Merge pull request #30172 from taosdata/merge/mainto3.0
merge: main to 3.0 branch
This commit is contained in:
Simon Guan
GitHub
commit
b3e38dbc96
12
README.md
12
README.md
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@ -29,9 +29,9 @@ English | [简体中文](README-CN.md) | [TDengine Cloud](https://cloud.tdengine
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1. [Introduction](#1-introduction)
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1. [Documentation](#2-documentation)
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1. [Prerequisites](#3-prerequisites)
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- [3.1 Prerequisites On Linux](#31-on-linux)
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- [3.2 Prerequisites On macOS](#32-on-macos)
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- [3.3 Prerequisites On Windows](#33-on-windows)
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- [3.1 Prerequisites On Linux](#31-prerequisites-on-linux)
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- [3.2 Prerequisites On macOS](#32-prerequisites-on-macos)
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- [3.3 Prerequisites On Windows](#33-prerequisites-on-windows)
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- [3.4 Clone the repo](#34-clone-the-repo)
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1. [Building](#4-building)
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- [4.1 Build on Linux](#41-build-on-linux)
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@ -86,7 +86,7 @@ At the moment, TDengine server supports running on Linux/Windows/MacOS systems.
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If you want to compile taosAdapter or taosKeeper, you need to install Go 1.18 or above.
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## 3.1 On Linux
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## 3.1 Prerequisites on Linux
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<details>
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@ -111,7 +111,7 @@ yum install -y zlib-static xz-devel snappy-devel jansson-devel pkgconfig libatom
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</details>
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## 3.2 On macOS
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## 3.2 Prerequisites on macOS
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<details>
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@ -125,7 +125,7 @@ brew install argp-standalone gflags pkgconfig
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</details>
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## 3.3 On Windows
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## 3.3 Prerequisites on Windows
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<details>
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@ -107,21 +107,23 @@ The header is the first line of the CSV file, with the following rules:
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(1) The header of the CSV can configure the following columns:
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| Number | Column Name | Description | Required | Default Behavior |
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| ------ | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| 1 | point_id | The id of the data point on the OPC UA server | Yes | None |
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| 2 | stable | The corresponding supertable for the data point in TDengine | Yes | None |
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| 3 | tbname | The corresponding subtable for the data point in TDengine | Yes | None |
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| 4 | enable | Whether to collect data from this point | No | Use the unified default value `1` for enable |
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| 5 | value_col | The column name in TDengine corresponding to the collected value of the data point | No | Use the unified default value `val` as the value_col |
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| 6 | value_transform | The transformation function executed in taosX for the collected value of the data point | No | Do not transform the collected value uniformly |
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| 7 | type | The data type of the collected value of the data point | No | Use the original type of the collected value as the data type in TDengine |
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| 8 | quality_col | The column name in TDengine corresponding to the quality of the collected value | No | Do not add a quality column in TDengine uniformly |
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| 9 | ts_col | The original timestamp column of the data point in TDengine | No | If both ts_col and received_ts_col are non-empty, use the former as the timestamp column; if one of ts_col or received_ts_col is non-empty, use the non-empty column as the timestamp column; if both are empty, use the original timestamp of the data point as the timestamp column with the default name `ts`. |
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| 10 | received_ts_col | The timestamp column in TDengine when the data point value is received | No | Same as above |
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| 11 | ts_transform | The transformation function executed in taosX for the original timestamp of the data point | No | Do not transform the original timestamp of the data point uniformly |
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| 12 | received_ts_transform | The transformation function executed in taosX for the received timestamp of the data point | No | Do not transform the received timestamp of the data point uniformly |
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| 13 | tag::VARCHAR(200)::name | The Tag column corresponding to the data point in TDengine. Here `tag` is a reserved keyword indicating that this column is a tag; `VARCHAR(200)` indicates the type of tag; `name` is the actual name of the tag. | No | If 1 or more tag columns are configured, use the configured tag columns; if no tag columns are configured and stable exists in TDengine, use the tags of the stable in TDengine; if no tag columns are configured and stable does not exist in TDengine, automatically add the following 2 tag columns: tag::VARCHAR(256)::point_id and tag::VARCHAR(256)::point_name |
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| Number | Column Name | Description | Required | Default Behavior |
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|--------|-------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -------- |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| 1 | point_id | The id of the data point on the OPC UA server | Yes | None |
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| 2 | stable | The corresponding supertable for the data point in TDengine | Yes | None |
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| 3 | tbname | The corresponding subtable for the data point in TDengine | Yes | None |
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| 4 | enable | Whether to collect data from this point | No | Use the unified default value `1` for enable |
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| 5 | value_col | The column name in TDengine corresponding to the collected value of the data point | No | Use the unified default value `val` as the value_col |
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| 6 | value_transform | The transformation function executed in taosX for the collected value of the data point | No | Do not transform the collected value uniformly |
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| 7 | type | The data type of the collected value of the data point | No | Use the original type of the collected value as the data type in TDengine |
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| 8 | quality_col | The column name in TDengine corresponding to the quality of the collected value | No | Do not add a quality column in TDengine uniformly |
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| 9 | ts_col | The original timestamp column of the data point in TDengine | No | ts_col, request_ts, received_ts these 3 columns, when there are more than 2 columns, the leftmost column is used as the primary key in TDengine. |
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| 10 | request_ts_col | The timestamp column in TDengine when the data point value is request | No | Same as above |
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| 11 | received_ts_col | The timestamp column in TDengine when the data point value is received | No | Same as above |
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| 12 | ts_transform | The transformation function executed in taosX for the original timestamp of the data point | No | Do not transform the original timestamp of the data point uniformly |
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| 13 | request_ts_transform | The transformation function executed in taosX for the request timestamp of the data point | No | Do not transform the original timestamp of the data point uniformly |
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| 14 | received_ts_transform | The transformation function executed in taosX for the received timestamp of the data point | No | Do not transform the received timestamp of the data point uniformly |
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| 15 | tag::VARCHAR(200)::name | The Tag column corresponding to the data point in TDengine. Here `tag` is a reserved keyword indicating that this column is a tag; `VARCHAR(200)` indicates the type of tag; `name` is the actual name of the tag. | No | If 1 or more tag columns are configured, use the configured tag columns; if no tag columns are configured and stable exists in TDengine, use the tags of the stable in TDengine; if no tag columns are configured and stable does not exist in TDengine, automatically add the following 2 tag columns: tag::VARCHAR(256)::point_id and tag::VARCHAR(256)::point_name |
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(2) In the CSV Header, there cannot be duplicate columns;
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@ -137,21 +139,23 @@ Each Row in the CSV file configures an OPC data point. The rules for Rows are as
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(1) Correspondence with columns in the Header
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| Number | Column in Header | Type of Value | Value Range | Mandatory | Default Value |
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| ------ | ----------------------- | ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------- | ------------------------ |
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| 1 | point_id | String | Strings like `ns=3;i=1005`, must meet the OPC UA ID specification, i.e., include ns and id parts | Yes | |
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| 2 | enable | int | 0: Do not collect this point, and delete the corresponding subtable in TDengine before the OPC DataIn task starts; 1: Collect this point, do not delete the subtable before the OPC DataIn task starts. | No | 1 |
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| 3 | stable | String | Any string that meets the TDengine supertable naming convention; if special character `.` exists, replace with underscore if `{type}` exists: if type in CSV file is not empty, replace with the value of type if type is empty, replace with the original type of the collected value | Yes | |
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| 4 | tbname | String | Any string that meets the TDengine subtable naming convention; for OPC UA: if `{ns}` exists, replace with ns from point_id if `{id}` exists, replace with id from point_id for OPC DA: if `{tag_name}` exists, replace with tag_name | Yes | |
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| 5 | value_col | String | Column name that meets TDengine naming convention | No | val |
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| 6 | value_transform | String | Expressions that meet the Rhai engine, for example: `(val + 10) / 1000 * 2.0`, `log(val) + 10`, etc.; | No | None |
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| 7 | type | String | Supported types include: b/bool/i8/tinyint/i16/small/inti32/int/i64/bigint/u8/tinyint unsigned/u16/smallint unsigned/u32/int unsigned/u64/bigint unsigned/f32/float/f64/double/timestamp/timestamp(ms)/timestamp(us)/timestamp(ns)/json | No | Original type of the data point value |
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| 8 | quality_col | String | Column name that meets TDengine naming convention | No | None |
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| 9 | ts_col | String | Column name that meets TDengine naming convention | No | ts |
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| 10 | received_ts_col | String | Column name that meets TDengine naming convention | No | rts |
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| 11 | ts_transform | String | Supports +, -, *, /, % operators, for example: ts / 1000* 1000, sets the last 3 digits of a timestamp in ms to 0; ts + 8 *3600* 1000, adds 8 hours to a timestamp in ms; ts - 8 *3600* 1000, subtracts 8 hours from a timestamp in ms; | No | None |
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| 12 | received_ts_transform | String | No | None | |
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| 13 | tag::VARCHAR(200)::name | String | The value inside a tag, when the tag type is VARCHAR, can be in Chinese | No | NULL |
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| Number | Column in Header | Type of Value | Value Range | Mandatory | Default Value |
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|--------|-------------------------| ------------- |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| --------- |---------------------------------------|
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| 1 | point_id | String | Strings like `ns=3;i=1005`, must meet the OPC UA ID specification, i.e., include ns and id parts | Yes | |
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| 2 | enable | int | 0: Do not collect this point, and delete the corresponding subtable in TDengine before the OPC DataIn task starts; 1: Collect this point, do not delete the subtable before the OPC DataIn task starts. | No | 1 |
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| 3 | stable | String | Any string that meets the TDengine supertable naming convention; if special character `.` exists, replace with underscore if `{type}` exists: if type in CSV file is not empty, replace with the value of type if type is empty, replace with the original type of the collected value | Yes | |
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| 4 | tbname | String | Any string that meets the TDengine subtable naming convention; for OPC UA: if `{ns}` exists, replace with ns from point_id if `{id}` exists, replace with id from point_id for OPC DA: if `{tag_name}` exists, replace with tag_name | Yes | |
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| 5 | value_col | String | Column name that meets TDengine naming convention | No | val |
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| 6 | value_transform | String | Expressions that meet the Rhai engine, for example: `(val + 10) / 1000 * 2.0`, `log(val) + 10`, etc.; | No | None |
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| 7 | type | String | Supported types include: b/bool/i8/tinyint/i16/small/inti32/int/i64/bigint/u8/tinyint unsigned/u16/smallint unsigned/u32/int unsigned/u64/bigint unsigned/f32/float/f64/double/timestamp/timestamp(ms)/timestamp(us)/timestamp(ns)/json | No | Original type of the data point value |
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| 8 | quality_col | String | Column name that meets TDengine naming convention | No | None |
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| 9 | ts_col | String | Column name that meets TDengine naming convention | No | ts |
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| 10 | request_ts_col | String | Column name that meets TDengine naming convention | No | qts |
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| 11 | received_ts_col | String | Column name that meets TDengine naming convention | No | rts |
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| 12 | ts_transform | String | Supports +, -, *, /, % operators, for example: ts / 1000* 1000, sets the last 3 digits of a timestamp in ms to 0; ts + 8 *3600* 1000, adds 8 hours to a timestamp in ms; ts - 8 *3600* 1000, subtracts 8 hours from a timestamp in ms; | No | None |
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| 13 | request_ts_transform | String | Supports +, -, *, /, % operators, for example: qts / 1000* 1000, sets the last 3 digits of a timestamp in ms to 0; qts + 8 *3600* 1000, adds 8 hours to a timestamp in ms; qts - 8 *3600* 1000, subtracts 8 hours from a timestamp in ms; | No | None |
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| 14 | received_ts_transform | String | Supports +, -, *, /, % operators, for example: qts / 1000* 1000, sets the last 3 digits of a timestamp in ms to 0; qts + 8 *3600* 1000, adds 8 hours to a timestamp in ms; qts - 8 *3600* 1000, subtracts 8 hours from a timestamp in ms; | None | None |
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| 15 | tag::VARCHAR(200)::name | String | The value inside a tag, when the tag type is VARCHAR, can be in Chinese | No | NULL |
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(2) `point_id` is unique throughout the DataIn task, meaning: in an OPC DataIn task, a data point can only be written to one subtable in TDengine. If you need to write a data point to multiple subtables, you need to create multiple OPC DataIn tasks;
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@ -171,7 +175,7 @@ Data points can be filtered by configuring **Root Node ID**, **Namespace**, **Re
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Configure **Supertable Name**, **Table Name** to specify the supertable and subtable where the data will be written.
