提交 0a85055d 编写于 作者: J jiacy-jcy

Merge branch '3.0' into test/jcy

cmake_minimum_required(VERSION 3.16)
cmake_minimum_required(VERSION 3.0)
project(
TDengine
......
cmake_minimum_required(VERSION 3.16)
cmake_minimum_required(VERSION 3.0)
set(CMAKE_VERBOSE_MAKEFILE OFF)
......
cmake_minimum_required(VERSION 3.16)
cmake_minimum_required(VERSION 3.0)
MESSAGE("Current system is ${CMAKE_SYSTEM_NAME}")
......
......@@ -4,6 +4,8 @@ title: 支持的数据类型
description: "TDengine 支持的数据类型: 时间戳、浮点型、JSON 类型等"
---
## 时间戳
使用 TDengine,最重要的是时间戳。创建并插入记录、查询历史记录的时候,均需要指定时间戳。时间戳有如下规则:
- 时间格式为 `YYYY-MM-DD HH:mm:ss.MS`,默认时间分辨率为毫秒。比如:`2017-08-12 18:25:58.128`
......@@ -12,39 +14,54 @@ description: "TDengine 支持的数据类型: 时间戳、浮点型、JSON 类
- Epoch Time:时间戳也可以是一个长整数,表示从格林威治时间 1970-01-01 00:00:00.000 (UTC/GMT) 开始的毫秒数(相应地,如果所在 Database 的时间精度设置为“微秒”,则长整型格式的时间戳含义也就对应于从格林威治时间 1970-01-01 00:00:00.000 (UTC/GMT) 开始的微秒数;纳秒精度逻辑类似。)
- 时间可以加减,比如 now-2h,表明查询时刻向前推 2 个小时(最近 2 小时)。数字后面的时间单位可以是 b(纳秒)、u(微秒)、a(毫秒)、s(秒)、m(分)、h(小时)、d(天)、w(周)。 比如 `select * from t1 where ts > now-2w and ts <= now-1w`,表示查询两周前整整一周的数据。在指定降采样操作(down sampling)的时间窗口(interval)时,时间单位还可以使用 n (自然月) 和 y (自然年)。
TDengine 缺省的时间戳精度是毫秒,但通过在 `CREATE DATABASE` 时传递的 PRECISION 参数也可以支持微秒和纳秒。(从 2.1.5.0 版本开始支持纳秒精度)
TDengine 缺省的时间戳精度是毫秒,但通过在 `CREATE DATABASE` 时传递的 PRECISION 参数也可以支持微秒和纳秒。
```sql
CREATE DATABASE db_name PRECISION 'ns';
```
## 数据类型
在 TDengine 中,普通表的数据模型中可使用以下 10 种数据类型。
在 TDengine 中,普通表的数据模型中可使用以下数据类型。
| # | **类型** | **Bytes** | **说明** |
| --- | :-------: | --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1 | TIMESTAMP | 8 | 时间戳。缺省精度毫秒,可支持微秒和纳秒。从格林威治时间 1970-01-01 00:00:00.000 (UTC/GMT) 开始,计时不能早于该时间。(从 2.0.18.0 版本开始,已经去除了这一时间范围限制)(从 2.1.5.0 版本开始支持纳秒精度) |
| 2 | INT | 4 | 整型,范围 [-2^31+1, 2^31-1], -2^31 用作 NULL |
| 3 | BIGINT | 8 | 长整型,范围 [-2^63+1, 2^63-1], -2^63 用作 NULL |
| 4 | FLOAT | 4 | 浮点型,有效位数 6-7,范围 [-3.4E38, 3.4E38] |
| 5 | DOUBLE | 8 | 双精度浮点型,有效位数 15-16,范围 [-1.7E308, 1.7E308] |
| 6 | BINARY | 自定义 | 记录单字节字符串,建议只用于处理 ASCII 可见字符,中文等多字节字符需使用 nchar。理论上,最长可以有 16374 字节。binary 仅支持字符串输入,字符串两端需使用单引号引用。使用时须指定大小,如 binary(20) 定义了最长为 20 个单字节字符的字符串,每个字符占 1 byte 的存储空间,总共固定占用 20 bytes 的空间,此时如果用户字符串超出 20 字节将会报错。对于字符串内的单引号,可以用转义字符反斜线加单引号来表示,即 `\’`。 |
| 7 | SMALLINT | 2 | 短整型, 范围 [-32767, 32767], -32768 用作 NULL |
| 8 | TINYINT | 1 | 单字节整型,范围 [-127, 127], -128 用作 NULL |
| 9 | BOOL | 1 | 布尔型,{true, false} |
| 10 | NCHAR | 自定义 | 记录包含多字节字符在内的字符串,如中文字符。每个 nchar 字符占用 4 bytes 的存储空间。字符串两端使用单引号引用,字符串内的单引号需用转义字符 `\’`。nchar 使用时须指定字符串大小,类型为 nchar(10) 的列表示此列的字符串最多存储 10 个 nchar 字符,会固定占用 40 bytes 的空间。如果用户字符串长度超出声明长度,将会报错。 |
| 11 | JSON | | json 数据类型, 只有 tag 可以是 json 格式 |
:::tip
TDengine 对 SQL 语句中的英文字符不区分大小写,自动转化为小写执行。因此用户大小写敏感的字符串及密码,需要使用单引号将字符串引起来。
| 1 | TIMESTAMP | 8 | 时间戳。缺省精度毫秒,可支持微秒和纳秒,详细说明见上节。 |
| 2 | INT | 4 | 整型,范围 [-2^31, 2^31-1] |
| 3 | INT UNSIGNED| 4| 无符号整数,[0, 2^32-1]
| 4 | BIGINT | 8 | 长整型,范围 [-2^63, 2^63-1] |
| 5 | BIGINT UNSIGNED | 8 | 长整型,范围 [0, 2^64-1] |
| 6 | FLOAT | 4 | 浮点型,有效位数 6-7,范围 [-3.4E38, 3.4E38] |
| 7 | DOUBLE | 8 | 双精度浮点型,有效位数 15-16,范围 [-1.7E308, 1.7E308] |
| 8 | BINARY | 自定义 | 记录单字节字符串,建议只用于处理 ASCII 可见字符,中文等多字节字符需使用 nchar。 |
| 9 | SMALLINT | 2 | 短整型, 范围 [-32768, 32767] |
| 10 | SMALLINT UNSIGNED | 2| 无符号短整型,范围 [0, 655357] |
| 11 | TINYINT | 1 | 单字节整型,范围 [-128, 127] |
| 12 | TINYINT UNSIGNED | 1 | 无符号单字节整型,范围 [0, 255] |
| 13 | BOOL | 1 | 布尔型,{true, false} |
| 14 | NCHAR | 自定义 | 记录包含多字节字符在内的字符串,如中文字符。每个 nchar 字符占用 4 bytes 的存储空间。字符串两端使用单引号引用,字符串内的单引号需用转义字符 `\’`。nchar 使用时须指定字符串大小,类型为 nchar(10) 的列表示此列的字符串最多存储 10 个 nchar 字符,会固定占用 40 bytes 的空间。如果用户字符串长度超出声明长度,将会报错。 |
| 15 | JSON | | json 数据类型, 只有 tag 可以是 json 格式 |
| 16 | VARCHAR | 自定义 | BINARY类型的别名 |
:::
:::note
虽然 BINARY 类型在底层存储上支持字节型的二进制字符,但不同编程语言对二进制数据的处理方式并不保证一致,因此建议在 BINARY 类型中只存储 ASCII 可见字符,而避免存储不可见字符。多字节的数据,例如中文字符,则需要使用 NCHAR 类型进行保存。如果强行使用 BINARY 类型保存中文字符,虽然有时也能正常读写,但并不带有字符集信息,很容易出现数据乱码甚至数据损坏等情况。
- TDengine 对 SQL 语句中的英文字符不区分大小写,自动转化为小写执行。因此用户大小写敏感的字符串及密码,需要使用单引号将字符串引起来。
- 虽然 BINARY 类型在底层存储上支持字节型的二进制字符,但不同编程语言对二进制数据的处理方式并不保证一致,因此建议在 BINARY 类型中只存储 ASCII 可见字符,而避免存储不可见字符。多字节的数据,例如中文字符,则需要使用 NCHAR 类型进行保存。如果强行使用 BINARY 类型保存中文字符,虽然有时也能正常读写,但并不带有字符集信息,很容易出现数据乱码甚至数据损坏等情况。
- BINARY 类型理论上最长可以有 16374 字节。binary 仅支持字符串输入,字符串两端需使用单引号引用。使用时须指定大小,如 binary(20) 定义了最长为 20 个单字节字符的字符串,每个字符占 1 byte 的存储空间,总共固定占用 20 bytes 的空间,此时如果用户字符串超出 20 字节将会报错。对于字符串内的单引号,可以用转义字符反斜线加单引号来表示,即 `\’`
- SQL 语句中的数值类型将依据是否存在小数点,或使用科学计数法表示,来判断数值类型是否为整型或者浮点型,因此在使用时要注意相应类型越界的情况。例如,9999999999999999999 会认为超过长整型的上边界而溢出,而 9999999999999999999.0 会被认为是有效的浮点数。
:::
:::note
SQL 语句中的数值类型将依据是否存在小数点,或使用科学计数法表示,来判断数值类型是否为整型或者浮点型,因此在使用时要注意相应类型越界的情况。例如,9999999999999999999 会认为超过长整型的上边界而溢出,而 9999999999999999999.0 会被认为是有效的浮点数。
:::
## 常量
TDengine支持多个类型的常量,细节如下表:
| # | **语法** | **类型** | **说明** |
| --- | :-------: | --------- | -------------------------------------- |
| 1 | [{+ \| -}]123 | BIGINT | 整型数值的字面量的类型均为BIGINT。如果用户输入超过了BIGINT的表示范围,TDengine 按BIGINT对数值进行截断。|
| 2 | 123.45 | DOUBLE | 浮点数值的字面量的类型均为DOUBLE。TDengine依据是否存在小数点,或使用科学计数法表示,来判断数值类型是否为整型或者浮点型,因此在使用时要注意相应类型越界的情况。例如,9999999999999999999会认为超过长整型的上边界而溢出,而9999999999999999999.0会被认为是有效的浮点数。|
| 3 | 1.2E3 | DOUBLE | 科学计数法的字面量的类型为DOUBLE。|
| 4 | 'abc' | BINARY | 单引号括住的内容为字符串字面值,其类型为BINARY,BINARY的size为实际的字符个数。对于字符串内的单引号,可以用转义字符反斜线加单引号来表示,即 \'。|
| 5 | "abc" | BINARY | 双引号括住的内容为字符串字面值,其类型为BINARY,BINARY的size为实际的字符个数。对于字符串内的双引号,可以用转义字符反斜线加单引号来表示,即 \"。 |
| 6 | TIMESTAMP {'literal' \| "literal"} | TIMESTAMP | TIMESTAMP关键字表示后面的字符串字面量需要被解释为TIMESTAMP类型。字符串需要满足YYYY-MM-DD HH:mm:ss.MS格式,其时间分辨率为当前数据库的时间分辨率。 |
| 7 | {TRUE \| FALSE} | BOOL | 布尔类型字面量。 |
| 8 | {'' \| "" \| '\t' \| "\t" \| ' ' \| " " \| NULL } | -- | 空值字面量。可以用于任意类型。|
......@@ -321,6 +321,32 @@ taos> SELECT HISTOGRAM(voltage, 'log_bin', '{"start": 1, "factor": 3, "count": 3
{"lower_bin":27, "upper_bin":inf, "count":1} |
```
### ELAPSED
```mysql
SELECT ELAPSED(field_name[, time_unit]) FROM { tb_name | stb_name } [WHERE clause] [INTERVAL(interval [, offset]) [SLIDING sliding]];
```
**功能说明**:elapsed函数表达了统计周期内连续的时间长度,和twa函数配合使用可以计算统计曲线下的面积。在通过INTERVAL子句指定窗口的情况下,统计在给定时间范围内的每个窗口内有数据覆盖的时间范围;如果没有INTERVAL子句,则返回整个给定时间范围内的有数据覆盖的时间范围。注意,ELAPSED返回的并不是时间范围的绝对值,而是绝对值除以time_unit所得到的单位个数。
**返回结果类型**:Double
**应用字段**:Timestamp类型
**支持的版本**:2.6.0.0 及以后的版本。
**适用于**: 表,超级表,嵌套查询的外层查询
**说明**
- field_name参数只能是表的第一列,即timestamp主键列。
