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Merge remote-tracking branch 'origin/3.0' into fix/mnode

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Shengliang Guan 2022-05-25 15:41:15 +08:00
parent 3a36979576 b1a6463206
commit 4bb91775f9
33 changed files with 383 additions and 248 deletions
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2
Jenkinsfile2
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@ -287,7 +287,7 @@ pipeline {
''' '''
sh ''' sh '''
cd ${WKC}/tests/parallel_test cd ${WKC}/tests/parallel_test
export DEFAULT_RETRY_TIME=2 export DEFAULT_RETRY_TIME=1
date date
timeout 2100 time ./run.sh -e -m /home/m.json -t /tmp/cases.task -b ${BRANCH_NAME} -l ${WKDIR}/log -o 480 timeout 2100 time ./run.sh -e -m /home/m.json -t /tmp/cases.task -b ${BRANCH_NAME} -l ${WKDIR}/log -o 480
''' '''

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docs-cn/04-concept/index.md
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@ -29,7 +29,7 @@ title: 数据模型和基本概念
<td>10.3</td> <td>10.3</td>
<td>219</td> <td>219</td>
<td>0.31</td> <td>0.31</td>
<td>Beijing.Chaoyang</td> <td>California.SanFrancisco</td>
<td>2</td> <td>2</td>
</tr> </tr>
<tr> <tr>
@ -38,7 +38,7 @@ title: 数据模型和基本概念
<td>10.2</td> <td>10.2</td>
<td>220</td> <td>220</td>
<td>0.23</td> <td>0.23</td>
<td>Beijing.Chaoyang</td> <td>California.SanFrancisco</td>
<td>3</td> <td>3</td>
</tr> </tr>
<tr> <tr>
@ -47,7 +47,7 @@ title: 数据模型和基本概念
<td>11.5</td> <td>11.5</td>
<td>221</td> <td>221</td>
<td>0.35</td> <td>0.35</td>
<td>Beijing.Haidian</td> <td>California.LosAngeles</td>
<td>3</td> <td>3</td>
</tr> </tr>
<tr> <tr>
@ -56,7 +56,7 @@ title: 数据模型和基本概念
<td>13.4</td> <td>13.4</td>
<td>223</td> <td>223</td>
<td>0.29</td> <td>0.29</td>
<td>Beijing.Haidian</td> <td>California.LosAngeles</td>
<td>2</td> <td>2</td>
</tr> </tr>
<tr> <tr>
@ -65,7 +65,7 @@ title: 数据模型和基本概念
<td>12.6</td> <td>12.6</td>
<td>218</td> <td>218</td>
<td>0.33</td> <td>0.33</td>
<td>Beijing.Chaoyang</td> <td>California.SanFrancisco</td>
<td>2</td> <td>2</td>
</tr> </tr>
<tr> <tr>
@ -74,7 +74,7 @@ title: 数据模型和基本概念
<td>11.8</td> <td>11.8</td>
<td>221</td> <td>221</td>
<td>0.28</td> <td>0.28</td>
<td>Beijing.Haidian</td> <td>California.LosAngeles</td>
<td>2</td> <td>2</td>
</tr> </tr>
<tr> <tr>
@ -83,7 +83,7 @@ title: 数据模型和基本概念
<td>10.3</td> <td>10.3</td>
<td>218</td> <td>218</td>
<td>0.25</td> <td>0.25</td>
<td>Beijing.Chaoyang</td> <td>California.SanFrancisco</td>
<td>3</td> <td>3</td>
</tr> </tr>
<tr> <tr>
@ -92,7 +92,7 @@ title: 数据模型和基本概念
<td>12.3</td> <td>12.3</td>
<td>221</td> <td>221</td>
<td>0.31</td> <td>0.31</td>
<td>Beijing.Chaoyang</td> <td>California.SanFrancisco</td>
<td>2</td> <td>2</td>
</tr> </tr>
</tbody> </tbody>

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docs-cn/05-get-started/index.md
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@ -132,7 +132,7 @@ Query OK, 2 row(s) in set (0.003128s)
taosBenchmark 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。 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; 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 ```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： 查询 groupId=10 的所有记录的平均值、最大值、最小值等：

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docs-cn/07-develop/02-model/index.mdx
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@ -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： 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 ```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 :::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。 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： 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 ```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。 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。

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docs-cn/07-develop/03-insert-data/02-influxdb-line.mdx
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@ -29,7 +29,7 @@ measurement,tag_set field_set timestamp
例如： 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 :::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) 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 ## 示例代码
<Tabs defaultValue="java" groupId="lang"> <Tabs defaultValue="java" groupId="lang">

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docs-cn/07-develop/03-insert-data/03-opentsdb-telnet.mdx
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@ -29,7 +29,7 @@ OpenTSDB 行协议同样采用一行字符串来表示一行数据。OpenTSDB
例如： 例如：
```txt ```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)。
@ -76,9 +76,9 @@ Query OK, 2 row(s) in set (0.002544s)
taos> select tbname, * from `meters.current`; taos> select tbname, * from `meters.current`;
tbname | ts | value | groupid | location | tbname | ts | value | groupid | location |
================================================================================================================================== ==================================================================================================================================
t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.249 | 10.800000000 | 3 | Beijing.Haidian | t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.249 | 10.800000000 | 3 | California.LosAngeles |
t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.250 | 11.300000000 | 3 | Beijing.Haidian | 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 | Beijing.Chaoyang | 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 | Beijing.Chaoyang | t_7e7b26dd860280242c6492a16... | 2022-03-28 09:56:51.250 | 12.600000000 | 2 | California.SanFrancisco |
Query OK, 4 row(s) in set (0.005399s) Query OK, 4 row(s) in set (0.005399s)
``` ```

