Merge branch '3.0' into cpwu/3.0
This commit is contained in:
cpwu
commit
dfcded5c5b
|
|
@ -269,7 +269,7 @@ pipeline {
|
|||
}
|
||||
}
|
||||
stage('linux test') {
|
||||
agent{label " slave3_0 || slave15 || slave16 || slave17 "}
|
||||
agent{label " worker03 || slave215 || slave217 || slave219 "}
|
||||
options { skipDefaultCheckout() }
|
||||
when {
|
||||
changeRequest()
|
||||
|
|
@ -287,9 +287,9 @@ pipeline {
|
|||
'''
|
||||
sh '''
|
||||
cd ${WKC}/tests/parallel_test
|
||||
export DEFAULT_RETRY_TIME=1
|
||||
export DEFAULT_RETRY_TIME=2
|
||||
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 ${BRANCH_NAME}_${BUILD_ID} -l ${WKDIR}/log -o 480
|
||||
'''
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -49,7 +49,7 @@ IF(${TD_WINDOWS})
|
|||
option(
|
||||
BUILD_TEST
|
||||
"If build unit tests using googletest"
|
||||
OFF
|
||||
ON
|
||||
)
|
||||
ELSE ()
|
||||
|
||||
|
|
|
|||
|
|
@ -243,7 +243,7 @@ void console(SRaftServer *pRaftServer) {
|
|||
|
||||
|
||||
} else if (strcmp(cmd, "dropnode") == 0) {
|
||||
char host[HOST_LEN];
|
||||
char host[HOST_LEN] = {0};
|
||||
uint32_t port;
|
||||
parseAddr(param1, host, HOST_LEN, &port);
|
||||
uint64_t rid = raftId(host, port);
|
||||
|
|
@ -258,7 +258,7 @@ void console(SRaftServer *pRaftServer) {
|
|||
|
||||
|
||||
} else if (strcmp(cmd, "put") == 0) {
|
||||
char buf[256];
|
||||
char buf[256] = {0};
|
||||
snprintf(buf, sizeof(buf), "%s--%s", param1, param2);
|
||||
putValue(&pRaftServer->raft, buf);
|
||||
|
||||
|
|
|
|||
|
|
@ -52,7 +52,7 @@ INSERT INTO d1001 VALUES (1538548685000, 10.3, 219, 0.31) (1538548695000, 12.6,
|
|||
|
||||
:::info
|
||||
|
||||
- 要提高写入效率，需要批量写入。一批写入的记录条数越多，插入效率就越高。但一条记录不能超过 16K，一条 SQL 语句总长度不能超过 1M 。
|
||||
- 要提高写入效率，需要批量写入。一批写入的记录条数越多，插入效率就越高。但一条记录不能超过 48K，一条 SQL 语句总长度不能超过 1M 。
|
||||
- TDengine 支持多线程同时写入，要进一步提高写入速度，一个客户端需要打开 20 个以上的线程同时写。但线程数达到一定数量后，无法再提高，甚至还会下降，因为线程频繁切换，带来额外开销。
|
||||
|
||||
:::
|
||||
|
|
|
|||
|
|
@ -145,7 +145,7 @@ void subscribe_callback(TAOS_SUB* tsub, TAOS_RES *res, void* param, int code) {
|
|||
taos_unsubscribe(tsub, keep);
|
||||
```
|
||||
|
||||
其第二个参数，用于决定是否在客户端保留订阅的进度信息。如果这个参数是**false**（**0**），那无论下次调用 `taos_subscribe` 时的 `restart` 参数是什么，订阅都只能重新开始。另外，进度信息的保存位置是 _{DataDir}/subscribe/_ 这个目录下，每个订阅有一个与其 `topic` 同名的文件，删掉某个文件，同样会导致下次创建其对应的订阅时只能重新开始。
|
||||
其第二个参数，用于决定是否在客户端保留订阅的进度信息。如果这个参数是**false**（**0**），那无论下次调用 `taos_subscribe` 时的 `restart` 参数是什么，订阅都只能重新开始。另外，进度信息的保存位置是 _{DataDir}/subscribe/_ 这个目录下（注：`taos.cfg` 配置文件中 `DataDir` 参数值默认为 **/var/lib/taos/**,但是 Windows 服务器上本身不存在该目录，所以需要在 Windows 的配置文件中修改 `DataDir` 参数值为相应的已存在目录"），每个订阅有一个与其 `topic` 同名的文件，删掉某个文件，同样会导致下次创建其对应的订阅时只能重新开始。
|
||||
|
||||
代码介绍完毕，我们来看一下实际的运行效果。假设：
|
||||
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ CREATE TABLE [IF NOT EXISTS] tb_name (timestamp_field_name TIMESTAMP, field1_nam
|
|||
|
||||
1. 表的第一个字段必须是 TIMESTAMP，并且系统自动将其设为主键；
|
||||
2. 表名最大长度为 192；
|
||||
3. 表的每行长度不能超过 16k 个字符;（注意：每个 BINARY/NCHAR 类型的列还会额外占用 2 个字节的存储位置）
|
||||
3. 表的每行长度不能超过 48KB;（注意：每个 BINARY/NCHAR 类型的列还会额外占用 2 个字节的存储位置）
|
||||
4. 子表名只能由字母、数字和下划线组成，且不能以数字开头，不区分大小写
|
||||
5. 使用数据类型 binary 或 nchar，需指定其最长的字节数，如 binary(20)，表示 20 字节；
|
||||
6. 为了兼容支持更多形式的表名，TDengine 引入新的转义符 "\`"，可以让表名与关键词不冲突，同时不受限于上述表名称合法性约束检查。但是同样具有长度限制要求。使用转义字符以后，不再对转义字符中的内容进行大小写统一。
|
||||
|
|
|
|||
|
|
@ -86,7 +86,7 @@ ALTER STABLE stb_name MODIFY COLUMN field_name data_type(length);
|
|||
ALTER STABLE stb_name ADD TAG new_tag_name tag_type;
|
||||
```
|
||||
|
||||
为 STable 增加一个新的标签，并指定新标签的类型。标签总数不能超过 128 个，总长度不超过 16k 个字符。
|
||||
为 STable 增加一个新的标签，并指定新标签的类型。标签总数不能超过 128 个，总长度不超过 16KB 。
|
||||
|
||||
### 删除标签
|
||||
|
||||
|
|
|
|||
|
|
@ -1766,6 +1766,8 @@ SELECT TIMEDIFF(ts_val1 | datetime_string1 | ts_col1, ts_val2 | datetime_string2
|
|||
1u(微秒)，1a(毫秒)，1s(秒)，1m(分)，1h(小时)，1d(天)。
|
||||
- 如果时间单位 time_unit 未指定， 返回的时间差值精度与当前 DATABASE 设置的时间精度一致。
|
||||
|
||||
**支持的版本**：2.6.0.0 及以后的版本。
|
||||
|
||||
**示例**：
|
||||
|
||||
```sql
|
||||
|
|
|
|||
|
|
@ -7,9 +7,9 @@ title: 边界限制
|
|||
|
||||
- 数据库名最大长度为 32。
|
||||
- 表名最大长度为 192，不包括数据库名前缀和分隔符
|
||||
- 每行数据最大长度 16k 个字符, 从 2.1.7.0 版本开始，每行数据最大长度 48k 个字符（注意：数据行内每个 BINARY/NCHAR 类型的列还会额外占用 2 个字节的存储位置）。
|
||||
- 每行数据最大长度 48KB （注意：数据行内每个 BINARY/NCHAR 类型的列还会额外占用 2 个字节的存储位置）。
|
||||
- 列名最大长度为 64，最多允许 4096 列，最少需要 2 列，第一列必须是时间戳。注：从 2.1.7.0 版本（不含)以前最多允许 4096 列
|
||||
- 标签名最大长度为 64，最多允许 128 个，至少要有 1 个标签，一个表中标签值的总长度不超过 16k 个字符。
|
||||
- 标签名最大长度为 64，最多允许 128 个，至少要有 1 个标签，一个表中标签值的总长度不超过 16KB 。
|
||||
- SQL 语句最大长度 1048576 个字符，也可通过客户端配置参数 maxSQLLength 修改，取值范围 65480 ~ 1048576。
|
||||
- SELECT 语句的查询结果，最多允许返回 4096 列（语句中的函数调用可能也会占用一些列空间），超限时需要显式指定较少的返回数据列，以避免语句执行报错。注： 2.1.7.0 版本（不含）之前为最多允许 1024 列
|
||||
- 库的数目，超级表的数目、表的数目，系统不做限制，仅受系统资源限制。
|
||||
|
|
|
|||
|
|
@ -23,17 +23,17 @@ title: TDengine 参数限制与保留关键字
|
|||
去掉了 `` ‘“`\ `` (单双引号、撇号、反斜杠、空格)
|
||||
|
||||
- 数据库名：不能包含“.”以及特殊字符，不能超过 32 个字符
|
||||
- 表名：不能包含“.”以及特殊字符，与所属数据库名一起，不能超过 192 个字符，每行数据最大长度 16k 个字符
|
||||
- 表的列名：不能包含特殊字符，不能超过 64 个字符
|
||||
- 表名：不能包含“.”以及特殊字符，与所属数据库名一起，不能超过 192 个字节 ，每行数据最大长度 48KB
|
||||
- 表的列名：不能包含特殊字符，不能超过 64 个字节
|
||||
- 数据库名、表名、列名，都不能以数字开头，合法的可用字符集是“英文字符、数字和下划线”
|
||||
- 表的列数：不能超过 1024 列，最少需要 2 列，第一列必须是时间戳（从 2.1.7.0 版本开始，改为最多支持 4096 列）
|
||||
- 记录的最大长度：包括时间戳 8 byte，不能超过 16KB（每个 BINARY/NCHAR 类型的列还会额外占用 2 个 byte 的存储位置）
|
||||
- 单条 SQL 语句默认最大字符串长度：1048576 byte，但可通过系统配置参数 maxSQLLength 修改，取值范围 65480 ~ 1048576 byte
|
||||
- 记录的最大长度：包括时间戳 8 字节，不能超过 48KB（每个 BINARY/NCHAR 类型的列还会额外占用 2 个 字节 的存储位置）
|
||||
- 单条 SQL 语句默认最大字符串长度：1048576 字节，但可通过系统配置参数 maxSQLLength 修改，取值范围 65480 ~ 1048576 字节
|
||||
- 数据库副本数：不能超过 3
|
||||
- 用户名：不能超过 23 个 byte
|
||||
- 用户密码：不能超过 15 个 byte
|
||||
- 用户名：不能超过 23 个 字节
|
||||
- 用户密码：不能超过 15 个 字节
|
||||
- 标签(Tags)数量：不能超过 128 个，可以 0 个
|
||||
- 标签的总长度：不能超过 16K byte
|
||||
- 标签的总长度：不能超过 16KB
|
||||
- 记录条数：仅受存储空间限制
|
||||
- 表的个数：仅受节点个数限制
|
||||
- 库的个数：仅受节点个数限制
|
||||
|
|
|
|||
|
|
@ -7,8 +7,6 @@ description: "TAOS SQL 支持的语法规则、主要查询功能、支持的 SQ
|
|||
|
||||
TAOS SQL 是用户对 TDengine 进行数据写入和查询的主要工具。TAOS SQL 为了便于用户快速上手，在一定程度上提供与标准 SQL 类似的风格和模式。严格意义上，TAOS SQL 并不是也不试图提供标准的 SQL 语法。此外，由于 TDengine 针对的时序性结构化数据不提供删除功能，因此在 TAO SQL 中不提供数据删除的相关功能。
|
||||
|
||||
TAOS SQL 不支持关键字的缩写，例如 DESCRIBE 不能缩写为 DESC。
|
||||
|
||||
本章节 SQL 语法遵循如下约定：
|
||||
|
||||
- <\> 里的内容是用户需要输入的，但不要输入 <\> 本身
|
||||
|
|
@ -37,4 +35,4 @@ import DocCardList from '@theme/DocCardList';
|
|||
import {useCurrentSidebarCategory} from '@docusaurus/theme-common';
|
||||
|
||||
<DocCardList items={useCurrentSidebarCategory().items}/>
|
||||
```
|
||||
```
|
||||
|
|
|
|||
|
|
@ -82,7 +82,7 @@ st,t1=3,t2=4,t3=t3 c1=3i64,c3="passit",c2=false,c4=4f64 1626006833639000000
|
|||
|
||||
:::tip
|
||||
无模式所有的处理逻辑，仍会遵循 TDengine 对数据结构的底层限制，例如每行数据的总长度不能超过
|
||||
16k 字节。这方面的具体限制约束请参见 [TAOS SQL 边界限制](/taos-sql/limit)
|
||||
48KB。这方面的具体限制约束请参见 [TAOS SQL 边界限制](/taos-sql/limit)
|
||||
|
||||
:::
|
||||
|
||||
|
|
|
|||
|
|
@ -33,15 +33,15 @@ title: 常见问题及反馈
|
|||
|
||||
### 2. Windows 平台下 JDBCDriver 找不到动态链接库，怎么办？
|
||||
|
||||
请看为此问题撰写的[技术博客](https://www.taosdata.com/blog/2019/12/03/950.html)。
|
||||
请看为此问题撰写的 [技术博客](https://www.taosdata.com/blog/2019/12/03/950.html)。
|
||||
|
||||
### 3. 创建数据表时提示 more dnodes are needed
|
||||
|
||||
请看为此问题撰写的[技术博客](https://www.taosdata.com/blog/2019/12/03/965.html)。
|
||||
请看为此问题撰写的 [技术博客](https://www.taosdata.com/blog/2019/12/03/965.html)。
|
||||
|
||||
### 4. 如何让 TDengine crash 时生成 core 文件？
|
||||
|
||||
请看为此问题撰写的[技术博客](https://www.taosdata.com/blog/2019/12/06/974.html)。
|
||||
请看为此问题撰写的 [技术博客](https://www.taosdata.com/blog/2019/12/06/974.html)。
|
||||
|
||||
### 5. 遇到错误“Unable to establish connection” 怎么办？
|
||||
|
||||
|
|
@ -128,19 +128,30 @@ properties.setProperty(TSDBDriver.LOCALE_KEY, "UTF-8");
|
|||
Connection = DriverManager.getConnection(url, properties);
|
||||
```
|
||||
|
||||
### 13.JDBC 报错： the executed SQL is not a DML or a DDL？
|
||||
### 13. Windows 系统下客户端无法正常显示中文字符？
|
||||
|
||||
Windows 系统中一般是采用 GBK/GB18030 存储中文字符，而 TDengine 的默认字符集为 UTF-8 ，在 Windows 系统中使用 TDengine 客户端时，客户端驱动会将字符统一转换为 UTF-8 编码后发送到服务端存储，因此在应用开发过程中，调用接口时正确配置当前的中文字符集即可。
|
||||
|
||||
【 v2.2.1.5以后版本 】在 Windows 10 环境下运行 TDengine 客户端命令行工具 taos 时，若无法正常输入、显示中文，可以对客户端 taos.cfg 做如下配置：
|
||||
|
||||
```
|
||||
locale C
|
||||
charset UTF-8
|
||||
```
|
||||
|
||||
### 14. JDBC 报错： the executed SQL is not a DML or a DDL？
|
||||
|
||||
请更新至最新的 JDBC 驱动，参考 [Java 连接器](/reference/connector/java)
|
||||
|
||||
### 14. taos connect failed, reason: invalid timestamp
|
||||
### 15. taos connect failed, reason: invalid timestamp
|
||||
|
||||
常见原因是服务器和客户端时间没有校准，可以通过和时间服务器同步的方式（Linux 下使用 ntpdate 命令，Windows 在系统时间设置中选择自动同步）校准。
|
||||
|
||||
### 15. 表名显示不全
|
||||
### 16. 表名显示不全
|
||||
|
||||
由于 taos shell 在终端中显示宽度有限，有可能比较长的表名显示不全，如果按照显示的不全的表名进行相关操作会发生 Table does not exist 错误。解决方法可以是通过修改 taos.cfg 文件中的设置项 maxBinaryDisplayWidth， 或者直接输入命令 set max_binary_display_width 100。或者在命令结尾使用 \G 参数来调整结果的显示方式。
|
||||
|
||||
### 16. 如何进行数据迁移？
|
||||
### 17. 如何进行数据迁移？
|
||||
|
||||
TDengine 是根据 hostname 唯一标志一台机器的，在数据文件从机器 A 移动机器 B 时，注意如下两件事：
|
||||
|
||||
|
|
@ -148,7 +159,7 @@ TDengine 是根据 hostname 唯一标志一台机器的，在数据文件从机
|
|||
- 2.0.7.0 及以后的版本，到/var/lib/taos/dnode 下，修复 dnodeEps.json 的 dnodeId 对应的 FQDN，重启。确保机器内所有机器的此文件是完全相同的。
|
||||
- 1.x 和 2.x 版本的存储结构不兼容，需要使用迁移工具或者自己开发应用导出导入数据。
|
||||
|
||||
### 17. 如何在命令行程序 taos 中临时调整日志级别
|
||||
### 18. 如何在命令行程序 taos 中临时调整日志级别
|
||||
|
||||
为了调试方便，从 2.0.16 版本开始，命令行程序 taos 新增了与日志记录相关的两条指令：
|
||||
|
||||
|
|
@ -169,7 +180,7 @@ ALTER LOCAL RESETLOG;
|
|||
|
||||
<a class="anchor" id="timezone"></a>
|
||||
|
||||
### 18. go 语言编写组件编译失败怎样解决？
|
||||
### 19. go 语言编写组件编译失败怎样解决？
|
||||
|
||||
TDengine 2.3.0.0 及之后的版本包含一个使用 go 语言开发的 taosAdapter 独立组件，需要单独运行，取代之前 taosd 内置的 httpd ，提供包含原 httpd 功能以及支持多种其他软件（Prometheus、Telegraf、collectd、StatsD 等）的数据接入功能。
|
||||
使用最新 develop 分支代码编译需要先 `git submodule update --init --recursive` 下载 taosAdapter 仓库代码后再编译。
|
||||
|
|
@ -184,7 +195,7 @@ go env -w GOPROXY=https://goproxy.cn,direct
|
|||
如果希望继续使用之前的内置 httpd，可以关闭 taosAdapter 编译，使用
|
||||
`cmake .. -DBUILD_HTTP=true` 使用原来内置的 httpd。
|
||||
|
||||
### 19. 如何查询数据占用的存储空间大小？
|
||||
### 20. 如何查询数据占用的存储空间大小？
|
||||
|
||||
默认情况下，TDengine 的数据文件存储在 /var/lib/taos ，日志文件存储在 /var/log/taos 。
|
||||
|
||||
|
|
@ -193,3 +204,50 @@ go env -w GOPROXY=https://goproxy.cn,direct
|
|||
若想查看单个数据库占用的大小，可在命令行程序 taos 内指定要查看的数据库后执行 `show vgroups;` ，通过得到的 VGroup id 去 /var/lib/taos/vnode 下查看包含的文件夹大小。
|
||||
|
||||
若仅仅想查看指定（超级）表的数据块分布及大小，可查看[_block_dist 函数](https://docs.taosdata.com/taos-sql/select/#_block_dist-%E5%87%BD%E6%95%B0)
|
||||
|
||||
### 21. 客户端连接串如何保证高可用？
|
||||
|
||||
请看为此问题撰写的 [技术博客](https://www.taosdata.com/blog/2021/04/16/2287.html)
|
||||
|
||||
### 22. 时间戳的时区信息是怎样处理的？
|
||||
|
||||
TDengine 中时间戳的时区总是由客户端进行处理，而与服务端无关。具体来说，客户端会对 SQL 语句中的时间戳进行时区转换，转为 UTC 时区（即 Unix 时间戳——Unix Timestamp）再交由服务端进行写入和查询；在读取数据时，服务端也是采用 UTC 时区提供原始数据，客户端收到后再根据本地设置，把时间戳转换为本地系统所要求的时区进行显示。
|
||||
|
||||
客户端在处理时间戳字符串时，会采取如下逻辑：
|
||||
|
||||
1. 在未做特殊设置的情况下，客户端默认使用所在操作系统的时区设置。
|
||||
2. 如果在 taos.cfg 中设置了 timezone 参数，则客户端会以这个配置文件中的设置为准。
|
||||
3. 如果在 C/C++/Java/Python 等各种编程语言的 Connector Driver 中，在建立数据库连接时显式指定了 timezone，那么会以这个指定的时区设置为准。例如 Java Connector 的 JDBC URL 中就有 timezone 参数。
|
||||
4. 在书写 SQL 语句时，也可以直接使用 Unix 时间戳（例如 `1554984068000`）或带有时区的时间戳字符串，也即以 RFC 3339 格式（例如 `2013-04-12T15:52:01.123+08:00`）或 ISO-8601 格式（例如 `2013-04-12T15:52:01.123+0800`）来书写时间戳，此时这些时间戳的取值将不再受其他时区设置的影响。
|
||||
|
||||
### 23. TDengine 2.0 都会用到哪些网络端口？
|
||||
|
||||
在 TDengine 2.0 版本中，会用到以下这些网络端口（以默认端口 6030 为前提进行说明，如果修改了配置文件中的设置，那么这里列举的端口都会随之出现变化），管理员可以参考这里的信息调整防火墙设置：
|
||||
|
||||
| 协议 | 默认端口 | 用途说明 | 修改方法 |
|
||||
| :--- | :-------- | :---------------------------------- | :------------------------------- |
|
||||
| TCP | 6030 | 客户端与服务端之间通讯。 | 由配置文件设置 serverPort 决定。 |
|
||||
| TCP | 6035 | 多节点集群的节点间通讯。 | 随 serverPort 端口变化。 |
|
||||
| TCP | 6040 | 多节点集群的节点间数据同步。 | 随 serverPort 端口变化。 |
|
||||
| TCP | 6041 | 客户端与服务端之间的 RESTful 通讯。 | 随 serverPort 端口变化。2.4.0.0 及以上版本由 taosAdapter 配置。 |
|
||||
| TCP | 6042 | Arbitrator 的服务端口。 | 随 Arbitrator 启动参数设置变化。 |
|
||||
| TCP | 6043 | TaosKeeper 监控服务端口。 | 随 TaosKeeper 启动参数设置变化。 |
|
||||
| TCP | 6044 | 支持 StatsD 的数据接入端口。 | 随 taosAdapter 启动参数设置变化（ 2.4.0.0 及以上版本）。 |
|
||||
| UDP | 6045 | 支持 collectd 数据接入端口。 | 随 taosAdapter 启动参数设置变化（ 2.4.0.0 及以上版本）。 |
|
||||
| TCP | 6060 | 企业版内 Monitor 服务的网络端口。 | |
|
||||
| UDP | 6030-6034 | 客户端与服务端之间通讯。 | 随 serverPort 端口变化。 |
|
||||
| UDP | 6035-6039 | 多节点集群的节点间通讯。 | 随 serverPort 端口变化。 |
|
||||
|
||||
### 24. 为什么 RESTful 接口无响应、Grafana 无法添加 TDengine 为数据源、TDengineGUI 选了 6041 端口还是无法连接成功？？
|
||||
|
||||
taosAdapter 从 TDengine 2.4.0.0 版本开始成为 TDengine 服务端软件的组成部分，是 TDengine 集群和应用程序之间的桥梁和适配器。在此之前 RESTful 接口等功能是由 taosd 内置的 HTTP 服务提供的，而如今要实现上述功能需要执行：```systemctl start taosadapter``` 命令来启动 taosAdapter 服务。
|
||||
|
||||
需要说明的是，taosAdapter 的日志路径 path 需要单独配置，默认路径是 /var/log/taos ；日志等级 logLevel 有 8 个等级，默认等级是 info ，配置成 panic 可关闭日志输出。请注意操作系统 / 目录的空间大小，可通过命令行参数、环境变量或配置文件来修改配置，默认配置文件是 /etc/taos/taosadapter.toml 。
|
||||
|
||||
有关 taosAdapter 组件的详细介绍请看文档：[taosAdapter](https://docs.taosdata.com/reference/taosadapter/)
|
||||
|
||||
### 25. 发生了 OOM 怎么办？
|
||||
|
||||
OOM 是操作系统的保护机制，当操作系统内存(包括 SWAP )不足时，会杀掉某些进程，从而保证操作系统的稳定运行。通常内存不足主要是如下两个原因导致，一是剩余内存小于 vm.min_free_kbytes ；二是程序请求的内存大于剩余内存。还有一种情况是内存充足但程序占用了特殊的内存地址，也会触发 OOM 。
|
||||
|
||||
TDengine 会预先为每个 VNode 分配好内存，每个 Database 的 VNode 个数受 maxVgroupsPerDb 影响，每个 VNode 占用的内存大小受 Blocks 和 Cache 影响。要防止 OOM，需要在项目建设之初合理规划内存，并合理设置 SWAP ，除此之外查询过量的数据也有可能导致内存暴涨，这取决于具体的查询语句。TDengine 企业版对内存管理做了优化，采用了新的内存分配器，对稳定性有更高要求的用户可以考虑选择企业版。
|
||||
|
|
|
|||
|
|
@ -130,7 +130,7 @@ After TDengine server is running，execute `taosBenchmark` (previously named tao
|
|||
taosBenchmark
|
||||
```
|
||||
|
||||
This command will create a super table "meters" under database "test". Under "meters", 10000 tables are created with names from "d0" to "d9999". Each table has 10000 rows and each row has four columns (ts, current, voltage, phase). Time stamp is starting from "2017-07-14 10:40:00 000" to "2017-07-14 10:40:09 999". Each table has tags "location" and "groupId". groupId is set 1 to 10 randomly, and location is set to "California.SanFrancisco" or "California.SanDieo".
|
||||
This command will create a super table "meters" under database "test". Under "meters", 10000 tables are created with names from "d0" to "d9999". Each table has 10000 rows and each row has four columns (ts, current, voltage, phase). Time stamp is starting from "2017-07-14 10:40:00 000" to "2017-07-14 10:40:09 999". Each table has tags "location" and "groupId". groupId is set 1 to 10 randomly, and location is set to "California.SanFrancisco" or "California.SanDiego".
|
||||
|
||||
This command will insert 100 million rows into the database quickly. Time to insert depends on the hardware configuration, it only takes a dozen seconds for a regular PC server.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
---
|
||||
sidebar_label: Connection
|
||||
title: Connect to TDengine
|
||||
sidebar_label: Connect
|
||||
title: Connect
|
||||
description: "This document explains how to establish connections to TDengine, and briefly introduces how to install and use TDengine connectors."
|
||||
---
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
---
|
||||
sidebar_label: SQL
|
||||
sidebar_label: Insert Using SQL
|
||||
title: Insert Using SQL
|
||||
---
|
||||
|
||||
|
|
@ -52,7 +52,7 @@ For more details about `INSERT` please refer to [INSERT](/taos-sql/insert).
|
|||
|
||||
:::info
|
||||
|
||||
- Inserting in batches can improve performance. Normally, the higher the batch size, the better the performance. Please note that a single row can't exceed 16K bytes and each SQL statement can't exceed 1MB.
|
||||
- Inserting in batches can improve performance. Normally, the higher the batch size, the better the performance. Please note that a single row can't exceed 48K bytes and each SQL statement can't exceed 1MB.
|
||||
- Inserting with multiple threads can also improve performance. However, depending on the system resources on the application side and the server side, when the number of inserting threads grows beyond a specific point the performance may drop instead of improving. The proper number of threads needs to be tested in a specific environment to find the best number.
|
||||
|
||||
:::
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
---
|
||||
title: Insert
|
||||
title: Insert Data
|
||||
---
|
||||
|
||||
TDengine supports multiple protocols of inserting data, including SQL, InfluxDB Line protocol, OpenTSDB Telnet protocol, and OpenTSDB JSON protocol. Data can be inserted row by row, or in batches. Data from one or more collection points can be inserted simultaneously. Data can be inserted with multiple threads, and out of order data and historical data can be inserted as well. InfluxDB Line protocol, OpenTSDB Telnet protocol and OpenTSDB JSON protocol are the 3 kinds of schemaless insert protocols supported by TDengine. It's not necessary to create STables and tables in advance if using schemaless protocols, and the schemas can be adjusted automatically based on the data being inserted.
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
label: Select Data
|
||||
label: Query Data
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
---
|
||||
Sidebar_label: Select
|
||||
title: Select
|
||||
Sidebar_label: Query data
|
||||
title: Query data
|
||||
description: "This chapter introduces major query functionalities and how to perform sync and async query using connectors."
|
||||
---
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
---
|
||||
sidebar_label: Subscription
|
||||
sidebar_label: Data Subscription
|
||||
description: "Lightweight service for data subscription and publishing. Time series data inserted into TDengine continuously can be pushed automatically to subscribing clients."
|
||||
title: Data Subscription
|
||||
---
|
||||
|
|
@ -151,7 +151,7 @@ void subscribe_callback(TAOS_SUB* tsub, TAOS_RES *res, void* param, int code) {
|
|||
taos_unsubscribe(tsub, keep);
|
||||
```
|
||||
|
||||
The second parameter `keep` is used to specify whether to keep the subscription progress on the client sde. If it is **false**, i.e. **0**, then subscription will be restarted from beginning regardless of the `restart` parameter's value when `taos_subscribe` is invoked again. The subscription progress information is stored in _{DataDir}/subscribe/_ , under which there is a file with the same name as `topic` for each subscription, the subscription will be restarted from the beginning if the corresponding progress file is removed.
|
||||
The second parameter `keep` is used to specify whether to keep the subscription progress on the client sde. If it is **false**, i.e. **0**, then subscription will be restarted from beginning regardless of the `restart` parameter's value when `taos_subscribe` is invoked again. The subscription progress information is stored in _{DataDir}/subscribe/_ , under which there is a file with the same name as `topic` for each subscription(Note: The default value of `DataDir` in the `taos.cfg` file is **/var/lib/taos/**. However, **/var/lib/taos/** does not exist on the Windows server. So you need to change the `DataDir` value to the corresponding existing directory."), the subscription will be restarted from the beginning if the corresponding progress file is removed.
|
||||
|
||||
Now let's see the effect of the above sample code, assuming below prerequisites have been done.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,24 +1,31 @@
|
|||
---
|
||||
sidebar_label: UDF
|
||||
title: User Defined Functions
|
||||
description: "Scalar functions and aggregate functions developed by users can be utilized by the query framework to expand the query capability"
|
||||
title: User Defined Functions(UDF)
|
||||
description: "Scalar functions and aggregate functions developed by users can be utilized by the query framework to expand query capability"
|
||||
---
|
||||
|
||||
In some use cases, the query capability required by application programs can't be achieved directly by builtin functions. With UDF, the functions developed by users can be utilized by query framework to meet some special requirements. UDF normally takes one column of data as input, but can also support the result of sub query as input.
|
||||
In some use cases, built-in functions are not adequate for the query capability required by application programs. With UDF, the functions developed by users can be utilized by the query framework to meet business and application requirements. UDF normally takes one column of data as input, but can also support the result of a sub-query as input.
|
||||
|
||||
From version 2.2.0.0, UDF programmed in C/C++ language can be supported by TDengine.
|
||||
From version 2.2.0.0, UDF written in C/C++ are supported by TDengine.
|
||||
|
||||
Two kinds of functions can be implemented by UDF: scalar function and aggregate function.
|
||||
|
||||
## Define UDF
|
||||
## Types of UDF
|
||||
|
||||
Two kinds of functions can be implemented by UDF: scalar functions and aggregate functions.
|
||||
|
||||
Scalar functions return multiple rows and aggregate functions return either 0 or 1 row.
|
||||
|
||||
In the case of a scalar function you only have to implement the "normal" function template.
|
||||
|
||||
In the case of an aggregate function, in addition to the "normal" function, you also need to implement the "merge" and "finalize" function templates even if the implementation is empty. This will become clear in the sections below.
|
||||
|
||||
### Scalar Function
|
||||
|
||||
Below function template can be used to define your own scalar function.
|
||||
As mentioned earlier, a scalar UDF only has to implement the "normal" function template. The function template below can be used to define your own scalar function.
|
||||
|
||||
`void udfNormalFunc(char* data, short itype, short ibytes, int numOfRows, long long* ts, char* dataOutput, char* interBuf, char* tsOutput, int* numOfOutput, short otype, short obytes, SUdfInit* buf)`
|
||||
|
||||
`udfNormalFunc` is the place holder of function name, a function implemented based on the above template can be used to perform scalar computation on data rows. The parameters are fixed to control the data exchange between UDF and TDengine.
|
||||
`udfNormalFunc` is the place holder for a function name. A function implemented based on the above template can be used to perform scalar computation on data rows. The parameters are fixed to control the data exchange between UDF and TDengine.
|
||||
|
||||
- Definitions of the parameters:
|
||||
|
||||
|
|
@ -30,20 +37,24 @@ Below function template can be used to define your own scalar function.
|
|||
- numOfRows：the number of rows in the input data
|
||||
- ts: the column of timestamp corresponding to the input data
|
||||
- dataOutput：the buffer for output data, total size is `oBytes * numberOfRows`
|
||||
- interBuf：the buffer for intermediate result, its size is specified by `BUFSIZE` parameter when creating a UDF. It's normally used when the intermediate result is not same as the final result, it's allocated and freed by TDengine.
|
||||
- interBuf：the buffer for an intermediate result. Its size is specified by the `BUFSIZE` parameter when creating a UDF. It's normally used when the intermediate result is not same as the final result. This buffer is allocated and freed by TDengine.
|
||||
- tsOutput：the column of timestamps corresponding to the output data; it can be used to output timestamp together with the output data if it's not NULL
|
||||
- numOfOutput：the number of rows in output data
|
||||
- buf：for the state exchange between UDF and TDengine
|
||||
|
||||
[add_one.c](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/add_one.c) is one example of the simplest UDF implementations, i.e. one instance of the above `udfNormalFunc` template. It adds one to each value of a column passed in which can be filtered using `where` clause and outputs the result.
|
||||
[add_one.c](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/add_one.c) is one example of a very simple UDF implementation, i.e. one instance of the above `udfNormalFunc` template. It adds one to each value of a passed in column, which can be filtered using the `where` clause, and outputs the result.
|
||||
|
||||
### Aggregate Function
|
||||
|
||||
Below function template can be used to define your own aggregate function.
|
||||
For aggregate UDF, as mentioned earlier you must implement a "normal" function template (described above) and also implement the "merge" and "finalize" templates.
|
||||
|
||||
`void abs_max_merge(char* data, int32_t numOfRows, char* dataOutput, int32_t* numOfOutput, SUdfInit* buf)`
|
||||
#### Merge Function Template
|
||||
|
||||
`udfMergeFunc` is the place holder of function name, the function implemented with the above template is used to aggregate the intermediate result, only can be used in the aggregate query for STable.
|
||||
The function template below can be used to define your own merge function for an aggregate UDF.
|
||||
|
||||
`void udfMergeFunc(char* data, int32_t numOfRows, char* dataOutput, int32_t* numOfOutput, SUdfInit* buf)`
|
||||
|
||||
`udfMergeFunc` is the place holder for a function name. The function implemented with the above template is used to aggregate intermediate results and can only be used in the aggregate query for STable.
|
||||
|
||||
Definitions of the parameters:
|
||||
|
||||
|
|
@ -53,17 +64,11 @@ Definitions of the parameters:
|
|||
- numOfOutput：number of rows in the output data
|
||||
- buf：for the state exchange between UDF and TDengine
|
||||
|
||||
[abs_max.c](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/abs_max.c) is an user defined aggregate function to get the maximum from the absolute value of a column.
|
||||
#### Finalize Function Template
|
||||
|
||||
The internal processing is that the data affected by the select statement will be divided into multiple row blocks and `udfNormalFunc`, i.e. `abs_max` in this case, is performed on each row block to generate the intermediate of each sub table, then `udfMergeFunc`, i.e. `abs_max_merge` in this case, is performed on the intermediate result of sub tables to aggregate to generate the final or intermediate result of STable. The intermediate result of STable is finally processed by `udfFinalizeFunc` to generate the final result, which contain either 0 or 1 row.
|
||||
The function template below can be used to finalize the result of your own UDF, normally used when interBuf is used.
|
||||
|
||||
Other typical scenarios, like covariance, can also be achieved by aggregate UDF.
|
||||
|
||||
### Finalize
|
||||
|
||||
Below function template can be used to finalize the result of your own UDF, normally used when interBuf is used.