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Configure **Primary Key Column**, choose `origin_ts` to use the original timestamp of the OPC data point as the primary key in TDengine; choose `received_ts` to use the data's reception timestamp as the primary key in TDengine. Configure **Primary Key Alias** to specify the name of the TDengine timestamp column.
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Configure **Primary Key Column**, choose `origin_ts` to use the original timestamp of the OPC data point as the primary key in TDengine; choose `request_ts` to use the data's request timestamp as the primary key in TDengine; choose `received_ts` to use the data's reception timestamp as the primary key in TDengine. Configure **Primary Key Alias** to specify the name of the TDengine timestamp column.
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<figure>
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<Image img={imgStep5} alt=""/>
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@ -81,21 +81,23 @@ The header is the first line of the CSV file, with the following rules:
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(1) The header of the CSV can configure the following columns:
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| No. | Column Name | Description | Required | Default Behavior |
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| ---- | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| 1 | tag_name | The id of the data point on the OPC DA server | Yes | None |
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| 2 | stable | The supertable in TDengine corresponding to the data point | Yes | None |
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| 3 | tbname | The subtable in TDengine corresponding to the data point | Yes | None |
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| 4 | enable | Whether to collect data from this point | No | Use a unified default value `1` for enable |
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| 5 | value_col | The column name in TDengine corresponding to the collected value of the data point | No | Use a unified default value `val` as the value_col |
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| 6 | value_transform | The transform function executed in taosX for the collected value of the data point | No | Do not perform a transform on the collected value |
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| 7 | type | The data type of the collected value of the data point | No | Use the original type of the collected value as the data type in TDengine |
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| 8 | quality_col | The column name in TDengine corresponding to the quality of the collected value | No | Do not add a quality column in TDengine |
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| 9 | ts_col | The timestamp column in TDengine corresponding to the original timestamp of the data point | No | If both ts_col and received_ts_col are non-empty, use the former as the timestamp column; if one of ts_col or received_ts_col is non-empty, use the non-empty column as the timestamp column; if both are empty, use the original timestamp of the data point as the timestamp column in TDengine, with the default column name ts. |
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| 10 | received_ts_col | The timestamp column in TDengine corresponding to the timestamp when the data point value was received | No | |
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| 11 | ts_transform | The transform function executed in taosX for the original timestamp of the data point | No | Do not perform a transform on the original timestamp of the data point |
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| 12 | received_ts_transform | The transform function executed in taosX for the received timestamp of the data point | No | Do not perform a transform on the received timestamp of the data point |
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| 13 | tag::VARCHAR(200)::name | The Tag column in TDengine corresponding to the data point. Where `tag` is a reserved keyword, indicating that this column is a tag column; `VARCHAR(200)` indicates the type of this tag, which can also be other legal types; `name` is the actual name of this tag. | No | If configuring more than one tag column, use the configured tag columns; if no tag columns are configured, and stable exists in TDengine, use the tags of the stable in TDengine; if no tag columns are configured, and stable does not exist in TDengine, automatically add the following two tag columns by default: tag::VARCHAR(256)::point_idtag::VARCHAR(256)::point_name |
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| No. | Column Name | Description | Required | Default Behavior |
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|-----|-------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| 1 | tag_name | The id of the data point on the OPC DA server | Yes | None |
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| 2 | stable | The supertable in TDengine corresponding to the data point | Yes | None |
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| 3 | tbname | The subtable in TDengine corresponding to the data point | Yes | None |
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| 4 | enable | Whether to collect data from this point | No | Use a unified default value `1` for enable |
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| 5 | value_col | The column name in TDengine corresponding to the collected value of the data point | No | Use a unified default value `val` as the value_col |
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| 6 | value_transform | The transform function executed in taosX for the collected value of the data point | No | Do not perform a transform on the collected value |
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| 7 | type | The data type of the collected value of the data point | No | Use the original type of the collected value as the data type in TDengine |
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| 8 | quality_col | The column name in TDengine corresponding to the quality of the collected value | No | Do not add a quality column in TDengine |
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| 9 | ts_col | The timestamp column in TDengine corresponding to the original timestamp of the data point | No | ts_col, request_ts, received_ts these 3 columns, when there are more than 2 columns, the leftmost column is used as the primary key in TDengine. |
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| 10 | request_ts_col | The timestamp column in TDengine corresponding to the timestamp when the data point value was request | No | Same as above |
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| 11 | received_ts_col | The timestamp column in TDengine corresponding to the timestamp when the data point value was received | No | Same as above |
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| 12 | ts_transform | The transform function executed in taosX for the original timestamp of the data point | No | Do not perform a transform on the original timestamp of the data point |
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| 13 | request_ts_transform | The transform function executed in taosX for the request timestamp of the data point | No | Do not perform a transform on the received timestamp of the data point |
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| 14 | received_ts_transform | The transform function executed in taosX for the received timestamp of the data point | No | Do not perform a transform on the received timestamp of the data point |
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| 15 | tag::VARCHAR(200)::name | The Tag column in TDengine corresponding to the data point. Where `tag` is a reserved keyword, indicating that this column is a tag column; `VARCHAR(200)` indicates the type of this tag, which can also be other legal types; `name` is the actual name of this tag. | No | If configuring more than one tag column, use the configured tag columns; if no tag columns are configured, and stable exists in TDengine, use the tags of the stable in TDengine; if no tag columns are configured, and stable does not exist in TDengine, automatically add the following two tag columns by default: tag::VARCHAR(256)::point_idtag::VARCHAR(256)::point_name |
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(2) In the CSV Header, there cannot be duplicate columns;
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@ -112,7 +114,7 @@ Each Row in the CSV file configures an OPC data point. The rules for Rows are as
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(1) Correspondence with columns in the Header
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| Number | Column in Header | Type of Value | Range of Values | Mandatory | Default Value |
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||||
| ------ | ----------------------- | ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------- | ------------------------ |
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|--------|-------------------------| ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------- | ------------------------ |
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| 1 | tag_name | String | Strings like `root.parent.temperature`, must meet the OPC DA ID specification | Yes | |
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| 2 | enable | int | 0: Do not collect this point, and delete the corresponding subtable in TDengine before the OPC DataIn task starts; 1: Collect this point, do not delete the subtable before the OPC DataIn task starts. | No | 1 |
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| 3 | stable | String | Any string that meets the TDengine supertable naming convention; if there are special characters `.`, replace with underscore. If `{type}` exists: if type in CSV file is not empty, replace with the value of type; if empty, replace with the original type of the collected value | Yes | |
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@ -122,10 +124,12 @@ Each Row in the CSV file configures an OPC data point. The rules for Rows are as
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| 7 | type | String | Supported types include: b/bool/i8/tinyint/i16/smallint/i32/int/i64/bigint/u8/tinyint unsigned/u16/smallint unsigned/u32/int unsigned/u64/bigint unsigned/f32/floatf64/double/timestamp/timestamp(ms)/timestamp(us)/timestamp(ns)/json | No | Original type of data point value |
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| 8 | quality_col | String | Column name that meets TDengine naming convention | No | None |
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| 9 | ts_col | String | Column name that meets TDengine naming convention | No | ts |
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| 10 | received_ts_col | String | Column name that meets TDengine naming convention | No | rts |
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| 11 | ts_transform | String | Supports +, -, *, /, % operators, for example: ts / 1000* 1000, sets the last 3 digits of a ms unit timestamp to 0; ts + 8 *3600* 1000, adds 8 hours to a ms precision timestamp; ts - 8 *3600* 1000, subtracts 8 hours from a ms precision timestamp; | No | None |
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| 12 | received_ts_transform | String | No | None | |
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| 13 | tag::VARCHAR(200)::name | String | The value in tag, when the tag type is VARCHAR, it can be in Chinese | No | NULL |
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||||
| 10 | request_ts_col | String | Column name that meets TDengine naming convention | No | rts |
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||||
| 11 | received_ts_col | String | Column name that meets TDengine naming convention | No | rts |
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| 12 | ts_transform | String | Supports +, -, *, /, % operators, for example: ts / 1000* 1000, sets the last 3 digits of a ms unit timestamp to 0; ts + 8 *3600* 1000, adds 8 hours to a ms precision timestamp; ts - 8 *3600* 1000, subtracts 8 hours from a ms precision timestamp; | No | None |
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| 13 | request_ts_transform | String | No | None | |
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| 14 | received_ts_transform | String | No | None | |
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| 15 | tag::VARCHAR(200)::name | String | The value in tag, when the tag type is VARCHAR, it can be in Chinese | No | NULL |
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(2) `tag_name` is unique throughout the DataIn task, that is: in an OPC DataIn task, a data point can only be written to one subtable in TDengine. If you need to write a data point to multiple subtables, you need to create multiple OPC DataIn tasks;
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@ -145,7 +149,7 @@ Data points can be filtered by configuring the **Root Node ID** and **Regular Ex
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|
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Configure **Supertable Name** and **Table Name** to specify the supertable and subtable where the data will be written.
|
||||
|
||||
Configure **Primary Key Column**, choosing `origin_ts` to use the original timestamp of the OPC data point as the primary key in TDengine; choosing `received_ts` to use the timestamp when the data is received as the primary key. Configure **Primary Key Alias** to specify the name of the TDengine timestamp column.
|
||||
Configure **Primary Key Column**, choosing `origin_ts` to use the original timestamp of the OPC data point as the primary key in TDengine; choosing `request_ts` to use the timestamp when the data is request as the primary key; choosing `received_ts` to use the timestamp when the data is received as the primary key. Configure **Primary Key Alias** to specify the name of the TDengine timestamp column.
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<figure>
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<Image img={imgStep4} alt=""/>
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@ -339,20 +339,272 @@ Helm operates Kubernetes using kubectl and kubeconfig configurations, which can
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The TDengine Chart has not yet been released to the Helm repository, it can currently be downloaded directly from GitHub:
|
||||
|
||||
```shell
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||||
wget https://github.com/taosdata/TDengine-Operator/raw/refs/heads/3.0/helm/tdengine-enterprise-3.5.0.tgz
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wget https://github.com/taosdata/TDengine-Operator/raw/refs/heads/3.0/helm/tdengine-3.5.0.tgz
|
||||
```
|
||||
|
||||
Note that it's for the enterprise edition, and the community edition is not yet available.
|
||||
|
||||
Follow the steps below to install the TDengine Chart:
|
||||
|
||||
```shell
|
||||
# Edit the values.yaml file to set the topology of the cluster
|
||||
vim values.yaml
|
||||
helm install tdengine tdengine-enterprise-3.5.0.tgz -f values.yaml
|
||||
helm install tdengine tdengine-3.5.0.tgz -f values.yaml
|
||||
```
|
||||
|
||||
#### Case 1: Simple 1-node Deployment
|
||||
If you are using community images, you can use the following command to install TDengine with Helm Chart:
|
||||
|
||||
<details>
|
||||
<summary>Helm Chart Use Cases for Community</summary>
|
||||
|
||||
#### Community Case 1: Simple 1-node Deployment
|
||||
|
||||
The following is a simple example of deploying a single-node TDengine cluster using Helm.
