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Merge remote-tracking branch 'refs/remotes/origin/3.0' into 3.0

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11
README.md
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@ -15,11 +15,11 @@
[![Coverage Status](https://coveralls.io/repos/github/taosdata/TDengine/badge.svg?branch=develop)](https://coveralls.io/github/taosdata/TDengine?branch=develop)
[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/4201/badge)](https://bestpractices.coreinfrastructure.org/projects/4201)
English | [简体中文](README-CN.md) | We are hiring, check [here](https://tdengine.com/careers)
English | [简体中文](README-CN.md) | [Lean more about TSDB](https://tdengine.com/tsdb)
# What is TDengine？
TDengine is an open source, high-performance, cloud native [time-series database](https://tdengine.com/tsdb/what-is-a-time-series-database/) optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors. TDengine differentiates itself from other time-seires databases with the following advantages:
TDengine is an open source, high-performance, cloud native [time-series database](https://tdengine.com/tsdb/) optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors. TDengine differentiates itself from other time-seires databases with the following advantages:
- **[High-Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression.
@ -33,6 +33,8 @@ TDengine is an open source, high-performance, cloud native [time-series database
- **[Open Source](https://tdengine.com/tdengine/open-source-time-series-database/)**: TDengine’s core modules, including cluster feature, are all available under open source licenses. It has gathered 18.8k stars on GitHub. There is an active developer community, and over 139k running instances worldwide.
For a full list of TDengine competitive advantages, please [check here](https://tdengine.com/tdengine/)
# Documentation
For user manual, system design and architecture, please refer to [TDengine Documentation](https://docs.tdengine.com) ([TDengine 文档](https://docs.taosdata.com))
@ -319,6 +321,7 @@ TDengine provides abundant developing tools for users to develop on TDengine. Fo
Please follow the [contribution guidelines](CONTRIBUTING.md) to contribute to the project.
# Join TDengine WeChat Group
# Join TDengine User Community
Add WeChat “tdengine” to join the group，you can communicate with other users.
- Join [TDengine Discord Channel](https://discord.com/invite/VZdSuUg4pS?utm_id=discord)
- Join wechat group by adding WeChat “tdengine”

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@ -87,7 +87,7 @@ IF ("${CPUTYPE}" STREQUAL "")
SET(TD_ARM_32 TRUE)
ADD_DEFINITIONS("-D_TD_ARM_")
ADD_DEFINITIONS("-D_TD_ARM_32")
ELSEIF (CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64")
ELSEIF (CMAKE_SYSTEM_PROCESSOR MATCHES "(aarch64)|(arm64)")
MESSAGE(STATUS "The current platform is aarch64")
SET(PLATFORM_ARCH_STR "arm64")
SET(TD_ARM_64 TRUE)

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cmake/cmake.version
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@ -2,7 +2,7 @@
IF (DEFINED VERNUMBER)
SET(TD_VER_NUMBER ${VERNUMBER})
ELSE ()
SET(TD_VER_NUMBER "3.0.0.1")
SET(TD_VER_NUMBER "3.0.1.1")
ENDIF ()
IF (DEFINED VERCOMPATIBLE)

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cmake/taosadapter_CMakeLists.txt.in
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@ -2,7 +2,7 @@
# taosadapter
ExternalProject_Add(taosadapter
GIT_REPOSITORY https://github.com/taosdata/taosadapter.git
GIT_TAG abed566
GIT_TAG 05fb2ff
SOURCE_DIR "${TD_SOURCE_DIR}/tools/taosadapter"
BINARY_DIR ""
#BUILD_IN_SOURCE TRUE

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cmake/taostools_CMakeLists.txt.in
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@ -2,7 +2,7 @@
# taos-tools
ExternalProject_Add(taos-tools
GIT_REPOSITORY https://github.com/taosdata/taos-tools.git
GIT_TAG a4d9b92
GIT_TAG 125c77a
SOURCE_DIR "${TD_SOURCE_DIR}/tools/taos-tools"
BINARY_DIR ""
#BUILD_IN_SOURCE TRUE

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cmake/taosws_CMakeLists.txt.in
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@ -2,7 +2,7 @@
# taosws-rs
ExternalProject_Add(taosws-rs
GIT_REPOSITORY https://github.com/taosdata/taos-connector-rust.git
GIT_TAG 6fc47d7
GIT_TAG e771403
SOURCE_DIR "${TD_SOURCE_DIR}/tools/taosws-rs"
BINARY_DIR ""
#BUILD_IN_SOURCE TRUE

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@ -4,7 +4,7 @@ sidebar_label: Documentation Home
slug: /
---
TDengine is an [open-source](https://tdengine.com/tdengine/open-source-time-series-database/), [cloud-native](https://tdengine.com/tdengine/cloud-native-time-series-database/) time-series database optimized for the Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors. This document is the TDengine user manual. It introduces the basic, as well as novel concepts, in TDengine, and also talks in detail about installation, features, SQL, APIs, operation, maintenance, kernel design, and other topics. It’s written mainly for architects, developers, and system administrators.
TDengine is an [open-source](https://tdengine.com/tdengine/open-source-time-series-database/), [cloud-native](https://tdengine.com/tdengine/cloud-native-time-series-database/) [time-series database](https://tdengine.com/tsdb/) optimized for the Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors. This document is the TDengine user manual. It introduces the basic, as well as novel concepts, in TDengine, and also talks in detail about installation, features, SQL, APIs, operation, maintenance, kernel design, and other topics. It’s written mainly for architects, developers, and system administrators.
To get an overview of TDengine, such as a feature list, benchmarks, and competitive advantages, please browse through the [Introduction](./intro) section.
@ -22,6 +22,8 @@ If you want to know more about TDengine tools, the REST API, and connectors for
If you are very interested in the internal design of TDengine, please read the chapter [Inside TDengine](./tdinternal), which introduces the cluster design, data partitioning, sharding, writing, and reading processes in detail. If you want to study TDengine code or even contribute code, please read this chapter carefully.
To get more general introduction about time series database, please read through [a series of articles](https://tdengine.com/tsdb/). To lean more competitive advantages about TDengine, please read through [a series of blogs](https://tdengine.com/tdengine/).
TDengine is an open-source database, and we would love for you to be a part of TDengine. If you find any errors in the documentation or see parts where more clarity or elaboration is needed, please click "Edit this page" at the bottom of each page to edit it directly.
Together, we make a difference!

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@ -3,7 +3,7 @@ title: Introduction
toc_max_heading_level: 2
---
TDengine is an open source, high-performance, cloud native [time-series database](https://tdengine.com/tsdb/) optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. Its code, including its cluster feature is open source under GNU AGPL v3.0. Besides the database engine, it provides [caching](../develop/cache), [stream processing](../develop/stream), [data subscription](../develop/tmq) and other functionalities to reduce the system complexity and cost of development and operation.
TDengine is an [open source](https://tdengine.com/tdengine/open-source-time-series-database/), [high-performance](https://tdengine.com/tdengine/high-performance-time-series-database/), [cloud native](https://tdengine.com/tdengine/cloud-native-time-series-database/) [time-series database](https://tdengine.com/tsdb/) optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. Its code, including its cluster feature is open source under GNU AGPL v3.0. Besides the database engine, it provides [caching](../develop/cache), [stream processing](../develop/stream), [data subscription](../develop/tmq) and other functionalities to reduce the system complexity and cost of development and operation.
This section introduces the major features, competitive advantages, typical use-cases and benchmarks to help you get a high level overview of TDengine.
@ -43,7 +43,7 @@ For more details on features, please read through the entire documentation.
## Competitive Advantages
By making full use of [characteristics of time series data](https://tdengine.com/tsdb/characteristics-of-time-series-data/), TDengine differentiates itself from other time series databases, with the following advantages.
By making full use of [characteristics of time series data](https://tdengine.com/tsdb/characteristics-of-time-series-data/), TDengine differentiates itself from other [time series databases](https://tdengine.com/tsdb), with the following advantages.
- **[High-Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression.
@ -127,3 +127,8 @@ As a high-performance, scalable and SQL supported time-series database, TDengine
- [TDengine vs OpenTSDB](https://tdengine.com/2019/09/12/710.html)
- [TDengine vs Cassandra](https://tdengine.com/2019/09/12/708.html)
- [TDengine vs InfluxDB](https://tdengine.com/2019/09/12/706.html)
## More readings
- [Introduction to Time-Series Database](https://tdengine.com/tsdb/)
- [Introduction to TDengine competitive advantages](https://tdengine.com/tdengine/)

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@ -6,101 +6,100 @@ In order to explain the basic concepts and provide some sample code, the TDengin
<div className="center-table">
<table>
<thead><tr>
<th>Device ID</th>
<th>Time Stamp</th>
<th colSpan="3">Collected Metrics</th>
<th colSpan="2">Tags</th>
<thead>
<tr>
<th rowSpan="2">Device ID</th>
<th rowSpan="2">Timestamp</th>
<th colSpan="3">Collected Metrics</th>
<th colSpan="2">Tags</th>
</tr>
<tr>
<th>Device ID</th>
<th>Time Stamp</th>
<th>current</th>
<th>voltage</th>
<th>phase</th>
<th>location</th>
<th>groupId</th>
</tr>
</thead>
<tbody>
<tr>
<td>d1001</td>
<td>1538548685000</td>
<td>10.3</td>
<td>219</td>
<td>0.31</td>
<td>California.SanFrancisco</td>
<td>2</td>
</tr>
<tr>
<td>d1002</td>
<td>1538548684000</td>
<td>10.2</td>
<td>220</td>
<td>0.23</td>
<td>California.SanFrancisco</td>
<td>3</td>
</tr>
<tr>
<td>d1003</td>
<td>1538548686500</td>
<td>11.5</td>
<td>221</td>
<td>0.35</td>
<td>California.LosAngeles</td>
<td>3</td>
</tr>
<tr>
<td>d1004</td>
<td>1538548685500</td>
<td>13.4</td>
<td>223</td>
<td>0.29</td>
<td>California.LosAngeles</td>
<td>2</td>
</tr>
<tr>
<td>d1001</td>
<td>1538548695000</td>
<td>12.6</td>
<td>218</td>
<td>0.33</td>
<td>California.SanFrancisco</td>
<td>2</td>
</tr>
<tr>
<td>d1004</td>
<td>1538548696600</td>
<td>11.8</td>
<td>221</td>
<td>0.28</td>
<td>California.LosAngeles</td>
<td>2</td>
</tr>
<tr>
<td>d1002</td>
<td>1538548696650</td>
<td>10.3</td>
<td>218</td>
<td>0.25</td>
<td>California.SanFrancisco</td>
<td>3</td>
</tr>
<tr>
<td>d1001</td>
<td>1538548696800</td>
<td>12.3</td>
<td>221</td>
<td>0.31</td>
<td>California.SanFrancisco</td>
<td>2</td>
</tr>
</tbody>
<tr>
<th>current</th>
<th>voltage</th>
<th>phase</th>
<th>location</th>
<th>groupid</th>
</tr>
</thead>
<tbody>
<tr>
<td>d1001</td>
<td>1538548685000</td>
<td>10.3</td>
<td>219</td>
<td>0.31</td>
<td>California.SanFrancisco</td>
<td>2</td>
</tr>
<tr>
<td>d1002</td>
<td>1538548684000</td>
<td>10.2</td>
<td>220</td>
<td>0.23</td>
<td>California.SanFrancisco</td>
<td>3</td>
</tr>
<tr>
<td>d1003</td>
<td>1538548686500</td>
<td>11.5</td>
<td>221</td>
<td>0.35</td>
<td>California.LosAngeles</td>
<td>3</td>
</tr>
<tr>
<td>d1004</td>
<td>1538548685500</td>
<td>13.4</td>
<td>223</td>
<td>0.29</td>
<td>California.LosAngeles</td>
<td>2</td>
</tr>
<tr>
<td>d1001</td>
<td>1538548695000</td>
<td>12.6</td>
<td>218</td>
<td>0.33</td>
<td>California.SanFrancisco</td>
<td>2</td>
</tr>
<tr>
<td>d1004</td>
<td>1538548696600</td>
<td>11.8</td>
<td>221</td>
<td>0.28</td>
<td>California.LosAngeles</td>
<td>2</td>
</tr>
<tr>
<td>d1002</td>
<td>1538548696650</td>
<td>10.3</td>
<td>218</td>
<td>0.25</td>
<td>California.SanFrancisco</td>
<td>3</td>
</tr>
<tr>
<td>d1001</td>
<td>1538548696800</td>
<td>12.3</td>
<td>221</td>
<td>0.31</td>
<td>California.SanFrancisco</td>
<td>2</td>
</tr>
</tbody>
</table>
<a href="#model_table1">Table 1: Smart meter example data</a>
</div>
Each row contains the device ID, time stamp, collected metrics (current, voltage, phase as above), and static tags (location and groupId in Table 1) associated with the devices. Each smart meter generates a row (measurement) in a pre-defined time interval or triggered by an external event. The device produces a sequence of measurements with associated time stamps.
Each row contains the device ID, timestamp, collected metrics (`current`, `voltage`, `phase` as above), and static tags (`location` and `groupid` in Table 1) associated with the devices. Each smart meter generates a row (measurement) in a pre-defined time interval or triggered by an external event. The device produces a sequence of measurements with associated timestamps.
## Metric
@ -112,22 +111,22 @@ Label/Tag refers to the static properties of sensors, equipment or other types o
## Data Collection Point
Data Collection Point (DCP) refers to hardware or software that collects metrics based on preset time periods or triggered by events. A data collection point can collect one or multiple metrics, but these metrics are collected at the same time and have the same time stamp. For some complex equipment, there are often multiple data collection points, and the sampling rate of each collection point may be different, and fully independent. For example, for a car, there could be a data collection point to collect GPS position metrics, a data collection point to collect engine status metrics, and a data collection point to collect the environment metrics inside the car. So in this example the car would have three data collection points. In the smart meters example, d1001, d1002, d1003, and d1004 are the data collection points.
Data Collection Point (DCP) refers to hardware or software that collects metrics based on preset time periods or triggered by events. A data collection point can collect one or multiple metrics, but these metrics are collected at the same time and have the same timestamp. For some complex equipment, there are often multiple data collection points, and the sampling rate of each collection point may be different, and fully independent. For example, for a car, there could be a data collection point to collect GPS position metrics, a data collection point to collect engine status metrics, and a data collection point to collect the environment metrics inside the car. So in this example the car would have three data collection points. In the smart meters example, d1001, d1002, d1003, and d1004 are the data collection points.
## Table
Since time-series data is most likely to be structured data, TDengine adopts the traditional relational database model to process them with a short learning curve. You need to create a database, create tables, then insert data points and execute queries to explore the data.
To make full use of time-series data characteristics, TDengine adopts a strategy of "**One Table for One Data Collection Point**". TDengine requires the user to create a table for each data collection point (DCP) to store collected time-series data. For example, if there are over 10 million smart meters, it means 10 million tables should be created. For the table above, 4 tables should be created for devices D1001, D1002, D1003, and D1004 to store the data collected. This design has several benefits:
To make full use of time-series data characteristics, TDengine adopts a strategy of "**One Table for One Data Collection Point**". TDengine requires the user to create a table for each data collection point (DCP) to store collected time-series data. For example, if there are over 10 million smart meters, it means 10 million tables should be created. For the table above, 4 tables should be created for devices d1001, d1002, d1003, and d1004 to store the data collected. This design has several benefits:
1. Since the metric data from different DCP are fully independent, the data source of each DCP is unique, and a table has only one writer. In this way, data points can be written in a lock-free manner, and the writing speed can be greatly improved.
2. For a DCP, the metric data generated by DCP is ordered by timestamp, so the write operation can be implemented by simple appending, which further greatly improves the data writing speed.
3. The metric data from a DCP is continuously stored, block by block. If you read data for a period of time, it can greatly reduce random read operations and improve read and query performance by orders of magnitude.
4. Inside a data block for a DCP, columnar storage is used, and different compression algorithms are used for different data types. Metrics generally don't vary as significantly between themselves over a time range as compared to other metrics, which allows for a higher compression rate.
If the metric data of multiple DCPs are traditionally written into a single table, due to uncontrollable network delays, the timing of the data from different DCPs arriving at the server cannot be guaranteed, write operations must be protected by locks, and metric data from one DCP cannot be guaranteed to be continuously stored together. ** One table for one data collection point can ensure the best performance of insert and query of a single data collection point to the greatest possible extent.**
If the metric data of multiple DCPs are traditionally written into a single table, due to uncontrollable network delays, the timing of the data from different DCPs arriving at the server cannot be guaranteed, write operations must be protected by locks, and metric data from one DCP cannot be guaranteed to be continuously stored together. **One table for one data collection point can ensure the best performance of insert and query of a single data collection point to the greatest possible extent.**
TDengine suggests using DCP ID as the table name (like D1001 in the above table). Each DCP may collect one or multiple metrics (like the current, voltage, phase as above). Each metric has a corresponding column in the table. The data type for a column can be int, float, string and others. In addition, the first column in the table must be a timestamp. TDengine uses the time stamp as the index, and won’t build the index on any metrics stored. Column wise storage is used.
TDengine suggests using DCP ID as the table name (like d1001 in the above table). Each DCP may collect one or multiple metrics (like the `current`, `voltage`, `phase` as above). Each metric has a corresponding column in the table. The data type for a column can be int, float, string and others. In addition, the first column in the table must be a timestamp. TDengine uses the timestamp as the index, and won’t build the index on any metrics stored. Column wise storage is used.
Complex devices, such as connected cars, may have multiple DCPs. In this case, multiple tables are created for a single device, one table per DCP.
@ -156,9 +155,16 @@ The relationship between a STable and the subtables created based on this STable
Queries can be executed on both a table (subtable) and a STable. For a query on a STable, TDengine will treat the data in all its subtables as a whole data set for processing. TDengine will first find the subtables that meet the tag filter conditions, then scan the time-series data of these subtables to perform aggregation operation, which reduces the number of data sets to be scanned which in turn greatly improves the performance of data aggregation across multiple DCPs. In essence, querying a supertable is a very efficient aggregate query on multiple DCPs of the same type.
In TDengine, it is recommended to use a subtable instead of a regular table for a DCP. In the smart meters example, we can create subtables like d1001, d1002, d1003, and d1004 under super table meters.
In TDengine, it is recommended to use a subtable instead of a regular table for a DCP. In the smart meters example, we can create subtables like d1001, d1002, d1003, and d1004 under super table `meters`.
To better understand the data model using metri, tags, super table and subtable, please refer to the diagram below which demonstrates the data model of the smart meters example. ![Meters Data Model Diagram](./supertable.webp)
To better understand the data model using metrics, tags, super table and subtable, please refer to the diagram below which demonstrates the data model of the smart meters example.
<figure>
![Meters Data Model Diagram](./supertable.webp)
<center><figcaption>Figure 1. Meters Data Model Diagram</figcaption></center>
</figure>
## Database
@ -172,4 +178,4 @@ FQDN (Fully Qualified Domain Name) is the full domain name of a specific compute
Each node of a TDengine cluster is uniquely identified by an End Point, which consists of an FQDN and a Port, such as h1.tdengine.com:6030. In this way, when the IP changes, we can still use the FQDN to dynamically find the node without changing any configuration of the cluster. In addition, FQDN is used to facilitate unified access to the same cluster from the Intranet and the Internet.
TDengine does not recommend using an IP address to access the cluster. FQDN is recommended for cluster management.
TDengine does not recommend using an IP address to access the cluster. FQDN is recommended for cluster management.

64
docs/en/05-get-started/01-docker.md
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@ -13,7 +13,7 @@ If Docker is already installed on your computer, run the following command:
docker run -d -p 6030:6030 -p 6041:6041 -p 6043-6049:6043-6049 -p 6043-6049:6043-6049/udp tdengine/tdengine
```
Note that TDengine Server uses TCP port 6030. Port 6041 is used by taosAdapter for the REST API service. Ports 6043 through 6049 are used by taosAdapter for other connectors. You can open these ports as needed.
Note that TDengine Server 3.0 uses TCP port 6030. Port 6041 is used by taosAdapter for the REST API service. Ports 6043 through 6049 are used by taosAdapter for other connectors. You can open these ports as needed.
Run the following command to ensure that your container is running:
@ -21,7 +21,7 @@ Run the following command to ensure that your container is running:
docker ps
```
Enter the container and open the bash shell:
Enter the container and open the `bash` shell:
```shell
docker exec -it <container name> bash
@ -31,68 +31,68 @@ You can now access TDengine or run other Linux commands.
Note: For information about installing docker, see the [official documentation](https://docs.docker.com/get-docker/).
## Insert Data into TDengine
You can use the `taosBenchmark` tool included with TDengine to write test data into your deployment.
To do so, run the following command:
```bash
$ taosBenchmark
```
This command creates the `meters` supertable in the `test` database. In the `meters` supertable, it then creates 10,000 subtables named `d0` to `d9999`. Each table has 10,000 rows and each row has four columns: `ts`, `current`, `voltage`, and `phase`. The timestamps of the data in these columns range from 2017-07-14 10:40:00 000 to 2017-07-14 10:40:09 999. Each table is randomly assigned a `groupId` tag from 1 to 10 and a `location` tag of either `Campbell`, `Cupertino`, `Los Angeles`, `Mountain View`, `Palo Alto`, `San Diego`, `San Francisco`, `San Jose`, `Santa Clara` or `Sunnyvale`.
The `taosBenchmark` command creates a deployment with 100 million data points that you can use for testing purposes. The time required depends on the hardware specifications of the local system.
You can customize the test deployment that taosBenchmark creates by specifying command-line parameters. For information about command-line parameters, run the `taosBenchmark --help` command. For more information about taosBenchmark, see [taosBenchmark](/reference/taosbenchmark).
## Open the TDengine CLI
On the container, run the following command to open the TDengine CLI:
On the container, run the following command to open the TDengine CLI:
```
$ taos
taos>
taos>
```
## Query Data in TDengine
## Test data insert performance
After using taosBenchmark to create your test deployment, you can run queries in the TDengine CLI to test its performance. For example:
After your TDengine Server is running normally, you can run the taosBenchmark utility to test its performance:
From the TDengine CLI query the number of rows in the `meters` supertable:
Start TDengine service and execute `taosBenchmark` (formerly named `taosdemo`) in a Linux or Windows terminal.
```bash
taosBenchmark
```
This command creates the `meters` supertable in the `test` database. In the `meters` supertable, it then creates 10,000 subtables named `d0` to `d9999`. Each table has 10,000 rows and each row has four columns: `ts`, `current`, `voltage`, and `phase`. The timestamps of the data in these columns range from 2017-07-14 10:40:00 000 to 2017-07-14 10:40:09 999. Each table is randomly assigned a `groupId` tag from 1 to 10 and a `location` tag of either `California.Campbell`, `California.Cupertino`, `California.LosAngeles`, `California.MountainView`, `California.PaloAlto`, `California.SanDiego`, `California.SanFrancisco`, `California.SanJose`, `California.SantaClara` or `California.Sunnyvale`.
The `taosBenchmark` command creates a deployment with 100 million data points that you can use for testing purposes. The time required to create the deployment depends on your hardware. On most modern servers, the deployment is created in ten to twenty seconds.
You can customize the test deployment that taosBenchmark creates by specifying command-line parameters. For information about command-line parameters, run the `taosBenchmark --help` command. For more information about taosBenchmark, see [taosBenchmark](../../reference/taosbenchmark).
## Test data query performance
After using `taosBenchmark` to create your test deployment, you can run queries in the TDengine CLI to test its performance:
From the TDengine CLI (taos) query the number of rows in the `meters` supertable:
```sql
select count(*) from test.meters;
SELECT COUNT(*) FROM test.meters;
```
Query the average, maximum, and minimum values of all 100 million rows of data:
```sql
select avg(current), max(voltage), min(phase) from test.meters;
SELECT AVG(current), MAX(voltage), MIN(phase) FROM test.meters;
```
Query the number of rows whose `location` tag is `San Francisco`:
Query the number of rows whose `location` tag is `California.SanFrancisco`:
```sql
select count(*) from test.meters where location="San Francisco";
SELECT COUNT(*) FROM test.meters WHERE location = "California.SanFrancisco";
```
Query the average, maximum, and minimum values of all rows whose `groupId` tag is `10`:
```sql
select avg(current), max(voltage), min(phase) from test.meters where groupId=10;
SELECT AVG(current), MAX(voltage), MIN(phase) FROM test.meters WHERE groupId = 10;
```
Query the average, maximum, and minimum values for table `d10` in 1 second intervals:
Query the average, maximum, and minimum values for table `d10` in 10 second intervals:
```sql
select first(ts), avg(current), max(voltage), min(phase) from test.d10 interval(1s);
SELECT FIRST(ts), AVG(current), MAX(voltage), MIN(phase) FROM test.d10 INTERVAL(10s);
```
In the query above you are selecting the first timestamp (ts) in the interval, another way of selecting this would be _wstart which will give the start of the time window. For more information about windowed queries, see [Time-Series Extensions](../../taos-sql/distinguished/).
In the query above you are selecting the first timestamp (ts) in the interval, another way of selecting this would be `\_wstart` which will give the start of the time window. For more information about windowed queries, see [Time-Series Extensions](../../taos-sql/distinguished/).
## Additional Information

108
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View File

@ -9,23 +9,24 @@ import PkgListV3 from "/components/PkgListV3";
For information about installing TDengine on Docker, see [Quick Install on Docker](../../get-started/docker). If you want to view the source code, build TDengine yourself, or contribute to the project, see the [TDengine GitHub repository](https://github.com/taosdata/TDengine).
The full package of TDengine includes the TDengine Server (`taosd`), TDengine Client (`taosc`), taosAdapter for connecting with third-party systems and providing a RESTful interface, a command-line interface, and some tools. Note that taosAdapter supports Linux only. In addition to connectors for multiple languages, TDengine also provides a [REST API](../../reference/rest-api) through [taosAdapter](../../reference/taosadapter).
The full package of TDengine includes the TDengine Server (`taosd`), TDengine Client (`taosc`), taosAdapter for connecting with third-party systems and providing a RESTful interface, a command-line interface (CLI, taos), and some tools. Note that taosAdapter supports Linux only. In addition to connectors for multiple languages, TDengine also provides a [REST API](../../reference/rest-api) through [taosAdapter](../../reference/taosadapter).
The standard server installation package includes `taos`, `taosd`, `taosAdapter`, `taosBenchmark`, and sample code. You can also download a lite package that includes only `taosd` and the C/C++ connector.
The standard server installation package includes `taos`, `taosd`, `taosAdapter`, `taosBenchmark`, and sample code. You can also download the Lite package that includes only `taosd` and the C/C++ connector.
The TDengine Community Edition is released as .deb and .rpm packages. The .deb package can be installed on Debian, Ubuntu, and derivative systems. The .rpm package can be installed on CentOS, RHEL, SUSE, and derivative systems. A .tar.gz package is also provided for enterprise customers, and you can install TDengine over `apt-get` as well. The .tar.tz package includes `taosdump` and the TDinsight installation script. If you want to use these utilities with the .deb or .rpm package, download and install taosTools separately. TDengine can also be installed on 64-bit Windows servers.
The TDengine Community Edition is released as Deb and RPM packages. The Deb package can be installed on Debian, Ubuntu, and derivative systems. The RPM package can be installed on CentOS, RHEL, SUSE, and derivative systems. A .tar.gz package is also provided for enterprise customers, and you can install TDengine over `apt-get` as well. The .tar.tz package includes `taosdump` and the TDinsight installation script. If you want to use these utilities with the Deb or RPM package, download and install taosTools separately. TDengine can also be installed on 64-bit Windows.
## Installation
<Tabs>
<TabItem label=".deb" value="debinst">
1. Download the .deb installation package.
<PkgListV3 type={6}/>
1. Download the Deb installation package.
<PkgListV3 type={6}/>
2. In the directory where the package is located, use `dpkg` to install the package:
> Please replace `<version>` with the corresponding version of the package downloaded
```bash
# Enter the name of the package that you downloaded.
sudo dpkg -i TDengine-server-<version>-Linux-x64.deb
```
@ -34,11 +35,12 @@ sudo dpkg -i TDengine-server-<version>-Linux-x64.deb
<TabItem label=".rpm" value="rpminst">
1. Download the .rpm installation package.
<PkgListV3 type={5}/>
<PkgListV3 type={5}/>
2. In the directory where the package is located, use rpm to install the package:
> Please replace `<version>` with the corresponding version of the package downloaded
```bash
# Enter the name of the package that you downloaded.
sudo rpm -ivh TDengine-server-<version>-Linux-x64.rpm
```
@ -47,11 +49,12 @@ sudo rpm -ivh TDengine-server-<version>-Linux-x64.rpm
<TabItem label=".tar.gz" value="tarinst">
1. Download the .tar.gz installation package.
<PkgListV3 type={0}/>
<PkgListV3 type={0}/>
2. In the directory where the package is located, use `tar` to decompress the package:
> Please replace `<version>` with the corresponding version of the package downloaded
```bash
# Enter the name of the package that you downloaded.
tar -zxvf TDengine-server-<version>-Linux-x64.tar.gz
```
@ -96,23 +99,23 @@ sudo apt-get install tdengine
This installation method is supported only for Debian and Ubuntu.
::::
</TabItem>
<TabItem label="Windows" value="windows">
<TabItem label="Windows" value="windows">
Note: TDengine only supports Windows Server 2016/2019 and windows 10/11 system versions on the windows platform.
Note: TDengine only supports Windows Server 2016/2019 and Windows 10/11 on the Windows platform.
1. Download the Windows installation package.
<PkgListV3 type={3}/>
<PkgListV3 type={3}/>
2. Run the downloaded package to install TDengine.
</TabItem>
</Tabs>
:::info
For information about TDengine releases, see [Release History](../../releases).
For information about TDengine releases, see [Release History](../../releases/tdengine).
:::
:::note
On the first node in your TDengine cluster, leave the `Enter FQDN:` prompt blank and press **Enter**. On subsequent nodes, you can enter the end point of the first dnode in the cluster. You can also configure this setting after you have finished installing TDengine.
On the first node in your TDengine cluster, leave the `Enter FQDN:` prompt blank and press **Enter**. On subsequent nodes, you can enter the endpoint of the first dnode in the cluster. You can also configure this setting after you have finished installing TDengine.
:::
@ -147,7 +150,7 @@ Active: inactive (dead)
After confirming that TDengine is running, run the `taos` command to access the TDengine CLI.
The following `systemctl` commands can help you manage TDengine:
The following `systemctl` commands can help you manage TDengine service:
- Start TDengine Server: `systemctl start taosd`
@ -159,7 +162,7 @@ The following `systemctl` commands can help you manage TDengine:
:::info
- The `systemctl` command requires _root_ privileges. If you are not logged in as the `root` user, use the `sudo` command.
- The `systemctl` command requires _root_ privileges. If you are not logged in as the _root_ user, use the `sudo` command.
- The `systemctl stop taosd` command does not instantly stop TDengine Server. The server is stopped only after all data in memory is flushed to disk. The time required depends on the cache size.
- If your system does not include `systemd`, you can run `/usr/local/taos/bin/taosd` to start TDengine manually.
@ -174,23 +177,9 @@ After the installation is complete, run `C:\TDengine\taosd.exe` to start TDengin
</TabItem>
</Tabs>
## Test data insert performance
## Command Line Interface (CLI)
After your TDengine Server is running normally, you can run the taosBenchmark utility to test its performance:
```bash
taosBenchmark
```
This command creates the `meters` supertable in the `test` database. In the `meters` supertable, it then creates 10,000 subtables named `d0` to `d9999`. Each table has 10,000 rows and each row has four columns: `ts`, `current`, `voltage`, and `phase`. The timestamps of the data in these columns range from 2017-07-14 10:40:00 000 to 2017-07-14 10:40:09 999. Each table is randomly assigned a `groupId` tag from 1 to 10 and a `location` tag of either `Campbell`, `Cupertino`, `Los Angeles`, `Mountain View`, `Palo Alto`, `San Diego`, `San Francisco`, `San Jose`, `Santa Clara` or `Sunnyvale`.
The `taosBenchmark` command creates a deployment with 100 million data points that you can use for testing purposes. The time required to create the deployment depends on your hardware. On most modern servers, the deployment is created in less than a minute.
You can customize the test deployment that taosBenchmark creates by specifying command-line parameters. For information about command-line parameters, run the `taosBenchmark --help` command. For more information about taosBenchmark, see [taosBenchmark](../../reference/taosbenchmark).
## Command Line Interface
You can use the TDengine CLI to monitor your TDengine deployment and execute ad hoc queries. To open the CLI, run the following command:
You can use the TDengine CLI to monitor your TDengine deployment and execute ad hoc queries. To open the CLI, you can execute `taos` in the Linux terminal where TDengine is installed, or you can run `taos.exe` in the `C:\TDengine` directory of the Windows terminal where TDengine is installed to start the TDengine command line.
```bash
taos
@ -205,52 +194,71 @@ taos>
For example, you can create and delete databases and tables and run all types of queries. Each SQL command must be end with a semicolon (;). For example:
```sql
create database demo;
use demo;
create table t (ts timestamp, speed int);
insert into t values ('2019-07-15 00:00:00', 10);
insert into t values ('2019-07-15 01:00:00', 20);
select * from t;
CREATE DATABASE demo;
USE demo;
CREATE TABLE t (ts TIMESTAMP, speed INT);
INSERT INTO t VALUES ('2019-07-15 00:00:00', 10);
INSERT INTO t VALUES ('2019-07-15 01:00:00', 20);
SELECT * FROM t;
ts | speed |
========================================
2019-07-15 00:00:00.000 | 10 |
2019-07-15 01:00:00.000 | 20 |
Query OK, 2 row(s) in set (0.003128s)
```
You can also can monitor the deployment status, add and remove user accounts, and manage running instances. You can run the TDengine CLI on either Linux or Windows machines. For more information, see [TDengine CLI](../../reference/taos-shell/).
## Test data insert performance
After your TDengine Server is running normally, you can run the taosBenchmark utility to test its performance:
Start TDengine service and execute `taosBenchmark` (formerly named `taosdemo`) in a Linux or Windows terminal.
```bash
taosBenchmark
```
This command creates the `meters` supertable in the `test` database. In the `meters` supertable, it then creates 10,000 subtables named `d0` to `d9999`. Each table has 10,000 rows and each row has four columns: `ts`, `current`, `voltage`, and `phase`. The timestamps of the data in these columns range from 2017-07-14 10:40:00 000 to 2017-07-14 10:40:09 999. Each table is randomly assigned a `groupId` tag from 1 to 10 and a `location` tag of either `California.Campbell`, `California.Cupertino`, `California.LosAngeles`, `California.MountainView`, `California.PaloAlto`, `California.SanDiego`, `California.SanFrancisco`, `California.SanJose`, `California.SantaClara` or `California.Sunnyvale`.
The `taosBenchmark` command creates a deployment with 100 million data points that you can use for testing purposes. The time required to create the deployment depends on your hardware. On most modern servers, the deployment is created in ten to twenty seconds.
You can customize the test deployment that taosBenchmark creates by specifying command-line parameters. For information about command-line parameters, run the `taosBenchmark --help` command. For more information about taosBenchmark, see [taosBenchmark](../../reference/taosbenchmark).
## Test data query performance
After using taosBenchmark to create your test deployment, you can run queries in the TDengine CLI to test its performance:
After using `taosBenchmark` to create your test deployment, you can run queries in the TDengine CLI to test its performance:
From the TDengine CLI query the number of rows in the `meters` supertable:
From the TDengine CLI (taos) query the number of rows in the `meters` supertable:
```sql
select count(*) from test.meters;
SELECT COUNT(*) FROM test.meters;
```
Query the average, maximum, and minimum values of all 100 million rows of data:
```sql
select avg(current), max(voltage), min(phase) from test.meters;
SELECT AVG(current), MAX(voltage), MIN(phase) FROM test.meters;
```
Query the number of rows whose `location` tag is `San Francisco`:
Query the number of rows whose `location` tag is `California.SanFrancisco`:
```sql
select count(*) from test.meters where location="San Francisco";
SELECT COUNT(*) FROM test.meters WHERE location = "California.SanFrancisco";
```
Query the average, maximum, and minimum values of all rows whose `groupId` tag is `10`:
```sql
select avg(current), max(voltage), min(phase) from test.meters where groupId=10;
SELECT AVG(current), MAX(voltage), MIN(phase) FROM test.meters WHERE groupId = 10;
```
Query the average, maximum, and minimum values for table `d10` in 1 second intervals:
Query the average, maximum, and minimum values for table `d10` in 10 second intervals:
```sql
select first(ts), avg(current), max(voltage), min(phase) from test.d10 interval(1s);
SELECT FIRST(ts), AVG(current), MAX(voltage), MIN(phase) FROM test.d10 INTERVAL(10s);
```
In the query above you are selecting the first timestamp (ts) in the interval, another way of selecting this would be _wstart which will give the start of the time window. For more information about windowed queries, see [Time-Series Extensions](../../taos-sql/distinguished/).
In the query above you are selecting the first timestamp (ts) in the interval, another way of selecting this would be `\_wstart` which will give the start of the time window. For more information about windowed queries, see [Time-Series Extensions](../../taos-sql/distinguished/).

