diff --git a/Jenkinsfile2 b/Jenkinsfile2 index 14c03068d7a32745bb269d07d7903da12253694b..db49ab27d76f03bbaab0e0bf4aeba74b2f7ae361 100644 --- a/Jenkinsfile2 +++ b/Jenkinsfile2 @@ -287,7 +287,7 @@ pipeline { ''' sh ''' cd ${WKC}/tests/parallel_test - export DEFAULT_RETRY_TIME=2 + export DEFAULT_RETRY_TIME=1 date timeout 2100 time ./run.sh -e -m /home/m.json -t /tmp/cases.task -b ${BRANCH_NAME} -l ${WKDIR}/log -o 480 ''' diff --git a/docs-cn/02-intro.md b/docs-cn/02-intro.md index 8daea48e3e8e1b083f5cb0c04af0bef8ddb83122..949c21472dd29d51f2703034bd38ab95037e09c6 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技术生态图](eco_system.webp)
图 1. TDengine技术生态图
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/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/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-continuous-query.mdx b/docs-cn/07-develop/05-continuous-query.mdx index 2fd1b3cc755188f513fe511541a84efa3558d3ea..b2223d15e33114d263b9833df51e4201bc01c772 100644 --- a/docs-cn/07-develop/05-continuous-query.mdx +++ b/docs-cn/07-develop/05-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/06-subscribe.mdx index d471c114e827d7c4b40195c2c1b3c8f6a9d26ed4..ad5561fa09087c4c562ac340506f56d756bd98b2 100644 --- a/docs-cn/07-develop/06-subscribe.mdx +++ b/docs-cn/07-develop/06-subscribe.mdx @@ -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/07-cache.md index fd31335310d62d792e5173e38a9aa778ee6c6c60..cc59c0353c0d12fb7a8f0f20254087d741361031 100644 --- a/docs-cn/07-develop/07-cache.md +++ b/docs-cn/07-develop/07-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/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/08-interval.md b/docs-cn/12-taos-sql/08-interval.md index d62e11b0dbd0ba49ceedb3807e05361f060969b3..7c796e0046c5a740d393d71861828eb30bb3a5cc 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) +![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) +![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) +![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/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/14-reference/03-connector/03-connector.mdx b/docs-cn/14-reference/03-connector/03-connector.mdx index c0e714f148a7821e070be38a5484484fdd747e9a..aac358bea0682a9bd0807f10dc0cb2d4ef1d7a7b 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) +![image-connector](./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..1c24afdc4404a887e83a2664a311e16378ef9283 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-connector](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： @@ -208,10 +208,10 @@ 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); ::: @@ -563,7 +563,7 @@ 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 +574,7 @@ 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 +633,7 @@ 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/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..5fc9a282815813a87c9c17b84e5ffafc2f4692e7 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) +![taosAdapter Architecture](taosAdapter-architecture.webp) ## taosAdapter 部署方法 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 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a/docs-cn/14-reference/07-tdinsight/assets/tdengine_dashboard.png and /dev/null differ diff --git a/docs-cn/14-reference/07-tdinsight/assets/tdengine_dashboard.webp b/docs-cn/14-reference/07-tdinsight/assets/tdengine_dashboard.webp new file mode 100644 index 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..d7511fde3b5b92b335d60026e56944b9e2b99398 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) +![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) +![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) +![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) +![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) +![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) +![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) +![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) +![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) +![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) +![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) +![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) +![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) +![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) +![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/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..f9f7a26aa1632a07406199d76b3ad4ef9f1ec3e0 100644 --- a/docs-cn/20-third-party/01-grafana.mdx +++ b/docs-cn/20-third-party/01-grafana.mdx @@ -64,15 +64,15 @@ GF_PLUGINS_ALLOW_LOADING_UNSIGNED_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： -![img](/img/connections/add_datasource1.jpg) +![img](./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) +![img](./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) +![img](./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。 - User：TDengine 用户名。 @@ -80,13 +80,13 @@ GF_PLUGINS_ALLOW_LOADING_UNSIGNED_PLUGINS=tdengine-datasource 点击 `Save & Test` 进行测试，成功会有如下提示： -![img](/img/connections/add_datasource4.jpg) +![img](./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) +![img](./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： @@ -96,7 +96,7 @@ 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) +![img](./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/)。 diff --git a/docs-cn/20-third-party/09-emq-broker.md b/docs-cn/20-third-party/09-emq-broker.md index f57ccb20e6517c51b55093d11fa767bef7d0c9a8..b9d099c145d89c4f8e8a3cfaa994bffa0085e280 100644 --- a/docs-cn/20-third-party/09-emq-broker.md +++ b/docs-cn/20-third-party/09-emq-broker.md @@ -45,25 +45,25 @@ MQTT 是流行的物联网数据传输协议，[EMQX](https://github.com/emqx/em 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) +![img](./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) +![img](./emqx/rule-engine.webp) ### 编辑 SQL 字段 -![img](./emqx/create-rule.png) +![img](./emqx/create-rule.webp) ### 新增“动作（action handler）” -![img](./emqx/add-action-handler.png) +![img](./emqx/add-action-handler.webp) ### 新增“资源（Resource）” -![img](./emqx/create-resource.png) +![img](./emqx/create-resource.webp) 选择“发送数据到 Web 服务“并点击“新建资源”按钮： @@ -71,13 +71,13 @@ MQTT 是流行的物联网数据传输协议，[EMQX](https://github.com/emqx/em 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。 -![img](./emqx/edit-resource.png) +![img](./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。 -![img](./emqx/edit-action.png) +![img](./emqx/edit-action.webp) ## 编写模拟测试程序 @@ -164,7 +164,7 @@ MQTT 是流行的物联网数据传输协议，[EMQX](https://github.com/emqx/em 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) +![img](./emqx/client-num.webp) ## 执行测试模拟发送 MQTT 数据 @@ -173,19 +173,19 @@ npm install mqtt mockjs --save --registry=https://registry.npm.taobao.org node mock.js ``` -![img](./emqx/run-mock.png) +![img](./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) +![img](./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) +![img](./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..058909ca48da8dd1bb627ea4f984f086dd8aaf8e 100644 --- a/docs-cn/20-third-party/11-kafka.md +++ b/docs-cn/20-third-party/11-kafka.md @@ -9,11 +9,11 @@ TDengine Kafka Connector 包含两个插件: TDengine Source Connector 和 TDeng 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/Kafka_Connect.png) +![](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) +![](kafka/streaming-integration-with-kafka-connect.webp) ## 什么是 Confluent？ @@ -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) +![](kafka/confluentPlatform.webp) Confluent 企业版提供了 `confluent` 命令行工具管理各个组件。 @@ -196,10 +196,10 @@ 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 +223,10 @@ 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 +275,7 @@ 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 +302,8 @@ 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 ...... ``` diff --git a/docs-cn/20-third-party/add_datasource1.webp b/docs-cn/20-third-party/add_datasource1.webp new file mode 100644 index 0000000000000000000000000000000000000000..211edc4457abd0db6b0ef64636d61d65b5f43db6 Binary files /dev/null and b/docs-cn/20-third-party/add_datasource1.webp differ diff --git a/docs-cn/20-third-party/add_datasource2.webp b/docs-cn/20-third-party/add_datasource2.webp new file mode 100644 index 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b/docs-cn/20-third-party/kafka/confluentPlatform.webp differ diff --git a/docs-cn/20-third-party/kafka/streaming-integration-with-kafka-connect.png b/docs-cn/20-third-party/kafka/streaming-integration-with-kafka-connect.png deleted file mode 100644 index 26d8a866d706180c900d69bb6f57ca2dff0047dd..0000000000000000000000000000000000000000 Binary files a/docs-cn/20-third-party/kafka/streaming-integration-with-kafka-connect.png and /dev/null differ diff --git a/docs-cn/20-third-party/kafka/streaming-integration-with-kafka-connect.webp 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..456d4bea910fab46965600639cc3dd634daa15b9 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架构示意图](./structure.webp)
图 1 TDengine架构示意图
@@ -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 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 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) +![多表聚合查询原理图](./multi_tables.webp)
图 5 多表聚合查询原理图
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groupid | location | ================================= - 0 | shanghai | + 0 | California.SanDieo | Query OK, 1 row(s) in set (0.003490s) ``` 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 4fb00afc52d65debb3c4e310ef0192f3f6215636..628e87dd59f3c7a3bb00d93ee21b82550993d9ae 100644 --- a/docs-en/02-intro/index.md +++ b/docs-en/02-intro/index.md @@ -54,7 +54,7 @@ With TDengine, the total cost of ownership of your time-series data platform can ## Technical Ecosystem This is how TDengine would be situated, in a typical time-series data processing platform: -![TDengine Technical Ecosystem ](eco_system.png) +![TDengine Technical Ecosystem ](eco_system.webp)
Figure 1. TDengine Technical Ecosystem
diff --git a/docs-en/04-concept/index.md b/docs-en/04-concept/index.md index d714bace1d978017910f3160df98a0cc28189f5d..850f705146c4829db579f14be1a686ef9052f678 100644 --- a/docs-en/04-concept/index.md +++ b/docs-en/04-concept/index.md @@ -29,7 +29,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin 10.3 219 0.31 -San Jose +California.SanFrancisco 2 @@ -38,7 +38,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin 10.2 220 0.23 -San Jose +California.SanFrancisco 3 @@ -47,7 +47,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin 11.5 221 0.35 -Mountain View +California.LosAngeles 3 @@ -56,7 +56,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin 13.4 223 0.29 -Mountain View +California.LosAngeles 2 @@ -65,7 +65,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin 12.6 218 0.33 -San Jose +California.SanFrancisco 2 @@ -74,7 +74,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin 11.8 221 0.28 -Mountain View +California.LosAngeles 2 @@ -83,7 +83,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin 10.3 218 0.25 -San Jose +California.SanFrancisco 3 @@ -92,7 +92,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin 12.3 221 0.31 -San Jose +California.SanFrancisco 2 diff --git a/docs-en/05-get-started/index.md b/docs-en/05-get-started/index.md index 596d42d32f2fcba51a4326cd96220b4055d11704..858dd6ac56e3a523220903fc63335dfdc573b752 100644 --- a/docs-en/05-get-started/index.md +++ b/docs-en/05-get-started/index.md @@ -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.SanDieo". 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/02-model/index.mdx b/docs-en/07-develop/02-model/index.mdx index 2b91dc548729efa8855f3fc015c56ab7a674dd36..bdeca37ec11f623112b372984a51c331ba36cabf 100644 --- a/docs-en/07-develop/02-model/index.mdx +++ b/docs-en/07-develop/02-model/index.mdx @@ -52,10 +52,10 @@ At most 4096 (or 1024 prior to version 2.1.7.0) columns are allowed in a STable. 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. ```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. 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. @@ -70,10 +70,10 @@ It's suggested to use the global unique ID of a data collection point as the tab 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 exist. ```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). 