode status for a period of time, the dnode will be treated as offline. If the dnode stays offline beyond the time configured by parameter `“offlineThreshold”`, the dnode will be forcibly removed from the cluster by mnode. If the number of replicas of vnodes on this dnode is greater than one, the system will automatically create new replicas on other dnodes to ensure the replica number. If there are other mnodes on this dnode and the number of mnodes replicas is greater than one, the system will automatically create new mnodes on other dnodes to ensure the replica number. -When new data nodes are added to the cluster, with new computing and storage resources are added, the system will automatically start the load balancing process. +When new data nodes are added to the cluster, with new computing and storage resources, the system will automatically start the load balancing process. The load balancing process does not require any manual intervention, and it is transparent to the application. **Note: load balancing is controlled by parameter “balance”, which determines to turn on/off automatic load balancing.** ## Data Writing and Replication Process -If a database has N replicas, thus a virtual node group has N virtual nodes, but only one as Master and all others are slaves. When the application writes a new record to system, only the Master vnode can accept the writing request. If a slave vnode receives a writing request, the system will notifies TAOSC to redirect. +If a database has N replicas, a virtual node group has N virtual nodes. But only one is the Master and all others are slaves. When the application writes a new record to system, only the Master vnode can accept the writing request. If a slave vnode receives a writing request, the system will notifies TAOSC to redirect. ### Master vnode Writing Process Master Vnode uses a writing process as follows: -![TDengine Master Writing Process](write_master.png) +![TDengine Database Master Writing Process](write_master.webp)
Figure 3: TDengine Master writing process
1. Master vnode receives the application data insertion request, verifies, and moves to next step; 2. If the system configuration parameter `“walLevel”` is greater than 0, vnode will write the original request packet into database log file WAL. If walLevel is set to 2 and fsync is set to 0, TDengine will make WAL data written immediately to ensure that even system goes down, all data can be recovered from database log file; 3. If there are multiple replicas, vnode will forward data packet to slave vnodes in the same virtual node group, and the forwarded packet has a version number with data; 4. Write into memory and add the record to “skip list”; -5. Master vnode returns a confirmation message to the application, indicating a successful writing. +5. Master vnode returns a confirmation message to the application, indicating a successful write. 6. If any of Step 2, 3 or 4 fails, the error will directly return to the application. ### Slave vnode Writing Process For a slave vnode, the write process as follows: -![TDengine Slave Writing Process](write_slave.png) +![TDengine Database Slave Writing Process](write_slave.webp)
Figure 4: TDengine Slave Writing Process
1. Slave vnode receives a data insertion request forwarded by Master vnode; @@ -146,19 +148,19 @@ For a slave vnode, the write process as follows: Compared with Master vnode, slave vnode has no forwarding or reply confirmation step, means two steps less. But writing into memory and WAL is exactly the same. -### Remote Disaster Recovery and IDC Migration +### Remote Disaster Recovery and IDC (Internet Data Center) Migration -As above Master and Slave processes discussed, TDengine adopts asynchronous replication for data synchronization. This method can greatly improve the writing performance, with no obvious impact from network delay. By configuring IDC and rack number for each physical node, it can be ensured that for a virtual node group, virtual nodes are composed of physical nodes from different IDC and different racks, thus implementing remote disaster recovery without other tools. +As discussed above, TDengine writes using Master and Slave processes. TDengine adopts asynchronous replication for data synchronization. This method can greatly improve write performance, with no obvious impact from network delay. By configuring IDC and rack number for each physical node, it can be ensured that for a virtual node group, virtual nodes are composed of physical nodes from different IDC and different racks, thus implementing remote disaster recovery without other tools. -On the other hand, TDengine supports dynamic modification of the replicas number. Once the number of replicas increases, the newly added virtual nodes will immediately enter the data synchronization process. After synchronization completed, added virtual nodes can provide services. In the synchronization process, master and other synchronized virtual nodes keep serving. With this feature, TDengine can provide IDC migration without service interruption. It is only necessary to add new physical nodes to the existing IDC cluster, and then remove old physical nodes after the data synchronization is completed. +On the other hand, TDengine supports dynamic modification of the replica number. Once the number of replicas increases, the newly added virtual nodes will immediately enter the data synchronization process. After synchronization is complete, added virtual nodes can provide services. In the synchronization process, master and other synchronized virtual nodes keep serving. With this feature, TDengine can provide IDC migration without service interruption. It is only necessary to add new physical nodes to the existing IDC cluster, and then remove old physical nodes after the data synchronization is completed. -However, the asynchronous replication has a tiny time window where data can be lost. The specific scenario is as follows: +However, the asynchronous replication has a very low probability scenario where data may be lost. The specific scenario is as follows: -1. Master vnode has finished its 5-step operations, confirmed the success of writing to APP, and then went down; +1. Master vnode has finished its 5-step operations, confirmed the success of writing to APP, and then goes down; 2. Slave vnode receives the write request, then processing fails before writing to the log in Step 2; 3. Slave vnode will become the new master, thus losing one record. -In theory, for asynchronous replication, there is no guarantee to prevent data loss. However, this window is extremely small, only if mater and slave fail at the same time, and just confirm the successful write to the application before. +In theory, for asynchronous replication, there is no guarantee to prevent data loss. However, this is an extremely low probability scenario as described above. Note: Remote disaster recovery and no-downtime IDC migration are only supported by Enterprise Edition. **Hint: This function is not available yet** @@ -171,43 +173,43 @@ When a vnode starts, the roles (master, slave) are uncertain, and the data is in 1. If there’s only one replica, it’s always master 2. When all replicas are online, the one with latest version is master 3. Over half of online nodes are virtual nodes, and some virtual node is slave, it will automatically become master -4. For 2 and 3, if multiple virtual nodes meet the requirement, the first vnode in virtual node group list will be selected as master +4. For 2 and 3, if multiple virtual nodes meet the requirement, the first vnode in virtual node group list will be selected as master. ### Synchronous Replication For scenarios with strong data consistency requirements, asynchronous data replication is not applicable, because there is a small probability of data loss. So, TDengine provides a synchronous replication mechanism for users. When creating a database, in addition to specifying the number of replicas, user also needs to specify a new parameter “quorum”. If quorum is greater than one, it means that every time the Master forwards a message to the replica, it needs to wait for “quorum-1” reply confirms before informing the application that data has been successfully written in slave. If “quorum-1” reply confirms are not received within a certain period of time, the master vnode will return an error to the application. -With synchronous replication, performance of system will decrease and latency will increase. Because metadata needs strong consistent, the default for data synchronization between mnodes is synchronous replication. +With synchronous replication, performance of system will decrease and latency will increase. Because metadata needs strong consistency, the default for data synchronization between mnodes is synchronous replication. ## Caching and Persistence ### Caching -TDengine adopts a time-driven cache management strategy (First-In-First-Out, FIFO), also known as a Write-driven Cache Management Mechanism. This strategy is different from the read-driven data caching mode (Least-Recent-Used, LRU), which directly put the most recently written data in the system buffer. When the buffer reaches a threshold, the earliest data are written to disk in batches. Generally speaking, for the use of IoT data, users are most concerned about the newly generated data, that is, the current status. TDengine takes full advantage of this feature to put the most recently arrived (current state) data in the buffer. +TDengine adopts a time-driven cache management strategy (First-In-First-Out, FIFO), also known as a Write-driven Cache Management Mechanism. This strategy is different from the read-driven data caching mode (Least-Recent-Used, LRU), which directly puts the most recently written data in the system buffer. When the buffer reaches a threshold, the earliest data are written to disk in batches. Generally speaking, for the use of IoT data, users are most concerned about the most recently generated data, that is, the current status. TDengine takes full advantage of this feature to put the most recently arrived (current state) data in the buffer. -TDengine provides millisecond-level data collecting capability to users through query functions. Putting the recently arrived data directly in the buffer can respond to users' analysis query for the latest piece or batch of data more quickly, and provide faster database query response capability as a whole. In this sense, **TDengine can be used as a data cache by setting appropriate configuration parameters without deploying Redis or other additional cache systems**, which can effectively simplify the system architecture and reduce the operation costs. It should be noted that after the TDengine is restarted, the buffer of the system will be emptied, the previously cached data will be written to disk in batches, and the previously cached data will not be reloaded into the buffer as so in a proprietary key-value cache system. +TDengine provides millisecond-level data collecting capability to users through query functions. Putting the recently arrived data directly in the buffer can respond to users' analysis query for the latest piece or batch of data more quickly, and provide faster database query response capability as a whole. In this sense, **TDengine can be used as a data cache by setting appropriate configuration parameters without deploying Redis or other additional cache systems**. This can effectively simplify the system architecture and reduce operational costs. It should be noted that after TDengine is restarted, the buffer of the system will be emptied, the previously cached data will be written to disk in batches, and the previously cached data will not be reloaded into the buffer. In this sense, TDengine's cache differs from proprietary key-value cache systems. Each vnode has its own independent memory, and it is composed of multiple memory blocks of fixed size, and different vnodes are completely isolated. When writing data, similar to the writing of logs, data is sequentially added to memory, but each vnode maintains its own skip list for quick search. When more than one third of the memory block are used, the disk writing operation will start, and the subsequent writing operation is carried out in a new memory block. By this design, one third of the memory blocks in a vnode keep the latest data, so as to achieve the purpose of caching and quick search. The number of memory blocks of a vnode is determined by the configuration parameter “blocks”, and the size of memory blocks is determined by the configuration parameter “cache”. ### Persistent Storage -TDengine uses a data-driven method to write the data from buffer into hard disk for persistent storage. When the cached data in vnode reaches a certain volume, TDengine will also pull up the disk-writing thread to write the cached data into persistent storage in order not to block subsequent data writing. TDengine will open a new database log file when the data is written, and delete the old database log file after written successfully to avoid unlimited log growth. +TDengine uses a data-driven method to write the data from buffer into hard disk for persistent storage. When the cached data in vnode reaches a certain volume, TDengine will pull up the disk-writing thread to write the cached data into persistent storage so that subsequent data writing is not blocked. TDengine will open a new database log file when the data is written, and delete the old database log file after successfull persistence, to avoid unlimited log growth. -To make full use of the characteristics of time-series data, TDengine splits the data stored in persistent storage by a vnode into multiple files, each file only saves data for a fixed number of days, which is determined by the system configuration parameter `“days”`. By so, for the given start and end date of a query, you can locate the data files to open immediately without any index, thus greatly speeding up reading operations. +To make full use of the characteristics of time-series data, TDengine splits the data stored in persistent storage by a vnode into multiple files, each file only saves data for a fixed number of days, which is determined by the system configuration parameter `“days”`. Thus for given start and end dates of a query, you can locate the data files to open immediately without any index. This greatly speeds up read operations. For time-series data, there is generally a retention policy, which is determined by the system configuration parameter `“keep”`. Data files exceeding this set number of days will be automatically deleted by the system to free up storage space. Given “days” and “keep” parameters, the total number of data files in a vnode is: keep/days. The total number of data files should not be too large or too small. 10 to 100 is appropriate. Based on this principle, reasonable days can be set. In the current version, parameter “keep” can be modified, but parameter “days” cannot be modified once it is set. -In each data file, the data of a table is stored by blocks. A table can have one or more data file blocks. In a file block, data is stored in columns, occupying a continuous storage space, thus greatly improving the reading speed. The size of file block is determined by the system parameter `“maxRows”` (the maximum number of records per block), and the default value is 4096. This value should not be too large or too small. If it is too large, the data locating in search will cost longer; if too small, the index of data block is too large, and the compression efficiency will be low with slower reading speed. +In each data file, the data of a table is stored in blocks. A table can have one or more data file blocks. In a file block, data is stored in columns, occupying a continuous storage space, thus greatly improving the reading speed. The size of file block is determined by the system parameter `“maxRows”` (the maximum number of records per block), and the default value is 4096. This value should not be too large or too small. If it is too large, data location for queries will take a longer tim. If it is too small, the index of data block is too large, and the compression efficiency will be low with slower reading speed. -Each data file (with a .data postfix) has a corresponding index file (with a .head postfix). The index file has summary information of a data block for each table, recording the offset of each data block in the data file, start and end time of data and other information, so as to lead system quickly locate the data to be found. Each data file also has a corresponding last file (with a .last postfix), which is designed to prevent data block fragmentation when written in disk. If the number of written records from a table does not reach the system configuration parameter `“minRows”` (minimum number of records per block), it will be stored in the last file first. When write to disk next time, the newly written records will be merged with the records in last file and then written into data file. +Each data file (with a .data postfix) has a corresponding index file (with a .head postfix). The index file has summary information of a data block for each table, recording the offset of each data block in the data file, start and end time of data and other information which allows the system to locate the data to be found very quickly. Each data file also has a corresponding last file (with a .last postfix), which is designed to prevent data block fragmentation when written in disk. If the number of written records from a table does not reach the system configuration parameter `“minRows”` (minimum number of records per block), it will be stored in the last file first. At the next write operation to the disk, the newly written records will be merged with the records in last file and then written into data file. -When data is written to disk, it is decided whether to compress the data according to system configuration parameter `“comp”`. TDengine provides three compression options: no compression, one-stage compression and two-stage compression, corresponding to comp values of 0, 1 and 2 respectively. One-stage compression is carried out according to the type of data. Compression algorithms include delta-delta coding, simple 8B method, zig-zag coding, LZ4 and other algorithms. Two-stage compression is based on one-stage compression and compressed by general compression algorithm, which has higher compression ratio. +When data is written to disk, the system decideswhether to compress the data based on the system configuration parameter `“comp”`. TDengine provides three compression options: no compression, one-stage compression and two-stage compression, corresponding to comp values of 0, 1 and 2 respectively. One-stage compression is carried out according to the type of data. Compression algorithms include delta-delta coding, simple 8B method, zig-zag coding, LZ4 and other algorithms. Two-stage compression is based on one-stage compression and compressed by general compression algorithm, which has higher compression ratio. ### Tiered Storage -By default, TDengine saves all data in /var/lib/taos directory, and the data files of each vnode are saved in a different directory under this directory. In order to expand the storage space, minimize the bottleneck of file reading and improve the data throughput rate, TDengine can configure the system parameter “dataDir” to allow multiple mounted hard disks to be used by system at the same time. In addition, TDengine also provides the function of tiered data storage, i.e. storage on different storage media according to the time stamps of data files. For example, the latest data is stored on SSD, the data for more than one week is stored on local hard disk, and the data for more than four weeks is stored on network storage device, thus reducing the storage cost and ensuring efficient data access. The movement of data on different storage media is automatically done by the system and completely transparent to applications. Tiered storage of data is also configured through the system parameter “dataDir”. +By default, TDengine saves all data in /var/lib/taos directory, and the data files of each vnode are saved in a different directory under this directory. In order to expand the storage space, minimize the bottleneck of file reading and improve the data throughput rate, TDengine can configure the system parameter “dataDir” to allow multiple mounted hard disks to be used by system at the same time. In addition, TDengine also provides the function of tiered data storage, i.e. storage on different storage media according to the time stamps of data files. For example, the latest data is stored on SSD, the data older than a week is stored on local hard disk, and data older than four weeks is stored on network storage device. This reduces storage costs and ensures efficient data access. The movement of data on different storage media is automatically done by the system and is completely transparent to applications. Tiered storage of data is also configured through the system parameter “dataDir”. dataDir format is as follows: ``` @@ -216,7 +218,7 @@ dataDir data_path [tier_level] Where data_path is the folder path of mount point and tier_level is the media storage-tier. The higher the media storage-tier, means the older the data file. Multiple hard disks can be mounted at the same storage-tier, and data files on the same storage-tier are distributed on all hard disks within the tier. TDengine supports up to 3 tiers of storage, so tier_level values are 0, 1, and 2. When configuring dataDir, there must be only one mount path without specifying tier_level, which is called special mount disk (path). The mount path defaults to level 0 storage media and contains special file links, which cannot be removed, otherwise it will have a devastating impact on the written data. -Suppose a physical node with six mountable hard disks/mnt/disk1,/mnt/disk2, …,/mnt/disk6, where disk1 and disk2 need to be designated as level 0 storage media, disk3 and disk4 are level 1 storage media, and disk5 and disk6 are level 2 storage media. Disk1 is a special mount disk, you can configure it in/etc/taos/taos.cfg as follows: +Suppose there is a physical node with six mountable hard disks/mnt/disk1,/mnt/disk2, …,/mnt/disk6, where disk1 and disk2 need to be designated as level 0 storage media, disk3 and disk4 are level 1 storage media, and disk5 and disk6 are level 2 storage media. Disk1 is a special mount disk, you can configure it in/etc/taos/taos.cfg as follows: ``` dataDir /mnt/disk1/taos @@ -233,11 +235,11 @@ Note: Tiered Storage is only supported in Enterprise Edition ## Data Query -TDengine provides a variety of query processing functions for tables and STables. In addition to common aggregation queries, TDengine also provides window queries and statistical aggregation functions for time-series data. The query processing of TDengine needs the collaboration of client, vnode and mnode. +TDengine provides a variety of query processing functions for tables and STables. In addition to common aggregation queries, TDengine also provides window queries and statistical aggregation functions for time-series data. Query processing in TDengine needs the collaboration of client, vnode and mnode. ### Single Table Query -The parsing and verification of SQL statements are completed on the client side. SQL statements are parsed and generate an Abstract Syntax Tree (AST), which is then checksummed. Then request metadata information (table metadata) for the table specified in the query from management node (mnode). +The parsing and verification of SQL statements are completed on the client side. SQL statements are parsed and generate an Abstract Syntax Tree (AST), which is then checksummed. Then metadata information (table metadata) for the table specified is requested in the query from management node (mnode). According to the End Point information in metadata information, the query request is serialized and sent to the data node (dnode) where the table is located. After receiving the query, the dnode identifies the virtual node (vnode) pointed to and forwards the message to the query execution queue of the vnode. The query execution thread of vnode establishes the basic query execution environment, immediately returns the query request and starts executing the query at the same time. @@ -245,9 +247,9 @@ When client obtains query result, the worker thread in query execution queue of ### Aggregation by Time Axis, Downsampling, Interpolation -The remarkable feature that time-series data is different from ordinary data is that each record has a timestamp, so aggregating data with timestamps on the time axis is an important and distinct feature from common databases. From this point of view, it is similar to the window query of stream computing engine. +Time-series data is different from ordinary data in that each record has a timestamp. So aggregating data by timestamps on the time axis is an important and distinct feature of time-series databases which is different from that of common databases. It is similar to the window query of stream computing engines. -The keyword `interval` is introduced into TDengine to split fixed length time windows on time axis, and the data are aggregated based on time windows, and the data within window range are aggregated as needed. For example: +The keyword `interval` is introduced into TDengine to split fixed length time windows on the time axis. The data is aggregated based on time windows, and the data within time window ranges is aggregated as needed. For example: ```mysql select count(*) from d1001 interval(1h); @@ -265,21 +267,21 @@ For the data collected by device D1001, the number of records per hour is counte ### Multi-table Aggregation Query -TDengine creates a separate table for each data collection point, but in practical applications, it is often necessary to aggregate data from different data collection points. In order to perform aggregation operations efficiently, TDengine introduces the concept of STable. STable is used to represent a specific type of data collection point. It is a table set containing multiple tables. The schema of each table in the set is the same, but each table has its own static tag. The tags can be multiple and be added, deleted and modified at any time. Applications can aggregate or statistically operate all or a subset of tables under a STABLE by specifying tag filters, thus greatly simplifying the development of applications. The process is shown in the following figure: +TDengine creates a separate table for each data collection point, but in practical applications, it is often necessary to aggregate data from different data collection points. In order to perform aggregation operations efficiently, TDengine introduces the concept of STable (super table). STable is used to represent a specific type of data collection point. It is a table set containing multiple tables. The schema of each table in the set is the same, but each table has its own static tag. There can be multiple tags which can be added, deleted and modified at any time. Applications can aggregate or statistically operate on all or a subset of tables under a STABLE by specifying tag filters. This greatly simplifies the development of applications. The process is shown in the following figure: -![Diagram of multi-table aggregation query](multi_tables.png) +![TDengine Database Diagram of multi-table aggregation query](multi_tables.webp)
Figure 5: Diagram of multi-table aggregation query
1. Application sends a query condition to system; 2. TAOSC sends the STable name to Meta Node(management node); 3. Management node sends the vnode list owned by the STable back to TAOSC; 4. TAOSC sends the computing request together with tag filters to multiple data nodes corresponding to these vnodes; -5. Each vnode first finds out the set of tables within its own node that meet the tag filters from memory, then scans the stored time-series data, completes corresponding aggregation calculations, and returns result to TAOSC; +5. Each vnode first finds the set of tables within its own node that meet the tag filters from memory, then scans the stored time-series data, completes corresponding aggregation calculations, and returns result to TAOSC; 6. TAOSC finally aggregates the results returned by multiple data nodes and send them back to application. -Since TDengine stores tag data and time-series data separately in vnode, by filtering tag data in memory, the set of tables that need to participate in aggregation operation is first found, which greatly reduces the volume of data scanned and improves aggregation speed. At the same time, because the data is distributed in multiple vnodes/dnodes, the aggregation operation is carried out concurrently in multiple vnodes, which further improves the aggregation speed. Aggregation functions for ordinary tables and most operations are applicable to STables. The syntax is exactly the same. Please see TAOS SQL for details. +Since TDengine stores tag data and time-series data separately in vnode, by filtering tag data in memory, the set of tables that need to participate in aggregation operation is first found, which reduces the volume of data to be scanned and improves aggregation speed. At the same time, because the data is distributed in multiple vnodes/dnodes, the aggregation operation is carried out concurrently in multiple vnodes, which further improves the aggregation speed. Aggregation functions for ordinary tables and most operations are applicable to STables. The syntax is exactly the same. Please see TAOS SQL for details. ### Precomputation -In order to effectively improve the performance of query processing, based-on the unchangeable feature of IoT data, statistical information of data stored in data block is recorded in the head of data block, including max value, min value, and sum. We call it a precomputing unit. If the query processing involves all the data of a whole data block, the pre-calculated results are directly used, and no need to read the data block contents at all. Since the amount of pre-calculated data is much smaller than the actual size of data block stored on disk, for query processing with disk IO as bottleneck, the use of pre-calculated results can greatly reduce the pressure of reading IO and accelerate the query process. The precomputation mechanism is similar to the index BRIN (Block Range Index) of PostgreSQL. +In order to effectively improve the performance of query processing, based-on the unchangeable feature of IoT data, statistical information of data stored in data block is recorded in the head of data block, including max value, min value, and sum. We call it a precomputing unit. If the query processing involves all the data of a whole data block, the pre-calculated results are directly used, and no need to read the data block contents at all. Since the amount of pre-calculated data is much smaller than the actual size of data block stored on disk, for query processing with disk IO as bottleneck, the use of pre-calculated results can greatly reduce the pressure of reading IO and accelerate the query process. 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Since it opened its source code in July 2019, it has won the favor of a large number of time-series data developers with its innovative data modeling design, convenient installation, easy-to-use programming interface, and powerful data writing and query performance. +TDengine is a big data platform designed and optimized for IoT (Internet of Things), Vehicle Telemetry, Industrial Internet, IT DevOps and other applications. Since it was open-sourced in July 2019, it has won the favor of a large number of time-series data developers with its innovative data modeling design, convenient installation, easy-to-use programming interface, and powerful data writing and query performance. IT DevOps metric data usually are time sensitive, for example: - System resource metrics: CPU, memory, IO, bandwidth, etc. - Software system metrics: health status, number of connections, number of requests, number of timeouts, number of errors, response time, service type, and other business-related metrics. -Current mainstream IT DevOps system usually include a data collection module, a data persistent module, and a visualization module; Telegraf and Grafana are one of the most popular data collection modules and visualization modules, respectively. The data persistent module is available in a wide range of options, with OpenTSDB or InfluxDB being the most popular. TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance. +Current mainstream IT DevOps system usually include a data collection module, a data persistent module, and a visualization module; Telegraf and Grafana are one of the most popular data collection modules and visualization modules, respectively. The data persistence module is available in a wide range of options, with OpenTSDB or InfluxDB being the most popular. TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance. -This article introduces how to quickly build a TDengine + Telegraf + Grafana based IT DevOps visualization system without writing even a single line of code and by simply modifying a few lines of configuration files. The architecture is as follows. +This article introduces how to quickly build a TDengine + Telegraf + Grafana based IT DevOps visualization system without writing even a single line of code and by simply modifying a few lines in configuration files. The architecture is as follows. -![IT-DevOps-Solutions-Telegraf.png](/img/IT-DevOps-Solutions-Telegraf.png) +![TDengine Database IT-DevOps-Solutions-Telegraf](./IT-DevOps-Solutions-Telegraf.webp) ## Installation steps @@ -73,11 +73,11 @@ sudo systemctl start telegraf Log in to the Grafana interface using a web browser at `IP:3000`, with the system's initial username and password being `admin/admin`. Click on the gear icon on the left and select `Plugins`, you should find the TDengine data source plugin icon. -Click on the plus icon on the left and select `Import` to get the data from `https://github.com/taosdata/grafanaplugin/blob/master/examples/telegraf/grafana/dashboards/telegraf-dashboard- v0.1.0.json`, download the dashboard JSON file and import it. You will then see the dashboard in the following screen. +Click on the plus icon on the left and select `Import` to get the data from `https://github.com/taosdata/grafanaplugin/blob/master/examples/telegraf/grafana/dashboards/telegraf-dashboard-v0.1.0.json`, download the dashboard JSON file and import it. You will then see the dashboard in the following screen. -![IT-DevOps-Solutions-telegraf-dashboard.png](/img/IT-DevOps-Solutions-telegraf-dashboard.png) +![TDengine Database IT-DevOps-Solutions-telegraf-dashboard](./IT-DevOps-Solutions-telegraf-dashboard.webp) ## Wrap-up -The above demonstrates how to quickly build a IT DevOps visualization system. Thanks to the new schemaless protocol parsing feature in TDengine version 2.4.0.0 and the powerful ecological software adaptation capability, users can build an efficient and easy-to-use IT DevOps visualization system in just a few minutes. +The above demonstrates how to quickly build a IT DevOps visualization system. Thanks to the new schemaless protocol parsing feature in TDengine version 2.4.0.0 and ability to integrate easily with a large software ecosystem, users can build an efficient and easy-to-use IT DevOps visualization system in just a few minutes. Please refer to the official documentation and product implementation cases for other features. diff --git a/docs-en/25-application/02-collectd.md b/docs-en/25-application/02-collectd.md index 2ac37618fafe11e71b215313e53f89b6c302f7cb..1733ed1b1af8c9375c3773d1ca86831396499a78 100644 --- a/docs-en/25-application/02-collectd.md +++ b/docs-en/25-application/02-collectd.md @@ -5,19 +5,19 @@ title: Quickly build an IT DevOps visualization system using TDengine + collectd ## Background -TDengine is a big data platform designed and optimized for IoT (Internet of Things), Vehicle Telematics, Industrial Internet, IT DevOps, etc. by TAOSData. Since it opened its source code in July 2019, it has won the favor of a large number of time-series data developers with its innovative data modeling design, convenient installation, easy-to-use programming interface, and powerful data writing and query performance. +TDengine is a big data platform designed and optimized for IoT (Internet of Things), Vehicle Telemetry, Industrial Internet, IT DevOps and other applications. Since it was open-sourced in July 2019, it has won the favor of a large number of time-series data developers with its innovative data modeling design, convenient installation, easy-to-use programming interface, and powerful data writing and query performance. IT DevOps metric data usually are time sensitive, for example: - System resource metrics: CPU, memory, IO, bandwidth, etc. - Software system metrics: health status, number of connections, number of requests, number of timeouts, number of errors, response time, service type, and other business-related metrics. -The current mainstream IT DevOps visualization system usually contains a data collection module, a data persistent module, and a visual display module. collectd/StatsD, as an old-fashion open source data collection tool, has a wide user base. However, collectd/StatsD has limited functionality, and often needs to be combined with Telegraf, Grafana, and a time-series database to build a complete monitoring system. +The current mainstream IT DevOps visualization system usually contains a data collection module, a data persistence module, and a visual display module. collectd/StatsD, as an old-fashion open source data collection tool, has a wide user base. However, collectd/StatsD has limited functionality, and often needs to be combined with Telegraf, Grafana, and a time-series database to build a complete monitoring system. The new version of TDengine supports multiple data protocols and can accept data from collectd and StatsD directly, and provides Grafana dashboard for graphical display. -This article introduces how to quickly build an IT DevOps visualization system based on TDengine + collectd / StatsD + Grafana without writing even a single line of code but by simply modifying a few lines of configuration files. The architecture is shown in the following figure. +This article introduces how to quickly build an IT DevOps visualization system based on TDengine + collectd / StatsD + Grafana without writing even a single line of code but by simply modifying a few lines in configuration files. The architecture is shown in the following figure. -![IT-DevOps-Solutions-Collectd-StatsD.png](/img/IT-DevOps-Solutions-Collectd-StatsD.png) +![TDengine Database IT-DevOps-Solutions-Collectd-StatsD](./IT-DevOps-Solutions-Collectd-StatsD.webp) ## Installation Steps @@ -83,22 +83,22 @@ Click on the gear icon on the left and select `Plugins`, you should find the TDe Download the dashboard json from `https://github.com/taosdata/grafanaplugin/blob/master/examples/collectd/grafana/dashboards/collect-metrics-with-tdengine-v0.1.0.json`, click the plus icon on the left and select Import, follow the instructions to import the JSON file. After that, you can see The dashboard can be seen in the following screen. -![IT-DevOps-Solutions-collectd-dashboard.png](/img/IT-DevOps-Solutions-collectd-dashboard.png) +![TDengine Database IT-DevOps-Solutions-collectd-dashboard](./IT-DevOps-Solutions-collectd-dashboard.webp) #### import collectd dashboard Download the dashboard json file from `https://github.com/taosdata/grafanaplugin/blob/master/examples/collectd/grafana/dashboards/collect-metrics-with-tdengine-v0.1.0.json`. Download the dashboard json file, click the plus icon on the left side and select `Import`, and follow the interface prompts to select the JSON file to import. After that, you can see dashboard with the following interface. -![IT-DevOps-Solutions-collectd-dashboard.png](/img/IT-DevOps-Solutions-collectd-dashboard.png) +![IT-DevOps-Solutions-collectd-dashboard](./IT-DevOps-Solutions-collectd-dashboard.webp) #### Importing the StatsD dashboard Download the dashboard json from `https://github.com/taosdata/grafanaplugin/blob/master/examples/statsd/dashboards/statsd-with-tdengine-v0.1.0.json`. Click on the plus icon on the left and select `Import`, and follow the interface prompts to import the JSON file. You will then see the dashboard in the following screen. -![IT-DevOps-Solutions-statsd-dashboard.png](/img/IT-DevOps-Solutions-statsd-dashboard.png) +![TDengine Database IT-DevOps-Solutions-statsd-dashboard](./IT-DevOps-Solutions-statsd-dashboard.webp) ## Wrap-up -TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance. Thanks to the new schemaless protocol parsing function in TDengine version 2.4.0.0 and the powerful ecological software adaptation capability, users can build an efficient and easy-to-use IT DevOps visualization system or adapt to an existing system in just a few minutes. +TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance. Thanks to the new schemaless protocol parsing feature in TDengine version 2.4.0.0 and ability to integrate easily with a large software ecosystem, users can build an efficient and easy-to-use IT DevOps visualization system, or adapt an existing system, in just a few minutes. For TDengine's powerful data writing and querying performance and other features, please refer to the official documentation and successful product implementation cases. diff --git a/docs-en/25-application/03-immigrate.md b/docs-en/25-application/03-immigrate.md index 4cfeb892d821a1e5b7d5250615e7122e64b9882d..4d47aec1d76014ba63f6be91004abcc3934769f7 100644 --- a/docs-en/25-application/03-immigrate.md +++ b/docs-en/25-application/03-immigrate.md @@ -3,10 +3,9 @@ sidebar_label: OpenTSDB Migration to TDengine title: Best Practices for Migrating OpenTSDB Applications to TDengine --- -As a distributed, scalable, HBase-based distributed time-series database software, thanks to its first-mover advantage, OpenTSDB has been introduced and widely used in DevOps by people. However, using new technologies like cloud computing, microservices, and containerization technology with rapid development. Enterprise-level services are becoming more and more diverse. The architecture is becoming more complex. +As a distributed, scalable, distributed time-series database platform based on HBase, and thanks to its first-mover advantage, OpenTSDB is widely used for monitoring in DevOps. However, as new technologies like cloud computing, microservices, and containerization technology has developed rapidly, Enterprise-level services are becoming more and more diverse and the architecture is becoming more complex. -From this situation, it increasingly plagues to use of OpenTSDB as a DevOps backend storage for monitoring by performance issues and delayed feature upgrades. The resulting increase in application deployment costs and reduced operational efficiency. -These problems are becoming increasingly severe as the system scales up. +As a result, as a DevOps backend for monitoring, OpenTSDB is plagued by performance issues and delayed feature upgrades. This has resulted in increased application deployment costs and reduced operational efficiency. These problems become increasingly severe as the system tries to scale up. To meet the fast-growing IoT big data market and technical needs, TAOSData developed an innovative big-data processing product, **TDengine**. @@ -14,14 +13,14 @@ After learning the advantages of many traditional relational databases and NoSQL Compared with OpenTSDB, TDengine has the following distinctive features. -- Performance of data writing and querying far exceeds that of OpenTSDB. -- Efficient compression mechanism for time-series data, which compresses less than 1/5 of the storage space on disk. -- The installation and deployment are straightforward. A single installation package can complete the installation and deployment and does not rely on other third-party software. The entire installation and deployment process in a few seconds; -- The built-in functions cover all of OpenTSDB's query functions. And support more time-series data query functions, scalar functions, and aggregation functions. And support advanced query functions such as multiple time-window aggregations, join query, expression operation, multiple group aggregation, user-defined sorting, and user-defined functions. Adopting SQL-like syntax rules is more straightforward and has no learning cost. +- Data writing and querying performance far exceeds that of OpenTSDB. +- Efficient compression mechanism for time-series data, which compresses to less than 1/5 of the storage space, on disk. +- The installation and deployment are straightforward. A single installation package can complete the installation and deployment and does not rely on other third-party software. The entire installation and deployment process takes a few seconds. +- The built-in functions cover all of OpenTSDB's query functions and TDengine supports more time-series data query functions, scalar functions, and aggregation functions. TDengine also supports advanced query functions such as multiple time-window aggregations, join query, expression operation, multiple group aggregation, user-defined sorting, and user-defined functions. With a SQL-like query language, querying is more straightforward and has no learning cost. - Supports up to 128 tags, with a total tag length of 16 KB. - In addition to the REST interface, it also provides interfaces to Java, Python, C, Rust, Go, C# and other languages. Its supports a variety of enterprise-class standard connector protocols such as JDBC. -If we migrate the applications originally running on OpenTSDB to TDengine, we will effectively reduce the compute and storage resource consumption and the number of deployed servers. And will also significantly reduce the operation and maintenance costs, making operation and maintenance management more straightforward and more accessible, and considerably reducing the total cost of ownership. Like OpenTSDB, TDengine has also been open-sourced, including the stand-alone version and the cluster version source code. So there is no need to be concerned about the vendor-lock problem. +Migrating applications originally running on OpenTSDB to TDengine, effectively reduces compute and storage resource consumption and the number of deployed servers. It also significantly reduces operation and maintenance costs, makes operation and maintenance management more straightforward and more accessible, and considerably reduces the total cost of ownership. Like OpenTSDB, TDengine has also been open-sourced. Both the stand-alone version and the cluster version are open-sourced and there is no need to be concerned about the vendor-lock problem. We will explain how to migrate OpenTSDB applications to TDengine quickly, securely, and reliably without coding, using the most typical DevOps scenarios. Subsequent chapters will go into more depth to facilitate migration for non-DevOps systems. @@ -32,9 +31,9 @@ We will explain how to migrate OpenTSDB applications to TDengine quickly, secure The following figure (Figure 1) shows the system's overall architecture for a typical DevOps application scenario. **Figure 1. Typical architecture in a DevOps scenario** -![IT-DevOps-Solutions-Immigrate-OpenTSDB-Arch](/img/IT-DevOps-Solutions-Immigrate-OpenTSDB-Arch.jpg "Figure 1. Typical architecture in a DevOps scenario") +![TDengine Database IT-DevOps-Solutions-Immigrate-OpenTSDB-Arch](./IT-DevOps-Solutions-Immigrate-OpenTSDB-Arch.webp "Figure 1. Typical architecture in a DevOps scenario") -In this application scenario, there are Agent tools deployed in the application environment to collect machine metrics, network metrics, and application metrics. Data collectors to aggregate information collected by agents, systems for persistent data storage and management, and tools for monitoring data visualization (e.g., Grafana, etc.). +In this application scenario, there are Agent tools deployed in the application environment to collect machine metrics, network metrics, and application metrics. There are also data collectors to aggregate information collected by agents, systems for persistent data storage and management, and tools for data visualization (e.g., Grafana, etc.). The agents deployed in the application nodes are responsible for providing operational metrics from different sources to collectd/Statsd. And collectd/StatsD is accountable for pushing the aggregated data to the OpenTSDB cluster system and then visualizing the data using the visualization kanban board software, Grafana. @@ -44,15 +43,15 @@ The agents deployed in the application nodes are responsible for providing opera First of all, please install TDengine. Download the latest stable version of TDengine from the official website and install it. For help with using various installation packages, please refer to the blog ["Installation and Uninstallation of TDengine Multiple Installation Packages"](https://www.taosdata.com/blog/2019/08/09/566.html). -Note that once the installation is complete, do not start the `taosd` service immediately, but after properly configuring the parameters. +Note that once the installation is complete, do not start the `taosd` service before properly configuring the parameters. - **Adjusting the data collector configuration** TDengine version 2.4 and later version includes `taosAdapter`. taosAdapter is a stateless, rapidly elastic, and scalable component. taosAdapter supports Influxdb's Line Protocol and OpenTSDB's telnet/JSON writing protocol specification, providing rich data access capabilities, effectively saving user migration costs and reducing the difficulty of user migration. -Users can flexibly deploy taosAdapter instances according to their requirements to rapidly improve the throughput of data writes in conjunction with the needs of scenarios and provide guarantees for data writes in different application scenarios. +Users can flexibly deploy taosAdapter instances, based on their requirements, to improve data writing throughput and provide guarantees for data writes in different application scenarios. -Through taosAdapter, users can directly push the data collected by `collectd` or `StatsD` to TDengine to achieve seamless migration of application scenarios, which is very easy and convenient. taosAdapter also supports Telegraf, Icinga, TCollector, and node_exporter data. For more details, please refer to [taosAdapter](/reference/taosadapter/). +Through taosAdapter, users can directly write the data collected by `collectd` or `StatsD` to TDengine to achieve easy, convenient and seamless migration in application scenarios. taosAdapter also supports Telegraf, Icinga, TCollector, and node_exporter data. For more details, please refer to [taosAdapter](/reference/taosadapter/). If using collectd, modify the configuration file in its default location `/etc/collectd/collectd.conf` to point to the IP address and port of the node where to deploy taosAdapter. For example, assuming the taosAdapter IP address is 192.168.1.130 and port 6046, configure it as follows. @@ -66,29 +65,29 @@ LoadPlugin write_tsdb ``` -You can use collectd and push the data to taosAdapter utilizing the push to OpenTSDB plugin. taosAdapter will call the API to write the data to TDengine, thus completing the writing of the data. If you are using StatsD, adjust the profile information accordingly. +You can use collectd and push the data to taosAdapter utilizing the write_tsdb plugin. taosAdapter will call the API to write the data to TDengine. If you are using StatsD, adjust the profile information accordingly. - **Tuning the Dashboard system** -After writing the data to TDengine properly, you can adapt Grafana to visualize the data written to TDengine. To obtain and use the Grafana plugin provided by TDengine, please refer to [Links to other tools](/third-party/grafana). +After writing the data to TDengine, you can configure Grafana to visualize the data written to TDengine. To obtain and use the Grafana plugin provided by TDengine, please refer to [Links to other tools](/third-party/grafana). TDengine provides two sets of Dashboard templates by default, and users only need to import the templates from the Grafana directory into Grafana to activate their use. **Importing Grafana Templates** Figure 2. -![](/img/IT-DevOps-Solutions-Immigrate-OpenTSDB-Dashboard.jpg "Figure 2. Importing a Grafana Template") +![TDengine Database IT-DevOps-Solutions-Immigrate-OpenTSDB-Dashboard](./IT-DevOps-Solutions-Immigrate-OpenTSDB-Dashboard.webp "Figure 2. Importing a Grafana Template") -After the above steps, you completed the migration to replace OpenTSDB with TDengine. You can see that the whole process is straightforward, there is no need to write any code, and only some configuration files need to be adjusted to meet the migration work. +With the above steps completed, you have finished replacing OpenTSDB with TDengine. You can see that the whole process is straightforward, there is no need to write any code, and only some configuration files need to be changed. ### 3. Post-migration architecture -After completing the migration, the figure below (Figure 3) shows the system's overall architecture. The whole process of the acquisition side, the data writing, and the monitoring and presentation side are all kept stable, except for a few configuration adjustments, which do not involve any critical changes or alterations. OpenTSDB to TDengine migration action, using TDengine more powerful processing power and query performance. +After completing the migration, the figure below (Figure 3) shows the system's overall architecture. The whole process of the acquisition side, the data writing, and the monitoring and presentation side are all kept stable. There are a few configuration adjustments, which do not involve any critical changes or alterations. Migrating to TDengine from OpenTSDB leads to powerful processing power and query performance. -In most DevOps scenarios, if you have a small OpenTSDB cluster (3 or fewer nodes) for providing the storage layer of DevOps and rely on OpenTSDB to give a data persistence layer and query capabilities, you can safely replace OpenTSDB with TDengine. TDengine will save more compute and storage resources. With the same compute resource allocation, a single TDengine can meet the service capacity provided by 3 to 5 OpenTSDB nodes. If the scale is more prominent, then TDengine clustering is required. - -Suppose your application is particularly complex, or the application domain is not a DevOps scenario. You can continue reading subsequent chapters for a more comprehensive and in-depth look at the advanced topics of migrating an OpenTSDB application to TDengine. +In most DevOps scenarios, if you have a small OpenTSDB cluster (3 or fewer nodes) which provides storage and data persistence layer in addition to query capability, you can safely replace OpenTSDB with TDengine. TDengine will save compute and storage resources. With the same compute resource allocation, a single TDengine can meet the service capacity provided by 3 to 5 OpenTSDB nodes. TDengine clustering may be required depending on the scale of the application. **Figure 3. System architecture after migration** -![IT-DevOps-Solutions-Immigrate-TDengine-Arch](/img/IT-DevOps-Solutions-Immigrate-TDengine-Arch.jpg "Figure 3. System architecture after migration completion") +![TDengine Database IT-DevOps-Solutions-Immigrate-TDengine-Arch](./IT-DevOps-Solutions-Immigrate-TDengine-Arch.webp "Figure 3. System architecture after migration completion") + +The following chapters provide a more comprehensive and in-depth look at the advanced topics of migrating an OpenTSDB application to TDengine. This will be useful if your application is particularly complex and is not a DevOps application. ## Migration evaluation and strategy for other scenarios @@ -96,26 +95,25 @@ Suppose your application is particularly complex, or the application domain is n This chapter describes the differences between OpenTSDB and TDengine at the system functionality level. After reading this chapter, you can fully evaluate whether you can migrate some complex OpenTSDB-based applications to TDengine, and what you should pay attention to after migration. -TDengine currently only supports Grafana for visual kanban rendering, so if your application uses front-end kanban boards other than Grafana (e.g., [TSDash](https://github.com/facebook/tsdash), [Status Wolf](https://github.com/box/StatusWolf), etc.). You cannot directly migrate those front-end kanbans to TDengine, and the front-end kanban will need to be ported to Grafana to work correctly. +TDengine currently only supports Grafana for visual kanban rendering, so if your application uses front-end kanban boards other than Grafana (e.g., [TSDash](https://github.com/facebook/tsdash), [Status Wolf](https://github.com/box/StatusWolf), etc.) you cannot directly migrate those front-end kanbans to TDengine. The front-end kanban will need to be ported to Grafana to work correctly. -TDengine version 2.3.0.x only supports collectd and StatsD as data collection aggregation software but will provide more data collection aggregation software in the future. If you use other data aggregators on the collection side, your application needs to be ported to these two data aggregation systems to write data correctly. +TDengine version 2.3.0.x only supports collectd and StatsD as data collection and aggregation software but future versions will provide support for more data collection and aggregation software in the future. If you use other data aggregators on the collection side, your application needs to be ported to these two data aggregation systems to write data correctly. In addition to the two data aggregator software protocols mentioned above, TDengine also supports writing data directly via InfluxDB's line protocol and OpenTSDB's data writing protocol, JSON format. You can rewrite the logic on the data push side to write data using the line protocols supported by TDengine. -In addition, if your application uses the following features of OpenTSDB, you need to understand the following considerations before migrating your application to TDengine. +In addition, if your application uses the following features of OpenTSDB, you need to take into account the following considerations before migrating your application to TDengine. 1. `/api/stats`: If your application uses this feature to monitor the service status of OpenTSDB, and you have built the relevant logic to link the processing in your application, then this part of the status reading and fetching logic needs to be re-adapted to TDengine. TDengine provides a new mechanism for handling cluster state monitoring to meet the monitoring and maintenance needs of your application. -2. `/api/tree`: If you rely on this feature of OpenTSDB for the hierarchical organization and maintenance of timelines, you cannot migrate it directly to TDengine, which uses a database -> super table -> sub-table hierarchy to organize and maintain timelines, with all timelines belonging to the same super table in the same system hierarchy, but it is possible to simulate a logical multi-level structure of the application through the unique construction of different tag values. -3. `Rollup And PreAggregates`: The use of Rollup and PreAggregates requires the application to decide where to access the Rollup results and, in some scenarios, to access the actual results. The opacity of this structure makes the application processing logic extraordinarily complex and not portable at all. We think this strategy is a compromise when the time-series database does not. -TDengine does not support automatic downsampling of multiple timelines and preaggregation (for a range of periods) for the time being. Still, thanks to its high-performance query processing logic can provide very high-performance query responses without relying on Rollup and preaggregation (for a range of periods), making your application query processing logic much more straightforward. -The logic is much simpler. -4. `Rate`: TDengine provides two functions to calculate the rate of change of values, namely `Derivative` (the result is consistent with the Derivative behavior of InfluxDB) and `IRate` (the result is compatible with the IRate function in Prometheus). However, the results of these two functions are slightly different from Rate, but the functions are more powerful overall. In addition, TDengine supports all the calculation functions provided by OpenTSDB, and TDengine's query functions are much more potent than those supported by OpenTSDB, which can significantly simplify the processing logic of your application. +2. `/api/tree`: If you rely on this feature of OpenTSDB for the hierarchical organization and maintenance of timelines, you cannot migrate it directly to TDengine, which uses a database -> super table -> sub-table hierarchy to organize and maintain timelines, with all timelines belonging to the same super table in the same system hierarchy. But it is possible to simulate a logical multi-level structure of the application through the unique construction of different tag values. +3. `Rollup And PreAggregates`: The use of Rollup and PreAggregates requires the application to decide where to access the Rollup results and, in some scenarios, to access the actual results. The opacity of this structure makes the application processing logic extraordinarily complex and not portable at all. +While TDengine does not currently support automatic downsampling of multiple timelines and preaggregation (for a range of periods), thanks to its high-performance query processing logic, it can provide very high-performance query responses without relying on Rollup and preaggregation (for a range of periods). This makes your application query processing logic straightforward and simple. +4. `Rate`: TDengine provides two functions to calculate the rate of change of values, namely `Derivative` (the result is consistent with the Derivative behavior of InfluxDB) and `IRate` (the result is compatible with the IRate function in Prometheus). However, the results of these two functions are slightly different from that of Rate. But the TDengine functions are more powerful. In addition, TDengine supports all the calculation functions provided by OpenTSDB. TDengine's query functions are much more powerful than those supported by OpenTSDB, which can significantly simplify the processing logic of your application. -Through the above introduction, I believe you should be able to understand the changes brought about by the migration of OpenTSDB to TDengine. And this information will also help you correctly determine whether you would migrate your application to TDengine to experience the powerful and convenient time-series data processing capability provided by TDengine. +With the above introduction, we believe you should be able to understand the changes brought about by the migration of OpenTSDB to TDengine. And this information will also help you correctly determine whether you should migrate your application to TDengine to experience the powerful and convenient time-series data processing capability provided by TDengine. ### 2. Migration strategy suggestion -First, the OpenTSDB-based system migration involves data schema design, system scale estimation, and data write end transformation, data streaming, and application adaptation; after that, the two systems will run in parallel for a while and then migrate the historical data to TDengine. Of course, if your application has some functions that strongly depend on the above OpenTSDB features and you do not want to stop using them, you can migrate the historical data to TDengine. -You can consider keeping the original OpenTSDB system running while starting TDengine to provide the primary services. +OpenTSDB-based system migration involves data schema design, system scale estimation, data write transformation, data streaming, and application changes. The two systems should run in parallel for a while and then the historical data should be migrated to TDengine if your application has some functions that strongly depend on the above OpenTSDB features and you do not want to stop using them. +You can also consider keeping the original OpenTSDB system running while using TDengine to provide the primary services. ## Data model design @@ -129,16 +127,19 @@ Let us now assume a DevOps scenario where we use collectd to collect the underly | 2 | swap | value | double | host | swap_type | swap_type_instance | source | n/a | | 3 | disk | value | double | host | disk_point | disk_instance | disk_type | source | -TDengine requires the data stored to have a data schema, i.e., you need to create a super table and specify the schema of the super table before writing the data. For data schema creation, you have two ways to do this: 1) Take advantage of TDengine's native data writing support for OpenTSDB by calling the TDengine API to write (text line or JSON format) -and automate the creation of single-value models. This approach does not require significant adjustments to the data writing application, nor does it require converting the written data format. +TDengine requires the data stored to have a data schema, i.e., you need to create a super table and specify the schema of the super table before writing the data. For data schema creation, you have two ways to do this: +1) Take advantage of TDengine's native data writing support for OpenTSDB by calling the TDengine API to write (text line or JSON format) and automate the creation of single-value models. This approach does not require significant adjustments to the data writing application, nor does it require converting the written data format. At the C level, TDengine provides the `taos_schemaless_insert()` function to write data in OpenTSDB format directly (in early version this function was named `taos_insert_lines()`). Please refer to the sample code `schemaless.c` in the installation package directory as reference. -(2) based on a complete understanding of TDengine's data model, to establish the mapping relationship between OpenTSDB and TDengine's data model adjustment manually. Considering that OpenTSDB is a single-value mapping model, recommended using the single-value model in TDengine. TDengine can support both multi-value and single-value models. +(2) Based on a thorough understanding of TDengine's data model, establish a mapping between OpenTSDB and TDengine's data model. Considering that OpenTSDB is a single-value mapping model, we recommended using the single-value model in TDengine for simplicity. But keep in mind that TDengine supports both multi-value and single-value models. - **Single-valued model**. -The steps are as follows: use the name of the metrics as the name of the TDengine super table, which build with two basic data columns - timestamp and value, and the label of the super table is equivalent to the label information of the metrics, and the number of labels is equal to the number of labels of the metrics. The names of sub-tables are named with fixed rules: `metric + '_' + tags1_value + '_' + tag2_value + '_' + tag3_value ...` as the sub-table name. +The steps are as follows: +- Use the name of the metrics as the name of the TDengine super table +- Build with two basic data columns - timestamp and value. The label of the super table is equivalent to the label information of the metrics, and the number of labels is equal to the number of labels of the metrics. +- The names of sub-tables are named with fixed rules: `metric + '_' + tags1_value + '_' + tag2_value + '_' + tag3_value ...` as the sub-table name. Create 3 super tables in TDengine. @@ -158,13 +159,13 @@ The final system will have about 340 sub-tables and three super-tables. Note tha - **Multi-value model** -Suppose you want to take advantage of TDengine's multi-value modeling capabilities. In that case, you need first to meet the requirements that different collection quantities have the same collection frequency and can reach the **data write side simultaneously via a message queue**, thus ensuring writing multiple metrics at once using SQL statements. The metric's name is used as the name of the super table to create a multi-column model of data that has the same collection frequency and can arrive simultaneously. The names of the sub-tables are named using a fixed rule. Each of the above metrics contains only one measurement value, so converting it into a multi-value model is impossible. +Ideally you should take advantage of TDengine's multi-value modeling capabilities. In that case, you first need to meet the requirement that different collection quantities have the same collection frequency and can reach the **data write side simultaneously via a message queue**, thus ensuring writing multiple metrics at once, using SQL statements. The metric's name is used as the name of the super table to create a multi-column model of data that has the same collection frequency and can arrive simultaneously. The sub-tables are named using a fixed rule. Each of the above metrics contains only one measurement value, so converting it into a multi-value model is impossible. ## Data triage and application adaptation -Subscribe data from the message queue and start the adapted writer to write the data. +Subscribe to the message queue and start writing data to TDengine. -After writing the data starts for a while, you can use SQL statements to check whether the amount of data written meets the expected writing requirements. Use the following SQL statement to count the amount of data. +After data has been written for a while, you can use SQL statements to check whether the amount of data written meets the expected writing requirements. Use the following SQL statement to count the amount of data. ```sql select count(*) from memory @@ -184,7 +185,7 @@ To facilitate historical data migration, we provide a plug-in for the data synch For the specific usage of DataX and how to use DataX to write data to TDengine, please refer to [DataX-based TDengine Data Migration Tool](https://www.taosdata.com/blog/2021/10/26/3156.html). -After migrating via DataX, we found that we can significantly improve the efficiency of migrating historical data by starting multiple processes and migrating numerous metrics simultaneously. The following are some records of the migration process. I wish to use these for application migration as a reference. +After migrating via DataX, we found that we can significantly improve the efficiency of migrating historical data by starting multiple processes and migrating numerous metrics simultaneously. The following are some records of the migration process. We provide these as a reference for application migration. | Number of datax instances (number of concurrent processes) | Migration record speed (pieces/second) | | ----------------------------- | ------------------- -- | @@ -202,13 +203,13 @@ Suppose you need to use the multi-value model for data writing. In that case, yo Manual migration of data requires attention to the following two issues: -1) When storing the exported data on the disk, the disk needs to have enough storage space to accommodate the exported data files fully. Adopting the partial import mode to avoid the shortage of disk file storage after the total amount of data is exported. Preferentially export the timelines belonging to the same super table. Then the exported data files are imported into the TDengine system. +1) When storing the exported data on the disk, the disk needs to have enough storage space to accommodate the exported data files fully. To avoid running out of disk space, you can adopt a partial import mode in which you preferentially export the timelines belonging to the same super table and then only those files are imported into TDengine. -2) Under the full load of the system, if there are enough remaining computing and IO resources, establish a multi-threaded importing to maximize the efficiency of data migration. Considering the vast load that data parsing brings to the CPU, it is necessary to control the maximum number of parallel tasks to avoid the overall overload of the system triggered by importing historical data. +2) Under the full load of the system, if there are enough remaining computing and IO resources, establish a multi-threaded import to maximize the efficiency of data migration. Considering the vast load that data parsing brings to the CPU, it is necessary to control the maximum number of parallel tasks to avoid overloading the system when importing historical data. Due to the ease of operation of TDengine itself, there is no need to perform index maintenance and data format change processing in the entire process. The whole process only needs to be executed sequentially. -When wholly importing the historical data into TDengine, the two systems run simultaneously and then switch the query request to TDengine to achieve seamless application switching. +While importing historical data into TDengine, the two systems should run simultaneously. Once all the data is migrated, switch the query request to TDengine to achieve seamless application switching. ## Appendix 1: OpenTSDB query function correspondence table @@ -222,12 +223,12 @@ Example: SELECT avg(val) FROM (SELECT first(val) FROM super_table WHERE ts >= startTime and ts <= endTime INTERVAL(20s) Fill(linear)) INTERVAL(20s) ``` -Remark: +Remarks: 1. The value in Interval needs to be the same as the interval value in the outer query. -2. The interpolation processing in TDengine needs to use subqueries to assist in the completion. As shown above, it is enough to specify the interpolation type in the inner query. Since the interpolation of the values ​​in OpenTSDB uses linear interpolation, use fill( in the interpolation clause. linear) to declare the interpolation type. The following functions with the exact interpolation calculation requirements are processed by this method. -3. The parameter 20s in Interval indicates that the inner query will generate results according to a time window of 20 seconds. In an actual query, it needs to adjust to the time interval between different records. It ensures that producing interpolation results equivalent to the original data. -4. Due to the particular interpolation strategy and mechanism of OpenTSDB, the method of the first interpolation and then calculation in the aggregate query (Aggregate) makes the calculation results impossible to be utterly consistent with TDengine. But in the case of downsampling (Downsample), TDengine and OpenTSDB can obtain consistent results (since OpenTSDB performs aggregation and downsampling queries). +2. Interpolation processing in TDengine uses subqueries to assist in completion. As shown above, it is enough to specify the interpolation type in the inner query. Since OpenTSDB uses linear interpolation, use `fill(linear)` to declare the interpolation type in TDengine. Some of the functions mentioned below have exactly the same interpolation calculation requirements. +3. The parameter 20s in Interval indicates that the inner query will generate results according to a time window of 20 seconds. In an actual query, it needs to adjust to the time interval between different records. It ensures that interpolation results are equivalent to the original data. +4. Due to the particular interpolation strategy and mechanism of OpenTSDB i.e. interpolation followed by aggregate calculation, it is impossible for the results to be completely consistent with those of TDengine. But in the case of downsampling (Downsample), TDengine and OpenTSDB can obtain consistent results (since OpenTSDB performs aggregation and downsampling queries). ### Count @@ -261,7 +262,7 @@ Select apercentile(col1, 50, “t-digest”) from table_name Remark: -1. During the approximate query processing, OpenTSDB uses the t-digest algorithm by default, so in order to obtain the same calculation result, the algorithm used needs to be specified in the `apercentile()` function. TDengine can support two different approximation processing algorithms, declared by "default" and "t-digest" respectively. +1. When calculating estimate percentiles, OpenTSDB uses the t-digest algorithm by default. In order to obtain the same calculation results in TDengine, the algorithm used needs to be specified in the `apercentile()` function. TDengine can support two different percentile calculation algorithms named "default" and "t-digest" respectively. ### First @@ -379,35 +380,34 @@ We still use the hypothetical environment from Chapter 4. There are three measur ### Storage resource estimation Assuming that the number of sensor devices that generate data and need to be stored is `n`, the frequency of data generation is `t` per second, and the length of each record is `L` bytes, the scale of data generated per day is `n * t * L` bytes. Assuming the compression ratio is `C`, the daily data size is `(n * t * L)/C` bytes. The storage resources are estimated to accommodate the data scale for 1.5 years. In the production environment, the compression ratio C of TDengine is generally between 5 and 7. -With additional 20% ​​redundancy, you can calculate the required storage resources: +With additional 20% redundancy, you can calculate the required storage resources: ```matlab (n * t * L) * (365 * 1.5) * (1+20%)/C ```` - -Combined with the above calculation formula, bring the parameters into the formula, and the raw data scale generated every year is 11.8TB without considering the label information. Note that since tag information is associated with each timeline in TDengine, not every record. The scale of the amount of data to be recorded is somewhat reduced relative to the generated data, and this part of label data can be ignored as a whole. Assuming a compression ratio of 5, the size of the retained data ends up being 2.56 TB. +Substituting in the above formula, the raw data generated every year is 11.8TB without considering the label information. Note that tag information is associated with each timeline in TDengine, not every record. The amount of data to be recorded is somewhat reduced relative to the generated data, and label data can be ignored as a whole. Assuming a compression ratio of 5, the size of the retained data ends up being 2.56 TB. ### Storage Device Selection Considerations -The hard disk should be capable of better random read performance. Considering using an SSD as much as possible is a better choice. A disk with better random read performance is a great help to improve the system's query performance and improve the query response performance as a whole system. To obtain better query performance, the performance index of the single-threaded random read IOPS of the hard disk device should not be lower than 1000, and it is better to reach 5000 IOPS or more. Recommend to use `fio` utility software to evaluate the running performance (please refer to Appendix 1 for specific usage) for the random IO read of the current device to confirm whether it can meet the requirements of random read of large files. +A disk with better random read performance, such as an SSD, improves the system's query performance and improves the query response performance of the whole system. To obtain better query performance, the performance index of the single-threaded random read IOPS of the hard disk device should not be lower than 1000, and it is better to reach 5000 IOPS or more. We recommend using `fio` utility software to evaluate the running performance (please refer to Appendix 1 for specific usage) for the random IO read of the current device to confirm whether it can meet the requirements of random read of large files. Hard disk writing performance has little effect on TDengine. The TDengine writing process adopts the append write mode, so as long as it has good sequential write performance, both SAS hard disks and SSDs in the general sense can well meet TDengine's requirements for disk write performance. ### Computational resource estimates -Due to the particularity of IoT data, after the frequency of data generation is consistent, the writing process of TDengine maintains a relatively fixed amount of resource consumption (computing and storage). According to the [TDengine Operation and Maintenance Guide](/operation/) description, the system consumes less than 1 CPU core at 22,000 writes per second. +Due to the characteristics of IoT data, when the frequency of data generation is consistent, the writing process of TDengine maintains a relatively fixed amount of resource consumption (computing and storage). According to the [TDengine Operation and Maintenance Guide](/operation/) description, the system consumes less than 1 CPU core at 22,000 writes per second. -In estimating the CPU resources consumed by the query, assuming that the application requires the database to provide 10,000 QPS, the CPU time consumed by each query is about 1 ms. The query provided by each core per second is 1,000 QPS, which satisfies 10,000 QPS. The query request requires at least 10 cores. For the system as a whole system to have less than 50% CPU load, the entire cluster needs twice as many as 10 cores or 20 cores. +In estimating the CPU resources consumed by the query, assuming that the application requires the database to provide 10,000 QPS, the CPU time consumed by each query is about 1 ms. The query provided by each core per second is 1,000 QPS, which satisfies 10,000 QPS. The query request requires at least 10 cores. For the system as a whole system to have less than 50% CPU load, the entire cluster needs twice as many cores i.e. 20 cores. ### Memory resource estimation -The database allocates 16MB\*3 buffer memory for each Vnode by default. If the cluster system includes 22 CPU cores, TDengine will create 22 Vnodes (virtual nodes) by default. Each Vnode contains 1000 tables, which can accommodate all the tables. Then it takes about 1.5 hours to write a block, which triggers the drop, and no adjustment is required. A total of 22 Vnodes require about 1GB of memory cache. Considering the memory needed for the query, assuming that the memory overhead of each query is about 50MB, the memory required for 500 queries concurrently is about 25GB. +The database allocates 16MB\*3 buffer memory for each Vnode by default. If the cluster system includes 22 CPU cores, TDengine will create 22 Vnodes (virtual nodes) by default. Each Vnode contains 1000 tables, which is more than enough to accommodate all the tables in our hypothetical scenario. Then it takes about 1.5 hours to write a block, which triggers persistence to disk without requiring any adjustment. A total of 22 Vnodes require about 1GB of memory cache. Considering the memory needed for the query, assuming that the memory overhead of each query is about 50MB, the memory required for 500 queries concurrently is about 25GB. In summary, using a single 16-core 32GB machine or a cluster of 2 8-core 16GB machines is enough. ## Appendix 3: Cluster Deployment and Startup -TDengine provides a wealth of help documents to explain many aspects of cluster installation and deployment. Here is the list of corresponding document for your reference. +TDengine provides a wealth of help documents to explain many aspects of cluster installation and deployment. Here is the list of documents for your reference. ### Cluster Deployment @@ -421,7 +421,7 @@ To ensure that the system can obtain the necessary information for regular opera FQDN, firstEp, secondEP, dataDir, logDir, tmpDir, serverPort. For the specific meaning and setting requirements of each parameter, please refer to the document "[TDengine Cluster Installation and Management](/cluster/)" -Follow the same steps to set parameters on the nodes that need running, start the taosd service, and then add Dnodes to the cluster. +Follow the same steps to set parameters on the other nodes, start the taosd service, and then add Dnodes to the cluster. Finally, start `taos` and execute the `show dnodes` command. If you can see all the nodes that have joined the cluster, the cluster building process was successfully completed. 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These two are the default directories used by TDengine, if they have been changed in your configuration, please use according to the actual configuration. It's recommended to firstly set `debugFlag` to 135 in `taos.cfg`, restart `taosd`, then reproduce the problem and collect logs. If you don't want to restart, an alternative way of setting `debugFlag` is executing `alter dnode debugFlag 135` command in TDengine CLI `taos`. During normal running, however, please make sure `debugFlag` is set to 131. +If the tips in FAQ don't help much, please submit an issue on [GitHub](https://github.com/taosdata/TDengine) to describe your problem. In your description please include the TDengine version, hardware and OS information, the steps to reproduce the problem and any other relevant information. It would be very helpful if you can package the contents in `/var/log/taos` and `/etc/taos` and upload. These two are the default directories used by TDengine. If you have changed the default directories in your configuration, please package the files in your configured directories. We recommended setting `debugFlag` to 135 in `taos.cfg`, restarting `taosd`, then reproducing the problem and collecting the logs. If you don't want to restart, an alternative way of setting `debugFlag` is executing `alter dnode debugFlag 135` command in TDengine CLI `taos`. During normal running, however, please make sure `debugFlag` is set to 131. ## Frequently Asked Questions ### 1. How to upgrade to TDengine 2.0 from older version? -version 2.x is not compatible with version 1.x regarding configuration file and data file, please do following before upgrading: +version 2.x is not compatible with version 1.x. With regard to the configuration and data files, please perform the following steps before upgrading. Please follow data integrity, security, backup and other relevant SOPs, best practices before removing/deleting any data. -1. Delete configuration files: `sudo rm -rf /etc/taos/taos.cfg` +1. Delete configuration files: `sudo rm -rf /etc/taos/taos.cfg` 2. Delete log files: `sudo rm -rf /var/log/taos/` 3. Delete data files if the data doesn't need to be kept: `sudo rm -rf /var/lib/taos/` -4. Install latests 2.x version -5. If the data needs to be kept and migrated to newer version, please contact professional service of TDengine for assistance +4. Install latest 2.x version +5. If the data needs to be kept and migrated to newer version, please contact professional service at TDengine for assistance. ### 2. How to handle "Unable to establish connection"？ -When the client is unable to connect to the server, you can try following ways to find out why. +When the client is unable to connect to the server, you can try the following ways to troubleshoot and resolve the problem. 