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docs-cn/07-develop/03-insert-data/04-opentsdb-json.mdx
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@ -42,10 +42,10 @@ OpenTSDB JSON 格式协议采用一个 JSON 字符串表示一行或多行数据
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。 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 :::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。 - 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。 - 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`; taos> select * from `meters.current`;
ts | value | groupid | location | ts | value | groupid | location |
=================================================================================================================== ===================================================================================================================
2022-03-28 09:56:51.249 | 10.300000000 | 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 | Beijing.Chaoyang | 2022-03-28 09:56:51.250 | 12.600000000 | 2.000000000 | California.SanFrancisco |
Query OK, 2 row(s) in set (0.004076s) Query OK, 2 row(s) in set (0.004076s)
``` ```

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docs-cn/07-develop/04-query-data/index.mdx
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@ -50,14 +50,14 @@ Query OK, 2 row(s) in set (0.001100s)
### 示例一 ### 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; taos> SELECT AVG(voltage) FROM meters GROUP BY location;
avg(voltage) | location | avg(voltage) | location |
============================================================= =============================================================
222.000000000 | Beijing.Haidian | 222.000000000 | California.LosAngeles |
219.200000000 | Beijing.Chaoyang | 219.200000000 | California.SanFrancisco |
Query OK, 2 row(s) in set (0.002136s) 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) 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) | ts | sum(current) |
====================================================== ======================================================
2018-10-03 14:38:04.000 | 10.199999809 | 2018-10-03 14:38:04.000 | 10.199999809 |

4
docs-cn/07-develop/05-continuous-query.mdx
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@ -34,8 +34,8 @@ SLIDING: 连续查询的时间窗口向前滑动的时间间隔
```sql ```sql
create table meters (ts timestamp, current float, voltage int, phase float) tags (location binary(64), groupId int); 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 D1001 using meters tags ("California.SanFrancisco", 2);
create table D1002 using meters tags ("Beijing.Haidian", 2); create table D1002 using meters tags ("California.LosAngeles", 2);
... ...
``` ```

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docs-cn/07-develop/06-subscribe.mdx
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@ -184,8 +184,8 @@ taos> use power;
# create super table "meters" # create super table "meters"
taos> create table meters(ts timestamp, current float, voltage int, phase int) tags(location binary(64), groupId int); 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" # create tabes using the schema defined by super table "meters"
taos> create table d1001 using meters tags ("Beijing.Chaoyang", 2); taos> create table d1001 using meters tags ("California.SanFrancisco", 2);
taos> create table d1002 using meters tags ("Beijing.Haidian", 2); taos> create table d1002 using meters tags ("California.LosAngeles", 2);
# insert some rows # 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 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); 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);
@ -193,13 +193,14 @@ 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; taos> select * from meters where current > 10;
ts | current | voltage | phase | location | groupid | ts | current | voltage | phase | location | groupid |
=========================================================================================================== ===========================================================================================================
2020-08-15 12:10:00.000 | 10.30000 | 220 | 1 | Beijing.Haidian | 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 | Beijing.Haidian | 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 | Beijing.Chaoyang | 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 | Beijing.Chaoyang | 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 | Beijing.Chaoyang | 2 | 2020-08-15 12:20:00.000 | 12.20000 | 220 | 1 | California.SanFrancisco | 2 |
Query OK, 5 row(s) in set (0.004896s) Query OK, 5 row(s) in set (0.004896s)
``` ```
### 示例代码 ### 示例代码
<Tabs defaultValue="java" groupId="lang"> <Tabs defaultValue="java" groupId="lang">
@ -231,11 +232,11 @@ Query OK, 5 row(s) in set (0.004896s)
示例程序会先消费符合查询条件的所有历史数据： 示例程序会先消费符合查询条件的所有历史数据：
```bash ```bash
ts: 1597464000000 current: 12.0 voltage: 220 phase: 1 location: Beijing.Chaoyang 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: Beijing.Chaoyang 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: Beijing.Chaoyang 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: Beijing.Haidian 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: Beijing.Haidian 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： 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： 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
``` ```

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docs-cn/07-develop/07-cache.md
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@ -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： 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 ```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。

10
docs-cn/12-taos-sql/05-insert.md
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@ -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： 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： 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： 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) 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); 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： 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： 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'; 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; taos> SHOW TABLES;
Query OK, 0 row(s) in set (0.000946s) 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) DB error: invalid SQL: 'a' (invalid timestamp) (0.039494s)