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..06f6387b8a378f7bf91972418c2b853ab217ad3b 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 @@ -29,7 +29,7 @@ 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 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 3656e5fd3bb40735ab0f59187463c913c87573a1..b83bbdf61e6f14c5badc51c0ab03b53ca62d5055 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 @@ -29,7 +29,7 @@ A single line of text is used in OpenTSDB line protocol to represent one row of 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. @@ -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..74267a344b56019b1a9004edb4621e280bf6473d 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 @@ -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/04-query-data/index.mdx b/docs-en/07-develop/04-query-data/index.mdx index 539b8867a7aa197fd9c80790ad63e7d9577bbc31..761fe1889b795b8ba49604ef6f67877201984148 100644 --- a/docs-en/07-develop/04-query-data/index.mdx +++ b/docs-en/07-develop/04-query-data/index.mdx @@ -58,14 +58,14 @@ In summary, for a STable, its subtables can be aggregated by a simple query on t ### 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 below SQL 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) ``` @@ -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, the 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 | diff --git a/docs-en/07-develop/05-continuous-query.mdx b/docs-en/07-develop/05-continuous-query.mdx index 6e7ec643078b500a33a06c7b60c99ed5c601c694..f233deba31db1aa12654badbb7f33c7b33a3d70a 100644 --- a/docs-en/07-develop/05-continuous-query.mdx +++ b/docs-en/07-develop/05-continuous-query.mdx @@ -34,8 +34,8 @@ In this section the use case of meters will be used to introduce how to use cont ```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); ``` The SQL statement below retrieves the average voltage for a one minute time window, with each time window moving forward by 30 seconds. diff --git a/docs-en/07-develop/06-subscribe.mdx b/docs-en/07-develop/06-subscribe.mdx index 964bb2fd8d8ca284e1c79013868a5c44fbeb1aed..3fa2d1280f3b97702c1c0912b55a6c32f9c6be37 100644 --- a/docs-en/07-develop/06-subscribe.mdx +++ b/docs-en/07-develop/06-subscribe.mdx @@ -187,8 +187,8 @@ 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); @@ -196,11 +196,11 @@ 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) ``` @@ -235,11 +235,11 @@ Query OK, 5 row(s) in set (0.004896s) The example programs first 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 +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. @@ -253,5 +253,5 @@ 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 +ts: 1651146662805 current: 12.4 voltage: 220 phase: 1 location: California.SanFrancisco groupid: 2 ``` diff --git a/docs-en/07-develop/07-cache.md b/docs-en/07-develop/07-cache.md index c99612ecfbf192d8ffb4e2edd33665ba9f994797..3d42e22eb3eb0369140e2782de5a01b60156423a 100644 --- a/docs-en/07-develop/07-cache.md +++ b/docs-en/07-develop/07-cache.md @@ -12,8 +12,8 @@ The memory space used by TDengine cache is fixed in size, according to the confi 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`. 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 Chaoyang district of Beijing. +`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 of California. ```sql -select last_row(voltage) from meters where location='Beijing.Chaoyang'; +select last_row(voltage) from meters where location='California.SanFrancisco'; ``` diff --git a/docs-en/12-taos-sql/05-insert.md b/docs-en/12-taos-sql/05-insert.md index 96e6a08ee17e0c72b15a35efc487a78ae4673017..d511bd5c9131c1431a4dcdbe130b50161a3bc6f9 100644 --- a/docs-en/12-taos-sql/05-insert.md +++ b/docs-en/12-taos-sql/05-insert.md @@ -69,7 +69,7 @@ INSERT INTO d1001 VALUES ('2021-07-13 14:06:34.630', 10.2, 219, 0.32) ('2021-07- 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. ```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. @@ -81,7 +81,7 @@ INSERT INTO d21001 USING meters (groupId) TAGS (2) VALUES ('2021-07-13 14:06:33. Multiple rows can also be inserted into same table in single SQL statement using this way. ```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); ``` @@ -110,13 +110,13 @@ INSERT INTO d1001 FILE '/tmp/csvfile.csv'; 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: ```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'; ``` @@ -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. diff --git a/docs-en/12-taos-sql/06-select.md b/docs-en/12-taos-sql/06-select.md index 11b181f65d4e7e0e7d47d04986b144ff362c879f..0a5dc7645f7d17fd536fe8ca9606c238b5bdba5c 100644 --- a/docs-en/12-taos-sql/06-select.md +++ b/docs-en/12-taos-sql/06-select.md @@ -39,15 +39,15 @@ 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) ``` @@ -102,8 +102,8 @@ Starting from version 2.0.14, tag columns can be selected together with data col 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) ``` @@ -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; @@ -323,7 +323,7 @@ Logical operations in below table can be used in `where` clause to filter the re - 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 2.1.4.0, operator `IN` can be used in where clause. For example, `WHERE city IN ('California.SanFrancisco', 'California.SanDieo')`. 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. ## Regular Expression diff --git a/docs-en/12-taos-sql/08-interval.md b/docs-en/12-taos-sql/08-interval.md index 5cc3fa8cb43749fd40b808699f82a8761525cc6a..2044ff4f61d9da6bdc1c07b5361b89050193aa96 100644 --- a/docs-en/12-taos-sql/08-interval.md +++ b/docs-en/12-taos-sql/08-interval.md @@ -10,7 +10,7 @@ Window related clauses are used to divide the data set to be queried into subset `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. -![Time Window](/img/sql/timewindow-1.png) +![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`. @@ -30,7 +30,7 @@ When the time length specified by `SLIDING` is same as that specified by `INTERV 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. -![Status Window](/img/sql/timewindow-3.png) +![Status Window](./timewindow-3.webp) `STATE_WINDOW` is used to specify the column based on which to define status window, for example： @@ -46,7 +46,7 @@ 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. -![Session Window](/img/sql/timewindow-2.png) +![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. 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/14-reference/03-connector/03-connector.mdx b/docs-en/14-reference/03-connector/03-connector.mdx index 6be914bdb4b701f478b6b8b27366d6ebb5a39ec8..38eba73d0983951901a26eee3962e89007f6d30a 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) +![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/java.mdx b/docs-en/14-reference/03-connector/java.mdx index 328907c4d781bdea8d30623e01d431cedbf8d0fa..530798af1143d2e611369579a945de295d248ab0 100644 --- a/docs-en/14-reference/03-connector/java.mdx +++ b/docs-en/14-reference/03-connector/java.mdx @@ -11,7 +11,7 @@ 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. -![tdengine-connector](tdengine-jdbc-connector.png) +![tdengine-connector](tdengine-jdbc-connector.webp) The preceding diagram shows two ways for a Java app to access TDengine via connector: @@ -206,10 +206,10 @@ 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 t1 using weather(ts, temperature) tags('California.SanFrancisco') values(now, 24.6); ::: @@ -565,7 +565,7 @@ 