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TDengine

taosdata / TDengine
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T
TDengine
提交 3c7469de 编写于 作者: H Haojun Liao
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[td-225] merge develop.

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.appveyor.yml
浏览文件 @ 3c7469de
version: 1.0.{build}
image:
- Visual Studio 2015
- macos
environment:
matrix:
- ARCH: amd64
- ARCH: x86
matrix:
exclude:
- image: macos
ARCH: x86
for:
-
matrix:
only:
- image: Visual Studio 2015
clone_folder: c:\dev\TDengine
clone_depth: 1
init:
- call "C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\vcvarsall.bat" %ARCH%
before_build:
- cd c:\dev\TDengine
- md build
build_script:
- cd build
- cmake -G "NMake Makefiles" ..
- nmake install
-
matrix:
only:
- image: macos
clone_depth: 1
build_script:
- mkdir debug
- cd debug
- cmake .. > /dev/null
- make > /dev/null
notifications:
- provider: Email
to:
- sangshuduo@gmail.com
on_build_success: true
on_build_failure: true
on_build_status_changed: true
version: 1.0.{build}
image:
- Visual Studio 2015
- macos
environment:
matrix:
- ARCH: amd64
- ARCH: x86
matrix:
exclude:
- image: macos
ARCH: x86
for:
-
matrix:
only:
- image: Visual Studio 2015
clone_folder: c:\dev\TDengine
clone_depth: 1
init:
- call "C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\vcvarsall.bat" %ARCH%
before_build:
- cd c:\dev\TDengine
- md build
build_script:
- cd build
- cmake -G "NMake Makefiles" .. -DBUILD_JDBC=false
- nmake install
-
matrix:
only:
- image: macos
clone_depth: 1
build_script:
- mkdir debug
- cd debug
- cmake .. > /dev/null
- make > /dev/null
notifications:
- provider: Email
to:
- sangshuduo@gmail.com
on_build_success: true
on_build_failure: true
on_build_status_changed: true
CMakeLists.txt
浏览文件 @ 3c7469de
......@@ -42,13 +42,6 @@ INCLUDE(cmake/env.inc)
INCLUDE(cmake/version.inc)
INCLUDE(cmake/install.inc)
IF (CMAKE_SYSTEM_NAME MATCHES "Linux")
SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -pipe -Wall -Wshadow -Werror")
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pipe -Wall -Wshadow -Werror")
ENDIF ()
MESSAGE(STATUS "CMAKE_C_FLAGS: ${CMAKE_C_FLAGS}")
MESSAGE(STATUS "CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}")
ADD_SUBDIRECTORY(deps)
ADD_SUBDIRECTORY(src)
ADD_SUBDIRECTORY(tests)
......
Jenkinsfile
浏览文件 @ 3c7469de
......@@ -119,16 +119,32 @@ pipeline {
abortPreviousBuilds()
}
sh'''
rm -rf ${WORKSPACE}.tes
cp -r ${WORKSPACE} ${WORKSPACE}.tes
cd ${WORKSPACE}.tes
git checkout develop
git pull
'''
script {
if (env.CHANGE_TARGET == 'master') {
sh '''
git checkout master
git pull origin master
'''
}
else {
sh '''
git checkout develop
git pull origin develop
'''
}
}
sh'''
git fetch origin +refs/pull/${CHANGE_ID}/merge
git checkout -qf FETCH_HEAD
'''
script{
env.skipstage=sh(script:"cd ${WORKSPACE}.tes && git --no-pager diff --name-only FETCH_HEAD develop|grep -v -E '.*md|//src//connector|Jenkinsfile|test-all.sh' || echo 0 ",returnStdout:true)
env.skipstage=sh(script:"cd ${WORKSPACE}.tes && git --no-pager diff --name-only FETCH_HEAD ${env.CHANGE_TARGET}|grep -v -E '.*md|//src//connector|Jenkinsfile|test-all.sh' || echo 0 ",returnStdout:true)
}
println env.skipstage
sh'''
......