32
docs-cn/12-taos-sql/06-select.md
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@ -40,15 +40,15 @@ Query OK, 3 row(s) in set (0.001165s)
taos> SELECT * FROM meters; taos> SELECT * FROM meters;
ts | current | voltage | phase | location | groupid | ts | current | voltage | phase | location | groupid |
===================================================================================================================================== =====================================================================================================================================
2018-10-03 14:38:05.500 | 11.80000 | 221 | 0.28000 | Beijing.Haidian | 2 | 2018-10-03 14:38:05.500 | 11.80000 | 221 | 0.28000 | California.LosAngeles | 2 |
2018-10-03 14:38:16.600 | 13.40000 | 223 | 0.29000 | Beijing.Haidian | 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 | Beijing.Haidian | 3 | 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 | Beijing.Haidian | 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 | Beijing.Chaoyang | 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 | Beijing.Chaoyang | 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 | Beijing.Chaoyang | 2 | 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 | Beijing.Chaoyang | 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 | Beijing.Chaoyang | 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) 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; taos> SELECT location, groupid, current FROM d1001 LIMIT 2;
location | groupid | current | location | groupid | current |
====================================================================== ======================================================================
Beijing.Chaoyang | 2 | 10.30000 | California.SanFrancisco | 2 | 10.30000 |
Beijing.Chaoyang | 2 | 12.60000 | California.SanFrancisco | 2 | 12.60000 |
Query OK, 2 row(s) in set (0.003112s) Query OK, 2 row(s) in set (0.003112s)
``` ```
@ -284,10 +284,10 @@ SELECT COUNT(TBNAME) FROM meters;
taos> SELECT TBNAME, location FROM meters; taos> SELECT TBNAME, location FROM meters;
tbname | location | tbname | location |
================================================================== ==================================================================
d1004 | Beijing.Haidian | d1004 | California.LosAngeles |
d1003 | Beijing.Haidian | d1003 | California.LosAngeles |
d1002 | Beijing.Chaoyang | d1002 | California.SanFrancisco |
d1001 | Beijing.Chaoyang | d1001 | California.SanFrancisco |
Query OK, 4 row(s) in set (0.000881s) Query OK, 4 row(s) in set (0.000881s)
taos> SELECT COUNT(tbname) FROM meters WHERE groupId > 2; taos> SELECT COUNT(tbname) FROM meters WHERE groupId > 2;
@ -335,7 +335,7 @@ Query OK, 1 row(s) in set (0.001091s)
- 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))`。 - 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 2.0.17.0 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。 - 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 ## 正则表达式过滤

10
docs-cn/14-reference/03-connector/java.mdx
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@ -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： - 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 ```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 // set table name
pstmt.setTableName("t5_" + i); pstmt.setTableName("t5_" + i);
// set tags // set tags
pstmt.setTagNString(0, "北京-abc"); pstmt.setTagNString(0, "California.SanFrancisco");
// set columns // set columns
ArrayList<Long> tsList = new ArrayList<>(); ArrayList<Long> tsList = new ArrayList<>();
@ -574,7 +574,7 @@ public class ParameterBindingDemo {
ArrayList<String> f1List = new ArrayList<>(); ArrayList<String> f1List = new ArrayList<>();
for (int j = 0; j < numOfRow; j++) { for (int j = 0; j < numOfRow; j++) {
f1List.add("北京-abc"); f1List.add("California.LosAngeles");
} }
pstmt.setNString(1, f1List, BINARY_COLUMN_SIZE); 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 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 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 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 { public static void main(String[] args) throws SQLException {
final String url = "jdbc:TAOS://" + host + ":6030/?user=root&password=taosdata"; final String url = "jdbc:TAOS://" + host + ":6030/?user=root&password=taosdata";

22
docs-cn/20-third-party/11-kafka.md vendored
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@ -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： 准备测试数据的文本文件，内容如下：
```txt title="test-data.txt" ```txt title="test-data.txt"
meters,location=Beijing.Haidian,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249000000 meters,location=California.LosAngeles,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=California.LosAngeles,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=California.LosAngeles,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=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。 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; taos> select * from meters;
ts | current | voltage | phase | groupid | location | 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 09:56:51.249000000 | 11.800000000 | 221.000000000 | 0.280000000 | 2 | California.LosAngeles |
2022-03-28 09:56:51.250000000 | 13.400000000 | 223.000000000 | 0.290000000 | 2 | Beijing.Haidian | 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 | Beijing.Haidian | 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 | Beijing.Haidian | 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) Query OK, 4 row(s) in set (0.004208s)
``` ```
@ -275,7 +275,7 @@ DROP DATABASE IF EXISTS test;
CREATE DATABASE test; CREATE DATABASE test;
USE test; USE test;
CREATE STABLE meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT); 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。 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="California.SanFrancisco",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=12.6f32,voltage=218i32,phase=0.33f32 1538548695000000000
...... ......
``` ```

4
docs-cn/27-train-faq/03-docker.md
View File

@ -209,7 +209,7 @@ curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'show databases;' 127.0.0
Press enter key to continue or Ctrl-C to stop Press enter key to continue or Ctrl-C to stop
``` ```
回车后，该命令将在数据库 test 下面自动创建一张超级表 meters，该超级表下有 1 万张表，表名为 "d0" 到 "d9999"，每张表有 1 万条记录，每条记录有 (ts, current, voltage, phase) 四个字段，时间戳从 "2017-07-14 10:40:00 000" 到 "2017-07-14 10:40:09 999"，每张表带有标签 location 和 groupId，groupId 被设置为 1 到 10， location 被设置为 "beijing" 或者 "shanghai"。 回车后，该命令将在数据库 test 下面自动创建一张超级表 meters，该超级表下有 1 万张表，表名为 "d0" 到 "d9999"，每张表有 1 万条记录，每条记录有 (ts, current, voltage, phase) 四个字段，时间戳从 "2017-07-14 10:40:00 000" 到 "2017-07-14 10:40:09 999"，每张表带有标签 location 和 groupId，groupId 被设置为 1 到 10， location 被设置为 "California.SanFrancisco" 或者 "California.SanDieo"。
最后共插入 1 亿条记录。 最后共插入 1 亿条记录。
@ -279,7 +279,7 @@ curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'show databases;' 127.0.0
$ taos> select groupid, location from test.d0; $ taos> select groupid, location from test.d0;
groupid | location | groupid | location |
================================= =================================
0 | shanghai | 0 | California.SanDieo |
Query OK, 1 row(s) in set (0.003490s) Query OK, 1 row(s) in set (0.003490s)
``` ```