|
||||
|
||||
```yaml
|
||||
# This example is a simple deployment with one server replica.
|
||||
name: "tdengine"
|
||||
|
||||
image:
|
||||
repository: # Leave a trailing slash for the repository, or "" for no repository
|
||||
server: tdengine/tdengine:latest
|
||||
|
||||
# Set timezone here, not in taoscfg
|
||||
timezone: "Asia/Shanghai"
|
||||
|
||||
labels:
|
||||
app: "tdengine"
|
||||
# Add more labels as needed.
|
||||
|
||||
services:
|
||||
server:
|
||||
type: ClusterIP
|
||||
replica: 1
|
||||
ports:
|
||||
# TCP range required
|
||||
tcp: [6041, 6030, 6060]
|
||||
# UDP range, optional
|
||||
udp:
|
||||
volumes:
|
||||
- name: data
|
||||
mountPath: /var/lib/taos
|
||||
spec:
|
||||
storageClassName: "local-path"
|
||||
accessModes: [ "ReadWriteOnce" ]
|
||||
resources:
|
||||
requests:
|
||||
storage: "10Gi"
|
||||
- name: log
|
||||
mountPath: /var/log/taos/
|
||||
spec:
|
||||
storageClassName: "local-path"
|
||||
accessModes: [ "ReadWriteOnce" ]
|
||||
resources:
|
||||
requests:
|
||||
storage: "10Gi"
|
||||
files:
|
||||
- name: cfg # must be lower case.
|
||||
mountPath: /etc/taos/taos.cfg
|
||||
content: |
|
||||
dataDir /var/lib/taos/
|
||||
logDir /var/log/taos/
|
||||
```
|
||||
|
||||
Let's explain the above configuration:
|
||||
|
||||
- name: The name of the deployment, here it is "tdengine".
|
||||
- image:
|
||||
- repository: The image repository address, remember to leave a trailing slash for the repository, or set it to an empty string to use docker.io.
|
||||
- server: The specific name and tag of the server image. You need to ask your business partner for the TDengine Enterprise image.
|
||||
- timezone: Set the timezone, here it is "Asia/Shanghai".
|
||||
- labels: Add labels to the deployment, here is an app label with the value "tdengine", more labels can be added as needed.
|
||||
- services:
|
||||
- server: Configure the server service.
|
||||
- type: The service type, here it is **ClusterIP**.
|
||||
- replica: The number of replicas, here it is 1.
|
||||
- ports: Configure the ports of the service.
|
||||
- tcp: The required TCP port range, here it is [6041, 6030, 6060].
|
||||
- udp: The optional UDP port range, which is not configured here.
|
||||
- volumes: Configure the volumes.
|
||||
- name: The name of the volume, here there are two volumes, data and log.
|
||||
- mountPath: The mount path of the volume.
|
||||
- spec: The specification of the volume.
|
||||
- storageClassName: The storage class name, here it is **local-path**.
|
||||
- accessModes: The access mode, here it is **ReadWriteOnce**.
|
||||
- resources.requests.storage: The requested storage size, here it is **10Gi**.
|
||||
- files: Configure the files to mount in TDengine server.
|
||||
- name: The name of the file, here it is **cfg**.
|
||||
- mountPath: The mount path of the file, which is **taos.cfg**.
|
||||
- content: The content of the file, here the **dataDir** and **logDir** are configured.
|
||||
|
||||
After configuring the values.yaml file, use the following command to install the TDengine Chart:
|
||||
|
||||
```shell
|
||||
helm install simple tdengine-3.5.0.tgz -f values.yaml
|
||||
```
|
||||
|
||||
After installation, you can see the instructions to see the status of the TDengine cluster:
|
||||
|
||||
```shell
|
||||
NAME: simple
|
||||
LAST DEPLOYED: Sun Feb 9 13:40:00 2025 default
|
||||
STATUS: deployed
|
||||
REVISION: 1
|
||||
TEST SUITE: None
|
||||
NOTES:
|
||||
1. Get first POD name:
|
||||
|
||||
export POD_NAME=$(kubectl get pods --namespace default \
|
||||
-l "app.kubernetes.io/name=tdengine,app.kubernetes.io/instance=simple" -o jsonpath="{.items[0].metadata.name}")
|
||||
|
||||
2. Show dnodes/mnodes:
|
||||
|
||||
kubectl --namespace default exec $POD_NAME -- taos -s "show dnodes; show mnodes"
|
||||
|
||||
3. Run into TDengine CLI:
|
||||
|
||||
kubectl --namespace default exec -it $POD_NAME -- taos
|
||||
```
|
||||
|
||||
Follow the instructions to check the status of the TDengine cluster:
|
||||
|
||||
```shell
|
||||
root@u1-58:/data1/projects/helm# kubectl --namespace default exec $POD_NAME -- taos -s "show dnodes; show mnodes"
|
||||
Welcome to the TDengine Command Line Interface, Client Version:3.3.5.8
|
||||
Copyright (c) 2023 by TDengine, all rights reserved.
|
||||
|
||||
taos> show dnodes; show mnodes
|
||||
id | endpoint | vnodes | support_vnodes | status | create_time | reboot_time | note |
|
||||
=============================================================================================================================================================================
|
||||
1 | oss-tdengine-0.oss-tdengine... | 0 | 21 | ready | 2025-03-12 19:05:42.224 | 2025-03-12 19:05:42.044 | |
|
||||
Query OK, 1 row(s) in set (0.002545s)
|
||||
|
||||
id | endpoint | role | status | create_time | role_time |
|
||||
==================================================================================================================================
|
||||
1 | oss-tdengine-0.oss-tdengine... | leader | ready | 2025-03-12 19:05:42.239 | 2025-03-12 19:05:42.137 |
|
||||
Query OK, 1 row(s) in set (0.001343s)
|
||||
```
|
||||
|
||||
To clean up the TDengine cluster, use the following command:
|
||||
|
||||
```shell
|
||||
helm uninstall simple
|
||||
kubectl delete pvc -l app.kubernetes.io/instance=simple
|
||||
```
|
||||
|
||||
#### Community Case 2: 3-replica Deployment with Single taosX
|
||||
|
||||
```yaml
|
||||
# This example shows how to deploy a 3-replica TDengine cluster with separate taosx/explorer service.
|
||||
# Users should know that the explorer/taosx service is not cluster-ready, so it is recommended to deploy it separately.
|
||||
name: "tdengine"
|
||||
|
||||
image:
|
||||
repository: # Leave a trailing slash for the repository, or "" for no repository
|
||||
server: tdengine/tdengine:latest
|
||||
|
||||
# Set timezone here, not in taoscfg
|
||||
timezone: "Asia/Shanghai"
|
||||
|
||||
labels:
|
||||
# Add more labels as needed.
|
||||
|
||||
services:
|
||||
server:
|
||||
type: ClusterIP
|
||||
replica: 3
|
||||
ports:
|
||||
# TCP range required
|
||||
tcp: [6041, 6030]
|
||||
# UDP range, optional
|
||||
udp:
|
||||
volumes:
|
||||
- name: data
|
||||
mountPath: /var/lib/taos
|
||||
spec:
|
||||
storageClassName: "local-path"
|
||||
accessModes: ["ReadWriteOnce"]
|
||||
resources:
|
||||
requests:
|
||||
storage: "10Gi"
|
||||
- name: log
|
||||
mountPath: /var/log/taos/
|
||||
spec:
|
||||
storageClassName: "local-path"
|
||||
accessModes: ["ReadWriteOnce"]
|
||||
resources:
|
||||
requests:
|
||||
storage: "10Gi"
|
||||
```
|
||||
|
||||
You can see that the configuration is similar to the first one, with the addition of the taosx configuration. The taosx service is configured with similar storage configuration as the server service, and the server service is configured with 3 replicas. Since the taosx service is not cluster-ready, it is recommended to deploy it separately.
|
||||
|
||||
After configuring the values.yaml file, use the following command to install the TDengine Chart:
|
||||
|
||||
```shell
|
||||
helm install replica3 tdengine-3.5.0.tgz -f values.yaml
|
||||
```
|
||||
|
||||
To clean up the TDengine cluster, use the following command:
|
||||
|
||||
```shell
|
||||
helm uninstall replica3
|
||||
kubectl delete pvc -l app.kubernetes.io/instance=replica3
|
||||
```
|
||||
|
||||
You can use the following command to expose the explorer service to the outside world with ingress:
|
||||
|
||||
```shell
|
||||
tee replica3-ingress.yaml <<EOF
|
||||
# This is a helm chart example for deploying 3 replicas of TDengine Explorer
|
||||
apiVersion: networking.k8s.io/v1
|
||||
kind: Ingress
|
||||
metadata:
|
||||
name: replica3-ingress
|
||||
namespace: default
|
||||
spec:
|
||||
rules:
|
||||
- host: replica3.local.tdengine.com
|
||||
http:
|
||||
paths:
|
||||
- path: /
|
||||
pathType: Prefix
|
||||
backend:
|
||||
service:
|
||||
name: replica3-tdengine-taosx
|
||||
port:
|
||||
number: 6060
|
||||
EOF
|
||||
|
||||
kubectl apply -f replica3-ingress.yaml
|
||||
```
|
||||
|
||||
Use `kubectl get ingress` to view the ingress service.
|
||||
|
||||
```shell
|
||||
root@server:/data1/projects/helm# kubectl get ingress
|
||||
NAME CLASS HOSTS ADDRESS PORTS AGE
|
||||
replica3-ingress nginx replica3.local.tdengine.com 192.168.1.58 80 48m
|
||||
```
|
||||
|
||||
You can configure the domain name resolution to point to the ingress service's external IP address. For example, add the following line to the hosts file:
|
||||
|
||||
```conf
|
||||
192.168.1.58 replica3.local.tdengine.com
|
||||
```
|
||||
|
||||
Now you can access the explorer service through the domain name `replica3.local.tdengine.com`.
|
||||
|
||||
```shell
|
||||
curl http://replica3.local.tdengine.com
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
With TDengine Enterprise images, you can use the following command to install TDengine with Helm Chart:
|
||||
|
||||
<details>
|
||||
<summary>Helm Chart Use Cases for Enterprise</summary>
|
||||
|
||||
#### Enterprise Case 1: Simple 1-node Deployment
|
||||
|
||||
The following is a simple example of deploying a single-node TDengine cluster using Helm.
|
||||
|
||||
|
|
@ -435,7 +687,7 @@ Let's explain the above configuration:
|
|||
After configuring the values.yaml file, use the following command to install the TDengine Chart:
|
||||
|
||||
```shell
|
||||
helm install simple tdengine-enterprise-3.5.0.tgz -f values.yaml
|
||||
helm install simple tdengine-3.5.0.tgz -f values.yaml
|
||||
```
|
||||
|
||||
After installation, you can see the instructions to see the status of the TDengine cluster:
|
||||
|
|
@ -487,7 +739,7 @@ helm uninstall simple
|
|||
kubectl delete pvc -l app.kubernetes.io/instance=simple
|
||||
```
|
||||
|
||||
#### Case 2: Tiered-Storage Deployment
|
||||
#### Enterprise Case 2: Tiered-Storage Deployment
|
||||
|
||||
The following is an example of deploying a TDengine cluster with tiered storage using Helm.
|
||||
|
||||
|
|
@ -563,10 +815,10 @@ You can see that the configuration is similar to the previous one, with the addi
|
|||
After configuring the values.yaml file, use the following command to install the TDengine Chart:
|
||||
|
||||
```shell
|
||||
helm install tiered tdengine-enterprise-3.5.0.tgz -f values.yaml
|
||||
helm install tiered tdengine-3.5.0.tgz -f values.yaml
|
||||
```
|
||||
|
||||
#### Case 3: 2-replica Deployment
|
||||
#### Enterprise Case 3: 2-replica Deployment
|
||||
|
||||
TDengine support 2-replica deployment with an arbitrator, which can be configured as follows:
|
||||
|
||||
|
|
@ -634,7 +886,7 @@ services:
|
|||
|
||||
You can see that the configuration is similar to the first one, with the addition of the arbitrator configuration. The arbitrator service is configured with the same storage as the server service, and the server service is configured with 2 replicas (the arbitrator should be 1 replica and not able to be changed).
|
||||
|
||||
#### Case 4: 3-replica Deployment with Single taosX
|
||||
#### Enterprise Case 4: 3-replica Deployment with Single taosX
|
||||
|
||||
```yaml
|
||||
# This example shows how to deploy a 3-replica TDengine cluster with separate taosx/explorer service.
|
||||
|
|
@ -761,7 +1013,7 @@ You can see that the configuration is similar to the first one, with the additio
|
|||
After configuring the values.yaml file, use the following command to install the TDengine Chart:
|
||||
|
||||
```shell
|
||||
helm install replica3 tdengine-enterprise-3.5.0.tgz -f values.yaml
|
||||
helm install replica3 tdengine-3.5.0.tgz -f values.yaml
|
||||
```
|
||||
|
||||
You can use the following command to expose the explorer service to the outside world with ingress:
|
||||
|
|
@ -810,3 +1062,5 @@ Now you can access the explorer service through the domain name `replica3.local.