1. Check the network - - Check if the hosts where the client and server are running can be accessible to each other, for example by `ping` command. - - Check if the TCP/UDP on port 6030-6042 are open for access if firewall is enabled. It's better to firstly disable firewall for diagnostics. - - Check if the FQDN and serverPort are configured correctly in `taos.cfg` used by the server side - - Check if the `firstEp` is set properly in the `taos.cfg` used by the client side + - Check if the hosts where the client and server are running are accessible to each other, for example by `ping` command. + - Check if the TCP/UDP on port 6030-6042 are open for access if firewall is enabled. If possible, disable the firewall for diagnostics, but please ensure that you are following security and other relevant protocols. + - Check if the FQDN and serverPort are configured correctly in `taos.cfg` used by the server side. + - Check if the `firstEp` is set properly in the `taos.cfg` used by the client side. 2. Make sure the client version and server version are same. 3. On server side, check the running status of `taosd` by executing `systemctl status taosd` . If your server is started using another way instead of `systemctl`, use the proper method to check whether the server process is running normally. -4. If using connector of Python, Java, Go, Rust, C#, node.JS on Linux to connect toe the server, please make sure `libtaos.so` is in directory `/usr/local/taos/driver` and `/usr/local/taos/driver` is in system lib search environment variable `LD_LIBRARY_PATH`. +4. If using connector of Python, Java, Go, Rust, C#, node.JS on Linux to connect to the server, please make sure `libtaos.so` is in directory `/usr/local/taos/driver` and `/usr/local/taos/driver` is in system lib search environment variable `LD_LIBRARY_PATH`. -5. If using connector on Windows, please make sure `C:\TDengine\driver\taos.dll` is in your system lib search path, it's suggested to put `taos.dll` under `C:\Windows\System32`. +5. If using connector on Windows, please make sure `C:\TDengine\driver\taos.dll` is in your system lib search path. We recommend putting `taos.dll` under `C:\Windows\System32`. 6. Some advanced network diagnostics tools @@ -45,7 +45,7 @@ When the client is unable to connect to the server, you can try following ways t Check whether a TCP port on server side is open: `nc -l {port}` Check whether a TCP port on client side is open: `nc {hostIP} {port}` - - On Windows system `Net-TestConnection -ComputerName {fqdn} -Port {port}` on PowerShell can be used to check whether the port on serer side is open for access. + - On Windows system `Test-NetConnection -ComputerName {fqdn} -Port {port}` on PowerShell can be used to check whether the port on server side is open for access. 7. TDengine CLI `taos` can also be used to check network, please refer to [TDengine CLI](/reference/taos-shell). diff --git a/docs-en/27-train-faq/03-docker.md b/docs-en/27-train-faq/03-docker.md index ba435a9307c1d6595579a295df83030c58ba0f22..afee13c1377b0b4331d6f7ec20251d1aa2db81a1 100644 --- a/docs-en/27-train-faq/03-docker.md +++ b/docs-en/27-train-faq/03-docker.md @@ -3,15 +3,15 @@ sidebar_label: TDengine in Docker title: Deploy TDengine in Docker --- -Even though it's not recommended to deploy TDengine using docker in production system, docker is still very useful in development environment, especially when your host is not Linux. From version 2.0.14.0, the official image of TDengine can support X86-64, X86, arm64, and rm32 . +We do not recommend deploying TDengine using Docker in a production system. However, Docker is still very useful in a development environment, especially when your host is not Linux. From version 2.0.14.0, the official image of TDengine can support X86-64, X86, arm64, and rm32 . -In this chapter a simple step by step guide of using TDengine in docker is introduced. +In this chapter we introduce a simple step by step guide to use TDengine in Docker. ## Install Docker -The installation of docker please refer to [Get Docker](https://docs.docker.com/get-docker/). +To install Docker please refer to [Get Docker](https://docs.docker.com/get-docker/). -After docker is installed, you can check whether Docker is installed properly by displaying Docker version. +After Docker is installed, you can check whether Docker is installed properly by displaying Docker version. ```bash $ docker -v @@ -27,7 +27,7 @@ $ docker run -d -p 6030-6049:6030-6049 -p 6030-6049:6030-6049/udp tdengine/tdeng 526aa188da767ae94b244226a2b2eec2b5f17dd8eff592893d9ec0cd0f3a1ccd ``` -In the above command, a docker container is started to run TDengine server, the port range 6030-6049 of the container is mapped to host port range 6030-6049. If port range 6030-6049 has been occupied on the host, please change to an available host port range. Regarding the requirements about ports on the host, please refer to [Port Configuration](/reference/config/#serverport). +In the above command, a docker container is started to run TDengine server, the port range 6030-6049 of the container is mapped to host port range 6030-6049. If port range 6030-6049 has been occupied on the host, please change to an available host port range. For port requirements on the host, please refer to [Port Configuration](/reference/config/#serverport). - **docker run**: Launch a docker container - **-d**: the container will run in background mode @@ -95,7 +95,7 @@ In TDengine CLI, SQL commands can be executed to create/drop databases, tables, ### Access TDengine from host -If `-p` used to map ports properly between host and container, it's also able to access TDengine in container from the host as long as `firstEp` is configured correctly for the client on host. +If option `-p` used to map ports properly between host and container, it's also able to access TDengine in container from the host as long as `firstEp` is configured correctly for the client on host. ``` $ taos @@ -118,7 +118,7 @@ Output is like below: {"status":"succ","head":["name","created_time","ntables","vgroups","replica","quorum","days","keep0,keep1,keep(D)","cache(MB)","blocks","minrows","maxrows","wallevel","fsync","comp","cachelast","precision","update","status"],"column_meta":[["name",8,32],["created_time",9,8],["ntables",4,4],["vgroups",4,4],["replica",3,2],["quorum",3,2],["days",3,2],["keep0,keep1,keep(D)",8,24],["cache(MB)",4,4],["blocks",4,4],["minrows",4,4],["maxrows",4,4],["wallevel",2,1],["fsync",4,4],["comp",2,1],["cachelast",2,1],["precision",8,3],["update",2,1],["status",8,10]],"data":[["test","2021-08-18 06:01:11.021",10000,4,1,1,10,"3650,3650,3650",16,6,100,4096,1,3000,2,0,"ms",0,"ready"],["log","2021-08-18 05:51:51.065",4,1,1,1,10,"30,30,30",1,3,100,4096,1,3000,2,0,"us",0,"ready"]],"rows":2} ``` -For details of REST API please refer to [REST API]](/reference/rest-api/). +For details of REST API please refer to [REST API](/reference/rest-api/). ### Run TDengine server and taosAdapter inside container @@ -265,13 +265,13 @@ Below is an example output: $ taos> select groupid, location from test.d0; groupid | location | ================================= - 0 | shanghai | + 0 | California.SanDiego | Query OK, 1 row(s) in set (0.003490s) ``` ### Access TDengine from 3rd party tools -A lot of 3rd party tools can be used to write data into TDengine through `taosAdapter` , for details please refer to [3rd party tools](/third-party/). +A lot of 3rd party tools can be used to write data into TDengine through `taosAdapter`, for details please refer to [3rd party tools](/third-party/). There is nothing different from the 3rd party side to access TDengine server inside a container, as long as the end point is specified correctly, the end point should be the FQDN and the mapped port of the host. diff --git a/docs-en/30-release/02-2.6.md b/docs-en/30-release/02-2.6.md new file mode 100644 index 0000000000000000000000000000000000000000..85b76d9999e211336b5859beab3fdfc7988f4fda --- /dev/null +++ b/docs-en/30-release/02-2.6.md @@ -0,0 +1,9 @@ +--- +title: 2.6 +--- + +[2.6.0.4](https://github.com/taosdata/TDengine/releases/tag/ver-2.6.0.4) + +[2.6.0.1](https://github.com/taosdata/TDengine/releases/tag/ver-2.6.0.1) + +[2.6.0.0](https://github.com/taosdata/TDengine/releases/tag/ver-2.6.0.0) diff --git a/docs-en/30-release/03-2.4.md b/docs-en/30-release/03-2.4.md new file mode 100644 index 0000000000000000000000000000000000000000..62580b327a3bd5098e1b7f1162a1c398ac2a5eff --- /dev/null +++ b/docs-en/30-release/03-2.4.md @@ -0,0 +1,29 @@ +--- +title: 2.4 +--- + +[2.4.0.26](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.26) + +[2.4.0.25](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.25) + +[2.4.0.24](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.24) + +[2.4.0.20](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.20) + +[2.4.0.18](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.18) + +[2.4.0.16](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.16) + +[2.4.0.14](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.14) + +[2.4.0.12](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.12) + +[2.4.0.10](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.10) + +[2.4.0.7](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.7) + +[2.4.0.5](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.5) + +[2.4.0.4](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.4) + +[2.4.0.0](https://github.com/taosdata/TDengine/releases/tag/ver-2.4.0.0) diff --git a/docs-en/30-release/_category_.yml b/docs-en/30-release/_category_.yml new file mode 100644 index 0000000000000000000000000000000000000000..4672fec6fd11d6e06b61e58ebdeb2cfb100ccc5e --- /dev/null +++ b/docs-en/30-release/_category_.yml @@ -0,0 +1 @@ +label: Releases diff --git a/docs-en/30-release/index.md b/docs-en/30-release/index.md new file mode 100644 index 0000000000000000000000000000000000000000..c01c99cdce0190fb04f88d55a09e8cc406d4d8b0 --- /dev/null +++ b/docs-en/30-release/index.md @@ -0,0 +1,10 @@ +--- +title: Releases +--- + +```mdx-code-block +import DocCardList from '@theme/DocCardList'; +import {useCurrentSidebarCategory} from '@docusaurus/theme-common'; + + +``` \ No newline at end of file diff --git a/docs-examples/c/async_query_example.c b/docs-examples/c/async_query_example.c index 262757f02b5c52f2d4402d363663db80bb38a54d..b370420b124a21b05f8e0b4041fb1461b1e2478a 100644 --- a/docs-examples/c/async_query_example.c +++ b/docs-examples/c/async_query_example.c @@ -182,14 +182,14 @@ int main() { // query callback ... // ts current voltage phase location groupid // numOfRow = 8 -// 1538548685000 10.300000 219 0.310000 beijing.chaoyang 2 -// 1538548695000 12.600000 218 0.330000 beijing.chaoyang 2 -// 1538548696800 12.300000 221 0.310000 beijing.chaoyang 2 -// 1538548696650 10.300000 218 0.250000 beijing.chaoyang 3 -// 1538548685500 11.800000 221 0.280000 beijing.haidian 2 -// 1538548696600 13.400000 223 0.290000 beijing.haidian 2 -// 1538548685000 10.800000 223 0.290000 beijing.haidian 3 -// 1538548686500 11.500000 221 0.350000 beijing.haidian 3 +// 1538548685500 11.800000 221 0.280000 california.losangeles 2 +// 1538548696600 13.400000 223 0.290000 california.losangeles 2 +// 1538548685000 10.800000 223 0.290000 california.losangeles 3 +// 1538548686500 11.500000 221 0.350000 california.losangeles 3 +// 1538548685000 10.300000 219 0.310000 california.sanfrancisco 2 +// 1538548695000 12.600000 218 0.330000 california.sanfrancisco 2 +// 1538548696800 12.300000 221 0.310000 california.sanfrancisco 2 +// 1538548696650 10.300000 218 0.250000 california.sanfrancisco 3 // numOfRow = 0 // no more data, close the connection. // ANCHOR_END: demo \ No newline at end of file diff --git a/docs-examples/c/insert_example.c b/docs-examples/c/insert_example.c index ca12be9314efbda707dbd05449c746794c209743..ce8fdc5b9372aec7b02d3c9254ec25c4c4f62adc 100644 --- a/docs-examples/c/insert_example.c +++ b/docs-examples/c/insert_example.c @@ -36,10 +36,10 @@ int main() { executeSQL(taos, "CREATE DATABASE power"); executeSQL(taos, "USE power"); executeSQL(taos, "CREATE STABLE meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT)"); - executeSQL(taos, "INSERT INTO d1001 USING meters TAGS(Beijing.Chaoyang, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000)" - "d1002 USING meters TAGS(Beijing.Chaoyang, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000)" - "d1003 USING meters TAGS(Beijing.Haidian, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000)" - "d1004 USING meters TAGS(Beijing.Haidian, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)"); + executeSQL(taos, "INSERT INTO d1001 USING meters TAGS(California.SanFrancisco, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000)" + "d1002 USING meters TAGS(California.SanFrancisco, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000)" + "d1003 USING meters TAGS(California.LosAngeles, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000)" + "d1004 USING meters TAGS(California.LosAngeles, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)"); taos_close(taos); taos_cleanup(); } diff --git a/docs-examples/c/json_protocol_example.c b/docs-examples/c/json_protocol_example.c index 182fd201308facc80c76f36cfa57580784d70413..9d276127a64c3d74322e30587ab2e319c29cbf65 100644 --- a/docs-examples/c/json_protocol_example.c +++ b/docs-examples/c/json_protocol_example.c @@ -29,11 +29,11 @@ int main() { executeSQL(taos, "USE test"); char *line = "[{\"metric\": \"meters.current\", \"timestamp\": 1648432611249, \"value\": 10.3, \"tags\": {\"location\": " - "\"Beijing.Chaoyang\", \"groupid\": 2}},{\"metric\": \"meters.voltage\", \"timestamp\": 1648432611249, " - "\"value\": 219, \"tags\": {\"location\": \"Beijing.Haidian\", \"groupid\": 1}},{\"metric\": \"meters.current\", " - "\"timestamp\": 1648432611250, \"value\": 12.6, \"tags\": {\"location\": \"Beijing.Chaoyang\", \"groupid\": " + "\"California.SanFrancisco\", \"groupid\": 2}},{\"metric\": \"meters.voltage\", \"timestamp\": 1648432611249, " + "\"value\": 219, \"tags\": {\"location\": \"California.LosAngeles\", \"groupid\": 1}},{\"metric\": \"meters.current\", " + "\"timestamp\": 1648432611250, \"value\": 12.6, \"tags\": {\"location\": \"California.SanFrancisco\", \"groupid\": " "2}},{\"metric\": \"meters.voltage\", \"timestamp\": 1648432611250, \"value\": 221, \"tags\": {\"location\": " - "\"Beijing.Haidian\", \"groupid\": 1}}]"; + "\"California.LosAngeles\", \"groupid\": 1}}]"; char *lines[] = {line}; TAOS_RES *res = taos_schemaless_insert(taos, lines, 1, TSDB_SML_JSON_PROTOCOL, TSDB_SML_TIMESTAMP_NOT_CONFIGURED); diff --git a/docs-examples/c/line_example.c b/docs-examples/c/line_example.c index 8dd4b1a5075369625645959da0476b76b9fbf290..ce39f8d9df744082a450ce246529bf56adebd1e0 100644 --- a/docs-examples/c/line_example.c +++ b/docs-examples/c/line_example.c @@ -27,10 +27,10 @@ int main() { executeSQL(taos, "DROP DATABASE IF EXISTS test"); executeSQL(taos, "CREATE DATABASE test"); executeSQL(taos, "USE test"); - char *lines[] = {"meters,location=Beijing.Haidian,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249", - "meters,location=Beijing.Haidian,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250", - "meters,location=Beijing.Haidian,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249", - "meters,location=Beijing.Haidian,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250"}; + char *lines[] = {"meters,location=California.LosAngeles,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249", + "meters,location=California.LosAngeles,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250", + "meters,location=California.LosAngeles,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249", + "meters,location=California.LosAngeles,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250"}; TAOS_RES *res = taos_schemaless_insert(taos, lines, 4, TSDB_SML_LINE_PROTOCOL, TSDB_SML_TIMESTAMP_MILLI_SECONDS); if (taos_errno(res) != 0) { printf("failed to insert schema-less data, reason: %s\n", taos_errstr(res)); diff --git a/docs-examples/c/multi_bind_example.c b/docs-examples/c/multi_bind_example.c index fe11df9caad3e216fbd0b1ff2f40a54fe3ba86e5..02e6568e9e88ac8703a4993ed406e770d23c2438 100644 --- a/docs-examples/c/multi_bind_example.c +++ b/docs-examples/c/multi_bind_example.c @@ -52,7 +52,7 @@ void insertData(TAOS *taos) { checkErrorCode(stmt, code, "failed to execute taos_stmt_prepare"); // bind table name and tags TAOS_BIND tags[2]; - char *location = "Beijing.Chaoyang"; + char *location = "California.SanFrancisco"; int groupId = 2; tags[0].buffer_type = TSDB_DATA_TYPE_BINARY; tags[0].buffer_length = strlen(location); diff --git a/docs-examples/c/query_example.c b/docs-examples/c/query_example.c index f88b2467ceb3d9bbeaf6b3beb6a24befd3e398c6..fcae95bcd45a282eaa3ae911b4115e6300c6af8e 100644 --- a/docs-examples/c/query_example.c +++ b/docs-examples/c/query_example.c @@ -139,5 +139,5 @@ int main() { // output: // ts current voltage phase location groupid -// 1648432611249 10.300000 219 0.310000 Beijing.Chaoyang 2 -// 1648432611749 12.600000 218 0.330000 Beijing.Chaoyang 2 \ No newline at end of file +// 1648432611249 10.300000 219 0.310000 California.SanFrancisco 2 +// 1648432611749 12.600000 218 0.330000 California.SanFrancisco 2 \ No newline at end of file diff --git a/docs-examples/c/stmt_example.c b/docs-examples/c/stmt_example.c index fab1506f953ef68050e4318406fa2ba1a0202929..28dae5f9d5ea2faec0aa3c0a784d39e252651c65 100644 --- a/docs-examples/c/stmt_example.c +++ b/docs-examples/c/stmt_example.c @@ -59,7 +59,7 @@ void insertData(TAOS *taos) { checkErrorCode(stmt, code, "failed to execute taos_stmt_prepare"); // bind table name and tags TAOS_BIND tags[2]; - char* location = "Beijing.Chaoyang"; + char* location = "California.SanFrancisco"; int groupId = 2; tags[0].buffer_type = TSDB_DATA_TYPE_BINARY; tags[0].buffer_length = strlen(location); diff --git a/docs-examples/c/telnet_line_example.c b/docs-examples/c/telnet_line_example.c index 913d433f6aec07b3bce115d45536ffa4b45a0481..da62da4ba492856b0d73a564c1bf9cdd60b5b742 100644 --- a/docs-examples/c/telnet_line_example.c +++ b/docs-examples/c/telnet_line_example.c @@ -28,14 +28,14 @@ int main() { executeSQL(taos, "CREATE DATABASE test"); executeSQL(taos, "USE test"); char *lines[] = { - "meters.current 1648432611249 10.3 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611250 12.6 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611249 10.8 location=Beijing.Haidian groupid=3", - "meters.current 1648432611250 11.3 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611249 219 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611250 218 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611249 221 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611250 217 location=Beijing.Haidian groupid=3", + "meters.current 1648432611249 10.3 location=California.SanFrancisco groupid=2", + "meters.current 1648432611250 12.6 location=California.SanFrancisco groupid=2", + "meters.current 1648432611249 10.8 location=California.LosAngeles groupid=3", + "meters.current 1648432611250 11.3 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611249 219 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611250 218 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611249 221 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611250 217 location=California.LosAngeles groupid=3", }; TAOS_RES *res = taos_schemaless_insert(taos, lines, 8, TSDB_SML_TELNET_PROTOCOL, TSDB_SML_TIMESTAMP_NOT_CONFIGURED); if (taos_errno(res) != 0) { diff --git a/docs-examples/csharp/AsyncQueryExample.cs b/docs-examples/csharp/AsyncQueryExample.cs index fe30d21efe82e8d1dc414bd4723227ca93bc944f..3dabbebd1630a207af2e1b1b11cc4ba15bdd94a9 100644 --- a/docs-examples/csharp/AsyncQueryExample.cs +++ b/docs-examples/csharp/AsyncQueryExample.cs @@ -224,15 +224,15 @@ namespace TDengineExample } //output: -//Connect to TDengine success -//8 rows async retrieved - -//1538548685000 | 10.3 | 219 | 0.31 | beijing.chaoyang | 2 | -//1538548695000 | 12.6 | 218 | 0.33 | beijing.chaoyang | 2 | -//1538548696800 | 12.3 | 221 | 0.31 | beijing.chaoyang | 2 | -//1538548696650 | 10.3 | 218 | 0.25 | beijing.chaoyang | 3 | -//1538548685500 | 11.8 | 221 | 0.28 | beijing.haidian | 2 | -//1538548696600 | 13.4 | 223 | 0.29 | beijing.haidian | 2 | -//1538548685000 | 10.8 | 223 | 0.29 | beijing.haidian | 3 | -//1538548686500 | 11.5 | 221 | 0.35 | beijing.haidian | 3 | -//async retrieve complete. \ No newline at end of file +// Connect to TDengine success +// 8 rows async retrieved + +// 1538548685500 | 11.8 | 221 | 0.28 | california.losangeles | 2 | +// 1538548696600 | 13.4 | 223 | 0.29 | california.losangeles | 2 | +// 1538548685000 | 10.8 | 223 | 0.29 | california.losangeles | 3 | +// 1538548686500 | 11.5 | 221 | 0.35 | california.losangeles | 3 | +// 1538548685000 | 10.3 | 219 | 0.31 | california.sanfrancisco | 2 | +// 1538548695000 | 12.6 | 218 | 0.33 | california.sanfrancisco | 2 | +// 1538548696800 | 12.3 | 221 | 0.31 | california.sanfrancisco | 2 | +// 1538548696650 | 10.3 | 218 | 0.25 | california.sanfrancisco | 3 | +// async retrieve complete. \ No newline at end of file diff --git a/docs-examples/csharp/InfluxDBLineExample.cs b/docs-examples/csharp/InfluxDBLineExample.cs index 7aad08825209db568d61e5963ec7a00034ab7ca7..7b4453f4ac0b14dd76d166e395bdacb46a5d3fbc 100644 --- a/docs-examples/csharp/InfluxDBLineExample.cs +++ b/docs-examples/csharp/InfluxDBLineExample.cs @@ -9,10 +9,10 @@ namespace TDengineExample IntPtr conn = GetConnection(); PrepareDatabase(conn); string[] lines = { - "meters,location=Beijing.Haidian,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249", - "meters,location=Beijing.Haidian,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250", - "meters,location=Beijing.Haidian,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249", - "meters,location=Beijing.Haidian,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250" + "meters,location=California.LosAngeles,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249", + "meters,location=California.LosAngeles,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250", + "meters,location=California.LosAngeles,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249", + "meters,location=California.LosAngeles,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250" }; IntPtr res = TDengine.SchemalessInsert(conn, lines, lines.Length, (int)TDengineSchemalessProtocol.TSDB_SML_LINE_PROTOCOL, (int)TDengineSchemalessPrecision.TSDB_SML_TIMESTAMP_MILLI_SECONDS); if (TDengine.ErrorNo(res) != 0) diff --git a/docs-examples/csharp/OptsJsonExample.cs b/docs-examples/csharp/OptsJsonExample.cs index d774a325afa1a8d93eb858f23dcd97dd29f8653d..2c41acc5c9628befda7eb4ad5c30af5b921de948 100644 --- a/docs-examples/csharp/OptsJsonExample.cs +++ b/docs-examples/csharp/OptsJsonExample.cs @@ -8,10 +8,10 @@ namespace TDengineExample { IntPtr conn = GetConnection(); PrepareDatabase(conn); - string[] lines = { "[{\"metric\": \"meters.current\", \"timestamp\": 1648432611249, \"value\": 10.3, \"tags\": {\"location\": \"Beijing.Chaoyang\", \"groupid\": 2}}," + - " {\"metric\": \"meters.voltage\", \"timestamp\": 1648432611249, \"value\": 219, \"tags\": {\"location\": \"Beijing.Haidian\", \"groupid\": 1}}, " + - "{\"metric\": \"meters.current\", \"timestamp\": 1648432611250, \"value\": 12.6, \"tags\": {\"location\": \"Beijing.Chaoyang\", \"groupid\": 2}}," + - " {\"metric\": \"meters.voltage\", \"timestamp\": 1648432611250, \"value\": 221, \"tags\": {\"location\": \"Beijing.Haidian\", \"groupid\": 1}}]" + string[] lines = { "[{\"metric\": \"meters.current\", \"timestamp\": 1648432611249, \"value\": 10.3, \"tags\": {\"location\": \"California.SanFrancisco\", \"groupid\": 2}}," + + " {\"metric\": \"meters.voltage\", \"timestamp\": 1648432611249, \"value\": 219, \"tags\": {\"location\": \"California.LosAngeles\", \"groupid\": 1}}, " + + "{\"metric\": \"meters.current\", \"timestamp\": 1648432611250, \"value\": 12.6, \"tags\": {\"location\": \"California.SanFrancisco\", \"groupid\": 2}}," + + " {\"metric\": \"meters.voltage\", \"timestamp\": 1648432611250, \"value\": 221, \"tags\": {\"location\": \"California.LosAngeles\", \"groupid\": 1}}]" }; IntPtr res = TDengine.SchemalessInsert(conn, lines, 1, (int)TDengineSchemalessProtocol.TSDB_SML_JSON_PROTOCOL, (int)TDengineSchemalessPrecision.TSDB_SML_TIMESTAMP_NOT_CONFIGURED); diff --git a/docs-examples/csharp/OptsTelnetExample.cs b/docs-examples/csharp/OptsTelnetExample.cs index 81608c32213fa0618a2ca6e0769aacf8e9c8e64d..bb752db1afbbb2ef68df9ca25314c8b91cd9a266 100644 --- a/docs-examples/csharp/OptsTelnetExample.cs +++ b/docs-examples/csharp/OptsTelnetExample.cs @@ -9,14 +9,14 @@ namespace TDengineExample IntPtr conn = GetConnection(); PrepareDatabase(conn); string[] lines = { - "meters.current 1648432611249 10.3 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611250 12.6 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611249 10.8 location=Beijing.Haidian groupid=3", - "meters.current 1648432611250 11.3 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611249 219 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611250 218 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611249 221 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611250 217 location=Beijing.Haidian groupid=3", + "meters.current 1648432611249 10.3 location=California.SanFrancisco groupid=2", + "meters.current 1648432611250 12.6 location=California.SanFrancisco groupid=2", + "meters.current 1648432611249 10.8 location=California.LosAngeles groupid=3", + "meters.current 1648432611250 11.3 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611249 219 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611250 218 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611249 221 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611250 217 location=California.LosAngeles groupid=3", }; IntPtr res = TDengine.SchemalessInsert(conn, lines, lines.Length, (int)TDengineSchemalessProtocol.TSDB_SML_TELNET_PROTOCOL, (int)TDengineSchemalessPrecision.TSDB_SML_TIMESTAMP_NOT_CONFIGURED); if (TDengine.ErrorNo(res) != 0) diff --git a/docs-examples/csharp/QueryExample.cs b/docs-examples/csharp/QueryExample.cs index f00e391100c7ce42177e2987f5b0b32dc02262c4..97f0c456d412e2ed608c345ba87469d3f5ccfc15 100644 --- a/docs-examples/csharp/QueryExample.cs +++ b/docs-examples/csharp/QueryExample.cs @@ -158,5 +158,5 @@ namespace TDengineExample // Connect to TDengine success // fieldCount=6 // ts current voltage phase location groupid -// 1648432611249 10.3 219 0.31 Beijing.Chaoyang 2 -// 1648432611749 12.6 218 0.33 Beijing.Chaoyang 2 \ No newline at end of file +// 1648432611249 10.3 219 0.31 California.SanFrancisco 2 +// 1648432611749 12.6 218 0.33 California.SanFrancisco 2 \ No newline at end of file diff --git a/docs-examples/csharp/SQLInsertExample.cs b/docs-examples/csharp/SQLInsertExample.cs index fa2e2a50daf06f4d948479e7f5b0df82c517f809..d5462c1062e01fd5c93bac983696d0350117ad92 100644 --- a/docs-examples/csharp/SQLInsertExample.cs +++ b/docs-examples/csharp/SQLInsertExample.cs @@ -15,10 +15,10 @@ namespace TDengineExample CheckRes(conn, res, "failed to change database"); res = TDengine.Query(conn, "CREATE STABLE power.meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT)"); CheckRes(conn, res, "failed to create stable"); - var sql = "INSERT INTO d1001 USING meters TAGS(Beijing.Chaoyang, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) " + - "d1002 USING power.meters TAGS(Beijing.Chaoyang, 3) VALUES('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) " + - "d1003 USING power.meters TAGS(Beijing.Haidian, 2) VALUES('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000)('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) " + - "d1004 USING power.meters TAGS(Beijing.Haidian, 3) VALUES('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000)('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)"; + var sql = "INSERT INTO d1001 USING meters TAGS(California.SanFrancisco, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) " + + "d1002 USING power.meters TAGS(California.SanFrancisco, 3) VALUES('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) " + + "d1003 USING power.meters TAGS(California.LosAngeles, 2) VALUES('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000)('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) " + + "d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000)('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)"; res = TDengine.Query(conn, sql); CheckRes(conn, res, "failed to insert data"); int affectedRows = TDengine.AffectRows(res); diff --git a/docs-examples/csharp/StmtInsertExample.cs b/docs-examples/csharp/StmtInsertExample.cs index d6e00dd4ac54ab8dbfc33b93896d19fc585e7642..6ade424b95d64529b7a40a782de13e3106d0c78a 100644 --- a/docs-examples/csharp/StmtInsertExample.cs +++ b/docs-examples/csharp/StmtInsertExample.cs @@ -21,7 +21,7 @@ namespace TDengineExample CheckStmtRes(res, "failed to prepare stmt"); // 2. bind table name and tags - TAOS_BIND[] tags = new TAOS_BIND[2] { TaosBind.BindBinary("Beijing.Chaoyang"), TaosBind.BindInt(2) }; + TAOS_BIND[] tags = new TAOS_BIND[2] { TaosBind.BindBinary("California.SanFrancisco"), TaosBind.BindInt(2) }; res = TDengine.StmtSetTbnameTags(stmt, "d1001", tags); CheckStmtRes(res, "failed to bind table name and tags"); diff --git a/docs-examples/go/connect/cgoexample/main.go b/docs-examples/go/connect/cgoexample/main.go index 8b9aba4ce4217c00605bc8796c788f3dd52805e6..ba7ed0f728a1cd546dbc3199ce4c0dc854ebee91 100644 --- a/docs-examples/go/connect/cgoexample/main.go +++ b/docs-examples/go/connect/cgoexample/main.go @@ -20,4 +20,4 @@ func main() { // use // var taosDSN = "root:taosdata@tcp(localhost:6030)/dbName" -// if you want to connect to a default database. +// if you want to connect a specified database named "dbName". diff --git a/docs-examples/go/connect/restexample/main.go b/docs-examples/go/connect/restexample/main.go index 9c05e7eed80dee4ae7e6b20637d265f388d7438d..1efc98b988c183c4c680884057bf2a72a9dd19e9 100644 --- a/docs-examples/go/connect/restexample/main.go +++ b/docs-examples/go/connect/restexample/main.go @@ -18,6 +18,6 @@ func main() { defer taos.Close() } -// use +// use // var taosDSN = "root:taosdata@http(localhost:6041)/dbName" -// if you want to connect to a default database. +// if you want to connect a specified database named "dbName". diff --git a/docs-examples/go/insert/json/main.go b/docs-examples/go/insert/json/main.go index 47d9e9984adc05896fb9954ad3deffde3764b836..6be375270e32a5091c015f88de52c9dda2246b59 100644 --- a/docs-examples/go/insert/json/main.go +++ b/docs-examples/go/insert/json/main.go @@ -25,10 +25,10 @@ func main() { defer conn.Close() prepareDatabase(conn) - payload := `[{"metric": "meters.current", "timestamp": 1648432611249, "value": 10.3, "tags": {"location": "Beijing.Chaoyang", "groupid": 2}}, - {"metric": "meters.voltage", "timestamp": 1648432611249, "value": 219, "tags": {"location": "Beijing.Haidian", "groupid": 1}}, - {"metric": "meters.current", "timestamp": 1648432611250, "value": 12.6, "tags": {"location": "Beijing.Chaoyang", "groupid": 2}}, - {"metric": "meters.voltage", "timestamp": 1648432611250, "value": 221, "tags": {"location": "Beijing.Haidian", "groupid": 1}}]` + payload := `[{"metric": "meters.current", "timestamp": 1648432611249, "value": 10.3, "tags": {"location": "California.SanFrancisco", "groupid": 2}}, + {"metric": "meters.voltage", "timestamp": 1648432611249, "value": 219, "tags": {"location": "California.LosAngeles", "groupid": 1}}, + {"metric": "meters.current", "timestamp": 1648432611250, "value": 12.6, "tags": {"location": "California.SanFrancisco", "groupid": 2}}, + {"metric": "meters.voltage", "timestamp": 1648432611250, "value": 221, "tags": {"location": "California.LosAngeles", "groupid": 1}}]` err = conn.OpenTSDBInsertJsonPayload(payload) if err != nil { diff --git a/docs-examples/go/insert/line/main.go b/docs-examples/go/insert/line/main.go index bbc41468fe5f13d3e6f896445bb88f3eba584d0f..c17e1a5270850e6a8b497e0dbec4ae714ee1e2d6 100644 --- a/docs-examples/go/insert/line/main.go +++ b/docs-examples/go/insert/line/main.go @@ -25,10 +25,10 @@ func main() { defer conn.Close() prepareDatabase(conn) var lines = []string{ - "meters,location=Beijing.Haidian,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249", - "meters,location=Beijing.Haidian,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250", - "meters,location=Beijing.Haidian,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249", - "meters,location=Beijing.Haidian,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250", + "meters,location=California.LosAngeles,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249", + "meters,location=California.LosAngeles,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250", + "meters,location=California.LosAngeles,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249", + "meters,location=California.LosAngeles,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250", } err = conn.InfluxDBInsertLines(lines, "ms") diff --git a/docs-examples/go/insert/sql/main.go b/docs-examples/go/insert/sql/main.go index 91386855334c1930af721e0b4f43395c6a6d8e82..6cd5f860e65f4fffd139668f69cc1772f5310eae 100644 --- a/docs-examples/go/insert/sql/main.go +++ b/docs-examples/go/insert/sql/main.go @@ -19,10 +19,10 @@ func createStable(taos *sql.DB) { } func insertData(taos *sql.DB) { - sql := `INSERT INTO power.d1001 USING power.meters TAGS(Beijing.Chaoyang, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) - power.d1002 USING power.meters TAGS(Beijing.Chaoyang, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) - power.d1003 USING power.meters TAGS(Beijing.Haidian, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) - power.d1004 USING power.meters TAGS(Beijing.Haidian, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)` + sql := `INSERT INTO power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) + power.d1002 USING power.meters TAGS(California.SanFrancisco, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) + power.d1003 USING power.meters TAGS(California.LosAngeles, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) + power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)` result, err := taos.Exec(sql) if err != nil { fmt.Println("failed to insert, err:", err) diff --git a/docs-examples/go/insert/stmt/main.go b/docs-examples/go/insert/stmt/main.go index c50200ebb427c4c64c2737cb8fe4c3d287551a34..7093fdf1e52bc5a14fc92cec995fd81e70717d9f 100644 --- a/docs-examples/go/insert/stmt/main.go +++ b/docs-examples/go/insert/stmt/main.go @@ -37,7 +37,7 @@ func main() { checkErr(err, "failed to create prepare statement") // bind table name and tags - tagParams := param.NewParam(2).AddBinary([]byte("Beijing.Chaoyang")).AddInt(2) + tagParams := param.NewParam(2).AddBinary([]byte("California.SanFrancisco")).AddInt(2) err = stmt.SetTableNameWithTags("d1001", tagParams) checkErr(err, "failed to execute SetTableNameWithTags") diff --git a/docs-examples/go/insert/telnet/main.go b/docs-examples/go/insert/telnet/main.go index 879e6d5cece74fd0b7c815dd34614dca3c9d4544..91fafbe71adbf60d9341b903f5a25708b7011852 100644 --- a/docs-examples/go/insert/telnet/main.go +++ b/docs-examples/go/insert/telnet/main.go @@ -25,14 +25,14 @@ func main() { defer conn.Close() prepareDatabase(conn) var lines = []string{ - "meters.current 1648432611249 10.3 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611250 12.6 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611249 10.8 location=Beijing.Haidian groupid=3", - "meters.current 1648432611250 11.3 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611249 219 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611250 218 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611249 221 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611250 217 location=Beijing.Haidian groupid=3", + "meters.current 1648432611249 10.3 location=California.SanFrancisco groupid=2", + "meters.current 1648432611250 12.6 location=California.SanFrancisco groupid=2", + "meters.current 1648432611249 10.8 location=California.LosAngeles groupid=3", + "meters.current 1648432611250 11.3 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611249 219 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611250 218 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611249 221 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611250 217 location=California.LosAngeles groupid=3", } err = conn.OpenTSDBInsertTelnetLines(lines) diff --git a/docs-examples/java/src/main/java/com/taos/example/JNIConnectExample.java b/docs-examples/java/src/main/java/com/taos/example/JNIConnectExample.java index c6ce2ef9785a010daa55ad29415f81711760cd57..84292f7e8682dbb8171c807da74a603f4ae8256e 100644 --- a/docs-examples/java/src/main/java/com/taos/example/JNIConnectExample.java +++ b/docs-examples/java/src/main/java/com/taos/example/JNIConnectExample.java @@ -22,4 +22,4 @@ public class JNIConnectExample { // use // String jdbcUrl = "jdbc:TAOS://localhost:6030/dbName?user=root&password=taosdata"; -// if you want to connect to a default database. \ No newline at end of file +// if you want to connect a specified database named "dbName". \ No newline at end of file diff --git a/docs-examples/java/src/main/java/com/taos/example/JSONProtocolExample.java b/docs-examples/java/src/main/java/com/taos/example/JSONProtocolExample.java index cb83424576a4fd7dfa09ea297294ed77b66bd12d..c8e649482fbd747cdc238daa9e7a237cf63295b6 100644 --- a/docs-examples/java/src/main/java/com/taos/example/JSONProtocolExample.java +++ b/docs-examples/java/src/main/java/com/taos/example/JSONProtocolExample.java @@ -23,10 +23,10 @@ public class JSONProtocolExample { } private static String getJSONData() { - return "[{\"metric\": \"meters.current\", \"timestamp\": 1648432611249, \"value\": 10.3, \"tags\": {\"location\": \"Beijing.Chaoyang\", \"groupid\": 2}}," + - " {\"metric\": \"meters.voltage\", \"timestamp\": 1648432611249, \"value\": 219, \"tags\": {\"location\": \"Beijing.Haidian\", \"groupid\": 1}}, " + - "{\"metric\": \"meters.current\", \"timestamp\": 1648432611250, \"value\": 12.6, \"tags\": {\"location\": \"Beijing.Chaoyang\", \"groupid\": 2}}," + - " {\"metric\": \"meters.voltage\", \"timestamp\": 1648432611250, \"value\": 221, \"tags\": {\"location\": \"Beijing.Haidian\", \"groupid\": 1}}]"; + return "[{\"metric\": \"meters.current\", \"timestamp\": 1648432611249, \"value\": 10.3, \"tags\": {\"location\": \"California.SanFrancisco\", \"groupid\": 2}}," + + " {\"metric\": \"meters.voltage\", \"timestamp\": 1648432611249, \"value\": 219, \"tags\": {\"location\": \"California.LosAngeles\", \"groupid\": 1}}, " + + "{\"metric\": \"meters.current\", \"timestamp\": 1648432611250, \"value\": 12.6, \"tags\": {\"location\": \"California.SanFrancisco\", \"groupid\": 2}}," + + " {\"metric\": \"meters.voltage\", \"timestamp\": 1648432611250, \"value\": 221, \"tags\": {\"location\": \"California.LosAngeles\", \"groupid\": 1}}]"; } public static void main(String[] args) throws SQLException { diff --git a/docs-examples/java/src/main/java/com/taos/example/LineProtocolExample.java b/docs-examples/java/src/main/java/com/taos/example/LineProtocolExample.java index 8a2eabe0a91f7966cc3cc6b7dfeeb71b71b88d92..990922b7a516bd32a7e299f5743bd1b5e321868a 100644 --- a/docs-examples/java/src/main/java/com/taos/example/LineProtocolExample.java +++ b/docs-examples/java/src/main/java/com/taos/example/LineProtocolExample.java @@ -12,11 +12,11 @@ import java.sql.Statement; public class LineProtocolExample { // format: measurement,tag_set field_set timestamp private static String[] lines = { - "meters,location=Beijing.Haidian,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249000", // micro + "meters,location=California.LosAngeles,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249000", // micro // seconds - "meters,location=Beijing.Haidian,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611249500", - "meters,location=Beijing.Haidian,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249300", - "meters,location=Beijing.Haidian,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611249800", + "meters,location=California.LosAngeles,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611249500", + "meters,location=California.LosAngeles,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249300", + "meters,location=California.LosAngeles,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611249800", }; private static Connection getConnection() throws SQLException { diff --git a/docs-examples/java/src/main/java/com/taos/example/RestInsertExample.java b/docs-examples/java/src/main/java/com/taos/example/RestInsertExample.java index de89f26cbe38f9343d60aeb8d3e9ce7f67c2e764..af97fe4373ca964260e5614f133f359e229b0e15 100644 --- a/docs-examples/java/src/main/java/com/taos/example/RestInsertExample.java +++ b/docs-examples/java/src/main/java/com/taos/example/RestInsertExample.java @@ -16,28 +16,28 @@ public class RestInsertExample { private static List getRawData() { return Arrays.asList( - "d1001,2018-10-03 14:38:05.000,10.30000,219,0.31000,Beijing.Chaoyang,2", - "d1001,2018-10-03 14:38:15.000,12.60000,218,0.33000,Beijing.Chaoyang,2", - "d1001,2018-10-03 14:38:16.800,12.30000,221,0.31000,Beijing.Chaoyang,2", - "d1002,2018-10-03 14:38:16.650,10.30000,218,0.25000,Beijing.Chaoyang,3", - "d1003,2018-10-03 14:38:05.500,11.80000,221,0.28000,Beijing.Haidian,2", - "d1003,2018-10-03 14:38:16.600,13.40000,223,0.29000,Beijing.Haidian,2", - "d1004,2018-10-03 14:38:05.000,10.80000,223,0.29000,Beijing.Haidian,3", - "d1004,2018-10-03 14:38:06.500,11.50000,221,0.35000,Beijing.Haidian,3" + "d1001,2018-10-03 14:38:05.000,10.30000,219,0.31000,California.SanFrancisco,2", + "d1001,2018-10-03 14:38:15.000,12.60000,218,0.33000,California.SanFrancisco,2", + "d1001,2018-10-03 14:38:16.800,12.30000,221,0.31000,California.SanFrancisco,2", + "d1002,2018-10-03 14:38:16.650,10.30000,218,0.25000,California.SanFrancisco,3", + "d1003,2018-10-03 14:38:05.500,11.80000,221,0.28000,California.LosAngeles,2", + "d1003,2018-10-03 14:38:16.600,13.40000,223,0.29000,California.LosAngeles,2", + "d1004,2018-10-03 14:38:05.000,10.80000,223,0.29000,California.LosAngeles,3", + "d1004,2018-10-03 14:38:06.500,11.50000,221,0.35000,California.LosAngeles,3" ); } /** * The generated SQL is: - * INSERT INTO power.d1001 USING power.meters TAGS(Beijing.Chaoyang, 2) VALUES('2018-10-03 14:38:05.000',10.30000,219,0.31000) - * power.d1001 USING power.meters TAGS(Beijing.Chaoyang, 2) VALUES('2018-10-03 14:38:15.000',12.60000,218,0.33000) - * power.d1001 USING power.meters TAGS(Beijing.Chaoyang, 2) VALUES('2018-10-03 14:38:16.800',12.30000,221,0.31000) - * power.d1002 USING power.meters TAGS(Beijing.Chaoyang, 3) VALUES('2018-10-03 14:38:16.650',10.30000,218,0.25000) - * power.d1003 USING power.meters TAGS(Beijing.Haidian, 2) VALUES('2018-10-03 14:38:05.500',11.80000,221,0.28000) - * power.d1003 USING power.meters TAGS(Beijing.Haidian, 2) VALUES('2018-10-03 14:38:16.600',13.40000,223,0.29000) - * power.d1004 USING power.meters TAGS(Beijing.Haidian, 3) VALUES('2018-10-03 14:38:05.000',10.80000,223,0.29000) - * power.d1004 USING power.meters TAGS(Beijing.Haidian, 3) VALUES('2018-10-03 14:38:06.500',11.50000,221,0.35000) + * INSERT INTO power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 14:38:05.000',10.30000,219,0.31000) + * power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 14:38:15.000',12.60000,218,0.33000) + * power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 14:38:16.800',12.30000,221,0.31000) + * power.d1002 USING power.meters TAGS(California.SanFrancisco, 3) VALUES('2018-10-03 14:38:16.650',10.30000,218,0.25000) + * power.d1003 USING power.meters TAGS(California.LosAngeles, 2) VALUES('2018-10-03 14:38:05.500',11.80000,221,0.28000) + * power.d1003 USING power.meters TAGS(California.LosAngeles, 2) VALUES('2018-10-03 14:38:16.600',13.40000,223,0.29000) + * power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES('2018-10-03 14:38:05.000',10.80000,223,0.29000) + * power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES('2018-10-03 14:38:06.500',11.50000,221,0.35000) */ private static String getSQL() { StringBuilder sb = new StringBuilder("INSERT INTO "); diff --git a/docs-examples/java/src/main/java/com/taos/example/RestQueryExample.java b/docs-examples/java/src/main/java/com/taos/example/RestQueryExample.java index b1a1d224c6d9af2b83ac039726dcdb49a33ec2b0..a3581a1f4733e8bf3e3f561bb6cab5a725d8a1c0 100644 --- a/docs-examples/java/src/main/java/com/taos/example/RestQueryExample.java +++ b/docs-examples/java/src/main/java/com/taos/example/RestQueryExample.java @@ -51,5 +51,5 @@ public class RestQueryExample { // possible output: // avg(voltage) location -// 222.0 Beijing.Haidian -// 219.0 Beijing.Chaoyang +// 222.0 California.LosAngeles +// 219.0 California.SanFrancisco diff --git a/docs-examples/java/src/main/java/com/taos/example/StmtInsertExample.java b/docs-examples/java/src/main/java/com/taos/example/StmtInsertExample.java index 2a7ccebf41cae1a22d7516966e2c6ffb10011b64..bbcc92b22f67c31384b0fb7a082975eaac2ff2bc 100644 --- a/docs-examples/java/src/main/java/com/taos/example/StmtInsertExample.java +++ b/docs-examples/java/src/main/java/com/taos/example/StmtInsertExample.java @@ -30,14 +30,14 @@ public class StmtInsertExample { private static List getRawData() { return Arrays.asList( - "d1001,2018-10-03 14:38:05.000,10.30000,219,0.31000,Beijing.Chaoyang,2", - "d1001,2018-10-03 14:38:15.000,12.60000,218,0.33000,Beijing.Chaoyang,2", - "d1001,2018-10-03 14:38:16.800,12.30000,221,0.31000,Beijing.Chaoyang,2", - "d1002,2018-10-03 14:38:16.650,10.30000,218,0.25000,Beijing.Chaoyang,3", - "d1003,2018-10-03 14:38:05.500,11.80000,221,0.28000,Beijing.Haidian,2", - "d1003,2018-10-03 14:38:16.600,13.40000,223,0.29000,Beijing.Haidian,2", - "d1004,2018-10-03 14:38:05.000,10.80000,223,0.29000,Beijing.Haidian,3", - "d1004,2018-10-03 14:38:06.500,11.50000,221,0.35000,Beijing.Haidian,3" + "d1001,2018-10-03 14:38:05.000,10.30000,219,0.31000,California.SanFrancisco,2", + "d1001,2018-10-03 14:38:15.000,12.60000,218,0.33000,California.SanFrancisco,2", + "d1001,2018-10-03 14:38:16.800,12.30000,221,0.31000,California.SanFrancisco,2", + "d1002,2018-10-03 14:38:16.650,10.30000,218,0.25000,California.SanFrancisco,3", + "d1003,2018-10-03 14:38:05.500,11.80000,221,0.28000,California.LosAngeles,2", + "d1003,2018-10-03 14:38:16.600,13.40000,223,0.29000,California.LosAngeles,2", + "d1004,2018-10-03 14:38:05.000,10.80000,223,0.29000,California.LosAngeles,3", + "d1004,2018-10-03 14:38:06.500,11.50000,221,0.35000,California.LosAngeles,3" ); } diff --git a/docs-examples/java/src/main/java/com/taos/example/TelnetLineProtocolExample.java b/docs-examples/java/src/main/java/com/taos/example/TelnetLineProtocolExample.java index 1431eccf16dabaac20f60ae7e971ef49707ba509..4c9368288df74f829121aeab5b925d1d083d29f0 100644 --- a/docs-examples/java/src/main/java/com/taos/example/TelnetLineProtocolExample.java +++ b/docs-examples/java/src/main/java/com/taos/example/TelnetLineProtocolExample.java @@ -11,14 +11,14 @@ import java.sql.Statement; public class TelnetLineProtocolExample { // format: =[ =] - private static String[] lines = { "meters.current 1648432611249 10.3 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611250 12.6 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611249 10.8 location=Beijing.Haidian groupid=3", - "meters.current 1648432611250 11.3 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611249 219 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611250 218 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611249 221 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611250 217 location=Beijing.Haidian groupid=3", + private static String[] lines = { "meters.current 1648432611249 10.3 location=California.SanFrancisco groupid=2", + "meters.current 1648432611250 12.6 location=California.SanFrancisco groupid=2", + "meters.current 1648432611249 10.8 location=California.LosAngeles groupid=3", + "meters.current 1648432611250 11.3 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611249 219 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611250 218 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611249 221 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611250 217 location=California.LosAngeles groupid=3", }; private static Connection getConnection() throws SQLException { diff --git a/docs-examples/java/src/test/java/com/taos/test/TestAll.java b/docs-examples/java/src/test/java/com/taos/test/TestAll.java index 92fe14a49d5f5ea5d7ea5f1d809867b3de0cc9d2..42db24485afec05298159f7b0c3a4e15835d98ed 100644 --- a/docs-examples/java/src/test/java/com/taos/test/TestAll.java +++ b/docs-examples/java/src/test/java/com/taos/test/TestAll.java @@ -23,16 +23,16 @@ public class TestAll { String jdbcUrl = "jdbc:TAOS://localhost:6030?user=root&password=taosdata"; try (Connection conn = DriverManager.getConnection(jdbcUrl)) { try (Statement stmt = conn.createStatement()) { - String sql = "INSERT INTO power.d1001 USING power.meters TAGS(Beijing.Chaoyang, 2) VALUES('2018-10-03 14:38:05.000',10.30000,219,0.31000)\n" + - " power.d1001 USING power.meters TAGS(Beijing.Chaoyang, 2) VALUES('2018-10-03 15:38:15.000',12.60000,218,0.33000)\n" + - " power.d1001 USING power.meters TAGS(Beijing.Chaoyang, 2) VALUES('2018-10-03 15:38:16.800',12.30000,221,0.31000)\n" + - " power.d1002 USING power.meters TAGS(Beijing.Chaoyang, 3) VALUES('2018-10-03 15:38:16.650',10.30000,218,0.25000)\n" + - " power.d1003 USING power.meters TAGS(Beijing.Haidian, 2) VALUES('2018-10-03 15:38:05.500',11.80000,221,0.28000)\n" + - " power.d1003 USING power.meters TAGS(Beijing.Haidian, 2) VALUES('2018-10-03 15:38:16.600',13.40000,223,0.29000)\n" + - " power.d1004 USING power.meters TAGS(Beijing.Haidian, 3) VALUES('2018-10-03 15:38:05.000',10.80000,223,0.29000)\n" + - " power.d1004 USING power.meters TAGS(Beijing.Haidian, 3) VALUES('2018-10-03 15:38:06.000',10.80000,223,0.29000)\n" + - " power.d1004 USING power.meters TAGS(Beijing.Haidian, 3) VALUES('2018-10-03 15:38:07.000',10.80000,223,0.29000)\n" + - " power.d1004 USING power.meters TAGS(Beijing.Haidian, 3) VALUES('2018-10-03 15:38:08.500',11.50000,221,0.35000)"; + String sql = "INSERT INTO power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 14:38:05.000',10.30000,219,0.31000)\n" + + " power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 15:38:15.000',12.60000,218,0.33000)\n" + + " power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 15:38:16.800',12.30000,221,0.31000)\n" + + " power.d1002 USING power.meters TAGS(California.SanFrancisco, 3) VALUES('2018-10-03 15:38:16.650',10.30000,218,0.25000)\n" + + " power.d1003 USING power.meters TAGS(California.LosAngeles, 2) VALUES('2018-10-03 15:38:05.500',11.80000,221,0.28000)\n" + + " power.d1003 USING power.meters TAGS(California.LosAngeles, 2) VALUES('2018-10-03 15:38:16.600',13.40000,223,0.29000)\n" + + " power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES('2018-10-03 15:38:05.000',10.80000,223,0.29000)\n" + + " power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES('2018-10-03 15:38:06.000',10.80000,223,0.29000)\n" + + " power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES('2018-10-03 15:38:07.000',10.80000,223,0.29000)\n" + + " power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES('2018-10-03 15:38:08.500',11.50000,221,0.35000)"; stmt.execute(sql); } diff --git a/docs-examples/node/nativeexample/influxdb_line_example.js b/docs-examples/node/nativeexample/influxdb_line_example.js index a9fc6d11df0b335b92bb3292baaa017cb4bc42ea..2050bee54506a3ee6fe7d89de97b3b41334dd4a6 100644 --- a/docs-examples/node/nativeexample/influxdb_line_example.js +++ b/docs-examples/node/nativeexample/influxdb_line_example.js @@ -13,10 +13,10 @@ function createDatabase() { function insertData() { const lines = [ - "meters,location=Beijing.Haidian,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249", - "meters,location=Beijing.Haidian,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250", - "meters,location=Beijing.Haidian,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249", - "meters,location=Beijing.Haidian,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250", + "meters,location=California.LosAngeles,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249", + "meters,location=California.LosAngeles,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250", + "meters,location=California.LosAngeles,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249", + "meters,location=California.LosAngeles,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250", ]; cursor.schemalessInsert( lines, diff --git a/docs-examples/node/nativeexample/insert_example.js b/docs-examples/node/nativeexample/insert_example.js index 85a353f889176655654d8c39c9a905054d3b6622..ade9d83158362cbf00a856b43a973de31def7601 100644 --- a/docs-examples/node/nativeexample/insert_example.js +++ b/docs-examples/node/nativeexample/insert_example.js @@ -11,10 +11,10 @@ try { cursor.execute( "CREATE STABLE meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT)" ); - var sql = `INSERT INTO power.d1001 USING power.meters TAGS(Beijing.Chaoyang, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) -power.d1002 USING power.meters TAGS(Beijing.Chaoyang, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) -power.d1003 USING power.meters TAGS(Beijing.Haidian, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) -power.d1004 USING power.meters TAGS(Beijing.Haidian, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)`; + var sql = `INSERT INTO power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) +power.d1002 USING power.meters TAGS(California.SanFrancisco, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) +power.d1003 USING power.meters TAGS(California.LosAngeles, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) +power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)`; cursor.execute(sql); } finally { cursor.close(); diff --git a/docs-examples/node/nativeexample/multi_bind_example.js b/docs-examples/node/nativeexample/multi_bind_example.js index d52581ec8e10c6edfbc8fc8f7ca78512b5c93d74..6ef8b30c097393fef8c6a2837f8683c736b363f1 100644 --- a/docs-examples/node/nativeexample/multi_bind_example.js +++ b/docs-examples/node/nativeexample/multi_bind_example.js @@ -25,7 +25,7 @@ function insertData() { // bind table name and tags let tagBind = new taos.TaosBind(2); - tagBind.bindBinary("Beijing.Chaoyang"); + tagBind.bindBinary("California.SanFrancisco"); tagBind.bindInt(2); cursor.stmtSetTbnameTags("d1001", tagBind.getBind()); diff --git a/docs-examples/node/nativeexample/opentsdb_json_example.js b/docs-examples/node/nativeexample/opentsdb_json_example.js index 6d436a8e9ebe0230bba22064e8fb6c180c14b5d1..2d78444a3f805bc77ab5e11925a28dd18fe221fe 100644 --- a/docs-examples/node/nativeexample/opentsdb_json_example.js +++ b/docs-examples/node/nativeexample/opentsdb_json_example.js @@ -17,25 +17,25 @@ function insertData() { metric: "meters.current", timestamp: 1648432611249, value: 10.3, - tags: { location: "Beijing.Chaoyang", groupid: 2 }, + tags: { location: "California.SanFrancisco", groupid: 2 }, }, { metric: "meters.voltage", timestamp: 1648432611249, value: 219, - tags: { location: "Beijing.Haidian", groupid: 1 }, + tags: { location: "California.LosAngeles", groupid: 1 }, }, { metric: "meters.current", timestamp: 1648432611250, value: 12.6, - tags: { location: "Beijing.Chaoyang", groupid: 2 }, + tags: { location: "California.SanFrancisco", groupid: 2 }, }, { metric: "meters.voltage", timestamp: 1648432611250, value: 221, - tags: { location: "Beijing.Haidian", groupid: 1 }, + tags: { location: "California.LosAngeles", groupid: 1 }, }, ]; diff --git a/docs-examples/node/nativeexample/opentsdb_telnet_example.js b/docs-examples/node/nativeexample/opentsdb_telnet_example.js index 01e79c2dcacd923cd708d1d228959a628d0ff26a..7f80f558838e18f07ad79e580e7d08638b74e940 100644 --- a/docs-examples/node/nativeexample/opentsdb_telnet_example.js +++ b/docs-examples/node/nativeexample/opentsdb_telnet_example.js @@ -13,14 +13,14 @@ function createDatabase() { function insertData() { const lines = [ - "meters.current 1648432611249 10.3 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611250 12.6 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611249 10.8 location=Beijing.Haidian groupid=3", - "meters.current 1648432611250 11.3 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611249 219 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611250 218 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611249 221 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611250 217 location=Beijing.Haidian groupid=3", + "meters.current 1648432611249 10.3 location=California.SanFrancisco groupid=2", + "meters.current 1648432611250 12.6 location=California.SanFrancisco groupid=2", + "meters.current 1648432611249 10.8 location=California.LosAngeles groupid=3", + "meters.current 1648432611250 11.3 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611249 219 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611250 218 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611249 221 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611250 217 location=California.LosAngeles groupid=3", ]; cursor.schemalessInsert( lines, diff --git a/docs-examples/node/nativeexample/param_bind_example.js b/docs-examples/node/nativeexample/param_bind_example.js index 9117f46c3eeabd9009b72fa9d4a8503e65884242..c7e04c71a0d19ff8666f3d43fe09109009741266 100644 --- a/docs-examples/node/nativeexample/param_bind_example.js +++ b/docs-examples/node/nativeexample/param_bind_example.js @@ -24,7 +24,7 @@ function insertData() { // bind table name and tags let tagBind = new taos.TaosBind(2); - tagBind.bindBinary("Beijing.Chaoyang"); + tagBind.bindBinary("California.SanFrancisco"); tagBind.bindInt(2); cursor.stmtSetTbnameTags("d1001", tagBind.getBind()); diff --git a/docs-examples/other/mock.js b/docs-examples/other/mock.js new file mode 100644 index 0000000000000000000000000000000000000000..136c5afa96425073fc29854708495e98d0b2e743 --- /dev/null +++ b/docs-examples/other/mock.js @@ -0,0 +1,78 @@ +// mock.js +const mqtt = require('mqtt') +const Mock = require('mockjs') +const EMQX_SERVER = 'mqtt://localhost:1883' +const CLIENT_NUM = 10 +const STEP = 5000 // Data interval in ms +const AWAIT = 5000 // Sleep time after data be written once to avoid data writing too fast +const CLIENT_POOL = [] +startMock() +function sleep(timer = 100) { + return new Promise(resolve => { + setTimeout(resolve, timer) + }) +} +async function startMock() { + const now = Date.now() + for (let i = 0; i < CLIENT_NUM; i++) { + const client = await createClient(`mock_client_${i}`) + CLIENT_POOL.push(client) + } + // last 24h every 5s + const last = 24 * 3600 * 1000 + for (let ts = now - last; ts <= now; ts += STEP) { + for (const client of CLIENT_POOL) { + const mockData = generateMockData() + const data = { + ...mockData, + id: client.clientId, + area: 0, + ts, + } + client.publish('sensor/data', JSON.stringify(data)) + } + const dateStr = new Date(ts).toLocaleTimeString() + console.log(`${dateStr} send success.`) + await sleep(AWAIT) + } + console.log(`Done, use ${(Date.now() - now) / 1000}s`) +} +/** + * Init a virtual mqtt client + * @param {string} clientId ClientID + */ +function createClient(clientId) { + return new Promise((resolve, reject) => { + const client = mqtt.connect(EMQX_SERVER, { + clientId, + }) + client.on('connect', () => { + console.log(`client ${clientId} connected`) + resolve(client) + }) + client.on('reconnect', () => { + console.log('reconnect') + }) + client.on('error', (e) => { + console.error(e) + reject(e) + }) + }) +} +/** +* Generate mock data +*/ +function generateMockData() { + return { + "temperature": parseFloat(Mock.Random.float(22, 100).toFixed(2)), + "humidity": parseFloat(Mock.Random.float(12, 86).toFixed(2)), + "volume": parseFloat(Mock.Random.float(20, 200).toFixed(2)), + "PM10": parseFloat(Mock.Random.float(0, 300).toFixed(2)), + "pm25": parseFloat(Mock.Random.float(0, 300).toFixed(2)), + "SO2": parseFloat(Mock.Random.float(0, 50).toFixed(2)), + "NO2": parseFloat(Mock.Random.float(0, 50).toFixed(2)), + "CO": parseFloat(Mock.Random.float(0, 50).toFixed(2)), + "area": Mock.Random.integer(0, 20), + "ts": 1596157444170, + } +} \ No newline at end of file diff --git a/docs-examples/php/connect.php b/docs-examples/php/connect.php index 5af77b9768e5c5ac4b774b433479a4ac8902beda..b825b447805a3923248042d2cdff79c51bdcdbe3 100644 --- a/docs-examples/php/connect.php +++ b/docs-examples/php/connect.php @@ -4,7 +4,7 @@ use TDengine\Connection; use TDengine\Exception\TDengineException; try { - // 实例化 + // instantiate $host = 'localhost'; $port = 6030; $username = 'root'; @@ -12,9 +12,9 @@ try { $dbname = null; $connection = new Connection($host, $port, $username, $password, $dbname); - // 连接 + // connect $connection->connect(); } catch (TDengineException $e) { - // 连接失败捕获异常 + // throw exception throw $e; } diff --git a/docs-examples/php/insert.php b/docs-examples/php/insert.php index 0d9cfc4843a2ec3e72d0ad128fa4c2650d6b9cf6..6e38fa0c46d31aa0a939d471ccbd255cfa453a16 100644 --- a/docs-examples/php/insert.php +++ b/docs-examples/php/insert.php @@ -4,7 +4,7 @@ use TDengine\Connection; use TDengine\Exception\TDengineException; try { - // 实例化 + // instantiate $host = 'localhost'; $port = 6030; $username = 'root'; @@ -12,22 +12,22 @@ try { $dbname = 'power'; $connection = new Connection($host, $port, $username, $password, $dbname); - // 连接 + // connect $connection->connect(); - // 插入 + // insert $connection->query('CREATE DATABASE if not exists power'); $connection->query('CREATE STABLE if not exists meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT)'); $resource = $connection->query(<<<'SQL' - INSERT INTO power.d1001 USING power.meters TAGS(Beijing.Chaoyang, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) - power.d1002 USING power.meters TAGS(Beijing.Chaoyang, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) - power.d1003 USING power.meters TAGS(Beijing.Haidian, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) - power.d1004 USING power.meters TAGS(Beijing.Haidian, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000) + INSERT INTO power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) + power.d1002 USING power.meters TAGS(California.SanFrancisco, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) + power.d1003 USING power.meters TAGS(California.LosAngeles, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) + power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000) SQL); - // 影响行数 + // get affected rows var_dump($resource->affectedRows()); } catch (TDengineException $e) { - // 捕获异常 + // throw exception throw $e; } diff --git a/docs-examples/php/insert_stmt.php b/docs-examples/php/insert_stmt.php index 5d4b4809d215d781807c21172982feff2171fe07..c927a9b0ced46461daeda0f53b27e2f9d67d5860 100644 --- a/docs-examples/php/insert_stmt.php +++ b/docs-examples/php/insert_stmt.php @@ -4,7 +4,7 @@ use TDengine\Connection; use TDengine\Exception\TDengineException; try { - // 实例化 + // instantiate $host = 'localhost'; $port = 6030; $username = 'root'; @@ -12,18 +12,18 @@ try { $dbname = 'power'; $connection = new Connection($host, $port, $username, $password, $dbname); - // 连接 + // connect $connection->connect(); - // 插入 + // insert $connection->query('CREATE DATABASE if not exists power'); $connection->query('CREATE STABLE if not exists meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT)'); $stmt = $connection->prepare('INSERT INTO ? USING meters TAGS(?, ?) VALUES(?, ?, ?, ?)'); - // 设置表名和标签 + // set table name and tags $stmt->setTableNameTags('d1001', [ - // 支持格式同参数绑定 - [TDengine\TSDB_DATA_TYPE_BINARY, 'Beijing.Chaoyang'], + // same format as parameter binding + [TDengine\TSDB_DATA_TYPE_BINARY, 'California.SanFrancisco'], [TDengine\TSDB_DATA_TYPE_INT, 2], ]); @@ -41,9 +41,9 @@ try { ]); $resource = $stmt->execute(); - // 影响行数 + // get affected rows var_dump($resource->affectedRows()); } catch (TDengineException $e) { - // 捕获异常 + // throw exception throw $e; } diff --git a/docs-examples/php/query.php b/docs-examples/php/query.php index 4e86a2cec7426887686049977a8647e786ac2744..2607940ea06a70eaa30e4c165c05bd72aa89857c 100644 --- a/docs-examples/php/query.php +++ b/docs-examples/php/query.php @@ -4,7 +4,7 @@ use TDengine\Connection; use TDengine\Exception\TDengineException; try { - // 实例化 + // instantiate $host = 'localhost'; $port = 6030; $username = 'root'; @@ -12,12 +12,12 @@ try { $dbname = 'power'; $connection = new Connection($host, $port, $username, $password, $dbname); - // 连接 + // connect $connection->connect(); $resource = $connection->query('SELECT ts, current FROM meters LIMIT 2'); var_dump($resource->fetch()); } catch (TDengineException $e) { - // 捕获异常 + // throw exception throw $e; } diff --git a/docs-examples/python/bind_param_example.py b/docs-examples/python/bind_param_example.py index 503a2eb5dd91a3516f87a4d3c1c3218cb6505236..6a67434f876f159cf32069a55e9527ca19034640 100644 --- a/docs-examples/python/bind_param_example.py +++ b/docs-examples/python/bind_param_example.py @@ -2,14 +2,14 @@ import taos from datetime import datetime # note: lines have already been sorted by table name -lines = [('d1001', '2018-10-03 14:38:05.000', 10.30000, 219, 0.31000, 'Beijing.Chaoyang', 2), - ('d1001', '2018-10-03 14:38:15.000', 12.60000, 218, 0.33000, 'Beijing.Chaoyang', 2), - ('d1001', '2018-10-03 14:38:16.800', 12.30000, 221, 0.31000, 'Beijing.Chaoyang', 2), - ('d1002', '2018-10-03 14:38:16.650', 10.30000, 218, 0.25000, 'Beijing.Chaoyang', 3), - ('d1003', '2018-10-03 14:38:05.500', 11.80000, 221, 0.28000, 'Beijing.Haidian', 2), - ('d1003', '2018-10-03 14:38:16.600', 13.40000, 223, 0.29000, 'Beijing.Haidian', 2), - ('d1004', '2018-10-03 14:38:05.000', 10.80000, 223, 0.29000, 'Beijing.Haidian', 3), - ('d1004', '2018-10-03 14:38:06.500', 11.50000, 221, 0.35000, 'Beijing.Haidian', 3)] +lines = [('d1001', '2018-10-03 14:38:05.000', 10.30000, 219, 0.31000, 'California.SanFrancisco', 2), + ('d1001', '2018-10-03 14:38:15.000', 12.60000, 218, 0.33000, 'California.SanFrancisco', 2), + ('d1001', '2018-10-03 14:38:16.800', 12.30000, 221, 0.31000, 'California.SanFrancisco', 2), + ('d1002', '2018-10-03 14:38:16.650', 10.30000, 218, 0.25000, 'California.SanFrancisco', 3), + ('d1003', '2018-10-03 14:38:05.500', 11.80000, 221, 0.28000, 'California.LosAngeles', 2), + ('d1003', '2018-10-03 14:38:16.600', 13.40000, 223, 0.29000, 'California.LosAngeles', 2), + ('d1004', '2018-10-03 14:38:05.000', 10.80000, 223, 0.29000, 'California.LosAngeles', 3), + ('d1004', '2018-10-03 14:38:06.500', 11.50000, 221, 0.35000, 'California.LosAngeles', 3)] def get_ts(ts: str): diff --git a/docs-examples/python/conn_native_pandas.py b/docs-examples/python/conn_native_pandas.py index 314759f7662c7bf4c9df2c8b3396ad3101c91cd4..56942ef57085766cd128b03cabb7a357587eab16 100644 --- a/docs-examples/python/conn_native_pandas.py +++ b/docs-examples/python/conn_native_pandas.py @@ -13,7 +13,7 @@ print(df.head(3)) # output: # RangeIndex(start=0, stop=8, step=1) # -# ts current voltage phase location groupid -# 0 2018-10-03 14:38:05.000 10.3 219 0.31 beijing.chaoyang 2 -# 1 2018-10-03 14:38:15.000 12.6 218 0.33 beijing.chaoyang 2 -# 2 2018-10-03 14:38:16.800 12.3 221 0.31 beijing.chaoyang 2 +# ts current ... location groupid +# 0 2018-10-03 14:38:05.500 11.8 ... california.losangeles 2 +# 1 2018-10-03 14:38:16.600 13.4 ... california.losangeles 2 +# 2 2018-10-03 14:38:05.000 10.8 ... california.losangeles 3 diff --git a/docs-examples/python/conn_rest_pandas.py b/docs-examples/python/conn_rest_pandas.py index 143e4275fa4eda685766297e4b90cba3935a574d..0164080cd5a05e72dce40b1d111ea423623ff9b2 100644 --- a/docs-examples/python/conn_rest_pandas.py +++ b/docs-examples/python/conn_rest_pandas.py @@ -11,9 +11,9 @@ print(type(df.ts[0])) print(df.head(3)) # output: -# # RangeIndex(start=0, stop=8, step=1) -# ts current ... location groupid -# 0 2018-10-03 14:38:05+08:00 10.3 ... beijing.chaoyang 2 -# 1 2018-10-03 14:38:15+08:00 12.6 ... beijing.chaoyang 2 -# 2 2018-10-03 14:38:16.800000+08:00 12.3 ... beijing.chaoyang 2 +# +# ts current ... location groupid +# 0 2018-10-03 06:38:05.500000+00:00 11.8 ... california.losangeles 2 +# 1 2018-10-03 06:38:16.600000+00:00 13.4 ... california.losangeles 2 +# 2 2018-10-03 06:38:05+00:00 10.8 ... california.losangeles 3 diff --git a/docs-examples/python/connect_rest_examples.py b/docs-examples/python/connect_rest_examples.py index a043d506b965bc31179dbb6f38749d196ab338ff..900ec1022ec81ac2db761d918d1ec11c9bb26852 100644 --- a/docs-examples/python/connect_rest_examples.py +++ b/docs-examples/python/connect_rest_examples.py @@ -1,10 +1,9 @@ # ANCHOR: connect from taosrest import connect, TaosRestConnection, TaosRestCursor -conn: TaosRestConnection = connect(host="localhost", +conn: TaosRestConnection = connect(url="http://localhost:6041", user="root", password="taosdata", - port=6041, timeout=30) # ANCHOR_END: connect @@ -16,10 +15,10 @@ cursor.execute("CREATE DATABASE power") cursor.execute("CREATE STABLE power.meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT)") # insert data -cursor.execute("""INSERT INTO power.d1001 USING power.meters TAGS(Beijing.Chaoyang, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) - power.d1002 USING power.meters TAGS(Beijing.Chaoyang, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) - power.d1003 USING power.meters TAGS(Beijing.Haidian, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) - power.d1004 USING power.meters TAGS(Beijing.Haidian, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)""") +cursor.execute("""INSERT INTO power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) + power.d1002 USING power.meters TAGS(California.SanFrancisco, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) + power.d1003 USING power.meters TAGS(California.LosAngeles, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) + power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)""") print("inserted row count:", cursor.rowcount) # query data @@ -38,8 +37,7 @@ for row in data: # inserted row count: 8 # queried row count: 3 # ['ts', 'current', 'voltage', 'phase', 'location', 'groupid'] -# [datetime.datetime(2018, 10, 3, 14, 38, 5, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 10.3, 219, 0.31, 'beijing.chaoyang', 2] -# [datetime.datetime(2018, 10, 3, 14, 38, 15, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 12.6, 218, 0.33, 'beijing.chaoyang', 2] -# [datetime.datetime(2018, 10, 3, 14, 38, 16, 800000, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 12.3, 221, 0.31, 'beijing.chaoyang', 2] - +# [datetime.datetime(2018, 10, 3, 14, 38, 5, 500000, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 11.8, 221, 0.28, 'california.losangeles', 2] +# [datetime.datetime(2018, 10, 3, 14, 38, 16, 600000, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 13.4, 223, 0.29, 'california.losangeles', 2] +# [datetime.datetime(2018, 10, 3, 14, 38, 5, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 10.8, 223, 0.29, 'california.losangeles', 3] # ANCHOR_END: basic diff --git a/docs-examples/python/json_protocol_example.py b/docs-examples/python/json_protocol_example.py index 5bb4d629bccf3d79e74b381d6259de86d6522315..58b38f3ff667bcbbd902434d3409441a4d2c5b45 100644 --- a/docs-examples/python/json_protocol_example.py +++ b/docs-examples/python/json_protocol_example.py @@ -3,12 +3,12 @@ import json import taos from taos import SmlProtocol, SmlPrecision -lines = [{"metric": "meters.current", "timestamp": 1648432611249, "value": 10.3, "tags": {"location": "Beijing.Chaoyang", "groupid": 2}}, +lines = [{"metric": "meters.current", "timestamp": 1648432611249, "value": 10.3, "tags": {"location": "California.SanFrancisco", "groupid": 2}}, {"metric": "meters.voltage", "timestamp": 1648432611249, "value": 219, - "tags": {"location": "Beijing.Haidian", "groupid": 1}}, + "tags": {"location": "California.LosAngeles", "groupid": 1}}, {"metric": "meters.current", "timestamp": 1648432611250, "value": 12.6, - "tags": {"location": "Beijing.Chaoyang", "groupid": 2}}, - {"metric": "meters.voltage", "timestamp": 1648432611250, "value": 221, "tags": {"location": "Beijing.Haidian", "groupid": 1}}] + "tags": {"location": "California.SanFrancisco", "groupid": 2}}, + {"metric": "meters.voltage", "timestamp": 1648432611250, "value": 221, "tags": {"location": "California.LosAngeles", "groupid": 1}}] def get_connection(): diff --git a/docs-examples/python/line_protocol_example.py b/docs-examples/python/line_protocol_example.py index 02baeb2104f9f48984b4d34afb5e67af641d4e32..735e8e7eb8aed1a8133de7a6de50bd50d076c472 100644 --- a/docs-examples/python/line_protocol_example.py +++ b/docs-examples/python/line_protocol_example.py @@ -1,10 +1,10 @@ import taos from taos import SmlProtocol, SmlPrecision -lines = ["meters,location=Beijing.Haidian,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249000", - "meters,location=Beijing.Haidian,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611249500", - "meters,location=Beijing.Haidian,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249300", - "meters,location=Beijing.Haidian,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611249800", +lines = ["meters,location=California.LosAngeles,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249000", + "meters,location=California.LosAngeles,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611249500", + "meters,location=California.LosAngeles,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249300", + "meters,location=California.LosAngeles,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611249800", ] diff --git a/docs-examples/python/multi_bind_example.py b/docs-examples/python/multi_bind_example.py index 1714121d72705ab8d619a41f3463af4aa3193871..205ba69fb267ae1781415e4f0995b41f908ceb17 100644 --- a/docs-examples/python/multi_bind_example.py +++ b/docs-examples/python/multi_bind_example.py @@ -3,10 +3,10 @@ from datetime import datetime # ANCHOR: bind_batch table_tags = { - "d1001": ('Beijing.Chaoyang', 2), - "d1002": ('Beijing.Chaoyang', 3), - "d1003": ('Beijing.Haidian', 2), - "d1004": ('Beijing.Haidian', 3) + "d1001": ('California.SanFrancisco', 2), + "d1002": ('California.SanFrancisco', 3), + "d1003": ('California.LosAngeles', 2), + "d1004": ('California.LosAngeles', 3) } table_values = { diff --git a/docs-examples/python/native_insert_example.py b/docs-examples/python/native_insert_example.py index 94d4888a8f5330b9e39d5ae051fcb68f9825505f..3b6b73cb2236c8d9d11019349f99f79135a5c1d6 100644 --- a/docs-examples/python/native_insert_example.py +++ b/docs-examples/python/native_insert_example.py @@ -1,13 +1,13 @@ import taos -lines = ["d1001,2018-10-03 14:38:05.000,10.30000,219,0.31000,Beijing.Chaoyang,2", - "d1004,2018-10-03 14:38:05.000,10.80000,223,0.29000,Beijing.Haidian,3", - "d1003,2018-10-03 14:38:05.500,11.80000,221,0.28000,Beijing.Haidian,2", - "d1004,2018-10-03 14:38:06.500,11.50000,221,0.35000,Beijing.Haidian,3", - "d1002,2018-10-03 14:38:16.650,10.30000,218,0.25000,Beijing.Chaoyang,3", - "d1001,2018-10-03 14:38:15.000,12.60000,218,0.33000,Beijing.Chaoyang,2", - "d1001,2018-10-03 14:38:16.800,12.30000,221,0.31000,Beijing.Chaoyang,2", - "d1003,2018-10-03 14:38:16.600,13.40000,223,0.29000,Beijing.Haidian,2"] +lines = ["d1001,2018-10-03 14:38:05.000,10.30000,219,0.31000,California.SanFrancisco,2", + "d1004,2018-10-03 14:38:05.000,10.80000,223,0.29000,California.LosAngeles,3", + "d1003,2018-10-03 14:38:05.500,11.80000,221,0.28000,California.LosAngeles,2", + "d1004,2018-10-03 14:38:06.500,11.50000,221,0.35000,California.LosAngeles,3", + "d1002,2018-10-03 14:38:16.650,10.30000,218,0.25000,California.SanFrancisco,3", + "d1001,2018-10-03 14:38:15.000,12.60000,218,0.33000,California.SanFrancisco,2", + "d1001,2018-10-03 14:38:16.800,12.30000,221,0.31000,California.SanFrancisco,2", + "d1003,2018-10-03 14:38:16.600,13.40000,223,0.29000,California.LosAngeles,2"] def get_connection() -> taos.TaosConnection: @@ -25,10 +25,10 @@ def create_stable(conn: taos.TaosConnection): # The generated SQL is: -# INSERT INTO d1001 USING meters TAGS(Beijing.Chaoyang, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) -# d1002 USING meters TAGS(Beijing.Chaoyang, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) -# d1003 USING meters TAGS(Beijing.Haidian, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) -# d1004 USING meters TAGS(Beijing.Haidian, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000) +# INSERT INTO d1001 USING meters TAGS(California.SanFrancisco, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) +# d1002 USING meters TAGS(California.SanFrancisco, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) +# d1003 USING meters TAGS(California.LosAngeles, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) +# d1004 USING meters TAGS(California.LosAngeles, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000) def get_sql(): global lines diff --git a/docs-examples/python/query_example.py b/docs-examples/python/query_example.py index 6d33c49c968d9210b475931b5d8cecca0ceff3e3..8afd7f07358d7e9c9a3677ee04f8eb92aae6856b 100644 --- a/docs-examples/python/query_example.py +++ b/docs-examples/python/query_example.py @@ -12,10 +12,10 @@ def query_api_demo(conn: taos.TaosConnection): # field count: 7 -# meta of files[1]: {name: ts, type: 9, bytes: 8} +# meta of fields[1]: {name: ts, type: 9, bytes: 8} # ======================Iterate on result========================= -# ('d1001', datetime.datetime(2018, 10, 3, 14, 38, 5), 10.300000190734863, 219, 0.3100000023841858, 'Beijing.Chaoyang', 2) -# ('d1001', datetime.datetime(2018, 10, 3, 14, 38, 15), 12.600000381469727, 218, 0.33000001311302185, 'Beijing.Chaoyang', 2) +# ('d1003', datetime.datetime(2018, 10, 3, 14, 38, 5, 500000), 11.800000190734863, 221, 0.2800000011920929, 'california.losangeles', 2) +# ('d1003', datetime.datetime(2018, 10, 3, 14, 38, 16, 600000), 13.399999618530273, 223, 0.28999999165534973, 'california.losangeles', 2) # ANCHOR_END: iter # ANCHOR: fetch_all @@ -29,8 +29,8 @@ def fetch_all_demo(conn: taos.TaosConnection): # row count: 2 # ===============all data=================== -# [{'ts': datetime.datetime(2018, 10, 3, 14, 38, 5), 'current': 10.300000190734863}, -# {'ts': datetime.datetime(2018, 10, 3, 14, 38, 15), 'current': 12.600000381469727}] +# [{'ts': datetime.datetime(2018, 10, 3, 14, 38, 5, 500000), 'current': 11.800000190734863}, +# {'ts': datetime.datetime(2018, 10, 3, 14, 38, 16, 600000), 'current': 13.399999618530273}] # ANCHOR_END: fetch_all if __name__ == '__main__': diff --git a/docs-examples/python/rest_client_example.py b/docs-examples/python/rest_client_example.py index 46d33a1d795f8c8dbb0b830061d43ed4510046ba..59c629df95fdc7dcee8d764d061afeb360b51dfc 100644 --- a/docs-examples/python/rest_client_example.py +++ b/docs-examples/python/rest_client_example.py @@ -1,6 +1,6 @@ from taosrest import RestClient -client = RestClient("localhost", 6041, "root", "taosdata") +client = RestClient("http://localhost:6041", user="root", password="taosdata") res: dict = client.sql("SELECT ts, current FROM power.meters LIMIT 1") print(res) diff --git a/docs-examples/python/telnet_line_protocol_example.py b/docs-examples/python/telnet_line_protocol_example.py index 072835109ee238940e6fe5880b72b2b04e0157fa..d812e186af86be6811ee7774f10458e46df1f39f 100644 --- a/docs-examples/python/telnet_line_protocol_example.py +++ b/docs-examples/python/telnet_line_protocol_example.py @@ -2,14 +2,14 @@ import taos from taos import SmlProtocol, SmlPrecision # format: =[ =] -lines = ["meters.current 1648432611249 10.3 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611250 12.6 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611249 10.8 location=Beijing.Haidian groupid=3", - "meters.current 1648432611250 11.3 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611249 219 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611250 218 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611249 221 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611250 217 location=Beijing.Haidian groupid=3", +lines = ["meters.current 1648432611249 10.3 location=California.SanFrancisco groupid=2", + "meters.current 1648432611250 12.6 location=California.SanFrancisco groupid=2", + "meters.current 1648432611249 10.8 location=California.LosAngeles groupid=3", + "meters.current 1648432611250 11.3 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611249 219 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611250 218 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611249 221 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611250 217 location=California.LosAngeles groupid=3", ] diff --git a/docs-examples/rust/nativeexample/examples/stmt_example.rs b/docs-examples/rust/nativeexample/examples/stmt_example.rs index a791a4135984a33dded145e8175d7ade57de8d77..190f8c1ef6d50a8e9c925178c1a9d31c22e3d4df 100644 --- a/docs-examples/rust/nativeexample/examples/stmt_example.rs +++ b/docs-examples/rust/nativeexample/examples/stmt_example.rs @@ -12,7 +12,7 @@ async fn main() -> Result<(), Error> { stmt.set_tbname_tags( "d1001", [ - Field::Binary(BString::from("Beijing.Chaoyang")), + Field::Binary(BString::from("California.SanFrancisco")), Field::Int(2), ], )?; diff --git a/docs-examples/rust/restexample/examples/insert_example.rs b/docs-examples/rust/restexample/examples/insert_example.rs index d7acc98d096fb3cd6bea22d6c5f6f0f5caea50af..9261536f627c297fc707708f88f57eed647dbf3e 100644 --- a/docs-examples/rust/restexample/examples/insert_example.rs +++ b/docs-examples/rust/restexample/examples/insert_example.rs @@ -5,10 +5,10 @@ async fn main() -> Result<(), Error> { let taos = TaosCfg::default().connect().expect("fail to connect"); taos.create_database("power").await?; taos.exec("CREATE STABLE power.meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT)").await?; - let sql = "INSERT INTO power.d1001 USING power.meters TAGS(Beijing.Chaoyang, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) - power.d1002 USING power.meters TAGS(Beijing.Chaoyang, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) - power.d1003 USING power.meters TAGS(Beijing.Haidian, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) - power.d1004 USING power.meters TAGS(Beijing.Haidian, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)"; + let sql = "INSERT INTO power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000) ('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000) + power.d1002 USING power.meters TAGS(California.SanFrancisco, 3) VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000) + power.d1003 USING power.meters TAGS(California.LosAngeles, 2) VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000) + power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)"; let result = taos.query(sql).await?; println!("{:?}", result); Ok(()) diff --git a/docs-examples/rust/schemalessexample/examples/influxdb_line_example.rs b/docs-examples/rust/schemalessexample/examples/influxdb_line_example.rs index e93888cc83d12f3bec7370a66e8a85d38cec42ad..64d1a3c9ac6037c16e3e1c3be0258e19cce632a0 100644 --- a/docs-examples/rust/schemalessexample/examples/influxdb_line_example.rs +++ b/docs-examples/rust/schemalessexample/examples/influxdb_line_example.rs @@ -5,10 +5,10 @@ fn main() { let taos = TaosCfg::default().connect().expect("fail to connect"); taos.raw_query("CREATE DATABASE test").unwrap(); taos.raw_query("USE test").unwrap(); - let lines = ["meters,location=Beijing.Haidian,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249", - "meters,location=Beijing.Haidian,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250", - "meters,location=Beijing.Haidian,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249", - "meters,location=Beijing.Haidian,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250"]; + let lines = ["meters,location=California.LosAngeles,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249", + "meters,location=California.LosAngeles,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611250", + "meters,location=California.LosAngeles,groupid=3 current=10.8,voltage=223,phase=0.29 1648432611249", + "meters,location=California.LosAngeles,groupid=3 current=11.3,voltage=221,phase=0.35 1648432611250"]; let affected_rows = taos .schemaless_insert( &lines, diff --git a/docs-examples/rust/schemalessexample/examples/opentsdb_json_example.rs b/docs-examples/rust/schemalessexample/examples/opentsdb_json_example.rs index 1d66bd1f2b1bcbe82dc3ee3e8e25ea4c521c81f0..e61691596704c8aaf979081429802df6e5aa86f9 100644 --- a/docs-examples/rust/schemalessexample/examples/opentsdb_json_example.rs +++ b/docs-examples/rust/schemalessexample/examples/opentsdb_json_example.rs @@ -6,10 +6,10 @@ fn main() { taos.raw_query("CREATE DATABASE test").unwrap(); taos.raw_query("USE test").unwrap(); let lines = [ - r#"[{"metric": "meters.current", "timestamp": 1648432611249, "value": 10.3, "tags": {"location": "Beijing.Chaoyang", "groupid": 2}}, - {"metric": "meters.voltage", "timestamp": 1648432611249, "value": 219, "tags": {"location": "Beijing.Haidian", "groupid": 1}}, - {"metric": "meters.current", "timestamp": 1648432611250, "value": 12.6, "tags": {"location": "Beijing.Chaoyang", "groupid": 2}}, - {"metric": "meters.voltage", "timestamp": 1648432611250, "value": 221, "tags": {"location": "Beijing.Haidian", "groupid": 1}}]"#, + r#"[{"metric": "meters.current", "timestamp": 1648432611249, "value": 10.3, "tags": {"location": "California.SanFrancisco", "groupid": 2}}, + {"metric": "meters.voltage", "timestamp": 1648432611249, "value": 219, "tags": {"location": "California.LosAngeles", "groupid": 1}}, + {"metric": "meters.current", "timestamp": 1648432611250, "value": 12.6, "tags": {"location": "California.SanFrancisco", "groupid": 2}}, + {"metric": "meters.voltage", "timestamp": 1648432611250, "value": 221, "tags": {"location": "California.LosAngeles", "groupid": 1}}]"#, ]; let affected_rows = taos diff --git a/docs-examples/rust/schemalessexample/examples/opentsdb_telnet_example.rs b/docs-examples/rust/schemalessexample/examples/opentsdb_telnet_example.rs index 18d7500714d9e41b1bebd490199d296ead3dc7c4..c8cab7655a24806e5c7659af80e83da383539c55 100644 --- a/docs-examples/rust/schemalessexample/examples/opentsdb_telnet_example.rs +++ b/docs-examples/rust/schemalessexample/examples/opentsdb_telnet_example.rs @@ -6,14 +6,14 @@ fn main() { taos.raw_query("CREATE DATABASE test").unwrap(); taos.raw_query("USE test").unwrap(); let lines = [ - "meters.current 1648432611249 10.3 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611250 12.6 location=Beijing.Chaoyang groupid=2", - "meters.current 1648432611249 10.8 location=Beijing.Haidian groupid=3", - "meters.current 1648432611250 11.3 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611249 219 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611250 218 location=Beijing.Chaoyang groupid=2", - "meters.voltage 1648432611249 221 location=Beijing.Haidian groupid=3", - "meters.voltage 1648432611250 217 location=Beijing.Haidian groupid=3", + "meters.current 1648432611249 10.3 location=California.SanFrancisco groupid=2", + "meters.current 1648432611250 12.6 location=California.SanFrancisco groupid=2", + "meters.current 1648432611249 10.8 location=California.LosAngeles groupid=3", + "meters.current 1648432611250 11.3 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611249 219 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611250 218 location=California.SanFrancisco groupid=2", + "meters.voltage 1648432611249 221 location=California.LosAngeles groupid=3", + "meters.voltage 1648432611250 217 location=California.LosAngeles groupid=3", ]; let affected_rows = taos .schemaless_insert( diff --git a/documentation/tdenginedocs-cn/administrator/index.html b/documentation/tdenginedocs-cn/administrator/index.html deleted file mode 100644 index eaaf04ff95fa69cb3bae47d0e574c4f1931e7719..0000000000000000000000000000000000000000 --- a/documentation/tdenginedocs-cn/administrator/index.html +++ /dev/null @@ -1,137 +0,0 @@ -文档 | 涛思数据
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系统管理