14
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View File

@ -348,19 +348,15 @@ SELECT ... FROM (SELECT ... FROM ...) ...;
:::info
- Only one layer of nesting is allowed, that means no sub query is allowed within a sub query
- The result set returned by the inner query will be used as a "virtual table" by the outer query. The "virtual table" can be renamed using `AS` keyword for easy reference in the outer query.
- Sub query is not allowed in continuous query.
- The result of a nested query is returned as a virtual table used by the outer query. It's recommended to give an alias to this table for the convenience of using it in the outer query.
- JOIN operation is allowed between tables/STables inside both inner and outer queries. Join operation can be performed on the result set of the inner query.
- UNION operation is not allowed in either inner query or outer query.
- The functions that can be used in the inner query are the same as those that can be used in a non-nested query.
- The features that can be used in the inner query are the same as those that can be used in a non-nested query.
- `ORDER BY` inside the inner query is unnecessary and will slow down the query performance significantly. It is best to avoid the use of `ORDER BY` inside the inner query.
- Compared to the non-nested query, the functionality that can be used in the outer query has the following restrictions:
- Functions
- If the result set returned by the inner query doesn't contain timestamp column, then functions relying on timestamp can't be used in the outer query, like `TOP`, `BOTTOM`, `FIRST`, `LAST`, `DIFF`.
- Functions that need to scan the data twice can't be used in the outer query, like `STDDEV`, `PERCENTILE`.
- `IN` operator is not allowed in the outer query but can be used in the inner query.
- `GROUP BY` is not supported in the outer query.
- If the result set returned by the inner query doesn't contain timestamp column, then functions relying on timestamp can't be used in the outer query, like INTERP,DERIVATIVE, IRATE, LAST_ROW, FIRST, LAST, TWA, STATEDURATION, TAIL, UNIQUE.
- If the result set returned by the inner query are not sorted in order by timestamp, then functions relying on data ordered by timestamp can't be used in the outer query, like LEASTSQUARES, ELAPSED, INTERP, DERIVATIVE, IRATE, TWA, DIFF, STATECOUNT, STATEDURATION, CSUM, MAVG, TAIL, UNIQUE.
- Functions that need to scan the data twice can't be used in the outer query, like PERCENTILE.
:::

44
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View File

@ -126,7 +126,7 @@ SELECT COS(field_name) FROM { tb_name | stb_name } [WHERE clause]
SELECT FLOOR(field_name) FROM { tb_name | stb_name } [WHERE clause];
```
**Description**: The rounded down value of a specific field
**Description**: The rounded down value of a specific field
**More explanations**: The restrictions are same as those of the `CEIL` function.
#### LOG
@ -173,7 +173,7 @@ SELECT POW(field_name, power) FROM { tb_name | stb_name } [WHERE clause]
SELECT ROUND(field_name) FROM { tb_name | stb_name } [WHERE clause];
```
**Description**: The rounded value of a specific field.
**Description**: The rounded value of a specific field.
**More explanations**: The restrictions are same as those of the `CEIL` function.
@ -434,7 +434,7 @@ SELECT TO_ISO8601(ts[, timezone]) FROM { tb_name | stb_name } [WHERE clause];
**More explanations**:
- You can specify a time zone in the following format: [z/Z, +/-hhmm, +/-hh, +/-hh:mm]。 For example, TO_ISO8601(1, "+00:00").
- If the input is a UNIX timestamp, the precision of the returned value is determined by the digits of the input timestamp
- If the input is a UNIX timestamp, the precision of the returned value is determined by the digits of the input timestamp
- If the input is a column of TIMESTAMP type, the precision of the returned value is same as the precision set for the current data base in use
@ -769,14 +769,14 @@ SELECT HISTOGRAM(field_name，bin_type, bin_description, normalized) FROM tb_nam
**Explanations**：
- bin_type: parameter to indicate the bucket type, valid inputs are: "user_input", "linear_bin", "log_bin"。
- 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]":
- 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 "linear_bin" descriptor generates a set of bins: [-inf, 1.0, 2.0, 4.0, 8.0, 16.0, +inf].
@ -862,9 +862,9 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } [WHERE where_condition] RA
- `INTERP` is used to get the value that matches the specified time slice from a column. If no such value exists an interpolation value will be returned based on `FILL` parameter.
- The input data of `INTERP` is the value of the specified column and a `where` clause can be used to filter the original data. If no `where` condition is specified then all original data is the input.
- The output time range of `INTERP` is specified by `RANGE(timestamp1,timestamp2)` parameter, with timestamp1<=timestamp2. timestamp1 is the starting point of the output time range and must be specified. timestamp2 is the ending point of the output time range and must be specified.
- The number of rows in the result set of `INTERP` is determined by the parameter `EVERY`. Starting from timestamp1, one interpolation is performed for every time interval specified `EVERY` parameter.
- Interpolation is performed based on `FILL` parameter.
- The output time range of `INTERP` is specified by `RANGE(timestamp1,timestamp2)` parameter, with timestamp1<=timestamp2. timestamp1 is the starting point of the output time range and must be specified. timestamp2 is the ending point of the output time range and must be specified.
- The number of rows in the result set of `INTERP` is determined by the parameter `EVERY`. Starting from timestamp1, one interpolation is performed for every time interval specified `EVERY` parameter.
- Interpolation is performed based on `FILL` parameter.
- `INTERP` can only be used to interpolate in single timeline. So it must be used with `partition by tbname` when it's used on a STable.
### LAST
@ -917,7 +917,7 @@ SELECT MAX(field_name) FROM { tb_name | stb_name } [WHERE clause];
**Return value type**:Same as the data type of the column being operated upon
**Applicable data types**: Numeric, Timestamp
**Applicable data types**: Numeric
**Applicable table types**: standard tables and supertables
@ -932,7 +932,7 @@ SELECT MIN(field_name) FROM {tb_name | stb_name} [WHERE clause];
**Return value type**:Same as the data type of the column being operated upon
**Applicable data types**: Numeric, Timestamp
**Applicable data types**: Numeric
**Applicable table types**: standard tables and supertables
@ -968,7 +968,7 @@ SELECT SAMPLE(field_name, K) FROM { tb_name | stb_name } [WHERE clause]
**Applicable table types**: standard tables and supertables
**More explanations**:
**More explanations**:
This function cannot be used in expression calculation.
- Must be used with `PARTITION BY tbname` when it's used on a STable to force the result on each single timeline
@ -1046,10 +1046,10 @@ SELECT CSUM(field_name) FROM { tb_name | stb_name } [WHERE clause]
**Applicable table types**: standard tables and supertables
**More explanations**:
**More explanations**:
- Arithmetic operation can't be performed on the result of `csum` function
- Can only be used with aggregate functions This function can be used with supertables and standard tables.
- Can only be used with aggregate functions This function can be used with supertables and standard tables.
- Must be used with `PARTITION BY tbname` when it's used on a STable to force the result on each single timeline
@ -1067,8 +1067,8 @@ SELECT DERIVATIVE(field_name, time_interval, ignore_negative) FROM tb_name [WHER
**Applicable table types**: standard tables and supertables
**More explanation**:
**More explanation**:
- It can be used together with `PARTITION BY tbname` against a STable.
- It can be used together with a selected column. For example: select \_rowts, DERIVATIVE() from。
@ -1086,7 +1086,7 @@ SELECT {DIFF(field_name, ignore_negative) | DIFF(field_name)} FROM tb_name [WHER
**Applicable table types**: standard tables and supertables
**More explanation**:
**More explanation**:
- The number of result rows is the number of rows subtracted by one, no output for the first row
- It can be used together with a selected column. For example: select \_rowts, DIFF() from。
@ -1123,9 +1123,9 @@ SELECT MAVG(field_name, K) FROM { tb_name | stb_name } [WHERE clause]
**Applicable table types**: standard tables and supertables
**More explanations**:
- Arithmetic operation can't be performed on the result of `MAVG`.
**More explanations**:
- Arithmetic operation can't be performed on the result of `MAVG`.
- Can only be used with data columns, can't be used with tags. - Can't be used with aggregate functions.
- Must be used with `PARTITION BY tbname` when it's used on a STable to force the result on each single timeline

40
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@ -5,11 +5,11 @@ title: Time-Series Extensions
As a purpose-built database for storing and processing time-series data, TDengine provides time-series-specific extensions to standard SQL.
These extensions include tag-partitioned queries and windowed queries.
These extensions include partitioned queries and windowed queries.
## Tag-Partitioned Queries
## Partitioned Queries
When you query a supertable, you may need to partition the supertable by tag and perform additional operations on a specific partition. In this case, you can use the following SQL clause:
When you query a supertable, you may need to partition the supertable by some dimensions and perform additional operations on a specific partition. In this case, you can use the following SQL clause:
```sql
PARTITION BY part_list
@ -17,22 +17,24 @@ PARTITION BY part_list
part_list can be any scalar expression, such as a column, constant, scalar function, or a combination of the preceding items.
A PARTITION BY clause with a tag is processed as follows:
A PARTITION BY clause is processed as follows:
- The PARTITION BY clause must occur after the WHERE clause and cannot be used with a JOIN clause.
- The PARTITION BY clause partitions the super table by the specified tag group, and the specified calculation is performed on each partition. The calculation performed is determined by the rest of the statement - a window clause, GROUP BY clause, or SELECT clause.
- You can use PARTITION BY together with a window clause or GROUP BY clause. In this case, the window or GROUP BY clause takes effect on every partition. For example, the following statement partitions the table by the location tag, performs downsampling over a 10 minute window, and returns the maximum value:
- The PARTITION BY clause must occur after the WHERE clause
- The PARTITION BY caluse partitions the data according to the specified dimentions, then perform computation on each partition. The performed computation is determined by the rest of the statement - a window clause, GROUP BY clause, or SELECT clause.
- The PARTITION BY clause can be used together with a window clause or GROUP BY clause. In this case, the window or GROUP BY clause takes effect on every partition. For example, the following statement partitions the table by the location tag, performs downsampling over a 10 minute window, and returns the maximum value:
```sql
select max(current) from meters partition by location interval(10m)
```
The most common usage of PARTITION BY is partitioning the data in subtables by tags then perform computation when querying data in a supertable. More specifically, `PARTITION BY TBNAME` partitions the data of each subtable into a single timeline, and this method facilitates the statistical analysis in many use cases of processing timeseries data.
## Windowed Queries
Aggregation by time window is supported in TDengine. For example, in the case where temperature sensors report the temperature every seconds, the average temperature for every 10 minutes can be retrieved by performing a query with a time window. Window related clauses are used to divide the data set to be queried into subsets and then aggregation is performed across the subsets. There are three kinds of windows: time window, status window, and session window. There are two kinds of time windows: sliding window and flip time/tumbling window. The query syntax is as follows:
```sql
SELECT function_list FROM tb_name
SELECT select_list FROM tb_name
[WHERE where_condition]
[SESSION(ts_col, tol_val)]
[STATE_WINDOW(col)]
@ -42,15 +44,9 @@ SELECT function_list FROM tb_name
The following restrictions apply:
### Restricted Functions
- Aggregate functions and select functions can be used in `function_list`, with each function having only one output. For example COUNT, AVG, SUM, STDDEV, LEASTSQUARES, PERCENTILE, MIN, MAX, FIRST, LAST. Functions having multiple outputs, such as DIFF or arithmetic operations can't be used.
- `LAST_ROW` can't be used together with window aggregate.
- Scalar functions, like CEIL/FLOOR, can't be used with window aggregate.
### Other Rules
- The window clause must occur after the PARTITION BY clause and before the GROUP BY clause. It cannot be used with a GROUP BY clause.
- The window clause must occur after the PARTITION BY clause. It cannot be used with a GROUP BY clause.
- SELECT clauses on windows can contain only the following expressions:
- Constants
- Aggregate functions
@ -82,7 +78,7 @@ These pseudocolumns occur after the aggregation clause.
1. A huge volume of interpolation output may be returned using `FILL`, so it's recommended to specify the time range when using `FILL`. The maximum number of interpolation values that can be returned in a single query is 10,000,000.
2. The result set is in ascending order of timestamp when you aggregate by time window.
3. If aggregate by window is used on STable, the aggregate function is performed on all the rows matching the filter conditions. If `GROUP BY` is not used in the query, the result set will be returned in ascending order of timestamp; otherwise the result set is not exactly in the order of ascending timestamp in each group.
3. If aggregate by window is used on STable, the aggregate function is performed on all the rows matching the filter conditions. If `PARTITION BY` is not used in the query, the result set will be returned in strict ascending order of timestamp; otherwise the result set will be returned in the order of ascending timestamp in each group.
:::
@ -112,9 +108,9 @@ When using time windows, note the following:
Please note that the `timezone` parameter should be configured to be the same value in the `taos.cfg` configuration file on client side and server side.
- The result set is in ascending order of timestamp when you aggregate by time window.
### Status Window
### State Window
In case of using integer, bool, or string to represent the status of a device at any given moment, continuous rows with the same status belong to a status window. Once the status changes, the status window closes. As shown in the following figure, there are two status windows according to status, [2019-04-28 14:22:07，2019-04-28 14:22:10] and [2019-04-28 14:22:11，2019-04-28 14:22:12]. Status window is not applicable to STable for now.
In case of using integer, bool, or string to represent the status of a device at any given moment, continuous rows with the same status belong to a status window. Once the status changes, the status window closes. As shown in the following figure, there are two state windows according to status, [2019-04-28 14:22:07，2019-04-28 14:22:10] and [2019-04-28 14:22:11，2019-04-28 14:22:12].
![TDengine Database Status Window](./timewindow-3.webp)
@ -124,13 +120,19 @@ In case of using integer, bool, or string to represent the status of a device at
SELECT COUNT(*), FIRST(ts), status FROM temp_tb_1 STATE_WINDOW(status);
```
Only care about the information of the status window when the status is 2. For example:
```
SELECT * FROM (SELECT COUNT(*) AS cnt, FIRST(ts) AS fst, status FROM temp_tb_1 STATE_WINDOW(status)) t WHERE status = 2;
```
### Session Window
The primary key, i.e. timestamp, is used to determine which session window a row belongs to. As shown in the figure below, if the limit of time interval for the session window is specified as 12 seconds, then the 6 rows in the figure constitutes 2 time windows, [2019-04-28 14:22:10，2019-04-28 14:22:30] and [2019-04-28 14:23:10，2019-04-28 14:23:30] because the time difference between 2019-04-28 14:22:30 and 2019-04-28 14:23:10 is 40 seconds, which exceeds the time interval limit of 12 seconds.
![TDengine Database Session Window](./timewindow-2.webp)
If the time interval between two continuous rows are within the time interval specified by `tol_value` they belong to the same session window; otherwise a new session window is started automatically. Session window is not supported on STable for now.
If the time interval between two continuous rows are within the time interval specified by `tol_value` they belong to the same session window; otherwise a new session window is started automatically.
```

146
docs/en/12-taos-sql/20-keywords.md
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@ -5,7 +5,9 @@ title: Reserved Keywords
## Keyword List
There are about 200 keywords reserved by TDengine, they can't be used as the name of database, STable or table with either upper case, lower case or mixed case. The following list shows all reserved keywords:
There are more than 200 keywords reserved by TDengine, they can't be used as the name of database, table, STable, subtable, column or tag with either upper case, lower case or mixed case. If you need to use these keywords, use the symbol `` ` `` to enclose the keywords, e.g. \`ADD\`.
The following list shows all reserved keywords:
### A
@ -14,15 +16,20 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
- ACCOUNTS
- ADD
- AFTER
- AGGREGATE
- ALL
- ALTER
- ANALYZE
- AND
- APPS
- AS
- ASC
- AT_ONCE
- ATTACH
### B
- BALANCE
- BEFORE
- BEGIN
- BETWEEN
@ -32,19 +39,27 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
- BITNOT
- BITOR
- BLOCKS
- BNODE
- BNODES
- BOOL
- BUFFER
- BUFSIZE
- BY
### C
- CACHE
- CACHELAST
- CACHEMODEL
- CACHESIZE
- CASCADE
- CAST
- CHANGE
- CLIENT_VERSION
- CLUSTER
- COLON
- COLUMN
- COMMA
- COMMENT
- COMP
- COMPACT
- CONCAT
@ -52,15 +67,18 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
- CONNECTION
- CONNECTIONS
- CONNS
- CONSUMER
- CONSUMERS
- CONTAINS
- COPY
- COUNT
- CREATE
- CTIME
- CURRENT_USER
### D
- DATABASE
- DATABASES
- DAYS
- DBS
- DEFERRED
- DELETE
@ -69,18 +87,23 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
- DESCRIBE
- DETACH
- DISTINCT
- DISTRIBUTED
- DIVIDE
- DNODE
- DNODES
- DOT
- DOUBLE
- DROP
- DURATION
### E
- EACH
- ENABLE
- END
- EQ
- EVERY
- EXISTS
- EXPIRED
- EXPLAIN
### F
@ -88,18 +111,20 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
- FAIL
- FILE
- FILL
- FIRST
- FLOAT
- FLUSH
- FOR
- FROM
- FSYNC
- FUNCTION
- FUNCTIONS
### G
- GE
- GLOB
- GRANT
- GRANTS
- GROUP
- GT
### H
@ -110,15 +135,18 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
- ID
- IF
- IGNORE
- IMMEDIA
- IMMEDIATE
- IMPORT
- IN
- INITIAL
- INDEX
- INDEXES
- INITIALLY
- INNER
- INSERT
- INSTEAD
- INT
- INTEGER
- INTERVA
- INTERVAL
- INTO
- IS
- ISNULL
@ -126,6 +154,7 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
### J
- JOIN
- JSON
### K
@ -135,46 +164,57 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
### L
- LE
- LAST
- LAST_ROW
- LICENCES
- LIKE
- LIMIT
- LINEAR
- LOCAL
- LP
- LSHIFT
- LT
### M
- MATCH
- MAX_DELAY
- MAXROWS
- MERGE
- META
- MINROWS
- MINUS
- MNODE
- MNODES
- MODIFY
- MODULES
### N
- NE
- NCHAR
- NEXT
- NMATCH
- NONE
- NOT
- NOTNULL
- NOW
- NULL
- NULLS
### O
- OF
- OFFSET
- ON
- OR
- ORDER
- OUTPUTTYPE
### P
- PARTITION
- PAGES
- PAGESIZE
- PARTITIONS
- PASS
- PLUS
- PORT
- PPS
- PRECISION
- PREV
@ -182,47 +222,63 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
### Q
- QNODE
- QNODES
- QTIME
- QUERIE
- QUERIES
- QUERY
- QUORUM
### R
- RAISE
- REM
- RANGE
- RATIO
- READ
- REDISTRIBUTE
- RENAME
- REPLACE
- REPLICA
- RESET
- RESTRIC
- RESTRICT
- RETENTIONS
- REVOKE
- ROLLUP
- ROW
- RP
- RSHIFT
### S
- SCHEMALESS
- SCORES
- SELECT
- SEMI
- SERVER_STATUS
- SERVER_VERSION
- SESSION
- SET
- SHOW
- SLASH
- SINGLE_STABLE
- SLIDING
- SLIMIT
- SMALLIN
- SMA
- SMALLINT
- SNODE
- SNODES
- SOFFSET
- STable
- STableS
- SPLIT
- STABLE
- STABLES
- STAR
- STATE
- STATEMEN
- STATE_WI
- STATE_WINDOW
- STATEMENT
- STORAGE
- STREAM
- STREAMS
- STRICT
- STRING
- SUBSCRIPTIONS
- SYNCDB
- SYSINFO
### T
@ -233,19 +289,24 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
- TBNAME
- TIMES
- TIMESTAMP
- TIMEZONE
- TINYINT
- TO
- TODAY
- TOPIC
- TOPICS
- TRANSACTION
- TRANSACTIONS
- TRIGGER
- TRIM
- TSERIES
- TTL
### U
- UMINUS
- UNION
- UNSIGNED
- UPDATE
- UPLUS
- USE
- USER
- USERS
@ -253,9 +314,13 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
### V
- VALUE
- VALUES
- VARCHAR
- VARIABLE
- VARIABLES
- VERBOSE
- VGROUP
- VGROUPS
- VIEW
- VNODES
@ -263,14 +328,25 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
### W
- WAL
- WAL_FSYNC_PERIOD
- WAL_LEVEL
- WAL_RETENTION_PERIOD
- WAL_RETENTION_SIZE
- WAL_ROLL_PERIOD
- WAL_SEGMENT_SIZE
- WATERMARK
- WHERE
- WINDOW_CLOSE
- WITH
- WRITE
### \_
- \_C0
- \_QSTART
- \_QSTOP
- \_QDURATION
- \_WSTART
- \_WSTOP
- \_QEND
- \_QSTART
- \_ROWTS
- \_WDURATION
- \_WEND
- \_WSTART