|
|||
```shell
|
||||
curl http://replica3.local.tdengine.com
|
||||
```
|
||||
|
||||
</details>
|
||||
|
|
|
|||
|
|
@ -276,6 +276,15 @@ TDengine supports INNER JOIN based on the timestamp primary key, with the follow
|
|||
5. Both sides of JOIN support subqueries.
|
||||
6. Does not support mixing with the FILL clause.
|
||||
|
||||
## INTERP
|
||||
The INTERP clause is a dedicated syntax for the INTERP function (../function/#interp). When an SQL statement contains an INTERP clause, it can only query the INTERP function and cannot be used with other functions. Additionally, the INTERP clause cannot be used simultaneously with window clauses (window_clause) or group by clauses (group_by_clause). The INTERP function must be used with the RANGE, EVERY, and FILL clauses; stream computing does not support the use of RANGE but requires the use of the EVERY and FILL keywords.
|
||||
- The output time range for INTERP is specified by the RANGE(timestamp1, timestamp2) field, which must satisfy timestamp1 \<= timestamp2. Here, timestamp1 is the start value of the output time range, i.e., if the conditions for interpolation are met at timestamp1, then timestamp1 is the first record output, and timestamp2 is the end value of the output time range, i.e., the timestamp of the last record output cannot be greater than timestamp2.
|
||||
- INTERP determines the number of results within the output time range based on the EVERY(time_unit) field, starting from timestamp1 and interpolating at fixed intervals of time (time_unit value), where time_unit can be time units: 1a (milliseconds), 1s (seconds), 1m (minutes), 1h (hours), 1d (days), 1w (weeks). For example, EVERY(500a) will interpolate the specified data every 500 milliseconds.
|
||||
- INTERP determines how to interpolate at each time point that meets the output conditions based on the FILL field. For how to use the FILL clause, refer to [FILL Clause](../time-series-extensions/)
|
||||
- INTERP can interpolate at a single time point specified in the RANGE field, in which case the EVERY field can be omitted. For example: SELECT INTERP(col) FROM tb RANGE('2023-01-01 00:00:00') FILL(linear).
|
||||
- INTERP query supports NEAR FILL mode, i.e., when FILL is needed, it uses the data closest to the current time point for interpolation. When the timestamps before and after are equally close to the current time slice, FILL the previous row's value. This mode is not supported in stream computing and window queries. For example: SELECT INTERP(col) FROM tb RANGE('2023-01-01 00:00:00', '2023-01-01 00:10:00') FILL(NEAR).(Supported from version 3.3.4.9).
|
||||
- INTERP `RANGE` clause supports the expansion of the time range (supported from version 3.3.4.9), such as `RANGE('2023-01-01 00:00:00', 10s)` means to find data 10s before and after the time point '2023-01-01 00:00:00' for interpolation, FILL PREV/NEXT/NEAR respectively means to look for data forward/backward/around the time point, if there is no data around the time point, then use the value specified by FILL for interpolation, therefore the FILL clause must specify a value at this time. For example: SELECT INTERP(col) FROM tb RANGE('2023-01-01 00:00:00', 10s) FILL(PREV, 1). Currently, only the combination of time point and time range is supported, not the combination of time interval and time range, i.e., RANGE('2023-01-01 00:00:00', '2023-02-01 00:00:00', 1h) is not supported. The specified time range rules are similar to EVERY, the unit cannot be year or month, the value cannot be 0, and cannot have quotes. When using this extension, other FILL modes except FILL PREV/NEXT/NEAR are not supported, and the EVERY clause cannot be specified.
|
||||
|
||||
## GROUP BY
|
||||
|
||||
If a GROUP BY clause is specified in the statement, the SELECT list can only contain the following expressions:
|
||||
|
|
|
|||
|
|
@ -1965,42 +1965,6 @@ FIRST(expr)
|
|||
- If all columns in the result set are NULL, no results are returned.
|
||||
- For tables with composite primary keys, if there are multiple entries with the smallest timestamp, only the data with the smallest composite primary key is returned.
|
||||
|
||||
### INTERP
|
||||
|
||||
```sql
|
||||
INTERP(expr [, ignore_null_values])
|
||||
|
||||
ignore_null_values: {
|
||||
0
|
||||
| 1
|
||||
}
|
||||
```
|
||||
|
||||
**Function Description**: Returns the record value or interpolated value of a specified column at a specified time slice. The ignore_null_values parameter can be 0 or 1, where 1 means to ignore NULL values, default is 0.
|
||||
|
||||
**Return Data Type**: Same as the field type.
|
||||
|
||||
**Applicable Data Types**: Numeric types.
|
||||
|
||||
**Applicable to**: Tables and supertables.
|
||||
|
||||
**Usage Instructions**
|
||||
|
||||
- INTERP is used to obtain the record value of a specified column at a specified time slice. If there is no row data that meets the conditions at that time slice, interpolation will be performed according to the settings of the FILL parameter.
|
||||
- The input data for INTERP is the data of the specified column, which can be filtered through conditional statements (where clause). If no filtering condition is specified, the input is all data.
|
||||
- INTERP SQL queries need to be used together with the RANGE, EVERY, and FILL keywords; stream computing cannot use RANGE, needs EVERY and FILL keywords together.
|
||||
- The output time range for INTERP is specified by the RANGE(timestamp1, timestamp2) field, which must satisfy timestamp1 \<= timestamp2. Here, timestamp1 is the start value of the output time range, i.e., if the conditions for interpolation are met at timestamp1, then timestamp1 is the first record output, and timestamp2 is the end value of the output time range, i.e., the timestamp of the last record output cannot be greater than timestamp2.
|
||||
- INTERP determines the number of results within the output time range based on the EVERY(time_unit) field, starting from timestamp1 and interpolating at fixed intervals of time (time_unit value), where time_unit can be time units: 1a (milliseconds), 1s (seconds), 1m (minutes), 1h (hours), 1d (days), 1w (weeks). For example, EVERY(500a) will interpolate the specified data every 500 milliseconds.
|
||||
- INTERP determines how to interpolate at each time point that meets the output conditions based on the FILL field. For how to use the FILL clause, refer to [FILL Clause](../time-series-extensions/)
|
||||
- INTERP can interpolate at a single time point specified in the RANGE field, in which case the EVERY field can be omitted. For example: SELECT INTERP(col) FROM tb RANGE('2023-01-01 00:00:00') FILL(linear).
|
||||
- When INTERP is applied to a supertable, it will sort all the subtable data under that supertable by primary key column and perform interpolation calculations, and can also be used with PARTITION BY tbname to force the results to a single timeline.
|
||||
- INTERP can be used with the pseudocolumn _irowts to return the timestamp corresponding to the interpolation point (supported from version 3.0.2.0).
|
||||
- INTERP can be used with the pseudocolumn _isfilled to display whether the return result is from the original record or generated by the interpolation algorithm (supported from version 3.0.3.0).
|
||||
- For queries on tables with composite primary keys, if there are data with the same timestamp, only the data with the smallest composite primary key participates in the calculation.
|
||||
- INTERP query supports NEAR FILL mode, i.e., when FILL is needed, it uses the data closest to the current time point for interpolation. When the timestamps before and after are equally close to the current time slice, FILL the previous row's value. This mode is not supported in stream computing and window queries. For example: SELECT INTERP(col) FROM tb RANGE('2023-01-01 00:00:00', '2023-01-01 00:10:00') FILL(NEAR).(Supported from version 3.3.4.9).
|
||||
- INTERP can only use the pseudocolumn `_irowts_origin` when using FILL PREV/NEXT/NEAR modes. `_irowts_origin` is supported from version 3.3.4.9.
|
||||
- INTERP `RANGE` clause supports the expansion of the time range (supported from version 3.3.4.9), For example, `RANGE('2023-01-01 00:00:00', 10s)` means that only data within 10s around the time point '2023-01-01 00:00:00' can be used for interpolation. `FILL PREV/NEXT/NEAR` respectively means to look for data forward/backward/around the time point. If there is no data around the time point, the default value specified by `FILL` is used for interpolation. Therefore the `FILL` clause must specify the default value at the same time. For example: SELECT INTERP(col) FROM tb RANGE('2023-01-01 00:00:00', 10s) FILL(PREV, 1). Starting from the 3.3.6.0 version, the combination of time period and time range is supported. When interpolating for each point within the time period, the time range requirement must be met. Prior versions only supported single time point and its time range. The available values for time range are similar to `EVERY`, the unit cannot be year or month, the value must be greater than 0, and cannot be in quotes. When using this extension, `FILL` modes other than `PREV/NEXT/NEAR` are not supported.
|
||||
|
||||
### LAST
|
||||
|
||||
```sql
|
||||
|
|
@ -2266,6 +2230,35 @@ ignore_option: {
|
|||
- When there is no composite primary key, if different subtables have data with the same timestamp, a "Duplicate timestamps not allowed" message will be displayed
|
||||
- When using composite primary keys, the timestamp and primary key combinations of different subtables may be the same, which row is used depends on which one is found first, meaning that the results of running diff() multiple times in this situation may vary.
|
||||
|
||||
### INTERP
|
||||
|
||||
```sql
|
||||
INTERP(expr [, ignore_null_values])
|
||||
|
||||
ignore_null_values: {
|
||||
0
|
||||
| 1
|
||||
}
|
||||
```
|
||||
|
||||
**Function Description**: Returns the record value or interpolated value of a specified column at a specified time slice. The ignore_null_values parameter can be 0 or 1, where 1 means to ignore NULL values, default is 0.
|
||||
|
||||
**Return Data Type**: Same as the field type.
|
||||
|
||||
**Applicable Data Types**: Numeric types.
|
||||
|
||||
**Applicable to**: Tables and supertables.
|
||||
|
||||
**Usage Instructions**
|
||||
|
||||
- INTERP is used to obtain the record value of a specified column at the specified time slice. It has a dedicated syntax (interp_clause) when used. For syntax introduction, see [reference link](../query-data/#interp).
|
||||
- When there is no row data that meets the conditions at the specified time slice, the INTERP function will interpolate according to the settings of the [FILL](../time-series-extensions/#fill-clause) parameter.
|
||||
- When INTERP is applied to a supertable, it will sort all the subtable data under that supertable by primary key column and perform interpolation calculations, and can also be used with PARTITION BY tbname to force the results to a single timeline.
|
||||
- INTERP can be used with the pseudocolumn _irowts to return the timestamp corresponding to the interpolation point (supported from version 3.0.2.0).
|
||||
- INTERP can be used with the pseudocolumn _isfilled to display whether the return result is from the original record or generated by the interpolation algorithm (supported from version 3.0.3.0).
|
||||
- INTERP can only use the pseudocolumn `_irowts_origin` when using FILL PREV/NEXT/NEAR modes. `_irowts_origin` is supported from version 3.3.4.9.
|
||||
- For queries on tables with composite primary keys, if there are data with the same timestamp, only the data with the smallest composite primary key participates in the calculation.
