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TDengine

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T
TDengine
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Merge branch '3.0' into feat/sangshuduo/TD-14141-update-taostools-for3.0

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CONTRIBUTING-CN.md 0 → 100644
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# 贡献指南
我们感谢所有开发者提交贡献。随时关注我们，Fork 存储库，报告错误，以及在 GitHub 上提交您的代码。但是，我们希望开发者遵循我们的指南，才能更好的做出贡献。
## 报告错误
- 任何用户都可以通过 **[GitHub issue tracker](https://github.com/taosdata/TDengine/issues)** 向我们报告错误。请您对所遇到的问题进行**详细描述**，最好提供重现错误的详细步骤。
- 欢迎提供包含由 Bug 生成的日志文件的附录。
## 需要强调的代码提交规则
- 在提交代码之前，需要**同意贡献者许可协议（CLA）**。点击 [TaosData CLA](https://cla-assistant.io/taosdata/TDengine) 阅读并签署协议。如果您不接受该协议，请停止提交。
- 请在 [GitHub issue tracker](https://github.com/taosdata/TDengine/issues) 中解决问题或添加注册功能。
- 如果在 [GitHub issue tracker](https://github.com/taosdata/TDengine/issues) 中没有找到相应的问题或功能，请**创建一个新的 issue**。
- 将代码提交到我们的存储库时，请创建**包含问题编号的 PR**。
## 贡献指南
1. 请用友好的语气书写。
2. **主动语态**总体上优于被动语态。主动语态中的句子会突出执行动作的人，而不是被动语态突出动作的接受者。
3. 文档写作建议
- 正确拼写产品名称 “TDengine”。 “TD” 用大写字母，“TD” 和 “engine” 之间没有空格 **（正确拼写：TDengine）**。
- 在句号或其他标点符号后只留一个空格。
4. 尽量**使用简单句**，而不是复杂句。
## 给贡献者的礼品
只要您是为 TDengine 做贡献的开发者，不管是代码贡献、修复 bug 或功能请求，还是文档更改，您都将会获得一份**特别的贡献者纪念品礼物**！
<p align="left">
<img
src="docs/assets/contributing-cup.jpg"
alt=""
width="200"
/>
<img
src="docs/assets/contributing-notebook.jpg"
alt=""
width="200"
/>
<img
src="docs/assets/contributing-shirt.jpg"
alt=""
width="200"
/>
TDengine 社区致力于让更多的开发者理解和使用它。
请填写**贡献者提交表**以选择您想收到的礼物。
- [贡献者提交表](https://page.ma.scrmtech.com/form/index?pf_uid=27715_2095&id=12100)
## 联系我们
如果您有什么问题需要解决，或者有什么问题需要解答，可以添加微信：TDengineECO
CONTRIBUTING.md
浏览文件 @ 324973bd
# Contributing
We appreciate contributions from all developers. Feel free to follow us, fork the repository, report bugs and even submit your code on GitHub. However, we would like developers to follow our guides to contribute for better corporation.
We appreciate contributions from all developers. Feel free to follow us, fork the repository, report bugs, and even submit your code on GitHub. However, we would like developers to follow the guidelines in this document to ensure effective cooperation.
## Report bugs
## Reporting a bug
Any users can report bugs to us through the [github issue tracker](https://github.com/taosdata/TDengine/issues). We appreciate a detailed description of the problem you met. It is better to provide the detailed steps on reproducing the bug. Otherwise, an appendix with log files generated by the bug is welcome.
- Any users can report bugs to us through the **[GitHub issue tracker](https://github.com/taosdata/TDengine/issues)**. We would appreciate if you could provide **a detailed description** of the problem you encountered, including steps to reproduce it.
## Read the contributor license agreement
- Attaching log files caused by the bug is really appreciated.
It is required to agree the Contributor Licence Agreement(CLA) before a user submitting his/her code patch. Follow the [TaosData CLA](https://www.taosdata.com/en/contributor/) link to read through the agreement.
## Guidelines for committing code
## Submit your code
- You must agree to the **Contributor License Agreement(CLA) before submitting your code patch**. Follow the **[TAOSData CLA](https://cla-assistant.io/taosdata/TDengine)** link to read through and sign the agreement. If you do not accept the agreement, your contributions cannot be accepted.
Before submitting your code, make sure to [read the contributor license agreement](#read-the-contributor-license-agreement) beforehand. If you don't accept the aggreement, please stop submitting. Your submission means you have accepted the agreement. Your submission should solve an issue or add a feature registered in the [github issue tracker](https://github.com/taosdata/TDengine/issues). If no corresponding issue or feature is found in the issue tracker, please create one. When submitting your code to our repository, please create a pull request with the issue number included.
- Please solve an issue or add a feature registered in the **[GitHub issue tracker](https://github.com/taosdata/TDengine/issues)**.
- If no corresponding issue or feature is found in the issue tracker, please **create one**.
- When submitting your code to our repository, please create a pull request with the **issue number** included.
## Guidelines for communicating
1. Please be **nice and polite** in the description.
2. **Active voice is better than passive voice in general**. Sentences in the active voice will highlight who is performing the action rather than the recipient of the action highlighted by the passive voice.
3. Documentation writing advice
- Spell the product name "TDengine" correctly. "TD" is written in capital letters, and there is no space between "TD" and "engine" (**Correct spelling: TDengine**).
- Please **capitalize the first letter** of every sentence.
- Leave **only one space** after periods or other punctuation marks.
- Use **American spelling**.
- When possible, **use second person** rather than first person (e.g.“You are recommended to use a reverse proxy such as Nginx.” rather than “We recommend to use a reverse proxy such as Nginx.”).
5. Use **simple sentences**, rather than complex sentences.
## Gifts for the contributors
Developers, as long as you contribute to TDengine, whether it's code contributions to fix bugs or feature requests, or documentation changes, **you are eligible for a very special Contributor Souvenir Gift!**
**You can choose one of the following gifts:**
<p align="left">
<img
src="docs/assets/contributing-cup.jpg"
alt=""
width="200"
/>
<img
src="docs/assets/contributing-notebook.jpg"
alt=""
width="200"
/>
<img
src="docs/assets/contributing-shirt.jpg"
alt=""
width="200"
/>
The TDengine community is committed to making TDengine accepted and used by more developers.
Just fill out the **Contributor Submission Form** to choose your desired gift.
- [Contributor Submission Form](https://page.ma.scrmtech.com/form/index?pf_uid=27715_2095&id=12100)
## Contact us
If you have any problems or questions that need help from us, please feel free to add our WeChat account: TDengineECO.
Jenkinsfile2
浏览文件 @ 324973bd
import hudson.model.Result
import hudson.model.*;
import jenkins.model.CauseOfInterruption
docs_only=0
node {
}
......@@ -29,6 +30,48 @@ def abort_previous(){
if (buildNumber > 1) milestone(buildNumber - 1)
milestone(buildNumber)
}
def check_docs() {
if (env.CHANGE_URL =~ /\/TDengine\//) {
sh '''
hostname
date
env
'''
sh '''
cd ${WKC}
git reset --hard
git clean -fxd
rm -rf examples/rust/
git remote prune origin
git fetch
'''
script {
sh '''
cd ${WKC}
git checkout ''' + env.CHANGE_TARGET + '''
'''
}
sh '''
cd ${WKC}
git pull >/dev/null
git fetch origin +refs/pull/${CHANGE_ID}/merge
git checkout -qf FETCH_HEAD
'''
def file_changed = sh (
script: '''
cd ${WKC}
git --no-pager diff --name-only FETCH_HEAD `git merge-base FETCH_HEAD ${CHANGE_TARGET}`|grep -v "^docs/en/"|grep -v "^docs/zh/" || :
''',
returnStdout: true
).trim()
if (file_changed == '') {
echo "docs PR"
docs_only=1
} else {
echo file_changed
}
}
}
def pre_test(){
sh '''
hostname
......@@ -43,6 +86,7 @@ def pre_test(){
cd ${WKC}
git reset --hard
git clean -fxd
rm -rf examples/rust/
git remote prune origin
git fetch
'''
......@@ -306,10 +350,27 @@ pipeline {
WKPY = '/var/lib/jenkins/workspace/taos-connector-python'
}
stages {
stage('check') {
when {
allOf {
not { expression { env.CHANGE_BRANCH =~ /docs\// }}
not { expression { env.CHANGE_URL =~ /\/TDinternal\// }}
}
}
parallel {
stage('check docs') {
agent{label " worker03 || slave215 || slave217 || slave219 || Mac_catalina "}
steps {
check_docs()
}
}
}
}
stage('run test') {
when {
allOf {
not { expression { env.CHANGE_BRANCH =~ /docs\// }}
expression { docs_only == 0 }
}
}
parallel {
......
README-CN.md
浏览文件 @ 324973bd
......@@ -21,17 +21,17 @@
TDengine 是一款开源、高性能、云原生的时序数据库 (Time-Series Database, TSDB)。TDengine 能被广泛运用于物联网、工业互联网、车联网、IT 运维、金融等领域。除核心的时序数据库功能外，TDengine 还提供缓存、数据订阅、流式计算等功能，是一极简的时序数据处理平台，最大程度的减小系统设计的复杂度，降低研发和运营成本。与其他时序数据库相比，TDengine 的主要优势如下：
- 高性能：通过创新的存储引擎设计，无论是数据写入还是查询，TDengine 的性能比通用数据库快 10 倍以上，也远超其他时序数据库，存储空间不及通用数据库的1/10。
- **高性能**：通过创新的存储引擎设计，无论是数据写入还是查询，TDengine 的性能比通用数据库快 10 倍以上，也远超其他时序数据库，存储空间不及通用数据库的1/10。
- 云原生：通过原生分布式的设计，充分利用云平台的优势，TDengine 提供了水平扩展能力，具备弹性、韧性和可观测性，支持k8s部署，可运行在公有云、私有云和混合云上。
- **云原生**：通过原生分布式的设计，充分利用云平台的优势，TDengine 提供了水平扩展能力，具备弹性、韧性和可观测性，支持k8s部署，可运行在公有云、私有云和混合云上。
- 极简时序数据平台：TDengine 内建消息队列、缓存、流式计算等功能，应用无需再集成 Kafka/Redis/HBase/Spark 等软件，大幅降低系统的复杂度，降低应用开发和运营成本。
- **极简时序数据平台**：TDengine 内建消息队列、缓存、流式计算等功能，应用无需再集成 Kafka/Redis/HBase/Spark 等软件，大幅降低系统的复杂度，降低应用开发和运营成本。
- 分析能力：支持 SQL，同时为时序数据特有的分析提供SQL扩展。通过超级表、存储计算分离、分区分片、预计算、自定义函数等技术，TDengine 具备强大的分析能力。
- **分析能力**：支持 SQL，同时为时序数据特有的分析提供SQL扩展。通过超级表、存储计算分离、分区分片、预计算、自定义函数等技术，TDengine 具备强大的分析能力。
- 简单易用：无任何依赖，安装、集群几秒搞定；提供REST以及各种语言连接器，与众多第三方工具无缝集成；提供命令行程序，便于管理和即席查询；提供各种运维工具。
- **简单易用**：无任何依赖，安装、集群几秒搞定；提供REST以及各种语言连接器，与众多第三方工具无缝集成；提供命令行程序，便于管理和即席查询；提供各种运维工具。
- 核心开源：TDengine 的核心代码包括集群功能全部开源，截止到2022年8月1日，全球超过 135.9k 个运行实例，GitHub Star 18.7k，Fork 4.4k，社区活跃。
- **核心开源**：TDengine 的核心代码包括集群功能全部开源，截止到2022年8月1日，全球超过 135.9k 个运行实例，GitHub Star 18.7k，Fork 4.4k，社区活跃。
# 文档
......
README.md
浏览文件 @ 324973bd
......@@ -15,28 +15,23 @@
[![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)
# What is TDengine？
TDengine is an open source, high-performance, cloud native 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 (Time-Series Database, TSDB).
TDengine can be optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT, IT operation and maintenance, finance and other fields. In addition to the core time series database functions, TDengine also provides functions such as caching, data subscription, and streaming computing. It is a minimalist time series data processing platform that minimizes the complexity of system design and reduces R&D and operating costs. Compared with other time series databases, the main advantages of TDengine are as follows:
- **High-Performance**: 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.
- High-Performance: 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.
- **Simplified Solution**: Through built-in caching, stream processing and data subscription features, TDengine provides a simplified solution for time-series data processing. It reduces system design complexity and operation costs significantly.
- Simplified Solution: Through built-in caching, stream processing and data subscription features, TDengine provides a simplified solution for time-series data processing. It reduces system design complexity and operation costs significantly.
- **Cloud Native**: Through native distributed design, sharding and partitioning, separation of compute and storage, RAFT, support for kubernetes deployment and full observability, TDengine is a cloud native Time-Series Database and can be deployed on public, private or hybrid clouds.
- Cloud Native: Through native distributed design, sharding and partitioning, separation of compute and storage, RAFT, support for kubernetes deployment and full observability, TDengine is a cloud native Time-Series Database and can be deployed on public, private or hybrid clouds.
- **Ease of Use**: For administrators, TDengine significantly reduces the effort to deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access.
- Ease of Use: For administrators, TDengine significantly reduces the effort to deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access.
- **Easy Data Analytics**: Through super tables, storage and compute separation, data partitioning by time interval, pre-computation and other means, TDengine makes it easy to explore, format, and get access to data in a highly efficient way.
- Easy Data Analytics: Through super tables, storage and compute separation, data partitioning by time interval, pre-computation and other means, TDengine makes it easy to explore, format, and get access to data in a highly efficient way.
- Open Source: TDengine’s core modules, including cluster feature, are all available under open source licenses. It has gathered 18.8k stars on GitHub, an active developer community, and over 137k running instances worldwide.
- **Open Source**: 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.
# Documentation
......@@ -44,14 +39,9 @@ For user manual, system design and architecture, please refer to [TDengine Docum
# Building
At the moment, TDengine server supports running on Linux, Windows systems.Any OS application can also choose the RESTful interface of taosAdapter to connect the taosd service . TDengine supports X64/ARM64 CPU , and it will support MIPS64, Alpha64, ARM32, RISC-V and other CPU architectures in the future.
You can choose to install through source code according to your needs, [container](https://docs.taosdata.com/get-started/docker/), [installation package](https://docs.taosdata.com/get-started/package/) or [Kubenetes](https://docs.taosdata.com/deployment/k8s/) to install. This quick guide only applies to installing from source.
You can choose to install through source code according to your needs, [container](https://docs.taosdata.com/get-started/docker/), [installation package](https://docs.taosdata.com/get-started/package/) or [Kubernetes](https://docs.taosdata.com/deployment/k8s/) to install. This quick guide only applies to installing from source.
TDengine provide a few useful tools such as taosBenchmark (was named taosdemo) and taosdump. They were part of TDengine. By default, TDengine compiling does not include taosTools. You can use `cmake .. -DBUILD_TOOLS=true` to make them be compiled with TDengine.
......@@ -67,7 +57,6 @@ sudo apt-get install -y gcc cmake build-essential git libssl-dev
#### Install build dependencies for taosTools
To build the [taosTools](https://github.com/taosdata/taos-tools) on Ubuntu/Debian, the following packages need to be installed.
```bash
......@@ -91,14 +80,13 @@ sudo dnf install -y gcc gcc-c++ make cmake epel-release git openssl-devel
#### Install build dependencies for taosTools on CentOS
#### CentOS 7.9
```
sudo yum install -y zlib-devel xz-devel snappy-devel jansson jansson-devel pkgconfig libatomic libstdc++-static openssl-devel
```
#### CentOS 8/Rocky Linux
#### CentOS 8/Rocky Linux
```
sudo yum install -y epel-release
......@@ -109,14 +97,14 @@ sudo yum install -y zlib-devel xz-devel snappy-devel jansson jansson-devel pkgco
Note: Since snappy lacks pkg-config support (refer to [link](https://github.com/google/snappy/pull/86)), it leads a cmake prompt libsnappy not found. But snappy still works well.
If the powertools installation fails, you can try to use:
If the PowerTools installation fails, you can try to use:
```
sudo yum config-manager --set-enabled Powertools
sudo yum config-manager --set-enabled powertools
```
### Setup golang environment
TDengine includes a few components like taosAdapter developed by Go language. Please refer to golang.org official documentation for golang environment setup.
Please use version 1.14+. For the user in China, we recommend using a proxy to accelerate package downloading.
......@@ -134,7 +122,7 @@ cmake .. -DBUILD_HTTP=false
### Setup rust environment
TDengine includes a few compoments developed by Rust language. Please refer to rust-lang.org official documentation for rust environment setup.
TDengine includes a few components developed by Rust language. Please refer to rust-lang.org official documentation for rust environment setup.
## Get the source codes
......@@ -145,7 +133,6 @@ git clone https://github.com/taosdata/TDengine.git
cd TDengine
```
You can modify the file ~/.gitconfig to use ssh protocol instead of https for better download speed. You will need to upload ssh public key to GitHub first. Please refer to GitHub official documentation for detail.
```
......@@ -155,14 +142,12 @@ You can modify the file ~/.gitconfig to use ssh protocol instead of https for be
## Special Note
[JDBC Connector](https://github.com/taosdata/taos-connector-jdbc)， [Go Connector](https://github.com/taosdata/driver-go)，[Python Connector](https://github.com/taosdata/taos-connector-python)，[Node.js Connector](https://github.com/taosdata/taos-connector-node)，[C# Connector](https://github.com/taosdata/taos-connector-dotnet) ，[Rust Connector](https://github.com/taosdata/taos-connector-rust) and [Grafana plugin](https://github.com/taosdata/grafanaplugin) has been moved to standalone repository.
## Build TDengine
### On Linux platform
You can run the bash script `build.sh` to build both TDengine and taosTools including taosBenchmark and taosdump as below:
```bash
......@@ -178,7 +163,6 @@ cmake .. -DBUILD_TOOLS=true
make
```
You can use Jemalloc as memory allocator instead of glibc:
```
......@@ -246,7 +230,7 @@ After building successfully, TDengine can be installed by
sudo make install
```
Users can find more information about directories installed on the system in the [directory and files](https://docs.taosdata.com/reference/directory/) section.
Users can find more information about directories installed on the system in the [directory and files](https://docs.taosdata.com/reference/directory/) section.
Installing from source code will also configure service management for TDengine.Users can also choose to [install from packages](https://docs.taosdata.com/get-started/package/) for it.
......@@ -318,7 +302,7 @@ Query OK, 2 row(s) in set (0.001700s)
## Official Connectors
TDengine provides abundant developing tools for users to develop on TDengine. include C/C++、Java、Python、Go、Node.js、C# 、RESTful ,Follow the links below to find your desired connectors and relevant documentation.
TDengine provides abundant developing tools for users to develop on TDengine. Follow the links below to find your desired connectors and relevant documentation.
- [Java](https://docs.taosdata.com/reference/connector/java/)
- [C/C++](https://docs.taosdata.com/reference/connector/cpp/)
......
cmake/cmake.define
浏览文件 @ 324973bd
......@@ -81,7 +81,7 @@ ENDIF ()
IF (TD_WINDOWS)
MESSAGE("${Yellow} set compiler flag for Windows! ${ColourReset}")
SET(COMMON_FLAGS "/w /D_WIN32 /DWIN32 /Zi")
SET(COMMON_FLAGS "/w /D_WIN32 /DWIN32 /Zi /MTd")
SET(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /MANIFEST:NO")
# IF (MSVC AND (MSVC_VERSION GREATER_EQUAL 1900))
# SET(COMMON_FLAGS "${COMMON_FLAGS} /Wv:18")
......@@ -92,6 +92,12 @@ IF (TD_WINDOWS)
IF (CMAKE_DEPFILE_FLAGS_CXX)
SET(CMAKE_DEPFILE_FLAGS_CXX "")
ENDIF ()
IF (CMAKE_C_FLAGS_DEBUG)
SET(CMAKE_C_FLAGS_DEBUG "" CACHE STRING "" FORCE)
ENDIF ()
IF (CMAKE_CXX_FLAGS_DEBUG)
SET(CMAKE_CXX_FLAGS_DEBUG "" CACHE STRING "" FORCE)
ENDIF ()
SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${COMMON_FLAGS}")
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${COMMON_FLAGS}")
......
cmake/cmake.install
浏览文件 @ 324973bd
IF (EXISTS /var/lib/taos/dnode/dnodeCfg.json)
INSTALL(CODE "MESSAGE(\"The default data directory /var/lib/taos contains old data of tdengine 2.x, please clear it before installing!\")")
ELSEIF (EXISTS C:/TDengine/data/dnode/dnodeCfg.json)
INSTALL(CODE "MESSAGE(\"The default data directory C:/TDengine/data contains old data of tdengine 2.x, please clear it before installing!\")")
ELSEIF (TD_LINUX)
IF (TD_LINUX)
SET(TD_MAKE_INSTALL_SH "${TD_SOURCE_DIR}/packaging/tools/make_install.sh")
INSTALL(CODE "MESSAGE(\"make install script: ${TD_MAKE_INSTALL_SH}\")")
INSTALL(CODE "execute_process(COMMAND bash ${TD_MAKE_INSTALL_SH} ${TD_SOURCE_DIR} ${PROJECT_BINARY_DIR} Linux ${TD_VER_NUMBER})")
ELSEIF (TD_WINDOWS)
SET(CMAKE_INSTALL_PREFIX C:/TDengine)
# INSTALL(DIRECTORY ${TD_SOURCE_DIR}/src/connector/go DESTINATION connector)
# INSTALL(DIRECTORY ${TD_SOURCE_DIR}/src/connector/nodejs DESTINATION connector)
# INSTALL(DIRECTORY ${TD_SOURCE_DIR}/src/connector/python DESTINATION connector)
# INSTALL(DIRECTORY ${TD_SOURCE_DIR}/src/connector/C\# DESTINATION connector)
# INSTALL(DIRECTORY ${TD_SOURCE_DIR}/examples DESTINATION .)
INSTALL(CODE "IF (NOT EXISTS ${CMAKE_INSTALL_PREFIX}/cfg/taos.cfg)
execute_process(COMMAND ${CMAKE_COMMAND} -E copy ${TD_SOURCE_DIR}/packaging/cfg/taos.cfg ${CMAKE_INSTALL_PREFIX}/cfg/taos.cfg)
ENDIF ()")
INSTALL(FILES ${TD_SOURCE_DIR}/include/client/taos.h DESTINATION include)
INSTALL(FILES ${TD_SOURCE_DIR}/include/util/taoserror.h DESTINATION include)
INSTALL(FILES ${TD_SOURCE_DIR}/include/libs/function/taosudf.h DESTINATION include)
INSTALL(FILES ${LIBRARY_OUTPUT_PATH}/taos.lib DESTINATION driver)
INSTALL(FILES ${LIBRARY_OUTPUT_PATH}/taos_static.lib DESTINATION driver)
INSTALL(FILES ${LIBRARY_OUTPUT_PATH}/taos.dll DESTINATION driver)
INSTALL(FILES ${EXECUTABLE_OUTPUT_PATH}/taos.exe DESTINATION .)