|
||||
|
||||
`void abs_max_finalize(char* dataOutput, char* interBuf, int* numOfOutput, SUdfInit* buf)`
|
||||
`void udfFinalizeFunc(char* dataOutput, char* interBuf, int* numOfOutput, SUdfInit* buf)`
|
||||
|
||||
`udfFinalizeFunc` is the place holder of function name, definitions of the parameter are as below:
|
||||
|
||||
|
|
@ -72,47 +77,64 @@ Below function template can be used to finalize the result of your own UDF, norm
|
|||
- numOfOutput：number of output data, can only be 0 or 1 for aggregate function
|
||||
- buf：for state exchange between UDF and TDengine
|
||||
|
||||
## UDF Conventions
|
||||
### Example abs_max.c
|
||||
|
||||
The naming of 3 kinds of UDF, i.e. udfNormalFunc, udfMergeFunc, and udfFinalizeFunc is required to have same prefix, i.e. the actual name of udfNormalFunc, which means udfNormalFunc doesn't need a suffix following the function name. While udfMergeFunc should be udfNormalFunc followed by `_merge`, udfFinalizeFunc should be udfNormalFunc followed by `_finalize`. The naming convention is part of UDF framework, TDengine follows this convention to invoke corresponding actual functions.\
|
||||
[abs_max.c](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/abs_max.c) is an example of a user defined aggregate function to get the maximum from the absolute values of a column.
|
||||
|
||||
According to the kind of UDF to implement, the functions that need to be implemented are different.
|
||||
The internal processing happens as follows. The results of the select statement are divided into multiple row blocks and `udfNormalFunc`, i.e. `abs_max` in this case, is performed on each row block to generate the intermediate results for each sub table. Then `udfMergeFunc`, i.e. `abs_max_merge` in this case, is performed on the intermediate result of sub tables to aggregate and generate the final or intermediate result of STable. The intermediate result of STable is finally processed by `udfFinalizeFunc`, i.e. `abs_max_finalize` in this example, to generate the final result, which contains either 0 or 1 row.
|
||||
|
||||
- Scalar function：udfNormalFunc is required
|
||||
- Aggregate function：udfNormalFunc, udfMergeFunc (if query on STable) and udfFinalizeFunc are required
|
||||
Other typical aggregation functions such as covariance, can also be implemented using aggregate UDF.
|
||||
|
||||
To be more accurate, assuming we want to implement a UDF named "foo". If the function is a scalar function, what we really need to implement is `foo`; if the function is aggregate function, we need to implement `foo`, `foo_merge`, and `foo_finalize`. For aggregate UDF, even though one of the three functions is not necessary, there must be an empty implementation.
|
||||
## UDF Naming Conventions
|
||||
|
||||
The naming convention for the 3 kinds of function templates required by UDF is as follows:
|
||||
- udfNormalFunc, udfMergeFunc, and udfFinalizeFunc are required to have same prefix, i.e. the actual name of udfNormalFunc. The udfNormalFunc doesn't need a suffix following the function name.
|
||||
- udfMergeFunc should be udfNormalFunc followed by `_merge`
|
||||
- udfFinalizeFunc should be udfNormalFunc followed by `_finalize`.
|
||||
|
||||
The naming convention is part of TDengine's UDF framework. TDengine follows this convention to invoke the corresponding actual functions.
|
||||
|
||||
Depending on whether you are creating a scalar UDF or aggregate UDF, the functions that you need to implement are different.
|
||||
|
||||
- Scalar function：udfNormalFunc is required.
|
||||
- Aggregate function：udfNormalFunc, udfMergeFunc (if query on STable) and udfFinalizeFunc are required.
|
||||
|
||||
For clarity, assuming we want to implement a UDF named "foo":
|
||||
- If the function is a scalar function, we only need to implement the "normal" function template and it should be named simply `foo`.
|
||||
- If the function is an aggregate function, we need to implement `foo`, `foo_merge`, and `foo_finalize`. Note that for aggregate UDF, even though one of the three functions is not necessary, there must be an empty implementation.
|
||||
|
||||
## Compile UDF
|
||||
|
||||
The source code of UDF in C can't be utilized by TDengine directly. UDF can only be loaded into TDengine after compiling to dynamically linked library.
|
||||
The source code of UDF in C can't be utilized by TDengine directly. UDF can only be loaded into TDengine after compiling to dynamically linked library (DLL).
|
||||
|
||||
For example, the example UDF `add_one.c` mentioned in previous sections need to be compiled into DLL using below command on Linux Shell.
|
||||
For example, the example UDF `add_one.c` mentioned earlier, can be compiled into DLL using the command below, in a Linux Shell.
|
||||
|
||||
```bash
|
||||
gcc -g -O0 -fPIC -shared add_one.c -o add_one.so
|
||||
```
|
||||
|
||||
The generated DLL file `dd_one.so` can be used later when creating UDF. It's recommended to use GCC not older than 7.5.
|
||||
The generated DLL file `add_one.so` can be used later when creating a UDF. It's recommended to use GCC not older than 7.5.
|
||||
|
||||
## Create and Use UDF
|
||||
|
||||
When a UDF is created in a TDengine instance, it is available across the databases in that instance.
|
||||
|
||||
### Create UDF
|
||||
|
||||
SQL command can be executed on the same hos where the generated UDF DLL resides to load the UDF DLL into TDengine, this operation can't be done through REST interface or web console. Once created, all the clients of the current TDengine can use these UDF functions in their SQL commands. UDF are stored in the management node of TDengine. The UDFs loaded in TDengine would be still available after TDengine is restarted.
|
||||
SQL command can be executed on the host where the generated UDF DLL resides to load the UDF DLL into TDengine. This operation cannot be done through REST interface or web console. Once created, any client of the current TDengine can use these UDF functions in their SQL commands. UDF are stored in the management node of TDengine. The UDFs loaded in TDengine would be still available after TDengine is restarted.
|
||||
|
||||
When creating UDF, it needs to be clarified as either scalar function or aggregate function. If the specified type is wrong, the SQL statements using the function would fail with error. Besides, the input type and output type don't need to be same in UDF, but the input data type and output data type need to be consistent with the UDF definition.
|
||||
When creating UDF, the type of UDF, i.e. a scalar function or aggregate function must be specified. If the specified type is wrong, the SQL statements using the function would fail with errors. The input type and output type don't need to be the same in UDF, but the input data type and output data type must be consistent with the UDF definition.
|
||||
|
||||
- Create Scalar Function
|
||||
|
||||
```sql
|
||||
CREATE FUNCTION ids(X) AS ids(Y) OUTPUTTYPE typename(Z) [ BUFSIZE B ];
|
||||
CREATE FUNCTION userDefinedFunctionName AS "/absolute/path/to/userDefinedFunctionName.so" OUTPUTTYPE <supported TDengine type> [BUFSIZE B];
|
||||
```
|
||||
|
||||
- ids(X)：the function name to be sued in SQL statement, must be consistent with the function name defined by `udfNormalFunc`
|
||||
- ids(Y)：the absolute path of the DLL file including the implementation of the UDF, the path needs to be quoted by single or double quotes
|
||||
- typename(Z)：the output data type, the value is the literal string of the type
|
||||
- B：the size of intermediate buffer, in bytes; it's an optional parameter and the range is [0,512]
|
||||
- userDefinedFunctionName：The function name to be used in SQL statement which must be consistent with the function name defined by `udfNormalFunc` and is also the name of the compiled DLL (.so file).
|
||||
- path：The absolute path of the DLL file including the name of the shared object file (.so). The path must be quoted with single or double quotes.
|
||||
- outputtype：The output data type, the value is the literal string of the supported TDengine data type.
|
||||
- B：the size of intermediate buffer, in bytes; it is an optional parameter and the range is [0,512].
|
||||
|
||||
For example, below SQL statement can be used to create a UDF from `add_one.so`.
|
||||
|
||||
|
|
@ -123,17 +145,17 @@ CREATE FUNCTION add_one AS "/home/taos/udf_example/add_one.so" OUTPUTTYPE INT;
|
|||
- Create Aggregate Function
|
||||
|
||||
```sql
|
||||
CREATE AGGREGATE FUNCTION ids(X) AS ids(Y) OUTPUTTYPE typename(Z) [ BUFSIZE B ];
|
||||
CREATE AGGREGATE FUNCTION userDefinedFunctionName AS "/absolute/path/to/userDefinedFunctionName.so" OUTPUTTYPE <supported TDengine data type> [ BUFSIZE B ];
|
||||
```
|
||||
|
||||
- ids(X)：the function name to be sued in SQL statement, must be consistent with the function name defined by `udfNormalFunc`
|
||||
- ids(Y)：the absolute path of the DLL file including the implementation of the UDF, the path needs to be quoted by single or double quotes
|
||||
- typename(Z)：the output data type, the value is the literal string of the type
|
||||
- userDefinedFunctionName：the function name to be used in SQL statement which must be consistent with the function name defined by `udfNormalFunc` and is also the name of the compiled DLL (.so file).
|
||||
- path：the absolute path of the DLL file including the name of the shared object file (.so). The path needs to be quoted by single or double quotes.
|
||||
- OUTPUTTYPE：the output data type, the value is the literal string of the type
|
||||
- B：the size of intermediate buffer, in bytes; it's an optional parameter and the range is [0,512]
|
||||
|
||||
For details about how to use intermediate result, please refer to example program [demo.c](https://github.com/taosdata/TDengine/blob/develop/tests/script/sh/demo.c).
|
||||
|
||||
For example, below SQL statement can be used to create a UDF rom `demo.so`.
|
||||
For example, below SQL statement can be used to create a UDF from `demo.so`.
|
||||
|
||||
```sql
|
||||
CREATE AGGREGATE FUNCTION demo AS "/home/taos/udf_example/demo.so" OUTPUTTYPE DOUBLE bufsize 14;
|
||||
|
|
@ -176,11 +198,11 @@ In current version there are some restrictions for UDF
|
|||
1. Only Linux is supported when creating and invoking UDF for both client side and server side
|
||||
2. UDF can't be mixed with builtin functions
|
||||
3. Only one UDF can be used in a SQL statement
|
||||
4. Single column is supported as input for UDF
|
||||
4. Only a single column is supported as input for UDF
|
||||
5. Once created successfully, UDF is persisted in MNode of TDengineUDF
|
||||
6. UDF can't be created through REST interface
|
||||
7. The function name used when creating UDF in SQL must be consistent with the function name defined in the DLL, i.e. the name defined by `udfNormalFunc`
|
||||
8. The name name of UDF name should not conflict with any of builtin functions
|
||||
8. The name of a UDF should not conflict with any of TDengine's built-in functions
|
||||
|
||||
## Examples
|
||||
|
||||
|
|
|
|||
|
|
@ -3,16 +3,16 @@ sidebar_label: Operation
|
|||
title: Manage DNODEs
|
||||
---
|
||||
|
||||
The previous section [Deployment](/cluster/deploy) introduced how to deploy and start a cluster from scratch. Once a cluster is ready, the dnode status in the cluster can be shown at any time, new dnode can be added to scale out the cluster, an existing dnode can be removed, even load balance can be performed manually.
|
||||
The previous section, [Deployment],(/cluster/deploy) showed you how to deploy and start a cluster from scratch. Once a cluster is ready, the status of dnode(s) in the cluster can be shown at any time. Dnodes can be managed from the TDengine CLI. New dnode(s) can be added to scale out the cluster, an existing dnode can be removed and you can even perform load balancing manually, if necessary.
|
||||
|
||||
:::note
|
||||
All the commands to be introduced in this chapter need to be run through TDengine CLI, sometimes it's necessary to use root privilege.
|
||||
All the commands introduced in this chapter must be run in the TDengine CLI - `taos`. Note that sometimes it is necessary to use root privilege.
|
||||
|
||||
:::
|
||||
|
||||
## Show DNODEs
|
||||
|
||||
The below command can be executed in TDengine CLI `taos` to list all dnodes in the cluster, including ID, end point (fqdn:port), status (ready, offline), number of vnodes, number of free vnodes, etc. It's suggested to execute this command to check after adding or removing a dnode.
|
||||
The below command can be executed in TDengine CLI `taos` to list all dnodes in the cluster, including ID, end point (fqdn:port), status (ready, offline), number of vnodes, number of free vnodes and so on. We recommend executing this command after adding or removing a dnode.
|
||||
|
||||
```sql
|
||||
SHOW DNODES;
|
||||
|
|
@ -30,7 +30,7 @@ Query OK, 1 row(s) in set (0.008298s)
|
|||
|
||||
## Show VGROUPs
|
||||
|
||||
To utilize system resources efficiently and provide scalability, data sharding is required. The data of each database is divided into multiple shards and stored in multiple vnodes. These vnodes may be located in different dnodes, scaling out can be achieved by adding more vnodes from more dnodes. Each vnode can only be used for a single DB, but one DB can have multiple vnodes. The allocation of vnode is scheduled automatically by mnode according to system resources of the dnodes.
|
||||
To utilize system resources efficiently and provide scalability, data sharding is required. The data of each database is divided into multiple shards and stored in multiple vnodes. These vnodes may be located on different dnodes. One way of scaling out is to add more vnodes on dnodes. Each vnode can only be used for a single DB, but one DB can have multiple vnodes. The allocation of vnode is scheduled automatically by mnode based on system resources of the dnodes.
|
||||
|
||||
Launch TDengine CLI `taos` and execute below command:
|
||||
|
||||
|
|
@ -87,7 +87,7 @@ taos> show dnodes;
|
|||
Query OK, 2 row(s) in set (0.001017s)
|
||||
```
|
||||
|
||||
It can be seen that the status of the new dnode is "offline", once the dnode is started and connects the firstEp of the cluster, execute the command again and get the example output below, from which it can be seen that two dnodes are both in "ready" status.
|
||||
It can be seen that the status of the new dnode is "offline". Once the dnode is started and connects to the firstEp of the cluster, you can execute the command again and get the example output below. As can be seen, both dnodes are in "ready" status.
|
||||
|
||||
```
|
||||
taos> show dnodes;
|
||||
|
|
@ -132,12 +132,12 @@ taos> show dnodes;
|
|||
Query OK, 1 row(s) in set (0.001137s)
|
||||
```
|
||||
|
||||
In the above example, when `show dnodes` is executed the first time, two dnodes are shown. Then `drop dnode 2` is executed, after that from the output of executing `show dnodes` again it can be seen that only the dnode with ID 1 is still in the cluster.
|
||||
In the above example, when `show dnodes` is executed the first time, two dnodes are shown. After `drop dnode 2` is executed, you can execute `show dnodes` again and it can be seen that only the dnode with ID 1 is still in the cluster.
|
||||
|
||||
:::note
|
||||
|
||||
- Once a dnode is dropped, it can't rejoin the cluster. To rejoin, the dnode needs to deployed again after cleaning up the data directory. Normally, before dropping a dnode, the data belonging to the dnode needs to be migrated to other place.
|
||||
- Please be noted that `drop dnode` is different from stopping `taosd` process. `drop dnode` just removes the dnode out of TDengine cluster. Only after a dnode is dropped, can the corresponding `taosd` process be stopped.
|
||||
- Once a dnode is dropped, it can't rejoin the cluster. To rejoin, the dnode needs to deployed again after cleaning up the data directory. Before dropping a dnode, the data belonging to the dnode MUST be migrated/backed up according to your data retention, data security or other SOPs.
|
||||
- Please note that `drop dnode` is different from stopping `taosd` process. `drop dnode` just removes the dnode out of TDengine cluster. Only after a dnode is dropped, can the corresponding `taosd` process be stopped.
|
||||
- Once a dnode is dropped, other dnodes in the cluster will be notified of the drop and will not accept the request from the dropped dnode.
|
||||
- dnodeID is allocated automatically and can't be manually modified. dnodeID is generated in ascending order without duplication.
|
||||
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ title: High Availability and Load Balancing
|
|||
|
||||
High availability of vnode and mnode can be achieved through replicas in TDengine.
|
||||
|
||||
The number of vnodes is associated with each DB, there can be multiple DBs in a TDengine cluster. A different number of replicas can be configured for each DB. When creating a database, the parameter `replica` is used to specify the number of replicas, the default value is 1. With single replica, the high availability of the system can't be guaranteed. Whenever one node is down, the data service will be unavailable. The number of dnodes in the cluster must NOT be lower than the number of replicas set for any DB, otherwise the `create table` operation would fail with error "more dnodes are needed". The SQL statement below is used to create a database named "demo" with 3 replicas.
|
||||
A TDengine cluster can have multiple databases. Each database has a number of vnodes associated with it. A different number of replicas can be configured for each DB. When creating a database, the parameter `replica` is used to specify the number of replicas. The default value for `replica` is 1. Naturally, a single replica cannot guarantee high availability since if one node is down, the data service is unavailable. Note that the number of dnodes in the cluster must NOT be lower than the number of replicas set for any DB, otherwise the `create table` operation will fail with error "more dnodes are needed". The SQL statement below is used to create a database named "demo" with 3 replicas.
|
||||
|
||||
```sql
|
||||
CREATE DATABASE demo replica 3;
|
||||
|
|
@ -15,19 +15,19 @@ CREATE DATABASE demo replica 3;
|
|||
|
||||
The data in a DB is divided into multiple shards and stored in multiple vgroups. The number of vnodes in each vgroup is determined by the number of replicas set for the DB. The vnodes in each vgroup store exactly the same data. For the purpose of high availability, the vnodes in a vgroup must be located in different dnodes on different hosts. As long as over half of the vnodes in a vgroup are in an online state, the vgroup is able to provide data access. Otherwise the vgroup can't provide data access for reading or inserting data.
|
||||
|
||||
There may be data for multiple DBs in a dnode. Once a dnode is down, multiple DBs may be affected. However, it's hard to say the cluster is guaranteed to work properly as long as over half of dnodes are online because vnodes are introduced and there may be complex mapping between vnodes and dnodes.
|
||||
There may be data for multiple DBs in a dnode. When a dnode is down, multiple DBs may be affected. While in theory, the cluster will provide data access for reading or inserting data if over half the vnodes in vgroups are online, because of the possibly complex mapping between vnodes and dnodes, it is difficult to guarantee that the cluster will work properly if over half of the dnodes are online.
|
||||
|
||||
## High Availability of Mnode
|
||||
|
||||
Each TDengine cluster is managed by `mnode`, which is a module of `taosd`. For the high availability of mnode, multiple mnodes can be configured using system parameter `numOfMNodes`, the valid time range is [1,3]. To make sure the data consistency between mnodes, the data replication between mnodes is performed in a synchronous way.
|
||||
Each TDengine cluster is managed by `mnode`, which is a module of `taosd`. For the high availability of mnode, multiple mnodes can be configured using system parameter `numOfMNodes`. The valid range for `numOfMnodes` is [1,3]. To ensure data consistency between mnodes, data replication between mnodes is performed synchronously.
|
||||
|
||||
There may be multiple dnodes in a cluster, but only one mnode can be started in each dnode. Which one or ones of the dnodes will be designated as mnodes is automatically determined by TDengine according to the cluster configuration and system resources. Command `show mnodes` can be executed in TDengine `taos` to show the mnodes in the cluster.
|
||||
There may be multiple dnodes in a cluster, but only one mnode can be started in each dnode. Which one or ones of the dnodes will be designated as mnodes is automatically determined by TDengine according to the cluster configuration and system resources. The command `show mnodes` can be executed in TDengine `taos` to show the mnodes in the cluster.
|
||||
|
||||
```sql
|
||||
SHOW MNODES;
|
||||
```
|
||||
|
||||
The end point and role/status (master, slave, unsynced, or offline) of all mnodes can be shown by the above command. When the first dnode is started in a cluster, there must be one mnode in this dnode, because there must be at least one mnode otherwise the cluster doesn't work. If `numOfMNodes` is configured to 2, another mnode will be started when the second dnode is launched.
|
||||
The end point and role/status (master, slave, unsynced, or offline) of all mnodes can be shown by the above command. When the first dnode is started in a cluster, there must be one mnode in this dnode. Without at least one mnode, the cluster cannot work. If `numOfMNodes` is configured to 2, another mnode will be started when the second dnode is launched.
|
||||
|
||||
For the high availability of mnode, `numOfMnodes` needs to be configured to 2 or a higher value. Because the data consistency between mnodes must be guaranteed, the replica confirmation parameter `quorum` is set to 2 automatically if `numOfMNodes` is set to 2 or higher.
|
||||
|
||||
|
|
@ -36,15 +36,16 @@ If high availability is important for your system, both vnode and mnode must be
|
|||
|
||||
:::
|
||||
|
||||
## Load Balance
|
||||
## Load Balancing
|
||||
|
||||
Load balance will be triggered in 3 cases without manual intervention.
|
||||
Load balancing will be triggered in 3 cases without manual intervention.
|
||||
|
||||
- When a new dnode is joined in the cluster, automatic load balancing may be triggered, some data from some dnodes may be transferred to the new dnode automatically.
|
||||
- When a new dnode joins the cluster, automatic load balancing may be triggered. Some data from other dnodes may be transferred to the new dnode automatically.
|
||||
- When a dnode is removed from the cluster, the data from this dnode will be transferred to other dnodes automatically.
|
||||
- When a dnode is too hot, i.e. too much data has been stored in it, automatic load balancing may be triggered to migrate some vnodes from this dnode to other dnodes.
|
||||
|
||||
:::tip
|
||||
Automatic load balancing is controlled by parameter `balance`, 0 means disabled and 1 means enabled.
|
||||
Automatic load balancing is controlled by the parameter `balance`, 0 means disabled and 1 means enabled. This is set in the file [taos.cfg](https://docs.tdengine.com/reference/config/#balance).
|
||||
|
||||
:::
|
||||
|
||||
|
|
@ -52,22 +53,22 @@ Automatic load balancing is controlled by parameter `balance`, 0 means disabled
|
|||
|
||||
When a dnode is offline, it can be detected by the TDengine cluster. There are two cases:
|
||||
|
||||
- The dnode becomes online again before the threshold configured in `offlineThreshold` is reached, it is still in the cluster and data replication is started automatically. The dnode can work properly after the data syncup is finished.
|
||||
- The dnode comes online before the threshold configured in `offlineThreshold` is reached. The dnode is still in the cluster and data replication is started automatically. The dnode can work properly after the data sync is finished.
|
||||
|
||||
- If the dnode has been offline over the threshold configured in `offlineThreshold` in `taos.cfg`, the dnode will be removed from the cluster automatically. A system alert will be generated and automatic load balancing will be triggered if `balance` is set to 1. When the removed dnode is restarted and becomes online, it will not join in the cluster automatically, it can only be joined manually by the system operator.
|
||||
- If the dnode has been offline over the threshold configured in `offlineThreshold` in `taos.cfg`, the dnode will be removed from the cluster automatically. A system alert will be generated and automatic load balancing will be triggered if `balance` is set to 1. When the removed dnode is restarted and becomes online, it will not join the cluster automatically. The system administrator has to manually join the dnode to the cluster.
|
||||
|
||||
:::note
|
||||
If all the vnodes in a vgroup (or mnodes in mnode group) are in offline or unsynced status, the master node can only be voted after all the vnodes or mnodes in the group become online and can exchange status, then the vgroup (or mnode group) is able to provide service.
|
||||
If all the vnodes in a vgroup (or mnodes in mnode group) are in offline or unsynced status, the master node can only be voted on, after all the vnodes or mnodes in the group become online and can exchange status. Following this, the vgroup (or mnode group) is able to provide service.
|
||||
|
||||
:::
|
||||
|
||||
## Arbitrator
|
||||
|
||||
If the number of replicas is set to an even number like 2, when half of the vnodes in a vgroup don't work a master node can't be voted. A similar case is also applicable to mnode if the number of mnodes is set to an even number like 2.
|
||||
The "arbitrator" component is used to address the special case when the number of replicas is set to an even number like 2,4 etc. If half of the vnodes in a vgroup don't work, it is impossible to vote and select a master node. This situation also applies to mnodes if the number of mnodes is set to an even number like 2,4 etc.
|
||||
|
||||
To resolve this problem, a new arbitrator component named `tarbitrator`, abbreviated for TDengine Arbitrator, was introduced. Arbitrator simulates a vnode or mnode but it's only responsible for network communication and doesn't handle any actual data access. As long as more than half of the vnode or mnode, including Arbitrator, are available the vnode group or mnode group can provide data insertion or query services normally.
|
||||
To resolve this problem, a new arbitrator component named `tarbitrator`, an abbreviation of TDengine Arbitrator, was introduced. The `tarbitrator` simulates a vnode or mnode but it's only responsible for network communication and doesn't handle any actual data access. As long as more than half of the vnode or mnode, including Arbitrator, are available the vnode group or mnode group can provide data insertion or query services normally.
|
||||
|
||||
Normally, it's suggested to configure a replica number of each DB or system parameter `numOfMNodes` to an odd number. However, if a user is very sensitive to storage space, a replica number of 2 plus arbitrator component can be used to achieve both lower cost of storage space and high availability.
|
||||
Normally, it's prudent to configure the replica number for each DB or system parameter `numOfMNodes` to be an odd number. However, if a user is very sensitive to storage space, a replica number of 2 plus arbitrator component can be used to achieve both lower cost of storage space and high availability.
|
||||
|
||||
Arbitrator component is installed with the server package. For details about how to install, please refer to [Install](/operation/pkg-install). The `-p` parameter of `tarbitrator` can be used to specify the port on which it provides service.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,17 +1,17 @@
|
|||
---
|
||||
title: Data Types
|
||||
description: "The data types supported by TDengine include timestamp, float, JSON, etc"
|
||||
description: "TDengine supports a variety of data types including timestamp, float, JSON and many others."
|
||||
---
|
||||
|
||||
When using TDengine to store and query data, the most important part of the data is timestamp. Timestamp must be specified when creating and inserting data rows or querying data, timestamp must follow the rules below:
|
||||
When using TDengine to store and query data, the most important part of the data is timestamp. Timestamp must be specified when creating and inserting data rows. Timestamp must follow the rules below:
|
||||
|
||||
- the format must be `YYYY-MM-DD HH:mm:ss.MS`, the default time precision is millisecond (ms), for example `2017-08-12 18:25:58.128`
|
||||
- internal function `now` can be used to get the current timestamp of the client side
|
||||
- the current timestamp of the client side is applied when `now` is used to insert data
|
||||
- The format must be `YYYY-MM-DD HH:mm:ss.MS`, the default time precision is millisecond (ms), for example `2017-08-12 18:25:58.128`
|
||||
- Internal function `now` can be used to get the current timestamp on the client side
|
||||
- The current timestamp of the client side is applied when `now` is used to insert data
|
||||
- Epoch Time：timestamp can also be a long integer number, which means the number of seconds, milliseconds or nanoseconds, depending on the time precision, from 1970-01-01 00:00:00.000 (UTC/GMT)
|
||||
- timestamp can be applied with add/subtract operation, for example `now-2h` means 2 hours back from the time at which query is executed，the unit can be b(nanosecond), u(microsecond), a(millisecond), s(second), m(minute), h(hour), d(day), or w(week). So `select * from t1 where ts > now-2w and ts <= now-1w` means the data between two weeks ago and one week ago. The time unit can also be n (calendar month) or y (calendar year) when specifying the time window for down sampling operation.
|
||||
- Add/subtract operations can be carried out on timestamps. For example `now-2h` means 2 hours prior to the time at which query is executed. The units of time in operations can be b(nanosecond), u(microsecond), a(millisecond), s(second), m(minute), h(hour), d(day), or w(week). So `select * from t1 where ts > now-2w and ts <= now-1w` means the data between two weeks ago and one week ago. The time unit can also be n (calendar month) or y (calendar year) when specifying the time window for down sampling operations.
|
||||
|
||||
Time precision in TDengine can be set by the `PRECISION` parameter when executing `CREATE DATABASE`, like below, the default time precision is millisecond.
|
||||
Time precision in TDengine can be set by the `PRECISION` parameter when executing `CREATE DATABASE`. The default time precision is millisecond. In the statement below, the precision is set to nanonseconds.
|
||||
|
||||
```sql
|
||||
CREATE DATABASE db_name PRECISION 'ns';
|
||||
|
|
@ -30,8 +30,8 @@ In TDengine, the data types below can be used when specifying a column or tag.
|
|||
| 7 | SMALLINT | 2 | Short integer, the value range is [-32767, 32767], while -32768 is treated as NULL |
|
||||
| 8 | TINYINT | 1 | Single-byte integer, the value range is [-127, 127], while -128 is treated as NULL |
|
||||
| 9 | BOOL | 1 | Bool, the value range is {true, false} |
|
||||
| 10 | NCHAR | User Defined| Multiple-Byte string that can include like Chinese characters. Each character of NCHAR type consumes 4 bytes storage. The string value should be quoted with single quotes. Literal single quote inside the string must be preceded with backslash, like `\’`. The length must be specified when defining a column or tag of NCHAR type, for example nchar(10) means it can store at most 10 characters of nchar type and will consume fixed storage of 40 bytes. An error will be reported if the string value exceeds the length defined. |
|
||||
| 11 | JSON | | json type can only be used on tag, a tag of json type is excluded with any other tags of any other type |
|
||||
| 10 | NCHAR | User Defined| Multi-Byte string that can include multi byte characters like Chinese characters. Each character of NCHAR type consumes 4 bytes storage. The string value should be quoted with single quotes. Literal single quote inside the string must be preceded with backslash, like `\’`. The length must be specified when defining a column or tag of NCHAR type, for example nchar(10) means it can store at most 10 characters of nchar type and will consume fixed storage of 40 bytes. An error will be reported if the string value exceeds the length defined. |
|
||||
| 11 | JSON | | JSON type can only be used on tags. A tag of json type is excluded with any other tags of any other type |
|
||||
|
||||
:::tip
|
||||
TDengine is case insensitive and treats any characters in the sql command as lower case by default, case sensitive strings must be quoted with single quotes.
|
||||
|
|
@ -39,7 +39,7 @@ TDengine is case insensitive and treats any characters in the sql command as low
|
|||
:::
|
||||
|
||||
:::note
|
||||
Only ASCII visible characters are suggested to be used in a column or tag of BINARY type. Multiple-byte characters must be stored in NCHAR type.
|
||||
Only ASCII visible characters are suggested to be used in a column or tag of BINARY type. Multi-byte characters must be stored in NCHAR type.
|
||||
|
||||
:::
|
||||
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ title: Database
|
|||
description: "create and drop database, show or change database parameters"
|
||||
---
|
||||
|
||||
## Create Datable
|
||||
## Create Database
|
||||
|
||||
```
|
||||
CREATE DATABASE [IF NOT EXISTS] db_name [KEEP keep] [DAYS days] [UPDATE 1];
|
||||
|
|
@ -12,11 +12,11 @@ CREATE DATABASE [IF NOT EXISTS] db_name [KEEP keep] [DAYS days] [UPDATE 1];
|
|||
|
||||
:::info
|
||||
|
||||
1. KEEP specifies the number of days for which the data in the database to be created will be kept, the default value is 3650 days, i.e. 10 years. The data will be deleted automatically once its age exceeds this threshold.
|
||||
1. KEEP specifies the number of days for which the data in the database will be retained. The default value is 3650 days, i.e. 10 years. The data will be deleted automatically once its age exceeds this threshold.
|
||||
2. UPDATE specifies whether the data can be updated and how the data can be updated.
|
||||
1. UPDATE set to 0 means update operation is not allowed, the data with an existing timestamp will be dropped silently.
|
||||
2. UPDATE set to 1 means the whole row will be updated, the columns for which no value is specified will be set to NULL
|
||||
3. UPDATE set to 2 means updating a part of columns for a row is allowed, the columns for which no value is specified will be kept as no change
|
||||
1. UPDATE set to 0 means update operation is not allowed. The update for data with an existing timestamp will be discarded silently and the original record in the database will be preserved as is.
|
||||
2. UPDATE set to 1 means the whole row will be updated. The columns for which no value is specified will be set to NULL.
|
||||
3. UPDATE set to 2 means updating a subset of columns for a row is allowed. The columns for which no value is specified will be kept unchanged.
|
||||
3. The maximum length of database name is 33 bytes.
|
||||
4. The maximum length of a SQL statement is 65,480 bytes.
|
||||
5. Below are the parameters that can be used when creating a database
|
||||
|
|
@ -35,7 +35,7 @@ CREATE DATABASE [IF NOT EXISTS] db_name [KEEP keep] [DAYS days] [UPDATE 1];
|
|||
- maxVgroupsPerDb: [Description](/reference/config/#maxvgroupsperdb)
|
||||
- comp: [Description](/reference/config/#comp)
|
||||
- precision: [Description](/reference/config/#precision)
|
||||
6. Please note that all of the parameters mentioned in this section can be configured in configuration file `taosd.cfg` at server side and used by default, the default parameters can be overriden if they are specified in `create database` statement.
|
||||
6. Please note that all of the parameters mentioned in this section are configured in configuration file `taos.cfg` on the TDengine server. If not specified in the `create database` statement, the values from taos.cfg are used by default. To override default parameters, they must be specified in the `create database` statement.
|
||||
|
||||
:::
|
||||
|
||||
|
|
@ -52,7 +52,7 @@ USE db_name;
|
|||
```
|
||||
|
||||
:::note
|
||||
This way is not applicable when using a REST connection
|
||||
This way is not applicable when using a REST connection. In a REST connection the database name must be specified before a table or stable name. For e.g. to query the stable "meters" in database "test" the query would be "SELECT count(*) from test.meters"
|
||||
|
||||
:::
|
||||
|
||||
|
|
@ -63,13 +63,13 @@ DROP DATABASE [IF EXISTS] db_name;
|
|||
```
|
||||
|
||||
:::note
|
||||
All data in the database will be deleted too. This command must be used with caution.
|
||||
All data in the database will be deleted too. This command must be used with extreme caution. Please follow your organization's data integrity, data backup, data security or any other applicable SOPs before using this command.
|
||||
|
||||
:::
|
||||
|
||||
## Change Database Configuration
|
||||
|
||||
Some examples are shown below to demonstrate how to change the configuration of a database. Please note that some configuration parameters can be changed after the database is created, but some others can't, for details of the configuration parameters of database please refer to [Configuration Parameters](/reference/config/).
|
||||
Some examples are shown below to demonstrate how to change the configuration of a database. Please note that some configuration parameters can be changed after the database is created, but some cannot. For details of the configuration parameters of database please refer to [Configuration Parameters](/reference/config/).
|
||||
|
||||
```
|
||||
ALTER DATABASE db_name COMP 2;
|
||||
|
|
@ -81,7 +81,7 @@ COMP parameter specifies whether the data is compressed and how the data is comp
|
|||
ALTER DATABASE db_name REPLICA 2;
|
||||
```
|
||||
|
||||
REPLICA parameter specifies the number of replications of the database.
|
||||
REPLICA parameter specifies the number of replicas of the database.
|
||||
|
||||
```
|
||||
ALTER DATABASE db_name KEEP 365;
|
||||
|
|
@ -124,4 +124,4 @@ SHOW DATABASES;
|
|||
SHOW CREATE DATABASE db_name;
|
||||
```
|
||||
|
||||
This command is useful when migrating the data from one TDengine cluster to another one. This command can be used to get the CREATE statement, which can be used in another TDengine to create the exact same database.
|
||||
This command is useful when migrating the data from one TDengine cluster to another. This command can be used to get the CREATE statement, which can be used in another TDengine instance to create the exact same database.
|
||||
|
|
|
|||
|
|
@ -12,10 +12,10 @@ CREATE TABLE [IF NOT EXISTS] tb_name (timestamp_field_name TIMESTAMP, field1_nam
|
|||
|
||||
:::info
|
||||
|
||||
1. The first column of a table must be of TIMESTAMP type, and it will be set as the primary key automatically
|
||||
1. The first column of a table MUST be of type TIMESTAMP. It is automatically set as the primary key.
|
||||
2. The maximum length of the table name is 192 bytes.
|
||||
3. The maximum length of each row is 16k bytes, please note that the extra 2 bytes used by each BINARY/NCHAR column are also counted.
|
||||
4. The name of the subtable can only consist of English characters, digits and underscore, and can't start with a digit. Table names are case insensitive.
|
||||
3. The maximum length of each row is 48k bytes, please note that the extra 2 bytes used by each BINARY/NCHAR column are also counted.
|
||||
4. The name of the subtable can only consist of characters from the English alphabet, digits and underscore. Table names can't start with a digit. Table names are case insensitive.
|
||||
5. The maximum length in bytes must be specified when using BINARY or NCHAR types.
|
||||
6. Escape character "\`" can be used to avoid the conflict between table names and reserved keywords, above rules will be bypassed when using escape character on table names, but the upper limit for the name length is still valid. The table names specified using escape character are case sensitive. Only ASCII visible characters can be used with escape character.
|
||||
For example \`aBc\` and \`abc\` are different table names but `abc` and `aBc` are same table names because they are both converted to `abc` internally.
|
||||
|
|
@ -44,7 +44,7 @@ The tags for which no value is specified will be set to NULL.
|
|||
CREATE TABLE [IF NOT EXISTS] tb_name1 USING stb_name TAGS (tag_value1, ...) [IF NOT EXISTS] tb_name2 USING stb_name TAGS (tag_value2, ...) ...;
|
||||
```
|
||||
|
||||
This can be used to create a lot of tables in a single SQL statement to accelerate the speed of the creating tables.
|
||||
This can be used to create a lot of tables in a single SQL statement while making table creation much faster.
|
||||
|
||||
:::info
|
||||
|
||||
|
|
@ -111,7 +111,7 @@ If a table is created using a super table as template, the table definition can
|
|||
ALTER TABLE tb_name MODIFY COLUMN field_name data_type(length);
|
||||
```
|
||||
|
||||
The type of a column is variable length, like BINARY or NCHAR, this can be used to change (or increase) the length of the column.
|
||||
If the type of a column is variable length, like BINARY or NCHAR, this command can be used to change the length of the column.
|
||||
|
||||
:::note
|
||||
If a table is created using a super table as template, the table definition can only be changed on the corresponding super table, and the change will be automatically applied to all the subtables created using this super table as template. For tables created in the normal way, the table definition can be changed directly on the table.