16
docs-en/04-concept/index.md
View File

@ -29,7 +29,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin
<td>10.3</td> <td>10.3</td>
<td>219</td> <td>219</td>
<td>0.31</td> <td>0.31</td>
<td>San Jose</td> <td>California.SanFrancisco</td>
<td>2</td> <td>2</td>
</tr> </tr>
<tr> <tr>
@ -38,7 +38,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin
<td>10.2</td> <td>10.2</td>
<td>220</td> <td>220</td>
<td>0.23</td> <td>0.23</td>
<td>San Jose</td> <td>California.SanFrancisco</td>
<td>3</td> <td>3</td>
</tr> </tr>
<tr> <tr>
@ -47,7 +47,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin
<td>11.5</td> <td>11.5</td>
<td>221</td> <td>221</td>
<td>0.35</td> <td>0.35</td>
<td>Mountain View</td> <td>California.LosAngeles</td>
<td>3</td> <td>3</td>
</tr> </tr>
<tr> <tr>
@ -56,7 +56,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin
<td>13.4</td> <td>13.4</td>
<td>223</td> <td>223</td>
<td>0.29</td> <td>0.29</td>
<td>Mountain View</td> <td>California.LosAngeles</td>
<td>2</td> <td>2</td>
</tr> </tr>
<tr> <tr>
@ -65,7 +65,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin
<td>12.6</td> <td>12.6</td>
<td>218</td> <td>218</td>
<td>0.33</td> <td>0.33</td>
<td>San Jose</td> <td>California.SanFrancisco</td>
<td>2</td> <td>2</td>
</tr> </tr>
<tr> <tr>
@ -74,7 +74,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin
<td>11.8</td> <td>11.8</td>
<td>221</td> <td>221</td>
<td>0.28</td> <td>0.28</td>
<td>Mountain View</td> <td>California.LosAngeles</td>
<td>2</td> <td>2</td>
</tr> </tr>
<tr> <tr>
@ -83,7 +83,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin
<td>10.3</td> <td>10.3</td>
<td>218</td> <td>218</td>
<td>0.25</td> <td>0.25</td>
<td>San Jose</td> <td>California.SanFrancisco</td>
<td>3</td> <td>3</td>
</tr> </tr>
<tr> <tr>
@ -92,7 +92,7 @@ In order to explain the basic concepts and provide some sample code, the TDengin
<td>12.3</td> <td>12.3</td>
<td>221</td> <td>221</td>
<td>0.31</td> <td>0.31</td>
<td>San Jose</td> <td>California.SanFrancisco</td>
<td>2</td> <td>2</td>
</tr> </tr>
</tbody> </tbody>

6
docs-en/05-get-started/index.md
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@ -130,7 +130,7 @@ After TDengine server is running，execute `taosBenchmark` (previously named tao
taosBenchmark 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. 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; 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 ```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: query the average, maximum, minimum of all rows with groupId=10:

8
docs-en/07-develop/02-model/index.mdx
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@ -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. 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 ```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. 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. 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 ```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). For more details please refer to [Create Table Automatically](/taos-sql/insert#automatically-create-table-when-inserting).

2
docs-en/07-develop/03-insert-data/02-influxdb-line.mdx
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@ -29,7 +29,7 @@ measurement,tag_set field_set timestamp
For example: 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 :::note

10
docs-en/07-develop/03-insert-data/03-opentsdb-telnet.mdx
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@ -29,7 +29,7 @@ A single line of text is used in OpenTSDB line protocol to represent one row of
For example: For example:
```txt ```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. 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`; taos> select tbname, * from `meters.current`;
tbname | ts | value | groupid | location | tbname | ts | value | groupid | location |
================================================================================================================================== ==================================================================================================================================
t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.249 | 10.800000000 | 3 | Beijing.Haidian | t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.249 | 10.800000000 | 3 | California.LoSangeles |
t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.250 | 11.300000000 | 3 | Beijing.Haidian | 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 | Beijing.Chaoyang | 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 | Beijing.Chaoyang | t_7e7b26dd860280242c6492a16... | 2022-03-28 09:56:51.250 | 12.600000000 | 2 | California.SanFrancisco |
Query OK, 4 row(s) in set (0.005399s) Query OK, 4 row(s) in set (0.005399s)
``` ```