INSTALL(FILES ${EXECUTABLE_OUTPUT_PATH}/taosd.exe DESTINATION .)
INSTALL(FILES ${EXECUTABLE_OUTPUT_PATH}/udfd.exe DESTINATION .)
IF (BUILD_TOOLS)
INSTALL(FILES ${EXECUTABLE_OUTPUT_PATH}/taosBenchmark.exe DESTINATION .)
ENDIF ()
IF (TD_MVN_INSTALLED)
INSTALL(FILES ${LIBRARY_OUTPUT_PATH}/taos-jdbcdriver-2.0.38-dist.jar DESTINATION connector/jdbc)
ENDIF ()
SET(TD_MAKE_INSTALL_SH "${TD_SOURCE_DIR}/packaging/tools/make_install.bat")
INSTALL(CODE "MESSAGE(\"make install script: ${TD_MAKE_INSTALL_SH}\")")
INSTALL(CODE "execute_process(COMMAND ${TD_MAKE_INSTALL_SH} :needAdmin ${TD_SOURCE_DIR} ${PROJECT_BINARY_DIR} Windows ${TD_VER_NUMBER})")
......
cmake/libuv_CMakeLists.txt.in
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......@@ -2,7 +2,7 @@
# libuv
ExternalProject_Add(libuv
GIT_REPOSITORY https://github.com/libuv/libuv.git
GIT_TAG v1.42.0
GIT_TAG v1.44.2
SOURCE_DIR "${TD_CONTRIB_DIR}/libuv"
BINARY_DIR "${TD_CONTRIB_DIR}/libuv"
CONFIGURE_COMMAND ""
......
cmake/rust-bindings_CMakeLists.txt.in 已删除 100644 → 0
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# rust-bindings
ExternalProject_Add(rust-bindings
GIT_REPOSITORY https://github.com/songtianyi/tdengine-rust-bindings.git
GIT_TAG 7ed7a97
SOURCE_DIR "${TD_SOURCE_DIR}/examples/rust"
BINARY_DIR "${TD_SOURCE_DIR}/examples/rust"
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
INSTALL_COMMAND ""
TEST_COMMAND ""
)
cmake/taosadapter_CMakeLists.txt.in
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......@@ -2,7 +2,7 @@
# taosadapter
ExternalProject_Add(taosadapter
GIT_REPOSITORY https://github.com/taosdata/taosadapter.git
GIT_TAG 3d21433
GIT_TAG abed566
SOURCE_DIR "${TD_SOURCE_DIR}/tools/taosadapter"
BINARY_DIR ""
#BUILD_IN_SOURCE TRUE
......
contrib/CMakeLists.txt
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......@@ -105,11 +105,6 @@ if(${BUILD_WITH_SQLITE})
cat("${TD_SUPPORT_DIR}/sqlite_CMakeLists.txt.in" ${CONTRIB_TMP_FILE})
endif(${BUILD_WITH_SQLITE})
# rust-bindings
if(${RUST_BINDINGS})
cat("${TD_SUPPORT_DIR}/rust-bindings_CMakeLists.txt.in" ${CONTRIB_TMP_FILE})
endif(${RUST_BINDINGS})
# lucene
if(${BUILD_WITH_LUCENE})
cat("${TD_SUPPORT_DIR}/lucene_CMakeLists.txt.in" ${CONTRIB_TMP_FILE})
......@@ -140,24 +135,6 @@ execute_process(COMMAND "${CMAKE_COMMAND}" -G "${CMAKE_GENERATOR}" .
WORKING_DIRECTORY "${TD_CONTRIB_DIR}/deps-download")
execute_process(COMMAND "${CMAKE_COMMAND}" --build .
WORKING_DIRECTORY "${TD_CONTRIB_DIR}/deps-download")
# clear submodule
execute_process(COMMAND git submodule deinit -f tools/taos-tools
WORKING_DIRECTORY "${TD_SOURCE_DIR}")
execute_process(COMMAND git rm --cached tools/taos-tools
WORKING_DIRECTORY "${TD_SOURCE_DIR}")
execute_process(COMMAND git submodule deinit -f tools/taosadapter
WORKING_DIRECTORY "${TD_SOURCE_DIR}")
execute_process(COMMAND git rm --cached tools/taosadapter
WORKING_DIRECTORY "${TD_SOURCE_DIR}")
execute_process(COMMAND git submodule deinit -f tools/taosws-rs
WORKING_DIRECTORY "${TD_SOURCE_DIR}")
execute_process(COMMAND git rm --cached tools/taosws-rs
WORKING_DIRECTORY "${TD_SOURCE_DIR}")
execute_process(COMMAND git submodule deinit -f examples/rust
WORKING_DIRECTORY "${TD_SOURCE_DIR}")
execute_process(COMMAND git rm --cached examples/rust
WORKING_DIRECTORY "${TD_SOURCE_DIR}")
# ================================================================================================
# Build
......@@ -273,7 +250,7 @@ endif(${BUILD_WITH_NURAFT})
# pthread
if(${BUILD_PTHREAD})
set(CMAKE_BUILD_TYPE release)
set(CMAKE_BUILD_TYPE debug)
add_definitions(-DPTW32_STATIC_LIB)
add_subdirectory(pthread EXCLUDE_FROM_ALL)
set_target_properties(libpthreadVC3 PROPERTIES OUTPUT_NAME pthread)
......@@ -354,9 +331,11 @@ endif(${BUILD_WITH_TRAFT})
# LIBUV
if(${BUILD_WITH_UV})
if (NOT ${CMAKE_SYSTEM_NAME} MATCHES "Windows")
MESSAGE("Windows need set no-sign-compare")
add_compile_options(-Wno-sign-compare)
if (TD_WINDOWS)
# There is no GetHostNameW function on win7.
file(READ "libuv/src/win/util.c" LIBUV_WIN_UTIL_CONTENT)
string(REPLACE "if (GetHostNameW(buf, UV_MAXHOSTNAMESIZE" "DWORD nSize = UV_MAXHOSTNAMESIZE;\n if (GetComputerNameW(buf, &nSize" LIBUV_WIN_UTIL_CONTENT "${LIBUV_WIN_UTIL_CONTENT}")
file(WRITE "libuv/src/win/util.c" "${LIBUV_WIN_UTIL_CONTENT}")
endif ()
add_subdirectory(libuv EXCLUDE_FROM_ALL)
endif(${BUILD_WITH_UV})
......
docs/en/01-index.md
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......@@ -4,7 +4,8 @@ sidebar_label: Documentation Home
slug: /
---
TDengine is a [high-performance](https://tdengine.com/fast), [scalable](https://tdengine.com/scalable) time series database with [SQL support](https://tdengine.com/sql-support). 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, cloud native 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. 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.
......@@ -12,7 +13,7 @@ TDengine greatly improves the efficiency of data ingestion, querying and storage
If you are a developer, please read the [“Developer Guide”](./develop) carefully. This section introduces the database connection, data modeling, data ingestion, query, continuous query, cache, data subscription, user-defined functions, and other functionality in detail. Sample code is provided for a variety of programming languages. In most cases, you can just copy and paste the sample code, make a few changes to accommodate your application, and it will work.
We live in the era of big data, and scale-up is unable to meet the growing needs of business. Any modern data system must have the ability to scale out, and clustering has become an indispensable feature of big data systems. Not only did the TDengine team develop the cluster feature, but also decided to open source this important feature. To learn how to deploy, manage and maintain a TDengine cluster please refer to ["cluster"](./cluster).
We live in the era of big data, and scale-up is unable to meet the growing needs of business. Any modern data system must have the ability to scale out, and clustering has become an indispensable feature of big data systems. Not only did the TDengine team develop the cluster feature, but also decided to open source this important feature. To learn how to deploy, manage and maintain a TDengine cluster please refer to ["cluster deployment"](../deployment).
TDengine uses ubiquitious SQL as its query language, which greatly reduces learning costs and migration costs. In addition to the standard SQL, TDengine has extensions to better support time series data analysis. These extensions include functions such as roll up, interpolation and time weighted average, among many others. The ["SQL Reference"](./taos-sql) chapter describes the SQL syntax in detail, and lists the various supported commands and functions.
......
docs/en/02-intro/index.md
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......@@ -3,7 +3,7 @@ title: Introduction
toc_max_heading_level: 2
---
TDengine is a high-performance, scalable time-series database with SQL support. 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/continuous-query), [data subscription](/develop/subscribe) and other functionalities to reduce the complexity and cost of development and operation.
TDengine is an open source, high-performance, cloud native time-series database 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.
......@@ -16,9 +16,9 @@ The major features are listed below:
3. Support for [all kinds of queries](/develop/query-data), including aggregation, nested query, downsampling, interpolation and others.
4. Support for [user defined functions](/develop/udf).
5. Support for [caching](/develop/cache). TDengine always saves the last data point in cache, so Redis is not needed in some scenarios.
6. Support for [continuous query](/develop/continuous-query).
7. Support for [data subscription](/develop/subscribe) with the capability to specify filter conditions.
8. Support for [cluster](/cluster/), with the capability of increasing processing power by adding more nodes. High availability is supported by replication.
6. Support for [continuous query](../develop/stream).
7. Support for [data subscription](../develop/tmq) with the capability to specify filter conditions.
8. Support for [cluster](../deployment/), with the capability of increasing processing power by adding more nodes. High availability is supported by replication.
9. Provides an interactive [command-line interface](/reference/taos-shell) for management, maintenance and ad-hoc queries.
10. Provides many ways to [import](/operation/import) and [export](/operation/export) data.
11. Provides [monitoring](/operation/monitor) on running instances of TDengine.
......@@ -31,25 +31,21 @@ For more details on features, please read through the entire documentation.
## Competitive Advantages
Time-series data is structured, not transactional, and is rarely deleted or updated. TDengine makes full use of [these characteristics of time series data](https://tdengine.com/2019/07/09/86.html) to build its own innovative storage engine and computing engine to differentiate 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, with the following advantages.
- **[High Performance](https://tdengine.com/fast)**: With an innovatively designed and purpose-built storage engine, TDengine outperforms other time series databases in data ingestion and querying while significantly reducing storage costs and compute costs.
- **High-Performance**: 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.
- **[Scalable](https://tdengine.com/scalable)**: TDengine provides out-of-box scalability and high-availability through its native distributed design. Nodes can be added through simple configuration to achieve greater data processing power. In addition, this feature is open source.
- **Simplified Solution**: Through built-in caching, stream processing and data subscription features, TDengine provides a simplified solution for time-series data processing. It reduces system design complexity and operation costs significantly.
- **[SQL Support](https://tdengine.com/sql-support)**: TDengine uses SQL as the query language, thereby reducing learning and migration costs, while adding SQL extensions to better handle time-series. Keeping NoSQL developers in mind, TDengine also supports convenient and flexible, schemaless data ingestion.
- **Cloud Native**: Through native distributed design, sharding and partitioning, separation of compute and storage, RAFT, support for kubernetes deployment and full observability, TDengine is a cloud native Time-Series Database and can be deployed on public, private or hybrid clouds.
- **All in One**: TDengine has built-in caching, stream processing and data subscription functions. It is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software in some scenarios. It makes the system architecture much simpler, cost-effective and easier to maintain.
- **Ease of Use**: For administrators, TDengine significantly reduces the effort to deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access.
- **Seamless Integration**: Without a single line of code, TDengine provide seamless, configurable integration with third-party tools such as Telegraf, Grafana, EMQX, Prometheus, StatsD, collectd, etc. More third-party tools are being integrated.
- **Easy Data Analytics**: Through super tables, storage and compute separation, data partitioning by time interval, pre-computation and other means, TDengine makes it easy to explore, format, and get access to data in a highly efficient way.
- **Zero Management**: Installation and cluster setup can be done in seconds. Data partitioning and sharding are executed automatically. TDengine’s running status can be monitored via Grafana or other DevOps tools.
- **Open Source**: TDengine’s core modules, including cluster feature, are all available under open source licenses. It has gathered over 19k stars on GitHub. There is an active developer community, and over 140k running instances worldwide.
- **Zero Learning Costs**: With SQL as the query language and support for ubiquitous tools like Python, Java, C/C++, Go, Rust, and Node.js connectors, and a REST API, there are zero learning costs.
- **Interactive Console**: TDengine provides convenient console access to the database, through a CLI, to run ad hoc queries, maintain the database, or manage the cluster, without any programming.
With TDengine, the total cost of ownership of your time-series data platform can be greatly reduced. 1: With its superior performance, the computing and storage resources are reduced significantly 2: With SQL support, it can be seamlessly integrated with many third party tools, and learning costs/migration costs are reduced significantly 3: With its simple architecture and zero management, the operation and maintenance costs are reduced.
With TDengine, the total cost of ownership of your time-series data platform can be greatly reduced. 1: With its superior performance, the computing and storage resources are reduced significantly；2: With SQL support, it can be seamlessly integrated with many third party tools, and learning costs/migration costs are reduced significantly；3: With its simplified solution and nearly zero management, the operation and maintenance costs are reduced significantly.
## Technical Ecosystem
This is how TDengine would be situated, in a typical time-series data processing platform:
......
docs/en/04-concept/index.md
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......@@ -2,7 +2,7 @@
title: Concepts
---
In order to explain the basic concepts and provide some sample code, the TDengine documentation smart meters as a typical time series use case. We assume the following: 1. Each smart meter collects three metrics i.e. current, voltage, and phase 2. There are multiple smart meters, and 3. Each meter has static attributes like location and group ID. Based on this, collected data will look similar to the following table:
In order to explain the basic concepts and provide some sample code, the TDengine documentation smart meters as a typical time series use case. We assume the following: 1. Each smart meter collects three metrics i.e. current, voltage, and phase; 2. There are multiple smart meters; 3. Each meter has static attributes like location and group ID. Based on this, collected data will look similar to the following table:
<div className="center-table">
<table>
......@@ -116,7 +116,7 @@ Data Collection Point (DCP) refers to hardware or software that collects metrics
## 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.
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:
......@@ -125,25 +125,28 @@ To make full use of time-series data characteristics, TDengine adopts a strategy
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.
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.
## Super Table (STable)
The design of one table for one data collection point will require a huge number of tables, which is difficult to manage. Furthermore, applications often need to take aggregation operations among DCPs, thus aggregation operations will become complicated. To support aggregation over multiple tables efficiently, the STable(Super Table) concept is introduced by TDengine.
STable is a template for a type of data collection point. A STable contains a set of data collection points (tables) that have the same schema or data structure, but with different static attributes (tags). To describe a STable, in addition to defining the table structure of the metrics, it is also necessary to define the schema of its tags. The data type of tags can be int, float, string, and there can be multiple tags, which can be added, deleted, or modified afterward. If the whole system has N different types of data collection points, N STables need to be established.
In the design of TDengine, **a table is used to represent a specific data collection point, and STable is used to represent a set of data collection points of the same type**.
In the design of TDengine, **a table is used to represent a specific data collection point, and STable is used to represent a set of data collection points of the same type**.
## Subtable
When creating a table for a specific data collection point, the user can use a STable as a template and specify the tag values of this specific DCP to create it. **The table created by using a STable as the template is called subtable** in TDengine. The difference between regular table and subtable is:
When creating a table for a specific data collection point, the user can use a STable as a template and specify the tag values of this specific DCP to create it. ** The table created by using a STable as the template is called subtable** in TDengine. The difference between regular table and subtable is:
1. Subtable is a table, all SQL commands applied on a regular table can be applied on subtable.
2. Subtable is a table with extensions, it has static tags (labels), and these tags can be added, deleted, and updated after it is created. But a regular table does not have tags.
3. A subtable belongs to only one STable, but a STable may have many subtables. Regular tables do not belong to a STable.
4. A regular table can not be converted into a subtable, and vice versa.
4. A regular table can not be converted into a subtable, and vice versa.
The relationship between a STable and the subtables created based on this STable is as follows:
......@@ -151,13 +154,13 @@ The relationship between a STable and the subtables created based on this STable
2. The schema of metrics or labels cannot be adjusted through subtables, and it can only be changed via STable. Changes to the schema of a STable takes effect immediately for all associated subtables.
3. STable defines only one template and does not store any data or label information by itself. Therefore, data cannot be written to a STable, only to subtables.
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.
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 TDengine, it is recommended to use a subtable instead of a regular table for a DCP.
## Database
A database is a collection of tables. TDengine allows a running instance to have multiple databases, and each database can be configured with different storage policies. Different types of DCPs often have different data characteristics, including the frequency of data collection, data retention time, the number of replications, the size of data blocks, whether data is allowed to be updated, and so on. In order for TDengine to work with maximum efficiency in various scenarios, TDengine recommends that STables with different data characteristics be created in different databases.
A database is a collection of tables. TDengine allows a running instance to have multiple databases, and each database can be configured with different storage policies. The [characteristics of time-series data](https://www.taosdata.com/blog/2019/07/09/86.html) from different data collection points may be different. Characteristics include collection frequency, retention policy and others which determine how you create and configure the database. For e.g. days to keep, number of replicas, data block size, whether data updates are allowed and other configurable parameters would be determined by the characteristics of your data and your business requirements. In order for TDengine to work with maximum efficiency in various scenarios, TDengine recommends that STables with different data characteristics be created in different databases.
In a database, there can be one or more STables, but a STable belongs to only one database. All tables owned by a STable are stored in only one database.
......@@ -167,4 +170,4 @@ 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.
docs/en/05-get-started/01-docker.md
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---
sidebar_label: Docker
title: 通过 Docker 快速体验 TDengine
title: Quick Install on Docker
---
:::info
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.
:::
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。
This document describes how to install TDengine in a Docker container and perform queries and inserts. To get started with TDengine in a non-containerized environment, see [Quick Install](../../get-started/package). 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).
## 启动 TDengine
## Run TDengine
如果已经安装了 docker， 只需执行下面的命令。
If Docker is already installed on your computer, run the following command:
```shell
docker run -d -p 6030:6030 -p 6041/6041 -p 6043-6049/6043-6049 -p 6043-6049:6043-6049/udp tdengine/tdengine
docker run -d -p 6030:6030 -p 6041:6041 -p 6043-6049:6043-6049 -p 6043-6049:6043-6049/udp tdengine/tdengine
```
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。
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.
确定该容器已经启动并且在正常运行
Run the following command to ensure that your container is running:
```shell
docker ps
```
进入该容器并执行 bash
Enter the container and open the bash shell:
```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
You can now access TDengine or run other Linux commands.
## 运行 TDengine CLI
Note: For information about installing docker, see the [official documentation](https://docs.docker.com/get-docker/).
进入容器，执行 taos
## Insert Data into TDengine
```
$ taos
Welcome to the TDengine shell from Linux, Client Version:3.0.0.0
Copyright (c) 2022 by TAOS Data, Inc. All rights reserved.
You can use the `taosBenchmark` tool included with TDengine to write test data into your deployment.
Server is Community Edition.
To do so, run the following command:
taos>
```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 ten and a `location` tag of either `California.SanFrancisco` or `California.SanDiego`.
## 写入数据
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.
可以使用 TDengine 的自带工具 taosBenchmark 快速体验 TDengine 的写入。
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).
