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TDengine
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CMakeLists.txt
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cmake_minimum_required(VERSION 3.16)
cmake_minimum_required(VERSION 3.0)
project(
TDengine
......
cmake/cmake.define
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cmake_minimum_required(VERSION 3.16)
cmake_minimum_required(VERSION 3.0)
set(CMAKE_VERBOSE_MAKEFILE OFF)
......
cmake/cmake.platform
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cmake_minimum_required(VERSION 3.16)
cmake_minimum_required(VERSION 3.0)
MESSAGE("Current system is ${CMAKE_SYSTEM_NAME}")
......
docs-cn/07-develop/03-insert-data/01-sql-writing.mdx
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......@@ -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。
:::
......
docs-cn/12-taos-sql/01-data-type.md
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......@@ -4,6 +4,8 @@ title: 支持的数据类型
description: "TDengine 支持的数据类型: 时间戳、浮点型、JSON 类型等"
---
## 时间戳
使用 TDengine，最重要的是时间戳。创建并插入记录、查询历史记录的时候，均需要指定时间戳。时间戳有如下规则：
- 时间格式为 `YYYY-MM-DD HH:mm:ss.MS`，默认时间分辨率为毫秒。比如：`2017-08-12 18:25:58.128`
......@@ -12,39 +14,59 @@ description: "TDengine 支持的数据类型: 时间戳、浮点型、JSON 类
- Epoch Time：时间戳也可以是一个长整数，表示从格林威治时间 1970-01-01 00:00:00.000 (UTC/GMT) 开始的毫秒数（相应地，如果所在 Database 的时间精度设置为“微秒”，则长整型格式的时间戳含义也就对应于从格林威治时间 1970-01-01 00:00:00.000 (UTC/GMT) 开始的微秒数；纳秒精度逻辑类似。）
- 时间可以加减，比如 now-2h，表明查询时刻向前推 2 个小时（最近 2 小时）。数字后面的时间单位可以是 b(纳秒)、u(微秒)、a(毫秒)、s(秒)、m(分)、h(小时)、d(天)、w(周)。 比如 `select * from t1 where ts > now-2w and ts <= now-1w`，表示查询两周前整整一周的数据。在指定降采样操作（down sampling）的时间窗口（interval）时，时间单位还可以使用 n (自然月) 和 y (自然年)。
TDengine 缺省的时间戳精度是毫秒，但通过在 `CREATE DATABASE` 时传递的 PRECISION 参数也可以支持微秒和纳秒。（从 2.1.5.0 版本开始支持纳秒精度）
TDengine 缺省的时间戳精度是毫秒，但通过在 `CREATE DATABASE` 时传递的 PRECISION 参数也可以支持微秒和纳秒。
```sql
CREATE DATABASE db_name PRECISION 'ns';
```
## 数据类型
在 TDengine 中，普通表的数据模型中可使用以下 10 种数据类型。
在 TDengine 中，普通表的数据模型中可使用以下数据类型。
| # | **类型** | **Bytes** | **说明** |
| --- | :-------: | --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1 | TIMESTAMP | 8 | 时间戳。缺省精度毫秒，可支持微秒和纳秒。从格林威治时间 1970-01-01 00:00:00.000 (UTC/GMT) 开始，计时不能早于该时间。（从 2.0.18.0 版本开始，已经去除了这一时间范围限制）（从 2.1.5.0 版本开始支持纳秒精度） |
| 2 | INT | 4 | 整型，范围 [-2^31+1, 2^31-1], -2^31 用作 NULL |
| 3 | BIGINT | 8 | 长整型，范围 [-2^63+1, 2^63-1], -2^63 用作 NULL |
| 4 | FLOAT | 4 | 浮点型，有效位数 6-7，范围 [-3.4E38, 3.4E38] |
| 5 | DOUBLE | 8 | 双精度浮点型，有效位数 15-16，范围 [-1.7E308, 1.7E308] |
| 6 | BINARY | 自定义 | 记录单字节字符串，建议只用于处理 ASCII 可见字符，中文等多字节字符需使用 nchar。理论上，最长可以有 16374 字节。binary 仅支持字符串输入，字符串两端需使用单引号引用。使用时须指定大小，如 binary(20) 定义了最长为 20 个单字节字符的字符串，每个字符占 1 byte 的存储空间，总共固定占用 20 bytes 的空间，此时如果用户字符串超出 20 字节将会报错。对于字符串内的单引号，可以用转义字符反斜线加单引号来表示，即 `\’`。 |
| 7 | SMALLINT | 2 | 短整型， 范围 [-32767, 32767], -32768 用作 NULL |
| 8 | TINYINT | 1 | 单字节整型，范围 [-127, 127], -128 用作 NULL |
| 9 | BOOL | 1 | 布尔型，{true, false} |
| 10 | NCHAR | 自定义 | 记录包含多字节字符在内的字符串，如中文字符。每个 nchar 字符占用 4 bytes 的存储空间。字符串两端使用单引号引用，字符串内的单引号需用转义字符 `\’`。nchar 使用时须指定字符串大小，类型为 nchar(10) 的列表示此列的字符串最多存储 10 个 nchar 字符，会固定占用 40 bytes 的空间。如果用户字符串长度超出声明长度，将会报错。 |
| 11 | JSON | | json 数据类型， 只有 tag 可以是 json 格式 |
:::tip
TDengine 对 SQL 语句中的英文字符不区分大小写，自动转化为小写执行。因此用户大小写敏感的字符串及密码，需要使用单引号将字符串引起来。
| 1 | TIMESTAMP | 8 | 时间戳。缺省精度毫秒，可支持微秒和纳秒，详细说明见上节。 |
| 2 | INT | 4 | 整型，范围 [-2^31, 2^31-1] |
| 3 | INT UNSIGNED| 4| 无符号整数，[0, 2^32-1]
| 4 | BIGINT | 8 | 长整型，范围 [-2^63, 2^63-1] |
| 5 | BIGINT UNSIGNED | 8 | 长整型，范围 [0, 2^64-1] |
| 6 | FLOAT | 4 | 浮点型，有效位数 6-7，范围 [-3.4E38, 3.4E38] |
| 7 | DOUBLE | 8 | 双精度浮点型，有效位数 15-16，范围 [-1.7E308, 1.7E308] |
| 8 | BINARY | 自定义 | 记录单字节字符串，建议只用于处理 ASCII 可见字符，中文等多字节字符需使用 nchar。 |
| 9 | SMALLINT | 2 | 短整型， 范围 [-32768, 32767] |
| 10 | SMALLINT UNSIGNED | 2| 无符号短整型，范围 [0, 655357] |
| 11 | TINYINT | 1 | 单字节整型，范围 [-128, 127] |
| 12 | TINYINT UNSIGNED | 1 | 无符号单字节整型，范围 [0, 255] |
| 13 | BOOL | 1 | 布尔型，{true, false} |
| 14 | NCHAR | 自定义 | 记录包含多字节字符在内的字符串，如中文字符。每个 nchar 字符占用 4 bytes 的存储空间。字符串两端使用单引号引用，字符串内的单引号需用转义字符 `\’`。nchar 使用时须指定字符串大小，类型为 nchar(10) 的列表示此列的字符串最多存储 10 个 nchar 字符，会固定占用 40 bytes 的空间。如果用户字符串长度超出声明长度，将会报错。 |
| 15 | JSON | | json 数据类型， 只有 tag 可以是 json 格式 |
| 16 | VARCHAR | 自定义 | BINARY类型的别名 |
:::
:::note
虽然 BINARY 类型在底层存储上支持字节型的二进制字符，但不同编程语言对二进制数据的处理方式并不保证一致，因此建议在 BINARY 类型中只存储 ASCII 可见字符，而避免存储不可见字符。多字节的数据，例如中文字符，则需要使用 NCHAR 类型进行保存。如果强行使用 BINARY 类型保存中文字符，虽然有时也能正常读写，但并不带有字符集信息，很容易出现数据乱码甚至数据损坏等情况。
- TDengine 对 SQL 语句中的英文字符不区分大小写，自动转化为小写执行。因此用户大小写敏感的字符串及密码，需要使用单引号将字符串引起来。
- 虽然 BINARY 类型在底层存储上支持字节型的二进制字符，但不同编程语言对二进制数据的处理方式并不保证一致，因此建议在 BINARY 类型中只存储 ASCII 可见字符，而避免存储不可见字符。多字节的数据，例如中文字符，则需要使用 NCHAR 类型进行保存。如果强行使用 BINARY 类型保存中文字符，虽然有时也能正常读写，但并不带有字符集信息，很容易出现数据乱码甚至数据损坏等情况。
- BINARY 类型理论上最长可以有 16374 字节。binary 仅支持字符串输入，字符串两端需使用单引号引用。使用时须指定大小，如 binary(20) 定义了最长为 20 个单字节字符的字符串，每个字符占 1 byte 的存储空间，总共固定占用 20 bytes 的空间，此时如果用户字符串超出 20 字节将会报错。对于字符串内的单引号，可以用转义字符反斜线加单引号来表示，即 `\’`。
- SQL 语句中的数值类型将依据是否存在小数点，或使用科学计数法表示，来判断数值类型是否为整型或者浮点型，因此在使用时要注意相应类型越界的情况。例如，9999999999999999999 会认为超过长整型的上边界而溢出，而 9999999999999999999.0 会被认为是有效的浮点数。
:::
## 常量
TDengine支持多个类型的常量，细节如下表：
| # | **语法** | **类型** | **说明** |
| --- | :-------: | --------- | -------------------------------------- |
| 1 | [{+ \| -}]123 | BIGINT | 整型数值的字面量的类型均为BIGINT。如果用户输入超过了BIGINT的表示范围，TDengine 按BIGINT对数值进行截断。|
| 2 | 123.45 | DOUBLE | 浮点数值的字面量的类型均为DOUBLE。TDengine依据是否存在小数点，或使用科学计数法表示，来判断数值类型是否为整型或者浮点型。|
| 3 | 1.2E3 | DOUBLE | 科学计数法的字面量的类型为DOUBLE。|
| 4 | 'abc' | BINARY | 单引号括住的内容为字符串字面值，其类型为BINARY，BINARY的size为实际的字符个数。对于字符串内的单引号，可以用转义字符反斜线加单引号来表示，即 \'。|
| 5 | "abc" | BINARY | 双引号括住的内容为字符串字面值，其类型为BINARY，BINARY的size为实际的字符个数。对于字符串内的双引号，可以用转义字符反斜线加单引号来表示，即 \"。 |
| 6 | TIMESTAMP {'literal' \| "literal"} | TIMESTAMP | TIMESTAMP关键字表示后面的字符串字面量需要被解释为TIMESTAMP类型。字符串需要满足YYYY-MM-DD HH:mm:ss.MS格式，其时间分辨率为当前数据库的时间分辨率。 |
| 7 | {TRUE \| FALSE} | BOOL | 布尔类型字面量。 |
| 8 | {'' \| "" \| '\t' \| "\t" \| ' ' \| " " \| NULL } | -- | 空值字面量。可以用于任意类型。|
:::note
SQL 语句中的数值类型将依据是否存在小数点，或使用科学计数法表示，来判断数值类型是否为整型或者浮点型，因此在使用时要注意相应类型越界的情况。例如，9999999999999999999 会认为超过长整型的上边界而溢出，而 9999999999999999999.0 会被认为是有效的浮点数。
- TDengine依据是否存在小数点，或使用科学计数法表示，来判断数值类型是否为整型或者浮点型，因此在使用时要注意相应类型越界的情况。例如，9999999999999999999会认为超过长整型的上边界而溢出，而9999999999999999999.0会被认为是有效的浮点数。
:::
docs-cn/12-taos-sql/03-table.md
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......@@ -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。
......
docs-cn/12-taos-sql/04-stable.md
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......@@ -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
......
docs-cn/12-taos-sql/07-function.md
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......@@ -261,6 +261,92 @@ taos> select hyperloglog(dbig) from shll;
Query OK, 1 row(s) in set (0.008388s)
```
### HISTOGRAM
```
SELECT HISTOGRAM(field_name，bin_type, bin_description, normalized) FROM tb_name [WHERE clause];
```
**功能说明**：统计数据按照用户指定区间的分布。
**返回结果类型**：如归一化参数 normalized 设置为 1，返回结果为双精度浮点类型 DOUBLE，否则为长整形 INT64。
**应用字段**：数值型字段。
**支持的版本**：2.6.0.0 及以后的版本。
**适用于**: 表和超级表。
**说明**：
1. bin_type 用户指定的分桶类型, 有效输入类型为"user_input“, ”linear_bin", "log_bin"。
2. bin_description 描述如何生成分桶区间，针对三种桶类型，分别为以下描述格式(均为 JSON 格式字符串)：
- "user_input": "[1, 3, 5, 7]"
用户指定 bin 的具体数值。
- "linear_bin": "{"start": 0.0, "width": 5.0, "count": 5, "infinity": true}"
"start" 表示数据起始点，"width" 表示每次 bin 偏移量, "count" 为 bin 的总数，"infinity" 表示是否添加（-inf, inf）作为区间起点跟终点，
生成区间为[-inf, 0.0, 5.0, 10.0, 15.0, 20.0, +inf]。
- "log_bin": "{"start":1.0, "factor": 2.0, "count": 5, "infinity": true}"
"start" 表示数据起始点，"factor" 表示按指数递增的因子，"count" 为 bin 的总数，"infinity" 表示是否添加（-inf, inf）作为区间起点跟终点，
生成区间为[-inf, 1.0, 2.0, 4.0, 8.0, 16.0, +inf]。
3. normalized 是否将返回结果归一化到 0~1 之间 。有效输入为 0 和 1。
**示例**：
```mysql
taos> SELECT HISTOGRAM(voltage, "user_input", "[1,3,5,7]", 1) FROM meters;
histogram(voltage, "user_input", "[1,3,5,7]", 1) |
=======================================================
{"lower_bin":1, "upper_bin":3, "count":0.333333} |
{"lower_bin":3, "upper_bin":5, "count":0.333333} |
{"lower_bin":5, "upper_bin":7, "count":0.333333} |
Query OK, 3 row(s) in set (0.004273s)
taos> SELECT HISTOGRAM(voltage, 'linear_bin', '{"start": 1, "width": 3, "count": 3, "infinity": false}', 0) FROM meters;
histogram(voltage, 'linear_bin', '{"start": 1, "width": 3, " |
===================================================================
{"lower_bin":1, "upper_bin":4, "count":3} |
{"lower_bin":4, "upper_bin":7, "count":3} |
{"lower_bin":7, "upper_bin":10, "count":3} |
Query OK, 3 row(s) in set (0.004887s)
taos> SELECT HISTOGRAM(voltage, 'log_bin', '{"start": 1, "factor": 3, "count": 3, "infinity": true}', 0) FROM meters;
histogram(voltage, 'log_bin', '{"start": 1, "factor": 3, "count" |
===================================================================
{"lower_bin":-inf, "upper_bin":1, "count":3} |
{"lower_bin":1, "upper_bin":3, "count":2} |
{"lower_bin":3, "upper_bin":9, "count":6} |
{"lower_bin":9, "upper_bin":27, "count":3} |
{"lower_bin":27, "upper_bin":inf, "count":1} |
```
### ELAPSED
```mysql
SELECT ELAPSED(field_name[, time_unit]) FROM { tb_name | stb_name } [WHERE clause] [INTERVAL(interval [, offset]) [SLIDING sliding]];
```
**功能说明**：elapsed函数表达了统计周期内连续的时间长度，和twa函数配合使用可以计算统计曲线下的面积。在通过INTERVAL子句指定窗口的情况下，统计在给定时间范围内的每个窗口内有数据覆盖的时间范围；如果没有INTERVAL子句，则返回整个给定时间范围内的有数据覆盖的时间范围。注意，ELAPSED返回的并不是时间范围的绝对值，而是绝对值除以time_unit所得到的单位个数。
**返回结果类型**：Double
**应用字段**：Timestamp类型
**支持的版本**：2.6.0.0 及以后的版本。
**适用于**: 表，超级表，嵌套查询的外层查询
**说明**：
- field_name参数只能是表的第一列，即timestamp主键列。
- 按time_unit参数指定的时间单位返回，最小是数据库的时间分辨率。time_unit参数未指定时，以数据库的时间分辨率为时间单位。
- 可以和interval组合使用，返回每个时间窗口的时间戳差值。需要特别注意的是，除第一个时间窗口和最后一个时间窗口外，中间窗口的时间戳差值均为窗口长度。
- order by asc/desc不影响差值的计算结果。
- 对于超级表，需要和group by tbname子句组合使用，不可以直接使用。
- 对于普通表，不支持和group by子句组合使用。
- 对于嵌套查询，仅当内层查询会输出隐式时间戳列时有效。例如select elapsed(ts) from (select diff(value) from sub1)语句，diff函数会让内层查询输出隐式时间戳列，此为主键列，可以用于elapsed函数的第一个参数。相反，例如select elapsed(ts) from (select * from sub1) 语句，ts列输出到外层时已经没有了主键列的含义，无法使用elapsed函数。此外，elapsed函数作为一个与时间线强依赖的函数，形如select elapsed(ts) from (select diff(value) from st group by tbname)尽管会返回一条计算结果，但并无实际意义，这种用法后续也将被限制。
- 不支持与leastsquares、diff、derivative、top、bottom、last_row、interp等函数混合使用。
## 选择函数
在使用所有的选择函数的时候，可以同时指定输出 ts 列或标签列（包括 tbname），这样就可以方便地知道被选出的值是源于哪个数据行的。
......@@ -698,7 +784,7 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } WHERE ts='timestamp' [FILL
SELECT TAIL(field_name, k, offset_val) FROM {tb_name | stb_name} [WHERE clause];
```
**功能说明**：返回跳过最后 offset_value 个，然后取连续 k 个记录，不忽略 NULL 值。offset_val 可以不输入。此时返回最后的 k 个记录。当有 offset_val 输入的情况下，该函数功能等效于 `order by ts desc LIMIT k OFFSET offset_val`。
**功能说明**：返回跳过最后 offset_val 个，然后取连续 k 个记录，不忽略 NULL 值。offset_val 可以不输入。此时返回最后的 k 个记录。当有 offset_val 输入的情况下，该函数功能等效于 `order by ts desc LIMIT k OFFSET offset_val`。
**参数范围**：k: [1,100] offset_val: [0,100]。
......@@ -1378,35 +1464,6 @@ SELECT ROUND(field_name) FROM { tb_name | stb_name } [WHERE clause];
- 该函数适用于内层查询和外层查询。
- 版本2.6.0.x后支持
### 四则运算
```
SELECT field_name [+|-|*|/|%][Value|field_name] FROM { tb_name | stb_name } [WHERE clause];
```
**功能说明**：统计表/超级表中某列或多列间的值加、减、乘、除、取余计算结果。
**返回数据类型**：双精度浮点数。
**应用字段**：不能应用在 timestamp、binary、nchar、bool 类型字段。
**适用于**：表、超级表。
**使用说明**：
- 支持两列或多列之间进行计算，可使用括号控制计算优先级；
- NULL 字段不参与计算，如果参与计算的某行中包含 NULL，该行的计算结果为 NULL。
```
taos> SELECT current + voltage * phase FROM d1001;
(current+(voltage*phase)) |
============================
78.190000713 |
84.540003240 |
80.810000718 |
Query OK, 3 row(s) in set (0.001046s)
```
### STATECOUNT
```
......
docs-cn/12-taos-sql/09-limit.md
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......@@ -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。
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......@@ -23,17 +23,17 @@ title: TDengine 参数限制与保留关键字
去掉了 `` ‘“`\ `` (单双引号、撇号、反斜杠、空格)
- 数据库名：不能包含“.”以及特殊字符，不能超过 32 个字符
- 表名：不能包含“.”以及特殊字符，与所属数据库名一起，不能超过 192 个字符，每行数据最大长度 16k 个字符
- 表的列名：不能包含特殊字符，不能超过 64 个字符
- 表名：不能包含“.”以及特殊字符，与所属数据库名一起，不能超过 192 个字节 ，每行数据最大长度 48KB
- 表的列名：不能包含特殊字符，不能超过 64 个字节
- 数据库名、表名、列名，都不能以数字开头，合法的可用字符集是“英文字符、数字和下划线”
- 表的列数：不能超过 1024 列，最少需要 2 列，第一列必须是时间戳（从 2.1.7.0 版本开始，改为最多支持 4096 列）
- 记录的最大长度：包括时间戳 8 byte，不能超过 16KB（每个 BINARY/NCHAR 类型的列还会额外占用 2 个 byte 的存储位置）
- 单条 SQL 语句默认最大字符串长度：1048576 byte，但可通过系统配置参数 maxSQLLength 修改，取值范围 65480 ~ 1048576 byte
- 记录的最大长度：包括时间戳 8 字节，不能超过 48KB（每个 BINARY/NCHAR 类型的列还会额外占用 2 个 字节 的存储位置）
- 单条 SQL 语句默认最大字符串长度：1048576 字节，但可通过系统配置参数 maxSQLLength 修改，取值范围 65480 ~ 1048576 字节
- 数据库副本数：不能超过 3
- 用户名：不能超过 23 个 byte
- 用户密码：不能超过 15 个 byte
- 用户名：不能超过 23 个 字节
- 用户密码：不能超过 15 个 字节
- 标签(Tags)数量：不能超过 128 个，可以 0 个
- 标签的总长度：不能超过 16K byte
- 标签的总长度：不能超过 16KB
- 记录条数：仅受存储空间限制
- 表的个数：仅受节点个数限制
- 库的个数：仅受节点个数限制
......@@ -85,3 +85,44 @@ title: TDengine 参数限制与保留关键字
| CONNECTIONS | HAVING | NOT | SOFFSET | VNODES |
| CONNS | ID | NOTNULL | STABLE | WAL |
| COPY | IF | NOW | STABLES | WHERE |
| _C0 | _QSTART | _QSTOP | _QDURATION | _WSTART |
| _WSTOP | _WDURATION | _ROWTS |
## 特殊说明
### TBNAME
`TBNAME` 可以视为超级表中一个特殊的标签，代表子表的表名。
获取一个超级表所有的子表名及相关的标签信息：
```mysql
SELECT TBNAME, location FROM meters;
统计超级表下辖子表数量：
```mysql
SELECT COUNT(TBNAME) FROM meters;
```
以上两个查询均只支持在WHERE条件子句中添加针对标签（TAGS）的过滤条件。例如：
```mysql
taos> SELECT TBNAME, location FROM meters;
tbname | location |
==================================================================
d1004 | California.SanFrancisco |
d1003 | California.SanFrancisco |
d1002 | California.LosAngeles |
d1001 | California.LosAngeles |
Query OK, 4 row(s) in set (0.000881s)
taos> SELECT COUNT(tbname) FROM meters WHERE groupId > 2;
count(tbname) |
========================
2 |
Query OK, 1 row(s) in set (0.001091s)
```
### _QSTART/_QSTOP/_QDURATION
表示查询过滤窗口的起始，结束以及持续时间。
### _WSTART/_WSTOP/_WDURATION
窗口切分聚合查询（例如 interval/session window/state window）中表示每个切分窗口的起始，结束以及持续时间。
### _c0/_ROWTS
_c0 _ROWTS 等价，表示表或超级表的第一列
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\ No newline at end of file
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---
sidebar_label: 运算符
title: 运算符
---
## 算术运算符
| # | **运算符** | **支持的类型** | **说明** |
| --- | :--------: | -------------- | -------------------------- |
| 1 | +, - | 数值类型 | 表达正数和负数，一元运算符 |
| 2 | +, - | 数值类型 | 表示加法和减法，二元运算符 |
| 3 | \*, / | 数值类型 | 表示乘法和除法，二元运算符 |
| 4 | % | 数值类型 | 表示取余运算，二元运算符 |
## 位运算符
| # | **运算符** | **支持的类型** | **说明** |
| --- | :--------: | -------------- | ------------------ |
| 1 | & | 数值类型 | 按位与，二元运算符 |
| 2 | \| | 数值类型 | 按位或，二元运算符 |
## JSON 运算符
`->` 运算符可以对 JSON 类型的列按键取值。->左侧是列标识符，右侧是键的字符串常量，如 col->'name'，返回键'name'的值。
## 集合运算符
集合运算符将两个查询的结果合并为一个结果。包含集合运算符的查询称之为复合查询。复合查询中每条查询的选择列表中的相应表达式在数量上必须匹配，并且必须位于同一数据类型组中（如数值类型或字符串类型）。
- 对于字符串类型数据，返回值的数据类型按如下方式确定：
- 如果具有相同的类型（都为 BINARY 或都为 NCHAR），则返回此类型，并以较大的长度作为返回值长度。
- 如果具有不同的类型，则返回 BINARY 类型，并以较大的长度（NCHAR 类型长度按四倍计算）作为返回值长度。
- 对于数值类型数据，返回值的数据类型是数字表达范围较大的那个。
TDengine 支持 `UNION ALL` 操作符。UNION ALL 将查询返回的结果集合并返回，并不去重。在同一个 SQL 语句中，UNION ALL 最多支持 100 个。
## 比较运算符
| # | **运算符** | **支持的类型** | **说明** |
| --- | :---------------: | -------------------------------------------------------------------- | -------------------- |
| 1 | = | 除 BLOB、MEDIUMBLOB 和 JSON 外的所有类型 | 相等 |
| 2 | <\>, != | 除 BLOB、MEDIUMBLOB 和 JSON 外的所有类型，且不可以为表的时间戳主键列 | 不相等 |
| 3 | \>, \< | 除 BLOB、MEDIUMBLOB 和 JSON 外的所有类型 | 大于，小于 |
| 4 | \>=, \<= | 除 BLOB、MEDIUMBLOB 和 JSON 外的所有类型 | 大于等于，小于等于 |
| 5 | IS [NOT] NULL | 所有类型 | 是否为空值 |
| 6 | [NOT] BETWEEN AND | 除 BOOL、BLOB、MEDIUMBLOB 和 JSON 外的所有类型 | 闭区间比较 |
| 7 | IN | 除 BLOB、MEDIUMBLOB 和 JSON 外的所有类型，且不可以为表的时间戳主键列 | 与列表内的任意值相等 |
| 8 | LIKE | BINARY、NCHAR 和 VARCHAR | 通配符匹配 |
| 9 | MATCH, NMATCH | BINARY、NCHAR 和 VARCHAR | 正则表达式匹配 |
| 10 | CONTAINS | JSON | JSON 中是否存在某键 |
LIKE 条件使用通配符字符串进行匹配检查，规则如下：
- '%'（百分号）匹配 0 到任意个字符；'\_'（下划线）匹配单个任意 ASCII 字符。
- 如果希望匹配字符串中原本就带有的 \_（下划线）字符，那么可以在通配符字符串中写作 \_，即加一个反斜线来进行转义。
- 通配符字符串最长不能超过 100 字节。不建议使用太长的通配符字符串，否则将有可能严重影响 LIKE 操作的执行性能。
MATCH 条件和 NMATCH 条件使用正则表达式进行匹配，规则如下：
- 支持符合 POSIX 规范的正则表达式，具体规范内容可参见 Regular Expressions。
- 只能针对子表名（即 tbname）、字符串类型的标签值进行正则表达式过滤，不支持普通列的过滤。
- 正则匹配字符串长度不能超过 128 字节。可以通过参数 maxRegexStringLen 设置和调整最大允许的正则匹配字符串，该参数是客户端配置参数，需要重启客户端才能生效
## 逻辑运算符
| # | **运算符** | **支持的类型** | **说明** |
| --- | :--------: | -------------- | --------------------------------------------------------------------------- |
| 1 | AND | BOOL | 逻辑与，如果两个条件均为 TRUE， 则返回 TRUE。如果任一为 FALSE，则返回 FALSE |
| 2 | OR | BOOL | 逻辑或，如果任一条件为 TRUE， 则返回 TRUE。如果两者都是 FALSE，则返回 FALSE |
TDengine 在计算逻辑条件时，会进行短路径优化，即对于 AND，第一个条件为 FALSE，则不再计算第二个条件，直接返回 FALSE；对于 OR，第一个条件为 TRUE，则不再计算第二个条件，直接返回 TRUE。
docs-cn/14-reference/03-connector/node.mdx
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......@@ -14,7 +14,6 @@ import NodeInfluxLine from "../../07-develop/03-insert-data/_js_line.mdx";
import NodeOpenTSDBTelnet from "../../07-develop/03-insert-data/_js_opts_telnet.mdx";
import NodeOpenTSDBJson from "../../07-develop/03-insert-data/_js_opts_json.mdx";
import NodeQuery from "../../07-develop/04-query-data/_js.mdx";
import NodeAsyncQuery from "../../07-develop/04-query-data/_js_async.mdx";
`td2.0-connector` 和 `td2.0-rest-connector` 是 TDengine 的官方 Node.js 语言连接器。Node.js 开发人员可以通过它开发可以存取 TDengine 集群数据的应用软件。
......@@ -189,14 +188,8 @@ let cursor = conn.cursor();
### 查询数据
#### 同步查询
<NodeQuery />
#### 异步查询
<NodeAsyncQuery />
## 更多示例程序
| 示例程序 | 示例程序描述 |
......
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......@@ -38,7 +38,7 @@ taosdump 有两种安装方式:
:::tip
- taosdump 1.4.1 之后的版本提供 `-I` 参数，用于解析 avro 文件 schema 和数据，如果指定 `-s` 参数将只解析 schema。
- taosdump 1.4.2 之后的备份使用 `-B` 参数指定的批次数，默认值为 16384，如果在某些环境下由于网络速度或磁盘性能不足导致 "Error actual dump .. batch .." 可以通过 `-B` 参数挑战为更小的值进行尝试。
- taosdump 1.4.2 之后的备份使用 `-B` 参数指定的批次数，默认值为 16384，如果在某些环境下由于网络速度或磁盘性能不足导致 "Error actual dump .. batch .." 可以通过 `-B` 参数调整为更小的值进行尝试。
:::
......