4
docs-en/07-develop/03-insert-data/04-opentsdb-json.mdx
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@ -93,7 +93,7 @@ Query OK, 2 row(s) in set (0.001954s)
taos> select * from `meters.current`; taos> select * from `meters.current`;
ts | value | groupid | location | ts | value | groupid | location |
=================================================================================================================== ===================================================================================================================
2022-03-28 09:56:51.249 | 10.300000000 | 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 | Beijing.Chaoyang | 2022-03-28 09:56:51.250 | 12.600000000 | 2.000000000 | California.SanFrancisco |
Query OK, 2 row(s) in set (0.004076s) Query OK, 2 row(s) in set (0.004076s)
``` ```

10
docs-en/07-develop/04-query-data/index.mdx
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@ -58,14 +58,14 @@ In summary, for a STable, its subtables can be aggregated by a simple query on t
### Example 1 ### 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; taos> SELECT AVG(voltage) FROM meters GROUP BY location;
avg(voltage) | location | avg(voltage) | location |
============================================================= =============================================================
222.000000000 | Beijing.Haidian | 222.000000000 | California.LoSangeles |
219.200000000 | Beijing.Chaoyang | 219.200000000 | California.SanFrancisco |
Query OK, 2 row(s) in set (0.002136s) 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) 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) | ts | sum(current) |
====================================================== ======================================================
2018-10-03 14:38:04.000 | 10.199999809 | 2018-10-03 14:38:04.000 | 10.199999809 |

4
docs-en/07-develop/05-continuous-query.mdx
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@ -34,8 +34,8 @@ In this section the use case of meters will be used to introduce how to use cont
```sql ```sql
create table meters (ts timestamp, current float, voltage int, phase float) tags (location binary(64), groupId int); 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 D1001 using meters tags ("California.SanFrancisco", 2);
create table D1002 using meters tags ("Beijing.Haidian", 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. The SQL statement below retrieves the average voltage for a one minute time window, with each time window moving forward by 30 seconds.

26
docs-en/07-develop/06-subscribe.mdx
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@ -187,8 +187,8 @@ taos> use power;
# create super table "meters" # create super table "meters"
taos> create table meters(ts timestamp, current float, voltage int, phase int) tags(location binary(64), groupId int); 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" # create tabes using the schema defined by super table "meters"
taos> create table d1001 using meters tags ("Beijing.Chaoyang", 2); taos> create table d1001 using meters tags ("California.SanFrancisco", 2);
taos> create table d1002 using meters tags ("Beijing.Haidian", 2); taos> create table d1002 using meters tags ("California.LoSangeles", 2);
# insert some rows # 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 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); 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; taos> select * from meters where current > 10;
ts | current | voltage | phase | location | groupid | ts | current | voltage | phase | location | groupid |
=========================================================================================================== ===========================================================================================================
2020-08-15 12:10:00.000 | 10.30000 | 220 | 1 | Beijing.Haidian | 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 | Beijing.Haidian | 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 | Beijing.Chaoyang | 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 | Beijing.Chaoyang | 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 | Beijing.Chaoyang | 2 | 2020-08-15 12:20:00.000 | 12.20000 | 220 | 1 | California.SanFrancisco | 2 |
Query OK, 5 row(s) in set (0.004896s) 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. The example programs first consume all historical data matching the criteria.
```bash ```bash
ts: 1597464000000 current: 12.0 voltage: 220 phase: 1 location: Beijing.Chaoyang 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: Beijing.Chaoyang 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: Beijing.Chaoyang 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: Beijing.Haidian 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: Beijing.Haidian 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. 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. 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
``` ```

4
docs-en/07-develop/07-cache.md
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@ -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. 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 ```sql
select last_row(voltage) from meters where location='Beijing.Chaoyang'; select last_row(voltage) from meters where location='California.SanFrancisco';
``` ```

10
docs-en/12-taos-sql/05-insert.md
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@ -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. 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 ```sql
INSERT INTO d21001 USING meters TAGS ('Beijing.Chaoyang', 2) VALUES ('2021-07-13 14:06:32.272', 10.2, 219, 0.32); INSERT INTO d21001 USING meters TAGS ('California.SanFrancisco', 2) VALUES ('2021-07-13 14:06:32.272', 10.2, 219, 0.32);
``` ```
It's not necessary to provide values for all tag when creating tables automatically, the tags without values provided will be set to NULL. It's not necessary to provide values for all 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. Multiple rows can also be inserted into same table in single SQL statement using this way.
```sql ```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) 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); 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: 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 ```sql
INSERT INTO d21001 USING meters TAGS ('Beijing.Chaoyang', 2) FILE '/tmp/csvfile.csv'; INSERT INTO d21001 USING meters TAGS ('California.SanFrancisco', 2) FILE '/tmp/csvfile.csv';
``` ```
Multiple tables can be automatically created and inserted in single SQL statement, like below: Multiple tables can be automatically created and inserted in single SQL statement, like below:
```sql ```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'; 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. Then, try to create table d1001 automatically when inserting data into it.
```sql ```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. 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.