54
docs/en/12-taos-sql/25-grant.md
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@ -9,15 +9,54 @@ This document describes how to manage permissions in TDengine.
## Create a User
```sql
CREATE USER use_name PASS 'password';
CREATE USER user_name PASS 'password' [SYSINFO {1|0}];
```
This statement creates a user account.
The maximum length of use_name is 23 bytes.
The maximum length of user_name is 23 bytes.
The maximum length of password is 128 bytes. The password can include leters, digits, and special characters excluding single quotation marks, double quotation marks, backticks, backslashes, and spaces. The password cannot be empty.
`SYSINFO` indicates whether the user is allowed to view system information. `1` means allowed, `0` means not allowed. System information includes server configuration, dnode, vnode, storage. The default value is `1`.
For example, we can create a user whose password is `123456` and is able to view system information.
```sql
taos> create user test pass '123456' sysinfo 1;
Query OK, 0 of 0 rows affected (0.001254s)
```
## View Users
To show the users in the system, please use
```sql
SHOW USERS;
```
This is an example:
```sql
taos> show users;
name | super | enable | sysinfo | create_time |
================================================================================
test | 0 | 1 | 1 | 2022-08-29 15:10:27.315 |
root | 1 | 1 | 1 | 2022-08-29 15:03:34.710 |
Query OK, 2 rows in database (0.001657s)
```
Alternatively, you can get the user information by querying a built-in table, INFORMATION_SCHEMA.INS_USERS. For example:
```sql
taos> select * from information_schema.ins_users;
name | super | enable | sysinfo | create_time |
================================================================================
test | 0 | 1 | 1 | 2022-08-29 15:10:27.315 |
root | 1 | 1 | 1 | 2022-08-29 15:03:34.710 |
Query OK, 2 rows in database (0.001953s)
```
## Delete a User
```sql
@ -40,6 +79,13 @@ alter_user_clause: {
- ENABLE: Specify whether the user is enabled or disabled. 1 indicates enabled and 0 indicates disabled.
- SYSINFO: Specify whether the user can query system information. 1 indicates that the user can query system information and 0 indicates that the user cannot query system information.
For example, you can use below command to disable user `test`:
```sql
taos> alter user test enable 0;
Query OK, 0 of 0 rows affected (0.001160s)
```
## Grant Permissions
@ -62,7 +108,7 @@ priv_level : {
}
```
Grant permissions to a user.
Grant permissions to a user, this feature is only available in enterprise edition.
Permissions are granted on the database level. You can grant read or write permissions.
@ -92,4 +138,4 @@ priv_level : {
```
Revoke permissions from a user.
Revoke permissions from a user, this feature is only available in enterprise edition.

25
docs/en/13-operation/10-monitor.md
View File

@ -2,7 +2,7 @@
title: TDengine Monitoring
---
After TDengine is started, a database named `log` is created automatically to help with monitoring. Information that includes CPU, memory and disk usage, bandwidth, number of requests, disk I/O speed, slow queries, is written into the `log` database at a predefined interval. Additionally, some important system operations, like logon, create user, drop database, and alerts and warnings generated in TDengine are written into the `log` database too. A system operator can view the data in `log` database from TDengine CLI or from a web console.
After TDengine is started, it automatically writes monitoring data including CPU, memory and disk usage, bandwidth, number of requests, disk I/O speed, slow queries, into a designated database at a predefined interval through taosKeeper. Additionally, some important system operations, like logon, create user, drop database, and alerts and warnings generated in TDengine are written into the `log` database too. A system operator can view the data in `log` database from TDengine CLI or from a web console.
The collection of the monitoring information is enabled by default, but can be disabled by parameter `monitor` in the configuration file.
@ -10,7 +10,7 @@ The collection of the monitoring information is enabled by default, but can be d
TDinsight is a complete solution which uses the monitoring database `log` mentioned previously, and Grafana, to monitor a TDengine cluster.
From version 2.3.3.0, more monitoring data has been added in the `log` database. Please refer to [TDinsight Grafana Dashboard](https://grafana.com/grafana/dashboards/15167) to learn more details about using TDinsight to monitor TDengine.
Please refer to [TDinsight Grafana Dashboard](../../reference/tdinsight) to learn more details about using TDinsight to monitor TDengine.
A script `TDinsight.sh` is provided to deploy TDinsight automatically.
@ -30,31 +30,14 @@ Prepare：
2. Grafana Alert Notification
There are two ways to setup Grafana alert notification.
You can use below command to setup Grafana alert notification.
- An existing Grafana Notification Channel can be specified with parameter `-E`, the notifier uid of the channel can be obtained by `curl -u admin:admin localhost:3000/api/alert-notifications |jq`
An existing Grafana Notification Channel can be specified with parameter `-E`, the notifier uid of the channel can be obtained by `curl -u admin:admin localhost:3000/api/alert-notifications |jq`
```bash
sudo ./TDinsight.sh -a http://localhost:6041 -u root -p taosdata -E <notifier uid>
```
- The AliCloud SMS alert built in TDengine data source plugin can be enabled with parameter `-s`, the parameters of enabling this plugin are listed below:
- `-I`: AliCloud SMS Key ID
- `-K`: AliCloud SMS Key Secret
- `-S`: AliCloud SMS Signature
- `-C`: SMS notification template
- `-T`: Input parameters in JSON format for the SMS notification template, for example`{"alarm_level":"%s","time":"%s","name":"%s","content":"%s"}`
- `-B`: List of mobile numbers to be notified
Below is an example of the full command using the AliCloud SMS alert.
```bash
sudo ./TDinsight.sh -a http://localhost:6041 -u root -p taosdata -s \
-I XXXXXXX -K XXXXXXXX -S taosdata -C SMS_1111111 -B 18900000000 \
-T '{"alarm_level":"%s","time":"%s","name":"%s","content":"%s"}'
```
Launch `TDinsight.sh` with the command above and restart Grafana, then open Dashboard `http://localhost:3000/d/tdinsight`.
For more use cases and restrictions please refer to [TDinsight](/reference/tdinsight/).

6
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View File

@ -155,15 +155,15 @@ async fn demo(taos: &Taos, db: &str) -> Result<(), Error> {
let inserted = taos.exec_many([
// create super table
"CREATE TABLE `meters` (`ts` TIMESTAMP, `current` FLOAT, `voltage` INT, `phase` FLOAT) \
TAGS (`groupid` INT, `location` BINARY(16))",
TAGS (`groupid` INT, `location` BINARY(24))",
// create child table
"CREATE TABLE `d0` USING `meters` TAGS(0, 'Los Angles')",
"CREATE TABLE `d0` USING `meters` TAGS(0, 'California.LosAngles')",
// insert into child table
"INSERT INTO `d0` values(now - 10s, 10, 116, 0.32)",
// insert with NULL values
"INSERT INTO `d0` values(now - 8s, NULL, NULL, NULL)",
// insert and automatically create table with tags if not exists
"INSERT INTO `d1` USING `meters` TAGS(1, 'San Francisco') values(now - 9s, 10.1, 119, 0.33)",
"INSERT INTO `d1` USING `meters` TAGS(1, 'California.SanFrancisco') values(now - 9s, 10.1, 119, 0.33)",
// insert many records in a single sql
"INSERT INTO `d1` values (now-8s, 10, 120, 0.33) (now - 6s, 10, 119, 0.34) (now - 4s, 11.2, 118, 0.322)",
]).await?;

2
docs/en/14-reference/03-connector/_linux_install.mdx
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@ -4,7 +4,7 @@ import PkgListV3 from "/components/PkgListV3";
<PkgListV3 type={1} sys="Linux" />
[All Downloads](../../releases)
[All Downloads](../../releases/tdengine)
2. Unzip

2
docs/en/14-reference/03-connector/_windows_install.mdx
View File

@ -4,7 +4,7 @@ import PkgListV3 from "/components/PkgListV3";
<PkgListV3 type={4} sys="Windows" />
[All Downloads](../../releases)
[All Downloads](../../releases/tdengine)
2. Execute the installer, select the default value as prompted, and complete the installation
3. Installation path

35
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@ -39,14 +39,14 @@ Comparing the connector support for TDengine functional features as follows.
### Using the native interface (taosc)
| **Functional Features** | **Java** | **Python** | **Go** | **C#** | **Node.js** | **Rust** |
| -------------- | -------- | ---------- | ------ | ------ | ----------- | -------- |
| **Connection Management** | Support | Support | Support | Support | Support | Support |
| **Regular Query** | Support | Support | Support | Support | Support | Support |
| **Parameter Binding** | Support | Support | Support | Support | Support | Support |
| ** TMQ ** | Support | Support | Support | Support | Support | Support |
| **Schemaless** | Support | Support | Support | Support | Support | Support |
| **DataFrame** | Not Supported | Support | Not Supported | Not Supported | Not Supported | Not Supported |
| **Functional Features** | **Java** | **Python** | **Go** | **C#** | **Node.js** | **Rust** |
| ----------------------------- | ------------- | ---------- | ------------- | ------------- | ------------- | ------------- |
| **Connection Management** | Support | Support | Support | Support | Support | Support |
| **Regular Query** | Support | Support | Support | Support | Support | Support |
| **Parameter Binding** | Support | Support | Support | Support | Support | Support |
| **Subscription (TMQ)** | Support | Support | Support | Support | Support | Support |
| **Schemaless** | Support | Support | Support | Support | Support | Support |
| **DataFrame** | Not Supported | Support | Not Supported | Not Supported | Not Supported | Not Supported |
:::info
The different database framework specifications for various programming languages do not mean that all C/C++ interfaces need a wrapper.
@ -54,16 +54,15 @@ The different database framework specifications for various programming language
### Use HTTP Interfaces (REST or WebSocket)
| **Functional Features** | **Java** | **Python** | **Go** | **C# (not supported yet)** | **Node.js** | **Rust** |
| ------------------------------ | -------- | ---------- | -------- | ------------------ | ----------- | -------- |
| **Connection Management** | Support | Support | Support | N/A | Support | Support |
| **Regular Query** | Support | Support | Support | N/A | Support | Support |
| **Continous Query ** | Support | Support | Support | N/A | Support | Support |
| **Parameter Binding** | Not supported | Not supported | Not supported | N/A | Not supported | Support |
| ** TMQ ** | Not supported | Not supported | Not supported | N/A | Not supported | Support |
| **Schemaless** | Not supported | Not supported | Not supported | N/A | Not supported | Not supported |
| **Bulk Pulling (based on WebSocket) **| Support | Support | Not Supported | N/A | Not Supported | Supported |
| **DataFrame** | Not supported | Support | Not supported | N/A | Not supported | Not supported |
| **Functional Features** | **Java** | **Python** | **Go** | **C#** | **Node.js** | **Rust** |
| -------------------------------------- | ------------- | --------------- | ------------- | ------------- | ------------- | ------------- |
| **Connection Management** | Support | Support | Support | Support | Support | Support |
| **Regular Query** | Support | Support | Support | Support | Support | Support |
| **Parameter Binding** | Not supported | Not supported | Not supported | Support | Not supported | Support |
| **Subscription (TMQ) ** | Not supported | Not supported | Not supported | Not supported | Not supported | Support |
| **Schemaless** | Not supported | Not supported | Not supported | Not supported | Not supported | Not supported |
| **Bulk Pulling (based on WebSocket) ** | Support | Support | Not Supported | support | Not Supported | Supported |
| **DataFrame** | Not supported | Support | Not supported | Not supported | Not supported | Not supported |
:::warning

2
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@ -30,7 +30,7 @@ taosAdapter provides the following features.
### Install taosAdapter
If you use the TDengine server, you don't need additional steps to install taosAdapter. You can download taosAdapter from [TDengine 3.0 released versions](../../releases) to download the TDengine server installation package. 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/3.0/BUILD.md) documentation.
If you use the TDengine server, you don't need additional steps to install taosAdapter. You can download taosAdapter from [TDengine 3.0 released versions](../../releases/tdengine) to download the TDengine server installation package. 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/3.0/BUILD.md) documentation.
### Start/Stop taosAdapter

8
docs/en/14-reference/07-tdinsight/assets/15146-tdengine-monitor-dashboard.json
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@ -211,7 +211,7 @@
],
"timeFrom": null,
"timeShift": null,
"title": "Leader MNode",
"title": "Master MNode",
"transformations": [
{
"id": "filterByValue",
@ -221,7 +221,7 @@
"config": {
"id": "regex",
"options": {
"value": "leader"
"value": "master"
}
},
"fieldName": "role"
@ -300,7 +300,7 @@
],
"timeFrom": null,
"timeShift": null,
"title": "Leader MNode Create Time",
"title": "Master MNode Create Time",
"transformations": [
{
"id": "filterByValue",
@ -310,7 +310,7 @@
"config": {
"id": "regex",
"options": {
"value": "leader"
"value": "master"
}
},
"fieldName": "role"

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8
docs/en/14-reference/07-tdinsight/assets/tdengine-grafana-7.x.json
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@ -153,7 +153,7 @@
],
"timeFrom": null,
"timeShift": null,
"title": "Leader MNode",
"title": "Master MNode",
"transformations": [
{
"id": "filterByValue",
@ -163,7 +163,7 @@
"config": {
"id": "regex",
"options": {
"value": "leader"
"value": "master"
}
},
"fieldName": "role"
@ -246,7 +246,7 @@
],
"timeFrom": null,
"timeShift": null,
"title": "Leader MNode Create Time",
"title": "Master MNode Create Time",
"transformations": [
{
"id": "filterByValue",
@ -256,7 +256,7 @@
"config": {
"id": "regex",
"options": {
"value": "leader"
"value": "master"
}
},
"fieldName": "role"

93
docs/en/14-reference/07-tdinsight/index.md
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@ -5,15 +5,23 @@ 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, 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.
After TDengine starts, it automatically writes many metrics in specific intervals into a designated 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-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`).
To deploy TDinsight, we need
- a single-node TDengine server or a multi-node TDengine cluster and a [Grafana] server are required. This dashboard requires TDengine 3.0.1.0 and above, with the monitoring feature enabled. For detailed configuration, please refer to [TDengine monitoring configuration](../config/#monitoring-parameters).
- taosAdapter has been instaleld and running, please refer to [taosAdapter](../taosadapter).
- taosKeeper has been installed and running, please refer to [taosKeeper](../taoskeeper).
Please record
- The endpoint of taosAdapter REST service, for example `http://tdengine.local:6041`
- Authentication of taosAdapter, e.g. user name and password
- The database name used by taosKeeper to store monitoring data
## Installing Grafana
We recommend using the latest [Grafana] version 7 or 8 here. You can install Grafana on any [supported operating system](https://grafana.com/docs/grafana/latest/installation/requirements/#supported-operating-systems) by following the [official Grafana documentation Instructions](https://grafana.com/docs/grafana/latest/installation/) to install [Grafana].
We recommend using the latest [Grafana] version 8 or 9 here. You can install Grafana on any [supported operating system](https://grafana.com/docs/grafana/latest/installation/requirements/#supported-operating-systems) by following the [official Grafana documentation Instructions](https://grafana.com/docs/grafana/latest/installation/) to install [Grafana].
### Installing Grafana on Debian or Ubuntu
@ -71,7 +79,7 @@ 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 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.
This script will automatically download the latest [Grafana TDengine data source plugin](https://github.com/taosdata/grafanaplugin/releases/latest) and [TDinsight dashboard](https://github.com/taosdata/grafanaplugin/blob/master/dashboards/TDinsightV3.json) 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.
@ -106,18 +114,6 @@ Install and configure TDinsight dashboard in Grafana on Ubuntu 18.04/20.04 syste
-E, --external-notifier <string> Apply external notifier uid to TDinsight dashboard.
Alibaba Cloud SMS as Notifier:
-s, --sms-enabled To enable tdengine-datasource plugin builtin Alibaba Cloud SMS webhook.
-N, --sms-notifier-name <string> Provisioning notifier name.[default: TDinsight Builtin SMS]
-U, --sms-notifier-uid <string> Provisioning notifier uid, use lowercase notifier name by default.
-D, --sms-notifier-is-default Set notifier as default.
-I, --sms-access-key-id <string> Alibaba Cloud SMS access key id
-K, --sms-access-key-secret <string> Alibaba Cloud SMS access key secret
-S, --sms-sign-name <string> Sign name
-C, --sms-template-code <string> Template code
-T, --sms-template-param <string> Template param, a escaped JSON string like '{"alarm_level":"%s","time":"%s","name":"%s","content":"%s"}'
-B, --sms-phone-numbers <string> Comma-separated numbers list, eg "189xxxxxxxx,132xxxxxxxx"
-L, --sms-listen-addr <string> [default: 127.0.0.1:9100]
```
Most command-line options can take effect the same as environment variables.
@ -136,17 +132,6 @@ Most command-line options can take effect the same as environment variables.
| -t | --tdinsight-title | TDINSIGHT_DASHBOARD_TITLE | TDinsight dashboard title. [Default: TDinsight] | -e | -tdinsight-title
| -e | --tdinsight-editable | TDINSIGHT_DASHBOARD_EDITABLE | If the dashboard is configured to be editable. [Default: false] | -e | --external
| -E | --external-notifier | EXTERNAL_NOTIFIER | Apply the external notifier uid to the TDinsight dashboard. | -s
| -s | --sms-enabled | SMS_ENABLED | Enable the tdengine-datasource plugin built into Alibaba Cloud SMS webhook. | -s
| -N | --sms-notifier-name | SMS_NOTIFIER_NAME | The name of the provisioning notifier. [Default: `TDinsight Builtin SMS`] | -U
| -U | --sms-notifier-uid | SMS_NOTIFIER_UID | "Notification Channel" `uid`, lowercase of the program name is used by default, other characters are replaced by "-". |-sms
| -D | --sms-notifier-is-default | SMS_NOTIFIER_IS_DEFAULT | Set built-in SMS notification to default value. |-sms-notifier-is-default
| -I | --sms-access-key-id | SMS_ACCESS_KEY_ID | Alibaba Cloud SMS access key id |
| -K | --sms-access-key-secret | SMS_ACCESS_KEY_SECRET | AliCloud SMS-access-secret-key |
| -S | --sms-sign-name | SMS_SIGN_NAME | Signature |
| -C | --sms-template-code | SMS_TEMPLATE_CODE | Template code |
| -T | --sms-template-param | SMS_TEMPLATE_PARAM | JSON template for template parameters |
| -B | --sms-phone-numbers | SMS_PHONE_NUMBERS | A comma-separated list of phone numbers, e.g. `"189xxxxxxxx,132xxxxxxxx"` |
| -L | --sms-listen-addr | SMS_LISTEN_ADDR | Built-in SMS webhook listener address, default is `127.0.0.1:9100` |
Suppose you start a TDengine database on host `tdengine` with HTTP API port `6041`, user `root1`, and password `pass5ord`. Execute the script.
@ -166,24 +151,10 @@ Use the `uid` value obtained above as `-E` input.
sudo ./TDinsight.sh -a http://tdengine:6041 -u root1 -p pass5ord -E existing-notifier
```
If you want to use the [Alibaba Cloud SMS](https://www.aliyun.com/product/sms) service as a notification channel, you should enable it with the `-s` flag add the following parameters.
- `-N`: Notification Channel name, default is `TDinsight Builtin SMS`.
- `-U`: Channel uid, default is lowercase of `name`, any other character is replaced with -, for the default `-N`, its uid is `tdinsight-builtin-sms`.
- `-I`: Alibaba Cloud SMS access key id.
- `-K`: Alibaba Cloud SMS access secret key.
- `-S`: Alibaba Cloud SMS signature.
- `-C`: Alibaba Cloud SMS template id.
- `-T`: Alibaba Cloud SMS template parameters, for JSON format template, example is as follows `'{"alarm_level":"%s", "time":"%s", "name":"%s", "content":"%s"}'`. There are four parameters: alarm level, time, name and alarm content.
- `-B`: a list of phone numbers, separated by a comma `,`.
If you want to monitor multiple TDengine clusters, you need to set up numerous TDinsight dashboards. Setting up non-default TDinsight requires some changes: the `-n` `-i` `-t` options need to be changed to non-default names, and `-N` and `-L` should also be changed if using the built-in SMS alerting feature.
```bash
sudo . /TDengine.sh -n TDengine-Env1 -a http://another:6041 -u root -p taosdata -i tdinsight-env1 -t 'TDinsight Env1'
# If using built-in SMS notifications
sudo . /TDengine.sh -n TDengine-Env1 -a http://another:6041 -u root -p taosdata -i tdinsight-env1 -t 'TDinsight Env1' \
-s -N 'Env1 SMS' -I xx -K xx -S xx -C SMS_XX -T '' -B 00000000000 -L 127.0.0.01:10611
```
Please note that the configuration data source, notification channel, and dashboard are not changeable on the front end. You should update the configuration again via this script or manually change the configuration file in the `/etc/grafana/provisioning` directory (this is the default directory for Grafana, use the `-P` option to change it as needed).
@ -249,21 +220,23 @@ Save and test. It will report 'TDengine Data source is working' under normal cir
### Importing dashboards
Point to **+** / **Create** - **import** (or `/dashboard/import` url).
In the page of configuring data source, click **Dashboards** tab.
![TDengine Database TDinsight Import Dashboard and Configuration](./assets/import_dashboard.webp)
Type the dashboard ID `15167` in the **Import via grafana.com** location and **Load**.
Choose `TDengine for 3.x` and click `import`.
![TDengine Database TDinsight Import via grafana.com](./assets/import-dashboard-15167.webp)
After the importing is done, `TDinsight for 3.x` dashboard is available on the page of `search dashboards by name`.
Once the import is complete, the full page view of TDinsight is shown below.
![TDengine Database TDinsight Import via grafana.com](./assets/import_dashboard_view.webp)
![TDengine Database TDinsight show](./assets/TDinsight-full.webp)
In the `TDinsight for 3.x` dashboard, choose the database used by taosKeeper to store monitoring data, you can see the monitoring result.
![TDengine Database TDinsight 选择数据库](./assets/select_dashboard_db.webp)
## TDinsight dashboard details
The TDinsight dashboard is designed to provide the usage and status of TDengine-related resources [dnodes, mnodes, vnodes](../../taos-sql/node/) or databases.
The TDinsight dashboard is designed to provide the usage and status of TDengine-related resources, e.g. dnodes, mnodes, vnodes and databases.
Details of the metrics are as follows.
@ -285,7 +258,6 @@ This section contains the current information and status of the cluster, the ale
- **Measuring Points Used**: The number of measuring points used to enable the alert rule (no data available in the community version, healthy by default).
- **Grants Expire Time**: the expiration time of the enterprise version of the enabled alert rule (no data available for the community version, healthy by default).
- **Error Rate**: Aggregate error rate (average number of errors per second) for alert-enabled clusters.
- **Variables**: `show variables` table display.
### DNodes Status
@ -294,7 +266,6 @@ This section contains the current information and status of the cluster, the ale
- **DNodes Status**: simple table view of `show dnodes`.
- **DNodes Lifetime**: the time elapsed since the dnode was created.
- **DNodes Number**: the number of DNodes changes.
- **Offline Reason**: if any dnode status is offline, the reason for offline is shown as a pie chart.
### MNode Overview
@ -309,7 +280,6 @@ This section contains the current information and status of the cluster, the ale
1. **Requests Rate(Inserts per Second)**: average number of inserts per second.
2. **Requests (Selects)**: number of query requests and change rate (count of second).
3. **Requests (HTTP)**: number of HTTP requests and request rate (count of second).
### Database
@ -319,9 +289,8 @@ Database usage, repeated for each value of the variable `$database` i.e. multipl
1. **STables**: number of super tables.
2. **Total Tables**: number of all tables.
3. **Sub Tables**: the number of all super table subtables.
4. **Tables**: graph of all normal table numbers over time.
5. **Tables Number Foreach VGroups**: The number of tables contained in each VGroups.
3. **Tables**: number of normal tables.
4. **Table number for each vgroup**: number of tables per vgroup.