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......@@ -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)
:::
......
docs-cn/20-third-party/11-kafka.md
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......@@ -7,7 +7,7 @@ TDengine Kafka Connector 包含两个插件: TDengine Source Connector 和 TDeng
## 什么是 Kafka Connect？
Kafka Connect 是 Apache Kafka 的一个组件，用于使其它系统，比如数据库、云服务、文件系统等能方便地连接到 Kafka。数据既可以通过 Kafka Connect 从其它系统流向 Kafka, 也可以通过 Kafka Connect 从 Kafka 流向其它系统。从其它系统读数据的插件称为 Source Connector, 写数据到其它系统的插件称为 Sink Connector。Source Connector 和 Sink Connector 都不会直接连接 Kafka Broker，Source Connector 把数据转交给 Kafka Connect。Sink Connector 从 Kafka Connect 接收数据。
Kafka Connect 是 [Apache Kafka](https://kafka.apache.org/) 的一个组件，用于使其它系统，比如数据库、云服务、文件系统等能方便地连接到 Kafka。数据既可以通过 Kafka Connect 从其它系统流向 Kafka, 也可以通过 Kafka Connect 从 Kafka 流向其它系统。从其它系统读数据的插件称为 Source Connector, 写数据到其它系统的插件称为 Sink Connector。Source Connector 和 Sink Connector 都不会直接连接 Kafka Broker，Source Connector 把数据转交给 Kafka Connect。Sink Connector 从 Kafka Connect 接收数据。
![TDengine Database Kafka Connector -- Kafka Connect structure](kafka/Kafka_Connect.webp)
......@@ -17,7 +17,7 @@ TDengine Source Connector 用于把数据实时地从 TDengine 读出来发送
## 什么是 Confluent？
Confluent 在 Kafka 的基础上增加很多扩展功能。包括：
[Confluent](https://www.confluent.io/) 在 Kafka 的基础上增加很多扩展功能。包括：
1. Schema Registry
2. REST 代理
......@@ -81,10 +81,10 @@ Development: false
git clone https://github.com:taosdata/kafka-connect-tdengine.git
cd kafka-connect-tdengine
mvn clean package
unzip -d $CONFLUENT_HOME/share/confluent-hub-components/ target/components/packages/taosdata-kafka-connect-tdengine-0.1.0.zip
unzip -d $CONFLUENT_HOME/share/java/ target/components/packages/taosdata-kafka-connect-tdengine-*.zip
```
以上脚本先 clone 项目源码，然后用 Maven 编译打包。打包完成后在 `target/components/packages/` 目录生成了插件的 zip 包。把这个 zip 包解压到安装插件的路径即可。安装插件的路径在配置文件 `$CONFLUENT_HOME/etc/kafka/connect-standalone.properties` 中。默认的路径为 `$CONFLUENT_HOME/share/confluent-hub-components/`。
以上脚本先 clone 项目源码，然后用 Maven 编译打包。打包完成后在 `target/components/packages/` 目录生成了插件的 zip 包。把这个 zip 包解压到安装插件的路径即可。上面的示例中使用了内置的插件安装路径： `$CONFLUENT_HOME/share/java/`。
### 用 confluent-hub 安装
......@@ -98,7 +98,7 @@ confluent local services start
```
:::note
一定要先安装插件再启动 Confluent, 否则会出现找不到类的错误。Kafka Connect 的日志（默认路径: /tmp/confluent.xxxx/connect/logs/connect.log）中会输出成功安装的插件，据此可判断插件是否安装成功。
一定要先安装插件再启动 Confluent, 否则加载插件会失败。
:::
:::tip
......@@ -125,6 +125,61 @@ Control Center is [UP]
清空数据可执行 `rm -rf /tmp/confluent.106668`。
:::
### 验证各个组件是否启动成功
输入命令：
```
confluent local services status
```
如果各组件都启动成功，会得到如下输出：
```
Connect is [UP]
Control Center is [UP]
Kafka is [UP]
Kafka REST is [UP]
ksqlDB Server is [UP]
Schema Registry is [UP]
ZooKeeper is [UP]
```
### 验证插件是否安装成功
在 Kafka Connect 组件完全启动后，可用以下命令列出成功加载的插件：
```
confluent local services connect plugin list
```
如果成功安装，会输出如下：
```txt {4,9}
Available Connect Plugins:
[
{
"class": "com.taosdata.kafka.connect.sink.TDengineSinkConnector",
"type": "sink",
"version": "1.0.0"
},
{
"class": "com.taosdata.kafka.connect.source.TDengineSourceConnector",
"type": "source",
"version": "1.0.0"
},
......
```
如果插件安装失败，请检查 Kafka Connect 的启动日志是否有异常信息，用以下命令输出日志路径：
```
echo `cat /tmp/confluent.current`/connect/connect.stdout
```
该命令的输出类似： `/tmp/confluent.104086/connect/connect.stdout`。
与日志文件 `connect.stdout` 同一目录，还有一个文件名为： `connect.properties`。在这个文件的末尾，可以看到最终生效的 `plugin.path`， 它是一系列用逗号分割的路径。如果插件安装失败，很可能是因为实际的安装路径不包含在 `plugin.path` 中。
## TDengine Sink Connector 的使用
TDengine Sink Connector 的作用是同步指定 topic 的数据到 TDengine。用户无需提前创建数据库和超级表。可手动指定目标数据库的名字（见配置参数 connection.database）， 也可按一定规则生成(见配置参数 connection.database.prefix)。
......@@ -144,7 +199,7 @@ vi sink-demo.properties
sink-demo.properties 内容如下：
```ini title="sink-demo.properties"
name=tdengine-sink-demo
name=TDengineSinkConnector
connector.class=com.taosdata.kafka.connect.sink.TDengineSinkConnector
tasks.max=1
topics=meters
......@@ -153,6 +208,7 @@ connection.user=root
connection.password=taosdata
connection.database=power
db.schemaless=line
data.precision=ns
key.converter=org.apache.kafka.connect.storage.StringConverter
value.converter=org.apache.kafka.connect.storage.StringConverter
```
......@@ -179,6 +235,7 @@ confluent local services connect connector load TDengineSinkConnector --config .
"connection.url": "jdbc:TAOS://127.0.0.1:6030",
"connection.user": "root",
"connector.class": "com.taosdata.kafka.connect.sink.TDengineSinkConnector",
"data.precision": "ns",
"db.schemaless": "line",
"key.converter": "org.apache.kafka.connect.storage.StringConverter",
"tasks.max": "1",
......@@ -223,10 +280,10 @@ Database changed.
taos> select * from meters;
ts | current | voltage | phase | groupid | location |
===============================================================================================================================================================
2022-03-28 09:56:51.249000000 | 11.800000000 | 221.000000000 | 0.280000000 | 2 | California.LosAngeles |
2022-03-28 09:56:51.250000000 | 13.400000000 | 223.000000000 | 0.290000000 | 2 | California.LosAngeles |
2022-03-28 09:56:51.249000000 | 10.800000000 | 223.000000000 | 0.290000000 | 3 | California.LosAngeles |
2022-03-28 09:56:51.250000000 | 11.300000000 | 221.000000000 | 0.350000000 | 3 | California.LosAngeles |
2022-03-28 09:56:51.249000000 | 11.800000000 | 221.000000000 | 0.280000000 | 2 | California.LosAngeles |
2022-03-28 09:56:51.250000000 | 13.400000000 | 223.000000000 | 0.290000000 | 2 | California.LosAngeles |
2022-03-28 09:56:51.249000000 | 10.800000000 | 223.000000000 | 0.290000000 | 3 | California.LosAngeles |
2022-03-28 09:56:51.250000000 | 11.300000000 | 221.000000000 | 0.350000000 | 3 | California.LosAngeles |
Query OK, 4 row(s) in set (0.004208s)
```
......@@ -356,21 +413,33 @@ confluent local services connect connector unload TDengineSourceConnector
2. `connection.database.prefix`： 当 connection.database 为 null 时, 目标数据库的前缀。可以包含占位符 '${topic}'。 比如 kafka_${topic}, 对于主题 'orders' 将写入数据库 'kafka_orders'。 默认 null。当为 null 时，目标数据库的名字和主题的名字是一致的。
3. `batch.size`: 分批写入每批记录数。当 Sink Connector 一次接收到的数据大于这个值时将分批写入。
4. `max.retries`: 发生错误时的最大重试次数。默认为 1。
5. `retry.backoff.ms`: 发送错误时重试的时间间隔。单位毫秒，默认 3000。
6. `db.schemaless`: 数据格式，必须指定为： line、json、telnet 中的一个。分别代表 InfluxDB 行协议格式、 OpenTSDB JSON 格式、 OpenTSDB Telnet 行协议格式。
5. `retry.backoff.ms`: 发送错误时重试的时间间隔。单位毫秒，默认为 3000。
6. `db.schemaless`: 数据格式，可选值为：
1. line ：代表 InfluxDB 行协议格式
2. json : 代表 OpenTSDB JSON 格式
3. telnet ：代表 OpenTSDB Telnet 行协议格式
7. `data.precision`: 使用 InfluxDB 行协议格式时，时间戳的精度。可选值为：
1. ms ： 表示毫秒
2. us ： 表示微秒
3. ns ： 表示纳秒。默认为纳秒。
### TDengine Source Connector 特有的配置
1. `connection.database`: 源数据库名称，无缺省值。
2. `topic.prefix`： 数据导入 kafka 后 topic 名称前缀。 使用 `topic.prefix` + `connection.database` 名称作为完整 topic 名。默认为空字符串 ""。
3. `timestamp.initial`: 数据同步起始时间。格式为'yyyy-MM-dd HH:mm:ss'。默认 "1970-01-01 00:00:00"。
4. `poll.interval.ms`: 拉取数据间隔，单位为 ms。默认 1000。
3. `timestamp.initial`: 数据同步起始时间。格式为'yyyy-MM-dd HH:mm:ss'。默认为 "1970-01-01 00:00:00"。
4. `poll.interval.ms`: 拉取数据间隔，单位为 ms。默认为 1000。
5. `fetch.max.rows` : 检索数据库时最大检索条数。 默认为 100。
6. `out.format`: 数据格式。取值 line 或 json。line 表示 InfluxDB Line 协议格式， json 表示 OpenTSDB JSON 格式。默认 line。
6. `out.format`: 数据格式。取值 line 或 json。line 表示 InfluxDB Line 协议格式， json 表示 OpenTSDB JSON 格式。默认为 line。
## 其他说明
1. 插件的安装位置可以自定义，请参考官方文档：https://docs.confluent.io/home/connect/self-managed/install.html#install-connector-manually。
2. 本教程的示例程序使用了 Confluent 平台，但是 TDengine Kafka Connector 本身同样适用于独立安装的 Kafka, 且配置方法相同。关于如何在独立安装的 Kafka 环境使用 Kafka Connect 插件， 请参考官方文档： https://kafka.apache.org/documentation/#connect。
## 问题反馈
https://github.com/taosdata/kafka-connect-tdengine/issues
无论遇到任何问题，都欢迎在本项目的 Github 仓库反馈： https://github.com/taosdata/kafka-connect-tdengine/issues。
## 参考
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docs-cn/27-train-faq/01-faq.md
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......@@ -222,21 +222,9 @@ TDengine 中时间戳的时区总是由客户端进行处理，而与服务端
### 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。 |
使用到的网络端口请看文档：[serverport](/reference/config/#serverport)
需要注意，文档上列举的端口号都是以默认端口 6030 为前提进行说明，如果修改了配置文件中的设置，那么列举的端口都会随之出现变化，管理员可以参考上述的信息调整防火墙设置。
### 24. 为什么 RESTful 接口无响应、Grafana 无法添加 TDengine 为数据源、TDengineGUI 选了 6041 端口还是无法连接成功？？
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......@@ -12,6 +12,6 @@ Between two major release versions, some beta versions may be delivered for user
For the details please refer to [Install and Uninstall](/operation/pkg-install)。
To see the details of versions, please refer to [Download List](https://www.taosdata.com/all-downloads) and [Release Notes](https://github.com/taosdata/TDengine/releases).
To see the details of versions, please refer to [Download List](https://tdengine.com/all-downloads) and [Release Notes](https://github.com/taosdata/TDengine/releases).
docs-en/07-develop/03-insert-data/01-sql-writing.mdx
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......@@ -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.
:::
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......@@ -3,6 +3,8 @@ title: Data Types
description: "TDengine supports a variety of data types including timestamp, float, JSON and many others."
---
## TIMESTAMP
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`
......@@ -17,33 +19,51 @@ Time precision in TDengine can be set by the `PRECISION` parameter when executin
CREATE DATABASE db_name PRECISION 'ns';
```
## Data Types
In TDengine, the data types below can be used when specifying a column or tag.
| # | **type** | **Bytes** | **Description** |
| --- | :-------: | --------- | ------------------------- |
| 1 | TIMESTAMP | 8 | Default precision is millisecond, microsecond and nanosecond are also supported |
| 2 | INT | 4 | Integer, the value range is [-2^31+1, 2^31-1], while -2^31 is treated as NULL |
| 3 | BIGINT | 8 | Long integer, the value range is [-2^63+1, 2^63-1], while -2^63 is treated as NULL |
| 4 | FLOAT | 4 | Floating point number, the effective number of digits is 6-7, the value range is [-3.4E38, 3.4E38] |
| 5 | DOUBLE | 8 | Double precision floating point number, the effective number of digits is 15-16, the value range is [-1.7E308, 1.7E308] |
| 6 | BINARY | User Defined | Single-byte string for ASCII visible characters. Length must be specified when defining a column or tag of binary type. The string length can be up to 16374 bytes. The string value must be quoted with single quotes. The literal single quote inside the string must be preceded with back slash like `\'` |
| 7 | SMALLINT | 2 | Short integer, the value range is [-32767, 32767], while -32768 is treated as NULL |
| 8 | TINYINT | 1 | Single-byte integer, the value range is [-127, 127], while -128 is treated as NULL |
| 9 | BOOL | 1 | Bool, the value range is {true, false} |
| 10 | NCHAR | User Defined| 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.
:::
| 2 | INT | 4 | Integer, the value range is [-2^31, 2^31-1] |
| 3 |INT UNSIGNED|4 | Unsigned integer, the value range is [0, 2^31-1] |
| 4 | BIGINT | 8 | Long integer, the value range is [-2^63, 2^63-1] |
| 5 | BIGINT UNSIGNED | 8 | Unsigned long integer, the value range is [0, 2^63-1] |
| 6 | FLOAT | 4 | Floating point number, the effective number of digits is 6-7, the value range is [-3.4E38, 3.4E38] |
| 7 | DOUBLE | 8 | Double precision floating point number, the effective number of digits is 15-16, the value range is [-1.7E308, 1.7E308] |
| 8 | BINARY | User Defined | Single-byte string for ASCII visible characters. Length must be specified when defining a column or tag of binary type. The string length can be up to 16374 bytes. The string value must be quoted with single quotes. The literal single quote inside the string must be preceded with back slash like `\'` |
| 9 | SMALLINT | 2 | Short integer, the value range is [-32768, 32767] |
| 10 | SMALLINT UNSIGNED | 2 | Unsigned short integer, the value range is [0, 32767] |
| 11 | TINYINT | 1 | Single-byte integer, the value range is [-128, 127] |
| 12 | TINYINT UNSIGNED | 1 | Unsigned single-byte integer, the value range is [0, 127] |
| 13 | BOOL | 1 | Bool, the value range is {true, false} |
| 14 | 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. |
| 15 | JSON | | JSON type can only be used on tags. A tag of json type is excluded with any other tags of any other type |
| 16 | VARCHAR | User Defined| Alias of BINARY type |
:::note
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.
- 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.
- 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.
- Numeric values in SQL statements will be determined as integer or float type according to whether there is decimal point or whether scientific notation is used, so attention must be paid to avoid overflow. For example, 9999999999999999999 will be considered as overflow because it exceeds the upper limit of long integer, but 9999999999999999999.0 will be considered as a legal float number.
:::
## Constants
TDengine supports constants of multiple data type.
| # | **Syntax** | **Type** | **Description** |
| --- | :-------: | --------- | -------------------------------------- |
| 1 | [{+ \| -}]123 | BIGINT | Numeric constants are treated as BIGINT type. The value will be truncated if it exceeds the range of BIGINT type. |
| 2 | 123.45 | DOUBLE | Floating number constants are treated as DOUBLE type. TDengine determines whether it's a floating number based on if decimal point or scientific notation is used. |
| 3 | 1.2E3 | DOUBLE | Constants in scientific notation are treated ad DOUBLE type. |
| 4 | 'abc' | BINARY | String constants enclosed by single quotes are treated as BINARY type. Its size is determined as the acutal length. Single quote itself can be included by preceding backslash, i.e. `\'`, in a string constant. |
| 5 | "abc" | BINARY | String constants enclosed by double quotes are treated as BINARY type. Its size is determined as the acutal length. Double quote itself can be included by preceding backslash, i.e. `\"`, in a string constant. |
| 6 | TIMESTAMP {'literal' \| "literal"} | TIMESTAMP | A string constant following `TIMESTAMP` keyword is treated as TIMESTAMP type. The string should be in the format of "YYYY-MM-DD HH:mm:ss.MS". Its time precision is same as that of the current database being used. |
| 7 | {TRUE \| FALSE} | BOOL | BOOL type contant. |
| 8 | {'' \| "" \| '\t' \| "\t" \| ' ' \| " " \| NULL } | -- | NULL constant, it can be used for any type.|
:::note
Numeric values in SQL statements will be determined as integer or float type according to whether there is decimal point or whether scientific notation is used, so attention must be paid to avoid overflow. For example, 9999999999999999999 will be considered as overflow because it exceeds the upper limit of long integer, but 9999999999999999999.0 will be considered as a legal float number.
- TDengine determines whether it's a floating number based on if decimal point or scientific notation is used. So whether the value is determined as overflow depends on both the value and the determined type. For example, 9999999999999999999 is determined as overflow because it exceeds the upper limit of BIGINT type, while 9999999999999999999.0 is considered as a valid floating number because it is within the range of DOUBLE type.
:::
docs-en/12-taos-sql/03-table.md
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......@@ -14,7 +14,7 @@ CREATE TABLE [IF NOT EXISTS] tb_name (timestamp_field_name TIMESTAMP, field1_nam
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.
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.
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......@@ -259,6 +259,100 @@ taos> select hyperloglog(dbig) from shll;
Query OK, 1 row(s) in set (0.008388s)
```
### HISTOGRAM
```
SELECT HISTOGRAM(field_name，bin_type, bin_description, normalized) FROM tb_name [WHERE clause];
```
**Description**：Returns count of data points in user-specified ranges.
**Return value type**：Double or INT64, depends on normalized parameter settings.
**Applicable column type**：Numerical types.
**Applicable versions**：Since version 2.6.0.0.
**Applicable table types**: table, STable
**Explanations**：
1. bin_type: parameter to indicate the bucket type, valid inputs are: "user_input", "linear_bin", "log_bin"。
2. bin_description: parameter to describe how to generate buckets，can be in the following JSON formats for each bin_type respectively:
- "user_input": "[1, 3, 5, 7]": User specified bin values.
- "linear_bin": "{"start": 0.0, "width": 5.0, "count": 5, "infinity": true}"
"start" - bin starting point.
"width" - bin offset.
"count" - number of bins generated.
"infinity" - whether to add（-inf, inf）as start/end point in generated set of bins.
The above "linear_bin" descriptor generates a set of bins: [-inf, 0.0, 5.0, 10.0, 15.0, 20.0, +inf].
- "log_bin": "{"start":1.0, "factor": 2.0, "count": 5, "infinity": true}"
"start" - bin starting point.
"factor" - exponential factor of bin offset.
"count" - number of bins generated.
"infinity" - whether to add（-inf, inf）as start/end point in generated range of bins.
The above "log_bin" descriptor generates a set of bins:[-inf, 1.0, 2.0, 4.0, 8.0, 16.0, +inf].
3. normalized: setting to 1/0 to turn on/off result normalization.
**Example**：
```mysql
taos> SELECT HISTOGRAM(voltage, "user_input", "[1,3,5,7]", 1) FROM meters;
histogram(voltage, "user_input", "[1,3,5,7]", 1) |
=======================================================
{"lower_bin":1, "upper_bin":3, "count":0.333333} |
{"lower_bin":3, "upper_bin":5, "count":0.333333} |
{"lower_bin":5, "upper_bin":7, "count":0.333333} |
Query OK, 3 row(s) in set (0.004273s)
taos> SELECT HISTOGRAM(voltage, 'linear_bin', '{"start": 1, "width": 3, "count": 3, "infinity": false}', 0) FROM meters;
histogram(voltage, 'linear_bin', '{"start": 1, "width": 3, " |
===================================================================
{"lower_bin":1, "upper_bin":4, "count":3} |
{"lower_bin":4, "upper_bin":7, "count":3} |
{"lower_bin":7, "upper_bin":10, "count":3} |
Query OK, 3 row(s) in set (0.004887s)
taos> SELECT HISTOGRAM(voltage, 'log_bin', '{"start": 1, "factor": 3, "count": 3, "infinity": true}', 0) FROM meters;
histogram(voltage, 'log_bin', '{"start": 1, "factor": 3, "count" |
===================================================================
{"lower_bin":-inf, "upper_bin":1, "count":3} |
{"lower_bin":1, "upper_bin":3, "count":2} |
{"lower_bin":3, "upper_bin":9, "count":6} |
{"lower_bin":9, "upper_bin":27, "count":3} |
{"lower_bin":27, "upper_bin":inf, "count":1} |
```
### ELAPSED
```mysql
SELECT ELAPSED(field_name[, time_unit]) FROM { tb_name | stb_name } [WHERE clause] [INTERVAL(interval [, offset]) [SLIDING sliding]];
```
**Description**：`elapsed` function can be used to calculate the continuous time length in which there is valid data. If it's used with `INTERVAL` clause, the returned result is the calcualted time length within each time window. If it's used without `INTERVAL` caluse, the returned result is the calculated time length within the specified time range. Please be noted that the return value of `elapsed` is the number of `time_unit` in the calculated time length.
**Return value type**：Double
**Applicable Column type**：Timestamp
**Applicable versions**：Sicne version 2.6.0.0
**Applicable tables**: table, STable, outter in nested query
**Explanations**：
- `field_name` parameter can only be the first column of a table, i.e. timestamp primary key.
- The minimum value of `time_unit` is the time precision of the database. If `time_unit` is not specified, the time precision of the database is used as the default ime unit.
- It can be used with `INTERVAL` to get the time valid time length of each time window. Please be noted that the return value is same as the time window for all time windows except for the first and the last time window.
- `order by asc/desc` has no effect on the result.
- `group by tbname` must be used together when `elapsed` is used against a STable.
- `group by` must NOT be used together when `elapsed` is used against a table or sub table.
- When used in nested query, it's only applicable when the inner query outputs an implicit timestamp column as the primary key. For example, `select elapsed(ts) from (select diff(value) from sub1)` is legal usage while `select elapsed(ts) from (select * from sub1)` is not.
- It can't be used with `leastsquares`, `diff`, `derivative`, `top`, `bottom`, `last_row`, `interp`.
## Selection Functions
When any select function is used, timestamp column or tag columns including `tbname` can be specified to show that the selected value are from which rows.
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| CONNECTIONS | HAVING | NOT | SOFFSET | VNODES |
| CONNS | ID | NOTNULL | STable | WAL |
| COPY | IF | NOW | STableS | WHERE |
| _C0 | _QSTART | _QSTOP | _QDURATION | _WSTART |
| _WSTOP | _WDURATION | _ROWTS |
## Explanations
### TBNAME
`TBNAME` can be considered as a special tag, which represents the name of the subtable, in a STable.
Get the table name and tag values of all subtables in a STable.
```mysql
SELECT TBNAME, location FROM meters;
Count the number of subtables in a STable.
```mysql
SELECT COUNT(TBNAME) FROM meters;
```
Only filter on TAGS can be used in WHERE clause in the above two query statements.
```mysql
taos> SELECT TBNAME, location FROM meters;
tbname | location |
==================================================================
d1004 | California.SanFrancisco |
d1003 | California.SanFrancisco |
d1002 | California.LosAngeles |
d1001 | California.LosAngeles |
Query OK, 4 row(s) in set (0.000881s)
taos> SELECT COUNT(tbname) FROM meters WHERE groupId > 2;
count(tbname) |
========================
2 |
Query OK, 1 row(s) in set (0.001091s)
```
### _QSTART/_QSTOP/_QDURATION
The start, stop and duration of a query time window.
### _WSTART/_WSTOP/_WDURATION
The start, stop and duration of aggegate query by time window, like interval, session window, state window.
### _c0/_ROWTS
_c0 is equal to _ROWTS, it means the first column of a table or STable.
docs-en/14-reference/03-connector/cpp.mdx
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......@@ -4,7 +4,7 @@ sidebar_label: C/C++
title: C/C++ Connector
---
C/C++ developers can use TDengine's client driver and the C/C++ connector, to develop their applications to connect to TDengine clusters for data writing, querying, and other functions. To use it, you need to include the TDengine header file _taos.h_, which lists the function prototypes of the provided APIs; the application also needs to link to the corresponding dynamic libraries on the platform where it is located.
C/C++ developers can use TDengine's client driver and the C/C++ connector, to develop their applications to connect to TDengine clusters for data writing, querying, and other functions. To use the C/C++ connector you must include the TDengine header file _taos.h_, which lists the function prototypes of the provided APIs. The application also needs to link to the corresponding dynamic libraries on the platform where it is located.
```c
#include <taos.h>
......@@ -26,7 +26,7 @@ Please refer to [list of supported platforms](/reference/connector#supported-pla
## Supported versions
The version number of the TDengine client driver and the version number of the TDengine server require one-to-one correspondence and recommend using the same version of client driver as what the TDengine server version is. Although a lower version of the client driver is compatible to work with a higher version of the server, if the first three version numbers are the same (i.e., only the fourth version number is different), but it is not recommended. It is strongly discouraged to use a higher version of the client driver to access a lower version of the TDengine server.
The version number of the TDengine client driver and the version number of the TDengine server should be the same. A lower version of the client driver is compatible with a higher version of the server, if the first three version numbers are the same (i.e., only the fourth version number is different). For e.g. if the client version is x.y.z.1 and the server version is x.y.z.2 the client and server are compatible. But in general we do not recommend using a lower client version with a newer server version. It is also strongly discouraged to use a higher version of the client driver to access a lower version of the TDengine server.
## Installation steps
......@@ -55,7 +55,7 @@ In the above example code, `taos_connect()` establishes a connection to port 603
:::note
- If not specified, when the return value of the API is an integer, _0_ means success, the others are error codes representing the reason for failure, and when the return value is a pointer, _NULL_ means failure.
- If not specified, when the return value of the API is an integer, _0_ means success. All others are error codes representing the reason for failure. When the return value is a pointer, _NULL_ means failure.
- All error codes and their corresponding causes are described in the `taoserror.h` file.
:::
......@@ -140,13 +140,12 @@ The base API is used to do things like create database connections and provide a
- `void taos_cleanup()`
Clean up the runtime environment and should be called before the application exits.
Cleans up the runtime environment and should be called before the application exits.
- ` int taos_options(TSDB_OPTION option, const void * arg, ...) `
Set client options, currently supports region setting (`TSDB_OPTION_LOCALE`), character set
(`TSDB_OPTION_CHARSET`), time zone
(`TSDB_OPTION_TIMEZONE`), configuration file path (`TSDB_OPTION_CONFIGDIR`) . The region setting, character set, and time zone default to the current settings of the operating system.
(`TSDB_OPTION_CHARSET`), time zone (`TSDB_OPTION_TIMEZONE`), configuration file path (`TSDB_OPTION_CONFIGDIR`). The region setting, character set, and time zone default to the current settings of the operating system.
- `char *taos_get_client_info()`
......@@ -159,7 +158,7 @@ The base API is used to do things like create database connections and provide a
- host: FQDN of any node in the TDengine cluster
- user: user name
- pass: password
- db: database name, if the user does not provide, it can also be connected correctly, the user can create a new database through this connection, if the user provides the database name, it means that the database user has already created, the default use of the database
- db: the database name. Even if the user does not provide this, the connection will still work correctly. The user can create a new database through this connection. If the user provides the database name, it means that the database has already been created and the connection can be used for regular operations on the database.
- port: the port the taosd program is listening on
NULL indicates a failure. The application needs to save the returned parameters for subsequent use.
......@@ -187,7 +186,7 @@ The APIs described in this subsection are all synchronous interfaces. After bein
- `TAOS_RES* taos_query(TAOS *taos, const char *sql)`
Executes an SQL command, either a DQL, DML, or DDL statement. The `taos` parameter is a handle obtained with `taos_connect()`. You can't tell if the result failed by whether the return value is `NULL`, but by parsing the error code in the result set with the `taos_errno()` function.
Executes an SQL command, either a DQL, DML, or DDL statement. The `taos` parameter is a handle obtained with `taos_connect()`. If the return value is `NULL` this does not necessarily indicate a failure. You can get the error code, if any, by parsing the error code in the result set with the `taos_errno()` function.
- `int taos_result_precision(TAOS_RES *res)`
......@@ -231,7 +230,7 @@ typedef struct taosField {
- ` void taos_free_result(TAOS_RES *res)`
Frees the query result set and the associated resources. Be sure to call this API to free the resources after the query is completed. Otherwise, it may lead to a memory leak in the application. However, note that the application will crash if you call a function like `taos_consume()` to get the query results after freeing the resources.
Frees the query result set and the associated resources. Be sure to call this API to free the resources after the query is completed. Failing to call this, may lead to a memory leak in the application. However, note that the application will crash if you call a function like `taos_consume()` to get the query results after freeing the resources.
- `char *taos_errstr(TAOS_RES *res)`
......@@ -242,7 +241,7 @@ typedef struct taosField {
Get the reason for the last API call failure. The return value is the error code.
:::note
TDengine version 2.0 and above recommends that each thread of a database application create a separate connection or a connection pool based on threads. It is not recommended to pass the connection (TAOS\*) structure to different threads for shared use in the application. Queries, writes, etc., issued based on TAOS structures are multi-thread safe, but state quantities such as "USE statement" may interfere between threads. In addition, the C connector can dynamically create new database-oriented connections on demand (this procedure is not visible to the user), and it is recommended that `taos_close()` be called only at the final exit of the program to close the connection.
TDengine version 2.0 and above recommends that each thread of a database application create a separate connection or a connection pool based on threads. It is not recommended to pass the connection (TAOS\*) structure to different threads for shared use in the application. Queries, writes, and other operations issued that are based on TAOS structures are multi-thread safe, but state quantities such as the "USE statement" may interfere between threads. In addition, the C connector can dynamically create new database-oriented connections on demand (this procedure is not visible to the user), and it is recommended that `taos_close()` be called only at the final exit of the program to close the connection.
:::
......@@ -274,12 +273,12 @@ All TDengine's asynchronous APIs use a non-blocking call pattern. Applications c
### Parameter Binding API
In addition to direct calls to `taos_query()` to perform queries, TDengine also provides a set of `bind` APIs that supports parameter binding, similar in style to MySQL, and currently only supports using a question mark `? ` to represent the parameter to be bound.
In addition to direct calls to `taos_query()` to perform queries, TDengine also provides a set of `bind` APIs that supports parameter binding, similar in style to MySQL. TDengine currently only supports using a question mark `? ` to represent the parameter to be bound.
Starting with versions 2.1.1.0 and 2.1.2.0, TDengine has significantly improved the bind APIs to support for data writing (INSERT) scenarios. This avoids the resource consumption of SQL syntax parsing when writing data through the parameter binding interface, thus significantly improving write performance in most cases. A typical operation, in this case, is as follows.