-

文件目录结构

-

安装TDengine后，默认会在操作系统中生成下列目录或文件：

-
10.3 219 0.31Beijing.ChaoyangCalifornia.SanFrancisco 2
10.2 220 0.23Beijing.ChaoyangCalifornia.SanFrancisco 3
11.5 221 0.35Beijing.HaidianCalifornia.LosAngeles 3
13.4 223 0.29Beijing.HaidianCalifornia.LosAngeles 2
12.6 218 0.33Beijing.ChaoyangCalifornia.SanFrancisco 2
11.8 221 0.28Beijing.HaidianCalifornia.LosAngeles 2
10.3 218 0.25Beijing.ChaoyangCalifornia.SanFrancisco 3
12.3 221 0.31Beijing.ChaoyangCalifornia.SanFrancisco 2
- - - - - - - - - - - - - - - - - - - - - - - - - - - - -
目录/文件说明
/etc/taos/taos.cfgTDengine默认[配置文件]
/usr/local/taos/driverTDengine动态链接库目录
/var/lib/taosTDengine默认数据文件目录,可通过[配置文件]修改位置.
/var/log/taosTDengine默认日志文件目录,可通过[配置文件]修改位置
/usr/local/taos/binTDengine可执行文件目录
-

可执行文件

-

TDengine的所有可执行文件默认存放在 /usr/local/taos/bin 目录下。其中包括：

-
    -
  • taosd：TDengine服务端可执行文件
    • -
  • taos： TDengine Shell可执行文件
    • -
  • taosdump：数据导出工具
    • -
  • rmtaos： 一个卸载TDengine的脚本, 请谨慎执行
    • -
-

您可以通过修改系统配置文件taos.cfg来配置不同的数据目录和日志目录

-

服务端配置

-

TDengine系统后台服务由taosd提供，可以在配置文件taos.cfg里修改配置参数，以满足不同场景的需求。配置文件的缺省位置在/etc/taos目录，可以通过taosd命令行执行参数-c指定配置文件目录。比如taosd -c /home/user来指定配置文件位于/home/user这个目录。

-

下面仅仅列出一些重要的配置参数，更多的参数请看配置文件里的说明。各个参数的详细介绍及作用请看前述章节。注意：配置修改后，需要重启taosd服务才能生效。

-
    -
  • internalIp: 对外提供服务的IP地址，默认取第一个IP地址
    • -
  • mgmtShellPort：管理节点与客户端通信使用的TCP/UDP端口号（默认值是6030）。此端口号在内向后连续的5个端口都会被UDP通信占用，即UDP占用[6030-6034]，同时TCP通信也会使用端口[6030]。
    • -
  • vnodeShellPort：数据节点与客户端通信使用的TCP/UDP端口号（默认值是6035）。此端口号在内向后连续的5个端口都会被UDP通信占用，即UDP占用[6035-6039]，同时TCP通信也会使用端口[6035]
    • -
  • httpPort：数据节点对外提供RESTful服务使用TCP，端口号[6020]
    • -
  • dataDir: 数据文件目录，缺省是/var/lib/taos
    • -
  • maxUsers：用户的最大数量
    • -
  • maxDbs：数据库的最大数量
    • -
  • maxTables：数据表的最大数量
    • -
  • enableMonitor: 系统监测标志位，0：关闭，1：打开
    • -
  • logDir: 日志文件目录，缺省是/var/log/taos
    • -
  • numOfLogLines：日志文件的最大行数
    • -
  • debugFlag: 系统debug日志开关，131：仅错误和报警信息，135：调试信息，143：非常详细的调试信息
    • -
-

不同应用场景的数据往往具有不同的数据特征，比如保留天数、副本数、采集频次、记录大小、采集点的数量、压缩等都可完全不同。为获得在存储上的最高效率，TDengine提供如下存储相关的系统配置参数：

-
    -
  • days：一个数据文件覆盖的时间长度，单位为天
    • -
  • keep：数据库中数据保留的天数
    • -
  • rows: 文件块中记录条数
    • -
  • comp: 文件压缩标志位，0：关闭，1:一阶段压缩，2:两阶段压缩
    • -
  • ctime：数据从写入内存到写入硬盘的最长时间间隔，单位为秒
    • -
  • clog：数据提交日志(WAL)的标志位，0为关闭，1为打开
    • -
  • tables：每个vnode允许创建表的最大数目
    • -
  • cache: 内存块的大小（字节数）
    • -
  • tblocks: 每张表最大的内存块数
    • -
  • ablocks: 每张表平均的内存块数
    • -
  • precision：时间戳为微秒的标志位，ms表示毫秒，us表示微秒
    • -
-

对于一个应用场景，可能有多种数据特征的数据并存，最佳的设计是将具有相同数据特征的表放在一个库里，这样一个应用有多个库，而每个库可以配置不同的存储参数，从而保证系统有最优的性能。TDengine容许应用在创建库时指定上述存储参数，如果指定，该参数就将覆盖对应的系统配置参数。举例，有下述SQL：

-
 create database demo days 10 cache 16000 ablocks 4
-

该SQL创建了一个库demo, 每个数据文件保留10天数据，内存块为16000字节，每个表平均占用4个内存块，而其他参数与系统配置完全一致。

-

客户端配置

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TDengine系统的前台交互客户端应用程序为taos，它与taosd共享同一个配置文件taos.cfg。运行taos时，使用参数-c指定配置文件目录，如taos -c /home/cfg，表示使用/home/cfg/目录下的taos.cfg配置文件中的参数，缺省目录是/etc/taos。更多taos的使用方法请见Shell命令行程序。本节主要讲解taos客户端应用在配置文件taos.cfg文件中使用到的参数。

-

客户端配置参数列表及解释

-
    -
  • masterIP：客户端默认发起请求的服务器的IP地址
    • -
  • charset：指明客户端所使用的字符集，默认值为UTF-8。TDengine存储nchar类型数据时使用的是unicode存储，因此客户端需要告知服务自己所使用的字符集，也即客户端所在系统的字符集。
    • -
  • locale：设置系统语言环境。Linux上客户端与服务端共享
    • -
  • defaultUser：默认登录用户，默认值root
    • -
  • defaultPass：默认登录密码，默认值taosdata
    • -
-

TCP/UDP端口，以及日志的配置参数，与server的配置参数完全一样。

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启动taos时，你也可以从命令行指定IP地址、端口号，用户名和密码，否则就从taos.cfg读取。

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用户管理

-

系统管理员可以在CLI界面里添加、删除用户，也可以修改密码。CLI里SQL语法如下：

-
CREATE USER user_name PASS ‘password’
-

创建用户，并制定用户名和密码，密码需要用单引号引起来

-
DROP USER user_name
-

删除用户，限root用户使用

-
ALTER USER user_name PASS ‘password’
-

修改用户密码, 为避免被转换为小写，密码需要用单引号引用

-
SHOW USERS
-

显示所有用户

-

数据导入

-

TDengine提供两种方便的数据导入功能，一种按脚本文件导入，一种按数据文件导入。

-

按脚本文件导入

-

TDengine的shell支持source filename命令，用于批量运行文件中的SQL语句。用户可将建库、建表、写数据等SQL命令写在同一个文件中，每条命令单独一行，在shell中运行source命令，即可按顺序批量运行文件中的SQL语句。以‘#’开头的SQL语句被认为是注释，shell将自动忽略。

-

按数据文件导入

-

TDengine也支持在shell对已存在的表从CSV文件中进行数据导入。每个CSV文件只属于一张表且CSV文件中的数据格式需与要导入表的结构相同。其语法如下

-
insert into tb1 file a.csv b.csv tb2 c.csv …
-import into tb1 file a.csv b.csv tb2 c.csv …
-

数据导出

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为方便数据导出，TDengine提供了两种导出方式，分别是按表导出和用taosdump导出。

-

按表导出CSV文件

-

如果用户需要导出一个表或一个STable中的数据，可在shell中运行

-
select * from <tb_name> >> a.csv
-

这样，表tb中的数据就会按照CSV格式导出到文件a.csv中。

-

用taosdump导出数据

-

TDengine提供了方便的数据库导出工具taosdump。用户可以根据需要选择导出所有数据库、一个数据库或者数据库中的一张表,所有数据或一时间段的数据，甚至仅仅表的定义。其用法如下：

-
    -
  • 导出数据库中的一张或多张表：taosdump [OPTION…] dbname tbname …
    • -
  • 导出一个或多个数据库： taosdump [OPTION…] --databases dbname…
    • -
  • 导出所有数据库（不含监控数据库）：taosdump [OPTION…] --all-databases
    • -
-

用户可通过运行taosdump --help获得更详细的用法说明

-

系统连接、任务查询管理

-

系统管理员可以从CLI查询系统的连接、正在进行的查询、流式计算，并且可以关闭连接、停止正在进行的查询和流式计算。CLI里SQL语法如下：

-
SHOW CONNECTIONS
-

显示数据库的连接，其中一列显示ip:port, 为连接的IP地址和端口号。

-
KILL CONNECTION <connection-id>
-

强制关闭数据库连接，其中的connection-id是SHOW CONNECTIONS中显示的 ip:port字串，如“192.168.0.1:42198”，拷贝粘贴即可。

-
SHOW QUERIES
-

显示数据查询，其中一列显示ip:port:id, 为发起该query应用的IP地址，端口号，以及系统分配的ID。

-
KILL QUERY <query-id>
-

强制关闭数据查询，其中query-id是SHOW QUERIES中显示的ip:port:id字串，如“192.168.0.1:42198:11”，拷贝粘贴即可。

-
SHOW STREAMS
-

显示流式计算，其中一列显示ip:port:id, 为启动该stream的IP地址、端口和系统分配的ID。

-
KILL STREAM <stream-id>
-

强制关闭流式计算，其中的中stream-id是SHOW STREAMS中显示的ip:port:id字串，如“192.168.0.1:42198:18”，拷贝粘贴即可。

-

系统监控

-

TDengine启动后，会自动创建一个监测数据库SYS，并自动将服务器的CPU、内存、硬盘空间、带宽、请求数、磁盘读写速度、慢查询等信息定时写入该数据库。TDengine还将重要的系统操作（比如登录、创建、删除数据库等）日志以及各种错误报警信息记录下来存放在SYS库里。系统管理员可以从CLI直接查看这个数据库，也可以在WEB通过图形化界面查看这些监测信息。

-

这些监测信息的采集缺省是打开的，但可以修改配置文件里的选项enableMonitor将其关闭或打开。

回去
\ No newline at end of file diff --git a/documentation/tdenginedocs-cn/advanced-features/index.html b/documentation/tdenginedocs-cn/advanced-features/index.html deleted file mode 100644 index b4953b4dd482341ce937f56b806b682ff7145409..0000000000000000000000000000000000000000 --- a/documentation/tdenginedocs-cn/advanced-features/index.html +++ /dev/null @@ -1,64 +0,0 @@ -文档 | 涛思数据
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高级功能

-

连续查询(Continuous Query)

-

连续查询是TDengine定期自动执行的查询，采用滑动窗口的方式进行计算，是一种简化的时间驱动的流式计算。针对库中的表或超级表，TDengine可提供定期自动执行的连续查询，用户可让TDengine推送查询的结果，也可以将结果再写回到TDengine中。每次执行的查询是一个时间窗口，时间窗口随着时间流动向前滑动。在定义连续查询的时候需要指定时间窗口（time window, 参数interval ）大小和每次前向增量时间（forward sliding times, 参数sliding）。

-

TDengine的连续查询采用时间驱动模式，可以直接使用TAOS SQL进行定义，不需要额外的操作。使用连续查询，可以方便快捷地按照时间窗口生成结果，从而对原始采集数据进行降采样（down sampling）。用户通过TAOS SQL定义连续查询以后，TDengine自动在最后的一个完整的时间周期末端拉起查询，并将计算获得的结果推送给用户或者写回TDengine。

-

TDengine提供的连续查询与普通流计算中的时间窗口计算具有以下区别：

-
    -

  • 不同于流计算的实时反馈计算结果，连续查询只在时间窗口关闭以后才开始计算。例如时间周期是1天，那么当天的结果只会在23:59:59以后才会生成。

    • -

  • 如果有历史记录写入到已经计算完成的时间区间，连续查询并不会重新进行计算，也不会重新将结果推送给用户。对于写回TDengine的模式，也不会更新已经存在的计算结果。

    • -

  • 使用连续查询推送结果的模式，服务端并不缓存客户端计算状态，也不提供Exactly-Once的语意保证。如果用户的应用端崩溃，再次拉起的连续查询将只会从再次拉起的时间开始重新计算最近的一个完整的时间窗口。如果使用写回模式，TDengine可确保数据写回的有效性和连续性。

    • -
-

使用连续查询

-

使用TAOS SQL定义连续查询的过程，需要调用API taos_stream在应用端启动连续查询。例如要对统计表FOO_TABLE 每1分钟统计一次记录数量，前向滑动的时间是30秒，SQL语句如下：

-
SELECT COUNT(*) 
-FROM FOO_TABLE 
-INTERVAL(1M) SLIDING(30S)
-

其中查询的时间窗口（time window）是1分钟，前向增量（forward sliding time）时间是30秒。也可以不使用sliding来指定前向滑动时间，此时系统将自动向前滑动一个查询时间窗口再开始下一次计算，即时间窗口长度等于前向滑动时间。

-
SELECT COUNT(*) 
-FROM FOO_TABLE 
-INTERVAL(1M)
-

如果需要将连续查询的计算结果写回到数据库中，可以使用如下的SQL语句

-
CREATE TABLE QUERY_RES 
-  AS 
-  SELECT COUNT(*) 
-  FROM FOO_TABLE 
-  INTERVAL(1M) SLIDING(30S)
-

此时系统将自动创建表QUERY_RES，然后将连续查询的结果写入到该表。需要注意的是，前向滑动时间不能大于时间窗口的范围。如果用户指定的前向滑动时间超过时间窗口范围，系统将自动将其设置为时间窗口的范围值。如上所示SQL语句，如果用户设置前向滑动时间超过1分钟，系统将强制将其设置为1分钟。

-

此外，TDengine还支持用户指定连续查询的结束时间，默认如果不输入结束时间，连续查询将永久运行，如果用户指定了结束时间，连续查询在系统时间达到指定的时间以后停止运行。如SQL所示，连续查询将运行1个小时，1小时之后连续查询自动停止。

-
CREATE TABLE QUERY_RES 
-  AS 
-  SELECT COUNT(*) 
-  FROM FOO_TABLE 
-  WHERE TS > NOW AND TS <= NOW + 1H 
-  INTERVAL(1M) SLIDING(30S)
-

此外，还需要注意的是查询时间窗口的最小值是10毫秒，没有时间窗口范围的上限。

-

管理连续查询

-

用户可在控制台中通过 show streams 命令来查看系统中全部运行的连续查询，并可以通过 kill stream 命令杀掉对应的连续查询。在写回模式中，如果用户可以直接将写回的表删除，此时连续查询也会自动停止并关闭。后续版本会提供更细粒度和便捷的连续查询管理命令。

-

数据订阅(Publisher/Subscriber)

-

基于数据天然的时间序列特性，TDengine的数据写入（insert）与消息系统的数据发布（pub）逻辑上一致，均可视为系统中插入一条带时间戳的新记录。同时，TDengine在内部严格按照数据时间序列单调递增的方式保存数据。本质上来说，TDengine中里每一张表均可视为一个标准的消息队列。

-

TDengine内嵌支持轻量级的消息订阅与推送服务。使用系统提供的API，用户可订阅数据库中的某一张表（或超级表）。订阅的逻辑和操作状态的维护均是由客户端完成，客户端定时轮询服务器是否有新的记录到达，有新的记录到达就会将结果反馈到客户。

-

TDengine的订阅与推送服务的状态是客户端维持，TDengine服务器并不维持。因此如果应用重启，从哪个时间点开始获取最新数据，由应用决定。

-

API说明

-

使用订阅的功能，主要API如下：

-
    -

  • TAOS_SUB *taos_subscribe(char *host, char *user, char *pass, char *db, char *table, int64_t time, int mseconds)

    该函数负责启动订阅服务。其中参数说明：

      • -

    • host：主机IP地址

      • -

    • user：数据库登录用户名

      • -

    • pass：密码

      • -

    • db：数据库名称

      • -

    • table：(超级) 表的名称

      • -

    • time：启动时间，Unix Epoch时间，单位为毫秒。从1970年1月1日起计算的毫秒数。如果设为0，表示从当前时间开始订阅

      • -

    • mseconds：查询数据库更新的时间间隔，单位为毫秒。一般设置为1000毫秒。返回值为指向TDengine_SUB 结构的指针，如果返回为空，表示失败。

      • -

  • TAOS_ROW taos_consume(TAOS_SUB *tsub) -

    该函数用来获取订阅的结果，用户应用程序将其置于一个无限循环语句。如果数据库有新记录到达，该API将返回该最新的记录。如果没有新的记录，该API将阻塞。如果返回值为空，说明系统出错。参数说明：

    • tsub：taos_subscribe的结构体指针。

  • void taos_unsubscribe(TAOS_SUB *tsub)

    取消订阅。应用程序退出时，务必调用该函数以避免资源泄露。

    • -

  • int taos_num_fields(TAOS_SUB *tsub)

    获取返回的一行记录中数据包含多少列。

    • -

  • TAOS_FIELD *taos_fetch_fields(TAOS_SUB *tsub)

    获取每列数据的属性（数据类型、名字、长度），与taos_num_subfileds配合使用，可解析返回的每行数据。

-

示例代码：请看安装包中的的示范程序

-

缓存 (Cache)

-

TDengine采用时间驱动缓存管理策略（First-In-First-Out，FIFO），又称为写驱动的缓存管理机制。这种策略有别于读驱动的数据缓存模式（Least-Recent-Use，LRU），直接将最近写入的数据保存在系统的缓存中。当缓存达到临界值的时候，将最早的数据批量写入磁盘。一般意义上来说，对于物联网数据的使用，用户最为关心最近产生的数据，即当前状态。TDengine充分利用了这一特性，将最近到达的（当前状态）数据保存在缓存中。

-

TDengine通过查询函数向用户提供毫秒级的数据获取能力。直接将最近到达的数据保存在缓存中，可以更加快速地响应用户针对最近一条或一批数据的查询分析，整体上提供更快的数据库查询响应能力。从这个意义上来说，可通过设置合适的配置参数将TDengine作为数据缓存来使用，而不需要再部署额外的缓存系统，可有效地简化系统架构，降低运维的成本。需要注意的是，TDengine重启以后系统的缓存将被清空，之前缓存的数据均会被批量写入磁盘，缓存的数据将不会像专门的Key-value缓存系统再将之前缓存的数据重新加载到缓存中。

-

TDengine分配固定大小的内存空间作为缓存空间，缓存空间可根据应用的需求和硬件资源配置。通过适当的设置缓存空间，TDengine可以提供极高性能的写入和查询的支持。TDengine中每个虚拟节点（virtual node）创建时分配独立的缓存池。每个虚拟节点管理自己的缓存池，不同虚拟节点间不共享缓存池。每个虚拟节点内部所属的全部表共享该虚拟节点的缓存池。

-

一个缓存池了有很多个缓存块，缓存的大小由缓存块的个数以及缓存块的大小决定。参数cacheBlockSize决定每个缓存块的大小，参数cacheNumOfBlocks决定每个虚拟节点可用缓存块数量。因此单个虚拟节点总缓存开销为cacheBlockSize x cacheNumOfBlocks。参数numOfBlocksPerMeter决定每张表可用缓存块的数量，TDengine要求每张表至少有2个缓存块可供使用，因此cacheNumOfBlocks的数值不应该小于虚拟节点中所包含的表数量的两倍，即cacheNumOfBlocks ≤ sessionsPerVnode x 2。一般情况下cacheBlockSize可以不用调整，使用系统默认值即可，缓存块需要存储至少几十条记录才能确保TDengine更有效率地进行数据写入。

-

你可以通过函数last快速获取一张表或一张超级表的最后一条记录，这样很便于在大屏显示各设备的实时状态或采集值。例如：

-
select degree from thermometer where location='beijing';
-

该SQL语句将获取所有位于北京的传感器最后记录的温度值。

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与其他工具的连接

-

Telegraf

-

TDengine能够与开源数据采集系统Telegraf快速集成，整个过程无需任何代码开发。

-

安装Telegraf

-

目前TDengine支持Telegraf 1.7.4以上的版本。用户可以根据当前的操作系统，到Telegraf官网下载安装包，并执行安装。下载地址如下：https://portal.influxdata.com/downloads

-

配置Telegraf

-

修改Telegraf配置文件/etc/telegraf/telegraf.conf中与TDengine有关的配置项。

-

在output plugins部分，增加[[outputs.http]]配置项：

-
    -
  • url：http://ip:6020/telegraf/udb，其中ip为TDengine集群的中任意一台服务器的IP地址，6020为TDengine RESTful接口的端口号，telegraf为固定关键字，udb为用于存储采集数据的数据库名称，可预先创建。
    • -
  • method: "POST"
    • -
  • username: 登录TDengine的用户名
    • -
  • password: 登录TDengine的密码
    • -
  • data_format: "json"
    • -
  • json_timestamp_units: "1ms"
    • -
-

在agent部分：

-
    -
  • hostname: 区分不同采集设备的机器名称，需确保其唯一性
    • -
  • metric_batch_size: 30，允许Telegraf每批次写入记录最大数量，增大其数量可以降低Telegraf的请求发送频率，但对于TDengine，该数值不能超过50
    • -
-

关于如何使用Telegraf采集数据以及更多有关使用Telegraf的信息，请参考Telegraf官方的文档。

-

Grafana

-

TDengine能够与开源数据可视化系统Grafana快速集成搭建数据监测报警系统，整个过程无需任何代码开发，TDengine中数据表中内容可以在仪表盘(DashBoard)上进行可视化展现。

-

安装Grafana

-

目前TDengine支持Grafana 5.2.4以上的版本。用户可以根据当前的操作系统，到Grafana官网下载安装包，并执行安装。下载地址如下：https://grafana.com/grafana/download

-

配置Grafana

-

TDengine的Grafana插件在安装包的/usr/local/taos/connector/grafana目录下。

-

以CentOS 7.2操作系统为例，将tdengine目录拷贝到/var/lib/grafana/plugins目录下，重新启动grafana即可。

-

使用Grafana

-

用户可以直接通过localhost:3000的网址，登录Grafana服务器(用户名/密码:admin/admin)，配置TDengine数据源，如下图所示，此时可以在下拉列表中看到TDengine数据源。

-

img

-

TDengine数据源中的HTTP配置里面的Host地址要设置为TDengine集群的中任意一台服务器的IP地址与TDengine RESTful接口的端口号(6020)。假设TDengine数据库与Grafana部署在同一机器，那么应输入：http://localhost:6020。

-

此外，还需配置登录TDengine的用户名与密码，然后点击下图中的Save&Test按钮保存。

-

img

-

然后，就可以在Grafana的数据源列表中看到刚创建好的TDengine的数据源：

-

img

-

基于上面的步骤，就可以在创建Dashboard的时候使用TDengine数据源，如下图所示：

-

img

-

然后，可以点击Add Query按钮增加一个新查询。

-

在INPUT SQL输入框中输入查询SQL语句，该SQL语句的结果集应为两行多列的曲线数据，例如SELECT count(*) FROM sys.cpu WHERE ts>=from and ts<​to interval(interval)。其中，from、to和interval为TDengine插件的内置变量，表示从Grafana插件面板获取的查询范围和时间间隔。

-

ALIAS BY输入框为查询的别名，点击GENERATE SQL 按钮可以获取发送给TDengine的SQL语句。如下图所示：

-

img

-

关于如何使用Grafana创建相应的监测界面以及更多有关使用Grafana的信息，请参考Grafana官方的文档。

-

Matlab

-

MatLab可以通过安装包内提供的JDBC Driver直接连接到TDengine获取数据到本地工作空间。

-

MatLab的JDBC接口适配

-

MatLab的适配有下面几个步骤，下面以Windows10上适配MatLab2017a为例：

-
    -
  • 将TDengine安装包内的驱动程序JDBCDriver-1.0.0-dist.jar拷贝到${matlab_root}\MATLAB\R2017a\java\jar\toolbox
    • -
  • 将TDengine安装包内的taos.lib文件拷贝至${matlab_ root _dir}\MATLAB\R2017a\lib\win64
    • -
  • 将新添加的驱动jar包加入MatLab的classpath。在${matlab_ root _dir}\MATLAB\R2017a\toolbox\local\classpath.txt文件中添加下面一行
    • -
-

​ $matlabroot/java/jar/toolbox/JDBCDriver-1.0.0-dist.jar

-
    -
  • 在${user_home}\AppData\Roaming\MathWorks\MATLAB\R2017a\下添加一个文件javalibrarypath.txt, 并在该文件中添加taos.dll的路径，比如您的taos.dll是在安装时拷贝到了C:\Windows\System32下，那么就应该在javalibrarypath.txt中添加如下一行：
    • -
-

​ C:\Windows\System32

-

在MatLab中连接TDengine获取数据

-

在成功进行了上述配置后，打开MatLab。

-
    -

  • 创建一个连接：

    -

    conn = database(‘db’, ‘root’, ‘taosdata’, ‘com.taosdata.jdbc.TSDBDriver’, ‘jdbc:TSDB://127.0.0.1:0/’)

    • -

  • 执行一次查询：

    -

    sql0 = [‘select * from tb’]

    -

    data = select(conn, sql0);

    • -

  • 插入一条记录:

    -

    sql1 = [‘insert into tb values (now, 1)’]

    -

    exec(conn, sql1)

    • -
-

更多例子细节请参考安装包内examples\Matlab\TDengineDemo.m文件。

-

R

-

R语言支持通过JDBC接口来连接TDengine数据库。首先需要安装R语言的JDBC包。启动R语言环境，然后执行以下命令安装R语言的JDBC支持库：

-
install.packages('rJDBC', repos='http://cran.us.r-project.org')
-

安装完成以后，通过执行library('RJDBC')命令加载 RJDBC 包：

-

然后加载TDengine的JDBC驱动：

-
drv<-JDBC("com.taosdata.jdbc.TSDBDriver","JDBCDriver-1.0.0-dist.jar", identifier.quote="\"")
-

如果执行成功，不会出现任何错误信息。之后通过以下命令尝试连接数据库：

-
conn<-dbConnect(drv,"jdbc:TSDB://192.168.0.1:0/?user=root&password=taosdata","root","taosdata")
-

注意将上述命令中的IP地址替换成正确的IP地址。如果没有任务错误的信息，则连接数据库成功，否则需要根据错误提示调整连接的命令。TDengine支持以下的 RJDBC 包中函数：

-
    -
  • dbWriteTable(conn, "test", iris, overwrite=FALSE, append=TRUE)：将数据框iris写入表test中，overwrite必须设置为false，append必须设为TRUE,且数据框iris要与表test的结构一致。
    • -
  • dbGetQuery(conn, "select count(*) from test")：查询语句
    • -
  • dbSendUpdate(conn, "use db")：执行任何非查询sql语句。例如dbSendUpdate(conn, "use db")， 写入数据dbSendUpdate(conn, "insert into t1 values(now, 99)")等。
    • -
  • dbReadTable(conn, "test")：读取表test中数据
    • -
  • dbDisconnect(conn)：关闭连接
    • -
  • dbRemoveTable(conn, "test")：删除表test
    • -
-

TDengine客户端暂不支持如下函数：

-
    -
  • dbExistsTable(conn, "test")：是否存在表test
    • -
  • dbListTables(conn)：显示连接中的所有表
    • -
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连接器

-

TDengine提供了丰富的应用程序开发接口，其中包括C/C++、JAVA、Python、RESTful、Go等，便于用户快速开发应用。

-

C/C++ Connector

-

C/C++的API类似于MySQL的C API。应用程序使用时，需要包含TDengine头文件 taos.h（安装后，位于/usr/local/taos/include）：

-
#include <taos.h>
-

在编译时需要链接TDengine动态库libtaos.so（安装后，位于/usr/local/taos/driver，gcc编译时，请加上 -ltaos）。 所有API都以返回-1或NULL均表示失败。

-

C/C++同步API

-

传统的数据库操作API，都属于同步操作。应用调用API后，一直处于阻塞状态，直到服务器返回结果。TDengine支持如下API：

-
    -

  • TAOS *taos_connect(char *ip, char *user, char *pass, char *db, int port)

    -

    创建数据库连接，初始化连接上下文。其中需要用户提供的参数包含：TDengine管理主节点的IP地址、用户名、密码、数据库名字和端口号。如果用户没有提供数据库名字，也可以正常连接，用户可以通过该连接创建新的数据库，如果用户提供了数据库名字，则说明该数据库用户已经创建好，缺省使用该数据库。返回值为空表示失败。应用程序需要保存返回的参数，以便后续API调用。

    • -

  • void taos_close(TAOS *taos)

    -

    关闭连接, 其中taos是taos_connect函数返回的指针。

    • -

  • int taos_query(TAOS *taos, char *sqlstr)

    -

    该API用来执行SQL语句，可以是DQL语句也可以是DML语句，或者DDL语句。其中的taos参数是通过taos_connect()获得的指针。返回值-1表示失败。

    • -

  • TAOS_RES *taos_use_result(TAOS *taos)

    -

    选择相应的查询结果集。

    • -

  • TAOS_ROW taos_fetch_row(TAOS_RES *res)

    -

    按行获取查询结果集中的数据。

    • -

  • int taos_num_fields(TAOS_RES *res)

    -

    获取查询结果集中的列数。

    • -

  • TAOS_FIELD *taos_fetch_fields(TAOS_RES *res)

    -

    获取查询结果集每列数据的属性（数据类型、名字、字节数），与taos_num_fileds配合使用，可用来解析taos_fetch_row返回的一个元组(一行)的数据。

    • -

  • void taos_free_result(TAOS_RES *res)

    -

    释放查询结果集以及相关的资源。查询完成后，务必调用该API释放资源，否则可能导致应用内存泄露。

    • -

  • void taos_init()

    -

    初始化环境变量。如果应用没有主动调用该API，那么应用在调用taos_connect时将自动调用。因此一般情况下应用程序无需手动调用该API。

    • -

  • char *taos_errstr(TAOS *taos)

    -

    获取最近一次API调用失败的原因,返回值为字符串。

    • -

  • char *taos_errno(TAOS *taos)

    -

    获取最近一次API调用失败的原因，返回值为错误代码。

    • -

  • int taos_options(TSDB_OPTION option, const void * arg, ...)

    -

    设置客户端选项，目前只支持时区设置（TSDB_OPTION_TIMEZONE）和编码设置（TSDB_OPTION_LOCALE）。时区和编码默认为操作系统当前设置。

    • -
-

上述12个API是C/C++接口中最重要的API，剩余的辅助API请参看taos.h文件。

-

注意：对于单个数据库连接，在同一时刻只能有一个线程使用该链接调用API，否则会有未定义的行为出现并可能导致客户端crash。客户端应用可以通过建立多个连接进行多线程的数据写入或查询处理。

-

C/C++异步API

-

同步API之外，TDengine还提供性能更高的异步调用API处理数据插入、查询操作。在软硬件环境相同的情况下，异步API处理数据插入的速度比同步API快2~4倍。异步API采用非阻塞式的调用方式，在系统真正完成某个具体数据库操作前，立即返回。调用的线程可以去处理其他工作，从而可以提升整个应用的性能。异步API在网络延迟严重的情况下，优点尤为突出。

-

异步API都需要应用提供相应的回调函数，回调函数参数设置如下：前两个参数都是一致的，第三个参数依不同的API而定。第一个参数param是应用调用异步API时提供给系统的，用于回调时，应用能够找回具体操作的上下文，依具体实现而定。第二个参数是SQL操作的结果集，如果为空，比如insert操作，表示没有记录返回，如果不为空，比如select操作，表示有记录返回。

-

异步API对于使用者的要求相对较高，用户可根据具体应用场景选择性使用。下面是三个重要的异步API：

-
    -

  • void taos_query_a(TAOS *taos, char *sqlstr, void (*fp)(void *param, TAOS_RES *, int code), void *param);

    -

    异步执行SQL语句。taos是调用taos_connect返回的数据库连接结构体。sqlstr是需要执行的SQL语句。fp是用户定义的回调函数。param是应用提供一个用于回调的参数。回调函数fp的第三个参数code用于指示操作是否成功，0表示成功，负数表示失败(调用taos_errstr获取失败原因)。应用在定义回调函数的时候，主要处理第二个参数TAOS_RES *，该参数是查询返回的结果集。

    • -

  • void taos_fetch_rows_a(TAOS_RES *res, void (*fp)(void *param, TAOS_RES *, int numOfRows), void *param);

    -

    批量获取异步查询的结果集，只能与taos_query_a配合使用。其中res是_taos_query_a回调时返回的结果集结构体指针，fp为回调函数。回调函数中的param是用户可定义的传递给回调函数的参数结构体。numOfRows表明有fetch数据返回的行数（numOfRows并不是本次查询满足查询条件的全部元组数量）。在回调函数中，应用可以通过调用taos_fetch_row前向迭代获取批量记录中每一行记录。读完一块内的所有记录后，应用需要在回调函数中继续调用taos_fetch_rows_a获取下一批记录进行处理，直到返回的记录数（numOfRows）为零（结果返回完成）或记录数为负值（查询出错）。

    • -

  • void taos_fetch_row_a(TAOS_RES *res, void (*fp)(void *param, TAOS_RES *, TAOS_ROW row), void *param);

    -

    异步获取一条记录。其中res是taos_query_a回调时返回的结果集结构体指针。fp为回调函数。param是应用提供的一个用于回调的参数。回调时，第三个参数TAOS_ROW指向一行记录。不同于taos_fetch_rows_a，应用无需调用同步API taos_fetch_row来获取一个元组，更加简单。数据提取性能不及批量获取的API。

    • -
-

TDengine的异步API均采用非阻塞调用模式。应用程序可以用多线程同时打开多张表，并可以同时对每张打开的表进行查询或者插入操作。需要指出的是，客户端应用必须确保对同一张表的操作完全串行化，即对同一个表的插入或查询操作未完成时（未返回时），不能够执行第二个插入或查询操作。

-

C/C++ 连续查询接口

-

TDengine提供时间驱动的实时流式计算API。可以每隔一指定的时间段，对一张或多张数据库的表(数据流)进行各种实时聚合计算操作。操作简单，仅有打开、关闭流的API。具体如下：

-
    -

  • TAOS_STREAM *taos_open_stream(TAOS *taos, char *sqlstr, void (*fp)(void *param, TAOS_RES *, TAOS_ROW row), int64_t stime, void *param)

    -

    该API用来创建数据流，其中taos是调用taos_connect返回的结构体指针；sqlstr是SQL查询语句（仅能使用查询语句）；fp是用户定义的回调函数指针，每次流式计算完成后，均回调该函数，用户可在该函数内定义其内部业务逻辑；param是应用提供的用于回调的一个参数，回调时，提供给应用；stime是流式计算开始的时间，如果是0，表示从现在开始，如果不为零，表示从指定的时间开始计算（UTC时间从1970/1/1算起的毫秒数）。返回值为NULL，表示创建成功，返回值不为空，表示成功。TDengine将查询的结果（TAOS_ROW）、查询状态（TAOS_RES）、用户定义参数（PARAM）传递给回调函数，在回调函数内，用户可以使用taos_num_fields获取结果集列数，taos_fetch_fields获取结果集每列数据的类型。

    • -

  • void taos_close_stream (TAOS_STREAM *tstr)

    -

    关闭数据流，其中提供的参数是taos_open_stream的返回值。用户停止流式计算的时候，务必关闭该数据流。

    • -
-

C/C++ 数据订阅接口

-

订阅API目前支持订阅一张表，并通过定期轮询的方式不断获取写入表中的最新数据。

-
    -

  • TAOS_SUB *taos_subscribe(char *host, char *user, char *pass, char *db, char *table, long time, int mseconds)

    -

    该API用来启动订阅，需要提供的参数包含：TDengine管理主节点的IP地址、用户名、密码、数据库、数据库表的名字；time是开始订阅消息的时间，是从1970年1月1日起计算的毫秒数，为长整型, 如果设为0，表示从当前时间开始订阅；mseconds为查询数据库更新的时间间隔，单位为毫秒，建议设为1000毫秒。返回值为一指向TDengine_SUB结构的指针，如果返回为空，表示失败。

    • -

  • TAOS_ROW taos_consume(TAOS_SUB *tsub)

    -

    该API用来获取最新消息，应用程序一般会将其置于一个无限循环语句中。其中参数tsub是taos_subscribe的返回值。如果数据库有新的记录，该API将返回，返回参数是一行记录。如果没有新的记录，该API将阻塞。如果返回值为空，说明系统出错，需要检查系统是否还在正常运行。

    • -

  • void taos_unsubscribe(TAOS_SUB *tsub)

    -

    该API用于取消订阅，参数tsub是taos_subscribe的返回值。应用程序退出时，需要调用该API，否则有资源泄露。

    • -

  • int taos_num_fields(TAOS_SUB *tsub)

    -

    该API用来获取返回的一排数据中数据的列数

    • -

  • TAOS_FIELD *taos_fetch_fields(TAOS_RES *res)

    -

    该API用来获取每列数据的属性（数据类型、名字、字节数），与taos_num_subfileds配合使用，可用来解析返回的一排数据。

    • -
-

Java Connector

-

JDBC接口

-

如果用户使用Java开发企业级应用，可选用TDengine提供的JDBC Driver来调用服务。TDengine提供的JDBC Driver是标准JDBC规范的子集，遵循JDBC 标准(3.0)API规范，支持现有的各种Java开发框架。目前TDengine的JDBC driver并未发布到在线依赖仓库比如maven的中心仓库。因此用户开发时，需要手动把驱动包taos-jdbcdriver-x.x.x-dist.jar安装到开发环境的依赖仓库中。

-

TDengine 的驱动程序包的在不同操作系统上依赖不同的本地函数库（均由C语言编写）。Linux系统上，依赖一个名为libtaos.so 的本地库，.so即"Shared Object"缩写。成功安装TDengine后，libtaos.so 文件会被自动拷贝至/usr/local/lib/taos目录下，该目录也包含在Linux上自动扫描路径上。Windows系统上，JDBC驱动程序依赖于一个名为taos.dll 的本地库，.dll是动态链接库"Dynamic Link Library"的缩写。Windows上成功安装客户端后，JDBC驱动程序包默认位于C:/TDengine/driver/JDBC/目录下;其依赖的动态链接库taos.dll文件位于C:/TDengine/driver/C目录下，taos.dll 会被自动拷贝至系统默认搜索路径C:/Windows/System32下。

-

TDengine的JDBC Driver遵循标准JDBC规范，开发人员可以参考Oracle官方的JDBC相关文档来找到具体的接口和方法的定义与用法。TDengine的JDBC驱动在连接配置和支持的方法上与传统数据库驱动稍有不同。

-

TDengine的JDBC URL规范格式为：

-

jdbc:TSDB://{host_ip}:{port}/{database_name}?[user={user}|&password={password}|&charset={charset}|&cfgdir={config_dir}|&locale={locale}|&timezone={timezone}]

-

其中，{}中的内容必须，[]中为可选。配置参数说明如下：

-
    -
  • user：登陆TDengine所用用户名；默认值root
    • -
  • password：用户登陆密码；默认值taosdata
    • -
  • charset：客户端使用的字符集；默认值为系统字符集
    • -
  • cfgdir：客户端配置文件目录路径；Linux OS上默认值/etc/taos ，Windows OS上默认值 C:/TDengine/cfg
    • -
  • locale：客户端语言环境；默认值系统当前locale
    • -
  • timezone：客户端使用的时区；默认值为系统当前时区
    • -
-

以上所有参数均可在调用java.sql.DriverManager类创建连接时指定，示例如下：

-
import java.sql.Connection;
-import java.sql.DriverManager;
-import java.util.Properties;
-import com.taosdata.jdbc.TSDBDriver;
-
-public Connection getConn() throws Exception{
-  Class.forName("com.taosdata.jdbc.TSDBDriver");
-  String jdbcUrl = "jdbc:TAOS://127.0.0.1:0/db?user=root&password=taosdata";
-  Properties connProps = new Properties();
-  connProps.setProperty(TSDBDriver.PROPERTY_KEY_USER, "root");
-  connProps.setProperty(TSDBDriver.PROPERTY_KEY_PASSWORD, "taosdata");
-  connProps.setProperty(TSDBDriver.PROPERTY_KEY_CONFIG_DIR, "/etc/taos");
-  connProps.setProperty(TSDBDriver.PROPERTY_KEY_CHARSET, "UTF-8");
-  connProps.setProperty(TSDBDriver.PROPERTY_KEY_LOCALE, "en_US.UTF-8");
-  connProps.setProperty(TSDBDriver.PROPERTY_KEY_TIMEZONE, "UTC-8");
-  Connection conn = DriverManager.getConnection(jdbcUrl, connProps);
-  return conn;
-}
-

这些配置参数中除了cfgdir外，均可在客户端配置文件taos.cfg中进行配置。调用java.sql.DriverManager时声明的配置参数优先级最高，JDBC URL的优先级次之，配置文件的优先级最低。例如charset同时在配置文件taos.cfg中配置，也在JDBC URL中配置，则使用JDBC URL中的配置值。

-

此外，尽管TDengine的JDBC驱动实现尽可能的与关系型数据库驱动保持一致，但时序空间数据库与关系对象型数据库服务的对象和技术特征的差异导致TDengine的Java API并不能与标准完全相同。对于有大量关系型数据库开发经验而初次接触TDengine的开发者来说，有以下一些值的注意的地方：

-
    -
  • TDengine不提供针对单条数据记录的删除和修改的操作，驱动中也没有支持相关方法
    • -
  • 目前TDengine不支持表间的join或union操作，因此也缺乏对该部分API的支持
    • -
  • TDengine支持批量写入，但是支持停留在SQL语句级别，而不是API级别，也就是说用户需要通过写特殊的SQL语句来实现批量
    • -
  • 目前TDengine不支持嵌套查询(nested query)，对每个Connection的实例，至多只能有一个打开的ResultSet实例；如果在ResultSet还没关闭的情况下执行了新的查询，TSDBJDBCDriver则会自动关闭上一个ResultSet
    • -
-

对于TDengine操作的报错信息，用户可使用JDBCDriver包里提供的枚举类TSDBError.java来获取error message和error code的列表。对于更多的具体操作的相关代码，请参考TDengine提供的使用示范项目JDBCDemo。

-

Python Connector

-

安装准备

-
  • 已安装TDengine, 如果客户端在Windows上，需要安装Windows 版本的TDengine客户端
    • -
  • 已安装python 2.7 or >= 3.4
    • -
  • 已安装pip
    • -

    安装

    -

    Linux

    -

    用户可以在源代码的src/connector/python文件夹下找到python2和python3的安装包， 然后通过pip命令安装

    -
    pip install src/connector/python/linux/python2/
    -

    或者

    -
    pip install src/connector/python/linux/python3/
    -

    Windows

    -

    在已安装Windows TDengine 客户端的情况下， 将文件"C:\TDengine\driver\taos.dll" 拷贝到 "C:\windows\system32" 目录下, 然后进入Windwos cmd 命令行界面

    -
    cd C:\TDengine\connector\python\windows
    -
    pip install python2\
    -

    或者

    -
    cd C:\TDengine\connector\python\windows
    -
    pip install python3\
    -

    * 如果机器上没有pip命令，用户可将src/connector/python/windows/python3或src/connector/python/windows/python2下的taos文件夹拷贝到应用程序的目录使用。

    -

    使用

    -

    代码示例

    -
  • 导入TDengine客户端模块：
    • -
    import taos
    -
  • 获取连接
    • -
    
-conn = taos.connect(host="127.0.0.1", user="root", password="taosdata", config="/etc/taos")
-c1 = conn.cursor()
-
    -

    * host 是TDengine 服务端所有IP, config 为客户端配置文件所在目录

    -
  • 写入数据
    • -
    
-import datetime
- 
-# 创建数据库
-c1.execute('create database db')
-c1.execute('use db')
-# 建表
-c1.execute('create table tb (ts timestamp, temperature int, humidity float)')
-# 插入数据
-start_time = datetime.datetime(2019, 11, 1)
-affected_rows = c1.execute('insert into tb values (\'%s\', 0, 0.0)' %start_time)
-# 批量插入数据
-time_interval = datetime.timedelta(seconds=60)
-sqlcmd = ['insert into tb values']
-for irow in range(1,11):
-  start_time += time_interval
-  sqlcmd.append('(\'%s\', %d, %f)' %(start_time, irow, irow*1.2))
-affected_rows = c1.execute(' '.join(sqlcmd))
-
    -
  • 查询数据
    • -
    -c1.execute('select * from tb')
-# 拉取查询结果
-data = c1.fetchall()
-# 返回的结果是一个列表，每一行构成列表的一个元素
-numOfRows = c1.rowcount
-numOfCols = c1.descriptions
-for irow in range(numOfRows):
-  print("Row%d: ts=%s, temperature=%d, humidity=%f" %(irow, data[irow][0], data[irow][1],data[irow][2])
-  
-# 直接使用cursor 循环拉取查询结果
-c1.execute('select * from tb')
-for data in c1:
-  print("ts=%s, temperature=%d, humidity=%f" %(data[0], data[1],data[2])
-
     -
  • 关闭连接
    • -
    -c1.close()
-conn.close()
-
     -

    帮助信息

    -

    用户可通过python的帮助信息直接查看模块的使用信息，或者参考code/examples/python中的示例程序。以下为部分常用类和方法:

    -
      -

    • TaosConnection 类

      -

      参考python中help(taos.TDengineConnection)

      • -

    • TaosCursor 类

      -

      参考python中help(taos.TDengineCursor)

      • -

    • connect 方法

      -

      用于生成taos.TDengineConnection的实例。

      • -
    -

    RESTful Connector

    -

    为支持各种不同类型平台的开发，TDengine提供符合REST设计标准的API，即RESTful API。为最大程度降低学习成本，不同于其他数据库RESTful API的设计方法，TDengine直接通过HTTP POST 请求BODY中包含的SQL语句来操作数据库，仅需要一个URL。

    -

    HTTP请求格式

    -

    ​ http://<ip>:<PORT>/rest/sql

    -

    ​ 参数说明：

    -

    ​ IP: 集群中的任一台主机

    -

    ​ PORT: 配置文件中httpPort配置项，缺省为6020

    -

    如：http://192.168.0.1:6020/rest/sql 是指向IP地址为192.168.0.1的URL.

    -

    HTTP请求的Header里需带有身份认证信息，TDengine单机版仅支持Basic认证机制。

    -

    HTTP请求的BODY里就是一个完整的SQL语句，SQL语句中的数据表应提供数据库前缀，例如\.\。如果表名不带数据库前缀，系统会返回错误。因为HTTP模块只是一个简单的转发，没有当前DB的概念。

    -

    使用curl来发起一个HTTP Request, 语法如下：

    -
    curl -H 'Authorization: Basic <TOKEN>' -d '<SQL>' <ip>:<PORT>/rest/sql
    -

    或者

    -
    curl -u username:password -d '<SQL>' <ip>:<PORT>/rest/sql
    -

    其中，TOKEN为{username}:{password}经过Base64编码之后的字符串，例如root:taosdata编码后为cm9vdDp0YW9zZGF0YQ==

    -

    HTTP返回格式

    -

    返回值为JSON格式，如下：

    -
    {
-    "status": "succ",
-    "head": ["column1","column2", …],
-    "data": [
-        ["2017-12-12 23:44:25.730", 1],
-        ["2017-12-12 22:44:25.728", 4]
-    ],
-    "rows": 2
-}
    -

    说明：

    -
      -
    • 第一行”status”告知操作结果是成功还是失败;
      • -
    • 第二行”head”是表的定义，如果不返回结果集，仅有一列“affected_rows”;
      • -
    • 第三行是具体返回的数据，一排一排的呈现。如果不返回结果集，仅[[affected_rows]]
      • -
    • 第四行”rows”表明总共多少行数据
      • -
    -

    使用示例

    -
      -

    • 在demo库里查询表t1的所有记录, curl如下：

      -

      curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'select * from demo.t1' 192.168.0.1:6020/rest/sql

      -

      返回值：

      • -
    -
    {
-    "status": "succ",
-    "head": ["column1","column2","column3"],
-    "data": [
-        ["2017-12-12 23:44:25.730", 1, 2.3],
-        ["2017-12-12 22:44:25.728", 4, 5.6]
-    ],
-    "rows": 2
-}
    -
      -

    • 创建库demo：

      -

      curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'create database demo' 192.168.0.1:6020/rest/sql

      -

      返回值：

      • -
    -
    {
-    "status": "succ",
-    "head": ["affected_rows"],
-    "data": [[1]],
-    "rows": 1,
-}
    -

    Go Connector

    -

    TDengine提供了GO驱动程序“taosSql”包。taosSql驱动包是基于GO的“database/sql/driver”接口的实现。用户可在安装后的/usr/local/taos/connector/go目录获得GO的客户端驱动程序。用户需将驱动包/usr/local/taos/connector/go/src/taosSql目录拷贝到应用程序工程的src目录下。然后在应用程序中导入驱动包，就可以使用“database/sql”中定义的接口访问TDengine：

    -
    import (
-    "database/sql"
-    _ "taosSql"
-)
    -

    taosSql驱动包内采用cgo模式，调用了TDengine的C/C++同步接口，与TDengine进行交互，因此，在数据库操作执行完成之前，客户端应用将处于阻塞状态。单个数据库连接，在同一时刻只能有一个线程调用API。客户应用可以建立多个连接，进行多线程的数据写入或查询处理。

    -

    更多使用的细节，请参考下载目录中的示例源码。

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    物联网典型场景

    -

    在典型的物联网、车联网、运维监测场景中，往往有多种不同类型的数据采集设备，采集一个到多个不同的物理量。而同一种采集设备类型，往往又有多个具体的采集设备分布在不同的地点。大数据处理系统就是要将各种采集的数据汇总，然后进行计算和分析。对于同一类设备，其采集的数据类似如下的表格：

    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Device IDTime StampValue 1Value 2Value 3Tag 1Tag 2
    D1001153854868500010.32190.31RedTesla
    D1002153854868400010.22200.23BlueBMW
    D1003153854868650011.52210.35BlackHonda
    D1004153854868550013.42230.29RedVolvo
    D1001153854869500012.62180.33RedTesla
    D1004153854869660011.82210.28BlackHonda
    -

    每一条记录都有设备ID，时间戳，采集的物理量，还有与每个设备相关的静态标签。每个设备是受外界的触发，或按照设定的周期采集数据。采集的数据点是时序的，是一个数据流。

    -

    数据特征

    -

    除时序特征外，仔细研究发现，物联网、车联网、运维监测类数据还具有很多其他明显的特征。

    -
      -
    1. 数据是结构化的；
      2. -
    2. 数据极少有更新或删除操作；
      3. -
    3. 无需传统数据库的事务处理；
      4. -
    4. 相对互联网应用，写多读少；
      5. -
    5. 流量平稳，根据设备数量和采集频次，可以预测出来；
      6. -
    6. 用户关注的是一段时间的趋势，而不是某一特点时间点的值；
      7. -
    7. 数据是有保留期限的；
      8. -
    8. 数据的查询分析一定是基于时间段和地理区域的；
      9. -
    9. 除存储查询外，还往往需要各种统计和实时计算操作；
      10. -
    10. 数据量巨大，一天采集的数据就可以超过100亿条。
      11. -
    -

    充分利用上述特征，TDengine采取了一特殊的优化的存储和计算设计来处理时序数据，能将系统处理能力显著提高。

    -

    关系型数据库模型

    -

    因为采集的数据一般是结构化数据，而且为降低学习门槛，TDengine采用传统的关系型数据库模型管理数据。因此用户需要先创建库，然后创建表，之后才能插入或查询数据。

    -

    一个设备一张表

    -

    为充分利用其数据的时序性和其他数据特点，TDengine要求对每个数据采集点单独建表（比如有一千万个智能电表，就需创建一千万张表，上述表格中的D1001, D1002, D1003, D1004都需单独建表），用来存储这个采集点所采集的时序数据。这种设计能保证一个采集点的数据在存储介质上是一块一块连续的，大幅减少随机读取操作，成数量级的提升读取和查询速度。而且由于不同数据采集设备产生数据的过程完全独立，每个设备只产生属于自己的数据，一张表也就只有一个写入者。这样每个表就可以采用无锁方式来写，写入速度就能大幅提升。同时，对于一个数据采集点而言，其产生的数据是时序的，因此写的操作可用追加的方式实现，进一步大幅提高数据写入速度。

    -

    数据建模最佳实践

    -

    表(Table)：TDengine 建议用数据采集点的名字(如上表中的D1001)来做表名。每个数据采集点可能同时采集多个物理量(如上表中的value1, value2, value3)，每个物理量对应一张表中的一列，数据类型可以是整型、浮点型、字符串等。除此之外，表的第一列必须是时间戳，即数据类型为 timestamp。有的设备有多组采集量，每一组的采集频次是不一样的，这是需要对同一个设备建多张表。对采集的数据，TDengine将自动按照时间戳建立索引，但对采集的物理量不建任何索引。数据是用列式存储方式保存。

    -

    超级表(Super Table)：对于同一类型的采集点，为保证Schema的一致性，而且为便于聚合统计操作，可以先定义超级表STable(详见第10章)，然后再定义表。每个采集点往往还有静态标签信息(如上表中的Tag 1, Tag 2)，比如设备型号、颜色等，这些静态信息不会保存在存储采集数据的数据节点中，而是通过超级表保存在元数据节点中。这些静态标签信息将作为过滤条件，用于采集点之间的数据聚合统计操作。

    -

    库(DataBase)：不同的数据采集点往往具有不同的数据特征，包括数据采集频率高低，数据保留时间长短，备份数目，单个字段大小等等。为让各种场景下TDengine都能最大效率的工作，TDengine建议将不同数据特征的表创建在不同的库里。创建一个库时，除SQL标准的选项外，应用还可以指定保留时长、数据备份的份数、cache大小、文件块大小、是否压缩等多种参数(详见第19章)。

    -

    Schemaless vs Schema: 与NoSQL的各种引擎相比，由于应用需要定义schema，插入数据的灵活性降低。但对于物联网、金融这些典型的时序数据场景，schema会很少变更，因此这个灵活性不够的设计就不成问题。相反，TDengine采用结构化数据来进行处理的方式将让查询、分析的性能成数量级的提升。

    -

    TDengine对库的数量、超级表的数量以及表的数量没有做任何限制，而且其多少不会对性能产生影响，应用按照自己的场景创建即可。

    -

    主要模块

    -

    如图所示，TDengine服务主要包含两大模块：管理节点模块(MGMT) 和 数据节点模块(DNODE)。整个TDengine还包含客户端模块。

    -

    -
    图 1 TDengine架构示意图

    -

    管理节点模块

    -

    管理节点模块主要负责元数据的存储和查询等工作，其中包括用户信息的管理、数据库和表信息的创建、删除以及查询等。应用连接TDengine时会首先连接到管理节点。在创建/删除数据库和表时，请求也会首先发送请求到管理节点模块。由管理节点模块首先创建/删除元数据信息，然后发送请求到数据节点模块进行分配/删除所需要的资源。在数据写入和查询时，应用同样会首先访问管理节点模块，获取元数据信息。然后根据元数据管理信息访问数据节点模块。

    -

    数据节点模块

    -

    写入数据的存储和查询工作是由数据节点模块负责。 为了更高效地利用资源，以及方便将来进行水平扩展，TDengine内部对数据节点进行了虚拟化，引入了虚拟节点(virtual node, 简称vnode）的概念，作为存储、资源分配以及数据备份的单元。如图2所示，在一个dnode上，通过虚拟化，可以将该dnode视为多个虚拟节点的集合。

    -

    创建一个库时，系统会自动分配vnode。每个vnode存储一定数量的表中的数据，但一个表只会存在于一个vnode里，不会跨vnode。一个vnode只会属于一个库，但一个库会有一到多个vnode。不同的vnode之间资源互不共享。每个虚拟节点都有自己的缓存，在硬盘上也有自己的存储目录。而同一vnode内部无论是缓存还是硬盘的存储都是共享的。通过虚拟化，TDengine可以将dnode上有限的物理资源合理地分配给不同的vnode，大大提高资源的利用率和并发度。一台物理机器上的虚拟节点个数可以根据其硬件资源进行配置。

    -

    -
    图 2 TDengine虚拟化

    -

    客户端模块

    -

    TDengine客户端模块主要负责将应用传来的请求（SQL语句）进行解析，转化为内部结构体再发送到服务端。TDengine的各种接口都是基于TDengine的客户端模块进行开发的。客户端模块与管理模块使用TCP/UDP通讯，端口号由系统参数mgmtShellPort配置, 缺省值为6030。客户端与数据节点模块也是使用TCP/UDP通讯，端口号由系统参数vnodeShellPort配置, 缺省值为6035。两个端口号均可通过系统配置文件taos.cfg进行个性化设置。

    -

    写入流程

    -

    TDengine的完整写入流程如图3所示。为了保证写入数据的安全性和完整性，TDengine在写入数据时采用[预写日志算法]。客户端发来的数据在经过验证以后，首先会写入预写日志中，以保证TDengine能够在断电等因素导致的服务重启时从预写日志中恢复数据，避免数据的丢失。写入预写日志后，数据会被写到对应的vnode的缓存中。随后，服务端会发送确认信息给客户端表示写入成功。TDengine中存在两种机制可以促使缓存中的数据写入到硬盘上进行持久化存储：

    -

    -
    图 3 TDengine写入流程

    -
      -
    1. 时间驱动的落盘：TDengine服务会定时将vnode缓存中的数据写入到硬盘上，默认为一个小时落一次盘。落盘间隔可在配置文件taos.cfg中通过参数commitTime配置。
      2. -
    2. 数据驱动的落盘：当vnode中缓存的数据达到一定规模时，为了不阻塞后续数据的写入，TDengine也会拉起落盘线程将缓存中的数据清空。数据驱动的落盘会刷新定时落盘的时间。
      3. -
    -

    TDengine在数据落盘时会打开新的预写日志文件，在落盘后则会删除老的预写日志文件，避免日志文件无限制的增长。TDengine对缓存按照先进先出的原则进行管理，以保证每个表的最新数据都在缓存中。

    -

    数据存储

    -

    TDengine将所有数据存储在/var/lib/taos/目录下，您可以通过系统配置参数dataDir进行个性化配置。

    -

    TDengine中的元数据信息包括TDengine中的数据库、表、用户等信息。每个超级表、以及每个表的标签数据也存放在这里。为提高访问速度，元数据全部有缓存。

    -

    TDengine中写入的数据在硬盘上是按时间维度进行分片的。同一个vnode中的表在同一时间范围内的数据都存放在同一文件组中。这一数据分片方式可以大大简化数据在时间维度的查询，提高查询速度。在默认配置下，硬盘上的每个数据文件存放10天数据。用户可根据需要修改系统配置参数daysPerFile进行个性化配置。

    -

    表中的数据都有保存时间，一旦超过保存时间（缺省是3650天），数据将被系统自动删除。您可以通过系统配置参数daysToKeep进行个性化设置。

    -

    数据在文件中是按块存储的。每个数据块只包含一张表的数据，且数据是按照时间主键递增排列的。数据在数据块中按列存储，这样使得同列的数据存放在一起，对于不同的数据类型还采用不同的压缩方法，大大提高压缩的比例，节省存储空间。

    -

    数据文件总共有三类文件，一类是data文件，它存放了真实的数据块，该文件只进行追加操作；一类文件是head文件, 它存放了其对应的data文件中数据块的索引信息；第三类是last文件，专门存储最后写入的数据，每次落盘操作时，这部分数据会与内存里的数据合并，并决定是否写入data文件还是last文件。

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    常见问题

    -

    1. 遇到错误"failed to connect to server", 我怎么办？

    -

    客户端遇到链接故障，请按照下面的步骤进行检查：

    -
      -
    1. 在服务器，执行 systemctl status taosd 检查taosd运行状态。如果没有运行，启动taosd
      2. -
    2. 确认客户端连接时指定了正确的服务器IP地址
      3. -
    3. ping服务器IP，如果没有反应，请检查你的网络
      4. -
    4. 检查防火墙设置，确认TCP/UDP 端口6030-6039 是打开的
      5. -
    5. 对于Linux上的JDBC（ODBC, Python, Go等接口类似）连接, 确保libtaos.so在目录/usr/local/lib/taos里, 并且/usr/local/lib/taos在系统库函数搜索路径LD_LIBRARY_PATH里
      6. -
    6. 对于windows上的JDBC, ODBC, Python, Go等连接，确保driver/c/taos.dll在你的系统搜索目录里 (建议taos.dll放在目录 C:\Windows\System32)
      7. -
    7. 如果仍不能排除连接故障，请使用命令行工具nc来分别判断指定端口的TCP和UDP连接是否通畅 -检查UDP端口连接是否工作：nc -vuz {hostIP} {port} -检查服务器侧TCP端口连接是否工作：nc -l {port} -检查客户端侧TCP端口链接是否工作：nc {hostIP} {port}
      8. -
    -

    2. 虽然语法正确，为什么我还是得到 "Invalid SQL" 错误

    -

    如果你确认语法正确，请检查SQL语句长度是否超过64K。如果超过，也会返回这个错误。

    -

    3. 为什么我删除超级表总是失败？

    -

    请确保超级表下已经没有其他表，否则系统不允许删除该超级表。

    -

    4. 是否支持validation queries?