进入容器，启动 taosBenchmark：
## Open the TDengine CLI
```bash
$ taosBenchmark
```
On the container, run the following command to open the TDengine CLI:
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。
```
$ taos
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。
taos>
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)。
```
## 体验查询
## Query Data in TDengine
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。。
After using taosBenchmark to create your test deployment, you can run queries in the TDengine CLI to test its performance. For example:
查询超级表下记录总条数：
From the TDengine CLI query the number of rows in the `meters` supertable:
```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：
Query the average, maximum, and minimum values of all 100 million rows of data:
```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：
Query the number of rows whose `location` tag is `San Francisco`:
```sql
taos> select count(*) from test.meters where location="San Francisco";
select count(*) from test.meters where location="San Francisco";
```
查询 groupId=10 的所有记录的平均值、最大值、最小值等：
Query the average, maximum, and minimum values of all rows whose `groupId` tag is `10`:
```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：
Query the average, maximum, and minimum values for table `d10` in 1 second intervals:
```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(1s);
```
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/).
## 其它
## Additional Information
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)
For more information about deploying TDengine in a Docker environment, see [Using TDengine in Docker](../../reference/docker).
docs/en/05-get-started/03-package.md
浏览文件 @ 324973bd
---
sidebar_label: 安装包
title: 使用安装包立即开始
sidebar_label: Package
title: Quick Install from Package
---
import Tabs from "@theme/Tabs";
import TabItem from "@theme/TabItem";
import PkgListV3 from "/components/PkgListV3";
:::info
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.
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 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.
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。
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.
## 安装
## Installation
<Tabs>
<TabItem value="apt-get" label="apt-get">
可以使用 apt-get 工具从官方仓库安装。
<TabItem label=".deb" value="debinst">
**安装包仓库**
1. Download the .deb installation package.
<PkgListV3 type={6}/>
2. In the directory where the package is located, use `dpkg` to install the package:
```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
# Enter the name of the package that you downloaded.
sudo dpkg -i TDengine-server-<version>-Linux-x64.deb
```
如果安装 Beta 版需要安装包仓库
</TabItem>
```bash
echo "deb [arch=amd64] http://repos.taosdata.com/tdengine-beta beta main" | sudo tee /etc/apt/sources.list.d/tdengine-beta.list
```
<TabItem label=".rpm" value="rpminst">
**使用 apt-get 命令安装**
1. Download the .rpm installation package.
<PkgListV3 type={5}/>
2. In the directory where the package is located, use rpm to install the package:
```bash
sudo apt-get update
apt-cache policy tdengine
sudo apt-get install tdengine
# Enter the name of the package that you downloaded.
sudo rpm -ivh TDengine-server-<version>-Linux-x64.rpm
```
:::tip
apt-get 方式只适用于 Debian 或 Ubuntu 系统
::::
</TabItem>
<TabItem label="Deb 安装" value="debinst">
1、从官网下载获得 deb 安装包，例如 TDengine-server-3.0.0.0-Linux-x64.deb；
2、进入到 TDengine-server-3.0.0.0-Linux-x64.deb 安装包所在目录，执行如下的安装命令：
<TabItem label=".tar.gz" value="tarinst">
1. Download the .tar.gz installation package.
<PkgListV3 type={0}/>
2. In the directory where the package is located, use `tar` to decompress the package:
```bash
sudo dpkg -i TDengine-server-3.0.0.0-Linux-x64.deb
# Enter the name of the package that you downloaded.
tar -zxvf TDengine-server-<version>-Linux-x64.tar.gz
```
</TabItem>
<TabItem label="RPM 安装" value="rpminst">
1、从官网下载获得 rpm 安装包，例如 TDengine-server-3.0.0.0-Linux-x64.rpm；
2、进入到 TDengine-server-3.0.0.0-Linux-x64.rpm 安装包所在目录，执行如下的安装命令：
In the directory to which the package was decompressed, run `install.sh`:
```bash
sudo rpm -ivh TDengine-server-3.0.0.0-Linux-x64.rpm
sudo ./install.sh
```
:::info
Users will be prompted to enter some configuration information when install.sh is executing. The interactive mode can be disabled by executing `./install.sh -e no`. `./install.sh -h` can show all parameters with detailed explanation.
:::
</TabItem>
<TabItem label="tar.gz 安装" value="tarinst">
<TabItem label="Windows" value="windows">
1. Download the Windows installation package.
<PkgListV3 type={3}/>
2. Run the downloaded package to install TDengine.
:::info
TDengine only supports Windows Server 2016/2019 and windows 10/11 system versions on the windows platform.
:::
</TabItem>
<TabItem value="apt-get" label="apt-get">
You can use `apt-get` to install TDengine from the official package repository.
1、从官网下载获得 tar.gz 安装包，例如 TDengine-server-3.0.0.0-Linux-x64.tar.gz；
2、进入到 TDengine-server-3.0.0.0-Linux-x64.tar.gz 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：
**Configure the package repository**
```bash
tar -zxvf TDengine-server-3.0.0.0-Linux-x64.tar.gz
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
```
解压后进入相应路径，执行
You can install beta versions by configuring the following repository:
```bash
sudo ./install.sh
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
```
:::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 TDengine with `apt-get`**
:::
```bash
sudo apt-get update
apt-cache policy tdengine
sudo apt-get install tdengine
```
:::tip
This installation method is supported only for Debian and Ubuntu.
::::
</TabItem>
</Tabs>
:::info
For information about TDengine releases, see [Release History](../../releases).
:::
:::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。
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.
:::
## 启动
## Quick Launch
安装后，请使用 `systemctl` 命令来启动 TDengine 的服务进程。
<Tabs>
<TabItem label="Linux" value="linux">
After the installation is complete, run the following command to start the TDengine service:
```bash
systemctl start taosd
```
检查服务是否正常工作：
Run the following command to confirm that TDengine is running normally:
```bash
systemctl status taosd
```
如果服务进程处于活动状态，则 status 指令会显示如下的相关信息：
Output similar to the following indicates that TDengine is running normally:
```
Active: active (running)
```
如果后台服务进程处于停止状态，则 status 指令会显示如下的相关信息：
Output similar to the following indicates that TDengine has not started successfully:
```
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。
After confirming that TDengine is running, run the `taos` command to access the TDengine CLI.
systemctl 命令汇总：
The following `systemctl` commands can help you manage TDengine:
- 启动服务进程：`systemctl start taosd`
- Start TDengine Server: `systemctl start taosd`
- 停止服务进程：`systemctl stop taosd`
- Stop TDengine Server: `systemctl stop taosd`
- 重启服务进程：`systemctl restart taosd`
- Restart TDengine Server: `systemctl restart taosd`
- 查看服务状态：`systemctl status taosd`
- Check TDengine Server status: `systemctl status taosd`
:::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 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。
- 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.
:::
## TDengine 命令行 (CLI)
</TabItem>
<TabItem label="Windows" value="windows">
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。
After the installation is complete, run `C:\TDengine\taosd.exe` to start TDengine Server.
</TabItem>
</Tabs>
## Test data insert performance
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:
```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：
The TDengine CLI displays a welcome message and version information to indicate that its connection to the TDengine service was successful. If an error message is displayed, see the [FAQ](/train-faq/faq) for troubleshooting information. At the following prompt, you can execute SQL commands.
```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：
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;
......@@ -170,52 +219,39 @@ select * from t;
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/)
## 使用 taosBenchmark 体验写入速度
启动 TDengine 的服务，在 Linux 终端执行 `taosBenchmark` （曾命名为 `taosdemo`）：
```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 "California.SanFrancisco" 或者 "California.LosAngeles"。
这条命令很快完成 1 亿条记录的插入。具体时间取决于硬件性能，即使在一台普通的 PC 服务器往往也仅需十几秒。
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)。
## 使用 TDengine CLI 体验查询速度
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 query performance
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。
After using taosBenchmark to create your test deployment, you can run queries in the TDengine CLI to test its performance:
查询超级表下记录总条数：
From the TDengine CLI query the number of rows in the `meters` supertable:
```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：
Query the average, maximum, and minimum values of all 100 million rows of data:
```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：
Query the number of rows whose `location` tag is `San Francisco`:
```sql
taos> select count(*) from test.meters where location="California.SanFrancisco";
select count(*) from test.meters where location="San Francisco";
```
查询 groupId=10 的所有记录的平均值、最大值、最小值等：
Query the average, maximum, and minimum values of all rows whose `groupId` tag is `10`:
```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：
Query the average, maximum, and minimum values for table `d10` in 1 second intervals:
```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(1s);
```
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/).
docs/en/05-get-started/_apt_get_install.mdx
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可以使用 apt-get 工具从官方仓库安装。
You can use `apt-get` to install TDengine from the official package repository.
**安装包仓库**
**Configure the package repository**
```
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
You can install beta versions by configuring the following package repository:
```
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**
**Install TDengine with `apt-get`**
```
sudo apt-get update
......@@ -22,5 +22,5 @@ sudo apt-get install tdengine
```
:::tip
apt-get 方式只适用于 Debian 或 Ubuntu 系统
This installation method is supported only for Debian and Ubuntu.
::::
docs/en/05-get-started/_pkg_install.mdx
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import PkgList from "/components/PkgList";
TDengine 的安装非常简单，从下载到安装成功仅仅只要几秒钟。
TDengine is easy to download and install.
为方便使用，从 2.4.0.10 开始，标准的服务端安装包包含了 taos、taosd、taosAdapter、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。
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.
在安装包格式上，我们提供 tar.gz, rpm 和 deb 格式，为企业客户提供 tar.gz 格式安装包，以方便在特定操作系统上使用。需要注意的是，rpm 和 deb 包不含 taosdump、taosBenchmark 和 TDinsight 安装脚本，这些工具需要通过安装 taosTool 包获得。
You can download the TDengine installation package in .rpm, .deb, or .tar.gz format. 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.
发布版本包括稳定版和 Beta 版，Beta 版含有更多新功能。正式上线或测试建议安装稳定版。您可以根据需要选择下载：
Between official releases, beta versions may be released that contain new features. Do not use beta versions for production or testing environments. Select the installation package appropriate for your system.
<PkgList type={0}/>
具体的安装方法，请参见[安装包的安装和卸载](/operation/pkg-install)。
For information about installing TDengine, see [Install and Uninstall](/operation/pkg-install).
下载其他组件、最新 Beta 版及之前版本的安装包，请点击[这里](https://www.taosdata.com/all-downloads)
For information about TDengine releases, see [All Downloads](https://tdengine.com/all-downloads)
查看 Release Notes, 请点击[这里](https://github.com/taosdata/TDengine/releases)
and [Release Notes](https://github.com/taosdata/TDengine/releases).
docs/en/05-get-started/index.md
浏览文件 @ 324973bd
---
title: 立即开始
description: '快速设置 TDengine 环境并体验其高效写入和查询'
title: Get Started
description: This article describes how to install TDengine and test its performance.
---
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。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](/reference/rest-api)。
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. In addition to connectors for multiple languages, TDengine also provides a [RESTful interface](/reference/rest-api) through [taosAdapter](/reference/taosadapter).
本章主要介绍如何利用 Docker 或者安装包快速设置 TDengine 环境并体验其高效写入和查询。
You can install and run TDengine on Linux and Windows machines as well as Docker containers.
```mdx-code-block
import DocCardList from '@theme/DocCardList';
......
docs/en/07-develop/01-connect/_connect_php.mdx 0 → 100644
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```php title="原生连接"
{{#include docs/examples/php/connect.php}}
```
docs/en/07-develop/01-connect/index.md
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---
sidebar_label: Connect
title: Connect
description: "This document explains how to establish connections to TDengine, and briefly introduces how to install and use TDengine connectors."
description: "This document explains how to establish connections to TDengine and how to install and use TDengine connectors."
---
import Tabs from "@theme/Tabs";
import TabItem from "@theme/TabItem";
import ConnJava from "./\_connect_java.mdx";
import ConnGo from "./\_connect_go.mdx";
import ConnRust from "./\_connect_rust.mdx";
import ConnNode from "./\_connect_node.mdx";
import ConnPythonNative from "./\_connect_python.mdx";
import ConnCSNative from "./\_connect_cs.mdx";
import ConnC from "./\_connect_c.mdx";
import ConnR from "./\_connect_r.mdx";
import InstallOnWindows from "../../14-reference/03-connector/\_linux_install.mdx";
import InstallOnLinux from "../../14-reference/03-connector/\_windows_install.mdx";
import VerifyLinux from "../../14-reference/03-connector/\_verify_linux.mdx";
import VerifyWindows from "../../14-reference/03-connector/\_verify_windows.mdx";
Any application programs running on any kind of platform can access TDengine through the REST API provided by TDengine. For details, please refer to [REST API](/reference/rest-api/). Additionally, application programs can use the connectors of multiple programming languages including C/C++, Java, Python, Go, Node.js, C#, Rust to access TDengine. This chapter describes how to establish a connection to TDengine and briefly introduces how to install and use connectors. TDengine community also provides connectors in LUA and PHP languages. For details about the connectors, please refer to [Connectors](/reference/connector/).
import ConnJava from "./_connect_java.mdx";
import ConnGo from "./_connect_go.mdx";
import ConnRust from "./_connect_rust.mdx";
import ConnNode from "./_connect_node.mdx";
import ConnPythonNative from "./_connect_python.mdx";
import ConnCSNative from "./_connect_cs.mdx";
import ConnC from "./_connect_c.mdx";
import ConnR from "./_connect_r.mdx";
import ConnPHP from "./_connect_php.mdx";
import InstallOnWindows from "../../14-reference/03-connector/_linux_install.mdx";
import InstallOnLinux from "../../14-reference/03-connector/_windows_install.mdx";
import VerifyLinux from "../../14-reference/03-connector/_verify_linux.mdx";
import VerifyWindows from "../../14-reference/03-connector/_verify_windows.mdx";
Any application running on any platform can access TDengine through the REST API provided by TDengine. For information, see [REST API](/reference/rest-api/). Applications can also use the connectors for various programming languages, including C/C++, Java, Python, Go, Node.js, C#, and Rust, to access TDengine. These connectors support connecting to TDengine clusters using both native interfaces (taosc). Some connectors also support connecting over a REST interface. Community developers have also contributed several unofficial connectors, such as the ADO.NET connector, the Lua connector, and the PHP connector.
## Establish Connection
There are two ways for a connector to establish connections to TDengine:
1. Connection through the REST API provided by the taosAdapter component, this way is called "REST connection" hereinafter.
2. Connection through the TDengine client driver (taosc), this way is called "Native connection" hereinafter.
1. REST connection through the REST API provided by the taosAdapter component.
2. Native connection through the TDengine client driver (taosc).
For REST and native connections, connectors provide similar APIs for performing operations and running SQL statements on your databases. The main difference is the method of establishing the connection, which is not visible to users.
Key differences：
1. The TDengine client driver (taosc) has the highest performance with all the features of TDengine like [Parameter Binding](/reference/connector/cpp#parameter-binding-api), [Subscription](/reference/connector/cpp#subscription-and-consumption-api), etc.
2. The TDengine client driver (taosc) is not supported across all platforms, and applications built on taosc may need to be modified when updating taosc to newer versions.
3. The REST connection is more accessible with cross-platform support, however it results in a 30% performance downgrade.
1. The TDengine client driver (taosc) has the highest performance with all the features of TDengine like [Parameter Binding](/reference/connector/cpp#parameter-binding-api), [Subscription](/reference/connector/cpp#subscription-and-consumption-api), etc.
## Install Client Driver taosc
......@@ -136,19 +137,19 @@ Node.js connector provides different ways of establishing connections by providi
1. Install Node.js Native Connector
```
npm install @tdengine/client
```
```
npm install @tdengine/client
```
:::note
It's recommend to use Node whose version is between `node-v12.8.0` and `node-v13.0.0`.
:::
:::
2. Install Node.js REST Connector
```
npm install @tdengine/rest
```
```
npm install @tdengine/rest
```
</TabItem>
<TabItem label="C#" value="csharp">
......@@ -222,7 +223,7 @@ phpize && ./configure && make -j && make install
**Specify TDengine Location：**
```shell
phpize && ./configure --with-tdengine-dir=/usr/local/Cellar/tdengine/2.4.0.0 && make -j && make install
phpize && ./configure --with-tdengine-dir=/usr/local/Cellar/tdengine/3.0.0.0 && make -j && make install
```
> `--with-tdengine-dir=` is followed by the TDengine installation location.
......@@ -236,14 +237,14 @@ phpize && ./configure --enable-swoole && make -j && make install
**Enable The Extension:**
Option One: Add `extension=tdengine` in `php.ini`
Option One: Add `extension=tdengine` in `php.ini`
Option Two: Specify the extension on CLI `php -d extension=tdengine test.php`
</TabItem>
</Tabs>
## Establish Connection
## Establish a connection
Prior to establishing connection, please make sure TDengine is already running and accessible. The following sample code assumes TDengine is running on the same host as the client program, with FQDN configured to "localhost" and serverPort configured to "6030".
......@@ -272,6 +273,9 @@ Prior to establishing connection, please make sure TDengine is already running a
<TabItem label="C" value="c">
<ConnC />
</TabItem>
<TabItem label="PHP" value="php">
<ConnPHP />
</TabItem>
</Tabs>
:::tip
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docs/en/07-develop/02-model/index.mdx
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title: Data Model
---
The data model employed by TDengine is similar to that of a relational database. You have to create databases and tables. You must design the data model based on your own business and application requirements. You should design the STable (an abbreviation for super table) schema to fit your data. This chapter will explain the big picture without getting into syntactical details.
The data model employed by TDengine is similar to that of a relational database. You have to create databases and tables. You must design the data model based on your own business and application requirements. You should design the [STable](/concept/#super-table-stable) (an abbreviation for super table) schema to fit your data. This chapter will explain the big picture without getting into syntactical details.
Note: before you read this chapter, please make sure you have already read through [Key Concepts](/concept/), since TDengine introduces new concepts like "one table for one [data collection point](/concept/#data-collection-point)" and "[super table](/concept/#super-table-stable)".
## Create Database
The [characteristics of time-series data](https://www.taosdata.com/blog/2019/07/09/86.html) from different data collection points may be different. Characteristics include collection frequency, retention policy and others which determine how you create and configure the database. For e.g. days to keep, number of replicas, data block size, whether data updates are allowed and other configurable parameters would be determined by the characteristics of your data and your business requirements. For TDengine to operate with the best performance, we strongly recommend that you create and configure different databases for data with different characteristics. This allows you, for example, to set up different storage and retention policies. When creating a database, there are a lot of parameters that can be configured such as, the days to keep data, the number of replicas, the number of memory blocks, time precision, the minimum and maximum number of rows in each data block, whether compression is enabled, the time range of the data in single data file and so on. Below is an example of the SQL statement to create a database.
The characteristics of time-series data from different data collection points may be different. Characteristics include collection frequency, retention policy and others which determine how you create and configure the database. For e.g. days to keep, number of replicas, data block size, whether data updates are allowed and other configurable parameters would be determined by the characteristics of your data and your business requirements. For TDengine to operate with the best performance, we strongly recommend that you create and configure different databases for data with different characteristics. This allows you, for example, to set up different storage and retention policies. When creating a database, there are a lot of parameters that can be configured such as, the days to keep data, the number of replicas, the size of the cache, time precision, the minimum and maximum number of rows in each data block, whether compression is enabled, the time range of the data in single data file and so on. An example is shown as follows:
```sql
CREATE DATABASE power KEEP 365 DURATION 10 BUFFER 16 VGROUPS 100 WAL 1;
CREATE DATABASE power KEEP 365 DURATION 10 BUFFER 16 WAL_LEVEL 1;
```
In the above SQL statement:
- a database named "power" will be created
- the data in it will be kept for 365 days, which means that data older than 365 days will be deleted automatically
- a database named "power" is created
- the data in it is retained for 365 days, which means that data older than 365 days will be deleted automatically
- a new data file will be created every 10 days
- the size of memory cache for writing is 16 MB
- data will be firstly written to WAL without FSYNC
For more details please refer to [Database](/taos-sql/database).
- the size of the write cache pool on each vnode is 16 MB
- the number of vgroups is 100
- WAL is enabled but fsync is disabled For more details please refer to [Database](/taos-sql/database).
After creating a database, the current database in use can be switched using SQL command `USE`. For example the SQL statement below switches the current database to `power`. Without the current database specified, table name must be preceded with the corresponding database name.
After creating a database, the current database in use can be switched using SQL command `USE`. For example the SQL statement below switches the current database to `power`.
```sql
USE power;
```
Without the current database specified, table name must be preceded with the corresponding database name.
:::note
- Any table or STable must belong to a database. To create a table or STable, the database it belongs to must be ready.
......@@ -39,14 +44,9 @@ USE power;
In a time-series application, there may be multiple kinds of data collection points. For example, in the electrical power system there are meters, transformers, bus bars, switches, etc. For easy and efficient aggregation of multiple tables, one STable needs to be created for each kind of data collection point. For example, for the meters in [table 1](/concept/#model_table1), the SQL statement below can be used to create the super table.
```sql
CREATE STable meters (ts timestamp, current float, voltage int, phase float) TAGS (location binary(64), groupId int);
CREATE STABLE meters (ts timestamp, current float, voltage int, phase float) TAGS (location binary(64), groupId int);
```
:::note
If you are using versions prior to 2.0.15, the `STable` keyword needs to be replaced with `TABLE`.