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 +576,7 @@ 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); @@ -635,7 +635,7 @@ 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-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..de42e8a883d8b195b9d342f761e39458e557dfac 100644 --- a/docs-en/14-reference/04-taosadapter.md +++ b/docs-en/14-reference/04-taosadapter.md @@ -24,7 +24,7 @@ taosAdapter provides the following features. ## taosAdapter architecture diagram -![taosAdapter Architecture](taosAdapter-architecture.png) +![taosAdapter Architecture](taosAdapter-architecture.webp) ## taosAdapter Deployment Method diff --git a/docs-en/14-reference/07-tdinsight/assets/TDinsight-1-cluster-status.png b/docs-en/14-reference/07-tdinsight/assets/TDinsight-1-cluster-status.png deleted file mode 100644 index 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a/docs-en/14-reference/07-tdinsight/index.md +++ b/docs-en/14-reference/07-tdinsight/index.md @@ -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) +![Add data source button](./assets/howto-add-datasource-button.webp) Search for and select **TDengine**. -![Add datasource](./assets/howto-add-datasource-tdengine.png) +![Add datasource](./assets/howto-add-datasource-tdengine.webp) Configure the TDengine datasource. -![Datasource Configuration](./assets/howto-add-datasource.png) +![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) +![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) +![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) +![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) +![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) +![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) +![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) +![tdinsight-mnodes-overview](./assets/TDinsight-3-mnodes.webp) 1. **MNodes Status**: a simple table view of `show mnodes`. 2. 2. **MNodes Number**: similar to `DNodes Number`, the number of MNodes changes. ### Request -![tdinsight-requests](./assets/TDinsight-4-requests.png) +![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). @@ -313,7 +313,7 @@ This section contains the current information and status of the cluster, the ale ### Database -![tdinsight-database](./assets/TDinsight-5-database.png) +![tdinsight-database](./assets/TDinsight-5-database.webp) Database usage, repeated for each value of the variable `$database` i.e. multiple rows per database. @@ -325,7 +325,7 @@ Database usage, repeated for each value of the variable `$database` i.e. multipl ### DNode Resource Usage -![dnode-usage](./assets/TDinsight-6-dnode-usage.png) +![dnode-usage](./assets/TDinsight-6-dnode-usage.webp) Data node resource usage display with repeated multiple rows for the variable `$fqdn` i.e., each data node. Includes. @@ -346,13 +346,13 @@ Data node resource usage display with repeated multiple rows for the variable `$ ### Login History -![Login History](./assets/TDinsight-7-login-history.png) +![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) +![taosadapter](./assets/TDinsight-8-taosadapter.webp) Support monitoring taosAdapter request statistics and status details. Includes. diff --git a/docs-en/14-reference/12-config/index.md b/docs-en/14-reference/12-config/index.md index c4e7cc523c400ea5be6610b64f1561246b1bfa24..1a84f1539938ed8456d1c21c6def97d89305914d 100644 --- a/docs-en/14-reference/12-config/index.md +++ b/docs-en/14-reference/12-config/index.md @@ -202,7 +202,7 @@ To handle the data insertion and data query from multiple timezones, Unix Timest 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 ``` 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..7239710e0aebdd95977d9b73a5a1a9fccd656542 100644 --- a/docs-en/20-third-party/01-grafana.mdx +++ b/docs-en/20-third-party/01-grafana.mdx @@ -62,15 +62,15 @@ GF_PLUGINS_ALLOW_LOADING_UNSIGNED_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. -![img](./grafana/add_datasource1.jpg) +![img](./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) +![img](./grafana/add_datasource2.webp) Enter the datasource configuration page, and follow the default prompts to modify the corresponding configuration. -![img](./grafana/add_datasource3.jpg) +![img](./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. @@ -78,13 +78,13 @@ Enter the datasource configuration page, and follow the default prompts to modif Click `Save & Test` to test. Follows are a success. -![img](./grafana/add_datasource4.jpg) +![img](./grafana/add_datasource4.webp) ### Create Dashboard Go back to the main interface to create the Dashboard, click Add Query to enter the panel query page: -![img](./grafana/create_dashboard1.jpg) +![img](./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. @@ -94,7 +94,7 @@ As shown above, select the `TDengine` data source in the `Query` and enter the c 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) +![img](./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/). diff --git a/docs-en/20-third-party/09-emq-broker.md b/docs-en/20-third-party/09-emq-broker.md index 13562ba7f720499c23771437c5c6ba0f61819456..560c6463b59b00a362023d6cfa44cf833419a9ea 100644 --- a/docs-en/20-third-party/09-emq-broker.md +++ b/docs-en/20-third-party/09-emq-broker.md @@ -44,25 +44,25 @@ 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) +![img](./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) +![img](./emqx/rule-engine.webp) ### Edit SQL fields -![img](./emqx/create-rule.png) +![img](./emqx/create-rule.webp) ### Add "action handler" -![img](./emqx/add-action-handler.png) +![img](./emqx/add-action-handler.webp) ### Add "Resource" -![img](./emqx/create-resource.png) +![img](./emqx/create-resource.webp) Select "Data to Web Service" and click the "New Resource" button. @@ -70,13 +70,13 @@ Select "Data to Web Service" and click the "New Resource" button. 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. -![img](./emqx/edit-resource.png) +![img](./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. -![img](./emqx/edit-action.png) +![img](./emqx/edit-action.webp) ## Compose program to mock data @@ -163,7 +163,7 @@ Edit the resource configuration to add the key/value pairing for Authorization. 