    -

    TDengine还没有一组专用的validation queries。然而建议你使用系统监测的数据库”log"来做。

    -

    5. 我可以删除或更新一条记录吗？

    -

    不能。因为TDengine是为联网设备采集的数据设计的，不容许修改。但TDengine提供数据保留策略，只要数据记录超过保留时长，就会被自动删除。

    -

    6. 我怎么创建超过250列的表？

    -

    TDengine最大允许创建250列的表。但是如果确实超过，我们建议按照数据特性，逻辑地将这个宽表分解成几个小表。

    -

    7. 最有效的写入数据的方法是什么？

    -

    批量插入。每条写入语句可以一张表同时插入多条记录，也可以同时插入多张表的记录。

    -

    8. windows系统下插入的nchar类数据中的汉字被解析成了乱码如何解决？

    -

    windows下插入nchar类的数据中如果有中文，请先确认系统的地区设置成了中国（在Control Panel里可以设置），这时cmd中的taos客户端应该已经可以正常工作了；如果是在IDE里开发Java应用，比如Eclipse， Intellij，请确认IDE里的文件编码为GBK（这是Java默认的编码类型），然后在生成Connection时，初始化客户端的配置，具体语句如下：

    -

    ​ Class.forName("com.taosdata.jdbc.TSDBDriver");

    -

    ​ Properties properties = new Properties();

    -

    ​ properties.setProperty(TSDBDriver.LOCALE_KEY, "UTF-8");

    -

    ​ Connection = DriverManager.getConnection(url, properties);

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    立即开始

    -

    快速上手

    -

    TDengine目前只支持在Linux系统上安装和运行。用户可根据需求选择通过源码或者安装包来安装。

    -

    通过源码安装

    -

    请参考我们的TDengine github主页下载源码并安装.

    -

    通过安装包安装

    -

    我们提供三种安装包，请选择你所需要的。TDengine的安装非常简单，从下载到安装成功仅仅只要几秒钟。

    - -

    目前，TDengine只支持在使用systemd做进程服务管理的linux系统上安装。其他linux系统的支持正在开发中。用which命令来检测系统中是否存在systemd:

    -
    which systemd
    -

    如果系统中不存在systemd命令，请考虑通过源码安装TDengine。

    -

    启动并体验TDengine

    -

    安装成功后，用户可使用systemctl命令来启动TDengine的服务进程。

    -
    systemctl start taosd
    -

    检查服务是否正常工作。

    -
    systemctl status taosd
    -

    如果TDengine服务正常工作，那么您可以通过TDengine的命令行程序taos来访问并体验TDengine。

    -

    注：systemctl 命令需要 root 权限来运行，如果您非 root 用户，请在命令前添加 sudo

    -

    TDengine命令行程序

    -

    执行TDengine命令行程序，您只要在Linux终端执行taos即可

    -
    taos
    -

    如果TDengine终端链接服务成功，将会打印出欢迎消息和版本信息。如果失败，则会打印错误消息出来（请参考FAQ来解决终端链接服务端失败的问题）。TDengine终端的提示符号如下：

    -
    taos>
    -

    在TDengine终端中，用户可以通过SQL命令来创建/删除数据库、表等，并进行插入查询操作。在终端中运行的SQL语句需要以分号结束来运行。示例：

    -
    create database db;
-use db;
-create table t (ts timestamp, speed int);
-insert into t values ('2019-07-15 00:00:00', 10);
-insert into t values ('2019-07-15 01:00:00', 20);
-select * from t;
-          ts          |   speed   |
-===================================
- 19-07-15 00:00:00.000|         10|
- 19-07-15 01:00:00.000|         20|
-Query OK, 2 row(s) in set (0.001700s)
    -

    除执行SQL语句外，系统管理员还可以从TDengine终端检查系统运行状态，添加删除用户账号等。

    -

    命令行参数

    -

    您可通过配置命令行参数来改变TDengine终端的行为。以下为常用的几个命令行参数：

    -
      -
    • -c, --config-dir: 指定配置文件目录，默认为/etc/taos
      • -
    • -h, --host: 指定服务的IP地址，默认为本地服务
      • -
    • -s, --commands: 在不进入终端的情况下运行TDengine命令
      • -
    • -u, -- user: 链接TDengine服务器的用户名，缺省为root
      • -
    • -p, --password: 链接TDengine服务器的密码，缺省为taosdata
      • -
    • -?, --help: 打印出所有命令行参数
      • -
    -

    示例：

    -
    taos -h 192.168.0.1 -s "use db; show tables;"
    -

    运行SQL命令脚本

    -

    TDengine终端可以通过source命令来运行SQL命令脚本.

    -
    taos> source <filename>;
    -

    Shell小技巧

    -
      -
    • 可以使用上下光标键查看已经历史输入的命令
      • -
    • 修改用户密码。在shell中使用alter user命令
      • -
    • ctrl+c 中止正在进行中的查询
      • -
    • 执行RESET QUERY CACHE清空本地缓存的表的schema
      • -
    -

    主要功能

    -

    TDengine的核心功能是时序数据库。除此之外，为减少研发的复杂度、系统维护的难度，TDengine还提供缓存、消息队列、订阅、流式计算等功能。更详细的功能如下：

    -
      -
    • 使用类SQL语言插入或查询数据
      • -
    • 支持C/C++, Java(JDBC), Python, Go, RESTful, and Node.JS 开发接口
      • -
    • 可通过Python/R/Matlab or TDengine shell做Ad Hoc查询分析
      • -
    • 通过定时连续查询支持基于滑动窗口的流式计算
      • -
    • 使用超级表来更灵活高效的聚合多个时间线的数据
      • -
    • 时间轴上聚合一个或多个时间线的数据
      • -
    • 支持数据订阅，一旦有新数据，就立即通知应用
      • -
    • 支持缓存，每个时间线或设备的最新数据都从内存里快速获取
      • -
    • 历史数据与实时数据处理完全透明，不用区别对待
      • -
    • 支持链接Telegraf, Grafana等第三方工具
      • -
    • 成套的配置和工具，让你更好的管理TDengine
      • -
    -

    对于企业版，TDengine还提供如下高级功能：

    -
      -
    • 线性水平扩展能力，以提供更高的处理速度和数据容量
      • -
    • 高可靠，无单点故障，提供运营商级别的服务
      • -
    • 多个副本自动同步，而且可以跨机房
      • -
    • 多级存储，让历史数据处理的成本更低
      • -
    • 用户友好的管理后台和工具，让管理更轻松简单
      • -
    -

    TDengine是专为物联网、车联网、工业互联网、运维监测等场景优化设计的时序数据处理引擎。与其他方案相比，它的插入查询速度都快10倍以上。单核一秒钟就能插入100万数据点，读出1000万数据点。由于采用列式存储和优化的压缩算法，存储空间不及普通数据库的1/10.

    -

    深入了解TDengine

    -

    请继续阅读文档来深入了解TDengine。

    回去
    diff --git a/documentation/tdenginedocs-cn/index.html b/documentation/tdenginedocs-cn/index.html deleted file mode 100644 index 17ee078d048fa102bcc364d55363a10bbd020776..0000000000000000000000000000000000000000 --- a/documentation/tdenginedocs-cn/index.html +++ /dev/null @@ -1 +0,0 @@ -文档 | 涛思数据

    TDengine文档

    TDengine是一个高效的存储、查询、分析时序大数据的平台，专为物联网、车联网、工业互联网、运维监测等优化而设计。您可以像使用关系型数据库MySQL一样来使用它，但建议您在使用前仔细阅读一遍下面的文档，特别是数据模型与超级表一节。除本文档之外，欢迎下载产品白皮书。

    立即开始

    数据模型和设计

    • 数据模型：关系型数据库模型，但要求每个采集设备单独建表
    • 主要模块：包含管理节点、数据节点和客户端，数据节点支持虚拟化
    • 写入流程：先写入WAL、之后写入缓存，再给应用确认
    • 数据存储：数据按时间段切片、采取列存、不同数据类型不同压缩算法

    TAOS SQL

    • 支持的数据类型：支持时间戳、整型、浮点型、布尔型、字符型等多种数据类型
    • 数据库管理：添加、删除、查看数据库
    • 表管理：添加、删除、查看、修改表
    • 数据写入：支持单表单条、多条、多表多条写入，支持历史数据写入
    • 数据查询：支持时间段、值过滤、排序、查询结果手动分页等
    • SQL函数：支持各种聚合函数、选择函数、计算函数，如avg, min, diff等
    • 时间维度聚合：将表中数据按照时间段进行切割后聚合，降维处理

    超级表STable：多表聚合

    高级功能

    连接器

    与其他工具的连接

    • Telegraf：将DevOps采集的数据发送到TDengine
    • Grafana：获取并可视化保存在TDengine的数据
    • Matlab：通过配置Matlab的JDBC数据源访问保存在TDengine的数据
    • R：通过配置R的JDBC数据源访问保存在TDengine的数据

    系统管理

    TDengine的技术设计

    • 存储设计：为时序数据专门优化设计的列式存储格式
    • 查询处理：高效的查询计算时序数据的方法
    • 集群设计：吸取NoSQL的优点，支持高可靠，支持线性扩展
    • 技术博客：更多的技术分析和架构设计文章

    培训和FAQ

    • FAQ：常见问题与答案
    • 应用案列：一些使用实例来解释如何使用TDengine
    回去
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    回去

    TDengine的技术设计

    -

    存储设计

    -

    TDengine的数据存储主要包含元数据的存储和写入数据的存储。以下章节详细介绍了TDengine各种数据的存储结构。

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    元数据的存储

    -

    TDengine中的元数据信息包括TDengine中的数据库，表，超级表等信息。元数据信息默认存放在 /var/lib/taos/mgmt/ 文件夹下。该文件夹的目录结构如下所示：

    -
    /var/lib/taos/
-      +--mgmt/
-          +--db.db
-          +--meters.db
-          +--user.db
-          +--vgroups.db
    -

    元数据在文件中按顺序排列。文件中的每条记录代表TDengine中的一个元数据机构（数据库、表等）。元数据文件只进行追加操作，即便是元数据的删除，也只是在数据文件中追加一条删除的记录。

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    写入数据的存储

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    TDengine中写入的数据在硬盘上是按时间维度进行分片的。同一个vnode中的表在同一时间范围内的数据都存放在同一文件组中，如下图中的v0f1804*文件。这一数据分片方式可以大大简化数据在时间维度的查询，提高查询速度。在默认配置下，硬盘上的每个文件存放10天数据。用户可根据需要调整数据库的 daysPerFile 配置项进行配置。 数据在文件中是按块存储的。每个数据块只包含一张表的数据，且数据是按照时间主键递增排列的。数据在数据块中按列存储，这样使得同类型的数据存放在一起，可以大大提高压缩的比例，节省存储空间。TDengine对不同类型的数据采用了不同的压缩算法进行压缩，以达到最优的压缩结果。TDengine使用的压缩算法包括simple8B、delta-of-delta、RLE以及LZ4等。

    -

    TDengine的数据文件默认存放在 /var/lib/taos/data/ 下。而 /var/lib/taos/tsdb/ 文件夹下存放了vnode的信息、vnode中表的信息以及数据文件的链接等。其完整目录结构如下所示：

    -
    /var/lib/taos/
-      +--tsdb/
-      |   +--vnode0
-      |        +--meterObj.v0
-      |        +--db/
-      |            +--v0f1804.head->/var/lib/taos/data/vnode0/v0f1804.head1
-      |            +--v0f1804.data->/var/lib/taos/data/vnode0/v0f1804.data
-      |            +--v0f1804.last->/var/lib/taos/data/vnode0/v0f1804.last1
-      |            +--v0f1805.head->/var/lib/taos/data/vnode0/v0f1805.head1
-      |            +--v0f1805.data->/var/lib/taos/data/vnode0/v0f1805.data
-      |            +--v0f1805.last->/var/lib/taos/data/vnode0/v0f1805.last1
-      |                   :
-      +--data/
-          +--vnode0/
-                +--v0f1804.head1
-                +--v0f1804.data
-                +--v0f1804.last1
-                +--v0f1805.head1
-                +--v0f1805.data
-                +--v0f1805.last1
-                        :
    -

    meterObj文件

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    每个vnode中只存在一个 meterObj 文件。该文件中存储了vnode的基本信息（创建时间，配置信息，vnode的统计信息等）以及该vnode中表的信息。其结构如下所示：

    -
    <文件开始>
-[文件头]
-[表记录1偏移量和长度]
-[表记录2偏移量和长度]
-...
-[表记录N偏移量和长度]
-[表记录1]
-[表记录2]
-...
-[表记录N]
-[表记录]
-<文件结尾>
    -

    其中，文件头大小为512字节，主要存放vnode的基本信息。每条表记录代表属于该vnode中的一张表在硬盘上的表示。

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    head文件

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    head文件中存放了其对应的data文件中数据块的索引信息。该文件组织形式如下：

    -
    <文件开始>
-[文件头]
-[表1偏移量]
-[表2偏移量]
-...
-[表N偏移量]
-[表1数据索引]
-[表2数据索引]
-...
-[表N数据索引]
-<文件结尾>
    -

    文件开头的偏移量列表表示对应表的数据索引块的开始位置在文件中的偏移量。每张表的数据索引信息在head文件中都是连续存放的。这也使得TDengine在读取单表数据时，可以将该表所有的数据块索引一次性读入内存，大大提高读取速度。表的数据索引块组织如下：

    -
    [索引块信息]
-[数据块1索引]
-[数据块2索引]
-...
-[数据块N索引]
    -

    其中，索引块信息中记录了数据块的个数等描述信息。每个数据块索引对应一个在data文件或last文件中的一个单独的数据块。索引信息中记录了数据块存放的文件、数据块起始位置的偏移量、数据块中数据时间主键的范围等。索引块中的数据块索引是按照时间范围顺序排放的，这也就是说，索引块M对应的数据块中的数据时间范围都大于索引块M-1的。这种预先排序的存储方式使得在TDengine在进行按照时间戳进行查询时可以使用折半查找算法，大大提高查询速度。

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    data文件

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    data文件中存放了真实的数据块。该文件只进行追加操作。其文件组织形式如下：

    -
    <文件开始>
-[文件头]
-[数据块1]
-[数据块2]
-...
-[数据块N]
-<文件结尾>
    -

    每个数据块只属于vnode中的一张表，且数据块中的数据按照时间主键排列。数据块中的数据按列组织排放，使得同一类型的数据排放在一起，方便压缩和读取。每个数据块的组织形式如下所示：

    -
    [列1信息]
-[列2信息]
-...
-[列N信息]
-[列1数据]
-[列2数据]
-...
-[列N数据]
    -

    列信息中包含该列的类型，列的压缩算法，列数据在文件中的偏移量以及长度等。除此之外，列信息中也包含该内存块中该列数据的预计算结果，从而在过滤查询时根据预计算结果判定是否读取数据块，大大提高读取速度。

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    last文件

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    为了防止数据块的碎片化，提高查询速度和压缩率，TDengine引入了last文件。当要落盘的数据块中的数据条数低于某个阈值时，TDengine会先将该数据块写入到last文件中进行暂时存储。当有新的数据需要落盘时，last文件中的数据会被读取出来与新数据组成新的数据块写入到data文件中。last文件的组织形式与data文件类似。

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    TDengine数据存储小结

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    TDengine通过其创新的架构和存储结构设计，有效提高了计算机资源的使用率。一方面，TDengine的虚拟化使得TDengine的水平扩展及备份非常容易。另一方面，TDengine将表中数据按时间主键排序存储且其列式存储的组织形式都使TDengine在写入、查询以及压缩方面拥有非常大的优势。

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    查询处理

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    概述

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    TDengine提供了多种多样针对表和超级表的查询处理功能，除了常规的聚合查询之外，还提供针对时序数据的窗口查询、统计聚合等功能。TDengine的查询处理需要客户端、管理节点、数据节点协同完成。 各组件包含的与查询处理相关的功能和模块如下：

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    客户端（Client App）。客户端包含TAOS SQL的解析（SQL Parser）和查询请求执行器（Query Executor），第二阶段聚合器（Result Merger），连续查询管理器（Continuous Query Manager）等主要功能模块构成。SQL解析器负责对SQL语句进行解析校验，并转化为抽象语法树，查询执行器负责将抽象语法树转化查询执行逻辑，并根据SQL语句查询条件，将其转换为针对管理节点元数据查询和针对数据节点的数据查询两级查询处理。由于TAOS SQL当前不提供复杂的嵌套查询和pipeline查询处理机制，所以不再需要查询计划优化、逻辑查询计划到物理查询计划转换等过程。第二阶段聚合器负责将各数据节点查询返回的独立结果进行二阶段聚合生成最后的结果。连续查询管理器则负责针对用户建立的连续查询进行管理，负责定时拉起查询请求并按需将结果写回TDengine或返回给客户应用。此外，客户端还负责查询失败后重试、取消查询请求、以及维持连接心跳、向管理节点上报查询状态等工作。

    -

    管理节点（Management Node）。管理节点保存了整个集群系统的全部数据的元数据信息，向客户端节点提供查询所需的数据的元数据，并根据集群的负载情况切分查询请求。通过超级表包含了通过该超级表创建的所有表的信息，因此查询处理器（Query Executor）负责针对标签（TAG）的查询处理，并将满足标签查询请求的表信息返回给客户端。此外，管理节点还维护集群的查询状态（Query Status Manager）维护，查询状态管理中在内存中临时保存有当前正在执行的全部查询，当客户端使用 show queries 命令的时候，将当前系统正在运行的查询信息返回客户端。

    -

    数据节点（Data Node）。数据节点保存了数据库中全部数据内容，并通过查询执行器、查询处理调度器、查询任务队列（Query Task Queue）进行查询处理的调度执行，从客户端接收到的查询处理请求都统一放置到处理队列中，查询执行器从队列中获得查询请求，并负责执行。通过查询优化器（Query Optimizer）对于查询进行基本的优化处理，以及通过数据节点的查询执行器（Query Executor）扫描符合条件的数据单元并返回计算结果。等接收客户端发出的查询请求，执行查询处理，并将结果返回。同时数据节点还需要响应来自管理节点的管理信息和命令，例如 kill query 命令以后，需要即刻停止执行的查询任务。

    -

    -
    图 1. 系统查询处理架构图(只包含查询相关组件)

    -

    普通查询处理

    -

    客户端、管理节点、数据节点协同完成TDengine的查询处理全流程。我们以一个具体的SQL查询为例，说明TDengine的查询处理流程。SQL语句向超级表FOO_SUPER_TABLE查询获取时间范围在2019年1月12日整天，标签TAG_LOC是'beijing'的表所包含的所有记录总数，SQL语句如下：

    -
    SELECT COUNT(*) 
-FROM FOO_SUPER_TABLE
-WHERE TAG_LOC = 'beijing' AND TS >= '2019-01-12 00:00:00' AND TS < '2019-01-13 00:00:00'
    -

    首先，客户端调用TAOS SQL解析器对SQL语句进行解析及合法性检查，然后生成语法树，并从中提取查询的对象 — 超级表 FOO_SUPER_TABLE ，然后解析器向管理节点（Management Node）请求其相应的元数据信息，并将过滤信息（TAG_LOC='beijing'）同时发送到管理节点。

    -

    管理节点接收元数据获取的请求，首先找到超级表 FOO_SUPER_TABLE 基础信息，然后应用查询条件来过滤通过该超级表创建的全部表，最后满足查询条件（TAG_LOC='beijing'），即 TAG_LOC 标签列是 'beijing' 的的通过其查询执行器将满足查询要求的对象（表或超级表）的元数据信息返回给客户端。

    -

    客户端获得了 FOO_SUPER_TABLE 的元数据信息后，查询执行器根据元数据中的数据分布，分别向保存有相应数据的节点发起查询请求，此时时间戳范围过滤条件（TS >= '2019-01-12 00:00:00' AND TS < '2019-01-13 00:00:00'）需要同时发送给全部的数据节点。

    -

    数据节点接收到发自客户端的查询，转化为内部结构并进行优化以后将其放入任务执行队列，等待查询执行器执行。当查询结果获得以后，将查询结果返回客户端。数据节点执行查询的过程均相互独立，完全只依赖于自身的数据和内容进行计算。

    -

    当所有查询涉及的数据节点返回结果后，客户端将每个数据节点查询的结果集再次进行聚合（针对本案例，即将所有结果再次进行累加），累加的结果即为最后的查询结果。第二阶段聚合并不是所有的查询都需要。例如，针对数据的列选取操作，实际上是不需要第二阶段聚合。

    -

    REST查询处理

    -

    在 C/C++ 、Python接口、 JDBC 接口之外，TDengine 还提供基于 HTTP 协议的 REST 接口。不同于使用应用客户端开发程序进行的开发。当用户使用 REST 接口的时候，所有的查询处理过程都是在服务器端来完成，用户的应用服务不会参与数据库的计算过程，查询处理完成后结果通过 HTTP的 JSON 格式返回给用户。

    -

    -
    图 2. REST查询架构

    -

    当用户使用基于HTTP的REST查询接口，HTTP的请求首先与位于数据节点的HTTP连接器（ Connector），建立连接，然后通过REST的签名机制，使用Token来确保请求的可靠性。对于数据节点，HTTP连接器接收到请求后，调用内嵌的客户端程序发起查询请求，内嵌客户端将解析通过HTTP连接器传递过来的SQL语句，解析该SQL语句并按需向管理节点请求元数据信息，然后向本机或集群中其他节点发送查询请求，最后按需聚合计算结果。HTTP连接器接收到请求SQL以后，后续的流程处理与采用应用客户端方式的查询处理完全一致。最后，还需要将查询的结果转换为JSON格式字符串，并通过HTTP 响应返回给客户端。

    -

    可以看到，在处理HTTP流程的整个过程中，用户应用不再参与到查询处理的过程中，只负责通过HTTP协议发送SQL请求并接收JSON格式的结果。同时还需要注意的是，每个数据节点均内嵌了一个HTTP连接器和客户端程序，因此请求集群中任何一个数据节点，该数据节点均能够通过HTTP协议返回用户的查询结果。

    -

    技术特征

    -

    由于TDengine采用数据和标签分离存储的模式，能够极大地降低标签数据存储的冗余度。标签数据直接关联到每个表，并采用全内存的结构进行管理和维护标签数据,全内存的结构提供快速的查询处理，千万级别规模的标签数据查询可以在毫秒级别返回。首先针对标签数据的过滤可以有效地降低第二阶段的查询涉及的数据规模。为有效地提升查询处理的性能，针对物联网数据的不可更改的特点，TDengine采用在每个保存的数据块上，都记录下该数据块中数据的最大值、最小值、和等统计数据。如果查询处理涉及整个数据块的全部数据，则直接使用预计算结果，不再读取数据块的内容。由于预计算模块的大小远小于磁盘上存储的具体数据的大小，对于磁盘IO为瓶颈的查询处理，使用预计算结果可以极大地减小读取IO，并加速查询处理的流程。

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    由于TDengine采用按列存储数据。当从磁盘中读取数据块进行计算的时候，按照查询列信息读取该列数据，并不需要读取其他不相关的数据，可以最小化读取数据。此外，由于采用列存储结构，数据节点针对数据的扫描采用该列数据块进行，可以充分利用CPU L2高速缓存，极大地加速数据扫描的速度。此外，对于某些查询，并不会等全部查询结果生成后再返回结果。例如，列选取查询，当第一批查询结果获得以后，数据节点直接将其返回客户端。同时，在查询处理过程中，系统在数据节点接收到查询请求以后马上返回客户端查询确认信息，并同时拉起查询处理过程，并等待查询执行完成后才返回给用户查询有响应。

    -

    TDengine集群设计

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    1：集群与主要逻辑单元

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    TDengine是基于硬件、软件系统不可靠、一定会有故障的假设进行设计的，是基于任何单台计算机都无足够能力处理海量数据的假设进行设计的。因此TDengine从研发的第一天起，就按照分布式高可靠架构进行设计，是完全去中心化的，是水平扩展的，这样任何单台或多台服务器宕机或软件错误都不影响系统的服务。通过节点虚拟化并辅以自动化负载均衡技术，TDengine能最大限度地利用异构集群中的计算和存储资源。而且只要数据副本数大于一，无论是硬软件的升级、还是IDC的迁移等都无需停止集群的服务，极大地保证系统的正常运行，并且降低了系统管理员和运维人员的工作量。

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    下面的示例图上有八个物理节点，每个物理节点被逻辑的划分为多个虚拟节点。下面对系统的基本概念进行介绍。

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    assets/nodes.png

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    物理节点(dnode)：集群中的一物理服务器或云平台上的一虚拟机。为安全以及通讯效率，一个物理节点可配置两张网卡，或两个IP地址。其中一张网卡用于集群内部通讯，其IP地址为privateIp, 另外一张网卡用于与集群外部应用的通讯，其IP地址为publicIp。在一些云平台（如阿里云），对外的IP地址是映射过来的，因此publicIp还有一个对应的内部IP地址internalIp(与privateIp不同)。对于只有一个IP地址的物理节点，publicIp, privateIp以及internalIp都是同一个地址，没有任何区别。一个dnode上有而且只有一个taosd实例运行。

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    虚拟数据节点(vnode)：在物理节点之上的可独立运行的基础逻辑单元，时序数据写入、存储、查询等操作逻辑都在虚拟节点中进行(图中V)，采集的时序数据就存储在vnode上。一个vnode包含固定数量的表。当创建一张新表时，系统会检查是否需要创建新的vnode。一个物理节点上能创建的vnode的数量取决于物理节点的硬件资源。一个vnode只属于一个DB，但一个DB可以有多个vnode。

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    虚拟数据节点组(vgroup): 位于不同物理节点的vnode可以组成一个虚拟数据节点组vnode group(如上图dnode0中的V0, dnode1中的V1, dnode6中的V2属于同一个虚拟节点组)。归属于同一个vgroup的虚拟节点采取master/slave的方式进行管理。写只能在master上进行，但采用asynchronous的方式将数据同步到slave，这样确保了一份数据在多个物理节点上有拷贝。如果master节点宕机，其他节点监测到后，将重新选举vgroup里的master, 新的master能继续处理数据请求，从而保证系统运行的可靠性。一个vgroup里虚拟节点个数就是数据的副本数。如果一个DB的副本数为N，系统必须有至少N个物理节点。副本数在创建DB时通过参数replica可以指定，缺省为1。使用TDengine, 数据的安全依靠多副本解决，因此不再需要昂贵的磁盘阵列等存储设备。

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    虚拟管理节点(mnode)：负责所有节点运行状态的监控和维护，以及节点之间的负载均衡（图中M）。同时，虚拟管理节点也负责元数据(包括用户、数据库、表、静态标签等)的存储和管理，因此也称为Meta Node。TDengine集群中可配置多个(最多不超过5个) mnode，它们自动构建成为一个管理节点集群(图中M0, M1, M2)。mnode间采用master/slave的机制进行管理，而且采取强一致方式进行数据同步。mnode集群的创建由系统自动完成，无需人工干预。每个dnode上至多有一个mnode，而且每个dnode都知道整个集群中所有mnode的IP地址。

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    taosc：一个软件模块，是TDengine给应用提供的驱动程序（driver）,内嵌于JDBC、ODBC driver中，或者C语言连接库里。应用都是通过taosc而不是直接来与整个集群进行交互的。这个模块负责获取并缓存元数据；将插入、查询等请求转发到正确的虚拟节点；在把结果返回给应用时，还需要负责最后一级的聚合、排序、过滤等操作。对于JDBC, ODBC, C/C++接口而言，这个模块是在应用所处的计算机上运行，但消耗的资源很小。为支持全分布式的REST接口，taosc在TDengine集群的每个dnode上都有一运行实例。

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    对外服务地址：TDengine集群可以容纳单台、多台甚至几千台物理节点。应用只需要向集群中任何一个物理节点的publicIp发起连接即可。启动CLI应用taos时，选项-h需要提供的就是publicIp。

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    master/secondIp：每一个dnode都需要配置一个masterIp。dnode启动后，将对配置的masterIp发起加入集群的连接请求。masterIp是已经创建的集群中的任何一个节点的privateIp，对于集群中的第一个节点，就是它自己的privateIp。为保证连接成功，每个dnode还可配置secondIp, 该IP地址也是已创建的集群中的任何一个节点的privateIp。如果一个节点连接masterIp失败,它将试图链接secondIp。

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    dnode启动后，会获知集群的mnode IP列表，并且定时向mnode发送状态信息。

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    vnode与mnode只是逻辑上的划分，都是执行程序taosd里的不同线程而已，无需安装不同的软件，做任何特殊的配置。最小的系统配置就是一个物理节点,vnode,mnode和taosc都存在而且都正常运行，但单一节点无法保证系统的高可靠。

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    2：一典型的操作流程

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    为解释vnode, mnode, taosc和应用之间的关系以及各自扮演的角色，下面对写入数据这个典型操作的流程进行剖析。

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    Picture1

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      -
    1. 应用通过JDBC、ODBC或其他API接口发起插入数据的请求。
      2. -
    2. taosc会检查缓存，看是有保存有该表的meta data。如果有，直接到第4步。如果没有，taosc将向mnode发出get meta-data请求。
      3. -
    3. mnode将该表的meta-data返回给taosc。Meta-data包含有该表的schema, 而且还有该表所属的vgroup信息（vnode ID以及所在的dnode的IP地址，如果副本数为N，就有N组vnodeID/IP)。如果taosc迟迟得不到mnode回应，而且存在多个mnode,taosc将向下一个mnode发出请求。
      4. -
    4. taosc向master vnode发起插入请求。
      5. -
    5. vnode插入数据后，给taosc一个应答，表示插入成功。如果taosc迟迟得不到vnode的回应，taosc会认为该节点已经离线。这种情况下，如果被插入的数据库有多个副本，taosc将向vgroup里下一个vnode发出插入请求。
      6. -
    6. taosc通知APP，写入成功。
      7. -
    -

    对于第二和第三步，taosc启动时，并不知道mnode的IP地址，因此会直接向配置的集群对外服务的IP地址发起请求。如果接收到该请求的dnode并没有配置mnode，该dnode会在回复的消息中告知mnode的IP地址列表（如果有多个dnodes，mnode的IP地址可以有多个），这样taosc会重新向新的mnode的IP地址发出获取meta-data的请求。

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    对于第四和第五步，没有缓存的情况下，taosc无法知道虚拟节点组里谁是master，就假设第一个vnodeID/IP就是master,向它发出请求。如果接收到请求的vnode并不是master,它会在回复中告知谁是master，这样taosc就向建议的master vnode发出请求。一旦得到插入成功的回复，taosc会缓存住master节点的信息。

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    上述是插入数据的流程，查询、计算的流程也完全一致。taosc把这些复杂的流程全部封装屏蔽了，因此应用无需处理重定向、获取meta data等细节，完全是透明的。

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    通过taosc缓存机制，只有在第一次对一张表操作时，才需要访问mnode, 因此mnode不会成为系统瓶颈。但因为schema有可能变化，而且vgroup有可能发生改变（比如负载均衡发生），因此taosc需要定时自动刷新缓存。

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    3：数据分区

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    vnode(虚拟数据节点)保存采集的时序数据，而且查询、计算都在这些节点上进行。为便于负载均衡、数据恢复、支持异构环境，TDengine将一个物理节点根据其计算和存储资源切分为多个vnode。这些vnode的管理是TDengine自动完成的，对应用完全透明。

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    对于单独一个数据采集点，无论其数据量多大，一个vnode（或vnode group, 如果副本数大于1）有足够的计算资源和存储资源来处理（如果每秒生成一条16字节的记录，一年产生的原始数据不到0.5G），因此TDengine将一张表的所有数据都存放在一个vnode里，而不会让同一个采集点的数据分布到两个或多个dnode上。而且一个vnode可存储多张表的数据，一个vnode可容纳的表的数目由配置参数tables指定，缺省为2000。设计上，一个vnode里所有的表都属于同一个DB。因此一个数据库DB需要的vnode或vgroup的个数等于：数据库表的数目/tables。

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    创建DB时，系统并不会马上分配资源。但当创建一张表时，系统将看是否有已经分配的vnode, 而且是否有空位，如果有，立即在该有空位的vnode创建表。如果没有，系统将从集群中，根据当前的负载情况，在一个dnode上创建一新的vnode, 然后创建表。如果DB有多个副本，系统不是只创建一个vnode，而是一个vgroup(虚拟数据节点组)。系统对vnode的数目没有任何限制，仅仅受限于物理节点本身的计算和存储资源。

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    参数tables的设置需要考虑具体场景，创建DB时，可以个性化指定该参数。该参数不宜过大，也不宜过小。过小，极端情况，就是每个数据采集点一个vnode, 这样导致系统数据文件过多。过大，虚拟化带来的优势就会丧失。给定集群计算资源的情况下，整个系统vnode的个数应该是CPU核的数目的两倍以上。

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    4：负载均衡

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    每个dnode(物理节点)都定时向 mnode(虚拟管理节点)报告其状态（包括硬盘空间、内存大小、CPU、网络、虚拟节点个数等），因此mnode了解整个集群的状态。基于整体状态，当mnode发现某个dnode负载过重，它会将dnode上的一个或多个vnode挪到其他dnode。在挪动过程中，对外服务继续进行，数据插入、查询和计算操作都不受影响。负载均衡操作结束后，应用也无需重启，将自动连接新的vnode。

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    如果mnode一段时间没有收到dnode的状态报告，mnode会认为这个dnode已经离线。如果离线时间超过一定时长（时长由配置参数offlineThreshold决定），该dnode将被mnode强制剔除出集群。该dnode上的vnodes如果副本数大于一，系统将自动在其他dnode上创建新的副本，以保证数据的副本数。

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    Note：目前集群功能仅仅限于企业版

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padding-left: 0.4em; - width:-webkit-calc(100%); - width:calc(100%); - border:solid 1px; - display: inline-block; - border-left:1px solid; - -webkit-border-radius:4px; - border-radius:4px; - -webkit-transition: border-left 0.2s; - -o-transition: border-left 0.2s; - transition: border-left 0.2s; - vertical-align: top; - font-weight:400; - border-color:inherit; - margin-bottom: 0.5rem; -} - -/*Other Text*/ -ul { - padding-left:30px; -} -p, li { - font-size:1em; - -} -p { - margin-bottom: 0.5rem; -} -/*Headers*/ -h1 { - font-size: 2.5rem; - line-height: 1.8; -} -h2 { - font-size: 1.7rem; - line-height: 1.8; -} -h3 { - font-size: 1.4rem; - line-height: 1.43; -} -h4 { - font-size: 1.25rem; -} -h5 { - font-size: 1rem; -} -h6 { - font-size: 1rem; - color: #777; -} -h1[b]::before,h2[b]::before, h3[b]::before { - content:""; - height:1em;; - display: block; - width:3px; - margin-left: -0.5em; - margin-top: 0.45em; - position: absolute; - background-color: var(--b1); -} -h1[b],h2[b], h3[b] { - padding-left: 0.5em -} -/* Navigation Bar */ -.logo { - height: 2.5rem; -} -a { - font-size:1em; -} -a:hover { - text-decoration: none; -} -a[l] { - color:var(--b2); - padding-bottom: 2px; - position: relative; - font-style: normal; - cursor: pointer; -} -a[l]:hover,a[l]:focus { - text-decoration: none; -} -a[l]::before { - content: ""; - left: 0; - background-color: var(--b2); - width: 0%; - height: 1px; - top:-webkit-calc(1em + 8px); - top:calc(1em + 8px); - position: absolute; - z-index: 2; - -webkit-transition: background-color 0.2s, height 0.2s, top 0.2s, width 0.2s; - -o-transition: background-color 0.2s, height 0.2s, top 0.2s, width 0.2s; - transition: background-color 0.2s, height 0.2s, top 0.2s, width 0.2s;; -} -a[l]:hover::before, a[l]:focus::before { - content: ""; - left: 0; - background-color: var(--b2); - width: 100%; - height: 1px; - top:-webkit-calc(1em + 8px); - top:calc(1em + 8px); - position: absolute; - z-index: 2; - -webkit-transition: background-color 0.2s, height 0.2s, top 0.2s, width 0.2s; - -o-transition: background-color 0.2s, height 0.2s, top 0.2s, width 0.2s; - transition: background-color 0.2s, height 0.2s, top 0.2s, width 0.2s; - text-decoration: none; -} -.navbar-brand { - margin-left: 10%; - padding-left: 15px; - color:var(--white) !important; -} -.navbar-nav { - top:0px; -} -.navbar { - background-color:var(--sg1); - z-index:10000; - padding-left: 0px; - padding-right: 0px; - padding-top:0.75rem; - padding-bottom: 0.75rem; -} -.navbar-toggler { - margin-right: -webkit-calc(2rem + 15px); - margin-right: calc(2rem + 15px); -} -.nav-link { - color:var(--white) !important; - line-height: 3.65rem; -} -.nav-item { - height:4.65rem; - font-size:1.1rem; - padding-left: 0.15rem; - padding-right: 0.15rem; - -webkit-transition: all 0.2s; - -o-transition: all 0.2s; - transition: all 0.2s; - border-bottom: 0rem solid var(--white); -} -.nav-item:hover { - border-bottom: 0.45rem solid var(--white); -} -.dropdown-menu { - top:4.1rem; - z-index:1000; - border-top:none; - border:none; - min-width: 120px; - margin-left:-1px; - -webkit-border-top-left-radius: 0; - border-top-left-radius: 0; - -webkit-border-top-right-radius: 0; - border-top-right-radius: 0; - -webkit-border-bottom-left-radius:0.25rem; - border-bottom-left-radius:0.25rem; - -webkit-border-bottom-right-radius:0.25rem; - border-bottom-right-radius:0.25rem; -} -.dropdown-menu.show { - -webkit-box-shadow: 0 4px 24px rgba(100, 109, 146, 0.15); - box-shadow: 0 4px 24px rgba(100, 109, 146, 0.15); -} -.dropdown-item { - color:var(--sg1); - background-color: var(--white); - -webkit-transition:all 0.2s; - -o-transition:all 0.2s; - transition:all 0.2s; - cursor:pointer; -} -.dropdown-item:hover, .dropdown-item:active { - background-color:var(--sg1); - color:var(--white) !important; -} -.dropdown-toggle::after { - display:none; -} -.dropdown a::after { - -webkit-transform: rotate(-90deg); - -ms-transform: rotate(-90deg); - transform: rotate(-90deg); - -webkit-transition: -webkit-transform 0.2s; - transition: -webkit-transform 0.2s; - -o-transition: transform 0.2s; - transition: transform 0.2s; - transition: transform 0.2s, -webkit-transform 0.2s; -} -.dropdown.show a::after { - -webkit-transform: rotate(0deg); - -ms-transform: rotate(0deg); - transform: rotate(0deg); -} -.navbar-nav .active { - border-bottom: 0.45rem solid var(--white); -} -.navbar-nav { - position: absolute; - right:-webkit-calc(10% + 15px); - right:calc(10% + 15px); -} -#language-dropdown .dropdown-menu{ - width:50px; -} -/*FOOTER*/ -footer { - background-color: var(--footer2); - padding-top: 1rem; -} -.page-footer { - padding-bottom: 2rem; -} -.footer-content, .footer-legal, .footer-contact { - width:80%; - margin-left: 10%; - padding-top:1rem; - color:var(--footer1); - font-size:0.8em; -} -.footer-content a { - color:var(--footer1); -} -.footer-content a { - color:var(--footer1); -} -.links-list { - text-align: left; - list-style: none; - padding: 0px; -} -.content-wrapper > .links-list { - padding-left:15px; -} -.links-list-title h4 { - font-size:1.2em; - font-weight:400; -} -.legal-links { - position: absolute; - right:-webkit-calc(10% + 15px); - right:calc(10% + 15px); -} -.legal-links a { - color:var(--footer1); -} -.links-list li { - height:2em; -} -.links-list li a::before, .legal-links a::before { - background-color:var(--footer1); -} -.links-list li a:hover::before, .legal-links a:hover::before { - background-color:var(--footer1); -} -.links-list .divider { - border-bottom: 1px solid var(--footer1); - opacity: 0.15; - height:0px; - margin-bottom: 0.3em; -} -.footer-divider { - border-bottom: 1px solid var(--footer1); - width:-webkit-calc(80% - 30px); - width:calc(80% - 30px); - margin-left: -webkit-calc(10% + 15px); - margin-left: calc(10% + 15px); -} -#social-media-links li { - height:2rem; - line-height:2rem; - display: inline-block; - font-size:1em; -} - -#social-media-links li:last-child::after { - content:""; -} -#social-media-links li::after { - content:" | "; -} -#social-media-links svg { - margin-left:2px;margin-right: 0.4rem; - width:20px; -} -#social-media-links svg path { - fill:var(--footer1); -} -#social-media-links li a::before { - left:1.9rem; - background-color:var(--footer1); -} -#social-media-links li a:hover::before, #social-media-links li a:focus::before { - left:1.9rem; - width: -webkit-calc(100% - 1.9rem); - width: calc(100% - 1.9rem); - background-color:var(--footer1); -} -#social-media-links ion-icon { - font-size:20px; - margin-right: 0.5rem; -} -#social-media-links svg { - font-size:20px; - margin-right: 0.5rem; -} -#email-subscribe-form { - width:-webkit-calc(100% - 160px); - width:calc(100% - 160px); -} -#email-subscribe-form input{ - width:-webkit-calc(100% - 4rem); - width:calc(100% - 4rem); - font-size:1.2em; - outline: none; - height:1.8em; - color:var(--sg1); - padding-left: 0.6em; - border:none; - display: inline-block; - border-left:0px solid var(--b1); - -webkit-border-radius:4px; - border-radius:4px; - -webkit-transition: border-left 0.2s; - -o-transition: border-left 0.2s; - transition: border-left 0.2s; - vertical-align: top; - font-weight:400; -} -#email-subscribe-form input:focus { - border-left:1rem solid var(--b1); - padding-top:2px; -} -#email-subscribe-form input:invalid, #email-subscribe-form input:invalid:focus { - border-color:var(--b1); -} -#email-subscribe-form input.invalid-input, #email-subscribe-form input.invalid-input:focus { - border-color:var(--red); -} -#email-subscribe-form input:valid, #email-subscribe-form input:valid:focus { - border-color:var(--green); -} -#email-subscribe-form button { - font-size:1.2em; - height:1.8em; - line-height: 1em; - float:right; - width:3rem; - padding:0; -} -form { - border-color:var(--b1); -} -form input:invalid, form input:invalid:focus { - border-color:inherit; -} -form input.invalid-input, form input.invalid-input:focus, form textarea.invalid-input, form textarea.invalid-input:focus { - border-color:var(--red); -} -form input:valid, form input:valid:focus { - border-color:var(--green); -} - -.sub-arrow { - width:1.2em; - fill:var(--b1); -} - - -@media only screen and (max-width:991px){ - .page-footer { - padding-left:20px; - padding-right:20px; - } - .footer-legal { - width:100%; - } - #legal-1 { - padding-left: 20px; - } - .legal-links { - right:20px; - } - .footer-content .col-xl-8, .footer-content .col-xl-4{ - padding-left:20px; - padding-right:20px; - } - .footer-content { - width:-webkit-calc(100% + 40px); - width:calc(100% + 40px); - } - .footer-divider { - width:100%; - margin-left: 0; - } -} - -/*SECTIONS AND CONTENT*/ -.content-wrapper { - width: 80%; - margin-left: 10%; - margin-top: 6rem; - margin-bottom: 3rem; - min-height: -webkit-calc(100vh - 187.7px - 74.45px); - min-height: calc(100vh - 187.7px - 74.45px); -} -.section { - /* border-bottom:2px solid rgba(0,0,0,0.2);*/ -} -.section-item { - -} -.section-title, -.section-item-title { - color:var(--b1); - -} -.container-fluid { - background-color: var(--white); -} -.center { - left:50%; - position: relative; -} -/*BUTTONS*/ -.btn-primary { - color:var(--b1); - background-color: var(--white); - border-color:var(--b1); - -webkit-box-shadow:0px 0px 0px 0px rgba(255,255,255,0.55); - box-shadow:0px 0px 0px 0px rgba(255,255,255,0.55); - -webkit-transition: all 0.2s; - -o-transition: all 0.2s; - transition: all 0.2s; -} -.btn-primary:hover,.btn-primary:focus { - color:var(--b1); - background-color: var(--white); - border-color:var(--b1); - -webkit-box-shadow:4px 4px 0px 0px var(--b1t); - box-shadow:4px 4px 0px 0px var(--b1t); - -webkit-transform: translate(-2px,-2px); - -ms-transform: translate(-2px,-2px); - transform: translate(-2px,-2px); -} -.btn-primary:active { - color:var(--b1) !important; - background-color: var(--white) !important; - border-color:var(--b1) !important; - -webkit-box-shadow:2px 2px 0px 0px var(--b1t); - box-shadow:2px 2px 0px 0px var(--b1t); - -webkit-transform: translate(-1px,-1px); - -ms-transform: translate(-1px,-1px); - transform: translate(-1px,-1px); -} -.btn-white { - color:var(--b1); - background-color: var(--white); - -webkit-transition: all 0.2s; - -o-transition: all 0.2s; - transition: all 0.2s; - -webkit-box-shadow:0px 0px 0px 0px rgba(255,255,255,0.55); - box-shadow:0px 0px 0px 0px rgba(255,255,255,0.55); -} -.btn-white:hover,.btn-white:focus { - color:var(--b1); - background-color: var(--white); - -webkit-box-shadow:4px 4px 0px 0px rgba(255,255,255,0.55); - box-shadow:4px 4px 0px 0px rgba(255,255,255,0.55); - -webkit-transform: translate(-2px,-2px); - -ms-transform: translate(-2px,-2px); - transform: translate(-2px,-2px); -} -.btn-white:active { - color:var(--b1) !important; - background-color: var(--white) !important; - -webkit-box-shadow:2px 2px 0px 0px rgba(255,255,255,0.55); - box-shadow:2px 2px 0px 0px rgba(255,255,255,0.55); - -webkit-transform: translate(-1px,-1px); - -ms-transform: translate(-1px,-1px); - transform: translate(-1px,-1px); -} -.btn-filled { - color:var(--white) !important; - background-color: var(--b1); - border-color:var(--b1); - -webkit-box-shadow:0px 0px 0px 0px rgba(255,255,255,0.55); - box-shadow:0px 0px 0px 0px rgba(255,255,255,0.55); - -webkit-transition: all 0.2s; - -o-transition: all 0.2s; - transition: all 0.2s; -} -.btn-filled:hover { - color:var(--white) !important;; - background-color: var(--b1); - border-color:var(--b1); - -webkit-box-shadow:4px 4px 0px 0px var(--b1t); - box-shadow:4px 4px 0px 0px var(--b1t); - -webkit-transform: translate(-2px,-2px); - -ms-transform: translate(-2px,-2px); - transform: translate(-2px,-2px); -} -.btn-filled:active { - color:var(--white) !important; - background-color: var(--b1) !important; - border-color:var(--b1) !important; - -webkit-box-shadow:2px 2px 0px 0px var(--b1t); - box-shadow:2px 2px 0px 0px var(--b1t); - -webkit-transform: translate(-1px,-1px); - -ms-transform: translate(-1px,-1px); - transform: translate(-1px,-1px); -} -/*Popup*/ -#popup-wrapper { - display: block; - position: absolute; - z-index:1000; - -webkit-transition:opacity 0.5s; - -o-transition:opacity 0.5s; - transition:opacity 0.5s; - opacity: 1; -} -#popup-page-cover { - display:none; - position: fixed; - height: 100vh; - width:100vw; - top:0;left:0; - background-color: rgba(131, 145, 174, 0.32); - z-index:1000; - -webkit-transition:opacity 0.5s; - -o-transition:opacity 0.5s; - transition:opacity 0.5s; - opacity:0; -} -#popup { - position: fixed; - display: none; - height:auto; - width:100px; - z-index: 1001; - max-width: -webkit-calc(100% - 30px); - max-width: calc(100% - 30px); - background-color: var(--white); - left:50%; - -webkit-transform:translate(-50%,-50%); - -ms-transform:translate(-50%,-50%); - transform:translate(-50%,-50%); - top:50%; - -webkit-transition:opacity 0.5s; - -o-transition:opacity 0.5s; - transition:opacity 0.5s; - opacity:0; - -webkit-border-radius:0.25rem; - border-radius:0.25rem; - -webkit-box-shadow: 0 12px 48px 0 rgba(0, 0, 0, 0.24); - box-shadow: 0 12px 48px 0 rgba(0, 0, 0, 0.24) -} -#close-popup { - position: absolute;right:1rem; - z-index: 1; - cursor: pointer; - top:0; -} -#close-popup svg { - margin-top:4px; -} -#close-popup::before { - content:""; - width:0px; - display: block; - position: absolute; - top:50%; - left:50%; - height:0px; - background-color:rgba(0,0,0,0.15); - -webkit-border-radius:50%; - border-radius:50%; - z-index:-1; - cursor: pointer; - -webkit-transition:all 0.2s; - -o-transition:all 0.2s; - transition:all 0.2s; -} -#close-popup:hover::before { - content:""; - width:32px;; - display: block; - position: absolute; - top:10px; - left:0px; - height:32px; - background-color:rgba(0,0,0,0.15); - -webkit-border-radius:50%; - border-radius:50%; -z-index:-1; -} -#popup-title { - padding-left: 1rem; - background-color:var(--b1); - color:var(--white); - font-weight:400; - font-size:1.6em; - width:100%; - display:block; - -webkit-border-radius:0.25rem 0.25rem 0 0; - border-radius:0.25rem 0.25rem 0 0; - padding-right:60px; - position: relative; -} -#popup-title-text { - line-height: 1.2; - display: inline-block; - padding-top: 9px; -} -#popup-content { - padding:1rem; - display: block; -} -#popup-title path { - fill:var(--white); -} -/*Banners*/ -.banner-content { - padding-right:32px; -} -.banner-wrapper { - width:100vw; - position: fixed; - top:4.3rem; - left:0; - z-index: 1000; -} -.banner { - background-color: var(--b1); - width:-webkit-calc(100% - 20px); - width:calc(100% - 20px); - margin: auto; - -webkit-border-radius:0.25rem; - border-radius:0.25rem; - padding:0.5rem; - color:var(--white); - font-size:1.6em; - margin-top: 1rem; - -webkit-box-shadow:0 4px 12px 0 rgba(0, 0, 0, 0.24); - box-shadow:0 4px 12px 0 rgba(0, 0, 0, 0.24); - opacity: 1; - -webkit-animation: bannerOpaque 0.2s; - animation: bannerOpaque 0.2s; -} -@-webkit-keyframes bannerOpaque { - from { - opacity:0 - } - to { - opacity:1; - } -} -@keyframes bannerOpaque { - from { - opacity:0 - } - to { - opacity:1; - } -} -.close-banner { - position: absolute;right:1rem; - z-index: 1; - cursor: pointer; - -webkit-transform: translate(0,-3px); - -ms-transform: translate(0,-3px); - transform: translate(0,-3px); -} -.close-banner::before { - content:""; - width:0px; - display: block; - position: absolute; - margin-top:26px; - left:50%; - height:0px; - background-color:rgba(0,0,0,0.15); - -webkit-border-radius:50%; - border-radius:50%; - z-index:-1; - cursor: pointer; - -webkit-transition:all 0.2s; - -o-transition:all 0.2s; - transition:all 0.2s; -} -.close-banner:hover::before { - content:""; - width:32px; - margin-top: 7px; - display: block; - position: absolute; - left:0px; - height:32px; - background-color:rgba(0,0,0,0.15); - -webkit-border-radius:50%; - border-radius:50%; -z-index:-1; -} -@media only screen and (max-width:991px) { - .banner { - font-size:1.2rem; - } -} -/*OTHER*/ -#globe-svg { - height:60px; - fill:#fefefe -} -#page-cover { - width:100vw; - top:-100vh; - left:0px; - -webkit-transition-delay: 0.3s; - -o-transition-delay: 0.3s; - transition-delay: 0.3s; - -webkit-transition:all 0.7s; - -o-transition:all 0.7s; - transition:all 0.7s; - height:100vh; - position: fixed; - z-index:1000; - background-color: rgba(54, 61, 75, 0.25); -} -#menu-button { - border:none; - outline:none; -} -#menu-bar { - -webkit-transition: all 0.15s; - -o-transition: all 0.15s; - transition: all 0.15s; -} -#close-bar { - -webkit-transition: all 0.15s; - -o-transition: all 0.15s; - transition: all 0.15s; - display: none; -} -#rect1 { - -webkit-transition: all 0.2s; - -o-transition: all 0.2s; - transition: all 0.2s; -} -#rect2 { --webkit-transition: all 0.2s; --o-transition: all 0.2s; -transition: all 0.2s; -} -#rect3 { --webkit-transition: all 0.2s; --o-transition: all 0.2s; -transition: all 0.2s; -} -@media only screen and (max-width: 991px) { - - .content-wrapper { - width:-webkit-calc(100%); - width:calc(100%); - left:0; - padding-left: 0; - margin-left:0; - margin-top:4.7rem; - } - .container-fluid { - padding-left:20px; - padding-right:20px; - } - .row { - margin-left:-20px; - margin-right:-20px; - } - #menu-button { - margin-right:20px; - padding:0px; - } - .navbar-brand { - margin-left: 20px; - padding-left: 0px; - } -} -.bot-logo { - margin-bottom:0.5rem; -} -@media only screen and (min-width:1200px){ - #page-cover { - display: none - } - .bot-logo { - margin-left:15px; - } -} -@media only screen and (max-width: 1199px) { - #globe-svg { - height:60px; - fill:var(--sg1); - } - .navbar-collapse.show { - -webkit-box-shadow:0px 10px 24px rgba(0,0,0,0.15) ; - box-shadow:0px 10px 24px rgba(0,0,0,0.15) ; - } - .nav-item:first-child { - border-top: 1px solid rgba(255,255,255,0.35); - } - #menu-button { - margin-right: -webkit-calc(10% + 15px); - margin-right: calc(10% + 15px); - padding:0; - } - #menu-button:hover { - background-color: transparent; - } - .nav-item { - height:auto; - border-bottom: 1px solid rgba(0,0,0,0.35); - padding-left: -webkit-calc(10% + 15px); - padding-left: calc(10% + 15px); - } - .nav-link{ - line-height: 3rem; - padding: 0px; - - } - .nav-link{ - color:var(--sg1) !important; - } - .nav-item:hover { - border-bottom: 1px solid rgba(0,0,0,0.35); - - } - .navbar-nav { - background-color: var(--white2); - margin-top: 15px; - } - .navbar-nav .active { - border-bottom: 1px solid rgba(0,0,0,0.35); - } - .nav-item:nth-child(even) { - /* - background-color:rgba(0,0,0,0.05); - padding-left: 1rem; - margin-left: -1rem; - */ - } - #navbarSupportedContent { - - } - #language-dropdown .dropdown-menu{ - width: -webkit-calc(80% + 4rem); - width: calc(80% + 4rem); - background-color: var(--white); - - } - .dropdown-menu { - border:none; - margin-top: -20px; - } - .nav-item:last-child { - border-bottom:none; - } - .dropdown-menu.show { - -webkit-box-shadow: 0 4px 24px rgba(100, 109, 146, 0.15); - box-shadow: 0 4px 24px rgba(100, 109, 146, 0.15); - margin-bottom:1rem; - margin-top:-0.5rem; - } - .dropdown-item { - padding-left: 15px; - font-weight:300; - } - .navbar-nav { - position: relative; - right:0rem; - } - .long-form input { - width:100%; - } -} -@media only screen and (max-width: 991px) { - .nav-item { - padding-left: 20px; - padding-right: 20px; - } - #language-dropdown{ - padding-left:20px; - } - #language-dropdown .dropdown-menu { - width:100%; - } - #menu-button { - margin-right: 20px; - padding:0; - } - .navbar { - padding-top: 0.25rem; - padding-bottom:0.25rem; - } - .logo { - height:1.8rem; - } - .anchor { - top: -55px; - } -} -@media only screen and (max-width:556px) { - #legal-1 { - width:100%; - } - .legal-links { - position: inherit; - margin-left: 20px; - margin-bottom: 1em; - } -} -@media only screen and (max-width:375px) { - #legal-1 p { - display: block; - } -} - -/*Footer media queries*/ -@media only screen and (max-width:830px) { -} -@media only screen and (max-width:650px) { -} -@media only screen and (max-width:352px) { -} - -.lds-ring { - display: inline-block; - position: relative; - width: 18px; - height: 18px; - padding-top:2px; -} -#email-subscribe-form .lds-ring { - padding-top:1px; -} -.lds-ring div { - -webkit-box-sizing: border-box; - box-sizing: border-box; - display: block; - position: absolute; - width: 18px; - height: 18px; - border: 2px solid var(--b2); - -webkit-border-radius: 50%; - border-radius: 50%; - -webkit-animation: lds-ring 1.2s cubic-bezier(0.5, 0, 0.5, 1) infinite; - animation: lds-ring 1.2s cubic-bezier(0.5, 0, 0.5, 1) infinite; - border-color: var(--b2) transparent transparent transparent; -} -.lds-ring div:nth-child(1) { - -webkit-animation-delay: -0.45s; - animation-delay: -0.45s; -} -.lds-ring div:nth-child(2) { - -webkit-animation-delay: -0.3s; - animation-delay: -0.3s; -} -.lds-ring div:nth-child(3) { - -webkit-animation-delay: -0.15s; - animation-delay: -0.15s; -} -@-webkit-keyframes lds-ring { - 0% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); - } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); - } -} -@keyframes lds-ring { - 0% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); - } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); - } -} -#email-subscribe-form .sub-arrow { - padding-top:2px; -} -.sub-arrow { - display: inline-block; -} -.sub-load { - display:none; -} diff --git a/documentation/tdenginedocs-cn/styles/base.min.css b/documentation/tdenginedocs-cn/styles/base.min.css deleted file mode 100644 index 7aa94277026265a64decb3717fdc680b8a338d59..0000000000000000000000000000000000000000 --- a/documentation/tdenginedocs-cn/styles/base.min.css +++ /dev/null @@ -1 +0,0 @@ -:root{--b1:rgb(0,118,206);--b1t:rgba(0,118,206,0.15);--b2:rgb(72,159,223);--sg-1:#b3b4b9;--sg0:#585c66;--sg1:rgb(51,56,68);--sg2:#2F333E;--sg3:#21242c;--black:#212529;--white:#fefefe;--white2:rgb(251, 251, 253);--white3:rgb(240,242,244);--footer1:#fefefe;--footer2:#333844;--red:#ea4741;--green:#72c156;--p1:#72c156;--p1t:rgba(114,193,86,0.15);--p2:#43b3ae;--p2t:rgba(70,161,168,0.15);--p3:#4997d0;--p3t:rgba(73,151,208,0.15)}html{font-size:12pt;background-color:var(--white)}body,body *{font-family:"Open Sans",Helvetica,'Hiragino Sans GB',sans-serif,"Apple Color Emoji";-webkit-font-smoothing:auto!important;-moz-osx-font-smoothing:auto!important;font-smooth:auto!important;letter-spacing:normal;line-height:1.6}body{-webkit-box-sizing:border-box;box-sizing:border-box;font-weight:300;color:var(--sg1);font-family:"Open Sans",Helvetica,sans-serif!important;background-color:var(--white)}strong{font-weight:600}.anchor{display:block;position:relative;z-index:-1;top:-10px}input{outline:0;-webkit-box-shadow:inset 0 0 0 0 transparent;box-shadow:inset 0 0 0 0 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40px);width:calc(100% + 40px)}.footer-divider{width:100%;margin-left:0}}.content-wrapper{width:80%;margin-left:10%;margin-top:6rem;margin-bottom:3rem;min-height:-webkit-calc(100vh - 187.7px - 74.45px);min-height:calc(100vh - 187.7px - 74.45px)}.section-item-title,.section-title{color:var(--b1)}.container-fluid{background-color:var(--white)}.center{left:50%;position:relative}.btn-primary{color:var(--b1);background-color:var(--white);border-color:var(--b1);-webkit-box-shadow:0 0 0 0 rgba(255,255,255,.55);box-shadow:0 0 0 0 rgba(255,255,255,.55);-webkit-transition:all .2s;-o-transition:all .2s;transition:all .2s}.btn-primary:focus,.btn-primary:hover{color:var(--b1);background-color:var(--white);border-color:var(--b1);-webkit-box-shadow:4px 4px 0 0 var(--b1t);box-shadow:4px 4px 0 0 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(max-width:991px){.banner{font-size:1.2rem}}#globe-svg{height:60px;fill:#fefefe}#page-cover{width:100vw;top:-100vh;left:0;-webkit-transition-delay:.3s;-o-transition-delay:.3s;transition-delay:.3s;-webkit-transition:all .7s;-o-transition:all .7s;transition:all .7s;height:100vh;position:fixed;z-index:1000;background-color:rgba(54,61,75,.25)}#menu-button{border:none;outline:0}#menu-bar{-webkit-transition:all .15s;-o-transition:all .15s;transition:all .15s}#close-bar{-webkit-transition:all .15s;-o-transition:all .15s;transition:all .15s;display:none}#rect1{-webkit-transition:all .2s;-o-transition:all .2s;transition:all .2s}#rect2{-webkit-transition:all .2s;-o-transition:all .2s;transition:all .2s}#rect3{-webkit-transition:all .2s;-o-transition:all .2s;transition:all .2s}@media only screen and (max-width:991px){.content-wrapper{width:-webkit-calc(100%);width:calc(100%);left:0;padding-left:0;margin-left:0;margin-top:4.7rem}.container-fluid{padding-left:20px;padding-right:20px}.row{margin-left:-20px;margin-right:-20px}#menu-button{margin-right:20px;padding:0}.navbar-brand{margin-left:20px;padding-left:0}}.bot-logo{margin-bottom:.5rem}@media only screen and (min-width:1200px){#page-cover{display:none}.bot-logo{margin-left:15px}}@media only screen and (max-width:1199px){#globe-svg{height:60px;fill:var(--sg1)}.navbar-collapse.show{-webkit-box-shadow:0 10px 24px rgba(0,0,0,.15);box-shadow:0 10px 24px rgba(0,0,0,.15)}.nav-item:first-child{border-top:1px solid rgba(255,255,255,.35)}#menu-button{margin-right:-webkit-calc(10% + 15px);margin-right:calc(10% + 15px);padding:0}#menu-button:hover{background-color:transparent}.nav-item{height:auto;border-bottom:1px solid rgba(0,0,0,.35);padding-left:-webkit-calc(10% + 15px);padding-left:calc(10% + 15px)}.nav-link{line-height:3rem;padding:0}.nav-link{color:var(--sg1)!important}.nav-item:hover{border-bottom:1px solid rgba(0,0,0,.35)}.navbar-nav{background-color:var(--white2);margin-top:15px}.navbar-nav .active{border-bottom:1px solid rgba(0,0,0,.35)}#language-dropdown .dropdown-menu{width:-webkit-calc(80% + 4rem);width:calc(80% + 4rem);background-color:var(--white)}.dropdown-menu{border:none;margin-top:-20px}.nav-item:last-child{border-bottom:none}.dropdown-menu.show{-webkit-box-shadow:0 4px 24px rgba(100,109,146,.15);box-shadow:0 4px 24px rgba(100,109,146,.15);margin-bottom:1rem;margin-top:-.5rem}.dropdown-item{padding-left:15px;font-weight:300}.navbar-nav{position:relative;right:0}.long-form input{width:100%}}@media only screen and (max-width:991px){.nav-item{padding-left:20px;padding-right:20px}#language-dropdown{padding-left:20px}#language-dropdown 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    回去

    超级表STable：多表聚合

    -

    TDengine要求每个数据采集点单独建表，这样能极大提高数据的插入/查询性能，但是导致系统中表的数量猛增，让应用对表的维护以及聚合、统计操作难度加大。为降低应用的开发难度，TDengine引入了超级表STable (Super Table)的概念。

    -

    什么是超级表

    -

    STable是同一类型数据采集点的抽象，是同类型采集实例的集合，包含多张数据结构一样的子表。每个STable为其子表定义了表结构和一组标签：表结构即表中记录的数据列及其数据类型；标签名和数据类型由STable定义，标签值记录着每个子表的静态信息，用以对子表进行分组过滤。子表本质上就是普通的表，由一个时间戳主键和若干个数据列组成，每行记录着具体的数据，数据查询操作与普通表完全相同；但子表与普通表的区别在于每个子表从属于一张超级表，并带有一组由STable定义的标签值。每种类型的采集设备可以定义一个STable。数据模型定义表的每列数据的类型，如温度、压力、电压、电流、GPS实时位置等，而标签信息属于Meta Data，如采集设备的序列号、型号、位置等，是静态的，是表的元数据。用户在创建表（数据采集点）时指定STable(采集类型)外，还可以指定标签的值，也可事后增加或修改。

    -

    TDengine扩展标准SQL语法用于定义STable，使用关键词tags指定标签信息。语法如下：

    -
    CREATE TABLE <stable_name> (<field_name> TIMESTAMP, field_name1 field_type,…)   TAGS(tag_name tag_type, …)
    -

    其中tag_name是标签名，tag_type是标签的数据类型。标签可以使用时间戳之外的其他TDengine支持的数据类型，标签的个数最多为6个，名字不能与系统关键词相同，也不能与其他列名相同。如：

    -
    create table thermometer (ts timestamp, degree float) 
-tags (location binary(20), type int)
    -

    上述SQL创建了一个名为thermometer的STable，带有标签location和标签type。

    -

    为某个采集点创建表时，可以指定其所属的STable以及标签的值，语法如下：

    -
    CREATE TABLE <tb_name> USING <stb_name> TAGS (tag_value1,...)
    -

    沿用上面温度计的例子，使用超级表thermometer建立单个温度计数据表的语句如下：

    -
    create table t1 using thermometer tags (‘beijing', 10)
    -

    上述SQL以thermometer为模板，创建了名为t1的表，这张表的Schema就是thermometer的Schema，但标签location值为'beijing'，标签type值为10。

    -

    用户可以使用一个STable创建数量无上限的具有不同标签的表，从这个意义上理解，STable就是若干具有相同数据模型，不同标签的表的集合。与普通表一样，用户可以创建、删除、查看超级表STable，大部分适用于普通表的查询操作都可运用到STable上，包括各种聚合和投影选择函数。除此之外，可以设置标签的过滤条件，仅对STbale中部分表进行聚合查询，大大简化应用的开发。

    -

    TDengine对表的主键（时间戳）建立索引，暂时不提供针对数据模型中其他采集量（比如温度、压力值）的索引。每个数据采集点会采集若干数据记录，但每个采集点的标签仅仅是一条记录，因此数据标签在存储上没有冗余，且整体数据规模有限。TDengine将标签数据与采集的动态数据完全分离存储，而且针对STable的标签建立了高性能内存索引结构，为标签提供全方位的快速操作支持。用户可按照需求对其进行增删改查（Create，Retrieve，Update，Delete，CRUD）操作。

    -

    STable从属于库，一个STable只属于一个库，但一个库可以有一到多个STable, 一个STable可有多个子表。

    -

    超级表管理

    -
      -

    • 创建超级表

      -
      CREATE TABLE <stable_name> (<field_name> TIMESTAMP, field_name1 field_type,…) TAGS(tag_name tag_type, …)
      -

      与创建表的SQL语法相似。但需指定TAGS字段的名称和类型。

      -

      说明：

      -
        -
      1. TAGS列总长度不能超过512 bytes；
        2. -
      2. TAGS列的数据类型不能是timestamp类型；
        3. -
      3. TAGS列名不能与其他列名相同;
        4. -
      4. TAGS列名不能为预留关键字.
      • -

    • 显示已创建的超级表

      -
      show stables;
      -

      查看数据库内全部STable，及其相关信息，包括STable的名称、创建时间、列数量、标签（TAG）数量、通过该STable建表的数量。

      • -

    • 删除超级表

      -
      DROP TABLE <stable_name>
      -

      Note: 删除STable时，所有通过该STable创建的表都将被删除。

      • -

    • 查看属于某STable并满足查询条件的表

      -
      SELECT TBNAME,[TAG_NAME,…] FROM <stable_name> WHERE <tag_name> <[=|=<|>=|<>] values..> ([AND|OR] …)
      -

      查看属于某STable并满足查询条件的表。说明：TBNAME为关键词，显示通过STable建立的子表表名，查询过程中可以使用针对标签的条件。

      -
      SELECT COUNT(TBNAME) FROM <stable_name> WHERE <tag_name> <[=|=<|>=|<>] values..> ([AND|OR] …)
      -

      统计属于某个STable并满足查询条件的子表的数量

      • -
    -

    写数据时自动建子表

    -

    在某些特殊场景中，用户在写数据时并不确定某个设备的表是否存在，此时可使用自动建表语法来实现写入数据时用超级表定义的表结构自动创建不存在的子表，若该表已存在则不会建立新表。注意：自动建表语句只能自动建立子表而不能建立超级表，这就要求超级表已经被事先定义好。自动建表语法跟insert/import语法非常相似，唯一区别是语句中增加了超级表和标签信息。具体语法如下：

    -
    INSERT INTO <tb_name> USING <stb_name> TAGS (<tag1_value>, ...) VALUES (field_value, ...) (field_value, ...) ...;
    -

    向表tb_name中插入一条或多条记录，如果tb_name这张表不存在，则会用超级表stb_name定义的表结构以及用户指定的标签值(即tag1_value…)来创建名为tb_name新表，并将用户指定的值写入表中。如果tb_name已经存在，则建表过程会被忽略，系统也不会检查tb_name的标签是否与用户指定的标签值一致，也即不会更新已存在表的标签。

    -
    INSERT INTO <tb1_name> USING <stb1_name> TAGS (<tag1_value1>, ...) VALUES (<field1_value1>, ...) (<field1_value2>, ...) ... <tb_name2> USING <stb_name2> TAGS(<tag1_value2>, ...) VALUES (<field1_value1>, ...) ...;
    -

    向多张表tb1_name，tb2_name等插入一条或多条记录，并分别指定各自的超级表进行自动建表。

    -

    STable中TAG管理

    -

    除了更新标签的值的操作是针对子表进行，其他所有的标签操作（添加标签、删除标签等）均只能作用于STable，不能对单个子表操作。对STable添加标签以后，依托于该STable建立的所有表将自动增加了一个标签，对于数值型的标签，新增加的标签的默认值是0.