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ Keyword `STable`, abbreviated for super table, is supported since version 2.0.15
|
|||
|
||||
:::
|
||||
|
||||
## Crate STable
|
||||
## Create STable
|
||||
|
||||
```
|
||||
CREATE STable [IF NOT EXISTS] stb_name (timestamp_field_name TIMESTAMP, field1_name data_type1 [, field2_name data_type2 ...]) TAGS (tag1_name tag_type1, tag2_name tag_type2 [, tag3_name tag_type3]);
|
||||
|
|
@ -19,7 +19,7 @@ The SQL statement of creating a STable is similar to that of creating a table, b
|
|||
|
||||
:::info
|
||||
|
||||
1. The tag types specified in TAGS should NOT be timestamp. Since 2.1.3.0 timestamp type can be used in TAGS column, but its value must be fixed and arithmetic operation can't be applied on it.
|
||||
1. A tag can be of type timestamp, since version 2.1.3.0, but its value must be fixed and arithmetic operations cannot be performed on it. Prior to version 2.1.3.0, tag types specified in TAGS could not be of type timestamp.
|
||||
2. The tag names specified in TAGS should NOT be the same as other columns.
|
||||
3. The tag names specified in TAGS should NOT be the same as any reserved keywords.(Please refer to [keywords](/taos-sql/keywords/)
|
||||
4. The maximum number of tags specified in TAGS is 128, there must be at least one tag, and the total length of all tag columns should NOT exceed 16KB.
|
||||
|
|
@ -76,7 +76,7 @@ ALTER STable stb_name DROP COLUMN field_name;
|
|||
ALTER STable stb_name MODIFY COLUMN field_name data_type(length);
|
||||
```
|
||||
|
||||
This command can be used to change (or increase, more specifically) the length of a column of variable length types, like BINARY or NCHAR.
|
||||
This command can be used to change (or more specifically, increase) the length of a column of variable length types, like BINARY or NCHAR.
|
||||
|
||||
## Change Tags of A STable
|
||||
|
||||
|
|
@ -94,7 +94,7 @@ This command is used to add a new tag for a STable and specify the tag type.
|
|||
ALTER STable stb_name DROP TAG tag_name;
|
||||
```
|
||||
|
||||
The tag will be removed automatically from all the subtables created using the super table as template once a tag is removed from a super table.
|
||||
The tag will be removed automatically from all the subtables, created using the super table as template, once a tag is removed from a super table.
|
||||
|
||||
### Change A Tag
|
||||
|
||||
|
|
@ -102,7 +102,7 @@ The tag will be removed automatically from all the subtables created using the s
|
|||
ALTER STable stb_name CHANGE TAG old_tag_name new_tag_name;
|
||||
```
|
||||
|
||||
The tag name will be changed automatically for all the subtables created using the super table as template once a tag name is changed for a super table.
|
||||
The tag name will be changed automatically for all the subtables, created using the super table as template, once a tag name is changed for a super table.
|
||||
|
||||
### Change Tag Length
|
||||
|
||||
|
|
@ -110,7 +110,7 @@ The tag name will be changed automatically for all the subtables created using t
|
|||
ALTER STable stb_name MODIFY TAG tag_name data_type(length);
|
||||
```
|
||||
|
||||
This command can be used to change (or increase, more specifically) the length of a tag of variable length types, like BINARY or NCHAR.
|
||||
This command can be used to change (or more specifically, increase) the length of a tag of variable length types, like BINARY or NCHAR.
|
||||
|
||||
:::note
|
||||
Changing tag values can be applied to only subtables. All other tag operations, like add tag, remove tag, however, can be applied to only STable. If a new tag is added for a STable, the tag will be added with NULL value for all its subtables.
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ SELECT select_expr [, select_expr ...]
|
|||
|
||||
## Wildcard
|
||||
|
||||
Wilcard \* can be used to specify all columns. The result includes only data columns for normal tables.
|
||||
Wildcard \* can be used to specify all columns. The result includes only data columns for normal tables.
|
||||
|
||||
```
|
||||
taos> SELECT * FROM d1001;
|
||||
|
|
@ -51,14 +51,14 @@ taos> SELECT * FROM meters;
|
|||
Query OK, 9 row(s) in set (0.002022s)
|
||||
```
|
||||
|
||||
Wildcard can be used with table name as prefix, both below SQL statements have same effects and return all columns.
|
||||
Wildcard can be used with table name as prefix. Both SQL statements below have the same effect and return all columns.
|
||||
|
||||
```SQL
|
||||
SELECT * FROM d1001;
|
||||
SELECT d1001.* FROM d1001;
|
||||
```
|
||||
|
||||
In JOIN query, however, with or without table name prefix will return different results. \* without table prefix will return all the columns of both tables, but \* with table name as prefix will return only the columns of that table.
|
||||
In a JOIN query, however, the results are different with or without a table name prefix. \* without table prefix will return all the columns of both tables, but \* with table name as prefix will return only the columns of that table.
|
||||
|
||||
```
|
||||
taos> SELECT * FROM d1001, d1003 WHERE d1001.ts=d1003.ts;
|
||||
|
|
@ -76,7 +76,7 @@ taos> SELECT d1001.* FROM d1001,d1003 WHERE d1001.ts = d1003.ts;
|
|||
Query OK, 1 row(s) in set (0.020443s)
|
||||
```
|
||||
|
||||
Wilcard \* can be used with some functions, but the result may be different depending on the function being used. For example, `count(*)` returns only one column, i.e. the number of rows; `first`, `last` and `last_row` return all columns of the selected row.
|
||||
Wildcard \* can be used with some functions, but the result may be different depending on the function being used. For example, `count(*)` returns only one column, i.e. the number of rows; `first`, `last` and `last_row` return all columns of the selected row.
|
||||
|
||||
```
|
||||
taos> SELECT COUNT(*) FROM d1001;
|
||||
|
|
@ -96,7 +96,7 @@ Query OK, 1 row(s) in set (0.000849s)
|
|||
|
||||
## Tags
|
||||
|
||||
Starting from version 2.0.14, tag columns can be selected together with data columns when querying sub tables. Please note that, however, wildcard \* doesn't represent any tag column, that means tag columns must be specified explicitly like the example below.
|
||||
Starting from version 2.0.14, tag columns can be selected together with data columns when querying sub tables. Please note however, that, wildcard \* cannot be used to represent any tag column. This means that tag columns must be specified explicitly like the example below.
|
||||
|
||||
```
|
||||
taos> SELECT location, groupid, current FROM d1001 LIMIT 2;
|
||||
|
|
@ -109,7 +109,7 @@ Query OK, 2 row(s) in set (0.003112s)
|
|||
|
||||
## Get distinct values
|
||||
|
||||
`DISTINCT` keyword can be used to get all the unique values of tag columns from a super table, it can also be used to get all the unique values of data columns from a table or subtable.
|
||||
`DISTINCT` keyword can be used to get all the unique values of tag columns from a super table. It can also be used to get all the unique values of data columns from a table or subtable.
|
||||
|
||||
```sql
|
||||
SELECT DISTINCT tag_name [, tag_name ...] FROM stb_name;
|
||||
|
|
@ -118,15 +118,15 @@ SELECT DISTINCT col_name [, col_name ...] FROM tb_name;
|
|||
|
||||
:::info
|
||||
|
||||
1. Configuration parameter `maxNumOfDistinctRes` in `taos.cfg` is used to control the number of rows to output. The minimum configurable value is 100,000, the maximum configurable value is 100,000,000, the default value is 1000,000. If the actual number of rows exceeds the value of this parameter, only the number of rows specified by this parameter will be output.
|
||||
2. It can't be guaranteed that the results selected by using `DISTINCT` on columns of `FLOAT` or `DOUBLE` are exactly unique because of the precision nature of floating numbers.
|
||||
1. Configuration parameter `maxNumOfDistinctRes` in `taos.cfg` is used to control the number of rows to output. The minimum configurable value is 100,000, the maximum configurable value is 100,000,000, the default value is 1,000,000. If the actual number of rows exceeds the value of this parameter, only the number of rows specified by this parameter will be output.
|
||||
2. It can't be guaranteed that the results selected by using `DISTINCT` on columns of `FLOAT` or `DOUBLE` are exactly unique because of the precision errors in floating point numbers.
|
||||
3. `DISTINCT` can't be used in the sub-query of a nested query statement, and can't be used together with aggregate functions, `GROUP BY` or `JOIN` in the same SQL statement.
|
||||
|
||||
:::
|
||||
|
||||
## Columns Names of Result Set
|
||||
|
||||
When using `SELECT`, the column names in the result set will be same as that in the select clause if `AS` is not used. `AS` can be used to rename the column names in the result set. For example
|
||||
When using `SELECT`, the column names in the result set will be the same as that in the select clause if `AS` is not used. `AS` can be used to rename the column names in the result set. For example
|
||||
|
||||
```
|
||||
taos> SELECT ts, ts AS primary_key_ts FROM d1001;
|
||||
|
|
@ -161,7 +161,7 @@ SELECT * FROM d1001;
|
|||
|
||||
## Special Query
|
||||
|
||||
Some special query functionalities can be performed without `FORM` sub-clause. For example, below statement can be used to get the current database in use.
|
||||
Some special query functions can be invoked without `FROM` sub-clause. For example, the statement below can be used to get the current database in use.
|
||||
|
||||
```
|
||||
taos> SELECT DATABASE();
|
||||
|
|
@ -181,7 +181,7 @@ taos> SELECT DATABASE();
|
|||
Query OK, 1 row(s) in set (0.000184s)
|
||||
```
|
||||
|
||||
Below statement can be used to get the version of client or server.
|
||||
The statement below can be used to get the version of client or server.
|
||||
|
||||
```
|
||||
taos> SELECT CLIENT_VERSION();
|
||||
|
|
@ -197,7 +197,7 @@ taos> SELECT SERVER_VERSION();
|
|||
Query OK, 1 row(s) in set (0.000077s)
|
||||
```
|
||||
|
||||
Below statement is used to check the server status. One integer, like `1`, is returned if the server status is OK, otherwise an error code is returned. This is compatible with the status check for TDengine from connection pool or 3rd party tools, and can avoid the problem of losing the connection from a connection pool when using the wrong heartbeat checking SQL statement.
|
||||
The statement below is used to check the server status. An integer, like `1`, is returned if the server status is OK, otherwise an error code is returned. This is compatible with the status check for TDengine from connection pool or 3rd party tools, and can avoid the problem of losing the connection from a connection pool when using the wrong heartbeat checking SQL statement.
|
||||
|
||||
```
|
||||
taos> SELECT SERVER_STATUS();
|
||||
|
|
@ -284,7 +284,7 @@ taos> SELECT COUNT(tbname) FROM meters WHERE groupId > 2;
|
|||
Query OK, 1 row(s) in set (0.001091s)
|
||||
```
|
||||
|
||||
- Wildcard \* can be used to get all columns, or specific column names can be specified. Arithmetic operation can be performed on columns of number types, columns can be renamed in the result set.
|
||||
- Wildcard \* can be used to get all columns, or specific column names can be specified. Arithmetic operation can be performed on columns of numerical types, columns can be renamed in the result set.
|
||||
- Arithmetic operation on columns can't be used in where clause. For example, `where a*2>6;` is not allowed but `where a>6/2;` can be used instead for the same purpose.
|
||||
- Arithmetic operation on columns can't be used as the objectives of select statement. For example, `select min(2*a) from t;` is not allowed but `select 2*min(a) from t;` can be used instead.
|
||||
- Logical operation can be used in `WHERE` clause to filter numeric values, wildcard can be used to filter string values.
|
||||
|
|
@ -318,13 +318,13 @@ Logical operations in below table can be used in the `where` clause to filter th
|
|||
- Operator `like` is used together with wildcards to match strings
|
||||
- '%' matches 0 or any number of characters, '\_' matches any single ASCII character.
|
||||
- `\_` is used to match the \_ in the string.
|
||||
- The maximum length of wildcard string is 100 bytes from version 2.1.6.1 (before that the maximum length is 20 bytes). `maxWildCardsLength` in `taos.cfg` can be used to control this threshold. Too long wildcard string may slowdown the execution performance of `LIKE` operator.
|
||||
- The maximum length of wildcard string is 100 bytes from version 2.1.6.1 (before that the maximum length is 20 bytes). `maxWildCardsLength` in `taos.cfg` can be used to control this threshold. A very long wildcard string may slowdown the execution performance of `LIKE` operator.
|
||||
- `AND` keyword can be used to filter multiple columns simultaneously. AND/OR operation can be performed on single or multiple columns from version 2.3.0.0. However, before 2.3.0.0 `OR` can't be used on multiple columns.
|
||||
- For timestamp column, only one condition can be used; for other columns or tags, `OR` keyword can be used to combine multiple logical operators. For example, `((value > 20 AND value < 30) OR (value < 12))`.
|
||||
- From version 2.3.0.0, multiple conditions can be used on timestamp column, but the result set can only contain single time range.
|
||||
- From version 2.0.17.0, operator `BETWEEN AND` can be used in where clause, for example `WHERE col2 BETWEEN 1.5 AND 3.25` means the filter condition is equal to "1.5 ≤ col2 ≤ 3.25".
|
||||
- From version 2.1.4.0, operator `IN` can be used in the where clause. For example, `WHERE city IN ('California.SanFrancisco', 'California.SanDiego')`. For bool type, both `{true, false}` and `{0, 1}` are allowed, but integers other than 0 or 1 are not allowed. FLOAT and DOUBLE types are impacted by floating 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 the where clause with keyword `match` or `nmatch`, the regular expression is case insensitive.
|
||||
- From version 2.1.4.0, operator `IN` can be used in the where clause. For example, `WHERE city IN ('California.SanFrancisco', 'California.SanDiego')`. For bool type, both `{true, false}` and `{0, 1}` are allowed, but integers other than 0 or 1 are not allowed. FLOAT and DOUBLE types are impacted by floating point precision errors. Only values that match the condition within the tolerance will be selected. Non-primary key column of timestamp type can be used with `IN`.
|
||||
- From version 2.3.0.0, regular expression is supported in the where clause with keyword `match` or `nmatch`. The regular expression is case insensitive.
|
||||
|
||||
## Regular Expression
|
||||
|
||||
|
|
@ -364,7 +364,7 @@ FROM temp_STable t1, temp_STable t2
|
|||
WHERE t1.ts = t2.ts AND t1.deviceid = t2.deviceid AND t1.status=0;
|
||||
```
|
||||
|
||||
Similary, join operation can be performed on the result set of multiple sub queries.
|
||||
Similarly, join operations can be performed on the result set of multiple sub queries.
|
||||
|
||||
:::note
|
||||
Restrictions on join operation:
|
||||
|
|
@ -380,7 +380,7 @@ Restrictions on join operation:
|
|||
|
||||
## Nested Query
|
||||
|
||||
Nested query is also called sub query, that means in a single SQL statement the result of inner query can be used as the data source of the outer query.
|
||||
Nested query is also called sub query. This means that in a single SQL statement the result of inner query can be used as the data source of the outer query.
|
||||
|
||||
From 2.2.0.0, unassociated sub query can be used in the `FROM` clause. Unassociated means the sub query doesn't use the parameters in the parent query. More specifically, in the `tb_name_list` of `SELECT` statement, an independent SELECT statement can be used. So a complete nested query looks like:
|
||||
|
||||
|
|
@ -390,14 +390,14 @@ SELECT ... FROM (SELECT ... FROM ...) ...;
|
|||
|
||||
:::info
|
||||
|
||||
- Only one layer of nesting is allowed, that means no sub query is allowed in a sub query
|
||||
- The result set returned by the inner query will be used as a "virtual table" by the outer query, the "virtual table" can be renamed using `AS` keyword for easy reference in the outer query.
|
||||
- Only one layer of nesting is allowed, that means no sub query is allowed within a sub query
|
||||
- The result set returned by the inner query will be used as a "virtual table" by the outer query. The "virtual table" can be renamed using `AS` keyword for easy reference in the outer query.
|
||||
- Sub query is not allowed in continuous query.
|
||||
- JOIN operation is allowed between tables/STables inside both inner and outer queries. Join operation can be performed on the result set of the inner query.
|
||||
- UNION operation is not allowed in either inner query or outer query.
|
||||
- The functionalities that can be used in the inner query is same as non-nested query.
|
||||
- `ORDER BY` inside the inner query doesn't make any sense but will slow down the query performance significantly, so please avoid such usage.
|
||||
- Compared to the non-nested query, the functionalities that can be used in the outer query have such restrictions as:
|
||||
- The functions that can be used in the inner query are the same as those that can be used in a non-nested query.
|
||||
- `ORDER BY` inside the inner query is unnecessary and will slow down the query performance significantly. It is best to avoid the use of `ORDER BY` inside the inner query.
|
||||
- Compared to the non-nested query, the functionality that can be used in the outer query has the following restrictions:
|
||||
- Functions
|
||||
- If the result set returned by the inner query doesn't contain timestamp column, then functions relying on timestamp can't be used in the outer query, like `TOP`, `BOTTOM`, `FIRST`, `LAST`, `DIFF`.
|
||||
- Functions that need to scan the data twice can't be used in the outer query, like `STDDEV`, `PERCENTILE`.
|
||||
|
|
@ -442,8 +442,8 @@ The sum of col1 and col2 for rows later than 2018-06-01 08:00:00.000 and whose c
|
|||
SELECT (col1 + col2) AS 'complex' FROM tb1 WHERE ts > '2018-06-01 08:00:00.000' AND col2 > 1.2 LIMIT 10 OFFSET 5;
|
||||
```
|
||||
|
||||
The rows in the past 10 minutes and whose col2 is bigger than 3.14 are selected and output to the result file `/home/testoutpu.csv` with below SQL statement:
|
||||
The rows in the past 10 minutes and whose col2 is bigger than 3.14 are selected and output to the result file `/home/testoutput.csv` with below SQL statement:
|
||||
|
||||
```SQL
|
||||
SELECT COUNT(*) FROM tb1 WHERE ts >= NOW - 10m AND col2 > 3.14 >> /home/testoutpu.csv;
|
||||
SELECT COUNT(*) FROM tb1 WHERE ts >= NOW - 10m AND col2 > 3.14 >> /home/testoutput.csv;
|
||||
```
|
||||
|
|
|
|||
|
|
@ -22,8 +22,8 @@ SELECT COUNT([*|field_name]) FROM tb_name [WHERE clause];
|
|||
|
||||
**More explanation**:
|
||||
|
||||
- Wildcard (\*) can be used to represent all columns, it's used to get the number of all rows
|
||||
- The number of non-NULL values will be returned if this function is used on a specific column
|
||||
- Wildcard (\*) is used to represent all columns. The `COUNT` function is used to get the total number of all rows.
|
||||
- The number of non-NULL values will be returned if this function is used on a specific column.
|
||||
|
||||
**Examples**:
|
||||
|
||||
|
|
@ -87,7 +87,7 @@ SELECT TWA(field_name) FROM tb_name WHERE clause;
|
|||
|
||||
**More explanations**:
|
||||
|
||||
- From version 2.1.3.0, function TWA can be used on stable with `GROUP BY`, i.e. timelines generated by `GROUP BY tbname` on a STable.
|
||||
- Since version 2.1.3.0, function TWA can be used on stable with `GROUP BY`, i.e. timelines generated by `GROUP BY tbname` on a STable.
|
||||
|
||||
### IRATE
|
||||
|
||||
|
|
@ -105,7 +105,7 @@ SELECT IRATE(field_name) FROM tb_name WHERE clause;
|
|||
|
||||
**More explanations**:
|
||||
|
||||
- From version 2.1.3.0, function IRATE can be used on stble with `GROUP BY`, i.e. timelines generated by `GROUP BY tbname` on a STable.
|
||||
- Since version 2.1.3.0, function IRATE can be used on stble with `GROUP BY`, i.e. timelines generated by `GROUP BY tbname` on a STable.
|
||||
|
||||
### SUM
|
||||
|
||||
|
|
@ -149,7 +149,7 @@ SELECT STDDEV(field_name) FROM tb_name [WHERE clause];
|
|||
|
||||
**Applicable column types**: Data types except for timestamp, binary, nchar and bool
|
||||
|
||||
**Applicable table types**: table, STable (starting from version 2.0.15.1)
|
||||
**Applicable table types**: table, STable (since version 2.0.15.1)
|
||||
|
||||
**Examples**:
|
||||
|
||||
|
|
@ -193,13 +193,13 @@ SELECT MODE(field_name) FROM tb_name [WHERE clause];
|
|||
|
||||
**Description**:The value which has the highest frequency of occurrence. NULL is returned if there are multiple values which have highest frequency of occurrence. It can't be used on timestamp column or tags.
|
||||
|
||||
**Return value type**:Same as the data type of the column being operated
|
||||
**Return value type**:Same as the data type of the column being operated upon
|
||||
|
||||
**Applicable column types**:Data types except for timestamp
|
||||
|
||||
**More explanations**:Considering the number of returned result set is unpredictable, it's suggested to limit the number of unique values to 100,000, otherwise error will be returned.
|
||||
|
||||
**Applicable version**:From version 2.6.0.0
|
||||
**Applicable version**:Since version 2.6.0.0
|
||||
|
||||
**Examples**:
|
||||
|
||||
|
|
@ -234,7 +234,7 @@ SELECT HYPERLOGLOG(field_name) FROM { tb_name | stb_name } [WHERE clause];
|
|||
|
||||
**More explanations**: The benefit of using hyperloglog algorithm is that the memory usage is under control when the data volume is huge. However, when the data volume is very small, the result may be not accurate, it's recommented to use `select count(data) from (select unique(col) as data from table)` in this case.
|
||||
|
||||
**Applicable versions**:From version 2.6.0.0
|
||||
**Applicable versions**:Since version 2.6.0.0
|
||||
|
||||
**Examples**:
|
||||
|
||||
|
|
@ -271,7 +271,7 @@ SELECT MIN(field_name) FROM {tb_name | stb_name} [WHERE clause];
|
|||
|
||||
**Description**: The minimum value of a specific column in a table or STable
|
||||
|
||||
**Return value type**: Same as the data type of the column being operated
|
||||
**Return value type**: Same as the data type of the column being operated upon
|
||||
|
||||
**Applicable column types**: Data types except for timestamp, binary, nchar and bool
|
||||
|
||||
|
|
@ -301,7 +301,7 @@ SELECT MAX(field_name) FROM { tb_name | stb_name } [WHERE clause];
|
|||
|
||||
**Description**: The maximum value of a specific column of a table or STable
|
||||
|
||||
**Return value type**: Same as the data type of the column being operated
|
||||
**Return value type**: Same as the data type of the column being operated upon
|
||||
|
||||
**Applicable column types**: Data types except for timestamp, binary, nchar and bool
|
||||
|
||||
|
|
@ -331,7 +331,7 @@ SELECT FIRST(field_name) FROM { tb_name | stb_name } [WHERE clause];
|
|||
|
||||
**Description**: The first non-null value of a specific column in a table or STable
|
||||
|
||||
**Return value type**: Same as the column being operated
|
||||
**Return value type**: Same as the column being operated upon
|
||||
|
||||
**Applicable column types**: Any data type
|
||||
|
||||
|
|
@ -341,7 +341,7 @@ SELECT FIRST(field_name) FROM { tb_name | stb_name } [WHERE clause];
|
|||
|
||||
- FIRST(\*) can be used to get the first non-null value of all columns
|
||||
- NULL will be returned if all the values of the specified column are all NULL
|
||||
- No result will NOT be returned if all the columns in the result set are all NULL
|
||||
- A result will NOT be returned if all the columns in the result set are all NULL
|
||||
|
||||
**Examples**:
|
||||
|
||||
|
|
@ -367,7 +367,7 @@ SELECT LAST(field_name) FROM { tb_name | stb_name } [WHERE clause];
|
|||
|
||||
**Description**: The last non-NULL value of a specific column in a table or STable
|
||||
|
||||
**Return value type**: Same as the column being operated
|
||||
**Return value type**: Same as the column being operated upon
|
||||
|
||||
**Applicable column types**: Any data type
|
||||
|
||||
|
|
@ -403,7 +403,7 @@ SELECT TOP(field_name, K) FROM { tb_name | stb_name } [WHERE clause];
|
|||
|
||||
**Description**: The greatest _k_ values of a specific column in a table or STable. If a value has multiple occurrences in the column but counting all of them in will exceed the upper limit _k_, then a part of them will be returned randomly.
|
||||
|
||||
**Return value type**: Same as the column being operated
|
||||
**Return value type**: Same as the column being operated upon
|
||||
|
||||
**Applicable column types**: Data types except for timestamp, binary, nchar and bool
|
||||
|
||||
|
|
@ -442,7 +442,7 @@ SELECT BOTTOM(field_name, K) FROM { tb_name | stb_name } [WHERE clause];
|
|||
|
||||
**Description**: The least _k_ values of a specific column in a table or STable. If a value has multiple occurrences in the column but counting all of them in will exceed the upper limit _k_, then a part of them will be returned randomly.
|
||||
|
||||
**Return value type**: Same as the column being operated
|
||||
**Return value type**: Same as the column being operated upon
|
||||
|
||||
**Applicable column types**: Data types except for timestamp, binary, nchar and bool
|
||||
|
||||
|
|
@ -549,7 +549,7 @@ SELECT LAST_ROW(field_name) FROM { tb_name | stb_name };
|
|||
|
||||
**Description**: The last row of a table or STable
|
||||
|
||||
**Return value type**: Same as the column being operated
|
||||
**Return value type**: Same as the column being operated upon
|
||||
|
||||
**Applicable column types**: Any data type
|
||||
|
||||
|
|
@ -576,7 +576,7 @@ SELECT LAST_ROW(field_name) FROM { tb_name | stb_name };
|
|||
Query OK, 1 row(s) in set (0.001042s)
|
||||
```
|
||||
|
||||
### INTERP [From version 2.3.1]
|
||||
### INTERP [Since version 2.3.1]
|
||||
|
||||
```
|
||||
SELECT INTERP(field_name) FROM { tb_name | stb_name } [WHERE where_condition] [ RANGE(timestamp1,timestamp2) ] [EVERY(interval)] [FILL ({ VALUE | PREV | NULL | LINEAR | NEXT})];
|
||||
|
|
@ -584,7 +584,7 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } [WHERE where_condition] [
|
|||
|
||||
**Description**: The value that matches the specified timestamp range is returned, if existing; or an interpolation value is returned.
|
||||
|
||||
**Return value type**: Same as the column being operated
|
||||
**Return value type**: Same as the column being operated upon
|
||||
|
||||
**Applicable column types**: Numeric data types
|
||||
|
||||
|
|
@ -593,7 +593,7 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } [WHERE where_condition] [
|
|||
**More explanations**
|
||||
|
||||
- `INTERP` is used to get the value that matches the specified time slice from a column. If no such value exists an interpolation value will be returned based on `FILL` parameter.
|
||||
- The input data of `INTERP` is the value of the specified column, `where` can be used to filter the original data. If no `where` condition is specified then all original data is the input.
|
||||
- The input data of `INTERP` is the value of the specified column and a `where` clause can be used to filter the original data. If no `where` condition is specified then all original data is the input.
|
||||
- The output time range of `INTERP` is specified by `RANGE(timestamp1,timestamp2)` parameter, with timestamp1<=timestamp2. timestamp1 is the starting point of the output time range and must be specified. timestamp2 is the ending point of the output time range and must be specified. If `RANGE` is not specified, then the timestamp of the first row that matches the filter condition is treated as timestamp1, the timestamp of the last row that matches the filter condition is treated as timestamp2.
|
||||
- The number of rows in the result set of `INTERP` is determined by the parameter `EVERY`. Starting from timestamp1, one interpolation is performed for every time interval specified `EVERY` parameter. If `EVERY` parameter is not used, the time windows will be considered as no ending timestamp, i.e. there is only one time window from timestamp1.
|
||||
- Interpolation is performed based on `FILL` parameter. No interpolation is performed if `FILL` is not used, that means either the original data that matches is returned or nothing is returned.
|
||||
|
|
@ -632,7 +632,7 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } [WHERE where_condition] [
|
|||
taos> SELECT INTERP(current) FROM t1 where ts >= '2017-07-14 17:00:00' and ts <= '2017-07-14 20:00:00' RANGE('2017-7-14 18:00:00','2017-7-14 19:00:00') EVERY(5s) FILL(LINEAR);
|
||||
```
|
||||
|
||||
### INTERP [Prior to version 2.3.1]
|
||||
### INTERP [Since version 2.0.15.0]
|
||||
|
||||
```
|
||||
SELECT INTERP(field_name) FROM { tb_name | stb_name } WHERE ts='timestamp' [FILL ({ VALUE | PREV | NULL | LINEAR | NEXT})];
|
||||
|
|
@ -640,7 +640,7 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } WHERE ts='timestamp' [FILL
|
|||
|
||||
**Description**: The value of a specific column that matches the specified time slice
|
||||
|
||||
**Return value type**: Same as the column being operated
|
||||
**Return value type**: Same as the column being operated upon
|
||||
|
||||
**Applicable column types**: Numeric data type
|
||||
|
||||
|
|
@ -648,7 +648,6 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } WHERE ts='timestamp' [FILL
|
|||
|
||||
**More explanations**:
|
||||
|
||||
- It can be used from version 2.0.15.0
|
||||
- Time slice must be specified. If there is no data matching the specified time slice, interpolation is performed based on `FILL` parameter. Conditions such as tags or `tbname` can be used `Where` clause can be used to filter data.
|
||||
- The timestamp specified must be within the time range of the data rows of the table or STable. If it is beyond the valid time range, nothing is returned even with `FILL` parameter.
|
||||
- `INTERP` can be used to query only single time point once. `INTERP` can be used with `EVERY` to get the interpolation value every time interval.
|
||||
|
|
@ -696,11 +695,11 @@ SELECT TAIL(field_name, k, offset_val) FROM {tb_name | stb_name} [WHERE clause];
|
|||
|
||||
**Parameter value range**: k: [1,100] offset_val: [0,100]
|
||||
|
||||
**Return value type**: Same as the column being operated
|
||||
**Return value type**: Same as the column being operated upon
|
||||
|
||||
**Applicable column types**: Any data type except form timestamp, i.e. the primary key
|
||||
|
||||
**Applicable versions**: From version 2.6.0.0
|
||||
**Applicable versions**: Since version 2.6.0.0
|
||||
|
||||
**Examples**:
|
||||
|
||||
|
|
@ -732,11 +731,11 @@ SELECT UNIQUE(field_name) FROM {tb_name | stb_name} [WHERE clause];
|
|||
|
||||
**Description**: The values that occur the first time in the specified column. The effect is similar to `distinct` keyword, but it can also be used to match tags or timestamp.
|
||||
|
||||
**Return value type**: Same as the column or tag being operated
|
||||
**Return value type**: Same as the column or tag being operated upon
|
||||
|
||||
**Applicable column types**: Any data types except for timestamp
|
||||
|
||||
**Applicable versions**: From version 2.6.0.0
|
||||
**Applicable versions**: Since version 2.6.0.0
|
||||
|
||||
**More explanations**:
|
||||
|
||||
|
|
@ -780,7 +779,7 @@ SELECT {DIFF(field_name, ignore_negative) | DIFF(field_name)} FROM tb_name [WHER
|
|||
|
||||
**Description**: The different of each row with its previous row for a specific column. `ignore_negative` can be specified as 0 or 1, the default value is 1 if it's not specified. `1` means negative values are ignored.
|
||||
|
||||
**Return value type**: Same as the column being operated
|
||||
**Return value type**: Same as the column being operated upon
|
||||
|
||||
**Applicable column types**: Data types except for timestamp, binary, nchar and bool
|
||||
|
||||
|
|
@ -789,8 +788,8 @@ SELECT {DIFF(field_name, ignore_negative) | DIFF(field_name)} FROM tb_name [WHER
|
|||
**More explanations**:
|
||||
|
||||
- The number of result rows is the number of rows subtracted by one, no output for the first row
|
||||
- From version 2.1.30, `DIFF` can be used on STable with `GROUP by tbname`
|
||||
- From version 2.6.0, `ignore_negative` parameter is supported
|
||||
- Since version 2.1.30, `DIFF` can be used on STable with `GROUP by tbname`
|
||||
- Since version 2.6.0, `ignore_negative` parameter is supported
|
||||
|
||||
**Examples**:
|
||||
|
||||
|
|
@ -874,7 +873,7 @@ Query OK, 1 row(s) in set (0.000836s)
|
|||
SELECT CEIL(field_name) FROM { tb_name | stb_name } [WHERE clause];
|
||||
```
|
||||
|
||||
**Description**: The round up value of a specific column
|
||||
**Description**: The rounded up value of a specific column
|
||||
|
||||
**Return value type**: Same as the column being used
|
||||
|
||||
|
|
@ -896,9 +895,9 @@ SELECT CEIL(field_name) FROM { tb_name | stb_name } [WHERE clause];
|
|||
SELECT FLOOR(field_name) FROM { tb_name | stb_name } [WHERE clause];
|
||||
```
|
||||
|
||||
**Description**: The round down value of a specific column
|
||||
**Description**: The rounded down value of a specific column
|
||||
|
||||
**More explanations**: The restrictions are same as `CEIL` function.
|
||||
**More explanations**: The restrictions are same as those of the `CEIL` function.
|
||||
|
||||
### ROUND
|
||||
|
||||
|
|
@ -906,7 +905,7 @@ SELECT FLOOR(field_name) FROM { tb_name | stb_name } [WHERE clause];
|
|||
SELECT ROUND(field_name) FROM { tb_name | stb_name } [WHERE clause];
|
||||
```
|
||||
|
||||
**Description**: The round value of a specific column.
|
||||
**Description**: The rounded value of a specific column.
|
||||
|
||||
**More explanations**: The restrictions are same as `CEIL` function.
|
||||
|
||||
|
|
@ -933,7 +932,7 @@ SELECT ROUND(field_name) FROM { tb_name | stb_name } [WHERE clause];
|
|||
- Can only be used with aggregate functions
|
||||
- `Group by tbname` must be used together on a STable to force the result on a single timeline
|
||||
|
||||
**Applicable versions**: From 2.3.0.x
|
||||
**Applicable versions**: Since 2.3.0.x
|
||||
|
||||
### MAVG
|
||||
|
||||
|
|
@ -958,7 +957,7 @@ SELECT ROUND(field_name) FROM { tb_name | stb_name } [WHERE clause];
|
|||
- Can't be used with aggregate functions.
|
||||
- Must be used with `GROUP BY tbname` when it's used on a STable to force the result on each single timeline.
|
||||
|
||||
**Applicable versions**: From 2.3.0.x
|
||||
**Applicable versions**: Since 2.3.0.x
|
||||
|
||||
### SAMPLE
|
||||
|
||||
|
|
@ -981,7 +980,7 @@ SELECT ROUND(field_name) FROM { tb_name | stb_name } [WHERE clause];
|
|||
- Arithmetic operation can't be operated on the result of `SAMPLE` function
|
||||
- Must be used with `Group by tbname` when it's used on a STable to force the result on each single timeline
|
||||
|
||||
**Applicable versions**: From 2.3.0.x
|
||||
**Applicable versions**: Since 2.3.0.x
|
||||
|
||||
### ASIN
|
||||
|
||||
|
|
@ -1460,8 +1459,8 @@ SELECT field_name [+|-|*|/|%][Value|field_name] FROM { tb_name | stb_name } [WH
|
|||
|
||||
**More explanations**:
|
||||
|
||||
- Arithmetic operations can be performed on two or more columns, `()` can be used to control the precedence
|
||||
- NULL doesn't participate the operation, if one of the operands is NULL then result is NULL
|
||||
- Arithmetic operations can be performed on two or more columns, Parentheses `()` can be used to control the order of precedence.