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) +![img](./emqx/client-num.webp) ## Execute tests to simulate sending MQTT data @@ -172,19 +172,19 @@ npm install mqtt mockjs --save ---registry=https://registry.npm.taobao.org node mock.js ``` -![img](./emqx/run-mock.png) +![img](./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) +![img](./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) +![img](./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..2da9a86b7d3def338497c9c0f3481918b566aaed 100644 --- a/docs-en/20-third-party/11-kafka.md +++ b/docs-en/20-third-party/11-kafka.md @@ -9,11 +9,11 @@ TDengine Kafka Connector contains two plugins: TDengine Source Connector and TDe 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/Kafka_Connect.png) +![](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) +![](kafka/streaming-integration-with-kafka-connect.webp) ## What is Confluent? @@ -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) +![](kafka/confluentPlatform.webp) Confluent Enterprise Edition provides the `confluent` command-line tool to manage various components. @@ -194,10 +194,10 @@ 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`. @@ -221,10 +221,10 @@ 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) ``` @@ -273,7 +273,7 @@ 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 +300,8 @@ 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 ...... ```` diff --git a/docs-en/20-third-party/emqx/add-action-handler.png 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following: -![TDengine architecture diagram](structure.png) +![TDengine 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. @@ -54,7 +54,7 @@ A complete TDengine system runs on one or more physical nodes. Logically, it inc 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](message.webp)
Figure 2: Typical process of TDengine
1. Application initiates a request to insert data through JDBC, ODBC, or other APIs. @@ -123,7 +123,7 @@ If a database has N replicas, thus a virtual node group has N virtual nodes, but Master Vnode uses a writing process as follows: -![TDengine Master Writing Process](write_master.png) +![TDengine 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; @@ -137,7 +137,7 @@ Master Vnode uses a writing process as follows: For a slave vnode, the write process as follows: -![TDengine Slave Writing Process](write_slave.png) +![TDengine Slave Writing Process](write_slave.webp)
Figure 4: TDengine Slave Writing Process
1. Slave vnode receives a data insertion request forwarded by Master vnode; @@ -267,7 +267,7 @@ For the data collected by device D1001, the number of records per hour is counte 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: -![Diagram of multi-table aggregation query](multi_tables.png) +![Diagram of multi-table aggregation query](multi_tables.webp)
Figure 5: Diagram of multi-table aggregation query
1. 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--git a/docs-en/21-tdinternal/write_slave.webp b/docs-en/21-tdinternal/write_slave.webp new file mode 100644 index 0000000000000000000000000000000000000000..7c45dec11b00e6a738de458f9e1bedacfad75a96 Binary files /dev/null and b/docs-en/21-tdinternal/write_slave.webp differ diff --git a/docs-en/25-application/01-telegraf.md b/docs-en/25-application/01-telegraf.md index 718e04ecd3dbd2a72feba3f5297d9da33a94ba83..07ab289ac2bbf44c219535fe128db69b34465c01 100644 --- a/docs-en/25-application/01-telegraf.md +++ b/docs-en/25-application/01-telegraf.md @@ -16,7 +16,7 @@ Current mainstream IT DevOps system usually include a data collection module, a 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. -![IT-DevOps-Solutions-Telegraf.png](/img/IT-DevOps-Solutions-Telegraf.png) +![IT-DevOps-Solutions-Telegraf.webp](./IT-DevOps-Solutions-Telegraf.webp) ## Installation steps @@ -75,7 +75,7 @@ Log in to the Grafana interface using a web browser at `IP:3000`, with the syste 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. -![IT-DevOps-Solutions-telegraf-dashboard.png](/img/IT-DevOps-Solutions-telegraf-dashboard.png) +![IT-DevOps-Solutions-telegraf-dashboard.webp](./IT-DevOps-Solutions-telegraf-dashboard.webp) ## Wrap-up diff --git a/docs-en/25-application/02-collectd.md b/docs-en/25-application/02-collectd.md index 2ac37618fafe11e71b215313e53f89b6c302f7cb..0ddea2855497f1dfdfce7a2aa6749e0c5ba1b9ff 100644 --- a/docs-en/25-application/02-collectd.md +++ b/docs-en/25-application/02-collectd.md @@ -17,7 +17,7 @@ The new version of TDengine supports multiple data protocols and can accept data 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. -![IT-DevOps-Solutions-Collectd-StatsD.png](/img/IT-DevOps-Solutions-Collectd-StatsD.png) +![IT-DevOps-Solutions-Collectd-StatsD.webp](./IT-DevOps-Solutions-Collectd-StatsD.webp) ## Installation Steps @@ -83,19 +83,19 @@ 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) +![IT-DevOps-Solutions-collectd-dashboard.webp](./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.webp](./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) +![IT-DevOps-Solutions-statsd-dashboard.webp](./IT-DevOps-Solutions-statsd-dashboard.webp) ## Wrap-up diff --git a/docs-en/25-application/03-immigrate.md b/docs-en/25-application/03-immigrate.md index 4cfeb892d821a1e5b7d5250615e7122e64b9882d..68d8a2b8cc25c80b8a647332df66874bee344715 100644 --- a/docs-en/25-application/03-immigrate.md +++ b/docs-en/25-application/03-immigrate.md @@ -32,7 +32,7 @@ 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") +![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.). @@ -75,7 +75,7 @@ After writing the data to TDengine properly, you can adapt Grafana to visualize 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") +![](./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. @@ -88,7 +88,7 @@ In most DevOps scenarios, if you have a small OpenTSDB cluster (3 or fewer nodes 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. **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") +![IT-DevOps-Solutions-Immigrate-TDengine-Arch](./IT-DevOps-Solutions-Immigrate-TDengine-Arch.webp "Figure 3. System architecture after migration completion") ## Migration evaluation and strategy for other scenarios diff --git a/docs-en/25-application/IT-DevOps-Solutions-Collectd-StatsD.webp b/docs-en/25-application/IT-DevOps-Solutions-Collectd-StatsD.webp new file mode 100644 index 0000000000000000000000000000000000000000..147a65b17bff2aa0e44faa206618bdce5664e1ca Binary files /dev/null and b/docs-en/25-application/IT-DevOps-Solutions-Collectd-StatsD.webp differ diff --git a/docs-en/25-application/IT-DevOps-Solutions-Immigrate-OpenTSDB-Arch.webp b/docs-en/25-application/IT-DevOps-Solutions-Immigrate-OpenTSDB-Arch.webp new file mode 100644 index 0000000000000000000000000000000000000000..3ca99c835b33df8845adf1b52d8fb8eb63076e82 Binary files /dev/null and b/docs-en/25-application/IT-DevOps-Solutions-Immigrate-OpenTSDB-Arch.webp differ diff --git a/docs-en/25-application/IT-DevOps-Solutions-Immigrate-OpenTSDB-Dashboard.webp