|
||||
|
||||
### IRATE
|
||||
|
||||
```sql
|
||||
|
|
|
|||
|
|
@ -89,21 +89,23 @@ Header 是 CSV 文件的第一行，规则如下：
|
|||
(1) CSV 的 Header 中可以配置以下列：
|
||||
|
||||
|
||||
| 序号 | 列名 | 描述 | 是否必填 | 默认行为 |
|
||||
| ---- | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| 1 | point_id | 数据点位在 OPC UA 服务器上的 id | 是 | 无 |
|
||||
| 2 | stable | 数据点位在 TDengine 中对应的超级表 | 是 | 无 |
|
||||
| 3 | tbname | 数据点位在 TDengine 中对应的子表 | 是 | 无 |
|
||||
| 4 | enable | 是否采集该点位的数据 | 否 | 使用统一的默认值`1`作为 enable 的值 |
|
||||
| 5 | value_col | 数据点位采集值在 TDengine 中对应的列名 | 否 | 使用统一的默认值`val` 作为 value_col 的值 |
|
||||
| 6 | value_transform | 数据点位采集值在 taosX 中执行的变换函数 | 否 | 统一不进行采集值的 transform |
|
||||
| 7 | type | 数据点位采集值的数据类型 | 否 | 统一使用采集值的原始类型作为 TDengine 中的数据类型 |
|
||||
| 8 | quality_col | 数据点位采集值质量在 TDengine 中对应的列名 | 否 | 统一不在 TDengine 添加 quality 列 |
|
||||
| 9 | ts_col | 数据点位的原始时间戳在 TDengine 中对应的时间戳列 | 否 | ts_col 和 received_ts_col 都非空时，使用前者作为时间戳列；ts_col 和 received_ts_col 有一列非空时，使用不为空的列作时间戳列；ts_col 和 received_ts_col 都为空时，使用数据点位原始时间戳作 TDengine 中的时间戳列，且列名为默认值`ts`。 |
|
||||
| 10 | received_ts_col | 接收到该点位采集值时的时间戳在 TDengine 中对应的时间戳列 | 否 | 同上 |
|
||||
| 11 | ts_transform | 数据点位时间戳在 taosX 中执行的变换函数 | 否 | 统一不进行数据点位原始时间戳的 transform |
|
||||
| 12 | received_ts_transform | 数据点位接收时间戳在 taosX 中执行的变换函数 | 否 | 统一不进行数据点位接收时间戳的 transform |
|
||||
| 13 | tag::VARCHAR(200)::name | 数据点位在 TDengine 中对应的 Tag 列。其中`tag` 为保留关键字，表示该列为一个 tag 列；`VARCHAR(200)` 表示该 tag 的类型，也可以是其它合法的类型；`name` 是该 tag 的实际名称。 | 否 | 配置 1 个以上的 tag 列，则使用配置的 tag 列；没有配置任何 tag 列，且 stable 在 TDengine 中存在，使用 TDengine 中的 stable 的 tag；没有配置任何 tag 列，且 stable 在 TDengine 中不存在，则默认自动添加以下 2 个 tag 列：tag::VARCHAR(256)::point_id 和 tag::VARCHAR(256)::point_name |
|
||||
| 序号 | 列名 | 描述 | 是否必填 | 默认行为 |
|
||||
|----|-------------------------|--------------------------------------------------------------------------------------------------------------------| -------- |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| 1 | point_id | 数据点位在 OPC UA 服务器上的 id | 是 | 无 |
|
||||
| 2 | stable | 数据点位在 TDengine 中对应的超级表 | 是 | 无 |
|
||||
| 3 | tbname | 数据点位在 TDengine 中对应的子表 | 是 | 无 |
|
||||
| 4 | enable | 是否采集该点位的数据 | 否 | 使用统一的默认值`1`作为 enable 的值 |
|
||||
| 5 | value_col | 数据点位采集值在 TDengine 中对应的列名 | 否 | 使用统一的默认值`val` 作为 value_col 的值 |
|
||||
| 6 | value_transform | 数据点位采集值在 taosX 中执行的变换函数 | 否 | 统一不进行采集值的 transform |
|
||||
| 7 | type | 数据点位采集值的数据类型 | 否 | 统一使用采集值的原始类型作为 TDengine 中的数据类型 |
|
||||
| 8 | quality_col | 数据点位采集值质量在 TDengine 中对应的列名 | 否 | 统一不在 TDengine 添加 quality 列 |
|
||||
| 9 | ts_col | 数据点位的原始时间戳在 TDengine 中对应的时间戳列 | 否 | ts_col，request_ts，received_ts 这 3 列，当有 2 列以上存在时，以最左侧的列作为 TDengine 中的主键。 |
|
||||
| 10 | request_ts_col | 请求到该点位采集值时的时间戳在 TDengine 中对应的时间戳列 | 否 | ts_col，request_ts，received_ts 这 3 列，当有 2 列以上存在时，以最左侧的列作为 TDengine 中的主键。 |
|
||||
| 11 | received_ts_col | 接收到该点位采集值时的时间戳在 TDengine 中对应的时间戳列 | 否 | ts_col，request_ts，received_ts 这 3 列，当有 2 列以上存在时，以最左侧的列作为 TDengine 中的主键。 |
|
||||
| 12 | ts_transform | 数据点位时间戳在 taosX 中执行的变换函数 | 否 | 统一不进行数据点位原始时间戳的 transform |
|
||||
| 13 | request_ts_transform | 数据点位接收时间戳在 taosX 中执行的变换函数 | 否 | 统一不进行数据点位接收时间戳的 transform |
|
||||
| 14 | received_ts_transform | 数据点位接收时间戳在 taosX 中执行的变换函数 | 否 | 统一不进行数据点位接收时间戳的 transform |
|
||||
| 15 | tag::VARCHAR(200)::name | 数据点位在 TDengine 中对应的 Tag 列。其中`tag` 为保留关键字，表示该列为一个 tag 列；`VARCHAR(200)` 表示该 tag 的类型，也可以是其它合法的类型；`name` 是该 tag 的实际名称。 | 否 | 配置 1 个以上的 tag 列，则使用配置的 tag 列；没有配置任何 tag 列，且 stable 在 TDengine 中存在，使用 TDengine 中的 stable 的 tag；没有配置任何 tag 列，且 stable 在 TDengine 中不存在，则默认自动添加以下 2 个 tag 列：tag::VARCHAR(256)::point_id 和 tag::VARCHAR(256)::point_name |
|
||||
|
||||
(2) CSV Header 中，不能有重复的列；
|
||||
|
||||
|
|
@ -120,21 +122,23 @@ CSV 文件中的每个 Row 配置一个 OPC 数据点位。Row 的规则如下
|
|||
(1) 与 Header 中的列有如下对应关系
|
||||
|
||||
|
||||
| 序号 | Header 中的列 | 值的类型 | 值的范围 | 是否必填 | 默认值 |
|
||||
| ---- | ----------------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | ------------------------ |
|
||||
| 1 | point_id | String | 类似`ns=3;i=1005`这样的字符串，要满足 OPC UA 的 ID 的规范，即：包含 ns 和 id 部分 | 是 | |
|
||||
| 2 | enable | int | 0：不采集该点位，且在 OPC DataIn 任务开始前，删除 TDengine 中点位对应的子表；1：采集该点位，在 OPC DataIn 任务开始前，不删除子表。 | 否 | 1 |
|
||||
| 3 | stable | String | 符合 TDengine 超级表命名规范的任何字符串；如果存在特殊字符`.`，使用下划线替换如果存在`{type}`，则：CSV 文件的 type 不为空，使用 type 的值进行替换CSV 文件的 type 为空，使用采集值的原始类型进行替换 | 是 | |
|
||||
| 4 | tbname | String | 符合 TDengine 子表命名规范的任何字符串；如果存在特殊字符`.`，使用下划线替换对于 OPC UA：如果存在`{ns}`，使用 point_id 中的 ns 替换如果存在`{id}`，使用 point_id 中的 id 替换对于 OPC DA：如果存在`{tag_name}`，使用 tag_name 替换 | 是 | |
|
||||
| 5 | value_col | String | 符合 TDengine 命名规范的列名 | 否 | val |
|
||||
| 6 | value_transform | String | 符合 Rhai 引擎的计算表达式，例如：`(val + 10) / 1000 * 2.0`，`log(val) + 10`等； | 否 | None |
|
||||
| 7 | type | String | 支持类型包括：b/bool/i8/tinyint/i16/small/inti32/int/i64/bigint/u8/tinyint unsigned/u16/smallint unsigned/u32/int unsigned/u64/bigint unsigned/f32/float/f64/double/timestamp/timestamp(ms)/timestamp(us)/timestamp(ns)/json | 否 | 数据点位采集值的原始类型 |
|
||||
| 8 | quality_col | String | 符合 TDengine 命名规范的列名 | 否 | None |
|
||||
| 9 | ts_col | String | 符合 TDengine 命名规范的列名 | 否 | ts |
|
||||
| 10 | received_ts_col | String | 符合 TDengine 命名规范的列名 | 否 | rts |
|
||||
| 11 | ts_transform | String | 支持 +、-、*、/、% 操作符，例如：ts / 1000 * 1000，将一个 ms 单位的时间戳的最后 3 位置为 0；ts + 8 * 3600 * 1000，将一个 ms 精度的时间戳，增加 8 小时；ts - 8 * 3600 * 1000，将一个 ms 精度的时间戳，减去 8 小时； | 否 | None |
|
||||
| 12 | received_ts_transform | String | 否 | None | |
|
||||
| 13 | tag::VARCHAR(200)::name | String | tag 里的值，当 tag 的类型是 VARCHAR 时，可以是中文 | 否 | NULL |
|
||||
| 序号 | Header 中的列 | 值的类型 | 值的范围 | 是否必填 | 默认值 |
|
||||
|----|-------------------------| -------- |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------| ------------------------ |
|
||||
| 1 | point_id | String | 类似`ns=3;i=1005`这样的字符串，要满足 OPC UA 的 ID 的规范，即：包含 ns 和 id 部分 | 是 | |
|
||||
| 2 | enable | int | 0：不采集该点位，且在 OPC DataIn 任务开始前，删除 TDengine 中点位对应的子表；1：采集该点位，在 OPC DataIn 任务开始前，不删除子表。 | 否 | 1 |
|
||||
| 3 | stable | String | 符合 TDengine 超级表命名规范的任何字符串；如果存在特殊字符`.`，使用下划线替换如果存在`{type}`，则：CSV 文件的 type 不为空，使用 type 的值进行替换CSV 文件的 type 为空，使用采集值的原始类型进行替换 | 是 | |
|
||||
| 4 | tbname | String | 符合 TDengine 子表命名规范的任何字符串；如果存在特殊字符`.`，使用下划线替换对于 OPC UA：如果存在`{ns}`，使用 point_id 中的 ns 替换如果存在`{id}`，使用 point_id 中的 id 替换对于 OPC DA：如果存在`{tag_name}`，使用 tag_name 替换 | 是 | |
|
||||
| 5 | value_col | String | 符合 TDengine 命名规范的列名 | 否 | val |
|
||||
| 6 | value_transform | String | 符合 Rhai 引擎的计算表达式，例如：`(val + 10) / 1000 * 2.0`，`log(val) + 10`等； | 否 | None |
|
||||
| 7 | type | String | 支持类型包括：b/bool/i8/tinyint/i16/small/inti32/int/i64/bigint/u8/tinyint unsigned/u16/smallint unsigned/u32/int unsigned/u64/bigint unsigned/f32/float/f64/double/timestamp/timestamp(ms)/timestamp(us)/timestamp(ns)/json | 否 | 数据点位采集值的原始类型 |
|
||||
| 8 | quality_col | String | 符合 TDengine 命名规范的列名 | 否 | None |
|
||||
| 9 | ts_col | String | 符合 TDengine 命名规范的列名 | 否 | ts |
|
||||
| 10 | request_ts_col | String | 符合 TDengine 命名规范的列名 | 否 | rts |
|
||||
| 11 | received_ts_col | String | 符合 TDengine 命名规范的列名 | 否 | rts |
|
||||
| 12 | ts_transform | String | 支持 +、-、*、/、% 操作符，例如：ts / 1000 * 1000，将一个 ms 单位的时间戳的最后 3 位置为 0；ts + 8 * 3600 * 1000，将一个 ms 精度的时间戳，增加 8 小时；ts - 8 * 3600 * 1000，将一个 ms 精度的时间戳，减去 8 小时； | 否 | None |