- 按time_unit参数指定的时间单位返回,最小是数据库的时间分辨率。time_unit参数未指定时,以数据库的时间分辨率为时间单位。
- 可以和interval组合使用,返回每个时间窗口的时间戳差值。需要特别注意的是,除第一个时间窗口和最后一个时间窗口外,中间窗口的时间戳差值均为窗口长度。
- order by asc/desc不影响差值的计算结果。
- 对于超级表,需要和group by tbname子句组合使用,不可以直接使用。
- 对于普通表,不支持和group by子句组合使用。
- 对于嵌套查询,仅当内层查询会输出隐式时间戳列时有效。例如select elapsed(ts) from (select diff(value) from sub1)语句,diff函数会让内层查询输出隐式时间戳列,此为主键列,可以用于elapsed函数的第一个参数。相反,例如select elapsed(ts) from (select * from sub1) 语句,ts列输出到外层时已经没有了主键列的含义,无法使用elapsed函数。此外,elapsed函数作为一个与时间线强依赖的函数,形如select elapsed(ts) from (select diff(value) from st group by tbname)尽管会返回一条计算结果,但并无实际意义,这种用法后续也将被限制。
- 不支持与leastsquares、diff、derivative、top、bottom、last_row、interp等函数混合使用。
## 选择函数
在使用所有的选择函数的时候,可以同时指定输出 ts 列或标签列(包括 tbname),这样就可以方便地知道被选出的值是源于哪个数据行的。
......
......@@ -85,3 +85,44 @@ title: TDengine 参数限制与保留关键字
| CONNECTIONS | HAVING | NOT | SOFFSET | VNODES |
| CONNS | ID | NOTNULL | STABLE | WAL |
| COPY | IF | NOW | STABLES | WHERE |
| _C0 | _QSTART | _QSTOP | _QDURATION | _WSTART |
| _WSTOP | _WDURATION |
## 特殊说明
### TBNAME
`TBNAME` 可以视为超级表中一个特殊的标签,代表子表的表名。
获取一个超级表所有的子表名及相关的标签信息:
```mysql
SELECT TBNAME, location FROM meters;
统计超级表下辖子表数量:
```mysql
SELECT COUNT(TBNAME) FROM meters;
```
以上两个查询均只支持在WHERE条件子句中添加针对标签(TAGS)的过滤条件。例如:
```mysql
taos> SELECT TBNAME, location FROM meters;
tbname | location |
==================================================================
d1004 | California.SanFrancisco |
d1003 | California.SanFrancisco |
d1002 | California.LosAngeles |
d1001 | California.LosAngeles |
Query OK, 4 row(s) in set (0.000881s)
taos> SELECT COUNT(tbname) FROM meters WHERE groupId > 2;
count(tbname) |
========================
2 |
Query OK, 1 row(s) in set (0.001091s)
```
### _QSTART/_QSTOP/_QDURATION
表示查询过滤窗口的起始,结束以及持续时间 (从2.6.0.0版本开始支持)
### _WSTART/_WSTOP/_WDURATION
窗口切分聚合查询(例如 interval/session window/state window)中表示每个切分窗口的起始,结束以及持续时间(从 2.6.0.0 版本开始支持)
### _c0
表示表或超级表的第一列
\ No newline at end of file
......@@ -7,7 +7,7 @@ TDengine Kafka Connector 包含两个插件: TDengine Source Connector 和 TDeng
## 什么是 Kafka Connect?
Kafka Connect 是 Apache Kafka 的一个组件,用于使其它系统,比如数据库、云服务、文件系统等能方便地连接到 Kafka。数据既可以通过 Kafka Connect 从其它系统流向 Kafka, 也可以通过 Kafka Connect 从 Kafka 流向其它系统。从其它系统读数据的插件称为 Source Connector, 写数据到其它系统的插件称为 Sink Connector。Source Connector 和 Sink Connector 都不会直接连接 Kafka Broker,Source Connector 把数据转交给 Kafka Connect。Sink Connector 从 Kafka Connect 接收数据。
Kafka Connect 是 [Apache Kafka](https://kafka.apache.org/) 的一个组件,用于使其它系统,比如数据库、云服务、文件系统等能方便地连接到 Kafka。数据既可以通过 Kafka Connect 从其它系统流向 Kafka, 也可以通过 Kafka Connect 从 Kafka 流向其它系统。从其它系统读数据的插件称为 Source Connector, 写数据到其它系统的插件称为 Sink Connector。Source Connector 和 Sink Connector 都不会直接连接 Kafka Broker,Source Connector 把数据转交给 Kafka Connect。Sink Connector 从 Kafka Connect 接收数据。
![TDengine Database Kafka Connector -- Kafka Connect structure](kafka/Kafka_Connect.webp)
......@@ -17,7 +17,7 @@ TDengine Source Connector 用于把数据实时地从 TDengine 读出来发送
## 什么是 Confluent?
Confluent 在 Kafka 的基础上增加很多扩展功能。包括:
[Confluent](https://www.confluent.io/) 在 Kafka 的基础上增加很多扩展功能。包括:
1. Schema Registry
2. REST 代理
......@@ -81,10 +81,10 @@ Development: false
git clone https://github.com:taosdata/kafka-connect-tdengine.git
cd kafka-connect-tdengine
mvn clean package
unzip -d $CONFLUENT_HOME/share/confluent-hub-components/ target/components/packages/taosdata-kafka-connect-tdengine-0.1.0.zip
unzip -d $CONFLUENT_HOME/share/java/ target/components/packages/taosdata-kafka-connect-tdengine-*.zip
```
以上脚本先 clone 项目源码,然后用 Maven 编译打包。打包完成后在 `target/components/packages/` 目录生成了插件的 zip 包。把这个 zip 包解压到安装插件的路径即可。安装插件的路径在配置文件 `$CONFLUENT_HOME/etc/kafka/connect-standalone.properties` 中。默认的路径为 `$CONFLUENT_HOME/share/confluent-hub-components/`
以上脚本先 clone 项目源码,然后用 Maven 编译打包。打包完成后在 `target/components/packages/` 目录生成了插件的 zip 包。把这个 zip 包解压到安装插件的路径即可。上面的示例中使用了内置的插件安装路径: `$CONFLUENT_HOME/share/java/`
### 用 confluent-hub 安装
......@@ -98,7 +98,7 @@ confluent local services start
```
:::note
一定要先安装插件再启动 Confluent, 否则会出现找不到类的错误。Kafka Connect 的日志(默认路径: /tmp/confluent.xxxx/connect/logs/connect.log)中会输出成功安装的插件,据此可判断插件是否安装成功
一定要先安装插件再启动 Confluent, 否则加载插件会失败
:::
:::tip
......@@ -125,6 +125,61 @@ Control Center is [UP]
清空数据可执行 `rm -rf /tmp/confluent.106668`
:::
### 验证各个组件是否启动成功
输入命令:
```
confluent local services status
```
如果各组件都启动成功,会得到如下输出:
```
Connect is [UP]
Control Center is [UP]
Kafka is [UP]
Kafka REST is [UP]
ksqlDB Server is [UP]
Schema Registry is [UP]
ZooKeeper is [UP]
```
### 验证插件是否安装成功
在 Kafka Connect 组件完全启动后,可用以下命令列出成功加载的插件:
```
confluent local services connect plugin list
```
如果成功安装,会输出如下:
```txt {4,9}
Available Connect Plugins:
[
{
"class": "com.taosdata.kafka.connect.sink.TDengineSinkConnector",
"type": "sink",
"version": "1.0.0"
},
{
"class": "com.taosdata.kafka.connect.source.TDengineSourceConnector",
"type": "source",
"version": "1.0.0"
},
......
```
如果插件安装失败,请检查 Kafka Connect 的启动日志是否有异常信息,用以下命令输出日志路径:
```
echo `cat /tmp/confluent.current`/connect/connect.stdout
```
该命令的输出类似: `/tmp/confluent.104086/connect/connect.stdout`
与日志文件 `connect.stdout` 同一目录,还有一个文件名为: `connect.properties`。在这个文件的末尾,可以看到最终生效的 `plugin.path`, 它是一系列用逗号分割的路径。如果插件安装失败,很可能是因为实际的安装路径不包含在 `plugin.path` 中。
## TDengine Sink Connector 的使用
TDengine Sink Connector 的作用是同步指定 topic 的数据到 TDengine。用户无需提前创建数据库和超级表。可手动指定目标数据库的名字(见配置参数 connection.database), 也可按一定规则生成(见配置参数 connection.database.prefix)。
......@@ -144,7 +199,7 @@ vi sink-demo.properties
sink-demo.properties 内容如下:
```ini title="sink-demo.properties"
name=tdengine-sink-demo
name=TDengineSinkConnector
connector.class=com.taosdata.kafka.connect.sink.TDengineSinkConnector
tasks.max=1
topics=meters
......@@ -153,6 +208,7 @@ connection.user=root
connection.password=taosdata
connection.database=power
db.schemaless=line
data.precision=ns
key.converter=org.apache.kafka.connect.storage.StringConverter
value.converter=org.apache.kafka.connect.storage.StringConverter
```
......@@ -179,6 +235,7 @@ confluent local services connect connector load TDengineSinkConnector --config .