README-CN.md
浏览文件 @ 3c7469de
......@@ -23,7 +23,7 @@ TDengine是涛思数据专为物联网、车联网、工业互联网、IT运维
TDengine是一个高效的存储、查询、分析时序大数据的平台，专为物联网、车联网、工业互联网、运维监测等优化而设计。您可以像使用关系型数据库MySQL一样来使用它，但建议您在使用前仔细阅读一遍下面的文档，特别是 [数据模型](https://www.taosdata.com/cn/documentation/architecture) 与 [数据建模](https://www.taosdata.com/cn/documentation/model)。除本文档之外，欢迎 [下载产品白皮书](https://www.taosdata.com/downloads/TDengine%20White%20Paper.pdf)。
# 生成
# 构建
TDengine目前2.0版服务器仅能在Linux系统上安装和运行，后续会支持Windows、macOS等系统。客户端可以在Windows或Linux上安装和运行。任何OS的应用也可以选择RESTful接口连接服务器taosd。CPU支持X64/ARM64/MIPS64/Alpha64，后续会支持ARM32、RISC-V等CPU架构。用户可根据需求选择通过[源码](https://www.taosdata.com/cn/getting-started/#通过源码安装)或者[安装包](https://www.taosdata.com/cn/getting-started/#通过安装包安装)来安装。本快速指南仅适用于通过源码安装。
......@@ -107,7 +107,7 @@ Go 连接器和 Grafana 插件在其他独立仓库，如果安装它们的话
git submodule update --init --recursive
```
## 生成 TDengine
## 构建 TDengine
### Linux 系统
......@@ -116,6 +116,12 @@ mkdir debug && cd debug
cmake .. && cmake --build .
```
您可以选择使用 Jemalloc 作为内存分配器，替代默认的 glibc：
```bash
apt install autoconf
cmake .. -DJEMALLOC_ENABLED=true
```
在X86-64、X86、arm64、arm32 和 mips64 平台上，TDengine 生成脚本可以自动检测机器架构。也可以手动配置 CPUTYPE 参数来指定 CPU 类型，如 aarch64 或 aarch32 等。
aarch64：
......
README.md
浏览文件 @ 3c7469de
......@@ -110,6 +110,12 @@ mkdir debug && cd debug
cmake .. && cmake --build .
```
You can use Jemalloc as memory allocator instead of glibc:
```
apt install autoconf
cmake .. -DJEMALLOC_ENABLED=true
```
TDengine build script can detect the host machine's architecture on X86-64, X86, arm64, arm32 and mips64 platform.
You can also specify CPUTYPE option like aarch64 or aarch32 too if the detection result is not correct:
......@@ -131,10 +137,10 @@ cmake .. -DCPUTYPE=mips64 && cmake --build .
### On Windows platform
If you use the Visual Studio 2013, please open a command window by executing "cmd.exe".
Please specify "x86_amd64" for 64 bits Windows or specify "x86" is for 32 bits Windows when you execute vcvarsall.bat.
Please specify "amd64" for 64 bits Windows or specify "x86" is for 32 bits Windows when you execute vcvarsall.bat.
```cmd
mkdir debug && cd debug
"C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\vcvarsall.bat" < x86_amd64 | x86 >
"C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\vcvarsall.bat" < amd64 | x86 >
cmake .. -G "NMake Makefiles"
nmake
```
......
cmake/define.inc
浏览文件 @ 3c7469de
......@@ -41,6 +41,10 @@ IF (TD_POWER)
ADD_DEFINITIONS(-D_TD_POWER_)
ENDIF ()
IF (TD_TQ)
ADD_DEFINITIONS(-D_TD_TQ_)
ENDIF ()
IF (TD_MEM_CHECK)
ADD_DEFINITIONS(-DTAOS_MEM_CHECK)
ENDIF ()
......@@ -57,7 +61,7 @@ IF (TD_LINUX_64)
ADD_DEFINITIONS(-D_M_X64)
ADD_DEFINITIONS(-D_TD_LINUX_64)
MESSAGE(STATUS "linux64 is defined")
SET(COMMON_FLAGS "-std=gnu99 -Wall -Werror -fPIC -gdwarf-2 -msse4.2 -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE")
SET(COMMON_FLAGS "-Wall -Werror -fPIC -gdwarf-2 -msse4.2 -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE")
ADD_DEFINITIONS(-DUSE_LIBICONV)
IF (JEMALLOC_ENABLED)
......@@ -70,7 +74,7 @@ IF (TD_LINUX_32)
ADD_DEFINITIONS(-D_TD_LINUX_32)
ADD_DEFINITIONS(-DUSE_LIBICONV)