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@ -39,15 +39,15 @@ The result includes both data columns and tag columns for super table.
taos> SELECT * FROM meters; taos> SELECT * FROM meters;
ts | current | voltage | phase | location | groupid | ts | current | voltage | phase | location | groupid |
===================================================================================================================================== =====================================================================================================================================
2018-10-03 14:38:05.500 | 11.80000 | 221 | 0.28000 | Beijing.Haidian | 2 | 2018-10-03 14:38:05.500 | 11.80000 | 221 | 0.28000 | California.LoSangeles | 2 |
2018-10-03 14:38:16.600 | 13.40000 | 223 | 0.29000 | Beijing.Haidian | 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 | Beijing.Haidian | 3 | 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 | Beijing.Haidian | 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 | Beijing.Chaoyang | 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 | Beijing.Chaoyang | 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 | Beijing.Chaoyang | 2 | 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 | Beijing.Chaoyang | 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 | Beijing.Chaoyang | 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) 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; taos> SELECT location, groupid, current FROM d1001 LIMIT 2;
location | groupid | current | location | groupid | current |
====================================================================== ======================================================================
Beijing.Chaoyang | 2 | 10.30000 | California.SanFrancisco | 2 | 10.30000 |
Beijing.Chaoyang | 2 | 12.60000 | California.SanFrancisco | 2 | 12.60000 |
Query OK, 2 row(s) in set (0.003112s) 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; taos> SELECT TBNAME, location FROM meters;
tbname | location | tbname | location |
================================================================== ==================================================================
d1004 | Beijing.Haidian | d1004 | California.LoSangeles |
d1003 | Beijing.Haidian | d1003 | California.LoSangeles |
d1002 | Beijing.Chaoyang | d1002 | California.SanFrancisco |
d1001 | Beijing.Chaoyang | d1001 | California.SanFrancisco |
Query OK, 4 row(s) in set (0.000881s) Query OK, 4 row(s) in set (0.000881s)
taos> SELECT COUNT(tbname) FROM meters WHERE groupId > 2; 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))`. - 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 2.3.0.0, 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 2.0.17.0, 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. - 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 ## Regular Expression

10
docs-en/14-reference/03-connector/java.mdx
View File

@ -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. - 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 ```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 // set table name
pstmt.setTableName("t5_" + i); pstmt.setTableName("t5_" + i);
// set tags // set tags
pstmt.setTagNString(0, "Beijing-abc"); pstmt.setTagNString(0, "California-abc");
// set columns // set columns
ArrayList<Long> tsList = new ArrayList<>(); ArrayList<Long> tsList = new ArrayList<>();
@ -576,7 +576,7 @@ public class ParameterBindingDemo {
ArrayList<String> f1List = new ArrayList<>(); ArrayList<String> f1List = new ArrayList<>();
for (int j = 0; j < numOfRow; j++) { for (int j = 0; j < numOfRow; j++) {
f1List.add("Beijing-abc"); f1List.add("California-abc");
} }
pstmt.setNString(1, f1List, BINARY_COLUMN_SIZE); 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 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 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 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 { public static void main(String[] args) throws SQLException {
final String url = "jdbc:TAOS://" + host + ":6030/?user=root&password=taosdata"; final String url = "jdbc:TAOS://" + host + ":6030/?user=root&password=taosdata";

2
docs-en/14-reference/12-config/index.md
View File

@ -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. 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 GMT-8
timezone Asia/Shanghai timezone Asia/Shanghai
``` ```

22
docs-en/20-third-party/11-kafka.md vendored
View File

@ -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: Prepare text file as test data, its content is following:
```txt title="test-data.txt" ```txt title="test-data.txt"
meters,location=Beijing.Haidian,groupid=2 current=11.8,voltage=221,phase=0.28 1648432611249000000 meters,location=California.LoSangeles,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=California.LoSangeles,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=California.LoSangeles,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=3 current=11.3,voltage=221,phase=0.35 1648432611250000000
``` ```
Use kafka-console-producer to write test data to the topic `meters`. Use kafka-console-producer to write test data to the topic `meters`.
@ -221,10 +221,10 @@ Database changed.
taos> select * from meters; taos> select * from meters;
ts | current | voltage | phase | groupid | location | 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 09:56:51.249000000 | 11.800000000 | 221.000000000 | 0.280000000 | 2 | California.LoSangeles |
2022-03-28 09:56:51.250000000 | 13.400000000 | 223.000000000 | 0.290000000 | 2 | Beijing.Haidian | 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 | Beijing.Haidian | 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 | Beijing.Haidian | 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) Query OK, 4 row(s) in set (0.004208s)
``` ```
@ -273,7 +273,7 @@ DROP DATABASE IF EXISTS test;
CREATE DATABASE test; CREATE DATABASE test;
USE test; USE test;
CREATE STABLE meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT); 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 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="California.SanFrancisco",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=12.6f32,voltage=218i32,phase=0.33f32 1538548695000000000
...... ......
```` ````

2
docs-en/27-train-faq/03-docker.md
View File

@ -265,7 +265,7 @@ Below is an example output:
$ taos> select groupid, location from test.d0; $ taos> select groupid, location from test.d0;
groupid | location | groupid | location |
================================= =================================
0 | shanghai | 0 | California.SanDieo |
Query OK, 1 row(s) in set (0.003490s) Query OK, 1 row(s) in set (0.003490s)
``` ```