### DNode Resource Usage
@ -356,12 +325,11 @@ Currently, only the number of logins per minute is reported.
Support monitoring taosAdapter request statistics and status details. Includes.
1. **http_request**: contains the total number of requests, the number of failed requests, and the number of requests being processed
2. **top 3 request endpoint**: data of the top 3 requests by endpoint group
3. **Memory Used**: taosAdapter memory usage
4. **latency_quantile(ms)**: quantile of (1, 2, 5, 9, 99) stages
5. **top 3 failed request endpoint**: data of the top 3 failed requests by endpoint grouping
6. **CPU Used**: taosAdapter CPU usage
1. **http_request_inflight**: number of real-time requests.
2. **http_request_total**: number of total requests.
3. **http_request_fail**: number of failed requets.
4. **CPU Used**: CPU usage of taosAdapter.
5. **Memory Used**: Memory usage of taosAdapter.
## Upgrade
@ -403,13 +371,6 @@ services:
TDENGINE_API: ${TDENGINE_API}
TDENGINE_USER: ${TDENGINE_USER}
TDENGINE_PASS: ${TDENGINE_PASS}
SMS_ACCESS_KEY_ID: ${SMS_ACCESS_KEY_ID}
SMS_ACCESS_KEY_SECRET: ${SMS_ACCESS_KEY_SECRET}
SMS_SIGN_NAME: ${SMS_SIGN_NAME}
SMS_TEMPLATE_CODE: ${SMS_TEMPLATE_CODE}
SMS_TEMPLATE_PARAM: '${SMS_TEMPLATE_PARAM}'
SMS_PHONE_NUMBERS: $SMS_PHONE_NUMBERS
SMS_LISTEN_ADDR: ${SMS_LISTEN_ADDR}
ports:
- 3000:3000
volumes:

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docs/en/20-third-party/13-Jupyter.md vendored Normal file
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@ -0,0 +1,98 @@
---
sidebar_label: JupyterLab
title: Connect JupyterLab to TDengine
---
JupyterLab is the next generation of the ubiquitous Jupyter Notebook. In this note we show you how to install the TDengine Python connector to connect to TDengine in JupyterLab. You can then insert data and perform queries against the TDengine instance within JupyterLab.
## Install JupyterLab
Installing JupyterLab is very easy. Installation instructions can be found at:
https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html.
If you don't feel like clicking on the link here are the instructions.
Jupyter's preferred Python package manager is pip, so we show the instructions for pip.
You can also use **conda** or **pipenv** if you are managing Python environments.
````
pip install jupyterlab
````
For **conda** you can run:
````
conda install -c conda-forge jupyterlab
````
For **pipenv** you can run:
````
pipenv install jupyterlab
pipenv shell
````
## Run JupyterLab
You can start JupyterLab from the command line by running:
````
jupyter lab
````
This will automatically launch your default browser and connect to your JupyterLab instance, usually on port 8888.
## Install the TDengine Python connector
You can now install the TDengine Python connector as follows.
Start a new Python kernel in JupyterLab.
If using **conda** run the following:
````
# Install a conda package in the current Jupyter kernel
import sys
!conda install --yes --prefix {sys.prefix} taospy
````
If using **pip** run the following:
````
# Install a pip package in the current Jupyter kernel
import sys
!{sys.executable} -m pip install taospy
````
## Connect to TDengine
You can find detailed examples to use the Python connector, in the TDengine documentation here.
Once you have installed the TDengine Python connector in your JupyterLab kernel, the process of connecting to TDengine is the same as that you would use if you weren't using JupyterLab.
Each TDengine instance, has a database called "log" which has monitoring information about the TDengine instance.
In the "log" database there is a [supertable](https://docs.tdengine.com/taos-sql/stable/) called "disks_info".
The structure of this table is as follows:
````
taos> desc disks_info;
Field | Type | Length | Note |
=================================================================================
ts | TIMESTAMP | 8 | |
datadir_l0_used | FLOAT | 4 | |
datadir_l0_total | FLOAT | 4 | |
datadir_l1_used | FLOAT | 4 | |
datadir_l1_total | FLOAT | 4 | |
datadir_l2_used | FLOAT | 4 | |
datadir_l2_total | FLOAT | 4 | |
dnode_id | INT | 4 | TAG |
dnode_ep | BINARY | 134 | TAG |
Query OK, 9 row(s) in set (0.000238s)
````
The code below is used to fetch data from this table into a pandas DataFrame.
````
import sys
import taos
import pandas
def sqlQuery(conn):
df: pandas.DataFrame = pandas.read_sql("select * from log.disks_info limit 500", conn)
print(df)
return df
conn = taos.connect()
result = sqlQuery(conn)
print(result)
````
TDengine has connectors for various languages including Node.js, Go, PHP and there are kernels for these languages which can be found [here](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels).

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docs/en/28-releases.md
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@ -1,9 +0,0 @@
---
sidebar_label: Releases
title: Released Versions
---
import Release from "/components/ReleaseV3";
<Release versionPrefix="3.0" />

17
docs/en/28-releases/01-tdengine.md Normal file
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@ -0,0 +1,17 @@
---
sidebar_label: TDengine
title: TDengine
description: TDengine release history, Release Notes and download links.
---
import Release from "/components/ReleaseV3";
## 3.0.1.1
<Release type="tdengine" version="3.0.1.1" />
## 3.0.1.0
<Release type="tdengine" version="3.0.1.0" />

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@ -0,0 +1,15 @@
---
sidebar_label: taosTools
title: taosTools
description: taosTools release history, Release Notes, download links.
---
import Release from "/components/ReleaseV3";
## 2.2.0
<Release type="tools" version="2.2.0" />
## 2.1.3
<Release type="tools" version="2.1.3" />

1
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@ -0,0 +1 @@
label: Releases

1
docs/examples/csharp/SQLInsertExample.cs
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@ -23,6 +23,7 @@ namespace TDengineExample
CheckRes(conn, res, "failed to insert data");
int affectedRows = TDengine.AffectRows(res);
Console.WriteLine("affectedRows " + affectedRows);
TDengine.FreeResult(res);
ExitProgram(conn, 0);
}

8
docs/examples/java/src/main/java/com/taos/example/SubscribeDemo.java
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@ -38,12 +38,12 @@ public class SubscribeDemo {
statement.executeUpdate("create database " + DB_NAME);
statement.executeUpdate("use " + DB_NAME);
statement.executeUpdate(
"CREATE TABLE `meters` (`ts` TIMESTAMP, `current` FLOAT, `voltage` INT) TAGS (`groupid` INT, `location` BINARY(16))");
statement.executeUpdate("CREATE TABLE `d0` USING `meters` TAGS(0, 'Los Angles')");
"CREATE TABLE `meters` (`ts` TIMESTAMP, `current` FLOAT, `voltage` INT) TAGS (`groupid` INT, `location` BINARY(24))");
statement.executeUpdate("CREATE TABLE `d0` USING `meters` TAGS(0, 'California.LosAngles')");
statement.executeUpdate("INSERT INTO `d0` values(now - 10s, 0.32, 116)");
statement.executeUpdate("INSERT INTO `d0` values(now - 8s, NULL, NULL)");
statement.executeUpdate(
"INSERT INTO `d1` USING `meters` TAGS(1, 'San Francisco') values(now - 9s, 10.1, 119)");
"INSERT INTO `d1` USING `meters` TAGS(1, 'California.SanFrancisco') values(now - 9s, 10.1, 119)");
statement.executeUpdate(
"INSERT INTO `d1` values (now-8s, 10, 120) (now - 6s, 10, 119) (now - 4s, 11.2, 118)");
// create topic
@ -75,4 +75,4 @@ public class SubscribeDemo {
}
timer.cancel();
}
}
}

4
docs/examples/java/src/main/java/com/taos/example/highvolume/MockDataSource.java
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@ -16,7 +16,7 @@ class MockDataSource implements Iterator {
private int currentTbId = -1;
// mock values
String[] location = {"LosAngeles", "SanDiego", "Hollywood", "Compton", "San Francisco"};
String[] location = {"California.LosAngeles", "California.SanDiego", "California.SanJose", "California.Campbell", "California.SanFrancisco"};
float[] current = {8.8f, 10.7f, 9.9f, 8.9f, 9.4f};
int[] voltage = {119, 116, 111, 113, 118};
float[] phase = {0.32f, 0.34f, 0.33f, 0.329f, 0.141f};
@ -50,4 +50,4 @@ class MockDataSource implements Iterator {
return sb.toString();
}
}
}

10
docs/examples/python/mockdatasource.py
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@ -3,11 +3,11 @@ import time
class MockDataSource:
samples = [
"8.8,119,0.32,LosAngeles,0",
"10.7,116,0.34,SanDiego,1",
"9.9,111,0.33,Hollywood,2",
"8.9,113,0.329,Compton,3",
"9.4,118,0.141,San Francisco,4"
"8.8,119,0.32,California.LosAngeles,0",
"10.7,116,0.34,California.SanDiego,1",
"9.9,111,0.33,California.SanJose,2",
"8.9,113,0.329,California.Campbell,3",
"9.4,118,0.141,California.SanFrancisco,4"
]
def __init__(self, tb_name_prefix, table_count):

2
docs/examples/rust/nativeexample/examples/stmt_example.rs
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@ -12,7 +12,7 @@ async fn main() -> anyhow::Result<()> {
// bind table name and tags
stmt.set_tbname_tags(
"d1001",
&[Value::VarChar("San Fransico".into()), Value::Int(2)],
&[Value::VarChar("California.SanFransico".into()), Value::Int(2)],
)?;
// bind values.
let values = vec![

6
docs/examples/rust/nativeexample/examples/subscribe_demo.rs
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@ -19,13 +19,13 @@ struct Record {
async fn prepare(taos: Taos) -> anyhow::Result<()> {
let inserted = taos.exec_many([
// create child table
"CREATE TABLE `d0` USING `meters` TAGS(0, 'Los Angles')",
"CREATE TABLE `d0` USING `meters` TAGS(0, 'California.LosAngles')",
// insert into child table
"INSERT INTO `d0` values(now - 10s, 10, 116, 0.32)",
// insert with NULL values
"INSERT INTO `d0` values(now - 8s, NULL, NULL, NULL)",
// insert and automatically create table with tags if not exists
"INSERT INTO `d1` USING `meters` TAGS(1, 'San Francisco') values(now - 9s, 10.1, 119, 0.33)",
"INSERT INTO `d1` USING `meters` TAGS(1, 'California.SanFrancisco') values(now - 9s, 10.1, 119, 0.33)",
// insert many records in a single sql
"INSERT INTO `d1` values (now-8s, 10, 120, 0.33) (now - 6s, 10, 119, 0.34) (now - 4s, 11.2, 118, 0.322)",
]).await?;
@ -48,7 +48,7 @@ async fn main() -> anyhow::Result<()> {
format!("CREATE DATABASE `{db}`"),
format!("USE `{db}`"),
// create super table
format!("CREATE TABLE `meters` (`ts` TIMESTAMP, `current` FLOAT, `voltage` INT, `phase` FLOAT) TAGS (`groupid` INT, `location` BINARY(16))"),
format!("CREATE TABLE `meters` (`ts` TIMESTAMP, `current` FLOAT, `voltage` INT, `phase` FLOAT) TAGS (`groupid` INT, `location` BINARY(24))"),
// create topic for subscription
format!("CREATE TOPIC tmq_meters with META AS DATABASE {db}")
])

8
docs/examples/rust/restexample/examples/insert_example.rs
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@ -14,14 +14,14 @@ async fn main() -> anyhow::Result<()> {
]).await?;
let inserted = taos.exec("INSERT INTO
power.d1001 USING power.meters TAGS('San Francisco', 2)
power.d1001 USING power.meters TAGS('California.SanFrancisco', 2)
VALUES ('2018-10-03 14:38:05.000', 10.30000, 219, 0.31000)
('2018-10-03 14:38:15.000', 12.60000, 218, 0.33000) ('2018-10-03 14:38:16.800', 12.30000, 221, 0.31000)
power.d1002 USING power.meters TAGS('San Francisco', 3)
power.d1002 USING power.meters TAGS('California.SanFrancisco', 3)
VALUES ('2018-10-03 14:38:16.650', 10.30000, 218, 0.25000)
power.d1003 USING power.meters TAGS('Los Angeles', 2)
power.d1003 USING power.meters TAGS('California.LosAngeles', 2)
VALUES ('2018-10-03 14:38:05.500', 11.80000, 221, 0.28000) ('2018-10-03 14:38:16.600', 13.40000, 223, 0.29000)
power.d1004 USING power.meters TAGS('Los Angeles', 3)
power.d1004 USING power.meters TAGS('California.LosAngeles', 3)
VALUES ('2018-10-03 14:38:05.000', 10.80000, 223, 0.29000) ('2018-10-03 14:38:06.500', 11.50000, 221, 0.35000)").await?;
assert_eq!(inserted, 8);

2
docs/zh/02-intro.md
View File

@ -48,7 +48,7 @@ TDengine 的主要功能如下：
- 多种[数据导出](../operation/export)方式
9. 工具
- 提供[交互式命令行程序（CLI）](../reference/taos-shell)，便于管理集群，检查系统状态，做即席查询
- 提供压力测试工具[taosBenchmark](../reference/taosbenchmark)，用于测试 TDengine 的性能
- 提供压力测试工具 [taosBenchmark](../reference/taosbenchmark)，用于测试 TDengine 的性能
10. 编程
- 提供各种语言的[连接器（Connector）](../connector): 如 [C/C++](../connector/cpp)、[Java](../connector/java)、[Go](../connector/go)、[Node.js](../connector/node)、[Rust](../connector/rust)、[Python](../connector/python)、[C#](../connector/csharp) 等
- 支持 [REST 接口](../connector/rest-api/)

231
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@ -4,119 +4,118 @@ title: 数据模型和基本概念
description: TDengine 的数据模型和基本概念
---
为了便于解释基本概念，便于撰写示例程序，整个 TDengine 文档以智能电表作为典型时序数据场景。假设每个智能电表采集电流、电压、相位三个量，有多个智能电表，每个电表有位置 location 和分组 group ID 的静态属性. 其采集的数据类似如下的表格：
为了便于解释基本概念，便于撰写示例程序，整个 TDengine 文档以智能电表作为典型时序数据场景。假设每个智能电表采集电流、电压、相位三个量，有多个智能电表，每个电表有位置 Location 和分组 Group ID 的静态属性. 其采集的数据类似如下的表格：
<div className="center-table">
<table>
<thead><tr>
<th>Device ID</th>
<th>Time Stamp</th>
<th colSpan="3">Collected Metrics</th>
<th colSpan="2">Tags</th>
<thead>
<tr>
<th rowSpan="2">Device ID</th>
<th rowSpan="2">Timestamp</th>
<th colSpan="3">Collected Metrics</th>
<th colSpan="2">Tags</th>
</tr>
<tr>
<th>Device ID</th>
<th>Time Stamp</th>
<th>current</th>
<th>voltage</th>
<th>phase</th>
<th>location</th>
<th>groupId</th>
</tr>
</thead>
<tbody>
<tr>
<td>d1001</td>
<td>1538548685000</td>
<td>10.3</td>
<td>219</td>
<td>0.31</td>
<td>California.SanFrancisco</td>
<td>2</td>
</tr>
<tr>
<td>d1002</td>
<td>1538548684000</td>
<td>10.2</td>
<td>220</td>
<td>0.23</td>
<td>California.SanFrancisco</td>
<td>3</td>
</tr>
<tr>
<td>d1003</td>
<td>1538548686500</td>
<td>11.5</td>
<td>221</td>
<td>0.35</td>
<td>California.LosAngeles</td>
<td>3</td>
</tr>
<tr>
<td>d1004</td>
<td>1538548685500</td>
<td>13.4</td>
<td>223</td>
<td>0.29</td>
<td>California.LosAngeles</td>
<td>2</td>
</tr>
<tr>
<td>d1001</td>
<td>1538548695000</td>
<td>12.6</td>
<td>218</td>
<td>0.33</td>
<td>California.SanFrancisco</td>
<td>2</td>
</tr>
<tr>
<td>d1004</td>
<td>1538548696600</td>
<td>11.8</td>
<td>221</td>
<td>0.28</td>
<td>California.LosAngeles</td>
<td>2</td>
</tr>
<tr>
<td>d1002</td>
<td>1538548696650</td>
<td>10.3</td>
<td>218</td>
<td>0.25</td>
<td>California.SanFrancisco</td>
<td>3</td>
</tr>
<tr>
<td>d1001</td>
<td>1538548696800</td>
<td>12.3</td>
<td>221</td>
<td>0.31</td>
<td>California.SanFrancisco</td>
<td>2</td>
</tr>
</tbody>
<tr>
<th>current</th>
<th>voltage</th>
<th>phase</th>
<th>location</th>
<th>groupid</th>
</tr>
</thead>
<tbody>
<tr>
<td>d1001</td>
<td>1538548685000</td>
<td>10.3</td>
<td>219</td>
<td>0.31</td>
<td>California.SanFrancisco</td>
<td>2</td>
</tr>
<tr>
<td>d1002</td>
<td>1538548684000</td>
<td>10.2</td>
<td>220</td>
<td>0.23</td>
<td>California.SanFrancisco</td>
<td>3</td>
</tr>
<tr>
<td>d1003</td>
<td>1538548686500</td>
<td>11.5</td>
<td>221</td>
<td>0.35</td>
<td>California.LosAngeles</td>
<td>3</td>
</tr>
<tr>
<td>d1004</td>
<td>1538548685500</td>
<td>13.4</td>
<td>223</td>
<td>0.29</td>
<td>California.LosAngeles</td>
<td>2</td>
</tr>
<tr>
<td>d1001</td>
<td>1538548695000</td>
<td>12.6</td>
<td>218</td>
<td>0.33</td>
<td>California.SanFrancisco</td>
<td>2</td>
</tr>
<tr>
<td>d1004</td>
<td>1538548696600</td>
<td>11.8</td>
<td>221</td>
<td>0.28</td>
<td>California.LosAngeles</td>
<td>2</td>
</tr>
<tr>
<td>d1002</td>
<td>1538548696650</td>
<td>10.3</td>
<td>218</td>
<td>0.25</td>
<td>California.SanFrancisco</td>
<td>3</td>
</tr>
<tr>
<td>d1001</td>
<td>1538548696800</td>
<td>12.3</td>
<td>221</td>
<td>0.31</td>
<td>California.SanFrancisco</td>
<td>2</td>
</tr>
</tbody>
</table>
<a href="#model_table1">表 1：智能电表数据示例</a>
<a name="#model_table1">表 1. 智能电表数据示例</a>
</div>
每一条记录都有设备 ID，时间戳，采集的物理量以及每个设备相关的静态标签。每个设备是受外界的触发，或按照设定的周期采集数据。采集的数据点是时序的，是一个数据流。
每一条记录都有设备 ID、时间戳、采集的物理量（如上表中的 `current`、`voltage` 和 `phase`）以及每个设备相关的静态标签（`location` 和 `groupid`）。每个设备是受外界的触发，或按照设定的周期采集数据。采集的数据点是时序的，是一个数据流。
## 采集量 (Metric)
## 采集量（Metric）
采集量是指传感器、设备或其他类型采集点采集的物理量，比如电流、电压、温度、压力、GPS 位置等，是随时间变化的，数据类型可以是整型、浮点型、布尔型，也可是字符串。随着时间的推移，存储的采集量的数据量越来越大。智能电表示例中的电流、电压、相位就是采集量。
## 标签 (Label/Tag)
## 标签（Label/Tag）
标签是指传感器、设备或其他类型采集点的静态属性，不是随时间变化的，比如设备型号、颜色、设备的所在地等，数据类型可以是任何类型。虽然是静态的，但 TDengine 容许用户修改、删除或增加标签值。与采集量不一样的是，随时间的推移，存储的标签的数据量不会有什么变化。智能电表示例中的location与groupId就是标签。
标签是指传感器、设备或其他类型采集点的静态属性，不是随时间变化的，比如设备型号、颜色、设备的所在地等，数据类型可以是任何类型。虽然是静态的，但 TDengine 容许用户修改、删除或增加标签值。与采集量不一样的是，随时间的推移，存储的标签的数据量不会有什么变化。智能电表示例中的 `location` 与 `groupid` 就是标签。
## 数据采集点 (Data Collection Point)
## 数据采集点（Data Collection Point）
数据采集点是指按照预设时间周期或受事件触发采集物理量的硬件或软件。一个数据采集点可以采集一个或多个采集量，**但这些采集量都是同一时刻采集的，具有相同的时间戳**。对于复杂的设备，往往有多个数据采集点，每个数据采集点采集的周期都可能不一样，而且完全独立，不同步。比如对于一台汽车，有数据采集点专门采集 GPS 位置，有数据采集点专门采集发动机状态，有数据采集点专门采集车内的环境，这样一台汽车就有三个数据采集点。智能电表示例中的d1001, d1002, d1003, d1004等就是数据采集点。
数据采集点是指按照预设时间周期或受事件触发采集物理量的硬件或软件。一个数据采集点可以采集一个或多个采集量，**但这些采集量都是同一时刻采集的，具有相同的时间戳**。对于复杂的设备，往往有多个数据采集点，每个数据采集点采集的周期都可能不一样，而且完全独立，不同步。比如对于一台汽车，有数据采集点专门采集 GPS 位置，有数据采集点专门采集发动机状态，有数据采集点专门采集车内的环境，这样一台汽车就有三个数据采集点。智能电表示例中的 d1001、d1002、d1003、d1004 等就是数据采集点。
## 表 (Table)
## 表（Table）
因为采集量一般是结构化数据，同时为降低学习门槛，TDengine 采用传统的关系型数据库模型管理数据。用户需要先创建库，然后创建表，之后才能插入或查询数据。
@ -129,50 +128,56 @@ description: TDengine 的数据模型和基本概念
如果采用传统的方式，将多个数据采集点的数据写入一张表，由于网络延时不可控，不同数据采集点的数据到达服务器的时序是无法保证的，写入操作是要有锁保护的，而且一个数据采集点的数据是难以保证连续存储在一起的。**采用一个数据采集点一张表的方式，能最大程度的保证单个数据采集点的插入和查询的性能是最优的。**
TDengine 建议用数据采集点的名字（如上表中的 D1001）来做表名。每个数据采集点可能同时采集多个采集量（如上表中的 current，voltage，phase），每个采集量对应一张表中的一列，数据类型可以是整型、浮点型、字符串等。除此之外，表的第一列必须是时间戳，即数据类型为 timestamp。对采集量，TDengine 将自动按照时间戳建立索引，但对采集量本身不建任何索引。数据用列式存储方式保存。
TDengine 建议用数据采集点的名字（如上表中的 d1001）来做表名。每个数据采集点可能同时采集多个采集量（如上表中的 `current`、`voltage` 和 `phase`），每个采集量对应一张表中的一列，数据类型可以是整型、浮点型、字符串等。除此之外，表的第一列必须是时间戳，即数据类型为 Timestamp。对采集量，TDengine 将自动按照时间戳建立索引，但对采集量本身不建任何索引。数据用列式存储方式保存。
对于复杂的设备，比如汽车，它有多个数据采集点，那么就需要为一台汽车建立多张表。
对于复杂的设备，比如汽车，它有多个数据采集点，那么就需要为一辆汽车建立多张表。
## 超级表 (STable)
## 超级表（STable）
由于一个数据采集点一张表，导致表的数量巨增，难以管理，而且应用经常需要做采集点之间的聚合操作，聚合的操作也变得复杂起来。为解决这个问题，TDengine 引入超级表（Super Table，简称为 STable）的概念。
超级表是指某一特定类型的数据采集点的集合。同一类型的数据采集点，其表的结构是完全一样的，但每个表（数据采集点）的静态属性（标签）是不一样的。描述一个超级表（某一特定类型的数据采集点的集合），除需要定义采集量的表结构之外，还需要定义其标签的 schema，标签的数据类型可以是整数、浮点数、字符串，标签可以有多个，可以事后增加、删除或修改。如果整个系统有 N 个不同类型的数据采集点，就需要建立 N 个超级表。
超级表是指某一特定类型的数据采集点的集合。同一类型的数据采集点，其表的结构是完全一样的，但每个表（数据采集点）的静态属性（标签）是不一样的。描述一个超级表（某一特定类型的数据采集点的集合），除需要定义采集量的表结构之外，还需要定义其标签的 Schema，标签的数据类型可以是整数、浮点数、字符串、JSON，标签可以有多个，可以事后增加、删除或修改。如果整个系统有 N 个不同类型的数据采集点，就需要建立 N 个超级表。
在 TDengine 的设计里，**表用来代表一个具体的数据采集点，超级表用来代表一组相同类型的数据采集点集合**。智能电表示例中，我们可以创建一个超级表meters.
在 TDengine 的设计里，**表用来代表一个具体的数据采集点，超级表用来代表一组相同类型的数据采集点集合**。智能电表示例中，我们可以创建一个超级表 `meters`.
## 子表 (Subtable)
## 子表（Subtable）
当为某个具体数据采集点创建表时，用户可以使用超级表的定义做模板，同时指定该具体采集点（表）的具体标签值来创建该表。**通过超级表创建的表称之为子表**。正常的表与子表的差异在于：
1. 子表就是表，因此所有正常表的SQL操作都可以在子表上执行。
1. 子表就是表，因此所有正常表的 SQL 操作都可以在子表上执行。
2. 子表在正常表的基础上有扩展，它是带有静态标签的，而且这些标签可以事后增加、删除、修改，而正常的表没有。
3. 子表一定属于一张超级表，但普通表不属于任何超级表
4. 普通表无法转为子表，子表也无法转为普通表。
超级表与与基于超级表建立的子表之间的关系表现在：
1. 一张超级表包含有多张子表，这些子表具有相同的采集量 schema，但带有不同的标签值。
1. 一张超级表包含有多张子表，这些子表具有相同的采集量 Schema，但带有不同的标签值。
2. 不能通过子表调整数据或标签的模式，对于超级表的数据模式修改立即对所有的子表生效。
3. 超级表只定义一个模板，自身不存储任何数据或标签信息。因此，不能向一个超级表写入数据，只能将数据写入子表中。
查询既可以在表上进行，也可以在超级表上进行。针对超级表的查询，TDengine 将把所有子表中的数据视为一个整体数据集进行处理，会先把满足标签过滤条件的表从超级表中找出来，然后再扫描这些表的时序数据，进行聚合操作，这样需要扫描的数据集会大幅减少，从而显著提高查询的性能。本质上，TDengine 通过对超级表查询的支持，实现了多个同类数据采集点的高效聚合。
TDengine系统建议给一个数据采集点建表，需要通过超级表建表，而不是建普通表。在智能电表的示例中，我们可以通过超级表meters创建子表d1001, d1002, d1003, d1004等。
TDengine 系统建议给一个数据采集点建表，需要通过超级表建表，而不是建普通表。在智能电表的示例中，我们可以通过超级表 meters 创建子表 d1001、d1002、d1003、d1004 等。
为了更好地理解超级与子表的关系，可以参考下面关于智能电表数据模型的示意图。 ![智能电表数据模型示意图](./supertable.webp)
为了更好地理解采集量、标签、超级与子表的关系，可以参考下面关于智能电表数据模型的示意图。
## 库 (database)
<figure>
![智能电表数据模型示意图](./supertable.webp)
<center><figcaption>图 1. 智能电表数据模型示意图</figcaption></center>
</figure>
## 库（Database）
库是指一组表的集合。TDengine 容许一个运行实例有多个库，而且每个库可以配置不同的存储策略。不同类型的数据采集点往往具有不同的数据特征，包括数据采集频率的高低，数据保留时间的长短，副本的数目，数据块的大小，是否允许更新数据等等。为了在各种场景下 TDengine 都能最大效率的工作，TDengine 建议将不同数据特征的超级表创建在不同的库里。
一个库里，可以有一到多个超级表，但一个超级表只属于一个库。一个超级表所拥有的子表全部存在一个库里。
## FQDN & End Point
## FQDN & Endpoint
FQDN (fully qualified domain name, 完全限定域名)是 Internet 上特定计算机或主机的完整域名。FQDN 由两部分组成:主机名和域名。例如，假设邮件服务器的 FQDN 可能是 mail.tdengine.com。主机名是 mail，主机位于域名 tdengine.com 中。DNS(Domain Name System)，负责将 FQDN 翻译成 IP，是互联网应用的寻址方式。对于没有 DNS 的系统，可以通过配置 hosts 文件来解决。
FQDN（Fully Qualified Domain Name，完全限定域名）是 Internet 上特定计算机或主机的完整域名。FQDN 由两部分组成:主机名和域名。例如，假设邮件服务器的 FQDN 可能是 mail.tdengine.com。主机名是 mail，主机位于域名 tdengine.com 中。DNS（Domain Name System），负责将 FQDN 翻译成 IP，是互联网应用的寻址方式。对于没有 DNS 的系统，可以通过配置 hosts 文件来解决。
TDengine 集群的每个节点是由 End Point 来唯一标识的，End Point 是由 FQDN 外加 Port 组成，比如 h1.tdengine.com:6030。这样当 IP 发生变化的时候，我们依然可以使用 FQDN 来动态找到节点，不需要更改集群的任何配置。而且采用 FQDN，便于内网和外网对同一个集群的统一访问。
TDengine 集群的每个节点是由 Endpoint 来唯一标识的，Endpoint 是由 FQDN 外加 Port 组成，比如 h1.tdengine.com:6030。这样当 IP 发生变化的时候，我们依然可以使用 FQDN 来动态找到节点，不需要更改集群的任何配置。而且采用 FQDN，便于内网和外网对同一个集群的统一访问。
TDengine 不建议采用直接的 IP 地址访问集群，不利于管理。不了解 FQDN 概念，请看博文[《一篇文章说清楚 TDengine 的 FQDN》](https://www.taosdata.com/blog/2020/09/11/1824.html)。

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@ -4,11 +4,11 @@ title: 通过 Docker 快速体验 TDengine
description: 使用 Docker 快速体验 TDengine 的高效写入和查询
---
本节首先介绍如何通过 Docker 快速体验 TDengine，然后介绍如何在 Docker 环境下体验 TDengine 的写入和查询功能。如果你不熟悉 Docker，请使用[安装包的方式快速体验](../../get-started/package/)。如果您希望为 TDengine 贡献代码或对内部技术实现感兴趣，请参考 [TDengine GitHub 主页](https://github.com/taosdata/TDengine) 下载源码构建和安装.
本节首先介绍如何通过 Docker 快速体验 TDengine，然后介绍如何在 Docker 环境下体验 TDengine 的写入和查询功能。如果你不熟悉 Docker，请使用[安装包的方式快速体验](../../get-started/package/)。如果您希望为 TDengine 贡献代码或对内部技术实现感兴趣，请参考 [TDengine GitHub 主页](https://github.com/taosdata/TDengine)下载源码构建和安装。
## 启动 TDengine
如果已经安装了 docker， 只需执行下面的命令。
如果已经安装了 Docker，只需执行下面的命令：
```shell
docker run -d -p 6030:6030 -p 6041:6041 -p 6043-6049:6043-6049 -p 6043-6049:6043-6049/udp tdengine/tdengine
@ -16,84 +16,84 @@ docker run -d -p 6030:6030 -p 6041:6041 -p 6043-6049:6043-6049 -p 6043-6049:6043
注意：TDengine 3.0 服务端仅使用 6030 TCP 端口。6041 为 taosAdapter 所使用提供 REST 服务端口。6043-6049 为 taosAdapter 提供第三方应用接入所使用端口，可根据需要选择是否打开。
确定该容器已经启动并且在正常运行
确定该容器已经启动并且在正常运行。
```shell
docker ps
```
进入该容器并执行 bash
进入该容器并执行 `bash`
```shell
docker exec -it <container name> bash
```
然后就可以执行相关的 Linux 命令操作和访问 TDengine
然后就可以执行相关的 Linux 命令操作和访问 TDengine。
注: Docker 工具自身的下载和使用请参考 [Docker 官网文档](https://docs.docker.com/get-docker/)。
注：Docker 工具自身的下载和使用请参考 [Docker 官网文档](https://docs.docker.com/get-docker/)。
## 运行 TDengine CLI
进入容器，执行 taos
进入容器，执行 `taos`：
```
$ taos
taos>
taos>
```
## 写入数据
## 使用 taosBenchmark 体验写入速度
可以使用 TDengine 的自带工具 taosBenchmark 快速体验 TDengine 的写入。
可以使用 TDengine 的自带工具 taosBenchmark 快速体验 TDengine 的写入速度。
进入容器，启动 taosBenchmark：
启动 TDengine 的服务，在 Linux 或 Windows 终端执行 `taosBenchmark`（曾命名为 `taosdemo`）：
```bash
$ taosBenchmark
```
```bash
$ taosBenchmark
```
该命令将在数据库 test 下面自动创建一张超级表 meters，该超级表下有 1 万张表，表名为 "d0" 到 "d9999"，每张表有 1 万条记录，每条记录有 (ts, current, voltage, phase) 四个字段，时间戳从 "2017-07-14 10:40:00 000" 到 "2017-07-14 10:40:09 999"，每张表带有标签 location 和 groupId，groupId 被设置为 1 到 10， location 被设置为 "San Francisco" 或者 "Los Angeles"等城市名称。
该命令将在数据库 `test` 下面自动创建一张超级表 `meters`，该超级表下有 1 万张表，表名为 `d0` 到 `d9999`，每张表有 1 万条记录，每条记录有 `ts`、`current`、`voltage`、`phase` 四个字段，时间戳从 2017-07-14 10:40:00 000 到 2017-07-14 10:40:09 999，每张表带有标签 `location` 和 `groupId`，groupId 被设置为 1 到 10，location 被设置为 `California.Campbell`、`California.Cupertino`、`California.LosAngeles`、`California.MountainView`、`California.PaloAlto`、`California.SanDiego`、`California.SanFrancisco`、`California.SanJose`、`California.SantaClara` 或者 `California.Sunnyvale`。
这条命令很快完成 1 亿条记录的插入。具体时间取决于硬件性能。
这条命令很快完成 1 亿条记录的插入。具体时间取决于硬件性能，即使在一台普通的 PC 服务器往往也仅需十几秒。
taosBenchmark 命令本身带有很多选项，配置表的数目、记录条数等等，您可以设置不同参数进行体验，请执行 `taosBenchmark --help` 详细列出。taosBenchmark 详细使用方法请参照 [taosBenchmark 参考手册](../../reference/taosbenchmark)。
taosBenchmark 命令本身带有很多选项，配置表的数目、记录条数等等，您可以设置不同参数进行体验，请执行 `taosBenchmark --help` 详细列出。taosBenchmark 详细使用方法请参照[如何使用 taosBenchmark 对 TDengine 进行性能测试](https://www.taosdata.com/2021/10/09/3111.html)和 [taosBenchmark 参考手册](../../reference/taosbenchmark)。
## 体验查询
## 使用 TDengine CLI 体验查询速度
使用上述 taosBenchmark 插入数据后，可以在 TDengine CLI 输入查询命令，体验查询速度。。
使用上述 `taosBenchmark` 插入数据后，可以在 TDengine CLI（taos）输入查询命令，体验查询速度。
查询超级表下记录总条数：
查询超级表 `meters` 下的记录总条数：
```sql
taos> select count(*) from test.meters;
SELECT COUNT(*) FROM test.meters;
```
查询 1 亿条记录的平均值、最大值、最小值等：
```sql
taos> select avg(current), max(voltage), min(phase) from test.meters;
SELECT AVG(current), MAX(voltage), MIN(phase) FROM test.meters;
```
查询 location="San Francisco" 的记录总条数：
查询 location = "California.SanFrancisco" 的记录总条数：
```sql
taos> select count(*) from test.meters where location="San Francisco";
SELECT COUNT(*) FROM test.meters WHERE location = "California.SanFrancisco";
```
查询 groupId=10 的所有记录的平均值、最大值、最小值等：
查询 groupId = 10 的所有记录的平均值、最大值、最小值等：
```sql
taos> select avg(current), max(voltage), min(phase) from test.meters where groupId=10;
SELECT AVG(current), MAX(voltage), MIN(phase) FROM test.meters WHERE groupId = 10;
```
对表 d10 按 10s 进行平均值、最大值和最小值聚合统计：
对表 `d10` 按 10 每秒进行平均值、最大值和最小值聚合统计：
```sql
taos> select avg(current), max(voltage), min(phase) from test.d10 interval(10s);
SELECT FIRST(ts), AVG(current), MAX(voltage), MIN(phase) FROM test.d10 INTERVAL(10s);
```
在上面的查询中，你选择的是区间内的第一个时间戳（ts），另一种选择方式是 `\_wstart`，它将给出时间窗口的开始。关于窗口查询的更多信息，参见[特色查询](../../taos-sql/distinguished/)。
## 其它
更多关于在 Docker 环境下使用 TDengine 的细节，请参考 [在 Docker 下使用 TDengine](../../reference/docker)
更多关于在 Docker 环境下使用 TDengine 的细节，请参考 [在 Docker 下使用 TDengine](../../reference/docker)。