MESSAGE(STATUS "linux32 is defined")
SET(COMMON_FLAGS "-std=gnu99 -Wall -Werror -fPIC -fsigned-char -munaligned-access -fpack-struct=8 -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE")
SET(COMMON_FLAGS "-Wall -Werror -fPIC -fsigned-char -munaligned-access -fpack-struct=8 -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE")
ENDIF ()
IF (TD_ARM_64)
......@@ -78,7 +82,7 @@ IF (TD_ARM_64)
ADD_DEFINITIONS(-D_TD_ARM_)
ADD_DEFINITIONS(-DUSE_LIBICONV)
MESSAGE(STATUS "arm64 is defined")
SET(COMMON_FLAGS "-std=gnu99 -Wall -Werror -fPIC -fsigned-char -fpack-struct=8 -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE")
SET(COMMON_FLAGS "-Wall -Werror -fPIC -fsigned-char -fpack-struct=8 -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE")
ENDIF ()
IF (TD_ARM_32)
......@@ -86,7 +90,7 @@ IF (TD_ARM_32)
ADD_DEFINITIONS(-D_TD_ARM_)
ADD_DEFINITIONS(-DUSE_LIBICONV)
MESSAGE(STATUS "arm32 is defined")
SET(COMMON_FLAGS "-std=gnu99 -Wall -Werror -fPIC -fsigned-char -fpack-struct=8 -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE -Wno-pointer-to-int-cast -Wno-int-to-pointer-cast -Wno-incompatible-pointer-types ")
SET(COMMON_FLAGS "-Wall -Werror -fPIC -fsigned-char -fpack-struct=8 -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE -Wno-pointer-to-int-cast -Wno-int-to-pointer-cast -Wno-incompatible-pointer-types ")
ENDIF ()
IF (TD_MIPS_64)
......@@ -94,7 +98,7 @@ IF (TD_MIPS_64)
ADD_DEFINITIONS(-D_TD_MIPS_64)
ADD_DEFINITIONS(-DUSE_LIBICONV)
MESSAGE(STATUS "mips64 is defined")
SET(COMMON_FLAGS "-std=gnu99 -Wall -Werror -fPIC -fsigned-char -fpack-struct=8 -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE")
SET(COMMON_FLAGS "-Wall -Werror -fPIC -fsigned-char -fpack-struct=8 -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE")
ENDIF ()
IF (TD_MIPS_32)
......@@ -102,7 +106,7 @@ IF (TD_MIPS_32)
ADD_DEFINITIONS(-D_TD_MIPS_32)
ADD_DEFINITIONS(-DUSE_LIBICONV)
MESSAGE(STATUS "mips32 is defined")
SET(COMMON_FLAGS "-std=gnu99 -Wall -Werror -fPIC -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE")
SET(COMMON_FLAGS "-Wall -Werror -fPIC -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE")
ENDIF ()
IF (TD_APLHINE)
......@@ -122,10 +126,14 @@ IF (TD_LINUX)
ADD_DEFINITIONS(-D_TD_NINGSI_60)
MESSAGE(STATUS "set ningsi macro to true")
ENDIF ()
SET(DEBUG_FLAGS "-O0 -g3 -DDEBUG")
IF (TD_MEMORY_SANITIZER)
SET(DEBUG_FLAGS "-fsanitize=address -fsanitize=undefined -fno-sanitize-recover=all -fsanitize=float-divide-by-zero -fsanitize=float-cast-overflow -fno-sanitize=null -fno-sanitize=alignment -static-libasan -O0 -g3 -DDEBUG")
ELSE ()
SET(DEBUG_FLAGS "-O0 -g3 -DDEBUG")
ENDIF ()
SET(RELEASE_FLAGS "-O3 -Wno-error")
IF (${COVER} MATCHES "true")
MESSAGE(STATUS "Test coverage mode, add extra flags")
SET(GCC_COVERAGE_COMPILE_FLAGS "-fprofile-arcs -ftest-coverage")
......@@ -144,8 +152,16 @@ IF (TD_DARWIN_64)
ADD_DEFINITIONS(-D_REENTRANT -D__USE_POSIX -D_LIBC_REENTRANT)
ADD_DEFINITIONS(-DUSE_LIBICONV)
MESSAGE(STATUS "darwin64 is defined")