|
||||
- NULL doesn't participate in the operation i.e. if one of the operands is NULL then result is NULL.
|
||||
|
||||
**Examples**:
|
||||
|
||||
|
|
@ -1586,7 +1585,7 @@ Query OK, 6 row(s) in set (0.002613s)
|
|||
|
||||
## Time Functions
|
||||
|
||||
From version 2.6.0.0, below time related functions can be used in TDengine.
|
||||
Since version 2.6.0.0, below time related functions can be used in TDengine.
|
||||
|
||||
### NOW
|
||||
|
||||
|
|
@ -1840,6 +1839,8 @@ SELECT TIMEDIFF(ts_val1 | datetime_string1 | ts_col1, ts_val2 | datetime_string2
|
|||
1u(microsecond),1a(millisecond),1s(second),1m(minute),1h(hour),1d(day).
|
||||
- The precision of the returned timestamp is same as the precision set for the current data base in use
|
||||
|
||||
**Applicable versions**:Since version 2.6.0.0
|
||||
|
||||
**Examples**:
|
||||
|
||||
```sql
|
||||
|
|
|
|||
|
|
@ -3,36 +3,36 @@ sidebar_label: Interval
|
|||
title: Aggregate by Time Window
|
||||
---
|
||||
|
||||
Aggregate by time window is supported in TDengine. For example, each temperature sensor reports the temperature every second, the average temperature every 10 minutes can be retrieved by query with time window.
|
||||
Window related clauses are used to divide the data set to be queried into subsets and then aggregate. There are three kinds of windows, time window, status window, and session window. There are two kinds of time windows, sliding window and flip time window.
|
||||
Aggregation by time window is supported in TDengine. For example, in the case where temperature sensors report the temperature every seconds, the average temperature for every 10 minutes can be retrieved by performing a query with a time window.
|
||||
Window related clauses are used to divide the data set to be queried into subsets and then aggregation is performed across the subsets. There are three kinds of windows: time window, status window, and session window. There are two kinds of time windows: sliding window and flip time/tumbling window.
|
||||
|
||||
## Time Window
|
||||
|
||||
`INTERVAL` clause is used to generate time windows of the same time interval, `SLIDING` is used to specify the time step for which the time window moves forward. The query is performed on one time window each time, and the time window moves forward with time. When defining continuous query both the size of time window and the step of forward sliding time need to be specified. As shown in the figure blow, [t0s, t0e] ，[t1s , t1e]， [t2s, t2e] are respectively the time ranges of three time windows on which continuous queries are executed. The time step for which time window moves forward is marked by `sliding time`. Query, filter and aggregate operations are executed on each time window respectively. When the time step specified by `SLIDING` is same as the time interval specified by `INTERVAL`, the sliding time window is actually a flip time window.
|
||||
The `INTERVAL` clause is used to generate time windows of the same time interval. The `SLIDING` parameter is used to specify the time step for which the time window moves forward. The query is performed on one time window each time, and the time window moves forward with time. When defining a continuous query, both the size of the time window and the step of forward sliding time need to be specified. As shown in the figure blow, [t0s, t0e] ，[t1s , t1e]， [t2s, t2e] are respectively the time ranges of three time windows on which continuous queries are executed. The time step for which time window moves forward is marked by `sliding time`. Query, filter and aggregate operations are executed on each time window respectively. When the time step specified by `SLIDING` is same as the time interval specified by `INTERVAL`, the sliding time window is actually a flip time/tumbling window.
|
||||
|
||||
|
||||
|
||||
`INTERVAL` and `SLIDING` should be used with aggregate functions and select functions. Below SQL statement is illegal because no aggregate or selection function is used with `INTERVAL`.
|
||||
`INTERVAL` and `SLIDING` should be used with aggregate functions and select functions. The SQL statement below is illegal because no aggregate or selection function is used with `INTERVAL`.
|
||||
|
||||
```
|
||||
SELECT * FROM temp_tb_1 INTERVAL(1m);
|
||||
```
|
||||
|
||||
The time step specified by `SLIDING` can't exceed the time interval specified by `INTERVAL`. Below SQL statement is illegal because the time length specified by `SLIDING` exceeds that specified by `INTERVAL`.
|
||||
The time step specified by `SLIDING` cannot exceed the time interval specified by `INTERVAL`. The SQL statement below is illegal because the time length specified by `SLIDING` exceeds that specified by `INTERVAL`.
|
||||
|
||||
```
|
||||
SELECT COUNT(*) FROM temp_tb_1 INTERVAL(1m) SLIDING(2m);
|
||||
```
|
||||
|
||||
When the time length specified by `SLIDING` is the same as that specified by `INTERVAL`, the sliding window is actually a flip window. The minimum time range specified by `INTERVAL` is 10 milliseconds (10a) prior to version 2.1.5.0. From version 2.1.5.0, the minimum time range by `INTERVAL` can be 1 microsecond (1u). However, if the DB precision is millisecond, the minimum time range is 1 millisecond (1a). Please note that the `timezone` parameter should be configured to be the same value in the `taos.cfg` configuration file on client side and server side.
|
||||
When the time length specified by `SLIDING` is the same as that specified by `INTERVAL`, the sliding window is actually a flip/tumbling window. The minimum time range specified by `INTERVAL` is 10 milliseconds (10a) prior to version 2.1.5.0. Since version 2.1.5.0, the minimum time range by `INTERVAL` can be 1 microsecond (1u). However, if the DB precision is millisecond, the minimum time range is 1 millisecond (1a). Please note that the `timezone` parameter should be configured to be the same value in the `taos.cfg` configuration file on client side and server side.
|
||||
|
||||
## Status Window
|
||||
|
||||
In case of using integer, bool, or string to represent the device status at a moment, the continuous rows with same status belong to same status window. Once the status changes, the status window closes. As shown in the following figure, there are two status windows according to status, [2019-04-28 14:22:07，2019-04-28 14:22:10] and [2019-04-28 14:22:11，2019-04-28 14:22:12]. Status window is not applicable to STable for now.
|
||||
In case of using integer, bool, or string to represent the status of a device at any given moment, continuous rows with the same status belong to a status window. Once the status changes, the status window closes. As shown in the following figure, there are two status windows according to status, [2019-04-28 14:22:07，2019-04-28 14:22:10] and [2019-04-28 14:22:11，2019-04-28 14:22:12]. Status window is not applicable to STable for now.
|
||||
|
||||
|
||||
|
||||
`STATE_WINDOW` is used to specify the column based on which to define status window, for example：
|
||||
`STATE_WINDOW` is used to specify the column on which the status window will be based. For example：
|
||||
|
||||
```
|
||||
SELECT COUNT(*), FIRST(ts), status FROM temp_tb_1 STATE_WINDOW(status);
|
||||
|
|
@ -44,7 +44,7 @@ SELECT COUNT(*), FIRST(ts), status FROM temp_tb_1 STATE_WINDOW(status);
|
|||
SELECT COUNT(*), FIRST(ts) FROM temp_tb_1 SESSION(ts, tol_val);
|
||||
```
|
||||
|
||||
The primary key, i.e. timestamp, is used to determine which session window the row belongs to. If the time interval between two adjacent rows is within the time range specified by `tol_val`, they belong to the same session window; otherwise they belong to two different time windows. As shown in the figure below, if the limit of time interval for the session window is specified as 12 seconds, then the 6 rows in the figure constitutes 2 time windows, [2019-04-28 14:22:10，2019-04-28 14:22:30] and [2019-04-28 14:23:10，2019-04-28 14:23:30], because the time difference between 2019-04-28 14:22:30 and 2019-04-28 14:23:10 is 40 seconds, which exceeds the time interval limit of 12 seconds.
|
||||
The primary key, i.e. timestamp, is used to determine which session window a row belongs to. If the time interval between two adjacent rows is within the time range specified by `tol_val`, they belong to the same session window; otherwise they belong to two different session windows. As shown in the figure below, if the limit of time interval for the session window is specified as 12 seconds, then the 6 rows in the figure constitutes 2 time windows, [2019-04-28 14:22:10，2019-04-28 14:22:30] and [2019-04-28 14:23:10，2019-04-28 14:23:30], because the time difference between 2019-04-28 14:22:30 and 2019-04-28 14:23:10 is 40 seconds, which exceeds the time interval limit of 12 seconds.
|
||||
|
||||
|
||||
|
||||
|
|
@ -73,7 +73,7 @@ SELECT function_list FROM stb_name
|
|||
|
||||
### Restrictions
|
||||
|
||||
- Aggregate functions and select functions can be used in `function_list`, with each function having only one output, for example COUNT, AVG, SUM, STDDEV, LEASTSQUARES, PERCENTILE, MIN, MAX, FIRST, LAST. Functions having multiple output can't be used, for example DIFF or arithmetic operations.
|
||||
- Aggregate functions and select functions can be used in `function_list`, with each function having only one output. For example COUNT, AVG, SUM, STDDEV, LEASTSQUARES, PERCENTILE, MIN, MAX, FIRST, LAST. Functions having multiple outputs, such as DIFF or arithmetic operations can't be used.
|
||||
- `LAST_ROW` can't be used together with window aggregate.
|
||||
- Scalar functions, like CEIL/FLOOR, can't be used with window aggregate.
|
||||
- `WHERE` clause can be used to specify the starting and ending time and other filter conditions
|
||||
|
|
@ -87,8 +87,8 @@ SELECT function_list FROM stb_name
|
|||
|
||||
:::info
|
||||
|
||||
1. Huge volume of interpolation output may be returned using `FILL`, so it's recommended to specify the time range when using `FILL`. The maximum interpolation values that can be returned in single query is 10,000,000.
|
||||
2. The result set is in ascending order of timestamp in aggregate by time window aggregate.
|
||||
1. A huge volume of interpolation output may be returned using `FILL`, so it's recommended to specify the time range when using `FILL`. The maximum number of interpolation values that can be returned in a single query is 10,000,000.
|
||||
2. The result set is in ascending order of timestamp when you aggregate by time window.
|
||||
3. If aggregate by window is used on STable, the aggregate function is performed on all the rows matching the filter conditions. If `GROUP BY` is not used in the query, the result set will be returned in ascending order of timestamp; otherwise the result set is not exactly in the order of ascending timestamp in each group.
|
||||
|
||||
:::
|
||||
|
|
@ -97,13 +97,13 @@ Aggregate by time window is also used in continuous query, please refer to [Cont
|
|||
|
||||
## Examples
|
||||
|
||||
The table of intelligent meters can be created by the SQL statement below：
|
||||
A table of intelligent meters can be created by the SQL statement below：
|
||||
|
||||
```sql
|
||||
CREATE TABLE meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT);
|
||||
```
|
||||
|
||||
The average current, maximum current and median of current in every 10 minutes for the past 24 hours can be calculated using the below SQL statement, with missing values filled with the previous non-NULL values.
|
||||
The average current, maximum current and median of current in every 10 minutes for the past 24 hours can be calculated using the SQL statement below, with missing values filled with the previous non-NULL values.
|
||||
|
||||
```
|
||||
SELECT AVG(current), MAX(current), APERCENTILE(current, 50) FROM meters
|
||||
|
|
|
|||
|
|
@ -4,8 +4,8 @@ title: Limits & Restrictions
|
|||
|
||||
## Naming Rules
|
||||
|
||||
1. Only English characters, digits and underscore are allowed
|
||||
2. Can't start with a digit
|
||||
1. Only characters from the English alphabet, digits and underscore are allowed
|
||||
2. Names cannot start with a digit
|
||||
3. Case insensitive without escape character "\`"
|
||||
4. Identifier with escape character "\`"
|
||||
To support more flexible table or column names, a new escape character "\`" is introduced. For more details please refer to [escape](/taos-sql/escape).
|
||||
|
|
@ -16,38 +16,38 @@ The legal character set is `[a-zA-Z0-9!?$%^&*()_–+={[}]:;@~#|<,>.?/]`.
|
|||
|
||||
## General Limits
|
||||
|
||||
- Maximum length of database name is 32 bytes
|
||||
- Maximum length of table name is 192 bytes, excluding the database name prefix and the separator
|
||||
- Maximum length of each data row is 48K bytes from version 2.1.7.0 , before which the limit is 16K bytes. Please note that the upper limit includes the extra 2 bytes consumed by each column of BINARY/NCHAR type.
|
||||
- Maximum of column name is 64.
|
||||
- Maximum length of database name is 32 bytes.
|
||||
- Maximum length of table name is 192 bytes, excluding the database name prefix and the separator.
|
||||
- Maximum length of each data row is 48K bytes since version 2.1.7.0 , before which the limit was 16K bytes. Please note that the upper limit includes the extra 2 bytes consumed by each column of BINARY/NCHAR type.
|
||||
- Maximum length of column name is 64.
|
||||
- Maximum number of columns is 4096. There must be at least 2 columns, and the first column must be timestamp.
|
||||
- Maximum length of tag name is 64.
|
||||
- Maximum number of tags is 128. There must be at least 1 tag. The total length of tag values should not exceed 16K bytes.
|
||||
- Maximum length of singe SQL statement is 1048576, i.e. 1 MB bytes. It can be configured in the parameter `maxSQLLength` in the client side, the applicable range is [65480, 1048576].
|
||||
- At most 4096 columns (or 1024 prior to 2.1.7.0) can be returned by `SELECT`, functions in the query statement may constitute columns. Error will be returned if the limit is exceeded.
|
||||
- Maximum numbers of databases, STables, tables are only depending on the system resources.
|
||||
- Maximum length of singe SQL statement is 1048576, i.e. 1 MB. It can be configured in the parameter `maxSQLLength` in the client side, the applicable range is [65480, 1048576].
|
||||
- At most 4096 columns (or 1024 prior to 2.1.7.0) can be returned by `SELECT`. Functions in the query statement constitute columns. An error is returned if the limit is exceeded.
|
||||
- Maximum numbers of databases, STables, tables are dependent only on the system resources.
|
||||
- Maximum of database name is 32 bytes, and it can't include "." or special characters.
|
||||
- Maximum replica number of database is 3
|
||||
- Maximum length of user name is 23 bytes
|
||||
- Maximum length of password is 15 bytes
|
||||
- Maximum number of rows depends on the storage space only.
|
||||
- Maximum number of tables depends on the number of nodes only.
|
||||
- Maximum number of databases depends on the number of nodes only.
|
||||
- Maximum number of vnodes for single database is 64.
|
||||
- Maximum number of replicas for a database is 3.
|
||||
- Maximum length of user name is 23 bytes.
|
||||
- Maximum length of password is 15 bytes.
|
||||
- Maximum number of rows depends only on the storage space.
|
||||
- Maximum number of tables depends only on the number of nodes.
|
||||
- Maximum number of databases depends only on the number of nodes.
|
||||
- Maximum number of vnodes for a single database is 64.
|
||||
|
||||
## Restrictions of `GROUP BY`
|
||||
|
||||
`GROUP BY` can be performed on tags and `TBNAME`. It can be performed on data columns too, with one restriction that only one column and the number of unique values on that column is lower than 100,000. Please note that `GROUP BY` can't be performed on float or double types.
|
||||
`GROUP BY` can be performed on tags and `TBNAME`. It can be performed on data columns too, with the only restriction being it can only be performed on one data column and the number of unique values in that column is lower than 100,000. Please note that `GROUP BY` cannot be performed on float or double types.
|
||||
|
||||
## Restrictions of `IS NOT NULL`
|
||||
|
||||
`IS NOT NULL` can be used on any data type of columns. The non-empty string evaluation expression, i.e. `<\>""` can only be used on non-numeric data types.
|
||||
`IS NOT NULL` can be used on any data type of columns. The non-empty string evaluation expression, i.e. `< > ""` can only be used on non-numeric data types.
|
||||
|
||||
## Restrictions of `ORDER BY`
|
||||
|
||||
- Only one `order by` is allowed for normal table and subtable.
|
||||
- At most two `order by` are allowed for STable, and the second one must be `ts`.
|
||||
- `order by tag` must be used with `group by tag` on same tag, this rule is also applicable to `tbname`.
|
||||
- `order by tag` must be used with `group by tag` on same tag. This rule is also applicable to `tbname`.
|
||||
- `order by column` must be used with `group by column` or `top/bottom` on same column. This rule is applicable to table and STable.
|
||||
- `order by ts` is applicable to table and STable.
|
||||
- If `order by ts` is used with `group by`, the result set is sorted using `ts` in each group.
|
||||
|
|
@ -56,7 +56,7 @@ The legal character set is `[a-zA-Z0-9!?$%^&*()_–+={[}]:;@~#|<,>.?/]`.
|
|||
|
||||
### Name Restrictions of Table/Column
|
||||
|
||||
The name of a table or column can only be composed of ASCII characters, digits and underscore, while it can't start with a digit. The maximum length is 192 bytes. Names are case insensitive. The name mentioned in this rule doesn't include the database name prefix and the separator.
|
||||
The name of a table or column can only be composed of ASCII characters, digits and underscore and it cannot start with a digit. The maximum length is 192 bytes. Names are case insensitive. The name mentioned in this rule doesn't include the database name prefix and the separator.
|
||||
|
||||
### Name Restrictions After Escaping
|
||||
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ title: JSON Type
|
|||
|
||||
## Syntax
|
||||
|
||||
1. Tag of JSON type
|
||||
1. Tag of type JSON
|
||||
|
||||
```sql
|
||||
create STable s1 (ts timestamp, v1 int) tags (info json);
|
||||
|
|
@ -12,7 +12,7 @@ title: JSON Type
|
|||
create table s1_1 using s1 tags ('{"k1": "v1"}');
|
||||
```
|
||||
|
||||
2. -> Operator of JSON
|
||||
2. "->" Operator of JSON
|
||||
|
||||
```sql
|
||||
select * from s1 where info->'k1' = 'v1';
|
||||
|
|
@ -20,7 +20,7 @@ title: JSON Type
|
|||
select info->'k1' from s1;
|
||||
```
|
||||
|
||||
3. contains Operator of JSON
|
||||
3. "contains" Operator of JSON
|
||||
|
||||
```sql
|
||||
select * from s1 where info contains 'k2';
|
||||
|
|
@ -30,7 +30,7 @@ title: JSON Type
|
|||
|
||||
## Applicable Operations
|
||||
|
||||
1. When JSON data type is used in `where`, `match/nmatch/between and/like/and/or/is null/is no null` can be used but `in` can't be used.
|
||||
1. When a JSON data type is used in `where`, `match/nmatch/between and/like/and/or/is null/is no null` can be used but `in` can't be used.
|
||||
|
||||
```sql
|
||||
select * from s1 where info->'k1' match 'v*';
|
||||
|
|
@ -42,9 +42,9 @@ title: JSON Type
|
|||
select * from s1 where info->'k1' is not null;
|
||||
```
|
||||
|
||||
2. Tag of JSON type can be used in `group by`, `order by`, `join`, `union all` and sub query, for example `group by json->'key'`
|
||||
2. A tag of JSON type can be used in `group by`, `order by`, `join`, `union all` and sub query; for example `group by json->'key'`
|
||||
|
||||
3. `Distinct` can be used with tag of JSON type
|
||||
3. `Distinct` can be used with a tag of type JSON
|
||||
|
||||
```sql
|
||||
select distinct info->'k1' from s1;
|
||||
|
|
@ -52,9 +52,9 @@ title: JSON Type
|
|||
|
||||
4. Tag Operations
|
||||
|
||||
The value of JSON tag can be altered. Please note that the full JSON will be overriden when doing this.
|
||||
The value of a JSON tag can be altered. Please note that the full JSON will be overriden when doing this.
|
||||
|
||||
The name of JSON tag can be altered. A tag of JSON type can't be added or removed. The column length of a JSON tag can't be changed.
|
||||
The name of a JSON tag can be altered. A tag of JSON type can't be added or removed. The column length of a JSON tag can't be changed.
|
||||
|
||||
## Other Restrictions
|
||||
|
||||
|
|
@ -64,17 +64,17 @@ title: JSON Type
|
|||
|
||||
- JSON format：
|
||||
|
||||
- The input string for JSON can be empty, i.e. "", "\t", or NULL, but can't be non-NULL string, bool or array.
|
||||
- object can be {}, and the whole JSON is empty if so. Key can be "", and it's ignored if so.
|
||||
- value can be int, double, string, boll or NULL, can't be array. Nesting is not allowed, that means value can't be another JSON.
|
||||
- The input string for JSON can be empty, i.e. "", "\t", or NULL, but it can't be non-NULL string, bool or array.
|
||||
- object can be {}, and the entire JSON is empty if so. Key can be "", and it's ignored if so.
|
||||
- value can be int, double, string, bool or NULL, and it can't be an array. Nesting is not allowed which means that the value of a key can't be JSON.
|
||||
- If one key occurs twice in JSON, only the first one is valid.
|
||||
- Escape characters are not allowed in JSON.
|
||||
|
||||
- NULL is returned if querying a key that doesn't exist in JSON.
|
||||
- NULL is returned when querying a key that doesn't exist in JSON.
|
||||
|
||||
- If a tag of JSON is the result of inner query, it can't be parsed and queried in the outer query.
|
||||
|
||||
For example, the below SQL statements are not supported.
|
||||
For example, the SQL statements below are not supported.
|
||||
|
||||
```sql;
|
||||
select jtag->'key' from (select jtag from STable);
|
||||
|
|
|
|||
|
|
@ -3,11 +3,9 @@ title: TDengine SQL
|
|||
description: "The syntax supported by TDengine SQL "
|
||||
---
|
||||
|
||||
This section explains the syntax to operating databases, tables, STables, inserting data, selecting data, functions and some tips that can be used in TDengine SQL. It would be easier to understand with some fundamental knowledge of SQL.
|
||||
This section explains the syntax of SQL to perform operations on databases, tables and STables, insert data, select data and use functions. We also provide some tips that can be used in TDengine SQL. If you have previous experience with SQL this section will be fairly easy to understand. If you do not have previous experience with SQL, you'll come to appreciate the simplicity and power of SQL.
|
||||
|
||||
TDengine SQL is the major interface for users to write data into or query from TDengine. For users to easily use, syntax similar to standard SQL is provided. However, please note that TDengine SQL is not standard SQL. For instance, TDengine doesn't provide the functionality of deleting time series data, thus corresponding statements are not provided in TDengine SQL.
|
||||
|
||||
TDengine SQL doesn't support abbreviation for keywords, for example `DESCRIBE` can't be abbreviated as `DESC`.
|
||||
TDengine SQL is the major interface for users to write data into or query from TDengine. For ease of use, the syntax is similar to that of standard SQL. However, please note that TDengine SQL is not standard SQL. For instance, TDengine doesn't provide a delete function for time series data and so corresponding statements are not provided in TDengine SQL.
|
||||
|
||||
Syntax Specifications used in this chapter:
|
||||
|
||||
|
|
@ -16,7 +14,7 @@ Syntax Specifications used in this chapter:
|
|||
- | means one of a few options, excluding | itself.
|
||||
- … means the item prior to it can be repeated multiple times.
|
||||
|
||||
To better demonstrate the syntax, usage and rules of TAOS SQL, hereinafter it's assumed that there is a data set of meters. Assuming each meter collects 3 data measurements: current, voltage, phase. The data model is shown below:
|
||||
To better demonstrate the syntax, usage and rules of TAOS SQL, hereinafter it's assumed that there is a data set of data from electric meters. Each meter collects 3 data measurements: current, voltage, phase. The data model is shown below:
|
||||
|
||||
```sql
|
||||
taos> DESCRIBE meters;
|
||||
|
|
@ -30,4 +28,4 @@ taos> DESCRIBE meters;
|
|||
groupid | INT | 4 | TAG |
|
||||
```
|
||||
|
||||
The data set includes the data collected by 4 meters, the corresponding table name is d1001, d1002, d1003, d1004 respectively based on the data model of TDengine.
|
||||
The data set includes the data collected by 4 meters, the corresponding table name is d1001, d1002, d1003 and d1004 based on the data model of TDengine.
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ description: Install, Uninstall, Start, Stop and Upgrade
|
|||
import Tabs from "@theme/Tabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
|
||||
TDengine community version provides dev and rpm packages for users to choose based on the system environment. deb supports Debian, Ubuntu and systems derived from them. rpm supports CentOS, RHEL, SUSE and systems derived from them. Furthermore, tar.gz package is provided for enterprise customers.
|
||||
TDengine community version provides deb and rpm packages for users to choose from, based on their system environment. The deb package supports Debian, Ubuntu and derivative systems. The rpm package supports CentOS, RHEL, SUSE and derivative systems. Furthermore, a tar.gz package is provided for TDengine Enterprise customers.
|
||||
|
||||
## Install
|
||||
|
||||
|
|
@ -124,7 +124,7 @@ taoskeeper is installed, enable it by `systemctl enable taoskeeper`
|
|||
```
|
||||
|
||||
:::info
|
||||
Some configuration will be prompted for users to provide when install.sh is executing, the interactive mode can be disabled by executing `./install.sh -e no`. `./install -h` can show all parameters and detailed explanation.
|
||||
Users will be prompted to enter some configuration information when install.sh is executing. The interactive mode can be disabled by executing `./install.sh -e no`. `./install.sh -h` can show all parameters with detailed explanation.
|
||||
|
||||
:::
|
||||
|
||||
|
|
@ -132,7 +132,7 @@ Some configuration will be prompted for users to provide when install.sh is exec
|
|||
</Tabs>
|
||||
|
||||
:::note
|
||||
When installing on the first node in the cluster, when "Enter FQDN:" is prompted, nothing needs to be provided. When installing on following nodes, when "Enter FQDN:" is prompted, the end point of the first dnode in the cluster can be input if it is already up; or just ignore it and configure later after installation is done.
|
||||
When installing on the first node in the cluster, at the "Enter FQDN:" prompt, nothing needs to be provided. When installing on subsequent nodes, at the "Enter FQDN:" prompt, you must enter the end point of the first dnode in the cluster if it is already up. You can also just ignore it and configure it later after installation is finished.
|
||||
|
||||
:::
|
||||
|
||||
|
|
@ -181,14 +181,14 @@ taosKeeper is removed successfully!
|
|||
|
||||
:::note
|
||||
|
||||
- It's strongly suggested not to use multiple kinds of installation packages on a single host TDengine
|
||||
- After deb package is installed, if the installation directory is removed manually so that uninstall or reinstall can't succeed, it can be resolved by cleaning up TDengine package information as in the command below and then reinstalling.
|
||||
- We strongly recommend not to use multiple kinds of installation packages on a single host TDengine.
|
||||
- After deb package is installed, if the installation directory is removed manually, uninstall or reinstall will not work. This issue can be resolved by using the command below which cleans up TDengine package information. You can then reinstall if needed.
|
||||
|
||||
```bash
|
||||
$ sudo rm -f /var/lib/dpkg/info/tdengine*
|
||||
```
|
||||
|
||||
- After rpm package is installed, if the installation directory is removed manually so that uninstall or reinstall can't succeed, it can be resolved by cleaning up TDengine package information as in the command below and then reinstalling.
|
||||
- After rpm package is installed, if the installation directory is removed manually, uninstall or reinstall will not work. This issue can be resolved by using the command below which cleans up TDengine package information. You can then reinstall if needed.
|
||||
|
||||
```bash
|
||||
$ sudo rpm -e --noscripts tdengine
|
||||
|
|
@ -219,7 +219,7 @@ lrwxrwxrwx 1 root root 13 Feb 22 09:34 log -> /var/log/taos/
|
|||
During the installation process:
|
||||
|
||||
- Configuration directory, data directory, and log directory are created automatically if they don't exist
|
||||
- The default configuration file is located at /etc/taos/taos.cfg, which is a copy of /usr/local/taos/cfg/taos.cfg if not existing
|
||||
- The default configuration file is located at /etc/taos/taos.cfg, which is a copy of /usr/local/taos/cfg/taos.cfg
|
||||
- The default data directory is /var/lib/taos, which is a soft link to /usr/local/taos/data
|
||||
- The default log directory is /var/log/taos, which is a soft link to /usr/local/taos/log
|
||||
- The executables at /usr/local/taos/bin are linked to /usr/bin
|
||||
|
|
@ -228,7 +228,7 @@ During the installation process:
|
|||
|
||||
:::note
|
||||
|
||||
- When TDengine is uninstalled, the configuration /etc/taos/taos.cfg, data directory /var/lib/taos, log directory /var/log/taos are kept. They can be deleted manually with caution because data can't be recovered
|
||||
- When TDengine is uninstalled, the configuration /etc/taos/taos.cfg, data directory /var/lib/taos, log directory /var/log/taos are kept. They can be deleted manually with caution, because data can't be recovered. Please follow data integrity, security, backup or relevant SOPs before deleting any data.
|
||||
- When reinstalling TDengine, if the default configuration file /etc/taos/taos.cfg exists, it will be kept and the configuration file in the installation package will be renamed to taos.cfg.orig and stored at /usr/local/taos/cfg to be used as configuration sample. Otherwise the configuration file in the installation package will be installed to /etc/taos/taos.cfg and used.
|
||||
|
||||
## Start and Stop
|
||||
|
|
@ -263,18 +263,19 @@ Active: inactive (dead)
|
|||
|
||||
There are two aspects in upgrade operation: upgrade installation package and upgrade a running server.
|
||||
|
||||
Upgrading package should follow the steps mentioned previously to first uninstall the old version then install the new version.
|
||||
To upgrade a package, follow the steps mentioned previously to first uninstall the old version then install the new version.
|
||||
|
||||
Upgrading a running server is much more complex. First please check the version number of the old version and the new version. The version number of TDengine consists of 4 sections, only if the first 3 section match can the old version be upgraded to the new version. The steps of upgrading a running server are as below:
|
||||
Upgrading a running server is much more complex. First please check the version number of the old version and the new version. The version number of TDengine consists of 4 sections, only if the first 3 sections match can the old version be upgraded to the new version. The steps of upgrading a running server are as below:
|
||||
|
||||
- Stop inserting data
|
||||
- Make sure all data are persisted into disk
|
||||
- Make sure all data is persisted to disk
|
||||
- Make some simple queries (Such as total rows in stables, tables and so on. Note down the values. Follow best practices and relevant SOPs.)
|
||||
- Stop the cluster of TDengine
|
||||
- Uninstall old version and install new version
|
||||
- Start the cluster of TDengine
|
||||
- Make some simple queries to make sure no data loss
|
||||
- Make some simple data insertion to make sure the cluster works well
|
||||
- Restore business data
|
||||
- Execute simple queries, such as the ones executed prior to installing the new package, to make sure there is no data loss
|
||||
- Run some simple data insertion statements to make sure the cluster works well
|
||||
- Restore business services
|
||||
|
||||
:::warning
|
||||
|
||||
|
|
|
|||
|
|
@ -2,17 +2,17 @@
|
|||
title: Resource Planning
|
||||
---
|
||||
|
||||
The computing and storage resources need to be planned if using TDengine to build an IoT platform. How to plan the CPU, memory and disk required will be described in this chapter.
|
||||
It is important to plan computing and storage resources if using TDengine to build an IoT, time-series or Big Data platform. How to plan the CPU, memory and disk resources required, will be described in this chapter.
|
||||
|
||||
## Memory Requirement of Server Side
|
||||
|
||||
The number of vgroups created for each database is the same as the number of CPU cores by default and can be configured by parameter `maxVgroupsPerDb`, each vnode in a vgroup stores one replica. Each vnode consumes a fixed size of memory, i.e. `blocks` \* `cache`. Besides, some memory is required for tag values associated with each table. A fixed amount of memory is required for each cluster. So, the memory required for each DB can be calculated using the formula below:
|
||||
By default, the number of vgroups created for each database is the same as the number of CPU cores. This can be configured by the parameter `maxVgroupsPerDb`. Each vnode in a vgroup stores one replica. Each vnode consumes a fixed amount of memory, i.e. `blocks` \* `cache`. In addition, some memory is required for tag values associated with each table. A fixed amount of memory is required for each cluster. So, the memory required for each DB can be calculated using the formula below:
|
||||
|
||||
```
|
||||
Database Memory Size = maxVgroupsPerDb * replica * (blocks * cache + 10MB) + numOfTables * (tagSizePerTable + 0.5KB)
|
||||
```
|
||||
|
||||
For example, assuming the default value of `maxVgroupPerDB` is 64, the default value of `cache` 16M, the default value of `blocks` is 6, there are 100,000 tables in a DB, the replica number is 1, total length of tag values is 256 bytes, the total memory required for this DB is: 64 \* 1 \* (16 \* 6 + 10) + 100000 \* (0.25 + 0.5) / 1000 = 6792M.
|
||||
For example, assuming the default value of `maxVgroupPerDB` is 64, the default value of `cache` is 16M, the default value of `blocks` is 6, there are 100,000 tables in a DB, the replica number is 1, total length of tag values is 256 bytes, the total memory required for this DB is: 64 \* 1 \* (16 \* 6 + 10) + 100000 \* (0.25 + 0.5) / 1000 = 6792M.
|
||||
|
||||
In the real operation of TDengine, we are more concerned about the memory used by each TDengine server process `taosd`.
|
||||
|
||||
|
|
@ -22,10 +22,10 @@ In the real operation of TDengine, we are more concerned about the memory used b
|
|||
|
||||
In the above formula:
|
||||
|
||||
1. "vnode_memory" of a `taosd` process is the memory used by all vnodes hosted by this `taosd` process. It can be roughly calculated by firstly adding up the total memory of all DBs whose memory usage can be derived according to the formula mentioned previously then dividing by number of dnodes and multiplying the number of replicas.
|
||||
1. "vnode_memory" of a `taosd` process is the memory used by all vnodes hosted by this `taosd` process. It can be roughly calculated by firstly adding up the total memory of all DBs whose memory usage can be derived according to the formula for Database Memory Size, mentioned above, then dividing by number of dnodes and multiplying the number of replicas.
|
||||
|
||||
```
|
||||
vnode_memory = sum(Database memory) / number_of_dnodes * replica
|
||||
vnode_memory = (sum(Database Memory Size) / number_of_dnodes) * replica
|
||||
```
|
||||
|
||||
2. "mnode_memory" of a `taosd` process is the memory consumed by a mnode. If there is one (and only one) mnode hosted in a `taosd` process, the memory consumed by "mnode" is "0.2KB \* the total number of tables in the cluster".
|
||||
|
|
@ -56,8 +56,8 @@ So, at least 3GB needs to be reserved for such a client.
|
|||
|
||||
The CPU resources required depend on two aspects:
|
||||
|
||||
- **Data Insertion** Each dnode of TDengine can process at least 10,000 insertion requests in one second, while each insertion request can have multiple rows. The computing resource consumed between inserting 1 row one time and inserting 10 rows one time is very small. So, the more the rows to insert one time, the higher the efficiency. Inserting in bach also exposes requirements for the client side which needs to cache rows and insert in batch once the cached rows reaches a threshold.
|
||||
- **Data Query** High efficiency query is provided in TDengine, but it's hard to estimate the CPU resource required because the queries used in different use cases and the frequency of queries vary significantly. It can only be verified with the query statements, query frequency, data size to be queried, etc provided by user.
|
||||
- **Data Insertion** Each dnode of TDengine can process at least 10,000 insertion requests in one second, while each insertion request can have multiple rows. The difference in computing resource consumed, between inserting 1 row at a time, and inserting 10 rows at a time is very small. So, the more the number of rows that can be inserted one time, the higher the efficiency. Inserting in batch also imposes requirements on the client side which needs to cache rows to insert in batch once the number of cached rows reaches a threshold.
|
||||
- **Data Query** High efficiency query is provided in TDengine, but it's hard to estimate the CPU resource required because the queries used in different use cases and the frequency of queries vary significantly. It can only be verified with the query statements, query frequency, data size to be queried, and other requirements provided by users.
|
||||
|
||||
In short, the CPU resource required for data insertion can be estimated but it's hard to do so for query use cases. In real operation, it's suggested to control CPU usage below 50%. If this threshold is exceeded, it's a reminder for system operator to add more nodes in the cluster to expand resources.