"connection.url": "jdbc:TAOS://127.0.0.1:6030",
"connection.user": "root",
"connector.class": "com.taosdata.kafka.connect.sink.TDengineSinkConnector",
"data.precision": "ns",
"db.schemaless": "line",
"key.converter": "org.apache.kafka.connect.storage.StringConverter",
"tasks.max": "1",
......@@ -223,10 +280,10 @@ Database changed.
taos> select * from meters;
ts | current | voltage | phase | groupid | location |
===============================================================================================================================================================
2022-03-28 09:56:51.249000000 | 11.800000000 | 221.000000000 | 0.280000000 | 2 | 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 |
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)
```
......@@ -356,21 +413,33 @@ confluent local services connect connector unload TDengineSourceConnector
2. `connection.database.prefix`: 当 connection.database 为 null 时, 目标数据库的前缀。可以包含占位符 '${topic}'。 比如 kafka_${topic}, 对于主题 'orders' 将写入数据库 'kafka_orders'。 默认 null。当为 null 时,目标数据库的名字和主题的名字是一致的。
3. `batch.size`: 分批写入每批记录数。当 Sink Connector 一次接收到的数据大于这个值时将分批写入。
4. `max.retries`: 发生错误时的最大重试次数。默认为 1。
5. `retry.backoff.ms`: 发送错误时重试的时间间隔。单位毫秒,默认 3000。
6. `db.schemaless`: 数据格式,必须指定为: line、json、telnet 中的一个。分别代表 InfluxDB 行协议格式、 OpenTSDB JSON 格式、 OpenTSDB Telnet 行协议格式。
5. `retry.backoff.ms`: 发送错误时重试的时间间隔。单位毫秒,默认为 3000。
6. `db.schemaless`: 数据格式,可选值为:
1. line :代表 InfluxDB 行协议格式
2. json : 代表 OpenTSDB JSON 格式
3. telnet :代表 OpenTSDB Telnet 行协议格式
7. `data.precision`: 使用 InfluxDB 行协议格式时,时间戳的精度。可选值为:
1. ms : 表示毫秒
2. us : 表示微秒
3. ns : 表示纳秒。默认为纳秒。
### TDengine Source Connector 特有的配置
1. `connection.database`: 源数据库名称,无缺省值。
2. `topic.prefix`: 数据导入 kafka 后 topic 名称前缀。 使用 `topic.prefix` + `connection.database` 名称作为完整 topic 名。默认为空字符串 ""。
3. `timestamp.initial`: 数据同步起始时间。格式为'yyyy-MM-dd HH:mm:ss'。默认 "1970-01-01 00:00:00"。
4. `poll.interval.ms`: 拉取数据间隔,单位为 ms。默认 1000。
3. `timestamp.initial`: 数据同步起始时间。格式为'yyyy-MM-dd HH:mm:ss'。默认 "1970-01-01 00:00:00"。
4. `poll.interval.ms`: 拉取数据间隔,单位为 ms。默认 1000。
5. `fetch.max.rows` : 检索数据库时最大检索条数。 默认为 100。
6. `out.format`: 数据格式。取值 line 或 json。line 表示 InfluxDB Line 协议格式, json 表示 OpenTSDB JSON 格式。默认 line。
6. `out.format`: 数据格式。取值 line 或 json。line 表示 InfluxDB Line 协议格式, json 表示 OpenTSDB JSON 格式。默认为 line。
## 其他说明
1. 插件的安装位置可以自定义,请参考官方文档:https://docs.confluent.io/home/connect/self-managed/install.html#install-connector-manually。
2. 本教程的示例程序使用了 Confluent 平台,但是 TDengine Kafka Connector 本身同样适用于独立安装的 Kafka, 且配置方法相同。关于如何在独立安装的 Kafka 环境使用 Kafka Connect 插件, 请参考官方文档: https://kafka.apache.org/documentation/#connect。
## 问题反馈
https://github.com/taosdata/kafka-connect-tdengine/issues
无论遇到任何问题,都欢迎在本项目的 Github 仓库反馈: https://github.com/taosdata/kafka-connect-tdengine/issues。
## 参考
......
......@@ -12,6 +12,6 @@ Between two major release versions, some beta versions may be delivered for user
For the details please refer to [Install and Uninstall](/operation/pkg-install)。
To see the details of versions, please refer to [Download List](https://www.taosdata.com/all-downloads) and [Release Notes](https://github.com/taosdata/TDengine/releases).
To see the details of versions, please refer to [Download List](https://tdengine.com/all-downloads) and [Release Notes](https://github.com/taosdata/TDengine/releases).
......@@ -327,6 +327,32 @@ taos> SELECT HISTOGRAM(voltage, 'log_bin', '{"start": 1, "factor": 3, "count": 3
{"lower_bin":27, "upper_bin":inf, "count":1} |
```
### ELAPSED
```mysql
SELECT ELAPSED(field_name[, time_unit]) FROM { tb_name | stb_name } [WHERE clause] [INTERVAL(interval [, offset]) [SLIDING sliding]];
```
**Description**`elapsed` function can be used to calculate the continuous time length in which there is valid data. If it's used with `INTERVAL` clause, the returned result is the calcualted time length within each time window. If it's used without `INTERVAL` caluse, the returned result is the calculated time length within the specified time range. Please be noted that the return value of `elapsed` is the number of `time_unit` in the calculated time length.
**Return value type**:Double
**Applicable Column type**:Timestamp
**Applicable versions**:Sicne version 2.6.0.0
**Applicable tables**: table, STable, outter in nested query
**Explanations**
- `field_name` parameter can only be the first column of a table, i.e. timestamp primary key.
- The minimum value of `time_unit` is the time precision of the database. If `time_unit` is not specified, the time precision of the database is used as the default ime unit.
- It can be used with `INTERVAL` to get the time valid time length of each time window. Please be noted that the return value is same as the time window for all time windows except for the first and the last time window.
- `order by asc/desc` has no effect on the result.
- `group by tbname` must be used together when `elapsed` is used against a STable.
- `group by` must NOT be used together when `elapsed` is used against a table or sub table.
- When used in nested query, it's only applicable when the inner query outputs an implicit timestamp column as the primary key. For example, `select elapsed(ts) from (select diff(value) from sub1)` is legal usage while `select elapsed(ts) from (select * from sub1)` is not.
- It can't be used with `leastsquares`, `diff`, `derivative`, `top`, `bottom`, `last_row`, `interp`.
## Selection Functions
When any select function is used, timestamp column or tag columns including `tbname` can be specified to show that the selected value are from which rows.
......
......@@ -46,3 +46,44 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
| CONNECTIONS | HAVING | NOT | SOFFSET | VNODES |
| CONNS | ID | NOTNULL | STable | WAL |
| COPY | IF | NOW | STableS | WHERE |
| _C0 | _QSTART | _QSTOP | _QDURATION | _WSTART |
| _WSTOP | _WDURATION |
## Explanations
### TBNAME
`TBNAME` can be considered as a special tag, which represents the name of the subtable, in STable.
Get the table name and tag values of all subtables in a STable.
```mysql
SELECT TBNAME, location FROM meters;
Count the number of subtables in a STable.
```mysql
SELECT COUNT(TBNAME) FROM meters;
```
Only filter on TAGS can be used in WHERE clause in the above two query statements.
```mysql
taos> SELECT TBNAME, location FROM meters;
tbname | location |
==================================================================
d1004 | California.SanFrancisco |
d1003 | California.SanFrancisco |
d1002 | California.LosAngeles |
d1001 | California.LosAngeles |
Query OK, 4 row(s) in set (0.000881s)
taos> SELECT COUNT(tbname) FROM meters WHERE groupId > 2;
count(tbname) |
========================
2 |
Query OK, 1 row(s) in set (0.001091s)
```
### _QSTART/_QSTOP/_QDURATION
The start, stop and duration of a query time window (Since version 2.6.0.0).
### _WSTART/_WSTOP/_WDURATION
The start, stop and duration of aggegate query by time window, like interval, session window, state window (Since version 2.6.0.0).
### _c0
The first column of a table or STable.
\ No newline at end of file
......@@ -4,7 +4,7 @@ sidebar_label: C/C++
title: C/C++ Connector
---
C/C++ developers can use TDengine's client driver and the C/C++ connector, to develop their applications to connect to TDengine clusters for data writing, querying, and other functions. To use it, you need to include the TDengine header file _taos.h_, which lists the function prototypes of the provided APIs; the application also needs to link to the corresponding dynamic libraries on the platform where it is located.
C/C++ developers can use TDengine's client driver and the C/C++ connector, to develop their applications to connect to TDengine clusters for data writing, querying, and other functions. To use the C/C++ connector you must include the TDengine header file _taos.h_, which lists the function prototypes of the provided APIs. The application also needs to link to the corresponding dynamic libraries on the platform where it is located.
```c
#include <taos.h>
......@@ -26,7 +26,7 @@ Please refer to [list of supported platforms](/reference/connector#supported-pla
## Supported versions
The version number of the TDengine client driver and the version number of the TDengine server require one-to-one correspondence and recommend using the same version of client driver as what the TDengine server version is. Although a lower version of the client driver is compatible to work with a higher version of the server, if the first three version numbers are the same (i.e., only the fourth version number is different), but it is not recommended. It is strongly discouraged to use a higher version of the client driver to access a lower version of the TDengine server.
The version number of the TDengine client driver and the version number of the TDengine server should be the same. A lower version of the client driver is compatible with a higher version of the server, if the first three version numbers are the same (i.e., only the fourth version number is different). For e.g. if the client version is x.y.z.1 and the server version is x.y.z.2 the client and server are compatible. But in general we do not recommend using a lower client version with a newer server version. It is also strongly discouraged to use a higher version of the client driver to access a lower version of the TDengine server.
## Installation steps
......@@ -55,7 +55,7 @@ In the above example code, `taos_connect()` establishes a connection to port 603
:::note
- If not specified, when the return value of the API is an integer, _0_ means success, the others are error codes representing the reason for failure, and when the return value is a pointer, _NULL_ means failure.
- If not specified, when the return value of the API is an integer, _0_ means success. All others are error codes representing the reason for failure. When the return value is a pointer, _NULL_ means failure.
- All error codes and their corresponding causes are described in the `taoserror.h` file.
:::
......@@ -140,13 +140,12 @@ The base API is used to do things like create database connections and provide a
- `void taos_cleanup()`
Clean up the runtime environment and should be called before the application exits.
Cleans up the runtime environment and should be called before the application exits.