115
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View File

@ -10,23 +10,24 @@ import PkgListV3 from "/components/PkgListV3";
您可以[用 Docker 立即体验](../../get-started/docker/) TDengine。如果您希望对 TDengine 贡献代码或对内部实现感兴趣，请参考我们的 [TDengine GitHub 主页](https://github.com/taosdata/TDengine) 下载源码构建和安装.
TDengine 完整的软件包包括服务端（taosd）、用于与第三方系统对接并提供 RESTful 接口的 taosAdapter、应用驱动（taosc）、命令行程序 (CLI，taos) 和一些工具软件。目前 taosAdapter 仅在 Linux 系统上安装和运行，后续将支持 Windows、macOS 等系统。TDengine 除了提供多种语言的连接器之外，还通过 [taosAdapter](../../reference/taosadapter/) 提供 [RESTful 接口](../../connector/rest-api/)。
TDengine 完整的软件包包括服务端（taosd）、应用驱动（taosc）、用于与第三方系统对接并提供 RESTful 接口的 taosAdapter、命令行程序（CLI，taos）和一些工具软件。目前 taosAdapter 仅在 Linux 系统上安装和运行，后续将支持 Windows、macOS 等系统。TDengine 除了提供多种语言的连接器之外，还通过 [taosAdapter](../../reference/taosadapter/) 提供 [RESTful 接口](../../connector/rest-api/)。
为方便使用，标准的服务端安装包包含了 taosd、taosAdapter、taosc、taos、taosdump、taosBenchmark、TDinsight 安装脚本和示例代码；如果您只需要用到服务端程序和客户端连接的 C/C++ 语言支持，也可以仅下载 lite 版本的安装包。
为方便使用，标准的服务端安装包包含了 taosd、taosAdapter、taosc、taos、taosdump、taosBenchmark、TDinsight 安装脚本和示例代码；如果您只需要用到服务端程序和客户端连接的 C/C++ 语言支持，也可以仅下载 Lite 版本的安装包。
在 Linux 系统上，TDengine 开源版本提供 deb 和 rpm 格式安装包，用户可以根据自己的运行环境选择合适的安装包。其中 deb 支持 Debian/Ubuntu 及衍生系统，rpm 支持 CentOS/RHEL/SUSE 及衍生系统。同时我们也为企业用户提供 tar.gz 格式安装包，也支持通过 `apt-get` 工具从线上进行安装。需要注意的是，rpm 和 deb 包不含 taosdump 和 TDinsight 安装脚本，这些工具需要通过安装 taosTool 包获得。TDengine 也提供 Windows x64 平台的安装包。
在 Linux 系统上，TDengine 社区版提供 Deb 和 RPM 格式安装包，用户可以根据自己的运行环境选择合适的安装包。其中 Deb 支持 Debian/Ubuntu 及其衍生系统，RPM 支持 CentOS/RHEL/SUSE 及其衍生系统。同时我们也为企业用户提供 tar.gz 格式安装包，也支持通过 `apt-get` 工具从线上进行安装。需要注意的是，RPM 和 Deb 包不含 `taosdump` 和 TDinsight 安装脚本，这些工具需要通过安装 taosTool 包获得。TDengine 也提供 Windows x64 平台的安装包。
## 安装
<Tabs>
<TabItem label="Deb 安装" value="debinst">
1. 从列表中下载获得 deb 安装包；
<PkgListV3 type={6}/>
1. 从列表中下载获得 Deb 安装包；
<PkgListV3 type={6}/>
2. 进入到安装包所在目录，执行如下的安装命令：
> 请将 `<version>` 替换为下载的安装包版本
```bash
# 替换为下载的安装包版本
sudo dpkg -i TDengine-server-<version>-Linux-x64.deb
```
@ -34,12 +35,13 @@ sudo dpkg -i TDengine-server-<version>-Linux-x64.deb
<TabItem label="RPM 安装" value="rpminst">
1. 从列表中下载获得 rpm 安装包；
<PkgListV3 type={5}/>
1. 从列表中下载获得 RPM 安装包；
<PkgListV3 type={5}/>
2. 进入到安装包所在目录，执行如下的安装命令：
> 请将 `<version>` 替换为下载的安装包版本
```bash
# 替换为下载的安装包版本
sudo rpm -ivh TDengine-server-<version>-Linux-x64.rpm
```
@ -48,44 +50,47 @@ sudo rpm -ivh TDengine-server-<version>-Linux-x64.rpm
<TabItem label="tar.gz 安装" value="tarinst">
1. 从列表中下载获得 tar.gz 安装包；
<PkgListV3 type={0}/>
2. 进入到安装包所在目录，先解压文件后，进入子目录，执行其中的 install.sh 安装脚本：
<PkgListV3 type={0}/>
2. 进入到安装包所在目录，使用 `tar` 解压安装包；
3. 进入到安装包所在目录，先解压文件后，进入子目录，执行其中的 install.sh 安装脚本。
> 请将 `<version>` 替换为下载的安装包版本
```bash
# 替换为下载的安装包版本
tar -zxvf TDengine-server-<version>-Linux-x64.tar.gz
```
解压后进入相应路径，执行
解压文件后，进入相应子目录，执行其中的 `install.sh` 安装脚本：
```bash
sudo ./install.sh
```
:::info
install.sh 安装脚本在执行过程中，会通过命令行交互界面询问一些配置信息。如果希望采取无交互安装方式，那么可以用 -e no 参数来执行 install.sh 脚本。运行 `./install.sh -h` 指令可以查看所有参数的详细说明信息。
install.sh 安装脚本在执行过程中，会通过命令行交互界面询问一些配置信息。如果希望采取无交互安装方式，那么可以运行 `./install.sh -e no`。运行 `./install.sh -h` 指令可以查看所有参数的详细说明信息。
:::
</TabItem>
<TabItem value="apt-get" label="apt-get">
可以使用 apt-get 工具从官方仓库安装。
**安装包仓库**
可以使用 `apt-get` 工具从官方仓库安装。
**配置包仓库**
```bash
wget -qO - http://repos.taosdata.com/tdengine.key | sudo apt-key add -
echo "deb [arch=amd64] http://repos.taosdata.com/tdengine-stable stable main" | sudo tee /etc/apt/sources.list.d/tdengine-stable.list
```
如果安装 Beta 版需要安装包仓库
如果安装 Beta 版需要安装包仓库：
```bash
wget -qO - http://repos.taosdata.com/tdengine.key | sudo apt-key add -
echo "deb [arch=amd64] http://repos.taosdata.com/tdengine-beta beta main" | sudo tee /etc/apt/sources.list.d/tdengine-beta.list
```
**使用 apt-get 命令安装**
**使用 `apt-get` 命令安装**
```bash
sudo apt-get update
@ -94,26 +99,26 @@ sudo apt-get install tdengine
```
:::tip
apt-get 方式只适用于 Debian 或 Ubuntu 系统
apt-get 方式只适用于 Debian 或 Ubuntu 系统。
::::
</TabItem>
<TabItem label="Windows 安装" value="windows">
<TabItem label="Windows 安装" value="windows">
注意：目前 TDengine 在 Windows 平台上只支持 Windows server 2016/2019 和 Windows 10/11 系统版本。
注意：目前 TDengine 在 Windows 平台上只支持 Windows Server 2016/2019 和 Windows 10/11。
1. 从列表中下载获得 exe 安装程序；
<PkgListV3 type={3}/>
<PkgListV3 type={3}/>
2. 运行可执行程序来安装 TDengine。
</TabItem>
</Tabs>
:::info
下载其他组件、最新 Beta 版及之前版本的安装包，请点击[发布历史页面](../../releases/tdengine)
下载其他组件、最新 Beta 版及之前版本的安装包，请点击[发布历史页面](../../releases/tdengine)。
:::
:::note
当安装第一个节点时，出现 Enter FQDN：提示的时候，不需要输入任何内容。只有当安装第二个或以后更多的节点时，才需要输入已有集群中任何一个可用节点的 FQDN，支持该新节点加入集群。当然也可以不输入，而是在新节点启动前，配置到新节点的配置文件中。
当安装第一个节点时，出现 `Enter FQDN:` 提示的时候，不需要输入任何内容。只有当安装第二个或以后更多的节点时，才需要输入已有集群中任何一个可用节点的 FQDN，支持该新节点加入集群。当然也可以不输入，而是在新节点启动前，配置到新节点的配置文件中。
:::
@ -148,7 +153,7 @@ Active: inactive (dead)
如果 TDengine 服务正常工作，那么您可以通过 TDengine 的命令行程序 `taos` 来访问并体验 TDengine。
systemctl 命令汇总：
如下 `systemctl` 命令可以帮助你管理 TDengine 服务：
- 启动服务进程：`systemctl start taosd`
@ -160,7 +165,7 @@ systemctl 命令汇总：
:::info
- systemctl 命令需要 _root_ 权限来运行，如果您非 _root_ 用户，请在命令前添加 sudo 。
- `systemctl` 命令需要 _root_ 权限来运行，如果您非 _root_ 用户，请在命令前添加 `sudo`。
- `systemctl stop taosd` 指令在执行后并不会马上停止 TDengine 服务，而是会等待系统中必要的落盘工作正常完成。在数据量很大的情况下，这可能会消耗较长时间。
- 如果系统中不支持 `systemd`，也可以用手动运行 `/usr/local/taos/bin/taosd` 方式启动 TDengine 服务。
@ -170,87 +175,93 @@ systemctl 命令汇总：
<TabItem label="Windows 系统" value="windows">
安装后，在 C:\TDengine 目录下，运行 taosd.exe 来启动 TDengine 服务进程。
安装后，在 `C:\TDengine` 目录下，运行 `taosd.exe` 来启动 TDengine 服务进程。
</TabItem>
</Tabs>
## TDengine 命令行 (CLI)
## TDengine 命令行（CLI）
为便于检查 TDengine 的状态，执行数据库 (Database) 的各种即席(Ad Hoc)查询，TDengine 提供一命令行应用程序(以下简称为 TDengine CLI) taos。要进入 TDengine 命令行，您只要在安装有 TDengine 的 Linux 终端执行 `taos` 即可，也可以在安装有 TDengine 的 Windows 终端的 C:\TDengine 目录下，运行 taos.exe 来启动 TDengine 命令行。
为便于检查 TDengine 的状态，执行数据库（Database）的各种即席（Ad Hoc）查询，TDengine 提供一命令行应用程序（以下简称为 TDengine CLI）taos。要进入 TDengine 命令行，您只要在安装有 TDengine 的 Linux 终端执行 `taos` 即可，也可以在安装有 TDengine 的 Windows 终端的 C:\TDengine 目录下，运行 taos.exe 来启动 TDengine 命令行。
```bash
taos
```
如果连接服务成功，将会打印出欢迎消息和版本信息。如果失败，则会打印错误消息出来（请参考 [FAQ](/train-faq/faq) 来解决终端连接服务端失败的问题）。 TDengine CLI 的提示符号如下：
如果连接服务成功，将会打印出欢迎消息和版本信息。如果失败，则会打印错误消息出来（请参考 [FAQ](/train-faq/faq) 来解决终端连接服务端失败的问题）。TDengine CLI 的提示符号如下：
```cmd
taos>
```
在 TDengine CLI 中，用户可以通过 SQL 命令来创建/删除数据库、表等，并进行数据库(database)插入查询操作。在终端中运行的 SQL 语句需要以分号结束来运行。示例：
在 TDengine CLI 中，用户可以通过 SQL 命令来创建/删除数据库、表等，并进行数据库（Database）插入查询操作。在终端中运行的 SQL 语句需要以分号（;）结束来运行。示例：
```sql
create database demo;
use demo;
create table t (ts timestamp, speed int);
insert into t values ('2019-07-15 00:00:00', 10);
insert into t values ('2019-07-15 01:00:00', 20);
select * from t;
CREATE DATABASE demo;
USE demo;
CREATE TABLE t (ts TIMESTAMP, speed INT);
INSERT INTO t VALUES ('2019-07-15 00:00:00', 10);
INSERT INTO t VALUES ('2019-07-15 01:00:00', 20);
SELECT * FROM t;
ts | speed |
========================================
2019-07-15 00:00:00.000 | 10 |
2019-07-15 01:00:00.000 | 20 |
Query OK, 2 row(s) in set (0.003128s)
```
除执行 SQL 语句外，系统管理员还可以从 TDengine CLI 进行检查系统运行状态、添加删除用户账号等操作。TDengine CLI 连同应用驱动也可以独立安装在 Linux 或 Windows 机器上运行，更多细节请参考 [这里](../../reference/taos-shell/)
除执行 SQL 语句外，系统管理员还可以从 TDengine CLI 进行检查系统运行状态、添加删除用户账号等操作。TDengine CLI 连同应用驱动也可以独立安装在 Linux 或 Windows 机器上运行，更多细节请参考 [TDengine 命令行](../../reference/taos-shell/)。
## 使用 taosBenchmark 体验写入速度
启动 TDengine 的服务，在 Linux 或 windows 终端执行 `taosBenchmark` （曾命名为 `taosdemo`）：
可以使用 TDengine 的自带工具 taosBenchmark 快速体验 TDengine 的写入速度。
启动 TDengine 的服务，在 Linux 或 Windows 终端执行 `taosBenchmark`（曾命名为 `taosdemo`）：
```bash
taosBenchmark
$ taosBenchmark
```
该命令将在数据库 test 下面自动创建一张超级表 meters，该超级表下有 1 万张表，表名为 "d0" 到 "d9999"，每张表有 1 万条记录，每条记录有 (ts, current, voltage, phase) 四个字段，时间戳从 "2017-07-14 10:40:00 000" 到 "2017-07-14 10:40:09 999"，每张表带有标签 location 和 groupId，groupId 被设置为 1 到 10， location 被设置为 "California.SanFrancisco" 或者 "California.LosAngeles"。
该命令将在数据库 `test` 下面自动创建一张超级表 `meters`，该超级表下有 1 万张表，表名为 `d0` 到 `d9999`，每张表有 1 万条记录，每条记录有 `ts`、`current`、`voltage`、`phase` 四个字段，时间戳从 2017-07-14 10:40:00 000 到 2017-07-14 10:40:09 999，每张表带有标签 `location` 和 `groupId`，groupId 被设置为 1 到 10，location 被设置为 `California.Campbell`、`California.Cupertino`、`California.LosAngeles`、`California.MountainView`、`California.PaloAlto`、`California.SanDiego`、`California.SanFrancisco`、`California.SanJose`、`California.SantaClara` 或者 `California.Sunnyvale`。
这条命令很快完成 1 亿条记录的插入。具体时间取决于硬件性能，即使在一台普通的 PC 服务器往往也仅需十几秒。
taosBenchmark 命令本身带有很多选项，配置表的数目、记录条数等等，您可以设置不同参数进行体验，请执行 `taosBenchmark --help` 详细列出。taosBenchmark 详细使用方法请参照 [如何使用 taosBenchmark 对 TDengine 进行性能测试](https://www.taosdata.com/2021/10/09/3111.html)。
taosBenchmark 命令本身带有很多选项，配置表的数目、记录条数等等，您可以设置不同参数进行体验，请执行 `taosBenchmark --help` 详细列出。taosBenchmark 详细使用方法请参照[如何使用 taosBenchmark 对 TDengine 进行性能测试](https://www.taosdata.com/2021/10/09/3111.html)和 [taosBenchmark 参考手册](../../reference/taosbenchmark)。
## 使用 TDengine CLI 体验查询速度
使用上述 taosBenchmark 插入数据后，可以在 TDengine CLI 输入查询命令，体验查询速度。
使用上述 `taosBenchmark` 插入数据后，可以在 TDengine CLI（taos）输入查询命令，体验查询速度。
查询超级表下记录总条数：
查询超级表 `meters` 下的记录总条数：
```sql
taos> select count(*) from test.meters;
SELECT COUNT(*) FROM test.meters;
```
查询 1 亿条记录的平均值、最大值、最小值等：
```sql
taos> select avg(current), max(voltage), min(phase) from test.meters;
SELECT AVG(current), MAX(voltage), MIN(phase) FROM test.meters;
```
查询 location="California.SanFrancisco" 的记录总条数：
查询 location = "California.SanFrancisco" 的记录总条数：
```sql
taos> select count(*) from test.meters where location="California.SanFrancisco";
SELECT COUNT(*) FROM test.meters WHERE location = "Calaifornia.SanFrancisco";
```
查询 groupId=10 的所有记录的平均值、最大值、最小值等：
查询 groupId = 10 的所有记录的平均值、最大值、最小值等：
```sql
taos> select avg(current), max(voltage), min(phase) from test.meters where groupId=10;
SELECT AVG(current), MAX(voltage), MIN(phase) FROM test.meters WHERE groupId = 10;
```
对表 d10 按 10s 进行平均值、最大值和最小值聚合统计：
对表 `d10` 按 10 每秒进行平均值、最大值和最小值聚合统计：
```sql
taos> select avg(current), max(voltage), min(phase) from test.d10 interval(10s);
SELECT FIRST(ts), AVG(current), MAX(voltage), MIN(phase) FROM test.d10 INTERVAL(10s);
```
在上面的查询中，你选择的是区间内的第一个时间戳（ts），另一种选择方式是 `\_wstart`，它将给出时间窗口的开始。关于窗口查询的更多信息，参见[特色查询](../../taos-sql/distinguished/)。

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@ -218,7 +218,7 @@ void Close()
```sql
DROP DATABASE IF EXISTS tmqdb;
CREATE DATABASE tmqdb;
CREATE TABLE tmqdb.stb (ts TIMESTAMP, c1 INT, c2 FLOAT, c3 VARCHAR(16) TAGS(t1 INT, t3 VARCHAR(16));
CREATE TABLE tmqdb.stb (ts TIMESTAMP, c1 INT, c2 FLOAT, c3 VARCHAR(16)) TAGS(t1 INT, t3 VARCHAR(16));
CREATE TABLE tmqdb.ctb0 USING tmqdb.stb TAGS(0, "subtable0");
CREATE TABLE tmqdb.ctb1 USING tmqdb.stb TAGS(1, "subtable1");
INSERT INTO tmqdb.ctb0 VALUES(now, 0, 0, 'a0')(now+1s, 0, 0, 'a00');

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@ -116,7 +116,7 @@ aggfn为函数名的占位符，需要修改为自己的函数名，如l2norm。
参数的具体含义是：
- inputDataBlock: 输入的数据块
- resultColumn: 输出列。输出列
- resultColumn: 输出列
### 聚合接口函数

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@ -4,7 +4,7 @@ sidebar_label: REST API
description: 详细介绍 TDengine 提供的 RESTful API.
---
为支持各种不同类型平台的开发，TDengine 提供符合 REST 设计标准的 API，即 REST API。为最大程度降低学习成本，不同于其他数据库 REST API 的设计方法，TDengine 直接通过 HTTP POST 请求 BODY 中包含的 SQL 语句来操作数据库，仅需要一个 URL。REST 连接器的使用参见 [视频教程](https://www.taosdata.com/blog/2020/11/11/1965.html)。
为支持各种不同类型平台的开发，TDengine 提供符合 RESTful 设计标准的 API，即 REST API。为最大程度降低学习成本，不同于其他数据库 REST API 的设计方法，TDengine 直接通过 HTTP POST 请求 BODY 中包含的 SQL 语句来操作数据库，仅需要一个 URL。REST API 的使用参见 [视频教程](https://www.taosdata.com/blog/2020/11/11/1965.html)。
:::note
与原生连接器的一个区别是，RESTful 接口是无状态的，因此 `USE db_name` 指令没有效果，所有对表名、超级表名的引用都需要指定数据库名前缀。支持在 RESTful URL 中指定 db_name，这时如果 SQL 语句中没有指定数据库名前缀的话，会使用 URL 中指定的这个 db_name。
@ -18,7 +18,7 @@ RESTful 接口不依赖于任何 TDengine 的库，因此客户端不需要安
在已经安装 TDengine 服务器端的情况下，可以按照如下方式进行验证。
下面以 Ubuntu 环境中使用 curl 工具（确认已经安装）来验证 RESTful 接口的正常，验证前请确认 taosAdapter 服务已开启，在 Linux 系统上此服务默认由 systemd 管理，使用命令 `systemctl start taosadapter` 启动。
下面以 Ubuntu 环境中使用 `curl` 工具（请确认已经安装）来验证 RESTful 接口是否工作正常，验证前请确认 taosAdapter 服务已开启，在 Linux 系统上此服务默认由 systemd 管理，使用命令 `systemctl start taosadapter` 启动。
下面示例是列出所有的数据库，请把 h1.taosdata.com 和 6041（缺省值）替换为实际运行的 TDengine 服务 FQDN 和端口号：

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@ -0,0 +1,38 @@
---
title: Schemaless API
sidebar_label: Schemaless API
description: 详细介绍 TDengine 提供的 Schemaless API.
---
TDengine 提供了兼容 InfluxDB (v1) 和 OpenTSDB 行协议的 Schemaless API。支持 InfluxDB（v1) 或 OpenTSDB 行协议写入数据的第三方软件无需修改代码，只要修改配置的 EndPoint URL 就可以直接把数据写入 TDengine 数据库。
### 兼容 InfluxDB 行协议写入的方法
您可以配置任何支持使用 InfluxDB（v1） 行协议的应用访问地址 `http://<fqdn>:6041/<APIEndPoint>` 来写入 InfluxDB 兼容格式的数据到 TDengine。EndPoint 如下：
```text
/influxdb/v1/write?<param1=value1>?<param2=value2>...
```
支持 InfluxDB 查询参数如下：
- `db` 指定 TDengine 使用的数据库名
- `precision` TDengine 使用的时间精度
- `u` TDengine 用户名
- `p` TDengine 密码
注意： 目前不支持 InfluxDB 的 token 验证方式，仅支持 Basic 验证和查询参数验证。
参考链接：[InfluxDB v1 写接口](https://docs.influxdata.com/influxdb/v2.0/reference/api/influxdb-1x/write/)
### 兼容 OpenTSDB 行协议写入的方法
您可以配置任何支持 OpenTSDB 行协议的应用访问地址 `http://<fqdn>:6041/<APIEndPoint>` 来写入 OpenTSDB 兼容格式的数据到 TDengine。EndPoint 如下：
```text
/opentsdb/v1/put/json/<db>
/opentsdb/v1/put/telnet/<db>
```
参考链接：
- [OpenTSDB JSON](http://opentsdb.net/docs/build/html/api_http/put.html)
- [OpenTSDB Telnet](http://opentsdb.net/docs/build/html/api_telnet/put.html)

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@ -155,15 +155,15 @@ async fn demo(taos: &Taos, db: &str) -> Result<(), Error> {
let inserted = taos.exec_many([
// create super table
"CREATE TABLE `meters` (`ts` TIMESTAMP, `current` FLOAT, `voltage` INT, `phase` FLOAT) \
TAGS (`groupid` INT, `location` BINARY(16))",
TAGS (`groupid` INT, `location` BINARY(24))",
// create child table
"CREATE TABLE `d0` USING `meters` TAGS(0, 'Los Angles')",
"CREATE TABLE `d0` USING `meters` TAGS(0, 'California.LosAngles')",
// insert into child table
"INSERT INTO `d0` values(now - 10s, 10, 116, 0.32)",
// insert with NULL values
"INSERT INTO `d0` values(now - 8s, NULL, NULL, NULL)",
// insert and automatically create table with tags if not exists
"INSERT INTO `d1` USING `meters` TAGS(1, 'San Francisco') values(now - 9s, 10.1, 119, 0.33)",
"INSERT INTO `d1` USING `meters` TAGS(1, 'California.SanFrancisco') values(now - 9s, 10.1, 119, 0.33)",
// insert many records in a single sql
"INSERT INTO `d1` values (now-8s, 10, 120, 0.33) (now - 6s, 10, 119, 0.34) (now - 4s, 11.2, 118, 0.322)",
]).await?;

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@ -41,14 +41,14 @@ TDengine 版本更新往往会增加新的功能特性，列表中的连接器
### 使用原生接口（taosc）
| **功能特性** | **Java** | **Python** | **Go** | **C#** | **Node.js** | **Rust** |
| -------------- | -------- | ---------- | ------ | ------ | ----------- | -------- |
| **连接管理** | 支持 | 支持 | 支持 | 支持 | 支持 | 支持 |
| **普通查询** | 支持 | 支持 | 支持 | 支持 | 支持 | 支持 |
| **参数绑定** | 支持 | 支持 | 支持 | 支持 | 支持 | 支持 |
| ** TMQ ** | 支持 | 支持 | 支持 | 支持 | 支持 | 支持 |
| **Schemaless** | 支持 | 支持 | 支持 | 支持 | 支持 | 支持 |
| **DataFrame** | 不支持 | 支持 | 不支持 | 不支持 | 不支持 | 不支持 |
| **功能特性** | **Java** | **Python** | **Go** | **C#** | **Node.js** | **Rust** |
| ------------------- | -------- | ---------- | ------ | ------ | ----------- | -------- |
| **连接管理** | 支持 | 支持 | 支持 | 支持 | 支持 | 支持 |
| **普通查询** | 支持 | 支持 | 支持 | 支持 | 支持 | 支持 |
| **参数绑定** | 支持 | 支持 | 支持 | 支持 | 支持 | 支持 |
| **数据订阅（TMQ）** | 支持 | 支持 | 支持 | 支持 | 支持 | 支持 |
| **Schemaless** | 支持 | 支持 | 支持 | 支持 | 支持 | 支持 |
| **DataFrame** | 不支持 | 支持 | 不支持 | 不支持 | 不支持 | 不支持 |
:::info
由于不同编程语言数据库框架规范不同，并不意味着所有 C/C++ 接口都需要对应封装支持。
@ -56,16 +56,15 @@ TDengine 版本更新往往会增加新的功能特性，列表中的连接器
### 使用 http (REST 或 WebSocket) 接口
| **功能特性** | **Java** | **Python** | **Go** | **C#（暂不支持）** | **Node.js** | **Rust** |
| ------------------------------ | -------- | ---------- | -------- | ------------------ | ----------- | -------- |
| **连接管理** | 支持 | 支持 | 支持 | N/A | 支持 | 支持 |
| **普通查询** | 支持 | 支持 | 支持 | N/A | 支持 | 支持 |
| **连续查询** | 支持 | 支持 | 支持 | N/A | 支持 | 支持 |
| **参数绑定** | 不支持 | 暂不支持 | 暂不支持 | N/A | 不支持 | 支持 |
| ** TMQ ** | 不支持 | 暂不支持 | 暂不支持 | N/A | 不支持 | 支持 |
| **Schemaless** | 暂不支持 | 暂不支持 | 暂不支持 | N/A | 不支持 | 暂不支持 |
| **批量拉取（基于 WebSocket）** | 支持 | 支持 | 暂不支持 | N/A | 不支持 | 支持 |
| **DataFrame** | 不支持 | 支持 | 不支持 | N/A | 不支持 | 不支持 |
| **功能特性** | **Java** | **Python** | **Go** | **C# ** | **Node.js** | **Rust** |
| ------------------------------ | -------- | ---------- | -------- | -------- | ----------- | -------- |
| **连接管理** | 支持 | 支持 | 支持 | 支持 | 支持 | 支持 |
| **普通查询** | 支持 | 支持 | 支持 | 支持 | 支持 | 支持 |
| **参数绑定** | 暂不支持 | 暂不支持 | 暂不支持 | 支持 | 暂不支持 | 支持 |
| **数据订阅（TMQ）** | 暂不支持 | 暂不支持 | 暂不支持 | 暂不支持 | 暂不支持 | 支持 |
| **Schemaless** | 暂不支持 | 暂不支持 | 暂不支持 | 暂不支持 | 暂不支持 | 暂不支持 |
| **批量拉取（基于 WebSocket）** | 支持 | 支持 | 暂不支持 | 支持 | 暂不支持 | 支持 |
| **DataFrame** | 不支持 | 支持 | 不支持 | 不支持 | 不支持 | 不支持 |
:::warning

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@ -168,7 +168,7 @@ Query OK, 8 row(s) in set (0.001154s)
## 删除数据节点
先停止要删除的数据节点的 taosd 进程，然后启动 CLI 程序 taos，执行：
启动 CLI 程序 taos，执行：
```sql
DROP DNODE "fqdn:port";

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@ -23,10 +23,7 @@ create_subtable_clause: {
}
create_definition:
col_name column_definition
column_definition:
type_name [comment 'string_value']
col_name column_type
table_options:
table_option ...

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@ -104,7 +104,7 @@ SELECT location, groupid, current FROM d1001 LIMIT 2;
### 结果去重
`DISINTCT` 关键字可以对结果集中的一列或多列进行去重，去除的列既可以是标签列也可以是数据列。
`DISTINCT` 关键字可以对结果集中的一列或多列进行去重，去除的列既可以是标签列也可以是数据列。
对标签列去重：
@ -356,7 +356,7 @@ SELECT ... FROM (SELECT ... FROM ...) ...;
- 与非嵌套的查询语句相比，外层查询所能支持的功能特性存在如下限制：
- 计算函数部分：
- 如果内层查询的结果数据未提供时间戳，那么计算过程隐式依赖时间戳的函数在外层会无法正常工作。例如：INTERP, DERIVATIVE, IRATE, LAST_ROW, FIRST, LAST, TWA, STATEDURATION, TAIL, UNIQUE。
- 如果内层查询的结果数据不是有效的时间序列，那么计算过程依赖数据为时间序列的函数在外层会无法正常工作。例如：LEASTSQUARES, ELAPSED, INTERP, DERIVATIVE, IRATE, TWA, DIFF, STATECOUNT, STATEDURATION, CSUM, MAVG, TAIL, UNIQUE。
- 如果内层查询的结果数据不是按时间戳有序，那么计算过程依赖数据按时间有序的函数在外层会无法正常工作。例如：LEASTSQUARES, ELAPSED, INTERP, DERIVATIVE, IRATE, TWA, DIFF, STATECOUNT, STATEDURATION, CSUM, MAVG, TAIL, UNIQUE。
- 计算过程需要两遍扫描的函数，在外层查询中无法正常工作。例如：此类函数包括：PERCENTILE。
:::

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@ -127,7 +127,7 @@ SELECT COS(field_name) FROM { tb_name | stb_name } [WHERE clause]
SELECT FLOOR(field_name) FROM { tb_name | stb_name } [WHERE clause];
```
**功能说明**：获得指定字段的向下取整数的结果。
**功能说明**：获得指定字段的向下取整数的结果。
其他使用说明参见 CEIL 函数描述。
#### LOG
@ -174,7 +174,7 @@ SELECT POW(field_name, power) FROM { tb_name | stb_name } [WHERE clause]
SELECT ROUND(field_name) FROM { tb_name | stb_name } [WHERE clause];
```
**功能说明**：获得指定字段的四舍五入的结果。
**功能说明**：获得指定字段的四舍五入的结果。
其他使用说明参见 CEIL 函数描述。
@ -435,7 +435,7 @@ SELECT TO_ISO8601(ts[, timezone]) FROM { tb_name | stb_name } [WHERE clause];
**使用说明**：
- timezone 参数允许输入的时区格式为: [z/Z, +/-hhmm, +/-hh, +/-hh:mm]。例如，TO_ISO8601(1, "+00:00")。
- 如果输入是表示 UNIX 时间戳的整形，返回格式精度由时间戳的位数决定;
- 如果输入是表示 UNIX 时间戳的整形，返回格式精度由时间戳的位数决定;
- 如果输入是 TIMESTAMP 类型的列，返回格式的时间戳精度与当前 DATABASE 设置的时间精度一致。
@ -770,14 +770,14 @@ SELECT HISTOGRAM(field_name，bin_type, bin_description, normalized) FROM tb_nam
**详细说明**：
- bin_type 用户指定的分桶类型, 有效输入类型为"user_input“, ”linear_bin", "log_bin"。
- 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]"
- 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）作为区间起点和终点，
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）作为区间起点和终点，
生成区间为[-inf, 1.0, 2.0, 4.0, 8.0, 16.0, +inf]。
@ -918,7 +918,7 @@ SELECT MAX(field_name) FROM { tb_name | stb_name } [WHERE clause];
**返回数据类型**：同应用的字段。
**适用数据类型**：数值类型，时间戳类型。
**适用数据类型**：数值类型。
**适用于**：表和超级表。
@ -933,7 +933,7 @@ SELECT MIN(field_name) FROM {tb_name | stb_name} [WHERE clause];
**返回数据类型**：同应用的字段。
**适用数据类型**：数值类型，时间戳类型。
**适用数据类型**：数值类型。
**适用于**：表和超级表。
@ -969,7 +969,7 @@ SELECT SAMPLE(field_name, K) FROM { tb_name | stb_name } [WHERE clause]
**适用于**：表和超级表。
**使用说明**：
**使用说明**：
- 不能参与表达式计算；该函数可以应用在普通表和超级表上；
- 使用在超级表上的时候，需要搭配 PARTITION by tbname 使用，将结果强制规约到单个时间线。
@ -1047,10 +1047,10 @@ SELECT CSUM(field_name) FROM { tb_name | stb_name } [WHERE clause]
**适用于**：表和超级表。
**使用说明**：
**使用说明**：
- 不支持 +、-、*、/ 运算，如 csum(col1) + csum(col2)。
- 只能与聚合（Aggregation）函数一起使用。 该函数可以应用在普通表和超级表上。
- 只能与聚合（Aggregation）函数一起使用。 该函数可以应用在普通表和超级表上。
- 使用在超级表上的时候，需要搭配 PARTITION BY tbname使用，将结果强制规约到单个时间线。
@ -1068,8 +1068,8 @@ SELECT DERIVATIVE(field_name, time_interval, ignore_negative) FROM tb_name [WHER
**适用于**：表和超级表。
**使用说明**:
**使用说明**:
- DERIVATIVE 函数可以在由 PARTITION BY 划分出单独时间线的情况下用于超级表（也即 PARTITION BY tbname）。
- 可以与选择相关联的列一起使用。 例如: select \_rowts, DERIVATIVE() from。
@ -1087,7 +1087,7 @@ SELECT {DIFF(field_name, ignore_negative) | DIFF(field_name)} FROM tb_name [WHER
**适用于**：表和超级表。
**使用说明**:
**使用说明**:
- 输出结果行数是范围内总行数减一，第一行没有结果输出。
- 可以与选择相关联的列一起使用。 例如: select \_rowts, DIFF() from。
@ -1124,9 +1124,9 @@ SELECT MAVG(field_name, K) FROM { tb_name | stb_name } [WHERE clause]
**适用于**：表和超级表。
**使用说明**：
- 不支持 +、-、*、/ 运算，如 mavg(col1, k1) + mavg(col2, k1);
**使用说明**：
- 不支持 +、-、*、/ 运算，如 mavg(col1, k1) + mavg(col2, k1);
- 只能与普通列，选择（Selection）、投影（Projection）函数一起使用，不能与聚合（Aggregation）函数一起使用；
- 使用在超级表上的时候，需要搭配 PARTITION BY tbname使用，将结果强制规约到单个时间线。