Starting with versions 2.1.1.0 and 2.1.2.0, TDengine has significantly improved the bind APIs to support data writing (INSERT) scenarios. This avoids the resource consumption of SQL syntax parsing when writing data through the parameter binding interface, thus significantly improving write performance in most cases. A typical operation, in this case, is as follows.
1. call `taos_stmt_init()` to create the parameter binding object.
2. call `taos_stmt_prepare()` to parse the INSERT statement. 3.
2. call `taos_stmt_prepare()` to parse the INSERT statement.
3. call `taos_stmt_set_tbname()` to set the table name if it is reserved in the INSERT statement but not the TAGS.
4. call `taos_stmt_set_tbname_tags()` to set the table name and TAGS values if the table name and TAGS are reserved in the INSERT statement (for example, if the INSERT statement takes an automatic table build).
5. call `taos_stmt_bind_param_batch()` to set the value of VALUES in multiple columns, or call `taos_stmt_bind_param()` to set the value of VALUES in a single row.
......@@ -383,7 +382,7 @@ In addition to writing data using the SQL method or the parameter binding API, w
**return value**
TAOS_RES structure, application can get error message by using `taos_errstr()` and also error code by using `taos_errno()`.
In some cases, the returned TAOS_RES is `NULL`, and it is still possible to call `taos_errno()` to safely get the error code information.
The returned TAOS_RES needs to be freed by the caller. Otherwise, a memory leak will occur.
The returned TAOS_RES needs to be freed by the caller in order to avoid memory leaks.
**Description**
The protocol type is enumerated and contains the following three formats.
......@@ -416,13 +415,13 @@ The Subscription API currently supports subscribing to one or more tables and co
This function is responsible for starting the subscription service, returning the subscription object on success and `NULL` on failure, with the following parameters.
- taos: the database connection that has been established
- restart: if the subscription already exists, whether to restart or continue the previous subscription
- topic: the topic of the subscription (i.e., the name). This parameter is the unique identifier of the subscription
- sql: the query statement of the subscription, this statement can only be _select_ statement, only the original data should be queried, only the data can be queried in time order
- fp: the callback function when the query result is received (the function prototype will be introduced later), only used when called asynchronously. This parameter should be passed `NULL` when called synchronously
- param: additional parameter when calling the callback function, the system API will pass it to the callback function as it is, without any processing
- interval: polling period in milliseconds. The callback function will be called periodically according to this parameter when called asynchronously. not recommended to set this parameter too small To avoid impact on system performance when called synchronously. If the interval between two calls to `taos_consume()` is less than this period, the API will block until the interval exceeds this period.
- taos: the database connection that has been established.
- restart: if the subscription already exists, whether to restart or continue the previous subscription.
- topic: the topic of the subscription (i.e., the name). This parameter is the unique identifier of the subscription.
- sql: the query statement of the subscription which can only be a _select_ statement. Only the original data should be queried, and data can only be queried in temporal order.
- fp: the callback function when the query result is received only used when called asynchronously. This parameter should be passed `NULL` when called synchronously. The function prototype is described below.
- param: additional parameter when calling the callback function. The system API will pass it to the callback function as is, without any processing.
- interval: polling period in milliseconds. The callback function will be called periodically according to this parameter when called asynchronously. The interval should not be too small to avoid impact on system performance when called synchronously. If the interval between two calls to `taos_consume()` is less than this period, the API will block until the interval exceeds this period.
- ` typedef void (*TAOS_SUBSCRIBE_CALLBACK)(TAOS_SUB* tsub, TAOS_RES *res, void* param, int code)`
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......@@ -179,9 +179,9 @@ namespace TDengineExample
1. "Unable to establish connection", "Unable to resolve FQDN"
Usually, it cause by the FQDN configuration is incorrect, you can refer to [How to understand TDengine's FQDN (Chinese)](https://www.taosdata.com/blog/2021/07/29/2741.html) to solve it. 2.
Usually, it's caused by an incorrect FQDN configuration. Please refer to this section in the [FAQ](https://docs.tdengine.com/2.4/train-faq/faq/#2-how-to-handle-unable-to-establish-connection) to troubleshoot.
Unhandled exception. System.DllNotFoundException: Unable to load DLL 'taos' or one of its dependencies: The specified module cannot be found.
2. Unhandled exception. System.DllNotFoundException: Unable to load DLL 'taos' or one of its dependencies: The specified module cannot be found.
This is usually because the program did not find the dependent client driver. The solution is to copy `C:\TDengine\driver\taos.dll` to the `C:\Windows\System32\` directory on Windows, and create the following soft link on Linux `ln -s /usr/local/taos/driver/libtaos.so.x.x .x.x /usr/lib/libtaos.so` will work.
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......@@ -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
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docs-en/14-reference/03-connector/java.mdx
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......@@ -9,19 +9,19 @@ description: TDengine Java based on JDBC API and provide both native and REST co
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
'taos-jdbcdriver' is TDengine's official Java language connector, which allows Java developers to develop applications that access the TDengine database. 'taos-jdbcdriver' implements the interface of the JDBC driver standard and provides two forms of connectors. One is to connect to a TDengine instance natively through the TDengine client driver (taosc), which supports functions including data writing, querying, subscription, schemaless writing, and bind interface. And the other is to connect to a TDengine instance through the REST interface provided by taosAdapter (2.4.0.0 and later). REST connections implement has a slight differences to compare the set of features implemented and native connections.
'taos-jdbcdriver' is TDengine's official Java language connector, which allows Java developers to develop applications that access the TDengine database. 'taos-jdbcdriver' implements the interface of the JDBC driver standard and provides two forms of connectors. One is to connect to a TDengine instance natively through the TDengine client driver (taosc), which supports functions including data writing, querying, subscription, schemaless writing, and bind interface. And the other is to connect to a TDengine instance through the REST interface provided by taosAdapter (2.4.0.0 and later). The implementation of the REST connection and those of the native connections have slight differences in features.
![TDengine Database tdengine-connector](tdengine-jdbc-connector.webp)
The preceding diagram shows two ways for a Java app to access TDengine via connector:
- JDBC native connection: Java applications use TSDBDriver on physical node 1 (pnode1) to call client-driven directly (`libtaos.so` or `taos.dll`) APIs to send writing and query requests to taosd instances located on physical node 2 (pnode2).
- JDBC REST connection: The Java application encapsulates the SQL as a REST request via RestfulDriver, sends it to the REST server of physical node 2 (taosAdapter), requests TDengine server through the REST server, and returns the result.
- JDBC REST connection: The Java application encapsulates the SQL as a REST request via RestfulDriver, sends it to the REST server (taosAdapter) on physical node 2. taosAdapter forwards the request to TDengine server and returns the result.
Using REST connection, which does not rely on TDengine client drivers.It can be cross-platform more convenient and flexible but introduce about 30% lower performance than native connection.
The REST connection, which does not rely on TDengine client drivers, is more convenient and flexible, in addition to being cross-platform. However the performance is about 30% lower than that of the native connection.
:::info
TDengine's JDBC driver implementation is as consistent as possible with the relational database driver. Still, there are differences in the use scenarios and technical characteristics of TDengine and relational object databases, so 'taos-jdbcdriver' also has some differences from traditional JDBC drivers. You need to pay attention to the following points when using:
TDengine's JDBC driver implementation is as consistent as possible with the relational database driver. Still, there are differences in the use scenarios and technical characteristics of TDengine and relational object databases. So 'taos-jdbcdriver' also has some differences from traditional JDBC drivers. It is important to keep the following points in mind:
- TDengine does not currently support delete operations for individual data records.
- Transactional operations are not currently supported.
......@@ -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
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......@@ -14,7 +14,6 @@ import NodeInfluxLine from "../../07-develop/03-insert-data/_js_line.mdx";
import NodeOpenTSDBTelnet from "../../07-develop/03-insert-data/_js_opts_telnet.mdx";
import NodeOpenTSDBJson from "../../07-develop/03-insert-data/_js_opts_json.mdx";
import NodeQuery from "../../07-develop/04-query-data/_js.mdx";
import NodeAsyncQuery from "../../07-develop/04-query-data/_js_async.mdx";
`td2.0-connector` and `td2.0-rest-connector` are the official Node.js language connectors for TDengine. Node.js developers can develop applications to access TDengine instance data.
......@@ -189,14 +188,8 @@ let cursor = conn.cursor();
### Query data
#### Synchronous queries
<NodeQuery />
#### asynchronous query
<NodeAsyncQuery />
## More Sample Programs
| Sample Programs | Sample Program Description |
......@@ -232,7 +225,7 @@ See [video tutorial](https://www.taosdata.com/blog/2020/11/11/1957.html) for the
2. "Unable to establish connection", "Unable to resolve FQDN"
Usually, root cause is the FQDN is not configured correctly. You can refer to [How to understand TDengine's FQDN (In Chinese)](https://www.taosdata.com/blog/2021/07/29/2741.html).
Usually, the root cause is an incorrect FQDN configuration. You can refer to this section in the [FAQ](https://docs.tdengine.com/2.4/train-faq/faq/#2-how-to-handle-unable-to-establish-connection) to troubleshoot.
## Important Updates
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`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
......@@ -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}}
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......@@ -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(? ,?)") ? ;
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docs-en/14-reference/04-taosadapter.md
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......@@ -30,7 +30,7 @@ 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 [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.
taosAdapter has been part of TDengine server software since TDengine v2.4.0.0. If you use the TDengine server, you don't need additional steps to install taosAdapter. You can download taosAdapter from [TDengine official website](https://tdengine.com/all-downloads/) to download the TDengine server installation package (taosAdapter is included in v2.4.0.0 and later version). If you need to deploy taosAdapter separately on another server other than the TDengine server, you should install the full TDengine server package on that server to install taosAdapter. If you need to build taosAdapter from source code, you can refer to the [Building taosAdapter]( https://github.com/taosdata/taosadapter/blob/develop/BUILD.md) documentation.
### Start/Stop taosAdapter
......@@ -38,7 +38,7 @@ On Linux systems, the taosAdapter service is managed by `systemd` by default. Yo
### Remove taosAdapter
Use the command `rmtaos` to remove the TDengine server software if you use tar.gz package or use package management command like rpm or apt to remove the TDengine server, including taosAdapter.
Use the command `rmtaos` to remove the TDengine server software if you use tar.gz package. If you installed using a .deb or .rpm package, use the corresponding command, for your package manager, like apt or rpm to remove the TDengine server, including taosAdapter.
### Upgrade taosAdapter
......@@ -240,7 +240,7 @@ node_export is an exporter of hardware and OS metrics exposed by the \*NIX kerne
## Memory usage optimization methods
taosAdapter will monitor its memory usage during operation and adjust it with two thresholds. Valid values range from -1 to 100 integers in percent of the system's physical memory.
taosAdapter will monitor its memory usage during operation and adjust it with two thresholds. Valid values are integers between 1 to 100, and represent a percentage of the system's physical memory.
- pauseQueryMemoryThreshold
- pauseAllMemoryThreshold
......@@ -276,7 +276,7 @@ Corresponding configuration parameter
monitor.pauseQueryMemoryThreshold memory threshold for no more queries Environment variable `TAOS_MONITOR_PAUSE_QUERY_MEMORY_THRESHOLD` (default 70)
```
You can adjust it according to the specific application scenario and operation strategy, and it is recommended to use operation monitoring software to monitor system memory status timely. The load balancer can also check the taosAdapter running status through this interface.
You should adjust this parameter based on your specific application scenario and operation strategy. We recommend using monitoring software to monitor system memory status. The load balancer can also check the taosAdapter running status through this interface.
## taosAdapter Monitoring Metrics
......@@ -325,7 +325,7 @@ You can also adjust the level of the taosAdapter log output by setting the `--lo
## How to migrate from older TDengine versions to taosAdapter
In TDengine server 2.2.x.x or earlier, the TDengine server process (taosd) contains an embedded HTTP service. As mentioned earlier, taosAdapter is a standalone software managed using `systemd` and has its process ID. And there are some configuration parameters and behaviors that are different between the two. See the following table for details.
In TDengine server 2.2.x.x or earlier, the TDengine server process (taosd) contains an embedded HTTP service. As mentioned earlier, taosAdapter is a standalone software managed using `systemd` and has its own process ID. There are some configuration parameters and behaviors that are different between the two. See the following table for details.
| **#** | **embedded httpd** | **taosAdapter** | **comment** |
| ----- | ------------------- | ------------------------------------ | ------------------------------------------------------------------ ------------------------------------------------------------------------ |
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docs-en/14-reference/05-taosbenchmark.md
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......@@ -7,7 +7,7 @@ description: "taosBenchmark (once called taosdemo ) is a tool for testing the pe
## Introduction
taosBenchmark (formerly taosdemo ) is a tool for testing the performance of TDengine products. taosBenchmark can test the performance of TDengine's insert, query, and subscription functions and simulate large amounts of data generated by many devices. taosBenchmark can flexibly control the number and type of databases, supertables, tag columns, number and type of data columns, and sub-tables, and types of databases, super tables, the number and types of data columns, the number of sub-tables, the amount of data per sub-table, the time interval for inserting data, the number of working threads, whether and how to insert disordered data, and so on. The installer provides taosdemo as a soft link to taosBenchmark for compatibility with past users.
taosBenchmark (formerly taosdemo ) is a tool for testing the performance of TDengine products. taosBenchmark can test the performance of TDengine's insert, query, and subscription functions and simulate large amounts of data generated by many devices. taosBenchmark can flexibly control the number and type of databases, supertables, tag columns, number and type of data columns, and sub-tables, and types of databases, super tables, the number and types of data columns, the number of sub-tables, the amount of data per sub-table, the time interval for inserting data, the number of working threads, whether and how to insert disordered data, and so on. The installer provides taosdemo as a soft link to taosBenchmark for compatibility and for the convenience of past users.
## Installation
......@@ -21,7 +21,7 @@ There are two ways to install taosBenchmark:
### Configuration and running methods
taosBenchmark supports two configuration methods: [Command-line arguments](#Command-line arguments in detailed) and [JSON configuration file](#Configuration file arguments in detailed). These two methods are mutually exclusive, and with only one command-line parameter, users can use `-f <json file>` to specify a configuration file when using a configuration file. When running taosBenchmark with command-line arguments and controlling its behavior, users should use other parameters for configuration rather than `-f` parameter. In addition, taosBenchmark offers a special way of running without parameters.
taosBenchmark supports two configuration methods: [Command-line arguments](#Command-line arguments in detailed) and [JSON configuration file](#Configuration file arguments in detailed). These two methods are mutually exclusive. Users can use `-f <json file>` to specify a configuration file. When running taosBenchmark with command-line arguments to control its behavior, users should use other parameters for configuration, but not the `-f` parameter. In addition, taosBenchmark offers a special way of running without parameters.
taosBenchmark supports complete performance testing of TDengine. taosBenchmark supports the TDengine functions in three categories: write, query, and subscribe. These three functions are mutually exclusive, and users can select only one of them each time taosBenchmark runs. It is important to note that the type of functionality to be tested is not configurable when using the command-line configuration method, which can only test writing performance. To test the query and subscription performance of the TDengine, you must use the configuration file method and specify the function type to test via the parameter `filetype` in the configuration file.
......@@ -35,7 +35,7 @@ Execute the following commands to quickly experience taosBenchmark's default con
taosBenchmark
```
When run without parameters, taosBenchmark connects to the TDengine cluster specified in `/etc/taos` by default and creates a database named test in TDengine, a super table named `meters` under the test database, and 10,000 tables under the super table with 10,000 records written to each table. Note that if there is already a test database, this table is not used. Note that if there is already a test database, this command will delete it first and create a new test database.
When run without parameters, taosBenchmark connects to the TDengine cluster specified in `/etc/taos` by default and creates a database named `test`, a super table named `meters` under the test database, and 10,000 tables under the super table with 10,000 records written to each table. Note that if there is already a database named "test" this command will delete it first and create a new database.
### Run with command-line configuration parameters
......@@ -45,7 +45,7 @@ The `-f <json file>` argument cannot be used when running taosBenchmark with com
taosBenchmark -I stmt -n 200 -t 100
```
The above command, `taosBenchmark` will create a database named `test`, create a super table `meters` in it, create 100 sub-tables in the super table and insert 200 records for each sub-table using parameter binding.
Using the above command, `taosBenchmark` will create a database named `test`, create a super table `meters` in it, create 100 sub-tables in the super table and insert 200 records for each sub-table using parameter binding.
### Run with the configuration file
......@@ -95,10 +95,10 @@ taosBenchmark -f <json file>
## Command-line argument in detailed
- **-f/--file <json file\>** :
specify the configuration file to use. This file includes All parameters. And users should not use this parameter with other parameters on the command-line. There is no default value.
specify the configuration file to use. This file includes All parameters. Users should not use this parameter with other parameters on the command-line. There is no default value.
- **-c/--config-dir <dir\>** :
specify the directory where the TDengine cluster configuration file. the default path is `/etc/taos`.
specify the directory where the TDengine cluster configuration file. The default path is `/etc/taos`.
- **-h/--host <host\>** :
Specify the FQDN of the TDengine server to connect to. The default value is localhost.
......@@ -272,13 +272,13 @@ The parameters for creating super tables are configured in `super_tables` in the
- **child_table_prefix** : The prefix of the child table name, mandatory configuration item, no default value.
- **escape_character**: specify the super table and child table names containing escape characters. By default is "no". The value can be "yes" or "no".
- **escape_character**: specify the super table and child table names containing escape characters. The value can be "yes" or "no". The default is "no".
- **auto_create_table**: only when insert_mode is taosc, rest, stmt, and childtable_exists is "no". "yes" means taosBenchmark will automatically create non-existent tables when inserting data; "no" means that taosBenchmark will create all tables before inserting.
- **batch_create_tbl_num** : the number of tables per batch when creating sub-tables, default is 10. Note: the actual number of batches may not be the same as this value when the executed SQL statement is larger than the maximum length supported, it will be automatically truncated and re-executed to continue creating.
- **batch_create_tbl_num** : the number of tables per batch when creating sub-tables, default is 10. Note: the actual number of batches may not be the same as this value. If the executed SQL statement is larger than the maximum length supported, it will be automatically truncated and re-executed to continue creating.
- **data_source**: specify the source of data-generating. Default is taosBenchmark randomly generated. Users can configure it as "rand" and "sample". When "sample" is used, taosBenchmark will use the data in the file specified by the `sample_file` parameter.
- **data_source**: specify the source of data-generation. Default is taosBenchmark randomly generated. Users can configure it as "rand" and "sample". When "sample" is used, taosBenchmark will use the data in the file specified by the `sample_file` parameter.
- **insert_mode**: insertion mode with options taosc, rest, stmt, sml, sml-rest, corresponding to normal write, restful interface write, parameter binding interface write, schemaless interface write, restful schemaless interface write (provided by taosAdapter). The default value is taosc.
......@@ -300,15 +300,15 @@ The parameters for creating super tables are configured in `super_tables` in the
- **partial_col_num**: If this value is a positive number n, only the first n columns are written to, only if insert_mode is taosc and rest, or all columns if n is 0.
- **disorder_ratio** : Specifies the percentage probability of disordered data in the value range [0,50]. The default is 0, which means there is no disorder data.
- **disorder_ratio** : Specifies the percentage probability of disordered (i.e. out-of-order) data in the value range [0,50]. The default is 0, which means there is no disorder data.
- **disorder_range** : Specifies the timestamp fallback range for the disordered data. The generated disorder timestamp is the timestamp that should be used in the non-disorder case minus a random value in this range. Valid only if the percentage of disordered data specified by `-O/--disorder` is greater than 0.
- **disorder_range** : Specifies the timestamp fallback range for the disordered data. The disordered timestamp is generated by subtracting a random value in this range, from the timestamp that would be used in the non-disorder case. Valid only if the percentage of disordered data specified by `-O/--disorder` is greater than 0.
- **timestamp_step**: The timestamp step for inserting data in each child table, in units consistent with the `precision` of the database, the default value is 1.
- **timestamp_step**: The timestamp step for inserting data in each child table, in units consistent with the `precision` of the database. For e.g. if the `precision` is milliseconds, the timestamp step will be in milliseconds. The default value is 1.
- **start_timestamp** : The timestamp start value of each sub-table, the default value is now.
- **sample_format**: The type of the sample data file, now only "csv" is supported.
- **sample_format**: The type of the sample data file; for now only "csv" is supported.
- **sample_file**: Specify a CSV format file as the data source. It only works when data_source is a sample. If the number of rows in the CSV file is less than or equal to prepared_rand, then taosBenchmark will read the CSV file data cyclically until it is the same as prepared_rand; otherwise, taosBenchmark will read only the rows with the number of prepared_rand. The final number of rows of data generated is the smaller of the two.
......@@ -341,7 +341,7 @@ The configuration parameters for specifying super table tag columns and data col
- **create_table_thread_count** : The number of threads to build the table, default is 8.
- **connection_pool_size** : The number of pre-established connections to the TDengine server. If not configured, it is the same number of threads specified.
- **connection_pool_size** : The number of pre-established connections to the TDengine server. If not configured, it is the same as number of threads specified.
- **result_file** : The path to the result output file, the default value is . /output.txt.
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---
title: taosdump
description: "taosdump is a tool application that supports backing up data from a running TDengine cluster and restoring the backed up data to the same or another running TDengine cluster."
description: "taosdump is a tool that supports backing up data from a running TDengine cluster and restoring the backed up data to the same, or another running TDengine cluster."
---
## Introduction
taosdump is a tool application that supports backing up data from a running TDengine cluster and restoring the backed up data to the same or another running TDengine cluster.
taosdump is a tool that supports backing up data from a running TDengine cluster and restoring the backed up data to the same, or another running TDengine cluster.
taosdump can back up a database, a super table, or a normal table as a logical data unit or backup data records in the database, super tables, and normal tables. When using taosdump, you can specify the directory path for data backup. If you do not specify a directory, taosdump will back up the data to the current directory by default.
Suppose the specified location already has data files. In that case, taosdump will prompt the user and exit immediately to avoid data overwriting which means that the same path can only be used for one backup.
Please be careful if you see a prompt for this.
If the specified location already has data files, taosdump will prompt the user and exit immediately to avoid data overwriting. This means that the same path can only be used for one backup.
Please be careful if you see a prompt for this and please ensure that you follow best practices and relevant SOPs for data integrity, backup and data security.
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.
......@@ -30,7 +31,7 @@ There are two ways to install taosdump:
2. backup multiple specified databases: use `-D db1,db2,... ` parameters;
3. back up some super or normal tables in the specified database: use `-dbname stbname1 stbname2 tbname1 tbname2 ... ` parameters. Note that the first parameter of this input sequence is the database name, and only one database is supported. The second and subsequent parameters are the names of super or normal tables in that database, separated by spaces.
4. back up the system log database: TDengine clusters usually contain a system database named `log`. The data in this database is the data that TDengine runs itself, and the taosdump will not back up the log database by default. If users need to back up the log database, users can use the `-a` or `-allow-sys` command-line parameter.
5. Loose mode backup: taosdump version 1.4.1 onwards provides `-n` and `-L` parameters for backing up data without using escape characters and "loose" mode, which can reduce the number of backups if table names, column names, tag names do not use This can reduce the backup data time and backup data footprint if table names, column names, and tag names do not use `escape character`. If you are unsure about using `-n` and `-L` conditions, please use the default parameters for "strict" mode backup. See the [official documentation](/taos-sql/escape) for a description of escaped characters.
5. Loose mode backup: taosdump version 1.4.1 onwards provides `-n` and `-L` parameters for backing up data without using escape characters and "loose" mode, which can reduce the number of backups if table names, column names, tag names do not use escape characters. This can also reduce the backup data time and backup data footprint. If you are unsure about using `-n` and `-L` conditions, please use the default parameters for "strict" mode backup. See the [official documentation](/taos-sql/escape) for a description of escaped characters.
:::tip
- taosdump versions after 1.4.1 provide the `-I` argument for parsing Avro file schema and data. If users specify `-s` then only taosdump will parse schema.
......@@ -58,7 +59,7 @@ Usage: taosdump [OPTION...] dbname [tbname ...]
or: taosdump [OPTION...] -i inpath
or: taosdump [OPTION...] -o outpath
-h, --host=HOST Server host dumping data from. Default is
-h, --host=HOST Server host from which to dump data. Default is
localhost.
-p, --password User password to connect to server. Default is
taosdata.
......@@ -71,10 +72,10 @@ Usage: taosdump [OPTION...] dbname [tbname ...]
-r, --resultFile=RESULTFILE DumpOut/In Result file path and name.
-a, --allow-sys Allow to dump system database
-A, --all-databases Dump all databases.
-D, --databases=DATABASES Dump inputted databases. Use comma to separate
databases' name.
-D, --databases=DATABASES Dump listed databases. Use comma to separate
database names.
-N, --without-property Dump database without its properties.
-s, --schemaonly Only dump tables' schema.
-s, --schemaonly Only dump table schemas.
-y, --answer-yes Input yes for prompt. It will skip data file
checking!
-d, --avro-codec=snappy Choose an avro codec among null, deflate, snappy,
......@@ -97,7 +98,7 @@ Usage: taosdump [OPTION...] dbname [tbname ...]
and try. The workable value is related to the
length of the row and type of table schema.
-I, --inspect inspect avro file content and print on screen
-L, --loose-mode Using loose mode if the table name and column name
-L, --loose-mode Use loose mode if the table name and column name
use letter and number only. Default is NOT.
-n, --no-escape No escape char '`'. Default is using it.
-T, --thread-num=THREAD_NUM Number of thread for dump in file. Default is
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......@@ -5,11 +5,11 @@ sidebar_label: TDinsight
TDinsight is a solution for monitoring TDengine using the builtin native monitoring database and [Grafana].
After TDengine starts, it will automatically create a monitoring database `log`. TDengine will automatically write many metrics in specific intervals into the `log` database. The metrics may include the server's CPU, memory, hard disk space, network bandwidth, number of requests, disk read/write speed, slow queries, other information like important system operations (user login, database creation, database deletion, etc.), and error alarms. With [Grafana] and [TDengine Data Source Plugin](https://github.com/taosdata/grafanaplugin/releases), TDinsight can visualize cluster status, node information, insertion and query requests, resource usage, etc., and also vnode, dnode, and mnode status, and exception alerts. Developers monitoring TDengine cluster operation status in real-time can be very convinient. This article will guide users to install the Grafana server, automatically install the TDengine data source plug-in, and deploy the TDinsight visualization panel through `TDinsight.sh` installation script.
After TDengine starts, it will automatically create a monitoring database `log`. TDengine will automatically write many metrics in specific intervals into the `log` database. The metrics may include the server's CPU, memory, hard disk space, network bandwidth, number of requests, disk read/write speed, slow queries, other information like important system operations (user login, database creation, database deletion, etc.), and error alarms. With [Grafana] and [TDengine Data Source Plugin](https://github.com/taosdata/grafanaplugin/releases), TDinsight can visualize cluster status, node information, insertion and query requests, resource usage, vnode, dnode, and mnode status, exception alerts and many other metrics. This is very convenient for developers who want to monitor TDengine cluster status in real-time. This article will guide users to install the Grafana server, automatically install the TDengine data source plug-in, and deploy the TDinsight visualization panel using the `TDinsight.sh` installation script.
## System Requirements
To deploy TDinsight, a single-node TDengine server or a multi-nodes TDengine cluster and a [Grafana] server are required. This dashboard requires TDengine 2.3.3.0 and above, with the `log` database enabled (`monitor = 1`).
To deploy TDinsight, a single-node TDengine server or a multi-node TDengine cluster and a [Grafana] server are required. This dashboard requires TDengine 2.3.3.0 and above, with the `log` database enabled (`monitor = 1`).
## Installing Grafana
......@@ -17,7 +17,7 @@ We recommend using the latest [Grafana] version 7 or 8 here. You can install Gra
### Installing Grafana on Debian or Ubuntu
For Debian or Ubuntu operating systems, we recommend the Grafana image repository and Use the following command to install from scratch.
For Debian or Ubuntu operating systems, we recommend the Grafana image repository and using the following command to install from scratch.
```bash
sudo apt-get install -y apt-transport-https
......@@ -71,7 +71,7 @@ chmod +x TDinsight.sh
./TDinsight.sh
```
This script will automatically download the latest [Grafana TDengine data source plugin](https://github.com/taosdata/grafanaplugin/releases/latest) and [TDinsight dashboard](https://grafana.com/grafana/dashboards/15167) with configurable parameters from the command-line options to the [Grafana Provisioning](https://grafana.com/docs/grafana/latest/administration/provisioning/) configuration file to automate deployment and updates, etc. With the alert setting options provided by this script, you can also get built-in support for AliCloud SMS alert notifications.
This script will automatically download the latest [Grafana TDengine data source plugin](https://github.com/taosdata/grafanaplugin/releases/latest) and [TDinsight dashboard](https://grafana.com/grafana/dashboards/15167) with configurable parameters for command-line options to the [Grafana Provisioning](https://grafana.com/docs/grafana/latest/administration/provisioning/) configuration file to automate deployment and updates, etc. With the alert setting options provided by this script, you can also get built-in support for AliCloud SMS alert notifications.
Assume you use TDengine and Grafana's default services on the same host. Run `. /TDinsight.sh` and open the Grafana browser window to see the TDinsight dashboard.
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......@@ -13,7 +13,7 @@ The TDengine image starts with the HTTP service activated by default, using the
docker run -d --name tdengine -p 6041:6041 tdengine/tdengine
```
The above command starts a container named "tdengine" and maps the HTTP service end 6041 to the host port 6041. You can verify that the HTTP service provided in this container is available using the following command.
The above command starts a container named "tdengine" and maps the HTTP service port 6041 to the host port 6041. You can verify that the HTTP service provided in this container is available using the following command.
```shell
curl -u root:taosdata -d "show databases" localhost:6041/rest/sql
......@@ -34,7 +34,7 @@ taos> show databases;
Query OK, 1 row(s) in set (0.002843s)
```
The TDengine server running in the container uses the container's hostname to establish a connection. Using TDengine CLI or various connectors (such as JDBC-JNI) to access the TDengine inside the container from outside the container is more complicated. So the above is the simplest way to access the TDengine service in the container and is suitable for some simple scenarios. Please refer to the next section if you want to access the TDengine service in the container from containerized using TDengine CLI or various connectors in some complex scenarios.
The TDengine server running in the container uses the container's hostname to establish a connection. Using TDengine CLI or various connectors (such as JDBC-JNI) to access the TDengine inside the container from outside the container is more complicated. So the above is the simplest way to access the TDengine service in the container and is suitable for some simple scenarios. Please refer to the next section if you want to access the TDengine service in the container from outside the container using TDengine CLI or various connectors for complex scenarios.
## Start TDengine on the host network
......@@ -42,7 +42,7 @@ The TDengine server running in the container uses the container's hostname to es
docker run -d --name tdengine --network host tdengine/tdengine
```
The above command starts TDengine on the host network and uses the host's FQDN to establish a connection instead of the container's hostname. It works too, like using `systemctl` to start TDengine on the host. If the TDengine client is already installed on the host, you can access it directly with the following command.