- ` int taos_options(TSDB_OPTION option, const void * arg, ...) `
Set client options, currently supports region setting (`TSDB_OPTION_LOCALE`), character set
(`TSDB_OPTION_CHARSET`), time zone
(`TSDB_OPTION_TIMEZONE`), configuration file path (`TSDB_OPTION_CONFIGDIR`) . The region setting, character set, and time zone default to the current settings of the operating system.
(`TSDB_OPTION_CHARSET`), time zone (`TSDB_OPTION_TIMEZONE`), configuration file path (`TSDB_OPTION_CONFIGDIR`). The region setting, character set, and time zone default to the current settings of the operating system.
- `char *taos_get_client_info()`
......@@ -159,7 +158,7 @@ The base API is used to do things like create database connections and provide a
- host: FQDN of any node in the TDengine cluster
- user: user name
- pass: password
- db: database name, if the user does not provide, it can also be connected correctly, the user can create a new database through this connection, if the user provides the database name, it means that the database user has already created, the default use of the database
- db: the database name. Even if the user does not provide this, the connection will still work correctly. The user can create a new database through this connection. If the user provides the database name, it means that the database has already been created and the connection can be used for regular operations on the database.
- port: the port the taosd program is listening on
NULL indicates a failure. The application needs to save the returned parameters for subsequent use.
......@@ -187,7 +186,7 @@ The APIs described in this subsection are all synchronous interfaces. After bein
- `TAOS_RES* taos_query(TAOS *taos, const char *sql)`
Executes an SQL command, either a DQL, DML, or DDL statement. The `taos` parameter is a handle obtained with `taos_connect()`. You can't tell if the result failed by whether the return value is `NULL`, but by parsing the error code in the result set with the `taos_errno()` function.
Executes an SQL command, either a DQL, DML, or DDL statement. The `taos` parameter is a handle obtained with `taos_connect()`. If the return value is `NULL` this does not necessarily indicate a failure. You can get the error code, if any, by parsing the error code in the result set with the `taos_errno()` function.
- `int taos_result_precision(TAOS_RES *res)`
......@@ -231,7 +230,7 @@ typedef struct taosField {
- ` void taos_free_result(TAOS_RES *res)`
Frees the query result set and the associated resources. Be sure to call this API to free the resources after the query is completed. Otherwise, it may lead to a memory leak in the application. However, note that the application will crash if you call a function like `taos_consume()` to get the query results after freeing the resources.
Frees the query result set and the associated resources. Be sure to call this API to free the resources after the query is completed. Failing to call this, may lead to a memory leak in the application. However, note that the application will crash if you call a function like `taos_consume()` to get the query results after freeing the resources.
- `char *taos_errstr(TAOS_RES *res)`
......@@ -242,7 +241,7 @@ typedef struct taosField {
Get the reason for the last API call failure. The return value is the error code.
:::note
TDengine version 2.0 and above recommends that each thread of a database application create a separate connection or a connection pool based on threads. It is not recommended to pass the connection (TAOS\*) structure to different threads for shared use in the application. Queries, writes, etc., issued based on TAOS structures are multi-thread safe, but state quantities such as "USE statement" may interfere between threads. In addition, the C connector can dynamically create new database-oriented connections on demand (this procedure is not visible to the user), and it is recommended that `taos_close()` be called only at the final exit of the program to close the connection.
TDengine version 2.0 and above recommends that each thread of a database application create a separate connection or a connection pool based on threads. It is not recommended to pass the connection (TAOS\*) structure to different threads for shared use in the application. Queries, writes, and other operations issued that are based on TAOS structures are multi-thread safe, but state quantities such as the "USE statement" may interfere between threads. In addition, the C connector can dynamically create new database-oriented connections on demand (this procedure is not visible to the user), and it is recommended that `taos_close()` be called only at the final exit of the program to close the connection.
:::
......@@ -274,12 +273,12 @@ All TDengine's asynchronous APIs use a non-blocking call pattern. Applications c
### Parameter Binding API
In addition to direct calls to `taos_query()` to perform queries, TDengine also provides a set of `bind` APIs that supports parameter binding, similar in style to MySQL, and currently only supports using a question mark `? ` to represent the parameter to be bound.
In addition to direct calls to `taos_query()` to perform queries, TDengine also provides a set of `bind` APIs that supports parameter binding, similar in style to MySQL. TDengine currently only supports using a question mark `? ` to represent the parameter to be bound.
Starting with versions 2.1.1.0 and 2.1.2.0, TDengine has significantly improved the bind APIs to support for data writing (INSERT) scenarios. This avoids the resource consumption of SQL syntax parsing when writing data through the parameter binding interface, thus significantly improving write performance in most cases. A typical operation, in this case, is as follows.
Starting with versions 2.1.1.0 and 2.1.2.0, TDengine has significantly improved the bind APIs to support data writing (INSERT) scenarios. This avoids the resource consumption of SQL syntax parsing when writing data through the parameter binding interface, thus significantly improving write performance in most cases. A typical operation, in this case, is as follows.
1. call `taos_stmt_init()` to create the parameter binding object.
2. call `taos_stmt_prepare()` to parse the INSERT statement. 3.
2. call `taos_stmt_prepare()` to parse the INSERT statement.
3. call `taos_stmt_set_tbname()` to set the table name if it is reserved in the INSERT statement but not the TAGS.
4. call `taos_stmt_set_tbname_tags()` to set the table name and TAGS values if the table name and TAGS are reserved in the INSERT statement (for example, if the INSERT statement takes an automatic table build).
5. call `taos_stmt_bind_param_batch()` to set the value of VALUES in multiple columns, or call `taos_stmt_bind_param()` to set the value of VALUES in a single row.
......@@ -383,7 +382,7 @@ In addition to writing data using the SQL method or the parameter binding API, w
**return value**
TAOS_RES structure, application can get error message by using `taos_errstr()` and also error code by using `taos_errno()`.
In some cases, the returned TAOS_RES is `NULL`, and it is still possible to call `taos_errno()` to safely get the error code information.
The returned TAOS_RES needs to be freed by the caller. Otherwise, a memory leak will occur.
The returned TAOS_RES needs to be freed by the caller in order to avoid memory leaks.
**Description**
The protocol type is enumerated and contains the following three formats.
......@@ -416,13 +415,13 @@ The Subscription API currently supports subscribing to one or more tables and co
This function is responsible for starting the subscription service, returning the subscription object on success and `NULL` on failure, with the following parameters.
- taos: the database connection that has been established
- restart: if the subscription already exists, whether to restart or continue the previous subscription
- topic: the topic of the subscription (i.e., the name). This parameter is the unique identifier of the subscription
- sql: the query statement of the subscription, this statement can only be _select_ statement, only the original data should be queried, only the data can be queried in time order
- fp: the callback function when the query result is received (the function prototype will be introduced later), only used when called asynchronously. This parameter should be passed `NULL` when called synchronously
- param: additional parameter when calling the callback function, the system API will pass it to the callback function as it is, without any processing
- interval: polling period in milliseconds. The callback function will be called periodically according to this parameter when called asynchronously. not recommended to set this parameter too small To avoid impact on system performance when called synchronously. If the interval between two calls to `taos_consume()` is less than this period, the API will block until the interval exceeds this period.
- taos: the database connection that has been established.
- restart: if the subscription already exists, whether to restart or continue the previous subscription.
- topic: the topic of the subscription (i.e., the name). This parameter is the unique identifier of the subscription.
- sql: the query statement of the subscription which can only be a _select_ statement. Only the original data should be queried, and data can only be queried in temporal order.
- fp: the callback function when the query result is received only used when called asynchronously. This parameter should be passed `NULL` when called synchronously. The function prototype is described below.
- param: additional parameter when calling the callback function. The system API will pass it to the callback function as is, without any processing.
- interval: polling period in milliseconds. The callback function will be called periodically according to this parameter when called asynchronously. The interval should not be too small to avoid impact on system performance when called synchronously. If the interval between two calls to `taos_consume()` is less than this period, the API will block until the interval exceeds this period.
- ` typedef void (*TAOS_SUBSCRIBE_CALLBACK)(TAOS_SUB* tsub, TAOS_RES *res, void* param, int code)`
......
......@@ -179,7 +179,7 @@ namespace TDengineExample
1. "Unable to establish connection", "Unable to resolve FQDN"
Usually, it cause by the FQDN configuration is incorrect, you can refer to [How to understand TDengine's FQDN (Chinese)](https://www.taosdata.com/blog/2021/07/29/2741.html) to solve it.
Usually, it's caused by an incorrect FQDN configuration. Please refer to this section in the [FAQ](https://docs.tdengine.com/2.4/train-faq/faq/#2-how-to-handle-unable-to-establish-connection) to troubleshoot.
2. Unhandled exception. System.DllNotFoundException: Unable to load DLL 'taos' or one of its dependencies: The specified module cannot be found.
......
......@@ -225,7 +225,7 @@ See [video tutorial](https://www.taosdata.com/blog/2020/11/11/1957.html) for the
2. "Unable to establish connection", "Unable to resolve FQDN"
Usually, root cause is the FQDN is not configured correctly. You can refer to [How to understand TDengine's FQDN (In Chinese)](https://www.taosdata.com/blog/2021/07/29/2741.html).
Usually, the root cause is an incorrect FQDN configuration. You can refer to this section in the [FAQ](https://docs.tdengine.com/2.4/train-faq/faq/#2-how-to-handle-unable-to-establish-connection) to troubleshoot.
## Important Updates
......
......@@ -7,7 +7,7 @@ TDengine Kafka Connector contains two plugins: TDengine Source Connector and TDe
## What is Kafka Connect?
Kafka Connect is a component of Apache Kafka that enables other systems, such as databases, cloud services, file systems, etc., to connect to Kafka easily. Data can flow from other software to Kafka via Kafka Connect and Kafka to other systems via Kafka Connect. Plugins that read data from other software are called Source Connectors, and plugins that write data to other software are called Sink Connectors. Neither Source Connector nor Sink Connector will directly connect to Kafka Broker, and Source Connector transfers data to Kafka Connect. Sink Connector receives data from Kafka Connect.
Kafka Connect is a component of [Apache Kafka](https://kafka.apache.org/) that enables other systems, such as databases, cloud services, file systems, etc., to connect to Kafka easily. Data can flow from other software to Kafka via Kafka Connect and Kafka to other systems via Kafka Connect. Plugins that read data from other software are called Source Connectors, and plugins that write data to other software are called Sink Connectors. Neither Source Connector nor Sink Connector will directly connect to Kafka Broker, and Source Connector transfers data to Kafka Connect. Sink Connector receives data from Kafka Connect.