211
source/libs/function/src/builtins.c
View File

@ -156,6 +156,14 @@ static int32_t translatePercentile(SFunctionNode* pFunc, char* pErrBuf, int32_t
return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); 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 para1Type = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type;
uint8_t para2Type = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 1))->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))) { 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) { static int32_t translateApercentile(SFunctionNode* pFunc, char* pErrBuf, int32_t len) {
int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList);
if (2 != paraNum && 3 != paraNum) { if (2 != numOfParams && 3 != numOfParams) {
return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName);
} }
@ -198,7 +206,7 @@ static int32_t translateApercentile(SFunctionNode* pFunc, char* pErrBuf, int32_t
pValue->notReserved = true; pValue->notReserved = true;
if (3 == paraNum) { if (3 == numOfParams) {
SNode* pPara3 = nodesListGetNode(pFunc->pParameterList, 2); SNode* pPara3 = nodesListGetNode(pFunc->pParameterList, 2);
if (QUERY_NODE_VALUE != nodeType(pPara3) || !validAperventileAlgo((SValueNode*)pPara3)) { if (QUERY_NODE_VALUE != nodeType(pPara3) || !validAperventileAlgo((SValueNode*)pPara3)) {
return buildFuncErrMsg(pErrBuf, len, TSDB_CODE_FUNC_FUNTION_ERROR, 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) { static int32_t translateTop(SFunctionNode* pFunc, char* pErrBuf, int32_t len) {
int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList);
if (2 != paraNum) { if (2 != numOfParams) {
return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); 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) { static int32_t translateElapsed(SFunctionNode* pFunc, char* pErrBuf, int32_t len) {
int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList);
if (1 != paraNum && 2 != paraNum) { if (1 != numOfParams && 2 != numOfParams) {
return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName);
} }
SNode* pPara = nodesListGetNode(pFunc->pParameterList, 0); //param0
if (QUERY_NODE_COLUMN != nodeType(pPara)) { SNode* pParaNode0 = nodesListGetNode(pFunc->pParameterList, 0);
if (QUERY_NODE_COLUMN != nodeType(pParaNode0)) {
return buildFuncErrMsg(pErrBuf, len, TSDB_CODE_FUNC_FUNTION_ERROR, 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; 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); 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}; pFunc->node.resType = (SDataType){.bytes = tDataTypes[TSDB_DATA_TYPE_DOUBLE].bytes, .type = TSDB_DATA_TYPE_DOUBLE};
return TSDB_CODE_SUCCESS; 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) { 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; uint8_t colType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, i))->resType.type;
if (!IS_NUMERIC_TYPE(colType)) { if (!IS_NUMERIC_TYPE(colType)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName);
} }
} }
pFunc->node.resType = (SDataType){.bytes = 64, .type = TSDB_DATA_TYPE_BINARY}; pFunc->node.resType = (SDataType){.bytes = 64, .type = TSDB_DATA_TYPE_BINARY};
return TSDB_CODE_SUCCESS; return TSDB_CODE_SUCCESS;
} }
static int32_t translateHistogram(SFunctionNode* pFunc, char* pErrBuf, int32_t len) { 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); 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; uint8_t colType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type;
if (!IS_NUMERIC_TYPE(colType)) { if (!IS_NUMERIC_TYPE(colType)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); 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 || 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, 2))->resType.type != TSDB_DATA_TYPE_BINARY ||
((SExprNode*)nodesListGetNode(pFunc->pParameterList, 3))->resType.type != TSDB_DATA_TYPE_BIGINT) { ((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) { 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); return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName);
} }
//param0
uint8_t colType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type; uint8_t colType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type;
if (!IS_NUMERIC_TYPE(colType)) { if (!IS_NUMERIC_TYPE(colType)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); 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 || 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_BIGINT &&
((SExprNode*)nodesListGetNode(pFunc->pParameterList, 2))->resType.type != TSDB_DATA_TYPE_DOUBLE)) { ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 2))->resType.type != TSDB_DATA_TYPE_DOUBLE)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); 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}; pFunc->node.resType = (SDataType){.bytes = tDataTypes[TSDB_DATA_TYPE_BIGINT].bytes, .type = TSDB_DATA_TYPE_BIGINT};
return TSDB_CODE_SUCCESS; return TSDB_CODE_SUCCESS;
} }
static int32_t translateStateDuration(SFunctionNode* pFunc, char* pErrBuf, int32_t len) { static int32_t translateStateDuration(SFunctionNode* pFunc, char* pErrBuf, int32_t len) {
int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList);
if (3 != paraNum && 4 != paraNum) { if (3 != numOfParams && 4 != numOfParams) {
return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName);
} }
//param0
uint8_t colType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type; uint8_t colType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type;
if (!IS_NUMERIC_TYPE(colType)) { if (!IS_NUMERIC_TYPE(colType)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); 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 || 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_BIGINT &&
((SExprNode*)nodesListGetNode(pFunc->pParameterList, 2))->resType.type != TSDB_DATA_TYPE_DOUBLE)) { ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 2))->resType.type != TSDB_DATA_TYPE_DOUBLE)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); 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); 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}; pFunc->node.resType = (SDataType){.bytes = tDataTypes[TSDB_DATA_TYPE_BIGINT].bytes, .type = TSDB_DATA_TYPE_BIGINT};
return TSDB_CODE_SUCCESS; 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); return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName);