The above command starts TDengine on the host network and uses the host's FQDN to establish a connection instead of the container's hostname. It is the equivalent of using `systemctl` to start TDengine on the host. If the TDengine client is already installed on the host, you can access it directly with the following command.
```shell
$ taos
......@@ -382,7 +382,7 @@ password: taosdata
Suppose you want to deploy multiple taosAdapters to improve throughput and provide high availability. In that case, the recommended configuration method uses a reverse proxy such as Nginx to offer a unified access entry. For specific configuration methods, please refer to the official documentation of Nginx. Here is an example:
```docker
ersion: "3"
version: "3"
networks:
inter:
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......@@ -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 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.
TDengine uses 13 continuous ports, both TCP and UDP, starting with the port specified by `serverPort`. You should ensure, in your firewall rules, that these ports are kept open. Below table describes the ports used by TDengine in details.
:::
......@@ -197,7 +197,7 @@ TDengine uses continuous 13 ports, both TCP and UDP, from the port specified by
| Default Value | TimeZone configured in the host |
:::info
To handle the data insertion and data query from multiple timezones, Unix Timestamp is used and stored TDengine. The timestamp generated from any timezones at same time is same in Unix timestamp. To make sure the time on client side can be converted to Unix timestamp correctly, the timezone must be set properly.
To handle the data insertion and data query from multiple timezones, Unix Timestamp is used and stored in TDengine. The timestamp generated from any timezones at same time is same in Unix timestamp. To make sure the time on client side can be converted to Unix timestamp correctly, the timezone must be set properly.
On Linux system, TDengine clients automatically obtain timezone from the host. Alternatively, the timezone can be configured explicitly in configuration file `taos.cfg` like below.
......@@ -209,7 +209,7 @@ timezone Asia/Shanghai
The above examples are all proper configuration for the timezone of UTC+8. On Windows system, however, `timezone Asia/Shanghai` is not supported, it must be set as `timezone UTC-8`.
The setting for timezone impacts the strings not in Unix timestamp, keywords or functions related to date/time, for example
The setting for timezone impacts strings that are not in Unix timestamp format and keywords or functions related to date/time. For example:
```sql
SELECT count(*) FROM table_name WHERE TS<'2019-04-11 12:01:08';
......@@ -227,7 +227,7 @@ If the timezone is UTC, it's equal to
SELECT count(*) FROM table_name WHERE TS<1554984068000;
```
To avoid the problems of using time strings, Unix timestamp can be used directly. Furthermore, time strings with timezone can be used in SQL statement, for example "2013-04-12T15:52:01.123+08:00" in RFC3339 format or "2013-04-12T15:52:01.123+0800" in ISO-8601 format, they are not influenced by timezone setting when converted to Unix timestamp.
To avoid the problems of using time strings, Unix timestamp can be used directly. Furthermore, time strings with timezone can be used in SQL statements. For example "2013-04-12T15:52:01.123+08:00" in RFC3339 format or "2013-04-12T15:52:01.123+0800" in ISO-8601 format are not influenced by timezone setting when converted to Unix timestamp.
:::
......@@ -244,7 +244,7 @@ A specific type "nchar" is provided in TDengine to store non-ASCII characters su
The characters input on the client side are encoded using the default system encoding, which is UTF-8 on Linux, or GB18030 or GBK on some systems in Chinese, POSIX in docker, CP936 on Windows in Chinese. The encoding of the operating system in use must be set correctly so that the characters in nchar type can be converted to UCS4-LE.
The locale definition standard on Linux is: <Language\>\_<Region\>.<charset\>, for example, in "zh_CN.UTF-8", "zh" means Chinese, "CN" means China mainland, "UTF-8" means charset. On Linux andMac OSX, the charset can be set by locale in the system. On Windows system another configuration parameter `charset` must be used to configure charset because the locale used on Windows is not POSIX standard. Of course, `charset` can also be used on Linux to specify the charset.
The locale definition standard on Linux is: <Language\>\_<Region\>.<charset\>, for example, in "zh_CN.UTF-8", "zh" means Chinese, "CN" means China mainland, "UTF-8" means charset. On Linux and Mac OSX, the charset can be set by locale in the system. On Windows system another configuration parameter `charset` must be used to configure charset because the locale used on Windows is not POSIX standard. Of course, `charset` can also be used on Linux to specify the charset.
:::
......@@ -263,7 +263,7 @@ On Linux, if `charset` is not set in `taos.cfg`, when `taos` is started, the cha
locale zh_CN.UTF-8
```
Besides, on Linux system, if the charset contained in `locale` is not consistent with that set by `charset`, the one who comes later in the configuration file is used.
On a Linux system, if the charset contained in `locale` is not consistent with that set by `charset`, the later setting in the configuration file takes precedence.
```title="Effective charset is GBK"
locale zh_CN.UTF-8
......@@ -778,7 +778,7 @@ To prevent system resource from being exhausted by multiple concurrent streams,
## HTTP Parameters
:::note
HTTP server had been provided by `taosd` prior to version 2.4.0.0, now is provided by `taosAdapter` after version 2.4.0.0.
HTTP service was provided by `taosd` prior to version 2.4.0.0 and is provided by `taosAdapter` after version 2.4.0.0.
The parameters described in this section are only application in versions prior to 2.4.0.0. If you are using any version from 2.4.0.0, please refer to [taosAdapter](/reference/taosadapter/).
:::
......
docs-en/14-reference/13-schemaless/13-schemaless.md
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---
title: Schemaless Writing
description: "The Schemaless write method eliminates the need to create super tables/sub tables in advance and automatically creates the storage structure corresponding to the data as it is written to the interface."
description: "The Schemaless write method eliminates the need to create super tables/sub tables in advance and automatically creates the storage structure corresponding to the data, as it is written to the interface."
---
In IoT applications, many data items are often collected for intelligent control, business analysis, device monitoring, etc. Due to the version 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.
In IoT applications, data is collected for many purposes such as intelligent control, business analysis, device monitoring and so on. Due to changes in business or functional requirements or changes in device hardware, the application logic and even the data collected may change. To provide the flexibility needed in such cases and in a rapidly changing IoT landscape, TDengine starting from version 2.2.0.0, provides a series of interfaces for the schemaless writing method. These interfaces eliminate the need to create super tables and subtables in advance by automatically creating the storage structure corresponding to the data as the data is written to the interface. When necessary, schemaless writing will automatically add the required columns to ensure that the data written by the user is stored correctly.
The schemaless writing method creates super tables and their corresponding 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.
The schemaless writing method creates super tables and their corresponding subtables. These are completely indistinguishable from the super tables and subtables created directly via SQL. You can write data directly to them via SQL statements. Note that the names of tables created by schemaless writing are based on fixed mapping rules for tag values, so they are not explicitly ideographic and they lack readability.
## Schemaless Writing Line Protocol
......@@ -76,8 +76,7 @@ If the subtable obtained by the parse line protocol does not exist, Schemaless c
8. Errors encountered throughout the processing will interrupt the writing process and return an error code.
:::tip
All processing logic of schemaless will still follow TDengine's underlying restrictions on data structures, such as the total length of each row of data cannot exceed
16k bytes. See [TAOS SQL Boundary Limits](/taos-sql/limit) for specific constraints in this area.
All processing logic of schemaless will still follow TDengine's underlying restrictions on data structures, such as the total length of each row of data cannot exceed 48k bytes. See [TAOS SQL Boundary Limits](/taos-sql/limit) for specific constraints in this area.
:::
## Time resolution recognition
......@@ -87,7 +86,7 @@ Three specified modes are supported in the schemaless writing process, as follow
| **Serial** | **Value** | **Description** |
| -------- | ------------------- | ------------------------------- |
| 1 | SML_LINE_PROTOCOL | InfluxDB Line Protocol |
| 2 | SML_TELNET_PROTOCOL | OpenTSDB Text Line Protocol | | 2 | SML_TELNET_PROTOCOL | OpenTSDB Text Line Protocol
| 2 | SML_TELNET_PROTOCOL | OpenTSDB Text Line Protocol |
| 3 | SML_JSON_PROTOCOL | JSON protocol format |
In the SML_LINE_PROTOCOL parsing mode, the user is required to specify the time resolution of the input timestamp. The available time resolutions are shown in the following table.
......@@ -106,8 +105,11 @@ In SML_TELNET_PROTOCOL and SML_JSON_PROTOCOL modes, the time precision is determ
## Data schema mapping rules
This section describes how data for line protocols are mapped to data with a schema. The data measurement in each line protocol is mapped to
The tag name in tag_set is the name of the tag in the data schema, and the name in field_set is the column's name. The following data is used as an example to illustrate the mapping rules.
This section describes how data for line protocols are mapped to data with a schema. The data measurement in each line protocol is mapped as follows:
- The tag name in tag_set is the name of the tag in the data schema
- The name in field_set is the column's name.
The following data is used as an example to illustrate the mapping rules.
```json
st,t1=3,t2=4,t3=t3 c1=3i64,c3="passit",c2=false,c4=4f64 1626006833639000000
......@@ -139,7 +141,7 @@ st,t1=3,t2=4,t3=t3 c1=3i64,c5="pass" 1626006833639000000
st,t1=3,t2=4,t3=t3 c1=3i64,c5="passit" 1626006833640000000
```
The first line of the line protocol parsing will declare column c5 is a BINARY(4) field, the second line data write will extract column c5 is still a BINARY column. Still, its width is 6, then you need to increase the width of the BINARY field to be able to accommodate the new string.
The first line of the line protocol parsing will declare column c5 is a BINARY(4) field. The second line data write will parse column c5 as a BINARY column. But in the second line, c5's width is 6 so you need to increase the width of the BINARY field to be able to accommodate the new string.
```json
st,t1=3,t2=4,t3=t3 c1=3i64 1626006833639000000
......
docs-en/14-reference/_collectd.mdx
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......@@ -25,7 +25,7 @@ The default database name written by taosAdapter is `collectd`. You can also mod
#collectd
collectd uses a plugin mechanism to write the collected monitoring data to different data storage software in various forms. tdengine supports both direct collection plugins and write_tsdb plugins.
#### is configured to receive data from the direct collection plugin
#### Configure the direct collection plugin
Modify the relevant configuration items in the collectd configuration file (default location /etc/collectd/collectd.conf).
......@@ -62,7 +62,7 @@ LoadPlugin write_tsdb
</Plugin>
```
Where <taosAdapter's host\> fills in the server's domain name or IP address running taosAdapter. <port for collectd write_tsdb plugin\> Fill in the data that taosAdapter uses to receive the collectd write_tsdb plugin (default is 6047).
Where <taosAdapter's host\> is the domain name or IP address of the server running taosAdapter. <port for collectd write_tsdb plugin\> Fill in the data that taosAdapter uses to receive the collectd write_tsdb plugin (default is 6047).
```text
LoadPlugin write_tsdb
......
docs-en/14-reference/_tcollector.mdx
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......@@ -17,7 +17,7 @@ password = "taosdata"
...
```
The taosAdapter writes to the database with the default name `tcollector`. You can also modify the taosAdapter configuration file dbs entry to specify a different name. user and password fill in the actual TDengine configuration values. After changing the configuration file, you need to restart the taosAdapter.
The taosAdapter writes to the database with the default name `tcollector`. You can also modify the taosAdapter configuration file dbs entry to specify a different name. Fill in the actual user and password for TDengine. After changing the configuration file, you need to restart the taosAdapter.
- You can also enable taosAdapter to receive tcollector data by using the taosAdapter command-line parameters or setting environment variables.
......@@ -25,7 +25,7 @@ The taosAdapter writes to the database with the default name `tcollector`. You c
To use TCollector, you need to download its [source code](https://github.com/OpenTSDB/tcollector). Its configuration items are in its source code. Note: TCollector differs significantly from version to version, so here is an example of the latest code for the current master branch (git commit: 37ae920).
Modify the contents of the `collectors/etc/config.py` and `tcollector.py` files. Change the address of the OpenTSDB host to the domain name or IP address of the server where taosAdapter is deployed, and change the port to the port that taosAdapter supports TCollector on (default is 6049).
Modify the contents of the `collectors/etc/config.py` and `tcollector.py` files. Change the address of the OpenTSDB host to the domain name or IP address of the server where taosAdapter is deployed, and change the port to the port on which taosAdapter supports TCollector (default is 6049).
Example of git diff output of source code changes.
......
docs-en/20-third-party/01-grafana.mdx
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......@@ -3,13 +3,13 @@ sidebar_label: Grafana
title: Grafana
---
TDengine can be quickly integrated with the open-source data visualization system [Grafana](https://www.grafana.com/) to build a data monitoring and alerting system. The whole process does not require any code development. And you can visualize the contents of the data tables in TDengine on a DashBoard.
TDengine can be quickly integrated with the open-source data visualization system [Grafana](https://www.grafana.com/) to build a data monitoring and alerting system. The whole process does not require any code development. And you can visualize the contents of the data tables in TDengine on a dashboard.
You can learn more about using the TDengine plugin on [GitHub](https://github.com/taosdata/grafanaplugin/blob/master/README.md).
## Prerequisites
In order for Grafana to add the TDengine data source successfully, the following preparations are required:
In order for Grafana to add the TDengine data source successfully, the following preparation is required:
1. The TDengine cluster is deployed and functioning properly
2. taosAdapter is installed and running properly. Please refer to the taosAdapter manual for details.
......@@ -36,7 +36,7 @@ GF_VERSION=3.1.4
wget https://github.com/taosdata/grafanaplugin/releases/download/v$GF_VERSION/tdengine-datasource-$GF_VERSION.zip
```
Take CentOS 7.2 for example, extract the plugin package to /var/lib/grafana/plugins directory, and restart grafana.
In CentOS 7.2 for example, extract the plugin package to /var/lib/grafana/plugins directory, and restart grafana.
```bash
sudo unzip tdengine-datasource-$GF_VERSION.zip -d /var/lib/grafana/plugins/
......@@ -76,13 +76,13 @@ Enter the datasource configuration page, and follow the default prompts to modif
- User: TDengine user name.
- Password: TDengine user password.
Click `Save & Test` to test. Follows are a success.
Click `Save & Test` to test. You should see a success message if the test worked.
![TDengine Database TDinsight plugin add database 4](./grafana/add_datasource4.webp)
### Create Dashboard
Go back to the main interface to create the Dashboard, click Add Query to enter the panel query page:
Go back to the main interface to create a dashboard and click Add Query to enter the panel query page:
![TDengine Database TDinsight plugin create dashboard 1](./grafana/create_dashboard1.webp)
......
docs-en/20-third-party/03-telegraf.md
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......@@ -5,7 +5,7 @@ title: Telegraf writing
import Telegraf from "../14-reference/_telegraf.mdx"
Telegraf is a viral metrics collection open-source software. Telegraf can collect the operation information of various components without writing any scripts to collect regularly, reducing the difficulty of data acquisition.
Telegraf is a viral, open-source, metrics collection software. Telegraf can collect the operation information of various components without having to write any scripts to collect regularly, reducing the difficulty of data acquisition.
Telegraf's data can be written to TDengine by simply adding the output configuration of Telegraf to the URL corresponding to taosAdapter and modifying several configuration items. The presence of Telegraf data in TDengine can take advantage of TDengine's efficient storage query performance and clustering capabilities for time-series data.
......
docs-en/20-third-party/05-collectd.md
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......@@ -6,7 +6,7 @@ title: collectd writing
import CollectD from "../14-reference/_collectd.mdx"
collectd is a daemon used to collect system performance metric data. collectd provides various storage mechanisms to store different values. It periodically counts system performance statistics number while the system is running and storing information. You can use this information to help identify current system performance bottlenecks and predict future system load.
collectd is a daemon used to collect system performance metric data. collectd provides various storage mechanisms to store different values. It periodically counts system performance statistics while the system is running and storing information. You can use this information to help identify current system performance bottlenecks and predict future system load.
You can write the data collected by collectd to TDengine by simply modifying the configuration of collectd to the domain name (or IP address) and corresponding port of the server running taosAdapter. It can take full advantage of TDengine's efficient storage query performance and clustering capability for time-series data.
......
docs-en/20-third-party/06-statsd.md
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......@@ -7,7 +7,7 @@ import StatsD from "../14-reference/_statsd.mdx"
StatsD is a simple daemon for aggregating application metrics, which has evolved rapidly in recent years into a unified protocol for collecting application performance metrics.
You can write StatsD data to TDengine by simply modifying in the configuration file of StatsD with the domain name (or IP address) of the server running taosAdapter and the corresponding port. It can take full advantage of TDengine's efficient storage query performance and clustering capabilities for time-series data.
You can write StatsD data to TDengine by simply modifying the configuration file of StatsD with the domain name (or IP address) of the server running taosAdapter and the corresponding port. It can take full advantage of TDengine's efficient storage query performance and clustering capabilities for time-series data.
## Prerequisites
......
docs-en/20-third-party/07-icinga2.md
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......@@ -5,7 +5,7 @@ title: icinga2 writing
import Icinga2 from "../14-reference/_icinga2.mdx"
icinga2 is an open-source software monitoring host and network initially developed from the Nagios network monitoring application. Currently, icinga2 is distributed under the GNU GPL v2 license.
icinga2 is an open-source, host and network monitoring software initially developed from the Nagios network monitoring application. Currently, icinga2 is distributed under the GNU GPL v2 license.
You can write the data collected by icinga2 to TDengine by simply modifying the icinga2 configuration to point to the taosAdapter server and the corresponding port, taking advantage of TDengine's efficient storage and query performance and clustering capabilities for time-series data.
......
docs-en/20-third-party/09-emq-broker.md
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......@@ -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, 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.).
MQTT is a popular IoT data transfer protocol. [EMQX](https://github.com/emqx/emqx) is an open-source MQTT Broker software. You can write MQTT data directly to TDengine without any code. You only need to setup "rules" in EMQX Dashboard to create a simple configuration. EMQX supports saving data to TDengine by sending data to a web service and provides a native TDengine driver for direct saving in the Enterprise Edition. Please refer to the [EMQX official documentation](https://www.emqx.io/docs/en/v4.4/rule/rule-engine.html) for details on how to use it.).
## Prerequisites
......
docs-en/20-third-party/11-kafka.md
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......@@ -7,7 +7,7 @@ TDengine Kafka Connector contains two plugins: TDengine Source Connector and TDe
## What is Kafka Connect?
Kafka Connect is a component of Apache Kafka that enables other systems, such as databases, cloud services, file systems, etc., to connect to Kafka easily. Data can flow from other software to Kafka via Kafka Connect and Kafka to other systems via Kafka Connect. Plugins that read data from other software are called Source Connectors, and plugins that write data to other software are called Sink Connectors. Neither Source Connector nor Sink Connector will directly connect to Kafka Broker, and Source Connector transfers data to Kafka Connect. Sink Connector receives data from Kafka Connect.
Kafka Connect is a component of [Apache Kafka](https://kafka.apache.org/) that enables other systems, such as databases, cloud services, file systems, etc., to connect to Kafka easily. Data can flow from other software to Kafka via Kafka Connect and Kafka to other systems via Kafka Connect. Plugins that read data from other software are called Source Connectors, and plugins that write data to other software are called Sink Connectors. Neither Source Connector nor Sink Connector will directly connect to Kafka Broker, and Source Connector transfers data to Kafka Connect. Sink Connector receives data from Kafka Connect.
![TDengine Database Kafka Connector -- Kafka Connect](kafka/Kafka_Connect.webp)
......@@ -17,7 +17,7 @@ TDengine Source Connector is used to read data from TDengine in real-time and se
## What is Confluent?
Confluent adds many extensions to Kafka. include:
[Confluent](https://www.confluent.io/) adds many extensions to Kafka. include:
1. Schema Registry
2. REST Proxy
......@@ -79,10 +79,10 @@ Development: false
git clone https://github.com:taosdata/kafka-connect-tdengine.git
cd kafka-connect-tdengine
mvn clean package
unzip -d $CONFLUENT_HOME/share/confluent-hub-components/ target/components/packages/taosdata-kafka-connect-tdengine-0.1.0.zip
unzip -d $CONFLUENT_HOME/share/java/ target/components/packages/taosdata-kafka-connect-tdengine-*.zip
```
The above script first clones the project source code and then compiles and packages it with Maven. After the package is complete, the zip package of the plugin is generated in the `target/components/packages/` directory. Unzip this zip package to the path where the plugin is installed. The path to install the plugin is in the configuration file `$CONFLUENT_HOME/etc/kafka/connect-standalone.properties`. The default path is `$CONFLUENT_HOME/share/confluent-hub-components/`.
The above script first clones the project source code and then compiles and packages it with Maven. After the package is complete, the zip package of the plugin is generated in the `target/components/packages/` directory. Unzip this zip package to plugin path. We used `$CONFLUENT_HOME/share/java/` above because it's a build in plugin path.
### Install with confluent-hub
......@@ -96,7 +96,7 @@ confluent local services start
```
:::note
Be sure to install the plugin before starting Confluent. Otherwise, there will be a class not found error. The log of Kafka Connect (default path: /tmp/confluent.xxxx/connect/logs/connect.log) will output the successfully installed plugin, which users can use to determine whether the plugin is installed successfully.
Be sure to install the plugin before starting Confluent. Otherwise, Kafka Connect will fail to discover the plugins.
:::
:::tip
......@@ -123,6 +123,59 @@ Control Center is [UP]
To clear data, execute `rm -rf /tmp/confluent.106668`.
:::
### Check Confluent Services Status
Use command bellow to check the status of all service:
```
confluent local services status
```
The expected output is:
```
Connect is [UP]
Control Center is [UP]
Kafka is [UP]
Kafka REST is [UP]
ksqlDB Server is [UP]
Schema Registry is [UP]
ZooKeeper is [UP]
```
### Check Successfully Loaded Plugin
After Kafka Connect was completely started, you can use bellow command to check if our plugins are installed successfully:
```
confluent local services connect plugin list
```
The output should contains `TDengineSinkConnector` and `TDengineSourceConnector` as bellow:
```
Available Connect Plugins:
[
{
"class": "com.taosdata.kafka.connect.sink.TDengineSinkConnector",
"type": "sink",
"version": "1.0.0"
},
{
"class": "com.taosdata.kafka.connect.source.TDengineSourceConnector",
"type": "source",
"version": "1.0.0"
},
......
```
If not, please check the log file of Kafka Connect. To view the log file path, please execute:
```
echo `cat /tmp/confluent.current`/connect/connect.stdout
```
It should produce a path like:`/tmp/confluent.104086/connect/connect.stdout`
Besides log file `connect.stdout` there is a file named `connect.properties`. At the end of this file you can see the effective `plugin.path` which is a series of paths joined by comma. If Kafka Connect not found our plugins, it's probably because the installed path is not included in `plugin.path`.
## The use of TDengine Sink Connector
The role of the TDengine Sink Connector is to synchronize the data of the specified topic to TDengine. Users do not need to create databases and super tables in advance. The name of the target database can be specified manually (see the configuration parameter connection.database), or it can be generated according to specific rules (see the configuration parameter connection.database.prefix).
......@@ -142,7 +195,7 @@ vi sink-demo.properties
sink-demo.properties' content is following:
```ini title="sink-demo.properties"
name=tdengine-sink-demo
name=TDengineSinkConnector
connector.class=com.taosdata.kafka.connect.sink.TDengineSinkConnector
tasks.max=1
topics=meters
......@@ -151,6 +204,7 @@ connection.user=root
connection.password=taosdata
connection.database=power
db.schemaless=line
data.precision=ns
key.converter=org.apache.kafka.connect.storage.StringConverter
value.converter=org.apache.kafka.connect.storage.StringConverter
```
......@@ -177,6 +231,7 @@ If the above command is executed successfully, the output is as follows:
"connection.url": "jdbc:TAOS://127.0.0.1:6030",
"connection.user": "root",
"connector.class": "com.taosdata.kafka.connect.sink.TDengineSinkConnector",
"data.precision": "ns",
"db.schemaless": "line",
"key.converter": "org.apache.kafka.connect.storage.StringConverter",
"tasks.max": "1",
......@@ -221,10 +276,10 @@ Database changed.
taos> select * from meters;
ts | current | voltage | phase | groupid | location |
===============================================================================================================================================================
2022-03-28 09:56:51.249000000 | 11.800000000 | 221.000000000 | 0.280000000 | 2 | California.LoSangeles |
2022-03-28 09:56:51.250000000 | 13.400000000 | 223.000000000 | 0.290000000 | 2 | California.LoSangeles |
2022-03-28 09:56:51.249000000 | 10.800000000 | 223.000000000 | 0.290000000 | 3 | California.LoSangeles |
2022-03-28 09:56:51.250000000 | 11.300000000 | 221.000000000 | 0.350000000 | 3 | California.LoSangeles |
2022-03-28 09:56:51.249000000 | 11.800000000 | 221.000000000 | 0.280000000 | 2 | California.LosAngeles |
2022-03-28 09:56:51.250000000 | 13.400000000 | 223.000000000 | 0.290000000 | 2 | California.LosAngeles |
2022-03-28 09:56:51.249000000 | 10.800000000 | 223.000000000 | 0.290000000 | 3 | California.LosAngeles |
2022-03-28 09:56:51.250000000 | 11.300000000 | 221.000000000 | 0.350000000 | 3 | California.LosAngeles |
Query OK, 4 row(s) in set (0.004208s)
```
......@@ -356,6 +411,7 @@ The following configuration items apply to TDengine Sink Connector and TDengine
4. `max.retries`: The maximum number of retries when an error occurs. Defaults to 1.
5. `retry.backoff.ms`: The time interval for retry when sending an error. The unit is milliseconds. The default is 3000.
6. `db.schemaless`: Data format, could be one of `line`, `json`, and `telnet`. Represent InfluxDB line protocol format, OpenTSDB JSON format, and OpenTSDB Telnet line protocol format.
7. `data.precision`: The time precision when use InfluxDB line protocol format data, could be one of `ms`, `us` and `ns`. The default is `ns`.
### TDengine Source Connector specific configuration
......@@ -366,7 +422,13 @@ The following configuration items apply to TDengine Sink Connector and TDengine
5. `fetch.max.rows`: The maximum number of rows retrieved when retrieving the database. Default is 100.
6. `out.format`: The data format. The value could be line or json. The line represents the InfluxDB Line protocol format, and json represents the OpenTSDB JSON format. Default is `line`.
## feedback
## Other notes
1. To install plugin to a customized location, refer to https://docs.confluent.io/home/connect/self-managed/install.html#install-connector-manually.
2. To use Kafka Connect without confluent, refer to https://kafka.apache.org/documentation/#connect.
## Feedback
https://github.com/taosdata/kafka-connect-tdengine/issues
......
docs-en/21-tdinternal/01-arch.md
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此差异已折叠。
docs-en/25-application/01-telegraf.md
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......@@ -5,16 +5,16 @@ title: Quickly Build IT DevOps Visualization System with TDengine + Telegraf + G
## Background
TDengine is a big data platform designed and optimized for IoT (Internet of Things), Vehicle Telematics, Industrial Internet, IT DevOps, etc. by TAOSData. Since it opened its source code in July 2019, it has won the favor of a large number of time-series data developers with its innovative data modeling design, convenient installation, easy-to-use programming interface, and powerful data writing and query performance.
TDengine is a big data platform designed and optimized for IoT (Internet of Things), Vehicle Telemetry, Industrial Internet, IT DevOps and other applications. Since it was open-sourced in July 2019, it has won the favor of a large number of time-series data developers with its innovative data modeling design, convenient installation, easy-to-use programming interface, and powerful data writing and query performance.
IT DevOps metric data usually are time sensitive, for example:
- System resource metrics: CPU, memory, IO, bandwidth, etc.
- Software system metrics: health status, number of connections, number of requests, number of timeouts, number of errors, response time, service type, and other business-related metrics.
Current mainstream IT DevOps system usually include a data collection module, a data persistent module, and a visualization module; Telegraf and Grafana are one of the most popular data collection modules and visualization modules, respectively. The data persistent module is available in a wide range of options, with OpenTSDB or InfluxDB being the most popular. TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance.
Current mainstream IT DevOps system usually include a data collection module, a data persistent module, and a visualization module; Telegraf and Grafana are one of the most popular data collection modules and visualization modules, respectively. The data persistence module is available in a wide range of options, with OpenTSDB or InfluxDB being the most popular. TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance.
This article introduces how to quickly build a TDengine + Telegraf + Grafana based IT DevOps visualization system without writing even a single line of code and by simply modifying a few lines of configuration files. The architecture is as follows.
This article introduces how to quickly build a TDengine + Telegraf + Grafana based IT DevOps visualization system without writing even a single line of code and by simply modifying a few lines in configuration files. The architecture is as follows.
![TDengine Database IT-DevOps-Solutions-Telegraf](./IT-DevOps-Solutions-Telegraf.webp)
......@@ -79,5 +79,5 @@ Click on the plus icon on the left and select `Import` to get the data from `htt
## Wrap-up
The above demonstrates how to quickly build a IT DevOps visualization system. Thanks to the new schemaless protocol parsing feature in TDengine version 2.4.0.0 and the powerful ecological software adaptation capability, users can build an efficient and easy-to-use IT DevOps visualization system in just a few minutes.
The above demonstrates how to quickly build a IT DevOps visualization system. Thanks to the new schemaless protocol parsing feature in TDengine version 2.4.0.0 and ability to integrate easily with a large software ecosystem, users can build an efficient and easy-to-use IT DevOps visualization system in just a few minutes.
Please refer to the official documentation and product implementation cases for other features.
docs-en/25-application/02-collectd.md
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......@@ -5,17 +5,17 @@ title: Quickly build an IT DevOps visualization system using TDengine + collectd
## Background
TDengine is a big data platform designed and optimized for IoT (Internet of Things), Vehicle Telematics, Industrial Internet, IT DevOps, etc. by TAOSData. Since it opened its source code in July 2019, it has won the favor of a large number of time-series data developers with its innovative data modeling design, convenient installation, easy-to-use programming interface, and powerful data writing and query performance.
TDengine is a big data platform designed and optimized for IoT (Internet of Things), Vehicle Telemetry, Industrial Internet, IT DevOps and other applications. Since it was open-sourced in July 2019, it has won the favor of a large number of time-series data developers with its innovative data modeling design, convenient installation, easy-to-use programming interface, and powerful data writing and query performance.
IT DevOps metric data usually are time sensitive, for example:
- System resource metrics: CPU, memory, IO, bandwidth, etc.
- Software system metrics: health status, number of connections, number of requests, number of timeouts, number of errors, response time, service type, and other business-related metrics.
The current mainstream IT DevOps visualization system usually contains a data collection module, a data persistent module, and a visual display module. collectd/StatsD, as an old-fashion open source data collection tool, has a wide user base. However, collectd/StatsD has limited functionality, and often needs to be combined with Telegraf, Grafana, and a time-series database to build a complete monitoring system.