|
||||
|
||||
|
|
@ -71,12 +71,12 @@ Raw DataSize = numOfTables * rowSizePerTable * rowsPerTable
|
|||
|
||||
For example, there are 10,000,000 meters, while each meter collects data every 15 minutes and the data size of each collection is 128 bytes, so the raw data size of one year is: 10000000 \* 128 \* 24 \* 60 / 15 \* 365 = 44.8512(TB). Assuming compression ratio is 5, the actual disk size is: 44.851 / 5 = 8.97024(TB).
|
||||
|
||||
Parameter `keep` can be used to set how long the data will be kept on disk. To further reduce storage cost, multiple storage levels can be enabled in TDengine, with the coldest data stored on the cheapest storage device, and this is transparent to application programs.
|
||||
Parameter `keep` can be used to set how long the data will be kept on disk. To further reduce storage cost, multiple storage levels can be enabled in TDengine, with the coldest data stored on the cheapest storage device. This is completely transparent to application programs.
|
||||
|
||||
To increase the performance, multiple disks can be setup for parallel data reading or data inserting. Please note that an expensive disk array is not necessary because replications are used in TDengine to provide high availability.
|
||||
To increase performance, multiple disks can be setup for parallel data reading or data inserting. Please note that an expensive disk array is not necessary because replications are used in TDengine to provide high availability.
|
||||
|
||||
## Number of Hosts
|
||||
|
||||
A host can be either physical or virtual. The total memory, total CPU, total disk required can be estimated according to the formulas mentioned previously. Then, according to the system resources that a single host can provide, assuming all hosts have the same resources, the number of hosts can be derived easily.
|
||||
A host can be either physical or virtual. The total memory, total CPU, total disk required can be estimated according to the formulae mentioned previously. Then, according to the system resources that a single host can provide, assuming all hosts have the same resources, the number of hosts can be derived easily.
|
||||
|
||||
**Quick Estimation for CPU, Memory and Disk** Please refer to [Resource Estimate](https://www.taosdata.com/config/config.html).
|
||||
|
|
|
|||
|
|
@ -7,26 +7,26 @@ title: Fault Tolerance & Disaster Recovery
|
|||
|
||||
TDengine uses **WAL**, i.e. Write Ahead Log, to achieve fault tolerance and high reliability.
|
||||
|
||||
When a data block is received by TDengine, the original data block is first written into WAL. The log in WAL will be deleted only after the data has been written into data files in the database. Data can be recovered from WAL in case the server is stopped abnormally due to any reason and then restarted.
|
||||
When a data block is received by TDengine, the original data block is first written into WAL. The log in WAL will be deleted only after the data has been written into data files in the database. Data can be recovered from WAL in case the server is stopped abnormally for any reason and then restarted.
|
||||
|
||||
There are 2 configuration parameters related to WAL:
|
||||
|
||||
- walLevel：
|
||||
- 0：wal is disabled;
|
||||
- 1：wal is enabled without fsync;
|
||||
- 2：wal is enabled with fsync.
|
||||
- fsync：only valid when walLevel is set to 2, it specifies the interval of invoking fsync. If set to 0, it means fsync is invoked immediately once WAL is written.
|
||||
- 0：wal is disabled
|
||||
- 1：wal is enabled without fsync
|
||||
- 2：wal is enabled with fsync
|
||||
- fsync：This parameter is only valid when walLevel is set to 2. It specifies the interval, in milliseconds, of invoking fsync. If set to 0, it means fsync is invoked immediately once WAL is written.
|
||||
|
||||
To achieve absolutely no data loss, walLevel needs to be set to 2 and fsync needs to be set to 1. The penalty is the performance of data ingestion downgrades. However, if the concurrent threads of data insertion on the client side can reach a big enough number, for example 50, the data ingestion performance would be still good enough, our verification shows that the drop is only 30% compared to fsync is set to 3,000 milliseconds.
|
||||
To achieve absolutely no data loss, walLevel should be set to 2 and fsync should be set to 1. There is a performance penalty to the data ingestion rate. However, if the concurrent data insertion threads on the client side can reach a big enough number, for example 50, the data ingestion performance will be still good enough. Our verification shows that the drop is only 30% when fsync is set to 3,000 milliseconds.
|
||||
|
||||
## Disaster Recovery
|
||||
|
||||
TDengine uses replications to provide high availability and disaster recovery capability.
|
||||
TDengine uses replication to provide high availability and disaster recovery capability.
|
||||
|
||||
TDengine cluster is managed by mnode. To make sure the high availability of mnode, multiple replicas can be configured by the system parameter `numOfMnodes`. The data replication between mnode replicas is performed in a synchronous way to guarantee the metadata consistency.
|
||||
A TDengine cluster is managed by mnode. To ensure the high availability of mnode, multiple replicas can be configured by the system parameter `numOfMnodes`. The data replication between mnode replicas is performed in a synchronous way to guarantee metadata consistency.
|
||||
|
||||
The number of replicas for the time series data in TDengine is associated with each database, there can be a lot of databases in a cluster while each database can be configured with a different number of replicas. When creating a database, parameter `replica` is used to configure the number of replications. To achieve high availability, `replica` needs to be higher than 1.
|
||||
The number of replicas for time series data in TDengine is associated with each database. There can be many databases in a cluster and each database can be configured with a different number of replicas. When creating a database, parameter `replica` is used to configure the number of replications. To achieve high availability, `replica` needs to be higher than 1.
|
||||
|
||||
The number of dnodes in a TDengine cluster must NOT be lower than the number of replicas for any database, otherwise it would fail when trying to create a table.
|
||||
|
||||
As long as the dnodes of a TDengine cluster are deployed on different physical machines and the replica number is set to bigger than 1, high availability can be achieved without any other assistance. If dnodes of TDengine cluster are deployed in geographically different data centers, disaster recovery can be achieved too.
|
||||
As long as the dnodes of a TDengine cluster are deployed on different physical machines and the replica number is higher than 1, high availability can be achieved without any other assistance. For disaster recovery, dnodes of a TDengine cluster should be deployed in geographically different data centers.
|
||||
|
|
|
|||
|
|
@ -2,11 +2,13 @@
|
|||
title: Data Export
|
||||
---
|
||||
|
||||
There are two ways of exporting data from a TDengine cluster, one is SQL statement in TDengine CLI, the other one is `taosdump`.
|
||||
There are two ways of exporting data from a TDengine cluster:
|
||||
- Using a SQL statement in TDengine CLI
|
||||
- Using the `taosdump` tool
|
||||
|
||||
## Export Using SQL
|
||||
|
||||
If you want to export the data of a table or a STable, please execute below SQL statement in TDengine CLI.
|
||||
If you want to export the data of a table or a STable, please execute the SQL statement below, in the TDengine CLI.
|
||||
|
||||
```sql
|
||||
select * from <tb_name> >> data.csv;
|
||||
|
|
@ -16,4 +18,4 @@ The data of table or STable specified by `tb_name` will be exported into a file
|
|||
|
||||
## Export Using taosdump
|
||||
|
||||
With `taosdump`, you can choose to export the data of all databases, a database, a table or a STable, you can also choose export the data within a time range, or even only export the schema definition of a table. For the details of using `taosdump` please refer to [Tool for exporting and importing data: taosdump](/reference/taosdump).
|
||||
With `taosdump`, you can choose to export the data of all databases, a database, a table or a STable, you can also choose to export the data within a time range, or even only export the schema definition of a table. For the details of using `taosdump` please refer to [Tool for exporting and importing data: taosdump](/reference/taosdump).
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ sidebar_label: Connections & Tasks
|
|||
title: Manage Connections and Query Tasks
|
||||
---
|
||||
|
||||
A system operator can use TDengine CLI to show the connections, ongoing queries, stream computing, and can close connection or stop ongoing query task or stream computing.
|
||||
A system operator can use the TDengine CLI to show connections, ongoing queries, stream computing, and can close connections or stop ongoing query tasks or stream computing.
|
||||
|
||||
## Show Connections
|
||||
|
||||
|
|
@ -13,7 +13,7 @@ SHOW CONNECTIONS;
|
|||
|
||||
One column of the output of the above SQL command is "ip:port", which is the end point of the client.
|
||||
|
||||
## Close Connections Forcedly
|
||||
## Force Close Connections
|
||||
|
||||
```sql
|
||||
KILL CONNECTION <connection-id>;
|
||||
|
|
@ -27,9 +27,9 @@ In the above SQL command, `connection-id` is from the first column of the output
|
|||
SHOW QUERIES;
|
||||
```
|
||||
|
||||
The first column of the output is query ID, which is composed of the corresponding connection ID and the sequence number of the current query task started on this connection, in format of "connection-id:query-no".
|
||||
The first column of the output is query ID, which is composed of the corresponding connection ID and the sequence number of the current query task started on this connection. The format is "connection-id:query-no".
|
||||
|
||||
## Close Queries Forcedly
|
||||
## Force Close Queries
|
||||
|
||||
```sql
|
||||
KILL QUERY <query-id>;
|
||||
|
|
@ -43,9 +43,9 @@ In the above SQL command, `query-id` is from the first column of the output of `
|
|||
SHOW STREAMS;
|
||||
```
|
||||
|
||||
The first column of the output is stream ID, which is composed of the connection ID and the sequence number of the current stream started on this connection, in the format of "connection-id:stream-no".
|
||||
The first column of the output is stream ID, which is composed of the connection ID and the sequence number of the current stream started on this connection. The format is "connection-id:stream-no".
|
||||
|
||||
## Close Continuous Query Forcedly
|
||||
## Force Close Continuous Query
|
||||
|
||||
```sql
|
||||
KILL STREAM <stream-id>;
|
||||
|
|
|
|||
|
|
@ -2,13 +2,13 @@
|
|||
title: TDengine Monitoring
|
||||
---
|
||||
|
||||
After TDengine is started, a database named `log` for monitoring is created automatically. The information about CPU, memory, disk, bandwidth, number of requests, disk I/O speed, slow query is written into `log` database on the basis of a predefined interval. Additionally, some important system operations, like logon, create user, drop database, and alerts and warnings generated in TDengine are written into the `log` database too. A system operator can view the data in `log` database from TDengine CLI or from a web console.
|
||||
After TDengine is started, a database named `log` is created automatically to help with monitoring. Information that includes CPU, memory and disk usage, bandwidth, number of requests, disk I/O speed, slow queries, is written into the `log` database at a predefined interval. Additionally, some important system operations, like logon, create user, drop database, and alerts and warnings generated in TDengine are written into the `log` database too. A system operator can view the data in `log` database from TDengine CLI or from a web console.
|
||||
|
||||
The collection of the monitoring information is enabled by default, but can be disabled by parameter `monitor` in the configuration file.
|
||||
|
||||
## TDinsight
|
||||
|
||||
TDinsight is a complete solution which uses the monitor database `log` mentioned previously and Grafana to monitor a TDengine cluster.
|
||||
TDinsight is a complete solution which uses the monitoring database `log` mentioned previously, and Grafana, to monitor a TDengine cluster.
|
||||
|
||||
From version 2.3.3.0, more monitoring data has been added in the `log` database. Please refer to [TDinsight Grafana Dashboard](https://grafana.com/grafana/dashboards/15167) to learn more details about using TDinsight to monitor TDengine.
|
||||
|
||||
|
|
|
|||
|
|
@ -4,13 +4,13 @@ title: Problem Diagnostics
|
|||
|
||||
## Network Connection Diagnostics
|
||||
|
||||
When the client is unable to access the server, the network connection between the client side and the server side needs to be checked to find out the root cause and resolve problems.
|
||||
When a TDengine client is unable to access a TDengine server, the network connection between the client side and the server side must be checked to find the root cause and resolve problems.
|
||||
|
||||
The diagnostic for network connection can be executed between Linux and Linux or between Linux and Windows.
|
||||
Diagnostics for network connections can be executed between Linux and Linux or between Linux and Windows.
|
||||
|
||||
Diagnostic steps：
|
||||
|
||||
1. If the port range to be diagnosed are being occupied by a `taosd` server process, please first stop `taosd.
|
||||
1. If the port range to be diagnosed is being occupied by a `taosd` server process, please first stop `taosd.
|
||||
2. On the server side, execute command `taos -n server -P <port> -l <pktlen>` to monitor the port range starting from the port specified by `-P` parameter with the role of "server".
|
||||
3. On the client side, execute command `taos -n client -h <fqdn of server> -P <port> -l <pktlen>` to send a testing package to the specified server and port.
|
||||
|
||||
|
|
@ -65,13 +65,13 @@ Output of the client side for the example is below:
|
|||
12/21 14:50:22.721274 0x7fc95d859200 UTL successed to test UDP port:6011
|
||||
```
|
||||
|
||||
The output needs to be checked carefully for the system operator to find out the root cause and solve the problem.
|
||||
The output needs to be checked carefully for the system operator to find the root cause and resolve the problem.
|
||||
|
||||
## Startup Status and RPC Diagnostic
|
||||
|
||||
`taos -n startup -h <fqdn of server>` can be used to check the startup status of a `taosd` process. This is a comman task for a system operator to do to determine whether `taosd` has been started successfully, especially in case of cluster.
|
||||
`taos -n startup -h <fqdn of server>` can be used to check the startup status of a `taosd` process. This is a common task which should be performed by a system operator, especially in the case of a cluster, to determine whether `taosd` has been started successfully.
|
||||
|
||||
`taos -n rpc -h <fqdn of server>` can be used to check whether the port of a started `taosd` can be accessed or not. If `taosd` process doesn't respond or is working abnormally, this command can be used to initiate a rpc communication with the specified fqdn to determine whether it's a network problem or `taosd` is abnormal.
|
||||
`taos -n rpc -h <fqdn of server>` can be used to check whether the port of a started `taosd` can be accessed or not. If `taosd` process doesn't respond or is working abnormally, this command can be used to initiate a rpc communication with the specified fqdn to determine whether it's a network problem or whether `taosd` is abnormal.
|
||||
|
||||
## Sync and Arbitrator Diagnostic
|
||||
|
||||
|
|
@ -80,13 +80,13 @@ taos -n sync -P 6040 -h <fqdn of server>
|
|||
taos -n sync -P 6042 -h <fqdn of server>
|
||||
```
|
||||
|
||||
The above commands can be executed on Linux Shell to check whether the port for sync is working well and whether the sync module on the server side is working well. Additionally, `-P 6042` is used to check whether the arbitrator is configured properly and is working well.
|
||||
The above commands can be executed in a Linux shell to check whether the port for sync is working well and whether the sync module on the server side is working well. Additionally, `-P 6042` is used to check whether the arbitrator is configured properly and is working well.
|
||||
|
||||
## Network Speed Diagnostic
|
||||
|
||||
`taos -n speed -h <fqdn of server> -P 6030 -N 10 -l 10000000 -S TCP`
|
||||
|
||||
From version 2.2.0.0, the above command can be executed on Linux Shell to test the network speed, it sends uncompressed package to a running `taosd` server process or a simulated server process started by `taos -n server` to test the network speed. Parameters can be used when testing network speed are as below:
|
||||
From version 2.2.0.0 onwards, the above command can be executed in a Linux shell to test network speed. The command sends uncompressed packages to a running `taosd` server process or a simulated server process started by `taos -n server` to test the network speed. Parameters can be used when testing network speed are as below:
|
||||
|
||||
-n：When set to "speed", it means testing network speed.
|
||||
-h：The FQDN or IP of the server process to be connected to; if not set, the FQDN configured in `taos.cfg` is used.
|
||||
|
|
@ -99,23 +99,23 @@ From version 2.2.0.0, the above command can be executed on Linux Shell to test t
|
|||
|
||||
`taos -n fqdn -h <fqdn of server>`
|
||||
|
||||
From version 2.2.0.0, the above command can be executed on Linux Shell to test the resolution speed of FQDN. It can be used to try to resolve a FQDN to an IP address and record the time spent in this process. The parameters that can be used for this purpose are as below:
|
||||
From version 2.2.0.0 onward, the above command can be executed in a Linux shell to test the resolution speed of FQDN. It can be used to try to resolve a FQDN to an IP address and record the time spent in this process. The parameters that can be used for this purpose are as below:
|
||||
|
||||
-n：When set to "fqdn", it means testing the speed of resolving FQDN.
|
||||
-h：The FQDN to be resolved. If not set, the `FQDN` parameter in `taos.cfg` is used by default.
|
||||
|
||||
## Server Log
|
||||
|
||||
The parameter `debugFlag` is used to control the log level of the `taosd` server process. The default value is 131, for debug purpose it needs to be escalated to 135 or 143.
|
||||
The parameter `debugFlag` is used to control the log level of the `taosd` server process. The default value is 131. For debugging and tracing, it needs to be set to either 135 or 143 respectively.
|
||||
|
||||
Once this parameter is set to 135 or 143, the log file grows very quickly especially when there is a huge volume of data insertion and data query requests. If all the logs are stored together, some important information may be missed very easily, so on server side important information is stored at different place from other logs.
|
||||
Once this parameter is set to 135 or 143, the log file grows very quickly especially when there is a huge volume of data insertion and data query requests. If all the logs are stored together, some important information may be missed very easily and so on the server side, important information is stored in a different place from other logs.
|
||||
|
||||
- The log at level of INFO, WARNING and ERROR is stored in `taosinfo` so that it is easy to find important information
|
||||
- The log at level of DEBUG (135) and TRACE (143) and other information not handled by `taosinfo` are stored in `taosdlog`
|
||||
|
||||
## Client Log
|
||||
|
||||
An independent log file, named as "taoslog+<seq num\>" is generated for each client program, i.e. a client process. The default value of `debugFlag` is also 131 and only logs at level of INFO/ERROR/WARNING are recorded, for debugging purposes it needs to be changed to 135 or 143 so that logs at DEBUG or TRACE level can be recorded.
|
||||
An independent log file, named as "taoslog+<seq num\>" is generated for each client program, i.e. a client process. The default value of `debugFlag` is also 131 and only logs at level of INFO/ERROR/WARNING are recorded. As stated above, for debugging and tracing, it needs to be changed to 135 or 143 respectively, so that logs at DEBUG or TRACE level can be recorded.
|
||||
|
||||
The maximum length of a single log file is controlled by parameter `numOfLogLines` and only 2 log files are kept for each `taosd` server process.
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
title: Administration
|
||||
---
|
||||
|
||||
This chapter is mainly written for system administrators, covering download, install/uninstall, data import/export, system monitoring, user management, connection management, etc. Capacity planning and system optimization are also covered.
|
||||
This chapter is mainly written for system administrators. It covers download, install/uninstall, data import/export, system monitoring, user management, connection management, capacity planning and system optimization.
|
||||
|
||||
```mdx-code-block
|
||||
import DocCardList from '@theme/DocCardList';
|
||||
|
|
|
|||
|
|
@ -2,23 +2,23 @@
|
|||
title: REST API
|
||||
---
|
||||
|
||||
To support the development of various types of platforms, TDengine provides an API that conforms to the REST principle, namely REST API. To minimize the learning cost, different from the other database REST APIs, TDengine directly requests the SQL command contained in the request BODY through HTTP POST to operate the database and only requires a URL.
|
||||
To support the development of various types of applications and platforms, TDengine provides an API that conforms to REST principles; namely REST API. To minimize the learning cost, unlike REST APIs for other database engines, TDengine allows insertion of SQL commands in the BODY of an HTTP POST request, to operate the database.
|
||||
|
||||
:::note
|
||||
One difference from the native connector is that the REST interface is stateless, so the `USE db_name` command has no effect. All references to table names and super table names need to specify the database name prefix. (Since version 2.2.0.0, it is supported to specify db_name in RESTful URL. If the database name prefix is not specified in the SQL command, the `db_name` specified in the URL will be used. Since version 2.4.0.0, REST service is provided by taosAdapter by default. And it requires that the `db_name` must be specified in the URL.)
|
||||
One difference from the native connector is that the REST interface is stateless and so the `USE db_name` command has no effect. All references to table names and super table names need to specify the database name in the prefix. (Since version 2.2.0.0, TDengine supports specification of the db_name in RESTful URL. If the database name prefix is not specified in the SQL command, the `db_name` specified in the URL will be used. Since version 2.4.0.0, REST service is provided by taosAdapter by default and it requires that the `db_name` must be specified in the URL.)
|
||||
:::
|
||||
|
||||
## Installation
|
||||
|
||||
The REST interface does not rely on any TDengine native library, so the client application does not need to install any TDengine libraries. The client application's development language supports the HTTP protocol is enough.
|
||||
The REST interface does not rely on any TDengine native library, so the client application does not need to install any TDengine libraries. The client application's development language only needs to support the HTTP protocol.
|
||||
|
||||
## Verification
|
||||
|
||||
If the TDengine server is already installed, it can be verified as follows:
|
||||
|
||||
The following is an Ubuntu environment using the `curl` tool (to confirm that it is installed) to verify that the REST interface is working.
|
||||
The following example is in an Ubuntu environment and uses the `curl` tool to verify that the REST interface is working. Note that the `curl` tool may need to be installed in your environment.
|
||||
|
||||
The following example lists all databases, replacing `h1.taosdata.com` and `6041` (the default port) with the actual running TDengine service FQDN and port number.
|
||||
The following example lists all databases on the host h1.taosdata.com. To use it in your environment, replace `h1.taosdata.com` and `6041` (the default port) with the actual running TDengine service FQDN and port number.
|
||||
|
||||
```html
|
||||
curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'show databases;' h1.taosdata.com:6041/rest/sql
|
||||
|
|
@ -89,7 +89,7 @@ For example, `http://h1.taos.com:6041/rest/sql/test` is a URL to `h1.taos.com:60
|
|||
|
||||
TDengine supports both Basic authentication and custom authentication mechanisms, and subsequent versions will provide a standard secure digital signature mechanism for authentication.
|
||||
|
||||
- The custom authentication information is as follows (Let's introduce token later)
|
||||
- The custom authentication information is as follows. More details about "token" later.
|
||||
|
||||
```
|
||||
Authorization: Taosd <TOKEN>
|
||||
|
|
@ -136,7 +136,7 @@ The return result is in JSON format, as follows:
|
|||
|
||||
Description:
|
||||
|
||||
- status: tell if the operation result is success or failure.
|
||||
- status: tells you whethre the operation result is success or failure.
|
||||
- head: the definition of the table, or just one column "affected_rows" if no result set is returned. (As of version 2.0.17.0, it is recommended not to rely on the head return value to determine the data column type but rather use column_meta. In later versions, the head item may be removed from the return value.)
|
||||
- column_meta: this item is added to the return value to indicate the data type of each column in the data with version 2.0.17.0 and later versions. Each column is described by three values: column name, column type, and type length. For example, `["current",6,4]` means that the column name is "current", the column type is 6, which is the float type, and the type length is 4, which is the float type with 4 bytes. If the column type is binary or nchar, the type length indicates the maximum length of content stored in the column, not the length of the specific data in this return value. When the column type is nchar, the type length indicates the number of Unicode characters that can be saved, not bytes.
|
||||
- data: The exact data returned, presented row by row, or just [[affected_rows]] if no result set is returned. The order of the data columns in each row of data is the same as that of the data columns described in column_meta.
|
||||
|
|
|
|||
|
|
@ -15,9 +15,9 @@ import GoOpenTSDBTelnet from "../../07-develop/03-insert-data/_go_opts_telnet.md
|
|||
import GoOpenTSDBJson from "../../07-develop/03-insert-data/_go_opts_json.mdx"
|
||||
import GoQuery from "../../07-develop/04-query-data/_go.mdx"
|
||||
|
||||
`driver-go` is the official Go language connector for TDengine, which implements the interface to the Go language [database/sql](https://golang.org/pkg/database/sql/) package. Go developers can use it to develop applications that access TDengine cluster data.
|
||||
`driver-go` is the official Go language connector for TDengine. It implements the [database/sql](https://golang.org/pkg/database/sql/) package, the generic Go language interface to SQL databases. Go developers can use it to develop applications that access TDengine cluster data.
|
||||
|
||||
`driver-go` provides two ways to establish connections. One is **native connection**, which connects to TDengine instances natively through the TDengine client driver (taosc), supporting data writing, querying, subscriptions, schemaless writing, and bind interface. The other is the **REST connection**, which connects to TDengine instances via the REST interface provided by taosAdapter. The set of features implemented by the REST connection differs slightly from the native connection.
|
||||
`driver-go` provides two ways to establish connections. One is **native connection**, which connects to TDengine instances natively through the TDengine client driver (taosc), supporting data writing, querying, subscriptions, schemaless writing, and bind interface. The other is the **REST connection**, which connects to TDengine instances via the REST interface provided by taosAdapter. The set of features implemented by the REST connection differs slightly from those implemented by the native connection.
|
||||
|
||||
This article describes how to install `driver-go` and connect to TDengine clusters and perform basic operations such as data query and data writing through `driver-go`.
|
||||
|
||||
|
|
@ -213,7 +213,7 @@ func main() {
|
|||
|
||||
Since the REST interface is stateless, the `use db` syntax will not work. You need to put the db name into the SQL command, e.g. `create table if not exists tb1 (ts timestamp, a int)` to `create table if not exists test.tb1 (ts timestamp, a int)` otherwise it will report the error `[0x217] Database not specified or available`.
|
||||
|
||||
You can also put the db name in the DSN by changing `root:taosdata@http(localhost:6041)/` to `root:taosdata@http(localhost:6041)/test`. This method is supported by taosAdapter in TDengine 2.4.0.5. is supported since TDengine 2.4.0.5. Executing the `create database` statement when the specified db does not exist will not report an error while executing other queries or writing against that db will report an error.
|
||||
You can also put the db name in the DSN by changing `root:taosdata@http(localhost:6041)/` to `root:taosdata@http(localhost:6041)/test`. This method is supported by taosAdapter since TDengine 2.4.0.5. Executing the `create database` statement when the specified db does not exist will not report an error while executing other queries or writing against that db will report an error.
|
||||
|
||||
The complete example is as follows.
|
||||
|
||||
|
|
@ -289,7 +289,7 @@ func main() {
|
|||
|
||||
6. `readBufferSize` parameter has no significant effect after being increased
|
||||
|
||||
If you increase `readBufferSize` will reduce the number of `syscall` calls when fetching results. If the query result is smaller, modifying this parameter will not improve significantly. If you increase the parameter value too much, the bottleneck will be parsing JSON data. If you need to optimize the query speed, you must adjust the value according to the actual situation to achieve the best query result.
|
||||
Increasing `readBufferSize` will reduce the number of `syscall` calls when fetching results. If the query result is smaller, modifying this parameter will not improve performance significantly. If you increase the parameter value too much, the bottleneck will be parsing JSON data. If you need to optimize the query speed, you must adjust the value based on the actual situation to achieve the best query performance.
|
||||
|
||||
7. `disableCompression` parameter is set to `false` when the query efficiency is reduced
|
||||
|
||||
|
|
|
|||
|
|
@ -9,19 +9,19 @@ description: TDengine Java based on JDBC API and provide both native and REST co
|
|||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
|
||||
'taos-jdbcdriver' is TDengine's official Java language connector, which allows Java developers to develop applications that access the TDengine database. 'taos-jdbcdriver' implements the interface of the JDBC driver standard and provides two forms of connectors. One is to connect to a TDengine instance natively through the TDengine client driver (taosc), which supports functions including data writing, querying, subscription, schemaless writing, and bind interface. And the other is to connect to a TDengine instance through the REST interface provided by taosAdapter (2.4.0.0 and later). REST connections implement has a slight differences to compare the set of features implemented and native connections.
|
||||
'taos-jdbcdriver' is TDengine's official Java language connector, which allows Java developers to develop applications that access the TDengine database. 'taos-jdbcdriver' implements the interface of the JDBC driver standard and provides two forms of connectors. One is to connect to a TDengine instance natively through the TDengine client driver (taosc), which supports functions including data writing, querying, subscription, schemaless writing, and bind interface. And the other is to connect to a TDengine instance through the REST interface provided by taosAdapter (2.4.0.0 and later). The implementation of the REST connection and those of the native connections have slight differences in features.
|
||||
|
||||
|
||||
|
||||
The preceding diagram shows two ways for a Java app to access TDengine via connector:
|
||||
|
||||
- JDBC native connection: Java applications use TSDBDriver on physical node 1 (pnode1) to call client-driven directly (`libtaos.so` or `taos.dll`) APIs to send writing and query requests to taosd instances located on physical node 2 (pnode2).
|
||||
- JDBC REST connection: The Java application encapsulates the SQL as a REST request via RestfulDriver, sends it to the REST server of physical node 2 (taosAdapter), requests TDengine server through the REST server, and returns the result.
|
||||
- JDBC REST connection: The Java application encapsulates the SQL as a REST request via RestfulDriver, sends it to the REST server (taosAdapter) on physical node 2. taosAdapter forwards the request to TDengine server and returns the result.
|
||||
|
||||
Using REST connection, which does not rely on TDengine client drivers.It can be cross-platform more convenient and flexible but introduce about 30% lower performance than native connection.
|
||||
The REST connection, which does not rely on TDengine client drivers, is more convenient and flexible, in addition to being cross-platform. However the performance is about 30% lower than that of the native connection.
|
||||
|
||||
:::info
|
||||
TDengine's JDBC driver implementation is as consistent as possible with the relational database driver. Still, there are differences in the use scenarios and technical characteristics of TDengine and relational object databases, so 'taos-jdbcdriver' also has some differences from traditional JDBC drivers. You need to pay attention to the following points when using:
|
||||
TDengine's JDBC driver implementation is as consistent as possible with the relational database driver. Still, there are differences in the use scenarios and technical characteristics of TDengine and relational object databases. So 'taos-jdbcdriver' also has some differences from traditional JDBC drivers. It is important to keep the following points in mind:
|
||||
|
||||
- TDengine does not currently support delete operations for individual data records.
|
||||
- Transactional operations are not currently supported.
|
||||
|
|
@ -88,7 +88,7 @@ Add following dependency in the `pom.xml` file of your Maven project:
|
|||
</TabItem>
|
||||
<TabItem value="source" label="Build from source code">
|
||||
|
||||
You can build Java connector from source code after clone TDengine project:
|
||||
You can build Java connector from source code after cloning the TDengine project:
|
||||
|
||||
```shell
|
||||
git clone https://github.com/taosdata/TDengine.git
|
||||
|
|
@ -96,7 +96,7 @@ cd TDengine/src/connector/jdbc
|
|||
mvn clean install -Dmaven.test.skip=true
|
||||
```
|
||||
|
||||
After compilation, a jar package of taos-jdbcdriver-2.0.XX-dist .jar is generated in the target directory, and the compiled jar file is automatically placed in the local Maven repository.
|
||||
After compilation, a jar package named taos-jdbcdriver-2.0.XX-dist.jar is generated in the target directory, and the compiled jar file is automatically placed in the local Maven repository.
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
|
@ -186,7 +186,7 @@ Connection conn = DriverManager.getConnection(jdbcUrl);
|
|||
|
||||
In the above example, a RestfulDriver with a JDBC REST connection is used to establish a connection to a database named `test` with hostname `taosdemo.com` on port `6041`. The URL specifies the user name as `root` and the password as `taosdata`.
|
||||
|
||||
There is no dependency on the client driver when Using a JDBC REST connection. Compared to a JDBC native connection, only the following are required: 1.
|
||||
There is no dependency on the client driver when Using a JDBC REST connection. Compared to a JDBC native connection, only the following are required:
|
||||
|
||||
1. driverClass specified as "com.taosdata.jdbc.rs.RestfulDriver".
|
||||
2. jdbcUrl starting with "jdbc:TAOS-RS://".
|
||||
|
|
@ -209,7 +209,7 @@ The configuration parameters in the URL are as follows.
|
|||
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('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 test using weather(ts, temperature) tags('California.SanFrancisco') values(now, 24.6);
|
||||
|
||||
:::
|
||||
|
||||
|
|
@ -271,7 +271,7 @@ If the configuration parameters are duplicated in the URL, Properties, or client
|
|||
2. Properties connProps
|
||||
3. the configuration file taos.cfg of the TDengine client driver when using a native connection
|
||||
|
||||
For example, if you specify the password as `taosdata` in the URL and specify the password as `taosdemo` in the Properties simultaneously. In this case, JDBC will use the password in the URL to establish the connection.
|
||||
For example, if you specify the password as `taosdata` in the URL and specify the password as `taosdemo` in the Properties simultaneously, JDBC will use the password in the URL to establish the connection.
|
||||
|
||||
## Usage examples
|
||||
|
||||
|
|
@ -323,7 +323,7 @@ while(resultSet.next()){
|
|||
}
|
||||
```
|
||||
|
||||
> The query is consistent with operating a relational database. When using subscripts to get the contents of the returned fields, starting from 1, it is recommended to use the field names to get them.
|
||||
> The query is consistent with operating a relational database. When using subscripts to get the contents of the returned fields, you have to start from 1. However, we recommend using the field names to get the values of the fields in the result set.
|
||||
|
||||
### Handling exceptions
|
||||
|
||||
|
|
@ -623,7 +623,7 @@ public void setNString(int columnIndex, ArrayList<String> list, int size) throws
|
|||
|
||||
### Schemaless Writing
|
||||
|
||||
Starting with version 2.2.0.0, TDengine has added the ability to schemaless writing. It is compatible with InfluxDB's Line Protocol, OpenTSDB's telnet line protocol, and OpenTSDB's JSON format protocol. See [schemaless writing](/reference/schemaless/) for details.
|
||||
Starting with version 2.2.0.0, TDengine has added the ability to perform schemaless writing. It is compatible with InfluxDB's Line Protocol, OpenTSDB's telnet line protocol, and OpenTSDB's JSON format protocol. See [schemaless writing](/reference/schemaless/) for details.
|
||||
|
||||
**Note**.
|
||||
|
||||
|
|
@ -666,16 +666,16 @@ The TDengine Java Connector supports subscription functionality with the followi
|
|||
#### Create subscriptions
|
||||
|
||||
```java
|
||||
TSDBSubscribe sub = ((TSDBConnection)conn).subscribe("topic", "select * from meters", false);
|
||||
TSDBSubscribe sub = ((TSDBConnection)conn).subscribe("topicname", "select * from meters", false);
|
||||
```
|
||||
|
||||
The three parameters of the `subscribe()` method have the following meanings.
|
||||
|
||||
- topic: the subscribed topic (i.e., name). This parameter is the unique identifier of the subscription
|
||||
- sql: the query statement of the subscription, this statement can only be `select` statement, only the original data should be queried, and you can query only the data in the positive time order
|
||||
- topicname: the name of the subscribed topic. This parameter is the unique identifier of the subscription.
|
||||
- sql: the query statement of the subscription. This statement can only be a `select` statement. Only original data can be queried, and you can query the data only temporal order.
|
||||
- restart: if the subscription already exists, whether to restart or continue the previous subscription
|
||||
|
||||
The above example will use the SQL command `select * from meters` to create a subscription named `topic`. If the subscription exists, it will continue the progress of the previous query instead of consuming all the data from the beginning.
|
||||
The above example will use the SQL command `select * from meters` to create a subscription named `topicname`. If the subscription exists, it will continue the progress of the previous query instead of consuming all the data from the beginning.
|
||||
|
||||
#### Subscribe to consume data
|
||||
|
||||
|
|
|
|||
|
|
@ -11,18 +11,18 @@ import TabItem from "@theme/TabItem";
|
|||
`taospy` is the official Python connector for TDengine. `taospy` provides a rich set of APIs that makes it easy for Python applications to access TDengine. `taospy` wraps both the [native interface](/reference/connector/cpp) and [REST interface](/reference/rest-api) of TDengine, which correspond to the `taos` and `taosrest` modules of the `taospy` package, respectively.
|
||||
In addition to wrapping the native and REST interfaces, `taospy` also provides a set of programming interfaces that conforms to the [Python Data Access Specification (PEP 249)](https://peps.python.org/pep-0249/). It is easy to integrate `taospy` with many third-party tools, such as [SQLAlchemy](https://www.sqlalchemy.org/) and [pandas](https://pandas.pydata.org/).
|
||||
|
||||
The connection to the server directly using the native interface provided by the client driver is referred to hereinafter as a "native connection"; the connection to the server using the REST interface provided by taosAdapter is referred to hereinafter as a "REST connection".
|
||||
The direct connection to the server using the native interface provided by the client driver is referred to hereinafter as a "native connection"; the connection to the server using the REST interface provided by taosAdapter is referred to hereinafter as a "REST connection".
|
||||
|
||||
The source code for the Python connector is hosted on [GitHub](https://github.com/taosdata/taos-connector-python).
|
||||
|
||||
## Supported Platforms
|
||||
|
||||
- The native connection [supported platforms](/reference/connector/#supported-platforms) is the same as the one supported by the TDengine client.
|
||||
- The [supported platforms](/reference/connector/#supported-platforms) for the native connection are the same as the ones supported by the TDengine client.