    -
      -

    • 添加新的标签

      -
      ALTER TABLE <stable_name> ADD TAG <new_tag_name> <TYPE>
      -

      为STable增加一个新的标签，并指定新标签的类型。标签总数不能超过6个。

      • -

    • 删除标签

      -
      ALTER TABLE <stable_name> DROP TAG <tag_name>
      -

      删除超级表的一个标签，从超级表删除某个标签后，该超级表下的所有子表也会自动删除该标签。

      -

      说明：第一列标签不能删除，至少需要为STable保留一个标签。

      • -

    • 修改标签名

      -
      ALTER TABLE <stable_name> CHANGE TAG <old_tag_name> <new_tag_name>
      -

      修改超级表的标签名，从超级表修改某个标签名后，该超级表下的所有子表也会自动更新该标签名。

      • -

    • 修改子表的标签值

      -
      ALTER TABLE <table_name> SET TAG <tag_name>=<new_tag_value>
      • -
    -

    STable多表聚合

    -

    针对所有的通过STable创建的子表进行多表聚合查询，支持按照全部的TAG值进行条件过滤，并可将结果按照TAGS中的值进行聚合，暂不支持针对binary类型的模糊匹配过滤。语法如下：

    -
    SELECT function<field_name>,… 
- FROM <stable_name> 
- WHERE <tag_name> <[=|<=|>=|<>] values..> ([AND|OR] …)
- INTERVAL (<time range>)
- GROUP BY <tag_name>, <tag_name>…
- ORDER BY <tag_name> <asc|desc>
- SLIMIT <group_limit>
- SOFFSET <group_offset>
- LIMIT <record_limit>
- OFFSET <record_offset>
    -

    说明：

    -

    超级表聚合查询，TDengine目前支持以下聚合\选择函数：sum、count、avg、first、last、min、max、top、bottom，以及针对全部或部分列的投影操作，使用方式与单表查询的计算过程相同。暂不支持其他类型的聚合计算和四则运算。当前所有的函数及计算过程均不支持嵌套的方式进行执行。

    -

    不使用GROUP BY的查询将会对超级表下所有满足筛选条件的表按时间进行聚合，结果输出默认是按照时间戳单调递增输出，用户可以使用ORDER BY _c0 ASC|DESC选择查询结果时间戳的升降排序；使用GROUP BY 的聚合查询会按照tags进行分组，并对每个组内的数据分别进行聚合，输出结果为各个组的聚合结果，组间的排序可以由ORDER BY 语句指定，每个分组内部，时间序列是单调递增的。

    -

    使用SLIMIT/SOFFSET语句指定组间分页，即指定结果集中输出的最大组数以及对组起始的位置。使用LIMIT/OFFSET语句指定组内分页，即指定结果集中每个组内最多输出多少条记录以及记录起始的位置。

    -

    STable使用示例

    -

    以温度传感器采集时序数据作为例，示范STable的使用。 在这个例子中，对每个温度计都会建立一张表，表名为温度计的ID，温度计读数的时刻记为ts，采集的值记为degree。通过tags给每个采集器打上不同的标签，其中记录温度计的地区和类型，以方便我们后面的查询。所有温度计的采集量都一样，因此我们用STable来定义表结构。

    -

    定义STable表结构并使用它创建子表

    -

    创建STable语句如下：

    -
    CREATE TABLE thermometer (ts timestamp, degree double) 
-TAGS(location binary(20), type int)
    -

    假设有北京，天津和上海三个地区的采集器共4个，温度采集器有3种类型，我们就可以对每个采集器建表如下：

    -
    CREATE TABLE therm1 USING thermometer TAGS ('beijing', 1);
-CREATE TABLE therm2 USING thermometer TAGS ('beijing', 2);
-CREATE TABLE therm3 USING thermometer TAGS ('tianjin', 1);
-CREATE TABLE therm4 USING thermometer TAGS ('shanghai', 3);
    -

    其中therm1，therm2，therm3，therm4是超级表thermometer四个具体的子表，也即普通的Table。以therm1为例，它表示采集器therm1的数据，表结构完全由thermometer定义，标签location=”beijing”, type=1表示therm1的地区是北京，类型是第1类的温度计。

    -

    写入数据

    -

    注意，写入数据时不能直接对STable操作，而是要对每张子表进行操作。我们分别向四张表therm1，therm2， therm3， therm4写入一条数据，写入语句如下：

    -
    INSERT INTO therm1 VALUES ('2018-01-01 00:00:00.000', 20);
-INSERT INTO therm2 VALUES ('2018-01-01 00:00:00.000', 21);
-INSERT INTO therm3 VALUES ('2018-01-01 00:00:00.000', 24);
-INSERT INTO therm4 VALUES ('2018-01-01 00:00:00.000', 23);
    -

    按标签聚合查询

    -

    查询位于北京(beijing)和天津(tianjing)两个地区的温度传感器采样值的数量count(*)、平均温度avg(degree)、最高温度max(degree)、最低温度min(degree)，并将结果按所处地域(location)和传感器类型(type)进行聚合。

    -
    SELECT COUNT(*), AVG(degree), MAX(degree), MIN(degree)
-FROM thermometer
-WHERE location='beijing' or location='tianjin'
-GROUP BY location, type
    -

    按时间周期聚合查询

    -

    查询仅位于北京以外地区的温度传感器最近24小时(24h)采样值的数量count(*)、平均温度avg(degree)、最高温度max(degree)和最低温度min(degree)，将采集结果按照10分钟为周期进行聚合，并将结果按所处地域(location)和传感器类型(type)再次进行聚合。

    -
    SELECT COUNT(*), AVG(degree), MAX(degree), MIN(degree)
-FROM thermometer
-WHERE location<>'beijing' and ts>=now-1d
-INTERVAL(10M)
-GROUP BY location, type
    回去
    diff --git a/documentation/tdenginedocs-cn/taos-sql/index.html b/documentation/tdenginedocs-cn/taos-sql/index.html deleted file mode 100644 index 207bfe03fd41fb91322c34b754e07fd77711881e..0000000000000000000000000000000000000000 --- a/documentation/tdenginedocs-cn/taos-sql/index.html +++ /dev/null @@ -1,388 +0,0 @@ -文档 | 涛思数据
    回去

    TAOS SQL

    -

    TDengine提供类似SQL语法，用户可以在TDengine Shell中使用SQL语句操纵数据库，也可以通过C/C++, Java(JDBC), Python, Go等各种程序来执行SQL语句。

    -

    本章节SQL语法遵循如下约定：

    -
      -
    • < > 里的内容是用户需要输入的，但不要输入<>本身
      • -
    • [ ]表示内容为可选项，但不能输入[]本身
      • -
    • | 表示多选一，选择其中一个即可，但不能输入|本身
      • -
    • … 表示前面的项可重复多个
      • -
    -

    支持的数据类型

    -

    使用TDengine，最重要的是时间戳。创建并插入记录、查询历史记录的时候，均需要指定时间戳。时间戳有如下规则：

    -
      -
    • 时间格式为YYYY-MM-DD HH:mm:ss.MS, 默认时间分辨率为毫秒。比如：2017-08-12 18:25:58.128
      • -
    • 内部函数now是服务器的当前时间
      • -
    • 插入记录时，如果时间戳为0，插入数据时使用服务器当前时间
      • -
    • Epoch Time: 时间戳也可以是一个长整数，表示从1970-01-01 08:00:00.000开始的毫秒数
      • -
    • 时间可以加减，比如 now-2h，表明查询时刻向前推2个小时(最近2小时)。数字后面的时间单位：a(毫秒), s(秒), m(分), h(小时), d(天)，w(周), n(月), y(年)。比如select * from t1 where ts > now-2w and ts <= now-1w, 表示查询两周前整整一周的数据
      • -
    -

    TDengine缺省的时间戳是毫秒精度，但通过修改配置参数enableMicrosecond就可支持微秒。

    -

    在TDengine中，普通表的数据模型中可使用以下10种数据类型。

    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    类型Bytes说明
    1TIMESTAMP8时间戳。最小精度毫秒。从格林威治时间1970-01-01 08:00:00.000开始，计时不能早于该时间。
    2INT4整型，范围 [-2^31+1, 2^31-1], -2^31被用作Null值
    3BIGINT8长整型，范围 [-2^59, 2^59]
    4FLOAT4浮点型，有效位数6-7，范围 [-3.4E38, 3.4E38]
    5DOUBLE8双精度浮点型，有效位数15-16，范围 [-1.7E308, 1.7E308]
    6BINARY自定义用于记录字符串，最长不能超过504 bytes。binary仅支持字符串输入，字符串两端使用单引号引用，否则英文全部自动转化为小写。使用时须指定大小，如binary(20)定义了最长为20个字符的字符串，每个字符占1byte的存储空间。如果用户字符串超出20字节，将被自动截断。对于字符串内的单引号，可以用转义字符反斜线加单引号来表示， 即 \’。
    7SMALLINT2短整型， 范围 [-32767, 32767]
    8TINYINT1单字节整型，范围 [-127, 127]
    9BOOL1布尔型，{true, false}
    10NCHAR自定义用于记录非ASCII字符串，如中文字符。每个nchar字符占用4bytes的存储空间。字符串两端使用单引号引用，字符串内的单引号需用转义字符 \’。nchar使用时须指定字符串大小，类型为nchar(10)的列表示此列的字符串最多存储10个nchar字符，会固定占用40bytes的空间。如用户字符串长度超出声明长度，则将被自动截断。
    -

    Tips: TDengine对SQL语句中的英文字符不区分大小写，自动转化为小写执行。因此用户大小写敏感的字符串及密码，需要使用单引号将字符串引起来。

    -

    数据库管理

    -
      -

    • 创建数据库

      -
      CREATE DATABASE [IF NOT EXISTS] db_name [KEEP keep]
      -

      创建数据库。KEEP是该数据库的数据保留多长天数，缺省是3650天(10年)，数据库会自动删除超过时限的数据。数据库还有更多与存储相关的配置参数，请参见系统管理。

      • -

    • 使用数据库

      -
      USE db_name
      -

      使用/切换数据库

      • -

    • 删除数据库

      -
      DROP DATABASE [IF EXISTS] db_name
      -

      删除数据库。所包含的全部数据表将被删除，谨慎使用

      • -

    • 显示系统所有数据库

      -
      SHOW DATABASES
      • -
    -

    表管理

    -
      -

    • 创建数据表

      -
      CREATE TABLE [IF NOT EXISTS] tb_name (timestamp_field_name TIMESTAMP, field1_name data_type1 [, field2_name data_type2 ...])
      -

      说明：1）表的第一个字段必须是TIMESTAMP，并且系统自动将其设为主键；2）表的每行长度不能超过4096字节；3）使用数据类型binary或nchar，需指定其最长的字节数，如binary(20)，表示20字节。

      • -

    • 删除数据表

      -
      DROP TABLE [IF EXISTS] tb_name
      • -

    • 显示当前数据库下的所有数据表信息

      -
      SHOW TABLES [LIKE tb_name_wildcar]
      -

      显示当前数据库下的所有数据表信息。说明：可在like中使用通配符进行名称的匹配。 通配符匹配：1）’%’ (百分号)匹配0到任意个字符；2）’_’下划线匹配一个字符。

      • -

    • 获取表的结构信息

      -
      DESCRIBE tb_name
      • -

    • 表增加列

      -
      ALTER TABLE tb_name ADD COLUMN field_name data_type
      • -

    • 表删除列

      -
      ALTER TABLE tb_name DROP COLUMN field_name
      -

      如果表是通过超级表创建，更改表结构的操作只能对超级表进行。同时针对超级表的结构更改对所有通过该结构创建的表生效。对于不是通过超级表创建的表，可以直接修改表结构

      -

      Tips：SQL语句中操作的当前数据库（通过use db_name的方式指定）中的表不需要指定表所属数据库。如果要操作非当前数据库中的表，需要采用“库名”.“表名”的方式。例如：demo.tb1，是指数据库demo中的表tb1。

      • -
    -

    数据写入

    -
      -

    • 插入一条记录

      -
      INSERT INTO tb_name VALUES (field_value, ...);
      -

      向表tb_name中插入一条记录

      • -

    • 插入一条记录，数据对应到指定的列

      -
      INSERT INTO tb_name (field1_name, ...) VALUES(field1_value, ...)
      -

      向表tb_name中插入一条记录，数据对应到指定的列。SQL语句中没有出现的列，数据库将自动填充为NULL。主键（时间戳）不能为NULL。

      • -

    • 插入多条记录

      -
      INSERT INTO tb_name VALUES (field1_value1, ...) (field1_value2, ...)...;
      -

      向表tb_name中插入多条记录

      • -

    • 按指定的列插入多条记录

      -
      INSERT INTO tb_name (field1_name, ...) VALUES(field1_value1, ...) (field1_value2, ...)
      -

      向表tb_name中按指定的列插入多条记录

      • -

    • 向多个表插入多条记录

      -
      INSERT INTO tb1_name VALUES (field1_value1, ...)(field1_value2, ...)... 
-            tb2_name VALUES (field1_value1, ...)(field1_value2, ...)...;
      -

      同时向表tb1_name和tb2_name中分别插入多条记录

      • -

    • 同时向多个表按列插入多条记录

      -
      INSERT INTO tb1_name (tb1_field1_name, ...) VALUES (field1_value1, ...) (field1_value1, ...)
-            tb2_name (tb2_field1_name, ...) VALUES(field1_value1, ...) (field1_value2, ...)
      -

      同时向表tb1_name和tb2_name中按列分别插入多条记录

      • -
    -

    注意：对同一张表，插入的新记录的时间戳必须递增，否则会跳过插入该条记录。如果时间戳为0，系统将自动使用服务器当前时间作为该记录的时间戳。

    -

    IMPORT：如果需要将时间戳小于最后一条记录时间的记录写入到数据库中，可使用IMPORT替代INSERT命令，IMPORT的语法与INSERT完全一样。如果同时IMPORT多条记录，需要保证一批记录是按时间戳排序好的。

    -

    数据查询

    -

    查询语法是：

    -
    SELECT {* | expr_list} FROM tb_name
-    [WHERE where_condition]
-    [ORDER BY _c0 { DESC | ASC }]
-    [LIMIT limit [, OFFSET offset]]
-    [>> export_file]
-
-SELECT function_list FROM tb_name
-    [WHERE where_condition]
-    [LIMIT limit [, OFFSET offset]]
-    [>> export_file]
    -
      -
    • 可以使用* 返回所有列，或指定列名。可以对数字列进行四则运算，可以给输出的列取列名
      • -
    • where语句可以使用各种逻辑判断来过滤数字值，或使用通配符来过滤字符串
      • -
    • 输出结果缺省按首列时间戳升序排序，但可以指定按降序排序(_c0指首列时间戳)。使用ORDER BY对其他字段进行排序为非法操作。
      • -
    • 参数LIMIT控制输出条数，OFFSET指定从第几条开始输出。LIMIT/OFFSET对结果集的执行顺序在ORDER BY之后。
      • -
    • 通过”>>"输出结果可以导出到指定文件
      • -
    -

    支持的条件过滤操作

    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    OperationNoteApplicable Data Types
    >larger thantimestamp and all numeric types
    <smaller thantimestamp and all numeric types
    >=larger than or equal totimestamp and all numeric types
    <=smaller than or equal totimestamp and all numeric types
    =equal toall types
    <>not equal toall types
    %match with any char sequencesbinary nchar
    _match with a single charbinary nchar
    -
      -
    1. 同时进行多个字段的范围过滤需要使用关键词AND进行连接不同的查询条件，暂不支持OR连接的查询条件。
      2. -
    2. 针对某一字段的过滤只支持单一区间的过滤条件。例如：value>20 and value<30是合法的过滤条件, 而Value<20 AND value<>5是非法的过滤条件。
      3. -
    -

    Some Examples

    -
      -

    • 对于下面的例子，表tb1用以下语句创建

      -
      CREATE TABLE tb1 (ts timestamp, col1 int, col2 float, col3 binary(50))
      • -

    • 查询tb1刚过去的一个小时的所有记录

      -
      SELECT * FROM tb1 WHERE ts >= NOW - 1h
      • -

    • 查询表tb1从2018-06-01 08:00:00.000 到2018-06-02 08:00:00.000时间范围，并且clo3的字符串是'nny'结尾的记录，结果按照时间戳降序

      -
      SELECT * FROM tb1 WHERE ts > '2018-06-01 08:00:00.000' AND ts <= '2018-06-02 08:00:00.000' AND col3 LIKE '%nny' ORDER BY ts DESC
      • -

    • 查询col1与col2的和，并取名complex, 时间大于2018-06-01 08:00:00.000, col2大于1.2，结果输出仅仅10条记录，从第5条开始

      -
      SELECT (col1 + col2) AS 'complex' FROM tb1 WHERE ts > '2018-06-01 08:00:00.000' and col2 > 1.2 LIMIT 10 OFFSET 5
      • -

    • 查询过去10分钟的记录，col2的值大于3.14，并且将结果输出到文件 /home/testoutpu.csv.

      -
      SELECT COUNT(*) FROM tb1 WHERE ts >= NOW - 10m AND col2 > 3.14 >> /home/testoutpu.csv
      • -
    -

    SQL函数

    -

    聚合函数

    -

    TDengine支持针对数据的聚合查询。提供支持的聚合和提取函数如下表：

    -
      -

    • COUNT

      -
      SELECT COUNT([*|field_name]) FROM tb_name [WHERE clause]
      -

      功能说明：统计表/超级表中记录行数或某列的非空值个数。
      -返回结果数据类型：长整型INT64。
      -应用字段：应用全部字段。
      -适用于：表、超级表。
      -说明：1）可以使用星号来替代具体的字段，使用星号()返回全部记录数量。2）针对同一表的（不包含NULL值）字段查询结果均相同。3）如果统计对象是具体的列，则返回该列中非NULL值的记录数量。

      • -

    • AVG

      -
      SELECT AVG(field_name) FROM tb_name [WHERE clause]
      -

      功能说明：统计表/超级表中某列的平均值。
      -返回结果数据类型：双精度浮点数Double。
      -应用字段：不能应用在timestamp、binary、nchar、bool字段。
      -适用于：表、超级表。

      • -

    • WAVG

      -
      SELECT WAVG(field_name) FROM tb_name WHERE clause
      -

      功能说明：统计表/超级表中某列在一段时间内的时间加权平均。
      -返回结果数据类型：双精度浮点数Double。
      -应用字段：不能应用在timestamp、binary、nchar、bool类型字段。
      -适用于：表、超级表。

      • -

    • SUM

      -
      SELECT SUM(field_name) FROM tb_name [WHERE clause]
      -

      功能说明：统计表/超级表中某列的和。
      -返回结果数据类型：双精度浮点数Double和长整型INT64。
      -应用字段：不能应用在timestamp、binary、nchar、bool类型字段。
      -适用于：表、超级表。

      • -

    • STDDEV

      -
      SELECT STDDEV(field_name) FROM tb_name [WHERE clause]
      -

      功能说明：统计表中某列的均方差。
      -返回结果数据类型：双精度浮点数Double。
      -应用字段：不能应用在timestamp、binary、nchar、bool类型字段。
      -适用于：表。

      • -

    • LEASTSQUARES

      -
      SELECT LEASTSQUARES(field_name) FROM tb_name [WHERE clause]
      -

      功能说明：统计表中某列的值是主键（时间戳）的拟合直线方程。
      -返回结果数据类型：字符串表达式（斜率, 截距）。
      -应用字段：不能应用在timestamp、binary、nchar、bool类型字段。
      -说明：自变量是时间戳，因变量是该列的值。
      -适用于：表。

      • -
    -

    选择函数

    -
      -

    • MIN

      -
      SELECT MIN(field_name) FROM {tb_name | stb_name} [WHERE clause]
      -

      功能说明：统计表/超级表中某列的值最小值。
      -返回结果数据类型：同应用的字段。
      -应用字段：不能应用在timestamp、binary、nchar、bool类型字段。

      • -

    • MAX

      -
      SELECT MAX(field_name) FROM { tb_name | stb_name } [WHERE clause]
      -

      功能说明：统计表/超级表中某列的值最大值。
      -返回结果数据类型：同应用的字段。
      -应用字段：不能应用在timestamp、binary、nchar、bool类型字段。

      • -

    • FIRST

      -
      SELECT FIRST(field_name) FROM { tb_name | stb_name } [WHERE clause]
      -

      功能说明：统计表/超级表中某列的值最先写入的非NULL值。
      -返回结果数据类型：同应用的字段。
      -应用字段：所有字段。
      -说明：1）如果要返回各个列的首个（时间戳最小）非NULL值，可以使用FIRST(*)；2) 如果结果集中的某列全部为NULL值，则该列的返回结果也是NULL；3) 如果结果集中所有列全部为NULL值，则不返回结果。

      • -

    • LAST

      -
      SELECT LAST(field_name) FROM { tb_name | stb_name } [WHERE clause]
      -

      功能说明：统计表/超级表中某列的值最后写入的非NULL值。
      -返回结果数据类型：同应用的字段。
      -应用字段：所有字段。
      -说明：1）如果要返回各个列的最后（时间戳最大）一个非NULL值，可以使用LAST(*)；2）如果结果集中的某列全部为NULL值，则该列的返回结果也是NULL；如果结果集中所有列全部为NULL值，则不返回结果。

      • -

    • TOP

      -
      SELECT TOP(field_name, K) FROM { tb_name | stb_name } [WHERE clause]
      -

      功能说明： 统计表/超级表中某列的值最大k个非NULL值。若多于k个列值并列最大，则返回时间戳小的。
      -返回结果数据类型：同应用的字段。
      -应用字段：不能应用在timestamp、binary、nchar、bool类型字段。
      -说明：1）k值取值范围1≤k≤100；2）系统同时返回该记录关联的时间戳列。

      • -

    • BOTTOM

      -
      SELECT BOTTOM(field_name, K) FROM { tb_name | stb_name } [WHERE clause]
      -

      功能说明：统计表/超级表中某列的值最小k个非NULL值。若多于k个列值并列最小，则返回时间戳小的。
      -返回结果数据类型：同应用的字段。
      -应用字段：不能应用在timestamp、binary、nchar、bool类型字段。
      -说明：1）k值取值范围1≤k≤100；2）系统同时返回该记录关联的时间戳列。

      • -

    • PERCENTILE

      -
      SELECT PERCENTILE(field_name, P) FROM { tb_name | stb_name } [WHERE clause]
      -

      功能说明：统计表中某列的值百分比分位数。
      -返回结果数据类型： 双精度浮点数Double。
      -应用字段：不能应用在timestamp、binary、nchar、bool类型字段。
      -说明：k值取值范围0≤k≤100，为0的时候等同于MIN，为100的时候等同于MAX。

      • -

    • LAST_ROW

      -
      SELECT LAST_ROW(field_name) FROM { tb_name | stb_name }
      -

      功能说明：返回表（超级表）的最后一条记录。
      -返回结果数据类型：同应用的字段。
      -应用字段：所有字段。
      -说明：与last函数不同，last_row不支持时间范围限制，强制返回最后一条记录。

      • -
    -

    计算函数

    -
      -

    • DIFF

      -
      SELECT DIFF(field_name) FROM tb_name [WHERE clause]
      -

      功能说明：统计表中某列的值与前一行对应值的差。
      -返回结果数据类型： 同应用字段。
      -应用字段：不能应用在timestamp、binary、nchar、bool类型字段。
      -说明：输出结果行数是范围内总行数减一，第一行没有结果输出。

      • -

    • SPREAD

      -
      SELECT SPREAD(field_name) FROM { tb_name | stb_name } [WHERE clause]
      -

      功能说明：统计表/超级表中某列的最大值和最小值之差。
      -返回结果数据类型： 双精度浮点数。
      -应用字段：不能应用在binary、nchar、bool类型字段。
      -说明：可用于TIMESTAMP字段，此时表示记录的时间覆盖范围。

      • -

    • 四则运算

      -
      SELECT field_name [+|-|*|/|%][Value|field_name] FROM { tb_name | stb_name }  [WHERE clause]
      -

      功能说明：统计表/超级表中某列或多列间的值加、减、乘、除、取余计算结果。
      -返回结果数据类型：双精度浮点数。
      -应用字段：不能应用在timestamp、binary、nchar、bool类型字段。
      -说明：1）支持两列或多列之间进行计算，可使用括号控制计算优先级;2）NULL字段不参与计算，如果参与计算的某行中包含NULL，该行的计算结果为NULL。

      • -
    -

    时间维度聚合

    -

    TDengine支持按时间段进行聚合，可以将表中数据按照时间段进行切割后聚合生成结果，比如温度传感器每秒采集一次数据，但需查询每隔10分钟的温度平均值。这个聚合适合于降维(down sample)操作, 语法如下：

    -
    SELECT function_list FROM tb_name 
-  [WHERE where_condition]
-  INTERVAL (interval)
-  [FILL ({NONE | VALUE | PREV | NULL | LINEAR})]
-
-SELECT function_list FROM stb_name 
-  [WHERE where_condition]
-  [FILL ({ VALUE | PREV | NULL | LINEAR})]
-  INTERVAL (interval)
-  [GROUP BY tags]
    -
      -
    • 聚合时间段的长度由关键词INTERVAL指定，最短时间间隔10毫秒（10a）。聚合查询中，能够同时执行的聚合和选择函数仅限于单个输出的函数：count、avg、sum 、stddev、leastsquares、percentile、min、max、first、last，不能使用具有多行输出结果的函数（例如：top、bottom、diff以及四则运算）。
      • -
    • WHERE语句可以指定查询的起止时间和其他过滤条件
      • -
    • FILL语句指定某一时间区间数据缺失的情况下的填充模式。填充模式包括以下几种：
      • -
    -
      -

    1. 不进行填充：NONE(默认填充模式)。

      2. -

    2. VALUE填充：固定值填充，此时需要指定填充的数值。例如：fill(value, 1.23)。

      3. -

    3. NULL填充：使用NULL填充数据。例如：fill(null)。

      4. -

    4. PREV填充：使用前一个非NULL值填充数据。例如：fill(prev)。

      5. -
    -

    说明：

    -
      -
    1. 使用FILL语句的时候可能生成大量的填充输出，务必指定查询的时间区间。针对每次查询，系统可返回不超过1千万条具有插值的结果。
      2. -
    2. 在时间维度聚合中，返回的结果中时间序列严格单调递增。
      3. -
    3. 如果查询对象是超级表，则聚合函数会作用于该超级表下满足值过滤条件的所有表的数据。如果查询中没有使用group by语句，则返回的结果按照时间序列严格单调递增；如果查询中使用了group by语句分组，则返回结果中每个group内不按照时间序列严格单调递增。
      4. -
    -

    示例：温度数据表的建表语句如下：

    -
    create table sensor(ts timestamp, degree double, pm25 smallint)
    -

    针对传感器采集的数据，以10分钟为一个阶段，计算过去24小时的温度数据的平均值、最大值、温度的中位数、以及随着时间变化的温度走势拟合直线。如果没有计算值，用前一个非NULL值填充。

    -
    SELECT AVG(degree),MAX(degree),LEASTSQUARES(degree), PERCENTILE(degree, 50) FROM sensor
-  WHERE TS>=NOW-1d
-  INTERVAL(10m)
-  FILL(PREV);
    回去
    \ No newline at end of file diff --git a/documentation/tdenginedocs-en/administrator/index.html b/documentation/tdenginedocs-en/administrator/index.html deleted file mode 100644 index 1615fbfb6a7aaedf208b2b0f959f08f9bb26cac8..0000000000000000000000000000000000000000 --- a/documentation/tdenginedocs-en/administrator/index.html +++ /dev/null @@ -1,137 +0,0 @@ -Documentation | Taos Data
    Back

    Administrator

    -

    Directory and Files

    -

    After TDengine is installed, by default, the following directories will be created:

    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Directory/FileDescription
    /etc/taos/taos.cfgTDengine configuration file
    /usr/local/taos/driverTDengine dynamic link library
    /var/lib/taosTDengine default data directory
    /var/log/taosTDengine default log directory
    /usr/local/taos/bin.TDengine executables
    -

    Executables

    -

    All TDengine executables are located at /usr/local/taos/bin , including:

    -
      -
    • taosd：TDengine server
      • -
    • taos： TDengine Shell, the command line interface.
      • -
    • taosdump：TDengine data export tool
      • -
    • rmtaos： a script to uninstall TDengine
      • -
    -

    You can change the data directory and log directory setting through the system configuration file

    -

    Configuration on Server

    -

    taosd is running on the server side, you can change the system configuration file taos.cfg to customize its behavior. By default, taos.cfg is located at /etc/taos, but you can specify the path to configuration file via the command line parameter -c. For example: taosd -c /home/user means the configuration file will be read from directory /home/user.

    -

    This section lists only the most important configuration parameters. Please check taos.cfg to find all the configurable parameters. Note: to make your new configurations work, you have to restart taosd after you change taos.cfg.

    -
      -
    • mgmtShellPort: TCP and UDP port between client and TDengine mgmt (default: 6030). Note: 5 successive UDP ports (6030-6034) starting from this number will be used.
      • -
    • vnodeShellPort: TCP and UDP port between client and TDengine vnode (default: 6035). Note: 5 successive UDP ports (6035-6039) starting from this number will be used.
      • -
    • httpPort: TCP port for RESTful service (default: 6020)
      • -
    • dataDir: data directory, default is /var/lib/taos
      • -
    • maxUsers: maximum number of users allowed
      • -
    • maxDbs: maximum number of databases allowed
      • -
    • maxTables: maximum number of tables allowed
      • -
    • enableMonitor: turn on/off system monitoring, 0: off, 1: on
      • -
    • logDir: log directory, default is /var/log/taos
      • -
    • numOfLogLines: maximum number of lines in the log file
      • -
    • debugFlag: log level, 131: only error and warnings, 135: all
      • -
    -

    In different scenarios, data characteristics are different. For example, the retention policy, data sampling period, record size, the number of devices, and data compression may be different. To gain the best performance, you can change the following configurations related to storage:

    -
      -
    • days: number of days to cover for a data file
      • -
    • keep: number of days to keep the data
      • -
    • rows: number of rows of records in a block in data file.
      • -
    • comp: compression algorithm, 0: off, 1: standard; 2: maximum compression
      • -
    • ctime: period (seconds) to flush data to disk
      • -
    • clog: flag to turn on/off Write Ahead Log, 0: off, 1: on
      • -
    • tables: maximum number of tables allowed in a vnode
      • -
    • cache: cache block size (bytes)
      • -
    • tblocks: maximum number of cache blocks for a table
      • -
    • abloks: average number of cache blocks for a table
      • -
    • precision: timestamp precision, us: microsecond ms: millisecond, default is ms
      • -
    -

    For an application, there may be multiple data scenarios. The best design is to put all data with the same characteristics into one database. One application may have multiple databases, and every database has its own configuration to maximize the system performance. You can specify the above configurations related to storage when you create a database. For example:

    -
    CREATE DATABASE demo DAYS 10 CACHE 16000 ROWS 2000
    -

    The above SQL statement will create a database demo, with 10 days for each data file, 16000 bytes for a cache block, and 2000 rows in a file block.

    -

    The configuration provided when creating a database will overwrite the configuration in taos.cfg.

    -

    Configuration on Client

    -

    taos is the TDengine shell and is a client that connects to taosd. TDengine uses the same configuration file taos.cfg for the client, with default location at /etc/taos. You can change it by specifying command line parameter -c when you run taos. For example, taos -c /home/user, it will read the configuration file taos.cfg from directory /home/user.

    -

    The parameters related to client configuration are listed below:

    -
      -
    • masterIP: IP address of TDengine server
      • -
    • charset: character set, default is the system . For data type nchar, TDengine uses unicode to store the data. Thus, the client needs to tell its character set.
      • -
    • locale: system language setting
      • -
    • defaultUser: default login user, default is root
      • -
    • defaultPass: default password, default is taosdata
      • -
    -

    For TCP/UDP port, and system debug/log configuration, it is the same as the server side.

    -

    For server IP, user name, password, you can always specify them in the command line when you run taos. If they are not specified, they will be read from the taos.cfg

    -

    User Management

    -

    System administrator (user root) can add, remove a user, or change the password from the TDengine shell. Commands are listed below:

    -

    Create a user, password shall be quoted with the single quote.

    -
    CREATE USER user_name PASS ‘password’
    -

    Remove a user

    -
    DROP USER user_name
    -

    Change the password for a user

    -
    ALTER USER user_name PASS ‘password’
    -

    List all users

    -
    SHOW USERS
    -

    Import Data

    -

    Inside the TDengine shell, you can import data into TDengine from either a script or CSV file

    -

    Import from Script

    -
    source <filename>
    -

    Inside the file, you can put all SQL statements there. Each SQL statement has a line. If a line starts with "#", it means comments, it will be skipped. The system will execute the SQL statements line by line automatically until the ends

    -

    Import from CVS

    -
    insert into tb1 file a.csv b.csv tb2 c.csv …
-import into tb1 file a.csv b.csv tb2 c.csv …
    -

    Each csv file contains records for only one table, and the data structure shall be the same as the defined schema for the table.

    -

    Export Data

    -

    You can export data either from TDengine shell or from tool taosdump.

    -

    Export from TDengine Shell

    -
    select * from <tb_name> >> a.csv
    -

    The above SQL statement will dump the query result set into a csv file.

    -

    Export Using taosdump

    -

    TDengine provides a data dumping tool taosdump. You can choose to dump a database, a table, all data or data only a time range, even only the metadata. For example:

    -
      -
    • Export one or more tables in a DB: taosdump [OPTION…] dbname tbname …
      • -
    • Export one or more DBs: taosdump [OPTION…] --databases dbname…
      • -
    • Export all DBs (excluding system DB): taosdump [OPTION…] --all-databases
      • -
    -

    run taosdump —help to get a full list of the options

    -

    Management of Connections, Streams, Queries

    -

    The system administrator can check, kill the ongoing connections, streams, or queries.

    -
    SHOW CONNECTIONS
    -

    It lists all connections, one column shows ip:port from the client.

    -
    KILL CONNECTION <connection-id>
    -

    It kills the connection, where connection-id is the ip:port showed by "SHOW CONNECTIONS". You can copy and paste it.

    -
    SHOW QUERIES
    -

    It shows the ongoing queries, one column ip:port:id shows the ip:port from the client, and id assigned by the system

    -
    KILL QUERY <query-id>
    -

    It kills the query, where query-id is the ip:port:id showed by "SHOW QUERIES". You can copy and paste it.

    -
    SHOW STREAMS
    -

    It shows the continuous queries, one column shows the ip:port:id, where ip:port is the connection from the client, and id assigned by the system.

    -
    KILL STREAM <stream-id>
    -

    It kills the continuous query, where stream-id is the ip:port:id showed by "SHOW STREAMS". You can copy and paste it.

    -

    System Monitor

    -

    TDengine runs a system monitor in the background. Once it is started, it will create a database sys automatically. System monitor collects the metric like CPU, memory, network, disk, number of requests periodically, and writes them into database sys. Also, TDengine will log all important actions, like login, logout, create database, drop database and so on, and write them into database sys.

    -

    You can check all the saved monitor information from database sys. By default, system monitor is turned on. But you can turn it off by changing the parameter in the configuration file.

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    Advanced Features

    -

    Continuous Query

    -

    Continuous Query is a query executed by TDengine periodically with a sliding window, it is a simplified stream computing driven by timers, not by events. Continuous query can be applied to a table or a STable, and the result set can be passed to the application directly via call back function, or written into a new table in TDengine. The query is always executed on a specified time window (window size is specified by parameter interval), and this window slides forward while time flows (the sliding period is specified by parameter sliding).

    -

    Continuous query is defined by TAOS SQL, there is nothing special. One of the best applications is downsampling. Once it is defined, at the end of each cycle, the system will execute the query, pass the result to the application or write it to a database.

    -

    If historical data pints are inserted into the stream, the query won't be re-executed, and the result set won't be updated. If the result set is passed to the application, the application needs to keep the status of continuous query, the server won't maintain it. If application re-starts, it needs to decide the time where the stream computing shall be started.

    -

    How to use continuous query

    -
      -

    • Pass result set to application

      -

      Application shall use API taos_stream (details in connector section) to start the stream computing. Inside the API, the SQL syntax is:

      -
      SELECT aggregation FROM [table_name | stable_name] 
-INTERVAL(window_size) SLIDING(period)
      -

      where the new keyword INTERVAL specifies the window size, and SLIDING specifies the sliding period. If parameter sliding is not specified, the sliding period will be the same as window size. The minimum window size is 10ms. The sliding period shall not be larger than the window size. If you set a value larger than the window size, the system will adjust it to window size automatically.

      -

      For example:

      -
      SELECT COUNT(*) FROM FOO_TABLE 
-INTERVAL(1M) SLIDING(30S)
      -

      The above SQL statement will count the number of records for the past 1-minute window every 30 seconds.

      • -

    • Save the result into a database

      -

      If you want to save the result set of stream computing into a new table, the SQL shall be:

      -
      CREATE TABLE table_name AS 
-SELECT aggregation from [table_name | stable_name]  
-INTERVAL(window_size) SLIDING(period)
      -

      Also, you can set the time range to execute the continuous query. If no range is specified, the continuous query will be executed forever. For example, the following continuous query will be executed from now and will stop in one hour.

      -
      CREATE TABLE QUERY_RES AS 
-SELECT COUNT(*) FROM FOO_TABLE 
-WHERE TS > NOW AND TS <= NOW + 1H 
-INTERVAL(1M) SLIDING(30S)
      • -
    -

    Manage the Continuous Query

    -

    Inside TDengine shell, you can use the command "show streams" to list the ongoing continuous queries, the command "kill stream" to kill a specific continuous query.

    -

    If you drop a table generated by the continuous query, the query will be removed too.

    -

    Publisher/Subscriber

    -

    Time series data is a sequence of data points over time. Inside a table, the data points are stored in order of timestamp. Also, there is a data retention policy, the data points will be removed once their lifetime is passed. From another view, a table in TDengine is just a standard message queue.

    -

    To reduce the development complexity and improve data consistency, TDengine provides the pub/sub functionality. To publish a message, you simply insert a record into a table. Compared with popular messaging tool Kafka, you subscribe to a table or a SQL query statement, instead of a topic. Once new data points arrive, TDengine will notify the application. The process is just like Kafka.

    -

    The detailed API will be introduced in the connectors section.

    -

    Caching

    -

    TDengine allocates a fixed-size buffer in memory, the newly arrived data will be written into the buffer first. Every device or table gets one or more memory blocks. For typical IoT scenarios, the hot data shall always be newly arrived data, they are more important for timely analysis. Based on this observation, TDengine manages the cache blocks in First-In-First-Out strategy. If no enough space in the buffer, the oldest data will be saved into hard disk first, then be overwritten by newly arrived data. TDengine also guarantees every device can keep at least one block of data in the buffer.

    -

    By this design, the application can retrieve the latest data from each device super-fast, since they are all available in memory. You can use last or last_row function to return the last data record. If the super table is used, it can be used to return the last data records of all or a subset of devices. For example, to retrieve the latest temperature from thermometers in located Beijing, execute the following SQL

    -
    select last(*) from thermometers where location=’beijing’
    -

    By this design, caching tool, like Redis, is not needed in the system. It will reduce the complexity of the system.

    -

    TDengine creates one or more virtual nodes(vnode) in each data node. Each vnode contains data for multiple tables and has its own buffer. The buffer of a vnode is fully separated from the buffer of another vnode, not shared. But the tables in a vnode share the same buffer.

    -

    System configuration parameter cacheBlockSize configures the cache block size in bytes, and another parameter cacheNumOfBlocks configures the number of cache blocks. The total memory for the buffer of a vnode is $cacheBlockSize \times cacheNumOfBlocks$. Another system parameter numOfBlocksPerMeter configures the maximum number of cache blocks a table can use. When you create a database, you can specify these parameters.

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    Back

    Connect with other tools

    -

    Telegraf

    -

    TDengine is easy to integrate with Telegraf, an open-source server agent for collecting and sending metrics and events, without more development.

    -

    Install Telegraf

    -

    At present, TDengine supports Telegraf newer than version 1.7.4. Users can go to the download link and choose the proper package to install on your system.

    -

    Configure Telegraf

    -

    Telegraf is configured by changing items in the configuration file /etc/telegraf/telegraf.conf.

    -

    In output plugins section，add [[outputs.http]] iterm：

    -
      -
    • url: http://ip:6020/telegraf/udb, in which ip is the IP address of any node in TDengine cluster. Port 6020 is the RESTful APT port used by TDengine. udb is the name of the database to save data, which needs to create beforehand.
      • -
    • method: "POST"
      • -
    • username: username to login TDengine
      • -
    • password: password to login TDengine
      • -
    • data_format: "json"
      • -
    • json_timestamp_units: "1ms"
      • -
    -

    In agent part：

    -
      -
    • hostname: used to distinguish different machines. Need to be unique.
      • -
    • metric_batch_size: 30，the maximum number of records allowed to write in Telegraf. The larger the value is, the less frequent requests are sent. For TDengine, the value should be less than 50.
      • -
    -

    Please refer to the Telegraf docs for more information.

    -

    Grafana

    -

    Grafana is an open-source system for time-series data display. It is easy to integrate TDengine and Grafana to build a monitor system. Data saved in TDengine can be fetched and shown on the Grafana dashboard.

    -

    Install Grafana

    -

    For now, TDengine only supports Grafana newer than version 5.2.4. Users can go to the Grafana download page for the proper package to download.

    -

    Configure Grafana

    -

    TDengine Grafana plugin is in the /usr/local/taos/connector/grafana directory. -Taking Centos 7.2 as an example, just copy TDengine directory to /var/lib/grafana/plugins directory and restart Grafana.

    -

    Use Grafana

    -

    Users can log in the Grafana server (username/password:admin/admin) through localhost:3000 to configure TDengine as the data source. As is shown in the picture below, TDengine as a data source option is shown in the box:

    -

    img

    -

    When choosing TDengine as the data source, the Host in HTTP configuration should be configured as the IP address of any node of a TDengine cluster. The port should be set as 6020. For example, when TDengine and Grafana are on the same machine, it should be configured as _http://localhost:6020.

    -

    Besides, users also should set the username and password used to log into TDengine. Then click Save&Test button to save.

    -

    img

    -

    Then, TDengine as a data source should show in the Grafana data source list.

    -

    img

    -

    Then, users can create Dashboards in Grafana using TDengine as the data source:

    -

    img

    -

    Click Add Query button to add a query and input the SQL command you want to run in the INPUT SQL text box. The SQL command should expect a two-row, multi-column result, such as SELECT count(*) FROM sys.cpu WHERE ts>=from and ts<​to interval(interval), in which, from, to and inteval are TDengine inner variables representing query time range and time interval.

    -

    ALIAS BY field is to set the query alias. Click GENERATE SQL to send the command to TDengine:

    -

    img

    -

    Please refer to the Grafana official document for more information about Grafana.

    -

    Matlab

    -

    Matlab can connect to and retrieve data from TDengine by TDengine JDBC Driver.

    -

    MatLab and TDengine JDBC adaptation

    -

    Several steps are required to adapt Matlab to TDengine. Taking adapting Matlab2017a on Windows10 as an example:

    -
      -
    1. Copy the file JDBCDriver-1.0.0-dist.jar in TDengine package to the directory ${matlab_root}\MATLAB\R2017a\java\jar\toolbox
      2. -
    2. Copy the file taos.lib in TDengine package to ${matlab root dir}\MATLAB\R2017a\lib\win64
      3. -
    3. Add the .jar package just copied to the Matlab classpath. Append the line below as the end of the file of ${matlab root dir}\MATLAB\R2017a\toolbox\local\classpath.txt
      4. -
    -

    ​ $matlabroot/java/jar/toolbox/JDBCDriver-1.0.0-dist.jar

    -
      -
    1. Create a file called javalibrarypath.txt in directory ${user_home}\AppData\Roaming\MathWorks\MATLAB\R2017a_, and add the _taos.dll path in the file. For example, if the file taos.dll is in the directory of C:\Windows\System32，then add the following line in file javalibrarypath.txt:
      2. -
    -

    ​ C:\Windows\System32

    -

    TDengine operations in Matlab

    -

    After correct configuration, open Matlab:

    -
      -

    • build a connection：

      -

      conn = database(‘db’, ‘root’, ‘taosdata’, ‘com.taosdata.jdbc.TSDBDriver’, ‘jdbc:TSDB://127.0.0.1:0/’)

      • -

    • Query：

      -

      sql0 = [‘select * from tb’]

      -

      data = select(conn, sql0);

      • -

    • Insert a record:

      -

      sql1 = [‘insert into tb values (now, 1)’]

      -

      exec(conn, sql1)

      • -
    -

    Please refer to the file examples\Matlab\TDengineDemo.m for more information.

    -

    R

    -

    Users can use R language to access the TDengine server with the JDBC interface. At first, install JDBC package in R:

    -
    install.packages('rJDBC', repos='http://cran.us.r-project.org')
    -

    Then use library function to load the package:

    -
    library('RJDBC')
    -

    Then load the TDengine JDBC driver:

    -
    drv<-JDBC("com.taosdata.jdbc.TSDBDriver","JDBCDriver-1.0.0-dist.jar", identifier.quote="\"")
    -

    If succeed, no error message will display. Then use the following command to try a database connection:

    -
    conn<-dbConnect(drv,"jdbc:TSDB://192.168.0.1:0/?user=root&password=taosdata","root","taosdata")
    -

    Please replace the IP address in the command above to the correct one. If no error message is shown, then the connection is established successfully. TDengine supports below functions in RJDBC package:

    -
      -
    • dbWriteTable(conn, "test", iris, overwrite=FALSE, append=TRUE): write the data in a data frame iris to the table test in the TDengine server. Parameter overwrite must be false. append must be TRUE and the schema of the data frame iris should be the same as the table test.
      • -
    • dbGetQuery(conn, "select count(*) from test"): run a query command
      • -
    • dbSendUpdate(conn, "use db"): run any non-query command.
      • -
    • dbReadTable(conn, "test"): read all the data in table test
      • -
    • dbDisconnect(conn): close a connection
      • -
    • dbRemoveTable(conn, "test"): remove table test
      • -
    -

    Below functions are not supported currently:

    -
      -
    • dbExistsTable(conn, "test"): if talbe test exists
      • -
    • dbListTables(conn): list all tables in the connection
      • -
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    TDengine connectors

    -

    TDengine provides many connectors for development, including C/C++, JAVA, Python, RESTful, Go, Node.JS, etc.

    -

    C/C++ API

    -

    C/C++ APIs are similar to the MySQL APIs. Applications should include TDengine head file taos.h to use C/C++ APIs by adding the following line in code:

    -
    #include <taos.h>
    -

    Make sure TDengine library libtaos.so is installed and use -ltaos option to link the library when compiling. The return values of all APIs are -1 or NULL for failure.

    -

    C/C++ sync API

    -

    Sync APIs are those APIs waiting for responses from the server after sending a request. TDengine has the following sync APIs:

    -
      -

    • TAOS *taos_connect(char *ip, char *user, char *pass, char *db, int port)

      -

      Open a connection to a TDengine server. The parameters are ip (IP address of the server), user (username to login), pass (password to login), db (database to use after connection) and port (port number to connect). The parameter db can be NULL for no database to use after connection. Otherwise, the database should exist before connection or a connection error is reported. The handle returned by this API should be kept for future use.

      • -

    • void taos_close(TAOS *taos)

      -

      Close a connection to a TDengine server by the handle returned by taos_connect`

      • -

    • int taos_query(TAOS *taos, char *sqlstr)

      -

      The API used to run a SQL command. The command can be DQL or DML. The parameter taos is the handle returned by taos_connect. Return value -1 means failure.

      • -

    • TAOS_RES *taos_use_result(TAOS *taos)

      -

      Use the result after running taos_query. The handle returned should be kept for future fetch.

      • -

    • TAOS_ROW taos_fetch_row(TAOS_RES *res)

      -

      Fetch a row of return results through res, the handle returned by taos_use_result.

      • -

    • int taos_num_fields(TAOS_RES *res)

      -

      Get the number of fields in the return result.

      • -

    • TAOS_FIELD *taos_fetch_fields(TAOS_RES *res)

      -

      Fetch the description of each field. The description includes the property of data type, field name, and bytes. The API should be used with taos_num_fields to fetch a row of data.

      • -

    • void taos_free_result(TAOS_RES *res)

      -

      Free the resources used by a result set. Make sure to call this API after fetching results or memory leak would happen.

      • -

    • void taos_init()

      -

      Initialize the environment variable used by TDengine client. The API is not necessary since it is called int taos_connect by default.

      • -

    • char *taos_errstr(TAOS *taos)

      -

      Return the reason of the last API call failure. The return value is a string.

      • -

    • int *taos_errno(TAOS *taos)

      -

      Return the error code of the last API call failure. The return value is an integer.

      • -

    • int taos_options(TSDB_OPTION option, const void * arg, ...)

      -

      Set client options. The parameter option supports values of TSDB_OPTION_CONFIGDIR (configuration directory), TSDB_OPTION_SHELL_ACTIVITY_TIMER, TSDB_OPTION_LOCALE (client locale) and TSDB_OPTION_TIMEZONE (client timezone).

      • -
    -

    The 12 APIs are the most important APIs frequently used. Users can check taos.h file for more API information.

    -

    Note: The connection to a TDengine server is not multi-thread safe. So a connection can only be used by one thread.

    -

    C/C++ async API

    -

    In addition to sync APIs, TDengine also provides async APIs, which are more efficient. Async APIs are returned right away without waiting for a response from the server, allowing the application to continute with other tasks without blocking. So async APIs are more efficient, especially useful when in a poor network.

    -

    All async APIs require callback functions. The callback functions have the format:

    -
    void fp(void *param, TAOS_RES * res, TYPE param3)
    -

    The first two parameters of the callback function are the same for all async APIs. The third parameter is different for different APIs. Generally, the first parameter is the handle provided to the API for action. The second parameter is a result handle.

    -
      -

    • void taos_query_a(TAOS *taos, char *sqlstr, void (*fp)(void *param, TAOS_RES *, int code), void *param);

      -

      The async query interface. taos is the handle returned by taos_connect interface. sqlstr is the SQL command to run. fp is the callback function. param is the parameter required by the callback function. The third parameter of the callback function code is 0 (for success) or a negative number (for failure, call taos_errstr to get the error as a string). Applications mainly handle with the second parameter, the returned result set.

      • -

    • void taos_fetch_rows_a(TAOS_RES *res, void (*fp)(void *param, TAOS_RES *, int numOfRows), void *param);

      -

      The async API to fetch a batch of rows, which should only be used with a taos_query_a call. The parameter res is the result handle returned by taos_query_a. fp is the callback function. param is a user-defined structure to pass to fp. The parameter numOfRows is the number of result rows in the current fetch cycle. In the callback function, applications should call taos_fetch_row to get records from the result handle. After getting a batch of results, applications should continue to call taos_fetch_rows_a API to handle the next batch, until the numOfRows is 0 (for no more data to fetch) or -1 (for failure).

      • -

    • void taos_fetch_row_a(TAOS_RES *res, void (*fp)(void *param, TAOS_RES *, TAOS_ROW row), void *param);

      -

      The async API to fetch a result row. res is the result handle. fp is the callback function. param is a user-defined structure to pass to fp. The third parameter of the callback function is a single result row, which is different from that of taos_fetch_rows_a API. With this API, it is not necessary to call taos_fetch_row to retrieve each result row, which is handier than taos_fetch_rows_a but less efficient.

      • -
    -

    Applications may apply operations on multiple tables. However, it is important to make sure the operations on the same table are serialized. That means after sending an insert request in a table to the server, no operations on the table are allowed before a response is received.

    -

    C/C++ continuous query interface

    -

    TDengine provides APIs for continuous query driven by time, which run queries periodically in the background. There are only two APIs:

    -
      -

    • TAOS_STREAM *taos_open_stream(TAOS *taos, char *sqlstr, void (*fp)(void *param, TAOS_RES *, TAOS_ROW row), int64_t stime, void *param, void (*callback)(void *));

      -

      The API is used to create a continuous query.

      • -

    • taos: the connection handle returned by taos_connect.

      • -

    • sqlstr: the SQL string to run. Only query commands are allowed.

      • -

    • fp: the callback function to run after a query

      • -

    • param: a parameter passed to fp

      • -

    • stime: the time of the stream starts in the form of epoch milliseconds. If 0 is given, the start time is set as the current time.

      • -

    • callback: a callback function to run when the continuous query stops automatically.

      -

      The API is expected to return a handle for success. Otherwise, a NULL pointer is returned.

      • -

    • void taos_close_stream (TAOS_STREAM *tstr)

      -

      Close the continuous query by the handle returned by taos_open_stream. Make sure to call this API when the continuous query is not needed anymore.

      • -
    -

    C/C++ subscription API

    -

    For the time being, TDengine supports subscription on one table. It is implemented through periodic pulling from a TDengine server.

    -
      -

    • TAOS_SUB *taos_subscribe(char *host, char *user, char *pass, char *db, char *table, long time, int mseconds) -The API is used to start a subscription session by given a handle. The parameters required are host (IP address of a TDenginer server), user (username), pass (password), db (database to use), table (table name to subscribe), time (start time to subscribe, 0 for now), mseconds (pulling period). If failed to open a subscription session, a NULL pointer is returned.

      • -

    • TAOS_ROW taos_consume(TAOS_SUB *tsub) -The API used to get the new data from a TDengine server. It should be put in an infinite loop. The parameter tsub is the handle returned by taos_subscribe. If new data are updated, the API will return a row of the result. Otherwise, the API is blocked until new data arrives. If NULL pointer is returned, it means an error occurs.

      • -

    • void taos_unsubscribe(TAOS_SUB *tsub) -Stop a subscription session by the handle returned by taos_subscribe.

      • -

    • int taos_num_fields(TAOS_SUB *tsub) -The API used to get the number of fields in a row.

      • -

    • TAOS_FIELD *taos_fetch_fields(TAOS_RES *res) -The API used to get the description of each column.

      • -
    -

    Java Connector

    -

    JDBC Interface

    -

    TDengine provides a JDBC driver taos-jdbcdriver-x.x.x.jar for Enterprise Java developers. TDengine's JDBC Driver is implemented as a subset of the standard JDBC 3.0 Specification and supports the most common Java development frameworks. The driver is currently not published to the online dependency repositories such as Maven Center Repository, and users should manually add the .jar file to their local dependency repository.

    -

    Please note the JDBC driver itself relies on a native library written in C. On a Linux OS, the driver relies on a libtaos.so native library, where .so stands for "Shared Object". After the successful installation of TDengine on Linux, libtaos.so should be automatically copied to /usr/local/lib/taos and added to the system's default search path. On a Windows OS, the driver relies on a taos.dll native library, where .dll stands for "Dynamic Link Library". After the successful installation of the TDengine client on Windows, the taos-jdbcdriver.jar file can be found in C:/TDengine/driver/JDBC; the taos.dll file can be found in C:/TDengine/driver/C and should have been automatically copied to the system's searching path C:/Windows/System32.

    -

    Developers can refer to the Oracle's official JDBC API documentation for detailed usage on classes and methods. However, there are some differences of connection configurations and supported methods in the driver implementation between TDengine and traditional relational databases.

    -

    For database connections, TDengine's JDBC driver has the following configurable parameters in the JDBC URL. The standard format of a TDengine JDBC URL is:

    -

    jdbc:TSDB://{host_ip}:{port}/{database_name}?[user={user}|&password={password}|&charset={charset}|&cfgdir={config_dir}|&locale={locale}|&timezone={timezone}]

    -

    where {} marks the required parameters and [] marks the optional. The usage of each parameter is pretty straightforward:

    -
      -
    • user - login user name for TDengine; by default, it's root
      • -
    • password - login password; by default, it's taosdata
      • -
    • charset - the client-side charset; by default, it's the operation system's charset
      • -
    • cfgdir - the directory of TDengine client configuration file; by default it's /etc/taos on Linux and C:\TDengine/cfg on Windows
      • -
    • locale - the language environment of TDengine client; by default, it's the operation system's locale
      • -
    • timezone - the timezone of the TDengine client; by default, it's the operation system's timezone
      • -
    -

    All parameters can be configured at the time when creating a connection using the java.sql.DriverManager class, for example:

    -
    import java.sql.Connection;
-import java.sql.DriverManager;
-import java.util.Properties;
-import com.taosdata.jdbc.TSDBDriver;
-
-public Connection getConn() throws Exception{
-    Class.forName("com.taosdata.jdbc.TSDBDriver");
-  String jdbcUrl = "jdbc:TAOS://127.0.0.1:0/db?user=root&password=taosdata";
-  Properties connProps = new Properties();
-  connProps.setProperty(TSDBDriver.PROPERTY_KEY_USER, "root");
-  connProps.setProperty(TSDBDriver.PROPERTY_KEY_PASSWORD, "taosdata");
-  connProps.setProperty(TSDBDriver.PROPERTY_KEY_CONFIG_DIR, "/etc/taos");
-  connProps.setProperty(TSDBDriver.PROPERTY_KEY_CHARSET, "UTF-8");
-  connProps.setProperty(TSDBDriver.PROPERTY_KEY_LOCALE, "en_US.UTF-8");
-  connProps.setProperty(TSDBDriver.PROPERTY_KEY_TIMEZONE, "UTC-8");
-  Connection conn = DriverManager.getConnection(jdbcUrl, connProps);
-  return conn;
-}
    -

    Except cfgdir, all the parameters listed above can also be configured in the configuration file. The properties specified when calling DriverManager.getConnection() has the highest priority among all configuration methods. The JDBC URL has the second-highest priority, and the configuration file has the lowest priority. The explicitly configured parameters in a method with higher priorities always overwrite that same parameter configured in methods with lower priorities. For example, if charset is explicitly configured as "UTF-8" in the JDBC URL and "GKB" in the taos.cfg file, then "UTF-8" will be used.

    -

    Although the JDBC driver is implemented following the JDBC standard as much as possible, there are major differences between TDengine and traditional databases in terms of data models that lead to the differences in the driver implementation. Here is a list of head-ups for developers who have plenty of experience on traditional databases but little on TDengine:

    -
      -
    • TDengine does NOT support updating or deleting a specific record, which leads to some unsupported methods in the JDBC driver
      • -
    • TDengine currently does not support join or union operations, and thus, is lack of support for associated methods in the JDBC driver
      • -
    • TDengine supports batch insertions which are controlled at the level of SQL statement writing instead of API calls
      • -
    • TDengine doesn't support nested queries and neither does the JDBC driver. Thus for each established connection to TDengine, there should be only one open result set associated with it
      • -
    -

    All the error codes and error messages can be found in TSDBError.java . For a more detailed coding example, please refer to the demo project JDBCDemo in TDengine's code examples.

    -

    Python Connector

    -

    Pre-requirement

    -
  • TDengine installed, TDengine-client installed if on Windows
    • -
  • python 2.7 or >= 3.4
    • -
  • pip installed
    • -

    Installation

    -

    Linux

    -

    Users can find python client packages in our source code directory src/connector/python. There are two directories corresponding to two python versions. Please choose the correct package to install. Users can use pip command to install:

    -
    pip install src/connector/python/linux/python2/
    -

    or

    -
    pip install src/connector/python/linux/python3/
    -

    Windows

    -

    Assumed the Windows TDengine client has been installed , copy the file "C:\TDengine\driver\taos.dll" to the folder "C:\windows\system32", and then enter the cmd Windows command interface

    -
    cd C:\TDengine\connector\python\windows
    -
    pip install python2\
    -

    or

    -
    cd C:\TDengine\connector\python\windows
    -
    pip install python3\
    -

    * If pip command is not installed on the system, users can choose to install pip or just copy the taos directory in the python client directory to the application directory to use.

    -

    Usage

    -

    Examples

    -
  • import TDengine module at first:
    • -
    import taos
    -
  • get the connection
    • -
    
-conn = taos.connect(host="127.0.0.1", user="root", password="taosdata", config="/etc/taos")
-c1 = conn.cursor()
-
    -

    * host is the IP of TDengine server, and config is the directory where exists the TDengine client configure file

    -
  • insert records into the database
    • -
    
-import datetime
- 
-# create a database
-c1.execute('create database db')
-c1.execute('use db')
-# create a table
-c1.execute('create table tb (ts timestamp, temperature int, humidity float)')
-# insert a record
-start_time = datetime.datetime(2019, 11, 1)
-affected_rows = c1.execute('insert into tb values (\'%s\', 0, 0.0)' %start_time)
-# insert multiple records in a batch
-time_interval = datetime.timedelta(seconds=60)
-sqlcmd = ['insert into tb values']
-for irow in range(1,11):
-  start_time += time_interval
-  sqlcmd.append('(\'%s\', %d, %f)' %(start_time, irow, irow*1.2))
-affected_rows = c1.execute(' '.join(sqlcmd))
-
    -
  • query the database
    • -
    -c1.execute('select * from tb')
-# fetch all returned results
-data = c1.fetchall()
-# data is a list of returned rows with each row being a tuple
-numOfRows = c1.rowcount
-numOfCols = c1.descriptions
-for irow in range(numOfRows):
-  print("Row%d: ts=%s, temperature=%d, humidity=%f" %(irow, data[irow][0], data[irow][1],data[irow][2])
-  
-# use the cursor as an iterator to retrieve all returned results
-c1.execute('select * from tb')
-for data in c1:
-  print("ts=%s, temperature=%d, humidity=%f" %(data[0], data[1],data[2])
-
     -
  • close the connection
    • -
    -c1.close()
-conn.close()
-
     -

    Help information

    -

    Users can get module information from Python help interface or refer to our [python code example](). We list the main classes and methods below:

    -
      -

    • TaosConnection class

      -

      Run help(taos.TaosConnection) in python terminal for details.

      • -

    • TaosCursor class

      -

      Run help(taos.TaosCursor) in python terminal for details.

      • -

    • connect method

      -

      Open a connection. Run help(taos.connect) in python terminal for details.

      • -
    -

    RESTful Connector

    -

    TDengine also provides RESTful API to satisfy developing on different platforms. Unlike other databases, TDengine RESTful API applies operations to the database through the SQL command in the body of HTTP POST request. What users are required to provide is just a URL.

    -

    For the time being, TDengine RESTful API uses a \ generated from username and password for identification. Safer identification methods will be provided in the future.

    -

    HTTP URL encoding

    -

    To use TDengine RESTful API, the URL should have the following encoding format:

    -
    http://<ip>:<PORT>/rest/sql
    -
      -
    • ip: IP address of any node in a TDengine cluster
      • -
    • PORT: TDengine HTTP service port. It is 6020 by default.
      • -
    -

    For example, the URL encoding http://192.168.0.1:6020/rest/sql used to send HTTP request to a TDengine server with IP address as 192.168.0.1.

    -

    It is required to add a token in an HTTP request header for identification.

    -
    Authorization: Basic <TOKEN>
    -

    The HTTP request body contains the SQL command to run. If the SQL command contains a table name, it should also provide the database name it belongs to in the form of <db_name>.<tb_name>. Otherwise, an error code is returned.

    -

    For example, use curl command to send a HTTP request:

    -
    curl -H 'Authorization: Basic <TOKEN>' -d '<SQL>' <ip>:<PORT>/rest/sql
    -

    or use

    -
    curl -u username:password -d '<SQL>' <ip>:<PORT>/rest/sql
    -

    where TOKEN is the encryted string of {username}:{password} using the Base64 algorithm, e.g. root:taosdata will be encoded as cm9vdDp0YW9zZGF0YQ==

    -

    HTTP response

    -

    The HTTP resonse is in JSON format as below:

    -
    {
-    "status": "succ",
-    "head": ["column1","column2", …],
-    "data": [
-        ["2017-12-12 23:44:25.730", 1],
-        ["2017-12-12 22:44:25.728", 4]
-    ],
-    "rows": 2
-}
    -

    Specifically,

    -
      -
    • status: the result of the operation, success or failure
      • -
    • head: description of returned result columns
      • -
    • data: the returned data array. If no data is returned, only an affected_rows field is listed
      • -
    • rows: the number of rows returned
      • -
    -

    Example

    -
      -

    • Use curl command to query all the data in table t1 of database demo:

      -

      curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'select * from demo.t1' 192.168.0.1:6020/rest/sql

      • -
    -

    The return value is like:

    -
    {
-    "status": "succ",
-    "head": ["column1","column2","column3"],
-    "data": [
-        ["2017-12-12 23:44:25.730", 1, 2.3],
-        ["2017-12-12 22:44:25.728", 4, 5.6]
-    ],
-    "rows": 2
-}
    -
      -

    • Use HTTP to create a database：

      -

      curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'create database demo' 192.168.0.1:6020/rest/sql

      -

      The return value should be:

      • -
    -
    {
-    "status": "succ",
-    "head": ["affected_rows"],
-    "data": [[1]],
-    "rows": 1,
-}
    -

    Go Connector

    -

    TDengine also provides a Go client package named taosSql for users to access TDengine with Go. The package is in /usr/local/taos/connector/go/src/taosSql by default if you installed TDengine. Users can copy the directory /usr/local/taos/connector/go/src/taosSql to the src directory of your project and import the package in the source code for use.

    -
    import (
-    "database/sql"
-    _ "taosSql"
-)
    -

    The taosSql package is in cgo form, which calls TDengine C/C++ sync interfaces. So a connection is allowed to be used by one thread at the same time. Users can open multiple connections for multi-thread operations.

    -

    Please refer the the demo code in the package for more information.

    -

    Node.js Connector

    -

    TDengine also provides a node.js connector package that is installable through npm. The package is also in our source code at src/connector/nodejs/. The following instructions are also available here

    -

    To get started, just type in the following to install the connector through npm.

    -
    npm install td-connector
    -

    It is highly suggested you use npm. If you don't have it installed, you can also just copy the nodejs folder from src/connector/nodejs/ into your node project folder.

    -

    To interact with TDengine, we make use of the node-gyp library. To install, you will need to install the following depending on platform (the following instructions are quoted from node-gyp)

    -

    On Unix

    -
      -
    • python (v2.7 recommended, v3.x.x is not supported)
      • -
    • make
      • -
    • A proper C/C++ compiler toolchain, like GCC
      • -
    -

    On macOS

    -
      -

    • python (v2.7 recommended, v3.x.x is not supported) (already installed on macOS)

      • -

    • Xcode

      • -

    • You also need to install the

      -
      Command Line Tools
      -

      via Xcode. You can find this under the menu

      -
      Xcode -> Preferences -> Locations
      -

      (or by running

      -
      xcode-select --install
      -

      in your Terminal)

      -
        -
      • This step will install gcc and the related toolchain containing make
      • -
    -

    On Windows

    -

    Option 1

    -

    Install all the required tools and configurations using Microsoft's windows-build-tools using npm install --global --production windows-build-tools from an elevated PowerShell or CMD.exe (run as Administrator).

    -

    Option 2

    -

    Install tools and configuration manually:

    -
      -
    • Install Visual C++ Build Environment: Visual Studio Build Tools (using "Visual C++ build tools" workload) or Visual Studio 2017 Community (using the "Desktop development with C++" workload)
      • -
    • Install Python 2.7 (v3.x.x is not supported), and run npm config set python python2.7 (or see below for further instructions on specifying the proper Python version and path.)
      • -
    • Launch cmd, npm config set msvs_version 2017
      • -
    -

    If the above steps didn't work for you, please visit Microsoft's Node.js Guidelines for Windows for additional tips.

    -

    To target native ARM64 Node.js on Windows 10 on ARM, add the components "Visual C++ compilers and libraries for ARM64" and "Visual C++ ATL for ARM64".

    -

    Usage

    -

    The following is a short summary of the basic usage of the connector, the full api and documentation can be found here

    -

    Connection

    -

    To use the connector, first require the library td-connector. Running the function taos.connect with the connection options passed in as an object will return a TDengine connection object. The required connection option is host, other options if not set, will be the default values as shown below.

    -

    A cursor also needs to be initialized in order to interact with TDengine from Node.js.

    -
    const taos = require('td-connector');
-var conn = taos.connect({host:"127.0.0.1", user:"root", password:"taosdata", config:"/etc/taos",port:0})
-var cursor = conn.cursor(); // Initializing a new cursor
    -

    To close a connection, run

    -
    conn.close();
    -

    Queries

    -

    We can now start executing simple queries through the cursor.query function, which returns a TaosQuery object.

    -
    var query = cursor.query('show databases;')
    -

    We can get the results of the queries through the query.execute() function, which returns a promise that resolves with a TaosResult object, which contains the raw data and additional functionalities such as pretty printing the results.

    -
    var promise = query.execute();
-promise.then(function(result) {
-  result.pretty(); //logs the results to the console as if you were in the taos shell
-});
    -

    You can also query by binding parameters to a query by filling in the question marks in a string as so. The query will automatically parse what was binded and convert it to the proper format for use with TDengine

    -
    var query = cursor.query('select * from meterinfo.meters where ts <= ? and areaid = ?;').bind(new Date(), 5);
-query.execute().then(function(result) {
-  result.pretty();
-})
    -

    The TaosQuery object can also be immediately executed upon creation by passing true as the second argument, returning a promise instead of a TaosQuery.

    -
    var promise = cursor.query('select * from meterinfo.meters where v1 = 30;', true)
-promise.then(function(result) {
-  result.pretty();
-})
    -

    Async functionality

    -

    Async queries can be performed using the same functions such as cursor.execute, cursor.query, but now with _a appended to them.

    -

    Say you want to execute an two async query on two seperate tables, using cursor.query_a, you can do that and get a TaosQuery object, which upon executing with the execute_a function, returns a promise that resolves with a TaosResult object.

    -
    var promise1 = cursor.query_a('select count(*), avg(v1), avg(v2) from meter1;').execute_a()
-var promise2 = cursor.query_a('select count(*), avg(v1), avg(v2) from meter2;').execute_a();
-promise1.then(function(result) {
-  result.pretty();
-})
-promise2.then(function(result) {
-  result.pretty();
-})
    -

    Example

    -

    An example of using the NodeJS connector to create a table with weather data and create and execute queries can be found here (The preferred method for using the connector)

    -

    An example of using the NodeJS connector to achieve the same things but without all the object wrappers that wrap around the data returned to achieve higher functionality can be found here

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    diff --git a/documentation/tdenginedocs-en/contributor_license_agreement/index.html b/documentation/tdenginedocs-en/contributor_license_agreement/index.html deleted file mode 100644 index a012993efef2539843d2f26cf91e5d805aa66490..0000000000000000000000000000000000000000 --- a/documentation/tdenginedocs-en/contributor_license_agreement/index.html +++ /dev/null @@ -1,20 +0,0 @@ -Documentation | Taos Data
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    Data Model and Architecture

    -

    Data Model

    -

    A Typical IoT Scenario

    -

    In a typical IoT scenario, there are many types of devices. Each device is collecting one or multiple metrics. For a specific type of device, the collected data looks like the table below:

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    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Device IDTime StampValue 1Value 2Value 3Tag 1Tag 2
    D1001153854868500010.32190.31RedTesla
    D1002153854868400010.22200.23BlueBMW
    D1003153854868650011.52210.35BlackHonda
    D1004153854868550013.42230.29RedVolvo
    D1001153854869500012.62180.33RedTesla
    D1004153854869660011.82210.28BlackHonda
    -

    Each data record has device ID, timestamp, the collected metrics, and static tags associated with the device. Each device generates a data record in a pre-defined timer or triggered by an event. It is a sequence of data points, like a stream.

    -

    Data Characteristics

    -

    Being a series of data points over time, data points generated by devices, sensors, servers, or applications have strong common characteristics.

    -
      -
    1. metric is always structured data;
      2. -
    2. there are rarely delete/update operations on collected data;
      3. -
    3. there is only one single data source for one device or sensor;
      4. -
    4. ratio of read/write is much lower than typical Internet application;
      5. -
    5. the user pays attention to the trend of data, not the specific value at a specific time;
      6. -
    6. there is always a data retention policy;
      7. -
    7. the data query is always executed in a given time range and a subset of devices;
      8. -
    8. real-time aggregation or analytics is mandatory;
      9. -
    9. traffic is predictable based on the number of devices and sampling frequency;
      10. -
    10. data volume is huge, a system may generate 10 billion data points in a day.
      11. -
    -

    By utilizing the above characteristics, TDengine designs the storage and computing engine in a special and optimized way for time-series data. The system efficiency is improved significantly.

    -

    Relational Database Model

    -

    Since time-series data is more likely to be structured data, TDengine adopts the traditional relational database model to process them. You need to create a database, create tables with schema definition, then insert data points and execute queries to explore the data. Standard SQL is used, there is no learning curve.

    -

    One Table for One Device

    -

    Due to different network latency, the data points from different devices may arrive at the server out of order. But for the same device, data points will arrive at the server in order if system is designed well. To utilize this special feature, TDengine requires the user to create a table for each device (time-stream). For example, if there are over 10,000 smart meters, 10,000 tables shall be created. For the table above, 4 tables shall be created for device D1001, D1002, D1003 and D1004, to store the data collected.

    -

    This strong requirement can guarantee the data points from a device can be saved in a continuous memory/hard disk space block by block. If queries are applied only on one device in a time range, this design will reduce the read latency significantly since a whole block is owned by one single device. Also, write latency can be significantly reduced too, since the data points generated by the same device will arrive in order, the new data point will be simply appended to a block. Cache block size and the rows of records in a file block can be configured to fit the scenarios.