The current mainstream IT DevOps visualization system usually contains a data collection module, a data persistence module, and a visual display module. collectd/StatsD, as an old-fashion open source data collection tool, has a wide user base. However, collectd/StatsD has limited functionality, and often needs to be combined with Telegraf, Grafana, and a time-series database to build a complete monitoring system.
The new version of TDengine supports multiple data protocols and can accept data from collectd and StatsD directly, and provides Grafana dashboard for graphical display.
This article introduces how to quickly build an IT DevOps visualization system based on TDengine + collectd / StatsD + Grafana without writing even a single line of code but by simply modifying a few lines of configuration files. The architecture is shown in the following figure.
This article introduces how to quickly build an IT DevOps visualization system based on TDengine + collectd / StatsD + Grafana without writing even a single line of code but by simply modifying a few lines in configuration files. The architecture is shown in the following figure.
![TDengine Database IT-DevOps-Solutions-Collectd-StatsD](./IT-DevOps-Solutions-Collectd-StatsD.webp)
......@@ -99,6 +99,6 @@ Download the dashboard json from `https://github.com/taosdata/grafanaplugin/blob
## Wrap-up
TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance. Thanks to the new schemaless protocol parsing function in TDengine version 2.4.0.0 and the powerful ecological software adaptation capability, users can build an efficient and easy-to-use IT DevOps visualization system or adapt to an existing system in just a few minutes.
TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance. Thanks to the new schemaless protocol parsing feature in TDengine version 2.4.0.0 and ability to integrate easily with a large software ecosystem, users can build an efficient and easy-to-use IT DevOps visualization system, or adapt an existing system, in just a few minutes.
For TDengine's powerful data writing and querying performance and other features, please refer to the official documentation and successful product implementation cases.
docs-en/27-train-faq/01-faq.md
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......@@ -5,38 +5,38 @@ title: Frequently Asked Questions
## Submit an Issue
If the tips in FAQ don't help much, please submit an issue on [GitHub](https://github.com/taosdata/TDengine) to describe your problem description, including TDengine version, hardware and OS information, the steps to reproduce the problem, etc. It would be very helpful if you package the contents in `/var/log/taos` and `/etc/taos` and upload. These two are the default directories used by TDengine, if they have been changed in your configuration, please use according to the actual configuration. It's recommended to firstly set `debugFlag` to 135 in `taos.cfg`, restart `taosd`, then reproduce the problem and collect logs. If you don't want to restart, an alternative way of setting `debugFlag` is executing `alter dnode <dnode_id> debugFlag 135` command in TDengine CLI `taos`. During normal running, however, please make sure `debugFlag` is set to 131.
If the tips in FAQ don't help much, please submit an issue on [GitHub](https://github.com/taosdata/TDengine) to describe your problem. In your description please include the TDengine version, hardware and OS information, the steps to reproduce the problem and any other relevant information. It would be very helpful if you can package the contents in `/var/log/taos` and `/etc/taos` and upload. These two are the default directories used by TDengine. If you have changed the default directories in your configuration, please package the files in your configured directories. We recommended setting `debugFlag` to 135 in `taos.cfg`, restarting `taosd`, then reproducing the problem and collecting the logs. If you don't want to restart, an alternative way of setting `debugFlag` is executing `alter dnode <dnode_id> debugFlag 135` command in TDengine CLI `taos`. During normal running, however, please make sure `debugFlag` is set to 131.
## Frequently Asked Questions
### 1. How to upgrade to TDengine 2.0 from older version?
version 2.x is not compatible with version 1.x regarding configuration file and data file, please do following before upgrading:
version 2.x is not compatible with version 1.x. With regard to the configuration and data files, please perform the following steps before upgrading. Please follow data integrity, security, backup and other relevant SOPs, best practices before removing/deleting any data.
1. Delete configuration files: `sudo rm -rf /etc/taos/taos.cfg`
1. Delete configuration files: `sudo rm -rf /etc/taos/taos.cfg`
2. Delete log files: `sudo rm -rf /var/log/taos/`
3. Delete data files if the data doesn't need to be kept: `sudo rm -rf /var/lib/taos/`
4. Install latests 2.x version
5. If the data needs to be kept and migrated to newer version, please contact professional service of TDengine for assistance
4. Install latest 2.x version
5. If the data needs to be kept and migrated to newer version, please contact professional service at TDengine for assistance.
### 2. How to handle "Unable to establish connection"？
When the client is unable to connect to the server, you can try following ways to find out why.
When the client is unable to connect to the server, you can try the following ways to troubleshoot and resolve the problem.
1. Check the network
- Check if the hosts where the client and server are running can be accessible to each other, for example by `ping` command.
- Check if the TCP/UDP on port 6030-6042 are open for access if firewall is enabled. It's better to firstly disable firewall for diagnostics.
- Check if the FQDN and serverPort are configured correctly in `taos.cfg` used by the server side
- Check if the `firstEp` is set properly in the `taos.cfg` used by the client side
- Check if the hosts where the client and server are running are accessible to each other, for example by `ping` command.
- Check if the TCP/UDP on port 6030-6042 are open for access if firewall is enabled. If possible, disable the firewall for diagnostics, but please ensure that you are following security and other relevant protocols.
- Check if the FQDN and serverPort are configured correctly in `taos.cfg` used by the server side.
- Check if the `firstEp` is set properly in the `taos.cfg` used by the client side.
2. Make sure the client version and server version are same.
3. On server side, check the running status of `taosd` by executing `systemctl status taosd` . If your server is started using another way instead of `systemctl`, use the proper method to check whether the server process is running normally.
4. If using connector of Python, Java, Go, Rust, C#, node.JS on Linux to connect toe the server, please make sure `libtaos.so` is in directory `/usr/local/taos/driver` and `/usr/local/taos/driver` is in system lib search environment variable `LD_LIBRARY_PATH`.
4. If using connector of Python, Java, Go, Rust, C#, node.JS on Linux to connect to the server, please make sure `libtaos.so` is in directory `/usr/local/taos/driver` and `/usr/local/taos/driver` is in system lib search environment variable `LD_LIBRARY_PATH`.
5. If using connector on Windows, please make sure `C:\TDengine\driver\taos.dll` is in your system lib search path, it's suggested to put `taos.dll` under `C:\Windows\System32`.
5. If using connector on Windows, please make sure `C:\TDengine\driver\taos.dll` is in your system lib search path. We recommend putting `taos.dll` under `C:\Windows\System32`.
6. Some advanced network diagnostics tools
......@@ -45,7 +45,7 @@ When the client is unable to connect to the server, you can try following ways t
Check whether a TCP port on server side is open: `nc -l {port}`
Check whether a TCP port on client side is open: `nc {hostIP} {port}`
- On Windows system `Net-TestConnection -ComputerName {fqdn} -Port {port}` on PowerShell can be used to check whether the port on serer side is open for access.
- On Windows system `Net-TestConnection -ComputerName {fqdn} -Port {port}` on PowerShell can be used to check whether the port on server side is open for access.
7. TDengine CLI `taos` can also be used to check network, please refer to [TDengine CLI](/reference/taos-shell).
......
docs-en/27-train-faq/03-docker.md
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......@@ -3,15 +3,15 @@ sidebar_label: TDengine in Docker
title: Deploy TDengine in Docker
---
Even though it's not recommended to deploy TDengine using docker in production system, docker is still very useful in development environment, especially when your host is not Linux. From version 2.0.14.0, the official image of TDengine can support X86-64, X86, arm64, and rm32 .
We do not recommend deploying TDengine using Docker in a production system. However, Docker is still very useful in a development environment, especially when your host is not Linux. From version 2.0.14.0, the official image of TDengine can support X86-64, X86, arm64, and rm32 .
In this chapter a simple step by step guide of using TDengine in docker is introduced.
In this chapter we introduce a simple step by step guide to use TDengine in Docker.
## Install Docker
The installation of docker please refer to [Get Docker](https://docs.docker.com/get-docker/).
To install Docker please refer to [Get Docker](https://docs.docker.com/get-docker/).
After docker is installed, you can check whether Docker is installed properly by displaying Docker version.
After Docker is installed, you can check whether Docker is installed properly by displaying Docker version.
```bash
$ docker -v
......@@ -27,7 +27,7 @@ $ docker run -d -p 6030-6049:6030-6049 -p 6030-6049:6030-6049/udp tdengine/tdeng
526aa188da767ae94b244226a2b2eec2b5f17dd8eff592893d9ec0cd0f3a1ccd
```
In the above command, a docker container is started to run TDengine server, the port range 6030-6049 of the container is mapped to host port range 6030-6049. If port range 6030-6049 has been occupied on the host, please change to an available host port range. Regarding the requirements about ports on the host, please refer to [Port Configuration](/reference/config/#serverport).
In the above command, a docker container is started to run TDengine server, the port range 6030-6049 of the container is mapped to host port range 6030-6049. If port range 6030-6049 has been occupied on the host, please change to an available host port range. For port requirements on the host, please refer to [Port Configuration](/reference/config/#serverport).
- **docker run**: Launch a docker container
- **-d**: the container will run in background mode
......@@ -95,7 +95,7 @@ In TDengine CLI, SQL commands can be executed to create/drop databases, tables,
### Access TDengine from host
If `-p` used to map ports properly between host and container, it's also able to access TDengine in container from the host as long as `firstEp` is configured correctly for the client on host.
If option `-p` used to map ports properly between host and container, it's also able to access TDengine in container from the host as long as `firstEp` is configured correctly for the client on host.
```
$ taos
......@@ -271,7 +271,7 @@ Below is an example output:
### Access TDengine from 3rd party tools
A lot of 3rd party tools can be used to write data into TDengine through `taosAdapter` , for details please refer to [3rd party tools](/third-party/).
A lot of 3rd party tools can be used to write data into TDengine through `taosAdapter`, for details please refer to [3rd party tools](/third-party/).
There is nothing different from the 3rd party side to access TDengine server inside a container, as long as the end point is specified correctly, the end point should be the FQDN and the mapped port of the host.
......
include/common/tmsg.h
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......@@ -1439,8 +1439,10 @@ typedef struct {
int32_t code;
} STaskDropRsp;
#define STREAM_TRIGGER_AT_ONCE 1
#define STREAM_TRIGGER_WINDOW_CLOSE 2
#define STREAM_TRIGGER_AT_ONCE_SMA 0
#define STREAM_TRIGGER_AT_ONCE 1
#define STREAM_TRIGGER_WINDOW_CLOSE 2
#define STREAM_TRIGGER_WINDOW_CLOSE_SMA 3
typedef struct {
char name[TSDB_TABLE_FNAME_LEN];
......
include/common/ttokendef.h
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......@@ -127,42 +127,42 @@
#define TK_BLOB 109
#define TK_VARBINARY 110
#define TK_DECIMAL 111
#define TK_DELAY 112
#define TK_FILE_FACTOR 113
#define TK_NK_FLOAT 114
#define TK_ROLLUP 115
#define TK_TTL 116
#define TK_SMA 117
#define TK_SHOW 118
#define TK_DATABASES 119
#define TK_TABLES 120
#define TK_STABLES 121
#define TK_MNODES 122
#define TK_MODULES 123
#define TK_QNODES 124
#define TK_FUNCTIONS 125
#define TK_INDEXES 126
#define TK_ACCOUNTS 127
#define TK_APPS 128
#define TK_CONNECTIONS 129
#define TK_LICENCE 130
#define TK_GRANTS 131
#define TK_QUERIES 132
#define TK_SCORES 133
#define TK_TOPICS 134
#define TK_VARIABLES 135
#define TK_BNODES 136
#define TK_SNODES 137
#define TK_CLUSTER 138
#define TK_TRANSACTIONS 139
#define TK_LIKE 140
#define TK_INDEX 141
#define TK_FULLTEXT 142
#define TK_FUNCTION 143
#define TK_INTERVAL 144
#define TK_TOPIC 145
#define TK_AS 146
#define TK_CGROUP 147
#define TK_FILE_FACTOR 112
#define TK_NK_FLOAT 113
#define TK_ROLLUP 114
#define TK_TTL 115
#define TK_SMA 116
#define TK_SHOW 117
#define TK_DATABASES 118
#define TK_TABLES 119
#define TK_STABLES 120
#define TK_MNODES 121
#define TK_MODULES 122
#define TK_QNODES 123
#define TK_FUNCTIONS 124
#define TK_INDEXES 125
#define TK_ACCOUNTS 126
#define TK_APPS 127
#define TK_CONNECTIONS 128
#define TK_LICENCE 129
#define TK_GRANTS 130
#define TK_QUERIES 131
#define TK_SCORES 132
#define TK_TOPICS 133
#define TK_VARIABLES 134
#define TK_BNODES 135
#define TK_SNODES 136
#define TK_CLUSTER 137
#define TK_TRANSACTIONS 138
#define TK_LIKE 139
#define TK_INDEX 140
#define TK_FULLTEXT 141
#define TK_FUNCTION 142
#define TK_INTERVAL 143
#define TK_TOPIC 144
#define TK_AS 145
#define TK_CONSUMER 146
#define TK_GROUP 147
#define TK_WITH 148
#define TK_SCHEMA 149
#define TK_DESC 150
......@@ -239,22 +239,21 @@
#define TK_PREV 221
#define TK_LINEAR 222
#define TK_NEXT 223
#define TK_GROUP 224
#define TK_HAVING 225
#define TK_ORDER 226
#define TK_SLIMIT 227
#define TK_SOFFSET 228
#define TK_LIMIT 229
#define TK_OFFSET 230
#define TK_ASC 231
#define TK_NULLS 232
#define TK_ID 233
#define TK_NK_BITNOT 234
#define TK_INSERT 235
#define TK_VALUES 236
#define TK_IMPORT 237
#define TK_NK_SEMI 238
#define TK_FILE 239
#define TK_HAVING 224
#define TK_ORDER 225
#define TK_SLIMIT 226
#define TK_SOFFSET 227
#define TK_LIMIT 228
#define TK_OFFSET 229
#define TK_ASC 230
#define TK_NULLS 231
#define TK_ID 232
#define TK_NK_BITNOT 233
#define TK_INSERT 234
#define TK_VALUES 235
#define TK_IMPORT 236
#define TK_NK_SEMI 237
#define TK_FILE 238
#define TK_NK_SPACE 300
#define TK_NK_COMMENT 301
......
include/libs/nodes/cmdnodes.h
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......@@ -80,8 +80,7 @@ typedef struct SAlterDatabaseStmt {
typedef struct STableOptions {
ENodeType type;
char comment[TSDB_TB_COMMENT_LEN];
int32_t delay;
float filesFactor;
double filesFactor;
SNodeList* pRollupFuncs;
int32_t ttl;
SNodeList* pSma;
......
include/libs/nodes/plannodes.h
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......@@ -59,6 +59,7 @@ typedef struct SScanLogicNode {
int8_t triggerType;
int64_t watermark;
int16_t tsColId;
double filesFactor;
} SScanLogicNode;
typedef struct SJoinLogicNode {
......@@ -113,6 +114,7 @@ typedef struct SWindowLogicNode {
SNode* pStateExpr;
int8_t triggerType;
int64_t watermark;
double filesFactor;
} SWindowLogicNode;
typedef struct SFillLogicNode {
......@@ -222,6 +224,7 @@ typedef struct STableScanPhysiNode {
int8_t triggerType;
int64_t watermark;
int16_t tsColId;
double filesFactor;
} STableScanPhysiNode;
typedef STableScanPhysiNode STableSeqScanPhysiNode;
......@@ -272,6 +275,7 @@ typedef struct SWinodwPhysiNode {
SNode* pTspk; // timestamp primary key
int8_t triggerType;
int64_t watermark;
double filesFactor;
} SWinodwPhysiNode;
typedef struct SIntervalPhysiNode {
......
include/libs/parser/parser.h
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......@@ -55,9 +55,9 @@ 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);
int32_t qParseSqlSyntax(SParseContext* pCxt, SQuery** pQuery, struct SCatalogReq* pCatalogReq);
int32_t qAnalyseSqlSemantic(SParseContext* pCxt, const struct SCatalogReq* pCatalogReq,
const struct SMetaData* pMetaData, SQuery* pQuery);
void qDestroyQuery(SQuery* pQueryNode);
......
include/libs/planner/planner.h
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......@@ -36,6 +36,7 @@ typedef struct SPlanContext {
int64_t watermark;
char* pMsg;
int32_t msgLen;
double filesFactor;
} SPlanContext;
// Create the physical plan for the query, according to the AST.
......
include/util/tdef.h
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......@@ -345,9 +345,6 @@ typedef enum ELogicConditionType {
#define TSDB_MIN_ROLLUP_FILE_FACTOR 0
#define TSDB_MAX_ROLLUP_FILE_FACTOR 1
#define TSDB_DEFAULT_ROLLUP_FILE_FACTOR 0.1
#define TSDB_MIN_ROLLUP_DELAY 1
#define TSDB_MAX_ROLLUP_DELAY 10
#define TSDB_DEFAULT_ROLLUP_DELAY 2
#define TSDB_MIN_TABLE_TTL 0
#define TSDB_DEFAULT_TABLE_TTL 0
......@@ -369,7 +366,11 @@ typedef enum ELogicConditionType {
#define PRIMARYKEY_TIMESTAMP_COL_ID 1
#define COL_REACH_END(colId, maxColId) ((colId) > (maxColId))
#ifdef WINDOWS
#define TSDB_MAX_RPC_THREADS 4 // windows pipe only support 4 connections.
#else
#define TSDB_MAX_RPC_THREADS 5
#endif
#define TSDB_QUERY_TYPE_NON_TYPE 0x00u // none type
#define TSDB_QUERY_TYPE_FREE_RESOURCE 0x01u // free qhandle at vnode
......
packaging/docker/Dockerfile
浏览文件 @ 9057abeb
FROM ubuntu:18.04
WORKDIR /root
ARG pkgFile
ARG dirName
ARG cpuType
RUN echo ${pkgFile} && echo ${dirName}
COPY ${pkgFile} /root/
RUN tar -zxf ${pkgFile}
WORKDIR /root/
RUN cd /root/${dirName}/ && /bin/bash install.sh -e no && cd /root
RUN rm /root/${pkgFile}
RUN rm -rf /root/${dirName}
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get clean && apt-get update && apt-get install -y locales tzdata netcat && locale-gen en_US.UTF-8
ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/lib" \
LC_CTYPE=en_US.UTF-8 \
LANG=en_US.UTF-8 \
LC_ALL=en_US.UTF-8
COPY ./bin/* /usr/bin/
ENV TINI_VERSION v0.19.0
RUN bash -c 'echo -e "Downloading tini-${cpuType} ..."'
ADD https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini-${cpuType} /tini
RUN chmod +x /tini
ENTRYPOINT ["/tini", "--", "/usr/bin/entrypoint.sh"]
CMD ["taosd"]
VOLUME [ "/var/lib/taos", "/var/log/taos", "/corefile" ]
FROM ubuntu:18.04
WORKDIR /root
ARG pkgFile
ARG dirName
ARG cpuType
RUN echo ${pkgFile} && echo ${dirName}
COPY ${pkgFile} /root/
ENV TINI_VERSION v0.19.0
ADD https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini-${cpuType} /tini
ENV DEBIAN_FRONTEND=noninteractive
WORKDIR /root/
RUN tar -zxf ${pkgFile} && cd /root/${dirName}/ && /bin/bash install.sh -e no && cd /root && rm /root/${pkgFile} && rm -rf /root/${dirName} && apt-get update && apt-get install -y locales tzdata netcat && locale-gen en_US.UTF-8 && apt-get clean && rm -rf /var/lib/apt/lists/ && chmod +x /tini
ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/lib" \
LC_CTYPE=en_US.UTF-8 \
LANG=en_US.UTF-8 \
LC_ALL=en_US.UTF-8
COPY ./bin/* /usr/bin/
ENTRYPOINT ["/tini", "--", "/usr/bin/entrypoint.sh"]
CMD ["taosd"]
VOLUME [ "/var/lib/taos", "/var/log/taos", "/corefile" ]
packaging/docker/bin/entrypoint.sh
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......@@ -11,39 +11,22 @@ DISABLE_ADAPTER=${TAOS_DISABLE_ADAPTER:-0}
unset TAOS_DISABLE_ADAPTER
# to get mnodeEpSet from data dir
DATA_DIR=${TAOS_DATA_DIR:-/var/lib/taos}
DATA_DIR=$(taosd -C|grep -E 'dataDir.*(\S+)' -o |head -n1|sed 's/dataDir *//')
DATA_DIR=${DATA_DIR:-/var/lib/taos}
# append env to custom taos.cfg
CFG_DIR=/tmp/taos
CFG_FILE=$CFG_DIR/taos.cfg
mkdir -p $CFG_DIR >/dev/null 2>&1
[ -f /etc/taos/taos.cfg ] && cat /etc/taos/taos.cfg | grep -E -v "^#|^\s*$" >$CFG_FILE
env-to-cfg >>$CFG_FILE
FQDN=$(cat $CFG_FILE | grep -E -v "^#|^$" | grep fqdn | tail -n1 | sed -E 's/.*fqdn\s+//')
FQDN=$(taosd -C|grep -E 'fqdn.*(\S+)' -o |head -n1|sed 's/fqdn *//')
# ensure the fqdn is resolved as localhost
grep "$FQDN" /etc/hosts >/dev/null || echo "127.0.0.1 $FQDN" >>/etc/hosts
FIRSET_EP=$(taosd -C|grep -E 'firstEp.*(\S+)' -o |head -n1|sed 's/firstEp *//')
# parse first ep host and port
FIRST_EP_HOST=${TAOS_FIRST_EP%:*}
FIRST_EP_PORT=${TAOS_FIRST_EP#*:}
FIRST_EP_HOST=${FIRSET_EP%:*}
FIRST_EP_PORT=${FIRSET_EP#*:}
# in case of custom server port
SERVER_PORT=$(cat $CFG_FILE | grep -E -v "^#|^$" | grep serverPort | tail -n1 | sed -E 's/.*serverPort\s+//')
SERVER_PORT=$(taosd -C|grep -E 'serverPort.*(\S+)' -o |head -n1|sed 's/serverPort *//')
SERVER_PORT=${SERVER_PORT:-6030}
# for other binaries like interpreters
if echo $1 | grep -E "taosd$" - >/dev/null; then
true # will run taosd
else
cp -f $CFG_FILE /etc/taos/taos.cfg || true
$@
exit $?
fi
set +e
ulimit -c unlimited
# set core files pattern, maybe failed
......@@ -62,22 +45,23 @@ fi
# if has mnode ep set or the host is first ep or not for cluster, just start.
if [ -f "$DATA_DIR/dnode/mnodeEpSet.json" ] ||
[ "$TAOS_FQDN" = "$FIRST_EP_HOST" ]; then
$@ -c $CFG_DIR
$@
# others will first wait the first ep ready.
else
if [ "$TAOS_FIRST_EP" = "" ]; then
echo "run TDengine with single node."
$@ -c $CFG_DIR
$@
exit $?
fi
while true; do
es=0
taos -h $FIRST_EP_HOST -P $FIRST_EP_PORT -n startup >/dev/null || es=$?
if [ "$es" -eq 0 ]; then
es=$(taos -h $FIRST_EP_HOST -P $FIRST_EP_PORT --check)
echo ${es}
if [ "${es%%:*}" -eq 2 ]; then
echo "execute create dnode"
taos -h $FIRST_EP_HOST -P $FIRST_EP_PORT -s "create dnode \"$FQDN:$SERVER_PORT\";"
break
fi
sleep 1s
done
$@ -c $CFG_DIR
$@
fi
packaging/docker/bin/taos-check 0 → 100644
浏览文件 @ 9057abeb
#!/bin/sh
es=$(taos --check)
code=${es%%:*}
if [ "$code" -ne "0" ] && [ "$code" -ne "4" ]; then
exit 0
fi
echo $es
exit 1
source/common/src/trow.c
浏览文件 @ 9057abeb
......@@ -605,6 +605,10 @@ static int32_t tdAppendKvRowToDataCol(STSRow *pRow, STSchema *pSchema, SDataCols
* @param pCols
*/
int32_t tdAppendSTSRowToDataCol(STSRow *pRow, STSchema *pSchema, SDataCols *pCols, bool isMerge) {
#ifdef TD_DEBUG_PRINT_TSDB_LOAD_DCOLS
printf("%s:%d ts: %" PRIi64 " sver:%d maxCols:%" PRIi16 " nCols:%" PRIi16 ", nRows:%d\n", __func__, __LINE__,
TD_ROW_KEY(pRow), TD_ROW_SVER(pRow), pCols->maxCols, pCols->numOfCols, pCols->numOfRows);
#endif
if (TD_IS_TP_ROW(pRow)) {
return tdAppendTpRowToDataCol(pRow, pSchema, pCols, isMerge);
} else if (TD_IS_KV_ROW(pRow)) {
......
source/dnode/mnode/impl/inc/mndDef.h
浏览文件 @ 9057abeb
......@@ -418,7 +418,7 @@ typedef struct {
char* ast;
char* physicalPlan;
SSchemaWrapper schema;
int32_t refConsumerCnt;
// int32_t refConsumerCnt;
} SMqTopicObj;
typedef struct {
......
source/dnode/mnode/impl/inc/mndScheduler.h
浏览文件 @ 9057abeb
......@@ -30,7 +30,7 @@ int32_t mndSchedInitSubEp(SMnode* pMnode, const SMqTopicObj* pTopic, SMqSubscrib
int32_t mndScheduleStream(SMnode* pMnode, STrans* pTrans, SStreamObj* pStream);
int32_t mndConvertRSmaTask(const char* ast, int64_t uid, int8_t triggerType, int64_t watermark, char** pStr,
int32_t* pLen);
int32_t* pLen, double filesFactor);
#ifdef __cplusplus
}
......
source/dnode/mnode/impl/src/mndConsumer.c
浏览文件 @ 9057abeb
......@@ -414,6 +414,7 @@ static int32_t mndProcessSubscribeReq(SRpcMsg *pMsg) {
goto SUBSCRIBE_OVER;
}
#if 0
// ref topic to prevent drop
// TODO make topic complete
SMqTopicObj topicObj = {0};
......@@ -422,6 +423,7 @@ static int32_t mndProcessSubscribeReq(SRpcMsg *pMsg) {
mInfo("subscribe topic %s by consumer %ld cgroup %s, refcnt %d", pTopic->name, consumerId, cgroup,
topicObj.refConsumerCnt);
if (mndSetTopicCommitLogs(pMnode, pTrans, &topicObj) != 0) goto SUBSCRIBE_OVER;
#endif
mndReleaseTopic(pMnode, pTopic);
}
......
source/dnode/mnode/impl/src/mndDb.c
浏览文件 @ 9057abeb
......@@ -1044,9 +1044,9 @@ static int32_t mndDropDb(SMnode *pMnode, SRpcMsg *pReq, SDbObj *pDb) {
if (mndSetDropDbRedoLogs(pMnode, pTrans, pDb) != 0) goto _OVER;
if (mndSetDropDbCommitLogs(pMnode, pTrans, pDb) != 0) goto _OVER;
/*if (mndDropOffsetByDB(pMnode, pTrans, pDb) != 0) goto _OVER;*/
/*if (mndDropSubByDB(pMnode, pTrans, pDb) != 0) goto _OVER;*/
/*if (mndDropTopicByDB(pMnode, pTrans, pDb) != 0) goto _OVER;*/
if (mndDropOffsetByDB(pMnode, pTrans, pDb) != 0) goto _OVER;
if (mndDropSubByDB(pMnode, pTrans, pDb) != 0) goto _OVER;
if (mndDropTopicByDB(pMnode, pTrans, pDb) != 0) goto _OVER;
if (mndSetDropDbRedoActions(pMnode, pTrans, pDb) != 0) goto _OVER;
SUserObj *pUser = mndAcquireUser(pMnode, pDb->createUser);
......
source/dnode/mnode/impl/src/mndOffset.c
浏览文件 @ 9057abeb
......@@ -21,6 +21,7 @@
#include "mndMnode.h"
#include "mndShow.h"
#include "mndStb.h"
#include "mndTopic.h"
#include "mndTrans.h"
#include "mndUser.h"
#include "mndVgroup.h"
......@@ -188,7 +189,15 @@ static int32_t mndProcessCommitOffsetReq(SRpcMsg *pMsg) {
bool create = false;
SMqOffsetObj *pOffsetObj = mndAcquireOffset(pMnode, key);
if (pOffsetObj == NULL) {
SMqTopicObj *pTopic = mndAcquireTopic(pMnode, pOffset->topicName);
if (pTopic == NULL) {
terrno = TSDB_CODE_MND_TOPIC_NOT_EXIST;
mError("submit offset to topic %s failed since %s", pOffset->topicName, terrstr());
continue;
}
pOffsetObj = taosMemoryMalloc(sizeof(SMqOffsetObj));
pOffsetObj->dbUid = pTopic->dbUid;
mndReleaseTopic(pMnode, pTopic);
memcpy(pOffsetObj->key, key, TSDB_PARTITION_KEY_LEN);
create = true;
}
......
source/dnode/mnode/impl/src/mndScheduler.c
浏览文件 @ 9057abeb
......@@ -36,7 +36,7 @@
extern bool tsStreamSchedV;
int32_t mndConvertRSmaTask(const char* ast, int64_t uid, int8_t triggerType, int64_t watermark, char** pStr,
int32_t* pLen) {
int32_t* pLen, double filesFactor) {
SNode* pAst = NULL;
SQueryPlan* pPlan = NULL;
terrno = TSDB_CODE_SUCCESS;
......@@ -58,6 +58,7 @@ int32_t mndConvertRSmaTask(const char* ast, int64_t uid, int8_t triggerType, int
.rSmaQuery = true,
.triggerType = triggerType,
.watermark = watermark,
.filesFactor = filesFactor,
};
if (qCreateQueryPlan(&cxt, &pPlan, NULL) < 0) {
terrno = TSDB_CODE_QRY_INVALID_INPUT;
......@@ -286,7 +287,7 @@ int32_t mndScheduleStream(SMnode* pMnode, STrans* pTrans, SStreamObj* pStream) {
pStream->tasks = taosArrayInit(totLevel, sizeof(void*));
bool hasExtraSink = false;
if (totLevel == 2) {
if (totLevel == 2 || strcmp(pStream->sourceDb, pStream->targetDb) != 0) {
SArray* taskOneLevel = taosArrayInit(0, sizeof(void*));
taosArrayPush(pStream->tasks, &taskOneLevel);
// add extra sink
......@@ -407,7 +408,7 @@ int32_t mndScheduleStream(SMnode* pMnode, STrans* pTrans, SStreamObj* pStream) {
/*pTask->dispatchMsgType = TDMT_VND_TASK_WRITE_EXEC;*/
pTask->dispatchMsgType = TDMT_VND_TASK_DISPATCH;
SDbObj* pDb = mndAcquireDb(pMnode, pStream->sourceDb);
SDbObj* pDb = mndAcquireDb(pMnode, pStream->targetDb);
ASSERT(pDb);
if (mndExtractDbInfo(pMnode, pDb, &pTask->shuffleDispatcher.dbInfo, NULL) < 0) {
sdbRelease(pSdb, pDb);
......