|
||||
- REST connections are supported on all platforms that can run Python.
|
||||
|
||||
## Version selection
|
||||
|
||||
We recommend using the latest version of `taospy`, regardless what the version of TDengine is.
|
||||
We recommend using the latest version of `taospy`, regardless of the version of TDengine.
|
||||
|
||||
## Supported features
|
||||
|
||||
|
|
@ -53,7 +53,7 @@ Earlier TDengine client software includes the Python connector. If the Python co
|
|||
|
||||
:::
|
||||
|
||||
#### to install `taospy`
|
||||
#### To install `taospy`
|
||||
|
||||
<Tabs>
|
||||
<TabItem label="Install from PyPI" value="pypi">
|
||||
|
|
@ -139,7 +139,7 @@ The FQDN above can be the FQDN of any dnode in the cluster, and the PORT is the
|
|||
</TabItem>
|
||||
<TabItem value="rest" label="REST connection" groupId="connect">
|
||||
|
||||
For REST connections and making sure the cluster is up, make sure the taosAdapter component is up. This can be tested using the following `curl ` command.
|
||||
For REST connections, make sure the cluster and taosAdapter component, are running. This can be tested using the following `curl ` command.
|
||||
|
||||
```
|
||||
curl -u root:taosdata http://<FQDN>:<PORT>/rest/sql -d "select server_version()"
|
||||
|
|
@ -312,7 +312,7 @@ For a more detailed description of the `sql()` method, please refer to [RestClie
|
|||
|
||||
### Exception handling
|
||||
|
||||
All database operations will be thrown directly if an exception occurs. The application is responsible for exception handling. For example:
|
||||
All errors from database operations are thrown directly as exceptions and the error message from the database is passed up the exception stack. The application is responsible for exception handling. For example:
|
||||
|
||||
```python
|
||||
{{#include docs-examples/python/handle_exception.py}}
|
||||
|
|
@ -320,7 +320,7 @@ All database operations will be thrown directly if an exception occurs. The appl
|
|||
|
||||
### About nanoseconds
|
||||
|
||||
Due to the current imperfection of Python's nanosecond support (see link below), the current implementation returns integers at nanosecond precision instead of the `datetime` type produced by `ms and `us`, which application developers will need to handle on their own. And it is recommended to use pandas' to_datetime(). The Python Connector may modify the interface in the future if Python officially supports nanoseconds in full.
|
||||
Due to the current imperfection of Python's nanosecond support (see link below), the current implementation returns integers at nanosecond precision instead of the `datetime` type produced by `ms` and `us`, which application developers will need to handle on their own. And it is recommended to use pandas' to_datetime(). The Python Connector may modify the interface in the future if Python officially supports nanoseconds in full.
|
||||
|
||||
1. https://stackoverflow.com/questions/10611328/parsing-datetime-strings-containing-nanoseconds
|
||||
2. https://www.python.org/dev/peps/pep-0564/
|
||||
|
|
@ -328,7 +328,7 @@ Due to the current imperfection of Python's nanosecond support (see link below),
|
|||
|
||||
## Frequently Asked Questions
|
||||
|
||||
Welcome to [ask questions or report questions] (https://github.com/taosdata/taos-connector-python/issues).
|
||||
Welcome to [ask questions or report questions](https://github.com/taosdata/taos-connector-python/issues).
|
||||
|
||||
## Important Update
|
||||
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ REST connections are supported on all platforms that can run Rust.
|
|||
|
||||
Please refer to [version support list](/reference/connector#version-support).
|
||||
|
||||
The Rust Connector is still under rapid development and is not guaranteed to be backward compatible before 1.0. Recommend to use TDengine version 2.4 or higher to avoid known issues.
|
||||
The Rust Connector is still under rapid development and is not guaranteed to be backward compatible before 1.0. We recommend using TDengine version 2.4 or higher to avoid known issues.
|
||||
|
||||
## Installation
|
||||
|
||||
|
|
@ -206,7 +206,7 @@ let conn: Taos = cfg.connect();
|
|||
|
||||
### Connection pooling
|
||||
|
||||
In complex applications, recommand to enable connection pool. Connection pool for [libtaos] is implemented using [r2d2].
|
||||
In complex applications, we recommend enabling connection pools. Connection pool for [libtaos] is implemented using [r2d2].
|
||||
|
||||
As follows, a connection pool with default parameters can be generated.
|
||||
|
||||
|
|
@ -269,7 +269,7 @@ The [Taos] structure is the connection manager in [libtaos] and provides two mai
|
|||
|
||||
Note that Rust asynchronous functions and an asynchronous runtime are required.
|
||||
|
||||
[Taos] provides partial Rust methodization of SQL to reduce the frequency of `format!` code blocks.
|
||||
[Taos] provides a few Rust methods that encapsulate SQL to reduce the frequency of `format!` code blocks.
|
||||
|
||||
- `.describe(table: &str)`: Executes `DESCRIBE` and returns a Rust data structure.
|
||||
- `.create_database(database: &str)`: Executes the `CREATE DATABASE` statement.
|
||||
|
|
@ -279,7 +279,7 @@ In addition, this structure is also the entry point for [Parameter Binding](#Par
|
|||
|
||||
### Bind Interface
|
||||
|
||||
Similar to the C interface, Rust provides the bind interface's wraping. First, create a bind object [Stmt] for a SQL command from the [Taos] object.
|
||||
Similar to the C interface, Rust provides the bind interface's wrapping. First, create a bind object [Stmt] for a SQL command from the [Taos] object.
|
||||
|
||||
```rust
|
||||
let mut stmt: Stmt = taos.stmt("insert into ? values(? ,?)") ? ;
|
||||
|
|
|
|||
|
|
@ -30,9 +30,9 @@ taosAdapter provides the following features.
|
|||
|
||||
### Install taosAdapter
|
||||
|
||||
taosAdapter has been part of TDengine server software since TDengine v2.4.0.0. If you use the TDengine server, you don't need additional steps to install taosAdapter. You can download taosAdapter from [TAOSData official website](https://taosdata.com/en/all-downloads/) to download the TDengine server installation package (taosAdapter is included in v2.4.0.0 and later version). If you need to deploy taosAdapter separately on another server other than the TDengine server, you should install the full TDengine on that server to install taosAdapter. If you need to build taosAdapter from source code, you can refer to the [Building taosAdapter]( https://github.com/taosdata/taosadapter/blob/develop/BUILD.md) documentation.
|
||||
taosAdapter has been part of TDengine server software since TDengine v2.4.0.0. If you use the TDengine server, you don't need additional steps to install taosAdapter. You can download taosAdapter from [TDengine official website](https://tdengine.com/all-downloads/) to download the TDengine server installation package (taosAdapter is included in v2.4.0.0 and later version). If you need to deploy taosAdapter separately on another server other than the TDengine server, you should install the full TDengine on that server to install taosAdapter. If you need to build taosAdapter from source code, you can refer to the [Building taosAdapter]( https://github.com/taosdata/taosadapter/blob/develop/BUILD.md) documentation.
|
||||
|
||||
### start/stop taosAdapter
|
||||
### Start/Stop taosAdapter
|
||||
|
||||
On Linux systems, the taosAdapter service is managed by `systemd` by default. You can use the command `systemctl start taosadapter` to start the taosAdapter service and use the command `systemctl stop taosadapter` to stop the taosAdapter service.
|
||||
|
||||
|
|
@ -153,8 +153,7 @@ See [example/config/taosadapter.toml](https://github.com/taosdata/taosadapter/bl
|
|||
|
||||
## Feature List
|
||||
|
||||
- Compatible with RESTful interfaces
|
||||
[https://www.taosdata.com/cn/documentation/connector#restful](https://www.taosdata.com/cn/documentation/connector#restful)
|
||||
- Compatible with RESTful interfaces [REST API](/reference/rest-api/)
|
||||
- Compatible with InfluxDB v1 write interface
|
||||
[https://docs.influxdata.com/influxdb/v2.0/reference/api/influxdb-1x/write/](https://docs.influxdata.com/influxdb/v2.0/reference/api/influxdb-1x/write/)
|
||||
- Compatible with OpenTSDB JSON and telnet format writes
|
||||
|
|
@ -187,7 +186,7 @@ You can use any client that supports the http protocol to write data to or query
|
|||
|
||||
### InfluxDB
|
||||
|
||||
You can use any client that supports the http protocol to access the Restful interface address `http://<fqdn>:6041/<APIEndPoint>` to write data in InfluxDB compatible format to TDengine. The EndPoint is as follows:
|
||||
You can use any client that supports the http protocol to access the RESTful interface address `http://<fqdn>:6041/<APIEndPoint>` to write data in InfluxDB compatible format to TDengine. The EndPoint is as follows:
|
||||
|
||||
```text
|
||||
/influxdb/v1/write
|
||||
|
|
@ -204,7 +203,7 @@ Note: InfluxDB token authorization is not supported at present. Only Basic autho
|
|||
|
||||
### OpenTSDB
|
||||
|
||||
You can use any client that supports the http protocol to access the Restful interface address `http://<fqdn>:6041/<APIEndPoint>` to write data in OpenTSDB compatible format to TDengine.
|
||||
You can use any client that supports the http protocol to access the RESTful interface address `http://<fqdn>:6041/<APIEndPoint>` to write data in OpenTSDB compatible format to TDengine.
|
||||
|
||||
```text
|
||||
/opentsdb/v1/put/json/:db
|
||||
|
|
|
|||
|
|
@ -12,14 +12,13 @@ taosdump can back up a database, a super table, or a normal table as a logical d
|
|||
Suppose the specified location already has data files. In that case, taosdump will prompt the user and exit immediately to avoid data overwriting which means that the same path can only be used for one backup.
|
||||
Please be careful if you see a prompt for this.
|
||||
|
||||
taosdump is a logical backup tool and should not be used to back up any raw data, environment settings,
|
||||
Users should not use taosdump to back up raw data, environment settings, hardware information, server configuration, or cluster topology. taosdump uses [Apache AVRO](https://avro.apache.org/) as the data file format to store backup data.
|
||||
|
||||
## Installation
|
||||
|
||||
There are two ways to install taosdump:
|
||||
|
||||
- Install the taosTools official installer. Please find taosTools from [All download links](https://www.taosdata.com/all-downloads) page and download and install it.
|
||||
- Install the taosTools official installer. Please find taosTools from [All download links](https://www.tdengine.com/all-downloads) page and download and install it.
|
||||
|
||||
- Compile taos-tools separately and install it. Please refer to the [taos-tools](https://github.com/taosdata/taos-tools) repository for details.
|
||||
|
||||
|
|
@ -28,14 +27,14 @@ There are two ways to install taosdump:
|
|||
### taosdump backup data
|
||||
|
||||
1. backing up all databases: specify `-A` or `-all-databases` parameter.
|
||||
2. backup multiple specified databases: use `-D db1,db2,... ` parameters; 3.
|
||||
2. backup multiple specified databases: use `-D db1,db2,... ` parameters;
|
||||
3. back up some super or normal tables in the specified database: use `-dbname stbname1 stbname2 tbname1 tbname2 ... ` parameters. Note that the first parameter of this input sequence is the database name, and only one database is supported. The second and subsequent parameters are the names of super or normal tables in that database, separated by spaces.
|
||||
4. back up the system log database: TDengine clusters usually contain a system database named `log`. The data in this database is the data that TDengine runs itself, and the taosdump will not back up the log database by default. If users need to back up the log database, users can use the `-a` or `-allow-sys` command-line parameter.
|
||||
5. Loose mode backup: taosdump version 1.4.1 onwards provides `-n` and `-L` parameters for backing up data without using escape characters and "loose" mode, which can reduce the number of backups if table names, column names, tag names do not use This can reduce the backup data time and backup data footprint if table names, column names, and tag names do not use `escape character`. If you are unsure about using `-n` and `-L` conditions, please use the default parameters for "strict" mode backup. See the [official documentation](/taos-sql/escape) for a description of escaped characters.
|
||||
|
||||
:::tip
|
||||
- taosdump versions after 1.4.1 provide the `-I` argument for parsing Avro file schema and data. If users specify `-s` then only taosdump will parse schema.
|
||||
- Backups after taosdump 1.4.2 use the batch count specified by the `-B` parameter. The default value is 16384. If, in some environments, low network speed or disk performance causes "Error actual dump ... batch ..." can be tried by challenging the `-B` parameter to a smaller value.
|
||||
- Backups after taosdump 1.4.2 use the batch count specified by the `-B` parameter. The default value is 16384. If, in some environments, low network speed or disk performance causes "Error actual dump ... batch ...", then try changing the `-B` parameter to a smaller value.
|
||||
|
||||
:::
|
||||
|
||||
|
|
@ -44,7 +43,7 @@ There are two ways to install taosdump:
|
|||
Restore the data file in the specified path: use the `-i` parameter plus the path to the data file. You should not use the same directory to backup different data sets, and you should not backup the same data set multiple times in the same path. Otherwise, the backup data will cause overwriting or multiple backups.
|
||||
|
||||
:::tip
|
||||
taosdump internally uses TDengine stmt binding API for writing recovery data and currently uses 16384 as one write batch for better data recovery performance. If there are more columns in the backup data, it may cause a "WAL size exceeds limit" error. You can try to adjust to a smaller value by using the `-B` parameter.
|
||||
taosdump internally uses TDengine stmt binding API for writing recovery data with a default batch size of 16384 for better data recovery performance. If there are more columns in the backup data, it may cause a "WAL size exceeds limit" error. You can try to adjust the batch size to a smaller value by using the `-B` parameter.
|
||||
|
||||
:::
|
||||
|
||||
|
|
|
|||
|
|
@ -61,7 +61,7 @@ sudo yum install \
|
|||
|
||||
## Automated deployment of TDinsight
|
||||
|
||||
We provide an installation script [`TDinsight.sh`](https://github.com/taosdata/grafanaplugin/releases/latest/download/TDinsight.sh) script to allow users to configure the installation automatically and quickly.
|
||||
We provide an installation script [`TDinsight.sh`](https://github.com/taosdata/grafanaplugin/releases/latest/download/TDinsight.sh) to allow users to configure the installation automatically and quickly.
|
||||
|
||||
You can download the script via `wget` or other tools:
|
||||
|
||||
|
|
@ -300,7 +300,7 @@ This section contains the current information and status of the cluster, the ale
|
|||
|
||||
|
||||
|
||||
1. **MNodes Status**: a simple table view of `show mnodes`. 2.
|
||||
1. **MNodes Status**: a simple table view of `show mnodes`.
|
||||
2. **MNodes Number**: similar to `DNodes Number`, the number of MNodes changes.
|
||||
|
||||
### Request
|
||||
|
|
@ -317,9 +317,9 @@ This section contains the current information and status of the cluster, the ale
|
|||
|
||||
Database usage, repeated for each value of the variable `$database` i.e. multiple rows per database.
|
||||
|
||||
1. **STables**: number of super tables. 2.
|
||||
2. **Total Tables**: number of all tables. 3.
|
||||
3. **Sub Tables**: the number of all super table sub-tables. 4.
|
||||
1. **STables**: number of super tables.
|
||||
2. **Total Tables**: number of all tables.
|
||||
3. **Sub Tables**: the number of all super table subtables.
|
||||
4. **Tables**: graph of all normal table numbers over time.
|
||||
5. **Tables Number Foreach VGroups**: The number of tables contained in each VGroups.
|
||||
|
||||
|
|
@ -330,18 +330,18 @@ Database usage, repeated for each value of the variable `$database` i.e. multipl
|
|||
Data node resource usage display with repeated multiple rows for the variable `$fqdn` i.e., each data node. Includes.
|
||||
|
||||
1. **Uptime**: the time elapsed since the dnode was created.
|
||||
2. **Has MNodes?**: whether the current dnode is a mnode. 3.
|
||||
3. **CPU Cores**: the number of CPU cores. 4.
|
||||
4. **VNodes Number**: the number of VNodes in the current dnode. 5.
|
||||
5. **VNodes Masters**: the number of vnodes in the master role. 6.
|
||||
2. **Has MNodes?**: whether the current dnode is a mnode.
|
||||
3. **CPU Cores**: the number of CPU cores.
|
||||
4. **VNodes Number**: the number of VNodes in the current dnode.
|
||||
5. **VNodes Masters**: the number of vnodes in the master role.
|
||||
6. **Current CPU Usage of taosd**: CPU usage rate of taosd processes.
|
||||
7. **Current Memory Usage of taosd**: memory usage of taosd processes.
|
||||
8. **Disk Used**: The total disk usage percentage of the taosd data directory.
|
||||
9. **CPU Usage**: Process and system CPU usage. 10.
|
||||
9. **CPU Usage**: Process and system CPU usage.
|
||||
10. **RAM Usage**: Time series view of RAM usage metrics.
|
||||
11. **Disk Used**: Disks used at each level of multi-level storage (default is level0).
|
||||
12. **Disk Increasing Rate per Minute**: Percentage increase or decrease in disk usage per minute.
|
||||
13. **Disk IO**: Disk IO rate. 14.
|
||||
13. **Disk IO**: Disk IO rate.
|
||||
14. **Net IO**: Network IO, the aggregate network IO rate in addition to the local network.
|
||||
|
||||
### Login History
|
||||
|
|
@ -376,7 +376,7 @@ TDinsight installed via the `TDinsight.sh` script can be cleaned up using the co
|
|||
To completely uninstall TDinsight during a manual installation, you need to clean up the following.
|
||||
|
||||
1. the TDinsight Dashboard in Grafana.
|
||||
2. the Data Source in Grafana. 3.
|
||||
2. the Data Source in Grafana.
|
||||
3. remove the `tdengine-datasource` plugin from the plugin installation directory.
|
||||
|
||||
## Integrated Docker Example
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
---
|
||||
title: TDengine Command Line (CLI)
|
||||
sidebar_label: TDengine CLI
|
||||
title: TDengine Command Line Interface (CLI)
|
||||
sidebar_label: Command Line Interface
|
||||
description: Instructions and tips for using the TDengine CLI
|
||||
---
|
||||
|
||||
The TDengine command-line application (hereafter referred to as `TDengine CLI`) is the most simplest way for users to manipulate and interact with TDengine instances.
|
||||
The TDengine command-line interface (hereafter referred to as `TDengine CLI`) is the simplest way for users to manipulate and interact with TDengine instances.
|
||||
|
||||
## Installation
|
||||
|
||||
If executed on the TDengine server-side, there is no need for additional installation steps to install TDengine CLI as it is already included and installed automatically. To run TDengine CLI on the environment which no TDengine server running, the TDengine client installation package needs to be installed first. For details, please refer to [connector](/reference/connector/).
|
||||
If executed on the TDengine server-side, there is no need for additional installation steps to install TDengine CLI as it is already included and installed automatically. To run TDengine CLI in an environment where no TDengine server is running, the TDengine client installation package needs to be installed first. For details, please refer to [connector](/reference/connector/).
|
||||
|
||||
## Execution
|
||||
|
||||
|
|
|
|||
|
|
@ -315,13 +315,13 @@ password: taosdata
|
|||
taoslog-td2:
|
||||
```
|
||||
|
||||
:::note
|
||||
:::note
|
||||
- The `VERSION` environment variable is used to set the tdengine image tag
|
||||
- `TAOS_FIRST_EP` must be set on the newly created instance so that it can join the TDengine cluster; if there is a high availability requirement, `TAOS_SECOND_EP` needs to be used at the same time
|
||||
- `TAOS_REPLICA` is used to set the default number of database replicas. Its value range is [1,3]
|
||||
We recommend setting with `TAOS_ARBITRATOR` to use arbitrator in a two-nodes environment.
|
||||
:::
|
||||
|
||||
We recommend setting it with `TAOS_ARBITRATOR` to use arbitrator in a two-nodes environment.
|
||||
|
||||
:::
|
||||
|
||||
2. Start the cluster
|
||||
|
||||
|
|
|
|||
|
|
@ -65,7 +65,7 @@ taos --dump-config
|
|||
| ------------- | ------------------------------------------------------------------------ |
|
||||
| Applicable | Server Only |
|
||||
| Meaning | The FQDN of the host where `taosd` will be started. It can be IP address |
|
||||
| Default Value | The first hostname configured for the hos |
|
||||
| Default Value | The first hostname configured for the host |
|
||||
| Note | It should be within 96 bytes |
|
||||
|
||||
### serverPort
|
||||
|
|
@ -78,7 +78,7 @@ taos --dump-config
|
|||
| Note | REST service is provided by `taosd` before 2.4.0.0 but by `taosAdapter` after 2.4.0.0, the default port of REST service is 6041 |
|
||||
|
||||
:::note
|
||||
TDengine uses continuous 13 ports, both TCP and TCP, from the port specified by `serverPort`. These ports need to be kept as open if firewall is enabled. Below table describes the ports used by TDengine in details.
|
||||
TDengine uses continuous 13 ports, both TCP and UDP, from the port specified by `serverPort`. These ports need to be kept open if firewall is enabled. Below table describes the ports used by TDengine in details.
|
||||
|
||||
:::
|
||||
|
||||
|
|
@ -182,8 +182,8 @@ TDengine uses continuous 13 ports, both TCP and TCP, from the port specified by
|
|||
| ------------- | -------------------------------------------- |
|
||||
| Applicable | Server Only |
|
||||
| Meaning | The maximum number of distinct rows returned |
|
||||
| Value Range | [100,000 - 100, 000, 000] |
|
||||
| Default Value | 100, 000 |
|
||||
| Value Range | [100,000 - 100,000,000] |
|
||||
| Default Value | 100,000 |
|
||||
| Note | After version 2.3.0.0 |
|
||||
|
||||
## Locale Parameters
|
||||
|
|
@ -240,7 +240,7 @@ To avoid the problems of using time strings, Unix timestamp can be used directly
|
|||
| Default Value | Locale configured in host |
|
||||
|
||||
:::info
|
||||
A specific type "nchar" is provided in TDengine to store non-ASCII characters such as Chinese, Japanese, Korean. The characters to be stored in nchar type are firstly encoded in UCS4-LE before sending to server side. To store non-ASCII characters correctly, the encoding format of the client side needs to be set properly.
|
||||
A specific type "nchar" is provided in TDengine to store non-ASCII characters such as Chinese, Japanese, and Korean. The characters to be stored in nchar type are firstly encoded in UCS4-LE before sending to server side. To store non-ASCII characters correctly, the encoding format of the client side needs to be set properly.
|
||||
|
||||
The characters input on the client side are encoded using the default system encoding, which is UTF-8 on Linux, or GB18030 or GBK on some systems in Chinese, POSIX in docker, CP936 on Windows in Chinese. The encoding of the operating system in use must be set correctly so that the characters in nchar type can be converted to UCS4-LE.
|
||||
|
||||
|
|
@ -779,7 +779,7 @@ To prevent system resource from being exhausted by multiple concurrent streams,
|
|||
|
||||
:::note
|
||||
HTTP server had been provided by `taosd` prior to version 2.4.0.0, now is provided by `taosAdapter` after version 2.4.0.0.
|
||||
The parameters described in this section are only application in versions prior to 2.4.0.0. If you are using any version from 2.4.0.0, please refer to [taosAdapter]](/reference/taosadapter/).
|
||||
The parameters described in this section are only application in versions prior to 2.4.0.0. If you are using any version from 2.4.0.0, please refer to [taosAdapter](/reference/taosadapter/).
|
||||
|
||||
:::
|
||||
|
||||
|
|
|
|||
|
|
@ -32,7 +32,7 @@ All executable files of TDengine are in the _/usr/local/taos/bin_ directory by d
|
|||
- _taosd-dump-cfg.gdb_: script to facilitate debugging of taosd's gdb execution.
|
||||
|
||||
:::note
|
||||
taosdump after version 2.4.0.0 require taosTools as a standalone installation. A few version taosBenchmark is include in taosTools too.
|
||||
taosdump after version 2.4.0.0 require taosTools as a standalone installation. A new version of taosBenchmark is include in taosTools too.
|
||||
:::
|
||||
|
||||
:::tip
|
||||
|
|
|
|||
|
|
@ -3,17 +3,17 @@ title: Schemaless Writing
|
|||
description: "The Schemaless write method eliminates the need to create super tables/sub tables in advance and automatically creates the storage structure corresponding to the data as it is written to the interface."
|
||||
---
|
||||
|
||||
In IoT applications, many data items are often collected for intelligent control, business analysis, device monitoring, etc. Due to the version upgrade of the application logic, or the hardware adjustment of the device itself, the data collection items may change more frequently. To facilitate the data logging work in such cases, TDengine starting from version 2.2.0.0, it provides a series of interfaces to the schemaless writing method, which eliminates the need to create super tables/sub tables in advance and automatically creates the storage structure corresponding to the data as the data is written to the interface. And when necessary, Schemaless writing will automatically add the required columns to ensure that the data written by the user is stored correctly.
|
||||
In IoT applications, many data items are often collected for intelligent control, business analysis, device monitoring, etc. Due to the version upgrades of the application logic, or the hardware adjustment of the devices themselves, the data collection items may change frequently. To facilitate the data logging work in such cases, TDengine starting from version 2.2.0.0 provides a series of interfaces to the schemaless writing method, which eliminate the need to create super tables and subtables in advance by automatically creating the storage structure corresponding to the data as the data is written to the interface. And when necessary, schemaless writing will automatically add the required columns to ensure that the data written by the user is stored correctly.
|
||||
|
||||
The schemaless writing method creates super tables and their corresponding sub-tables completely indistinguishable from the super tables and sub-tables created directly via SQL. You can write data directly to them via SQL statements. Note that the names of tables created by schemaless writing are based on fixed mapping rules for tag values, so they are not explicitly ideographic and lack readability.
|
||||
The schemaless writing method creates super tables and their corresponding subtables completely indistinguishable from the super tables and subtables created directly via SQL. You can write data directly to them via SQL statements. Note that the names of tables created by schemaless writing are based on fixed mapping rules for tag values, so they are not explicitly ideographic and lack readability.
|
||||
|
||||
## Schemaless Writing Line Protocol
|
||||
|
||||
TDengine's schemaless writing line protocol supports to be compatible with InfluxDB's Line Protocol, OpenTSDB's telnet line protocol, and OpenTSDB's JSON format protocol. However, when using these three protocols, you need to specify in the API the standard of the parsing protocol to be used for the input content.
|
||||
TDengine's schemaless writing line protocol supports InfluxDB's Line Protocol, OpenTSDB's telnet line protocol, and OpenTSDB's JSON format protocol. However, when using these three protocols, you need to specify in the API the standard of the parsing protocol to be used for the input content.
|
||||
|
||||
For the standard writing protocols of InfluxDB and OpenTSDB, please refer to the documentation of each protocol. The following is a description of TDengine's extended protocol, based on InfluxDB's line protocol first. They allow users to control the (super table) schema more granularly.
|
||||
|
||||
With the following formatting conventions, Schemaless writing uses a single string to express a data row (multiple rows can be passed into the writing API at once to enable bulk writing).
|
||||
With the following formatting conventions, schemaless writing uses a single string to express a data row (multiple rows can be passed into the writing API at once to enable bulk writing).
|
||||
|
||||
```json
|
||||
measurement,tag_set field_set timestamp
|
||||
|
|
@ -23,7 +23,7 @@ where :
|
|||
|
||||
- measurement will be used as the data table name. It will be separated from tag_set by a comma.
|
||||
- tag_set will be used as tag data in the format `<tag_key>=<tag_value>,<tag_key>=<tag_value>`, i.e. multiple tags' data can be separated by a comma. It is separated from field_set by space.
|
||||
- field_set will be used as normal column data in the format of `<field_key>=<field_value>,<field_key>=<field_value>`, again using a comma to separate multiple normal columns of data. It is separated from the timestamp by space.
|
||||
- field_set will be used as normal column data in the format of `<field_key>=<field_value>,<field_key>=<field_value>`, again using a comma to separate multiple normal columns of data. It is separated from the timestamp by a space.
|
||||
- The timestamp is the primary key corresponding to the data in this row.
|
||||
|
||||
All data in tag_set is automatically converted to the NCHAR data type and does not require double quotes (").
|
||||
|
|
@ -32,7 +32,7 @@ In the schemaless writing data line protocol, each data item in the field_set ne
|
|||
|
||||
- If there are English double quotes on both sides, it indicates the BINARY(32) type. For example, `"abc"`.
|
||||
- If there are double quotes on both sides and an L prefix, it means NCHAR(32) type. For example, `L"error message"`.
|
||||
- Spaces, equal signs (=), commas (,), and double quotes (") need to be escaped with a backslash (\) in front. (All refer to the ASCII character)
|
||||
- Spaces, equal signs (=), commas (,), and double quotes (") need to be escaped with a backslash (\\) in front. (All refer to the ASCII character)
|
||||
- Numeric types will be distinguished from data types by the suffix.
|
||||
|
||||
| **Serial number** | **Postfix** | **Mapping type** | **Size (bytes)** |
|
||||
|
|
@ -58,26 +58,26 @@ Note that if the wrong case is used when describing the data type suffix, or if
|
|||
|
||||
Schemaless writes process row data according to the following principles.
|
||||
|
||||
1. You can use the following rules to generate the sub-table names: first, combine the measurement name and the key and value of the label into the next string:
|
||||
1. You can use the following rules to generate the subtable names: first, combine the measurement name and the key and value of the label into the next string:
|
||||
|
||||
```json
|
||||
"measurement,tag_key1=tag_value1,tag_key2=tag_value2"
|
||||
```
|
||||
|
||||
Note that tag_key1, tag_key2 are not the original order of the tags entered by the user but the result of using the tag names in ascending order of the strings. Therefore, tag_key1 is not the first tag entered in the line protocol.
|
||||
The string's MD5 hash value "md5_val" is calculated after the ranking is completed. The calculation result is then combined with the string to generate the table name: "t_md5_val". "t*" is a fixed prefix that every table generated by this mapping relationship has. 2.
|
||||
The string's MD5 hash value "md5_val" is calculated after the ranking is completed. The calculation result is then combined with the string to generate the table name: "t_md5_val". "t*" is a fixed prefix that every table generated by this mapping relationship has.
|
||||
|
||||
2. If the super table obtained by parsing the line protocol does not exist, this super table is created.
|
||||
If the sub-table obtained by the parse line protocol does not exist, Schemaless creates the sub-table according to the sub-table name determined in steps 1 or 2. 4.
|
||||
If the subtable obtained by the parse line protocol does not exist, Schemaless creates the sub-table according to the subtable name determined in steps 1 or 2.
|
||||
4. If the specified tag or regular column in the data row does not exist, the corresponding tag or regular column is added to the super table (only incremental).
|
||||
5. If there are some tag columns or regular columns in the super table that are not specified to take values in a data row, then the values of these columns are set to NULL.
|
||||
6. For BINARY or NCHAR columns, if the length of the value provided in a data row exceeds the column type limit, the maximum length of characters allowed to be stored in the column is automatically increased (only incremented and not decremented) to ensure complete preservation of the data.
|
||||
7. If the specified data sub-table already exists, and the specified tag column takes a value different from the saved value this time, the value in the latest data row overwrites the old tag column take value.
|
||||
7. If the specified data subtable already exists, and the specified tag column takes a value different from the saved value this time, the value in the latest data row overwrites the old tag column take value.
|
||||
8. Errors encountered throughout the processing will interrupt the writing process and return an error code.
|
||||
|
||||
:::tip
|
||||
All processing logic of schemaless will still follow TDengine's underlying restrictions on data structures, such as the total length of each row of data cannot exceed
|
||||
16k bytes. See [TAOS SQL Boundary Limits](/taos-sql/limit) for specific constraints in this area.
|
||||
48k bytes. See [TAOS SQL Boundary Limits](/taos-sql/limit) for specific constraints in this area.
|
||||
:::
|
||||
|
||||
## Time resolution recognition
|
||||
|
|
|
|||
|
|
@ -2,11 +2,11 @@
|
|||
title: Reference
|
||||
---
|
||||
|
||||
The reference guide is the detailed introduction to TDengine, various TDengine's connectors in different languages, and the tools that come with it.
|
||||
The reference guide is a detailed introduction to TDengine including various TDengine connectors in different languages, and the tools that come with TDengine.
|
||||
|
||||
```mdx-code-block
|
||||
import DocCardList from '@theme/DocCardList';
|
||||
import {useCurrentSidebarCategory} from '@docusaurus/theme-common';
|
||||
|
||||
<DocCardList items={useCurrentSidebarCategory().items}/>
|
||||
```
|
||||
```
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ You can download The Grafana plugin for TDengine from <https://github.com/taosda
|
|||
|
||||
Recommend using the [``grafana-cli`` command-line tool](https://grafana.com/docs/grafana/latest/administration/cli/) for plugin installation.
|
||||
|
||||
``bash
|
||||
```bash
|
||||
sudo -u grafana grafana-cli \
|
||||
--pluginUrl https://github.com/taosdata/grafanaplugin/releases/download/v3.1.4/tdengine-datasource-3.1.4.zip \
|
||||
plugins install tdengine-datasource
|
||||
|
|
@ -88,7 +88,7 @@ Go back to the main interface to create the Dashboard, click Add Query to enter
|
|||
|
||||
As shown above, select the `TDengine` data source in the `Query` and enter the corresponding SQL in the query box below for query.
|
||||
|
||||
- INPUT SQL: enter the statement to be queried (the result set of the SQL statement should be two columns and multiple rows), for example: `select avg(mem_system) from log.dn where ts >= $from and ts < $to interval($interval)`, where, from, to and interval are built-in variables of the TDengine plugin, indicating the range and time interval of queries fetched from the Grafana plugin panel. In addition to the built-in variables, ` custom template variables are also supported.
|
||||
- INPUT SQL: enter the statement to be queried (the result set of the SQL statement should be two columns and multiple rows), for example: `select avg(mem_system) from log.dn where ts >= $from and ts < $to interval($interval)`, where, from, to and interval are built-in variables of the TDengine plugin, indicating the range and time interval of queries fetched from the Grafana plugin panel. In addition to the built-in variables, custom template variables are also supported.
|
||||
- ALIAS BY: This allows you to set the current query alias.
|
||||
- GENERATE SQL: Clicking this button will automatically replace the corresponding variables and generate the final executed statement.
|
||||
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ sidebar_label: EMQX Broker
|
|||
title: EMQX Broker writing
|
||||
---
|
||||
|
||||
MQTT is a popular IoT data transfer protocol, [EMQX](https://github.com/emqx/emqx) is an open-source MQTT Broker software, without any code, only need to use "rules" in EMQX Dashboard to do simple configuration. You can write MQTT data directly to TDengine. EMQX supports saving data to TDengine by sending it to web services and provides a native TDengine driver for direct saving in the Enterprise Edition. Please refer to the [EMQX official documentation](https://www.emqx.io/docs/en/v4.4/rule/rule-engine.html) for details on how to use it. tdengine).
|
||||
MQTT is a popular IoT data transfer protocol, [EMQX](https://github.com/emqx/emqx) is an open-source MQTT Broker software, you can write MQTT data directly to TDengine without any code, you only need to use "rules" in EMQX Dashboard to create a simple configuration. EMQX supports saving data to TDengine by sending it to web services and provides a native TDengine driver for direct saving in the Enterprise Edition. Please refer to the [EMQX official documentation](https://www.emqx.io/docs/en/v4.4/rule/rule-engine.html) for details on how to use it.).
|
||||
|
||||
## Prerequisites
|
||||
|
||||
|
|
|
|||
|
|
@ -228,7 +228,7 @@ taos> select * from meters;
|
|||
Query OK, 4 row(s) in set (0.004208s)
|
||||
```
|
||||
|
||||
If you see the above data, the synchronization is successful. If not, check the logs of Kafka Connect. For detailed description of configuration parameters, see [Configuration Reference](#Configuration Reference).
|
||||
If you see the above data, the synchronization is successful. If not, check the logs of Kafka Connect. For detailed description of configuration parameters, see [Configuration Reference](#configuration-reference).
|
||||
|
||||
## The use of TDengine Source Connector
|
||||
|
||||
|
|
|
|||
|
|
@ -118,7 +118,7 @@ Output is like below:
|
|||
{"status":"succ","head":["name","created_time","ntables","vgroups","replica","quorum","days","keep0,keep1,keep(D)","cache(MB)","blocks","minrows","maxrows","wallevel","fsync","comp","cachelast","precision","update","status"],"column_meta":[["name",8,32],["created_time",9,8],["ntables",4,4],["vgroups",4,4],["replica",3,2],["quorum",3,2],["days",3,2],["keep0,keep1,keep(D)",8,24],["cache(MB)",4,4],["blocks",4,4],["minrows",4,4],["maxrows",4,4],["wallevel",2,1],["fsync",4,4],["comp",2,1],["cachelast",2,1],["precision",8,3],["update",2,1],["status",8,10]],"data":[["test","2021-08-18 06:01:11.021",10000,4,1,1,10,"3650,3650,3650",16,6,100,4096,1,3000,2,0,"ms",0,"ready"],["log","2021-08-18 05:51:51.065",4,1,1,1,10,"30,30,30",1,3,100,4096,1,3000,2,0,"us",0,"ready"]],"rows":2}
|
||||
```
|
||||
|
||||
For details of REST API please refer to [REST API]](/reference/rest-api/).