:::
Similar to creating a regular table, when creating a STable, the name and schema need to be provided. In the STable schema, the first column must always be a timestamp (like ts in the example), and the other columns (like current, voltage and phase in the example) are the data collected. The remaining columns can [contain data of type](/taos-sql/data-type/) integer, float, double, string etc. In addition, the schema for tags, like location and groupId in the example, must be provided. The tag type can be integer, float, string, etc. Tags are essentially the static properties of a data collection point. For example, properties like the location, device type, device group ID, manager ID are tags. Tags in the schema can be added, removed or updated. Please refer to [STable](/taos-sql/stable) for more details.
For each kind of data collection point, a corresponding STable must be created. There may be many STables in an application. For electrical power system, we need to create a STable respectively for meters, transformers, busbars, switches. There may be multiple kinds of data collection points on a single device, for example there may be one data collection point for electrical data like current and voltage and another data collection point for environmental data like temperature, humidity and wind direction. Multiple STables are required for these kinds of devices.
......@@ -63,13 +63,8 @@ CREATE TABLE d1001 USING meters TAGS ("California.SanFrancisco", 2);
In the above SQL statement, "d1001" is the table name, "meters" is the STable name, followed by the value of tag "Location" and the value of tag "groupId", which are "California.SanFrancisco" and "2" respectively in the example. The tag values can be updated after the table is created. Please refer to [Tables](/taos-sql/table) for details.
In the TDengine system, it's recommended to create a table for a data collection point via STable. A table created via STable is called subtable in some parts of the TDengine documentation. All SQL commands applied on regular tables can be applied on subtables.
:::tip
It's suggested to use the globally unique ID of a data collection point as the table name. For example the device serial number could be used as a unique ID. If a unique ID doesn't exist, multiple IDs that are not globally unique can be combined to form a globally unique ID. It's not recommended to use a globally unique ID as tag value.
:::
## Create Table Automatically
In some circumstances, it's unknown whether the table already exists when inserting rows. The table can be created automatically using the SQL statement below, and nothing will happen if the table already exists.
......@@ -84,8 +79,6 @@ For more details please refer to [Create Table Automatically](/taos-sql/insert#a
## Single Column vs Multiple Column
A multiple columns data model is supported in TDengine. As long as multiple metrics are collected by the same data collection point at the same time, i.e. the timestamps are identical, these metrics can be put in a single STable as columns.
However, there is another kind of design, i.e. single column data model in which a table is created for each metric. This means that a STable is required for each kind of metric. For example in a single column model, 3 STables would be required for current, voltage and phase.
A multiple columns data model is supported in TDengine. As long as multiple metrics are collected by the same data collection point at the same time, i.e. the timestamps are identical, these metrics can be put in a single STable as columns. However, there is another kind of design, i.e. single column data model in which a table is created for each metric. This means that a STable is required for each kind of metric. For example in a single column model, 3 STables would be required for current, voltage and phase.
It's recommended to use a multiple column data model as much as possible because insert and query performance is higher. In some cases, however, the collected metrics may vary frequently and so the corresponding STable schema needs to be changed frequently too. In such cases, it's more convenient to use single column data model.
docs/en/07-develop/03-insert-data/01-sql-writing.mdx
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---
sidebar_label: Insert Using SQL
title: Insert Using SQL
---
......@@ -19,13 +18,14 @@ import CsSQL from "./_cs_sql.mdx";
import CsStmt from "./_cs_stmt.mdx";
import CSQL from "./_c_sql.mdx";
import CStmt from "./_c_stmt.mdx";
import PhpSQL from "./_php_sql.mdx";
import PhpStmt from "./_php_stmt.mdx";
## Introduction
Application programs can execute `INSERT` statement through connectors to insert rows. The TDengine CLI can also be used to manually insert data.
### Insert Single Row
The below SQL statement is used to insert one row into table "d1001".
```sql
......@@ -42,7 +42,7 @@ INSERT INTO d1001 VALUES (1538548684000, 10.2, 220, 0.23) (1538548696650, 10.3,
### Insert into Multiple Tables
Data can be inserted into multiple tables in single SQL statement. The example below inserts 2 rows into table "d1001" and 1 row into table "d1002".
Data can be inserted into multiple tables in the same SQL statement. The example below inserts 2 rows into table "d1001" and 1 row into table "d1002".
```sql
INSERT INTO d1001 VALUES (1538548685000, 10.3, 219, 0.31) (1538548695000, 12.6, 218, 0.33) d1002 VALUES (1538548696800, 12.3, 221, 0.31);
......@@ -52,19 +52,19 @@ For more details about `INSERT` please refer to [INSERT](/taos-sql/insert).
:::info
- Inserting in batches can improve performance. Normally, the higher the batch size, the better the performance. Please note that a single row can't exceed 48 KB bytes and each SQL statement can't exceed 1 MB.
- Inserting with multiple threads can also improve performance. However, depending on the system resources on the application side and the server side, when the number of inserting threads grows beyond a specific point the performance may drop instead of improving. The proper number of threads needs to be tested in a specific environment to find the best number. The proper number of threads may be impacted by the system resources on the server side, the system resources on the client side, the table schemas, etc.
- Inserting in batches can improve performance. The higher the batch size, the better the performance. Please note that a single row can't exceed 48K bytes and each SQL statement can't exceed 1MB.
- Inserting with multiple threads can also improve performance. However, at a certain point, increasing the number of threads no longer offers any benefit and can even decrease performance due to the overhead involved in frequent thread switching. The optimal number of threads for a system depends on the processing capabilities and configuration of the server, the configuration of the database, the data schema, and the batch size for writing data. In general, more powerful clients and servers can support higher numbers of concurrently writing threads. Given a sufficiently powerful server, a higher number of vgroups for a database also increases the number of concurrent writes. Finally, a simpler data schema enables more concurrent writes as well.
:::
:::warning
- If the timestamp for the row to be inserted already exists in the table, the old data will be overritten by the new values for the columns for which new values are provided, columns for which no new values are provided are not impacted.
- The timestamp to be inserted must be newer than the timestamp of subtracting current time by the parameter `KEEP`. If `KEEP` is set to 3650 days, then the data older than 3650 days ago can't be inserted. The timestamp to be inserted can't be newer than the timestamp of current time plus parameter `DURATION`. If `DAYS` is set to 2, the data newer than 2 days later can't be inserted.
- If the timestamp of a new record already exists in a table, columns with new data for that timestamp replace old data with new data, while columns without new data are not affected.
- The timestamp to be inserted must be newer than the timestamp of subtracting current time by the parameter `KEEP`. If `KEEP` is set to 3650 days, then the data older than 3650 days ago can't be inserted. The timestamp to be inserted cannot be newer than the timestamp of current time plus parameter `DURATION`. If `DURATION` is set to 2, the data newer than 2 days later can't be inserted.
:::
## Examples
## Sample program
### Insert Using SQL
......@@ -90,6 +90,9 @@ For more details about `INSERT` please refer to [INSERT](/taos-sql/insert).
<TabItem label="C" value="c">
<CSQL />
</TabItem>
<TabItem label="PHP" value="php">
<PhpSQL />
</TabItem>
</Tabs>
:::note
......@@ -101,7 +104,7 @@ For more details about `INSERT` please refer to [INSERT](/taos-sql/insert).
### Insert with Parameter Binding
TDengine also provides API support for parameter binding. Similar to MySQL, only `?` can be used in these APIs to represent the parameters to bind. Parameter binding support for inserting data has improved significantly to improve the insert performance by avoiding the cost of parsing SQL statements.
TDengine also provides API support for parameter binding. Similar to MySQL, only `?` can be used in these APIs to represent the parameters to bind. This avoids the resource consumption of SQL syntax parsing when writing data through the parameter binding interface, thus significantly improving write performance in most cases.
Parameter binding is available only with native connection.
......@@ -127,4 +130,8 @@ Parameter binding is available only with native connection.
<TabItem label="C" value="c">
<CStmt />
</TabItem>
<TabItem label="PHP" value="php">
<PhpStmt />
</TabItem>
</Tabs>
docs/en/07-develop/03-insert-data/02-influxdb-line.mdx
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......@@ -21,15 +21,15 @@ In the InfluxDB Line protocol format, a single line of text is used to represent
measurement,tag_set field_set timestamp
```
- `measurement` will be used as the name of the STable
- `tag_set` will be used as tags, with format like `<tag_key>=<tag_value>,<tag_key>=<tag_value>`
- `field_set`will be used as data columns, with format like `<field_key>=<field_value>,<field_key>=<field_value>`
- `measurement` will be used as the name of the STable Enter a comma (,) between `measurement` and `tag_set`.
- `tag_set` will be used as tags, with format like `<tag_key>=<tag_value>,<tag_key>=<tag_value>` Enter a space between `tag_set` and `field_set`.
- `field_set`will be used as data columns, with format like `<field_key>=<field_value>,<field_key>=<field_value>` Enter a space between `field_set` and `timestamp`.
- `timestamp` is the primary key timestamp corresponding to this row of data
For example:
```
meters,location=California.LoSangeles,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611249500
meters,location=California.LosAngeles,groupid=2 current=13.4,voltage=223,phase=0.29 1648432611249500
```
:::note
......@@ -42,7 +42,6 @@ meters,location=California.LoSangeles,groupid=2 current=13.4,voltage=223,phase=0
For more details please refer to [InfluxDB Line Protocol](https://docs.influxdata.com/influxdb/v2.0/reference/syntax/line-protocol/) and [TDengine Schemaless](/reference/schemaless/#Schemaless-Line-Protocol)
## Examples
<Tabs defaultValue="java" groupId="lang">
......
docs/en/07-develop/03-insert-data/03-opentsdb-telnet.mdx
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......@@ -17,19 +17,19 @@ import CTelnet from "./_c_opts_telnet.mdx";
A single line of text is used in OpenTSDB line protocol to represent one row of data. OpenTSDB employs a single column data model, so each line can only contain a single data column. There can be multiple tags. Each line contains 4 parts as below:
```
```txt
<metric> <timestamp> <value> <tagk_1>=<tagv_1>[ <tagk_n>=<tagv_n>]
```
- `metric` will be used as the STable name.
- `timestamp` is the timestamp of current row of data. The time precision will be determined automatically based on the length of the timestamp. Second and millisecond time precision are supported.
- `value` is a metric which must be a numeric value, the corresponding column name is "_value".
- `value` is a metric which must be a numeric value, The corresponding column name is "value".
- The last part is the tag set separated by spaces, all tags will be converted to nchar type automatically.
For example:
```txt
meters.current 1648432611250 11.3 location=California.LoSangeles groupid=3
meters.current 1648432611250 11.3 location=California.LosAngeles groupid=3
```
Please refer to [OpenTSDB Telnet API](http://opentsdb.net/docs/build/html/api_telnet/put.html) for more details.
......@@ -63,7 +63,7 @@ Please refer to [OpenTSDB Telnet API](http://opentsdb.net/docs/build/html/api_te
taos> use test;
Database changed.
taos> show STables;
taos> show stables;
name | created_time | columns | tags | tables |
============================================================================================
meters.current | 2022-03-30 17:04:10.877 | 2 | 2 | 2 |
......@@ -73,8 +73,8 @@ Query OK, 2 row(s) in set (0.002544s)
taos> select tbname, * from `meters.current`;
tbname | _ts | _value | groupid | location |
==================================================================================================================================
t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.249 | 10.800000000 | 3 | California.LoSangeles |
t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.250 | 11.300000000 | 3 | California.LoSangeles |
t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.249 | 10.800000000 | 3 | California.LosAngeles |
t_0e7bcfa21a02331c06764f275... | 2022-03-28 09:56:51.250 | 11.300000000 | 3 | California.LosAngeles |
t_7e7b26dd860280242c6492a16... | 2022-03-28 09:56:51.249 | 10.300000000 | 2 | California.SanFrancisco |
t_7e7b26dd860280242c6492a16... | 2022-03-28 09:56:51.250 | 12.600000000 | 2 | California.SanFrancisco |
Query OK, 4 row(s) in set (0.005399s)
......
docs/en/07-develop/03-insert-data/04-opentsdb-json.mdx
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......@@ -15,37 +15,37 @@ import CJson from "./_c_opts_json.mdx";
## Introduction
A JSON string is used in OpenTSDB JSON to represent one or more rows of data, for example:
A JSON string is used in OpenTSDB JSON to represent one or more rows of data, for example: For example:
```json
[
{
"metric": "sys.cpu.nice",
"timestamp": 1346846400,
"value": 18,
"tags": {
"host": "web01",
"dc": "lga"
}
},
{
"metric": "sys.cpu.nice",
"timestamp": 1346846400,
"value": 9,
"tags": {
"host": "web02",
"dc": "lga"
}
{
"metric": "sys.cpu.nice",
"timestamp": 1346846400,
"value": 18,
"tags": {
"host": "web01",
"dc": "lga"
}
},
{
"metric": "sys.cpu.nice",
"timestamp": 1346846400,
"value": 9,
"tags": {
"host": "web02",
"dc": "lga"
}
}
]
```
Similar to OpenTSDB line protocol, `metric` will be used as the STable name, `timestamp` is the timestamp to be used, `value` represents the metric collected, `tags` are the tag sets.
Please refer to [OpenTSDB HTTP API](http://opentsdb.net/docs/build/html/api_http/put.html) for more details.
:::note
- In JSON protocol, strings will be converted to nchar type and numeric values will be converted to double type.
- Only data in array format is accepted and so an array must be used even if there is only one row.
......@@ -74,13 +74,13 @@ Please refer to [OpenTSDB HTTP API](http://opentsdb.net/docs/build/html/api_http
</TabItem>
</Tabs>
The above sample code will created 2 STables automatically while each STable has 2 rows of data.
2 STables will be created automatically and each STable has 2 rows of data in the above sample code.
```cmd
taos> use test;
Database changed.
taos> show STables;
taos> show stables;
name | created_time | columns | tags | tables |
============================================================================================
meters.current | 2022-03-29 16:05:25.193 | 2 | 2 | 1 |
......
docs/en/07-develop/03-insert-data/_go_stmt.mdx
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......@@ -3,6 +3,6 @@
```
:::tip
`github.com/taosdata/driver-go/v2/wrapper` module in driver-go is the wrapper for C API, it can be used to insert data with parameter binding.
`github.com/taosdata/driver-go/v3/wrapper` module in driver-go is the wrapper for C API, it can be used to insert data with parameter binding.
:::
docs/en/07-develop/03-insert-data/_php_sql.mdx 0 → 100644
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```php
{{#include docs/examples/php/insert.php}}
```
docs/en/07-develop/03-insert-data/_php_stmt.mdx 0 → 100644
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```php
{{#include docs/examples/php/insert_stmt.php}}
```
docs/en/07-develop/04-query-data/_php.mdx 0 → 100644
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```go
{{#include docs/examples/php/query.php}}
```
docs/en/07-develop/04-query-data/index.mdx
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---
Sidebar_label: Query data
title: Query data
title: Query Data
description: "This chapter introduces major query functionalities and how to perform sync and async query using connectors."
---
......@@ -13,6 +12,7 @@ import RustQuery from "./_rust.mdx";
import NodeQuery from "./_js.mdx";
import CsQuery from "./_cs.mdx";
import CQuery from "./_c.mdx";
import PhpQuery from "./_php.mdx";
import PyAsync from "./_py_async.mdx";
import NodeAsync from "./_js_async.mdx";
import CsAsync from "./_cs_async.mdx";
......@@ -24,9 +24,8 @@ SQL is used by TDengine as its query language. Application programs can send SQL
- Query on single column or multiple columns
- Filter on tags or data columns：>, <, =, <\>, like
- Grouping of results: `Group By`
- Sorting of results: `Order By`
- Limit the number of results: `Limit/Offset`
- Grouping of results: `Group By` - Sorting of results: `Order By` - Limit the number of results: `Limit/Offset`
- Windowed aggregate queries for time windows (interval), session windows (session), and state windows (state_window)
- Arithmetic on columns of numeric types or aggregate results
- Join query with timestamp alignment
- Aggregate functions: count, max, min, avg, sum, twa, stddev, leastsquares, top, bottom, first, last, percentile, apercentile, last_row, spread, diff
......@@ -34,10 +33,6 @@ SQL is used by TDengine as its query language. Application programs can send SQL
For example, the SQL statement below can be executed in TDengine CLI `taos` to select records with voltage greater than 215 and limit the output to only 2 rows.
```sql
select * from d1001 where voltage > 215 order by ts desc limit 2;
```
```title=Output
taos> select * from d1001 where voltage > 215 order by ts desc limit 2;
ts | current | voltage | phase |
======================================================================================
......@@ -46,89 +41,88 @@ taos> select * from d1001 where voltage > 215 order by ts desc limit 2;
Query OK, 2 row(s) in set (0.001100s)
```
To meet the requirements of varied use cases, some special functions have been added in TDengine. Some examples are `twa` (Time Weighted Average), `spread` (The difference between the maximum and the minimum), and `last_row` (the last row). Furthermore, continuous query is also supported in TDengine.
To meet the requirements of varied use cases, some special functions have been added in TDengine. Some examples are `twa` (Time Weighted Average), `spread` (The difference between the maximum and the minimum), and `last_row` (the last row).
For detailed query syntax please refer to [Select](/taos-sql/select).
For detailed query syntax, see [Select](../../taos-sql/select).
## Aggregation among Tables
In most use cases, there are always multiple kinds of data collection points. A new concept, called STable (abbreviation for super table), is used in TDengine to represent one type of data collection point, and a subtable is used to represent a specific data collection point of that type. Tags are used by TDengine to represent the static properties of data collection points. A specific data collection point has its own values for static properties. By specifying filter conditions on tags, aggregation can be performed efficiently among all the subtables created via the same STable, i.e. same type of data collection points. Aggregate functions applicable for tables can be used directly on STables; the syntax is exactly the same.
In summary, records across subtables can be aggregated by a simple query on their STable. It is like a join operation. However, tables belonging to different STables can not be aggregated.
### Example 1
In TDengine CLI `taos`, use the SQL below to get the average voltage of all the meters in California grouped by location.
```
taos> SELECT AVG(voltage) FROM meters GROUP BY location;
avg(voltage) | location |
=============================================================
222.000000000 | California.LosAngeles |
219.200000000 | California.SanFrancisco |
Query OK, 2 row(s) in set (0.002136s)
taos> SELECT AVG(voltage), location FROM meters GROUP BY location;
avg(voltage) | location |
===============================================================================================
219.200000000 | California.SanFrancisco |
221.666666667 | California.LosAngeles |
Query OK, 2 rows in database (0.005995s)
```
### Example 2
In TDengine CLI `taos`, use the SQL below to get the number of rows and the maximum current in the past 24 hours from meters whose groupId is 2.
In TDengine CLI `taos`, use the SQL below to get the number of rows and the maximum current from meters whose groupId is 2.
```
taos> SELECT count(*), max(current) FROM meters where groupId = 2 and ts > now - 24h;
taos> SELECT count(*), max(current) FROM meters where groupId = 2;
count(*) | max(current) |
==================================
5 | 13.4 |
Query OK, 1 row(s) in set (0.002136s)
```
Join queries are only allowed between subtables of the same STable. In [Select](/taos-sql/select), all query operations are marked as to whether they support STables or not.
In [Select](../../taos-sql/select), all query operations are marked as to whether they support STables or not.
## Down Sampling and Interpolation
In IoT use cases, down sampling is widely used to aggregate data by time range. The `INTERVAL` keyword in TDengine can be used to simplify the query by time window. For example, the SQL statement below can be used to get the sum of current every 10 seconds from meters table d1001.
```
taos> SELECT sum(current) FROM d1001 INTERVAL(10s);
ts | sum(current) |
taos> SELECT _wstart, sum(current) FROM d1001 INTERVAL(10s);
_wstart | sum(current) |
======================================================
2018-10-03 14:38:00.000 | 10.300000191 |
2018-10-03 14:38:10.000 | 24.900000572 |
Query OK, 2 row(s) in set (0.000883s)
Query OK, 2 rows in database (0.003139s)
```
Down sampling can also be used for STable. For example, the below SQL statement can be used to get the sum of current from all meters in California.
```
taos> SELECT SUM(current) FROM meters where location like "California%" INTERVAL(1s);
ts | sum(current) |
taos> SELECT _wstart, SUM(current) FROM meters where location like "California%" INTERVAL(1s);
_wstart | sum(current) |
======================================================
2018-10-03 14:38:04.000 | 10.199999809 |
2018-10-03 14:38:05.000 | 32.900000572 |
2018-10-03 14:38:05.000 | 23.699999809 |
2018-10-03 14:38:06.000 | 11.500000000 |
2018-10-03 14:38:15.000 | 12.600000381 |
2018-10-03 14:38:16.000 | 36.000000000 |
Query OK, 5 row(s) in set (0.001538s)
2018-10-03 14:38:16.000 | 34.400000572 |
Query OK, 5 rows in database (0.007413s)
```
Down sampling also supports time offset. For example, the below SQL statement can be used to get the sum of current from all meters but each time window must start at the boundary of 500 milliseconds.