    -

    Best Practices

    -

    Table: TDengine suggests to use device ID as the table name (like D1001 in the above diagram). Each device may collect one or more metrics (like value1, valu2, valu3 in the diagram). Each metric has a column in the table, the metric name can be used as the column name. The data type for a column can be int, float, double, tinyint, bigint, bool or binary. Sometimes, a device may have multiple metric group, each group have different sampling period, you shall create a table for each group for each device. The first column in the table must be time stamp. TDengine uses time stamp as the index, and won’t build the index on any metrics stored.

    -

    Tags: to support aggregation over multiple tables efficiently, STable(Super Table) concept is introduced by TDengine. A STable is used to represent the same type of device. The schema is used to define the collected metrics(like value1, value2, value3 in the diagram), and tags are used to define the static attributes for each table or device(like tag1, tag2 in the diagram). A table is created via STable with a specific tag value. All or a subset of tables in a STable can be aggregated by filtering tag values.

    -

    Database: different types of devices may generate data points in different patterns and shall be processed differently. For example, sampling frequency, data retention policy, replication number, cache size, record size, the compression algorithm may be different. To make the system more efficient, TDengine suggests creating a different database with unique configurations for different scenarios

    -

    Schemaless vs Schema: compared with NoSQL database, since a table with schema definition shall be created before the data points can be inserted, flexibilities are not that good, especially when the schema is changed. But in most IoT scenarios, the schema is well defined and is rarely changed, the loss of flexibilities won’t be a big pain to developers or the administrator. TDengine allows the application to change the schema in a second even there is a huge amount of historical data when schema has to be changed.

    -

    TDengine does not impose a limitation on the number of tables, STables, or databases. You can create any number of STable or databases to fit the scenarios.

    -

    Architecture

    -

    There are two main modules in TDengine server as shown in Picture 1: Management Module (MGMT) and Data Module(DNODE). The whole TDengine architecture also includes a TDengine Client Module.

    -

    -
    Picture 1 TDengine Architecture

    -

    MGMT Module

    -

    The MGMT module deals with the storage and querying on metadata, which includes information about users, databases, and tables. Applications will connect to the MGMT module at first when connecting the TDengine server. When creating/dropping databases/tables, The request is sent to the MGMT module at first to create/delete metadata. Then the MGMT module will send requests to the data module to allocate/free resources required. In the case of writing or querying, applications still need to visit MGMT module to get meta data, according to which, then access the DNODE module.

    -

    DNODE Module

    -

    The DNODE module is responsible for storing and querying data. For the sake of future scaling and high-efficient resource usage, TDengine applies virtualization on resources it uses. TDengine introduces the concept of virtual node (vnode), which is the unit of storage, resource allocation and data replication (enterprise edition). As is shown in Picture 2, TDengine treats each data node as an aggregation of vnodes.

    -

    When a DB is created, the system will allocate a vnode. Each vnode contains multiple tables, but a table belongs to only one vnode. Each DB has one or mode vnodes, but one vnode belongs to only one DB. Each vnode contains all the data in a set of tables. Vnodes have their own cache, directory to store data. Resources between different vnodes are exclusive with each other, no matter cache or file directory. However, resources in the same vnode are shared between all the tables in it. By virtualization, TDengine can distribute resources reasonably to each vnode and improve resource usage and concurrency. The number of vnodes on a dnode is configurable according to its hardware resources.

    -

    -
    Picture 2 TDengine Virtualization

    -

    Client Module

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    TDengine client module accepts requests (mainly in SQL form) from applications and converts the requests to internal representations and sends to the server side. TDengine supports multiple interfaces, which are all built on top of TDengine client module.

    -

    For the communication between client and MGMT module, TCP/UDP is used, the port is set by the parameter mgmtShellPort in system configuration file taos.cfg, default is 6030. For the communication between client and DNODE module, TCP/UDP is used, the port is set by the parameter vnodeShellPort in the system configuration file, default is 6035.

    -

    Writing Process

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    Picture 3 shows the full writing process of TDengine. TDengine uses Writing Ahead Log strategy to assure data security and integrity. Data received from the client is written to the commit log at first. When TDengine recovers from crashes caused by power lose or other situations, the commit log is used to recover data. After writting to commit log, data will be wrtten to the corresponding vnode cache, then an acknowledgment is sent to the application. There are two mechanisms that can flush data in cache to disk for persistent storage:

    -
      -
    1. Flush driven by timer: There is a backend timer which flushes data in cache periodically to disks. The period is configurable via parameter commitTime in system configuration file taos.cfg.
      2. -
    2. Flush driven by data: Data in the cache is also flushed to disks when the left buffer size is below a threshold. Flush driven by data can reset the timer of flush driven by the timer.
      3. -
    -

    -
    Picture 3 TDengine Writting Process

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    New commit log file will be opened when the committing process begins. When the committing process finishes, the old commit file will be removed.

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    Data Storage

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    TDengine data are saved in /var/lib/taos directory by default. It can be changed to other directories by setting the parameter dataDir in system configuration file taos.cfg.

    -

    TDengine's metadata includes the database, table, user, super table and tag information. To reduce the latency, metadata are all buffered in the cache.

    -

    Data records saved in tables are sharded according to the time range. Data of tables in the same vnode in a certain time range are saved in the same file group. This sharding strategy can effectively improve data searching speed. By default, one group of files contain data in 10 days, which can be configured by daysPerFile in the configuration file or by DAYS keyword in CREATE DATABASE clause.

    -

    Data records are removed automatically once their lifetime is passed. The lifetime is configurable via parameter daysToKeep in the system configuration file. The default value is 3650 days.

    -

    Data in files are blockwise. A data block only contains one table's data. Records in the same data block are sorted according to the primary timestamp. To improve the compression ratio, records are stored column by column, and the different compression algorithm is applied based on each column's data type.

    Back
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    FAQ

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    1. When encountered with the error "failed to connect to server", what can I do?

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    The client may encounter connection errors. Please follow the steps below for troubleshooting:

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      -
    1. On the server side, execute systemctl status taosd to check the status of taosd service. If taosd is not running, start it and retry connecting.
      2. -
    2. Make sure you have used the correct server IP address to connect to.
      3. -
    3. Ping the server. If no response is received, check your network connection.
      4. -
    4. Check the firewall setting, make sure the TCP/UDP ports from 6030-6039 are enabled.
      5. -
    5. For JDBC, ODBC, Python, Go connections on Linux, make sure the native library libtaos.so are located at /usr/local/lib/taos, and /usr/local/lib/taos is in the LD_LIBRARY_PATH.
      6. -
    6. For JDBC, ODBC, Python, Go connections on Windows, make sure driver/c/taos.dll is in the system search path (or you can copy taos.dll into C:\Windows\System32)
      7. -
    7. If the above steps can not help, try the network diagnostic tool nc to check if TCP/UDP port works - check UDP port：nc -vuz {hostIP} {port} - check TCP port on server: nc -l {port} - check TCP port on client: nc {hostIP} {port}
      8. -
    -

    2. Why I get "Invalid SQL" error when a query is syntactically correct?

    -

    If you are sure your query has correct syntax, please check the length of the SQL string, it shall be less than 64KB.

    -

    3. Why I could not delete a super table?

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    Please make sure there are no tables under the super table. You could not delete a super table which still has associated tables.

    -

    4. Does TDengine support validation queries?

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    For the time being, TDengine does not have a specific set of validation queries. However, TDengine comes with a system monitoring database named 'sys', which can usually be used as a validation query object.

    -

    5. Can I delete or update a record that has been written into TDengine?

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    The answer is NO. The design of TDengine is based on the assumption that records are generated by the connected devices, you won't be allowed to change it. But TDengine provides a retention policy, the data records will be removed once their lifetime is passed.

    -

    6. How do I create a table with more than 250 columns?

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    For a single table, the maximum number of columns is 250. If for some reason, 250 columns are still not quite enough, our suggestion is to split the huge table into several smaller ones.

    -

    7. What is the most efficient way to write data to TDengine?

    -

    TDengine supports several different writing regimes. The most efficient way to write data to TDengine is to use batch inserting. For details on batch insertion syntax, please refer to Taos SQL

    Back
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    Getting Started

    -

    Quick Start

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    At the moment, TDengine only runs on Linux. You can set up and install it either from the source code or the packages. It takes only a few seconds from download to run it successfully.

    -

    Install from Source

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    Please visit our github page for instructions on installation from the source code.

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    Install from Package

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    Three different packages are provided, please pick up the one you like.

    - -

    For the time being, TDengine only supports installation on Linux systems using systemd as the service manager. To check if your system has systemd, use the which command.

    -
    which systemd
    -

    If the systemd command is not found, please install from source code.

    -

    Running TDengine

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    After installation, start the TDengine service by the systemctl command.

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    systemctl start taosd
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    Then check if the server is working now.

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    systemctl status taosd
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    If the service is running successfully, you can play around through TDengine shell taos, the command line interface tool located in directory /usr/local/bin/taos

    -

    Note: The systemctl command needs the root privilege. Use sudo if you are not the root user.

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    TDengine Shell

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    To launch TDengine shell, the command line interface, in a Linux terminal, type:

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    taos
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    The welcome message is printed if the shell connects to TDengine server successfully, otherwise, an error message will be printed (refer to our FAQ page for troubleshooting the connection error). The TDengine shell prompt is:

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    taos>
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    In the TDengine shell, you can create databases, create tables and insert/query data with SQL. Each query command ends with a semicolon. It works like MySQL, for example:

    -
    create database db;
-use db;
-create table t (ts timestamp, speed int);
-insert into t values ('2019-07-15 10:00:00', 10);
-insert into t values ('2019-07-15 10:01:05', 20);
-select * from t;
-          ts          |   speed   |
-===================================
- 19-07-15 10:00:00.000|         10|
- 19-07-15 10:01:05.000|         20|
-Query OK, 2 row(s) in set (0.001700s)
    -

    Besides the SQL commands, the system administrator can check system status, add or delete accounts, and manage the servers.

    -

    Shell Command Line Parameters

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    You can run taos command with command line options to fit your needs. Some frequently used options are listed below:

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    • -c, --config-dir: set the configuration directory. It is /etc/taos by default
      • -
    • -h, --host: set the IP address of the server it will connect to, Default is localhost
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    • -s, --commands: set the command to run without entering the shell
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    • -u, -- user: user name to connect to server. Default is root
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    • -p, --password: password. Default is 'taosdata'
      • -
    • -?, --help: get a full list of supported options
      • -
    -

    Examples:

    -
    taos -h 192.168.0.1 -s "use db; show tables;"
    -

    Run Batch Commands

    -

    Inside TDengine shell, you can run batch commands in a file with source command.

    -
    taos> source <filename>;
    -

    Tips

    -
      -
    • Use up/down arrow key to check the command history
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    • To change the default password, use "alter user" command
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    • ctrl+c to interrupt any queries
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    • To clean the cached schema of tables or STables, execute command RESET QUERY CACHE
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    -

    Major Features

    -

    The core functionality of TDengine is the time-series database. To reduce the development and management complexity, and to improve the system efficiency further, TDengine also provides caching, pub/sub messaging system, and stream computing functionalities. It provides a full stack for IoT big data platform. The detailed features are listed below:

    -
      -

    • SQL like query language used to insert or explore data

      • -

    • C/C++, Java(JDBC), Python, Go, RESTful, and Node.JS interfaces for development

      • -

    • Ad hoc queries/analysis via Python/R/Matlab or TDengine shell

      • -

    • Continuous queries to support sliding-window based stream computing

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    • Super table to aggregate multiple time-streams efficiently with flexibility

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    • Aggregation over a time window on one or multiple time-streams

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    • Built-in messaging system to support publisher/subscriber model

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    • Built-in cache for each time stream to make latest data available as fast as light speed

      • -

    • Transparent handling of historical data and real-time data

      • -

    • Integrating with Telegraf, Grafana and other tools seamlessly

      • -

    • A set of tools or configuration to manage TDengine

      • -
    -

    For enterprise edition, TDengine provides more advanced features below:

    -
      -

    • Linear scalability to deliver higher capacity/throughput

      • -

    • High availability to guarantee the carrier-grade service

      • -

    • Built-in replication between nodes which may span multiple geographical sites

      • -

    • Multi-tier storage to make historical data management simpler and cost-effective

      • -

    • Web-based management tools and other tools to make maintenance simpler

      • -
    -

    TDengine is specially designed and optimized for time-series data processing in IoT, connected cars, Industrial IoT, IT infrastructure and application monitoring, and other scenarios. Compared with other solutions, it is 10x faster on insert/query speed. With a single-core machine, over 20K requestes can be processed, millions data points can be ingested, and over 10 million data points can be retrieved in a second. Via column-based storage and tuned compression algorithm for different data types, less than 1/10 storage space is required.

    -

    Explore More on TDengine

    -

    Please read through the whole documentation to learn more about TDengine.

    Back
    diff --git a/documentation/tdenginedocs-en/index.html b/documentation/tdenginedocs-en/index.html deleted file mode 100644 index ebb728a0dfcc0faa972ae9620cf302ce2b76e580..0000000000000000000000000000000000000000 --- a/documentation/tdenginedocs-en/index.html +++ /dev/null @@ -1 +0,0 @@ -Documentation | Taos Data

    Documentation

    TDengine is a highly efficient platform to store, query, and analyze time-series data. It works like a relational database, but you are strongly suggested to read through the following documentation before you experience it.

    Getting Started

    • Quick Start: download, install and experience TDengine in a few seconds
    • TDengine Shell: command-line interface to access TDengine server
    • Major Features: insert/query, aggregation, cache, pub/sub, continuous query

    Data Model and Architecture

    • Data Model: relational database model, but one table for one device with static tags
    • Architecture: Management Module, Data Module, Client Module
    • Writing Process: records recieved are written to WAL, cache, then ack is sent back to client
    • Data Storage: records are sharded in the time range, and stored column by column

    TAOS SQL

    • Data Types: support timestamp, int, float, double, binary, nchar, bool, and other types
    • Database Management: add, drop, check databases
    • Table Management: add, drop, check, alter tables
    • Inserting Records: insert one or more records into tables, historical records can be imported
    • Data Query: query data with time range and filter conditions, support limit/offset
    • SQL Functions: support aggregation, selector, transformation functions
    • Downsampling: aggregate data in successive time windows, support interpolation

    Super Table

    Advanced Features

    • Continuous Query: query executed by TDengine periodically with a sliding window
    • Publisher/Subscriber: subscribe to the newly arrived data like a typical messaging system
    • Caching: the newly arrived data of each device/table will always be cached

    Connector

    • C/C++ Connector: primary method to connect to the server through libtaos client library
    • Java Connector: driver for connecting to the server from Java applications using the JDBC API
    • Python Connector: driver for connecting to the server from Python applications
    • RESTful Connector: a simple way to interact with TDengine via HTTP
    • Go Connector: driver for connecting to the server from Go applications
    • Node.js Connector: driver for connecting to the server from node applications

    Connections with Other Tools

    • Telegraf: pass the collected DevOps metrics to TDengine
    • Grafana: query the data saved in TDengine and visualize them
    • Matlab: access TDengine server from Matlab via JDBC
    • R: access TDengine server from R via JDBC

    Administrator

    More on System Architecture

    Tutorials & FAQ

    • FAQ: a list of frequently asked questions and answers
    • Use cases: a few typical cases to explain how to use TDengine in IoT platform
    Back
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    Back

    TDengine System Architecture

    -

    Storage Design

    -

    TDengine data mainly include metadata and data that we will introduce in the following sections.

    -

    Metadata Storage

    -

    Metadata include the information of databases, tables, etc. Metadata files are saved in /var/lib/taos/mgmt/ directory by default. The directory tree is as below:

    -
    /var/lib/taos/
-      +--mgmt/
-          +--db.db
-          +--meters.db
-          +--user.db
-          +--vgroups.db
    -

    A metadata structure (database, table, etc.) is saved as a record in a metadata file. All metadata files are appended only, and even a drop operation adds a deletion record at the end of the file.

    -

    Data storage

    -

    Data in TDengine are sharded according to the time range. Data of tables in the same vnode in a certain time range are saved in the same filegroup, such as files v0f1804*. This sharding strategy can effectively improve data searching speed. By default, a group of files contains data in 10 days, which can be configured by *daysPerFile* in the configuration file or by DAYS keyword in CREATE DATABASE clause. Data in files are blockwised. A data block only contains one table's data. Records in the same data block are sorted according to the primary timestamp, which helps to improve the compression rate and save storage. The compression algorithms used in TDengine include simple8B, delta-of-delta, RLE, LZ4, etc.

    -

    By default, TDengine data are saved in /var/lib/taos/data/ directory. /var/lib/taos/tsdb/ directory contains vnode informations and data file linkes.

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    /var/lib/taos/
-      +--tsdb/
-      |   +--vnode0
-      |        +--meterObj.v0
-      |        +--db/
-      |            +--v0f1804.head->/var/lib/taos/data/vnode0/v0f1804.head1
-      |            +--v0f1804.data->/var/lib/taos/data/vnode0/v0f1804.data
-      |            +--v0f1804.last->/var/lib/taos/data/vnode0/v0f1804.last1
-      |            +--v0f1805.head->/var/lib/taos/data/vnode0/v0f1805.head1
-      |            +--v0f1805.data->/var/lib/taos/data/vnode0/v0f1805.data
-      |            +--v0f1805.last->/var/lib/taos/data/vnode0/v0f1805.last1
-      |                   :
-      +--data/
-          +--vnode0/
-                +--v0f1804.head1
-                +--v0f1804.data
-                +--v0f1804.last1
-                +--v0f1805.head1
-                +--v0f1805.data
-                +--v0f1805.last1
-                        :
    -

    meterObj file

    -

    There are only one meterObj file in a vnode. Informations bout the vnode, such as created time, configuration information, vnode statistic informations are saved in this file. It has the structure like below:

    -
    <start_of_file>
-[file_header]
-[table_record1_offset&length]
-[table_record2_offset&length]
-...
-[table_recordN_offset&length]
-[table_record1]
-[table_record2]
-...
-[table_recordN]
-<end_of_file>
    -

    The file header takes 512 bytes, which mainly contains informations about the vnode. Each table record is the representation of a table on disk.

    -

    head file

    -

    The head files contain the index of data blocks in the data file. The inner organization is as below:

    -
    <start_of_file>
-[file_header]
-[table1_offset]
-[table2_offset]
-...
-[tableN_offset]
-[table1_index_block]
-[table2_index_block]
-...
-[tableN_index_block]
-<end_of_file>
    -

    The table offset array in the head file saves the information about the offsets of each table index block. Indices on data blocks in the same table are saved continuously. This also makes it efficient to load data indices on the same table. The data index block has a structure like:

    -
    [index_block_info]
-[block1_index]
-[block2_index]
-...
-[blockN_index]
    -

    The index block info part contains the information about the index block such as the number of index blocks, etc. Each block index corresponds to a real data block in the data file or last file. Information about the location of the real data block, the primary timestamp range of the data block, etc. are all saved in the block index part. The block indices are sorted in ascending order according to the primary timestamp. So we can apply algorithms such as the binary search on the data to efficiently search blocks according to time.

    -

    data file

    -

    The data files store the real data block. They are append-only. The organization is as:

    -
    <start_of_file>
-[file_header]
-[block1]
-[block2]
-...
-[blockN]
-<end_of_file>
    -

    A data block in data files only belongs to a table in the vnode and the records in a data block are sorted in ascending order according to the primary timestamp key. Data blocks are column-oriented. Data in the same column are stored contiguously, which improves reading speed and compression rate because of their similarity. A data block has the following organization:

    -
    [column1_info]
-[column2_info]
-...
-[columnN_info]
-[column1_data]
-[column2_data]
-...
-[columnN_data]
    -

    The column info part includes information about column types, column compression algorithm, column data offset and length in the data file, etc. Besides, pre-calculated results of the column data in the block are also in the column info part, which helps to improve reading speed by avoiding loading data block necessarily.

    -

    last file

    -

    To avoid storage fragment and to import query speed and compression rate, TDengine introduces an extra file, the last file. When the number of records in a data block is lower than a threshold, TDengine will flush the block to the last file for temporary storage. When new data comes, the data in the last file will be merged with the new data and form a larger data block and written to the data file. The organization of the last file is similar to the data file.

    -

    Summary

    -

    The innovation in architecture and storage design of TDengine improves resource usage. On the one hand, the virtualization makes it easy to distribute resources between different vnodes and for future scaling. On the other hand, sorted and column-oriented storage makes TDengine have a great advantage in writing, querying and compression.

    -

    Query Design

    -

    Introduction

    -

    TDengine provides a variety of query functions for both tables and super tables. In addition to regular aggregate queries, it also provides time window based query and statistical aggregation for time series data. TDengine's query processing requires the client app, management node, and data node to work together. The functions and modules involved in query processing included in each component are as follows:

    -

    Client (Client App). The client development kit, embed in a client application, consists of TAOS SQL parser and query executor, the second-stage aggregator (Result Merger), continuous query manager and other major functional modules. The SQL parser is responsible for parsing and verifying the SQL statement and converting it into an abstract syntax tree. The query executor is responsible for transforming the abstract syntax tree into the query execution logic and creates the metadata query according to the query condition of the SQL statement. Since TAOS SQL does not currently include complex nested queries and pipeline query processing mechanism, there is no longer need for query plan optimization and physical query plan conversions. The second-stage aggregator is responsible for performing the aggregation of the independent results returned by query involved data nodes at the client side to generate final results. The continuous query manager is dedicated to managing the continuous queries created by users, including issuing fixed-interval query requests and writing the results back to TDengine or returning to the client application as needed. Also, the client is also responsible for retrying after the query fails, canceling the query request, and maintaining the connection heartbeat and reporting the query status to the management node.

    -

    Management Node. The management node keeps the metadata of all the data of the entire cluster system, provides the metadata of the data required for the query from the client node, and divides the query request according to the load condition of the cluster. The super table contains information about all the tables created according to the super table, so the query processor (Query Executor) of the management node is responsible for the query processing of the tags of tables and returns the table information satisfying the tag query. Besides, the management node maintains the query status of the cluster in the Query Status Manager component, in which the metadata of all queries that are currently executing are temporarily stored in-memory buffer. When the client issues show queries command to management node, current running queries information is returned to the client.

    -

    Data Node. The data node, responsible for storing all data of the database, consists of query executor, query processing scheduler, query task queue, and other related components. Once the query requests from the client received, they are put into query task queue and waiting to be processed by query executor. The query executor extracts the query request from the query task queue and invokes the query optimizer to perform the basic optimization for the query execution plan. And then query executor scans the qualified data blocks in both cache and disk to obtain qualified data and return the calculated results. Besides, the data node also needs to respond to management information and commands from the management node. For example, after the kill query received from the management node, the query task needs to be stopped immediately.

    -

    -
    Fig 1. System query processing architecture diagram (only query related components)

    -

    Query Process Design

    -

    The client, the management node, and the data node cooperate to complete the entire query processing of TDengine. Let's take a concrete SQL query as an example to illustrate the whole query processing flow. The SQL statement is to query on super table FOO_SUPER_TABLE to get the total number of records generated on January 12, 2019, from the table, of which TAG_LOC equals to 'beijing'. The SQL statement is as follows:

    -
    SELECT COUNT(*) 
-FROM FOO_SUPER_TABLE
-WHERE TAG_LOC = 'beijing' AND TS >= '2019-01-12 00:00:00' AND TS < '2019-01-13 00:00:00'
    -

    First, the client invokes the TAOS SQL parser to parse and validate the SQL statement, then generates a syntax tree, and extracts the object of the query - the super table FOO_SUPER_TABLE, and then the parser sends requests with filtering information (TAG_LOC='beijing') to management node to get the corresponding metadata about FOO_SUPER_TABLE.

    -

    Once the management node receives the request for metadata acquisition, first finds the super table FOO_SUPER_TABLE basic information, and then applies the query condition (TAG_LOC='beijing') to filter all the related tables created according to it. And finally, the query executor returns the metadata information that satisfies the query request to the client.

    -

    After the client obtains the metadata information of FOO_SUPER_TABLE, the query executor initiates a query request with timestamp range filtering condition (TS >= '2019- 01-12 00:00:00' AND TS < '2019-01-13 00:00:00') to all nodes that hold the corresponding data according to the information about data distribution in metadata.

    -

    The data node receives the query sent from the client, converts it into an internal structure and puts it into the query task queue to be executed by query executor after optimizing the execution plan. When the query result is obtained, the query result is returned to the client. It should be noted that the data nodes perform the query process independently of each other, and rely solely on their data and content for processing.

    -

    When all data nodes involved in the query return results, the client aggregates the result sets from each data node. In this case, all results are accumulated to generate the final query result. The second stage of aggregation is not always required for all queries. For example, a column selection query does not require a second-stage aggregation at all.

    -

    REST Query Process

    -

    In addition to C/C++, Python, and JDBC interface, TDengine also provides a REST interface based on the HTTP protocol, which is different from using the client application programming interface. When the user uses the REST interface, all the query processing is completed on the server-side, and the user's application is not involved in query processing anymore. After the query processing is completed, the result is returned to the client through the HTTP JSON string.

    -

    -
    Fig. 2 REST query architecture

    -

    When a client uses an HTTP-based REST query interface, the client first establishes a connection with the HTTP connector at the data node and then uses the token to ensure the reliability of the request through the REST signature mechanism. For the data node, after receiving the request, the HTTP connector invokes the embedded client program to initiate a query processing, and then the embedded client parses the SQL statement from the HTTP connector and requests the management node to get metadata as needed. After that, the embedded client sends query requests to the same data node or other nodes in the cluster and aggregates the calculation results on demand. Finally, you also need to convert the result of the query into a JSON format string and return it to the client via an HTTP response. After the HTTP connector receives the request SQL, the subsequent process processing is completely consistent with the query processing using the client application development kit.

    -

    It should be noted that during the entire processing, the client application is no longer involved in, and is only responsible for sending SQL requests through the HTTP protocol and receiving the results in JSON format. Besides, each data node is embedded with an HTTP connector and a client, so any data node in the cluster received requests from a client, the data node can initiate the query and return the result to the client through the HTTP protocol, with transfer the request to other data nodes.

    -

    Technology

    -

    Because TDengine stores data and tags value separately, the tag value is kept in the management node and directly associated with each table instead of records, resulting in a great reduction of the data storage. Therefore, the tag value can be managed by a fully in-memory structure. First, the filtering of the tag data can drastically reduce the data size involved in the second phase of the query. The query processing for the data is performed at the data node. TDengine takes advantage of the immutable characteristics of IoT data by calculating the maximum, minimum, and other statistics of the data in one data block on each saved data block, to effectively improve the performance of query processing. If the query process involves all the data of the entire data block, the pre-computed result is used directly, and the content of the data block is no longer needed. Since the size of disk space required to store the pre-computation result is much smaller than the size of the specific data, the pre-computation result can greatly reduce the disk IO and speed up the query processing.

    -

    TDengine employs column-oriented data storage techniques. When the data block is involved to be loaded from the disk for calculation, only the required column is read according to the query condition, and the read overhead can be minimized. The data of one column is stored in a contiguous memory block and therefore can make full use of the CPU L2 cache to greatly speed up the data scanning. Besides, TDengine utilizes the eagerly responding mechanism and returns a partial result before the complete result is acquired. For example, when the first batch of results is obtained, the data node immediately returns it directly to the client in case of a column select query.

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    \ No newline at end of file diff --git a/documentation/tdenginedocs-en/styles/base.css b/documentation/tdenginedocs-en/styles/base.css deleted file mode 100644 index 564b587eb166c7fdca9f4d95070a4b16d743744a..0000000000000000000000000000000000000000 --- a/documentation/tdenginedocs-en/styles/base.css +++ /dev/null @@ -1,1112 +0,0 @@ -:root { - --b1:rgb(0,118,206);/*#0077bf*//*PANTONE 2174 C*/ - --b1t:rgba(0,118,206,0.15); - --b2:rgb(72,159,223);/*PANTONE 2171 C*//*OLD:#4193c5*/ - --sg-1:#b3b4b9; - --sg0:#585c66; - --sg1:rgb(51,56,68); - --sg2:#2F333E; - --sg3:#21242c; - --black: #212529; - --white: #fefefe; /*rgb(254,254,254)*/ - --white2:rgb(251, 251, 253); /*#fafbfc*/ - --white3:rgb(240,242,244); - --footer1:#fefefe; - --footer2:#333844; - --red:#ea4741; - --green:#72c156; - /*PRODUCT COLORS*/ - --p1:#72c156;/*#30D387;*/ - --p1t:rgba(114,193,86,0.15); - --p2:#43b3ae;/*rgb(70,161,168);/*#46a1a8*//*#D879D0;*/ - --p2t:rgba(70,161,168,0.15); - --p3:#4997d0;/*#30B7E8;*/ - --p3t:rgba(73,151,208,0.15); -} -/*@font-face{font-family:"Open Sans";src:url(fonts/Light/OpenSans-Light.woff2?v=1.101) format("woff2"),url(fonts/Light/OpenSans-Light.woff?v=1.101) format("woff");font-weight:300;font-style:normal}@font-face{font-family:"Open Sans";src:url(fonts/LightItalic/OpenSans-LightItalic.woff2?v=1.101) format("woff2"),url(fonts/LightItalic/OpenSans-LightItalic.woff?v=1.101) format("woff");font-weight:300;font-style:italic}@font-face{font-family:"Open Sans";src:url(fonts/Regular/OpenSans-Regular.woff2?v=1.101) format("woff2"),url(fonts/Regular/OpenSans-Regular.woff?v=1.101) format("woff");font-weight:400;font-style:normal}@font-face{font-family:"Open Sans";src:url(fonts/Italic/OpenSans-Italic.woff2?v=1.101) format("woff2"),url(fonts/Italic/OpenSans-Italic.woff?v=1.101) format("woff");font-weight:400;font-style:italic}@font-face{font-family:"Open Sans";src:url(fonts/SemiBold/OpenSans-SemiBold.woff2?v=1.101) format("woff2"),url(fonts/SemiBold/OpenSans-SemiBold.woff?v=1.101) format("woff");font-weight:600;font-style:normal}@font-face{font-family:"Open Sans";src:url(fonts/SemiBoldItalic/OpenSans-SemiBoldItalic.woff2?v=1.101) format("woff2"),url(fonts/SemiBoldItalic/OpenSans-SemiBoldItalic.woff?v=1.101) format("woff");font-weight:600;font-style:italic}@font-face{font-family:"Open Sans";src:url(fonts/Bold/OpenSans-Bold.woff2?v=1.101) format("woff2"),url(fonts/Bold/OpenSans-Bold.woff?v=1.101) format("woff");font-weight:700;font-style:normal}@font-face{font-family:"Open Sans";src:url(fonts/BoldItalic/OpenSans-BoldItalic.woff2?v=1.101) format("woff2"),url(fonts/BoldItalic/OpenSans-BoldItalic.woff?v=1.101) format("woff");font-weight:700;font-style:italic}@font-face{font-family:"Open Sans";src:url(fonts/ExtraBold/OpenSans-ExtraBold.woff2?v=1.101) format("woff2"),url(fonts/ExtraBold/OpenSans-ExtraBold.woff?v=1.101) format("woff");font-weight:800;font-style:normal}@font-face{font-family:"Open Sans";src:url(fonts/ExtraBoldItalic/OpenSans-ExtraBoldItalic.woff2?v=1.101) format("woff2"),url(fonts/ExtraBoldItalic/OpenSans-ExtraBoldItalic.woff?v=1.101) format("woff");font-weight:800;font-style:italic}*/ -html { - font-size:12pt; /*20px*/ - background-color: var(--white); -} -body, body * { - font-family: "Open Sans", Helvetica,'Hiragino Sans GB', sans-serif,"Apple Color Emoji"; - -webkit-font-smoothing:auto !important; - -moz-osx-font-smoothing:auto !important; - font-smooth: auto !important; - letter-spacing: normal; - line-height: 1.6; -} -body{ - -webkit-box-sizing: border-box; - box-sizing: border-box; - font-weight: 300; - color:var(--sg1); - font-family: "Open Sans", Helvetica, sans-serif !important; - background-color: var(--white); - -} -strong { - font-weight:600; -} -.anchor { - display: block; - position: relative; - z-index: -1; - top: -10px; -} -/* FORMS */ -input { - outline: none; - -webkit-box-shadow: inset 0px 0px 0px 0px transparent; - box-shadow: inset 0px 0px 0px 0px transparent; -} -input[type='text'], input[type='submit'],textarea { - -webkit-appearance: none; -} -input[l]{ - font-size:inherit; - outline: none; - - color:var(--sg1); - padding-left: 0.4em; - width:-webkit-calc(100%); - width:calc(100%); - border:solid 1px; - display: inline-block; - border-left:1px solid; - -webkit-border-radius:4px; - border-radius:4px; - -webkit-transition: border-left 0.2s; - -o-transition: border-left 0.2s; - transition: border-left 0.2s; - vertical-align: top; - font-weight:400; - border-color:inherit; - margin-bottom: 0.5rem; -} -input[l]:focus { - border-left:1rem solid; -} -input[l]:focus { - width:-webkit-calc(auto); - width:calc(auto); -} -input[plain]:valid { - border-color: var(--b1); -} -input[plain]:focus:valid { - border-color: var(--b1); -} -textarea { - -webkit-box-shadow: inset 0px 0px 0px 0px transparent; - box-shadow: inset 0px 0px 0px 0px transparent; -} -textarea[l] { - font-size:inherit; - outline: none; - - color:var(--sg1); - padding-left: 0.4em; - width:-webkit-calc(100%); - width:calc(100%); - border:solid 1px; - display: inline-block; - border-left:1px solid; - -webkit-border-radius:4px; - border-radius:4px; - -webkit-transition: border-left 0.2s; - -o-transition: border-left 0.2s; - transition: border-left 0.2s; - vertical-align: top; - font-weight:400; - border-color:inherit; - margin-bottom: 0.5rem; -} - -/*Other Text*/ -ul { - padding-left:30px; -} -p, li { - font-size:1em; - -} -p { - margin-bottom: 0.5rem; -} -/*Headers*/ -h1 { - font-size: 2.5rem; - line-height: 1.8; -} -h2 { - font-size: 1.7rem; - line-height: 1.8; -} -h3 { - font-size: 1.4rem; - line-height: 1.43; -} -h4 { - font-size: 1.25rem; -} -h5 { - font-size: 1rem; -} -h6 { - font-size: 1rem; - color: #777; -} -h1[b]::before,h2[b]::before, h3[b]::before { - content:""; - height:1em;; - display: block; - width:3px; - margin-left: -0.5em; - margin-top: 0.45em; - position: absolute; - background-color: var(--b1); -} -h1[b],h2[b], h3[b] { - padding-left: 0.5em -} -/* Navigation Bar */ -.logo { - height: 2.5rem; -} -a { - font-size:1em; -} -a:hover { - text-decoration: none; -} -a[l] { - color:var(--b2); - padding-bottom: 2px; - position: relative; - font-style: normal; - cursor: pointer; -} -a[l]:hover,a[l]:focus { - text-decoration: none; -} -a[l]::before { - content: ""; - left: 0; - background-color: var(--b2); - width: 0%; - height: 1px; - top:-webkit-calc(1em + 8px); - top:calc(1em + 8px); - position: absolute; - z-index: 2; - -webkit-transition: background-color 0.2s, height 0.2s, top 0.2s, width 0.2s; - -o-transition: background-color 0.2s, height 0.2s, top 0.2s, width 0.2s; - transition: background-color 0.2s, height 0.2s, top 0.2s, width 0.2s;; -} -a[l]:hover::before, a[l]:focus::before { - content: ""; - left: 0; - background-color: var(--b2); - width: 100%; - height: 1px; - top:-webkit-calc(1em + 8px); - top:calc(1em + 8px); - position: absolute; - z-index: 2; - -webkit-transition: background-color 0.2s, height 0.2s, top 0.2s, width 0.2s; - -o-transition: background-color 0.2s, height 0.2s, top 0.2s, width 0.2s; - transition: background-color 0.2s, height 0.2s, top 0.2s, width 0.2s; - text-decoration: none; -} -.navbar-brand { - margin-left: 10%; - padding-left: 15px; - color:var(--white) !important; -} -.navbar-nav { - top:0px; -} -.navbar { - background-color:var(--sg1); - z-index:10000; - padding-left: 0px; - padding-right: 0px; - padding-top:0.75rem; - padding-bottom: 0.75rem; -} -.navbar-toggler { - margin-right: -webkit-calc(2rem + 15px); - margin-right: calc(2rem + 15px); -} -.nav-link { - color:var(--white) !important; - line-height: 3.65rem; -} -.nav-item { - height:4.65rem; - font-size:1.1rem; - padding-left: 0.15rem; - padding-right: 0.15rem; - -webkit-transition: all 0.2s; - -o-transition: all 0.2s; - transition: all 0.2s; - border-bottom: 0rem solid var(--white); -} -.nav-item:hover { - border-bottom: 0.45rem solid var(--white); -} -.dropdown-menu { - top:4.1rem; - z-index:1000; - border-top:none; - border:none; - min-width: 120px; - margin-left:-1px; - -webkit-border-top-left-radius: 0; - border-top-left-radius: 0; - -webkit-border-top-right-radius: 0; - border-top-right-radius: 0; - -webkit-border-bottom-left-radius:0.25rem; - border-bottom-left-radius:0.25rem; - -webkit-border-bottom-right-radius:0.25rem; - border-bottom-right-radius:0.25rem; -} -.dropdown-menu.show { - -webkit-box-shadow: 0 4px 24px rgba(100, 109, 146, 0.15); - box-shadow: 0 4px 24px rgba(100, 109, 146, 0.15); -} -.dropdown-item { - color:var(--sg1); - background-color: var(--white); - -webkit-transition:all 0.2s; - -o-transition:all 0.2s; - transition:all 0.2s; - cursor:pointer; -} -.dropdown-item:hover, .dropdown-item:active { - background-color:var(--sg1); - color:var(--white) !important; -} -.dropdown-toggle::after { - display:none; -} -.dropdown a::after { - -webkit-transform: rotate(-90deg); - -ms-transform: rotate(-90deg); - transform: rotate(-90deg); - -webkit-transition: -webkit-transform 0.2s; - transition: -webkit-transform 0.2s; - -o-transition: transform 0.2s; - transition: transform 0.2s; - transition: transform 0.2s, -webkit-transform 0.2s; -} -.dropdown.show a::after { - -webkit-transform: rotate(0deg); - -ms-transform: rotate(0deg); - transform: rotate(0deg); -} -.navbar-nav .active { - border-bottom: 0.45rem solid var(--white); -} -.navbar-nav { - position: absolute; - right:-webkit-calc(10% + 15px); - right:calc(10% + 15px); -} -#language-dropdown .dropdown-menu{ - width:50px; -} -/*FOOTER*/ -footer { - background-color: var(--footer2); - padding-top: 1rem; -} -.page-footer { - padding-bottom: 2rem; -} -.footer-content, .footer-legal, .footer-contact { - width:80%; - margin-left: 10%; - padding-top:1rem; - color:var(--footer1); - font-size:0.8em; -} -.footer-content a { - color:var(--footer1); -} -.footer-content a { - color:var(--footer1); -} -.links-list { - text-align: left; - list-style: none; - padding: 0px; -} -.content-wrapper > .links-list { - padding-left:15px; -} -.links-list-title h4 { - font-size:1.2em; - font-weight:400; -} -.legal-links { - position: absolute; - right:-webkit-calc(10% + 15px); - right:calc(10% + 15px); -} -.legal-links a { - color:var(--footer1); -} -.links-list li { - height:2em; -} -.links-list li a::before, .legal-links a::before { - background-color:var(--footer1); -} -.links-list li a:hover::before, .legal-links a:hover::before { - background-color:var(--footer1); -} -.links-list .divider { - border-bottom: 1px solid var(--footer1); - opacity: 0.15; - height:0px; - margin-bottom: 0.3em; -} -.footer-divider { - border-bottom: 1px solid var(--footer1); - width:-webkit-calc(80% - 30px); - width:calc(80% - 30px); - margin-left: -webkit-calc(10% + 15px); - margin-left: calc(10% + 15px); -} -#social-media-links li { - height:2rem; - line-height:2rem; - display: inline-block; - font-size:1em; -} - -#social-media-links li:last-child::after { - content:""; -} -#social-media-links li::after { - content:" | "; -} -#social-media-links svg { - margin-left:2px;margin-right: 0.4rem; - width:20px; -} -#social-media-links svg path { - fill:var(--footer1); -} -#social-media-links li a::before { - left:1.9rem; - background-color:var(--footer1); -} -#social-media-links li a:hover::before, #social-media-links li a:focus::before { - left:1.9rem; - width: -webkit-calc(100% - 1.9rem); - width: calc(100% - 1.9rem); - background-color:var(--footer1); -} -#social-media-links ion-icon { - font-size:20px; - margin-right: 0.5rem; -} -#social-media-links svg { - font-size:20px; - margin-right: 0.5rem; -} -#email-subscribe-form { - width:-webkit-calc(100% - 160px); - width:calc(100% - 160px); -} -#email-subscribe-form input{ - width:-webkit-calc(100% - 4rem); - width:calc(100% - 4rem); - font-size:1.2em; - outline: none; - height:1.8em; - color:var(--sg1); - padding-left: 0.6em; - border:none; - display: inline-block; - border-left:0px solid var(--b1); - -webkit-border-radius:4px; - border-radius:4px; - -webkit-transition: border-left 0.2s; - -o-transition: border-left 0.2s; - transition: border-left 0.2s; - vertical-align: top; - font-weight:400; -} -#email-subscribe-form input:focus { - border-left:1rem solid var(--b1); - padding-top:2px; -} -#email-subscribe-form input:invalid, #email-subscribe-form input:invalid:focus { - border-color:var(--b1); -} -#email-subscribe-form input.invalid-input, #email-subscribe-form input.invalid-input:focus { - border-color:var(--red); -} -#email-subscribe-form input:valid, #email-subscribe-form input:valid:focus { - border-color:var(--green); -} -#email-subscribe-form button { - font-size:1.2em; - height:1.8em; - line-height: 1em; - float:right; - width:3rem; - padding:0; -} -form { - border-color:var(--b1); -} -form input:invalid, form input:invalid:focus { - border-color:inherit; -} -form input.invalid-input, form input.invalid-input:focus, form textarea.invalid-input, form textarea.invalid-input:focus { - border-color:var(--red); -} -form input:valid, form input:valid:focus { - border-color:var(--green); -} - -.sub-arrow { - width:1.2em; - fill:var(--b1); -} - - -@media only screen and (max-width:991px){ - .page-footer { - padding-left:20px; - padding-right:20px; - } - .footer-legal { - width:100%; - } - #legal-1 { - padding-left: 20px; - } - .legal-links { - right:20px; - } - .footer-content .col-xl-8, .footer-content .col-xl-4{ - padding-left:20px; - padding-right:20px; - } - .footer-content { - width:-webkit-calc(100% + 40px); - width:calc(100% + 40px); - } - .footer-divider { - width:100%; - margin-left: 0; - } -} - -/*SECTIONS AND CONTENT*/ -.content-wrapper { - width: 80%; - margin-left: 10%; - margin-top: 6rem; - margin-bottom: 3rem; - min-height: -webkit-calc(100vh - 187.7px - 74.45px); - min-height: calc(100vh - 187.7px - 74.45px); -} -.section { - /* border-bottom:2px solid rgba(0,0,0,0.2);*/ -} -.section-item { - -} -.section-title, -.section-item-title { - color:var(--b1); - -} -.container-fluid { - background-color: var(--white); -} -.center { - left:50%; - position: relative; -} -/*BUTTONS*/ -.btn-primary { - color:var(--b1); - background-color: var(--white); - border-color:var(--b1); - -webkit-box-shadow:0px 0px 0px 0px rgba(255,255,255,0.55); - box-shadow:0px 0px 0px 0px rgba(255,255,255,0.55); - -webkit-transition: all 0.2s; - -o-transition: all 0.2s; - transition: all 0.2s; -} -.btn-primary:hover,.btn-primary:focus { - color:var(--b1); - background-color: var(--white); - border-color:var(--b1); - -webkit-box-shadow:4px 4px 0px 0px var(--b1t); - box-shadow:4px 4px 0px 0px var(--b1t); - -webkit-transform: translate(-2px,-2px); - -ms-transform: translate(-2px,-2px); - transform: translate(-2px,-2px); -} -.btn-primary:active { - color:var(--b1) !important; - background-color: var(--white) !important; - border-color:var(--b1) !important; - -webkit-box-shadow:2px 2px 0px 0px var(--b1t); - box-shadow:2px 2px 0px 0px var(--b1t); - -webkit-transform: translate(-1px,-1px); - -ms-transform: translate(-1px,-1px); - transform: translate(-1px,-1px); -} -.btn-white { - color:var(--b1); - background-color: var(--white); - -webkit-transition: all 0.2s; - -o-transition: all 0.2s; - transition: all 0.2s; - -webkit-box-shadow:0px 0px 0px 0px rgba(255,255,255,0.55); - box-shadow:0px 0px 0px 0px rgba(255,255,255,0.55); -} -.btn-white:hover,.btn-white:focus { - color:var(--b1); - background-color: var(--white); - -webkit-box-shadow:4px 4px 0px 0px rgba(255,255,255,0.55); - box-shadow:4px 4px 0px 0px rgba(255,255,255,0.55); - -webkit-transform: translate(-2px,-2px); - -ms-transform: translate(-2px,-2px); - transform: translate(-2px,-2px); -} -.btn-white:active { - color:var(--b1) !important; - background-color: var(--white) !important; - -webkit-box-shadow:2px 2px 0px 0px rgba(255,255,255,0.55); - box-shadow:2px 2px 0px 0px rgba(255,255,255,0.55); - -webkit-transform: translate(-1px,-1px); - -ms-transform: translate(-1px,-1px); - transform: translate(-1px,-1px); -} -.btn-filled { - color:var(--white) !important; - background-color: var(--b1); - border-color:var(--b1); - -webkit-box-shadow:0px 0px 0px 0px rgba(255,255,255,0.55); - box-shadow:0px 0px 0px 0px rgba(255,255,255,0.55); - -webkit-transition: all 0.2s; - -o-transition: all 0.2s; - transition: all 0.2s; -} -.btn-filled:hover { - color:var(--white) !important;; - background-color: var(--b1); - border-color:var(--b1); - -webkit-box-shadow:4px 4px 0px 0px var(--b1t); - box-shadow:4px 4px 0px 0px var(--b1t); - -webkit-transform: translate(-2px,-2px); - -ms-transform: translate(-2px,-2px); - transform: translate(-2px,-2px); -} -.btn-filled:active { - color:var(--white) !important; - background-color: var(--b1) !important; - border-color:var(--b1) !important; - -webkit-box-shadow:2px 2px 0px 0px var(--b1t); - box-shadow:2px 2px 0px 0px var(--b1t); - -webkit-transform: translate(-1px,-1px); - -ms-transform: translate(-1px,-1px); - transform: translate(-1px,-1px); -} -/*Popup*/ -#popup-wrapper { - display: block; - position: absolute; - z-index:1000; - -webkit-transition:opacity 0.5s; - -o-transition:opacity 0.5s; - transition:opacity 0.5s; - opacity: 1; -} -#popup-page-cover { - display:none; - position: fixed; - height: 100vh; - width:100vw; - top:0;left:0; - background-color: rgba(131, 145, 174, 0.32); - z-index:1000; - -webkit-transition:opacity 0.5s; - -o-transition:opacity 0.5s; - transition:opacity 0.5s; - opacity:0; -} -#popup { - position: fixed; - display: none; - height:auto; - width:100px; - z-index: 1001; - max-width: -webkit-calc(100% - 30px); - max-width: calc(100% - 30px); - background-color: var(--white); - left:50%; - -webkit-transform:translate(-50%,-50%); - -ms-transform:translate(-50%,-50%); - transform:translate(-50%,-50%); - top:50%; - -webkit-transition:opacity 0.5s; - -o-transition:opacity 0.5s; - transition:opacity 0.5s; - opacity:0; - -webkit-border-radius:0.25rem; - border-radius:0.25rem; - -webkit-box-shadow: 0 12px 48px 0 rgba(0, 0, 0, 0.24); - box-shadow: 0 12px 48px 0 rgba(0, 0, 0, 0.24) -} -#close-popup { - position: absolute;right:1rem; - z-index: 1; - cursor: pointer; - top:0; -} -#close-popup svg { - margin-top:4px; -} -#close-popup::before { - content:""; - width:0px; - display: block; - position: absolute; - top:50%; - left:50%; - height:0px; - background-color:rgba(0,0,0,0.15); - -webkit-border-radius:50%; - border-radius:50%; - z-index:-1; - cursor: pointer; - -webkit-transition:all 0.2s; - -o-transition:all 0.2s; - transition:all 0.2s; -} -#close-popup:hover::before { - content:""; - width:32px;; - display: block; - position: absolute; - top:10px; - left:0px; - height:32px; - background-color:rgba(0,0,0,0.15); - -webkit-border-radius:50%; - border-radius:50%; -z-index:-1; -} -#popup-title { - padding-left: 1rem; - background-color:var(--b1); - color:var(--white); - font-weight:400; - font-size:1.6em; - width:100%; - display:block; - -webkit-border-radius:0.25rem 0.25rem 0 0; - border-radius:0.25rem 0.25rem 0 0; - padding-right:60px; - position: relative; -} -#popup-title-text { - line-height: 1.2; - display: inline-block; - padding-top: 9px; -} -#popup-content { - padding:1rem; - display: block; -} -#popup-title path { - fill:var(--white); -} -/*Banners*/ -.banner-content { - padding-right:32px; -} -.banner-wrapper { - width:100vw; - position: fixed; - top:4.3rem; - left:0; - z-index: 1000; -} -.banner { - background-color: var(--b1); - width:-webkit-calc(100% - 20px); - width:calc(100% - 20px); - margin: auto; - -webkit-border-radius:0.25rem; - border-radius:0.25rem; - padding:0.5rem; - color:var(--white); - font-size:1.6em; - margin-top: 1rem; - -webkit-box-shadow:0 4px 12px 0 rgba(0, 0, 0, 0.24); - box-shadow:0 4px 12px 0 rgba(0, 0, 0, 0.24); - opacity: 1; - -webkit-animation: bannerOpaque 0.2s; - animation: bannerOpaque 0.2s; -} -@-webkit-keyframes bannerOpaque { - from { - opacity:0 - } - to { - opacity:1; - } -} -@keyframes bannerOpaque { - from { - opacity:0 - } - to { - opacity:1; - } -} -.close-banner { - position: absolute;right:1rem; - z-index: 1; - cursor: pointer; - -webkit-transform: translate(0,-3px); - -ms-transform: translate(0,-3px); - transform: translate(0,-3px); -} -.close-banner::before { - content:""; - width:0px; - display: block; - position: absolute; - margin-top:26px; - left:50%; - height:0px; - background-color:rgba(0,0,0,0.15); - -webkit-border-radius:50%; - border-radius:50%; - z-index:-1; - cursor: pointer; - -webkit-transition:all 0.2s; - -o-transition:all 0.2s; - transition:all 0.2s; -} -.close-banner:hover::before { - content:""; - width:32px; - margin-top: 7px; - display: block; - position: absolute; - left:0px; - height:32px; - background-color:rgba(0,0,0,0.15); - -webkit-border-radius:50%; - border-radius:50%; -z-index:-1; -} -@media only screen and (max-width:991px) { - .banner { - font-size:1.2rem; - } -} -/*OTHER*/ -#globe-svg { - height:60px; - fill:#fefefe -} -#page-cover { - width:100vw; - top:-100vh; - left:0px; - -webkit-transition-delay: 0.3s; - -o-transition-delay: 0.3s; - transition-delay: 0.3s; - -webkit-transition:all 0.7s; - -o-transition:all 0.7s; - transition:all 0.7s; - height:100vh; - position: fixed; - z-index:1000; - background-color: rgba(54, 61, 75, 0.25); -} -#menu-button { - border:none; - outline:none; -} -#menu-bar { - -webkit-transition: all 0.15s; - -o-transition: all 0.15s; - transition: all 0.15s; -} -#close-bar { - -webkit-transition: all 0.15s; - -o-transition: all 0.15s; - transition: all 0.15s; - display: none; -} -#rect1 { - -webkit-transition: all 0.2s; - -o-transition: all 0.2s; - transition: all 0.2s; -} -#rect2 { --webkit-transition: all 0.2s; --o-transition: all 0.2s; -transition: all 0.2s; -} -#rect3 { --webkit-transition: all 0.2s; --o-transition: all 0.2s; -transition: all 0.2s; -} -@media only screen and (max-width: 991px) { - - .content-wrapper { - width:-webkit-calc(100%); - width:calc(100%); - left:0; - padding-left: 0; - margin-left:0; - margin-top:4.7rem; - } - .container-fluid { - padding-left:20px; - padding-right:20px; - } - .row { - margin-left:-20px; - margin-right:-20px; - } - #menu-button { - margin-right:20px; - padding:0px; - } - .navbar-brand { - margin-left: 20px; - padding-left: 0px; - } -} -.bot-logo { - margin-bottom:0.5rem; -} -@media only screen and (min-width:1200px){ - #page-cover { - display: none - } - .bot-logo { - margin-left:15px; - } -} -@media only screen and (max-width: 1199px) { - #globe-svg { - height:60px; - fill:var(--sg1); - } - .navbar-collapse.show { - -webkit-box-shadow:0px 10px 24px rgba(0,0,0,0.15) ; - box-shadow:0px 10px 24px rgba(0,0,0,0.15) ; - } - .nav-item:first-child { - border-top: 1px solid rgba(255,255,255,0.35); - } - #menu-button { - margin-right: -webkit-calc(10% + 15px); - margin-right: calc(10% + 15px); - padding:0; - } - #menu-button:hover { - background-color: transparent; - } - .nav-item { - height:auto; - border-bottom: 1px solid rgba(0,0,0,0.35); - padding-left: -webkit-calc(10% + 15px); - padding-left: calc(10% + 15px); - } - .nav-link{ - line-height: 3rem; - padding: 0px; - - } - .nav-link{ - color:var(--sg1) !important; - } - .nav-item:hover { - border-bottom: 1px solid rgba(0,0,0,0.35); - - } - .navbar-nav { - background-color: var(--white2); - margin-top: 15px; - } - .navbar-nav .active { - border-bottom: 1px solid rgba(0,0,0,0.35); - } - .nav-item:nth-child(even) { - /* - background-color:rgba(0,0,0,0.05); - padding-left: 1rem; - margin-left: -1rem; - */ - } - #navbarSupportedContent { - - } - #language-dropdown .dropdown-menu{ - width: -webkit-calc(80% + 4rem); - width: calc(80% + 4rem); - background-color: var(--white); - - } - .dropdown-menu { - border:none; - margin-top: -20px; - } - .nav-item:last-child { - border-bottom:none; - } - .dropdown-menu.show { - -webkit-box-shadow: 0 4px 24px rgba(100, 109, 146, 0.15); - box-shadow: 0 4px 24px rgba(100, 109, 146, 0.15); - margin-bottom:1rem; - margin-top:-0.5rem; - } - .dropdown-item { - padding-left: 15px; - font-weight:300; - } - .navbar-nav { - position: relative; - right:0rem; - } - .long-form input { - width:100%; - } -} -@media only screen and (max-width: 991px) { - .nav-item { - padding-left: 20px; - padding-right: 20px; - } - #language-dropdown{ - padding-left:20px; - } - #language-dropdown .dropdown-menu { - width:100%; - } - #menu-button { - margin-right: 20px; - padding:0; - } - .navbar { - padding-top: 0.25rem; - padding-bottom:0.25rem; - } - .logo { - height:1.8rem; - } - .anchor { - top: -55px; - } -} -@media only screen and (max-width:556px) { - #legal-1 { - width:100%; - } - .legal-links { - position: inherit; - margin-left: 20px; - margin-bottom: 1em; - } -} -@media only screen and (max-width:375px) { - #legal-1 p { - display: block; - } -} - -/*Footer media queries*/ -@media only screen and (max-width:830px) { -} -@media only screen and (max-width:650px) { -} -@media only screen and (max-width:352px) { -} - -.lds-ring { - display: inline-block; - position: relative; - width: 18px; - height: 18px; - padding-top:2px; -} -#email-subscribe-form .lds-ring { - padding-top:1px; -} -.lds-ring div { - -webkit-box-sizing: border-box; - box-sizing: border-box; - display: block; - position: absolute; - width: 18px; - height: 18px; - border: 2px solid var(--b2); - -webkit-border-radius: 50%; - border-radius: 50%; - -webkit-animation: lds-ring 1.2s cubic-bezier(0.5, 0, 0.5, 1) infinite; - animation: lds-ring 1.2s cubic-bezier(0.5, 0, 0.5, 1) infinite; - border-color: var(--b2) transparent transparent transparent; -} -.lds-ring div:nth-child(1) { - -webkit-animation-delay: -0.45s; - animation-delay: -0.45s; -} -.lds-ring div:nth-child(2) { - -webkit-animation-delay: -0.3s; - animation-delay: -0.3s; -} -.lds-ring div:nth-child(3) { - -webkit-animation-delay: -0.15s; - animation-delay: -0.15s; -} -@-webkit-keyframes lds-ring { - 0% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); - } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); - } -} -@keyframes lds-ring { - 0% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); - } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); - } -} -#email-subscribe-form .sub-arrow { - padding-top:2px; -} -.sub-arrow { - display: inline-block; -} -.sub-load { - display:none; -} diff --git a/documentation/tdenginedocs-en/styles/base.min.css b/documentation/tdenginedocs-en/styles/base.min.css deleted file mode 100644 index 7aa94277026265a64decb3717fdc680b8a338d59..0000000000000000000000000000000000000000 --- a/documentation/tdenginedocs-en/styles/base.min.css +++ /dev/null @@ -1 +0,0 @@ -:root{--b1:rgb(0,118,206);--b1t:rgba(0,118,206,0.15);--b2:rgb(72,159,223);--sg-1:#b3b4b9;--sg0:#585c66;--sg1:rgb(51,56,68);--sg2:#2F333E;--sg3:#21242c;--black:#212529;--white:#fefefe;--white2:rgb(251, 251, 253);--white3:rgb(240,242,244);--footer1:#fefefe;--footer2:#333844;--red:#ea4741;--green:#72c156;--p1:#72c156;--p1t:rgba(114,193,86,0.15);--p2:#43b3ae;--p2t:rgba(70,161,168,0.15);--p3:#4997d0;--p3t:rgba(73,151,208,0.15)}html{font-size:12pt;background-color:var(--white)}body,body *{font-family:"Open Sans",Helvetica,'Hiragino Sans GB',sans-serif,"Apple Color Emoji";-webkit-font-smoothing:auto!important;-moz-osx-font-smoothing:auto!important;font-smooth:auto!important;letter-spacing:normal;line-height:1.6}body{-webkit-box-sizing:border-box;box-sizing:border-box;font-weight:300;color:var(--sg1);font-family:"Open Sans",Helvetica,sans-serif!important;background-color:var(--white)}strong{font-weight:600}.anchor{display:block;position:relative;z-index:-1;top:-10px}input{outline:0;-webkit-box-shadow:inset 0 0 0 0 transparent;box-shadow:inset 0 0 0 0 transparent}input[type=submit],input[type=text],textarea{-webkit-appearance:none}input[l]{font-size:inherit;outline:0;color:var(--sg1);padding-left:.4em;width:-webkit-calc(100%);width:calc(100%);border:solid 1px;display:inline-block;border-left:1px solid;-webkit-border-radius:4px;border-radius:4px;-webkit-transition:border-left .2s;-o-transition:border-left .2s;transition:border-left .2s;vertical-align:top;font-weight:400;border-color:inherit;margin-bottom:.5rem}input[l]:focus{border-left:1rem solid}input[l]:focus{width:-webkit-calc(auto);width:calc(auto)}input[plain]:valid{border-color:var(--b1)}input[plain]:focus:valid{border-color:var(--b1)}textarea{-webkit-box-shadow:inset 0 0 0 0 transparent;box-shadow:inset 0 0 0 0 transparent}textarea[l]{font-size:inherit;outline:0;color:var(--sg1);padding-left:.4em;width:-webkit-calc(100%);width:calc(100%);border:solid 1px;display:inline-block;border-left:1px solid;-webkit-border-radius:4px;border-radius:4px;-webkit-transition:border-left .2s;-o-transition:border-left .2s;transition:border-left .2s;vertical-align:top;font-weight:400;border-color:inherit;margin-bottom:.5rem}ul{padding-left:30px}li,p{font-size:1em}p{margin-bottom:.5rem}h1{font-size:2.5rem;line-height:1.8}h2{font-size:1.7rem;line-height:1.8}h3{font-size:1.4rem;line-height:1.43}h4{font-size:1.25rem}h5{font-size:1rem}h6{font-size:1rem;color:#777}h1[b]::before,h2[b]::before,h3[b]::before{content:"";height:1em;display:block;width:3px;margin-left:-.5em;margin-top:.45em;position:absolute;background-color:var(--b1)}h1[b],h2[b],h3[b]{padding-left:.5em}.logo{height:2.5rem}a{font-size:1em}a:hover{text-decoration:none}a[l]{color:var(--b2);padding-bottom:2px;position:relative;font-style:normal;cursor:pointer}a[l]:focus,a[l]:hover{text-decoration:none}a[l]::before{content:"";left:0;background-color:var(--b2);width:0%;height:1px;top:-webkit-calc(1em + 8px);top:calc(1em + 8px);position:absolute;z-index:2;-webkit-transition:background-color .2s,height .2s,top .2s,width .2s;-o-transition:background-color .2s,height .2s,top .2s,width .2s;transition:background-color .2s,height .2s,top .2s,width .2s}a[l]:focus::before,a[l]:hover::before{content:"";left:0;background-color:var(--b2);width:100%;height:1px;top:-webkit-calc(1em + 8px);top:calc(1em + 8px);position:absolute;z-index:2;-webkit-transition:background-color .2s,height .2s,top .2s,width .2s;-o-transition:background-color .2s,height .2s,top .2s,width .2s;transition:background-color .2s,height .2s,top .2s,width .2s;text-decoration:none}.navbar-brand{margin-left:10%;padding-left:15px;color:var(--white)!important}.navbar-nav{top:0}.navbar{background-color:var(--sg1);z-index:10000;padding-left:0;padding-right:0;padding-top:.75rem;padding-bottom:.75rem}.navbar-toggler{margin-right:-webkit-calc(2rem + 15px);margin-right:calc(2rem + 15px)}.nav-link{color:var(--white)!important;line-height:3.65rem}.nav-item{height:4.65rem;font-size:1.1rem;padding-left:.15rem;padding-right:.15rem;-webkit-transition:all .2s;-o-transition:all .2s;transition:all .2s;border-bottom:0 solid var(--white)}.nav-item:hover{border-bottom:.45rem solid var(--white)}.dropdown-menu{top:4.1rem;z-index:1000;border-top:none;border:none;min-width:120px;margin-left:-1px;-webkit-border-top-left-radius:0;border-top-left-radius:0;-webkit-border-top-right-radius:0;border-top-right-radius:0;-webkit-border-bottom-left-radius:.25rem;border-bottom-left-radius:.25rem;-webkit-border-bottom-right-radius:.25rem;border-bottom-right-radius:.25rem}.dropdown-menu.show{-webkit-box-shadow:0 4px 24px rgba(100,109,146,.15);box-shadow:0 4px 24px rgba(100,109,146,.15)}.dropdown-item{color:var(--sg1);background-color:var(--white);-webkit-transition:all .2s;-o-transition:all .2s;transition:all .2s;cursor:pointer}.dropdown-item:active,.dropdown-item:hover{background-color:var(--sg1);color:var(--white)!important}.dropdown-toggle::after{display:none}.dropdown a::after{-webkit-transform:rotate(-90deg);-ms-transform:rotate(-90deg);transform:rotate(-90deg);-webkit-transition:-webkit-transform .2s;transition:-webkit-transform .2s;-o-transition:transform .2s;transition:transform .2s;transition:transform .2s,-webkit-transform .2s}.dropdown.show a::after{-webkit-transform:rotate(0);-ms-transform:rotate(0);transform:rotate(0)}.navbar-nav .active{border-bottom:.45rem solid var(--white)}.navbar-nav{position:absolute;right:-webkit-calc(10% + 15px);right:calc(10% + 15px)}#language-dropdown .dropdown-menu{width:50px}footer{background-color:var(--footer2);padding-top:1rem}.page-footer{padding-bottom:2rem}.footer-contact,.footer-content,.footer-legal{width:80%;margin-left:10%;padding-top:1rem;color:var(--footer1);font-size:.8em}.footer-content a{color:var(--footer1)}.footer-content a{color:var(--footer1)}.links-list{text-align:left;list-style:none;padding:0}.content-wrapper>.links-list{padding-left:15px}.links-list-title h4{font-size:1.2em;font-weight:400}.legal-links{position:absolute;right:-webkit-calc(10% + 15px);right:calc(10% + 15px)}.legal-links a{color:var(--footer1)}.links-list li{height:2em}.legal-links a::before,.links-list li a::before{background-color:var(--footer1)}.legal-links a:hover::before,.links-list li a:hover::before{background-color:var(--footer1)}.links-list .divider{border-bottom:1px solid var(--footer1);opacity:.15;height:0;margin-bottom:.3em}.footer-divider{border-bottom:1px solid var(--footer1);width:-webkit-calc(80% - 30px);width:calc(80% - 30px);margin-left:-webkit-calc(10% + 15px);margin-left:calc(10% + 15px)}#social-media-links li{height:2rem;line-height:2rem;display:inline-block;font-size:1em}#social-media-links li:last-child::after{content:""}#social-media-links li::after{content:" | "}#social-media-links svg{margin-left:2px;margin-right:.4rem;width:20px}#social-media-links svg path{fill:var(--footer1)}#social-media-links li a::before{left:1.9rem;background-color:var(--footer1)}#social-media-links li a:focus::before,#social-media-links li a:hover::before{left:1.9rem;width:-webkit-calc(100% - 1.9rem);width:calc(100% - 1.9rem);background-color:var(--footer1)}#social-media-links ion-icon{font-size:20px;margin-right:.5rem}#social-media-links svg{font-size:20px;margin-right:.5rem}#email-subscribe-form{width:-webkit-calc(100% - 160px);width:calc(100% - 160px)}#email-subscribe-form input{width:-webkit-calc(100% - 4rem);width:calc(100% - 4rem);font-size:1.2em;outline:0;height:1.8em;color:var(--sg1);padding-left:.6em;border:none;display:inline-block;border-left:0 solid var(--b1);-webkit-border-radius:4px;border-radius:4px;-webkit-transition:border-left .2s;-o-transition:border-left .2s;transition:border-left .2s;vertical-align:top;font-weight:400}#email-subscribe-form input:focus{border-left:1rem solid var(--b1);padding-top:2px}#email-subscribe-form input:invalid,#email-subscribe-form input:invalid:focus{border-color:var(--b1)}#email-subscribe-form input.invalid-input,#email-subscribe-form input.invalid-input:focus{border-color:var(--red)}#email-subscribe-form input:valid,#email-subscribe-form input:valid:focus{border-color:var(--green)}#email-subscribe-form button{font-size:1.2em;height:1.8em;line-height:1em;float:right;width:3rem;padding:0}form{border-color:var(--b1)}form input:invalid,form input:invalid:focus{border-color:inherit}form input.invalid-input,form input.invalid-input:focus,form textarea.invalid-input,form textarea.invalid-input:focus{border-color:var(--red)}form input:valid,form input:valid:focus{border-color:var(--green)}.sub-arrow{width:1.2em;fill:var(--b1)}@media only screen and (max-width:991px){.page-footer{padding-left:20px;padding-right:20px}.footer-legal{width:100%}#legal-1{padding-left:20px}.legal-links{right:20px}.footer-content .col-xl-4,.footer-content .col-xl-8{padding-left:20px;padding-right:20px}.footer-content{width:-webkit-calc(100% + 40px);width:calc(100% + 40px)}.footer-divider{width:100%;margin-left:0}}.content-wrapper{width:80%;margin-left:10%;margin-top:6rem;margin-bottom:3rem;min-height:-webkit-calc(100vh - 187.7px - 74.45px);min-height:calc(100vh - 187.7px - 74.45px)}.section-item-title,.section-title{color:var(--b1)}.container-fluid{background-color:var(--white)}.center{left:50%;position:relative}.btn-primary{color:var(--b1);background-color:var(--white);border-color:var(--b1);-webkit-box-shadow:0 0 0 0 rgba(255,255,255,.55);box-shadow:0 0 0 0 rgba(255,255,255,.55);-webkit-transition:all .2s;-o-transition:all .2s;transition:all .2s}.btn-primary:focus,.btn-primary:hover{color:var(--b1);background-color:var(--white);border-color:var(--b1);-webkit-box-shadow:4px 4px 0 0 var(--b1t);box-shadow:4px 4px 0 0 var(--b1t);-webkit-transform:translate(-2px,-2px);-ms-transform:translate(-2px,-2px);transform:translate(-2px,-2px)}.btn-primary:active{color:var(--b1)!important;background-color:var(--white)!important;border-color:var(--b1)!important;-webkit-box-shadow:2px 2px 0 0 var(--b1t);box-shadow:2px 2px 0 0 var(--b1t);-webkit-transform:translate(-1px,-1px);-ms-transform:translate(-1px,-1px);transform:translate(-1px,-1px)}.btn-white{color:var(--b1);background-color:var(--white);-webkit-transition:all .2s;-o-transition:all .2s;transition:all .2s;-webkit-box-shadow:0 0 0 0 rgba(255,255,255,.55);box-shadow:0 0 0 0 rgba(255,255,255,.55)}.btn-white:focus,.btn-white:hover{color:var(--b1);background-color:var(--white);-webkit-box-shadow:4px 4px 0 0 rgba(255,255,255,.55);box-shadow:4px 4px 0 0 rgba(255,255,255,.55);-webkit-transform:translate(-2px,-2px);-ms-transform:translate(-2px,-2px);transform:translate(-2px,-2px)}.btn-white:active{color:var(--b1)!important;background-color:var(--white)!important;-webkit-box-shadow:2px 2px 0 0 rgba(255,255,255,.55);box-shadow:2px 2px 0 0 rgba(255,255,255,.55);-webkit-transform:translate(-1px,-1px);-ms-transform:translate(-1px,-1px);transform:translate(-1px,-1px)}.btn-filled{color:var(--white)!important;background-color:var(--b1);border-color:var(--b1);-webkit-box-shadow:0 0 0 0 rgba(255,255,255,.55);box-shadow:0 0 0 0 rgba(255,255,255,.55);-webkit-transition:all .2s;-o-transition:all .2s;transition:all .2s}.btn-filled:hover{color:var(--white)!important;background-color:var(--b1);border-color:var(--b1);-webkit-box-shadow:4px 4px 0 0 var(--b1t);box-shadow:4px 4px 0 0 var(--b1t);-webkit-transform:translate(-2px,-2px);-ms-transform:translate(-2px,-2px);transform:translate(-2px,-2px)}.btn-filled:active{color:var(--white)!important;background-color:var(--b1)!important;border-color:var(--b1)!important;-webkit-box-shadow:2px 2px 0 0 var(--b1t);box-shadow:2px 2px 0 0 var(--b1t);-webkit-transform:translate(-1px,-1px);-ms-transform:translate(-1px,-1px);transform:translate(-1px,-1px)}#popup-wrapper{display:block;position:absolute;z-index:1000;-webkit-transition:opacity .5s;-o-transition:opacity .5s;transition:opacity .5s;opacity:1}#popup-page-cover{display:none;position:fixed;height:100vh;width:100vw;top:0;left:0;background-color:rgba(131,145,174,.32);z-index:1000;-webkit-transition:opacity .5s;-o-transition:opacity .5s;transition:opacity .5s;opacity:0}#popup{position:fixed;display:none;height:auto;width:100px;z-index:1001;max-width:-webkit-calc(100% - 30px);max-width:calc(100% - 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div:nth-child(2){-webkit-animation-delay:-.3s;animation-delay:-.3s}.lds-ring div:nth-child(3){-webkit-animation-delay:-.15s;animation-delay:-.15s}@-webkit-keyframes lds-ring{0%{-webkit-transform:rotate(0);transform:rotate(0)}100%{-webkit-transform:rotate(360deg);transform:rotate(360deg)}}@keyframes lds-ring{0%{-webkit-transform:rotate(0);transform:rotate(0)}100%{-webkit-transform:rotate(360deg);transform:rotate(360deg)}}#email-subscribe-form .sub-arrow{padding-top:2px}.sub-arrow{display:inline-block}.sub-load{display:none} \ No newline at end of file diff --git a/documentation/tdenginedocs-en/super-table/index.html b/documentation/tdenginedocs-en/super-table/index.html deleted file mode 100644 index 0e47a7bb9b4a170c70f9b484096f3af625865961..0000000000000000000000000000000000000000 --- a/documentation/tdenginedocs-en/super-table/index.html +++ /dev/null @@ -1,95 +0,0 @@ -Documentation | Taos Data
    Back

    STable: Super Table

    -

    "One Table for One Device" design can improve the insert/query performance significantly for a single device. But it has a side effect, the aggregation of multiple tables becomes hard. To reduce the complexity and improve the efficiency, TDengine introduced a new concept: STable (Super Table).