|
||||
For details of REST API please refer to [REST API](/reference/rest-api/).
|
||||
|
||||
### Run TDengine server and taosAdapter inside container
|
||||
|
||||
|
|
@ -265,7 +265,7 @@ Below is an example output:
|
|||
$ taos> select groupid, location from test.d0;
|
||||
groupid | location |
|
||||
=================================
|
||||
0 | California.SanDieo |
|
||||
0 | California.SanDiego |
|
||||
Query OK, 1 row(s) in set (0.003490s)
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -182,14 +182,14 @@ int main() {
|
|||
// query callback ...
|
||||
// ts current voltage phase location groupid
|
||||
// numOfRow = 8
|
||||
// 1538548685000 10.300000 219 0.310000 beijing.chaoyang 2
|
||||
// 1538548695000 12.600000 218 0.330000 beijing.chaoyang 2
|
||||
// 1538548696800 12.300000 221 0.310000 beijing.chaoyang 2
|
||||
// 1538548696650 10.300000 218 0.250000 beijing.chaoyang 3
|
||||
// 1538548685500 11.800000 221 0.280000 beijing.haidian 2
|
||||
// 1538548696600 13.400000 223 0.290000 beijing.haidian 2
|
||||
// 1538548685000 10.800000 223 0.290000 beijing.haidian 3
|
||||
// 1538548686500 11.500000 221 0.350000 beijing.haidian 3
|
||||
// 1538548685500 11.800000 221 0.280000 california.losangeles 2
|
||||
// 1538548696600 13.400000 223 0.290000 california.losangeles 2
|
||||
// 1538548685000 10.800000 223 0.290000 california.losangeles 3
|
||||
// 1538548686500 11.500000 221 0.350000 california.losangeles 3
|
||||
// 1538548685000 10.300000 219 0.310000 california.sanfrancisco 2
|
||||
// 1538548695000 12.600000 218 0.330000 california.sanfrancisco 2
|
||||
// 1538548696800 12.300000 221 0.310000 california.sanfrancisco 2
|
||||
// 1538548696650 10.300000 218 0.250000 california.sanfrancisco 3
|
||||
// numOfRow = 0
|
||||
// no more data, close the connection.
|
||||
// ANCHOR_END: demo
|
||||
|
|
@ -224,15 +224,15 @@ namespace TDengineExample
|
|||
}
|
||||
|
||||
//output:
|
||||
//Connect to TDengine success
|
||||
//8 rows async retrieved
|
||||
// Connect to TDengine success
|
||||
// 8 rows async retrieved
|
||||
|
||||
//1538548685000 | 10.3 | 219 | 0.31 | beijing.chaoyang | 2 |
|
||||
//1538548695000 | 12.6 | 218 | 0.33 | beijing.chaoyang | 2 |
|
||||
//1538548696800 | 12.3 | 221 | 0.31 | beijing.chaoyang | 2 |
|
||||
//1538548696650 | 10.3 | 218 | 0.25 | beijing.chaoyang | 3 |
|
||||
//1538548685500 | 11.8 | 221 | 0.28 | beijing.haidian | 2 |
|
||||
//1538548696600 | 13.4 | 223 | 0.29 | beijing.haidian | 2 |
|
||||
//1538548685000 | 10.8 | 223 | 0.29 | beijing.haidian | 3 |
|
||||
//1538548686500 | 11.5 | 221 | 0.35 | beijing.haidian | 3 |
|
||||
//async retrieve complete.
|
||||
// 1538548685500 | 11.8 | 221 | 0.28 | california.losangeles | 2 |
|
||||
// 1538548696600 | 13.4 | 223 | 0.29 | california.losangeles | 2 |
|
||||
// 1538548685000 | 10.8 | 223 | 0.29 | california.losangeles | 3 |
|
||||
// 1538548686500 | 11.5 | 221 | 0.35 | california.losangeles | 3 |
|
||||
// 1538548685000 | 10.3 | 219 | 0.31 | california.sanfrancisco | 2 |
|
||||
// 1538548695000 | 12.6 | 218 | 0.33 | california.sanfrancisco | 2 |
|
||||
// 1538548696800 | 12.3 | 221 | 0.31 | california.sanfrancisco | 2 |
|
||||
// 1538548696650 | 10.3 | 218 | 0.25 | california.sanfrancisco | 3 |
|
||||
// async retrieve complete.
|
||||
|
|
@ -20,4 +20,4 @@ func main() {
|
|||
|
||||
// use
|
||||
// var taosDSN = "root:taosdata@tcp(localhost:6030)/dbName"
|
||||
// if you want to connect to a default database.
|
||||
// if you want to connect a specified database named "dbName".
|
||||
|
|
|
|||
|
|
@ -18,6 +18,6 @@ func main() {
|
|||
defer taos.Close()
|
||||
}
|
||||
|
||||
// use
|
||||
// use
|
||||
// var taosDSN = "root:taosdata@http(localhost:6041)/dbName"
|
||||
// if you want to connect to a default database.
|
||||
// if you want to connect a specified database named "dbName".
|
||||
|
|
|
|||
|
|
@ -22,4 +22,4 @@ public class JNIConnectExample {
|
|||
|
||||
// use
|
||||
// String jdbcUrl = "jdbc:TAOS://localhost:6030/dbName?user=root&password=taosdata";
|
||||
// if you want to connect to a default database.
|
||||
// if you want to connect a specified database named "dbName".
|
||||
|
|
@ -13,7 +13,7 @@ print(df.head(3))
|
|||
# output:
|
||||
# RangeIndex(start=0, stop=8, step=1)
|
||||
# <class 'pandas._libs.tslibs.timestamps.Timestamp'>
|
||||
# ts current voltage phase location groupid
|
||||
# 0 2018-10-03 14:38:05.000 10.3 219 0.31 beijing.chaoyang 2
|
||||
# 1 2018-10-03 14:38:15.000 12.6 218 0.33 beijing.chaoyang 2
|
||||
# 2 2018-10-03 14:38:16.800 12.3 221 0.31 beijing.chaoyang 2
|
||||
# ts current ... location groupid
|
||||
# 0 2018-10-03 14:38:05.500 11.8 ... california.losangeles 2
|
||||
# 1 2018-10-03 14:38:16.600 13.4 ... california.losangeles 2
|
||||
# 2 2018-10-03 14:38:05.000 10.8 ... california.losangeles 3
|
||||
|
|
|
|||
|
|
@ -11,9 +11,9 @@ print(type(df.ts[0]))
|
|||
print(df.head(3))
|
||||
|
||||
# output:
|
||||
# <class 'datetime.datetime'>
|
||||
# RangeIndex(start=0, stop=8, step=1)
|
||||
# ts current ... location groupid
|
||||
# 0 2018-10-03 14:38:05+08:00 10.3 ... beijing.chaoyang 2
|
||||
# 1 2018-10-03 14:38:15+08:00 12.6 ... beijing.chaoyang 2
|
||||
# 2 2018-10-03 14:38:16.800000+08:00 12.3 ... beijing.chaoyang 2
|
||||
# <class 'pandas._libs.tslibs.timestamps.Timestamp'>
|
||||
# ts current ... location groupid
|
||||
# 0 2018-10-03 06:38:05.500000+00:00 11.8 ... california.losangeles 2
|
||||
# 1 2018-10-03 06:38:16.600000+00:00 13.4 ... california.losangeles 2
|
||||
# 2 2018-10-03 06:38:05+00:00 10.8 ... california.losangeles 3
|
||||
|
|
|
|||
|
|
@ -38,8 +38,7 @@ for row in data:
|
|||
# inserted row count: 8
|
||||
# queried row count: 3
|
||||
# ['ts', 'current', 'voltage', 'phase', 'location', 'groupid']
|
||||
# [datetime.datetime(2018, 10, 3, 14, 38, 5, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 10.3, 219, 0.31, 'beijing.chaoyang', 2]
|
||||
# [datetime.datetime(2018, 10, 3, 14, 38, 15, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 12.6, 218, 0.33, 'beijing.chaoyang', 2]
|
||||
# [datetime.datetime(2018, 10, 3, 14, 38, 16, 800000, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 12.3, 221, 0.31, 'beijing.chaoyang', 2]
|
||||
|
||||
# [datetime.datetime(2018, 10, 3, 14, 38, 5, 500000, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 11.8, 221, 0.28, 'california.losangeles', 2]
|
||||
# [datetime.datetime(2018, 10, 3, 14, 38, 16, 600000, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 13.4, 223, 0.29, 'california.losangeles', 2]
|
||||
# [datetime.datetime(2018, 10, 3, 14, 38, 5, tzinfo=datetime.timezone(datetime.timedelta(seconds=28800), '+08:00')), 10.8, 223, 0.29, 'california.losangeles', 3]
|
||||
# ANCHOR_END: basic
|
||||
|
|
|
|||
|
|
@ -5,9 +5,9 @@ from taos import SmlProtocol, SmlPrecision
|
|||
|
||||
lines = [{"metric": "meters.current", "timestamp": 1648432611249, "value": 10.3, "tags": {"location": "California.SanFrancisco", "groupid": 2}},
|
||||
{"metric": "meters.voltage", "timestamp": 1648432611249, "value": 219,
|
||||
"tags": {"location": "California.LosAngeles", "groupid": 1}},
|
||||
"tags": {"location": "California.LosAngeles", "groupid": 1}},
|
||||
{"metric": "meters.current", "timestamp": 1648432611250, "value": 12.6,
|
||||
"tags": {"location": "California.SanFrancisco", "groupid": 2}},
|
||||
"tags": {"location": "California.SanFrancisco", "groupid": 2}},
|
||||
{"metric": "meters.voltage", "timestamp": 1648432611250, "value": 221, "tags": {"location": "California.LosAngeles", "groupid": 1}}]
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -12,10 +12,10 @@ def query_api_demo(conn: taos.TaosConnection):
|
|||
|
||||
|
||||
# field count: 7
|
||||
# meta of files[1]: {name: ts, type: 9, bytes: 8}
|
||||
# meta of fields[1]: {name: ts, type: 9, bytes: 8}
|
||||
# ======================Iterate on result=========================
|
||||
# ('d1001', datetime.datetime(2018, 10, 3, 14, 38, 5), 10.300000190734863, 219, 0.3100000023841858, 'California.SanFrancisco', 2)
|
||||
# ('d1001', datetime.datetime(2018, 10, 3, 14, 38, 15), 12.600000381469727, 218, 0.33000001311302185, 'California.SanFrancisco', 2)
|
||||
# ('d1003', datetime.datetime(2018, 10, 3, 14, 38, 5, 500000), 11.800000190734863, 221, 0.2800000011920929, 'california.losangeles', 2)
|
||||
# ('d1003', datetime.datetime(2018, 10, 3, 14, 38, 16, 600000), 13.399999618530273, 223, 0.28999999165534973, 'california.losangeles', 2)
|
||||
# ANCHOR_END: iter
|
||||
|
||||
# ANCHOR: fetch_all
|
||||
|
|
@ -29,8 +29,8 @@ def fetch_all_demo(conn: taos.TaosConnection):
|
|||
|
||||
# row count: 2
|
||||
# ===============all data===================
|
||||
# [{'ts': datetime.datetime(2018, 10, 3, 14, 38, 5), 'current': 10.300000190734863},
|
||||
# {'ts': datetime.datetime(2018, 10, 3, 14, 38, 15), 'current': 12.600000381469727}]
|
||||
# [{'ts': datetime.datetime(2018, 10, 3, 14, 38, 5, 500000), 'current': 11.800000190734863},
|
||||
# {'ts': datetime.datetime(2018, 10, 3, 14, 38, 16, 600000), 'current': 13.399999618530273}]
|
||||
# ANCHOR_END: fetch_all
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
|||
|
|
@ -105,12 +105,14 @@ typedef struct SColumnInfoData {
|
|||
} SColumnInfoData;
|
||||
|
||||
typedef struct SQueryTableDataCond {
|
||||
STimeWindow twindow;
|
||||
//STimeWindow twindow;
|
||||
int32_t order; // desc|asc order to iterate the data block
|
||||
int32_t numOfCols;
|
||||
SColumnInfo *colList;
|
||||
bool loadExternalRows; // load external rows or not
|
||||
int32_t type; // data block load type:
|
||||
int32_t numOfTWindows;
|
||||
STimeWindow *twindows;
|
||||
} SQueryTableDataCond;
|
||||
|
||||
void* blockDataDestroy(SSDataBlock* pBlock);
|
||||
|
|
|
|||
|
|
@ -230,7 +230,7 @@ SSDataBlock* createOneDataBlock(const SSDataBlock* pDataBlock, bool copyData);
|
|||
void blockDebugShowData(const SArray* dataBlocks);
|
||||
|
||||
int32_t buildSubmitReqFromDataBlock(SSubmitReq** pReq, const SArray* pDataBlocks, STSchema* pTSchema, int32_t vgId,
|
||||
tb_uid_t uid, tb_uid_t suid);
|
||||
tb_uid_t suid);
|
||||
|
||||
SSubmitReq* tdBlockToSubmit(const SArray* pBlocks, const STSchema* pSchema, bool createTb, int64_t suid,
|
||||
const char* stbFullName, int32_t vgId);
|
||||
|
|
@ -299,4 +299,3 @@ static FORCE_INLINE void blockCompressEncode(const SSDataBlock* pBlock, char* da
|
|||
#endif
|
||||
|
||||
#endif /*_TD_COMMON_EP_H_*/
|
||||
|
||||
|
|
|
|||
|
|
@ -479,12 +479,8 @@ int32_t tDecodeSEpSet(SDecoder* pDecoder, SEpSet* pEp);
|
|||
int32_t taosEncodeSEpSet(void** buf, const SEpSet* pEp);
|
||||
void* taosDecodeSEpSet(const void* buf, SEpSet* pEp);
|
||||
|
||||
typedef struct {
|
||||
SEpSet epSet;
|
||||
} SMEpSet;
|
||||
|
||||
int32_t tSerializeSMEpSet(void* buf, int32_t bufLen, SMEpSet* pReq);
|
||||
int32_t tDeserializeSMEpSet(void* buf, int32_t buflen, SMEpSet* pReq);
|
||||
int32_t tSerializeSEpSet(void* buf, int32_t bufLen, const SEpSet* pEpset);
|
||||
int32_t tDeserializeSEpSet(void* buf, int32_t buflen, SEpSet* pEpset);
|
||||
|
||||
typedef struct {
|
||||
int8_t connType;
|
||||
|
|
@ -656,6 +652,9 @@ typedef struct {
|
|||
|
||||
typedef struct {
|
||||
int32_t code;
|
||||
char tbFName[TSDB_TABLE_FNAME_LEN];
|
||||
int32_t sversion;
|
||||
int32_t tversion;
|
||||
} SQueryTableRsp;
|
||||
|
||||
int32_t tSerializeSQueryTableRsp(void* buf, int32_t bufLen, SQueryTableRsp* pRsp);
|
||||
|
|
|
|||
|
|
@ -182,8 +182,6 @@ enum {
|
|||
TD_DEF_MSG_TYPE(TDMT_VND_MQ_DISCONNECT, "vnode-mq-disconnect", NULL, NULL)
|
||||
TD_DEF_MSG_TYPE(TDMT_VND_MQ_VG_CHANGE, "vnode-mq-vg-change", SMqRebVgReq, SMqRebVgRsp)
|
||||
TD_DEF_MSG_TYPE(TDMT_VND_MQ_VG_DELETE, "vnode-mq-vg-delete", SMqVDeleteReq, SMqVDeleteRsp)
|
||||
TD_DEF_MSG_TYPE(TDMT_VND_RES_READY, "vnode-res-ready", NULL, NULL)
|
||||
TD_DEF_MSG_TYPE(TDMT_VND_TASKS_STATUS, "vnode-tasks-status", NULL, NULL)
|
||||
TD_DEF_MSG_TYPE(TDMT_VND_CANCEL_TASK, "vnode-cancel-task", NULL, NULL)
|
||||
TD_DEF_MSG_TYPE(TDMT_VND_DROP_TASK, "vnode-drop-task", NULL, NULL)
|
||||
TD_DEF_MSG_TYPE(TDMT_VND_CREATE_TOPIC, "vnode-create-topic", NULL, NULL)
|
||||
|
|
|
|||
|
|
@ -81,7 +81,7 @@ int32_t mndGetLoad(SMnode *pMnode, SMnodeLoad *pLoad);
|
|||
* @param pMsg The request msg.
|
||||
* @return int32_t 0 for success, -1 for failure.
|
||||
*/
|
||||
int32_t mndProcessMsg(SRpcMsg *pMsg);
|
||||
int32_t mndProcessRpcMsg(SRpcMsg *pMsg);
|
||||
int32_t mndProcessSyncMsg(SRpcMsg *pMsg);
|
||||
|
||||
/**
|
||||
|
|
|
|||
|
|
@ -26,14 +26,17 @@ extern "C" {
|
|||
typedef struct SQnode SQnode;
|
||||
|
||||
typedef struct {
|
||||
int64_t numOfStartTask;
|
||||
int64_t numOfStopTask;
|
||||
int64_t numOfRecvedFetch;
|
||||
int64_t numOfSentHb;
|
||||
int64_t numOfSentFetch;
|
||||
int64_t numOfTaskInQueue;
|
||||
int64_t numOfProcessedQuery;
|
||||
int64_t numOfProcessedCQuery;
|
||||
int64_t numOfProcessedFetch;
|
||||
int64_t numOfProcessedDrop;
|
||||
int64_t memSizeInCache;
|
||||
int64_t dataSizeSend;
|
||||
int64_t dataSizeRecv;
|
||||
int64_t numOfQueryInQueue;
|
||||
int64_t numOfFetchInQueue;
|
||||
int64_t numOfErrors;
|
||||
int64_t waitTimeInQueryQUeue;
|
||||
int64_t waitTimeInFetchQUeue;
|
||||
} SQnodeLoad;
|
||||
|
||||
typedef struct {
|
||||
|
|
@ -71,10 +74,10 @@ int32_t qndGetLoad(SQnode *pQnode, SQnodeLoad *pLoad);
|
|||
* @param pQnode The qnode object.
|
||||
* @param pMsg The request message
|
||||
*/
|
||||
int32_t qndProcessQueryMsg(SQnode *pQnode, SRpcMsg *pMsg);
|
||||
int32_t qndProcessQueryMsg(SQnode *pQnode, int64_t ts, SRpcMsg *pMsg);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
||||
#endif /*_TD_QNODE_H_*/
|
||||
#endif /*_TD_QNODE_H_*/
|
||||
|
|
|
|||
|
|
@ -52,23 +52,31 @@ typedef struct SUserAuthInfo {
|
|||
AUTH_TYPE type;
|
||||
} SUserAuthInfo;
|
||||
|
||||
typedef struct SDbInfo {
|
||||
int32_t vgVer;
|
||||
int32_t tbNum;
|
||||
int64_t dbId;
|
||||
} SDbInfo;
|
||||
|
||||
typedef struct SCatalogReq {
|
||||
SArray *pTableMeta; // element is SNAME
|
||||
SArray *pDbVgroup; // element is db full name
|
||||
SArray *pDbCfg; // element is db full name
|
||||
SArray *pDbInfo; // element is db full name
|
||||
SArray *pTableMeta; // element is SNAME
|
||||
SArray *pTableHash; // element is SNAME
|
||||
SArray *pUdf; // element is udf name
|
||||
SArray *pDbCfg; // element is db full name
|
||||
SArray *pIndex; // element is index name
|
||||
SArray *pUser; // element is SUserAuthInfo
|
||||
bool qNodeRequired; // valid qnode
|
||||
} SCatalogReq;
|
||||
|
||||
typedef struct SMetaData {
|
||||
SArray *pTableMeta; // SArray<STableMeta*>
|
||||
SArray *pDbVgroup; // SArray<SArray<SVgroupInfo>*>
|
||||
SArray *pDbCfg; // SArray<SDbCfgInfo>
|
||||
SArray *pDbInfo; // SArray<SDbInfo>
|
||||
SArray *pTableMeta; // SArray<STableMeta*>
|
||||
SArray *pTableHash; // SArray<SVgroupInfo>
|
||||
SArray *pUdfList; // SArray<SFuncInfo>
|
||||
SArray *pDbCfg; // SArray<SDbCfgInfo>
|
||||
SArray *pIndex; // SArray<SIndexInfo>
|
||||
SArray *pUser; // SArray<bool>
|
||||
SArray *pQnodeList; // SArray<SQueryNodeAddr>
|
||||
|
|
@ -269,6 +277,8 @@ int32_t catalogChkAuth(SCatalog* pCtg, void *pRpc, const SEpSet* pMgmtEps, const
|
|||
|
||||
int32_t catalogUpdateUserAuthInfo(SCatalog* pCtg, SGetUserAuthRsp* pAuth);
|
||||
|
||||
int32_t catalogUpdateVgEpSet(SCatalog* pCtg, const char* dbFName, int32_t vgId, SEpSet *epSet);
|
||||
|
||||
|
||||
int32_t ctgdLaunchAsyncCall(SCatalog* pCtg, void *pTrans, const SEpSet* pMgmtEps, uint64_t reqId);
|
||||
|
||||
|
|
|
|||
|
|
@ -23,6 +23,9 @@ extern "C" {
|
|||
#include "function.h"
|
||||
#include "querynodes.h"
|
||||
|
||||
#define FUNC_AGGREGATE_UDF_ID 5001
|
||||
#define FUNC_SCALAR_UDF_ID 5002
|
||||
|
||||
typedef enum EFunctionType {
|
||||
// aggregate function
|
||||
FUNCTION_TYPE_APERCENTILE = 1,
|
||||
|
|
@ -126,21 +129,12 @@ typedef enum EFunctionType {
|
|||
struct SqlFunctionCtx;
|
||||
struct SResultRowEntryInfo;
|
||||
struct STimeWindow;
|
||||
struct SCatalog;
|
||||
|
||||
typedef struct SFmGetFuncInfoParam {
|
||||
struct SCatalog* pCtg;
|
||||
void* pRpc;
|
||||
const SEpSet* pMgmtEps;
|
||||
char* pErrBuf;
|
||||
int32_t errBufLen;
|
||||
} SFmGetFuncInfoParam;
|
||||
|
||||
int32_t fmFuncMgtInit();
|
||||
|
||||
void fmFuncMgtDestroy();
|
||||
|
||||
int32_t fmGetFuncInfo(SFmGetFuncInfoParam* pParam, SFunctionNode* pFunc);
|
||||
int32_t fmGetFuncInfo(SFunctionNode* pFunc, char* pMsg, int32_t msgLen);
|
||||
|
||||
bool fmIsBuiltinFunc(const char* pFunc);
|
||||
|
||||
|
|
|
|||
|
|
@ -192,11 +192,16 @@ void indexTermDestroy(SIndexTerm* p);
|
|||
void indexInit();
|
||||
|
||||
/* index filter */
|
||||
typedef struct SIndexMetaArg {
|
||||
void* metaHandle;
|
||||
uint64_t suid;
|
||||
} SIndexMetaArg;
|
||||
|
||||
typedef enum { SFLT_NOT_INDEX, SFLT_COARSE_INDEX, SFLT_ACCURATE_INDEX } SIdxFltStatus;
|
||||
|
||||
SIdxFltStatus idxGetFltStatus(SNode* pFilterNode);
|
||||
|
||||
int32_t doFilterTag(const SNode* pFilterNode, SArray* result);
|
||||
int32_t doFilterTag(const SNode* pFilterNode, SIndexMetaArg* metaArg, SArray* result);
|
||||
/*
|
||||
* destory index env
|
||||
*
|
||||
|
|
|
|||
|
|
@ -56,6 +56,9 @@ typedef struct SScanLogicNode {
|
|||
int8_t intervalUnit;
|
||||
int8_t slidingUnit;
|
||||
SNode* pTagCond;
|
||||
int8_t triggerType;
|
||||
int64_t watermark;
|
||||
int16_t tsColId;
|
||||
} SScanLogicNode;
|
||||
|
||||
typedef struct SJoinLogicNode {
|
||||
|
|
@ -216,6 +219,9 @@ typedef struct STableScanPhysiNode {
|
|||
int64_t sliding;
|
||||
int8_t intervalUnit;
|
||||
int8_t slidingUnit;
|
||||
int8_t triggerType;
|
||||
int64_t watermark;
|
||||
int16_t tsColId;
|
||||
} STableScanPhysiNode;
|
||||
|
||||
typedef STableScanPhysiNode STableSeqScanPhysiNode;
|
||||
|
|
|
|||
|
|
@ -322,21 +322,22 @@ typedef enum EQueryExecMode {
|
|||
} EQueryExecMode;
|
||||
|
||||
typedef struct SQuery {
|
||||
ENodeType type;
|
||||
EQueryExecMode execMode;
|
||||
bool haveResultSet;
|
||||
SNode* pRoot;
|
||||
int32_t numOfResCols;
|
||||
SSchema* pResSchema;
|
||||
int8_t precision;
|
||||
SCmdMsgInfo* pCmdMsg;
|
||||
int32_t msgType;
|
||||
SArray* pDbList;
|
||||
SArray* pTableList;
|
||||
bool showRewrite;
|
||||
int32_t placeholderNum;
|
||||
SArray* pPlaceholderValues;
|
||||
SNode* pPrepareRoot;
|
||||
ENodeType type;
|
||||
EQueryExecMode execMode;
|
||||
bool haveResultSet;
|
||||
SNode* pRoot;
|
||||
int32_t numOfResCols;
|
||||
SSchema* pResSchema;
|
||||
int8_t precision;
|
||||
SCmdMsgInfo* pCmdMsg;
|
||||
int32_t msgType;
|
||||
SArray* pDbList;
|
||||
SArray* pTableList;
|
||||
bool showRewrite;
|
||||
int32_t placeholderNum;
|
||||
SArray* pPlaceholderValues;
|
||||
SNode* pPrepareRoot;
|
||||
struct SParseMetaCache* pMetaCache;
|
||||
} SQuery;
|
||||
|
||||
void nodesWalkSelectStmt(SSelectStmt* pSelect, ESqlClause clause, FNodeWalker walker, void* pContext);
|
||||
|
|
|
|||
|
|
@ -23,6 +23,9 @@ extern "C" {
|
|||
#include "query.h"
|
||||
#include "querynodes.h"
|
||||
|
||||
struct SCatalogReq;
|
||||
struct SMetaData;
|
||||
|
||||
typedef struct SStmtCallback {
|
||||
TAOS_STMT* pStmt;
|
||||
int32_t (*getTbNameFn)(TAOS_STMT*, char**);
|
||||
|
|
@ -45,11 +48,17 @@ typedef struct SParseContext {
|
|||
SStmtCallback* pStmtCb;
|
||||
const char* pUser;
|
||||
bool isSuperUser;
|
||||
bool async;
|
||||
} SParseContext;
|
||||
|
||||
int32_t qParseSql(SParseContext* pCxt, SQuery** pQuery);
|
||||
bool qIsInsertSql(const char* pStr, size_t length);
|
||||
|
||||
// for async mode
|
||||
int32_t qSyntaxParseSql(SParseContext* pCxt, SQuery** pQuery, struct SCatalogReq* pCatalogReq);
|
||||
int32_t qSemanticAnalysisSql(SParseContext* pCxt, const struct SCatalogReq* pCatalogReq,
|
||||
const struct SMetaData* pMetaData, SQuery* pQuery);
|
||||
|
||||
void qDestroyQuery(SQuery* pQueryNode);
|
||||
|
||||
int32_t qExtractResultSchema(const SNode* pRoot, int32_t* numOfCols, SSchema** pSchema);
|
||||
|
|
|
|||
|
|
@ -43,6 +43,12 @@ typedef enum {
|
|||
TASK_TYPE_TEMP,
|
||||
} ETaskType;
|
||||
|
||||
typedef enum {
|
||||
TARGET_TYPE_MNODE = 1,
|
||||
TARGET_TYPE_VNODE,
|
||||
TARGET_TYPE_OTHER,
|
||||
} ETargetType;
|
||||
|
||||
typedef struct STableComInfo {
|
||||
uint8_t numOfTags; // the number of tags in schema
|
||||
uint8_t precision; // the number of precision
|
||||
|
|
@ -126,11 +132,18 @@ typedef struct SDataBuf {
|
|||
void* handle;
|
||||
} SDataBuf;
|
||||
|
||||
typedef struct STargetInfo {
|
||||
ETargetType type;
|
||||
char dbFName[TSDB_DB_FNAME_LEN]; // used to update db's vgroup epset
|
||||
int32_t vgId;
|
||||
} STargetInfo;
|
||||
|
||||
typedef int32_t (*__async_send_cb_fn_t)(void* param, const SDataBuf* pMsg, int32_t code);
|
||||
typedef int32_t (*__async_exec_fn_t)(void* param);
|
||||
|
||||
typedef struct SMsgSendInfo {
|
||||
__async_send_cb_fn_t fp; // async callback function
|
||||
STargetInfo target; // for update epset
|
||||
void* param;
|
||||
uint64_t requestId;
|
||||
uint64_t requestObjRefId;
|
||||
|
|
|
|||
|
|
@ -52,32 +52,24 @@ typedef struct {
|
|||
|
||||
int32_t qWorkerInit(int8_t nodeType, int32_t nodeId, SQWorkerCfg *cfg, void **qWorkerMgmt, const SMsgCb *pMsgCb);
|
||||
|
||||
int32_t qWorkerProcessQueryMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg);
|
||||
int32_t qWorkerProcessQueryMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg, int64_t ts);
|
||||
|
||||
int32_t qWorkerProcessCQueryMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg);
|
||||
int32_t qWorkerProcessCQueryMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg, int64_t ts);
|
||||
|
||||
int32_t qWorkerProcessDataSinkMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg);
|
||||
int32_t qWorkerProcessFetchMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg, int64_t ts);
|
||||
|
||||
int32_t qWorkerProcessReadyMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg);
|
||||
int32_t qWorkerProcessFetchRsp(void *node, void *qWorkerMgmt, SRpcMsg *pMsg, int64_t ts);
|
||||
|
||||
int32_t qWorkerProcessStatusMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg);
|
||||
int32_t qWorkerProcessCancelMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg, int64_t ts);
|
||||
|
||||
int32_t qWorkerProcessFetchMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg);
|
||||
int32_t qWorkerProcessDropMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg, int64_t ts);
|
||||
|
||||
int32_t qWorkerProcessFetchRsp(void *node, void *qWorkerMgmt, SRpcMsg *pMsg);
|
||||
|
||||
int32_t qWorkerProcessCancelMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg);
|
||||
|
||||
int32_t qWorkerProcessDropMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg);
|
||||
|
||||
int32_t qWorkerProcessHbMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg);
|
||||
|
||||
int32_t qWorkerProcessShowMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg);
|
||||
|
||||
int32_t qWorkerProcessShowFetchMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg);
|
||||
int32_t qWorkerProcessHbMsg(void *node, void *qWorkerMgmt, SRpcMsg *pMsg, int64_t ts);
|
||||
|
||||
void qWorkerDestroy(void **qWorkerMgmt);
|
||||
|
||||
int64_t qWorkerGetWaitTimeInQueue(void *qWorkerMgmt, EQueueType type);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -66,12 +66,6 @@ typedef struct SSyncCfg {
|
|||
SNodeInfo nodeInfo[TSDB_MAX_REPLICA];
|
||||
} SSyncCfg;
|
||||
|
||||
typedef struct SSnapshot {
|
||||
void* data;
|
||||
SyncIndex lastApplyIndex;
|
||||
SyncTerm lastApplyTerm;
|
||||
} SSnapshot;
|
||||
|
||||
typedef struct SFsmCbMeta {
|
||||
SyncIndex index;
|
||||
bool isWeak;
|
||||
|
|
@ -93,6 +87,12 @@ typedef struct SReConfigCbMeta {
|
|||
uint64_t flag;
|
||||
} SReConfigCbMeta;
|
||||
|
||||
typedef struct SSnapshot {
|
||||
void *data;
|
||||
SyncIndex lastApplyIndex;
|
||||
SyncTerm lastApplyTerm;
|
||||
} SSnapshot;
|
||||
|
||||
typedef struct SSyncFSM {
|
||||
void* data;
|
||||
|
||||
|
|
@ -101,23 +101,17 @@ typedef struct SSyncFSM {
|
|||
void (*FpRollBackCb)(struct SSyncFSM* pFsm, const SRpcMsg* pMsg, SFsmCbMeta cbMeta);
|
||||
|
||||
void (*FpRestoreFinishCb)(struct SSyncFSM* pFsm);
|
||||
int32_t (*FpGetSnapshot)(struct SSyncFSM* pFsm, SSnapshot* pSnapshot);
|
||||
|
||||
// if (*ppIter == NULL)
|
||||
// *ppIter = new iter;
|
||||
// else
|
||||
// *ppIter.next();
|
||||
//
|
||||
// if success, return 0. else return error code
|
||||
int32_t (*FpSnapshotRead)(struct SSyncFSM* pFsm, const SSnapshot* pSnapshot, void** ppIter, char** ppBuf,
|
||||
int32_t* len);
|
||||
|
||||
// apply data into fsm
|
||||
int32_t (*FpSnapshotApply)(struct SSyncFSM* pFsm, const SSnapshot* pSnapshot, char* pBuf, int32_t len);
|
||||
|
||||
void (*FpReConfigCb)(struct SSyncFSM* pFsm, SSyncCfg newCfg, SReConfigCbMeta cbMeta);
|
||||
|
||||
// int32_t (*FpRestoreSnapshot)(struct SSyncFSM* pFsm, const SSnapshot* snapshot);
|
||||
int32_t (*FpGetSnapshot)(struct SSyncFSM* pFsm, SSnapshot* pSnapshot);
|
||||
|
||||
int32_t (*FpSnapshotStartRead)(struct SSyncFSM* pFsm, void** ppReader);
|
||||
int32_t (*FpSnapshotStopRead)(struct SSyncFSM* pFsm, void* pReader);
|
||||
int32_t (*FpSnapshotDoRead)(struct SSyncFSM* pFsm, void* pReader, void** ppBuf, int32_t* len);
|
||||
|
||||
int32_t (*FpSnapshotStartWrite)(struct SSyncFSM* pFsm, void** ppWriter);
|
||||
int32_t (*FpSnapshotStopWrite)(struct SSyncFSM* pFsm, void* pWriter, bool isApply);
|
||||
int32_t (*FpSnapshotDoWrite)(struct SSyncFSM* pFsm, void* pWriter, void* pBuf, int32_t len);
|
||||
|
||||
} SSyncFSM;
|
||||
|
||||
|
|
|
|||
|
|
@ -69,6 +69,7 @@ int32_t* taosGetErrno();
|
|||
#define TSDB_CODE_DUP_KEY TAOS_DEF_ERROR_CODE(0, 0x0027)
|
||||
#define TSDB_CODE_NEED_RETRY TAOS_DEF_ERROR_CODE(0, 0x0028)
|
||||
#define TSDB_CODE_OUT_OF_RPC_MEMORY_QUEUE TAOS_DEF_ERROR_CODE(0, 0x0029)
|
||||
#define TSDB_CODE_INVALID_TIMESTAMP TAOS_DEF_ERROR_CODE(0, 0x0030)
|
||||
|
||||
#define TSDB_CODE_REF_NO_MEMORY TAOS_DEF_ERROR_CODE(0, 0x0040)
|
||||
#define TSDB_CODE_REF_FULL TAOS_DEF_ERROR_CODE(0, 0x0041)
|
||||
|
|
@ -643,6 +644,7 @@ int32_t* taosGetErrno();
|
|||
#define TSDB_CODE_PAR_INVALID_DROP_COL TAOS_DEF_ERROR_CODE(0, 0x2651)
|
||||
#define TSDB_CODE_PAR_INVALID_COL_JSON TAOS_DEF_ERROR_CODE(0, 0x2652)
|
||||
#define TSDB_CODE_PAR_VALUE_TOO_LONG TAOS_DEF_ERROR_CODE(0, 0x2653)
|
||||
#define TSDB_CODE_PAR_INTERNAL_ERROR TAOS_DEF_ERROR_CODE(0, 0x2654)
|
||||
|
||||
//planner
|
||||
#define TSDB_CODE_PLAN_INTERNAL_ERROR TAOS_DEF_ERROR_CODE(0, 0x2700)
|
||||
|
|
@ -654,7 +656,7 @@ int32_t* taosGetErrno();
|
|||
#define TSDB_CODE_FUNC_FUNTION_PARA_NUM TAOS_DEF_ERROR_CODE(0, 0x2801)
|
||||
#define TSDB_CODE_FUNC_FUNTION_PARA_TYPE TAOS_DEF_ERROR_CODE(0, 0x2802)
|
||||
#define TSDB_CODE_FUNC_FUNTION_PARA_VALUE TAOS_DEF_ERROR_CODE(0, 0x2803)
|
||||
#define TSDB_CODE_FUNC_INVALID_FUNTION TAOS_DEF_ERROR_CODE(0, 0x2804)
|
||||
#define TSDB_CODE_FUNC_NOT_BUILTIN_FUNTION TAOS_DEF_ERROR_CODE(0, 0x2804)
|
||||
|
||||
//udf
|
||||
#define TSDB_CODE_UDF_STOPPING TAOS_DEF_ERROR_CODE(0, 0x2901)
|
||||
|
|
|
|||
|
|
@ -247,7 +247,7 @@ typedef enum ELogicConditionType {
|
|||
#define TSDB_EP_LEN (TSDB_FQDN_LEN + 6)
|
||||
#define TSDB_IPv4ADDR_LEN 16
|
||||
#define TSDB_FILENAME_LEN 128
|
||||
#define TSDB_SHOW_SQL_LEN 512
|
||||
#define TSDB_SHOW_SQL_LEN 1024
|
||||
#define TSDB_SLOW_QUERY_SQL_LEN 512
|
||||
#define TSDB_SHOW_SUBQUERY_LEN 1000
|
||||
|
||||
|
|
|
|||
|
|
@ -46,6 +46,7 @@ typedef struct {
|
|||
void *ahandle;
|
||||
int32_t workerId;
|
||||
int32_t threadNum;
|
||||
int64_t timestamp;
|
||||
} SQueueInfo;
|
||||
|
||||
typedef enum {
|
||||
|
|
@ -80,7 +81,7 @@ int32_t taosAddIntoQset(STaosQset *qset, STaosQueue *queue, void *ahandle);
|
|||
void taosRemoveFromQset(STaosQset *qset, STaosQueue *queue);
|
||||
int32_t taosGetQueueNumber(STaosQset *qset);
|
||||
|
||||
int32_t taosReadQitemFromQset(STaosQset *qset, void **ppItem, void **ahandle, FItem *itemFp);
|
||||
int32_t taosReadQitemFromQset(STaosQset *qset, void **ppItem, int64_t *ts, void **ahandle, FItem *itemFp);
|
||||
int32_t taosReadAllQitemsFromQset(STaosQset *qset, STaosQall *qall, void **ahandle, FItems *itemsFp);
|
||||
void taosResetQsetThread(STaosQset *qset, void *pItem);
|
||||
|
||||
|
|
|
|||
|
|
@ -394,8 +394,8 @@ int32_t validateSversion(SRequestObj* pRequest, void* res) {
|
|||
if (NULL == blk->tblFName || 0 == blk->tblFName[0]) {
|
||||
continue;
|
||||
}
|
||||
|
||||
STbSVersion tbSver = {.tbFName = blk->tblFName, .sver = blk->sver};
|
||||
|
||||
STbSVersion tbSver = {.tbFName = blk->tblFName, .sver = blk->sver};
|
||||
taosArrayPush(pArray, &tbSver);
|
||||
}
|
||||
} else if (TDMT_VND_QUERY == pRequest->type) {
|
||||
|
|
@ -552,12 +552,12 @@ int32_t refreshMeta(STscObj* pTscObj, SRequestObj* pRequest) {
|
|||
|
||||
int32_t removeMeta(STscObj* pTscObj, SArray* tbList) {
|
||||
SCatalog* pCatalog = NULL;
|
||||
int32_t tbNum = taosArrayGetSize(tbList);
|
||||
int32_t code = catalogGetHandle(pTscObj->pAppInfo->clusterId, &pCatalog);
|
||||
int32_t tbNum = taosArrayGetSize(tbList);
|
||||
int32_t code = catalogGetHandle(pTscObj->pAppInfo->clusterId, &pCatalog);
|
||||
if (code != TSDB_CODE_SUCCESS) {
|
||||
return code;
|
||||
}
|
||||
|
||||
|
||||
for (int32_t i = 0; i < tbNum; ++i) {
|
||||
SName* pTbName = taosArrayGet(tbList, i);
|
||||
catalogRemoveTableMeta(pCatalog, pTbName);
|
||||
|
|
@ -566,7 +566,6 @@ int32_t removeMeta(STscObj* pTscObj, SArray* tbList) {
|
|||
return TSDB_CODE_SUCCESS;
|
||||
}
|
||||
|
||||
|
||||
SRequestObj* execQuery(STscObj* pTscObj, const char* sql, int sqlLen) {
|
||||
SRequestObj* pRequest = NULL;
|
||||
int32_t retryNum = 0;
|
||||
|
|
@ -589,7 +588,7 @@ SRequestObj* execQuery(STscObj* pTscObj, const char* sql, int sqlLen) {
|
|||
if (NEED_CLIENT_RM_TBLMETA_REQ(pRequest->type)) {
|
||||
removeMeta(pTscObj, pRequest->tableList);
|
||||
}
|
||||
|
||||
|
||||
return pRequest;
|
||||
}
|
||||
|
||||
|
|
@ -730,28 +729,55 @@ static void destroySendMsgInfo(SMsgSendInfo* pMsgBody) {
|
|||
taosMemoryFreeClear(pMsgBody);
|
||||
}
|
||||
|
||||
bool persistConnForSpecificMsg(void* parenct, tmsg_t msgType) {
|
||||
return msgType == TDMT_VND_QUERY_RSP || msgType == TDMT_VND_FETCH_RSP || msgType == TDMT_VND_RES_READY_RSP ||
|
||||
msgType == TDMT_VND_QUERY_HEARTBEAT_RSP;
|
||||
void updateTargetEpSet(SMsgSendInfo* pSendInfo, STscObj* pTscObj, SRpcMsg* pMsg, SEpSet* pEpSet) {
|
||||
if (NULL == pEpSet) {
|
||||
return;
|
||||
}
|
||||
|
||||
switch (pSendInfo->target.type) {
|
||||
case TARGET_TYPE_MNODE:
|
||||
if (NULL == pTscObj) {
|
||||
tscError("mnode epset changed but not able to update it, reqObjRefId:%" PRIx64, pSendInfo->requestObjRefId);
|
||||
return;
|
||||
}
|
||||
|
||||
updateEpSet_s(&pTscObj->pAppInfo->mgmtEp, pEpSet);
|
||||
break;
|
||||
case TARGET_TYPE_VNODE: {
|
||||
if (NULL == pTscObj) {
|
||||
tscError("vnode epset changed but not able to update it, reqObjRefId:%" PRIx64, pSendInfo->requestObjRefId);
|
||||
return;
|
||||
}
|
||||
|
||||
SCatalog* pCatalog = NULL;
|
||||
int32_t code = catalogGetHandle(pTscObj->pAppInfo->clusterId, &pCatalog);
|
||||
if (code != TSDB_CODE_SUCCESS) {
|
||||
tscError("fail to get catalog handle, clusterId:%" PRIx64 ", error %s", pTscObj->pAppInfo->clusterId, tstrerror(code));
|
||||
return;
|
||||
}
|
||||
|
||||
catalogUpdateVgEpSet(pCatalog, pSendInfo->target.dbFName, pSendInfo->target.vgId, pEpSet);
|
||||
break;
|
||||
}
|
||||
default:
|
||||
tscDebug("epset changed, not updated, msgType %s", TMSG_INFO(pMsg->msgType));
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void processMsgFromServer(void* parent, SRpcMsg* pMsg, SEpSet* pEpSet) {
|
||||
SMsgSendInfo* pSendInfo = (SMsgSendInfo*)pMsg->info.ahandle;
|
||||
assert(pMsg->info.ahandle != NULL);
|
||||
SRequestObj* pRequest = NULL;
|
||||
STscObj* pTscObj = NULL;
|
||||
|
||||
if (pSendInfo->requestObjRefId != 0) {
|
||||
SRequestObj* pRequest = (SRequestObj*)taosAcquireRef(clientReqRefPool, pSendInfo->requestObjRefId);
|
||||
assert(pRequest->self == pSendInfo->requestObjRefId);
|
||||
|
||||
pRequest->metric.rsp = taosGetTimestampUs();
|
||||
|
||||
//STscObj* pTscObj = pRequest->pTscObj;
|
||||
//if (pEpSet) {
|
||||
// if (!isEpsetEqual(&pTscObj->pAppInfo->mgmtEp.epSet, pEpSet)) {
|
||||
// updateEpSet_s(&pTscObj->pAppInfo->mgmtEp, pEpSet);
|
||||
// }
|
||||
//}
|
||||
|
||||
pTscObj = pRequest->pTscObj;
|
||||
/*
|
||||
* There is not response callback function for submit response.