![TDengine Database Kafka Connector -- Kafka Connect](kafka/Kafka_Connect.webp)
......@@ -17,7 +17,7 @@ TDengine Source Connector is used to read data from TDengine in real-time and se
## What is Confluent?
Confluent adds many extensions to Kafka. include:
[Confluent](https://www.confluent.io/) adds many extensions to Kafka. include:
1. Schema Registry
2. REST Proxy
......@@ -79,10 +79,10 @@ Development: false
git clone https://github.com:taosdata/kafka-connect-tdengine.git
cd kafka-connect-tdengine
mvn clean package
unzip -d $CONFLUENT_HOME/share/confluent-hub-components/ target/components/packages/taosdata-kafka-connect-tdengine-0.1.0.zip
unzip -d $CONFLUENT_HOME/share/java/ target/components/packages/taosdata-kafka-connect-tdengine-*.zip
```
The above script first clones the project source code and then compiles and packages it with Maven. After the package is complete, the zip package of the plugin is generated in the `target/components/packages/` directory. Unzip this zip package to the path where the plugin is installed. The path to install the plugin is in the configuration file `$CONFLUENT_HOME/etc/kafka/connect-standalone.properties`. The default path is `$CONFLUENT_HOME/share/confluent-hub-components/`.
The above script first clones the project source code and then compiles and packages it with Maven. After the package is complete, the zip package of the plugin is generated in the `target/components/packages/` directory. Unzip this zip package to plugin path. We used `$CONFLUENT_HOME/share/java/` above because it's a build in plugin path.
### Install with confluent-hub
......@@ -96,7 +96,7 @@ confluent local services start
```
:::note
Be sure to install the plugin before starting Confluent. Otherwise, there will be a class not found error. The log of Kafka Connect (default path: /tmp/confluent.xxxx/connect/logs/connect.log) will output the successfully installed plugin, which users can use to determine whether the plugin is installed successfully.
Be sure to install the plugin before starting Confluent. Otherwise, Kafka Connect will fail to discover the plugins.
:::
:::tip
......@@ -123,6 +123,59 @@ Control Center is [UP]
To clear data, execute `rm -rf /tmp/confluent.106668`.
:::
### Check Confluent Services Status
Use command bellow to check the status of all service:
```
confluent local services status
```
The expected output is:
```
Connect is [UP]
Control Center is [UP]
Kafka is [UP]
Kafka REST is [UP]
ksqlDB Server is [UP]
Schema Registry is [UP]
ZooKeeper is [UP]
```
### Check Successfully Loaded Plugin
After Kafka Connect was completely started, you can use bellow command to check if our plugins are installed successfully:
```
confluent local services connect plugin list
```
The output should contains `TDengineSinkConnector` and `TDengineSourceConnector` as bellow:
```
Available Connect Plugins:
[
{
"class": "com.taosdata.kafka.connect.sink.TDengineSinkConnector",
"type": "sink",
"version": "1.0.0"
},
{
"class": "com.taosdata.kafka.connect.source.TDengineSourceConnector",
"type": "source",
"version": "1.0.0"
},
......
```
If not, please check the log file of Kafka Connect. To view the log file path, please execute:
```
echo `cat /tmp/confluent.current`/connect/connect.stdout
```
It should produce a path like:`/tmp/confluent.104086/connect/connect.stdout`
Besides log file `connect.stdout` there is a file named `connect.properties`. At the end of this file you can see the effective `plugin.path` which is a series of paths joined by comma. If Kafka Connect not found our plugins, it's probably because the installed path is not included in `plugin.path`.
## The use of TDengine Sink Connector
The role of the TDengine Sink Connector is to synchronize the data of the specified topic to TDengine. Users do not need to create databases and super tables in advance. The name of the target database can be specified manually (see the configuration parameter connection.database), or it can be generated according to specific rules (see the configuration parameter connection.database.prefix).
......@@ -142,7 +195,7 @@ vi sink-demo.properties
sink-demo.properties' content is following:
```ini title="sink-demo.properties"
name=tdengine-sink-demo
name=TDengineSinkConnector
connector.class=com.taosdata.kafka.connect.sink.TDengineSinkConnector
tasks.max=1
topics=meters
......@@ -151,6 +204,7 @@ connection.user=root
connection.password=taosdata
connection.database=power
db.schemaless=line
data.precision=ns
key.converter=org.apache.kafka.connect.storage.StringConverter
value.converter=org.apache.kafka.connect.storage.StringConverter
```
......@@ -177,6 +231,7 @@ If the above command is executed successfully, the output is as follows:
"connection.url": "jdbc:TAOS://127.0.0.1:6030",
"connection.user": "root",
"connector.class": "com.taosdata.kafka.connect.sink.TDengineSinkConnector",
"data.precision": "ns",
"db.schemaless": "line",
"key.converter": "org.apache.kafka.connect.storage.StringConverter",
"tasks.max": "1",
......@@ -221,10 +276,10 @@ 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 | 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 |
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)
```
......@@ -356,6 +411,7 @@ The following configuration items apply to TDengine Sink Connector and TDengine
4. `max.retries`: The maximum number of retries when an error occurs. Defaults to 1.
5. `retry.backoff.ms`: The time interval for retry when sending an error. The unit is milliseconds. The default is 3000.
6. `db.schemaless`: Data format, could be one of `line`, `json`, and `telnet`. Represent InfluxDB line protocol format, OpenTSDB JSON format, and OpenTSDB Telnet line protocol format.
7. `data.precision`: The time precision when use InfluxDB line protocol format data, could be one of `ms`, `us` and `ns`. The default is `ns`.
### TDengine Source Connector specific configuration
......@@ -366,7 +422,13 @@ The following configuration items apply to TDengine Sink Connector and TDengine
5. `fetch.max.rows`: The maximum number of rows retrieved when retrieving the database. Default is 100.
6. `out.format`: The data format. The value could be line or json. The line represents the InfluxDB Line protocol format, and json represents the OpenTSDB JSON format. Default is `line`.
## feedback
## Other notes
1. To install plugin to a customized location, refer to https://docs.confluent.io/home/connect/self-managed/install.html#install-connector-manually.
2. To use Kafka Connect without confluent, refer to https://kafka.apache.org/documentation/#connect.
## Feedback
https://github.com/taosdata/kafka-connect-tdengine/issues
......
......@@ -5,38 +5,38 @@ title: Frequently Asked Questions
## Submit an Issue
If the tips in FAQ don't help much, please submit an issue on [GitHub](https://github.com/taosdata/TDengine) to describe your problem description, including TDengine version, hardware and OS information, the steps to reproduce the problem, etc. It would be very helpful if you package the contents in `/var/log/taos` and `/etc/taos` and upload. These two are the default directories used by TDengine, if they have been changed in your configuration, please use according to the actual configuration. It's recommended to firstly set `debugFlag` to 135 in `taos.cfg`, restart `taosd`, then reproduce the problem and collect logs. If you don't want to restart, an alternative way of setting `debugFlag` is executing `alter dnode <dnode_id> debugFlag 135` command in TDengine CLI `taos`. During normal running, however, please make sure `debugFlag` is set to 131.
If the tips in FAQ don't help much, please submit an issue on [GitHub](https://github.com/taosdata/TDengine) to describe your problem. In your description please include the TDengine version, hardware and OS information, the steps to reproduce the problem and any other relevant information. It would be very helpful if you can package the contents in `/var/log/taos` and `/etc/taos` and upload. These two are the default directories used by TDengine. If you have changed the default directories in your configuration, please package the files in your configured directories. We recommended setting `debugFlag` to 135 in `taos.cfg`, restarting `taosd`, then reproducing the problem and collecting the logs. If you don't want to restart, an alternative way of setting `debugFlag` is executing `alter dnode <dnode_id> debugFlag 135` command in TDengine CLI `taos`. During normal running, however, please make sure `debugFlag` is set to 131.
## Frequently Asked Questions
### 1. How to upgrade to TDengine 2.0 from older version?
version 2.x is not compatible with version 1.x regarding configuration file and data file, please do following before upgrading:
version 2.x is not compatible with version 1.x. With regard to the configuration and data files, please perform the following steps before upgrading. Please follow data integrity, security, backup and other relevant SOPs, best practices before removing/deleting any data.
1. Delete configuration files: `sudo rm -rf /etc/taos/taos.cfg`
1. Delete configuration files: `sudo rm -rf /etc/taos/taos.cfg`
2. Delete log files: `sudo rm -rf /var/log/taos/`
3. Delete data files if the data doesn't need to be kept: `sudo rm -rf /var/lib/taos/`
4. Install latests 2.x version
5. If the data needs to be kept and migrated to newer version, please contact professional service of TDengine for assistance
4. Install latest 2.x version
5. If the data needs to be kept and migrated to newer version, please contact professional service at TDengine for assistance.
### 2. How to handle "Unable to establish connection"?
When the client is unable to connect to the server, you can try following ways to find out why.
When the client is unable to connect to the server, you can try the following ways to troubleshoot and resolve the problem.
1. Check the network
- Check if the hosts where the client and server are running can be accessible to each other, for example by `ping` command.
- Check if the TCP/UDP on port 6030-6042 are open for access if firewall is enabled. It's better to firstly disable firewall for diagnostics.
- Check if the FQDN and serverPort are configured correctly in `taos.cfg` used by the server side
- Check if the `firstEp` is set properly in the `taos.cfg` used by the client side
- Check if the hosts where the client and server are running are accessible to each other, for example by `ping` command.
- Check if the TCP/UDP on port 6030-6042 are open for access if firewall is enabled. If possible, disable the firewall for diagnostics, but please ensure that you are following security and other relevant protocols.
- Check if the FQDN and serverPort are configured correctly in `taos.cfg` used by the server side.
- Check if the `firstEp` is set properly in the `taos.cfg` used by the client side.
2. Make sure the client version and server version are same.
3. On server side, check the running status of `taosd` by executing `systemctl status taosd` . If your server is started using another way instead of `systemctl`, use the proper method to check whether the server process is running normally.
4. If using connector of Python, Java, Go, Rust, C#, node.JS on Linux to connect toe the server, please make sure `libtaos.so` is in directory `/usr/local/taos/driver` and `/usr/local/taos/driver` is in system lib search environment variable `LD_LIBRARY_PATH`.
4. If using connector of Python, Java, Go, Rust, C#, node.JS on Linux to connect to the server, please make sure `libtaos.so` is in directory `/usr/local/taos/driver` and `/usr/local/taos/driver` is in system lib search environment variable `LD_LIBRARY_PATH`.
5. If using connector on Windows, please make sure `C:\TDengine\driver\taos.dll` is in your system lib search path, it's suggested to put `taos.dll` under `C:\Windows\System32`.
5. If using connector on Windows, please make sure `C:\TDengine\driver\taos.dll` is in your system lib search path. We recommend putting `taos.dll` under `C:\Windows\System32`.