10
docs/zh/12-taos-sql/12-distinguished.md
View File

@ -46,7 +46,7 @@ SELECT select_list FROM tb_name
### 窗口子句的规则
- 窗口子句位于数据切分子句之后，GROUP BY 子句之前，且不可以和 GROUP BY 子句一起使用。
- 窗口子句位于数据切分子句之后，不可以和 GROUP BY 子句一起使用。
- 窗口子句将数据按窗口进行切分，对每个窗口进行 SELECT 列表中的表达式的计算，SELECT 列表中的表达式只能包含：
- 常量。
- _wstart伪列、_wend伪列和_wduration伪列。
@ -71,7 +71,7 @@ FILL 语句指定某一窗口区间数据缺失的情况下的填充模式。填
1. 使用 FILL 语句的时候可能生成大量的填充输出，务必指定查询的时间区间。针对每次查询，系统可返回不超过 1 千万条具有插值的结果。
2. 在时间维度聚合中，返回的结果中时间序列严格单调递增。
3. 如果查询对象是超级表，则聚合函数会作用于该超级表下满足值过滤条件的所有表的数据。如果查询中没有使用 PARTITION BY 语句，则返回的结果按照时间序列严格单调递增；如果查询中使用了 PARTITION BY 语句分组，则返回结果中每个 PARTITION 内不按照时间序列严格单调递增。
3. 如果查询对象是超级表，则聚合函数会作用于该超级表下满足值过滤条件的所有表的数据。如果查询中没有使用 PARTITION BY 语句，则返回的结果按照时间序列严格单调递增；如果查询中使用了 PARTITION BY 语句分组，则返回结果中每个 PARTITION 内按照时间序列严格单调递增。
:::
@ -113,6 +113,12 @@ SELECT COUNT(*) FROM temp_tb_1 INTERVAL(1m) SLIDING(2m);
SELECT COUNT(*), FIRST(ts), status FROM temp_tb_1 STATE_WINDOW(status);
```
仅关心 status 为 2 时的状态窗口的信息。例如：
```
SELECT * FROM (SELECT COUNT(*) AS cnt, FIRST(ts) AS fst, status FROM temp_tb_1 STATE_WINDOW(status)) t WHERE status = 2;
```
### 会话窗口
会话窗口根据记录的时间戳主键的值来确定是否属于同一个会话。如下图所示，如果设置时间戳的连续的间隔小于等于 12 秒，则以下 6 条记录构成 2 个会话窗口，分别是：[2019-04-28 14:22:10，2019-04-28 14:22:30]和[2019-04-28 14:23:10，2019-04-28 14:23:30]。因为 2019-04-28 14:22:30 与 2019-04-28 14:23:10 之间的时间间隔是 40 秒，超过了连续时间间隔（12 秒）。

144
docs/zh/12-taos-sql/20-keywords.md
View File

@ -6,7 +6,8 @@ description: TDengine 保留关键字的详细列表
## 保留关键字
目前 TDengine 有将近 200 个内部保留关键字，这些关键字无论大小写如果需要用作库名、表名、STable 名、数据列名及标签列名等，需要使用符合``将关键字括起来使用，例如`ADD`。
目前 TDengine 有 200 多个内部保留关键字，这些关键字如果需要用作库名、表名、超级表名、子表名、数据列名及标签列名等，无论大小写，需要使用符号 `` ` `` 将关键字括起来使用，例如 \`ADD\`。
关键字列表如下：
### A
@ -16,15 +17,20 @@ description: TDengine 保留关键字的详细列表
- ACCOUNTS
- ADD
- AFTER
- AGGREGATE
- ALL
- ALTER
- ANALYZE
- AND
- APPS
- AS
- ASC
- AT_ONCE
- ATTACH
### B
- BALANCE
- BEFORE
- BEGIN
- BETWEEN
@ -34,19 +40,27 @@ description: TDengine 保留关键字的详细列表
- BITNOT
- BITOR
- BLOCKS
- BNODE
- BNODES
- BOOL
- BUFFER
- BUFSIZE
- BY
### C
- CACHE
- CACHELAST
- CACHEMODEL
- CACHESIZE
- CASCADE
- CAST
- CHANGE
- CLIENT_VERSION
- CLUSTER
- COLON
- COLUMN
- COMMA
- COMMENT
- COMP
- COMPACT
- CONCAT
@ -54,15 +68,18 @@ description: TDengine 保留关键字的详细列表
- CONNECTION
- CONNECTIONS
- CONNS
- CONSUMER
- CONSUMERS
- CONTAINS
- COPY
- COUNT
- CREATE
- CTIME
- CURRENT_USER
### D
- DATABASE
- DATABASES
- DAYS
- DBS
- DEFERRED
- DELETE
@ -71,18 +88,23 @@ description: TDengine 保留关键字的详细列表
- DESCRIBE
- DETACH
- DISTINCT
- DISTRIBUTED
- DIVIDE
- DNODE
- DNODES
- DOT
- DOUBLE
- DROP
- DURATION
### E
- EACH
- ENABLE
- END
- EQ
- EVERY
- EXISTS
- EXPIRED
- EXPLAIN
### F
@ -90,18 +112,20 @@ description: TDengine 保留关键字的详细列表
- FAIL
- FILE
- FILL
- FIRST
- FLOAT
- FLUSH
- FOR
- FROM
- FSYNC
- FUNCTION
- FUNCTIONS
### G
- GE
- GLOB
- GRANT
- GRANTS
- GROUP
- GT
### H
@ -112,15 +136,18 @@ description: TDengine 保留关键字的详细列表
- ID
- IF
- IGNORE
- IMMEDIA
- IMMEDIATE
- IMPORT
- IN
- INITIAL
- INDEX
- INDEXES
- INITIALLY
- INNER
- INSERT
- INSTEAD
- INT
- INTEGER
- INTERVA
- INTERVAL
- INTO
- IS
- ISNULL
@ -128,6 +155,7 @@ description: TDengine 保留关键字的详细列表
### J
- JOIN
- JSON
### K
@ -137,46 +165,57 @@ description: TDengine 保留关键字的详细列表
### L
- LE
- LAST
- LAST_ROW
- LICENCES
- LIKE
- LIMIT
- LINEAR
- LOCAL
- LP
- LSHIFT
- LT
### M
- MATCH
- MAX_DELAY
- MAXROWS
- MERGE
- META
- MINROWS
- MINUS
- MNODE
- MNODES
- MODIFY
- MODULES
### N
- NE
- NCHAR
- NEXT
- NMATCH
- NONE
- NOT
- NOTNULL
- NOW
- NULL
- NULLS
### O
- OF
- OFFSET
- ON
- OR
- ORDER
- OUTPUTTYPE
### P
- PARTITION
- PAGES
- PAGESIZE
- PARTITIONS
- PASS
- PLUS
- PORT
- PPS
- PRECISION
- PREV
@ -184,47 +223,63 @@ description: TDengine 保留关键字的详细列表
### Q
- QNODE
- QNODES
- QTIME
- QUERIE
- QUERIES
- QUERY
- QUORUM
### R
- RAISE
- REM
- RANGE
- RATIO
- READ
- REDISTRIBUTE
- RENAME
- REPLACE
- REPLICA
- RESET
- RESTRIC
- RESTRICT
- RETENTIONS
- REVOKE
- ROLLUP
- ROW
- RP
- RSHIFT
### S
- SCHEMALESS
- SCORES
- SELECT
- SEMI
- SERVER_STATUS
- SERVER_VERSION
- SESSION
- SET
- SHOW
- SLASH
- SINGLE_STABLE
- SLIDING
- SLIMIT
- SMALLIN
- SMA
- SMALLINT
- SNODE
- SNODES
- SOFFSET
- STable
- STableS
- SPLIT
- STABLE
- STABLES
- STAR
- STATE
- STATEMEN
- STATE_WI
- STATE_WINDOW
- STATEMENT
- STORAGE
- STREAM
- STREAMS
- STRICT
- STRING
- SUBSCRIPTIONS
- SYNCDB
- SYSINFO
### T
@ -235,20 +290,24 @@ description: TDengine 保留关键字的详细列表
- TBNAME
- TIMES
- TIMESTAMP
- TIMEZONE
- TINYINT
- TO
- TODAY
- TOPIC
- TOPICS
- TRANSACTION
- TRANSACTIONS
- TRIGGER
- TRIM
- TSERIES
- TTL
### U
- UMINUS
- UNION
- UNSIGNED
- UPDATE
- UPLUS
- USE
- USER
- USERS
@ -256,9 +315,13 @@ description: TDengine 保留关键字的详细列表
### V
- VALUE
- VALUES
- VARCHAR
- VARIABLE
- VARIABLES
- VERBOSE
- VGROUP
- VGROUPS
- VIEW
- VNODES
@ -266,14 +329,25 @@ description: TDengine 保留关键字的详细列表
### W
- WAL
- WAL_FSYNC_PERIOD
- WAL_LEVEL
- WAL_RETENTION_PERIOD
- WAL_RETENTION_SIZE
- WAL_ROLL_PERIOD
- WAL_SEGMENT_SIZE
- WATERMARK
- WHERE
- WINDOW_CLOSE
- WITH
- WRITE
### \_
- \_C0
- \_QSTART
- \_QSTOP
- \_QDURATION
- \_WSTART
- \_WSTOP
- \_QEND
- \_QSTART
- \_ROWTS
- \_WDURATION
- \_WEND
- \_WSTART

2
docs/zh/14-reference/04-taosadapter.md
View File

@ -196,7 +196,7 @@ AllowWebSockets
- `u` TDengine 用户名
- `p` TDengine 密码
注意： 目前不支持 InfluxDB 的 token 验证方式只支持 Basic 验证和查询参数验证。
注意： 目前不支持 InfluxDB 的 token 验证方式，仅支持 Basic 验证和查询参数验证。
### OpenTSDB

6
docs/zh/14-reference/14-taosKeeper.md
View File

@ -79,7 +79,7 @@ password = "taosdata"
# 需要被监控的 taosAdapter
[taosAdapter]
address = ["127.0.0.1:6041","192.168.1.95:6041"]
address = ["127.0.0.1:6041"]
[metrics]
# 监控指标前缀
@ -92,7 +92,7 @@ cluster = "production"
database = "log"
# 指定需要监控的普通表
tables = ["normal_table"]
tables = []
```
### 获取监控指标
@ -141,4 +141,4 @@ taos_cluster_info_dnodes_total{cluster_id="5981392874047724755"} 1
# HELP taos_cluster_info_first_ep
# TYPE taos_cluster_info_first_ep gauge
taos_cluster_info_first_ep{cluster_id="5981392874047724755",value="hlb:6030"} 1
```
```

2
docs/zh/21-tdinternal/01-arch.md
View File

@ -26,7 +26,7 @@ TDengine 分布式架构的逻辑结构图如下：
**管理节点（mnode）：** 一个虚拟的逻辑单元，负责所有数据节点运行状态的监控和维护，以及节点之间的负载均衡（图中 M）。同时，管理节点也负责元数据（包括用户、数据库、超级表等）的存储和管理，因此也称为 Meta Node。TDengine 集群中可配置多个（最多不超过 3 个）mnode，它们自动构建成为一个虚拟管理节点组（图中 M1，M2，M3）。mnode 支持多副本，采用 RAFT 一致性协议，保证系统的高可用与高可靠，任何数据更新操作只能在 Leader 上进行。mnode 集群的第一个节点在集群部署时自动完成，其他节点的创建与删除由用户通过 SQL 命令完成。每个 dnode 上至多有一个 mnode，由所属的数据节点的 EP 来唯一标识。每个 dnode 通过内部消息交互自动获取整个集群中所有 mnode 所在的 dnode 的 EP。
**弹性计算节点（qnode）：** 一个虚拟的逻辑单元，运行查询计算任务，也包括基于系统表来实现的 show 命令（图中 Q）。集群中可配置多个 qnode，在整个集群内部共享使用（图中 Q1，Q2，Q3）。qnode 不与具体的 DB 绑定，即一个 qnode 可以同时执行多个 DB 的查询任务。每个 dnode 上至多有一个 qnode，由所属的数据节点的 EP 来唯一标识。客户端通过与 mnode 交互，获取可用的 qnode 列表，当没有可用的 qnode 时，计算任务在 vnode 中执行。
**计算节点（qnode）：** 一个虚拟的逻辑单元，运行查询计算任务，也包括基于系统表来实现的 show 命令（图中 Q）。集群中可配置多个 qnode，在整个集群内部共享使用（图中 Q1，Q2，Q3）。qnode 不与具体的 DB 绑定，即一个 qnode 可以同时执行多个 DB 的查询任务。每个 dnode 上至多有一个 qnode，由所属的数据节点的 EP 来唯一标识。客户端通过与 mnode 交互，获取可用的 qnode 列表，当没有可用的 qnode 时，计算任务在 vnode 中执行。当一个查询执行时，依赖执行计划，调度器会安排一个或多个 qnode 来一起执行。qnode 能从 vnode 获取数据，也可以将自己的计算结果发给其他 qnode 做进一步的处理。通过引入独立的计算节点，TDengine 实现了存储和计算分离。
**流计算节点（snode）：** 一个虚拟的逻辑单元，只运行流计算任务（图中 S）。集群中可配置多个 snode，在整个集群内部共享使用（图中 S1，S2，S3）。snode 不与具体的 stream 绑定，即一个 snode 可以同时执行多个 stream 的计算任务。每个 dnode 上至多有一个 snode，由所属的数据节点的 EP 来唯一标识。由 mnode 调度可用的 snode 完成流计算任务，当没有可用的 snode 时，流计算任务在 vnode 中执行。

9
docs/zh/28-releases/01-tdengine.md
View File

@ -6,11 +6,12 @@ description: TDengine 发布历史、Release Notes 及下载链接
import Release from "/components/ReleaseV3";
## 3.0.0.1
<Release type="tdengine" version="3.0.0.1" />
## 3.0.1.1
<!-- ## 3.0.0.0
<Release type="tdengine" version="3.0.1.1" />
<Release type="tdengine" version="3.0.0.0" /> -->
## 3.0.1.0
<Release type="tdengine" version="3.0.1.0" />

8
docs/zh/28-releases/02-tools.md
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@ -6,6 +6,10 @@ description: taosTools 的发布历史、Release Notes 和下载链接
import Release from "/components/ReleaseV3";
## 2.1.2
## 2.2.0
<Release type="tools" version="2.1.2" />
<Release type="tools" version="2.2.0" />
## 2.1.3
<Release type="tools" version="2.1.3" />

33
include/common/tcommon.h
View File

@ -45,8 +45,8 @@ enum {
// clang-format on
typedef struct {
TSKEY ts;
uint64_t groupId;
TSKEY ts;
} SWinKey;
static inline int SWinKeyCmpr(const void* pKey1, int kLen1, const void* pKey2, int kLen2) {
@ -68,6 +68,37 @@ static inline int SWinKeyCmpr(const void* pKey1, int kLen1, const void* pKey2, i
return 0;
}
typedef struct {
uint64_t groupId;
TSKEY ts;
int32_t exprIdx;
} STupleKey;
static inline int STupleKeyCmpr(const void* pKey1, int kLen1, const void* pKey2, int kLen2) {
STupleKey* pTuple1 = (STupleKey*)pKey1;
STupleKey* pTuple2 = (STupleKey*)pKey2;
if (pTuple1->groupId > pTuple2->groupId) {
return 1;
} else if (pTuple1->groupId < pTuple2->groupId) {
return -1;
}
if (pTuple1->ts > pTuple2->ts) {
return 1;
} else if (pTuple1->ts < pTuple2->ts) {
return -1;
}
if (pTuple1->exprIdx > pTuple2->exprIdx) {
return 1;
} else if (pTuple1->exprIdx < pTuple2->exprIdx) {
return -1;
}
return 0;
}
enum {
TMQ_MSG_TYPE__DUMMY = 0,
TMQ_MSG_TYPE__POLL_RSP,

42
include/common/tdataformat.h
View File

@ -36,8 +36,13 @@ typedef struct STSRow2 STSRow2;
typedef struct STSRowBuilder STSRowBuilder;
typedef struct STagVal STagVal;
typedef struct STag STag;
typedef struct SColData SColData;
// bitmap
#define HAS_NONE ((uint8_t)0x1)
#define HAS_NULL ((uint8_t)0x2)
#define HAS_VALUE ((uint8_t)0x4)
// bitmap ================================
const static uint8_t BIT2_MAP[4][4] = {{0b00000000, 0b00000001, 0b00000010, 0},
{0b00000000, 0b00000100, 0b00001000, 2},
{0b00000000, 0b00010000, 0b00100000, 4},
@ -51,21 +56,21 @@ const static uint8_t BIT2_MAP[4][4] = {{0b00000000, 0b00000001, 0b00000010, 0},
#define SET_BIT2(p, i, v) ((p)[(i) >> 2] = (p)[(i) >> 2] & N1(BIT2_MAP[(i)&3][3]) | BIT2_MAP[(i)&3][(v)])
#define GET_BIT2(p, i) (((p)[(i) >> 2] >> BIT2_MAP[(i)&3][3]) & ((uint8_t)3))
// STSchema
// STSchema ================================
int32_t tTSchemaCreate(int32_t sver, SSchema *pSchema, int32_t nCols, STSchema **ppTSchema);
void tTSchemaDestroy(STSchema *pTSchema);
// SValue
// SValue ================================
int32_t tPutValue(uint8_t *p, SValue *pValue, int8_t type);
int32_t tGetValue(uint8_t *p, SValue *pValue, int8_t type);
int tValueCmprFn(const SValue *pValue1, const SValue *pValue2, int8_t type);
// SColVal
// SColVal ================================
#define COL_VAL_NONE(CID, TYPE) ((SColVal){.cid = (CID), .type = (TYPE), .isNone = 1})
#define COL_VAL_NULL(CID, TYPE) ((SColVal){.cid = (CID), .type = (TYPE), .isNull = 1})
#define COL_VAL_VALUE(CID, TYPE, V) ((SColVal){.cid = (CID), .type = (TYPE), .value = (V)})
// STSRow2
// STSRow2 ================================
#define TSROW_LEN(PROW, V) tGetI32v((uint8_t *)(PROW)->data, (V) ? &(V) : NULL)
#define TSROW_SVER(PROW, V) tGetI32v((PROW)->data + TSROW_LEN(PROW, NULL), (V) ? &(V) : NULL)
@ -77,7 +82,7 @@ int32_t tTSRowToArray(STSRow2 *pRow, STSchema *pTSchema, SArray **ppArray);
int32_t tPutTSRow(uint8_t *p, STSRow2 *pRow);
int32_t tGetTSRow(uint8_t *p, STSRow2 **ppRow);
// STSRowBuilder
// STSRowBuilder ================================
#define tsRowBuilderInit() ((STSRowBuilder){0})
#define tsRowBuilderClear(B) \
do { \
@ -86,7 +91,7 @@ int32_t tGetTSRow(uint8_t *p, STSRow2 **ppRow);
} \
} while (0)
// STag
// STag ================================
int32_t tTagNew(SArray *pArray, int32_t version, int8_t isJson, STag **ppTag);
void tTagFree(STag *pTag);
bool tTagIsJson(const void *pTag);
@ -100,7 +105,16 @@ void tTagSetCid(const STag *pTag, int16_t iTag, int16_t cid);
void debugPrintSTag(STag *pTag, const char *tag, int32_t ln); // TODO: remove
int32_t parseJsontoTagData(const char *json, SArray *pTagVals, STag **ppTag, void *pMsgBuf);
// STRUCT =================
// SColData ================================
void tColDataDestroy(void *ph);
void tColDataInit(SColData *pColData, int16_t cid, int8_t type, int8_t smaOn);
void tColDataClear(SColData *pColData);
int32_t tColDataAppendValue(SColData *pColData, SColVal *pColVal);
void tColDataGetValue(SColData *pColData, int32_t iVal, SColVal *pColVal);
uint8_t tColDataGetBitValue(SColData *pColData, int32_t iVal);
int32_t tColDataCopy(SColData *pColDataSrc, SColData *pColDataDest);
// STRUCT ================================
struct STColumn {
col_id_t colId;
int8_t type;
@ -166,6 +180,18 @@ struct SColVal {
SValue value;
};
struct SColData {
int16_t cid;
int8_t type;
int8_t smaOn;
int32_t nVal;
uint8_t flag;
uint8_t *pBitMap;
int32_t *aOffset;
int32_t nData;
uint8_t *pData;
};
#pragma pack(push, 1)
struct STagVal {
// char colName[TSDB_COL_NAME_LEN]; // only used for tmq_get_meta

4
include/common/tmsg.h
View File

@ -787,6 +787,7 @@ typedef struct {
int32_t sstTrigger;
int16_t hashPrefix;
int16_t hashSuffix;
int32_t tsdbPageSize;
} SCreateDbReq;
int32_t tSerializeSCreateDbReq(void* buf, int32_t bufLen, SCreateDbReq* pReq);
@ -1200,6 +1201,7 @@ typedef struct {
int16_t sstTrigger;
int16_t hashPrefix;
int16_t hashSuffix;
int32_t tsdbPageSize;
} SCreateVnodeReq;
int32_t tSerializeSCreateVnodeReq(void* buf, int32_t bufLen, SCreateVnodeReq* pReq);
@ -2954,7 +2956,7 @@ static FORCE_INLINE void* tDecodeSMqSubTopicEp(void* buf, SMqSubTopicEp* pTopicE
}
static FORCE_INLINE void tDeleteSMqSubTopicEp(SMqSubTopicEp* pSubTopicEp) {
// taosMemoryFree(pSubTopicEp->schema.pSchema);
if (pSubTopicEp->schema.nCols) taosMemoryFreeClear(pSubTopicEp->schema.pSchema);
taosArrayDestroy(pSubTopicEp->vgs);
}

2
include/common/tmsgdef.h
View File

@ -272,6 +272,8 @@ enum {
TD_DEF_MSG_TYPE(TDMT_SYNC_LEADER_TRANSFER, "sync-leader-transfer", NULL, NULL)
TD_DEF_MSG_TYPE(TDMT_SYNC_SET_MNODE_STANDBY, "set-mnode-standby", NULL, NULL)
TD_DEF_MSG_TYPE(TDMT_SYNC_SET_VNODE_STANDBY, "set-vnode-standby", NULL, NULL)
TD_DEF_MSG_TYPE(TDMT_SYNC_HEARTBEAT, "sync-heartbeat", NULL, NULL)
TD_DEF_MSG_TYPE(TDMT_SYNC_HEARTBEAT_REPLY, "sync-heartbeat-reply", NULL, NULL)
TD_DEF_MSG_TYPE(TDMT_SYNC_MAX_MSG, "sync-max", NULL, NULL)
#if defined(TD_MSG_NUMBER_)