```
taos> SELECT SUM(current) FROM meters INTERVAL(1s, 500a);
ts | sum(current) |
taos> SELECT _wstart, SUM(current) FROM meters INTERVAL(1s, 500a);
_wstart | sum(current) |
======================================================
2018-10-03 14:38:04.500 | 11.189999809 |
2018-10-03 14:38:05.500 | 31.900000572 |
2018-10-03 14:38:06.500 | 11.600000000 |
2018-10-03 14:38:15.500 | 12.300000381 |
2018-10-03 14:38:16.500 | 35.000000000 |
Query OK, 5 row(s) in set (0.001521s)
2018-10-03 14:38:03.500 | 10.199999809 |
2018-10-03 14:38:04.500 | 10.300000191 |
2018-10-03 14:38:05.500 | 13.399999619 |
2018-10-03 14:38:06.500 | 11.500000000 |
2018-10-03 14:38:14.500 | 12.600000381 |
2018-10-03 14:38:16.500 | 34.400000572 |
Query OK, 6 rows in database (0.005515s)
```
In many use cases, it's hard to align the timestamp of the data collected by each collection point. However, a lot of algorithms like FFT require the data to be aligned with same time interval and application programs have to handle this by themselves. In TDengine, it's easy to achieve the alignment using down sampling.
Interpolation can be performed in TDengine if there is no data in a time range.
For more details please refer to [Aggregate by Window](/taos-sql/interval).
For more information, see [Aggregate by Window](../../taos-sql/distinguished).
## Examples
......@@ -158,6 +152,9 @@ In the section describing [Insert](/develop/insert-data/sql-writing), a database
<TabItem label="C" value="c">
<CQuery />
</TabItem>
<TabItem label="PHP" value="php">
<PhpQuery />
</TabItem>
</Tabs>
:::note
......
docs/en/07-develop/06-continuous-query.mdx 已删除 100644 → 0
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---
sidebar_label: Continuous Query
description: "Continuous query is a query that's executed automatically at a predefined frequency to provide aggregate query capability by time window. It is essentially simplified, time driven, stream computing."
title: "Continuous Query"
---
A continuous query is a query that's executed automatically at a predefined frequency to provide aggregate query capability by time window. It is essentially simplified, time driven, stream computing. A continuous query can be performed on a table or STable in TDengine. The results of a continuous query can be pushed to clients or written back to TDengine. Each query is executed on a time window, which moves forward with time. The size of time window and the forward sliding time need to be specified with parameter `INTERVAL` and `SLIDING` respectively.
A continuous query in TDengine is time driven, and can be defined using TAOS SQL directly without any extra operations. With a continuous query, the result can be generated based on a time window to achieve down sampling of the original data. Once a continuous query is defined using TAOS SQL, the query is automatically executed at the end of each time window and the result is pushed back to clients or written to TDengine.
There are some differences between continuous query in TDengine and time window computation in stream computing：
- The computation is performed and the result is returned in real time in stream computing, but the computation in continuous query is only started when a time window closes. For example, if the time window is 1 day, then the result will only be generated at 23:59:59.
- If a historical data row is written in to a time window for which the computation has already finished, the computation will not be performed again and the result will not be pushed to client applications again. If the results have already been written into TDengine, they will not be updated.
- In continuous query, if the result is pushed to a client, the client status is not cached on the server side and Exactly-once is not guaranteed by the server. If the client program crashes, a new time window will be generated from the time where the continuous query is restarted. If the result is written into TDengine, the data written into TDengine can be guaranteed as valid and continuous.
## Syntax
```sql
[CREATE TABLE AS] SELECT select_expr [, select_expr ...]
FROM {tb_name_list}
[WHERE where_condition]
[INTERVAL(interval_val [, interval_offset]) [SLIDING sliding_val]]
```
INTERVAL: The time window for which continuous query is performed
SLIDING: The time step for which the time window moves forward each time
## How to Use
In this section the use case of meters will be used to introduce how to use continuous query. Assume the STable and subtables have been created using the SQL statements below.
```sql
create table meters (ts timestamp, current float, voltage int, phase float) tags (location binary(64), groupId int);
create table D1001 using meters tags ("California.SanFrancisco", 2);
create table D1002 using meters tags ("California.LosAngeles", 2);
```
The SQL statement below retrieves the average voltage for a one minute time window, with each time window moving forward by 30 seconds.
```sql
select avg(voltage) from meters interval(1m) sliding(30s);
```
Whenever the above SQL statement is executed, all the existing data will be computed again. If the computation needs to be performed every 30 seconds automatically to compute on the data in the past one minute, the above SQL statement needs to be revised as below, in which `{startTime}` stands for the beginning timestamp in the latest time window.
```sql
select avg(voltage) from meters where ts > {startTime} interval(1m) sliding(30s);
```
An easier way to achieve this is to prepend `create table {tableName} as` before the `select`.
```sql
create table avg_vol as select avg(voltage) from meters interval(1m) sliding(30s);
```
A table named as `avg_vol` will be created automatically, then every 30 seconds the `select` statement will be executed automatically on the data in the past 1 minute, i.e. the latest time window, and the result is written into table `avg_vol`. The client program just needs to query from table `avg_vol`. For example:
```sql
taos> select * from avg_vol;
ts | avg_voltage_ |
===================================================
2020-07-29 13:37:30.000 | 222.0000000 |
2020-07-29 13:38:00.000 | 221.3500000 |
2020-07-29 13:38:30.000 | 220.1700000 |
2020-07-29 13:39:00.000 | 223.0800000 |
```
Please note that the minimum allowed time window is 10 milliseconds, and there is no upper limit.
It's possible to specify the start and end time of a continuous query. If the start time is not specified, the timestamp of the first row will be considered as the start time; if the end time is not specified, the continuous query will be performed indefinitely, otherwise it will be terminated once the end time is reached. For example, the continuous query in the SQL statement below will be started from now and terminated one hour later.
```sql
create table avg_vol as select avg(voltage) from meters where ts > now and ts <= now + 1h interval(1m) sliding(30s);
```
`now` in the above SQL statement stands for the time when the continuous query is created, not the time when the computation is actually performed. To avoid the trouble caused by a delay in receiving data as much as possible, the actual computation in a continuous query is started after a little delay. That means, once a time window closes, the computation is not started immediately. Normally, the result are available after a little time, normally within one minute, after the time window closes.
## How to Manage
`show streams` command can be used in the TDengine CLI `taos` to show all the continuous queries in the system, and `kill stream` can be used to terminate a continuous query.
docs/en/07-develop/06-stream.md 0 → 100644
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---
sidebar_label: Stream Processing
description: "The TDengine stream processing engine combines data inserts, preprocessing, analytics, real-time computation, and alerting into a single component."
title: Stream Processing
---
Raw time-series data is often cleaned and preprocessed before being permanently stored in a database. In a traditional time-series solution, this generally requires the deployment of stream processing systems such as Kafka or Flink. However, the complexity of such systems increases the cost of development and maintenance.
With the stream processing engine built into TDengine, you can process incoming data streams in real time and define stream transformations in SQL. Incoming data is automatically processed, and the results are pushed to specified tables based on triggering rules that you define. This is a lightweight alternative to complex processing engines that returns computation results in milliseconds even in high throughput scenarios.
The stream processing engine includes data filtering, scalar function computation (including user-defined functions), and window aggregation, with support for sliding windows, session windows, and event windows. Stream processing can write data to supertables from other supertables, standard tables, or subtables. When you create a stream, the target supertable is automatically created. New data is then processed and written to that supertable according to the rules defined for the stream. You can use PARTITION BY statements to partition the data by table name or tag. Separate partitions are then written to different subtables within the target supertable.
TDengine stream processing supports the aggregation of supertables that are deployed across multiple vnodes. It can also handle out-of-order writes and includes a watermark mechanism that determines the extent to which out-of-order data is accepted by the system. You can configure whether to drop or reprocess out-of-order data through the **ignore expired** parameter.
For more information, see [Stream Processing](../../taos-sql/stream).
## Create a Stream
```sql
CREATE STREAM [IF NOT EXISTS] stream_name [stream_options] INTO stb_name AS subquery
stream_options: {
TRIGGER [AT_ONCE | WINDOW_CLOSE | MAX_DELAY time]
WATERMARK time
IGNORE EXPIRED [0 | 1]
}
```
For more information, see [Stream Processing](../../taos-sql/stream).
## Usage Scenario 1
It is common that smart electrical meter systems for businesses generate millions of data points that are widely dispersed and not ordered. The time required to clean and convert this data makes efficient, real-time processing impossible for traditional solutions. This scenario shows how you can configure TDengine stream processing to drop data points over 220 V, find the maximum voltage for 5 second windows, and output this data to a table.
### Create a Database for Raw Data
A database including one supertable and four subtables is created as follows:
```sql
DROP DATABASE IF EXISTS power;
CREATE DATABASE power;
USE power;
CREATE STABLE meters (ts timestamp, current float, voltage int, phase float) TAGS (location binary(64), groupId int);
CREATE TABLE d1001 USING meters TAGS ("California.SanFrancisco", 2);
CREATE TABLE d1002 USING meters TAGS ("California.SanFrancisco", 3);
CREATE TABLE d1003 USING meters TAGS ("California.LosAngeles", 2);
CREATE TABLE d1004 USING meters TAGS ("California.LosAngeles", 3);
```
### Create a Stream
```sql
create stream current_stream into current_stream_output_stb as select _wstart as start, _wend as end, max(current) as max_current from meters where voltage <= 220 interval (5s);
```
### Write Data
```sql
insert into d1001 values("2018-10-03 14:38:05.000", 10.30000, 219, 0.31000);
insert into d1001 values("2018-10-03 14:38:15.000", 12.60000, 218, 0.33000);
insert into d1001 values("2018-10-03 14:38:16.800", 12.30000, 221, 0.31000);
insert into d1002 values("2018-10-03 14:38:16.650", 10.30000, 218, 0.25000);
insert into d1003 values("2018-10-03 14:38:05.500", 11.80000, 221, 0.28000);
insert into d1003 values("2018-10-03 14:38:16.600", 13.40000, 223, 0.29000);
insert into d1004 values("2018-10-03 14:38:05.000", 10.80000, 223, 0.29000);
insert into d1004 values("2018-10-03 14:38:06.500", 11.50000, 221, 0.35000);
```
### Query the Results
```sql
taos> select start, end, max_current from current_stream_output_stb;
start | end | max_current |
===========================================================================
2018-10-03 14:38:05.000 | 2018-10-03 14:38:10.000 | 10.30000 |
2018-10-03 14:38:15.000 | 2018-10-03 14:38:20.000 | 12.60000 |
Query OK, 2 rows in database (0.018762s)
```
## Usage Scenario 2
In this scenario, the active power and reactive power are determined from the data gathered in the previous scenario. The location and name of each meter are concatenated with a period (.) between them, and the data set is partitioned by meter name and written to a new database.
### Create a Database for Raw Data
The procedure from the previous scenario is used to create the database.
### Create a Stream
```sql
create stream power_stream into power_stream_output_stb as select ts, concat_ws(".", location, tbname) as meter_location, current*voltage*cos(phase) as active_power, current*voltage*sin(phase) as reactive_power from meters partition by tbname;
```
### Write data
The procedure from the previous scenario is used to write the data.
### Query the Results
```sql
taos> select ts, meter_location, active_power, reactive_power from power_stream_output_stb;
ts | meter_location | active_power | reactive_power |
===================================================================================================================
2018-10-03 14:38:05.000 | California.LosAngeles.d1004 | 2307.834596289 | 688.687331847 |
2018-10-03 14:38:06.500 | California.LosAngeles.d1004 | 2387.415754896 | 871.474763418 |
2018-10-03 14:38:05.500 | California.LosAngeles.d1003 | 2506.240411679 | 720.680274962 |
2018-10-03 14:38:16.600 | California.LosAngeles.d1003 | 2863.424274422 | 854.482390839 |
2018-10-03 14:38:05.000 | California.SanFrancisco.d1001 | 2148.178871730 | 688.120784090 |
2018-10-03 14:38:15.000 | California.SanFrancisco.d1001 | 2598.589176205 | 890.081451418 |
2018-10-03 14:38:16.800 | California.SanFrancisco.d1001 | 2588.728381186 | 829.240910475 |
2018-10-03 14:38:16.650 | California.SanFrancisco.d1002 | 2175.595991997 | 555.520860397 |
Query OK, 8 rows in database (0.014753s)
```
docs/en/07-develop/07-subscribe.mdx 已删除 100644 → 0
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---
sidebar_label: Data Subscription
description: "Lightweight service for data subscription and publishing. Time series data inserted into TDengine continuously can be pushed automatically to subscribing clients."
title: Data Subscription
---
import Tabs from "@theme/Tabs";
import TabItem from "@theme/TabItem";
import Java from "./_sub_java.mdx";
import Python from "./_sub_python.mdx";
import Go from "./_sub_go.mdx";
import Rust from "./_sub_rust.mdx";
import Node from "./_sub_node.mdx";
import CSharp from "./_sub_cs.mdx";
import CDemo from "./_sub_c.mdx";
## Introduction
Due to the nature of time series data, data insertion into TDengine is similar to data publishing in message queues. Data is stored in ascending order of timestamp inside TDengine, and so each table in TDengine can essentially be considered as a message queue.
A lightweight service for data subscription and publishing is built into TDengine. With the API provided by TDengine, client programs can use `select` statements to subscribe to data from one or more tables. The subscription and state maintenance is performed on the client side. The client programs poll the server to check whether there is new data, and if so the new data will be pushed back to the client side. If the client program is restarted, where to start retrieving new data is up to the client side.
There are 3 major APIs related to subscription provided in the TDengine client driver.
```c
taos_subscribe
taos_consume
taos_unsubscribe
```
For more details about these APIs please refer to [C/C++ Connector](/reference/connector/cpp). Their usage will be introduced below using the use case of meters, in which the schema of STable and subtables from the previous section [Continuous Query](/develop/continuous-query) are used. Full sample code can be found [here](https://github.com/taosdata/TDengine/blob/master/examples/c/subscribe.c).
If we want to get a notification and take some actions if the current exceeds a threshold, like 10A, from some meters, there are two ways:
The first way is to query each sub table and record the last timestamp matching the criteria. Then after some time, query the data later than the recorded timestamp, and repeat this process. The SQL statements for this way are as below.
```sql
select * from D1001 where ts > {last_timestamp1} and current > 10;
select * from D1002 where ts > {last_timestamp2} and current > 10;
...
```
The above way works, but the problem is that the number of `select` statements increases with the number of meters. Additionally, the performance of both client side and server side will be unacceptable once the number of meters grows to a big enough number.
A better way is to query on the STable, only one `select` is enough regardless of the number of meters, like below:
```sql
select * from meters where ts > {last_timestamp} and current > 10;
```
However, this presents a new problem in how to choose `last_timestamp`. First, the timestamp when the data is generated is different from the timestamp when the data is inserted into the database, sometimes the difference between them may be very big. Second, the time when the data from different meters arrives at the database may be different too. If the timestamp of the "slowest" meter is used as `last_timestamp` in the query, the data from other meters may be selected repeatedly; but if the timestamp of the "fastest" meter is used as `last_timestamp`, some data from other meters may be missed.
All the problems mentioned above can be resolved easily using the subscription functionality provided by TDengine.
The first step is to create subscription using `taos_subscribe`.
```c
TAOS_SUB* tsub = NULL;
if (async) {
　　// create an asynchronous subscription, the callback function will be called every 1s
　　tsub = taos_subscribe(taos, restart, topic, sql, subscribe_callback, &blockFetch, 1000);
} else {
　　// create an synchronous subscription, need to call 'taos_consume' manually
　　tsub = taos_subscribe(taos, restart, topic, sql, NULL, NULL, 0);
}
```
The subscription in TDengine can be either synchronous or asynchronous. In the above sample code, the value of variable `async` is determined from the CLI input, then it's used to create either an async or sync subscription. Sync subscription means the client program needs to invoke `taos_consume` to retrieve data, and async subscription means another thread created by `taos_subscribe` internally invokes `taos_consume` to retrieve data and pass the data to `subscribe_callback` for processing. `subscribe_callback` is a callback function provided by the client program. You should not perform time consuming operations in the callback function.
The parameter `taos` is an established connection. Nothing special needs to be done for thread safety for synchronous subscription. For asynchronous subscription, the taos_subscribe function should be called exclusively by the current thread, to avoid unpredictable errors.
The parameter `sql` is a `select` statement in which the `where` clause can be used to specify filter conditions. In our example, we can subscribe to the records in which the current exceeds 10A, with the following SQL statement:
```sql
select * from meters where current > 10;
```
Please note that, all the data will be processed because no start time is specified. If we only want to process data for the past day, a time related condition can be added:
```sql
select * from meters where ts > now - 1d and current > 10;
```
The parameter `topic` is the name of the subscription. The client application must guarantee that the name is unique. However, it doesn't have to be globally unique because subscription is implemented in the APIs on the client side.
If the subscription named as `topic` doesn't exist, the parameter `restart` will be ignored. If the subscription named as `topic` has been created before by the client program, when the client program is restarted with the subscription named `topic`, parameter `restart` is used to determine whether to retrieve data from the beginning or from the last point where the subscription was broken.
If the value of `restart` is **true** (i.e. a non-zero value), data will be retrieved from the beginning. If it is **false** (i.e. zero), the data already consumed before will not be processed again.
The last parameter of `taos_subscribe` is the polling interval in units of millisecond. In sync mode, if the time difference between two continuous invocations to `taos_consume` is smaller than the interval specified by `taos_subscribe`, `taos_consume` will be blocked until the interval is reached. In async mode, this interval is the minimum interval between two invocations to the call back function.
The second to last parameter of `taos_subscribe` is used to pass arguments to the call back function. `taos_subscribe` doesn't process this parameter and simply passes it to the call back function. This parameter is simply ignored in sync mode.
After a subscription is created, its data can be consumed and processed. Shown below is the sample code to consume data in sync mode, in the else condition of `if (async)`.
```c
if (async) {
　　getchar();
} else while(1) {
　　TAOS_RES* res = taos_consume(tsub);
　　if (res == NULL) {
　　　　printf("failed to consume data.");
　　　　break;
　　} else {
　　　　print_result(res, blockFetch);
　　　　getchar();
　　}
}
```
In the above sample code in the else condition, there is an infinite loop. Each time carriage return is entered `taos_consume` is invoked. The return value of `taos_consume` is the selected result set. In the above sample, `print_result` is used to simplify the printing of the result set. It is similar to `taos_use_result`. Below is the implementation of `print_result`.
```c
void print_result(TAOS_RES* res, int blockFetch) {
　　TAOS_ROW row = NULL;
　　int num_fields = taos_num_fields(res);
　　TAOS_FIELD* fields = taos_fetch_fields(res);
　　int nRows = 0;
　　if (blockFetch) {
　　　　nRows = taos_fetch_block(res, &row);
　　　　for (int i = 0; i < nRows; i++) {
　　　　　　char temp[256];
　　　　　　taos_print_row(temp, row + i, fields, num_fields);
　　　　　　puts(temp);
　　　　}
　　} else {
　　　　while ((row = taos_fetch_row(res))) {
　　　　　　char temp[256];
　　　　　　taos_print_row(temp, row, fields, num_fields);
　　　　　　puts(temp);
　　　　　　nRows++;
　　　　}
　　}
　　printf("%d rows consumed.\n", nRows);
}
```
In the above code `taos_print_row` is used to process the data consumed. All matching rows are printed.
In async mode, consuming data is simpler as shown below.
```c
void subscribe_callback(TAOS_SUB* tsub, TAOS_RES *res, void* param, int code) {
　　print_result(res, *(int*)param);
}
```
`taos_unsubscribe` can be invoked to terminate a subscription.
```c
taos_unsubscribe(tsub, keep);
```
The second parameter `keep` is used to specify whether to keep the subscription progress on the client sde. If it is **false**, i.e. **0**, then subscription will be restarted from beginning regardless of the `restart` parameter's value when `taos_subscribe` is invoked again. The subscription progress information is stored in _{DataDir}/subscribe/_ , under which there is a file with the same name as `topic` for each subscription(Note: The default value of `DataDir` in the `taos.cfg` file is **/var/lib/taos/**. However, **/var/lib/taos/** does not exist on the Windows server. So you need to change the `DataDir` value to the corresponding existing directory."), the subscription will be restarted from the beginning if the corresponding progress file is removed.
Now let's see the effect of the above sample code, assuming below prerequisites have been done.
- The sample code has been downloaded to local system
- TDengine has been installed and launched properly on same system
- The database, STable, and subtables required in the sample code are ready
Launch the command below in the directory where the sample code resides to compile and start the program.
```bash
make
./subscribe -sql='select * from meters where current > 10;'
```
After the program is started, open another terminal and launch TDengine CLI `taos`, then use the below SQL commands to insert a row whose current is 12A into table **D1001**.
```sql
use test;
insert into D1001 values(now, 12, 220, 1);
```
Then, this row of data will be shown by the example program on the first terminal because its current exceeds 10A. More data can be inserted for you to observe the output of the example program.