6. Some advanced network diagnostics tools
......@@ -45,7 +45,7 @@ When the client is unable to connect to the server, you can try following ways t
Check whether a TCP port on server side is open: `nc -l {port}`
Check whether a TCP port on client side is open: `nc {hostIP} {port}`
- On Windows system `Net-TestConnection -ComputerName {fqdn} -Port {port}` on PowerShell can be used to check whether the port on serer side is open for access.
- On Windows system `Net-TestConnection -ComputerName {fqdn} -Port {port}` on PowerShell can be used to check whether the port on server side is open for access.
7. TDengine CLI `taos` can also be used to check network, please refer to [TDengine CLI](/reference/taos-shell).
......
......@@ -3,15 +3,15 @@ sidebar_label: TDengine in Docker
title: Deploy TDengine in Docker
---
Even though it's not recommended to deploy TDengine using docker in production system, docker is still very useful in development environment, especially when your host is not Linux. From version 2.0.14.0, the official image of TDengine can support X86-64, X86, arm64, and rm32 .
We do not recommend deploying TDengine using Docker in a production system. However, Docker is still very useful in a development environment, especially when your host is not Linux. From version 2.0.14.0, the official image of TDengine can support X86-64, X86, arm64, and rm32 .
In this chapter a simple step by step guide of using TDengine in docker is introduced.
In this chapter we introduce a simple step by step guide to use TDengine in Docker.
## Install Docker
The installation of docker please refer to [Get Docker](https://docs.docker.com/get-docker/).
To install Docker please refer to [Get Docker](https://docs.docker.com/get-docker/).
After docker is installed, you can check whether Docker is installed properly by displaying Docker version.
After Docker is installed, you can check whether Docker is installed properly by displaying Docker version.
```bash
$ docker -v
......@@ -27,7 +27,7 @@ $ docker run -d -p 6030-6049:6030-6049 -p 6030-6049:6030-6049/udp tdengine/tdeng
526aa188da767ae94b244226a2b2eec2b5f17dd8eff592893d9ec0cd0f3a1ccd
```
In the above command, a docker container is started to run TDengine server, the port range 6030-6049 of the container is mapped to host port range 6030-6049. If port range 6030-6049 has been occupied on the host, please change to an available host port range. Regarding the requirements about ports on the host, please refer to [Port Configuration](/reference/config/#serverport).
In the above command, a docker container is started to run TDengine server, the port range 6030-6049 of the container is mapped to host port range 6030-6049. If port range 6030-6049 has been occupied on the host, please change to an available host port range. For port requirements on the host, please refer to [Port Configuration](/reference/config/#serverport).
- **docker run**: Launch a docker container
- **-d**: the container will run in background mode
......@@ -95,7 +95,7 @@ In TDengine CLI, SQL commands can be executed to create/drop databases, tables,
### Access TDengine from host
If `-p` used to map ports properly between host and container, it's also able to access TDengine in container from the host as long as `firstEp` is configured correctly for the client on host.
If option `-p` used to map ports properly between host and container, it's also able to access TDengine in container from the host as long as `firstEp` is configured correctly for the client on host.
```
$ taos
......@@ -271,7 +271,7 @@ Below is an example output:
### Access TDengine from 3rd party tools
A lot of 3rd party tools can be used to write data into TDengine through `taosAdapter` , for details please refer to [3rd party tools](/third-party/).
A lot of 3rd party tools can be used to write data into TDengine through `taosAdapter`, for details please refer to [3rd party tools](/third-party/).
There is nothing different from the 3rd party side to access TDengine server inside a container, as long as the end point is specified correctly, the end point should be the FQDN and the mapped port of the host.
......
FROM ubuntu:18.04
WORKDIR /root
ARG pkgFile
ARG dirName
ARG cpuType
RUN echo ${pkgFile} && echo ${dirName}
COPY ${pkgFile} /root/
RUN tar -zxf ${pkgFile}
WORKDIR /root/
RUN cd /root/${dirName}/ && /bin/bash install.sh -e no && cd /root
RUN rm /root/${pkgFile}
RUN rm -rf /root/${dirName}
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get clean && apt-get update && apt-get install -y locales tzdata netcat && locale-gen en_US.UTF-8
ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/lib" \
LC_CTYPE=en_US.UTF-8 \
LANG=en_US.UTF-8 \
LC_ALL=en_US.UTF-8
COPY ./bin/* /usr/bin/
ENV TINI_VERSION v0.19.0
RUN bash -c 'echo -e "Downloading tini-${cpuType} ..."'
ADD https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini-${cpuType} /tini
RUN chmod +x /tini
ENTRYPOINT ["/tini", "--", "/usr/bin/entrypoint.sh"]
CMD ["taosd"]
VOLUME [ "/var/lib/taos", "/var/log/taos", "/corefile" ]
FROM ubuntu:18.04
WORKDIR /root
ARG pkgFile
ARG dirName
ARG cpuType
RUN echo ${pkgFile} && echo ${dirName}
COPY ${pkgFile} /root/
ENV TINI_VERSION v0.19.0
ADD https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini-${cpuType} /tini
ENV DEBIAN_FRONTEND=noninteractive
WORKDIR /root/
RUN tar -zxf ${pkgFile} && cd /root/${dirName}/ && /bin/bash install.sh -e no && cd /root && rm /root/${pkgFile} && rm -rf /root/${dirName} && apt-get update && apt-get install -y locales tzdata netcat && locale-gen en_US.UTF-8 && apt-get clean && rm -rf /var/lib/apt/lists/ && chmod +x /tini
ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/lib" \
LC_CTYPE=en_US.UTF-8 \
LANG=en_US.UTF-8 \
LC_ALL=en_US.UTF-8
COPY ./bin/* /usr/bin/
ENTRYPOINT ["/tini", "--", "/usr/bin/entrypoint.sh"]
CMD ["taosd"]
VOLUME [ "/var/lib/taos", "/var/log/taos", "/corefile" ]
......@@ -11,39 +11,22 @@ DISABLE_ADAPTER=${TAOS_DISABLE_ADAPTER:-0}
unset TAOS_DISABLE_ADAPTER
# to get mnodeEpSet from data dir
DATA_DIR=${TAOS_DATA_DIR:-/var/lib/taos}
DATA_DIR=$(taosd -C|grep -E 'dataDir.*(\S+)' -o |head -n1|sed 's/dataDir *//')
DATA_DIR=${DATA_DIR:-/var/lib/taos}
# append env to custom taos.cfg
CFG_DIR=/tmp/taos
CFG_FILE=$CFG_DIR/taos.cfg
mkdir -p $CFG_DIR >/dev/null 2>&1
[ -f /etc/taos/taos.cfg ] && cat /etc/taos/taos.cfg | grep -E -v "^#|^\s*$" >$CFG_FILE
env-to-cfg >>$CFG_FILE
FQDN=$(cat $CFG_FILE | grep -E -v "^#|^$" | grep fqdn | tail -n1 | sed -E 's/.*fqdn\s+//')
FQDN=$(taosd -C|grep -E 'fqdn.*(\S+)' -o |head -n1|sed 's/fqdn *//')
# ensure the fqdn is resolved as localhost
grep "$FQDN" /etc/hosts >/dev/null || echo "127.0.0.1 $FQDN" >>/etc/hosts
FIRSET_EP=$(taosd -C|grep -E 'firstEp.*(\S+)' -o |head -n1|sed 's/firstEp *//')
# parse first ep host and port
FIRST_EP_HOST=${TAOS_FIRST_EP%:*}
FIRST_EP_PORT=${TAOS_FIRST_EP#*:}
FIRST_EP_HOST=${FIRSET_EP%:*}
FIRST_EP_PORT=${FIRSET_EP#*:}
# in case of custom server port
SERVER_PORT=$(cat $CFG_FILE | grep -E -v "^#|^$" | grep serverPort | tail -n1 | sed -E 's/.*serverPort\s+//')
SERVER_PORT=$(taosd -C|grep -E 'serverPort.*(\S+)' -o |head -n1|sed 's/serverPort *//')
SERVER_PORT=${SERVER_PORT:-6030}
# for other binaries like interpreters
if echo $1 | grep -E "taosd$" - >/dev/null; then
true # will run taosd
else
cp -f $CFG_FILE /etc/taos/taos.cfg || true
$@
exit $?
fi
set +e
ulimit -c unlimited
# set core files pattern, maybe failed
......@@ -62,22 +45,23 @@ fi
# if has mnode ep set or the host is first ep or not for cluster, just start.
if [ -f "$DATA_DIR/dnode/mnodeEpSet.json" ] ||
[ "$TAOS_FQDN" = "$FIRST_EP_HOST" ]; then
$@ -c $CFG_DIR
$@
# others will first wait the first ep ready.
else
if [ "$TAOS_FIRST_EP" = "" ]; then
echo "run TDengine with single node."
$@ -c $CFG_DIR
$@
exit $?
fi
while true; do
es=0
taos -h $FIRST_EP_HOST -P $FIRST_EP_PORT -n startup >/dev/null || es=$?
if [ "$es" -eq 0 ]; then
es=$(taos -h $FIRST_EP_HOST -P $FIRST_EP_PORT --check)
echo ${es}
if [ "${es%%:*}" -eq 2 ]; then
echo "execute create dnode"
taos -h $FIRST_EP_HOST -P $FIRST_EP_PORT -s "create dnode \"$FQDN:$SERVER_PORT\";"
break
fi
sleep 1s
done
$@ -c $CFG_DIR
$@
fi
#!/bin/sh
es=$(taos --check)
code=${es%%:*}
if [ "$code" -ne "0" ] && [ "$code" -ne "4" ]; then
exit 0
fi
echo $es
exit 1
......@@ -5,7 +5,9 @@ target_link_libraries(
PUBLIC sut
)
add_test(
NAME dbTest
COMMAND dbTest
)
if(NOT TD_WINDOWS)
add_test(
NAME dbTest
COMMAND dbTest
)
endif(NOT TD_WINDOWS)
......@@ -5,7 +5,9 @@ target_link_libraries(
PUBLIC sut
)
add_test(
NAME smaTest
COMMAND smaTest
)
if(NOT TD_WINDOWS)
add_test(
NAME smaTest
COMMAND smaTest
)
endif(NOT TD_WINDOWS)
......@@ -5,7 +5,9 @@ target_link_libraries(
PUBLIC sut
)
add_test(
NAME stbTest
COMMAND stbTest
)
if(NOT TD_WINDOWS)
add_test(
NAME stbTest
COMMAND stbTest
)
endif(NOT TD_WINDOWS)
\ No newline at end of file
......@@ -17,9 +17,7 @@ TARGET_INCLUDE_DIRECTORIES(
PUBLIC "${TD_SOURCE_DIR}/source/libs/parser/inc"
PRIVATE "${TD_SOURCE_DIR}/source/libs/scalar/inc"
)
if(NOT TD_WINDOWS)
add_test(
NAME scalarTest
COMMAND scalarTest
)
endif(NOT TD_WINDOWS)
add_test(
NAME scalarTest
COMMAND scalarTest
)
......@@ -2498,7 +2498,7 @@ TEST(ScalarFunctionTest, tanFunction_column) {
code = tanFunction(pInput, 1, pOutput);
ASSERT_EQ(code, TSDB_CODE_SUCCESS);
for (int32_t i = 0; i < rowNum; ++i) {
ASSERT_EQ(*((double *)colDataGetData(pOutput->columnData, i)), result[i]);
ASSERT_NEAR(*((double *)colDataGetData(pOutput->columnData, i)), result[i], 1e-15);
PRINTF("tiny_int after TAN:%f\n", *((double *)colDataGetData(pOutput->columnData, i)));
}
scltDestroyDataBlock(pInput);
......@@ -2517,7 +2517,7 @@ TEST(ScalarFunctionTest, tanFunction_column) {
code = tanFunction(pInput, 1, pOutput);
ASSERT_EQ(code, TSDB_CODE_SUCCESS);
for (int32_t i = 0; i < rowNum; ++i) {
ASSERT_EQ(*((double *)colDataGetData(pOutput->columnData, i)), result[i]);
ASSERT_NEAR(*((double *)colDataGetData(pOutput->columnData, i)), result[i], 1e-15);
PRINTF("float after TAN:%f\n", *((double *)colDataGetData(pOutput->columnData, i)));
}
......