|
||||
| 13 | request_ts_transform | String | 支持 +、-、*、/、% 操作符，例如：qts / 1000 * 1000，将一个 ms 单位的时间戳的最后 3 位置为 0；qts + 8 * 3600 * 1000，将一个 ms 精度的时间戳，增加 8 小时；qts - 8 * 3600 * 1000，将一个 ms 精度的时间戳，减去 8 小时； | 否 | None |
|
||||
| 14 | received_ts_transform | String | 支持 +、-、*、/、% 操作符，例如：rts / 1000 * 1000，将一个 ms 单位的时间戳的最后 3 位置为 0；rts + 8 * 3600 * 1000，将一个 ms 精度的时间戳，增加 8 小时；rts - 8 * 3600 * 1000，将一个 ms 精度的时间戳，减去 8 小时； | 否 | None |
|
||||
| 15 | tag::VARCHAR(200)::name | String | tag 里的值，当 tag 的类型是 VARCHAR 时，可以是中文 | 否 | NULL |
|
||||
|
||||
(2) point_id 在整个 DataIn 任务中是唯一的，即：在一个 OPC DataIn 任务中，一个数据点位只能被写入到 TDengine 的一张子表。如果需要将一个数据点位写入多张子表，需要建多个 OPC DataIn 任务；
|
||||
|
||||
|
|
@ -154,7 +158,7 @@ CSV 文件中的每个 Row 配置一个 OPC 数据点位。Row 的规则如下
|
|||
|
||||
通过配置 **超级表名**、**表名称**，指定数据要写入的超级表、子表。
|
||||
|
||||
配置**主键列**，选择 origin_ts 表示使用 OPC 点位数据的原始时间戳作 TDengine 中的主键；选择 received_ts 表示使用数据的接收时间戳作 TDengine 中的主键。配置**主键别名**，指定 TDengine 时间戳列的名称。
|
||||
配置**主键列**，选择 origin_ts 表示使用 OPC 点位数据的原始时间戳作 TDengine 中的主键；选择 request_ts 表示使用数据的请求时间戳作 TDengine 中的主键；选择 received_ts 表示使用数据的接收时间戳作 TDengine 中的主键。配置**主键别名**，指定 TDengine 时间戳列的名称。
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -65,21 +65,23 @@ Header 是 CSV 文件的第一行，规则如下：
|
|||
(1) CSV 的 Header 中可以配置以下列：
|
||||
|
||||
|
||||
| 序号 | 列名 | 描述 | 是否必填 | 默认行为 |
|
||||
| ---- | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| 1 | tag_name | 数据点位在 OPC DA 服务器上的 id | 是 | 无 |
|
||||
| 2 | stable | 数据点位在 TDengine 中对应的超级表 | 是 | 无 |
|
||||
| 3 | tbname | 数据点位在 TDengine 中对应的子表 | 是 | 无 |
|
||||
| 4 | enable | 是否采集该点位的数据 | 否 | 使用统一的默认值`1`作为 enable 的值 |
|
||||
| 5 | value_col | 数据点位采集值在 TDengine 中对应的列名 | 否 | 使用统一的默认值`val` 作为 value_col 的值 |
|
||||
| 6 | value_transform | 数据点位采集值在 taosX 中执行的变换函数 | 否 | 统一不进行采集值的 transform |
|
||||
| 7 | type | 数据点位采集值的数据类型 | 否 | 统一使用采集值的原始类型作为 TDengine 中的数据类型 |
|
||||
| 8 | quality_col | 数据点位采集值质量在 TDengine 中对应的列名 | 否 | 统一不在 TDengine 添加 quality 列 |
|
||||
| 9 | ts_col | 数据点位的原始时间戳在 TDengine 中对应的时间戳列 | 否 | ts_col 和 received_ts_col 都非空时，使用前者作为时间戳列；ts_col 和 received_ts_col 有一列非空时，使用不为空的列作时间戳列；ts_col 和 received_ts_col 都为空时，使用数据点位原始时间戳作 TDengine 中的时间戳列，且列名为默认值ts。 |
|
||||
| 10 | received_ts_col | 接收到该点位采集值时的时间戳在 TDengine 中对应的时间戳列 | 否 | |
|
||||
| 11 | ts_transform | 数据点位时间戳在 taosX 中执行的变换函数 | 否 | 统一不进行数据点位原始时间戳的 transform |
|
||||
| 12 | received_ts_transform | 数据点位接收时间戳在 taosX 中执行的变换函数 | 否 | 统一不进行数据点位接收时间戳的 transform |
|
||||
| 13 | tag::VARCHAR(200)::name | 数据点位在 TDengine 中对应的 Tag 列。其中`tag` 为保留关键字，表示该列为一个 tag 列；`VARCHAR(200)` 表示该 tag 的类型，也可以是其它合法的类型；`name` 是该 tag 的实际名称。 | 否 | 配置 1 个以上的 tag 列，则使用配置的 tag 列；没有配置任何 tag 列，且 stable 在 TDengine 中存在，使用 TDengine 中的 stable 的 tag；没有配置任何 tag 列，且 stable 在 TDengine 中不存在，则默认自动添加以下 2 个 tag 列：tag::VARCHAR(256)::point_idtag::VARCHAR(256)::point_name |
|
||||
| 序号 | 列名 | 描述 | 是否必填 | 默认行为 |
|
||||
|----|-------------------------|-------------------------------------------------------------------------------------------------------------------| -------- |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| 1 | tag_name | 数据点位在 OPC DA 服务器上的 id | 是 | 无 |
|
||||
| 2 | stable | 数据点位在 TDengine 中对应的超级表 | 是 | 无 |
|
||||
| 3 | tbname | 数据点位在 TDengine 中对应的子表 | 是 | 无 |
|
||||
| 4 | enable | 是否采集该点位的数据 | 否 | 使用统一的默认值`1`作为 enable 的值 |
|
||||
| 5 | value_col | 数据点位采集值在 TDengine 中对应的列名 | 否 | 使用统一的默认值`val` 作为 value_col 的值 |
|
||||
| 6 | value_transform | 数据点位采集值在 taosX 中执行的变换函数 | 否 | 统一不进行采集值的 transform |
|
||||
| 7 | type | 数据点位采集值的数据类型 | 否 | 统一使用采集值的原始类型作为 TDengine 中的数据类型 |
|
||||
| 8 | quality_col | 数据点位采集值质量在 TDengine 中对应的列名 | 否 | 统一不在 TDengine 添加 quality 列 |
|
||||
| 9 | ts_col | 数据点位的原始时间戳在 TDengine 中对应的时间戳列 | 否 | ts_col，request_ts，received_ts 这 3 列，当有 2 列以上存在时，以最左侧的列作为 TDengine 中的主键 |
|
||||
| 10 | request_ts_col | 请求该点位采集值时的时间戳在 TDengine 中对应的时间戳列 | 否 | ts_col，request_ts，received_ts 这 3 列，当有 2 列以上存在时，以最左侧的列作为 TDengine 中的主键 |
|
||||
| 11 | received_ts_col | 接收到该点位采集值时的时间戳在 TDengine 中对应的时间戳列 | 否 | ts_col，request_ts，received_ts 这 3 列，当有 2 列以上存在时，以最左侧的列作为 TDengine 中的主键 |
|
||||
| 12 | ts_transform | 数据点位时间戳在 taosX 中执行的变换函数 | 否 | 统一不进行数据点位原始时间戳的 transform |
|
||||
| 13 | request_ts_transform | 数据点位请求时间戳在 taosX 中执行的变换函数 | 否 | 统一不进行数据点位原始时间戳的 transform |
|
||||
| 14 | received_ts_transform | 数据点位接收时间戳在 taosX 中执行的变换函数 | 否 | 统一不进行数据点位接收时间戳的 transform |
|
||||
| 15 | tag::VARCHAR(200)::name | 数据点位在 TDengine 中对应的 Tag 列。其中`tag` 为保留关键字，表示该列为一个 tag 列；`VARCHAR(200)` 表示该 tag 的类型，也可以是其它合法的类型；`name` 是该 tag 的实际名称。 | 否 | 配置 1 个以上的 tag 列，则使用配置的 tag 列；没有配置任何 tag 列，且 stable 在 TDengine 中存在，使用 TDengine 中的 stable 的 tag；没有配置任何 tag 列，且 stable 在 TDengine 中不存在，则默认自动添加以下 2 个 tag 列：tag::VARCHAR(256)::point_idtag::VARCHAR(256)::point_name |
|
||||
|
||||
(2) CSV Header 中，不能有重复的列；
|
||||
|
||||
|
|
@ -96,21 +98,23 @@ CSV 文件中的每个 Row 配置一个 OPC 数据点位。Row 的规则如下
|
|||
(1) 与 Header 中的列有如下对应关系
|
||||
|
||||
|
||||
| 序号 | Header 中的列 | 值的类型 | 值的范围 | 是否必填 | 默认值 |
|
||||
| ---- | ----------------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | ------------------------ |
|
||||
| 1 | tag_name | String | 类似`root.parent.temperature`这样的字符串，要满足 OPC DA 的 ID 规范 | 是 | |
|
||||
| 2 | enable | int | 0：不采集该点位，且在 OPC DataIn 任务开始前，删除 TDengine 中点位对应的子表；1：采集该点位，在 OPC DataIn 任务开始前，不删除子表。 | 否 | 1 |
|
||||
| 3 | stable | String | 符合 TDengine 超级表命名规范的任何字符串；如果存在特殊字符`.`，使用下划线替换如果存在`{type}`，则：CSV 文件的 type 不为空，使用 type 的值进行替换CSV 文件的 type 为空，使用采集值的原始类型进行替换 | 是 | |
|
||||
| 4 | tbname | String | 符合 TDengine 子表命名规范的任何字符串；如果存在特殊字符`.`，使用下划线替换对于 OPC UA：如果存在`{ns}`，使用 point_id 中的 ns 替换如果存在`{id}`，使用 point_id 中的 id 替换对于 OPC DA：如果存在`{tag_name}`，使用 tag_name 替换 | 是 | |
|
||||
| 5 | value_col | String | 符合 TDengine 命名规范的列名 | 否 | val |
|
||||
| 6 | value_transform | String | 符合 Rhai 引擎的计算表达式，例如：`(val + 10) / 1000 * 2.0`，`log(val) + 10`等； | 否 | None |
|
||||
| 7 | type | String | 支持类型包括：b/bool/i8/tinyint/i16/smallint/i32/int/i64/bigint/u8/tinyint unsigned/u16/smallint unsigned/u32/int unsigned/u64/bigint unsigned/f32/floatf64/double/timestamp/timestamp(ms)/timestamp(us)/timestamp(ns)/json | 否 | 数据点位采集值的原始类型 |
|
||||
| 8 | quality_col | String | 符合 TDengine 命名规范的列名 | 否 | None |
|
||||
| 9 | ts_col | String | 符合 TDengine 命名规范的列名 | 否 | ts |
|
||||
| 10 | received_ts_col | String | 符合 TDengine 命名规范的列名 | 否 | rts |
|
||||
| 11 | ts_transform | String | 支持 +、-、*、/、% 操作符，例如：ts / 1000 * 1000，将一个 ms 单位的时间戳的最后 3 位置为 0；ts + 8 * 3600 * 1000，将一个 ms 精度的时间戳，增加 8 小时；ts - 8 * 3600 * 1000，将一个 ms 精度的时间戳，减去 8 小时； | 否 | None |
|
||||
| 12 | received_ts_transform | String | 否 | None | |
|
||||
| 13 | tag::VARCHAR(200)::name | String | tag 里的值，当 tag 的类型是 VARCHAR 时，可以是中文 | 否 | NULL |
|
||||
| 序号 | Header 中的列 | 值的类型 | 值的范围 | 是否必填 | 默认值 |
|
||||
|----|-------------------------| -------- |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------|--------------|
|
||||
| 1 | tag_name | String | 类似`root.parent.temperature`这样的字符串，要满足 OPC DA 的 ID 规范 | 是 | |
|
||||
| 2 | enable | int | 0：不采集该点位，且在 OPC DataIn 任务开始前，删除 TDengine 中点位对应的子表；1：采集该点位，在 OPC DataIn 任务开始前，不删除子表。 | 否 | 1 |
|
||||
| 3 | stable | String | 符合 TDengine 超级表命名规范的任何字符串；如果存在特殊字符`.`，使用下划线替换如果存在`{type}`，则：CSV 文件的 type 不为空，使用 type 的值进行替换CSV 文件的 type 为空，使用采集值的原始类型进行替换 | 是 | |