## Examples
The example program below demonstrates how to subscribe, using connectors, to data rows in which current exceeds 10A.
### Prepare Data
```bash
# create database "power"
taos> create database power;
# use "power" as the database in following operations
taos> use power;
# create super table "meters"
taos> create table meters(ts timestamp, current float, voltage int, phase int) tags(location binary(64), groupId int);
# create tabes using the schema defined by super table "meters"
taos> create table d1001 using meters tags ("California.SanFrancisco", 2);
taos> create table d1002 using meters tags ("California.LoSangeles", 2);
# insert some rows
taos> insert into d1001 values("2020-08-15 12:00:00.000", 12, 220, 1),("2020-08-15 12:10:00.000", 12.3, 220, 2),("2020-08-15 12:20:00.000", 12.2, 220, 1);
taos> insert into d1002 values("2020-08-15 12:00:00.000", 9.9, 220, 1),("2020-08-15 12:10:00.000", 10.3, 220, 1),("2020-08-15 12:20:00.000", 11.2, 220, 1);
# filter out the rows in which current is bigger than 10A
taos> select * from meters where current > 10;
ts | current | voltage | phase | location | groupid |
===========================================================================================================
2020-08-15 12:10:00.000 | 10.30000 | 220 | 1 | California.LoSangeles | 2 |
2020-08-15 12:20:00.000 | 11.20000 | 220 | 1 | California.LoSangeles | 2 |
2020-08-15 12:00:00.000 | 12.00000 | 220 | 1 | California.SanFrancisco | 2 |
2020-08-15 12:10:00.000 | 12.30000 | 220 | 2 | California.SanFrancisco | 2 |
2020-08-15 12:20:00.000 | 12.20000 | 220 | 1 | California.SanFrancisco | 2 |
Query OK, 5 row(s) in set (0.004896s)
```
### Example Programs
<Tabs defaultValue="java" groupId="lang">
<TabItem label="Java" value="java">
<Java />
</TabItem>
<TabItem label="Python" value="Python">
<Python />
</TabItem>
{/* <TabItem label="Go" value="go">
<Go/>
</TabItem> */}
<TabItem label="Rust" value="rust">
<Rust />
</TabItem>
{/* <TabItem label="Node.js" value="nodejs">
<Node/>
</TabItem>
<TabItem label="C#" value="csharp">
<CSharp/>
</TabItem> */}
<TabItem label="C" value="c">
<CDemo />
</TabItem>
</Tabs>
### Run the Examples
The example programs first consume all historical data matching the criteria.
```bash
ts: 1597464000000 current: 12.0 voltage: 220 phase: 1 location: California.SanFrancisco groupid : 2
ts: 1597464600000 current: 12.3 voltage: 220 phase: 2 location: California.SanFrancisco groupid : 2
ts: 1597465200000 current: 12.2 voltage: 220 phase: 1 location: California.SanFrancisco groupid : 2
ts: 1597464600000 current: 10.3 voltage: 220 phase: 1 location: California.LoSangeles groupid : 2
ts: 1597465200000 current: 11.2 voltage: 220 phase: 1 location: California.LoSangeles groupid : 2
```
Next, use TDengine CLI to insert a new row.
```
# taos
taos> use power;
taos> insert into d1001 values(now, 12.4, 220, 1);
```
Because the current in the inserted row exceeds 10A, it will be consumed by the example program.
```
ts: 1651146662805 current: 12.4 voltage: 220 phase: 1 location: California.SanFrancisco groupid: 2
```
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---
sidebar_label: Cache
title: Cache
description: "Caching System inside TDengine"
sidebar_label: Caching
title: Caching
description: "This document describes the caching component of TDengine."
---
To achieve the purpose of high performance data writing and querying, TDengine employs a lot of caching technologies in both server side and client side.
TDengine uses various kinds of caching techniques to efficiently write and query data. This document describes the caching component of TDengine.
## Write Cache
The cache management policy in TDengine is First-In-First-Out (FIFO). FIFO is also known as insert driven cache management policy and it is different from read driven cache management, which is more commonly known as Least-Recently-Used (LRU). FIFO simply stores the latest data in cache and flushes the oldest data in cache to disk, when the cache usage reaches a threshold. In IoT use cases, it is the current state i.e. the latest or most recent data that is important. The cache policy in TDengine, like much of the design and architecture of TDengine, is based on the nature of IoT data.
TDengine uses an insert-driven cache management policy, known as first in, first out (FIFO). This policy differs from read-driven "least recently used (LRU)" cache management. A FIFO policy stores the latest data in cache and flushes the oldest data from cache to disk when the cache usage reaches a threshold. In IoT use cases, the most recent data or the current state is most important. The cache policy in TDengine, like much of the design and architecture of TDengine, is based on the nature of IoT data.
The memory space used by each vnode as write cache is determined when creating a database. Parameter `vgroups` and `buffer` can be used to specify the number of vnode and the size of write cache for each vnode when creating the database. Then, the total size of write cache for this database is `vgroups * buffer`.
When you create a database, you can configure the size of the write cache on each vnode. The **vgroups** parameter determines the number of vgroups that process data in the database, and the **buffer** parameter determines the size of the write cache for each vnode.
```sql
create database db0 vgroups 100 buffer 16MB
```
The above statement creates a database of 100 vnodes while each vnode has a write cache of 16MB.
Even though in theory it's always better to have a larger cache, the extra effect would be very minor once the size of cache grows beyond a threshold. So normally it's enough to use the default value of `buffer` parameter.
In theory, larger cache sizes are always better. However, at a certain point, it becomes impossible to improve performance by increasing cache size. In most scenarios, you can retain the default cache settings.
## Read Cache
When creating a database, it's also possible to specify whether to cache the latest data of each sub table, using parameter `cachelast`. There are 3 cases:
- 0: No cache for latest data
- 1: The last row of each table is cached, `last_row` function can benefit significantly from it
- 2: The latest non-NULL value of each column for each table is cached, `last` function can benefit very much when there is no `where`, `group by`, `order by` or `interval` clause
- 3: Bot hthe last row and the latest non-NULL value of each column for each table are cached, identical to the behavior of both 1 and 2 are set together
When you create a database, you can configure whether the latest data from every subtable is cached. To do so, set the *cachelast* parameter as follows:
- 0: Caching is disabled.
- 1: The latest row of data in each subtable is cached. This option significantly improves the performance of the `LAST_ROW` function
- 2: The latest non-null value in each column of each subtable is cached. This option significantly improves the performance of the `LAST` function in normal situations, such as WHERE, ORDER BY, GROUP BY, and INTERVAL statements.
- 3: Rows and columns are both cached. This option is equivalent to simultaneously enabling options 1 and 2.
## Meta Cache
## Metadata Cache
To process data writing and querying efficiently, each vnode caches the metadata that's already retrieved. Parameters `pages` and `pagesize` are used to specify the size of metadata cache for each vnode.
To improve query and write performance, each vnode caches the metadata that it receives. When you create a database, you can configure the size of the metadata cache through the *pages* and *pagesize* parameters.
```sql
create database db0 pages 128 pagesize 16kb
```
The above statement will create a database db0 each of whose vnode is allocated a meta cache of `128 * 16 KB = 2 MB` .
The preceding SQL statement creates 128 pages on each vnode in the `db0` database. Each page has a 16 KB metadata cache.
## File System Cache
TDengine utilizes WAL to provide basic reliability. The essential of WAL is to append data in a disk file, so the file system cache also plays an important role in the writing performance. Parameter `wal` can be used to specify the policy of writing WAL, there are 2 cases:
- 1: Write data to WAL without calling fsync, the data is actually written to the file system cache without flushing immediately, in this way you can get better write performance
- 2: Write data to WAL and invoke fsync, the data is immediately flushed to disk, in this way you can get higher reliability
TDengine implements data reliability by means of a write-ahead log (WAL). Writing data to the WAL is essentially writing data to the disk in an ordered, append-only manner. For this reason, the file system cache plays an important role in write performance. When you create a database, you can use the *wal* parameter to choose higher performance or higher reliability.
- 1: This option writes to the WAL but does not enable fsync. New data written to the WAL is stored in the file system cache but not written to disk. This provides better performance.
- 2: This option writes to the WAL and enables fsync. New data written to the WAL is immediately written to disk. This provides better data reliability.
## Client Cache
To improve the overall efficiency of processing data, besides the above caches, the core library `libtaos.so` (also referred to as `taosc`) which all client programs depend on also has its own cache. `taosc` caches the metadata of the databases, super tables, tables that the invoking client has accessed, plus other critical metadata such as the cluster topology.
In addition to the server-side caching discussed previously, the core client library `libtaos.so` also makes use of caching. TDengine Client caches the metadata of all databases, supertables, and subtables that it has accessed, as well as the cluster topology.
When multiple client programs are accessing a TDengine cluster, if one of the clients modifies some metadata, the cache may become invalid in other clients. If this case happens, the client programs need to "reset query cache" to invalidate the whole cache so that `taosc` is enforced to repull the metadata it needs to rebuild the cache.
If a client modifies certain metadata while multiple clients are simultaneously accessing a TDengine cluster, the metadata caches on each client may fail or become out of sync. If this occurs, run the `reset query cache` command on the affected clientsto force them to obtain fresh metadata and reset their caches.
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```c
{{#include docs/examples/c/subscribe_demo.c}}
```
\ No newline at end of file
{{#include docs/examples/c/tmq_example.c}}
```
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```java
{{#include docs/examples/java/src/main/java/com/taos/example/SubscribeDemo.java}}
{{#include docs/examples/java/src/main/java/com/taos/example/MetersDeserializer.java}}
{{#include docs/examples/java/src/main/java/com/taos/example/Meters.java}}
```
:::note
For now Java connector doesn't provide asynchronous subscription, but `TimerTask` can be used to achieve similar purpose.
:::
\ No newline at end of file
```java
{{#include docs/examples/java/src/main/java/com/taos/example/MetersDeserializer.java}}
```
```java
{{#include docs/examples/java/src/main/java/com/taos/example/Meters.java}}
```
\ No newline at end of file
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```py
{{#include docs/examples/python/subscribe_demo.py}}
```
\ No newline at end of file
{{#include docs/examples/python/tmq_example.py}}
```
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title: Developer Guide
---
To develop an application to process time-series data using TDengine, we recommend taking the following steps:
1. Choose the method to connect to TDengine. No matter what programming language you use, you can always use the REST interface to access TDengine, but you can also use connectors unique to each programming language.
2. Design the data model based on your own use cases. Learn the [concepts](/concept/) of TDengine including "one table for one data collection point" and the "super table" (STable) concept; learn about static labels, collected metrics, and subtables. Depending on the characteristics of your data and your requirements, you may decide to create one or more databases, and you should design the STable schema to fit your data.
Before creating an application to process time-series data with TDengine, consider the following:
1. Choose the method to connect to TDengine. TDengine offers a REST API that can be used with any programming language. It also has connectors for a variety of languages.
2. Design the data model based on your own use cases. Consider the main [concepts](/concept/) of TDengine, including "one table per data collection point" and the supertable. Learn about static labels, collected metrics, and subtables. Depending on the characteristics of your data and your requirements, you decide to create one or more databases and design a supertable schema that fit your data.
3. Decide how you will insert data. TDengine supports writing using standard SQL, but also supports schemaless writing, so that data can be written directly without creating tables manually.
4. Based on business requirements, find out what SQL query statements need to be written. You may be able to repurpose any existing SQL.
5. If you want to run real-time analysis based on time series data, including various dashboards, it is recommended that you use the TDengine continuous query feature instead of deploying complex streaming processing systems such as Spark or Flink.
5. If you want to run real-time analysis based on time series data, including various dashboards, use the TDengine stream processing component instead of deploying complex systems such as Spark or Flink.
6. If your application has modules that need to consume inserted data, and they need to be notified when new data is inserted, it is recommended that you use the data subscription function provided by TDengine without the need to deploy Kafka.
7. In many use cases (such as fleet management), the application needs to obtain the latest status of each data collection point. It is recommended that you use the cache function of TDengine instead of deploying Redis separately.
8. If you find that the SQL functions of TDengine cannot meet your requirements, then you can use user-defined functions to solve the problem.
......
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---
title: Deployment
---
## Prerequisites
### Step 1
The FQDN of all hosts must be setup properly. All FQDNs need to be configured in the /etc/hosts file on each host. You must confirm that each FQDN can be accessed from any other host, you can do this by using the `ping` command.
The command `hostname -f` can be executed to get the hostname on any host. `ping <FQDN>` command can be executed on each host to check whether any other host is accessible from it. If any host is not accessible, the network configuration, like /etc/hosts or DNS configuration, needs to be checked and revised, to make any two hosts accessible to each other.
:::note
- The host where the client program runs also needs to be configured properly for FQDN, to make sure all hosts for client or server can be accessed from any other. In other words, the hosts where the client is running are also considered as a part of the cluster.
- Please ensure that your firewall rules do not block TCP/UDP on ports 6030-6042 on all hosts in the cluster.
:::
### Step 2
If any previous version of TDengine has been installed and configured on any host, the installation needs to be removed and the data needs to be cleaned up. For details about uninstalling please refer to [Install and Uninstall](/operation/pkg-install). To clean up the data, please use `rm -rf /var/lib/taos/\*` assuming the `dataDir` is configured as `/var/lib/taos`.
:::note
As a best practice, before cleaning up any data files or directories, please ensure that your data has been backed up correctly, if required by your data integrity, backup, security, or other standard operating protocols (SOP).
:::
### Step 3
Now it's time to install TDengine on all hosts but without starting `taosd`. Note that the versions on all hosts should be same. If you are prompted to input the existing TDengine cluster, simply press carriage return to ignore the prompt. `install.sh -e no` can also be used to disable this prompt. For details please refer to [Install and Uninstall](/operation/pkg-install).
### Step 4
Now each physical node (referred to, hereinafter, as `dnode` which is an abbreviation for "data node") of TDengine needs to be configured properly. Please note that one dnode doesn't stand for one host. Multiple TDengine dnodes can be started on a single host as long as they are configured properly without conflicting. More specifically each instance of the configuration file `taos.cfg` stands for a dnode. Assuming the first dnode of TDengine cluster is "h1.taosdata.com:6030", its `taos.cfg` is configured as following.
```c
// firstEp is the end point to connect to when any dnode starts
firstEp h1.taosdata.com:6030
// must be configured to the FQDN of the host where the dnode is launched
fqdn h1.taosdata.com
// the port used by the dnode, default is 6030
serverPort 6030
// only necessary when replica is configured to an even number
#arbitrator ha.taosdata.com:6042
```
`firstEp` and `fqdn` must be configured properly. In `taos.cfg` of all dnodes in TDengine cluster, `firstEp` must be configured to point to same address, i.e. the first dnode of the cluster. `fqdn` and `serverPort` compose the address of each node itself. If you want to start multiple TDengine dnodes on a single host, please make sure all other configurations like `dataDir`, `logDir`, and other resources related parameters are not conflicting.
For all the dnodes in a TDengine cluster, the below parameters must be configured exactly the same, any node whose configuration is different from dnodes already in the cluster can't join the cluster.
| **#** | **Parameter** | **Definition** |
| ----- | -------------- | ------------------------------------------------------------- |
| 1 | statusInterval | The time interval for which dnode reports its status to mnode |
| 2 | timezone | Time Zone where the server is located |
| 3 | locale | Location code of the system |
| 4 | charset | Character set of the system |
## Start Cluster
In the following example we assume that first dnode has FQDN h1.taosdata.com and the second dnode has FQDN h2.taosdata.com.
### Start The First DNODE
Start the first dnode following the instructions in [Get Started](/get-started/). Then launch TDengine CLI `taos` and execute command `show dnodes`, the output is as following for example:
```
Welcome to the TDengine shell from Linux, Client Version:3.0.0.0
Copyright (c) 2022 by TAOS Data, Inc. All rights reserved.
Server is Enterprise trial Edition, ver:3.0.0.0 and will never expire.
taos> show dnodes;
id | endpoint | vnodes | support_vnodes | status | create_time | note |
============================================================================================================================================
1 | h1.taosdata.com:6030 | 0 | 1024 | ready | 2022-07-16 10:50:42.673 | |
Query OK, 1 rows affected (0.007984s)
taos>
```
From the above output, it is shown that the end point of the started dnode is "h1.taosdata.com:6030", which is the `firstEp` of the cluster.
### Start Other DNODEs
There are a few steps necessary to add other dnodes in the cluster.
Let's assume we are starting the second dnode with FQDN, h2.taosdata.com. Firstly we make sure the configuration is correct.
```c
// firstEp is the end point to connect to when any dnode starts
firstEp h1.taosdata.com:6030
// must be configured to the FQDN of the host where the dnode is launched
fqdn h2.taosdata.com
// the port used by the dnode, default is 6030
serverPort 6030
```
Secondly, we can start `taosd` as instructed in [Get Started](/get-started/).
Then, on the first dnode i.e. h1.taosdata.com in our example, use TDengine CLI `taos` to execute the following command to add the end point of the dnode in the cluster. In the command "fqdn:port" should be quoted using double quotes.
```sql
CREATE DNODE "h2.taos.com:6030";
```
Then on the first dnode h1.taosdata.com, execute `show dnodes` in `taos` to show whether the second dnode has been added in the cluster successfully or not.
```sql
SHOW DNODES;
```
If the status of the newly added dnode is offline, please check:
- Whether the `taosd` process is running properly or not
- In the log file `taosdlog.0` to see whether the fqdn and port are correct
The above process can be repeated to add more dnodes in the cluster.
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---
sidebar_label: Operation
title: Manage DNODEs
---
The previous section, [Deployment],(/cluster/deploy) showed you how to deploy and start a cluster from scratch. Once a cluster is ready, the status of dnode(s) in the cluster can be shown at any time. Dnodes can be managed from the TDengine CLI. New dnode(s) can be added to scale out the cluster, an existing dnode can be removed and you can even perform load balancing manually, if necessary.
:::note
All the commands introduced in this chapter must be run in the TDengine CLI - `taos`. Note that sometimes it is necessary to use root privilege.
:::
## Show DNODEs
The below command can be executed in TDengine CLI `taos` to list all dnodes in the cluster, including ID, end point (fqdn:port), status (ready, offline), number of vnodes, number of free vnodes and so on. We recommend executing this command after adding or removing a dnode.
```sql
SHOW DNODES;
```
Below is the example output of this command.
```
taos> show dnodes;
id | end_point | vnodes | cores | status | role | create_time | offline reason |
======================================================================================================================================
1 | localhost:6030 | 9 | 8 | ready | any | 2022-04-15 08:27:09.359 | |
Query OK, 1 row(s) in set (0.008298s)
```
## Show VGROUPs
To utilize system resources efficiently and provide scalability, data sharding is required. The data of each database is divided into multiple shards and stored in multiple vnodes. These vnodes may be located on different dnodes. One way of scaling out is to add more vnodes on dnodes. Each vnode can only be used for a single DB, but one DB can have multiple vnodes. The allocation of vnode is scheduled automatically by mnode based on system resources of the dnodes.
Launch TDengine CLI `taos` and execute below command:
```sql
USE SOME_DATABASE;
SHOW VGROUPS;
```
The output is like below:
taos> use db;
Database changed.
taos> show vgroups;
vgId | tables | status | onlines | v1_dnode | v1_status | compacting |
==========================================================================================
14 | 38000 | ready | 1 | 1 | leader | 0 |
15 | 38000 | ready | 1 | 1 | leader | 0 |
16 | 38000 | ready | 1 | 1 | leader | 0 |
17 | 38000 | ready | 1 | 1 | leader | 0 |
18 | 37001 | ready | 1 | 1 | leader | 0 |
19 | 37000 | ready | 1 | 1 | leader | 0 |
20 | 37000 | ready | 1 | 1 | leader | 0 |
21 | 37000 | ready | 1 | 1 | leader | 0 |
Query OK, 8 row(s) in set (0.001154s)
````
## Add DNODE
Launch TDengine CLI `taos` and execute the command below to add the end point of a new dnode into the EPI (end point) list of the cluster. "fqdn:port" must be quoted using double quotes.
```sql
CREATE DNODE "fqdn:port";
````
The example output is as below:
```
taos> create dnode "localhost:7030";
Query OK, 0 of 0 row(s) in database (0.008203s)
taos> show dnodes;
id | end_point | vnodes | cores | status | role | create_time | offline reason |
======================================================================================================================================
1 | localhost:6030 | 9 | 8 | ready | any | 2022-04-15 08:27:09.359 | |
2 | localhost:7030 | 0 | 0 | offline | any | 2022-04-19 08:11:42.158 | status not received |
Query OK, 2 row(s) in set (0.001017s)
```
It can be seen that the status of the new dnode is "offline". Once the dnode is started and connects to the firstEp of the cluster, you can execute the command again and get the example output below. As can be seen, both dnodes are in "ready" status.
```
taos> show dnodes;
id | end_point | vnodes | cores | status | role | create_time | offline reason |
======================================================================================================================================
1 | localhost:6030 | 3 | 8 | ready | any | 2022-04-15 08:27:09.359 | |
2 | localhost:7030 | 6 | 8 | ready | any | 2022-04-19 08:14:59.165 | |
Query OK, 2 row(s) in set (0.001316s)
```
## Drop DNODE
Launch TDengine CLI `taos` and execute the command below to drop or remove a dnode from the cluster. In the command, you can get `dnodeId` from `show dnodes`.