from math import floor
from random import randint, random
from numpy import equal
import taos
import sys
import datetime
import inspect
from util.log import *
from util.sql import *
from util.cases import *
class TDTestCase:
updatecfgDict = {'debugFlag': 143 ,"cDebugFlag":143,"uDebugFlag":143 ,"rpcDebugFlag":143 , "tmrDebugFlag":143 ,
"jniDebugFlag":143 ,"simDebugFlag":143,"dDebugFlag":143, "dDebugFlag":143,"vDebugFlag":143,"mDebugFlag":143,"qDebugFlag":143,
"wDebugFlag":143,"sDebugFlag":143,"tsdbDebugFlag":143,"tqDebugFlag":143 ,"fsDebugFlag":143 ,"fnDebugFlag":143}
def init(self, conn, logSql):
tdLog.debug(f"start to excute {__file__}")
tdSql.init(conn.cursor())
def prepare_datas(self):
tdSql.execute(
'''create table stb1
(ts timestamp, c1 int, c2 bigint, c3 smallint, c4 tinyint, c5 float, c6 double, c7 bool, c8 binary(16),c9 nchar(32), c10 timestamp)
tags (t1 int)
'''
)
tdSql.execute(
'''
create table t1
(ts timestamp, c1 int, c2 bigint, c3 smallint, c4 tinyint, c5 float, c6 double, c7 bool, c8 binary(16),c9 nchar(32), c10 timestamp)
'''
)
for i in range(4):
tdSql.execute(f'create table ct{i+1} using stb1 tags ( {i+1} )')
for i in range(9):
tdSql.execute(
f"insert into ct1 values ( now()-{i*10}s, {1*i}, {11111*i}, {111*i}, {11*i}, {1.11*i}, {11.11*i}, {i%2}, 'binary{i}', 'nchar{i}', now()+{1*i}a )"
)
tdSql.execute(
f"insert into ct4 values ( now()-{i*90}d, {1*i}, {11111*i}, {111*i}, {11*i}, {1.11*i}, {11.11*i}, {i%2}, 'binary{i}', 'nchar{i}', now()+{1*i}a )"
)
tdSql.execute("insert into ct1 values (now()-45s, 0, 0, 0, 0, 0, 0, 0, 'binary0', 'nchar0', now()+8a )")
tdSql.execute("insert into ct1 values (now()+10s, 9, -99999, -999, -99, -9.99, -99.99, 1, 'binary9', 'nchar9', now()+9a )")
tdSql.execute("insert into ct1 values (now()+15s, 9, -99999, -999, -99, -9.99, NULL, 1, 'binary9', 'nchar9', now()+9a )")
tdSql.execute("insert into ct1 values (now()+20s, 9, -99999, -999, NULL, -9.99, -99.99, 1, 'binary9', 'nchar9', now()+9a )")
tdSql.execute("insert into ct4 values (now()-810d, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL ) ")
tdSql.execute("insert into ct4 values (now()-400d, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL ) ")
tdSql.execute("insert into ct4 values (now()+90d, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL ) ")
tdSql.execute(
f'''insert into t1 values
( '2020-04-21 01:01:01.000', NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL )
( '2020-10-21 01:01:01.000', 1, 11111, 111, 11, 1.11, 11.11, 1, "binary1", "nchar1", now()+1a )
( '2020-12-31 01:01:01.000', 2, 22222, 222, 22, 2.22, 22.22, 0, "binary2", "nchar2", now()+2a )
( '2021-01-01 01:01:06.000', 3, 33333, 333, 33, 3.33, 33.33, 0, "binary3", "nchar3", now()+3a )
( '2021-05-07 01:01:10.000', 4, 44444, 444, 44, 4.44, 44.44, 1, "binary4", "nchar4", now()+4a )
( '2021-07-21 01:01:01.000', NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL )
( '2021-09-30 01:01:16.000', 5, 55555, 555, 55, 5.55, 55.55, 0, "binary5", "nchar5", now()+5a )
( '2022-02-01 01:01:20.000', 6, 66666, 666, 66, 6.66, 66.66, 1, "binary6", "nchar6", now()+6a )
( '2022-10-28 01:01:26.000', 7, 00000, 000, 00, 0.00, 00.00, 1, "binary7", "nchar7", "1970-01-01 08:00:00.000" )
( '2022-12-01 01:01:30.000', 8, -88888, -888, -88, -8.88, -88.88, 0, "binary8", "nchar8", "1969-01-01 01:00:00.000" )
( '2022-12-31 01:01:36.000', 9, -99999999999999999, -999, -99, -9.99, -999999999999999999999.99, 1, "binary9", "nchar9", "1900-01-01 00:00:00.000" )
( '2023-02-21 01:01:01.000', NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL )
'''
)
def test_errors(self):
error_sql_lists = [
"select unique from t1",
"select unique(123--123)==1 from t1",
"select unique(123,123) from t1",
"select unique(c1,ts) from t1",
"select unique(c1,c1,ts) from t1",
"select unique(c1) as 'd1' from t1",
"select unique(c1 ,c2 ) from t1",
"select unique(c1 ,NULL) from t1",
"select unique(,) from t1;",
"select unique(floor(c1) ab from t1)",
"select unique(c1) as int from t1",
"select unique('c1') from t1",
"select unique(NULL) from t1",
"select unique('') from t1",
"select unique(c%) from t1",
"select unique(t1) from t1",
"select unique(True) from t1",
"select unique(c1) , count(c1) from t1",
"select unique(c1) , avg(c1) from t1",
"select unique(c1) , min(c1) from t1",
"select unique(c1) , spread(c1) from t1",
"select unique(c1) , diff(c1) from t1",
"select unique(c1) , abs(c1) from t1",
"select unique(c1) , c1 from t1",
"select unique from stb1 partition by tbname",
"select unique(123--123)==1 from stb1 partition by tbname",
"select unique(123) from stb1 partition by tbname",
"select unique(c1,ts) from stb1 partition by tbname",
"select unique(c1,c1,ts) from stb1 partition by tbname",
"select unique(c1) as 'd1' from stb1 partition by tbname",
"select unique(c1 ,c2 ) from stb1 partition by tbname",
"select unique(c1 ,NULL) from stb1 partition by tbname",
"select unique(,) from stb1 partition by tbname;",
"select unique(floor(c1) ab from stb1 partition by tbname)",
"select unique(c1) as int from stb1 partition by tbname",
"select unique('c1') from stb1 partition by tbname",
"select unique(NULL) from stb1 partition by tbname",
"select unique('') from stb1 partition by tbname",
"select unique(c%) from stb1 partition by tbname",
#"select unique(t1) from stb1 partition by tbname",
"select unique(True) from stb1 partition by tbname",
"select unique(c1) , count(c1) from stb1 partition by tbname",
"select unique(c1) , avg(c1) from stb1 partition by tbname",
"select unique(c1) , min(c1) from stb1 partition by tbname",
"select unique(c1) , spread(c1) from stb1 partition by tbname",
"select unique(c1) , diff(c1) from stb1 partition by tbname",
"select unique(c1) , abs(c1) from stb1 partition by tbname",
"select unique(c1) , c1 from stb1 partition by tbname"
]
for error_sql in error_sql_lists:
tdSql.error(error_sql)
pass
def support_types(self):
other_no_value_types = [
"select unique(ts) from t1" ,
"select unique(c7) from t1",
"select unique(c8) from t1",
"select unique(c9) from t1",
"select unique(ts) from ct1" ,
"select unique(c7) from ct1",
"select unique(c8) from ct1",
"select unique(c9) from ct1",
"select unique(ts) from ct3" ,
"select unique(c7) from ct3",
"select unique(c8) from ct3",
"select unique(c9) from ct3",
"select unique(ts) from ct4" ,
"select unique(c7) from ct4",
"select unique(c8) from ct4",
"select unique(c9) from ct4",
"select unique(ts) from stb1 partition by tbname" ,
"select unique(c7) from stb1 partition by tbname",
"select unique(c8) from stb1 partition by tbname",
"select unique(c9) from stb1 partition by tbname"
]
for type_sql in other_no_value_types:
tdSql.query(type_sql)
tdLog.info("support type ok , sql is : %s"%type_sql)
type_sql_lists = [
"select unique(c1) from t1",
"select unique(c2) from t1",
"select unique(c3) from t1",
"select unique(c4) from t1",
"select unique(c5) from t1",
"select unique(c6) from t1",
"select unique(c1) from ct1",
"select unique(c2) from ct1",
"select unique(c3) from ct1",
"select unique(c4) from ct1",
"select unique(c5) from ct1",
"select unique(c6) from ct1",
"select unique(c1) from ct3",
"select unique(c2) from ct3",
"select unique(c3) from ct3",
"select unique(c4) from ct3",
"select unique(c5) from ct3",
"select unique(c6) from ct3",
"select unique(c1) from stb1 partition by tbname",
"select unique(c2) from stb1 partition by tbname",
"select unique(c3) from stb1 partition by tbname",
"select unique(c4) from stb1 partition by tbname",
"select unique(c5) from stb1 partition by tbname",
"select unique(c6) from stb1 partition by tbname",
"select unique(c6) as alisb from stb1 partition by tbname",
"select unique(c6) alisb from stb1 partition by tbname",
]
for type_sql in type_sql_lists:
tdSql.query(type_sql)
def check_unique_table(self , unique_sql):
# unique_sql = "select unique(c1) from ct1"
origin_sql = unique_sql.replace("unique(","").replace(")","")
tdSql.query(unique_sql)
unique_result = tdSql.queryResult
unique_datas = []
for elem in unique_result:
unique_datas.append(elem[0])
tdSql.query(origin_sql)
origin_result = tdSql.queryResult
origin_datas = []
for elem in origin_result:
origin_datas.append(elem[0])
pre_unique = []
for elem in origin_datas:
if elem in pre_unique:
continue
else:
pre_unique.append(elem)
if pre_unique == unique_datas:
tdLog.info(" unique query check pass , unique sql is: %s" %unique_sql)
else:
tdLog.exit(" unique query check fail , unique sql is: %s " %unique_sql)
def basic_unique_function(self):
# basic query
tdSql.query("select c1 from ct3")
tdSql.checkRows(0)
tdSql.query("select c1 from t1")
tdSql.checkRows(12)
tdSql.query("select c1 from stb1")
tdSql.checkRows(25)
# used for empty table , ct3 is empty
tdSql.query("select unique(c1) from ct3")
tdSql.checkRows(0)
tdSql.query("select unique(c2) from ct3")
tdSql.checkRows(0)
tdSql.query("select unique(c3) from ct3")
tdSql.checkRows(0)
tdSql.query("select unique(c4) from ct3")
tdSql.checkRows(0)
tdSql.query("select unique(c5) from ct3")
tdSql.checkRows(0)
tdSql.query("select unique(c6) from ct3")
# will support _rowts mix with
# tdSql.query("select unique(c6),_rowts from ct3")
# auto check for t1 table
# used for regular table
tdSql.query("select unique(c1) from t1")
tdSql.query("desc t1")
col_lists_rows = tdSql.queryResult
col_lists = []
for col_name in col_lists_rows:
col_lists.append(col_name[0])
for col in col_lists:
self.check_unique_table(f"select unique({col}) from t1")
# unique with super tags
tdSql.query("select unique(c1) from ct1")
tdSql.checkRows(10)
tdSql.query("select unique(c1) from ct4")
tdSql.checkRows(10)
tdSql.error("select unique(c1),tbname from ct1")
tdSql.error("select unique(c1),t1 from ct1")
# unique with common col
tdSql.error("select unique(c1) ,ts from ct1")
tdSql.error("select unique(c1) ,c1 from ct1")
# unique with scalar function
tdSql.error("select unique(c1) ,abs(c1) from ct1")
tdSql.error("select unique(c1) , unique(c2) from ct1")
tdSql.error("select unique(c1) , abs(c2)+2 from ct1")
# unique with aggregate function
tdSql.error("select unique(c1) ,sum(c1) from ct1")
tdSql.error("select unique(c1) ,max(c1) from ct1")
tdSql.error("select unique(c1) ,csum(c1) from ct1")
tdSql.error("select unique(c1) ,count(c1) from ct1")
# unique with filter where
tdSql.query("select unique(c1) from ct4 where c1 is null")
tdSql.checkData(0, 0, None)
tdSql.query("select unique(c1) from ct4 where c1 >2 ")
tdSql.checkData(0, 0, 8)
tdSql.checkData(1, 0, 7)
tdSql.checkData(2, 0, 6)
tdSql.checkData(5, 0, 3)
tdSql.query("select unique(c1) from ct4 where c2 between 0 and 99999")
tdSql.checkData(0, 0, 8)
tdSql.checkData(1, 0, 7)
tdSql.checkData(2, 0, 6)
tdSql.checkData(3, 0, 5)
tdSql.checkData(4, 0, 4)
tdSql.checkData(5, 0, 3)
tdSql.checkData(6, 0, 2)
tdSql.checkData(7, 0, 1)
tdSql.checkData(8, 0, 0)
# unique with union all
tdSql.query("select unique(c1) from ct4 union all select c1 from ct1")
tdSql.checkRows(23)
tdSql.query("select unique(c1) from ct4 union all select distinct(c1) from ct4")
tdSql.checkRows(20)
tdSql.query("select unique(c2) from ct4 union all select abs(c2)/2 from ct4")
tdSql.checkRows(22)
# unique with join
# prepare join datas with same ts
tdSql.execute(" use db ")
tdSql.execute(" create stable st1 (ts timestamp , num int) tags(ind int)")
tdSql.execute(" create table tb1 using st1 tags(1)")
tdSql.execute(" create table tb2 using st1 tags(2)")
tdSql.execute(" create stable st2 (ts timestamp , num int) tags(ind int)")
tdSql.execute(" create table ttb1 using st2 tags(1)")
tdSql.execute(" create table ttb2 using st2 tags(2)")
start_ts = 1622369635000 # 2021-05-30 18:13:55
for i in range(10):
ts_value = start_ts+i*1000
tdSql.execute(f" insert into tb1 values({ts_value} , {i})")
tdSql.execute(f" insert into tb2 values({ts_value} , {i})")
tdSql.execute(f" insert into ttb1 values({ts_value} , {i})")
tdSql.execute(f" insert into ttb2 values({ts_value} , {i})")
tdSql.query("select unique(tb2.num) from tb1, tb2 where tb1.ts=tb2.ts ")
tdSql.checkRows(10)
tdSql.checkData(0,0,0)
tdSql.checkData(1,0,1)
tdSql.checkData(2,0,2)
tdSql.checkData(9,0,9)
tdSql.query("select unique(tb2.num) from tb1, tb2 where tb1.ts=tb2.ts union all select unique(tb1.num) from tb1, tb2 where tb1.ts=tb2.ts ")
tdSql.checkRows(20)
tdSql.checkData(0,0,0)
tdSql.checkData(1,0,1)
tdSql.checkData(2,0,2)
tdSql.checkData(9,0,9)
# nest query
# tdSql.query("select unique(c1) from (select c1 from ct1)")
tdSql.query("select c1 from (select unique(c1) c1 from ct4)")
tdSql.checkRows(10)
tdSql.checkData(0, 0, None)
tdSql.checkData(1, 0, 8)
tdSql.checkData(9, 0, 0)
tdSql.query("select sum(c1) from (select unique(c1) c1 from ct1)")
tdSql.checkRows(1)
tdSql.checkData(0, 0, 45)
tdSql.query("select sum(c1) from (select distinct(c1) c1 from ct1) union all select sum(c1) from (select unique(c1) c1 from ct1)")
tdSql.checkRows(2)
tdSql.checkData(0, 0, 45)
tdSql.checkData(1, 0, 45)
tdSql.query("select 1-abs(c1) from (select unique(c1) c1 from ct4)")
tdSql.checkRows(10)
tdSql.checkData(0, 0, None)
tdSql.checkData(1, 0, -7.000000000)
# bug for stable
#partition by tbname
# tdSql.query(" select unique(c1) from stb1 partition by tbname ")
# tdSql.checkRows(21)
# tdSql.query(" select unique(c1) from stb1 partition by tbname ")
# tdSql.checkRows(21)
# group by
tdSql.error("select unique(c1) from ct1 group by c1")
tdSql.error("select unique(c1) from ct1 group by tbname")
# super table
def check_boundary_values(self):
tdSql.execute("drop database if exists bound_test")
tdSql.execute("create database if not exists bound_test")
tdSql.execute("use bound_test")
tdSql.execute(
"create table stb_bound (ts timestamp, c1 int, c2 bigint, c3 smallint, c4 tinyint, c5 float, c6 double, c7 bool, c8 binary(32),c9 nchar(32), c10 timestamp) tags (t1 int);"
)
tdSql.execute(f'create table sub1_bound using stb_bound tags ( 1 )')
tdSql.execute(
f"insert into sub1_bound values ( now()-1s, 2147483647, 9223372036854775807, 32767, 127, 3.40E+38, 1.7e+308, True, 'binary_tb1', 'nchar_tb1', now() )"
)
tdSql.execute(
f"insert into sub1_bound values ( now(), 2147483646, 9223372036854775806, 32766, 126, 3.40E+38, 1.7e+308, True, 'binary_tb1', 'nchar_tb1', now() )"
)
tdSql.execute(
f"insert into sub1_bound values ( now(), -2147483646, -9223372036854775806, -32766, -126, -3.40E+38, -1.7e+308, True, 'binary_tb1', 'nchar_tb1', now() )"
)
tdSql.execute(
f"insert into sub1_bound values ( now(), 2147483643, 9223372036854775803, 32763, 123, 3.39E+38, 1.69e+308, True, 'binary_tb1', 'nchar_tb1', now() )"
)
tdSql.execute(
f"insert into sub1_bound values ( now(), -2147483643, -9223372036854775803, -32763, -123, -3.39E+38, -1.69e+308, True, 'binary_tb1', 'nchar_tb1', now() )"
)
tdSql.error(
f"insert into sub1_bound values ( now()+1s, 2147483648, 9223372036854775808, 32768, 128, 3.40E+38, 1.7e+308, True, 'binary_tb1', 'nchar_tb1', now() )"
)
tdSql.query("select unique(c2) from sub1_bound")
tdSql.checkRows(5)
tdSql.checkData(0,0,9223372036854775807)
def run(self): # sourcery skip: extract-duplicate-method, remove-redundant-fstring
tdSql.prepare()
tdLog.printNoPrefix("==========step1:create table ==============")
self.prepare_datas()
tdLog.printNoPrefix("==========step2:test errors ==============")
self.test_errors()
tdLog.printNoPrefix("==========step3:support types ============")
self.support_types()
tdLog.printNoPrefix("==========step4: floor basic query ============")
self.basic_unique_function()
tdLog.printNoPrefix("==========step5: floor boundary query ============")
self.check_boundary_values()
def stop(self):
tdSql.close()
tdLog.success(f"{__file__} successfully executed")
tdCases.addLinux(__file__, TDTestCase())
tdCases.addWindows(__file__, TDTestCase())
......@@ -80,6 +80,7 @@ python3 ./test.py -f 2-query/mavg.py
python3 ./test.py -f 2-query/diff.py
python3 ./test.py -f 2-query/sample.py
python3 ./test.py -f 2-query/function_diff.py
python3 ./test.py -f 2-query/unique.py
python3 ./test.py -f 7-tmq/basic5.py
python3 ./test.py -f 7-tmq/subscribeDb.py
......@@ -91,4 +92,3 @@ python3 ./test.py -f 7-tmq/subscribeStb1.py
python3 ./test.py -f 7-tmq/subscribeStb2.py
python3 ./test.py -f 7-tmq/subscribeStb3.py
python3 ./test.py -f 7-tmq/subscribeStb4.py
python3 ./test.py -f 7-tmq/subscribeStb2.py
\ No newline at end of file
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