|
||||
| 4 | tbname | String | 符合 TDengine 子表命名规范的任何字符串；如果存在特殊字符`.`，使用下划线替换对于 OPC UA：如果存在`{ns}`，使用 point_id 中的 ns 替换如果存在`{id}`，使用 point_id 中的 id 替换对于 OPC DA：如果存在`{tag_name}`，使用 tag_name 替换 | 是 | |
|
||||
| 5 | value_col | String | 符合 TDengine 命名规范的列名 | 否 | val |
|
||||
| 6 | value_transform | String | 符合 Rhai 引擎的计算表达式，例如：`(val + 10) / 1000 * 2.0`，`log(val) + 10`等； | 否 | None |
|
||||
| 7 | type | String | 支持类型包括：b/bool/i8/tinyint/i16/smallint/i32/int/i64/bigint/u8/tinyint unsigned/u16/smallint unsigned/u32/int unsigned/u64/bigint unsigned/f32/floatf64/double/timestamp/timestamp(ms)/timestamp(us)/timestamp(ns)/json | 否 | 数据点位采集值的原始类型 |
|
||||
| 8 | quality_col | String | 符合 TDengine 命名规范的列名 | 否 | None |
|
||||
| 9 | ts_col | String | 符合 TDengine 命名规范的列名 | 否 | ts |
|
||||
| 10 | request_ts_col | String | 符合 TDengine 命名规范的列名 | 否 | qts |
|
||||
| 11 | received_ts_col | String | 符合 TDengine 命名规范的列名 | 否 | rts |
|
||||
| 12 | ts_transform | String | 支持 +、-、*、/、% 操作符，例如：ts / 1000 * 1000，将一个 ms 单位的时间戳的最后 3 位置为 0；ts + 8 * 3600 * 1000，将一个 ms 精度的时间戳，增加 8 小时；ts - 8 * 3600 * 1000，将一个 ms 精度的时间戳，减去 8 小时； | 否 | None |
|
||||
| 13 | request_ts_transform | String | 支持 +、-、*、/、% 操作符，例如：ts / 1000 * 1000，将一个 ms 单位的时间戳的最后 3 位置为 0；qts + 8 * 3600 * 1000，将一个 ms 精度的时间戳，增加 8 小时；qts - 8 * 3600 * 1000，将一个 ms 精度的时间戳，减去 8 小时； | 否 | None |
|
||||
| 14 | received_ts_transform | String | 支持 +、-、*、/、% 操作符，例如：ts / 1000 * 1000，将一个 ms 单位的时间戳的最后 3 位置为 0；rts + 8 * 3600 * 1000，将一个 ms 精度的时间戳，增加 8 小时；rts - 8 * 3600 * 1000，将一个 ms 精度的时间戳，减去 8 小时； | 否 | None |
|
||||
| 15 | tag::VARCHAR(200)::name | String | tag 里的值，当 tag 的类型是 VARCHAR 时，可以是中文 | 否 | NULL |
|
||||
|
||||
(2) tag_name 在整个 DataIn 任务中是唯一的，即：在一个 OPC DataIn 任务中，一个数据点位只能被写入到 TDengine 的一张子表。如果需要将一个数据点位写入多张子表，需要建多个 OPC DataIn 任务；
|
||||
|
||||
|
|
@ -130,7 +134,7 @@ CSV 文件中的每个 Row 配置一个 OPC 数据点位。Row 的规则如下
|
|||
|
||||
通过配置 **超级表名**、**表名称**，指定数据要写入的超级表、子表。
|
||||
|
||||
配置**主键列**，选择 origin_ts 表示使用 OPC 点位数据的原始时间戳作 TDengine 中的主键；选择 received_ts 表示使用数据的接收时间戳作 TDengine 中的主键。配置**主键别名**，指定 TDengine 时间戳列的名称。
|
||||
配置**主键列**，选择 origin_ts 表示使用 OPC 点位数据的原始时间戳作 TDengine 中的主键；选择 request_ts 表示使用数据的请求时间戳作 TDengine 中的主键；选择 received_ts 表示使用数据的接收时间戳作 TDengine 中的主键。配置**主键别名**，指定 TDengine 时间戳列的名称。
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -294,7 +294,6 @@ taosBenchmark -f <json file>
|
|||
|
||||
查询指定表（可以指定超级表、子表或普通表）的配置参数在 `specified_table_query` 中设置。
|
||||
|
||||
|
||||
- **mixed_query**：混合查询开关。
|
||||
“yes”: 开启 “混合查询”。
|
||||
“no” : 关闭 “混合查询” ，即 “普通查询”。
|
||||
|
|
|
|||
|
|
@ -276,6 +276,16 @@ TDengine 支持基于时间戳主键的 INNER JOIN，规则如下：
|
|||
5. JOIN 两侧均支持子查询。
|
||||
6. 不支持与 FILL 子句混合使用。
|
||||
|
||||
## INTERP
|
||||
|
||||
interp 子句是 INTERP 函数(../function/#interp)的专用语法，当 SQL 语句中存在 interp 子句时，只能查询 INTERP 函数而不能与其他函数一起查询，同时 interp 子句与窗口子句(window_clause)、分组子句(group_by_clause)也不能同时使用。INTERP 函数在使用时需要与 RANGE、EVERY 和 FILL 子句一起使用；流计算不支持使用 RANGE，但需要与 EVERY 和 FILL 关键字一起使用。
|
||||
- INTERP 的输出时间范围根据 RANGE(timestamp1, timestamp2) 字段来指定，需满足 timestamp1 \<= timestamp2。其中 timestamp1 为输出时间范围的起始值，即如果 timestamp1 时刻符合插值条件则 timestamp1 为输出的第一条记录，timestamp2 为输出时间范围的结束值，即输出的最后一条记录的 timestamp 不能大于 timestamp2。
|
||||
- INTERP 根据 EVERY(time_unit) 字段来确定输出时间范围内的结果条数，即从 timestamp1 开始每隔固定长度的时间（time_unit 值）进行插值，time_unit 可取值时间单位：1a(毫秒)、1s(秒)、1m(分)、1h(小时)、1d(天)、1w(周)。例如 EVERY(500a) 将对于指定数据每500毫秒间隔进行一次插值。
|
||||
- INTERP 根据 FILL 字段来决定在每个符合输出条件的时刻如何进行插值。关于 FILL 子句如何使用请参考 [FILL 子句](./distinguished#fill-子句)
|
||||
- INTERP 可以在 RANGE 字段中只指定唯一的时间戳对单个时间点进行插值，在这种情况下，EVERY 字段可以省略。例如 `SELECT INTERP(col) FROM tb RANGE('2023-01-01 00:00:00') FILL(linear)`。
|
||||
- INTERP 查询支持 NEAR FILL 模式，即当需要 FILL 时，使用距离当前时间点最近的数据进行插值，当前后时间戳与当前时间断面一样近时，FILL 前一行的值. 此模式在流计算中和窗口查询中不支持。例如 `SELECT INTERP(col) FROM tb RANGE('2023-01-01 00:00:00', '2023-01-01 00:10:00') FILL(NEAR)` (v3.3.4.9 及以后支持)。
|
||||
- INTERP `RANGE`子句支持时间范围的扩展(v3.3.4.9 及以后支持)，如`RANGE('2023-01-01 00:00:00', 10s)`表示在时间点 '2023-01-01 00:00:00' 查找前后 10s 的数据进行插值，FILL PREV/NEXT/NEAR 分别表示从时间点向前/向后/前后查找数据，若时间点周围没有数据，则使用 FILL 指定的值进行插值，因此此时 FILL 子句必须指定值。例如 `SELECT INTERP(col) FROM tb RANGE('2023-01-01 00:00:00', 10s) FILL(PREV, 1)`。目前仅支持时间点和时间范围的组合，不支持时间区间和时间范围的组合，即不支持 `RANGE('2023-01-01 00:00:00', '2023-02-01 00:00:00', 1h)`。所指定的时间范围规则与 EVERY 类似，单位不能是年或月，值不能为 0，不能带引号。使用该扩展时，不支持除 `FILL PREV/NEXT/NEAR` 外的其他 FILL 模式，且不能指定 EVERY 子句。
|
||||
|
||||
## GROUP BY
|
||||
|
||||
如果在语句中同时指定了 GROUP BY 子句，那么 SELECT 列表只能包含如下表达式：
|
||||
|
|
|
|||
|
|
@ -1889,42 +1889,6 @@ FIRST(expr)
|
|||
- 如果结果集中所有列全部为 NULL 值，则不返回结果。
|
||||
- 对于存在复合主键的表的查询，若最小时间戳的数据有多条，则只有对应的复合主键最小的数据被返回。
|
||||
|
||||
### INTERP
|
||||
|
||||
```sql
|
||||
INTERP(expr [, ignore_null_values])
|
||||
|
||||
ignore_null_values: {
|
||||
0
|
||||
| 1
|
||||
}
|
||||
```
|
||||
|
||||
**功能说明**：返回指定时间截面指定列的记录值或插值。ignore_null_values 参数的值可以是 0 或 1，为 1 时表示忽略 NULL 值，缺省值为 0。
|
||||
|
||||
**返回数据类型**：同字段类型。
|
||||
|
||||
**适用数据类型**：数值类型。
|
||||
|
||||
**适用于**：表和超级表。
|
||||
|
||||
**使用说明**
|
||||
|
||||
- INTERP 用于在指定时间断面获取指定列的记录值，如果该时间断面不存在符合条件的行数据，那么会根据 FILL 参数的设定进行插值。
|
||||
- INTERP 的输入数据为指定列的数据，可以通过条件语句（where 子句）来对原始列数据进行过滤，如果没有指定过滤条件则输入为全部数据。
|
||||
- INTERP SQL 查询需要同时与 RANGE、EVERY 和 FILL 关键字一起使用；流计算不能使用 RANGE，需要 EVERY 和 FILL 关键字一起使用。
|
||||
- INTERP 的输出时间范围根据 RANGE(timestamp1, timestamp2) 字段来指定，需满足 timestamp1 \<= timestamp2。其中 timestamp1 为输出时间范围的起始值，即如果 timestamp1 时刻符合插值条件则 timestamp1 为输出的第一条记录，timestamp2 为输出时间范围的结束值，即输出的最后一条记录的 timestamp 不能大于 timestamp2。
|
||||
- INTERP 根据 EVERY(time_unit) 字段来确定输出时间范围内的结果条数，即从 timestamp1 开始每隔固定长度的时间（time_unit 值）进行插值，time_unit 可取值时间单位：1a(毫秒)，1s(秒)，1m(分)，1h(小时)，1d(天)，1w(周)。例如 EVERY(500a) 将对于指定数据每500毫秒间隔进行一次插值。
|
||||
- INTERP 根据 FILL 字段来决定在每个符合输出条件的时刻如何进行插值。关于 FILL 子句如何使用请参考 [FILL 子句](../distinguished/#fill-子句)
|
||||
- INTERP 可以在 RANGE 字段中只指定唯一的时间戳对单个时间点进行插值，在这种情况下，EVERY 字段可以省略。例如 SELECT INTERP(col) FROM tb RANGE('2023-01-01 00:00:00') FILL(linear)。
|
||||
- INTERP 作用于超级表时，会将该超级表下的所有子表数据按照主键列排序后进行插值计算，也可以搭配 PARTITION BY tbname 使用，将结果强制规约到单个时间线。
|
||||
- INTERP 可以与伪列 _irowts 一起使用，返回插值点所对应的时间戳(v3.0.2.0 以后支持)。
|
||||
- INTERP 可以与伪列 _isfilled 一起使用，显示返回结果是否为原始记录或插值算法产生的数据(v3.0.3.0 以后支持)。
|
||||
- INTERP 对于带复合主键的表的查询，若存在相同时间戳的数据，则只有对应的复合主键最小的数据参与运算。
|
||||
- INTERP 查询支持 NEAR FILL 模式，即当需要 FILL 时，使用距离当前时间点最近的数据进行插值，当前后时间戳与当前时间断面一样近时，FILL 前一行的值。此模式在流计算中和窗口查询中不支持。例如 SELECT INTERP(col) FROM tb RANGE('2023-01-01 00:00:00', '2023-01-01 00:10:00') FILL(NEAR)(v3.3.4.9 及以后支持)。
|
||||
- INTERP 只有在使用 FILL PREV/NEXT/NEAR 模式时才可以使用伪列 `_irowts_origin`。`_irowts_origin` 在 v3.3.4.9 以后支持。
|
||||
- INTERP `RANGE`子句从 v3.3.4.9 开始支持时间范围的扩展，如 `RANGE('2023-01-01 00:00:00', 10s)` 表示只能使用时间点 '2023-01-01 00:00:00' 周边 10s 内的数据进行插值，FILL PREV/NEXT/NEAR 分别表示从时间点开始向前/向后/前后在时间范围内查找数据，若时间点周边在指定时间范围内没有数据，则使用 FILL 指定的默认值进行插值，因此此时 FILL 子句必须同时指定默认值。例如 SELECT INTERP(col) FROM tb RANGE('2023-01-01 00:00:00', 10s) FILL(PREV, 1)。从 v3.3.6.0 开始支持时间区间和时间范围的组合，对于时间区间内的每个断面进行插值时都需要满足时间范围的要求，在此之前的版本仅支持时间点和时间范围的组合。时间范围的值域规则与 EVERY 类似，单位不能是年或月，值必须大于 0，不能带引号。使用该扩展时，不支持除 `FILL PREV/NEXT/NEAR` 外的其他 FILL 模式。
|
||||
|
||||
### LAST
|
||||
|
||||
```sql
|
||||
|
|
@ -2187,6 +2151,34 @@ ignore_option: {