    -

    What is a Super Table

    -

    STable is an abstract and a template for a type of device. A STable contains a set of devices (tables) that have the same schema or data structure. Besides the shared schema, a STable has a set of tags, like the model, serial number and so on. Tags are used to record the static attributes for the devices and are used to group a set of devices (tables) for aggregation. Tags are metadata of a table and can be added, deleted or changed.

    -

    TDengine does not save tags as a part of the data points collected. Instead, tags are saved as metadata. Each table has a set of tags. To improve query performance, tags are all cached and indexed. One table can only belong to one STable, but one STable may contain many tables.

    -

    Like a table, you can create, show, delete and describe STables. Most query operations on tables can be applied to STable too, including the aggregation and selector functions. For queries on a STable, if no tags filter, the operations are applied to all the tables created via this STable. If there is a tag filter, the operations are applied only to a subset of the tables which satisfy the tag filter conditions. It will be very convenient to use tags to put devices into different groups for aggregation.

    -

    Create a STable

    -

    Similiar to creating a standard table, syntax is:

    -
    CREATE TABLE <stable_name> (<field_name> TIMESTAMP, field_name1 field_type, ...) TAGS(tag_name tag_type, ...)
    -

    New keyword "tags" is introduced, where tag_name is the tag name, and tag_type is the associated data type.

    -

    Note:

    -
      -
    1. The bytes of all tags together shall be less than 512
      2. -
    2. Tag's data type can not be time stamp or nchar
      3. -
    3. Tag name shall be different from the field name
      4. -
    4. Tag name shall not be the same as system keywords
      5. -
    5. Maximum number of tags is 6
      6. -
    -

    For example:

    -
    create table thermometer (ts timestamp, degree float) 
-tags (location binary(20), type int)
    -

    The above statement creates a STable thermometer with two tag "location" and "type"

    -

    Create a Table via STable

    -

    To create a table for a device, you can use a STable as its template and assign the tag values. The syntax is:

    -
    CREATE TABLE <tb_name> USING <stb_name> TAGS (tag_value1,...)
    -

    You can create any number of tables via a STable, and each table may have different tag values. For example, you create five tables via STable thermometer below:

    -
     create table t1 using thermometer tags ('beijing', 10);
- create table t2 using thermometer tags ('beijing', 20);
- create table t3 using thermometer tags ('shanghai', 10);
- create table t4 using thermometer tags ('shanghai', 20);
- create table t5 using thermometer tags ('new york', 10);
    -

    Aggregate Tables via STable

    -

    You can group a set of tables together by specifying the tags filter condition, then apply the aggregation operations. The result set can be grouped and ordered based on tag value. Syntax is:

    -
    SELECT function<field_name>, ... 
- FROM <stable_name> 
- WHERE <tag_name> <[=|<=|>=|<>] values..> ([AND|OR] ...
- INTERVAL (<time range>)
- GROUP BY <tag_name>, <tag_name> ... 
- ORDER BY <tag_name> <asc|desc>
- SLIMIT <group_limit>
- SOFFSET <group_offset>
- LIMIT <record_limit>
- OFFSET <record_offset>
    -

    For the time being, STable supports only the following aggregation/selection functions: sum, count, avg, first, last, min, max, top, bottom, and the projection operations, the same syntax as a standard table. Arithmetic operations are not supported, embedded queries not either.

    -

    INTERVAL is used for the aggregation over a time range.

    -

    If GROUP BY is not used, the aggregation is applied to all the selected tables, and the result set is output in ascending order of the timestamp, but you can use "ORDER BY _c0 ASC|DESC" to specify the order you like.

    -

    If GROUP BY is used, the aggregation is applied to groups based on tags. Each group is aggregated independently. Result set is a group of aggregation results. The group order is decided by ORDER BY . Inside each group, the result set is in the ascending order of the time stamp.

    -

    SLIMIT/SOFFSET are used to limit the number of groups and starting group number.

    -

    LIMIT/OFFSET are used to limit the number of records in a group and the starting rows.

    -

    Example 1:

    -

    Check the average, maximum, and minimum temperatures of Beijing and Shanghai, and group the result set by location and type. The SQL statement shall be:

    -
    SELECT COUNT(*), AVG(degree), MAX(degree), MIN(degree)
-FROM thermometer
-WHERE location='beijing' or location='tianjin'
-GROUP BY location, type
    -

    Example 2:

    -

    List the number of records, average, maximum, and minimum temperature every 10 minutes for the past 24 hours for all the thermometers located in Beijing with type 10. The SQL statement shall be:

    -
    SELECT COUNT(*), AVG(degree), MAX(degree), MIN(degree)
-FROM thermometer
-WHERE name='beijing' and type=10 and ts>=now-1d
-INTERVAL(10M)
    -

    Create Table Automatically

    -

    Insert operation will fail if the table is not created yet. But for STable, TDengine can create the table automatically if the application provides the STable name, table name and tags' value when inserting data points. The syntax is:

    -
    INSERT INTO <tb_name> USING <stb_name> TAGS (<tag1_value>, ...) VALUES (field_value, ...) (field_value, ...) ... <tb_name2> USING <stb_name2> TAGS(<tag1_value2>, ...) VALUES (<field1_value1>, ...) ...;
    -

    When inserting data points into table tb_name, the system will check if table tb_name is created or not. If it is already created, the data points will be inserted as usual. But if the table is not created yet, the system will create the table tb_bame using STable stb_name as the template with the tags. Multiple tables can be specified in the SQL statement.

    -

    Management of STables

    -

    After you can create a STable, you can describe, delete, change STables. This section lists all the supported operations.

    -

    Show STables in current DB

    -
    show stables;
    -

    It lists all STables in current DB, including the name, created time, number of fileds, number of tags, and number of tables which are created via this STable.

    -

    Describe a STable

    -
    DESCRIBE <stable_name>
    -

    It lists the STable's schema and tags

    -

    Drop a STable

    -
    DROP TABLE <stable_name>
    -

    To delete a STable, all the tables created via this STable will be deleted.

    -

    List the Associated Tables of a STable

    -
    SELECT TBNAME,[TAG_NAME, ...] FROM <stable_name> WHERE <tag_name> <[=|=<|>=|<>] values..> ([AND|OR] ...)
    -

    It will list all the tables which satisfy the tag filter conditions. The tables are all created from this specific STable. TBNAME is a new keyword introduced, it is the table name associated with the STable.

    -
    SELECT COUNT(TBNAME) FROM <stable_name> WHERE <tag_name> <[=|=<|>=|<>] values..> ([AND|OR] ...)
    -

    The above SQL statement will list the number of tables in a STable, which satisfy the filter condition.

    -

    Management of Tags

    -

    You can add, delete and change the tags for a STable, and you can change the tag value of a table. The SQL commands are listed below.

    -

    Add a Tag

    -
    ALTER TABLE <stable_name> ADD TAG <new_tag_name> <TYPE>
    -

    It adds a new tag to the STable with a data type. The maximum number of tags is 6.

    -

    Drop a Tag

    -
    ALTER TABLE <stable_name> DROP TAG <tag_name>
    -

    It drops a tag from a STable. The first tag could not be deleted, and there must be at least one tag.

    -

    Change a Tag's Name

    -
    ALTER TABLE <stable_name> CHANGE TAG <old_tag_name> <new_tag_name>
    -

    It changes the name of a tag from old to new.

    -

    Change the Tag's Value

    -
    ALTER TABLE <table_name> SET TAG <tag_name>=<new_tag_value>
    -

    It changes a table's tag value to a new one.

    Back
    diff --git a/documentation/tdenginedocs-en/taos-sql/index.html b/documentation/tdenginedocs-en/taos-sql/index.html deleted file mode 100644 index 6b73a70cd63d7abfac6706acda0f7b55630b6f0e..0000000000000000000000000000000000000000 --- a/documentation/tdenginedocs-en/taos-sql/index.html +++ /dev/null @@ -1,423 +0,0 @@ -Documentation | Taos Data
    Back

    TAOS SQL

    -

    TDengine provides a SQL like query language to insert or query data. You can execute the SQL statements through TDengine Shell, or through C/C++, Java(JDBC), Python, Restful, Go APIs to interact with the taosd service.

    -

    Before reading through, please have a look at the conventions used for syntax descriptions here in this documentation.

    -
      -
    • Squared brackets ("[]") indicate optional arguments or clauses
      • -
    • Curly braces ("{}") indicate that one member from a set of choices in the braces must be chosen
      • -
    • A single verticle line ("|") works a separator for multiple optional args or clauses
      • -
    • Dots ("…") means repeating for as many times
      • -
    -

    Data Types

    -

    Timestamp

    -

    The timestamp is the most important data type in TDengine. The first column of each table must be TIMESTAMP type, but other columns can also be TIMESTAMP type. The following rules for timestamp:

    -
      -

    • String Format: 'YYYY-MM-DD HH:mm:ss.MS', which represents the year, month, day, hour, minute and second and milliseconds. For example,'2017-08-12 18:52:58.128' is a valid timestamp string. Note: timestamp string must be quoted by either single quote or double quote.

      • -

    • Epoch Time: a timestamp value can also be a long integer representing milliseconds since the epoch. For example, the values in the above example can be represented as an epoch 1502535178128 in milliseconds. Please note the epoch time doesn't need any quotes.

      • -

    • Internal FunctionNOW : this is the current time of the server

      • -

    • If timestamp is 0 when inserting a record, timestamp will be set to the current time of the server

      • -

    • Arithmetic operations can be applied to timestamp. For example: now-2h represents a timestamp which is 2 hours ago from the current server time. Units include a (milliseconds), s (seconds), m (minutes), h (hours), d (days), w (weeks), n (months), y (years). NOW can be used in either insertions or queries.

      • -
    -

    Default time precision is millisecond, you can change it to microseocnd by setting parameter enableMicrosecond in system configuration. For epoch time, the long integer shall be microseconds since the epoch. For the above string format, MS shall be six digits.

    -

    Data Types

    -

    The full list of data types is listed below. For string types of data, we will use M to indicate the maximum length of that type.

    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Data TypeBytesNote
    1TINYINT1A nullable integer type with a range of [-127, 127]​
    2SMALLINT2A nullable integer type with a range of [-32767, 32767]​
    3INT4A nullable integer type with a range of [-2^31+1, 2^31-1 ]
    4BIGINT8A nullable integer type with a range of [-2^59, 2^59 ]​
    5FLOAT4A standard nullable float type with 6 -7 significant digits and a range of [-3.4E38, 3.4E38]
    6DOUBLE8A standard nullable double float type with 15-16 significant digits and a range of [-1.7E308, 1.7E308]​
    7BOOL1A nullable boolean type, [true, false]
    8TIMESTAMP8A nullable timestamp type with the same usage as the primary column timestamp
    9BINARY(M)MA nullable string type whose length is M, any exceeded chars will be automatically truncated. This type of string only supports ASCii encoded chars.
    10NCHAR(M)4 * MA nullable string type whose length is M, any exceeded chars will be truncated. The NCHAR type supports Unicode encoded chars.
    -

    All the keywords in a SQL statement are case-insensitive, but strings values are case-sensitive and must be quoted by a pair of ' or ". To quote a ' or a " , you can use the escape character \.

    -

    Database Management

    -
      -

    • Create a Database

      -
      CREATE DATABASE [IF NOT EXISTS] db_name [KEEP keep]
      -

      Option: KEEP is used for data retention policy. The data records will be removed once keep-days are passed. There are more parameters related to DB storage, please check system configuration.

      • -

    • Use a Database

      -
      USE db_name
      -

      Use or switch the current database.

      • -

    • Drop a Database

      -
      DROP DATABASE [IF EXISTS] db_name
      -

      Remove a database, all the tables inside the DB will be removed too, be careful.

      • -

    • List all Databases

      -
      SHOW DATABASES
      • -
    -

    Table Management

    -
      -

    • Create a Table

      -
      CREATE TABLE [IF NOT EXISTS] tb_name (timestamp_field_name TIMESTAMP, field1_name data_type1 [, field2_name data_type2 ...])
      -

      Note: 1) the first column must be timstamp, and system will set it as the primary key; 2) the record size is limited to 4096 bytes; 3) for binary or nachr data type, the length shall be specified, for example, binary(20), it means 20 bytes.

      • -

    • Drop a Table

      -
      DROP TABLE [IF EXISTS] tb_name
      • -

    • **List all Tables **

      -
      SHOW TABLES [LIKE tb_name_wildcar]
      -

      It shows all tables in the current DB. Note: wildcard character can be used in the table name to filter tables. Wildcard character: 1) ’%’ means 0 to any number of characters; 2）’_’ underscore means exactly one character.

      • -

    • Print Table Schema

      -
      DESCRIBE tb_name
      • -

    • Add a Column

      -
      ALTER TABLE tb_name ADD COLUMN field_name data_type
      • -

    • Drop a Column

      -
      ALTER TABLE tb_name DROP COLUMN field_name
      -

      If the table is created via [Super Table](), the schema can only be changed via STable. But for tables not created from STable, you can change their schema directly.

      • -
    -

    Tips: You can apply an operation on a table not in the current DB by concatenating DB name with the character '.', then with table name. For example, 'demo.tb1' means the operation is applied to table tb1 in DB demo although demo is not the current selected DB.

    -

    Inserting Records

    -
      -

    • Insert a Record

      -
      INSERT INTO tb_name VALUES (field_value, ...);
      -

      Insert a data record into table tb_name

      • -

    • Insert a Record with Selected Columns

      -
      INSERT INTO tb_name (field1_name, ...) VALUES(field1_value, ...)
      -

      Insert a data record into table tb_name, with data in selected columns. If a column is not selected, the system will put NULL there. First column (time stamp ) cant not be null, it must be inserted.

      • -

    • Insert a Batch of Records

      -
      INSERT INTO tb_name VALUES (field1_value1, ...) (field1_value2, ...)...;
      -

      Insert multiple data records to the table

      • -

    • Insert a Batch of Records with Selected Columns

      -
      INSERT INTO tb_name (field1_name, ...) VALUES(field1_value1, ...) (field1_value2, ...)
      • -

    • Insert Records into Multiple Tables

      -
      INSERT INTO tb1_name VALUES (field1_value1, ...)(field1_value2, ...)... 
-            tb2_name VALUES (field1_value1, ...)(field1_value2, ...)...;
      -

      Insert data records into table tb1_name and tb2_name

      • -

    • Insert Records into Multiple Tables with Selected Columns

      -
      INSERT INTO tb1_name (tb1_field1_name, ...) VALUES (field1_value1, ...) (field1_value1, ...)
-            tb2_name (tb2_field1_name, ...) VALUES(field1_value1, ...) (field1_value2, ...)
      • -
    -

    Note: For a table, the new record must have timestamp bigger than the last data record, otherwise, it will be thrown away. If timestamp is 0, the time stamp will be set to the system time on server.

    -

    IMPORT: If you do want to insert a historical data record into a table, use IMPORT command instead of INSERT. IMPORT has the same syntax as INSERT. If you want to import a batch of historical records, the records shall be ordered in the timestamp, otherwise, TDengine won't handle it in the right way.

    -

    Data Query

    -

    Query Syntax:

    -
    SELECT {* | expr_list} FROM tb_name
-    [WHERE where_condition]
-    [ORDER BY _c0 { DESC | ASC }]
-    [LIMIT limit [, OFFSET offset]]
-    [>> export_file]
-
-SELECT function_list FROM tb_name
-    [WHERE where_condition]
-    [LIMIT limit [, OFFSET offset]]
-    [>> export_file]
    -
      -
    • To query a table, use * to select all data from a table; or a specified list of expressions expr_list of columns. The SQL expression can contain alias and arithmetic operations between numeric typed columns.
      • -
    • For the WHERE conditions, use logical operations to filter the timestamp column and all numeric columns, and wild cards to filter the two string typed columns.
      • -
    • Sort the result set by the first column timestamp _c0 (or directly use the timestamp column name) in either descending or ascending order (by default). "Order by" could not be applied to other columns.
      • -
    • Use LIMIT and OFFSET to control the number of rows returned and the starting position of the retrieved rows. LIMIT/OFFSET is applied after "order by" operations.
      • -
    • Export the retrieved result set into a CSV file using >>. The target file's full path should be explicitly specified in the statement.
      • -
    -

    Supported Operations of Data Filtering:

    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    OperationNoteApplicable Data Types
    >larger thantimestamp and all numeric types
    <smaller thantimestamp and all numeric types
    >=larger than or equal totimestamp and all numeric types
    <=smaller than or equal totimestamp and all numeric types
    =equal toall types
    <>not equal toall types
    %match with any char sequencesbinary nchar
    _match with a single charbinary nchar
    -
      -
    1. For two or more conditions, only AND is supported, OR is not supported yet.
      2. -
    2. For filtering, only a single range is supported. For example, value>20 and value<30 is valid condition, but value<20 AND value<>5 is invalid condition
      3. -
    -

    Some Examples

    -
      -

    • For the examples below, table tb1 is created via the following statements

      -
      CREATE TABLE tb1 (ts timestamp, col1 int, col2 float, col3 binary(50))
      • -

    • Query all the records in tb1 in the last hour:

      -
      SELECT * FROM tb1 WHERE ts >= NOW - 1h
      • -

    • Query all the records in tb1 between 2018-06-01 08:00:00.000 and 2018-06-02 08:00:00.000, and filter out only the records whose col3 value ends with 'nny', and sort the records by their timestamp in a descending order:

      -
      SELECT * FROM tb1 WHERE ts > '2018-06-01 08:00:00.000' AND ts <= '2018-06-02 08:00:00.000' AND col3 LIKE '%nny' ORDER BY ts DESC
      • -

    • Query the sum of col1 and col2 as alias 'complex_metric', and filter on the timestamp and col2 values. Limit the number of returned rows to 10, and offset the result by 5.

      -
      SELECT (col1 + col2) AS 'complex_metric' FROM tb1 WHERE ts > '2018-06-01 08:00:00.000' and col2 > 1.2 LIMIT 10 OFFSET 5
      • -

    • Query the number of records in tb1 in the last 10 minutes, whose col2 value is larger than 3.14, and export the result to file /home/testoutpu.csv.

      -
      SELECT COUNT(*) FROM tb1 WHERE ts >= NOW - 10m AND col2 > 3.14 >> /home/testoutpu.csv
      • -
    -

    SQL Functions

    -

    Aggregation Functions

    -

    TDengine supports aggregations over numerical values, they are listed below:

    -
      -

    • COUNT

      -
      SELECT COUNT([*|field_name]) FROM tb_name [WHERE clause]
      -

      Function: return the number of rows.
      -Return Data Type: integer.
      -Applicable Data Types: all.
      -Applied to: table/STable.
      -Note: 1) * can be used for all columns, as long as a column has non-NULL value, it will be counted; 2) If it is on a specific column, only rows with non-NULL value will be counted

      • -

    • AVG

      -
      SELECT AVG(field_name) FROM tb_name [WHERE clause]
      -

      Function: return the average value of a specific column.
      -Return Data Type: double.
      -Applicable Data Types: all types except timestamp, binary, nchar, bool.
      -Applied to: table/STable.

      • -

    • WAVG

      -
      SELECT WAVG(field_name) FROM tb_name WHERE clause
      -

      Function: return the time-weighted average value of a specific column
      -Return Data Type: double
      -Applicable Data Types: all types except timestamp, binary, nchar, bool
      -Applied to: table/STable

      • -

    • SUM

      -
      SELECT SUM(field_name) FROM tb_name [WHERE clause]
      -

      Function: return the sum of a specific column.
      -Return Data Type: long integer or double.
      -Applicable Data Types: all types except timestamp, binary, nchar, bool.
      -Applied to: table/STable.

      • -

    • STDDEV

      -
      SELECT STDDEV(field_name) FROM tb_name [WHERE clause]
      -

      Function: return the standard deviation of a specific column.
      -Return Data Type: double.
      -Applicable Data Types: all types except timestamp, binary, nchar, bool.
      -Applied to: table.

      • -

    • LEASTSQUARES

      -
      SELECT LEASTSQUARES(field_name) FROM tb_name [WHERE clause]
      -

      Function: performs a linear fit to the primary timestamp and the specified column. -Return Data Type: return a string of the coefficient and the interception of the fitted line.
      -Applicable Data Types: all types except timestamp, binary, nchar, bool.
      -Applied to: table.
      -Note: The timestmap is taken as the independent variable while the specified column value is taken as the dependent variables.

      • -
    -

    Selector Functions

    -
      -

    • MIN

      -
      SELECT MIN(field_name) FROM {tb_name | stb_name} [WHERE clause]
      -

      Function: return the minimum value of a specific column.
      -Return Data Type: the same data type.
      -Applicable Data Types: all types except timestamp, binary, nchar, bool.
      -Applied to: table/STable.

      • -

    • MAX

      -
      SELECT MAX(field_name) FROM { tb_name | stb_name } [WHERE clause]
      -

      Function: return the maximum value of a specific column.
      -Return Data Type: the same data type.
      -Applicable Data Types: all types except timestamp, binary, nchar, bool.
      -Applied to: table/STable.

      • -

    • FIRST

      -
      SELECT FIRST(field_name) FROM { tb_name | stb_name } [WHERE clause]
      -

      Function: return the first non-NULL value.
      -Return Data Type: the same data type.
      -Applicable Data Types: all types.
      -Applied to: table/STable.
      -Note: To return all columns, use first(*).

      • -

    • LAST

      -
      SELECT LAST(field_name) FROM { tb_name | stb_name } [WHERE clause]
      -

      Function: return the last non-NULL value.
      -Return Data Type: the same data type.
      -Applicable Data Types: all types.
      -Applied to: table/STable.
      -Note: To return all columns, use last(*).

      • -

    • TOP

      -
      SELECT TOP(field_name, K) FROM { tb_name | stb_name } [WHERE clause]
      -

      Function: return the k largest values.
      -Return Data Type: the same data type.
      -Applicable Data Types: all types except timestamp, binary, nchar, bool.
      -Applied to: table/STable.
      -Note: 1) valid range of K: 1≤k≤100; 2) the associated time stamp will be returned too.

      • -

    • BOTTOM

      -
      SELECT BOTTOM(field_name, K) FROM { tb_name | stb_name } [WHERE clause]
      -

      Function: return the k smallest values.
      -Return Data Type: the same data type.
      -Applicable Data Types: all types except timestamp, binary, nchar, bool.
      -Applied to: table/STable.
      -Note: 1) valid range of K: 1≤k≤100; 2) the associated timestamp will be returned too.

      • -

    • PERCENTILE

      -
      SELECT PERCENTILE(field_name, P) FROM { tb_name | stb_name } [WHERE clause]
      -

      Function: the value of the specified column below which P percent of the data points fall.
      -Return Data Type: the same data type.
      -Applicable Data Types: all types except timestamp, binary, nchar, bool.
      -Applied to: table/STable.
      -Note: The range of P is [0, 100]. When P=0 , PERCENTILE returns the equal value as MIN; when P=100, PERCENTILE returns the equal value as MAX.

      • -

    • LAST_ROW

      -
      SELECT LAST_ROW(field_name) FROM { tb_name | stb_name }
      -

      Function: return the last row.
      -Return Data Type: the same data type.
      -Applicable Data Types: all types.
      -Applied to: table/STable.
      -Note: different from last, last_row returns the last row even it has NULL value.

      • -
    -

    Transformation Functions

    -
      -

    • DIFF

      -
      SELECT DIFF(field_name) FROM tb_name [WHERE clause]
      -

      Function: return the difference between successive values of the specified column.
      -Return Data Type: the same data type.
      -Applicable Data Types: all types except timestamp, binary, nchar, bool.
      -Applied to: table.

      • -

    • SPREAD

      -
      SELECT SPREAD(field_name) FROM { tb_name | stb_name } [WHERE clause]
      -

      Function: return the difference between the maximum and the mimimum value.
      -Return Data Type: the same data type.
      -Applicable Data Types: all types except timestamp, binary, nchar, bool.
      -Applied to: table/STable.
      -Note: spread gives the range of data variation in a table/supertable; it is equivalent to MAX() - MIN()

      • -

    • Arithmetic Operations

      -
      SELECT field_name [+|-|*|/|%][Value|field_name] FROM { tb_name | stb_name }  [WHERE clause]
      -

      Function: arithmetic operations on the selected columns.
      -Return Data Type: double.
      -Applicable Data Types: all types except timestamp, binary, nchar, bool.
      -Applied to: table/STable.
      -Note: 1) bracket can be used for operation priority; 2) If a column has NULL value, the result is NULL.

      • -
    -

    Downsampling

    -

    Time-series data are usually sampled by sensors at a very high frequency, but more often we are only interested in the downsampled, aggregated data of each timeline. TDengine provides a convenient way to downsample the highly frequently sampled data points as well as filling the missing data with a variety of interpolation choices.

    -
    SELECT function_list FROM tb_name 
-  [WHERE where_condition]
-  INTERVAL (interval)
-  [FILL ({NONE | VALUE | PREV | NULL | LINEAR})]
-
-SELECT function_list FROM stb_name 
-  [WHERE where_condition]
-  [GROUP BY tags]
-  INTERVAL (interval)
-  [FILL ({ VALUE | PREV | NULL | LINEAR})]
    -

    The downsampling time window is defined by interval, which is at least 10 milliseconds. The query returns a new series of downsampled data that has a series of fixed timestamps with an increment of interval.

    -

    For the time being, only function count, avg, sum, stddev, leastsquares, percentile, min, max, first, last are supported. Functions that may return multiple rows are not supported.

    -

    You can also use FILL to interpolate the intervals that don't contain any data.FILL currently supports four different interpolation strategies which are listed below:

    -
    - - - - - - - - - - - - - - - - - - - - - - - - -
    InterpolationUsage
    FILL(VALUE, val1 [, val2, ...])Interpolate with specified constants
    FILL(PREV)Interpolate with the value at the previous timestamp
    FILL(LINEAR)Linear interpolation with the non-null values at the previous timestamp and at the next timestamp
    FILL(NULL)Interpolate with NULL value
    -

    A few downsampling examples:

    -
      -

    • Find the number of data points, the maximum value of col1 and minimum value of col2 in a tb1 for every 10 minutes in the last 5 hours:

      -
      SELECT COUNT(*), MAX(col1), MIN(col2) FROM tb1 WHERE ts > NOW - 5h INTERVAL (10m)
      • -

    • Fill the above downsampling results using constant-value interpolation:

      -
      SELECT COUNT(*), MAX(col1), MIN(col2) FROM tb1 WHERE ts > NOW - 5h INTERVAL(10m) FILL(VALUE, 0, 1, -1)
      -

      Note that the number of constant values in FILL() should be equal or fewer than the number of functions in the SELECT clause. Exceeding fill constants will be ignored.

      • -

    • Fill the above downsampling results using PREV interpolation:

      -
      SELECT COUNT(*), MAX(col1), MIN(col2) FROM tb1 WHERE ts > NOW - 5h INTERVAL(10m) FILL(PREV)
      -

      This will interpolate missing data points with the value at the previous timestamp.

      • -

    • Fill the above downsampling results using NULL interpolation:

      -
      SELECT COUNT(*), MAX(col1), MIN(col2) FROM tb1 WHERE ts > NOW - 5h INTERVAL(10m) FILL(NULL)
      -

      Fill NULL to the interpolated data points.

      • -
    -

    Notes:

    -
      -
    1. FILL can generate tons of interpolated data points if the interval is small and the queried time range is large. So always remember to specify a time range when using interpolation. For each query with interpolation, the result set can not exceed 10,000,000 records.
      2. -
    2. The result set will always be sorted by time in ascending order.
      3. -
    3. If the query object is a supertable, then all the functions will be applied to all the tables that qualify the WHERE conditions. If the GROUP BY clause is also applied, the result set will be sorted ascendingly by time in each single group, otherwise, the result set will be sorted ascendingly by time as a whole.
      4. -
    Back
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1.7.4以上的版本。用户可以根据当前的操作系统，到Telegraf官网下载安装包，并执行安装。下载地址如下：https://portal.influxdata.com/downloads - -### 配置Telegraf - -修改Telegraf配置文件/etc/telegraf/telegraf.conf中与TDengine有关的配置项。 - -在output plugins部分，增加[[outputs.http]]配置项： - -- url：http://ip:6020/telegraf/udb，其中ip为TDengine集群的中任意一台服务器的IP地址，6020为TDengine RESTful接口的端口号，telegraf为固定关键字，udb为用于存储采集数据的数据库名称，可预先创建。 -- method: "POST" -- username: 登录TDengine的用户名 -- password: 登录TDengine的密码 -- data_format: "json" -- json_timestamp_units: "1ms" - -在agent部分： - -- hostname: 区分不同采集设备的机器名称，需确保其唯一性 -- metric_batch_size: 30，允许Telegraf每批次写入记录最大数量，增大其数量可以降低Telegraf的请求发送频率，但对于TDengine，该数值不能超过50 - -关于如何使用Telegraf采集数据以及更多有关使用Telegraf的信息，请参考Telegraf官方的[文档](https://docs.influxdata.com/telegraf/v1.11/)。 - -## Grafana - -TDengine能够与开源数据可视化系统[Grafana](https://www.grafana.com/)快速集成搭建数据监测报警系统，整个过程无需任何代码开发，TDengine中数据表中内容可以在仪表盘(DashBoard)上进行可视化展现。 - -### 安装Grafana - -目前TDengine支持Grafana 5.2.4以上的版本。用户可以根据当前的操作系统，到Grafana官网下载安装包，并执行安装。下载地址如下：https://grafana.com/grafana/download。 - -### 配置Grafana - -TDengine的Grafana插件在安装包的/usr/local/taos/connector/grafana目录下。 - -以CentOS 7.2操作系统为例，将tdengine目录拷贝到/var/lib/grafana/plugins目录下，重新启动grafana即可。 - -### 使用 Grafana - -#### 配置数据源 - -用户可以直接通过 localhost:3000 的网址，登录 Grafana 服务器(用户名/密码:admin/admin)，通过左侧 `Configuration -> Data Sources` 可以添加数据源，如下图所示： - -![img](../assets/add_datasource1.jpg) - -点击 `Add data source` 可进入新增数据源页面，在查询框中输入 TDengine 可选择添加，如下图所示： - -![img](../assets/add_datasource2.jpg) - -进入数据源配置页面，按照默认提示修改相应配置即可： - -![img](../assets/add_datasource3.jpg) - -* Host： TDengine 集群的中任意一台服务器的 IP 地址与 TDengine RESTful 接口的端口号(6020)，默认 http://localhost:6020。 -* User：TDengine 用户名。 -* Password：TDengine 用户密码。 - -点击 `Save & Test` 进行测试，成功会有如下提示： - -![img](../assets/add_datasource4.jpg) - -#### 创建 Dashboard - -回到主界面创建 Dashboard，点击 Add Query 进入面板查询页面： - -![img](../assets/create_dashboard1.jpg) - -如上图所示，在 Query 中选中 `TDengine` 数据源，在下方查询框可输入相应 sql 进行查询，具体说明如下： - -* INPUT SQL：输入要查询的语句（该 SQL 语句的结果集应为两列多行），例如：`select avg(mem_system) from log.dn where ts >= $from and ts < $to interval($interval)` ，其中，from、to 和 interval 为 TDengine插件的内置变量，表示从Grafana插件面板获取的查询范围和时间间隔。除了内置变量外，`也支持可以使用自定义模板变量`。 -* ALIAS BY：可设置当前查询别名。 -* GENERATE SQL： 点击该按钮会自动替换相应变量，并生成最终执行的语句。 - -按照默认提示查询当前 TDengine 部署所在服务器指定间隔系统内存平均使用量如下： - -![img](../assets/create_dashboard2.jpg) - -> 关于如何使用Grafana创建相应的监测界面以及更多有关使用Grafana的信息，请参考Grafana官方的[文档](https://grafana.com/docs/)。 - -#### 导入 Dashboard - -在 Grafana 插件目录 /usr/local/taos/connector/grafana/tdengine/dashboard/ 下提供了一个 `tdengine-grafana.json` 可导入的 dashboard。 - -点击左侧 `Import` 按钮，并上传 `tdengine-grafana.json` 文件： - -![img](../assets/import_dashboard1.jpg) - -导入完成之后可看到如下效果： - -![img](../assets/import_dashboard2.jpg) - - -## Matlab - -MatLab可以通过安装包内提供的JDBC Driver直接连接到TDengine获取数据到本地工作空间。 - -### MatLab的JDBC接口适配 - -MatLab的适配有下面几个步骤，下面以Windows10上适配MatLab2017a为例： - -- 将TDengine安装包内的驱动程序JDBCDriver-1.0.0-dist.jar拷贝到${matlab_root}\MATLAB\R2017a\java\jar\toolbox -- 将TDengine安装包内的taos.lib文件拷贝至${matlab_ root _dir}\MATLAB\R2017a\lib\win64 -- 将新添加的驱动jar包加入MatLab的classpath。在${matlab_ root _dir}\MATLAB\R2017a\toolbox\local\classpath.txt文件中添加下面一行 - -​ `$matlabroot/java/jar/toolbox/JDBCDriver-1.0.0-dist.jar` - -- 在${user_home}\AppData\Roaming\MathWorks\MATLAB\R2017a\下添加一个文件javalibrarypath.txt, 并在该文件中添加taos.dll的路径，比如您的taos.dll是在安装时拷贝到了C:\Windows\System32下，那么就应该在javalibrarypath.txt中添加如下一行： - -​ `C:\Windows\System32` - -### 在MatLab中连接TDengine获取数据 - -在成功进行了上述配置后，打开MatLab。 - -- 创建一个连接： - - `conn = database(‘db’, ‘root’, ‘taosdata’, ‘com.taosdata.jdbc.TSDBDriver’, ‘jdbc:TSDB://127.0.0.1:0/’)` - -- 执行一次查询： - - `sql0 = [‘select * from tb’]` - - `data = select(conn, sql0);` - -- 插入一条记录: - - `sql1 = [‘insert into tb values (now, 1)’]` - - `exec(conn, sql1)` - -更多例子细节请参考安装包内examples\Matlab\TDengineDemo.m文件。 - -## R - -R语言支持通过JDBC接口来连接TDengine数据库。首先需要安装R语言的JDBC包。启动R语言环境，然后执行以下命令安装R语言的JDBC支持库： - -```R -install.packages('rJDBC', repos='http://cran.us.r-project.org') -``` - -安装完成以后，通过执行`library('RJDBC')`命令加载 _RJDBC_ 包： - -然后加载TDengine的JDBC驱动： - -```R -drv<-JDBC("com.taosdata.jdbc.TSDBDriver","JDBCDriver-1.0.0-dist.jar", identifier.quote="\"") -``` -如果执行成功，不会出现任何错误信息。之后通过以下命令尝试连接数据库： - -```R -conn<-dbConnect(drv,"jdbc:TSDB://192.168.0.1:0/?user=root&password=taosdata","root","taosdata") -``` - -注意将上述命令中的IP地址替换成正确的IP地址。如果没有任务错误的信息，则连接数据库成功，否则需要根据错误提示调整连接的命令。TDengine支持以下的 _RJDBC_ 包中函数： - - -- dbWriteTable(conn, "test", iris, overwrite=FALSE, append=TRUE)：将数据框iris写入表test中，overwrite必须设置为false，append必须设为TRUE,且数据框iris要与表test的结构一致。 -- dbGetQuery(conn, "select count(*) from test")：查询语句 -- dbSendUpdate(conn, "use db")：执行任何非查询sql语句。例如dbSendUpdate(conn, "use db")， 写入数据dbSendUpdate(conn, "insert into t1 values(now, 99)")等。 -- dbReadTable(conn, "test")：读取表test中数据 -- dbDisconnect(conn)：关闭连接 -- dbRemoveTable(conn, "test")：删除表test - -TDengine客户端暂不支持如下函数： -- dbExistsTable(conn, "test")：是否存在表test -- dbListTables(conn)：显示连接中的所有表 - - diff --git a/documentation/webdocs/markdowndocs/Connections with other Tools.md b/documentation/webdocs/markdowndocs/Connections with other Tools.md deleted file mode 100644 index 8be05698497184aee2c41a60e32f39b636e2070e..0000000000000000000000000000000000000000 --- a/documentation/webdocs/markdowndocs/Connections with other Tools.md +++ /dev/null @@ -1,167 +0,0 @@ -# Connect with other tools - -## Telegraf - -TDengine is easy to integrate with [Telegraf](https://www.influxdata.com/time-series-platform/telegraf/), an open-source server agent for collecting and sending metrics and events, without more development. - -### Install Telegraf - -At present, TDengine supports Telegraf newer than version 1.7.4. Users can go to the [download link] and choose the proper package to install on your system. - -### Configure Telegraf - -Telegraf is configured by changing items in the configuration file */etc/telegraf/telegraf.conf*. - - -In **output plugins** section，add _[[outputs.http]]_ iterm： - -- _url_: http://ip:6020/telegraf/udb, in which _ip_ is the IP address of any node in TDengine cluster. Port 6020 is the RESTful APT port used by TDengine. _udb_ is the name of the database to save data, which needs to create beforehand. -- _method_: "POST" -- _username_: username to login TDengine -- _password_: password to login TDengine -- _data_format_: "json" -- _json_timestamp_units_: "1ms" - -In **agent** part： - -- hostname: used to distinguish different machines. Need to be unique. -- metric_batch_size: 30，the maximum number of records allowed to write in Telegraf. The larger the value is, the less frequent requests are sent. For TDengine, the value should be less than 50. - -Please refer to the [Telegraf docs](https://docs.influxdata.com/telegraf/v1.11/) for more information. - -## Grafana - -[Grafana] is an open-source system for time-series data display. It is easy to integrate TDengine and Grafana to build a monitor system. Data saved in TDengine can be fetched and shown on the Grafana dashboard. - -### Install Grafana - -For now, TDengine only supports Grafana newer than version 5.2.4. Users can go to the [Grafana download page] for the proper package to download. - -### Configure Grafana - -TDengine Grafana plugin is in the _/usr/local/taos/connector/grafana_ directory. -Taking Centos 7.2 as an example, just copy TDengine directory to _/var/lib/grafana/plugins_ directory and restart Grafana. - -### Use Grafana - -Users can log in the Grafana server (username/password:admin/admin) through localhost:3000 to configure TDengine as the data source. As is shown in the picture below, TDengine as a data source option is shown in the box: - - -![img](../assets/clip_image001.png) - -When choosing TDengine as the data source, the Host in HTTP configuration should be configured as the IP address of any node of a TDengine cluster. The port should be set as 6020. For example, when TDengine and Grafana are on the same machine, it should be configured as _http://localhost:6020. - - -Besides, users also should set the username and password used to log into TDengine. Then click _Save&Test_ button to save. - -![img](../assets/clip_image001-2474914.png) - -Then, TDengine as a data source should show in the Grafana data source list. - -![img](../assets/clip_image001-2474939.png) - - -Then, users can create Dashboards in Grafana using TDengine as the data source: - - -![img](../assets/clip_image001-2474961.png) - - - -Click _Add Query_ button to add a query and input the SQL command you want to run in the _INPUT SQL_ text box. The SQL command should expect a two-row, multi-column result, such as _SELECT count(*) FROM sys.cpu WHERE ts>=from and ts<​to interval(interval)_, in which, _from_, _to_ and _inteval_ are TDengine inner variables representing query time range and time interval. - - -_ALIAS BY_ field is to set the query alias. Click _GENERATE SQL_ to send the command to TDengine: - -![img](../assets/clip_image001-2474987.png) - -Please refer to the [Grafana official document] for more information about Grafana. - - -## Matlab - -Matlab can connect to and retrieve data from TDengine by TDengine JDBC Driver. - -### MatLab and TDengine JDBC adaptation - -Several steps are required to adapt Matlab to TDengine. Taking adapting Matlab2017a on Windows10 as an example: - -1. Copy the file _JDBCDriver-1.0.0-dist.jar_ in TDengine package to the directory _${matlab_root}\MATLAB\R2017a\java\jar\toolbox_ -2. Copy the file _taos.lib_ in TDengine package to _${matlab_ root _dir}\MATLAB\R2017a\lib\win64_ -3. Add the .jar package just copied to the Matlab classpath. Append the line below as the end of the file of _${matlab_ root _dir}\MATLAB\R2017a\toolbox\local\classpath.txt_ - -​ `$matlabroot/java/jar/toolbox/JDBCDriver-1.0.0-dist.jar` - -4. Create a file called _javalibrarypath.txt_ in directory _${user_home}\AppData\Roaming\MathWorks\MATLAB\R2017a\_, and add the _taos.dll_ path in the file. For example, if the file _taos.dll_ is in the directory of _C:\Windows\System32_，then add the following line in file *javalibrarypath.txt*: - -​ `C:\Windows\System32` - -### TDengine operations in Matlab - -After correct configuration, open Matlab: - -- build a connection： - - `conn = database(‘db’, ‘root’, ‘taosdata’, ‘com.taosdata.jdbc.TSDBDriver’, ‘jdbc:TSDB://127.0.0.1:0/’)` - -- Query： - - `sql0 = [‘select * from tb’]` - - `data = select(conn, sql0);` - -- Insert a record: - - `sql1 = [‘insert into tb values (now, 1)’]` - - `exec(conn, sql1)` - -Please refer to the file _examples\Matlab\TDengineDemo.m_ for more information. - -## R - -Users can use R language to access the TDengine server with the JDBC interface. At first, install JDBC package in R: - -```R -install.packages('rJDBC', repos='http://cran.us.r-project.org') -``` - -Then use _library_ function to load the package: - -```R -library('RJDBC') -``` - -Then load the TDengine JDBC driver: - -```R -drv<-JDBC("com.taosdata.jdbc.TSDBDriver","JDBCDriver-1.0.0-dist.jar", identifier.quote="\"") -``` -If succeed, no error message will display. Then use the following command to try a database connection: - -```R -conn<-dbConnect(drv,"jdbc:TSDB://192.168.0.1:0/?user=root&password=taosdata","root","taosdata") -``` - -Please replace the IP address in the command above to the correct one. If no error message is shown, then the connection is established successfully. TDengine supports below functions in _RJDBC_ package: - - -- _dbWriteTable(conn, "test", iris, overwrite=FALSE, append=TRUE)_: write the data in a data frame _iris_ to the table _test_ in the TDengine server. Parameter _overwrite_ must be _false_. _append_ must be _TRUE_ and the schema of the data frame _iris_ should be the same as the table _test_. -- _dbGetQuery(conn, "select count(*) from test")_: run a query command -- _dbSendUpdate(conn, "use db")_: run any non-query command. -- _dbReadTable(conn, "test"_): read all the data in table _test_ -- _dbDisconnect(conn)_: close a connection -- _dbRemoveTable(conn, "test")_: remove table _test_ - -Below functions are **not supported** currently: -- _dbExistsTable(conn, "test")_: if talbe _test_ exists -- _dbListTables(conn)_: list all tables in the connection - - -[Telegraf]: www.taosdata.com -[download link]: https://portal.influxdata.com/downloads -[Telegraf document]: www.taosdata.com -[Grafana]: https://grafana.com -[Grafana download page]: https://grafana.com/grafana/download -[Grafana official document]: https://grafana.com/docs/ - diff --git a/documentation/webdocs/markdowndocs/Connector.md b/documentation/webdocs/markdowndocs/Connector.md deleted file mode 100644 index fcd6976cb0ee9dcfe44926db7bb327f09e82e39f..0000000000000000000000000000000000000000 --- a/documentation/webdocs/markdowndocs/Connector.md +++ /dev/null @@ -1,918 +0,0 @@ -# TDengine connectors - -TDengine provides many connectors for development, including C/C++, JAVA, Python, RESTful, Go, Node.JS, etc. - -NOTE: All APIs which require a SQL string as parameter, including but not limit to `taos_query`, `taos_query_a`, `taos_subscribe` in the C/C++ Connector and their counterparts in other connectors, can ONLY process one SQL statement at a time. If more than one SQL statements are provided, their behaviors are undefined. - -## C/C++ API - -C/C++ APIs are similar to the MySQL APIs. Applications should include TDengine head file _taos.h_ to use C/C++ APIs by adding the following line in code: -```C -#include -``` -Make sure TDengine library _libtaos.so_ is installed and use _-ltaos_ option to link the library when compiling. In most cases, if the return value of an API is integer, it return _0_ for success and other values as an error code for failure; if the return value is pointer, then _NULL_ is used for failure. - - -### C/C++ sync API - -Sync APIs are those APIs waiting for responses from the server after sending a request. TDengine has the following sync APIs: - - -- `TAOS *taos_connect(char *ip, char *user, char *pass, char *db, int port)` - - Open a connection to a TDengine server. The parameters are _ip_ (IP address of the server), _user_ (username to login), _pass_ (password to login), _db_ (database to use after connection) and _port_ (port number to connect). The parameter _db_ can be NULL for no database to use after connection. Otherwise, the database should exist before connection or a connection error is reported. The handle returned by this API should be kept for future use. - -- `void taos_close(TAOS *taos)` - - Close a connection to a TDengine server by the handle returned by _taos_connect_` - - -- `int taos_query(TAOS *taos, char *sqlstr)` - - The API used to run a SQL command. The command can be DQL or DML. The parameter _taos_ is the handle returned by _taos_connect_. Return value _-1_ means failure. - - -- `TAOS_RES *taos_use_result(TAOS *taos)` - - Use the result after running _taos_query_. The handle returned should be kept for future fetch. - - -- `TAOS_ROW taos_fetch_row(TAOS_RES *res)` - - Fetch a row of return results through _res_, the handle returned by _taos_use_result_. - - -- `int taos_num_fields(TAOS_RES *res)` - - Get the number of fields in the return result. - - -- `TAOS_FIELD *taos_fetch_fields(TAOS_RES *res)` - - Fetch the description of each field. The description includes the property of data type, field name, and bytes. The API should be used with _taos_num_fields_ to fetch a row of data. - - -- `void taos_free_result(TAOS_RES *res)` - - Free the resources used by a result set. Make sure to call this API after fetching results or memory leak would happen. - - -- `void taos_init()` - - Initialize the environment variable used by TDengine client. The API is not necessary since it is called int _taos_connect_ by default. - - -- `char *taos_errstr(TAOS *taos)` - - Return the reason of the last API call failure. The return value is a string. - - -- `int *taos_errno(TAOS *taos)` - - Return the error code of the last API call failure. The return value is an integer. - - -- `int taos_options(TSDB_OPTION option, const void * arg, ...)` - - Set client options. The parameter _option_ supports values of _TSDB_OPTION_CONFIGDIR_ (configuration directory), _TSDB_OPTION_SHELL_ACTIVITY_TIMER_, _TSDB_OPTION_LOCALE_ (client locale) and _TSDB_OPTION_TIMEZONE_ (client timezone). - -The 12 APIs are the most important APIs frequently used. Users can check _taos.h_ file for more API information. - -**Note**: The connection to a TDengine server is not multi-thread safe. So a connection can only be used by one thread. - -### C/C++ parameter binding API - -TDengine also provides parameter binding APIs, like MySQL, only question mark `?` can be used to represent a parameter in these APIs. - -- `TAOS_STMT* taos_stmt_init(TAOS *taos)` - - Create a TAOS_STMT to represent the prepared statement for other APIs. - -- `int taos_stmt_prepare(TAOS_STMT *stmt, const char *sql, unsigned long length)` - - Parse SQL statement _sql_ and bind result to _stmt_ , if _length_ larger than 0, its value is used to determine the length of _sql_, the API auto detects the actual length of _sql_ otherwise. - -- `int taos_stmt_bind_param(TAOS_STMT *stmt, TAOS_BIND *bind)` - - Bind values to parameters. _bind_ points to an array, the element count and sequence of the array must be identical as the parameters of the SQL statement. The usage of _TAOS_BIND_ is same as _MYSQL_BIND_ in MySQL, its definition is as below: - - ```c - typedef struct TAOS_BIND { - int buffer_type; - void * buffer; - unsigned long buffer_length; // not used in TDengine - unsigned long *length; - int * is_null; - int is_unsigned; // not used in TDengine - int * error; // not used in TDengine - } TAOS_BIND; - ``` - -- `int taos_stmt_add_batch(TAOS_STMT *stmt)` - - Add bound parameters to batch, client can call `taos_stmt_bind_param` again after calling this API. Note this API only support _insert_ / _import_ statements, it returns an error in other cases. - -- `int taos_stmt_execute(TAOS_STMT *stmt)` - - Execute the prepared statement. This API can only be called once for a statement at present. - -- `TAOS_RES* taos_stmt_use_result(TAOS_STMT *stmt)` - - Acquire the result set of an executed statement. The usage of the result is same as `taos_use_result`, `taos_free_result` must be called after one you are done with the result set to release resources. - -- `int taos_stmt_close(TAOS_STMT *stmt)` - - Close the statement, release all resources. - - -### C/C++ async API - -In addition to sync APIs, TDengine also provides async APIs, which are more efficient. Async APIs are returned right away without waiting for a response from the server, allowing the application to continute with other tasks without blocking. So async APIs are more efficient, especially useful when in a poor network. - -All async APIs require callback functions. The callback functions have the format: -```C -void fp(void *param, TAOS_RES * res, TYPE param3) -``` -The first two parameters of the callback function are the same for all async APIs. The third parameter is different for different APIs. Generally, the first parameter is the handle provided to the API for action. The second parameter is a result handle. - -- `void taos_query_a(TAOS *taos, char *sqlstr, void (*fp)(void *param, TAOS_RES *, int code), void *param);` - - The async query interface. _taos_ is the handle returned by _taos_connect_ interface. _sqlstr_ is the SQL command to run. _fp_ is the callback function. _param_ is the parameter required by the callback function. The third parameter of the callback function _code_ is _0_ (for success) or a negative number (for failure, call taos_errstr to get the error as a string). Applications mainly handle with the second parameter, the returned result set. - - -- `void taos_fetch_rows_a(TAOS_RES *res, void (*fp)(void *param, TAOS_RES *, int numOfRows), void *param);` - - The async API to fetch a batch of rows, which should only be used with a _taos_query_a_ call. The parameter _res_ is the result handle returned by _taos_query_a_. _fp_ is the callback function. _param_ is a user-defined structure to pass to _fp_. The parameter _numOfRows_ is the number of result rows in the current fetch cycle. In the callback function, applications should call _taos_fetch_row_ to get records from the result handle. After getting a batch of results, applications should continue to call _taos_fetch_rows_a_ API to handle the next batch, until the _numOfRows_ is _0_ (for no more data to fetch) or _-1_ (for failure). - - -- `void taos_fetch_row_a(TAOS_RES *res, void (*fp)(void *param, TAOS_RES *, TAOS_ROW row), void *param);` - - The async API to fetch a result row. _res_ is the result handle. _fp_ is the callback function. _param_ is a user-defined structure to pass to _fp_. The third parameter of the callback function is a single result row, which is different from that of _taos_fetch_rows_a_ API. With this API, it is not necessary to call _taos_fetch_row_ to retrieve each result row, which is handier than _taos_fetch_rows_a_ but less efficient. - - -Applications may apply operations on multiple tables. However, **it is important to make sure the operations on the same table are serialized**. That means after sending an insert request in a table to the server, no operations on the table are allowed before a response is received. - -### C/C++ continuous query interface - -TDengine provides APIs for continuous query driven by time, which run queries periodically in the background. There are only two APIs: - - -- `TAOS_STREAM *taos_open_stream(TAOS *taos, char *sqlstr, void (*fp)(void *param, TAOS_RES *, TAOS_ROW row), int64_t stime, void *param, void (*callback)(void *));` - - The API is used to create a continuous query. - * _taos_: the connection handle returned by _taos_connect_. - * _sqlstr_: the SQL string to run. Only query commands are allowed. - * _fp_: the callback function to run after a query - * _param_: a parameter passed to _fp_ - * _stime_: the time of the stream starts in the form of epoch milliseconds. If _0_ is given, the start time is set as the current time. - * _callback_: a callback function to run when the continuous query stops automatically. - - The API is expected to return a handle for success. Otherwise, a NULL pointer is returned. - - -- `void taos_close_stream (TAOS_STREAM *tstr)` - - Close the continuous query by the handle returned by _taos_open_stream_. Make sure to call this API when the continuous query is not needed anymore. - - -### C/C++ subscription API - -For the time being, TDengine supports subscription on one or multiple tables. It is implemented through periodic pulling from a TDengine server. - -* `TAOS_SUB *taos_subscribe(TAOS* taos, int restart, const char* topic, const char *sql, TAOS_SUBSCRIBE_CALLBACK fp, void *param, int interval)` - - The API is used to start a subscription session, it returns the subscription object on success and `NULL` in case of failure, the parameters are: - * **taos**: The database connnection, which must be established already. - * **restart**: `Zero` to continue a subscription if it already exits, other value to start from the beginning. - * **topic**: The unique identifier of a subscription. - * **sql**: A sql statement for data query, it can only be a `select` statement, can only query for raw data, and can only query data in ascending order of the timestamp field. - * **fp**: A callback function to receive query result, only used in asynchronization mode and should be `NULL` in synchronization mode, please refer below for its prototype. - * **param**: User provided additional parameter for the callback function. - * **interval**: Pulling interval in millisecond. Under asynchronization mode, API will call the callback function `fp` in this interval, system performance will be impacted if this interval is too short. Under synchronization mode, if the duration between two call to `taos_consume` is less than this interval, the second call blocks until the duration exceed this interval. - -* `typedef void (*TAOS_SUBSCRIBE_CALLBACK)(TAOS_SUB* tsub, TAOS_RES *res, void* param, int code)` - - Prototype of the callback function, the parameters are: - * tsub: The subscription object. - * res: The query result. - * param: User provided additional parameter when calling `taos_subscribe`. - * code: Error code in case of failures. - -* `TAOS_RES *taos_consume(TAOS_SUB *tsub)` - - The API used to get the new data from a TDengine server. It should be put in an loop. The parameter `tsub` is the handle returned by `taos_subscribe`. This API should only be called in synchronization mode. If the duration between two call to `taos_consume` is less than pulling interval, the second call blocks until the duration exceed the interval. The API returns the new rows if new data arrives, or empty rowset otherwise, and if there's an error, it returns `NULL`. - -* `void taos_unsubscribe(TAOS_SUB *tsub, int keepProgress)` - - Stop a subscription session by the handle returned by `taos_subscribe`. If `keepProgress` is **not** zero, the subscription progress information is kept and can be reused in later call to `taos_subscribe`, the information is removed otherwise. - -## Java Connector - -To Java delevopers, TDengine provides `taos-jdbcdriver` according to the JDBC(3.0) API. Users can find and download it through [Sonatype Repository][1]. - -Since the native language of TDengine is C, the necessary TDengine library should be checked before using the taos-jdbcdriver: - -* libtaos.so (Linux) - After TDengine is installed successfully, the library `libtaos.so` will be automatically copied to the `/usr/lib/`, which is the system's default search path. - -* taos.dll (Windows) - After TDengine client is installed, the library `taos.dll` will be automatically copied to the `C:/Windows/System32`, which is the system's default search path. - -> Note: Please make sure that [TDengine Windows client][14] has been installed if developing on Windows. Now although TDengine client would be defaultly installed together with TDengine server, it can also be installed [alone][15]. - -Since TDengine is time-series database, there are still some differences compared with traditional databases in using TDengine JDBC driver: -* TDengine doesn't allow to delete/modify a single record, and thus JDBC driver also has no such method. -* No support for transaction -* No support for union between tables -* No support for nested query，`There is at most one open ResultSet for each Connection. Thus, TSDB JDBC Driver will close current ResultSet if it is not closed and a new query begins`. - -## Version list of TAOS-JDBCDriver and required TDengine and JDK - -| taos-jdbcdriver | TDengine | JDK | -| --- | --- | --- | -| 1.0.3 | 1.6.1.x or higher | 1.8.x | -| 1.0.2 | 1.6.1.x or higher | 1.8.x | -| 1.0.1 | 1.6.1.x or higher | 1.8.x | - -## DataType in TDengine and Java - -The datatypes in TDengine include timestamp, number, string and boolean, which are converted as follows in Java: - -| TDengine | Java | -| --- | --- | -| TIMESTAMP | java.sql.Timestamp | -| INT | java.lang.Integer | -| BIGINT | java.lang.Long | -| FLOAT | java.lang.Float | -| DOUBLE | java.lang.Double | -| SMALLINT, TINYINT |java.lang.Short | -| BOOL | java.lang.Boolean | -| BINARY, NCHAR | java.lang.String | - -## How to get TAOS-JDBC Driver - -### maven repository - -taos-jdbcdriver has been published to [Sonatype Repository][1]: -* [sonatype][8] -* [mvnrepository][9] -* [maven.aliyun][10] - -Using the following pom.xml for maven projects - -```xml - - - com.taosdata.jdbc - taos-jdbcdriver - 1.0.3 - - -``` - -### JAR file from the source code - -After downloading the [TDengine][3] source code, execute `mvn clean package` in the directory `src/connector/jdbc` and then the corresponding jar file is generated. - -## Usage - -### get the connection - -```java -Class.forName("com.taosdata.jdbc.TSDBDriver"); -String jdbcUrl = "jdbc:TAOS://127.0.0.1:6030/log?user=root&password=taosdata"; -Connection conn = DriverManager.getConnection(jdbcUrl); -``` -> `6030` is the default port and `log` is the default database for system monitor. - -A normal JDBC URL looks as follows: -`jdbc:TAOS://{host_ip}:{port}/[database_name]?[user={user}|&password={password}|&charset={charset}|&cfgdir={config_dir}|&locale={locale}|&timezone={timezone}]` - -values in `{}` are necessary while values in `[]` are optional。Each option in the above URL denotes: - -* user：user name for login, defaultly root。 -* password：password for login，defaultly taosdata。 -* charset：charset for client，defaultly system charset -* cfgdir：log directory for client, defaultly _/etc/taos/_ on Linux and _C:/TDengine/cfg_ on Windows。 -* locale：language for client，defaultly system locale。 -* timezone：timezone for client，defaultly system timezone。 - -The options above can be configures (`ordered by priority`): -1. JDBC URL - - As explained above. -2. java.sql.DriverManager.getConnection(String jdbcUrl, Properties connProps) -```java -public Connection getConn() throws Exception{ - Class.forName("com.taosdata.jdbc.TSDBDriver"); - String jdbcUrl = "jdbc:TAOS://127.0.0.1:0/log?user=root&password=taosdata"; - Properties connProps = new Properties(); - connProps.setProperty(TSDBDriver.PROPERTY_KEY_USER, "root"); - connProps.setProperty(TSDBDriver.PROPERTY_KEY_PASSWORD, "taosdata"); - connProps.setProperty(TSDBDriver.PROPERTY_KEY_CONFIG_DIR, "/etc/taos"); - connProps.setProperty(TSDBDriver.PROPERTY_KEY_CHARSET, "UTF-8"); - connProps.setProperty(TSDBDriver.PROPERTY_KEY_LOCALE, "en_US.UTF-8"); - connProps.setProperty(TSDBDriver.PROPERTY_KEY_TIME_ZONE, "UTC-8"); - Connection conn = DriverManager.getConnection(jdbcUrl, connProps); - return conn; -} -``` - -3. Configuration file (taos.cfg) - - Default configuration file is _/var/lib/taos/taos.cfg_ On Linux and _C:\TDengine\cfg\taos.cfg_ on Windows -```properties -# client default username -# defaultUser root - -# client default password -# defaultPass taosdata - -# default system charset -# charset UTF-8 - -# system locale -# locale en_US.UTF-8 -``` -> More options can refer to [client configuration][13] - -### Create databases and tables - -```java -Statement stmt = conn.createStatement(); - -// create database -stmt.executeUpdate("create database if not exists db"); - -// use database -stmt.executeUpdate("use db"); - -// create table -stmt.executeUpdate("create table if not exists tb (ts timestamp, temperature int, humidity float)"); -``` -> Note: if no step like `use db`, the name of database must be added as prefix like _db.tb_ when operating on tables - -### Insert data - -```java -// insert data -int affectedRows = stmt.executeUpdate("insert into tb values(now, 23, 10.3) (now + 1s, 20, 9.3)"); - -System.out.println("insert " + affectedRows + " rows."); -``` -> _now_ is the server time. -> _now+1s_ is 1 second later than current server time. The time unit includes: _a_(millisecond), _s_(second), _m_(minute), _h_(hour), _d_(day), _w_(week), _n_(month), _y_(year). - -### Query database - -```java -// query data -ResultSet resultSet = stmt.executeQuery("select * from tb"); - -Timestamp ts = null; -int temperature = 0; -float humidity = 0; -while(resultSet.next()){ - - ts = resultSet.getTimestamp(1); - temperature = resultSet.getInt(2); - humidity = resultSet.getFloat("humidity"); - - System.out.printf("%s, %d, %s\n", ts, temperature, humidity); -} -``` -> query is consistent with relational database. The subscript start with 1 when retrieving return results. It is recommended to use the column name to retrieve results. - -### Close all - -```java -resultSet.close(); -stmt.close(); -conn.close(); -``` -> `please make sure the connection is closed to avoid the error like connection leakage` - -## Using connection pool - -**HikariCP** - -* dependence in pom.xml： -```xml - - com.zaxxer - HikariCP - 3.4.1 - -``` - -* Examples： -```java - public static void main(String[] args) throws SQLException { - HikariConfig config = new HikariConfig(); - config.setJdbcUrl("jdbc:TAOS://127.0.0.1:6030/log"); - config.setUsername("root"); - config.setPassword("taosdata"); - - config.setMinimumIdle(3); //minimum number of idle connection - config.setMaximumPoolSize(10); //maximum number of connection in the pool - config.setConnectionTimeout(10000); //maximum wait milliseconds for get connection from pool - config.setIdleTimeout(60000); // max idle time for recycle idle connection - config.setConnectionTestQuery("describe log.dn"); //validation query - config.setValidationTimeout(3000); //validation query timeout - - HikariDataSource ds = new HikariDataSource(config); //create datasource - - Connection connection = ds.getConnection(); // get connection - Statement statement = connection.createStatement(); // get statement - - //query or insert - // ... - - connection.close(); // put back to conneciton pool -} -``` -> The close() method will not close the connection from HikariDataSource.getConnection(). Instead, the connection is put back to the connection pool. -> More instructions can refer to [User Guide][5] - -**Druid** - -* dependency in pom.xml： - -```xml - - com.alibaba - druid - 1.1.20 - -``` - -* Examples： -```java -public static void main(String[] args) throws Exception { - Properties properties = new Properties(); - properties.put("driverClassName","com.taosdata.jdbc.TSDBDriver"); - properties.put("url","jdbc:TAOS://127.0.0.1:6030/log"); - properties.put("username","root"); - properties.put("password","taosdata"); - - properties.put("maxActive","10"); //maximum number of connection in the pool - properties.put("initialSize","3");//initial number of connection - properties.put("maxWait","10000");//maximum wait milliseconds for get connection from pool - properties.put("minIdle","3");//minimum number of connection in the pool - - properties.put("timeBetweenEvictionRunsMillis","3000");// the interval milliseconds to test connection - - properties.put("minEvictableIdleTimeMillis","60000");//the minimum milliseconds to keep idle - properties.put("maxEvictableIdleTimeMillis","90000");//the maximum milliseconds to keep idle - - properties.put("validationQuery","describe log.dn"); //validation query - properties.put("testWhileIdle","true"); // test connection while idle - properties.put("testOnBorrow","false"); // don't need while testWhileIdle is true - properties.put("testOnReturn","false"); // don't need while testWhileIdle is true - - //create druid datasource - DataSource ds = DruidDataSourceFactory.createDataSource(properties); - Connection connection = ds.getConnection(); // get connection - Statement statement = connection.createStatement(); // get statement - - //query or insert - // ... - - connection.close(); // put back to conneciton pool -} -``` -> More instructions can refer to [User Guide][6] - -**Notice** -* TDengine `v1.6.4.1` provides a function `select server_status()` to check heartbeat. It is highly recommended to use this function for `Validation Query`. - -As follows，`1` will be returned if `select server_status()` is successfully executed。 -```shell -taos> select server_status(); -server_status()| -================ -1 | -Query OK, 1 row(s) in set (0.000141s) -``` - -## Integrated with framework - -* Please refer to [SpringJdbcTemplate][11] if using taos-jdbcdriver in Spring JdbcTemplate -* Please refer to [springbootdemo][12] if using taos-jdbcdriver in Spring JdbcTemplate - -## FAQ - -* java.lang.UnsatisfiedLinkError: no taos in java.library.path - - **Cause**：The application program cannot find Library function _taos_ - - **Answer**：Copy `C:\TDengine\driver\taos.dll` to `C:\Windows\System32\` on Windows and make a soft link through ` ln -s /usr/local/taos/driver/libtaos.so.x.x.x.x /usr/lib/libtaos.so` on Linux. - -* java.lang.UnsatisfiedLinkError: taos.dll Can't load AMD 64 bit on a IA 32-bit platform - - **Cause**：Currently TDengine only support 64bit JDK - - **Answer**：re-install 64bit JDK. - -* For other questions, please refer to [Issues][7] - - -## Python Connector - -### Pre-requirement -* TDengine installed, TDengine-client installed if on Windows [(Windows TDengine client installation)](https://www.taosdata.com/cn/documentation/connector/#Windows客户端及程序接口) -* python 2.7 or >= 3.4 -* pip installed - -### Installation -#### Linux - -Users can find python client packages in our source code directory _src/connector/python_. There are two directories corresponding to two python versions. Please choose the correct package to install. Users can use _pip_ command to install: - -```cmd -pip install src/connector/python/linux/python3/ -``` - -or - -``` -pip install src/connector/python/linux/python2/ -``` -#### Windows -Assumed the Windows TDengine client has been installed , copy the file "C:\TDengine\driver\taos.dll" to the folder "C:\windows\system32", and then enter the _cmd_ Windows command interface -``` -cd C:\TDengine\connector\python\windows -pip install python3\ -``` -or -``` -cd C:\TDengine\connector\python\windows -pip install python2\ -``` -*If _pip_ command is not installed on the system, users can choose to install pip or just copy the _taos_ directory in the python client directory to the application directory to use. - -### Usage -#### Examples -* import TDengine module - -```python -import taos -``` -* get the connection -```python -conn = taos.connect(host="127.0.0.1", user="root", password="taosdata", config="/etc/taos") -c1 = conn.cursor() -``` -*host is the IP of TDengine server, and config is the directory where exists the TDengine client configure file -* insert records into the database -```python -import datetime - -# create a database -c1.execute('create database db') -c1.execute('use db') -# create a table -c1.execute('create table tb (ts timestamp, temperature int, humidity float)') -# insert a record -start_time = datetime.datetime(2019, 11, 1) -affected_rows = c1.execute('insert into tb values (\'%s\', 0, 0.0)' %start_time) -# insert multiple records in a batch -time_interval = datetime.timedelta(seconds=60) -sqlcmd = ['insert into tb values'] -for irow in range(1,11): - start_time += time_interval - sqlcmd.append('(\'%s\', %d, %f)' %(start_time, irow, irow*1.2)) -affected_rows = c1.execute(' '.join(sqlcmd)) -``` -* query the database -```python -c1.execute('select * from tb') -# fetch all returned results -data = c1.fetchall() -# data is a list of returned rows with each row being a tuple -numOfRows = c1.rowcount -numOfCols = len(c1.description) -for irow in range(numOfRows): - print("Row%d: ts=%s, temperature=%d, humidity=%f" %(irow, data[irow][0], data[irow][1],data[irow][2])) - -# use the cursor as an iterator to retrieve all returned results -c1.execute('select * from tb') -for data in c1: - print("ts=%s, temperature=%d, humidity=%f" %(data[0], data[1],data[2]) -``` - -* create a subscription -```python -# Create a subscription with topic 'test' and consumption interval 1000ms. -# The first argument is True means to restart the subscription; -# if the subscription with topic 'test' has already been created, then pass -# False to this argument means to continue the existing subscription. -sub = conn.subscribe(True, "test", "select * from meters;", 1000) -``` - -* consume a subscription -```python -data = sub.consume() -for d in data: - print(d) -``` - -* close the subscription -```python -sub.close() -``` - -* close the connection -```python -c1.close() -conn.close() -``` -#### Help information - -Users can get module information from Python help interface or refer to our [python code example](). We list the main classes and methods below: - -- _TDengineConnection_ class - - Run `help(taos.TDengineConnection)` in python terminal for details. - -- _TDengineCursor_ class - - Run `help(taos.TDengineCursor)` in python terminal for details. - -- connect method - - Open a connection. Run `help(taos.connect)` in python terminal for details. - -## RESTful Connector - -TDengine also provides RESTful API to satisfy developing on different platforms. Unlike other databases, TDengine RESTful API applies operations to the database through the SQL command in the body of HTTP POST request. What users are required to provide is just a URL. - - -For the time being, TDengine RESTful API uses a _\_ generated from username and password for identification. Safer identification methods will be provided in the future. - - -### HTTP URL encoding - -To use TDengine RESTful API, the URL should have the following encoding format: -``` -http://:/rest/sql -``` -- _ip_: IP address of any node in a TDengine cluster -- _PORT_: TDengine HTTP service port. It is 6020 by default. - -For example, the URL encoding _http://192.168.0.1:6020/rest/sql_ used to send HTTP request to a TDengine server with IP address as 192.168.0.1. - -It is required to add a token in an HTTP request header for identification. - -``` -Authorization: Basic -``` - -The HTTP request body contains the SQL command to run. If the SQL command contains a table name, it should also provide the database name it belongs to in the form of `.`. Otherwise, an error code is returned. - -For example, use _curl_ command to send a HTTP request: - -``` -curl -H 'Authorization: Basic ' -d '' :/rest/sql -``` - -or use - -``` -curl -u username:password -d '' :/rest/sql -``` - -where `TOKEN` is the encryted string of `{username}:{password}` using the Base64 algorithm, e.g. `root:taosdata` will be encoded as `cm9vdDp0YW9zZGF0YQ==` - -### HTTP response - -The HTTP resonse is in JSON format as below: - -``` -{ - "status": "succ", - "head": ["column1","column2", …], - "data": [ - ["2017-12-12 23:44:25.730", 1], - ["2017-12-12 22:44:25.728", 4] - ], - "rows": 2 -} -``` -Specifically, -- _status_: the result of the operation, success or failure -- _head_: description of returned result columns -- _data_: the returned data array. If no data is returned, only an _affected_rows_ field is listed -- _rows_: the number of rows returned - -### Example - -- Use _curl_ command to query all the data in table _t1_ of database _demo_: - - `curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'select * from demo.t1' 192.168.0.1:6020/rest/sql` - -The return value is like: - -``` -{ - "status": "succ", - "head": ["column1","column2","column3"], - "data": [ - ["2017-12-12 23:44:25.730", 1, 2.3], - ["2017-12-12 22:44:25.728", 4, 5.6] - ], - "rows": 2 -} -``` - -- Use HTTP to create a database： - - `curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'create database demo' 192.168.0.1:6020/rest/sql` - - The return value should be: - -``` -{ - "status": "succ", - "head": ["affected_rows"], - "data": [[1]], - "rows": 1, -} -``` - -## Go Connector - -TDengine also provides a Go client package named _taosSql_ for users to access TDengine with Go. The package is in _/usr/local/taos/connector/go/src/taosSql_ by default if you installed TDengine. Users can copy the directory _/usr/local/taos/connector/go/src/taosSql_ to the _src_ directory of your project and import the package in the source code for use. - -```Go -import ( - "database/sql" - _ "taosSql" -) -``` - -The _taosSql_ package is in _cgo_ form, which calls TDengine C/C++ sync interfaces. So a connection is allowed to be used by one thread at the same time. Users can open multiple connections for multi-thread operations. - -Please refer the the demo code in the package for more information. - -## Node.js Connector - -TDengine also provides a node.js connector package that is installable through [npm](https://www.npmjs.com/). The package is also in our source code at *src/connector/nodejs/*. The following instructions are also available [here](https://github.com/taosdata/tdengine/tree/master/src/connector/nodejs) - -To get started, just type in the following to install the connector through [npm](https://www.npmjs.com/). - -```cmd -npm install td-connector -``` - -It is highly suggested you use npm. If you don't have it installed, you can also just copy the nodejs folder from *src/connector/nodejs/* into your node project folder. - -To interact with TDengine, we make use of the [node-gyp](https://github.com/nodejs/node-gyp) library. To install, you will need to install the following depending on platform (the following instructions are quoted from node-gyp) - -### On Unix - -- `python` (`v2.7` recommended, `v3.x.x` is **not** supported) -- `make` -- A proper C/C++ compiler toolchain, like [GCC](https://gcc.gnu.org) - -### On macOS - -- `python` (`v2.7` recommended, `v3.x.x` is **not** supported) (already installed on macOS) - -- Xcode - - - You also need to install the - - ``` - Command Line Tools - ``` - - via Xcode. You can find this under the menu - - ``` - Xcode -> Preferences -> Locations - ``` - - (or by running - - ``` - xcode-select --install - ``` - - in your Terminal) - - - This step will install `gcc` and the related toolchain containing `make` - -### On Windows - -#### Option 1 - -Install all the required tools and configurations using Microsoft's [windows-build-tools](https://github.com/felixrieseberg/windows-build-tools) using `npm install --global --production windows-build-tools` from an elevated PowerShell or CMD.exe (run as Administrator). - -#### Option 2 - -Install tools and configuration manually: - -- Install Visual C++ Build Environment: [Visual Studio Build Tools](https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools) (using "Visual C++ build tools" workload) or [Visual Studio 2017 Community](https://visualstudio.microsoft.com/pl/thank-you-downloading-visual-studio/?sku=Community) (using the "Desktop development with C++" workload) -- Install [Python 2.7](https://www.python.org/downloads/) (`v3.x.x` is not supported), and run `npm config set python python2.7` (or see below for further instructions on specifying the proper Python version and path.) -- Launch cmd, `npm config set msvs_version 2017` - -If the above steps didn't work for you, please visit [Microsoft's Node.js Guidelines for Windows](https://github.com/Microsoft/nodejs-guidelines/blob/master/windows-environment.md#compiling-native-addon-modules) for additional tips. - -To target native ARM64 Node.js on Windows 10 on ARM, add the components "Visual C++ compilers and libraries for ARM64" and "Visual C++ ATL for ARM64". - -### Usage - -The following is a short summary of the basic usage of the connector, the full api and documentation can be found [here](http://docs.taosdata.com/node) - -#### Connection - -To use the connector, first require the library ```td-connector```. Running the function ```taos.connect``` with the connection options passed in as an object will return a TDengine connection object. The required connection option is ```host```, other options if not set, will be the default values as shown below. - -A cursor also needs to be initialized in order to interact with TDengine from Node.js. - -```javascript -const taos = require('td-connector'); -var conn = taos.connect({host:"127.0.0.1", user:"root", password:"taosdata", config:"/etc/taos",port:0}) -var cursor = conn.cursor(); // Initializing a new cursor -``` - -To close a connection, run - -```javascript -conn.close(); -``` - -#### Queries - -We can now start executing simple queries through the ```cursor.query``` function, which returns a TaosQuery object. - -```javascript -var query = cursor.query('show databases;') -``` - -We can get the results of the queries through the ```query.execute()``` function, which returns a promise that resolves with a TaosResult object, which contains the raw data and additional functionalities such as pretty printing the results. - -```javascript -var promise = query.execute(); -promise.then(function(result) { - result.pretty(); //logs the results to the console as if you were in the taos shell -}); -``` - -You can also query by binding parameters to a query by filling in the question marks in a string as so. The query will automatically parse what was binded and convert it to the proper format for use with TDengine - -```javascript -var query = cursor.query('select * from meterinfo.meters where ts <= ? and areaid = ?;').bind(new Date(), 5); -query.execute().then(function(result) { - result.pretty(); -}) -``` - -The TaosQuery object can also be immediately executed upon creation by passing true as the second argument, returning a promise instead of a TaosQuery. - -```javascript -var promise = cursor.query('select * from meterinfo.meters where v1 = 30;', true) -promise.then(function(result) { - result.pretty(); -}) -``` -#### Async functionality - -Async queries can be performed using the same functions such as `cursor.execute`, `TaosQuery.execute`, but now with `_a` appended to them. - -Say you want to execute an two async query on two seperate tables, using `cursor.query`, you can do that and get a TaosQuery object, which upon executing with the `execute_a` function, returns a promise that resolves with a TaosResult object. - -```javascript -var promise1 = cursor.query('select count(*), avg(v1), avg(v2) from meter1;').execute_a() -var promise2 = cursor.query('select count(*), avg(v1), avg(v2) from meter2;').execute_a(); -promise1.then(function(result) { - result.pretty(); -}) -promise2.then(function(result) { - result.pretty(); -}) -``` - - -### Example - -An example of using the NodeJS connector to create a table with weather data and create and execute queries can be found [here](https://github.com/taosdata/TDengine/tree/master/tests/examples/nodejs/node-example.js) (The preferred method for using the connector) - -An example of using the NodeJS connector to achieve the same things but without all the object wrappers that wrap around the data returned to achieve higher functionality can be found [here](https://github.com/taosdata/TDengine/tree/master/tests/examples/nodejs/node-example-raw.js) - -[1]: https://search.maven.org/artifact/com.taosdata.jdbc/taos-jdbcdriver -[2]: https://mvnrepository.com/artifact/com.taosdata.jdbc/taos-jdbcdriver -[3]: https://github.com/taosdata/TDengine -[4]: https://www.taosdata.com/blog/2019/12/03/jdbcdriver%e6%89%be%e4%b8%8d%e5%88%b0%e5%8a%a8%e6%80%81%e9%93%be%e6%8e%a5%e5%ba%93/ -[5]: https://github.com/brettwooldridge/HikariCP -[6]: https://github.com/alibaba/druid -[7]: https://github.com/taosdata/TDengine/issues -[8]: https://search.maven.org/artifact/com.taosdata.jdbc/taos-jdbcdriver -[9]: https://mvnrepository.com/artifact/com.taosdata.jdbc/taos-jdbcdriver -[10]: https://maven.aliyun.com/mvn/search -[11]: https://github.com/taosdata/TDengine/tree/develop/tests/examples/JDBC/SpringJdbcTemplate -[12]: https://github.com/taosdata/TDengine/tree/develop/tests/examples/JDBC/springbootdemo -[13]: https://www.taosdata.com/cn/documentation/administrator/#%E5%AE%A2%E6%88%B7%E7%AB%AF%E9%85%8D%E7%BD%AE -[14]: https://www.taosdata.com/cn/documentation/connector/#Windows%E5%AE%A2%E6%88%B7%E7%AB%AF%E5%8F%8A%E7%A8%8B%E5%BA%8F%E6%8E%A5%E5%8F%A3 -[15]: https://www.taosdata.com/cn/getting-started/#%E5%BF%AB%E9%80%9F%E4%B8%8A%E6%89%8B diff --git a/documentation/webdocs/markdowndocs/Contributor_License_Agreement.md b/documentation/webdocs/markdowndocs/Contributor_License_Agreement.md deleted file mode 100644 index 8c158da4c5958384064b9993de6643be86b94fee..0000000000000000000000000000000000000000 --- a/documentation/webdocs/markdowndocs/Contributor_License_Agreement.md +++ /dev/null @@ -1,35 +0,0 @@ -# TaosData Contributor License Agreement - -This TaosData Contributor License Agreement (CLA) applies to any contribution you make to any TaosData projects. If you are representing your employing organization to sign this agreement, please warrant that you have the authority to grant the agreement. - -## Terms - -**"TaosData"**, **"we"**, **"our"** and **"us"** means TaosData, inc. - -**"You"** and **"your"** means you or the organization you are on behalf of to sign this agreement. - -**"Contribution"** means any original work you, or the organization you represent submit to TaosData for any project in any manner. - -## Copyright License - -All rights of your Contribution submitted to TaosData in any manner are granted to TaosData and recipients of software distributed by TaosData. 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If you want, you can provide for a fee. - -**I agree and accept on behalf of myself and behalf of my organization:** \ No newline at end of file diff --git a/documentation/webdocs/markdowndocs/Data model and architecture-ch.md b/documentation/webdocs/markdowndocs/Data model and architecture-ch.md deleted file mode 100644 index f17b015172095be051d6fe78c47db458ca2c797f..0000000000000000000000000000000000000000 --- a/documentation/webdocs/markdowndocs/Data model and architecture-ch.md +++ /dev/null @@ -1,100 +0,0 @@ -# 数据模型和设计 - -## 数据模型 - -### 物联网典型场景 - -在典型的物联网、车联网、运维监测场景中，往往有多种不同类型的数据采集设备，采集一个到多个不同的物理量。而同一种采集设备类型，往往又有多个具体的采集设备分布在不同的地点。大数据处理系统就是要将各种采集的数据汇总，然后进行计算和分析。对于同一类设备，其采集的数据类似如下的表格： - -| Device ID | Time Stamp | Value 1 | Value 2 | Value 3 | Tag 1 | Tag 2 | -| :-------: | :-----------: | :-----: | :-----: | :-----: | :---: | :---: | -| D1001 | 1538548685000 | 10.3 | 219 | 0.31 | Red | Tesla | -| D1002 | 1538548684000 | 10.2 | 220 | 0.23 | Blue | BMW | -| D1003 | 1538548686500 | 11.5 | 221 | 0.35 | Black | Honda | -| D1004 | 1538548685500 | 13.4 | 223 | 0.29 | Red | Volvo | -| D1001 | 1538548695000 | 12.6 | 218 | 0.33 | Red | Tesla | -| D1004 | 1538548696600 | 11.8 | 221 | 0.28 | Black | Honda | - -每一条记录都有设备ID，时间戳，采集的物理量，还有与每个设备相关的静态标签。每个设备是受外界的触发，或按照设定的周期采集数据。采集的数据点是时序的，是一个数据流。 - -### 数据特征 - -除时序特征外，仔细研究发现，物联网、车联网、运维监测类数据还具有很多其他明显的特征。 - -1. 数据是结构化的； -2. 数据极少有更新或删除操作； -3. 无需传统数据库的事务处理； -4. 相对互联网应用，写多读少； -5. 流量平稳，根据设备数量和采集频次，可以预测出来； -6. 用户关注的是一段时间的趋势，而不是某一特点时间点的值； -7. 数据是有保留期限的； -8. 数据的查询分析一定是基于时间段和地理区域的； -9. 除存储查询外，还往往需要各种统计和实时计算操作； -10. 数据量巨大，一天采集的数据就可以超过100亿条。 - -充分利用上述特征，TDengine采取了一特殊的优化的存储和计算设计来处理时序数据，能将系统处理能力显著提高。 - -### 关系型数据库模型 - -因为采集的数据一般是结构化数据，而且为降低学习门槛，TDengine采用传统的关系型数据库模型管理数据。因此用户需要先创建库，然后创建表，之后才能插入或查询数据。 - -### 一个设备一张表 - -为充分利用其数据的时序性和其他数据特点，TDengine要求**对每个数据采集点单独建表**（比如有一千万个智能电表，就需创建一千万张表，上述表格中的D1001, D1002, D1003, D1004都需单独建表），用来存储这个采集点所采集的时序数据。这种设计能保证一个采集点的数据在存储介质上是一块一块连续的，大幅减少随机读取操作，成数量级的提升读取和查询速度。而且由于不同数据采集设备产生数据的过程完全独立，每个设备只产生属于自己的数据，一张表也就只有一个写入者。这样每个表就可以采用无锁方式来写，写入速度就能大幅提升。同时，对于一个数据采集点而言，其产生的数据是时序的，因此写的操作可用追加的方式实现，进一步大幅提高数据写入速度。 - -### 数据建模最佳实践 - -**表(Table)**：TDengine 建议用数据采集点的名字(如上表中的D1001)来做表名。每个数据采集点可能同时采集多个物理量(如上表中的value1, value2, value3)，每个物理量对应一张表中的一列，数据类型可以是整型、浮点型、字符串等。除此之外，表的第一列必须是时间戳，即数据类型为 timestamp。有的设备有多组采集量，每一组的采集频次是不一样的，这是需要对同一个设备建多张表。对采集的数据，TDengine将自动按照时间戳建立索引，但对采集的物理量不建任何索引。数据是用列式存储方式保存。 - -**超级表(Super Table)**：对于同一类型的采集点，为保证Schema的一致性，而且为便于聚合统计操作，可以先定义超级表STable(详见第10章)，然后再定义表。每个采集点往往还有静态标签信息(如上表中的Tag 1, Tag 2)，比如设备型号、颜色等，这些静态信息不会保存在存储采集数据的数据节点中，而是通过超级表保存在元数据节点中。这些静态标签信息将作为过滤条件，用于采集点之间的数据聚合统计操作。 - -**库(DataBase)**：不同的数据采集点往往具有不同的数据特征，包括数据采集频率高低，数据保留时间长短，备份数目，单个字段大小等等。为让各种场景下TDengine都能最大效率的工作，TDengine建议将不同数据特征的表创建在不同的库里。创建一个库时，除SQL标准的选项外，应用还可以指定保留时长、数据备份的份数、cache大小、文件块大小、是否压缩等多种参数(详见第19章)。 - -**Schemaless vs Schema**: 与NoSQL的各种引擎相比，由于应用需要定义schema，插入数据的灵活性降低。但对于物联网、金融这些典型的时序数据场景，schema会很少变更，因此这个灵活性不够的设计就不成问题。相反，TDengine采用结构化数据来进行处理的方式将让查询、分析的性能成数量级的提升。 - -TDengine对库的数量、超级表的数量以及表的数量没有做任何限制，而且其多少不会对性能产生影响，应用按照自己的场景创建即可。 - -## 主要模块 -如图所示，TDengine服务主要包含两大模块：**管理节点模块(MGMT)** 和 **数据节点模块(DNODE)**。整个TDengine还包含**客户端模块**。 - -
    -
    图 1 TDengine架构示意图
    - -### 管理节点模块 -管理节点模块主要负责元数据的存储和查询等工作，其中包括用户信息的管理、数据库和表信息的创建、删除以及查询等。应用连接TDengine时会首先连接到管理节点。在创建/删除数据库和表时，请求也会首先发送请求到管理节点模块。由管理节点模块首先创建/删除元数据信息，然后发送请求到数据节点模块进行分配/删除所需要的资源。在数据写入和查询时，应用同样会首先访问管理节点模块，获取元数据信息。然后根据元数据管理信息访问数据节点模块。 - -### 数据节点模块 -写入数据的存储和查询工作是由数据节点模块负责。 为了更高效地利用资源，以及方便将来进行水平扩展，TDengine内部对数据节点进行了虚拟化，引入了虚拟节点(virtual node, 简称vnode）的概念，作为存储、资源分配以及数据备份的单元。如图2所示，在一个dnode上，通过虚拟化，可以将该dnode视为多个虚拟节点的集合。 - -创建一个库时，系统会自动分配vnode。每个vnode存储一定数量的表中的数据，但一个表只会存在于一个vnode里，不会跨vnode。一个vnode只会属于一个库，但一个库会有一到多个vnode。不同的vnode之间资源互不共享。每个虚拟节点都有自己的缓存，在硬盘上也有自己的存储目录。而同一vnode内部无论是缓存还是硬盘的存储都是共享的。通过虚拟化，TDengine可以将dnode上有限的物理资源合理地分配给不同的vnode，大大提高资源的利用率和并发度。一台物理机器上的虚拟节点个数可以根据其硬件资源进行配置。 - -
    -
    图 2 TDengine虚拟化
    - -### 客户端模块 -TDengine客户端模块主要负责将应用传来的请求（SQL语句）进行解析，转化为内部结构体再发送到服务端。TDengine的各种接口都是基于TDengine的客户端模块进行开发的。客户端模块与管理模块使用TCP/UDP通讯，端口号由系统参数mgmtShellPort配置, 缺省值为6030。客户端与数据节点模块也是使用TCP/UDP通讯，端口号由系统参数vnodeShellPort配置, 缺省值为6035。两个端口号均可通过系统配置文件taos.cfg进行个性化设置。 - -## 写入流程 -TDengine的完整写入流程如图3所示。为了保证写入数据的安全性和完整性，TDengine在写入数据时采用[预写日志算法]。客户端发来的数据在经过验证以后，首先会写入预写日志中，以保证TDengine能够在断电等因素导致的服务重启时从预写日志中恢复数据，避免数据的丢失。写入预写日志后，数据会被写到对应的vnode的缓存中。随后，服务端会发送确认信息给客户端表示写入成功。TDengine中存在两种机制可以促使缓存中的数据写入到硬盘上进行持久化存储： - -
    -
    图 3 TDengine写入流程
    - -1. **时间驱动的落盘**：TDengine服务会定时将vnode缓存中的数据写入到硬盘上，默认为一个小时落一次盘。落盘间隔可在配置文件taos.cfg中通过参数commitTime配置。 -2. **数据驱动的落盘**：当vnode中缓存的数据达到一定规模时，为了不阻塞后续数据的写入，TDengine也会拉起落盘线程将缓存中的数据清空。数据驱动的落盘会刷新定时落盘的时间。 - -TDengine在数据落盘时会打开新的预写日志文件，在落盘后则会删除老的预写日志文件，避免日志文件无限制的增长。TDengine对缓存按照先进先出的原则进行管理，以保证每个表的最新数据都在缓存中。 - -## 数据存储 - -TDengine将所有数据存储在/var/lib/taos/目录下，您可以通过系统配置参数dataDir进行个性化配置。 - -TDengine中的元数据信息包括TDengine中的数据库、表、用户等信息。每个超级表、以及每个表的标签数据也存放在这里。为提高访问速度，元数据全部有缓存。 - -TDengine中写入的数据在硬盘上是按时间维度进行分片的。同一个vnode中的表在同一时间范围内的数据都存放在同一文件组中。这一数据分片方式可以大大简化数据在时间维度的查询，提高查询速度。在默认配置下，硬盘上的每个数据文件存放10天数据。用户可根据需要修改系统配置参数daysPerFile进行个性化配置。 - -表中的数据都有保存时间，一旦超过保存时间（缺省是3650天），数据将被系统自动删除。您可以通过系统配置参数daysToKeep进行个性化设置。 - -数据在文件中是按块存储的。每个数据块只包含一张表的数据，且数据是按照时间主键递增排列的。数据在数据块中按列存储，这样使得同列的数据存放在一起，对于不同的数据类型还采用不同的压缩方法，大大提高压缩的比例，节省存储空间。 - -数据文件总共有三类文件，一类是data文件，它存放了真实的数据块，该文件只进行追加操作；一类文件是head文件, 它存放了其对应的data文件中数据块的索引信息；第三类是last文件，专门存储最后写入的数据，每次落盘操作时，这部分数据会与内存里的数据合并，并决定是否写入data文件还是last文件。 \ No newline at end of file diff --git a/documentation/webdocs/markdowndocs/Data model and architecture.md b/documentation/webdocs/markdowndocs/Data model and architecture.md deleted file mode 100644 index 1cf503f3c1694b65d4e181a8439e96d1735f8c56..0000000000000000000000000000000000000000 --- a/documentation/webdocs/markdowndocs/Data model and architecture.md +++ /dev/null @@ -1,101 +0,0 @@ -# Data Model and Architecture -## Data Model - -### A Typical IoT Scenario - -In a typical IoT scenario, there are many types of devices. Each device is collecting one or multiple metrics. For a specific type of device, the collected data could look like the table below: - -| Device ID | Time Stamp | Value 1 | Value 2 | Value 3 | Tag 1 | Tag 2 | -| :-------: | :-----------: | :-----: | :-----: | :-----: | :---: | :---: | -| D1001 | 1538548685000 | 10.3 | 219 | 0.31 | Red | Tesla | -| D1002 | 1538548684000 | 10.2 | 220 | 0.23 | Blue | BMW | -| D1003 | 1538548686500 | 11.5 | 221 | 0.35 | Black | Honda | -| D1004 | 1538548685500 | 13.4 | 223 | 0.29 | Red | Volvo | -| D1001 | 1538548695000 | 12.6 | 218 | 0.33 | Red | Tesla | -| D1004 | 1538548696600 | 11.8 | 221 | 0.28 | Black | Honda | - -Each data record contains the device ID, timestamp, collected metrics, and static tags associated with the device. Each device generates a data record in a pre-defined timer or triggered by an event. It is a sequence of data points like a stream. - -### Data Characteristics - -As the data points are a series of data points over time, the data points generated by devices, sensors, servers, and/or applications have some strong common characteristics: - -1. metrics are always structured data; -2. there are rarely delete/update operations on collected data; -3. there is only one single data source for one device or sensor; -4. ratio of read/write is much lower than typical Internet applications; -5. the user pays attention to the trend of data, not a specific value at a specific time; -6. there is always a data retention policy; -7. the data query is always executed in a given time range and a subset of devices; -8. real-time aggregation or analytics is mandatory; -9. traffic is predictable based on the number of devices and sampling frequency; -10. data volume is huge, a system may generate 10 billion data points in a day. - -By utilizing the above characteristics, TDengine designs the storage and computing engine in a special and optimized way for time-series data, resulting in massive improvements in system efficiency. - -### Relational Database Model - -Since time-series data is most likely to be structured data, TDengine adopts the traditional relational database model to process them. You need to create a database, create tables with schema definitions, then insert data points and execute queries to explore the data. Standard SQL is used, making it easy for anyone to get started and eliminating any learning curve. - -### One Table for One Device - -Due to different network latencies, the data points from different devices may arrive to the server out of order. But for the same device, data points will arrive to the server in order if the system is designed well. To utilize this special feature, TDengine requires the user to create a table for each device (time-stream). For example, if there are over 10,000 smart meters, 10,000 tables shall be created. For the table above, 4 tables shall be created for device D1001, D1002, D1003, and D1004 to store the data collected. - -This strong requirement can guarantee that all data points from a device can be saved in a continuous memory/hard disk space block by block. If queries are applied only on one device in a time range, this design will reduce the read latency significantly since a whole block is owned by one single device. Additionally, write latency can be significantly reduced too as the data points generated by the same device will arrive in order, the new data point will be simply appended to a block. Cache block size and the rows of records in a file block can be configured to fit different scenarios for optimal efficiency. - -### Best Practices - -**Table**: TDengine suggests to use device ID as the table name (like D1001 in the above diagram). Each device may collect one or more metrics (like value1, value2, value3 in the diagram). Each metric has a column in the table, the metric name can be used as the column name. The data type for a column can be int, float, double, tinyint, bigint, bool or binary. Sometimes, a device may have multiple metric groups, each group containing different sampling periods, so for best practice you should create a table for each group for each device. The first column in the table must be a time stamp. TDengine uses the time stamp as the index, and won’t build the index on any metrics stored. - -**Tags:** To support aggregation over multiple tables efficiently, the [STable(Super Table)](../super-table) concept is introduced by TDengine. A STable is used to represent the same type of device. The schema is used to define the collected metrics (like value1, value2, value3 in the diagram), and tags are used to define the static attributes for each table or device (like tag1, tag2 in the diagram). A table is created via STable with a specific tag value. All or a subset of tables in a STable can be aggregated by filtering tag values. - -**Database:** Different types of devices may generate data points in different patterns and should be processed differently. For example, sampling frequency, data retention policy, replication number, cache size, record size, the compression algorithm may be different. To make the system more efficient, TDengine suggests creating a different database with unique configurations for different scenarios. - -**Schemaless vs Schema:** Compared with NoSQL databases, since a table with schema definitions must be created before the data points can be inserted, flexibilities are not that good, especially when the schema is changed. But in most IoT scenarios, the schema is well defined and is rarely changed, the loss of flexibility won't pose any impact to developers or administrators. TDengine allows the application to change the schema in a second even there is a huge amount of historical data when schema has to be changed. - -TDengine does not impose a limitation on the number of tables, [STables](../super-table), or databases. You can create any number of STable or databases to fit different scenarios. - -## Architecture - -There are two main modules in TDengine server as shown in Picture 1: **Management Module (MGMT)** and **Data Module(DNODE)**. The whole TDengine architecture also includes a **TDengine Client Module**. - -
    -
    Picture 1 TDengine Architecture
    -### MGMT Module -The MGMT module deals with the storage and querying on metadata, which includes information about users, databases, and tables. Applications will connect to the MGMT module at first when connecting the TDengine server. When creating/dropping databases/tables, The request is sent to the MGMT module at first to create/delete metadata. Then the MGMT module will send requests to the data module to allocate/free resources required. In the case of writing or querying, applications still need to visit the MGMT module to get meta data, according to which, then access the DNODE module. - -### DNODE Module -The DNODE module is responsible for storing and querying data. For the sake of future scaling and high-efficient resource usage, TDengine applies virtualization on resources it uses. TDengine introduces the concept of a virtual node (vnode), which is the unit of storage, resource allocation and data replication (enterprise edition). As is shown in Picture 2, TDengine treats each data node as an aggregation of vnodes. - -When a DB is created, the system will allocate a vnode. Each vnode contains multiple tables, but a table belongs to only one vnode. Each DB has one or mode vnodes, but one vnode belongs to only one DB. Each vnode contains all the data in a set of tables. Vnodes have their own cache and directory to store data. Resources between different vnodes are exclusive with each other, no matter cache or file directory. However, resources in the same vnode are shared between all the tables in it. Through virtualization, TDengine can distribute resources reasonably to each vnode and improve resource usage and concurrency. The number of vnodes on a dnode is configurable according to its hardware resources. - -
    -
    Picture 2 TDengine Virtualization
    - -### Client Module -TDengine client module accepts requests (mainly in SQL form) from applications and converts the requests to internal representations and sends to the server side. TDengine supports multiple interfaces, which are all built on top of TDengine client module. - -For the communication between client and MGMT module, TCP/UDP is used, the port is set by the parameter `mgmtShellPort` in system configuration file `taos.cfg`, default is 6030. For communication between the client and the DNODE module, TCP/UDP is used, the port is set by the parameter `vnodeShellPort` in the system configuration file, default is 6035. - -## Writing Process -Picture 3 shows the full writing process of TDengine. TDengine uses the [Writing Ahead Log] (http://en.wikipedia.org/wiki/Write-ahead_logging) strategy to assure data security and integrity. Data received from the client is written to the commit log at first. When TDengine recovers from crashes caused by power loss or other situations, the commit log is used to recover data. After writting to the commit log, data will be wrtten to the corresponding vnode cache, then an acknowledgment is sent to the application. There are two mechanisms that can flush data in cache to disk for persistent storage: - -1. **Flush driven by timer**: There is a backend timer which flushes data in cache periodically to disks. The period is configurable via parameter commitTime in system configuration file taos.cfg. -2. **Flush driven by data**: Data in the cache is also flushed to disks when the left buffer size is below a threshold. Flush driven by data can reset the timer of flush driven by the timer. - -
    -
    Picture 3 TDengine Writting Process
    - -New commit log files will be opened when the committing process begins. When the committing process finishes, the old commit file will be removed. - -## Data Storage - -TDengine data are saved in _/var/lib/taos_ directory by default. It can be changed to other directories by setting the parameter `dataDir` in system configuration file taos.cfg. - -TDengine's metadata includes the database, table, user, super table and tag information. To reduce the latency, metadata are all buffered in the cache. - -Data records saved in tables are sharded according to the time range. Data from tables in the same vnode in a certain time range are saved in the same file group. This sharding strategy can effectively improve data search speed. By default, one group of files contain data in 10 days, which can be configured by `daysPerFile` in the configuration file or by the *DAYS* keyword in *CREATE DATABASE* clause. - -Data records are removed automatically once their lifetime is passed. The lifetime is configurable via parameter daysToKeep in the system configuration file. The default value is 3650 days. - -Data in files are blockwise. A data block only contains one table's data. Records in the same data block are sorted according to the primary timestamp. To improve the compression ratio, records are stored column by column, and different compression algorithms are applied based on each column's data type. \ No newline at end of file diff --git a/documentation/webdocs/markdowndocs/More on System Architecture-ch.md b/documentation/webdocs/markdowndocs/More on System Architecture-ch.md deleted file mode 100644 index 77ce9638884a00bafbb8ddbe90ff9839a9cb266e..0000000000000000000000000000000000000000 --- a/documentation/webdocs/markdowndocs/More on System Architecture-ch.md +++ /dev/null @@ -1,248 +0,0 @@ -# TDengine的技术设计 - -## 存储设计 - -TDengine的数据存储主要包含**元数据的存储**和**写入数据的存储**。以下章节详细介绍了TDengine各种数据的存储结构。 - -### 元数据的存储 - -TDengine中的元数据信息包括TDengine中的数据库，表，超级表等信息。元数据信息默认存放在 _/var/lib/taos/mgmt/_ 文件夹下。该文件夹的目录结构如下所示： -``` -/var/lib/taos/ - +--mgmt/ - +--db.db - +--meters.db - +--user.db - +--vgroups.db -``` -元数据在文件中按顺序排列。文件中的每条记录代表TDengine中的一个元数据机构（数据库、表等）。元数据文件只进行追加操作，即便是元数据的删除，也只是在数据文件中追加一条删除的记录。 - -### 写入数据的存储 - -TDengine中写入的数据在硬盘上是按时间维度进行分片的。同一个vnode中的表在同一时间范围内的数据都存放在同一文件组中，如下图中的v0f1804*文件。这一数据分片方式可以大大简化数据在时间维度的查询，提高查询速度。在默认配置下，硬盘上的每个文件存放10天数据。用户可根据需要调整数据库的 _daysPerFile_ 配置项进行配置。 数据在文件中是按块存储的。每个数据块只包含一张表的数据，且数据是按照时间主键递增排列的。数据在数据块中按列存储，这样使得同类型的数据存放在一起，可以大大提高压缩的比例，节省存储空间。TDengine对不同类型的数据采用了不同的压缩算法进行压缩，以达到最优的压缩结果。TDengine使用的压缩算法包括simple8B、delta-of-delta、RLE以及LZ4等。 - -TDengine的数据文件默认存放在 */var/lib/taos/data/* 下。而 */var/lib/taos/tsdb/* 文件夹下存放了vnode的信息、vnode中表的信息以及数据文件的链接等。其完整目录结构如下所示： -``` -/var/lib/taos/ - +--tsdb/ - | +--vnode0 - | +--meterObj.v0 - | +--db/ - | +--v0f1804.head->/var/lib/taos/data/vnode0/v0f1804.head1 - | +--v0f1804.data->/var/lib/taos/data/vnode0/v0f1804.data - | +--v0f1804.last->/var/lib/taos/data/vnode0/v0f1804.last1 - | +--v0f1805.head->/var/lib/taos/data/vnode0/v0f1805.head1 - | +--v0f1805.data->/var/lib/taos/data/vnode0/v0f1805.data - | +--v0f1805.last->/var/lib/taos/data/vnode0/v0f1805.last1 - | : - +--data/ - +--vnode0/ - +--v0f1804.head1 - +--v0f1804.data - +--v0f1804.last1 - +--v0f1805.head1 - +--v0f1805.data - +--v0f1805.last1 - : -``` - -#### meterObj文件 -每个vnode中只存在一个 _meterObj_ 文件。该文件中存储了vnode的基本信息（创建时间，配置信息，vnode的统计信息等）以及该vnode中表的信息。其结构如下所示： -``` -<文件开始> -[文件头] -[表记录1偏移量和长度] -[表记录2偏移量和长度] -... -[表记录N偏移量和长度] -[表记录1] -[表记录2] -... -[表记录N] -[表记录] -<文件结尾> -``` -其中，文件头大小为512字节，主要存放vnode的基本信息。每条表记录代表属于该vnode中的一张表在硬盘上的表示。 - -#### head文件 -head文件中存放了其对应的data文件中数据块的索引信息。该文件组织形式如下： -``` -<文件开始> -[文件头] -[表1偏移量] -[表2偏移量] -... -[表N偏移量] -[表1数据索引] -[表2数据索引] -... -[表N数据索引] -<文件结尾> -``` -文件开头的偏移量列表表示对应表的数据索引块的开始位置在文件中的偏移量。每张表的数据索引信息在head文件中都是连续存放的。这也使得TDengine在读取单表数据时，可以将该表所有的数据块索引一次性读入内存，大大提高读取速度。表的数据索引块组织如下： -``` -[索引块信息] -[数据块1索引] -[数据块2索引] -... -[数据块N索引] -``` -其中，索引块信息中记录了数据块的个数等描述信息。每个数据块索引对应一个在data文件或last文件中的一个单独的数据块。索引信息中记录了数据块存放的文件、数据块起始位置的偏移量、数据块中数据时间主键的范围等。索引块中的数据块索引是按照时间范围顺序排放的，这也就是说，索引块M对应的数据块中的数据时间范围都大于索引块M-1的。这种预先排序的存储方式使得在TDengine在进行按照时间戳进行查询时可以使用折半查找算法，大大提高查询速度。 - -#### data文件 -data文件中存放了真实的数据块。该文件只进行追加操作。其文件组织形式如下： -``` -<文件开始> -[文件头] -[数据块1] -[数据块2] -... -[数据块N] -<文件结尾> -``` -每个数据块只属于vnode中的一张表，且数据块中的数据按照时间主键排列。数据块中的数据按列组织排放，使得同一类型的数据排放在一起，方便压缩和读取。每个数据块的组织形式如下所示： -``` -[列1信息] -[列2信息] -... -[列N信息] -[列1数据] -[列2数据] -... -[列N数据] -``` -列信息中包含该列的类型，列的压缩算法，列数据在文件中的偏移量以及长度等。除此之外，列信息中也包含该内存块中该列数据的预计算结果，从而在过滤查询时根据预计算结果判定是否读取数据块，大大提高读取速度。 - -#### last文件 -为了防止数据块的碎片化，提高查询速度和压缩率，TDengine引入了last文件。当要落盘的数据块中的数据条数低于某个阈值时，TDengine会先将该数据块写入到last文件中进行暂时存储。当有新的数据需要落盘时，last文件中的数据会被读取出来与新数据组成新的数据块写入到data文件中。last文件的组织形式与data文件类似。 - -### TDengine数据存储小结 -TDengine通过其创新的架构和存储结构设计，有效提高了计算机资源的使用率。一方面，TDengine的虚拟化使得TDengine的水平扩展及备份非常容易。另一方面，TDengine将表中数据按时间主键排序存储且其列式存储的组织形式都使TDengine在写入、查询以及压缩方面拥有非常大的优势。 - - -## 查询处理 - -### 概述 - -TDengine提供了多种多样针对表和超级表的查询处理功能，除了常规的聚合查询之外，还提供针对时序数据的窗口查询、统计聚合等功能。TDengine的查询处理需要客户端、管理节点、数据节点协同完成。 各组件包含的与查询处理相关的功能和模块如下： - -客户端（Client App）。客户端包含TAOS SQL的解析（SQL Parser）和查询请求执行器（Query Executor），第二阶段聚合器（Result Merger），连续查询管理器（Continuous Query Manager）等主要功能模块构成。SQL解析器负责对SQL语句进行解析校验，并转化为抽象语法树，查询执行器负责将抽象语法树转化查询执行逻辑，并根据SQL语句查询条件，将其转换为针对管理节点元数据查询和针对数据节点的数据查询两级查询处理。由于TAOS SQL当前不提供复杂的嵌套查询和pipeline查询处理机制，所以不再需要查询计划优化、逻辑查询计划到物理查询计划转换等过程。第二阶段聚合器负责将各数据节点查询返回的独立结果进行二阶段聚合生成最后的结果。连续查询管理器则负责针对用户建立的连续查询进行管理，负责定时拉起查询请求并按需将结果写回TDengine或返回给客户应用。此外，客户端还负责查询失败后重试、取消查询请求、以及维持连接心跳、向管理节点上报查询状态等工作。 - -管理节点（Management Node）。管理节点保存了整个集群系统的全部数据的元数据信息，向客户端节点提供查询所需的数据的元数据，并根据集群的负载情况切分查询请求。通过超级表包含了通过该超级表创建的所有表的信息，因此查询处理器（Query Executor）负责针对标签（TAG）的查询处理，并将满足标签查询请求的表信息返回给客户端。此外，管理节点还维护集群的查询状态（Query Status Manager）维护，查询状态管理中在内存中临时保存有当前正在执行的全部查询，当客户端使用 *show queries* 命令的时候，将当前系统正在运行的查询信息返回客户端。 - -数据节点（Data Node）。数据节点保存了数据库中全部数据内容，并通过查询执行器、查询处理调度器、查询任务队列（Query Task Queue）进行查询处理的调度执行，从客户端接收到的查询处理请求都统一放置到处理队列中，查询执行器从队列中获得查询请求，并负责执行。通过查询优化器（Query Optimizer）对于查询进行基本的优化处理，以及通过数据节点的查询执行器（Query Executor）扫描符合条件的数据单元并返回计算结果。等接收客户端发出的查询请求，执行查询处理，并将结果返回。同时数据节点还需要响应来自管理节点的管理信息和命令，例如 *kill query* 命令以后，需要即刻停止执行的查询任务。 - -
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    图 1. 系统查询处理架构图(只包含查询相关组件)
    - -### 普通查询处理 - -客户端、管理节点、数据节点协同完成TDengine的查询处理全流程。我们以一个具体的SQL查询为例，说明TDengine的查询处理流程。SQL语句向超级表*FOO_SUPER_TABLE*查询获取时间范围在2019年1月12日整天，标签TAG_LOC是'beijing'的表所包含的所有记录总数，SQL语句如下： - -```sql -SELECT COUNT(*) -FROM FOO_SUPER_TABLE -WHERE TAG_LOC = 'beijing' AND TS >= '2019-01-12 00:00:00' AND TS < '2019-01-13 00:00:00' -``` - -首先，客户端调用TAOS SQL解析器对SQL语句进行解析及合法性检查，然后生成语法树，并从中提取查询的对象 — 超级表 *FOO_SUPER_TABLE* ，然后解析器向管理节点（Management Node）请求其相应的元数据信息，并将过滤信息（TAG_LOC='beijing'）同时发送到管理节点。 - -管理节点接收元数据获取的请求，首先找到超级表 *FOO_SUPER_TABLE* 基础信息，然后应用查询条件来过滤通过该超级表创建的全部表，最后满足查询条件（TAG_LOC='beijing'），即 *TAG_LOC* 标签列是 'beijing' 的的通过其查询执行器将满足查询要求的对象（表或超级表）的元数据信息返回给客户端。 - -客户端获得了 *FOO_SUPER_TABLE* 的元数据信息后，查询执行器根据元数据中的数据分布，分别向保存有相应数据的节点发起查询请求，此时时间戳范围过滤条件（TS >= '2019-01-12 00:00:00' AND TS < '2019-01-13 00:00:00'）需要同时发送给全部的数据节点。 - -数据节点接收到发自客户端的查询，转化为内部结构并进行优化以后将其放入任务执行队列，等待查询执行器执行。当查询结果获得以后，将查询结果返回客户端。数据节点执行查询的过程均相互独立，完全只依赖于自身的数据和内容进行计算。 - -当所有查询涉及的数据节点返回结果后，客户端将每个数据节点查询的结果集再次进行聚合（针对本案例，即将所有结果再次进行累加），累加的结果即为最后的查询结果。第二阶段聚合并不是所有的查询都需要。例如，针对数据的列选取操作，实际上是不需要第二阶段聚合。 - -### REST查询处理 - -在 C/C++ 、Python接口、 JDBC 接口之外，TDengine 还提供基于 HTTP 协议的 REST 接口。不同于使用应用客户端开发程序进行的开发。当用户使用 REST 接口的时候，所有的查询处理过程都是在服务器端来完成，用户的应用服务不会参与数据库的计算过程，查询处理完成后结果通过 HTTP的 JSON 格式返回给用户。 - -
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    图 2. REST查询架构
    - -当用户使用基于HTTP的REST查询接口，HTTP的请求首先与位于数据节点的HTTP连接器（ Connector），建立连接，然后通过REST的签名机制，使用Token来确保请求的可靠性。对于数据节点，HTTP连接器接收到请求后，调用内嵌的客户端程序发起查询请求，内嵌客户端将解析通过HTTP连接器传递过来的SQL语句，解析该SQL语句并按需向管理节点请求元数据信息，然后向本机或集群中其他节点发送查询请求，最后按需聚合计算结果。HTTP连接器接收到请求SQL以后，后续的流程处理与采用应用客户端方式的查询处理完全一致。最后，还需要将查询的结果转换为JSON格式字符串，并通过HTTP 响应返回给客户端。 - -可以看到，在处理HTTP流程的整个过程中，用户应用不再参与到查询处理的过程中，只负责通过HTTP协议发送SQL请求并接收JSON格式的结果。同时还需要注意的是，每个数据节点均内嵌了一个HTTP连接器和客户端程序，因此请求集群中任何一个数据节点，该数据节点均能够通过HTTP协议返回用户的查询结果。 - -### 技术特征 - -由于TDengine采用数据和标签分离存储的模式，能够极大地降低标签数据存储的冗余度。标签数据直接关联到每个表，并采用全内存的结构进行管理和维护标签数据,全内存的结构提供快速的查询处理，千万级别规模的标签数据查询可以在毫秒级别返回。首先针对标签数据的过滤可以有效地降低第二阶段的查询涉及的数据规模。为有效地提升查询处理的性能，针对物联网数据的不可更改的特点，TDengine采用在每个保存的数据块上，都记录下该数据块中数据的最大值、最小值、和等统计数据。如果查询处理涉及整个数据块的全部数据，则直接使用预计算结果，不再读取数据块的内容。由于预计算模块的大小远小于磁盘上存储的具体数据的大小，对于磁盘IO为瓶颈的查询处理，使用预计算结果可以极大地减小读取IO，并加速查询处理的流程。 - -由于TDengine采用按列存储数据。当从磁盘中读取数据块进行计算的时候，按照查询列信息读取该列数据，并不需要读取其他不相关的数据，可以最小化读取数据。此外，由于采用列存储结构，数据节点针对数据的扫描采用该列数据块进行，可以充分利用CPU L2高速缓存，极大地加速数据扫描的速度。此外，对于某些查询，并不会等全部查询结果生成后再返回结果。例如，列选取查询，当第一批查询结果获得以后，数据节点直接将其返回客户端。同时，在查询处理过程中，系统在数据节点接收到查询请求以后马上返回客户端查询确认信息，并同时拉起查询处理过程，并等待查询执行完成后才返回给用户查询有响应。 - -## 集群设计 - -### 1：集群与主要逻辑单元 - -TDengine是基于硬件、软件系统不可靠、一定会有故障的假设进行设计的，是基于任何单台计算机都无足够能力处理海量数据的假设进行设计的。因此TDengine从研发的第一天起，就按照分布式高可靠架构进行设计，是完全去中心化的，是水平扩展的，这样任何单台或多台服务器宕机或软件错误都不影响系统的服务。通过节点虚拟化并辅以自动化负载均衡技术，TDengine能最大限度地利用异构集群中的计算和存储资源。而且只要数据副本数大于一，无论是硬软件的升级、还是IDC的迁移等都无需停止集群的服务，极大地保证系统的正常运行，并且降低了系统管理员和运维人员的工作量。 - -下面的示例图上有八个物理节点，每个物理节点被逻辑的划分为多个虚拟节点。下面对系统的基本概念进行介绍。 - - - -![assets/nodes.png](../assets/nodes.png) - -**物理节点(dnode)**：集群中的一物理服务器或云平台上的一虚拟机。为安全以及通讯效率，一个物理节点可配置两张网卡，或两个IP地址。其中一张网卡用于集群内部通讯，其IP地址为**privateIp**, 另外一张网卡用于与集群外部应用的通讯，其IP地址为**publicIp**。在一些云平台（如阿里云），对外的IP地址是映射过来的，因此publicIp还有一个对应的内部IP地址**internalIp**(与privateIp不同)。对于只有一个IP地址的物理节点，publicIp, privateIp以及internalIp都是同一个地址，没有任何区别。一个dnode上有而且只有一个taosd实例运行。 - -**虚拟数据节点(vnode)**：在物理节点之上的可独立运行的基础逻辑单元，时序数据写入、存储、查询等操作逻辑都在虚拟节点中进行(图中V)，采集的时序数据就存储在vnode上。一个vnode包含固定数量的表。当创建一张新表时，系统会检查是否需要创建新的vnode。一个物理节点上能创建的vnode的数量取决于物理节点的硬件资源。一个vnode只属于一个DB，但一个DB可以有多个vnode。 - -**虚拟数据节点组(vgroup)**: 位于不同物理节点的vnode可以组成一个虚拟数据节点组vnode group(如上图dnode0中的V0, dnode1中的V1, dnode6中的V2属于同一个虚拟节点组)。归属于同一个vgroup的虚拟节点采取master/slave的方式进行管理。写只能在master上进行，但采用asynchronous的方式将数据同步到slave，这样确保了一份数据在多个物理节点上有拷贝。如果master节点宕机，其他节点监测到后，将重新选举vgroup里的master, 新的master能继续处理数据请求，从而保证系统运行的可靠性。一个vgroup里虚拟节点个数就是数据的副本数。如果一个DB的副本数为N，系统必须有至少N个物理节点。副本数在创建DB时通过参数replica可以指定，缺省为1。使用TDengine, 数据的安全依靠多副本解决，因此不再需要昂贵的磁盘阵列等存储设备。 - -**虚拟管理节点(mnode)**：负责所有节点运行状态的监控和维护，以及节点之间的负载均衡（图中M）。同时，虚拟管理节点也负责元数据(包括用户、数据库、表、静态标签等)的存储和管理，因此也称为Meta Node。TDengine集群中可配置多个(最多不超过5个) mnode，它们自动构建成为一个管理节点集群(图中M0, M1, M2)。mnode间采用master/slave的机制进行管理，而且采取强一致方式进行数据同步。mnode集群的创建由系统自动完成，无需人工干预。每个dnode上至多有一个mnode，而且每个dnode都知道整个集群中所有mnode的IP地址。 - -**taosc**：一个软件模块，是TDengine给应用提供的驱动程序（driver）,内嵌于JDBC、ODBC driver中，或者C语言连接库里。应用都是通过taosc而不是直接来与整个集群进行交互的。这个模块负责获取并缓存元数据；将插入、查询等请求转发到正确的虚拟节点；在把结果返回给应用时，还需要负责最后一级的聚合、排序、过滤等操作。对于JDBC, ODBC, C/C++接口而言，这个模块是在应用所处的计算机上运行，但消耗的资源很小。为支持全分布式的REST接口，taosc在TDengine集群的每个dnode上都有一运行实例。 - -**对外服务地址**：TDengine集群可以容纳单台、多台甚至几千台物理节点。应用只需要向集群中任何一个物理节点的publicIp发起连接即可。启动CLI应用taos时，选项-h需要提供的就是publicIp。 - -**master/secondIp**：每一个dnode都需要配置一个masterIp。dnode启动后，将对配置的masterIp发起加入集群的连接请求。masterIp是已经创建的集群中的任何一个节点的privateIp，对于集群中的第一个节点，就是它自己的privateIp。为保证连接成功，每个dnode还可配置secondIp, 该IP地址也是已创建的集群中的任何一个节点的privateIp。如果一个节点连接masterIp失败,它将试图链接secondIp。 - -dnode启动后，会获知集群的mnode IP列表，并且定时向mnode发送状态信息。 - -vnode与mnode只是逻辑上的划分，都是执行程序taosd里的不同线程而已，无需安装不同的软件，做任何特殊的配置。最小的系统配置就是一个物理节点,vnode,mnode和taosc都存在而且都正常运行，但单一节点无法保证系统的高可靠。 - -### 2：一典型的操作流程 - -为解释vnode, mnode, taosc和应用之间的关系以及各自扮演的角色，下面对写入数据这个典型操作的流程进行剖析。 - - - -![Picture1](../assets/Picture2.png) - - - -1. 应用通过JDBC、ODBC或其他API接口发起插入数据的请求。 -2. taosc会检查缓存，看是有保存有该表的meta data。如果有，直接到第4步。如果没有，taosc将向mnode发出get meta-data请求。 -3. mnode将该表的meta-data返回给taosc。Meta-data包含有该表的schema, 而且还有该表所属的vgroup信息（vnode ID以及所在的dnode的IP地址，如果副本数为N，就有N组vnodeID/IP)。如果taosc迟迟得不到mnode回应，而且存在多个mnode,taosc将向下一个mnode发出请求。 -4. taosc向master vnode发起插入请求。 -5. vnode插入数据后，给taosc一个应答，表示插入成功。如果taosc迟迟得不到vnode的回应，taosc会认为该节点已经离线。这种情况下，如果被插入的数据库有多个副本，taosc将向vgroup里下一个vnode发出插入请求。 -6. taosc通知APP，写入成功。 - -对于第二和第三步，taosc启动时，并不知道mnode的IP地址，因此会直接向配置的集群对外服务的IP地址发起请求。如果接收到该请求的dnode并没有配置mnode，该dnode会在回复的消息中告知mnode的IP地址列表（如果有多个dnodes，mnode的IP地址可以有多个），这样taosc会重新向新的mnode的IP地址发出获取meta-data的请求。 - -对于第四和第五步，没有缓存的情况下，taosc无法知道虚拟节点组里谁是master，就假设第一个vnodeID/IP就是master,向它发出请求。如果接收到请求的vnode并不是master,它会在回复中告知谁是master，这样taosc就向建议的master vnode发出请求。一旦得到插入成功的回复，taosc会缓存住master节点的信息。 - -上述是插入数据的流程，查询、计算的流程也完全一致。taosc把这些复杂的流程全部封装屏蔽了，因此应用无需处理重定向、获取meta data等细节，完全是透明的。 - -通过taosc缓存机制，只有在第一次对一张表操作时，才需要访问mnode, 因此mnode不会成为系统瓶颈。但因为schema有可能变化，而且vgroup有可能发生改变（比如负载均衡发生），因此taosc需要定时自动刷新缓存。 - -### 3：数据分区 - -vnode(虚拟数据节点)保存采集的时序数据，而且查询、计算都在这些节点上进行。为便于负载均衡、数据恢复、支持异构环境，TDengine将一个物理节点根据其计算和存储资源切分为多个vnode。这些vnode的管理是TDengine自动完成的，对应用完全透明。 - -对于单独一个数据采集点，无论其数据量多大，一个vnode（或vnode group, 如果副本数大于1）有足够的计算资源和存储资源来处理（如果每秒生成一条16字节的记录，一年产生的原始数据不到0.5G），因此TDengine将一张表的所有数据都存放在一个vnode里，而不会让同一个采集点的数据分布到两个或多个dnode上。而且一个vnode可存储多张表的数据，一个vnode可容纳的表的数目由配置参数tables指定，缺省为2000。设计上，一个vnode里所有的表都属于同一个DB。因此一个数据库DB需要的vnode或vgroup的个数等于：数据库表的数目/tables。 - -创建DB时，系统并不会马上分配资源。但当创建一张表时，系统将看是否有已经分配的vnode, 而且是否有空位，如果有，立即在该有空位的vnode创建表。如果没有，系统将从集群中，根据当前的负载情况，在一个dnode上创建一新的vnode, 然后创建表。如果DB有多个副本，系统不是只创建一个vnode，而是一个vgroup(虚拟数据节点组)。系统对vnode的数目没有任何限制，仅仅受限于物理节点本身的计算和存储资源。 - -参数tables的设置需要考虑具体场景，创建DB时，可以个性化指定该参数。该参数不宜过大，也不宜过小。过小，极端情况，就是每个数据采集点一个vnode, 这样导致系统数据文件过多。过大，虚拟化带来的优势就会丧失。给定集群计算资源的情况下，整个系统vnode的个数应该是CPU核的数目的两倍以上。 - -### 4：负载均衡 - -每个dnode(物理节点)都定时向 mnode(虚拟管理节点)报告其状态（包括硬盘空间、内存大小、CPU、网络、虚拟节点个数等），因此mnode了解整个集群的状态。基于整体状态，当mnode发现某个dnode负载过重，它会将dnode上的一个或多个vnode挪到其他dnode。在挪动过程中，对外服务继续进行，数据插入、查询和计算操作都不受影响。负载均衡操作结束后，应用也无需重启，将自动连接新的vnode。 - -如果mnode一段时间没有收到dnode的状态报告，mnode会认为这个dnode已经离线。如果离线时间超过一定时长（时长由配置参数offlineThreshold决定），该dnode将被mnode强制剔除出集群。该dnode上的vnodes如果副本数大于一，系统将自动在其他dnode上创建新的副本，以保证数据的副本数。 - - - -**Note：**目前集群功能仅仅限于企业版 diff --git a/documentation/webdocs/markdowndocs/More on System Architecture.md b/documentation/webdocs/markdowndocs/More on System Architecture.md deleted file mode 100644 index d7a38b99a3ae5a630509f3ef0f0ffdc97d3aaaf1..0000000000000000000000000000000000000000 --- a/documentation/webdocs/markdowndocs/More on System Architecture.md +++ /dev/null @@ -1,176 +0,0 @@ -# TDengine System Architecture - -## Storage Design - -TDengine data mainly include **metadata** and **data** that we will introduce in the following sections. - -### Metadata Storage - -Metadata include the information of databases, tables, etc. Metadata files are saved in _/var/lib/taos/mgmt/_ directory by default. The directory tree is as below: -``` -/var/lib/taos/ - +--mgmt/ - +--db.db - +--meters.db - +--user.db - +--vgroups.db -``` - -A metadata structure (database, table, etc.) is saved as a record in a metadata file. All metadata files are appended only, and even a drop operation adds a deletion record at the end of the file. - -### Data storage - -Data in TDengine are sharded according to the time range. Data of tables in the same vnode in a certain time range are saved in the same filegroup, such as files v0f1804*. This sharding strategy can effectively improve data searching speed. By default, a group of files contains data in 10 days, which can be configured by *daysPerFile* in the configuration file or by *DAYS* keyword in *CREATE DATABASE* clause. Data in files are blockwised. A data block only contains one table's data. Records in the same data block are sorted according to the primary timestamp, which helps to improve the compression rate and save storage. The compression algorithms used in TDengine include simple8B, delta-of-delta, RLE, LZ4, etc. - -By default, TDengine data are saved in */var/lib/taos/data/* directory. _/var/lib/taos/tsdb/_ directory contains vnode informations and data file linkes. - -``` -/var/lib/taos/ - +--tsdb/ - | +--vnode0 - | +--meterObj.v0 - | +--db/ - | +--v0f1804.head->/var/lib/taos/data/vnode0/v0f1804.head1 - | +--v0f1804.data->/var/lib/taos/data/vnode0/v0f1804.data - | +--v0f1804.last->/var/lib/taos/data/vnode0/v0f1804.last1 - | +--v0f1805.head->/var/lib/taos/data/vnode0/v0f1805.head1 - | +--v0f1805.data->/var/lib/taos/data/vnode0/v0f1805.data - | +--v0f1805.last->/var/lib/taos/data/vnode0/v0f1805.last1 - | : - +--data/ - +--vnode0/ - +--v0f1804.head1 - +--v0f1804.data - +--v0f1804.last1 - +--v0f1805.head1 - +--v0f1805.data - +--v0f1805.last1 - : -``` - -#### meterObj file -There are only one meterObj file in a vnode. Informations bout the vnode, such as created time, configuration information, vnode statistic informations are saved in this file. It has the structure like below: - -``` - -[file_header] -[table_record1_offset&length] -[table_record2_offset&length] -... -[table_recordN_offset&length] -[table_record1] -[table_record2] -... -[table_recordN] - -``` -The file header takes 512 bytes, which mainly contains informations about the vnode. Each table record is the representation of a table on disk. - -#### head file -The _head_ files contain the index of data blocks in the _data_ file. The inner organization is as below: -``` - -[file_header] -[table1_offset] -[table2_offset] -... -[tableN_offset] -[table1_index_block] -[table2_index_block] -... -[tableN_index_block] - -``` -The table offset array in the _head_ file saves the information about the offsets of each table index block. Indices on data blocks in the same table are saved continuously. This also makes it efficient to load data indices on the same table. The data index block has a structure like: - -``` -[index_block_info] -[block1_index] -[block2_index] -... -[blockN_index] -``` -The index block info part contains the information about the index block such as the number of index blocks, etc. Each block index corresponds to a real data block in the _data_ file or _last_ file. Information about the location of the real data block, the primary timestamp range of the data block, etc. are all saved in the block index part. The block indices are sorted in ascending order according to the primary timestamp. So we can apply algorithms such as the binary search on the data to efficiently search blocks according to time. - -#### data file -The _data_ files store the real data block. They are append-only. The organization is as: -``` - -[file_header] -[block1] -[block2] -... -[blockN] - -``` -A data block in _data_ files only belongs to a table in the vnode and the records in a data block are sorted in ascending order according to the primary timestamp key. Data blocks are column-oriented. Data in the same column are stored contiguously, which improves reading speed and compression rate because of their similarity. A data block has the following organization: - -``` -[column1_info] -[column2_info] -... -[columnN_info] -[column1_data] -[column2_data] -... -[columnN_data] -``` -The column info part includes information about column types, column compression algorithm, column data offset and length in the _data_ file, etc. Besides, pre-calculated results of the column data in the block are also in the column info part, which helps to improve reading speed by avoiding loading data block necessarily. - -#### last file -To avoid storage fragment and to import query speed and compression rate, TDengine introduces an extra file, the _last_ file. When the number of records in a data block is lower than a threshold, TDengine will flush the block to the _last_ file for temporary storage. When new data comes, the data in the _last_ file will be merged with the new data and form a larger data block and written to the _data_ file. The organization of the _last_ file is similar to the _data_ file. - -### Summary -The innovation in architecture and storage design of TDengine improves resource usage. On the one hand, the virtualization makes it easy to distribute resources between different vnodes and for future scaling. On the other hand, sorted and column-oriented storage makes TDengine have a great advantage in writing, querying and compression. - -## Query Design - -#### Introduction - -TDengine provides a variety of query functions for both tables and super tables. In addition to regular aggregate queries, it also provides time window based query and statistical aggregation for time series data. TDengine's query processing requires the client app, management node, and data node to work together. The functions and modules involved in query processing included in each component are as follows: - -Client (Client App). The client development kit, embed in a client application, consists of TAOS SQL parser and query executor, the second-stage aggregator (Result Merger), continuous query manager and other major functional modules. The SQL parser is responsible for parsing and verifying the SQL statement and converting it into an abstract syntax tree. The query executor is responsible for transforming the abstract syntax tree into the query execution logic and creates the metadata query according to the query condition of the SQL statement. Since TAOS SQL does not currently include complex nested queries and pipeline query processing mechanism, there is no longer need for query plan optimization and physical query plan conversions. The second-stage aggregator is responsible for performing the aggregation of the independent results returned by query involved data nodes at the client side to generate final results. The continuous query manager is dedicated to managing the continuous queries created by users, including issuing fixed-interval query requests and writing the results back to TDengine or returning to the client application as needed. Also, the client is also responsible for retrying after the query fails, canceling the query request, and maintaining the connection heartbeat and reporting the query status to the management node. - -Management Node. The management node keeps the metadata of all the data of the entire cluster system, provides the metadata of the data required for the query from the client node, and divides the query request according to the load condition of the cluster. The super table contains information about all the tables created according to the super table, so the query processor (Query Executor) of the management node is responsible for the query processing of the tags of tables and returns the table information satisfying the tag query. Besides, the management node maintains the query status of the cluster in the Query Status Manager component, in which the metadata of all queries that are currently executing are temporarily stored in-memory buffer. When the client issues *show queries* command to management node, current running queries information is returned to the client. - -Data Node. The data node, responsible for storing all data of the database, consists of query executor, query processing scheduler, query task queue, and other related components. Once the query requests from the client received, they are put into query task queue and waiting to be processed by query executor. The query executor extracts the query request from the query task queue and invokes the query optimizer to perform the basic optimization for the query execution plan. And then query executor scans the qualified data blocks in both cache and disk to obtain qualified data and return the calculated results. Besides, the data node also needs to respond to management information and commands from the management node. For example, after the *kill query* received from the management node, the query task needs to be stopped immediately. - -
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    Fig 1. System query processing architecture diagram (only query related components)
    - -#### Query Process Design - -The client, the management node, and the data node cooperate to complete the entire query processing of TDengine. Let's take a concrete SQL query as an example to illustrate the whole query processing flow. The SQL statement is to query on super table *FOO_SUPER_TABLE* to get the total number of records generated on January 12, 2019, from the table, of which TAG_LOC equals to 'beijing'. The SQL statement is as follows: - -```sql -SELECT COUNT(*) -FROM FOO_SUPER_TABLE -WHERE TAG_LOC = 'beijing' AND TS >= '2019-01-12 00:00:00' AND TS < '2019-01-13 00:00:00' -``` - -First, the client invokes the TAOS SQL parser to parse and validate the SQL statement, then generates a syntax tree, and extracts the object of the query - the super table *FOO_SUPER_TABLE*, and then the parser sends requests with filtering information (TAG_LOC='beijing') to management node to get the corresponding metadata about *FOO_SUPER_TABLE*. - -Once the management node receives the request for metadata acquisition, first finds the super table *FOO_SUPER_TABLE* basic information, and then applies the query condition (TAG_LOC='beijing') to filter all the related tables created according to it. And finally, the query executor returns the metadata information that satisfies the query request to the client. - -After the client obtains the metadata information of *FOO_SUPER_TABLE*, the query executor initiates a query request with timestamp range filtering condition (TS >= '2019- 01-12 00:00:00' AND TS < '2019-01-13 00:00:00') to all nodes that hold the corresponding data according to the information about data distribution in metadata. - -The data node receives the query sent from the client, converts it into an internal structure and puts it into the query task queue to be executed by query executor after optimizing the execution plan. When the query result is obtained, the query result is returned to the client. It should be noted that the data nodes perform the query process independently of each other, and rely solely on their data and content for processing. - -When all data nodes involved in the query return results, the client aggregates the result sets from each data node. In this case, all results are accumulated to generate the final query result. The second stage of aggregation is not always required for all queries. For example, a column selection query does not require a second-stage aggregation at all. - -#### REST Query Process - -In addition to C/C++, Python, and JDBC interface, TDengine also provides a REST interface based on the HTTP protocol, which is different from using the client application programming interface. When the user uses the REST interface, all the query processing is completed on the server-side, and the user's application is not involved in query processing anymore. After the query processing is completed, the result is returned to the client through the HTTP JSON string. - -
    -
    Fig. 2 REST query architecture
    - -When a client uses an HTTP-based REST query interface, the client first establishes a connection with the HTTP connector at the data node and then uses the token to ensure the reliability of the request through the REST signature mechanism. For the data node, after receiving the request, the HTTP connector invokes the embedded client program to initiate a query processing, and then the embedded client parses the SQL statement from the HTTP connector and requests the management node to get metadata as needed. After that, the embedded client sends query requests to the same data node or other nodes in the cluster and aggregates the calculation results on demand. Finally, you also need to convert the result of the query into a JSON format string and return it to the client via an HTTP response. After the HTTP connector receives the request SQL, the subsequent process processing is completely consistent with the query processing using the client application development kit. - -It should be noted that during the entire processing, the client application is no longer involved in, and is only responsible for sending SQL requests through the HTTP protocol and receiving the results in JSON format. Besides, each data node is embedded with an HTTP connector and a client, so any data node in the cluster received requests from a client, the data node can initiate the query and return the result to the client through the HTTP protocol, with transfer the request to other data nodes. - -#### Technology - -Because TDengine stores data and tags value separately, the tag value is kept in the management node and directly associated with each table instead of records, resulting in a great reduction of the data storage. Therefore, the tag value can be managed by a fully in-memory structure. First, the filtering of the tag data can drastically reduce the data size involved in the second phase of the query. The query processing for the data is performed at the data node. TDengine takes advantage of the immutable characteristics of IoT data by calculating the maximum, minimum, and other statistics of the data in one data block on each saved data block, to effectively improve the performance of query processing. If the query process involves all the data of the entire data block, the pre-computed result is used directly, and the content of the data block is no longer needed. Since the size of disk space required to store the pre-computation result is much smaller than the size of the specific data, the pre-computation result can greatly reduce the disk IO and speed up the query processing. - -TDengine employs column-oriented data storage techniques. When the data block is involved to be loaded from the disk for calculation, only the required column is read according to the query condition, and the read overhead can be minimized. The data of one column is stored in a contiguous memory block and therefore can make full use of the CPU L2 cache to greatly speed up the data scanning. Besides, TDengine utilizes the eagerly responding mechanism and returns a partial result before the complete result is acquired. For example, when the first batch of results is obtained, the data node immediately returns it directly to the client in case of a column select query. \ No newline at end of file diff --git a/documentation/webdocs/markdowndocs/Super Table-ch.md b/documentation/webdocs/markdowndocs/Super Table-ch.md deleted file mode 100644 index f45f2f91b1ae6cf7e5ab3c8270de5c3c6bca6c97..0000000000000000000000000000000000000000 --- a/documentation/webdocs/markdowndocs/Super Table-ch.md +++ /dev/null @@ -1,225 +0,0 @@ -# 超级表STable：多表聚合 - -TDengine要求每个数据采集点单独建表。独立建表的模式能够避免写入过程中的同步加锁，因此能够极大地提升数据的插入/查询性能。但是独立建表意味着系统中表的数量与采集点的数量在同一个量级。如果采集点众多，将导致系统中表的数量也非常庞大，让应用对表的维护以及聚合、统计操作难度加大。为降低应用的开发难度，TDengine引入了超级表(Super Table, 简称为STable)的概念。 - -## 什么是超级表 - -超级表是同一类型数据采集点的抽象，是同类型采集实例的集合，包含多张数据结构一样的子表。每个STable为其子表定义了表结构和一组标签：表结构即表中记录的数据列及其数据类型；标签名和数据类型由STable定义，标签值记录着每个子表的静态信息，用以对子表进行分组过滤。子表本质上就是普通的表，由一个时间戳主键和若干个数据列组成，每行记录着具体的数据，数据查询操作与普通表完全相同；但子表与普通表的区别在于每个子表从属于一张超级表，并带有一组由STable定义的标签值。每种类型的采集设备可以定义一个STable。数据模型定义表的每列数据的类型，如温度、压力、电压、电流、GPS实时位置等，而标签信息属于Meta Data，如采集设备的序列号、型号、位置等，是静态的，是表的元数据。用户在创建表（数据采集点）时指定STable(采集类型)外，还可以指定标签的值，也可事后增加或修改。 - -TDengine扩展标准SQL语法用于定义STable，使用关键词tags指定标签信息。语法如下： - -```mysql -CREATE TABLE ( TIMESTAMP, field_name1 field_type,…) TAGS(tag_name tag_type, …) -``` - -其中tag_name是标签名，tag_type是标签的数据类型。标签可以使用时间戳之外的其他TDengine支持的数据类型，标签的个数最多为32个，名字不能与系统关键词相同，也不能与其他列名相同。如： - -```mysql -CREATE TABLE thermometer (ts timestamp, degree float) -TAGS (location binary(20), type int) -``` - -上述SQL创建了一个名为thermometer的STable，带有标签location和标签type。 - -为某个采集点创建表时，可以指定其所属的STable以及标签的值，语法如下： - -```mysql -CREATE TABLE USING TAGS (tag_value1,...) -``` - -沿用上面温度计的例子，使用超级表thermometer建立单个温度计数据表的语句如下： - -```mysql -CREATE TABLE t1 USING thermometer TAGS ('beijing', 10) -``` - -上述SQL以thermometer为模板，创建了名为t1的表，这张表的Schema就是thermometer的Schema，但标签location值为'beijing'，标签type值为10。 - -用户可以使用一个STable创建数量无上限的具有不同标签的表，从这个意义上理解，STable就是若干具有相同数据模型，不同标签的表的集合。与普通表一样，用户可以创建、删除、查看超级表STable，大部分适用于普通表的查询操作都可运用到STable上，包括各种聚合和投影选择函数。除此之外，可以设置标签的过滤条件，仅对STbale中部分表进行聚合查询，大大简化应用的开发。 - -TDengine对表的主键（时间戳）建立索引，暂时不提供针对数据模型中其他采集量（比如温度、压力值）的索引。每个数据采集点会采集若干数据记录，但每个采集点的标签仅仅是一条记录，因此数据标签在存储上没有冗余，且整体数据规模有限。TDengine将标签数据与采集的动态数据完全分离存储，而且针对STable的标签建立了高性能内存索引结构，为标签提供全方位的快速操作支持。用户可按照需求对其进行增删改查（Create，Retrieve，Update，Delete，CRUD）操作。 - -STable从属于库，一个STable只属于一个库，但一个库可以有一到多个STable, 一个STable可有多个子表。 - -## 超级表管理 - -- 创建超级表 - - ```mysql - CREATE TABLE ( TIMESTAMP, field_name1 field_type,…) TAGS(tag_name tag_type, …) - ``` - - 与创建表的SQL语法相似。但需指定TAGS字段的名称和类型。 - - 说明： - - 1. TAGS列总长度不能超过16k bytes； - 2. TAGS列的数据类型不能是timestamp； - 3. TAGS列名不能与其他列名相同; - 4. TAGS列名不能为预留关键字. - 5. TAGS总数的上限是128. - -- 显示已创建的超级表 - - ```mysql - show stables; - ``` - - 查看数据库内全部STable，及其相关信息，包括STable的名称、创建时间、列数量、标签（TAG）数量、通过该STable建表的数量。 - -- 删除超级表 - - ```mysql - DROP TABLE - ``` - - Note: 删除STable时，所有通过该STable创建的表都将被删除。 - -- 查看属于某STable并满足查询条件的表 - - ```mysql - SELECT TBNAME,[TAG_NAME,…] FROM WHERE <[=|<=|>=|<>] values..> ([AND|OR] …) - ``` - - 查看属于某STable并满足查询条件的表。说明：TBNAME为关键词，显示通过STable建立的子表表名，查询过程中可以使用针对标签的条件。 - - ```mysql - SELECT COUNT(TBNAME) FROM WHERE <[=|<=|>=|<>] values..> ([AND|OR] …) - ``` - - 统计属于某个STable并满足查询条件的子表的数量 - -## 写数据时自动建子表 - -在某些特殊场景中，用户在写数据时并不确定某个设备的表是否存在，此时可使用自动建表语法来实现写入数据时里用超级表定义的表结构自动创建不存在的子表，若该表已存在则不会建立新表。注意：自动建表语句只能自动建立子表而不能建立超级表，这就要求超级表已经被事先定义好。自动建表语法跟insert/import语法非常相似，唯一区别是语句中增加了超级表和标签信息。具体语法如下： - -```mysql -INSERT INTO USING TAGS (, ...) VALUES (field_value, ...) (field_value, ...) ...; -``` - -向表tb_name中插入一条或多条记录，如果tb_name这张表不存在，则会用超级表stb_name定义的表结构以及用户指定的标签值(即tag1_value…)来创建名为tb_name新表，并将用户指定的值写入表中。如果tb_name已经存在，则建表过程会被忽略，系统也不会检查tb_name的标签是否与用户指定的标签值一致，也即不会更新已存在表的标签。 - -```mysql -INSERT INTO USING TAGS (, ...) VALUES (, ...) (, ...) ... USING TAGS(, ...) VALUES (, ...) ...; -``` - -向多张表tb1_name，tb2_name等插入一条或多条记录，并分别指定各自的超级表进行自动建表。 - -## STable中TAG管理 - -除了更新标签的值的操作是针对子表进行，其他所有的标签操作（添加标签、删除标签等）均只能作用于STable，不能对单个子表操作。对STable添加标签以后，依托于该STable建立的所有表将自动增加了一个标签，对于数值型的标签，新增加的标签的默认值是0. - -- 添加新的标签 - - ```mysql - ALTER TABLE ADD TAG - ``` - - 为STable增加一个新的标签，并指定新标签的类型。标签总数不能超过128个。 - -- 删除标签 - - ```mysql - ALTER TABLE DROP TAG - ``` - - 删除超级表的一个标签，从超级表删除某个标签后，该超级表下的所有子表也会自动删除该标签。 - - 说明：第一列标签不能删除，至少需要为STable保留一个标签。 - -- 修改标签名 - - ```mysql - ALTER TABLE CHANGE TAG - ``` - - 修改超级表的标签名，从超级表修改某个标签名后，该超级表下的所有子表也会自动更新该标签名。 - -- 修改子表的标签值 - - ```mysql - ALTER TABLE SET TAG = - ``` - -## STable多表聚合 - -针对所有的通过STable创建的子表进行多表聚合查询，支持按照全部的TAG值进行条件过滤，并可将结果按照TAGS中的值进行聚合，暂不支持针对binary类型的模糊匹配过滤。语法如下： - -```mysql -SELECT function,… - FROM - WHERE <[=|<=|>=|<>] values..> ([AND|OR] …) - INTERVAL (