```sql
DROP DNODE "fqdn:port";
```
or
```sql
DROP DNODE dnodeId;
```
The example output is below：
```
taos> show dnodes;
id | end_point | vnodes | cores | status | role | create_time | offline reason |
======================================================================================================================================
1 | localhost:6030 | 9 | 8 | ready | any | 2022-04-15 08:27:09.359 | |
2 | localhost:7030 | 0 | 0 | offline | any | 2022-04-19 08:11:42.158 | status not received |
Query OK, 2 row(s) in set (0.001017s)
taos> drop dnode 2;
Query OK, 0 of 0 row(s) in database (0.000518s)
taos> show dnodes;
id | end_point | vnodes | cores | status | role | create_time | offline reason |
======================================================================================================================================
1 | localhost:6030 | 9 | 8 | ready | any | 2022-04-15 08:27:09.359 | |
Query OK, 1 row(s) in set (0.001137s)
```
In the above example, when `show dnodes` is executed the first time, two dnodes are shown. After `drop dnode 2` is executed, you can execute `show dnodes` again and it can be seen that only the dnode with ID 1 is still in the cluster.
:::note
- Once a dnode is dropped, it can't rejoin the cluster. To rejoin, the dnode needs to deployed again after cleaning up the data directory. Before dropping a dnode, the data belonging to the dnode MUST be migrated/backed up according to your data retention, data security or other SOPs.
- Please note that `drop dnode` is different from stopping `taosd` process. `drop dnode` just removes the dnode out of TDengine cluster. Only after a dnode is dropped, can the corresponding `taosd` process be stopped.
- Once a dnode is dropped, other dnodes in the cluster will be notified of the drop and will not accept the request from the dropped dnode.
- dnodeID is allocated automatically and can't be manually modified. dnodeID is generated in ascending order without duplication.
:::
docs/en/10-deployment/01-deploy.md 0 → 100644
浏览文件 @ 324973bd
---
sidebar_label: Manual Deployment
title: Manual Deployment and Management
---
## Prerequisites
### Step 0
The FQDN of all hosts must be setup properly. For e.g. FQDNs may have to be configured in the /etc/hosts file on each host. You must confirm that each FQDN can be accessed from any other host. For e.g. you can do this by using the `ping` command. If you have a DNS server on your network, contact your network administrator for assistance.
### Step 1
If any previous version of TDengine has been installed and configured on any host, the installation needs to be removed and the data needs to be cleaned up. For details about uninstalling please refer to [Install and Uninstall](/operation/pkg-install). To clean up the data, please use `rm -rf /var/lib/taos/\*` assuming the `dataDir` is configured as `/var/lib/taos`.
:::note
FQDN information is written to file. If you have started TDengine without configuring or changing the FQDN, ensure that data is backed up or no longer needed before running the `rm -rf /var/lib\taos/\*` command.
:::
:::note
- The host where the client program runs also needs to be configured properly for FQDN, to make sure all hosts for client or server can be accessed from any other. In other words, the hosts where the client is running are also considered as a part of the cluster.
:::
### Step 2
- Please ensure that your firewall rules do not block TCP/UDP on ports 6030-6042 on all hosts in the cluster.
### Step 3
Now it's time to install TDengine on all hosts but without starting `taosd`. Note that the versions on all hosts should be same. If you are prompted to input the existing TDengine cluster, simply press carriage return to ignore the prompt.
### Step 4
Now each physical node (referred to, hereinafter, as `dnode` which is an abbreviation for "data node") of TDengine needs to be configured properly.
To get the hostname on any host, the command `hostname -f` can be executed.
`ping <FQDN>` command can be executed on each host to check whether any other host is accessible from it. If any host is not accessible, the network configuration, like /etc/hosts or DNS configuration, needs to be checked and revised, to make any two hosts accessible to each other. Hosts that are not accessible to each other cannot form a cluster.
On the physical machine running the application, ping the dnode that is running taosd. If the dnode is not accessible, the application cannot connect to taosd. In this case, verify the DNS and hosts settings on the physical node running the application.
The end point of each dnode is the output hostname and port, such as h1.taosdata.com:6030.
### Step 5
Modify the TDengine configuration file `/etc/taos/taos.cfg` on each node. Assuming the first dnode of TDengine cluster is "h1.taosdata.com:6030", its `taos.cfg` is configured as following.
```c
// firstEp is the end point to connect to when any dnode starts
firstEp h1.taosdata.com:6030
// must be configured to the FQDN of the host where the dnode is launched
fqdn h1.taosdata.com
// the port used by the dnode, default is 6030
serverPort 6030
```
`firstEp` and `fqdn` must be configured properly. In `taos.cfg` of all dnodes in TDengine cluster, `firstEp` must be configured to point to same address, i.e. the first dnode of the cluster. `fqdn` and `serverPort` compose the address of each node itself. Retain the default values for other parameters.
For all the dnodes in a TDengine cluster, the below parameters must be configured exactly the same, any node whose configuration is different from dnodes already in the cluster can't join the cluster.
| **#** | **Parameter** | **Definition** |
| ----- | ------------------ | ------------------------------------------- |
| 1 | statusInterval | The interval by which dnode reports its status to mnode |
| 2 | timezone | Timezone |
| 3 | locale | System region and encoding |
| 4 | charset | Character set |
## Start Cluster
The first dnode can be started following the instructions in [Get Started](/get-started/). Then TDengine CLI `taos` can be launched to execute command `show dnodes`, the output is as following for example:
```
taos> show dnodes;
id | endpoint | vnodes | support_vnodes | status | create_time | note |
============================================================================================================================================
1 | h1.taosdata.com:6030 | 0 | 1024 | ready | 2022-07-16 10:50:42.673 | |
Query OK, 1 rows affected (0.007984s)
```
From the above output, it is shown that the end point of the started dnode is "h1.taosdata.com:6030", which is the `firstEp` of the cluster.
## Add DNODE
There are a few steps necessary to add other dnodes in the cluster.
Second, we can start `taosd` as instructed in [Get Started](/get-started/).
Then, on the first dnode i.e. h1.taosdata.com in our example, use TDengine CLI `taos` to execute the following command:
```sql
CREATE DNODE "h2.taos.com:6030";
````
This adds the end point of the new dnode (from Step 4) into the end point list of the cluster. In the command "fqdn:port" should be quoted using double quotes. Change `"h2.taos.com:6030"` to the end point of your new dnode.
Then on the first dnode h1.taosdata.com, execute `show dnodes` in `taos`
```sql
SHOW DNODES;
```
to show whether the second dnode has been added in the cluster successfully or not. If the status of the newly added dnode is offline, please check:
- Whether the `taosd` process is running properly or not
- In the log file `taosdlog.0` to see whether the fqdn and port are correct and add the correct end point if not.
The above process can be repeated to add more dnodes in the cluster.
:::tip
Any node that is in the cluster and online can be the firstEp of new nodes.
Nodes use the firstEp parameter only when joining a cluster for the first time. After a node has joined the cluster, it stores the latest mnode in its end point list and no longer makes use of firstEp.
However, firstEp is used by clients that connect to the cluster. For example, if you run `taos shell` without arguments, it connects to the firstEp by default.
Two dnodes that are launched without a firstEp value operate independently of each other. It is not possible to add one dnode to the other dnode and form a cluster. It is also not possible to form two independent clusters into a new cluster.
:::
## Show DNODEs
The below command can be executed in TDengine CLI `taos`
```sql
SHOW DNODES;
```
to list all dnodes in the cluster, including ID, end point (fqdn:port), status (ready, offline), number of vnodes, number of free vnodes and so on. We recommend executing this command after adding or removing a dnode.
Below is the example output of this command.
```
taos> show dnodes;
id | endpoint | vnodes | support_vnodes | status | create_time | note |
============================================================================================================================================
1 | trd01:6030 | 100 | 1024 | ready | 2022-07-15 16:47:47.726 | |
Query OK, 1 rows affected (0.006684s)
```
## Show VGROUPs
To utilize system resources efficiently and provide scalability, data sharding is required. The data of each database is divided into multiple shards and stored in multiple vnodes. These vnodes may be located on different dnodes. One way of scaling out is to add more vnodes on dnodes. Each vnode can only be used for a single DB, but one DB can have multiple vnodes. The allocation of vnode is scheduled automatically by mnode based on system resources of the dnodes.
Launch TDengine CLI `taos` and execute below command:
```sql
USE SOME_DATABASE;
SHOW VGROUPS;
```
The example output is below:
```
taos> use db;
Database changed.
taos> show vgroups;
vgroup_id | db_name | tables | v1_dnode | v1_status | v2_dnode | v2_status | v3_dnode | v3_status | status | nfiles | file_size | tsma |
================================================================================================================================================================================================
2 | db | 0 | 1 | leader | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 0 |
3 | db | 0 | 1 | leader | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 0 |
4 | db | 0 | 1 | leader | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 0 |
Query OK, 8 row(s) in set (0.001154s)
```
## Drop DNODE
Before running the TDengine CLI, ensure that the taosd process has been stopped on the dnode that you want to delete.
```sql
DROP DNODE "fqdn:port";
```
or
```sql
DROP DNODE dnodeId;
```
to drop or remove a dnode from the cluster. In the command, you can get `dnodeId` from `show dnodes`.
:::warning
- Once a dnode is dropped, it can't rejoin the cluster. To rejoin, the dnode needs to deployed again after cleaning up the data directory. Before dropping a dnode, the data belonging to the dnode MUST be migrated/backed up according to your data retention, data security or other SOPs.
- Please note that `drop dnode` is different from stopping `taosd` process. `drop dnode` just removes the dnode out of TDengine cluster. Only after a dnode is dropped, can the corresponding `taosd` process be stopped.
- Once a dnode is dropped, other dnodes in the cluster will be notified of the drop and will not accept the request from the dropped dnode.
- dnodeID is allocated automatically and can't be manually modified. dnodeID is generated in ascending order without duplication.
:::
docs/en/10-deployment/03-k8s.md 0 → 100644
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---
sidebar_label: Kubernetes
title: Deploying a TDengine Cluster in Kubernetes
---
TDengine is a cloud-native time-series database that can be deployed on Kubernetes. This document gives a step-by-step description of how you can use YAML files to create a TDengine cluster and introduces common operations for TDengine in a Kubernetes environment.
## Prerequisites
Before deploying TDengine on Kubernetes, perform the following:
* Install and configure minikube, kubectl, and helm.
* Install and deploy Kubernetes and ensure that it can be accessed and used normally. Update any container registries or other services as necessary.
You can download the configuration files in this document from [GitHub](https://github.com/taosdata/TDengine-Operator/tree/3.0/src/tdengine).
## Configure the service
Create a service configuration file named `taosd-service.yaml`. Record the value of `metadata.name` (in this example, `taos`) for use in the next step. Add the ports required by TDengine:
```yaml
---
apiVersion: v1
kind: Service
metadata:
name: "taosd"
labels:
app: "tdengine"
spec:
ports:
- name: tcp6030
- protocol: "TCP"
port: 6030
- name: tcp6041
- protocol: "TCP"
port: 6041
selector:
app: "tdengine"
```
## Configure the service as StatefulSet
Configure the TDengine service as a StatefulSet.
Create the `tdengine.yaml` file and set `replicas` to 3. In this example, the region is set to Asia/Shanghai and 10 GB of standard storage are allocated per node. You can change the configuration based on your environment and business requirements.
```yaml
---
apiVersion: apps/v1
kind: StatefulSet
metadata:
name: "tdengine"
labels:
app: "tdengine"
spec:
serviceName: "taosd"
replicas: 3
updateStrategy:
type: RollingUpdate
selector:
matchLabels:
app: "tdengine"
template:
metadata:
name: "tdengine"
labels:
app: "tdengine"
spec:
containers:
- name: "tdengine"
image: "tdengine/tdengine:3.0.0.0"
imagePullPolicy: "IfNotPresent"
ports:
- name: tcp6030
- protocol: "TCP"
containerPort: 6030
- name: tcp6041
- protocol: "TCP"
containerPort: 6041
env:
# POD_NAME for FQDN config
- name: POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name
# SERVICE_NAME and NAMESPACE for fqdn resolve
- name: SERVICE_NAME
value: "taosd"
- name: STS_NAME
value: "tdengine"
- name: STS_NAMESPACE
valueFrom:
fieldRef:
fieldPath: metadata.namespace
# TZ for timezone settings, we recommend to always set it.
- name: TZ
value: "Asia/Shanghai"
# TAOS_ prefix will configured in taos.cfg, strip prefix and camelCase.
- name: TAOS_SERVER_PORT
value: "6030"
# Must set if you want a cluster.
- name: TAOS_FIRST_EP
value: "$(STS_NAME)-0.$(SERVICE_NAME).$(STS_NAMESPACE).svc.cluster.local:$(TAOS_SERVER_PORT)"
# TAOS_FQND should always be setted in k8s env.
- name: TAOS_FQDN
value: "$(POD_NAME).$(SERVICE_NAME).$(STS_NAMESPACE).svc.cluster.local"
volumeMounts:
- name: taosdata
mountPath: /var/lib/taos
readinessProbe:
exec:
command:
- taos-check
initialDelaySeconds: 5
timeoutSeconds: 5000
livenessProbe:
exec:
command:
- taos-check
initialDelaySeconds: 15
periodSeconds: 20
volumeClaimTemplates:
- metadata:
name: taosdata
spec:
accessModes:
- "ReadWriteOnce"
storageClassName: "standard"
resources:
requests:
storage: "10Gi"
```
## Use kubectl to deploy TDengine
Run the following commands:
```bash
kubectl apply -f taosd-service.yaml
kubectl apply -f tdengine.yaml
```
The preceding configuration generates a TDengine cluster with three nodes in which dnodes are automatically configured. You can run the `show dnodes` command to query the nodes in the cluster:
```bash
kubectl exec -i -t tdengine-0 -- taos -s "show dnodes"
kubectl exec -i -t tdengine-1 -- taos -s "show dnodes"
kubectl exec -i -t tdengine-2 -- taos -s "show dnodes"
```
The output is as follows:
```
taos> show dnodes
id | endpoint | vnodes | support_vnodes | status | create_time | note |
============================================================================================================================================
1 | tdengine-0.taosd.default.sv... | 0 | 256 | ready | 2022-08-10 13:14:57.285 | |
2 | tdengine-1.taosd.default.sv... | 0 | 256 | ready | 2022-08-10 13:15:11.302 | |
3 | tdengine-2.taosd.default.sv... | 0 | 256 | ready | 2022-08-10 13:15:23.290 | |
Query OK, 3 rows in database (0.003655s)
```
## Enable port forwarding
The kubectl port forwarding feature allows applications to access the TDengine cluster running on Kubernetes.
```
kubectl port-forward tdengine-0 6041:6041 &
```
Use curl to verify that the TDengine REST API is working on port 6041:
```
$ curl -u root:taosdata -d "show databases" 127.0.0.1:6041/rest/sql
Handling connection for 6041
{"code":0,"column_meta":[["name","VARCHAR",64],["create_time","TIMESTAMP",8],["vgroups","SMALLINT",2],["ntables","BIGINT",8],["replica","TINYINT",1],["strict","VARCHAR",4],["duration","VARCHAR",10],["keep","VARCHAR",32],["buffer","INT",4],["pagesize","INT",4],["pages","INT",4],["minrows","INT",4],["maxrows","INT",4],["comp","TINYINT",1],["precision","VARCHAR",2],["status","VARCHAR",10],["retention","VARCHAR",60],["single_stable","BOOL",1],["cachemodel","VARCHAR",11],["cachesize","INT",4],["wal_level","TINYINT",1],["wal_fsync_period","INT",4],["wal_retention_period","INT",4],["wal_retention_size","BIGINT",8],["wal_roll_period","INT",4],["wal_segment_size","BIGINT",8]],"data":[["information_schema",null,null,16,null,null,null,null,null,null,null,null,null,null,null,"ready",null,null,null,null,null,null,null,null,null,null],["performance_schema",null,null,10,null,null,null,null,null,null,null,null,null,null,null,"ready",null,null,null,null,null,null,null,null,null,null]],"rows":2}
```
## Enable the dashboard for visualization
The minikube dashboard command enables visualized cluster management.
```
$ minikube dashboard
* Verifying dashboard health ...
* Launching proxy ...
* Verifying proxy health ...
* Opening http://127.0.0.1:46617/api/v1/namespaces/kubernetes-dashboard/services/http:kubernetes-dashboard:/proxy/ in your default browser...
http://127.0.0.1:46617/api/v1/namespaces/kubernetes-dashboard/services/http:kubernetes-dashboard:/proxy/
```
In some public clouds, minikube cannot be remotely accessed if it is bound to 127.0.0.1. In this case, use the kubectl proxy command to map the port to 0.0.0.0. Then, you can access the dashboard by using a web browser to open the dashboard URL above on the public IP address and port of the virtual machine.
```
$ kubectl proxy --accept-hosts='^.*$' --address='0.0.0.0'
```
## Scaling Out Your Cluster
TDengine clusters can scale automatically:
```bash
kubectl scale statefulsets tdengine --replicas=4
```
The preceding command increases the number of replicas to 4. After running this command, query the pod status:
```bash
kubectl get pods -l app=tdengine
```
The output is as follows:
```
NAME READY STATUS RESTARTS AGE
tdengine-0 1/1 Running 0 161m
tdengine-1 1/1 Running 0 161m
tdengine-2 1/1 Running 0 32m
tdengine-3 1/1 Running 0 32m
```
The status of all pods is Running. Once the pod status changes to Ready, you can check the dnode status:
```bash
kubectl exec -i -t tdengine-3 -- taos -s "show dnodes"
```
The following output shows that the TDengine cluster has been expanded to 4 replicas:
```
taos> show dnodes
id | endpoint | vnodes | support_vnodes | status | create_time | note |
============================================================================================================================================
1 | tdengine-0.taosd.default.sv... | 0 | 256 | ready | 2022-08-10 13:14:57.285 | |
2 | tdengine-1.taosd.default.sv... | 0 | 256 | ready | 2022-08-10 13:15:11.302 | |
3 | tdengine-2.taosd.default.sv... | 0 | 256 | ready | 2022-08-10 13:15:23.290 | |
4 | tdengine-3.taosd.default.sv... | 0 | 256 | ready | 2022-08-10 13:33:16.039 | |
Query OK, 4 rows in database (0.008377s)
```
## Scaling In Your Cluster
When you scale in a TDengine cluster, your data is migrated to different nodes. You must run the drop dnodes command in TDengine to remove dnodes before scaling in your Kubernetes environment.
Note: In a Kubernetes StatefulSet service, the newest pods are always removed first. For this reason, when you scale in your TDengine cluster, ensure that you drop the newest dnodes.
```
$ kubectl exec -i -t tdengine-0 -- taos -s "drop dnode 4"
```
```bash
$ kubectl exec -it tdengine-0 -- taos -s "show dnodes"
taos> show dnodes
id | endpoint | vnodes | support_vnodes | status | create_time | note |
============================================================================================================================================
1 | tdengine-0.taosd.default.sv... | 0 | 256 | ready | 2022-08-10 13:14:57.285 | |
2 | tdengine-1.taosd.default.sv... | 0 | 256 | ready | 2022-08-10 13:15:11.302 | |
3 | tdengine-2.taosd.default.sv... | 0 | 256 | ready | 2022-08-10 13:15:23.290 | |
Query OK, 3 rows in database (0.004861s)
```
Verify that the dnode have been successfully removed by running the `kubectl exec -i -t tdengine-0 -- taos -s "show dnodes"` command. Then run the following command to remove the pod:
```
kubectl scale statefulsets tdengine --replicas=3
```
The newest pod in the deployment is removed. Run the `kubectl get pods -l app=tdengine` command to query the pod status:
```
$ kubectl get pods -l app=tdengine
NAME READY STATUS RESTARTS AGE
tdengine-0 1/1 Running 0 4m7s
tdengine-1 1/1 Running 0 3m55s
tdengine-2 1/1 Running 0 2m28s
```
After the pod has been removed, manually delete the PersistentVolumeClaim (PVC). Otherwise, future scale-outs will attempt to use existing data.
```bash
$ kubectl delete pvc taosdata-tdengine-3
```
Your cluster has now been safely scaled in, and you can scale it out again as necessary.