SET(COMMON_FLAGS "-std=gnu99 -Wall -Werror -Wno-missing-braces -fPIC -msse4.2 -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE")
SET(DEBUG_FLAGS "-O0 -g3 -DDEBUG")
IF ("${CPUTYPE}" STREQUAL "apple_m1")
SET(COMMON_FLAGS "-Wall -Werror -Wno-missing-braces -fPIC -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE")
ELSE ()
SET(COMMON_FLAGS "-Wall -Werror -Wno-missing-braces -fPIC -msse4.2 -D_FILE_OFFSET_BITS=64 -D_LARGE_FILE")
ENDIF ()
IF (TD_MEMORY_SANITIZER)
SET(DEBUG_FLAGS "-fsanitize=address -fsanitize=undefined -fno-sanitize-recover=all -fsanitize=float-divide-by-zero -fsanitize=float-cast-overflow -fno-sanitize=null -fno-sanitize=alignment -O0 -g3 -DDEBUG")
ELSE ()
SET(DEBUG_FLAGS "-O0 -g3 -DDEBUG")
ENDIF ()
SET(RELEASE_FLAGS "-Og")
INCLUDE_DIRECTORIES(${TD_COMMUNITY_DIR}/deps/cJson/inc)
INCLUDE_DIRECTORIES(${TD_COMMUNITY_DIR}/deps/lz4/inc)
......@@ -164,8 +180,15 @@ IF (TD_WINDOWS)
IF (MSVC AND (MSVC_VERSION GREATER_EQUAL 1900))
SET(COMMON_FLAGS "${COMMON_FLAGS} /Wv:18")
ENDIF ()
SET(DEBUG_FLAGS "/fsanitize=thread /fsanitize=leak /fsanitize=memory /fsanitize=undefined /fsanitize=hwaddress /Zi /W3 /GL")
SET(RELEASE_FLAGS "/W0 /O3 /GL")
IF (TD_MEMORY_SANITIZER)
MESSAGE("memory sanitizer detected as true")
SET(DEBUG_FLAGS "/fsanitize=address /Zi /W3 /GL")
ELSE ()
MESSAGE("memory sanitizer detected as false")
SET(DEBUG_FLAGS "/Zi /W3 /GL")
ENDIF ()
SET(RELEASE_FLAGS "/W0 /O2 /GL") # MSVC only support O2
ENDIF ()
INCLUDE_DIRECTORIES(${TD_COMMUNITY_DIR}/deps/pthread)
......@@ -174,7 +197,7 @@ IF (TD_WINDOWS)
INCLUDE_DIRECTORIES(${TD_COMMUNITY_DIR}/deps/wepoll/inc)
INCLUDE_DIRECTORIES(${TD_COMMUNITY_DIR}/deps/MsvcLibX/include)
INCLUDE_DIRECTORIES(${TD_COMMUNITY_DIR}/deps/lua/src)
ENDIF ()
ENDIF ()
IF (TD_WINDOWS_64)
ADD_DEFINITIONS(-D_M_X64)
......@@ -189,6 +212,10 @@ IF (TD_WINDOWS_32)
MESSAGE(STATUS "windows32 is defined")
ENDIF ()
IF (TD_LINUX)
SET(COMMON_FLAGS "${COMMON_FLAGS} -pipe -Wshadow")
ENDIF ()
INCLUDE_DIRECTORIES(${TD_COMMUNITY_DIR}/src/inc)
INCLUDE_DIRECTORIES(${TD_COMMUNITY_DIR}/src/os/inc)
INCLUDE_DIRECTORIES(${TD_COMMUNITY_DIR}/src/util/inc)
......
cmake/env.inc
浏览文件 @ 3c7469de
......@@ -14,11 +14,13 @@ MESSAGE(STATUS "Project binary files output path: " ${PROJECT_BINARY_DIR})
MESSAGE(STATUS "Project executable files output path: " ${EXECUTABLE_OUTPUT_PATH})
MESSAGE(STATUS "Project library files output path: " ${LIBRARY_OUTPUT_PATH})
FIND_PROGRAM(TD_MVN_INSTALLED mvn)
IF (TD_MVN_INSTALLED)
MESSAGE(STATUS "MVN is installed and JDBC will be compiled")
ELSE ()
MESSAGE(STATUS "MVN is not installed and JDBC is not compiled")
IF (TD_BUILD_JDBC)
FIND_PROGRAM(TD_MVN_INSTALLED mvn)
IF (TD_MVN_INSTALLED)
MESSAGE(STATUS "MVN is installed and JDBC will be compiled")
ELSE ()
MESSAGE(STATUS "MVN is not installed and JDBC is not compiled")
ENDIF ()
ENDIF ()
#
......@@ -33,13 +35,13 @@ ENDIF ()
# Set compiler options
SET(COMMON_C_FLAGS "${COMMON_FLAGS} -std=gnu99")
SET(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} ${COMMON_FLAGS} ${DEBUG_FLAGS}")
SET(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} ${COMMON_FLAGS} ${RELEASE_FLAGS}")