source/dnode/mnode/impl/src/mndStb.c
浏览文件 @ 9057abeb
......@@ -397,13 +397,13 @@ static void *mndBuildVCreateStbReq(SMnode *pMnode, SVgObj *pVgroup, SStbObj *pSt
req.pRSmaParam.xFilesFactor = pStb->xFilesFactor;
req.pRSmaParam.delay = pStb->delay;
if (pStb->ast1Len > 0) {
if (mndConvertRSmaTask(pStb->pAst1, pStb->uid, 0, 0, &req.pRSmaParam.qmsg1, &req.pRSmaParam.qmsg1Len) !=
if (mndConvertRSmaTask(pStb->pAst1, pStb->uid, 0, 0, &req.pRSmaParam.qmsg1, &req.pRSmaParam.qmsg1Len, req.pRSmaParam.xFilesFactor) !=
TSDB_CODE_SUCCESS) {
return NULL;
}
}
if (pStb->ast2Len > 0) {
if (mndConvertRSmaTask(pStb->pAst2, pStb->uid, 0, 0, &req.pRSmaParam.qmsg2, &req.pRSmaParam.qmsg2Len) !=
if (mndConvertRSmaTask(pStb->pAst2, pStb->uid, 0, 0, &req.pRSmaParam.qmsg2, &req.pRSmaParam.qmsg2Len, req.pRSmaParam.xFilesFactor) !=
TSDB_CODE_SUCCESS) {
return NULL;
}
......
source/dnode/mnode/impl/src/mndStream.c
浏览文件 @ 9057abeb
......@@ -393,6 +393,15 @@ static int32_t mndCreateStream(SMnode *pMnode, SRpcMsg *pReq, SCMCreateStreamReq
streamObj.trigger = pCreate->triggerType;
streamObj.waterMark = pCreate->watermark;
if (streamObj.targetSTbName[0]) {
pDb = mndAcquireDbByStb(pMnode, streamObj.targetSTbName);
if (pDb == NULL) {
terrno = TSDB_CODE_MND_DB_NOT_SELECTED;
return -1;
}
tstrncpy(streamObj.targetDb, pDb->name, TSDB_DB_FNAME_LEN);
}
STrans *pTrans = mndTransCreate(pMnode, TRN_POLICY_ROLLBACK, TRN_CONFLICT_GLOBAL, pReq);
if (pTrans == NULL) {
mError("stream:%s, failed to create since %s", pCreate->name, terrstr());
......
source/dnode/mnode/impl/src/mndSubscribe.c
浏览文件 @ 9057abeb
......@@ -157,6 +157,7 @@ static int32_t mndPersistSubChangeVgReq(SMnode *pMnode, STrans *pTrans, const SM
int32_t vgId = pRebVg->pVgEp->vgId;
SVgObj *pVgObj = mndAcquireVgroup(pMnode, vgId);
if (pVgObj == NULL) {
ASSERT(0);
taosMemoryFree(buf);
return -1;
}
......@@ -451,6 +452,7 @@ static int32_t mndPersistRebResult(SMnode *pMnode, SRpcMsg *pMsg, const SMqRebOu
taosArrayPush(pConsumerNew->rebNewTopics, &topic);
mndReleaseConsumer(pMnode, pConsumerOld);
if (mndSetConsumerCommitLogs(pMnode, pTrans, pConsumerNew) != 0) {
ASSERT(0);
goto REB_FAIL;
}
}
......@@ -469,9 +471,11 @@ static int32_t mndPersistRebResult(SMnode *pMnode, SRpcMsg *pMsg, const SMqRebOu
taosArrayPush(pConsumerNew->rebRemovedTopics, &topic);
mndReleaseConsumer(pMnode, pConsumerOld);
if (mndSetConsumerCommitLogs(pMnode, pTrans, pConsumerNew) != 0) {
ASSERT(0);
goto REB_FAIL;
}
}
#if 0
if (consumerNum) {
char topic[TSDB_TOPIC_FNAME_LEN];
char cgroup[TSDB_CGROUP_LEN];
......@@ -486,9 +490,13 @@ static int32_t mndPersistRebResult(SMnode *pMnode, SRpcMsg *pMsg, const SMqRebOu
pTopic->refConsumerCnt = topicObj.refConsumerCnt;
mInfo("subscribe topic %s unref %d consumer cgroup %s, refcnt %d", pTopic->name, consumerNum, cgroup,
topicObj.refConsumerCnt);
if (mndSetTopicCommitLogs(pMnode, pTrans, &topicObj) != 0) goto REB_FAIL;
if (mndSetTopicCommitLogs(pMnode, pTrans, &topicObj) != 0) {
ASSERT(0);
goto REB_FAIL;
}
}
}
#endif
// 4. TODO commit log: modification log
......@@ -496,7 +504,10 @@ static int32_t mndPersistRebResult(SMnode *pMnode, SRpcMsg *pMsg, const SMqRebOu
mndTransSetCb(pTrans, TRANS_START_FUNC_MQ_REB, TRANS_STOP_FUNC_MQ_REB, NULL, 0);
// 6. execution
if (mndTransPrepare(pMnode, pTrans) != 0) goto REB_FAIL;
if (mndTransPrepare(pMnode, pTrans) != 0) {
ASSERT(0);
goto REB_FAIL;
}
mndTransDrop(pTrans);
return 0;
......
source/dnode/mnode/impl/src/mndTopic.c
浏览文件 @ 9057abeb
......@@ -15,6 +15,7 @@
#include "mndTopic.h"
#include "mndAuth.h"
#include "mndConsumer.h"
#include "mndDb.h"
#include "mndDnode.h"
#include "mndMnode.h"
......@@ -121,7 +122,7 @@ SSdbRaw *mndTopicActionEncode(SMqTopicObj *pTopic) {
SDB_SET_BINARY(pRaw, dataPos, swBuf, schemaLen, TOPIC_ENCODE_OVER);
}
SDB_SET_INT32(pRaw, dataPos, pTopic->refConsumerCnt, TOPIC_ENCODE_OVER);
/*SDB_SET_INT32(pRaw, dataPos, pTopic->refConsumerCnt, TOPIC_ENCODE_OVER);*/
SDB_SET_RESERVE(pRaw, dataPos, MND_TOPIC_RESERVE_SIZE, TOPIC_ENCODE_OVER);
SDB_SET_DATALEN(pRaw, dataPos, TOPIC_ENCODE_OVER);
......@@ -221,7 +222,7 @@ SSdbRow *mndTopicActionDecode(SSdbRaw *pRaw) {
pTopic->schema.pSchema = NULL;
}
SDB_GET_INT32(pRaw, dataPos, &pTopic->refConsumerCnt, TOPIC_DECODE_OVER);
/*SDB_GET_INT32(pRaw, dataPos, &pTopic->refConsumerCnt, TOPIC_DECODE_OVER);*/
SDB_GET_RESERVE(pRaw, dataPos, MND_TOPIC_RESERVE_SIZE, TOPIC_DECODE_OVER);
......@@ -253,7 +254,7 @@ static int32_t mndTopicActionUpdate(SSdb *pSdb, SMqTopicObj *pOldTopic, SMqTopic
atomic_exchange_64(&pOldTopic->updateTime, pNewTopic->updateTime);
atomic_exchange_32(&pOldTopic->version, pNewTopic->version);
atomic_store_32(&pOldTopic->refConsumerCnt, pNewTopic->refConsumerCnt);
/*atomic_store_32(&pOldTopic->refConsumerCnt, pNewTopic->refConsumerCnt);*/
/*taosWLockLatch(&pOldTopic->lock);*/
......@@ -327,7 +328,7 @@ static int32_t mndCreateTopic(SMnode *pMnode, SRpcMsg *pReq, SCMCreateTopicReq *
topicObj.version = 1;
topicObj.sql = strdup(pCreate->sql);
topicObj.sqlLen = strlen(pCreate->sql) + 1;
topicObj.refConsumerCnt = 0;
/*topicObj.refConsumerCnt = 0;*/
if (pCreate->ast && pCreate->ast[0]) {
topicObj.ast = strdup(pCreate->ast);
......@@ -492,8 +493,8 @@ static int32_t mndDropTopic(SMnode *pMnode, STrans *pTrans, SRpcMsg *pReq, SMqTo
}
static int32_t mndProcessDropTopicReq(SRpcMsg *pReq) {
SMnode *pMnode = pReq->info.node;
/*SSdb *pSdb = pMnode->pSdb;*/
SMnode *pMnode = pReq->info.node;
SSdb *pSdb = pMnode->pSdb;
SMDropTopicReq dropReq = {0};
if (tDeserializeSMDropTopicReq(pReq->pCont, pReq->contLen, &dropReq) != 0) {
......@@ -513,12 +514,36 @@ static int32_t mndProcessDropTopicReq(SRpcMsg *pReq) {
}
}
void *pIter = NULL;
SMqConsumerObj *pConsumer;
while (1) {
pIter = sdbFetch(pSdb, SDB_CONSUMER, pIter, (void **)&pConsumer);
if (pIter == NULL) break;
if (pConsumer->status == MQ_CONSUMER_STATUS__LOST_REBD) continue;
int32_t sz = taosArrayGetSize(pConsumer->assignedTopics);
for (int32_t i = 0; i < sz; i++) {
char *name = taosArrayGetP(pConsumer->assignedTopics, i);
if (strcmp(name, pTopic->name) == 0) {
mndReleaseConsumer(pMnode, pConsumer);
mndReleaseTopic(pMnode, pTopic);
terrno = TSDB_CODE_MND_TOPIC_SUBSCRIBED;
mError("topic:%s, failed to drop since subscribed by consumer %ld from cgroup %s", dropReq.name,
pConsumer->consumerId, pConsumer->cgroup);
return -1;
}
}
sdbRelease(pSdb, pConsumer);
}
#if 0
if (pTopic->refConsumerCnt != 0) {
mndReleaseTopic(pMnode, pTopic);
terrno = TSDB_CODE_MND_TOPIC_SUBSCRIBED;
mError("topic:%s, failed to drop since %s", dropReq.name, terrstr());
return -1;
}
#endif
STrans *pTrans = mndTransCreate(pMnode, TRN_POLICY_ROLLBACK, TRN_CONFLICT_GLOBAL, pReq);
if (pTrans == NULL) {
......
source/dnode/mnode/impl/test/db/CMakeLists.txt
浏览文件 @ 9057abeb
......@@ -5,7 +5,9 @@ target_link_libraries(
PUBLIC sut
)
add_test(
NAME dbTest
COMMAND dbTest
)
if(NOT TD_WINDOWS)
add_test(
NAME dbTest
COMMAND dbTest
)
endif(NOT TD_WINDOWS)
source/dnode/mnode/impl/test/sma/CMakeLists.txt
浏览文件 @ 9057abeb
......@@ -5,7 +5,9 @@ target_link_libraries(
PUBLIC sut
)
add_test(
NAME smaTest
COMMAND smaTest
)
if(NOT TD_WINDOWS)
add_test(
NAME smaTest
COMMAND smaTest
)
endif(NOT TD_WINDOWS)
source/dnode/mnode/impl/test/stb/CMakeLists.txt
浏览文件 @ 9057abeb
......@@ -5,7 +5,9 @@ target_link_libraries(
PUBLIC sut
)
add_test(
NAME stbTest
COMMAND stbTest
)
if(NOT TD_WINDOWS)
add_test(
NAME stbTest
COMMAND stbTest
)
endif(NOT TD_WINDOWS)
\ No newline at end of file
source/dnode/vnode/src/inc/tsdb.h
浏览文件 @ 9057abeb
......@@ -79,13 +79,14 @@ struct STsdb {
struct STable {
uint64_t tid;
uint64_t uid;
STSchema *pSchema;
STSchema *pSchema; // latest schema
STSchema *pCacheSchema; // cached cache
};
#define TABLE_TID(t) (t)->tid
#define TABLE_UID(t) (t)->uid
int tsdbPrepareCommit(STsdb *pTsdb);
int tsdbPrepareCommit(STsdb *pTsdb);
typedef enum {
TSDB_FILE_HEAD = 0, // .head
TSDB_FILE_DATA, // .data
......@@ -181,13 +182,15 @@ int tsdbUnlockRepo(STsdb *pTsdb);
static FORCE_INLINE STSchema *tsdbGetTableSchemaImpl(STsdb *pTsdb, STable *pTable, bool lock, bool copy,
int32_t version) {
if ((version != -1) && (schemaVersion(pTable->pSchema) != version)) {
taosMemoryFreeClear(pTable->pSchema);
pTable->pSchema = metaGetTbTSchema(REPO_META(pTsdb), pTable->uid, version);
if ((version < 0) || (schemaVersion(pTable->pSchema) == version)) {
return pTable->pSchema;
}
return pTable->pSchema;
if (!pTable->pCacheSchema || (schemaVersion(pTable->pCacheSchema) != version)) {
taosMemoryFreeClear(pTable->pCacheSchema);
pTable->pCacheSchema = metaGetTbTSchema(REPO_META(pTsdb), pTable->uid, version);
}
return pTable->pCacheSchema;
}
// tsdbMemTable.h
......
source/dnode/vnode/src/inc/vnodeInt.h
浏览文件 @ 9057abeb
......@@ -149,7 +149,7 @@ int32_t tdUpdateExpireWindow(SSma* pSma, SSubmitReq* pMsg, int64_t version);
int32_t tdProcessTSmaCreate(SSma* pSma, int64_t version, const char* msg);
int32_t tdProcessTSmaInsert(SSma* pSma, int64_t indexUid, const char* msg);
int32_t tdProcessRSmaCreate(SSma* pSma, SMeta* pMeta, SVCreateStbReq* pReq, SMsgCb* pMsgCb);
int32_t tdProcessRSmaCreate(SVnode *pVnode, SVCreateStbReq* pReq);
int32_t tdProcessRSmaSubmit(SSma* pSma, void* pMsg, int32_t inputType);
int32_t tdFetchTbUidList(SSma* pSma, STbUidStore** ppStore, tb_uid_t suid, tb_uid_t uid);
int32_t tdUpdateTbUidList(SSma* pSma, STbUidStore* pUidStore);
......
source/dnode/vnode/src/meta/metaQuery.c
浏览文件 @ 9057abeb
......@@ -178,6 +178,7 @@ SSchemaWrapper *metaGetTableSchema(SMeta *pMeta, tb_uid_t uid, int32_t sver, boo
if (me.type == TSDB_SUPER_TABLE) {
pSchema = tCloneSSchemaWrapper(&me.stbEntry.schemaRow);
} else if (me.type == TSDB_NORMAL_TABLE) {
pSchema = tCloneSSchemaWrapper(&me.ntbEntry.schemaRow);
} else {
ASSERT(0);
}
......@@ -299,7 +300,7 @@ STSchema *metaGetTbTSchema(SMeta *pMeta, tb_uid_t uid, int32_t sver) {
pSW = metaGetTableSchema(pMeta, quid, sver, 0);
if (!pSW) return NULL;
tdInitTSchemaBuilder(&sb, sver);
tdInitTSchemaBuilder(&sb, pSW->version);
for (int i = 0; i < pSW->nCols; i++) {
pSchema = pSW->pSchema + i;
tdAddColToSchema(&sb, pSchema->type, pSchema->flags, pSchema->colId, pSchema->bytes);
......
source/dnode/vnode/src/sma/smaRollup.c
浏览文件 @ 9057abeb
......@@ -165,7 +165,10 @@ int32_t tdFetchTbUidList(SSma *pSma, STbUidStore **ppStore, tb_uid_t suid, tb_ui
* @param pReq
* @return int32_t
*/
int32_t tdProcessRSmaCreate(SSma *pSma, SMeta *pMeta, SVCreateStbReq *pReq, SMsgCb *pMsgCb) {
int32_t tdProcessRSmaCreate(SVnode *pVnode, SVCreateStbReq *pReq) {
SSma *pSma = pVnode->pSma;
SMeta *pMeta = pVnode->pMeta;
SMsgCb *pMsgCb = &pVnode->msgCb;
if (!pReq->rollup) {
smaTrace("vgId:%d return directly since no rollup for stable %s %" PRIi64, SMA_VID(pSma), pReq->name, pReq->suid);
return TSDB_CODE_SUCCESS;
......@@ -210,6 +213,7 @@ int32_t tdProcessRSmaCreate(SSma *pSma, SMeta *pMeta, SVCreateStbReq *pReq, SMsg
.reader = pReadHandle,
.meta = pMeta,
.pMsgCb = pMsgCb,
.vnode = pVnode,
};
if (param->qmsg1) {
......@@ -441,7 +445,7 @@ static int32_t tdExecuteRSma(SSma *pSma, const void *pMsg, int32_t inputType, tb
if (inputType == STREAM_DATA_TYPE_SUBMIT_BLOCK) {
// TODO: use the proper schema instead of 0, and cache STSchema in cache
STSchema *pTSchema = metaGetTbTSchema(SMA_META(pSma), suid, 1);
STSchema *pTSchema = metaGetTbTSchema(SMA_META(pSma), suid, -1);
if (!pTSchema) {
terrno = TSDB_CODE_TDB_IVD_TB_SCHEMA_VERSION;
return TSDB_CODE_FAILED;
......
source/dnode/vnode/src/tsdb/tsdbCommit.c
浏览文件 @ 9057abeb
......@@ -84,8 +84,8 @@ static int tsdbMergeBlockData(SCommitH *pCommith, SCommitIter *pIter, SDataCols
static void tsdbResetCommitTable(SCommitH *pCommith);
static void tsdbCloseCommitFile(SCommitH *pCommith, bool hasError);
static bool tsdbCanAddSubBlock(SCommitH *pCommith, SBlock *pBlock, SMergeInfo *pInfo);
static void tsdbLoadAndMergeFromCache(STsdb *pTsdb, SDataCols *pDataCols, int *iter, SCommitIter *pCommitIter, SDataCols *pTarget,
TSKEY maxKey, int maxRows, int8_t update);
static void tsdbLoadAndMergeFromCache(STsdb *pTsdb, SDataCols *pDataCols, int *iter, SCommitIter *pCommitIter,
SDataCols *pTarget, TSKEY maxKey, int maxRows, int8_t update);
int tsdbWriteBlockIdx(SDFile *pHeadf, SArray *pIdxA, void **ppBuf);
int tsdbApplyRtnOnFSet(STsdb *pRepo, SDFileSet *pSet, SRtn *pRtn) {
......@@ -466,7 +466,7 @@ static int tsdbCreateCommitIters(SCommitH *pCommith) {
pTbData = (STbData *)pNode->pData;
pCommitIter = pCommith->iters + i;
pTSchema = metaGetTbTSchema(REPO_META(pRepo), pTbData->uid, 1); // TODO: schema version
pTSchema = metaGetTbTSchema(REPO_META(pRepo), pTbData->uid, -1);
if (pTSchema) {
pCommitIter->pIter = tSkipListCreateIter(pTbData->pData);
......@@ -475,7 +475,8 @@ static int tsdbCreateCommitIters(SCommitH *pCommith) {
pCommitIter->pTable = (STable *)taosMemoryMalloc(sizeof(STable));
pCommitIter->pTable->uid = pTbData->uid;
pCommitIter->pTable->tid = pTbData->uid;
pCommitIter->pTable->pSchema = pTSchema; // metaGetTbTSchema(REPO_META(pRepo), pTbData->uid, 0);
pCommitIter->pTable->pSchema = pTSchema;
pCommitIter->pTable->pCacheSchema = NULL;
}
}
tSkipListDestroyIter(pSlIter);
......@@ -490,6 +491,7 @@ static void tsdbDestroyCommitIters(SCommitH *pCommith) {
tSkipListDestroyIter(pCommith->iters[i].pIter);
if (pCommith->iters[i].pTable) {
tdFreeSchema(pCommith->iters[i].pTable->pSchema);
tdFreeSchema(pCommith->iters[i].pTable->pCacheSchema);
taosMemoryFreeClear(pCommith->iters[i].pTable);
}
}
......@@ -914,7 +916,7 @@ static int tsdbMoveBlkIdx(SCommitH *pCommith, SBlockIdx *pIdx) {
while (bidx < nBlocks) {
if (!pTSchema && !tsdbCommitIsSameFile(pCommith, bidx)) {
// Set commit table
pTSchema = metaGetTbTSchema(REPO_META(pTsdb), pIdx->uid, 1); // TODO: schema version
pTSchema = metaGetTbTSchema(REPO_META(pTsdb), pIdx->uid, -1); // TODO: schema version
if (!pTSchema) {
terrno = TSDB_CODE_OUT_OF_MEMORY;
return -1;
......@@ -948,7 +950,7 @@ static int tsdbMoveBlkIdx(SCommitH *pCommith, SBlockIdx *pIdx) {
}
static int tsdbSetCommitTable(SCommitH *pCommith, STable *pTable) {
STSchema *pSchema = tsdbGetTableSchemaImpl(TSDB_COMMIT_REPO(pCommith),pTable, false, false, -1);
STSchema *pSchema = tsdbGetTableSchemaImpl(TSDB_COMMIT_REPO(pCommith), pTable, false, false, -1);
pCommith->pTable = pTable;
......@@ -1422,8 +1424,8 @@ static int tsdbMergeBlockData(SCommitH *pCommith, SCommitIter *pIter, SDataCols
int biter = 0;
while (true) {
tsdbLoadAndMergeFromCache(TSDB_COMMIT_REPO(pCommith), pCommith->readh.pDCols[0], &biter, pIter, pCommith->pDataCols, keyLimit, defaultRows,
pCfg->update);
tsdbLoadAndMergeFromCache(TSDB_COMMIT_REPO(pCommith), pCommith->readh.pDCols[0], &biter, pIter, pCommith->pDataCols,
keyLimit, defaultRows, pCfg->update);
if (pCommith->pDataCols->numOfRows == 0) break;
......@@ -1447,8 +1449,8 @@ static int tsdbMergeBlockData(SCommitH *pCommith, SCommitIter *pIter, SDataCols
return 0;
}
static void tsdbLoadAndMergeFromCache(STsdb *pTsdb, SDataCols *pDataCols, int *iter, SCommitIter *pCommitIter, SDataCols *pTarget,
TSKEY maxKey, int maxRows, int8_t update) {
static void tsdbLoadAndMergeFromCache(STsdb *pTsdb, SDataCols *pDataCols, int *iter, SCommitIter *pCommitIter,
SDataCols *pTarget, TSKEY maxKey, int maxRows, int8_t update) {
TSKEY key1 = INT64_MAX;
TSKEY key2 = INT64_MAX;
TSKEY lastKey = TSKEY_INITIAL_VAL;
......
source/dnode/vnode/src/tsdb/tsdbRead.c
浏览文件 @ 9057abeb
......@@ -1395,7 +1395,7 @@ static int32_t handleDataMergeIfNeeded(STsdbReadHandle* pTsdbReadHandle, SBlock*
}
if (pTsdbReadHandle->outputCapacity >= binfo.rows) {
ASSERT(cur->blockCompleted);
ASSERT(cur->blockCompleted || cur->mixBlock);
}
if (cur->rows == binfo.rows) {
......
source/dnode/vnode/src/vnd/vnodeSvr.c
浏览文件 @ 9057abeb
......@@ -360,7 +360,7 @@ static int vnodeProcessCreateStbReq(SVnode *pVnode, int64_t version, void *pReq,
goto _err;
}
tdProcessRSmaCreate(pVnode->pSma, pVnode->pMeta, &req, &pVnode->msgCb);
tdProcessRSmaCreate(pVnode, &req);
tDecoderClear(&coder);
return 0;
......
source/libs/executor/inc/executorimpl.h
浏览文件 @ 9057abeb
......@@ -160,7 +160,7 @@ struct SOperatorInfo;
//struct SOptrBasicInfo;
typedef int32_t (*__optr_encode_fn_t)(struct SOperatorInfo* pOperator, char** result, int32_t* length);
typedef int32_t (*__optr_decode_fn_t)(struct SOperatorInfo* pOperator, char* result, int32_t length);
typedef int32_t (*__optr_decode_fn_t)(struct SOperatorInfo* pOperator, char* result);
typedef int32_t (*__optr_open_fn_t)(struct SOperatorInfo* pOptr);
typedef SSDataBlock* (*__optr_fn_t)(struct SOperatorInfo* pOptr);
......@@ -440,6 +440,7 @@ typedef struct STimeWindowSupp {
int64_t waterMark;
TSKEY maxTs;
SColumnInfoData timeWindowData; // query time window info for scalar function execution.
SHashObj *winMap;
} STimeWindowAggSupp;
typedef struct SIntervalAggOperatorInfo {
......@@ -758,7 +759,7 @@ SOperatorInfo* createDataBlockInfoScanOperator(void* dataReader, SExecTaskInfo*
SOperatorInfo* createStreamScanOperatorInfo(void* pDataReader, SReadHandle* pHandle,
SArray* pTableIdList, STableScanPhysiNode* pTableScanNode, SExecTaskInfo* pTaskInfo,
STimeWindowAggSupp* pTwSup, int16_t tsColId);
STimeWindowAggSupp* pTwSup);
SOperatorInfo* createFillOperatorInfo(SOperatorInfo* downstream, SExprInfo* pExpr, int32_t numOfCols,
......@@ -821,7 +822,7 @@ int32_t createExecTaskInfoImpl(SSubplan* pPlan, SExecTaskInfo** pTaskInfo, SRead
int32_t getOperatorExplainExecInfo(SOperatorInfo* operatorInfo, SExplainExecInfo** pRes, int32_t* capacity,
int32_t* resNum);
int32_t aggDecodeResultRow(SOperatorInfo* pOperator, char* result, int32_t length);
int32_t aggDecodeResultRow(SOperatorInfo* pOperator, char* result);
int32_t aggEncodeResultRow(SOperatorInfo* pOperator, char** result, int32_t* length);
STimeWindow getActiveTimeWindow(SDiskbasedBuf* pBuf, SResultRowInfo* pResultRowInfo, int64_t ts,
......@@ -837,6 +838,8 @@ SResultWindowInfo* getSessionTimeWindow(SArray* pWinInfos, TSKEY ts, int64_t gap
int32_t updateSessionWindowInfo(SResultWindowInfo* pWinInfo, TSKEY* pTs, int32_t rows,
int32_t start, int64_t gap, SHashObj* pStDeleted);
bool functionNeedToExecute(SqlFunctionCtx* pCtx);
int64_t getSmaWaterMark(int64_t interval, double filesFactor);
bool isSmaStream(int8_t triggerType);
int32_t compareTimeWindow(const void* p1, const void* p2, const void* param);
#ifdef __cplusplus
......
source/libs/executor/src/executorimpl.c
浏览文件 @ 9057abeb
......@@ -2767,7 +2767,7 @@ static SSDataBlock* concurrentlyLoadRemoteDataImpl(SOperatorInfo* pOperator, SEx
code = setSDataBlockFromFetchRsp(pExchangeInfo->pResult, pLoadInfo, pTableRsp->numOfRows, pTableRsp->data,
pTableRsp->compLen, pTableRsp->numOfCols, startTs, &pDataInfo->totalRows, NULL);
if (code != 0) {
taosMemoryFreeClear(pDataInfo->pRsp);
taosMemoryFreeClear(pDataInfo->pRsp);
goto _error;
}
......@@ -2788,7 +2788,7 @@ static SSDataBlock* concurrentlyLoadRemoteDataImpl(SOperatorInfo* pOperator, SEx
pDataInfo->status = EX_SOURCE_DATA_NOT_READY;
code = doSendFetchDataRequest(pExchangeInfo, pTaskInfo, i);
if (code != TSDB_CODE_SUCCESS) {
taosMemoryFreeClear(pDataInfo->pRsp);
taosMemoryFreeClear(pDataInfo->pRsp);
goto _error;
}
}
......@@ -2895,7 +2895,7 @@ static SSDataBlock* seqLoadRemoteData(SOperatorInfo* pOperator) {
pDataInfo->totalRows, pLoadInfo->totalRows);
pDataInfo->status = EX_SOURCE_DATA_EXHAUSTED;
pExchangeInfo->current += 1;
pExchangeInfo->current += 1;
taosMemoryFreeClear(pDataInfo->pRsp);
continue;
}
......@@ -2922,7 +2922,7 @@ static SSDataBlock* seqLoadRemoteData(SOperatorInfo* pOperator) {
}
pOperator->resultInfo.totalRows += pRes->info.rows;
taosMemoryFreeClear(pDataInfo->pRsp);
taosMemoryFreeClear(pDataInfo->pRsp);
return pExchangeInfo->pResult;
}
}
......@@ -3384,7 +3384,7 @@ int32_t getTableScanInfo(SOperatorInfo* pOperator, int32_t* order, int32_t* scan
// todo add more information about exchange operation
int32_t type = pOperator->operatorType;
if (type == QUERY_NODE_PHYSICAL_PLAN_EXCHANGE || type == QUERY_NODE_PHYSICAL_PLAN_SYSTABLE_SCAN ||
type == QUERY_NODE_PHYSICAL_PLAN_STREAM_SCAN) {
type == QUERY_NODE_PHYSICAL_PLAN_STREAM_SCAN || type == QUERY_NODE_PHYSICAL_PLAN_TAG_SCAN) {
*order = TSDB_ORDER_ASC;
*scanFlag = MAIN_SCAN;
return TSDB_CODE_SUCCESS;
......@@ -3448,14 +3448,14 @@ static int32_t doOpenAggregateOptr(SOperatorInfo* pOperator) {
}
#if 0 // test for encode/decode result info
if(pOperator->encodeResultRow){
if(pOperator->fpSet.encodeResultRow){
char *result = NULL;
int32_t length = 0;
SAggSupporter *pSup = &pAggInfo->aggSup;
pOperator->encodeResultRow(pOperator, pSup, pInfo, &result, &length);
pOperator->fpSet.encodeResultRow(pOperator, &result, &length);
SAggSupporter* pSup = &pAggInfo->aggSup;
taosHashClear(pSup->pResultRowHashTable);
pInfo->resultRowInfo.size = 0;
pOperator->decodeResultRow(pOperator, pSup, pInfo, result, length);
pOperator->fpSet.decodeResultRow(pOperator, result);
if(result){
taosMemoryFree(result);
}
......@@ -3499,14 +3499,15 @@ static SSDataBlock* getAggregateResult(SOperatorInfo* pOperator) {
}
int32_t aggEncodeResultRow(SOperatorInfo* pOperator, char** result, int32_t* length) {
if(result == NULL || length == NULL){
if (result == NULL || length == NULL) {
return TSDB_CODE_TSC_INVALID_INPUT;
}
SOptrBasicInfo* pInfo = (SOptrBasicInfo*)(pOperator->info);
SAggSupporter* pSup = (SAggSupporter*)POINTER_SHIFT(pOperator->info, sizeof(SOptrBasicInfo));
int32_t size = taosHashGetSize(pSup->pResultRowHashTable);
size_t keyLen = sizeof(uint64_t) * 2; // estimate the key length
int32_t totalSize = sizeof(int32_t) + sizeof(int32_t) + size * (sizeof(int32_t) + keyLen + sizeof(int32_t) + pSup->resultRowSize);
SAggSupporter* pSup = (SAggSupporter*)POINTER_SHIFT(pOperator->info, sizeof(SOptrBasicInfo));
int32_t size = taosHashGetSize(pSup->pResultRowHashTable);
size_t keyLen = sizeof(uint64_t) * 2; // estimate the key length
int32_t totalSize =
sizeof(int32_t) + sizeof(int32_t) + size * (sizeof(int32_t) + keyLen + sizeof(int32_t) + pSup->resultRowSize);
*result = (char*)taosMemoryCalloc(1, totalSize);
if (*result == NULL) {
......@@ -3567,17 +3568,20 @@ int32_t aggEncodeResultRow(SOperatorInfo* pOperator, char** result, int32_t* len
return TDB_CODE_SUCCESS;
}
int32_t aggDecodeResultRow(SOperatorInfo* pOperator, char* result, int32_t length) {
if(result == NULL || length <= 0){
int32_t aggDecodeResultRow(SOperatorInfo* pOperator, char* result) {
if (result == NULL) {
return TSDB_CODE_TSC_INVALID_INPUT;
}
SOptrBasicInfo* pInfo = (SOptrBasicInfo*)(pOperator->info);
SAggSupporter* pSup = (SAggSupporter*)POINTER_SHIFT(pOperator->info, sizeof(SOptrBasicInfo));
SAggSupporter* pSup = (SAggSupporter*)POINTER_SHIFT(pOperator->info, sizeof(SOptrBasicInfo));
// int32_t size = taosHashGetSize(pSup->pResultRowHashTable);
int32_t count = *(int32_t*)(result);
int32_t length = *(int32_t*)(result);
int32_t offset = sizeof(int32_t);
int32_t count = *(int32_t*)(result + offset);
offset += sizeof(int32_t);
while (count-- > 0 && length > offset) {
int32_t keyLen = *(int32_t*)(result + offset);
offset += sizeof(int32_t);
......@@ -4509,8 +4513,8 @@ SOperatorInfo* createOperatorTree(SPhysiNode* pPhyNode, SExecTaskInfo* pTaskInfo
} else if (QUERY_NODE_PHYSICAL_PLAN_STREAM_SCAN == type) {
SScanPhysiNode* pScanPhyNode = (SScanPhysiNode*)pPhyNode; // simple child table.