|
|||
- 当没有复合主键时，如果不同的子表有相同时间戳的数据，会提示 "Duplicate timestamps not allowed"。
|
||||
- 当使用复合主键时，不同子表的时间戳和主键组合可能相同，使用哪一行取决于先找到哪一行，这意味着在这种情况下多次运行 diff() 的结果可能会不同。
|
||||
|
||||
### INTERP
|
||||
|
||||
```sql
|
||||
INTERP(expr [, ignore_null_values])
|
||||
|
||||
ignore_null_values: {
|
||||
0
|
||||
| 1
|
||||
}
|
||||
```
|
||||
|
||||
**功能说明**：返回指定时间截面指定列的记录值或插值。ignore_null_values 参数的值可以是 0 或 1，为 1 时表示忽略 NULL 值，缺省值为 0。
|
||||
|
||||
**返回数据类型**：同字段类型。
|
||||
|
||||
**适用数据类型**：数值类型。
|
||||
|
||||
**适用于**：表和超级表。
|
||||
|
||||
**使用说明**
|
||||
- INTERP 用于在指定时间断面获取指定列的记录值，使用时有专用语法(interp_clause)，语法介绍[参考链接](../select/#interp) 。
|
||||
- 当指定时间断面不存在符合条件的行数据时，INTERP 函数会根据 [FILL](../distinguished/#fill-子句) 参数的设定进行插值。
|
||||
- INTERP 作用于超级表时，会将该超级表下的所有子表数据按照主键列排序后进行插值计算，也可以搭配 PARTITION BY tbname 使用，将结果强制规约到单个时间线。
|
||||
- INTERP 可以与伪列 `_irowts` 一起使用，返回插值点所对应的时间戳(v3.0.2.0 以后支持)。
|
||||
- INTERP 可以与伪列 `_isfilled` 一起使用，显示返回结果是否为原始记录或插值算法产生的数据(v3.0.3.0 以后支持)。
|
||||
- 只有在使用 FILL PREV/NEXT/NEAR 模式时才可以使用伪列 `_irowts_origin`, 用于返回 `interp` 函数所使用的原始数据的时间戳列。若范围内无值, 则返回 NULL。`_irowts_origin` 在 v3.3.4.9 以后支持。
|
||||
- 对于带复合主键的表的查询，若存在相同时间戳的数据，则只有对应的复合主键最小的数据参与运算。
|
||||
|
||||
### IRATE
|
||||
|
||||
```sql
|
||||
|
|
|
|||
|
|
@ -17,7 +17,9 @@
|
|||
#include "function.h"
|
||||
#include "functionMgt.h"
|
||||
#include "operator.h"
|
||||
#include "query.h"
|
||||
#include "querytask.h"
|
||||
#include "taoserror.h"
|
||||
#include "tchecksum.h"
|
||||
#include "tcommon.h"
|
||||
#include "tcompare.h"
|
||||
|
|
@ -986,6 +988,11 @@ static void doStateWindowAggImpl(SOperatorInfo* pOperator, SStateWindowOperatorI
|
|||
if (colDataIsNull(pStateColInfoData, pBlock->info.rows, j, pAgg)) {
|
||||
continue;
|
||||
}
|
||||
if (pStateColInfoData->pData == NULL) {
|
||||
qError("%s:%d state column data is null", __FILE__, __LINE__);
|
||||
pTaskInfo->code = TSDB_CODE_QRY_EXECUTOR_INTERNAL_ERROR;
|
||||
T_LONG_JMP(pTaskInfo->env, TSDB_CODE_QRY_EXECUTOR_INTERNAL_ERROR);
|
||||
}
|
||||
|
||||
char* val = colDataGetData(pStateColInfoData, j);
|
||||
|
||||
|
|
|
|||
|
|
@ -6610,12 +6610,12 @@ int32_t blockDBUsageFinalize(SqlFunctionCtx* pCtx, SSDataBlock* pBlock) {
|
|||
|
||||
uint64_t totalDiskSize = pData->dataInDiskSize;
|
||||
uint64_t rawDataSize = pData->rawDataSize;
|
||||
double compressRadio = 0;
|
||||
double compressRatio = 0;
|
||||
if (rawDataSize != 0) {
|
||||
compressRadio = totalDiskSize * 100 / (double)rawDataSize;
|
||||
len = tsnprintf(varDataVal(st), sizeof(st) - VARSTR_HEADER_SIZE, "Compress_radio=[%.2f%]", compressRadio);
|
||||
compressRatio = totalDiskSize * 100 / (double)rawDataSize;
|
||||
len = tsnprintf(varDataVal(st), sizeof(st) - VARSTR_HEADER_SIZE, "Compress_ratio=[%.2f%]", compressRatio);
|
||||
} else {
|
||||
len = tsnprintf(varDataVal(st), sizeof(st) - VARSTR_HEADER_SIZE, "Compress_radio=[NULL]");
|
||||
len = tsnprintf(varDataVal(st), sizeof(st) - VARSTR_HEADER_SIZE, "Compress_ratio=[NULL]");
|
||||
}
|
||||
|
||||
varDataSetLen(st, len);
|
||||
|
|
|
|||
|
|
@ -2258,9 +2258,17 @@ static int32_t createWindowPhysiNodeFinalize(SPhysiPlanContext* pCxt, SNodeList*
|
|||
SDataBlockDescNode* pChildTupe = (((SPhysiNode*)nodesListGetNode(pChildren, 0))->pOutputDataBlockDesc);
|
||||
// push down expression to pOutputDataBlockDesc of child node
|
||||
if (TSDB_CODE_SUCCESS == code && NULL != pPrecalcExprs) {
|
||||
code = setListSlotId(pCxt, pChildTupe->dataBlockId, -1, pPrecalcExprs, &pWindow->pExprs);
|
||||
SNodeList* pOutput;
|
||||
code = setListSlotId(pCxt, pChildTupe->dataBlockId, -1, pPrecalcExprs, &pOutput);
|
||||
if (TSDB_CODE_SUCCESS == code) {
|
||||
code = addDataBlockSlots(pCxt, pWindow->pExprs, pChildTupe);
|
||||
code = addDataBlockSlots(pCxt, pOutput, pChildTupe);
|
||||
}
|
||||
if (TSDB_CODE_SUCCESS == code) {
|
||||
if (pWindow->pExprs == NULL) {
|
||||
pWindow->pExprs = pOutput;
|
||||
} else {
|
||||
code = nodesListAppendList(pWindow->pExprs, pOutput);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -182,7 +182,7 @@ void compressImplTestByAlg(void* pVal, int8_t type, int32_t num, uint32_t
|
|||
|
||||
DEFINE_VAR(cmprAlg)
|
||||
int32_t len = compres.compFunc(pVal, bytes, num, px, externalSize, cmprAlg, pBuf, externalSize);
|
||||
printf("encode:%s, compress alg:%s, type:%s, compresess size: %d, actual size: %d, radio: %f\n", end[l1], alg[l2],
|
||||
printf("encode:%s, compress alg:%s, type:%s, compresess size: %d, actual size: %d, ratio: %f\n", end[l1], alg[l2],
|
||||
compres.name, len, bytes, (float)len / bytes);
|
||||
char* pOutput = static_cast<char*>(taosMemoryCalloc(1, externalSize));
|
||||
memset(pBuf, 0, externalSize);
|
||||
|
|
|
|||
|
|
@ -203,7 +203,23 @@ class TDTestCase:
|
|||
tdSql.execute("insert into t0 values(now, 3,NULL,3,3,3,3,3,3,3)", queryTimes=1)
|
||||
tdSql.query("select first(c2) from t0 session(ts, 1s) order by ts", queryTimes=1)
|
||||
|
||||
def ts6079(self):
|
||||
ts = 1741757485230
|
||||
tdSql.execute("drop database if exists ts6079")
|
||||
tdSql.execute("create database ts6079 vgroups 2 replica 1")
|
||||
tdSql.execute("CREATE STABLE ts6079.`meters` (`ts` TIMESTAMP, `current` FLOAT, `voltage` INT, `phase` FLOAT) TAGS (`groupid` INT, `location` VARCHAR(24))")
|
||||
for tableIndex in range(10):
|
||||
tdSql.execute(f"CREATE TABLE ts6079.t{tableIndex} USING ts6079.meters TAGS ({tableIndex}, 'tb{tableIndex}')")
|
||||
for num in range(10):
|
||||
tdSql.execute(f"INSERT INTO ts6079.t{tableIndex} VALUES({ts + num}, {num * 1.0}, {215 + num}, 0.0)")
|
||||
|
||||
tdSql.query("select _wstart ,first(ts),last(ts),count(*),to_char(ts, 'yyyymmdd') as ts from ts6079.meters partition by to_char(ts, 'yyyymmdd') as ts state_window(cast(current as varchar(2)));")
|
||||
tdSql.checkRows(10)
|
||||
tdSql.checkData(0, 3, 10)
|
||||
|
||||
|
||||
def run(self):
|
||||
self.ts6079()
|
||||
self.test_crash_for_session_window()
|
||||
self.test_crash_for_state_window1()
|
||||
self.test_crash_for_state_window2()
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@
|
|||
# expressly provided by the written permission from Jianhui Tao
|
||||
#
|
||||
###################################################################
|
||||
import time
|
||||
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
|
|
@ -90,6 +91,17 @@ class TDTestCase:
|
|||
return True
|
||||
|
||||
def restart_stream(self):
|
||||
st = time.time()
|
||||
while True:
|
||||
sql = 'select status from information_schema.ins_stream_tasks where status<>"ready" '
|
||||
if len(tdSql.getResult(sql)) != 0:
|
||||
time.sleep(1)
|
||||
tdLog.info("wait for task to be ready, 1s")
|
||||
else:
|
||||
et = time.time()
|
||||
tdLog.info(f"wait for tasks to be ready: {et-st}s")
|
||||
break
|
||||
|
||||
tdLog.debug("========restart stream========")
|
||||
for i in range(5):
|
||||
tdSql.execute("pause stream s1")
|
||||
|
|
|
|||
|
|
@ -8,4 +8,4 @@ ENDIF(${BUILD_TEST})
|
|||
# ADD_SUBDIRECTORY(comparisonTest/tdengine)
|
||||
IF(NOT "${TSZ_ENABLED}" MATCHES "false")
|
||||
ADD_SUBDIRECTORY(TSZ)
|
||||
ENDIF()
|
||||
ENDIF()
|
||||
|
|
|
|||
Loading…
Reference in New Issue