SET(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} ${COMMON_C_FLAGS} ${DEBUG_FLAGS}")
SET(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} ${COMMON_C_FLAGS} ${RELEASE_FLAGS}")
# Set c++ compiler options
# SET(COMMON_CXX_FLAGS "${COMMON_FLAGS} -std=c++11")
# SET(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} ${COMMON_CXX_FLAGS} ${DEBUG_FLAGS}")
# SET(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} ${COMMON_CXX_FLAGS} ${RELEASE_FLAGS}")
SET(COMMON_CXX_FLAGS "${COMMON_FLAGS} -std=c++11 -Wno-unused-function")
SET(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} ${COMMON_CXX_FLAGS} ${DEBUG_FLAGS}")
SET(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} ${COMMON_CXX_FLAGS} ${RELEASE_FLAGS}")
IF (${CMAKE_BUILD_TYPE} MATCHES "Debug")
SET(CMAKE_BUILD_TYPE "Debug")
......@@ -55,4 +57,4 @@ ELSE ()
SET(CMAKE_BUILD_TYPE "Debug")
MESSAGE(STATUS "Build Debug Version as default")
ENDIF()
ENDIF ()
\ No newline at end of file
ENDIF ()
cmake/input.inc
浏览文件 @ 3c7469de
......@@ -46,6 +46,9 @@ ENDIF ()
IF (${DBNAME} MATCHES "power")
SET(TD_POWER TRUE)
MESSAGE(STATUS "power is true")
ELSEIF (${DBNAME} MATCHES "tq")
SET(TD_TQ TRUE)
MESSAGE(STATUS "tq is true")
ENDIF ()
IF (${DLLTYPE} MATCHES "go")
......@@ -77,3 +80,14 @@ IF (${JEMALLOC_ENABLED} MATCHES "true")
SET(TD_JEMALLOC_ENABLED TRUE)
MESSAGE(STATUS "build with jemalloc enabled")
ENDIF ()
SET(TD_BUILD_JDBC TRUE)
IF (${BUILD_JDBC} MATCHES "false")
SET(TD_BUILD_JDBC FALSE)
ENDIF ()
SET(TD_MEMORY_SANITIZER FALSE)
IF (${MEMORY_SANITIZER} MATCHES "true")
SET(TD_MEMORY_SANITIZER TRUE)
ENDIF ()
cmake/install.inc
浏览文件 @ 3c7469de
......@@ -32,7 +32,7 @@ ELSEIF (TD_WINDOWS)
#INSTALL(TARGETS taos RUNTIME DESTINATION driver)
#INSTALL(TARGETS shell RUNTIME DESTINATION .)
IF (TD_MVN_INSTALLED)
INSTALL(FILES ${LIBRARY_OUTPUT_PATH}/taos-jdbcdriver-2.0.29.jar DESTINATION connector/jdbc)
INSTALL(FILES ${LIBRARY_OUTPUT_PATH}/taos-jdbcdriver-2.0.32-dist.jar DESTINATION connector/jdbc)
ENDIF ()
ELSEIF (TD_DARWIN)
SET(TD_MAKE_INSTALL_SH "${TD_COMMUNITY_DIR}/packaging/tools/make_install.sh")
......
cmake/platform.inc
浏览文件 @ 3c7469de
......@@ -108,6 +108,10 @@ IF ("${CPUTYPE}" STREQUAL "")
SET(TD_LINUX TRUE)
SET(TD_LINUX_64 FALSE)
SET(TD_MIPS_64 TRUE)
ELSEIF (CMAKE_SYSTEM_PROCESSOR MATCHES "arm64")
SET(CPUTYPE "apple_m1")
MESSAGE(STATUS "Set CPUTYPE to apple silicon m1")
SET(TD_ARM_64 TRUE)
ENDIF ()
ELSE ()
......@@ -153,5 +157,5 @@ ELSEIF (${OSTYPE} MATCHES "Alpine")
MESSAGE(STATUS "input osType: Alpine")
SET(TD_APLHINE TRUE)
ELSE ()
MESSAGE(STATUS "input osType unknown: " ${OSTYPE})
MESSAGE(STATUS "The user specified osType is unknown: " ${OSTYPE})
ENDIF ()
cmake/version.inc
浏览文件 @ 3c7469de
......@@ -4,13 +4,13 @@ PROJECT(TDengine)
IF (DEFINED VERNUMBER)
SET(TD_VER_NUMBER ${VERNUMBER})
ELSE ()
SET(TD_VER_NUMBER "2.1.1.0")
SET(TD_VER_NUMBER "2.1.3.0")
ENDIF ()
IF (DEFINED VERCOMPATIBLE)
SET(TD_VER_COMPATIBLE ${VERCOMPATIBLE})
ELSE ()
SET(TD_VER_COMPATIBLE "2.0.0.0")
SET(TD_VER_COMPATIBLE "1.0.0.0")
ENDIF ()
find_program(HAVE_GIT NAMES git)
......