STableScanPhysiNode* pTableScanNode = (STableScanPhysiNode*)pPhyNode;
STimeWindowAggSupp twSup = {.waterMark = pTableScanNode->watermark,
.calTrigger = pTableScanNode->triggerType, .maxTs = INT64_MIN};
STimeWindowAggSupp twSup = {
.waterMark = pTableScanNode->watermark, .calTrigger = pTableScanNode->triggerType, .maxTs = INT64_MIN};
tsdbReaderT pDataReader = NULL;
if (pHandle->vnode) {
pDataReader = doCreateDataReader(pTableScanNode, pHandle, pTableListInfo, (uint64_t)queryId, taskId, pTagCond);
......@@ -4524,9 +4528,9 @@ SOperatorInfo* createOperatorTree(SPhysiNode* pPhyNode, SExecTaskInfo* pTaskInfo
} else {
qDebug("%s pDataReader is not NULL", GET_TASKID(pTaskInfo));
}
SArray* tableIdList = extractTableIdList(pTableListInfo);
SArray* tableIdList = extractTableIdList(pTableListInfo);
SOperatorInfo* pOperator = createStreamScanOperatorInfo(pDataReader, pHandle,
tableIdList, pTableScanNode, pTaskInfo, &twSup, pTableScanNode->tsColId);
tableIdList, pTableScanNode, pTaskInfo, &twSup);
taosArrayDestroy(tableIdList);
return pOperator;
......@@ -4627,7 +4631,19 @@ SOperatorInfo* createOperatorTree(SPhysiNode* pPhyNode, SExecTaskInfo* pTaskInfo
STimeWindowAggSupp as = {.waterMark = pIntervalPhyNode->window.watermark,
.calTrigger = pIntervalPhyNode->window.triggerType,
.maxTs = INT64_MIN};
.maxTs = INT64_MIN,
.winMap = NULL,};
if (isSmaStream(pIntervalPhyNode->window.triggerType)) {
if (FLT_LESS(pIntervalPhyNode->window.filesFactor, 1.000000)) {
as.calTrigger = STREAM_TRIGGER_AT_ONCE_SMA;
} else {
_hash_fn_t hashFn = taosGetDefaultHashFunction(TSDB_DATA_TYPE_TIMESTAMP);
as.winMap = taosHashInit(64, hashFn, true, HASH_NO_LOCK);
as.waterMark = getSmaWaterMark(interval.interval,
pIntervalPhyNode->window.filesFactor);
as.calTrigger = STREAM_TRIGGER_WINDOW_CLOSE_SMA;
}
}
int32_t tsSlotId = ((SColumnNode*)pIntervalPhyNode->window.pTspk)->slotId;
pOptr = createIntervalOperatorInfo(ops[0], pExprInfo, num, pResBlock, &interval, tsSlotId, &as, pTaskInfo);
......@@ -4993,25 +5009,25 @@ _error:
return NULL;
}
int32_t encodeOperator(SOperatorInfo* ops, char** result, int32_t *length){
int32_t encodeOperator(SOperatorInfo* ops, char** result, int32_t* length) {
int32_t code = TDB_CODE_SUCCESS;
char *pCurrent = NULL;
char* pCurrent = NULL;
int32_t currLength = 0;
if(ops->fpSet.encodeResultRow){
if(result == NULL || length == NULL){
if (ops->fpSet.encodeResultRow) {
if (result == NULL || length == NULL) {
return TSDB_CODE_TSC_INVALID_INPUT;
}
code = ops->fpSet.encodeResultRow(ops, &pCurrent, &currLength);
if(code != TDB_CODE_SUCCESS){
if(*result != NULL){
if (code != TDB_CODE_SUCCESS) {
if (*result != NULL) {
taosMemoryFree(*result);
*result = NULL;
}
return code;
}
if(*result == NULL){
if (*result == NULL) {
*result = (char*)taosMemoryCalloc(1, currLength + sizeof(int32_t));
if (*result == NULL) {
taosMemoryFree(pCurrent);
......@@ -5019,9 +5035,9 @@ int32_t encodeOperator(SOperatorInfo* ops, char** result, int32_t *length){
}
memcpy(*result + sizeof(int32_t), pCurrent, currLength);
*(int32_t*)(*result) = currLength + sizeof(int32_t);
}else{
} else {
int32_t sizePre = *(int32_t*)(*result);
char* tmp = (char*)taosMemoryRealloc(*result, sizePre + currLength);
char* tmp = (char*)taosMemoryRealloc(*result, sizePre + currLength);
if (tmp == NULL) {
taosMemoryFree(pCurrent);
taosMemoryFree(*result);
......@@ -5038,31 +5054,33 @@ int32_t encodeOperator(SOperatorInfo* ops, char** result, int32_t *length){
for (int32_t i = 0; i < ops->numOfDownstream; ++i) {
code = encodeOperator(ops->pDownstream[i], result, length);
if(code != TDB_CODE_SUCCESS){
if (code != TDB_CODE_SUCCESS) {
return code;
}
}
return TDB_CODE_SUCCESS;
}
int32_t decodeOperator(SOperatorInfo* ops, char* result, int32_t length){
int32_t decodeOperator(SOperatorInfo* ops, char* result, int32_t length) {
int32_t code = TDB_CODE_SUCCESS;
if(ops->fpSet.decodeResultRow){
if(result == NULL || length <= 0){
if (ops->fpSet.decodeResultRow) {
if (result == NULL) {
return TSDB_CODE_TSC_INVALID_INPUT;
}
char* data = result + 2 * sizeof(int32_t);
int32_t dataLength = *(int32_t*)(result + sizeof(int32_t));
code = ops->fpSet.decodeResultRow(ops, data, dataLength - sizeof(int32_t));
if(code != TDB_CODE_SUCCESS){
ASSERT(length == *(int32_t*)result);
char* data = result + sizeof(int32_t);
code = ops->fpSet.decodeResultRow(ops, data);
if (code != TDB_CODE_SUCCESS) {
return code;
}
int32_t totalLength = *(int32_t*)result;
if(totalLength == dataLength + sizeof(int32_t)) { // the last data
int32_t dataLength = *(int32_t*)data;
if (totalLength == dataLength + sizeof(int32_t)) { // the last data
result = NULL;
length = 0;
}else{
} else {
result += dataLength;
*(int32_t*)(result) = totalLength - dataLength;
length = totalLength - dataLength;
......@@ -5071,7 +5089,7 @@ int32_t decodeOperator(SOperatorInfo* ops, char* result, int32_t length){
for (int32_t i = 0; i < ops->numOfDownstream; ++i) {
code = decodeOperator(ops->pDownstream[i], result, length);
if(code != TDB_CODE_SUCCESS){
if (code != TDB_CODE_SUCCESS) {
return code;
}
}
......@@ -5294,3 +5312,18 @@ int32_t initStreamAggSupporter(SStreamAggSupporter* pSup, const char* pKey) {
}
return createDiskbasedBuf(&pSup->pResultBuf, pageSize, bufSize, pKey, TD_TMP_DIR_PATH);
}
int64_t getSmaWaterMark(int64_t interval, double filesFactor) {
int64_t waterMark = 0;
ASSERT(FLT_GREATEREQUAL(filesFactor,0.000000));
waterMark = -1 * filesFactor;
return waterMark;
}
bool isSmaStream(int8_t triggerType) {
if (triggerType == STREAM_TRIGGER_AT_ONCE ||
triggerType == STREAM_TRIGGER_WINDOW_CLOSE) {
return false;
}
return true;
}
source/libs/executor/src/groupoperator.c
浏览文件 @ 9057abeb
......@@ -318,7 +318,20 @@ static SSDataBlock* hashGroupbyAggregate(SOperatorInfo* pOperator) {
// updateNumOfRowsInResultRows(pInfo->binfo.pCtx, pOperator->numOfExprs, &pInfo->binfo.resultRowInfo,
// pInfo->binfo.rowCellInfoOffset);
// }
#if 0
if(pOperator->fpSet.encodeResultRow){
char *result = NULL;
int32_t length = 0;
pOperator->fpSet.encodeResultRow(pOperator, &result, &length);
SAggSupporter* pSup = &pInfo->aggSup;
taosHashClear(pSup->pResultRowHashTable);
pInfo->binfo.resultRowInfo.size = 0;
pOperator->fpSet.decodeResultRow(pOperator, result);
if(result){
taosMemoryFree(result);
}
}
#endif
blockDataEnsureCapacity(pRes, pOperator->resultInfo.capacity);
initGroupedResultInfo(&pInfo->groupResInfo, pInfo->aggSup.pResultRowHashTable, 0);
......
source/libs/executor/src/scanoperator.c
浏览文件 @ 9057abeb
......@@ -875,7 +875,7 @@ static SSDataBlock* doStreamBlockScan(SOperatorInfo* pOperator) {
if (rows == 0) {
pOperator->status = OP_EXEC_DONE;
} else if (pInfo->pUpdateInfo) {
SSDataBlock* upRes = getUpdateDataBlock(pInfo, true); // TODO(liuyao) get invertible from plan
SSDataBlock* upRes = getUpdateDataBlock(pInfo, true);
if (upRes) {
pInfo->pUpdateRes = upRes;
if (upRes->info.type == STREAM_REPROCESS) {
......@@ -894,7 +894,7 @@ static SSDataBlock* doStreamBlockScan(SOperatorInfo* pOperator) {
SOperatorInfo* createStreamScanOperatorInfo(void* pDataReader, SReadHandle* pHandle,
SArray* pTableIdList, STableScanPhysiNode* pTableScanNode, SExecTaskInfo* pTaskInfo,
STimeWindowAggSupp* pTwSup, int16_t tsColId) {
STimeWindowAggSupp* pTwSup) {
SStreamBlockScanInfo* pInfo = taosMemoryCalloc(1, sizeof(SStreamBlockScanInfo));
SOperatorInfo* pOperator = taosMemoryCalloc(1, sizeof(SOperatorInfo));
if (pInfo == NULL || pOperator == NULL) {
......@@ -939,8 +939,12 @@ SOperatorInfo* createStreamScanOperatorInfo(void* pDataReader, SReadHandle* pHan
goto _error;
}
pInfo->primaryTsIndex = tsColId;
if (pSTInfo->interval.interval > 0) {
if (isSmaStream(pTableScanNode->triggerType)) {
pTwSup->waterMark = getSmaWaterMark(pSTInfo->interval.interval,
pTableScanNode->filesFactor);
}
pInfo->primaryTsIndex = 0; // pTableScanNode->tsColId;
if (pSTInfo->interval.interval > 0 && pDataReader) {
pInfo->pUpdateInfo = updateInfoInitP(&pSTInfo->interval, pTwSup->waterMark);
} else {
pInfo->pUpdateInfo = NULL;
......
source/libs/executor/src/timewindowoperator.c
浏览文件 @ 9057abeb
......@@ -748,10 +748,14 @@ static void hashIntervalAgg(SOperatorInfo* pOperatorInfo, SResultRowInfo* pResul
longjmp(pTaskInfo->env, TSDB_CODE_QRY_OUT_OF_MEMORY);
}
if (pInfo->execModel == OPTR_EXEC_MODEL_STREAM &&
(pInfo->twAggSup.calTrigger == STREAM_TRIGGER_AT_ONCE ||
pInfo->twAggSup.calTrigger == 0) ) {
saveResult(pResult, tableGroupId, pUpdated);
if (pInfo->execModel == OPTR_EXEC_MODEL_STREAM) {
if (pInfo->twAggSup.calTrigger == STREAM_TRIGGER_AT_ONCE ||
pInfo->twAggSup.calTrigger == STREAM_TRIGGER_AT_ONCE_SMA) {
saveResult(pResult, tableGroupId, pUpdated);
}
if (pInfo->twAggSup.winMap) {
taosHashRemove(pInfo->twAggSup.winMap, &win.skey, sizeof(TSKEY));
}
}
int32_t forwardStep = 0;
......@@ -824,10 +828,14 @@ static void hashIntervalAgg(SOperatorInfo* pOperatorInfo, SResultRowInfo* pResul
longjmp(pTaskInfo->env, TSDB_CODE_QRY_OUT_OF_MEMORY);
}
if (pInfo->execModel == OPTR_EXEC_MODEL_STREAM &&
(pInfo->twAggSup.calTrigger == STREAM_TRIGGER_AT_ONCE ||
pInfo->twAggSup.calTrigger == 0) ) {
saveResult(pResult, tableGroupId, pUpdated);
if (pInfo->execModel == OPTR_EXEC_MODEL_STREAM) {
if (pInfo->twAggSup.calTrigger == STREAM_TRIGGER_AT_ONCE ||
pInfo->twAggSup.calTrigger == STREAM_TRIGGER_AT_ONCE_SMA) {
saveResult(pResult, tableGroupId, pUpdated);
}
if (pInfo->twAggSup.winMap) {
taosHashRemove(pInfo->twAggSup.winMap, &win.skey, sizeof(TSKEY));
}
}
ekey = ascScan? nextWin.ekey:nextWin.skey;
......@@ -880,14 +888,14 @@ static int32_t doOpenIntervalAgg(SOperatorInfo* pOperator) {
hashIntervalAgg(pOperator, &pInfo->binfo.resultRowInfo, pBlock, pBlock->info.groupId, NULL);
#if 0 // test for encode/decode result info
if(pOperator->encodeResultRow){
if(pOperator->fpSet.encodeResultRow){
char *result = NULL;
int32_t length = 0;
SAggSupporter *pSup = &pInfo->aggSup;
pOperator->encodeResultRow(pOperator, pSup, &pInfo->binfo, &result, &length);
pOperator->fpSet.encodeResultRow(pOperator, &result, &length);
taosHashClear(pSup->pResultRowHashTable);
pInfo->binfo.resultRowInfo.size = 0;
pOperator->decodeResultRow(pOperator, pSup, &pInfo->binfo, result, length);
pOperator->fpSet.decodeResultRow(pOperator, result);
if(result){
taosMemoryFree(result);
}
......@@ -1172,15 +1180,23 @@ static int32_t closeIntervalWindow(SHashObj *pHashMap, STimeWindowAggSupp *pSup,
void* key = taosHashGetKey(pIte, &keyLen);
uint64_t groupId = *(uint64_t*) key;
ASSERT(keyLen == GET_RES_WINDOW_KEY_LEN(sizeof(TSKEY)));
TSKEY ts = *(uint64_t*) ((char*)key + sizeof(uint64_t));
TSKEY ts = *(int64_t*) ((char*)key + sizeof(uint64_t));
SResultRowInfo dumyInfo;
dumyInfo.cur.pageId = -1;
STimeWindow win = getActiveTimeWindow(NULL, &dumyInfo, ts, pInterval,
pInterval->precision, NULL);
if (win.ekey < pSup->maxTs - pSup->waterMark) {
if (pSup->calTrigger == STREAM_TRIGGER_WINDOW_CLOSE_SMA) {
if (taosHashGet(pSup->winMap, &win.skey, sizeof(TSKEY))) {
continue;
}
}
char keyBuf[GET_RES_WINDOW_KEY_LEN(sizeof(TSKEY))];
SET_RES_WINDOW_KEY(keyBuf, &ts, sizeof(TSKEY), groupId);
taosHashRemove(pHashMap, keyBuf, keyLen);
if (pSup->calTrigger != STREAM_TRIGGER_AT_ONCE_SMA &&
pSup->calTrigger != STREAM_TRIGGER_WINDOW_CLOSE_SMA) {
taosHashRemove(pHashMap, keyBuf, keyLen);
}
SResKeyPos* pos = taosMemoryMalloc(sizeof(SResKeyPos) + sizeof(uint64_t));
if (pos == NULL) {
return TSDB_CODE_OUT_OF_MEMORY;
......@@ -1192,6 +1208,7 @@ static int32_t closeIntervalWindow(SHashObj *pHashMap, STimeWindowAggSupp *pSup,
taosMemoryFree(pos);
return TSDB_CODE_OUT_OF_MEMORY;
}
taosHashPut(pSup->winMap, &win.skey, sizeof(TSKEY), NULL, 0);
}
}
return TSDB_CODE_SUCCESS;
......@@ -1248,7 +1265,8 @@ static SSDataBlock* doStreamIntervalAgg(SOperatorInfo* pOperator) {
&pInfo->interval, pClosed);
finalizeUpdatedResult(pOperator->numOfExprs, pInfo->aggSup.pResultBuf, pClosed,
pInfo->binfo.rowCellInfoOffset);
if (pInfo->twAggSup.calTrigger == STREAM_TRIGGER__WINDOW_CLOSE) {
if (pInfo->twAggSup.calTrigger == STREAM_TRIGGER_WINDOW_CLOSE ||
pInfo->twAggSup.calTrigger == STREAM_TRIGGER_WINDOW_CLOSE_SMA) {
taosArrayAddAll(pUpdated, pClosed);
}
taosArrayDestroy(pClosed);
......@@ -2412,7 +2430,7 @@ int32_t closeSessionWindow(SArray *pWins, STimeWindowAggSupp *pTwSup, SArray *pC
return TSDB_CODE_OUT_OF_MEMORY;
}
pSeWin->isClosed = true;
if (calTrigger == STREAM_TRIGGER__WINDOW_CLOSE) {
if (calTrigger == STREAM_TRIGGER_WINDOW_CLOSE) {
pSeWin->isOutput = true;
}
}
......@@ -2486,7 +2504,7 @@ static SSDataBlock* doStreamSessionWindowAgg(SOperatorInfo* pOperator) {
SArray* pUpdated = taosArrayInit(16, POINTER_BYTES);
copyUpdateResult(pStUpdated, pUpdated, pBInfo->pRes->info.groupId);
taosHashCleanup(pStUpdated);
if (pInfo->twAggSup.calTrigger == STREAM_TRIGGER__WINDOW_CLOSE) {
if (pInfo->twAggSup.calTrigger == STREAM_TRIGGER_WINDOW_CLOSE) {
taosArrayAddAll(pUpdated, pClosed);
}
......
source/libs/function/src/builtins.c
浏览文件 @ 9057abeb
......@@ -445,6 +445,11 @@ static int32_t translateStateCount(SFunctionNode* pFunc, char* pErrBuf, int32_t
}
// param0
SNode* pParaNode0 = nodesListGetNode(pFunc->pParameterList, 0);
if (QUERY_NODE_COLUMN != nodeType(pParaNode0)) {
return buildFuncErrMsg(pErrBuf, len, TSDB_CODE_FUNC_FUNTION_ERROR,
"The input parameter of STATECOUNT function can only be column");
}
uint8_t colType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type;
if (!IS_NUMERIC_TYPE(colType)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName);
......@@ -480,6 +485,11 @@ static int32_t translateStateDuration(SFunctionNode* pFunc, char* pErrBuf, int32
}
// param0
SNode* pParaNode0 = nodesListGetNode(pFunc->pParameterList, 0);
if (QUERY_NODE_COLUMN != nodeType(pParaNode0)) {
return buildFuncErrMsg(pErrBuf, len, TSDB_CODE_FUNC_FUNTION_ERROR,
"The input parameter of STATEDURATION function can only be column");
}
uint8_t colType = ((SExprNode*)nodesListGetNode(pFunc->pParameterList, 0))->resType.type;
if (!IS_NUMERIC_TYPE(colType)) {
return invaildFuncParaTypeErrMsg(pErrBuf, len, pFunc->functionName);
......@@ -693,7 +703,7 @@ static int32_t translateFirstLast(SFunctionNode* pFunc, char* pErrBuf, int32_t l
static int32_t translateUnique(SFunctionNode* pFunc, char* pErrBuf, int32_t len) {
if (1 != LIST_LENGTH(pFunc->pParameterList)) {
return TSDB_CODE_SUCCESS;
return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName);
}
SNode* pPara = nodesListGetNode(pFunc->pParameterList, 0);
......@@ -1181,7 +1191,7 @@ const SBuiltinFuncDefinition funcMgtBuiltins[] = {
.finalizeFunc = functionFinalize
},
{
.name = "state_count",
.name = "statecount",
.type = FUNCTION_TYPE_STATE_COUNT,
.classification = FUNC_MGT_INDEFINITE_ROWS_FUNC,
.translateFunc = translateStateCount,
......@@ -1191,7 +1201,7 @@ const SBuiltinFuncDefinition funcMgtBuiltins[] = {
.finalizeFunc = NULL
},
{
.name = "state_duration",
.name = "stateduration",
.type = FUNCTION_TYPE_STATE_DURATION,
.classification = FUNC_MGT_INDEFINITE_ROWS_FUNC | FUNC_MGT_TIMELINE_FUNC,
.translateFunc = translateStateDuration,
......
source/libs/function/src/builtinsimpl.c
浏览文件 @ 9057abeb
......@@ -3776,6 +3776,7 @@ static void tailAssignResult(STailItem* pItem, char *data, int32_t colBytes, TSK
if (isNull) {
pItem->isNull = true;
} else {
pItem->isNull = false;
memcpy(pItem->data, data, colBytes);
}
}
......
source/libs/nodes/src/nodesCloneFuncs.c
浏览文件 @ 9057abeb
......@@ -305,6 +305,7 @@ static SNode* logicNodeCopy(const SLogicNode* pSrc, SLogicNode* pDst) {
CLONE_NODE_FIELD(pConditions);
CLONE_NODE_LIST_FIELD(pChildren);
COPY_SCALAR_FIELD(optimizedFlag);
COPY_SCALAR_FIELD(precision);
return (SNode*)pDst;
}
......@@ -328,6 +329,10 @@ static SNode* logicScanCopy(const SScanLogicNode* pSrc, SScanLogicNode* pDst) {
COPY_SCALAR_FIELD(intervalUnit);
COPY_SCALAR_FIELD(slidingUnit);
CLONE_NODE_FIELD(pTagCond);
COPY_SCALAR_FIELD(triggerType);
COPY_SCALAR_FIELD(watermark);
COPY_SCALAR_FIELD(tsColId);
COPY_SCALAR_FIELD(filesFactor);
return (SNode*)pDst;
}
......@@ -384,6 +389,7 @@ static SNode* logicWindowCopy(const SWindowLogicNode* pSrc, SWindowLogicNode* pD
CLONE_NODE_FIELD(pStateExpr);
COPY_SCALAR_FIELD(triggerType);
COPY_SCALAR_FIELD(watermark);
COPY_SCALAR_FIELD(filesFactor);
return (SNode*)pDst;
}
......
source/libs/nodes/src/nodesCodeFuncs.c
浏览文件 @ 9057abeb
......@@ -1133,6 +1133,7 @@ static const char* jkTableScanPhysiPlanSlidingUnit = "slidingUnit";
static const char* jkTableScanPhysiPlanTriggerType = "triggerType";
static const char* jkTableScanPhysiPlanWatermark = "watermark";
static const char* jkTableScanPhysiPlanTsColId = "tsColId";
static const char* jkTableScanPhysiPlanFilesFactor = "FilesFactor";
static int32_t physiTableScanNodeToJson(const void* pObj, SJson* pJson) {
const STableScanPhysiNode* pNode = (const STableScanPhysiNode*)pObj;
......@@ -1183,6 +1184,9 @@ static int32_t physiTableScanNodeToJson(const void* pObj, SJson* pJson) {
if (TSDB_CODE_SUCCESS == code) {
code = tjsonAddIntegerToObject(pJson, jkTableScanPhysiPlanTsColId, pNode->tsColId);
}
if (TSDB_CODE_SUCCESS == code) {
code = tjsonAddDoubleToObject(pJson, jkTableScanPhysiPlanFilesFactor, pNode->filesFactor);
}
return code;
}
......@@ -1242,7 +1246,9 @@ static int32_t jsonToPhysiTableScanNode(const SJson* pJson, void* pObj) {
if (TSDB_CODE_SUCCESS == code) {
tjsonGetNumberValue(pJson, jkTableScanPhysiPlanTsColId, pNode->tsColId, code);
}
if (TSDB_CODE_SUCCESS == code) {
code = tjsonGetDoubleValue(pJson, jkTableScanPhysiPlanFilesFactor, &pNode->filesFactor);
}
return code;
}
......@@ -1496,6 +1502,7 @@ static const char* jkWindowPhysiPlanFuncs = "Funcs";
static const char* jkWindowPhysiPlanTsPk = "TsPk";
static const char* jkWindowPhysiPlanTriggerType = "TriggerType";
static const char* jkWindowPhysiPlanWatermark = "Watermark";
static const char* jkWindowPhysiPlanFilesFactor = "FilesFactor";
static int32_t physiWindowNodeToJson(const void* pObj, SJson* pJson) {
const SWinodwPhysiNode* pNode = (const SWinodwPhysiNode*)pObj;
......@@ -1516,6 +1523,9 @@ static int32_t physiWindowNodeToJson(const void* pObj, SJson* pJson) {
if (TSDB_CODE_SUCCESS == code) {
code = tjsonAddIntegerToObject(pJson, jkWindowPhysiPlanWatermark, pNode->watermark);
}
if (TSDB_CODE_SUCCESS == code) {
code = tjsonAddDoubleToObject(pJson, jkWindowPhysiPlanFilesFactor, pNode->filesFactor);
}
return code;
}
......@@ -1541,6 +1551,9 @@ static int32_t jsonToPhysiWindowNode(const SJson* pJson, void* pObj) {
tjsonGetNumberValue(pJson, jkWindowPhysiPlanWatermark, pNode->watermark, code);
;
}
if (TSDB_CODE_SUCCESS == code) {
code = tjsonGetDoubleValue(pJson, jkWindowPhysiPlanFilesFactor, &pNode->filesFactor);
}
return code;
}
......
source/libs/parser/inc/parAst.h
浏览文件 @ 9057abeb
......@@ -59,7 +59,6 @@ typedef enum EDatabaseOptionType {
typedef enum ETableOptionType {
TABLE_OPTION_COMMENT = 1,
TABLE_OPTION_DELAY,
TABLE_OPTION_FILE_FACTOR,
TABLE_OPTION_ROLLUP,
TABLE_OPTION_TTL,
......
source/libs/parser/inc/parInt.h
浏览文件 @ 9057abeb
......@@ -24,6 +24,7 @@ extern "C" {
#include "parUtil.h"
#include "parser.h"
int32_t parseInsertSyntax(SParseContext* pContext, SQuery** pQuery);
int32_t parseInsertSql(SParseContext* pContext, SQuery** pQuery);
int32_t parse(SParseContext* pParseCxt, SQuery** pQuery);
int32_t collectMetaKey(SParseContext* pParseCxt, SQuery* pQuery);
......
source/libs/parser/inc/parUtil.h
浏览文件 @ 9057abeb
......@@ -65,12 +65,15 @@ int32_t trimString(const char* src, int32_t len, char* dst, int32_t dlen);
int32_t buildCatalogReq(const SParseMetaCache* pMetaCache, SCatalogReq* pCatalogReq);
int32_t putMetaDataToCache(const SCatalogReq* pCatalogReq, const SMetaData* pMetaData, SParseMetaCache* pMetaCache);
int32_t reserveTableMetaInCache(int32_t acctId, const char* pDb, const char* pTable, SParseMetaCache* pMetaCache);
int32_t reserveTableMetaInCacheExt(const SName* pName, SParseMetaCache* pMetaCache);
int32_t reserveDbVgInfoInCache(int32_t acctId, const char* pDb, SParseMetaCache* pMetaCache);
int32_t reserveTableVgroupInCache(int32_t acctId, const char* pDb, const char* pTable, SParseMetaCache* pMetaCache);
int32_t reserveTableVgroupInCacheExt(const SName* pName, SParseMetaCache* pMetaCache);
int32_t reserveDbVgVersionInCache(int32_t acctId, const char* pDb, SParseMetaCache* pMetaCache);
int32_t reserveDbCfgInCache(int32_t acctId, const char* pDb, SParseMetaCache* pMetaCache);
int32_t reserveUserAuthInCache(int32_t acctId, const char* pUser, const char* pDb, AUTH_TYPE type,
SParseMetaCache* pMetaCache);
int32_t reserveUserAuthInCacheExt(const char* pUser, const SName* pName, AUTH_TYPE type, SParseMetaCache* pMetaCache);
int32_t reserveUdfInCache(const char* pFunc, SParseMetaCache* pMetaCache);
int32_t getTableMetaFromCache(SParseMetaCache* pMetaCache, const SName* pName, STableMeta** pMeta);
int32_t getDbVgInfoFromCache(SParseMetaCache* pMetaCache, const char* pDbFName, SArray** pVgInfo);
......@@ -78,7 +81,7 @@ int32_t getTableVgroupFromCache(SParseMetaCache* pMetaCache, const SName* pName,
int32_t getDbVgVersionFromCache(SParseMetaCache* pMetaCache, const char* pDbFName, int32_t* pVersion, int64_t* pDbId,
int32_t* pTableNum);
int32_t getDbCfgFromCache(SParseMetaCache* pMetaCache, const char* pDbFName, SDbCfgInfo* pInfo);
int32_t getUserAuthFromCache(SParseMetaCache* pMetaCache, const char* pUser, const char* pDb, AUTH_TYPE type,
int32_t getUserAuthFromCache(SParseMetaCache* pMetaCache, const char* pUser, const char* pDbFName, AUTH_TYPE type,
bool* pPass);
int32_t getUdfInfoFromCache(SParseMetaCache* pMetaCache, const char* pFunc, SFuncInfo* pInfo);
......