b/docs-en/25-application/IT-DevOps-Solutions-Immigrate-OpenTSDB-Dashboard.webp new file mode 100644 index 0000000000000000000000000000000000000000..04811f61b9b318e129552d87cd48eabf6e99feab Binary files /dev/null and b/docs-en/25-application/IT-DevOps-Solutions-Immigrate-OpenTSDB-Dashboard.webp differ diff --git a/docs-en/25-application/IT-DevOps-Solutions-Immigrate-TDengine-Arch.webp b/docs-en/25-application/IT-DevOps-Solutions-Immigrate-TDengine-Arch.webp new file mode 100644 index 0000000000000000000000000000000000000000..36930068758556f4de5b58321804a96401c64b22 Binary files /dev/null and b/docs-en/25-application/IT-DevOps-Solutions-Immigrate-TDengine-Arch.webp differ diff --git a/docs-en/25-application/IT-DevOps-Solutions-Telegraf.webp b/docs-en/25-application/IT-DevOps-Solutions-Telegraf.webp new file mode 100644 index 0000000000000000000000000000000000000000..fd5461ec9b37be66cac4c17fb1f81fec76158330 Binary files /dev/null and b/docs-en/25-application/IT-DevOps-Solutions-Telegraf.webp differ diff --git a/docs-en/25-application/IT-DevOps-Solutions-collectd-dashboard.webp b/docs-en/25-application/IT-DevOps-Solutions-collectd-dashboard.webp new file mode 100644 index 0000000000000000000000000000000000000000..879c27a1a5843c714ff3c33c1dccfa32a2154b82 Binary files /dev/null and b/docs-en/25-application/IT-DevOps-Solutions-collectd-dashboard.webp differ diff --git a/docs-en/25-application/IT-DevOps-Solutions-statsd-dashboard.webp b/docs-en/25-application/IT-DevOps-Solutions-statsd-dashboard.webp new file mode 100644 index 0000000000000000000000000000000000000000..1d4c655970b5f3fcb3be2d65d67eb42f08f35862 Binary files /dev/null and b/docs-en/25-application/IT-DevOps-Solutions-statsd-dashboard.webp differ diff --git a/docs-en/25-application/IT-DevOps-Solutions-telegraf-dashboard.webp b/docs-en/25-application/IT-DevOps-Solutions-telegraf-dashboard.webp new file mode 100644 index 0000000000000000000000000000000000000000..105afcdb8312b23675f62ff6339d5e737b5cd958 Binary files /dev/null and b/docs-en/25-application/IT-DevOps-Solutions-telegraf-dashboard.webp differ diff --git a/docs-en/27-train-faq/03-docker.md b/docs-en/27-train-faq/03-docker.md index ba435a9307c1d6595579a295df83030c58ba0f22..3f560bcfef6119480b5499649cee1602656dbd6f 100644 --- a/docs-en/27-train-faq/03-docker.md +++ b/docs-en/27-train-faq/03-docker.md @@ -265,7 +265,7 @@ Below is an example output: $ 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/source/libs/function/src/builtins.c b/source/libs/function/src/builtins.c index b76ca1ec053ee55a1c24fd9df179f9fa73ea3cb3..b891e9a37ab5fdb34d689da22a5d0dad5c7fb1b9 100644 --- a/source/libs/function/src/builtins.c +++ b/source/libs/function/src/builtins.c @@ -156,6 +156,14 @@ static int32_t translatePercentile(SFunctionNode* pFunc, char* pErrBuf, int32_t return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); } + SValueNode* pValue = (SValueNode*)nodesListGetNode(pFunc->pParameterList, 1); + + if (pValue->datum.i < 0 || pValue->datum.i > 100) { + return invaildFuncParaValueErrMsg(pErrBuf, len, pFunc->functionName); + } + + pValue->notReserved = true; + uint8_t para1Type = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type; uint8_t para2Type = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 1))->resType.type; if (!IS_NUMERIC_TYPE(para1Type) || (!IS_SIGNED_NUMERIC_TYPE(para2Type) && !IS_UNSIGNED_NUMERIC_TYPE(para2Type))) { @@ -175,8 +183,8 @@ static bool validAperventileAlgo(const SValueNode* pVal) { } static int32_t translateApercentile(SFunctionNode* pFunc, char* pErrBuf, int32_t len) { - int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); - if (2 != paraNum && 3 != paraNum) { + int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList); + if (2 != numOfParams && 3 != numOfParams) { return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); } @@ -190,15 +198,15 @@ static int32_t translateApercentile(SFunctionNode* pFunc, char* pErrBuf, int32_t if (nodeType(pParamNode) != QUERY_NODE_VALUE) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); } - + SValueNode* pValue = (SValueNode*)pParamNode; if (pValue->datum.i < 0 || pValue->datum.i > 100) { return invaildFuncParaValueErrMsg(pErrBuf, len, pFunc->functionName); } pValue->notReserved = true; - - if (3 == paraNum) { + + if (3 == numOfParams) { SNode* pPara3 = nodesListGetNode(pFunc->pParameterList, 2); if (QUERY_NODE_VALUE != nodeType(pPara3) || !validAperventileAlgo((SValueNode*)pPara3)) { return buildFuncErrMsg(pErrBuf, len, TSDB_CODE_FUNC_FUNTION_ERROR, @@ -218,8 +226,8 @@ static int32_t translateTbnameColumn(SFunctionNode* pFunc, char* pErrBuf, int32_ } static int32_t translateTop(SFunctionNode* pFunc, char* pErrBuf, int32_t len) { - int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); - if (2 != paraNum) { + int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList); + if (2 != numOfParams) { return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); } @@ -263,15 +271,16 @@ static int32_t translateSpread(SFunctionNode* pFunc, char* pErrBuf, int32_t len) } static int32_t translateElapsed(SFunctionNode* pFunc, char* pErrBuf, int32_t len) { - int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); - if (1 != paraNum && 2 != paraNum) { + int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList); + if (1 != numOfParams && 2 != numOfParams) { return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); } - SNode* pPara = nodesListGetNode(pFunc->pParameterList, 0); - if (QUERY_NODE_COLUMN != nodeType(pPara)) { + //param0 + SNode* pParaNode0 = nodesListGetNode(pFunc->pParameterList, 0); + if (QUERY_NODE_COLUMN != nodeType(pParaNode0)) { return buildFuncErrMsg(pErrBuf, len, TSDB_CODE_FUNC_FUNTION_ERROR, - "The input parameter of ELAPSED function can only be column"); + "The first parameter of ELAPSED function can only be column"); } uint8_t paraType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type; @@ -279,6 +288,23 @@ static int32_t translateElapsed(SFunctionNode* pFunc, char* pErrBuf, int32_t len return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); } + //param1 + if (2 == numOfParams) { + SNode* pParamNode1 = nodesListGetNode(pFunc->pParameterList, 1); + if (QUERY_NODE_VALUE != nodeType(pParamNode1)) { + return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); + } + + SValueNode* pValue = (SValueNode*)pParamNode1; + + pValue->notReserved = true; + + uint8_t paraType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 1))->resType.type; + if (!IS_INTEGER_TYPE(paraType)) { + return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); + } + } + pFunc->node.resType = (SDataType){.bytes = tDataTypes[TSDB_DATA_TYPE_DOUBLE].bytes, .type = TSDB_DATA_TYPE_DOUBLE}; return TSDB_CODE_SUCCESS; } @@ -290,26 +316,58 @@ static int32_t translateLeastSQR(SFunctionNode* pFunc, char* pErrBuf, int32_t le } for (int32_t i = 0; i < numOfParams; ++i) { + SNode* pParamNode = nodesListGetNode(pFunc->pParameterList, i); + if (i > 0) { //param1 & param2 + if (QUERY_NODE_VALUE != nodeType(pParamNode)) { + return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); + } + + SValueNode* pValue = (SValueNode*)pParamNode; + + pValue->notReserved = true; + } + uint8_t colType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, i))->resType.type; if (!IS_NUMERIC_TYPE(colType)) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); } } + pFunc->node.resType = (SDataType){.bytes = 64, .type = TSDB_DATA_TYPE_BINARY}; return TSDB_CODE_SUCCESS; } static int32_t translateHistogram(SFunctionNode* pFunc, char* pErrBuf, int32_t len) { - if (4 != LIST_LENGTH(pFunc->pParameterList)) { + int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList); + if (4 != numOfParams) { return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); } + //param0 + SNode* pParaNode0 = nodesListGetNode(pFunc->pParameterList, 0); + if (QUERY_NODE_COLUMN != nodeType(pParaNode0)) { + return buildFuncErrMsg(pErrBuf, len, TSDB_CODE_FUNC_FUNTION_ERROR, + "The first parameter of HISTOGRAM function can only be column"); + } + uint8_t colType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type; if (!IS_NUMERIC_TYPE(colType)) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); } + //param1 ~ param3 + for (int32_t i = 1; i < numOfParams; ++i) { + SNode* pParamNode = nodesListGetNode(pFunc->pParameterList, i); + if (QUERY_NODE_VALUE != nodeType(pParamNode)) { + return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); + } + + SValueNode* pValue = (SValueNode*)pParamNode; + + pValue->notReserved = true; + } + if (((SExprNode*)nodesListGetNode(pFunc->pParameterList, 1))->resType.type != TSDB_DATA_TYPE_BINARY || ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 2))->resType.type != TSDB_DATA_TYPE_BINARY || ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 3))->resType.type != TSDB_DATA_TYPE_BIGINT) { @@ -336,46 +394,75 @@ static int32_t translateHLL(SFunctionNode* pFunc, char* pErrBuf, int32_t len) { } static int32_t translateStateCount(SFunctionNode* pFunc, char* pErrBuf, int32_t len) { - if (3 != LIST_LENGTH(pFunc->pParameterList)) { + int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList); + if (3 != numOfParams) { return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); } + //param0 uint8_t colType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type; if (!IS_NUMERIC_TYPE(colType)) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); } + //param1 & param2 + for (int32_t i = 1; i < numOfParams; ++i) { + SNode* pParamNode = nodesListGetNode(pFunc->pParameterList, i); + if (QUERY_NODE_VALUE != nodeType(pParamNode)) { + return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); + } + + SValueNode* pValue = (SValueNode*)pParamNode; + + pValue->notReserved = true; + } + if (((SExprNode*)nodesListGetNode(pFunc->pParameterList, 1))->resType.type != TSDB_DATA_TYPE_BINARY || (((SExprNode*)nodesListGetNode(pFunc->pParameterList, 2))->resType.type != TSDB_DATA_TYPE_BIGINT && ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 2))->resType.type != TSDB_DATA_TYPE_DOUBLE)) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); } + //set result type pFunc->node.resType = (SDataType){.bytes = tDataTypes[TSDB_DATA_TYPE_BIGINT].bytes, .type = TSDB_DATA_TYPE_BIGINT}; return TSDB_CODE_SUCCESS; } static int32_t translateStateDuration(SFunctionNode* pFunc, char* pErrBuf, int32_t len) { - int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); - if (3 != paraNum && 4 != paraNum) { + int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList); + if (3 != numOfParams && 4 != numOfParams) { return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); } + //param0 uint8_t colType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type; if (!IS_NUMERIC_TYPE(colType)) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); } + //param1, param2 & param3 + for (int32_t i = 1; i < numOfParams; ++i) { + SNode* pParamNode = nodesListGetNode(pFunc->pParameterList, i); + if (QUERY_NODE_VALUE != nodeType(pParamNode)) { + return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); + } + + SValueNode* pValue = (SValueNode*)pParamNode; + + pValue->notReserved = true; + } + if (((SExprNode*)nodesListGetNode(pFunc->pParameterList, 1))->resType.type != TSDB_DATA_TYPE_BINARY || (((SExprNode*)nodesListGetNode(pFunc->pParameterList, 2))->resType.type != TSDB_DATA_TYPE_BIGINT && ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 2))->resType.type != TSDB_DATA_TYPE_DOUBLE)) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); } - if (paraNum == 4 && ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 3))->resType.type != TSDB_DATA_TYPE_BIGINT) { + if (numOfParams == 4 && ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 3))->resType.type != TSDB_DATA_TYPE_BIGINT) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); } + //set result type pFunc->node.resType = (SDataType){.bytes = tDataTypes[TSDB_DATA_TYPE_BIGINT].bytes, .type = TSDB_DATA_TYPE_BIGINT}; return TSDB_CODE_SUCCESS; } @@ -416,13 +503,28 @@ static int32_t translateMavg(SFunctionNode* pFunc, char* pErrBuf, int32_t len) { return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); } - SNode* pPara = nodesListGetNode(pFunc->pParameterList, 0); - if (QUERY_NODE_COLUMN != nodeType(pPara)) { + //param0 + SNode* pParaNode0 = nodesListGetNode(pFunc->pParameterList, 0); + if (QUERY_NODE_COLUMN != nodeType(pParaNode0)) { return buildFuncErrMsg(pErrBuf, len, TSDB_CODE_FUNC_FUNTION_ERROR, - "The input parameter of MAVG function can only be column"); + "The first parameter of MAVG function can only be column"); } uint8_t colType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type; + + //param1 + SNode* pParamNode1 = nodesListGetNode(pFunc->pParameterList, 1); + if (QUERY_NODE_VALUE != nodeType(pParamNode1)) { + return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); + } + + SValueNode* pValue = (SValueNode*)pParamNode1; + if (pValue->datum.i < 1 || pValue->datum.i > 1000) { + return invaildFuncParaValueErrMsg(pErrBuf, len, pFunc->functionName); + } + + pValue->notReserved = true; + uint8_t paraType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 1))->resType.type; if (!IS_NUMERIC_TYPE(colType) || !IS_INTEGER_TYPE(paraType)) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); @@ -437,24 +539,41 @@ static int32_t translateSample(SFunctionNode* pFunc, char* pErrBuf, int32_t len) return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); } - SNode* pPara = nodesListGetNode(pFunc->pParameterList, 0); - if (QUERY_NODE_COLUMN != nodeType(pPara)) { + //param0 + SNode* pParamNode0 = nodesListGetNode(pFunc->pParameterList, 0); + if (QUERY_NODE_COLUMN != nodeType(pParamNode0)) { return buildFuncErrMsg(pErrBuf, len, TSDB_CODE_FUNC_FUNTION_ERROR, - "The input parameter of SAMPLE function can only be column"); + "The first parameter of SAMPLE function can only be column"); } + SExprNode* pCol = (SExprNode*)nodesListGetNode(pFunc->pParameterList, 0); + uint8_t colType = pCol->resType.type; + + //param1 + SNode* pParamNode1 = nodesListGetNode(pFunc->pParameterList, 1); + if (QUERY_NODE_VALUE != nodeType(pParamNode1)) { + return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); + } + + SValueNode* pValue = (SValueNode*)pParamNode1; + if (pValue->datum.i < 1 || pValue->datum.i > 1000) { + return invaildFuncParaValueErrMsg(pErrBuf, len, pFunc->functionName); + } + + pValue->notReserved = true; + uint8_t paraType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 1))->resType.type; if (!IS_INTEGER_TYPE(paraType)) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); } - SExprNode* pCol = (SExprNode*)nodesListGetNode(pFunc->pParameterList, 0); - uint8_t colType = pCol->resType.type; + //set result type if (IS_VAR_DATA_TYPE(colType)) { pFunc->node.resType = (SDataType){.bytes = pCol->resType.bytes, .type = colType}; } else { pFunc->node.resType = (SDataType){.bytes = tDataTypes[colType].bytes, .type = colType}; } + return TSDB_CODE_SUCCESS; } @@ -464,21 +583,37 @@ static int32_t translateTail(SFunctionNode* pFunc, char* pErrBuf, int32_t len) { return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); } + //param0 SNode* pPara = nodesListGetNode(pFunc->pParameterList, 0); if (QUERY_NODE_COLUMN != nodeType(pPara)) { return buildFuncErrMsg(pErrBuf, len, TSDB_CODE_FUNC_FUNTION_ERROR, - "The input parameter of TAIL function can only be column"); + "The first parameter of TAIL function can only be column"); } + SExprNode* pCol = (SExprNode*)nodesListGetNode(pFunc->pParameterList, 0); + uint8_t colType = pCol->resType.type; + //param1 & param2 for (int32_t i = 1; i < numOfParams; ++i) { + SNode* pParamNode = nodesListGetNode(pFunc->pParameterList, i); + if (QUERY_NODE_VALUE != nodeType(pParamNode)) { + return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); + } + + SValueNode* pValue = (SValueNode*)pParamNode; + + if (pValue->datum.i < ((i > 1) ? 0 : 1) || pValue->datum.i > 1000) { + return invaildFuncParaValueErrMsg(pErrBuf, len, pFunc->functionName); + } + + pValue->notReserved = true; + uint8_t paraType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, i))->resType.type; if (!IS_INTEGER_TYPE(paraType)) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); } } - SExprNode* pCol = (SExprNode*)nodesListGetNode(pFunc->pParameterList, 0); - uint8_t colType = pCol->resType.type; + //set result type if (IS_VAR_DATA_TYPE(colType)) { pFunc->node.resType = (SDataType){.bytes = pCol->resType.bytes, .type = colType}; } else { @@ -552,8 +687,8 @@ static int32_t translateLength(SFunctionNode* pFunc, char* pErrBuf, int32_t len) static int32_t translateConcatImpl(SFunctionNode* pFunc, char* pErrBuf, int32_t len, int32_t minParaNum, int32_t maxParaNum, bool hasSep) { - int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); - if (paraNum < minParaNum || paraNum > maxParaNum) { + int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList); + if (numOfParams < minParaNum || numOfParams > maxParaNum) { return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); } @@ -562,7 +697,7 @@ static int32_t translateConcatImpl(SFunctionNode* pFunc, char* pErrBuf, int32_t int32_t sepBytes = 0; /* For concat/concat_ws function, if params have NCHAR type, promote the final result to NCHAR */ - for (int32_t i = 0; i < paraNum; ++i) { + for (int32_t i = 0; i < numOfParams; ++i) { SNode* pPara = nodesListGetNode(pFunc->pParameterList, i); uint8_t paraType = ((SExprNode*)pPara)->resType.type; if (!IS_VAR_DATA_TYPE(paraType)) { @@ -573,7 +708,7 @@ static int32_t translateConcatImpl(SFunctionNode* pFunc, char* pErrBuf, int32_t } } - for (int32_t i = 0; i < paraNum; ++i) { + for (int32_t i = 0; i < numOfParams; ++i) { SNode* pPara = nodesListGetNode(pFunc->pParameterList, i); uint8_t paraType = ((SExprNode*)pPara)->resType.type; int32_t paraBytes = ((SExprNode*)pPara)->resType.bytes; @@ -589,7 +724,7 @@ static int32_t translateConcatImpl(SFunctionNode* pFunc, char* pErrBuf, int32_t } if (hasSep) { - resultBytes += sepBytes * (paraNum - 3); + resultBytes += sepBytes * (numOfParams - 3); } pFunc->node.resType = (SDataType){.bytes = resultBytes, .type = resultType}; @@ -605,8 +740,8 @@ static int32_t translateConcatWs(SFunctionNode* pFunc, char* pErrBuf, int32_t le } static int32_t translateSubstr(SFunctionNode* pFunc, char* pErrBuf, int32_t len) { - int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); - if (2 != paraNum && 3 != paraNum) { + int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList); + if (2 != numOfParams && 3 != numOfParams) { return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); } @@ -615,7 +750,7 @@ static int32_t translateSubstr(SFunctionNode* pFunc, char* pErrBuf, int32_t len) if (!IS_VAR_DATA_TYPE(pPara1->resType.type) || !IS_INTEGER_TYPE(para2Type)) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); } - if (3 == paraNum) { + if (3 == numOfParams) { uint8_t para3Type = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 1))->resType.type; if (!IS_INTEGER_TYPE(para3Type)) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); @@ -692,8 +827,8 @@ static int32_t translateTimeTruncate(SFunctionNode* pFunc, char* pErrBuf, int32_ } static int32_t translateTimeDiff(SFunctionNode* pFunc, char* pErrBuf, int32_t len) { - int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); - if (2 != paraNum && 3 != paraNum) { + int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList); + if (2 != numOfParams && 3 != numOfParams) { return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); } @@ -704,7 +839,7 @@ static int32_t translateTimeDiff(SFunctionNode* pFunc, char* pErrBuf, int32_t le } } - if (3 == paraNum) { + if (3 == numOfParams) { if (!IS_INTEGER_TYPE(((SExprNode*)nodesListGetNode(pFunc->pParameterList, 2))->resType.type)) { return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); }