} }
SNode* pPara = nodesListGetNode(pFunc->pParameterList, 0); //param0
if (QUERY_NODE_COLUMN != nodeType(pPara)) { SNode* pParaNode0 = nodesListGetNode(pFunc->pParameterList, 0);
if (QUERY_NODE_COLUMN != nodeType(pParaNode0)) {
return buildFuncErrMsg(pErrBuf, len, TSDB_CODE_FUNC_FUNTION_ERROR, 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; 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; uint8_t paraType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 1))->resType.type;
if (!IS_NUMERIC_TYPE(colType) || !IS_INTEGER_TYPE(paraType)) { if (!IS_NUMERIC_TYPE(colType) || !IS_INTEGER_TYPE(paraType)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); 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); return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName);
} }
SNode* pPara = nodesListGetNode(pFunc->pParameterList, 0); //param0
if (QUERY_NODE_COLUMN != nodeType(pPara)) { SNode* pParamNode0 = nodesListGetNode(pFunc->pParameterList, 0);
if (QUERY_NODE_COLUMN != nodeType(pParamNode0)) {
return buildFuncErrMsg(pErrBuf, len, TSDB_CODE_FUNC_FUNTION_ERROR, 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; uint8_t paraType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 1))->resType.type;
if (!IS_INTEGER_TYPE(paraType)) { if (!IS_INTEGER_TYPE(paraType)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName);
} }
SExprNode* pCol = (SExprNode*)nodesListGetNode(pFunc->pParameterList, 0); //set result type
uint8_t colType = pCol->resType.type;
if (IS_VAR_DATA_TYPE(colType)) { if (IS_VAR_DATA_TYPE(colType)) {
pFunc->node.resType = (SDataType){.bytes = pCol->resType.bytes, .type = colType}; pFunc->node.resType = (SDataType){.bytes = pCol->resType.bytes, .type = colType};
} else { } else {
pFunc->node.resType = (SDataType){.bytes = tDataTypes[colType].bytes, .type = colType}; pFunc->node.resType = (SDataType){.bytes = tDataTypes[colType].bytes, .type = colType};
} }
return TSDB_CODE_SUCCESS; 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); return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName);
} }
//param0
SNode* pPara = nodesListGetNode(pFunc->pParameterList, 0); SNode* pPara = nodesListGetNode(pFunc->pParameterList, 0);
if (QUERY_NODE_COLUMN != nodeType(pPara)) { if (QUERY_NODE_COLUMN != nodeType(pPara)) {
return buildFuncErrMsg(pErrBuf, len, TSDB_CODE_FUNC_FUNTION_ERROR, 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);
} }
for (int32_t i = 1; i < numOfParams; ++i) { 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; uint8_t paraType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, i))->resType.type;
if (!IS_INTEGER_TYPE(paraType)) { if (!IS_INTEGER_TYPE(paraType)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName);
} }
} }
SExprNode* pCol = (SExprNode*)nodesListGetNode(pFunc->pParameterList, 0); //set result type
uint8_t colType = pCol->resType.type;
if (IS_VAR_DATA_TYPE(colType)) { if (IS_VAR_DATA_TYPE(colType)) {
pFunc->node.resType = (SDataType){.bytes = pCol->resType.bytes, .type = colType}; pFunc->node.resType = (SDataType){.bytes = pCol->resType.bytes, .type = colType};
} else { } 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, static int32_t translateConcatImpl(SFunctionNode* pFunc, char* pErrBuf, int32_t len, int32_t minParaNum,
int32_t maxParaNum, bool hasSep) { int32_t maxParaNum, bool hasSep) {
int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList);
if (paraNum < minParaNum || paraNum > maxParaNum) { if (numOfParams < minParaNum || numOfParams > maxParaNum) {
return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName);
} }
@ -562,7 +697,7 @@ static int32_t translateConcatImpl(SFunctionNode* pFunc, char* pErrBuf, int32_t
int32_t sepBytes = 0; int32_t sepBytes = 0;
/* For concat/concat_ws function, if params have NCHAR type, promote the final result to NCHAR */ /* 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); SNode* pPara = nodesListGetNode(pFunc->pParameterList, i);
uint8_t paraType = ((SExprNode*)pPara)->resType.type; uint8_t paraType = ((SExprNode*)pPara)->resType.type;
if (!IS_VAR_DATA_TYPE(paraType)) { 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); SNode* pPara = nodesListGetNode(pFunc->pParameterList, i);
uint8_t paraType = ((SExprNode*)pPara)->resType.type; uint8_t paraType = ((SExprNode*)pPara)->resType.type;
int32_t paraBytes = ((SExprNode*)pPara)->resType.bytes; int32_t paraBytes = ((SExprNode*)pPara)->resType.bytes;
@ -589,7 +724,7 @@ static int32_t translateConcatImpl(SFunctionNode* pFunc, char* pErrBuf, int32_t
} }
if (hasSep) { if (hasSep) {
resultBytes += sepBytes * (paraNum - 3); resultBytes += sepBytes * (numOfParams - 3);
} }
pFunc->node.resType = (SDataType){.bytes = resultBytes, .type = resultType}; 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) { static int32_t translateSubstr(SFunctionNode* pFunc, char* pErrBuf, int32_t len) {
int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList);
if (2 != paraNum && 3 != paraNum) { if (2 != numOfParams && 3 != numOfParams) {
return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); 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)) { if (!IS_VAR_DATA_TYPE(pPara1->resType.type) || !IS_INTEGER_TYPE(para2Type)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName);
} }
if (3 == paraNum) { if (3 == numOfParams) {
uint8_t para3Type = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 1))->resType.type; uint8_t para3Type = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 1))->resType.type;
if (!IS_INTEGER_TYPE(para3Type)) { if (!IS_INTEGER_TYPE(para3Type)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); 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) { static int32_t translateTimeDiff(SFunctionNode* pFunc, char* pErrBuf, int32_t len) {
int32_t paraNum = LIST_LENGTH(pFunc->pParameterList); int32_t numOfParams = LIST_LENGTH(pFunc->pParameterList);
if (2 != paraNum && 3 != paraNum) { if (2 != numOfParams && 3 != numOfParams) {
return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName); 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)) { if (!IS_INTEGER_TYPE(((SExprNode*)nodesListGetNode(pFunc->pParameterList, 2))->resType.type)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName); return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName);
} }