469
include/common/ttokendef.h
View File

@ -89,240 +89,241 @@
#define TK_KEEP 71
#define TK_PAGES 72
#define TK_PAGESIZE 73
#define TK_PRECISION 74
#define TK_REPLICA 75
#define TK_STRICT 76
#define TK_VGROUPS 77
#define TK_SINGLE_STABLE 78
#define TK_RETENTIONS 79
#define TK_SCHEMALESS 80
#define TK_WAL_LEVEL 81
#define TK_WAL_FSYNC_PERIOD 82
#define TK_WAL_RETENTION_PERIOD 83
#define TK_WAL_RETENTION_SIZE 84
#define TK_WAL_ROLL_PERIOD 85
#define TK_WAL_SEGMENT_SIZE 86
#define TK_SST_TRIGGER 87
#define TK_TABLE_PREFIX 88
#define TK_TABLE_SUFFIX 89
#define TK_NK_COLON 90
#define TK_TABLE 91
#define TK_NK_LP 92
#define TK_NK_RP 93
#define TK_STABLE 94
#define TK_ADD 95
#define TK_COLUMN 96
#define TK_MODIFY 97
#define TK_RENAME 98
#define TK_TAG 99
#define TK_SET 100
#define TK_NK_EQ 101
#define TK_USING 102
#define TK_TAGS 103
#define TK_COMMENT 104
#define TK_BOOL 105
#define TK_TINYINT 106
#define TK_SMALLINT 107
#define TK_INT 108
#define TK_INTEGER 109
#define TK_BIGINT 110
#define TK_FLOAT 111
#define TK_DOUBLE 112
#define TK_BINARY 113
#define TK_TIMESTAMP 114
#define TK_NCHAR 115
#define TK_UNSIGNED 116
#define TK_JSON 117
#define TK_VARCHAR 118
#define TK_MEDIUMBLOB 119
#define TK_BLOB 120
#define TK_VARBINARY 121
#define TK_DECIMAL 122
#define TK_MAX_DELAY 123
#define TK_WATERMARK 124
#define TK_ROLLUP 125
#define TK_TTL 126
#define TK_SMA 127
#define TK_FIRST 128
#define TK_LAST 129
#define TK_SHOW 130
#define TK_DATABASES 131
#define TK_TABLES 132
#define TK_STABLES 133
#define TK_MNODES 134
#define TK_MODULES 135
#define TK_QNODES 136
#define TK_FUNCTIONS 137
#define TK_INDEXES 138
#define TK_ACCOUNTS 139
#define TK_APPS 140
#define TK_CONNECTIONS 141
#define TK_LICENCES 142
#define TK_GRANTS 143
#define TK_QUERIES 144
#define TK_SCORES 145
#define TK_TOPICS 146
#define TK_VARIABLES 147
#define TK_BNODES 148
#define TK_SNODES 149
#define TK_CLUSTER 150
#define TK_TRANSACTIONS 151
#define TK_DISTRIBUTED 152
#define TK_CONSUMERS 153
#define TK_SUBSCRIPTIONS 154
#define TK_VNODES 155
#define TK_LIKE 156
#define TK_INDEX 157
#define TK_FUNCTION 158
#define TK_INTERVAL 159
#define TK_TOPIC 160
#define TK_AS 161
#define TK_WITH 162
#define TK_META 163
#define TK_CONSUMER 164
#define TK_GROUP 165
#define TK_DESC 166
#define TK_DESCRIBE 167
#define TK_RESET 168
#define TK_QUERY 169
#define TK_CACHE 170
#define TK_EXPLAIN 171
#define TK_ANALYZE 172
#define TK_VERBOSE 173
#define TK_NK_BOOL 174
#define TK_RATIO 175
#define TK_NK_FLOAT 176
#define TK_OUTPUTTYPE 177
#define TK_AGGREGATE 178
#define TK_BUFSIZE 179
#define TK_STREAM 180
#define TK_INTO 181
#define TK_TRIGGER 182
#define TK_AT_ONCE 183
#define TK_WINDOW_CLOSE 184
#define TK_IGNORE 185
#define TK_EXPIRED 186
#define TK_KILL 187
#define TK_CONNECTION 188
#define TK_TRANSACTION 189
#define TK_BALANCE 190
#define TK_VGROUP 191
#define TK_MERGE 192
#define TK_REDISTRIBUTE 193
#define TK_SPLIT 194
#define TK_DELETE 195
#define TK_INSERT 196
#define TK_NULL 197
#define TK_NK_QUESTION 198
#define TK_NK_ARROW 199
#define TK_ROWTS 200
#define TK_TBNAME 201
#define TK_QSTART 202
#define TK_QEND 203
#define TK_QDURATION 204
#define TK_WSTART 205
#define TK_WEND 206
#define TK_WDURATION 207
#define TK_CAST 208
#define TK_NOW 209
#define TK_TODAY 210
#define TK_TIMEZONE 211
#define TK_CLIENT_VERSION 212
#define TK_SERVER_VERSION 213
#define TK_SERVER_STATUS 214
#define TK_CURRENT_USER 215
#define TK_COUNT 216
#define TK_LAST_ROW 217
#define TK_BETWEEN 218
#define TK_IS 219
#define TK_NK_LT 220
#define TK_NK_GT 221
#define TK_NK_LE 222
#define TK_NK_GE 223
#define TK_NK_NE 224
#define TK_MATCH 225
#define TK_NMATCH 226
#define TK_CONTAINS 227
#define TK_IN 228
#define TK_JOIN 229
#define TK_INNER 230
#define TK_SELECT 231
#define TK_DISTINCT 232
#define TK_WHERE 233
#define TK_PARTITION 234
#define TK_BY 235
#define TK_SESSION 236
#define TK_STATE_WINDOW 237
#define TK_SLIDING 238
#define TK_FILL 239
#define TK_VALUE 240
#define TK_NONE 241
#define TK_PREV 242
#define TK_LINEAR 243
#define TK_NEXT 244
#define TK_HAVING 245
#define TK_RANGE 246
#define TK_EVERY 247
#define TK_ORDER 248
#define TK_SLIMIT 249
#define TK_SOFFSET 250
#define TK_LIMIT 251
#define TK_OFFSET 252
#define TK_ASC 253
#define TK_NULLS 254
#define TK_ABORT 255
#define TK_AFTER 256
#define TK_ATTACH 257
#define TK_BEFORE 258
#define TK_BEGIN 259
#define TK_BITAND 260
#define TK_BITNOT 261
#define TK_BITOR 262
#define TK_BLOCKS 263
#define TK_CHANGE 264
#define TK_COMMA 265
#define TK_COMPACT 266
#define TK_CONCAT 267
#define TK_CONFLICT 268
#define TK_COPY 269
#define TK_DEFERRED 270
#define TK_DELIMITERS 271
#define TK_DETACH 272
#define TK_DIVIDE 273
#define TK_DOT 274
#define TK_EACH 275
#define TK_END 276
#define TK_FAIL 277
#define TK_FILE 278
#define TK_FOR 279
#define TK_GLOB 280
#define TK_ID 281
#define TK_IMMEDIATE 282
#define TK_IMPORT 283
#define TK_INITIALLY 284
#define TK_INSTEAD 285
#define TK_ISNULL 286
#define TK_KEY 287
#define TK_NK_BITNOT 288
#define TK_NK_SEMI 289
#define TK_NOTNULL 290
#define TK_OF 291
#define TK_PLUS 292
#define TK_PRIVILEGE 293
#define TK_RAISE 294
#define TK_REPLACE 295
#define TK_RESTRICT 296
#define TK_ROW 297
#define TK_SEMI 298
#define TK_STAR 299
#define TK_STATEMENT 300
#define TK_STRING 301
#define TK_TIMES 302
#define TK_UPDATE 303
#define TK_VALUES 304
#define TK_VARIABLE 305
#define TK_VIEW 306
#define TK_WAL 307
#define TK_TSDB_PAGESIZE 74
#define TK_PRECISION 75
#define TK_REPLICA 76
#define TK_STRICT 77
#define TK_VGROUPS 78
#define TK_SINGLE_STABLE 79
#define TK_RETENTIONS 80
#define TK_SCHEMALESS 81
#define TK_WAL_LEVEL 82
#define TK_WAL_FSYNC_PERIOD 83
#define TK_WAL_RETENTION_PERIOD 84
#define TK_WAL_RETENTION_SIZE 85
#define TK_WAL_ROLL_PERIOD 86
#define TK_WAL_SEGMENT_SIZE 87
#define TK_STT_TRIGGER 88
#define TK_TABLE_PREFIX 89
#define TK_TABLE_SUFFIX 90
#define TK_NK_COLON 91
#define TK_TABLE 92
#define TK_NK_LP 93
#define TK_NK_RP 94
#define TK_STABLE 95
#define TK_ADD 96
#define TK_COLUMN 97
#define TK_MODIFY 98
#define TK_RENAME 99
#define TK_TAG 100
#define TK_SET 101
#define TK_NK_EQ 102
#define TK_USING 103
#define TK_TAGS 104
#define TK_COMMENT 105
#define TK_BOOL 106
#define TK_TINYINT 107
#define TK_SMALLINT 108
#define TK_INT 109
#define TK_INTEGER 110
#define TK_BIGINT 111
#define TK_FLOAT 112
#define TK_DOUBLE 113
#define TK_BINARY 114
#define TK_TIMESTAMP 115
#define TK_NCHAR 116
#define TK_UNSIGNED 117
#define TK_JSON 118
#define TK_VARCHAR 119
#define TK_MEDIUMBLOB 120
#define TK_BLOB 121
#define TK_VARBINARY 122
#define TK_DECIMAL 123
#define TK_MAX_DELAY 124
#define TK_WATERMARK 125
#define TK_ROLLUP 126
#define TK_TTL 127
#define TK_SMA 128
#define TK_FIRST 129
#define TK_LAST 130
#define TK_SHOW 131
#define TK_DATABASES 132
#define TK_TABLES 133
#define TK_STABLES 134
#define TK_MNODES 135
#define TK_MODULES 136
#define TK_QNODES 137
#define TK_FUNCTIONS 138
#define TK_INDEXES 139
#define TK_ACCOUNTS 140
#define TK_APPS 141
#define TK_CONNECTIONS 142
#define TK_LICENCES 143
#define TK_GRANTS 144
#define TK_QUERIES 145
#define TK_SCORES 146
#define TK_TOPICS 147
#define TK_VARIABLES 148
#define TK_BNODES 149
#define TK_SNODES 150
#define TK_CLUSTER 151
#define TK_TRANSACTIONS 152
#define TK_DISTRIBUTED 153
#define TK_CONSUMERS 154
#define TK_SUBSCRIPTIONS 155
#define TK_VNODES 156
#define TK_LIKE 157
#define TK_INDEX 158
#define TK_FUNCTION 159
#define TK_INTERVAL 160
#define TK_TOPIC 161
#define TK_AS 162
#define TK_WITH 163
#define TK_META 164
#define TK_CONSUMER 165
#define TK_GROUP 166
#define TK_DESC 167
#define TK_DESCRIBE 168
#define TK_RESET 169
#define TK_QUERY 170
#define TK_CACHE 171
#define TK_EXPLAIN 172
#define TK_ANALYZE 173
#define TK_VERBOSE 174
#define TK_NK_BOOL 175
#define TK_RATIO 176
#define TK_NK_FLOAT 177
#define TK_OUTPUTTYPE 178
#define TK_AGGREGATE 179
#define TK_BUFSIZE 180
#define TK_STREAM 181
#define TK_INTO 182
#define TK_TRIGGER 183
#define TK_AT_ONCE 184
#define TK_WINDOW_CLOSE 185
#define TK_IGNORE 186
#define TK_EXPIRED 187
#define TK_KILL 188
#define TK_CONNECTION 189
#define TK_TRANSACTION 190
#define TK_BALANCE 191
#define TK_VGROUP 192
#define TK_MERGE 193
#define TK_REDISTRIBUTE 194
#define TK_SPLIT 195
#define TK_DELETE 196
#define TK_INSERT 197
#define TK_NULL 198
#define TK_NK_QUESTION 199
#define TK_NK_ARROW 200
#define TK_ROWTS 201
#define TK_TBNAME 202
#define TK_QSTART 203
#define TK_QEND 204
#define TK_QDURATION 205
#define TK_WSTART 206
#define TK_WEND 207
#define TK_WDURATION 208
#define TK_CAST 209
#define TK_NOW 210
#define TK_TODAY 211
#define TK_TIMEZONE 212
#define TK_CLIENT_VERSION 213
#define TK_SERVER_VERSION 214
#define TK_SERVER_STATUS 215
#define TK_CURRENT_USER 216
#define TK_COUNT 217
#define TK_LAST_ROW 218
#define TK_BETWEEN 219
#define TK_IS 220
#define TK_NK_LT 221
#define TK_NK_GT 222
#define TK_NK_LE 223
#define TK_NK_GE 224
#define TK_NK_NE 225
#define TK_MATCH 226
#define TK_NMATCH 227
#define TK_CONTAINS 228
#define TK_IN 229
#define TK_JOIN 230
#define TK_INNER 231
#define TK_SELECT 232
#define TK_DISTINCT 233
#define TK_WHERE 234
#define TK_PARTITION 235
#define TK_BY 236
#define TK_SESSION 237
#define TK_STATE_WINDOW 238
#define TK_SLIDING 239
#define TK_FILL 240
#define TK_VALUE 241
#define TK_NONE 242
#define TK_PREV 243
#define TK_LINEAR 244
#define TK_NEXT 245
#define TK_HAVING 246
#define TK_RANGE 247
#define TK_EVERY 248
#define TK_ORDER 249
#define TK_SLIMIT 250
#define TK_SOFFSET 251
#define TK_LIMIT 252
#define TK_OFFSET 253
#define TK_ASC 254
#define TK_NULLS 255
#define TK_ABORT 256
#define TK_AFTER 257
#define TK_ATTACH 258
#define TK_BEFORE 259
#define TK_BEGIN 260
#define TK_BITAND 261
#define TK_BITNOT 262
#define TK_BITOR 263
#define TK_BLOCKS 264
#define TK_CHANGE 265
#define TK_COMMA 266
#define TK_COMPACT 267
#define TK_CONCAT 268
#define TK_CONFLICT 269
#define TK_COPY 270
#define TK_DEFERRED 271
#define TK_DELIMITERS 272
#define TK_DETACH 273
#define TK_DIVIDE 274
#define TK_DOT 275
#define TK_EACH 276
#define TK_END 277
#define TK_FAIL 278
#define TK_FILE 279
#define TK_FOR 280
#define TK_GLOB 281
#define TK_ID 282
#define TK_IMMEDIATE 283
#define TK_IMPORT 284
#define TK_INITIALLY 285
#define TK_INSTEAD 286
#define TK_ISNULL 287
#define TK_KEY 288
#define TK_NK_BITNOT 289
#define TK_NK_SEMI 290
#define TK_NOTNULL 291
#define TK_OF 292
#define TK_PLUS 293
#define TK_PRIVILEGE 294
#define TK_RAISE 295
#define TK_REPLACE 296
#define TK_RESTRICT 297
#define TK_ROW 298
#define TK_SEMI 299
#define TK_STAR 300
#define TK_STATEMENT 301
#define TK_STRING 302
#define TK_TIMES 303
#define TK_UPDATE 304
#define TK_VALUES 305
#define TK_VARIABLE 306
#define TK_VIEW 307
#define TK_WAL 308
#define TK_NK_SPACE 300
#define TK_NK_COMMENT 301

145
include/libs/function/function.h
View File

@ -34,66 +34,69 @@ typedef struct SFuncExecEnv {
int32_t calcMemSize;
} SFuncExecEnv;
typedef bool (*FExecGetEnv)(struct SFunctionNode* pFunc, SFuncExecEnv* pEnv);
typedef bool (*FExecInit)(struct SqlFunctionCtx *pCtx, struct SResultRowEntryInfo* pResultCellInfo);
typedef bool (*FExecGetEnv)(struct SFunctionNode *pFunc, SFuncExecEnv *pEnv);
typedef bool (*FExecInit)(struct SqlFunctionCtx *pCtx, struct SResultRowEntryInfo *pResultCellInfo);
typedef int32_t (*FExecProcess)(struct SqlFunctionCtx *pCtx);
typedef int32_t (*FExecFinalize)(struct SqlFunctionCtx *pCtx, SSDataBlock* pBlock);
typedef int32_t (*FExecFinalize)(struct SqlFunctionCtx *pCtx, SSDataBlock *pBlock);
typedef int32_t (*FScalarExecProcess)(SScalarParam *pInput, int32_t inputNum, SScalarParam *pOutput);
typedef int32_t (*FExecCombine)(struct SqlFunctionCtx *pDestCtx, struct SqlFunctionCtx *pSourceCtx);
typedef struct SScalarFuncExecFuncs {
FExecGetEnv getEnv;
FExecGetEnv getEnv;
FScalarExecProcess process;
} SScalarFuncExecFuncs;
typedef struct SFuncExecFuncs {
FExecGetEnv getEnv;
FExecInit init;
FExecProcess process;
FExecGetEnv getEnv;
FExecInit init;
FExecProcess process;
FExecFinalize finalize;
FExecCombine combine;
FExecCombine combine;
} SFuncExecFuncs;
#define MAX_INTERVAL_TIME_WINDOW 1000000 // maximum allowed time windows in final results
#define MAX_INTERVAL_TIME_WINDOW 1000000 // maximum allowed time windows in final results
#define TOP_BOTTOM_QUERY_LIMIT 100
#define FUNCTIONS_NAME_MAX_LENGTH 16
typedef struct SResultRowEntryInfo {
bool initialized:1; // output buffer has been initialized
bool complete:1; // query has completed
uint8_t isNullRes:6; // the result is null
uint16_t numOfRes; // num of output result in current buffer. NOT NULL RESULT
bool initialized : 1; // output buffer has been initialized
bool complete : 1; // query has completed
uint8_t isNullRes : 6; // the result is null
uint16_t numOfRes; // num of output result in current buffer. NOT NULL RESULT
} SResultRowEntryInfo;
// determine the real data need to calculated the result
enum {
BLK_DATA_NOT_LOAD = 0x0,
BLK_DATA_SMA_LOAD = 0x1,
BLK_DATA_NOT_LOAD = 0x0,
BLK_DATA_SMA_LOAD = 0x1,
BLK_DATA_DATA_LOAD = 0x3,
BLK_DATA_FILTEROUT = 0x4, // discard current data block since it is not qualified for filter
BLK_DATA_FILTEROUT = 0x4, // discard current data block since it is not qualified for filter
};
enum {
MAIN_SCAN = 0x0u,
REVERSE_SCAN = 0x1u, // todo remove it
REPEAT_SCAN = 0x2u, //repeat scan belongs to the master scan
MERGE_STAGE = 0x20u,
MAIN_SCAN = 0x0u,
REVERSE_SCAN = 0x1u, // todo remove it
REPEAT_SCAN = 0x2u, // repeat scan belongs to the master scan
MERGE_STAGE = 0x20u,
};
typedef struct SPoint1 {
int64_t key;
union{double val; char* ptr;};
int64_t key;
union {
double val;
char *ptr;
};
} SPoint1;
struct SqlFunctionCtx;
struct SResultRowEntryInfo;
//for selectivity query, the corresponding tag value is assigned if the data is qualified
// for selectivity query, the corresponding tag value is assigned if the data is qualified
typedef struct SSubsidiaryResInfo {
int16_t num;
int32_t rowLen;
char* buf; // serialize data buffer
int16_t num;
int32_t rowLen;
char *buf; // serialize data buffer
struct SqlFunctionCtx **pCtx;
} SSubsidiaryResInfo;
@ -106,69 +109,70 @@ typedef struct SResultDataInfo {
} SResultDataInfo;
#define GET_RES_INFO(ctx) ((ctx)->resultInfo)
#define GET_ROWCELL_INTERBUF(_c) ((void*) ((char*)(_c) + sizeof(SResultRowEntryInfo)))
#define GET_ROWCELL_INTERBUF(_c) ((void *)((char *)(_c) + sizeof(SResultRowEntryInfo)))
typedef struct SInputColumnInfoData {
int32_t totalRows; // total rows in current columnar data
int32_t startRowIndex; // handle started row index
int32_t numOfRows; // the number of rows needs to be handled
int32_t numOfInputCols; // PTS is not included
bool colDataAggIsSet;// if agg is set or not
SColumnInfoData *pPTS; // primary timestamp column
int32_t totalRows; // total rows in current columnar data
int32_t startRowIndex; // handle started row index
int32_t numOfRows; // the number of rows needs to be handled
int32_t numOfInputCols; // PTS is not included
bool colDataAggIsSet; // if agg is set or not
SColumnInfoData *pPTS; // primary timestamp column
SColumnInfoData **pData;
SColumnDataAgg **pColumnDataAgg;
uint64_t uid; // table uid, used to set the tag value when building the final query result for selectivity functions.
uint64_t uid; // table uid, used to set the tag value when building the final query result for selectivity functions.
} SInputColumnInfoData;
typedef struct SSerializeDataHandle {
struct SDiskbasedBuf* pBuf;
struct SDiskbasedBuf *pBuf;
int32_t currentPage;
void *pState;
} SSerializeDataHandle;
// sql function runtime context
typedef struct SqlFunctionCtx {
SInputColumnInfoData input;
SResultDataInfo resDataInfo;
uint32_t order; // data block scanner order: asc|desc
uint8_t scanFlag; // record current running step, default: 0
int16_t functionId; // function id
char *pOutput; // final result output buffer, point to sdata->data
int32_t numOfParams;
SFunctParam *param; // input parameter, e.g., top(k, 20), the number of results for top query is kept in param
SColumnInfoData *pTsOutput; // corresponding output buffer for timestamp of each result, e.g., top/bottom*/
int32_t offset;
struct SResultRowEntryInfo *resultInfo;
SSubsidiaryResInfo subsidiaries;
SPoint1 start;
SPoint1 end;
SFuncExecFuncs fpSet;
SScalarFuncExecFuncs sfp;
struct SExprInfo *pExpr;
struct SSDataBlock *pSrcBlock;
struct SSDataBlock *pDstBlock; // used by indefinite rows function to set selectivity
SSerializeDataHandle saveHandle;
bool isStream;
SInputColumnInfoData input;
SResultDataInfo resDataInfo;
uint32_t order; // data block scanner order: asc|desc
uint8_t scanFlag; // record current running step, default: 0
int16_t functionId; // function id
char *pOutput; // final result output buffer, point to sdata->data
int32_t numOfParams;
SFunctParam *param; // input parameter, e.g., top(k, 20), the number of results for top query is kept in param
SColumnInfoData *pTsOutput; // corresponding output buffer for timestamp of each result, e.g., top/bottom*/
int32_t offset;
struct SResultRowEntryInfo *resultInfo;
SSubsidiaryResInfo subsidiaries;
SPoint1 start;
SPoint1 end;
SFuncExecFuncs fpSet;
SScalarFuncExecFuncs sfp;
struct SExprInfo *pExpr;
struct SSDataBlock *pSrcBlock;
struct SSDataBlock *pDstBlock; // used by indefinite rows function to set selectivity
SSerializeDataHandle saveHandle;
bool isStream;
char udfName[TSDB_FUNC_NAME_LEN];
char udfName[TSDB_FUNC_NAME_LEN];
} SqlFunctionCtx;
enum {
TEXPR_BINARYEXPR_NODE= 0x1,
TEXPR_BINARYEXPR_NODE = 0x1,
TEXPR_UNARYEXPR_NODE = 0x2,
};
typedef struct tExprNode {
int32_t nodeType;
union {
struct {// function node
char functionName[FUNCTIONS_NAME_MAX_LENGTH]; // todo refactor
int32_t functionId;
int32_t num;
struct SFunctionNode *pFunctNode;
struct { // function node
char functionName[FUNCTIONS_NAME_MAX_LENGTH]; // todo refactor
int32_t functionId;
int32_t num;
struct SFunctionNode *pFunctNode;
} _function;
struct {
struct SNode* pRootNode;
struct SNode *pRootNode;
} _optrRoot;
};
} tExprNode;
@ -182,17 +186,18 @@ struct SScalarParam {
int32_t numOfRows;
};
void cleanupResultRowEntry(struct SResultRowEntryInfo* pCell);
int32_t getNumOfResult(SqlFunctionCtx* pCtx, int32_t num, SSDataBlock* pResBlock);
bool isRowEntryCompleted(struct SResultRowEntryInfo* pEntry);
bool isRowEntryInitialized(struct SResultRowEntryInfo* pEntry);
void cleanupResultRowEntry(struct SResultRowEntryInfo *pCell);
int32_t getNumOfResult(SqlFunctionCtx *pCtx, int32_t num, SSDataBlock *pResBlock);
bool isRowEntryCompleted(struct SResultRowEntryInfo *pEntry);
bool isRowEntryInitialized(struct SResultRowEntryInfo *pEntry);
typedef struct SPoint {
int64_t key;
void * val;
void *val;
} SPoint;
int32_t taosGetLinearInterpolationVal(SPoint* point, int32_t outputType, SPoint* point1, SPoint* point2, int32_t inputType);
int32_t taosGetLinearInterpolationVal(SPoint *point, int32_t outputType, SPoint *point1, SPoint *point2,
int32_t inputType);
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// udf api

1
include/libs/nodes/cmdnodes.h
View File

@ -64,6 +64,7 @@ typedef struct SDatabaseOptions {
int64_t keep[3];
int32_t pages;
int32_t pagesize;
int32_t tsdbPageSize;
char precisionStr[3];
int8_t precision;
int8_t replica;

3
include/libs/nodes/nodes.h
View File

@ -320,6 +320,9 @@ int32_t nodesStringToNode(const char* pStr, SNode** pNode);
int32_t nodesListToString(const SNodeList* pList, bool format, char** pStr, int32_t* pLen);
int32_t nodesStringToList(const char* pStr, SNodeList** pList);
int32_t nodesNodeToMsg(const SNode* pNode, char** pMsg, int32_t* pLen);
int32_t nodesMsgToNode(const char* pStr, int32_t len, SNode** pNode);
int32_t nodesNodeToSQL(SNode* pNode, char* buf, int32_t bufSize, int32_t* len);
char* nodesGetNameFromColumnNode(SNode* pNode);
int32_t nodesGetOutputNumFromSlotList(SNodeList* pSlots);

4
include/libs/nodes/plannodes.h
View File

@ -151,6 +151,8 @@ typedef struct SVnodeModifyLogicNode {
SArray* pDataBlocks;
SVgDataBlocks* pVgDataBlocks;
SNode* pAffectedRows; // SColumnNode
SNode* pStartTs; // SColumnNode
SNode* pEndTs; // SColumnNode
uint64_t tableId;
uint64_t stableId;
int8_t tableType; // table type
@ -525,6 +527,8 @@ typedef struct SDataDeleterNode {
char tsColName[TSDB_COL_NAME_LEN];
STimeWindow deleteTimeRange;
SNode* pAffectedRows;
SNode* pStartTs;
SNode* pEndTs;
} SDataDeleterNode;
typedef struct SSubplan {

2
include/libs/nodes/querynodes.h
View File

@ -315,6 +315,8 @@ typedef struct SDeleteStmt {
SNode* pFromTable; // FROM clause
SNode* pWhere; // WHERE clause
SNode* pCountFunc; // count the number of rows affected
SNode* pFirstFunc; // the start timestamp when the data was actually deleted
SNode* pLastFunc; // the end timestamp when the data was actually deleted
SNode* pTagCond; // pWhere divided into pTagCond and timeRange
STimeWindow timeRange;
uint8_t precision;

6
include/libs/planner/planner.h
View File

@ -52,10 +52,14 @@ int32_t qSetSubplanExecutionNode(SSubplan* pSubplan, int32_t groupId, SDownstrea
void qClearSubplanExecutionNode(SSubplan* pSubplan);
// Convert to subplan to string for the scheduler to send to the executor
// Convert to subplan to display string for the scheduler to send to the executor
int32_t qSubPlanToString(const SSubplan* pSubplan, char** pStr, int32_t* pLen);
int32_t qStringToSubplan(const char* pStr, SSubplan** pSubplan);
// Convert to subplan to msg for the scheduler to send to the executor
int32_t qSubPlanToMsg(const SSubplan* pSubplan, char** pStr, int32_t* pLen);
int32_t qMsgToSubplan(const char* pStr, int32_t len, SSubplan** pSubplan);
char* qQueryPlanToString(const SQueryPlan* pPlan);
SQueryPlan* qStringToQueryPlan(const char* pStr);

75
include/libs/stream/streamState.h Normal file
View File

@ -0,0 +1,75 @@
/*
* Copyright (c) 2019 TAOS Data, Inc. <jhtao@taosdata.com>
*
* This program is free software: you can use, redistribute, and/or modify
* it under the terms of the GNU Affero General Public License, version 3
* or later ("AGPL"), as published by the Free Software Foundation.
*
* This program is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
* FITNESS FOR A PARTICULAR PURPOSE.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include "tdatablock.h"
#include "tdbInt.h"
#ifdef __cplusplus
extern "C" {
#endif
#ifndef _STREAM_STATE_H_
#define _STREAM_STATE_H_
typedef struct SStreamTask SStreamTask;
// incremental state storage
typedef struct {
SStreamTask* pOwner;
TDB* db;
TTB* pStateDb;
TTB* pFuncStateDb;
TXN txn;
} SStreamState;
SStreamState* streamStateOpen(char* path, SStreamTask* pTask);
void streamStateClose(SStreamState* pState);
int32_t streamStateBegin(SStreamState* pState);
int32_t streamStateCommit(SStreamState* pState);
int32_t streamStateAbort(SStreamState* pState);
typedef struct {
TBC* pCur;
} SStreamStateCur;
int32_t streamStateFuncPut(SStreamState* pState, const STupleKey* key, const void* value, int32_t vLen);
int32_t streamStateFuncGet(SStreamState* pState, const STupleKey* key, void** pVal, int32_t* pVLen);
int32_t streamStateFuncDel(SStreamState* pState, const STupleKey* key);
int32_t streamStatePut(SStreamState* pState, const SWinKey* key, const void* value, int32_t vLen);
int32_t streamStateGet(SStreamState* pState, const SWinKey* key, void** pVal, int32_t* pVLen);
int32_t streamStateDel(SStreamState* pState, const SWinKey* key);
int32_t streamStateAddIfNotExist(SStreamState* pState, const SWinKey* key, void** pVal, int32_t* pVLen);
int32_t streamStateReleaseBuf(SStreamState* pState, const SWinKey* key, void* pVal);
void streamFreeVal(void* val);
SStreamStateCur* streamStateGetCur(SStreamState* pState, const SWinKey* key);
SStreamStateCur* streamStateSeekKeyNext(SStreamState* pState, const SWinKey* key);
SStreamStateCur* streamStateSeekKeyPrev(SStreamState* pState, const SWinKey* key);
void streamStateFreeCur(SStreamStateCur* pCur);
int32_t streamStateGetKVByCur(SStreamStateCur* pCur, SWinKey* pKey, const void** pVal, int32_t* pVLen);
int32_t streamStateSeekFirst(SStreamState* pState, SStreamStateCur* pCur);
int32_t streamStateSeekLast(SStreamState* pState, SStreamStateCur* pCur);
int32_t streamStateCurNext(SStreamState* pState, SStreamStateCur* pCur);
int32_t streamStateCurPrev(SStreamState* pState, SStreamStateCur* pCur);
#ifdef __cplusplus
}
#endif
#endif /* ifndef _STREAM_STATE_H_ */

40
include/libs/stream/tstream.h
View File

@ -16,6 +16,7 @@
#include "executor.h"
#include "os.h"
#include "query.h"
#include "streamState.h"
#include "tdatablock.h"
#include "tdbInt.h"
#include "tmsg.h"
@ -263,14 +264,6 @@ typedef struct {
SArray* checkpointVer;
} SStreamRecoveringState;
// incremental state storage
typedef struct {
SStreamTask* pOwner;
TDB* db;
TTB* pStateDb;
TXN txn;
} SStreamState;
typedef struct SStreamTask {
int64_t streamId;
int32_t taskId;
@ -540,37 +533,6 @@ int32_t streamMetaCommit(SStreamMeta* pMeta);
int32_t streamMetaRollBack(SStreamMeta* pMeta);
int32_t streamLoadTasks(SStreamMeta* pMeta);
SStreamState* streamStateOpen(char* path, SStreamTask* pTask);
void streamStateClose(SStreamState* pState);
int32_t streamStateBegin(SStreamState* pState);
int32_t streamStateCommit(SStreamState* pState);
int32_t streamStateAbort(SStreamState* pState);
typedef struct {
TBC* pCur;
} SStreamStateCur;
#if 1
int32_t streamStatePut(SStreamState* pState, const SWinKey* key, const void* value, int32_t vLen);
int32_t streamStateGet(SStreamState* pState, const SWinKey* key, void** pVal, int32_t* pVLen);
int32_t streamStateDel(SStreamState* pState, const SWinKey* key);
void streamFreeVal(void* val);
SStreamStateCur* streamStateGetCur(SStreamState* pState, const SWinKey* key);
SStreamStateCur* streamStateSeekKeyNext(SStreamState* pState, const SWinKey* key);
SStreamStateCur* streamStateSeekKeyPrev(SStreamState* pState, const SWinKey* key);
void streamStateFreeCur(SStreamStateCur* pCur);
int32_t streamStateGetKVByCur(SStreamStateCur* pCur, SWinKey* pKey, const void** pVal, int32_t* pVLen);
int32_t streamStateSeekFirst(SStreamState* pState, SStreamStateCur* pCur);
int32_t streamStateSeekLast(SStreamState* pState, SStreamStateCur* pCur);
int32_t streamStateCurNext(SStreamState* pState, SStreamStateCur* pCur);
int32_t streamStateCurPrev(SStreamState* pState, SStreamStateCur* pCur);
#endif
#ifdef __cplusplus
}
#endif

64
include/libs/sync/syncTools.h
View File

@ -444,6 +444,70 @@ void syncAppendEntriesReplyPrint2(char* s, const SyncAppendEntriesReply* pMsg);
void syncAppendEntriesReplyLog(const SyncAppendEntriesReply* pMsg);
void syncAppendEntriesReplyLog2(char* s, const SyncAppendEntriesReply* pMsg);
// ---------------------------------------------
typedef struct SyncHeartbeat {
uint32_t bytes;
int32_t vgId;
uint32_t msgType;
SRaftId srcId;
SRaftId destId;
// private data
SyncTerm term;
SyncIndex commitIndex;
SyncTerm privateTerm;
} SyncHeartbeat;
SyncHeartbeat* syncHeartbeatBuild(int32_t vgId);
void syncHeartbeatDestroy(SyncHeartbeat* pMsg);
void syncHeartbeatSerialize(const SyncHeartbeat* pMsg, char* buf, uint32_t bufLen);
void syncHeartbeatDeserialize(const char* buf, uint32_t len, SyncHeartbeat* pMsg);
char* syncHeartbeatSerialize2(const SyncHeartbeat* pMsg, uint32_t* len);
SyncHeartbeat* syncHeartbeatDeserialize2(const char* buf, uint32_t len);
void syncHeartbeat2RpcMsg(const SyncHeartbeat* pMsg, SRpcMsg* pRpcMsg);
void syncHeartbeatFromRpcMsg(const SRpcMsg* pRpcMsg, SyncHeartbeat* pMsg);
SyncHeartbeat* syncHeartbeatFromRpcMsg2(const SRpcMsg* pRpcMsg);
cJSON* syncHeartbeat2Json(const SyncHeartbeat* pMsg);
char* syncHeartbeat2Str(const SyncHeartbeat* pMsg);
// for debug ----------------------
void syncHeartbeatPrint(const SyncHeartbeat* pMsg);
void syncHeartbeatPrint2(char* s, const SyncHeartbeat* pMsg);
void syncHeartbeatLog(const SyncHeartbeat* pMsg);
void syncHeartbeatLog2(char* s, const SyncHeartbeat* pMsg);
// ---------------------------------------------
typedef struct SyncHeartbeatReply {
uint32_t bytes;
int32_t vgId;
uint32_t msgType;
SRaftId srcId;
SRaftId destId;
// private data
SyncTerm term;
SyncTerm privateTerm;
int64_t startTime;
} SyncHeartbeatReply;
SyncHeartbeatReply* syncHeartbeatReplyBuild(int32_t vgId);
void syncHeartbeatReplyDestroy(SyncHeartbeatReply* pMsg);
void syncHeartbeatReplySerialize(const SyncHeartbeatReply* pMsg, char* buf, uint32_t bufLen);
void syncHeartbeatReplyDeserialize(const char* buf, uint32_t len, SyncHeartbeatReply* pMsg);
char* syncHeartbeatReplySerialize2(const SyncHeartbeatReply* pMsg, uint32_t* len);
SyncHeartbeatReply* syncHeartbeatReplyDeserialize2(const char* buf, uint32_t len);
void syncHeartbeatReply2RpcMsg(const SyncHeartbeatReply* pMsg, SRpcMsg* pRpcMsg);
void syncHeartbeatReplyFromRpcMsg(const SRpcMsg* pRpcMsg, SyncHeartbeatReply* pMsg);
SyncHeartbeatReply* syncHeartbeatReplyFromRpcMsg2(const SRpcMsg* pRpcMsg);
cJSON* syncHeartbeatReply2Json(const SyncHeartbeatReply* pMsg);
char* syncHeartbeatReply2Str(const SyncHeartbeatReply* pMsg);
// for debug ----------------------
void syncHeartbeatReplyPrint(const SyncHeartbeatReply* pMsg);
void syncHeartbeatReplyPrint2(char* s, const SyncHeartbeatReply* pMsg);
void syncHeartbeatReplyLog(const SyncHeartbeatReply* pMsg);
void syncHeartbeatReplyLog2(char* s, const SyncHeartbeatReply* pMsg);
// ---------------------------------------------
typedef struct SyncApplyMsg {
uint32_t bytes;