source/libs/parser/inc/sql.y
浏览文件 @ 9057abeb
......@@ -313,7 +313,6 @@ tags_def(A) ::= TAGS NK_LP column_def_list(B) NK_RP.
table_options(A) ::= . { A = createDefaultTableOptions(pCxt); }
table_options(A) ::= table_options(B) COMMENT NK_STRING(C). { A = setTableOption(pCxt, B, TABLE_OPTION_COMMENT, &C); }
table_options(A) ::= table_options(B) DELAY NK_INTEGER(C). { A = setTableOption(pCxt, B, TABLE_OPTION_DELAY, &C); }
table_options(A) ::= table_options(B) FILE_FACTOR NK_FLOAT(C). { A = setTableOption(pCxt, B, TABLE_OPTION_FILE_FACTOR, &C); }
table_options(A) ::= table_options(B) ROLLUP NK_LP func_name_list(C) NK_RP. { A = setTableOption(pCxt, B, TABLE_OPTION_ROLLUP, C); }
table_options(A) ::= table_options(B) TTL NK_INTEGER(C). { A = setTableOption(pCxt, B, TABLE_OPTION_TTL, &C); }
......@@ -408,7 +407,7 @@ cmd ::= CREATE TOPIC not_exists_opt(A)
cmd ::= CREATE TOPIC not_exists_opt(A)
topic_name(B) topic_options(D) AS db_name(C). { pCxt->pRootNode = createCreateTopicStmt(pCxt, A, &B, NULL, &C, D); }
cmd ::= DROP TOPIC exists_opt(A) topic_name(B). { pCxt->pRootNode = createDropTopicStmt(pCxt, A, &B); }
cmd ::= DROP CGROUP exists_opt(A) cgroup_name(B) ON topic_name(C). { pCxt->pRootNode = createDropCGroupStmt(pCxt, A, &B, &C); }
cmd ::= DROP CONSUMER GROUP exists_opt(A) cgroup_name(B) ON topic_name(C). { pCxt->pRootNode = createDropCGroupStmt(pCxt, A, &B, &C); }
topic_options(A) ::= . { A = createTopicOptions(pCxt); }
topic_options(A) ::= topic_options(B) WITH TABLE. { ((STopicOptions*)B)->withTable = true; A = B; }
......
source/libs/parser/src/parAstCreater.c
浏览文件 @ 9057abeb
......@@ -857,7 +857,6 @@ SNode* createDefaultTableOptions(SAstCreateContext* pCxt) {
CHECK_PARSER_STATUS(pCxt);
STableOptions* pOptions = nodesMakeNode(QUERY_NODE_TABLE_OPTIONS);
CHECK_OUT_OF_MEM(pOptions);
pOptions->delay = TSDB_DEFAULT_ROLLUP_DELAY;
pOptions->filesFactor = TSDB_DEFAULT_ROLLUP_FILE_FACTOR;
pOptions->ttl = TSDB_DEFAULT_TABLE_TTL;
return (SNode*)pOptions;
......@@ -867,7 +866,6 @@ SNode* createAlterTableOptions(SAstCreateContext* pCxt) {
CHECK_PARSER_STATUS(pCxt);
STableOptions* pOptions = nodesMakeNode(QUERY_NODE_TABLE_OPTIONS);
CHECK_OUT_OF_MEM(pOptions);
pOptions->delay = -1;
pOptions->filesFactor = -1;
pOptions->ttl = -1;
return (SNode*)pOptions;
......@@ -882,11 +880,8 @@ SNode* setTableOption(SAstCreateContext* pCxt, SNode* pOptions, ETableOptionType
sizeof(((STableOptions*)pOptions)->comment));
}
break;
case TABLE_OPTION_DELAY:
((STableOptions*)pOptions)->delay = taosStr2Int32(((SToken*)pVal)->z, NULL, 10);
break;
case TABLE_OPTION_FILE_FACTOR:
((STableOptions*)pOptions)->filesFactor = taosStr2Float(((SToken*)pVal)->z, NULL);
((STableOptions*)pOptions)->filesFactor = taosStr2Double(((SToken*)pVal)->z, NULL);
break;
case TABLE_OPTION_ROLLUP:
((STableOptions*)pOptions)->pRollupFuncs = pVal;
......
source/libs/parser/src/parAstParser.c
浏览文件 @ 9057abeb
......@@ -333,68 +333,22 @@ static int32_t collectMetaKeyFromQuery(SCollectMetaKeyCxt* pCxt, SNode* pStmt) {
return collectMetaKeyFromSetOperator(pCxt, (SSetOperator*)pStmt);
case QUERY_NODE_SELECT_STMT:
return collectMetaKeyFromSelect(pCxt, (SSelectStmt*)pStmt);
case QUERY_NODE_VNODE_MODIF_STMT:
case QUERY_NODE_CREATE_DATABASE_STMT:
case QUERY_NODE_DROP_DATABASE_STMT:
case QUERY_NODE_ALTER_DATABASE_STMT:
break;
case QUERY_NODE_CREATE_TABLE_STMT:
return collectMetaKeyFromCreateTable(pCxt, (SCreateTableStmt*)pStmt);
case QUERY_NODE_CREATE_SUBTABLE_CLAUSE:
break;
case QUERY_NODE_CREATE_MULTI_TABLE_STMT:
return collectMetaKeyFromCreateMultiTable(pCxt, (SCreateMultiTableStmt*)pStmt);
case QUERY_NODE_DROP_TABLE_CLAUSE:
case QUERY_NODE_DROP_TABLE_STMT:
case QUERY_NODE_DROP_SUPER_TABLE_STMT:
break;
case QUERY_NODE_ALTER_TABLE_STMT:
return collectMetaKeyFromAlterTable(pCxt, (SAlterTableStmt*)pStmt);
case QUERY_NODE_CREATE_USER_STMT:
case QUERY_NODE_ALTER_USER_STMT:
case QUERY_NODE_DROP_USER_STMT:
break;
case QUERY_NODE_USE_DATABASE_STMT:
return collectMetaKeyFromUseDatabase(pCxt, (SUseDatabaseStmt*)pStmt);
case QUERY_NODE_CREATE_DNODE_STMT:
case QUERY_NODE_DROP_DNODE_STMT:
case QUERY_NODE_ALTER_DNODE_STMT:
break;
case QUERY_NODE_CREATE_INDEX_STMT:
return collectMetaKeyFromCreateIndex(pCxt, (SCreateIndexStmt*)pStmt);
case QUERY_NODE_DROP_INDEX_STMT:
case QUERY_NODE_CREATE_QNODE_STMT:
case QUERY_NODE_DROP_QNODE_STMT:
case QUERY_NODE_CREATE_BNODE_STMT:
case QUERY_NODE_DROP_BNODE_STMT:
case QUERY_NODE_CREATE_SNODE_STMT:
case QUERY_NODE_DROP_SNODE_STMT:
case QUERY_NODE_CREATE_MNODE_STMT:
case QUERY_NODE_DROP_MNODE_STMT:
break;
case QUERY_NODE_CREATE_TOPIC_STMT:
return collectMetaKeyFromCreateTopic(pCxt, (SCreateTopicStmt*)pStmt);
case QUERY_NODE_DROP_TOPIC_STMT:
case QUERY_NODE_DROP_CGROUP_STMT:
case QUERY_NODE_ALTER_LOCAL_STMT:
break;
case QUERY_NODE_EXPLAIN_STMT:
return collectMetaKeyFromExplain(pCxt, (SExplainStmt*)pStmt);
case QUERY_NODE_DESCRIBE_STMT:
case QUERY_NODE_RESET_QUERY_CACHE_STMT:
case QUERY_NODE_COMPACT_STMT:
case QUERY_NODE_CREATE_FUNCTION_STMT:
case QUERY_NODE_DROP_FUNCTION_STMT:
break;
case QUERY_NODE_CREATE_STREAM_STMT:
return collectMetaKeyFromCreateStream(pCxt, (SCreateStreamStmt*)pStmt);
case QUERY_NODE_DROP_STREAM_STMT:
case QUERY_NODE_MERGE_VGROUP_STMT:
case QUERY_NODE_REDISTRIBUTE_VGROUP_STMT:
case QUERY_NODE_SPLIT_VGROUP_STMT:
case QUERY_NODE_SYNCDB_STMT:
case QUERY_NODE_GRANT_STMT:
case QUERY_NODE_REVOKE_STMT:
case QUERY_NODE_SHOW_DNODES_STMT:
return collectMetaKeyFromShowDnodes(pCxt, (SShowStmt*)pStmt);
case QUERY_NODE_SHOW_MNODES_STMT:
......@@ -407,8 +361,6 @@ static int32_t collectMetaKeyFromQuery(SCollectMetaKeyCxt* pCxt, SNode* pStmt) {
return collectMetaKeyFromShowSnodes(pCxt, (SShowStmt*)pStmt);
case QUERY_NODE_SHOW_BNODES_STMT:
return collectMetaKeyFromShowBnodes(pCxt, (SShowStmt*)pStmt);
case QUERY_NODE_SHOW_CLUSTER_STMT:
break;
case QUERY_NODE_SHOW_DATABASES_STMT:
return collectMetaKeyFromShowDatabases(pCxt, (SShowStmt*)pStmt);
case QUERY_NODE_SHOW_FUNCTIONS_STMT:
......@@ -429,25 +381,8 @@ static int32_t collectMetaKeyFromQuery(SCollectMetaKeyCxt* pCxt, SNode* pStmt) {
return collectMetaKeyFromShowVgroups(pCxt, (SShowStmt*)pStmt);
case QUERY_NODE_SHOW_TOPICS_STMT:
return collectMetaKeyFromShowTopics(pCxt, (SShowStmt*)pStmt);
case QUERY_NODE_SHOW_CONSUMERS_STMT:
case QUERY_NODE_SHOW_SUBSCRIBES_STMT:
case QUERY_NODE_SHOW_SMAS_STMT:
case QUERY_NODE_SHOW_CONFIGS_STMT:
case QUERY_NODE_SHOW_CONNECTIONS_STMT:
case QUERY_NODE_SHOW_QUERIES_STMT:
case QUERY_NODE_SHOW_VNODES_STMT:
case QUERY_NODE_SHOW_APPS_STMT:
case QUERY_NODE_SHOW_SCORES_STMT:
case QUERY_NODE_SHOW_VARIABLE_STMT:
case QUERY_NODE_SHOW_CREATE_DATABASE_STMT:
case QUERY_NODE_SHOW_CREATE_TABLE_STMT:
case QUERY_NODE_SHOW_CREATE_STABLE_STMT:
break;
case QUERY_NODE_SHOW_TRANSACTIONS_STMT:
return collectMetaKeyFromShowTransactions(pCxt, (SShowStmt*)pStmt);
case QUERY_NODE_KILL_CONNECTION_STMT:
case QUERY_NODE_KILL_QUERY_STMT:
case QUERY_NODE_KILL_TRANSACTION_STMT:
default:
break;
}
......
source/libs/parser/src/parInsert.c
浏览文件 @ 9057abeb
......@@ -64,6 +64,7 @@ typedef struct SInsertParseContext {
int32_t totalNum;
SVnodeModifOpStmt* pOutput;
SStmtCallback* pStmtCb;
SParseMetaCache* pMetaCache;
} SInsertParseContext;
typedef int32_t (*_row_append_fn_t)(SMsgBuf* pMsgBuf, const void* value, int32_t len, void* param);
......@@ -92,15 +93,15 @@ typedef struct SMemParam {
} \
} while (0)
static int32_t skipInsertInto(SInsertParseContext* pCxt) {
static int32_t skipInsertInto(char** pSql, SMsgBuf* pMsg) {
SToken sToken;
NEXT_TOKEN(pCxt->pSql, sToken);
NEXT_TOKEN(*pSql, sToken);
if (TK_INSERT != sToken.type) {
return buildSyntaxErrMsg(&pCxt->msg, "keyword INSERT is expected", sToken.z);
return buildSyntaxErrMsg(pMsg, "keyword INSERT is expected", sToken.z);
}
NEXT_TOKEN(pCxt->pSql, sToken);
NEXT_TOKEN(*pSql, sToken);
if (TK_INTO != sToken.type) {
return buildSyntaxErrMsg(&pCxt->msg, "keyword INTO is expected", sToken.z);
return buildSyntaxErrMsg(pMsg, "keyword INTO is expected", sToken.z);
}
return TSDB_CODE_SUCCESS;
}
......@@ -212,7 +213,7 @@ static int32_t createSName(SName* pName, SToken* pTableName, int32_t acctId, con
return buildInvalidOperationMsg(pMsgBuf, msg4);
}
char tbname[TSDB_TABLE_FNAME_LEN] = {0};
char tbname[TSDB_TABLE_FNAME_LEN] = {0};
strncpy(tbname, p + 1, tbLen);
/*tbLen = */ strdequote(tbname);
......@@ -250,25 +251,46 @@ static int32_t createSName(SName* pName, SToken* pTableName, int32_t acctId, con
return code;
}
static int32_t getTableMetaImpl(SInsertParseContext* pCxt, SName* name, char* dbFname, bool isStb) {
static int32_t checkAuth(SInsertParseContext* pCxt, char* pDbFname, bool* pPass) {
SParseContext* pBasicCtx = pCxt->pComCxt;
if (NULL != pCxt->pMetaCache) {
return getUserAuthFromCache(pCxt->pMetaCache, pBasicCtx->pUser, pDbFname, AUTH_TYPE_WRITE, pPass);
}
return catalogChkAuth(pBasicCtx->pCatalog, pBasicCtx->pTransporter, &pBasicCtx->mgmtEpSet, pBasicCtx->pUser, pDbFname,
AUTH_TYPE_WRITE, pPass);
}
static int32_t getTableSchema(SInsertParseContext* pCxt, SName* pTbName, bool isStb, STableMeta** pTableMeta) {
SParseContext* pBasicCtx = pCxt->pComCxt;
if (NULL != pCxt->pMetaCache) {
return getTableMetaFromCache(pCxt->pMetaCache, pTbName, pTableMeta);
}
if (isStb) {
return catalogGetSTableMeta(pBasicCtx->pCatalog, pBasicCtx->pTransporter, &pBasicCtx->mgmtEpSet, pTbName,
pTableMeta);
}
return catalogGetTableMeta(pBasicCtx->pCatalog, pBasicCtx->pTransporter, &pBasicCtx->mgmtEpSet, pTbName, pTableMeta);
}
static int32_t getTableVgroup(SInsertParseContext* pCxt, SName* pTbName, SVgroupInfo* pVg) {
SParseContext* pBasicCtx = pCxt->pComCxt;
if (NULL != pCxt->pMetaCache) {
return getTableVgroupFromCache(pCxt->pMetaCache, pTbName, pVg);
}
return catalogGetTableHashVgroup(pBasicCtx->pCatalog, pBasicCtx->pTransporter, &pBasicCtx->mgmtEpSet, pTbName, pVg);
}
static int32_t getTableMetaImpl(SInsertParseContext* pCxt, SName* name, char* dbFname, bool isStb) {
bool pass = false;
CHECK_CODE(catalogChkAuth(pBasicCtx->pCatalog, pBasicCtx->pTransporter, &pBasicCtx->mgmtEpSet, pBasicCtx->pUser,
dbFname, AUTH_TYPE_WRITE, &pass));
CHECK_CODE(checkAuth(pCxt, dbFname, &pass));
if (!pass) {
return TSDB_CODE_PAR_PERMISSION_DENIED;
}
if (isStb) {
CHECK_CODE(catalogGetSTableMeta(pBasicCtx->pCatalog, pBasicCtx->pTransporter, &pBasicCtx->mgmtEpSet, name,
&pCxt->pTableMeta));
} else {
CHECK_CODE(catalogGetTableMeta(pBasicCtx->pCatalog, pBasicCtx->pTransporter, &pBasicCtx->mgmtEpSet, name,
&pCxt->pTableMeta));
ASSERT(pCxt->pTableMeta->tableInfo.rowSize > 0);
CHECK_CODE(getTableSchema(pCxt, name, isStb, &pCxt->pTableMeta));
if (!isStb) {
SVgroupInfo vg;
CHECK_CODE(
catalogGetTableHashVgroup(pBasicCtx->pCatalog, pBasicCtx->pTransporter, &pBasicCtx->mgmtEpSet, name, &vg));
CHECK_CODE(getTableVgroup(pCxt, name, &vg));
CHECK_CODE(taosHashPut(pCxt->pVgroupsHashObj, (const char*)&vg.vgId, sizeof(vg.vgId), (char*)&vg, sizeof(vg)));
}
return TSDB_CODE_SUCCESS;
......@@ -777,7 +799,7 @@ static int32_t KvRowAppend(SMsgBuf* pMsgBuf, const void* value, int32_t len, voi
if (errno == E2BIG) {
return generateSyntaxErrMsg(pMsgBuf, TSDB_CODE_PAR_VALUE_TOO_LONG, pa->schema->name);
}
char buf[512] = {0};
snprintf(buf, tListLen(buf), " taosMbsToUcs4 error:%s", strerror(errno));
return buildSyntaxErrMsg(pMsgBuf, buf, value);
......@@ -857,10 +879,8 @@ static int32_t cloneTableMeta(STableMeta* pSrc, STableMeta** pDst) {
static int32_t storeTableMeta(SInsertParseContext* pCxt, SHashObj* pHash, SName* pTableName, const char* pName,
int32_t len, STableMeta* pMeta) {
SVgroupInfo vg;
SParseContext* pBasicCtx = pCxt->pComCxt;
CHECK_CODE(
catalogGetTableHashVgroup(pBasicCtx->pCatalog, pBasicCtx->pTransporter, &pBasicCtx->mgmtEpSet, pTableName, &vg));
SVgroupInfo vg;
CHECK_CODE(getTableVgroup(pCxt, pTableName, &vg));
CHECK_CODE(taosHashPut(pCxt->pVgroupsHashObj, (const char*)&vg.vgId, sizeof(vg.vgId), (char*)&vg, sizeof(vg)));
pMeta->uid = 0;
......@@ -1082,9 +1102,9 @@ static void destroyInsertParseContext(SInsertParseContext* pCxt) {
// VALUES (field1_value, ...) [(field1_value2, ...) ...] | FILE csv_file_path
// [...];
static int32_t parseInsertBody(SInsertParseContext* pCxt) {
int32_t tbNum = 0;
char tbFName[TSDB_TABLE_FNAME_LEN];
bool autoCreateTbl = false;
int32_t tbNum = 0;
char tbFName[TSDB_TABLE_FNAME_LEN];
bool autoCreateTbl = false;
// for each table
while (1) {
......@@ -1186,8 +1206,8 @@ static int32_t parseInsertBody(SInsertParseContext* pCxt) {
return TSDB_CODE_TSC_OUT_OF_MEMORY;
}
memcpy(tags, &pCxt->tags, sizeof(pCxt->tags));
(*pCxt->pStmtCb->setInfoFn)(pCxt->pStmtCb->pStmt, pCxt->pTableMeta, tags, tbFName, autoCreateTbl, pCxt->pVgroupsHashObj,
pCxt->pTableBlockHashObj);
(*pCxt->pStmtCb->setInfoFn)(pCxt->pStmtCb->pStmt, pCxt->pTableMeta, tags, tbFName, autoCreateTbl,
pCxt->pVgroupsHashObj, pCxt->pTableBlockHashObj);
memset(&pCxt->tags, 0, sizeof(pCxt->tags));
pCxt->pVgroupsHashObj = NULL;
......@@ -1245,12 +1265,11 @@ int32_t parseInsertSql(SParseContext* pContext, SQuery** pQuery) {
if (NULL == *pQuery) {
return TSDB_CODE_OUT_OF_MEMORY;
}
(*pQuery)->execMode = QUERY_EXEC_MODE_SCHEDULE;
(*pQuery)->haveResultSet = false;
(*pQuery)->msgType = TDMT_VND_SUBMIT;
(*pQuery)->pRoot = (SNode*)context.pOutput;
}
(*pQuery)->execMode = QUERY_EXEC_MODE_SCHEDULE;
(*pQuery)->haveResultSet = false;
(*pQuery)->msgType = TDMT_VND_SUBMIT;
(*pQuery)->pRoot = (SNode*)context.pOutput;
if (NULL == (*pQuery)->pTableList) {
(*pQuery)->pTableList = taosArrayInit(taosHashGetSize(context.pTableNameHashObj), sizeof(SName));
......@@ -1261,7 +1280,7 @@ int32_t parseInsertSql(SParseContext* pContext, SQuery** pQuery) {
context.pOutput->payloadType = PAYLOAD_TYPE_KV;
int32_t code = skipInsertInto(&context);
int32_t code = skipInsertInto(&context.pSql, &context.msg);
if (TSDB_CODE_SUCCESS == code) {
code = parseInsertBody(&context);
}
......@@ -1276,6 +1295,171 @@ int32_t parseInsertSql(SParseContext* pContext, SQuery** pQuery) {
return code;
}
typedef struct SInsertParseSyntaxCxt {
SParseContext* pComCxt;
char* pSql;
SMsgBuf msg;
SParseMetaCache* pMetaCache;
} SInsertParseSyntaxCxt;
static int32_t skipParentheses(SInsertParseSyntaxCxt* pCxt) {
SToken sToken;
while (1) {
NEXT_TOKEN(pCxt->pSql, sToken);
if (TK_NK_RP == sToken.type) {
break;
}
if (0 == sToken.n) {
return buildSyntaxErrMsg(&pCxt->msg, ") expected", NULL);
}
}
return TSDB_CODE_SUCCESS;
}
static int32_t skipBoundColumns(SInsertParseSyntaxCxt* pCxt) { return skipParentheses(pCxt); }
// pSql -> (field1_value, ...) [(field1_value2, ...) ...]
static int32_t skipValuesClause(SInsertParseSyntaxCxt* pCxt) {
int32_t numOfRows = 0;
SToken sToken;
while (1) {
int32_t index = 0;
NEXT_TOKEN_KEEP_SQL(pCxt->pSql, sToken, index);
if (TK_NK_LP != sToken.type) {
break;
}
pCxt->pSql += index;
CHECK_CODE(skipParentheses(pCxt));
++numOfRows;
}
if (0 == numOfRows) {
return buildSyntaxErrMsg(&pCxt->msg, "no any data points", NULL);
}
return TSDB_CODE_SUCCESS;
}
static int32_t skipTagsClause(SInsertParseSyntaxCxt* pCxt) { return skipParentheses(pCxt); }
// pSql -> [(tag1_name, ...)] TAGS (tag1_value, ...)
static int32_t skipUsingClause(SInsertParseSyntaxCxt* pCxt) {
SToken sToken;
NEXT_TOKEN(pCxt->pSql, sToken);
if (TK_NK_LP == sToken.type) {
CHECK_CODE(skipBoundColumns(pCxt));
NEXT_TOKEN(pCxt->pSql, sToken);
}
if (TK_TAGS != sToken.type) {
return buildSyntaxErrMsg(&pCxt->msg, "TAGS is expected", sToken.z);
}
// pSql -> (tag1_value, ...)
NEXT_TOKEN(pCxt->pSql, sToken);
if (TK_NK_LP != sToken.type) {
return buildSyntaxErrMsg(&pCxt->msg, "( is expected", sToken.z);
}
CHECK_CODE(skipTagsClause(pCxt));
return TSDB_CODE_SUCCESS;
}
static int32_t collectTableMetaKey(SInsertParseSyntaxCxt* pCxt, SToken* pTbToken) {
SName name;
CHECK_CODE(createSName(&name, pTbToken, pCxt->pComCxt->acctId, pCxt->pComCxt->db, &pCxt->msg));
CHECK_CODE(reserveUserAuthInCacheExt(pCxt->pComCxt->pUser, &name, AUTH_TYPE_WRITE, pCxt->pMetaCache));
CHECK_CODE(reserveTableMetaInCacheExt(&name, pCxt->pMetaCache));
CHECK_CODE(reserveTableVgroupInCacheExt(&name, pCxt->pMetaCache));
return TSDB_CODE_SUCCESS;
}
static int32_t parseInsertBodySyntax(SInsertParseSyntaxCxt* pCxt) {
bool hasData = false;
// for each table
while (1) {
SToken sToken;
// pSql -> tb_name ...
NEXT_TOKEN(pCxt->pSql, sToken);
// no data in the sql string anymore.
if (sToken.n == 0) {
if (sToken.type && pCxt->pSql[0]) {
return buildSyntaxErrMsg(&pCxt->msg, "invalid charactor in SQL", sToken.z);
}
if (!hasData) {
return buildInvalidOperationMsg(&pCxt->msg, "no data in sql");
}
break;
}
hasData = false;
SToken tbnameToken = sToken;
NEXT_TOKEN(pCxt->pSql, sToken);
// USING clause
if (TK_USING == sToken.type) {
NEXT_TOKEN(pCxt->pSql, sToken);
CHECK_CODE(collectTableMetaKey(pCxt, &sToken));
CHECK_CODE(skipUsingClause(pCxt));
NEXT_TOKEN(pCxt->pSql, sToken);
} else {
CHECK_CODE(collectTableMetaKey(pCxt, &tbnameToken));
}
if (TK_NK_LP == sToken.type) {
// pSql -> field1_name, ...)
CHECK_CODE(skipBoundColumns(pCxt));
NEXT_TOKEN(pCxt->pSql, sToken);
}
if (TK_VALUES == sToken.type) {
// pSql -> (field1_value, ...) [(field1_value2, ...) ...]
CHECK_CODE(skipValuesClause(pCxt));
hasData = true;
continue;
}
// FILE csv_file_path
if (TK_FILE == sToken.type) {
// pSql -> csv_file_path
NEXT_TOKEN(pCxt->pSql, sToken);
if (0 == sToken.n || (TK_NK_STRING != sToken.type && TK_NK_ID != sToken.type)) {
return buildSyntaxErrMsg(&pCxt->msg, "file path is required following keyword FILE", sToken.z);
}
hasData = true;
continue;
}
return buildSyntaxErrMsg(&pCxt->msg, "keyword VALUES or FILE is expected", sToken.z);
}
return TSDB_CODE_SUCCESS;
}
int32_t parseInsertSyntax(SParseContext* pContext, SQuery** pQuery) {
SInsertParseSyntaxCxt context = {.pComCxt = pContext,
.pSql = (char*)pContext->pSql,
.msg = {.buf = pContext->pMsg, .len = pContext->msgLen},
.pMetaCache = taosMemoryCalloc(1, sizeof(SParseMetaCache))};
if (NULL == context.pMetaCache) {
return TSDB_CODE_OUT_OF_MEMORY;
}
int32_t code = skipInsertInto(&context.pSql, &context.msg);
if (TSDB_CODE_SUCCESS == code) {
code = parseInsertBodySyntax(&context);
}
if (TSDB_CODE_SUCCESS == code) {
*pQuery = taosMemoryCalloc(1, sizeof(SQuery));
if (NULL == *pQuery) {
return TSDB_CODE_OUT_OF_MEMORY;
}
TSWAP((*pQuery)->pMetaCache, context.pMetaCache);
}
return code;
}
int32_t qCreateSName(SName* pName, const char* pTableName, int32_t acctId, char* dbName, char* msgBuf,
int32_t msgBufLen) {
SMsgBuf msg = {.buf = msgBuf, .len = msgBufLen};
......
source/libs/parser/src/parTokenizer.c
浏览文件 @ 9057abeb
......@@ -53,7 +53,6 @@ static SKeyword keywordTable[] = {
{"CACHE", TK_CACHE},
{"CACHELAST", TK_CACHELAST},
{"CAST", TK_CAST},
{"CGROUP", TK_CGROUP},
{"CLUSTER", TK_CLUSTER},
{"COLUMN", TK_COLUMN},
{"COMMENT", TK_COMMENT},
......@@ -62,13 +61,13 @@ static SKeyword keywordTable[] = {
{"CONNS", TK_CONNS},
{"CONNECTION", TK_CONNECTION},
{"CONNECTIONS", TK_CONNECTIONS},
{"CONSUMER", TK_CONSUMER},
{"COUNT", TK_COUNT},
{"CREATE", TK_CREATE},
{"DATABASE", TK_DATABASE},
{"DATABASES", TK_DATABASES},
{"DAYS", TK_DAYS},
{"DBS", TK_DBS},
{"DELAY", TK_DELAY},
{"DESC", TK_DESC},
{"DESCRIBE", TK_DESCRIBE},
{"DISTINCT", TK_DISTINCT},
......
source/libs/parser/src/parUtil.c
浏览文件 @ 9057abeb
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source/libs/parser/src/parser.c
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source/libs/parser/src/sql.c
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tests/pytest/util/common.py
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tests/pytest/util/dnodes.py
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tests/script/jenkins/basic.txt
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tests/script/tsim/stable/disk.sim
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tests/system-test/1-insert/performanceQuery.json 0 → 100644
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tests/system-test/1-insert/test_stmt_set_tbname_tag.py 0 → 100644
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tests/system-test/2-query/apercentile.py 0 → 100644
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tests/system-test/2-query/avg.py 0 → 100644
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tests/system-test/2-query/bottom.py
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tests/system-test/2-query/elapsed.py 0 → 100644
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tests/system-test/2-query/function_diff.py 0 → 100644
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tests/system-test/2-query/mavg.py 0 → 100644
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tests/system-test/2-query/nestedQuery_str.py 0 → 100755
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tests/system-test/2-query/sample.py 0 → 100644
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tests/system-test/2-query/stateduration.py 0 → 100644
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tests/system-test/2-query/top.py
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tests/system-test/2-query/unique.py 0 → 100644
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tests/system-test/fulltest.sh 100755 → 100644
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tests/system-test/insert.json 已删除 100644 → 0
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tests/system-test/test.py
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tests/test/c/CMakeLists.txt
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tests/test/c/sdbDump.c
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此差异已折叠。
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