|
||||
* The actual inserted number of points is the first number.
|
||||
|
|
@ -768,6 +794,8 @@ void processMsgFromServer(void* parent, SRpcMsg* pMsg, SEpSet* pEpSet) {
|
|||
taosReleaseRef(clientReqRefPool, pSendInfo->requestObjRefId);
|
||||
}
|
||||
|
||||
updateTargetEpSet(pSendInfo, pTscObj, pMsg, pEpSet);
|
||||
|
||||
SDataBuf buf = {.len = pMsg->contLen, .pData = NULL, .handle = pMsg->info.handle};
|
||||
|
||||
if (pMsg->contLen > 0) {
|
||||
|
|
@ -1221,6 +1249,8 @@ void resetConnectDB(STscObj* pTscObj) {
|
|||
int32_t setQueryResultFromRsp(SReqResultInfo* pResultInfo, const SRetrieveTableRsp* pRsp, bool convertUcs4) {
|
||||
assert(pResultInfo != NULL && pRsp != NULL);
|
||||
|
||||
taosMemoryFreeClear(pResultInfo->pRspMsg);
|
||||
|
||||
pResultInfo->pRspMsg = (const char*)pRsp;
|
||||
pResultInfo->pData = (void*)pRsp->data;
|
||||
pResultInfo->numOfRows = htonl(pRsp->numOfRows);
|
||||
|
|
|
|||
|
|
@ -125,11 +125,15 @@ static const SSysDbTableSchema userStbsSchema[] = {
|
|||
|
||||
static const SSysDbTableSchema streamSchema[] = {
|
||||
{.name = "stream_name", .bytes = SYSTABLE_SCH_DB_NAME_LEN, .type = TSDB_DATA_TYPE_VARCHAR},
|
||||
{.name = "user_name", .bytes = 23, .type = TSDB_DATA_TYPE_VARCHAR},
|
||||
{.name = "dest_table", .bytes = SYSTABLE_SCH_DB_NAME_LEN, .type = TSDB_DATA_TYPE_VARCHAR},
|
||||
{.name = "create_time", .bytes = 8, .type = TSDB_DATA_TYPE_TIMESTAMP},
|
||||
{.name = "sql", .bytes = 1024, .type = TSDB_DATA_TYPE_VARCHAR},
|
||||
};
|
||||
{.name = "sql", .bytes = TSDB_SHOW_SQL_LEN + VARSTR_HEADER_SIZE, .type = TSDB_DATA_TYPE_VARCHAR},
|
||||
{.name = "status", .bytes = 20 + VARSTR_HEADER_SIZE, .type = TSDB_DATA_TYPE_BINARY},
|
||||
{.name = "source_db", .bytes = SYSTABLE_SCH_DB_NAME_LEN, .type = TSDB_DATA_TYPE_VARCHAR},
|
||||
{.name = "target_db", .bytes = SYSTABLE_SCH_DB_NAME_LEN, .type = TSDB_DATA_TYPE_VARCHAR},
|
||||
{.name = "target_table", .bytes = SYSTABLE_SCH_TABLE_NAME_LEN, .type = TSDB_DATA_TYPE_VARCHAR},
|
||||
{.name = "watermark", .bytes = 8, .type = TSDB_DATA_TYPE_BIGINT},
|
||||
{.name = "trigger", .bytes = 4, .type = TSDB_DATA_TYPE_INT},
|
||||
};
|
||||
|
||||
static const SSysDbTableSchema userTblsSchema[] = {
|
||||
{.name = "table_name", .bytes = SYSTABLE_SCH_TABLE_NAME_LEN, .type = TSDB_DATA_TYPE_VARCHAR},
|
||||
|
|
|
|||
|
|
@ -611,6 +611,7 @@ int32_t blockDataFromBuf1(SSDataBlock* pBlock, const char* buf, size_t capacity)
|
|||
|
||||
for (int32_t i = 0; i < numOfCols; ++i) {
|
||||
SColumnInfoData* pCol = taosArrayGet(pBlock->pDataBlock, i);
|
||||
pCol->hasNull = true;
|
||||
|
||||
if (IS_VAR_DATA_TYPE(pCol->info.type)) {
|
||||
size_t metaSize = capacity * sizeof(int32_t);
|
||||
|
|
@ -1153,7 +1154,9 @@ void colInfoDataCleanup(SColumnInfoData* pColumn, uint32_t numOfRows) {
|
|||
if (IS_VAR_DATA_TYPE(pColumn->info.type)) {
|
||||
pColumn->varmeta.length = 0;
|
||||
} else {
|
||||
memset(pColumn->nullbitmap, 0, BitmapLen(numOfRows));
|
||||
if (pColumn->nullbitmap != NULL) {
|
||||
memset(pColumn->nullbitmap, 0, BitmapLen(numOfRows));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -1290,8 +1293,8 @@ static void doShiftBitmap(char* nullBitmap, size_t n, size_t total) {
|
|||
|
||||
static void colDataTrimFirstNRows(SColumnInfoData* pColInfoData, size_t n, size_t total) {
|
||||
if (IS_VAR_DATA_TYPE(pColInfoData->info.type)) {
|
||||
memmove(pColInfoData->varmeta.offset, &pColInfoData->varmeta.offset[n], (total - n));
|
||||
memset(&pColInfoData->varmeta.offset[total - n - 1], 0, n);
|
||||
memmove(pColInfoData->varmeta.offset, &pColInfoData->varmeta.offset[n], (total - n) * sizeof(int32_t));
|
||||
memset(&pColInfoData->varmeta.offset[total - n], 0, n);
|
||||
} else {
|
||||
int32_t bytes = pColInfoData->info.bytes;
|
||||
memmove(pColInfoData->pData, ((char*)pColInfoData->pData + n * bytes), (total - n) * bytes);
|
||||
|
|
@ -1460,7 +1463,7 @@ static char* formatTimestamp(char* buf, int64_t val, int precision) {
|
|||
}
|
||||
|
||||
void blockDebugShowData(const SArray* dataBlocks) {
|
||||
char pBuf[128];
|
||||
char pBuf[128] = {0};
|
||||
int32_t sz = taosArrayGetSize(dataBlocks);
|
||||
for (int32_t i = 0; i < sz; i++) {
|
||||
SSDataBlock* pDataBlock = taosArrayGet(dataBlocks, i);
|
||||
|
|
@ -1508,14 +1511,11 @@ void blockDebugShowData(const SArray* dataBlocks) {
|
|||
* @param pReq
|
||||
* @param pDataBlocks
|
||||
* @param vgId
|
||||
* @param uid set as parameter temporarily // TODO: remove this parameter, and the executor should set uid in
|
||||
* SDataBlock->info.uid
|
||||
* @param suid // TODO: check with Liao whether suid response is reasonable
|
||||
*
|
||||
* TODO: colId should be set
|
||||
*/
|
||||
int32_t buildSubmitReqFromDataBlock(SSubmitReq** pReq, const SArray* pDataBlocks, STSchema* pTSchema, int32_t vgId,
|
||||
tb_uid_t uid, tb_uid_t suid) {
|
||||
int32_t buildSubmitReqFromDataBlock(SSubmitReq** pReq, const SArray* pDataBlocks, STSchema* pTSchema, int32_t vgId, tb_uid_t suid) {
|
||||
int32_t sz = taosArrayGetSize(pDataBlocks);
|
||||
int32_t bufSize = sizeof(SSubmitReq);
|
||||
for (int32_t i = 0; i < sz; ++i) {
|
||||
|
|
@ -1551,7 +1551,7 @@ int32_t buildSubmitReqFromDataBlock(SSubmitReq** pReq, const SArray* pDataBlocks
|
|||
|
||||
SSubmitBlk* pSubmitBlk = POINTER_SHIFT(pDataBuf, msgLen);
|
||||
pSubmitBlk->suid = suid;
|
||||
pSubmitBlk->uid = uid;
|
||||
pSubmitBlk->uid = pDataBlock->info.groupId;
|
||||
pSubmitBlk->numOfRows = rows;
|
||||
|
||||
++numOfBlks;
|
||||
|
|
@ -1562,6 +1562,7 @@ int32_t buildSubmitReqFromDataBlock(SSubmitReq** pReq, const SArray* pDataBlocks
|
|||
tdSRowResetBuf(&rb, POINTER_SHIFT(pDataBuf, msgLen)); // set row buf
|
||||
printf("|");
|
||||
bool isStartKey = false;
|
||||
int32_t offset = 0;
|
||||
for (int32_t k = 0; k < colNum; ++k) { // iterate by column
|
||||
SColumnInfoData* pColInfoData = taosArrayGet(pDataBlock->pDataBlock, k);
|
||||
void* var = POINTER_SHIFT(pColInfoData->pData, j * pColInfoData->info.bytes);
|
||||
|
|
@ -1570,18 +1571,18 @@ int32_t buildSubmitReqFromDataBlock(SSubmitReq** pReq, const SArray* pDataBlocks
|
|||
if (!isStartKey) {
|
||||
isStartKey = true;
|
||||
tdAppendColValToRow(&rb, PRIMARYKEY_TIMESTAMP_COL_ID, TSDB_DATA_TYPE_TIMESTAMP, TD_VTYPE_NORM, var, true,
|
||||
0, 0);
|
||||
offset, k);
|
||||
|
||||
} else {
|
||||
tdAppendColValToRow(&rb, 2, TSDB_DATA_TYPE_TIMESTAMP, TD_VTYPE_NORM, var, true, 8, k);
|
||||
break;
|
||||
tdAppendColValToRow(&rb, 2, TSDB_DATA_TYPE_TIMESTAMP, TD_VTYPE_NORM, var, true, offset, k);
|
||||
}
|
||||
break;
|
||||
case TSDB_DATA_TYPE_NCHAR: {
|
||||
tdAppendColValToRow(&rb, 2, TSDB_DATA_TYPE_NCHAR, TD_VTYPE_NORM, var, true, 8, k);
|
||||
tdAppendColValToRow(&rb, 2, TSDB_DATA_TYPE_NCHAR, TD_VTYPE_NORM, var, true, offset, k);
|
||||
break;
|
||||
}
|
||||
case TSDB_DATA_TYPE_VARCHAR: { // TSDB_DATA_TYPE_BINARY
|
||||
tdAppendColValToRow(&rb, 2, TSDB_DATA_TYPE_VARCHAR, TD_VTYPE_NORM, var, true, 8, k);
|
||||
tdAppendColValToRow(&rb, 2, TSDB_DATA_TYPE_VARCHAR, TD_VTYPE_NORM, var, true, offset, k);
|
||||
break;
|
||||
}
|
||||
case TSDB_DATA_TYPE_VARBINARY:
|
||||
|
|
@ -1593,13 +1594,14 @@ int32_t buildSubmitReqFromDataBlock(SSubmitReq** pReq, const SArray* pDataBlocks
|
|||
break;
|
||||
default:
|
||||
if (pColInfoData->info.type < TSDB_DATA_TYPE_MAX && pColInfoData->info.type > TSDB_DATA_TYPE_NULL) {
|
||||
tdAppendColValToRow(&rb, 2, pColInfoData->info.type, TD_VTYPE_NORM, var, true, 8, k);
|
||||
tdAppendColValToRow(&rb, 2, pColInfoData->info.type, TD_VTYPE_NORM, var, true, offset, k);
|
||||
} else {
|
||||
printf("the column type %" PRIi16 " is undefined\n", pColInfoData->info.type);
|
||||
TASSERT(0);
|
||||
}
|
||||
break;
|
||||
}
|
||||
offset += TYPE_BYTES[pColInfoData->info.type];
|
||||
}
|
||||
dataLen += TD_ROW_LEN(rb.pBuf);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -293,7 +293,7 @@ int32_t taosAddClientLogCfg(SConfig *pCfg) {
|
|||
if (cfgAddInt32(pCfg, "jniDebugFlag", jniDebugFlag, 0, 255, 1) != 0) return -1;
|
||||
if (cfgAddInt32(pCfg, "simDebugFlag", 143, 0, 255, 1) != 0) return -1;
|
||||
if (cfgAddInt32(pCfg, "debugFlag", 0, 0, 255, 1) != 0) return -1;
|
||||
if (cfgAddInt32(pCfg, "idxDebugFlag", 0, 0, 255, 1) != 0) return -1;
|
||||
if (cfgAddInt32(pCfg, "idxDebugFlag", idxDebugFlag, 0, 255, 1) != 0) return -1;
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -665,22 +665,24 @@ void tFreeSMAltertbReq(SMAlterStbReq *pReq) {
|
|||
taosArrayDestroy(pReq->pFields);
|
||||
pReq->pFields = NULL;
|
||||
}
|
||||
int32_t tSerializeSMEpSet(void *buf, int32_t bufLen, SMEpSet *pReq) {
|
||||
|
||||
int32_t tSerializeSEpSet(void *buf, int32_t bufLen, const SEpSet *pEpset) {
|
||||
SEncoder encoder = {0};
|
||||
tEncoderInit(&encoder, buf, bufLen);
|
||||
if (tStartEncode(&encoder) < 0) return -1;
|
||||
if (tEncodeSEpSet(&encoder, &pReq->epSet) < 0) return -1;
|
||||
if (tEncodeSEpSet(&encoder, pEpset) < 0) return -1;
|
||||
|
||||
tEndEncode(&encoder);
|
||||
int32_t tlen = encoder.pos;
|
||||
tEncoderClear(&encoder);
|
||||
return tlen;
|
||||
}
|
||||
int32_t tDeserializeSMEpSet(void *buf, int32_t bufLen, SMEpSet *pReq) {
|
||||
|
||||
int32_t tDeserializeSEpSet(void *buf, int32_t bufLen, SEpSet *pEpset) {
|
||||
SDecoder decoder = {0};
|
||||
tDecoderInit(&decoder, buf, bufLen);
|
||||
if (tStartDecode(&decoder) < 0) return -1;
|
||||
if (tDecodeSEpSet(&decoder, &pReq->epSet) < 0) return -1;
|
||||
if (tDecodeSEpSet(&decoder, pEpset) < 0) return -1;
|
||||
|
||||
tEndDecode(&decoder);
|
||||
tDecoderClear(&decoder);
|
||||
|
|
@ -3507,31 +3509,6 @@ int32_t tDeserializeSSchedulerHbRsp(void *buf, int32_t bufLen, SSchedulerHbRsp *
|
|||
|
||||
void tFreeSSchedulerHbRsp(SSchedulerHbRsp *pRsp) { taosArrayDestroy(pRsp->taskStatus); }
|
||||
|
||||
int32_t tSerializeSQueryTableRsp(void *buf, int32_t bufLen, SQueryTableRsp *pRsp) {
|
||||
SEncoder encoder = {0};
|
||||
tEncoderInit(&encoder, buf, bufLen);
|
||||
|
||||
if (tStartEncode(&encoder) < 0) return -1;
|
||||
if (tEncodeI32(&encoder, pRsp->code) < 0) return -1;
|
||||
tEndEncode(&encoder);
|
||||
|
||||
int32_t tlen = encoder.pos;
|
||||
tEncoderClear(&encoder);
|
||||
return tlen;
|
||||
}
|
||||
|
||||
int32_t tDeserializeSQueryTableRsp(void *buf, int32_t bufLen, SQueryTableRsp *pRsp) {
|
||||
SDecoder decoder = {0};
|
||||
tDecoderInit(&decoder, buf, bufLen);
|
||||
|
||||
if (tStartDecode(&decoder) < 0) return -1;
|
||||
if (tDecodeI32(&decoder, &pRsp->code) < 0) return -1;
|
||||
tEndDecode(&decoder);
|
||||
|
||||
tDecoderClear(&decoder);
|
||||
return 0;
|
||||
}
|
||||
|
||||
int32_t tSerializeSVCreateTbBatchRsp(void *buf, int32_t bufLen, SVCreateTbBatchRsp *pRsp) {
|
||||
// SEncoder encoder = {0};
|
||||
// tEncoderInit(&encoder, buf, bufLen);
|
||||
|
|
|
|||
|
|
@ -1191,9 +1191,9 @@ bool tdGetTpRowDataOfCol(STSRowIter *pIter, col_type_t colType, int32_t offset,
|
|||
}
|
||||
|
||||
static FORCE_INLINE int32_t compareKvRowColId(const void *key1, const void *key2) {
|
||||
if (*(int16_t *)key1 > ((SColIdx *)key2)->colId) {
|
||||
if (*(col_id_t *)key1 > ((SKvRowIdx *)key2)->colId) {
|
||||
return 1;
|
||||
} else if (*(int16_t *)key1 < ((SColIdx *)key2)->colId) {
|
||||
} else if (*(col_id_t *)key1 < ((SKvRowIdx *)key2)->colId) {
|
||||
return -1;
|
||||
} else {
|
||||
return 0;
|
||||
|
|
|
|||
|
|
@ -521,21 +521,21 @@ int32_t convertStringToTimestamp(int16_t type, char *inputData, int64_t timePrec
|
|||
if (type == TSDB_DATA_TYPE_BINARY || type == TSDB_DATA_TYPE_VARBINARY) {
|
||||
newColData = taosMemoryCalloc(1, charLen + 1);
|
||||
memcpy(newColData, varDataVal(inputData), charLen);
|
||||
bool ret = taosParseTime(newColData, timeVal, charLen, (int32_t)timePrec, tsDaylight);
|
||||
int32_t ret = taosParseTime(newColData, timeVal, charLen, (int32_t)timePrec, tsDaylight);
|
||||
if (ret != TSDB_CODE_SUCCESS) {
|
||||
taosMemoryFree(newColData);
|
||||
return ret;
|
||||
return TSDB_CODE_INVALID_TIMESTAMP;
|
||||
}
|
||||
taosMemoryFree(newColData);
|
||||
} else if (type == TSDB_DATA_TYPE_NCHAR) {
|
||||
newColData = taosMemoryCalloc(1, charLen / TSDB_NCHAR_SIZE + 1);
|
||||
newColData = taosMemoryCalloc(1, charLen + TSDB_NCHAR_SIZE);
|
||||
int len = taosUcs4ToMbs((TdUcs4 *)varDataVal(inputData), charLen, newColData);
|
||||
if (len < 0){
|
||||
taosMemoryFree(newColData);
|
||||
return TSDB_CODE_FAILED;
|
||||
}
|
||||
newColData[len] = 0;
|
||||
bool ret = taosParseTime(newColData, timeVal, len + 1, (int32_t)timePrec, tsDaylight);
|
||||
int32_t ret = taosParseTime(newColData, timeVal, len + 1, (int32_t)timePrec, tsDaylight);
|
||||
if (ret != TSDB_CODE_SUCCESS) {
|
||||
taosMemoryFree(newColData);
|
||||
return ret;
|
||||
|
|
@ -783,7 +783,7 @@ int64_t taosTimeTruncate(int64_t t, const SInterval* pInterval, int32_t precisio
|
|||
// 2020-07-03 17:48:42
|
||||
// and the parameter can also be a variable.
|
||||
const char* fmtts(int64_t ts) {
|
||||
static char buf[96];
|
||||
static char buf[96] = {0};
|
||||
size_t pos = 0;
|
||||
struct tm tm;
|
||||
|
||||
|
|
|
|||
|
|
@ -40,7 +40,7 @@ static void mmProcessQueue(SQueueInfo *pInfo, SRpcMsg *pMsg) {
|
|||
break;
|
||||
default:
|
||||
pMsg->info.node = pMgmt->pMnode;
|
||||
code = mndProcessMsg(pMsg);
|
||||
code = mndProcessRpcMsg(pMsg);
|
||||
}
|
||||
|
||||
if (IsReq(pMsg) && pMsg->info.handle != NULL && code != TSDB_CODE_ACTION_IN_PROGRESS) {
|
||||
|
|
|
|||
|
|
@ -16,7 +16,11 @@
|
|||
#define _DEFAULT_SOURCE
|
||||
#include "qmInt.h"
|
||||
|
||||
void qmGetMonitorInfo(SQnodeMgmt *pMgmt, SMonQmInfo *qmInfo) {}
|
||||
void qmGetMonitorInfo(SQnodeMgmt *pMgmt, SMonQmInfo *qmInfo) {
|
||||
SQnodeLoad qload = {0};
|
||||
qndGetLoad(pMgmt->pQnode, &qload);
|
||||
|
||||
}
|
||||
|
||||
int32_t qmProcessGetMonitorInfoReq(SQnodeMgmt *pMgmt, SRpcMsg *pMsg) {
|
||||
SMonQmInfo qmInfo = {0};
|
||||
|
|
@ -101,8 +105,6 @@ SArray *qmGetMsgHandles() {
|
|||
if (dmSetMgmtHandle(pArray, TDMT_VND_FETCH_RSP, qmPutNodeMsgToFetchQueue, 1) == NULL) goto _OVER;
|
||||
if (dmSetMgmtHandle(pArray, TDMT_VND_QUERY_HEARTBEAT, qmPutNodeMsgToFetchQueue, 1) == NULL) goto _OVER;
|
||||
|
||||
if (dmSetMgmtHandle(pArray, TDMT_VND_RES_READY, qmPutNodeMsgToFetchQueue, 1) == NULL) goto _OVER;
|
||||
if (dmSetMgmtHandle(pArray, TDMT_VND_TASKS_STATUS, qmPutNodeMsgToFetchQueue, 1) == NULL) goto _OVER;
|
||||
if (dmSetMgmtHandle(pArray, TDMT_VND_CANCEL_TASK, qmPutNodeMsgToFetchQueue, 1) == NULL) goto _OVER;
|
||||
if (dmSetMgmtHandle(pArray, TDMT_VND_DROP_TASK, qmPutNodeMsgToFetchQueue, 1) == NULL) goto _OVER;
|
||||
|
||||
|
|
|
|||
|
|
@ -36,7 +36,7 @@ static void qmProcessQueue(SQueueInfo *pInfo, SRpcMsg *pMsg) {
|
|||
code = qmProcessGetMonitorInfoReq(pMgmt, pMsg);
|
||||
break;
|
||||
default:
|
||||
code = qndProcessQueryMsg(pMgmt->pQnode, pMsg);
|
||||
code = qndProcessQueryMsg(pMgmt->pQnode, pInfo->timestamp, pMsg);
|
||||
break;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -292,8 +292,6 @@ SArray *vmGetMsgHandles() {
|
|||
if (dmSetMgmtHandle(pArray, TDMT_VND_MQ_CONNECT, vmPutNodeMsgToWriteQueue, 0) == NULL) goto _OVER;
|
||||
if (dmSetMgmtHandle(pArray, TDMT_VND_MQ_DISCONNECT, vmPutNodeMsgToWriteQueue, 0) == NULL) goto _OVER;
|
||||
// if (dmSetMgmtHandle(pArray, TDMT_VND_MQ_SET_CUR, vmPutNodeMsgToWriteQueue, 0)== NULL) goto _OVER;
|
||||
if (dmSetMgmtHandle(pArray, TDMT_VND_RES_READY, vmPutNodeMsgToFetchQueue, 0) == NULL) goto _OVER;
|
||||
if (dmSetMgmtHandle(pArray, TDMT_VND_TASKS_STATUS, vmPutNodeMsgToFetchQueue, 0) == NULL) goto _OVER;
|
||||
if (dmSetMgmtHandle(pArray, TDMT_VND_CANCEL_TASK, vmPutNodeMsgToFetchQueue, 0) == NULL) goto _OVER;
|
||||
if (dmSetMgmtHandle(pArray, TDMT_VND_DROP_TASK, vmPutNodeMsgToFetchQueue, 0) == NULL) goto _OVER;
|
||||
if (dmSetMgmtHandle(pArray, TDMT_VND_CREATE_STB, vmPutNodeMsgToWriteQueue, 0) == NULL) goto _OVER;
|
||||
|
|
|
|||
|
|
@ -62,8 +62,10 @@ static void dmProcessRpcMsg(SDnode *pDnode, SRpcMsg *pRpc, SEpSet *pEpSet) {
|
|||
dmProcessNetTestReq(pDnode, pRpc);
|
||||
return;
|
||||
} else if (pRpc->msgType == TDMT_MND_SYSTABLE_RETRIEVE_RSP || pRpc->msgType == TDMT_VND_FETCH_RSP) {
|
||||
qWorkerProcessFetchRsp(NULL, NULL, pRpc);
|
||||
qWorkerProcessFetchRsp(NULL, NULL, pRpc, 0);
|
||||
return;
|
||||
} else if (pRpc->msgType == TDMT_MND_STATUS_RSP && pEpSet != NULL) {
|
||||
dmSetMnodeEpSet(&pDnode->data, pEpSet);
|
||||
} else {
|
||||
}
|
||||
|
||||
|
|
@ -204,29 +206,28 @@ static inline void dmSendRsp(SRpcMsg *pMsg) {
|
|||
}
|
||||
|
||||
static void dmBuildMnodeRedirectRsp(SDnode *pDnode, SRpcMsg *pMsg) {
|
||||
SMEpSet msg = {0};
|
||||
dmGetMnodeEpSetForRedirect(&pDnode->data, pMsg, &msg.epSet);
|
||||
SEpSet epSet = {0};
|
||||
dmGetMnodeEpSetForRedirect(&pDnode->data, pMsg, &epSet);
|
||||
|
||||
int32_t contLen = tSerializeSMEpSet(NULL, 0, &msg);
|
||||
int32_t contLen = tSerializeSEpSet(NULL, 0, &epSet);
|
||||
pMsg->pCont = rpcMallocCont(contLen);
|
||||
if (pMsg->pCont == NULL) {
|
||||
pMsg->code = TSDB_CODE_OUT_OF_MEMORY;
|
||||
} else {
|
||||
tSerializeSMEpSet(pMsg->pCont, contLen, &msg);
|
||||
tSerializeSEpSet(pMsg->pCont, contLen, &epSet);
|
||||
pMsg->contLen = contLen;
|
||||
}
|
||||
}
|
||||
|
||||
static inline void dmSendRedirectRsp(SRpcMsg *pMsg, const SEpSet *pNewEpSet) {
|
||||
SRpcMsg rsp = {.code = TSDB_CODE_RPC_REDIRECT, .info = pMsg->info};
|
||||
SMEpSet msg = {.epSet = *pNewEpSet};
|
||||
int32_t contLen = tSerializeSMEpSet(NULL, 0, &msg);
|
||||
int32_t contLen = tSerializeSEpSet(NULL, 0, pNewEpSet);
|
||||
|
||||
rsp.pCont = rpcMallocCont(contLen);
|
||||
if (rsp.pCont == NULL) {
|
||||
terrno = TSDB_CODE_OUT_OF_MEMORY;
|
||||
} else {
|
||||
tSerializeSMEpSet(rsp.pCont, contLen, &msg);
|
||||
tSerializeSEpSet(rsp.pCont, contLen, pNewEpSet);
|
||||
rsp.contLen = contLen;
|
||||
}
|
||||
dmSendRsp(&rsp);
|
||||
|
|
|
|||
|
|
@ -326,6 +326,7 @@ void dmGetMnodeEpSetForRedirect(SDnodeData *pData, SRpcMsg *pMsg, SEpSet *pEpSet
|
|||
}
|
||||
|
||||
void dmSetMnodeEpSet(SDnodeData *pData, SEpSet *pEpSet) {
|
||||
if (memcmp(pEpSet, &pData->mnodeEps, sizeof(SEpSet)) == 0) return;
|
||||
taosThreadRwlockWrlock(&pData->lock);
|
||||
pData->mnodeEps = *pEpSet;
|
||||
taosThreadRwlockUnlock(&pData->lock);
|
||||
|
|
|
|||
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Reference in New Issue