8
include/libs/tfs/tfs.h
View File

@ -69,6 +69,14 @@ void tfsUpdateSize(STfs *pTfs);
*/
SDiskSize tfsGetSize(STfs *pTfs);
/**
* @brief Get level of multi-tier storage.
*
* @param pTfs
* @return int32_t
*/
int32_t tfsGetLevel(STfs *pTfs);
/**
* @brief Allocate an existing available tier level from fs.
*

2
include/util/taoserror.h
View File

@ -285,6 +285,7 @@ int32_t* taosGetErrno();
#define TSDB_CODE_MND_TOPIC_SUBSCRIBED TAOS_DEF_ERROR_CODE(0, 0x03EB)
#define TSDB_CODE_MND_CGROUP_USED TAOS_DEF_ERROR_CODE(0, 0x03EC)
#define TSDB_CODE_MND_TOPIC_MUST_BE_DELETED TAOS_DEF_ERROR_CODE(0, 0x03ED)
#define TSDB_CODE_MND_IN_REBALANCE TAOS_DEF_ERROR_CODE(0, 0x03EF)
// mnode-stream
#define TSDB_CODE_MND_STREAM_ALREADY_EXIST TAOS_DEF_ERROR_CODE(0, 0x03F0)
@ -577,6 +578,7 @@ int32_t* taosGetErrno();
#define TSDB_CODE_FUNC_FUNTION_PARA_TYPE TAOS_DEF_ERROR_CODE(0, 0x2802)
#define TSDB_CODE_FUNC_FUNTION_PARA_VALUE TAOS_DEF_ERROR_CODE(0, 0x2803)
#define TSDB_CODE_FUNC_NOT_BUILTIN_FUNTION TAOS_DEF_ERROR_CODE(0, 0x2804)
#define TSDB_CODE_FUNC_DUP_TIMESTAMP TAOS_DEF_ERROR_CODE(0, 0x2805)
//udf
#define TSDB_CODE_UDF_STOPPING TAOS_DEF_ERROR_CODE(0, 0x2901)

7
include/util/tdef.h
View File

@ -300,6 +300,9 @@ typedef enum ELogicConditionType {
#define TSDB_DEFAULT_PAGES_PER_VNODE 256
#define TSDB_MIN_PAGESIZE_PER_VNODE 1 // unit KB
#define TSDB_MAX_PAGESIZE_PER_VNODE 16384
#define TSDB_DEFAULT_TSDB_PAGESIZE 4
#define TSDB_MIN_TSDB_PAGESIZE 1 // unit KB
#define TSDB_MAX_TSDB_PAGESIZE 16384
#define TSDB_DEFAULT_PAGESIZE_PER_VNODE 4
#define TSDB_MIN_DAYS_PER_FILE 60 // unit minute
#define TSDB_MAX_DAYS_PER_FILE (3650 * 1440)
@ -359,8 +362,8 @@ typedef enum ELogicConditionType {
#define TSDB_DB_SCHEMALESS_ON 1
#define TSDB_DB_SCHEMALESS_OFF 0
#define TSDB_DEFAULT_DB_SCHEMALESS TSDB_DB_SCHEMALESS_OFF
#define TSDB_MIN_SST_TRIGGER 1
#define TSDB_MAX_SST_TRIGGER 128
#define TSDB_MIN_STT_TRIGGER 1
#define TSDB_MAX_STT_TRIGGER 16
#define TSDB_DEFAULT_SST_TRIGGER 8
#define TSDB_MIN_HASH_PREFIX 0
#define TSDB_MAX_HASH_PREFIX 128

6
include/util/tsched.h
View File

@ -31,7 +31,6 @@ typedef struct SSchedMsg {
void *thandle;
} SSchedMsg;
typedef struct {
char label[TSDB_LABEL_LEN];
tsem_t emptySem;
@ -48,7 +47,6 @@ typedef struct {
void *pTimer;
} SSchedQueue;
/**
* Create a thread-safe ring-buffer based task queue and return the instance. A thread
* pool will be created to consume the messages in the queue.
@ -57,7 +55,7 @@ typedef struct {
* @param label the label of the queue
* @return the created queue scheduler
*/
void *taosInitScheduler(int32_t capacity, int32_t numOfThreads, const char *label, SSchedQueue* pSched);
void *taosInitScheduler(int32_t capacity, int32_t numOfThreads, const char *label, SSchedQueue *pSched);
/**
* Create a thread-safe ring-buffer based task queue and return the instance.
@ -83,7 +81,7 @@ void taosCleanUpScheduler(void *queueScheduler);
* @param queueScheduler the queue scheduler instance
* @param pMsg the message for the task
*/
void taosScheduleTask(void *queueScheduler, SSchedMsg *pMsg);
int taosScheduleTask(void *queueScheduler, SSchedMsg *pMsg);
#ifdef __cplusplus
}

13
include/util/tutil.h
View File

@ -69,7 +69,18 @@ static FORCE_INLINE void taosEncryptPass_c(uint8_t *inBuf, size_t len, char *tar
memcpy(target, buf, TSDB_PASSWORD_LEN);
}
#define taosGetTbHashVal(tbname, tblen, method, prefix, suffix) MurmurHash3_32((tbname), (tblen))
static FORCE_INLINE int32_t taosGetTbHashVal(const char *tbname, int32_t tblen, int32_t method, int32_t prefix,
int32_t suffix) {
if (prefix == 0 && suffix == 0) {
return MurmurHash3_32(tbname, tblen);
} else {
if (tblen <= (prefix + suffix)) {
return MurmurHash3_32(tbname, tblen);
} else {
return MurmurHash3_32(tbname + prefix, tblen - prefix - suffix);
}
}
}
#ifdef __cplusplus
}

107
packaging/MPtestJenkinsfile
View File

@ -5,13 +5,6 @@ def sync_source(branch_name) {
echo ''' + branch_name + '''
'''
sh '''
cd ${TDINTERNAL_ROOT_DIR}
git reset --hard
git fetch || git fetch
git checkout ''' + branch_name + ''' -f
git branch
git pull || git pull
git log | head -n 20
cd ${TDENGINE_ROOT_DIR}
git reset --hard
git fetch || git fetch
@ -64,17 +57,12 @@ pipeline {
defaultValue:'2.1.2',
description: 'This number of baseVerison is generally not modified.Now it is 3.0.0.1'
)
string (
name:'nasPassword',
defaultValue:'password',
description: 'the pasword of the NAS server which has installPackage-192.168.1.131'
)
}
environment{
WORK_DIR = '/var/lib/jenkins/workspace'
TDINTERNAL_ROOT_DIR = '/var/lib/jenkins/workspace/TDinternal'
TDENGINE_ROOT_DIR = '/var/lib/jenkins/workspace/TDinternal/community'
BRANCH_NAME = '3.0'
BRANCH_NAME = 'test/chr/TD-14699'
TD_SERVER_TAR = "TDengine-server-${version}-Linux-x64.tar.gz"
BASE_TD_SERVER_TAR = "TDengine-server-${baseVersion}-Linux-x64.tar.gz"
@ -107,7 +95,7 @@ pipeline {
}
stages {
stage ('RUN') {
stage ('Test Server') {
parallel {
stage('ubuntu16') {
agent{label " ubuntu16 "}
@ -116,17 +104,17 @@ pipeline {
sync_source("${BRANCH_NAME}")
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_SERVER_TAR} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server ${nasPassword}
bash testpackage.sh ${TD_SERVER_TAR} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server
python3 checkPackageRuning.py
'''
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_SERVER_LITE_TAR} ${version} ${BASE_TD_SERVER_LITE_TAR} ${baseVersion} server ${nasPassword}
bash testpackage.sh ${TD_SERVER_LITE_TAR} ${version} ${BASE_TD_SERVER_LITE_TAR} ${baseVersion} server
python3 checkPackageRuning.py
'''
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_SERVER_DEB} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server ${nasPassword}
bash testpackage.sh ${TD_SERVER_DEB} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server
python3 checkPackageRuning.py
'''
}
@ -139,24 +127,21 @@ pipeline {
sync_source("${BRANCH_NAME}")
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_SERVER_TAR} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server ${nasPassword}
bash testpackage.sh ${TD_SERVER_TAR} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server
python3 checkPackageRuning.py
'''
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_SERVER_LITE_TAR} ${version} ${BASE_TD_SERVER_LITE_TAR} ${baseVersion} server ${nasPassword}
bash testpackage.sh ${TD_SERVER_LITE_TAR} ${version} ${BASE_TD_SERVER_LITE_TAR} ${baseVersion} server
python3 checkPackageRuning.py
'''
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_SERVER_DEB} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server ${nasPassword}
python3 checkPackageRuning.py
'''
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_CLIENT_TAR} ${version} ${BASE_TD_CLIENT_TAR} ${baseVersion} client ${nasPassword}
bash testpackage.sh ${TD_SERVER_DEB} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server
python3 checkPackageRuning.py
dpkg -r tdengine
'''
}
}
}
@ -167,17 +152,17 @@ pipeline {
sync_source("${BRANCH_NAME}")
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_SERVER_TAR} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server ${nasPassword}
bash testpackage.sh ${TD_SERVER_TAR} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server
python3 checkPackageRuning.py
'''
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_SERVER_LITE_TAR} ${version} ${BASE_TD_SERVER_LITE_TAR} ${baseVersion} server ${nasPassword}
bash testpackage.sh ${TD_SERVER_LITE_TAR} ${version} ${BASE_TD_SERVER_LITE_TAR} ${baseVersion} server
python3 checkPackageRuning.py
'''
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_SERVER_RPM} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server ${nasPassword}
bash testpackage.sh ${TD_SERVER_RPM} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server
python3 checkPackageRuning.py
'''
}
@ -190,28 +175,23 @@ pipeline {
sync_source("${BRANCH_NAME}")
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_SERVER_TAR} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server ${nasPassword}
bash testpackage.sh ${TD_SERVER_TAR} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server
python3 checkPackageRuning.py
'''
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_SERVER_LITE_TAR} ${version} ${BASE_TD_SERVER_LITE_TAR} ${baseVersion} server ${nasPassword}
bash testpackage.sh ${TD_SERVER_LITE_TAR} ${version} ${BASE_TD_SERVER_LITE_TAR} ${baseVersion} server
python3 checkPackageRuning.py
'''
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_SERVER_RPM} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server ${nasPassword}
bash testpackage.sh ${TD_SERVER_RPM} ${version} ${BASE_TD_SERVER_TAR} ${baseVersion} server
python3 checkPackageRuning.py
'''
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_CLIENT_LITE_TAR} ${version} ${BASE_TD_CLIENT_LITE_TAR} ${baseVersion} client ${nasPassword}
python3 checkPackageRuning.py
'''
sudo rpm -e tdengine
'''
}
}
}
}
stage('arm64') {
agent{label 'linux_arm64'}
steps {
@ -219,18 +199,53 @@ pipeline {
sync_source("${BRANCH_NAME}")
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_SERVER_ARM_TAR} ${version} ${BASE_TD_SERVER_ARM_TAR} ${baseVersion} server ${nasPassword}
bash testpackage.sh ${TD_SERVER_ARM_TAR} ${version} ${BASE_TD_SERVER_ARM_TAR} ${baseVersion} server
python3 checkPackageRuning.py
'''
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_CLIENT_ARM_TAR} ${version} ${BASE_TD_CLIENT_ARM_TAR} ${baseVersion} client ${nasPassword}
python3 checkPackageRuning.py
'''
}
}
}
}
}
}
stage ('Test Client') {
parallel {
stage('ubuntu18') {
agent{label " ubuntu18 "}
steps {
timeout(time: 30, unit: 'MINUTES'){
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_CLIENT_TAR} ${version} ${BASE_TD_CLIENT_TAR} ${baseVersion} client
python3 checkPackageRuning.py 192.168.0.21
'''
}
}
}
stage('centos8') {
agent{label " centos8_3 "}
steps {
timeout(time: 30, unit: 'MINUTES'){
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_CLIENT_LITE_TAR} ${version} ${BASE_TD_CLIENT_LITE_TAR} ${baseVersion} client
python3 checkPackageRuning.py 192.168.0.24
'''
}
}
}
}
}
stage('arm64-client') {
agent{label " linux_arm64 "}
steps {
timeout(time: 30, unit: 'MINUTES'){
sh '''
cd ${TDENGINE_ROOT_DIR}/packaging
bash testpackage.sh ${TD_CLIENT_ARM_TAR} ${version} ${BASE_TD_CLIENT_ARM_TAR} ${baseVersion} client
python3 checkPackageRuning.py 192.168.0.21
'''
}
}
}
}
}

23
packaging/checkPackageRuning.py
View File

@ -19,12 +19,19 @@ import subprocess
# from this import d
import time
if( len(sys.argv)>1 ):
serverHost=sys.argv[1]
else:
serverHost="localhost"
# install taospy
out = subprocess.getoutput("pip3 show taospy|grep Version| awk -F ':' '{print $2}' ")
print("taospy version %s "%out)
if (out == "" ):
os.system("pip install git+https://github.com/taosdata/taos-connector-python.git")
os.system("pip3 install git+https://github.com/taosdata/taos-connector-python.git")
print("install taos python connector")
else:
os.system("pip3 install --upgrade taospy ")
@ -32,19 +39,19 @@ else:
# start taosd prepare
os.system("rm -rf /var/lib/taos/*")
os.system("systemctl restart taosd ")
# os.system("rm -rf /var/lib/taos/*")
# os.system("systemctl restart taosd ")
# wait a moment ,at least 5 seconds
time.sleep(5)
# prepare data by taosBenchmark
os.system("taosBenchmark -y -n 100 -t 100")
os.system("taosBenchmark -y -n 100 -t 100 -h %s "%serverHost )
import taos
conn = taos.connect(host="localhost",
conn = taos.connect(host="%s"%serverHost,
user="root",
password="taosdata",
database="test",
@ -80,15 +87,15 @@ os.system("rm -rf /tmp/dumpdata/*")
# dump data out
print("taosdump dump out data")
os.system("taosdump -o /tmp/dumpdata -D test -y ")
os.system("taosdump -o /tmp/dumpdata -D test -y -h %s "%serverHost)
# drop database of test
print("drop database test")
os.system(" taos -s ' drop database test ;' ")
os.system(" taos -s ' drop database test ;' -h %s "%serverHost)
# dump data in
print("taosdump dump data in")
os.system("taosdump -i /tmp/dumpdata -y ")
os.system("taosdump -i /tmp/dumpdata -y -h %s "%serverHost)
result = conn.query("SELECT count(*) from test.meters")

2
packaging/debRpmAutoInstall.sh
View File

@ -11,3 +11,5 @@ expect "*one:"
send "\r"
expect "*skip:"
send "\r"
expect eof

122
packaging/testpackage.sh
View File

@ -7,7 +7,6 @@ originPackageName=$3
originversion=$4
testFile=$5
subFile="taos.tar.gz"
password=$6
# Color setting
RED='\033[41;30m'
@ -68,11 +67,37 @@ fi
}
function wgetFile {
file=$1
if [ ! -f ${file} ];then
echoColor BD "wget https://www.taosdata.com/assets-download/3.0/${file}"
wget https://www.taosdata.com/assets-download/3.0/${file}
else
echoColor YD "${file} already exists "
fi
}
function newPath {
buildPath=$1
if [ ! -d ${buildPath} ] ;then
echoColor BD "mkdir -p ${buildPath}"
mkdir -p ${buildPath}
else
echoColor YD "${buildPath} already exists"
fi
}
echoColor G "===== install basesoft ====="
cmdInstall tree
cmdInstall wget
cmdInstall sshpass
cmdInstall expect
echoColor G "===== Uninstall all components of TDeingne ====="
@ -97,11 +122,14 @@ echoColor G "===== new workroom path ====="
installPath="/usr/local/src/packageTest"
oriInstallPath="/usr/local/src/packageTest/3.1"
if [ ! -d ${installPath} ] ;then
echoColor BD "mkdir -p ${installPath}"
mkdir -p ${installPath}
else
echoColor YD "${installPath} already exists"
newPath ${installPath}
newPath ${oriInstallPath}
if [ -d ${oriInstallPath}/${originTdpPath} ] ;then
echoColor BD "rm -rf ${oriInstallPath}/${originTdpPath}/*"
rm -rf ${oriInstallPath}/${originTdpPath}/*
fi
if [ -d ${installPath}/${tdPath} ] ;then
@ -109,33 +137,13 @@ if [ -d ${installPath}/${tdPath} ] ;then
rm -rf ${installPath}/${tdPath}/*
fi
if [ ! -d ${oriInstallPath} ] ;then
echoColor BD "mkdir -p ${oriInstallPath}"
mkdir -p ${oriInstallPath}
else
echoColor YD "${oriInstallPath} already exists"
fi
if [ -d ${oriInstallPath}/${originTdpPath} ] ;then
echoColor BD "rm -rf ${oriInstallPath}/${originTdpPath}/*"
rm -rf ${oriInstallPath}/${originTdpPath}/*
fi
echoColor G "===== download installPackage ====="
# cd ${installPath}
# wget https://www.taosdata.com/assets-download/3.0/${packgeName}
# cd ${oriInstallPath}
# wget https://www.taosdata.com/assets-download/3.0/${originPackageName}
cd ${installPath} && wgetFile ${packgeName}
cd ${oriInstallPath} && wgetFile ${originPackageName}
cd ${installPath}
cp -r ${scriptDir}/debRpmAutoInstall.sh .
if [ ! -f {packgeName} ];then
echoColor BD "sshpass -p ${password} scp -oStrictHostKeyChecking=no 192.168.1.131:/nas/TDengine3/v${version}/community/${packgeName} ."
sshpass -p ${password} scp -oStrictHostKeyChecking=no -oStrictHostKeyChecking=no 192.168.1.131:/nas/TDengine3/v${version}/community/${packgeName} .
fi
packageSuffix=$(echo ${packgeName} | awk -F '.' '{print $NF}')
@ -181,8 +189,7 @@ elif [[ ${packgeName} =~ "tar" ]];then
cd ${oriInstallPath}
if [ ! -f {originPackageName} ];then
echoColor YD "download base installPackage"
echoColor BD "sshpass -p ${password} scp -oStrictHostKeyChecking=no 192.168.1.131:/nas/TDengine3/v${originversion}/community/${originPackageName} ."
sshpass -p ${password} scp -oStrictHostKeyChecking=no 192.168.1.131:/nas/TDengine3/v${originversion}/community/${originPackageName} .
wgetFile ${originPackageName}
fi
echoColor YD "unzip the base installation package"
echoColor BD "tar -xf ${originPackageName}" && tar -xf ${originPackageName}
@ -222,24 +229,45 @@ fi
cd ${installPath}
if ([[ ${packgeName} =~ "Lite" ]] && [[ ${packgeName} =~ "tar" ]]) || [[ ${packgeName} =~ "client" ]] ;then
if [[ ${packgeName} =~ "Lite" ]] || ([[ ${packgeName} =~ "x64" ]] && [[ ${packgeName} =~ "client" ]]) || ([[ ${packgeName} =~ "deb" ]] && [[ ${packgeName} =~ "server" ]]) || ([[ ${packgeName} =~ "rpm" ]] && [[ ${packgeName} =~ "server" ]]) ;then
echoColor G "===== install taos-tools when package is lite or client ====="
cd ${installPath}
sshpass -p ${password} scp -oStrictHostKeyChecking=no 192.168.1.131:/nas/TDengine3/v${version}/community/taosTools-2.1.2-Linux-x64.tar.gz .
# wget https://www.taosdata.com/assets-download/3.0/taosTools-2.1.2-Linux-x64.tar.gz
tar xf taosTools-2.1.2-Linux-x64.tar.gz
cd taosTools-2.1.2 && bash install-taostools.sh
elif [[ ${packgeName} =~ "Lite" ]] && [[ ${packgeName} =~ "deb" ]] ;then
echoColor G "===== install taos-tools when package is lite or client ====="
wgetFile taosTools-2.1.3-Linux-x64.tar.gz .
tar xf taosTools-2.1.3-Linux-x64.tar.gz
cd taosTools-2.1.3 && bash install-taostools.sh
elif ([[ ${packgeName} =~ "arm64" ]] && [[ ${packgeName} =~ "client" ]]);then
echoColor G "===== install taos-tools arm when package is arm64-client ====="
cd ${installPath}
sshpass -p ${password} scp -oStrictHostKeyChecking=no 192.168.1.131:/nas/TDengine3/v${version}/community/taosTools-2.1.2-Linux-x64.tar.gz .
tar xf taosTools-2.1.2-Linux-x64.tar.gz
cd taosTools-2.1.2 && bash install-taostools.sh
elif [[ ${packgeName} =~ "Lite" ]] && [[ ${packgeName} =~ "rpm" ]] ;then
echoColor G "===== install taos-tools when package is lite or client ====="
cd ${installPath}
sshpass -p ${password} scp -oStrictHostKeyChecking=no -oStrictHostKeyChecking=no 192.168.1.131:/nas/TDengine3/v${version}/community/taosTools-2.1.2-Linux-x64.tar.gz .
tar xf taosTools-2.1.2-Linux-x64.tar.gz
cd taosTools-2.1.2 && bash install-taostools.sh
wgetFile taosTools-2.1.3-Linux-arm64.tar.gz .
tar xf taosTools-2.1.3-Linux-arm64.tar.gz
cd taosTools-2.1.3 && bash install-taostools.sh
fi
echoColor G "===== start TDengine ====="
if [[ ${packgeName} =~ "server" ]] ;then
echoColor BD " rm -rf /var/lib/taos/* && systemctl restart taosd "
rm -rf /var/lib/taos/*
systemctl restart taosd
fi
# if ([[ ${packgeName} =~ "Lite" ]] && [[ ${packgeName} =~ "tar" ]]) || [[ ${packgeName} =~ "client" ]] ;then
# echoColor G "===== install taos-tools when package is lite or client ====="
# cd ${installPath}
# wgetFile taosTools-2.1.2-Linux-x64.tar.gz .
# tar xf taosTools-2.1.2-Linux-x64.tar.gz
# cd taosTools-2.1.2 && bash install-taostools.sh
# elif [[ ${packgeName} =~ "Lite" ]] && [[ ${packgeName} =~ "deb" ]] ;then
# echoColor G "===== install taos-tools when package is lite or client ====="
# cd ${installPath}
# wgetFile taosTools-2.1.2-Linux-x64.tar.gz .
# tar xf taosTools-2.1.2-Linux-x64.tar.gz
# cd taosTools-2.1.2 && bash install-taostools.sh
# elif [[ ${packgeName} =~ "Lite" ]] && [[ ${packgeName} =~ "rpm" ]] ;then
# echoColor G "===== install taos-tools when package is lite or client ====="
# cd ${installPath}
# wgetFile taosTools-2.1.2-Linux-x64.tar.gz .
# tar xf taosTools-2.1.2-Linux-x64.tar.gz
# cd taosTools-2.1.2 && bash install-taostools.sh
# fi

20
packaging/tools/install.sh
View File

@ -840,14 +840,20 @@ function updateProduct() {
echo
echo -e "${GREEN_DARK}To configure ${productName} ${NC}: edit ${cfg_install_dir}/${configFile}"
echo -e "${GREEN_DARK}To configure Adapter (if has) ${NC}: edit ${cfg_install_dir}/${adapterName}.toml"
[ -f ${configDir}/taosadapter.toml ] && [ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To configure Taos Adapter ${NC}: edit ${configDir}/taosadapter.toml"
if ((${service_mod} == 0)); then
echo -e "${GREEN_DARK}To start ${productName} ${NC}: ${csudo}systemctl start ${serverName}${NC}"
[ -f ${service_config_dir}/taosadapter.service ] && [ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To start Taos Adatper ${NC}: ${csudo}systemctl start taosadapter ${NC}"
elif ((${service_mod} == 1)); then
echo -e "${GREEN_DARK}To start ${productName} ${NC}: ${csudo}service ${serverName} start${NC}"
[ -f ${service_config_dir}/taosadapter.service ] && [ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To start Taos Adapter ${NC}: ${csudo}service taosadapter start${NC}"
else
echo -e "${GREEN_DARK}To start Adapter (if has)${NC}: ${adapterName} &${NC}"
echo -e "${GREEN_DARK}To start ${productName} ${NC}: ./${serverName}${NC}"
[ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To start Taos Adapter ${NC}: taosadapter &${NC}"
fi
if [ ${openresty_work} = 'true' ]; then
@ -926,14 +932,20 @@ function installProduct() {
# Ask if to start the service
echo
echo -e "${GREEN_DARK}To configure ${productName} ${NC}: edit ${cfg_install_dir}/${configFile}"
echo -e "${GREEN_DARK}To configure ${adapterName} (if has) ${NC}: edit ${cfg_install_dir}/${adapterName}.toml"
[ -f ${configDir}/taosadapter.toml ] && [ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To configure Taos Adapter ${NC}: edit ${configDir}/taosadapter.toml"
if ((${service_mod} == 0)); then
echo -e "${GREEN_DARK}To start ${productName} ${NC}: ${csudo}systemctl start ${serverName}${NC}"
[ -f ${service_config_dir}/taosadapter.service ] && [ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To start Taos Adatper ${NC}: ${csudo}systemctl start taosadapter ${NC}"
elif ((${service_mod} == 1)); then
echo -e "${GREEN_DARK}To start ${productName} ${NC}: ${csudo}service ${serverName} start${NC}"
[ -f ${service_config_dir}/taosadapter.service ] && [ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To start Taos Adapter ${NC}: ${csudo}service taosadapter start${NC}"
else
echo -e "${GREEN_DARK}To start Adapter (if has)${NC}: ${adapterName} &${NC}"
echo -e "${GREEN_DARK}To start ${productName} ${NC}: ${serverName}${NC}"
[ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To start Taos Adapter ${NC}: taosadapter &${NC}"
fi
if [ ! -z "$firstEp" ]; then

20
packaging/tools/make_install.sh
View File

@ -609,14 +609,20 @@ function update_TDengine() {
echo
echo -e "${GREEN_DARK}To configure ${productName} ${NC}: edit ${configDir}/${configFile}"
echo -e "${GREEN_DARK}To configure Taos Adapter (if has) ${NC}: edit ${configDir}/taosadapter.toml"
[ -f ${configDir}/taosadapter.toml ] && [ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To configure Taos Adapter ${NC}: edit ${configDir}/taosadapter.toml"
if ((${service_mod} == 0)); then
echo -e "${GREEN_DARK}To start ${productName} ${NC}: ${csudo}systemctl start ${serverName}${NC}"
[ -f ${service_config_dir}/taosadapter.service ] && [ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To start Taos Adatper ${NC}: ${csudo}systemctl start taosadapter ${NC}"
elif ((${service_mod} == 1)); then
echo -e "${GREEN_DARK}To start ${productName} ${NC}: ${csudo}service ${serverName} start${NC}"
[ -f ${service_config_dir}/taosadapter.service ] && [ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To start Taos Adapter ${NC}: ${csudo}service taosadapter start${NC}"
else
echo -e "${GREEN_DARK}To start Taos Adapter (if has)${NC}: taosadapter &${NC}"
echo -e "${GREEN_DARK}To start ${productName} ${NC}: ${serverName}${NC}"
[ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To start Taos Adapter ${NC}: taosadapter &${NC}"
fi
echo -e "${GREEN_DARK}To access ${productName} ${NC}: use ${GREEN_UNDERLINE}${clientName}${NC} in shell${NC}"
@ -649,14 +655,20 @@ function install_TDengine() {
echo -e "\033[44;32;1m${productName} is installed successfully!${NC}"
echo
echo -e "${GREEN_DARK}To configure ${productName} ${NC}: edit ${configDir}/${configFile}"
echo -e "${GREEN_DARK}To configure taosadapter (if has) ${NC}: edit ${configDir}/taosadapter.toml"
[ -f ${configDir}/taosadapter.toml ] && [ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To configure Taos Adapter ${NC}: edit ${configDir}/taosadapter.toml"
if ((${service_mod} == 0)); then
echo -e "${GREEN_DARK}To start ${productName} ${NC}: ${csudo}systemctl start ${serverName}${NC}"
[ -f ${service_config_dir}/taosadapter.service ] && [ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To start Taos Adapter ${NC}: ${csudo}systemctl start taosadapter ${NC}"
elif ((${service_mod} == 1)); then
echo -e "${GREEN_DARK}To start ${productName} ${NC}: ${csudo}service ${serverName} start${NC}"
[ -f ${service_config_dir}/taosadapter.service ] && [ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To start Taos Adapter ${NC}: ${csudo}service taosadapter start${NC}"
else
echo -e "${GREEN_DARK}To start Taos Adapter (if has)${NC}: taosadapter &${NC}"
echo -e "${GREEN_DARK}To start ${productName} ${NC}: ./${serverName}${NC}"
[ -f ${installDir}/bin/taosadapter ] && \
echo -e "${GREEN_DARK}To start Taos Adapter ${NC}: taosadapter &${NC}"
fi
echo -e "${GREEN_DARK}To access ${productName} ${NC}: use ${GREEN_UNDERLINE}${clientName}${NC} in shell${NC}"

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