```bash
$ kubectl scale statefulsets tdengine --replicas=4
statefulset.apps/tdengine scaled
it@k8s-2:~/TDengine-Operator/src/tdengine$ kubectl get pods -l app=tdengine
NAME READY STATUS RESTARTS AGE
tdengine-0 1/1 Running 0 35m
tdengine-1 1/1 Running 0 34m
tdengine-2 1/1 Running 0 12m
tdengine-3 0/1 ContainerCreating 0 4s
it@k8s-2:~/TDengine-Operator/src/tdengine$ kubectl get pods -l app=tdengine
NAME READY STATUS RESTARTS AGE
tdengine-0 1/1 Running 0 35m
tdengine-1 1/1 Running 0 34m
tdengine-2 1/1 Running 0 12m
tdengine-3 0/1 Running 0 7s
it@k8s-2:~/TDengine-Operator/src/tdengine$ kubectl exec -it tdengine-0 -- taos -s "show dnodes"
taos> show dnodes
id | endpoint | vnodes | support_vnodes | status | create_time | offline reason |
======================================================================================================================================
1 | tdengine-0.taosd.default.sv... | 0 | 4 | ready | 2022-07-25 17:38:49.012 | |
2 | tdengine-1.taosd.default.sv... | 1 | 4 | ready | 2022-07-25 17:39:01.517 | |
5 | tdengine-2.taosd.default.sv... | 0 | 4 | ready | 2022-07-25 18:01:36.479 | |
6 | tdengine-3.taosd.default.sv... | 0 | 4 | ready | 2022-07-25 18:13:54.411 | |
Query OK, 4 row(s) in set (0.001348s)
```
## Remove a TDengine Cluster
To fully remove a TDengine cluster, you must delete its statefulset, svc, configmap, and pvc entries:
```bash
kubectl delete statefulset -l app=tdengine
kubectl delete svc -l app=tdengine
kubectl delete pvc -l app=tdengine
kubectl delete configmap taoscfg
```
## Troubleshooting
### Error 1
If you remove a pod without first running `drop dnode`, some TDengine nodes will go offline.
```
$ kubectl exec -it tdengine-0 -- taos -s "show dnodes"
taos> show dnodes
id | endpoint | vnodes | support_vnodes | status | create_time | offline reason |
======================================================================================================================================
1 | tdengine-0.taosd.default.sv... | 0 | 4 | ready | 2022-07-25 17:38:49.012 | |
2 | tdengine-1.taosd.default.sv... | 1 | 4 | ready | 2022-07-25 17:39:01.517 | |
5 | tdengine-2.taosd.default.sv... | 0 | 4 | offline | 2022-07-25 18:01:36.479 | status msg timeout |
6 | tdengine-3.taosd.default.sv... | 0 | 4 | offline | 2022-07-25 18:13:54.411 | status msg timeout |
Query OK, 4 row(s) in set (0.001323s)
```
### Error 2
If the number of nodes after a scale-in is less than the value of the replica parameter, the cluster will go down:
Create a database with replica set to 2 and add data.
```bash
kubectl exec -i -t tdengine-0 -- \
taos -s \
"create database if not exists test replica 2;
use test;
create table if not exists t1(ts timestamp, n int);
insert into t1 values(now, 1)(now+1s, 2);"
```
Scale in to one node:
```bash
kubectl scale statefulsets tdengine --replicas=1
```
In the TDengine CLI, you can see that no database operations succeed:
```
taos> show dnodes;
id | end_point | vnodes | cores | status | role | create_time | offline reason |
======================================================================================================================================
1 | tdengine-0.taosd.default.sv... | 2 | 40 | ready | any | 2021-06-01 15:55:52.562 | |
2 | tdengine-1.taosd.default.sv... | 1 | 40 | offline | any | 2021-06-01 15:56:07.212 | status msg timeout |
Query OK, 2 row(s) in set (0.000845s)
taos> show dnodes;
id | end_point | vnodes | cores | status | role | create_time | offline reason |
======================================================================================================================================
1 | tdengine-0.taosd.default.sv... | 2 | 40 | ready | any | 2021-06-01 15:55:52.562 | |
2 | tdengine-1.taosd.default.sv... | 1 | 40 | offline | any | 2021-06-01 15:56:07.212 | status msg timeout |
Query OK, 2 row(s) in set (0.000837s)
taos> use test;
Database changed.
taos> insert into t1 values(now, 3);
DB error: Unable to resolve FQDN (0.013874s)
```
docs/en/10-deployment/05-helm.md 0 → 100644
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---
sidebar_label: Helm
title: Use Helm to deploy TDengine
---
Helm is a package manager for Kubernetes that can provide more capabilities in deploying on Kubernetes.
## Install Helm
```bash
curl -fsSL -o get_helm.sh \
https://raw.githubusercontent.com/helm/helm/master/scripts/get-helm-3
chmod +x get_helm.sh
./get_helm.sh
```
Helm uses the kubectl and kubeconfig configurations to perform Kubernetes operations. For more information, see the Rancher configuration for Kubernetes installation.
## Install TDengine Chart
To use TDengine Chart, download it from GitHub:
```bash
wget https://github.com/taosdata/TDengine-Operator/raw/3.0/helm/tdengine-3.0.0.tgz
```
Query the storageclass of your Kubernetes deployment:
```bash
kubectl get storageclass
```
With minikube, the default value is standard.
Use Helm commands to install TDengine:
```bash
helm install tdengine tdengine-3.0.0.tgz \
--set storage.className=<your storage class name>
```
You can configure a small storage size in minikube to ensure that your deployment does not exceed your available disk space.
```bash
helm install tdengine tdengine-3.0.0.tgz \
--set storage.className=standard \
--set storage.dataSize=2Gi \
--set storage.logSize=10Mi
```
After TDengine is deployed, TDengine Chart outputs information about how to use TDengine:
```bash
export POD_NAME=$(kubectl get pods --namespace default \
-l "app.kubernetes.io/name=tdengine,app.kubernetes.io/instance=tdengine" \
-o jsonpath="{.items[0].metadata.name}")
kubectl --namespace default exec $POD_NAME -- taos -s "show dnodes; show mnodes"
kubectl --namespace default exec -it $POD_NAME -- taos
```
You can test the deployment by creating a table:
```bash
kubectl --namespace default exec $POD_NAME -- \
taos -s "create database test;
use test;
create table t1 (ts timestamp, n int);
insert into t1 values(now, 1)(now + 1s, 2);
select * from t1;"
```
## Configuring Values
You can configure custom parameters in TDengine with the `values.yaml` file.
Run the `helm show values` command to see all parameters supported by TDengine Chart.
```bash
helm show values tdengine-3.0.0.tgz
```
Save the output of this command as `values.yaml`. Then you can modify this file with your desired values and use it to deploy a TDengine cluster:
```bash
helm install tdengine tdengine-3.0.0.tgz -f values.yaml
```
The parameters are described as follows:
```yaml
# Default values for tdengine.
# This is a YAML-formatted file.
# Declare variables to be passed into helm templates.
replicaCount: 1
image:
prefix: tdengine/tdengine
#pullPolicy: Always
# Overrides the image tag whose default is the chart appVersion.
# tag: "3.0.0.0"
service:
# ClusterIP is the default service type, use NodeIP only if you know what you are doing.
type: ClusterIP
ports:
# TCP range required
tcp: [6030, 6041, 6042, 6043, 6044, 6046, 6047, 6048, 6049, 6060]
# UDP range
udp: [6044, 6045]
# Set timezone here, not in taoscfg
timezone: "Asia/Shanghai"
resources:
# We usually recommend not to specify default resources and to leave this as a conscious
# choice for the user. This also increases chances charts run on environments with little
# resources, such as Minikube. If you do want to specify resources, uncomment the following
# lines, adjust them as necessary, and remove the curly braces after 'resources:'.
# limits:
# cpu: 100m
# memory: 128Mi
# requests:
# cpu: 100m
# memory: 128Mi
storage:
# Set storageClassName for pvc. K8s use default storage class if not set.
#
className: ""
dataSize: "100Gi"
logSize: "10Gi"
nodeSelectors:
taosd:
# node selectors
clusterDomainSuffix: ""
# Config settings in taos.cfg file.
#
# The helm/k8s support will use environment variables for taos.cfg,
# converting an upper-snake-cased variable like `TAOS_DEBUG_FLAG`,
# to a camelCase taos config variable `debugFlag`.
#
# See the variable list at https://www.taosdata.com/cn/documentation/administrator .
#
# Note:
# 1. firstEp/secondEp: should not be setted here, it's auto generated at scale-up.
# 2. serverPort: should not be setted, we'll use the default 6030 in many places.
# 3. fqdn: will be auto generated in kubenetes, user should not care about it.
# 4. role: currently role is not supported - every node is able to be mnode and vnode.
#
# Btw, keep quotes "" around the value like below, even the value will be number or not.
taoscfg:
# Starts as cluster or not, must be 0 or 1.
# 0: all pods will start as a seperate TDengine server
# 1: pods will start as TDengine server cluster. [default]
CLUSTER: "1"
# number of replications, for cluster only
TAOS_REPLICA: "1"
# number of days per DB file
# TAOS_DAYS: "10"
# number of days to keep DB file, default is 10 years.
#TAOS_KEEP: "3650"
# cache block size (Mbyte)
#TAOS_CACHE: "16"
# number of cache blocks per vnode
#TAOS_BLOCKS: "6"
# minimum rows of records in file block
#TAOS_MIN_ROWS: "100"
# maximum rows of records in file block
#TAOS_MAX_ROWS: "4096"
#
# TAOS_NUM_OF_THREADS_PER_CORE: number of threads per CPU core
#TAOS_NUM_OF_THREADS_PER_CORE: "1.0"
#
# TAOS_NUM_OF_COMMIT_THREADS: number of threads to commit cache data
#TAOS_NUM_OF_COMMIT_THREADS: "4"
#
# TAOS_RATIO_OF_QUERY_CORES:
# the proportion of total CPU cores available for query processing
# 2.0: the query threads will be set to double of the CPU cores.
# 1.0: all CPU cores are available for query processing [default].
# 0.5: only half of the CPU cores are available for query.
# 0.0: only one core available.
#TAOS_RATIO_OF_QUERY_CORES: "1.0"
#
# TAOS_KEEP_COLUMN_NAME:
# the last_row/first/last aggregator will not change the original column name in the result fields
#TAOS_KEEP_COLUMN_NAME: "0"
# enable/disable backuping vnode directory when removing vnode
#TAOS_VNODE_BAK: "1"
# enable/disable installation / usage report
#TAOS_TELEMETRY_REPORTING: "1"
# enable/disable load balancing
#TAOS_BALANCE: "1"
# max timer control blocks
#TAOS_MAX_TMR_CTRL: "512"
# time interval of system monitor, seconds
#TAOS_MONITOR_INTERVAL: "30"
# number of seconds allowed for a dnode to be offline, for cluster only
#TAOS_OFFLINE_THRESHOLD: "8640000"
# RPC re-try timer, millisecond
#TAOS_RPC_TIMER: "1000"
# RPC maximum time for ack, seconds.
#TAOS_RPC_MAX_TIME: "600"
# time interval of dnode status reporting to mnode, seconds, for cluster only
#TAOS_STATUS_INTERVAL: "1"
# time interval of heart beat from shell to dnode, seconds
#TAOS_SHELL_ACTIVITY_TIMER: "3"
# minimum sliding window time, milli-second
#TAOS_MIN_SLIDING_TIME: "10"
# minimum time window, milli-second
#TAOS_MIN_INTERVAL_TIME: "10"
# maximum delay before launching a stream computation, milli-second
#TAOS_MAX_STREAM_COMP_DELAY: "20000"
# maximum delay before launching a stream computation for the first time, milli-second
#TAOS_MAX_FIRST_STREAM_COMP_DELAY: "10000"
# retry delay when a stream computation fails, milli-second
#TAOS_RETRY_STREAM_COMP_DELAY: "10"
# the delayed time for launching a stream computation, from 0.1(default, 10% of whole computing time window) to 0.9
#TAOS_STREAM_COMP_DELAY_RATIO: "0.1"
# max number of vgroups per db, 0 means configured automatically
#TAOS_MAX_VGROUPS_PER_DB: "0"
# max number of tables per vnode
#TAOS_MAX_TABLES_PER_VNODE: "1000000"
# the number of acknowledgments required for successful data writing
#TAOS_QUORUM: "1"
# enable/disable compression
#TAOS_COMP: "2"
# write ahead log (WAL) level, 0: no wal; 1: write wal, but no fysnc; 2: write wal, and call fsync
#TAOS_WAL_LEVEL: "1"
# if walLevel is set to 2, the cycle of fsync being executed, if set to 0, fsync is called right away
#TAOS_FSYNC: "3000"
# the compressed rpc message, option:
# -1 (no compression)
# 0 (all message compressed),
# > 0 (rpc message body which larger than this value will be compressed)
#TAOS_COMPRESS_MSG_SIZE: "-1"
# max length of an SQL
#TAOS_MAX_SQL_LENGTH: "1048576"
# the maximum number of records allowed for super table time sorting
#TAOS_MAX_NUM_OF_ORDERED_RES: "100000"
# max number of connections allowed in dnode
#TAOS_MAX_SHELL_CONNS: "5000"
# max number of connections allowed in client
#TAOS_MAX_CONNECTIONS: "5000"
# stop writing logs when the disk size of the log folder is less than this value
#TAOS_MINIMAL_LOG_DIR_G_B: "0.1"
# stop writing temporary files when the disk size of the tmp folder is less than this value
#TAOS_MINIMAL_TMP_DIR_G_B: "0.1"
# if disk free space is less than this value, taosd service exit directly within startup process
#TAOS_MINIMAL_DATA_DIR_G_B: "0.1"
# One mnode is equal to the number of vnode consumed
#TAOS_MNODE_EQUAL_VNODE_NUM: "4"
# enbale/disable http service
#TAOS_HTTP: "1"
# enable/disable system monitor
#TAOS_MONITOR: "1"
# enable/disable recording the SQL statements via restful interface
#TAOS_HTTP_ENABLE_RECORD_SQL: "0"
# number of threads used to process http requests
#TAOS_HTTP_MAX_THREADS: "2"
# maximum number of rows returned by the restful interface
#TAOS_RESTFUL_ROW_LIMIT: "10240"
# The following parameter is used to limit the maximum number of lines in log files.
# max number of lines per log filters
# numOfLogLines 10000000
# enable/disable async log
#TAOS_ASYNC_LOG: "0"
#
# time of keeping log files, days
#TAOS_LOG_KEEP_DAYS: "0"
# The following parameters are used for debug purpose only.
# debugFlag 8 bits mask: FILE-SCREEN-UNUSED-HeartBeat-DUMP-TRACE_WARN-ERROR
# 131: output warning and error
# 135: output debug, warning and error
# 143: output trace, debug, warning and error to log
# 199: output debug, warning and error to both screen and file
# 207: output trace, debug, warning and error to both screen and file
#
# debug flag for all log type, take effect when non-zero value\
#TAOS_DEBUG_FLAG: "143"
# enable/disable recording the SQL in taos client
#TAOS_ENABLE_RECORD_SQL: "0"
# generate core file when service crash
#TAOS_ENABLE_CORE_FILE: "1"
# maximum display width of binary and nchar fields in the shell. The parts exceeding this limit will be hidden
#TAOS_MAX_BINARY_DISPLAY_WIDTH: "30"
# enable/disable stream (continuous query)
#TAOS_STREAM: "1"
# in retrieve blocking model, only in 50% query threads will be used in query processing in dnode
#TAOS_RETRIEVE_BLOCKING_MODEL: "0"
# the maximum allowed query buffer size in MB during query processing for each data node
# -1 no limit (default)
# 0 no query allowed, queries are disabled
#TAOS_QUERY_BUFFER_SIZE: "-1"
```
## Scaling Out
For information about scaling out your deployment, see Kubernetes. Additional Helm-specific is described as follows.
First, obtain the name of the StatefulSet service for your deployment.
```bash
export STS_NAME=$(kubectl get statefulset \
-l "app.kubernetes.io/name=tdengine" \
-o jsonpath="{.items[0].metadata.name}")
```
You can scale out your deployment by adding replicas. The following command scales a deployment to three nodes:
```bash
kubectl scale --replicas 3 statefulset/$STS_NAME
```
Run the `show dnodes` and `show mnodes` commands to check whether the scale-out was successful.
## Scaling In
:::warning
Exercise caution when scaling in a cluster.
:::
Determine which dnodes you want to remove and drop them manually.
```bash
kubectl --namespace default exec $POD_NAME -- \
cat /var/lib/taos/dnode/dnodeEps.json \
| jq '.dnodeInfos[1:] |map(.dnodeFqdn + ":" + (.dnodePort|tostring)) | .[]' -r
kubectl --namespace default exec $POD_NAME -- taos -s "show dnodes"
kubectl --namespace default exec $POD_NAME -- taos -s 'drop dnode "<you dnode in list>"'
```
## Remove a TDengine Cluster
You can use Helm to remove your cluster:
```bash
helm uninstall tdengine
```
However, Helm does not remove PVCs automatically. After you remove your cluster, manually remove all PVCs.
docs/en/10-cluster/index.md → docs/en/10-deployment/index.md
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---
title: Cluster
keywords: ["cluster", "high availability", "load balance", "scale out"]
title: Deployment
---
TDengine has a native distributed design and provides the ability to scale out. A few nodes can form a TDengine cluster. If you need higher processing power, you just need to add more nodes into the cluster. TDengine uses virtual node technology to virtualize a node into multiple virtual nodes to achieve load balancing. At the same time, TDengine can group virtual nodes on different nodes into virtual node groups, and use the replication mechanism to ensure the high availability of the system. The cluster feature of TDengine is completely open source.
This chapter mainly introduces cluster deployment, maintenance, and how to achieve high availability and load balancing.
This document describes how to manually deploy a cluster on a host as well as how to deploy on Kubernetes and by using Helm.
```mdx-code-block
import DocCardList from '@theme/DocCardList';
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......@@ -4,8 +4,7 @@ description: "Delete data from table or Stable"
title: Delete Data
---
TDengine provides the functionality of deleting data from a table or STable according to specified time range, it can be used to cleanup abnormal data generated due to device failure. Please be noted that this functionality is only available in Enterprise version, please refer to [TDengine Enterprise Edition](https://tdengine.com/products#enterprise-edition-link)
TDengine provides the functionality of deleting data from a table or STable according to specified time range, it can be used to cleanup abnormal data generated due to device failure.
**Syntax:**
......@@ -16,21 +15,21 @@ DELETE FROM [ db_name. ] tb_name [WHERE condition];
**Description:** Delete data from a table or STable
**Parameters:**
- `db_name`: Optional parameter, specifies the database in which the table exists; if not specified, the current database will be used.
- `tb_name`: Mandatory parameter, specifies the table name from which data will be deleted, it can be normal table, subtable or STable.
- `condition`: Optional parameter, specifies the data filter condition. If no condition is specified all data will be deleted, so please be cautions to delete data without any condition. The condition used here is only applicable to the first column, i.e. the timestamp column. If the table is a STable, the condition is also applicable to tag columns.
- `condition`: Optional parameter, specifies the data filter condition. If no condition is specified all data will be deleted, so please be cautions to delete data without any condition. The condition used here is only applicable to the first column, i.e. the timestamp column.
**More Explanations:**
The data can't be recovered once deleted, so please be cautious to use the functionality of deleting data. It's better to firstly make sure the data to be deleted using `select` then execute `delete`.
The data can't be recovered once deleted, so please be cautious to use the functionality of deleting data. It's better to firstly make sure the data to be deleted using `select` then execute `delete`.
**Example:**
`meters` is a STable, in which `groupid` is a tag column of int type. Now we want to delete the data older than 2021-10-01 10:40:00.100 and `groupid` is 1. The SQL for this purpose is like below:
`meters` is a STable, in which `groupid` is a tag column of int type. Now we want to delete the data older than 2021-10-01 10:40:00.100. You can perform this action by running the following SQL statement:
```sql
delete from meters where ts < '2021-10-01 10:40:00.100' and groupid=1 ;
delete from meters where ts < '2021-10-01 10:40:00.100' ;
```
The output is:
......
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......@@ -2,7 +2,7 @@
title: Escape Characters
---
Below table is the list of escape characters used in TDengine.
## Escape Characters
| Escape Character | **Actual Meaning** |
| :--------------: | ------------------------ |
......@@ -15,11 +15,6 @@ Below table is the list of escape characters used in TDengine.
| `\%` | % see below for details |
| `\_` | \_ see below for details |
:::note
Escape characters are available from version 2.4.0.4 .
:::
## Restrictions
1. If there are escape characters in identifiers (database name, table name, column name)
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- 已安装客户端驱动（使用原生连接必须安装，使用 REST 连接无需安装）
:::info
由于 TDengine 的客户端驱动使用 C 语言编写，使用原生连接时需要加载系统对应安装在本地的客户端驱动共享库文件，通常包含在 TDengine 安装包。TDengine Linux 服务端安装包附带了 TDengine 客户端，也可以单独安装 [Linux 客户端](/get-started/) 。在 Windows 环境开发时需要安装 TDengine 对应的 [Windows 客户端](https://www.taosdata.com/cn/all-downloads/#TDengine-Windows-Client) 。
- libtaos.so: 在 Linux 系统中成功安装 TDengine 后，依赖的 Linux 版客户端驱动 libtaos.so 文件会被自动拷贝至 /usr/lib/libtaos.so，该目录包含在 Linux 自动扫描路径上，无需单独指定。
- taos.dll: 在 Windows 系统中安装完客户端之后，依赖的 Windows 版客户端驱动 taos.dll 文件会自动拷贝到系统默认搜索路径 C:/Windows/System32 下，同样无需要单独指定。
:::
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