deps/CMakeLists.txt
浏览文件 @ 3c7469de
......@@ -10,7 +10,10 @@ ADD_SUBDIRECTORY(cJson)
ADD_SUBDIRECTORY(wepoll)
ADD_SUBDIRECTORY(MsvcLibX)
ADD_SUBDIRECTORY(rmonotonic)
ADD_SUBDIRECTORY(lua)
IF (TD_LINUX_64)
ADD_SUBDIRECTORY(lua)
ENDIF ()
IF (TD_LINUX AND TD_MQTT)
ADD_SUBDIRECTORY(MQTT-C)
......@@ -21,7 +24,7 @@ IF (TD_DARWIN AND TD_MQTT)
ENDIF ()
IF (TD_LINUX_64 AND JEMALLOC_ENABLED)
MESSAGE("setup dpes/jemalloc, current source dir:" ${CMAKE_CURRENT_SOURCE_DIR})
MESSAGE("setup deps/jemalloc, current source dir:" ${CMAKE_CURRENT_SOURCE_DIR})
MESSAGE("binary dir:" ${CMAKE_BINARY_DIR})
include(ExternalProject)
ExternalProject_Add(jemalloc
......
documentation20/cn/00.index/docs.md
浏览文件 @ 3c7469de
......@@ -15,6 +15,7 @@ TDengine是一个高效的存储、查询、分析时序大数据的平台，专
* [命令行程序TAOS](/getting-started#console)：访问TDengine的简便方式
* [极速体验](/getting-started#demo)：运行示例程序，快速体验高效的数据插入、查询
* [支持平台列表](/getting-started#platforms)：TDengine服务器和客户端支持的平台列表
* [Kubernetes部署](https://taosdata.github.io/TDengine-Operator/zh/index.html)：TDengine在Kubernetes环境进行部署的详细说明
## [整体架构](/architecture)
......@@ -41,7 +42,7 @@ TDengine是一个高效的存储、查询、分析时序大数据的平台，专
* [数据写入](/taos-sql#insert)：支持单表单条、多条、多表多条写入，支持历史数据写入
* [数据查询](/taos-sql#select)：支持时间段、值过滤、排序、查询结果手动分页等
* [SQL函数](/taos-sql#functions)：支持各种聚合函数、选择函数、计算函数，如avg, min, diff等
* [时间维度聚合](/taos-sql#aggregation)：将表中数据按照时间段进行切割后聚合，降维处理
* [窗口切分聚合](/taos-sql#aggregation)：将表中数据按照时间段等方式进行切割后聚合，降维处理
* [边界限制](/taos-sql#limitation)：库、表、SQL等边界限制条件
* [错误码](/taos-sql/error-code)：TDengine 2.0 错误码以及对应的十进制码
......@@ -62,7 +63,7 @@ TDengine是一个高效的存储、查询、分析时序大数据的平台，专
## [高级功能](/advanced-features)
* [连续查询(Continuous Query)](/advanced-features#continuous-query)：基于滑动窗口，定时自动的对数据流进行查询计算
* [数据订阅(Publisher/Subscriber)](/advanced-features#subscribe)：象典型的消息队列，应用可订阅接收到的最新数据
* [数据订阅(Publisher/Subscriber)](/advanced-features#subscribe)：类似典型的消息队列，应用可订阅接收到的最新数据
* [缓存(Cache)](/advanced-features#cache)：每个设备最新的数据都会缓存在内存中，可快速获取
* [报警监测](/advanced-features#alert)：根据配置规则，自动监测超限行为数据，并主动推送
......@@ -80,7 +81,7 @@ TDengine是一个高效的存储、查询、分析时序大数据的平台，专
## [与其他工具的连接](/connections)
* [Grafana](/connections#grafana)：获取并可视化保存在TDengine的数据
* [Matlab](/connections#matlab)：通过配置Matlab的JDBC数据源访问保存在TDengine的数据
* [MATLAB](/connections#matlab)：通过配置MATLAB的JDBC数据源访问保存在TDengine的数据
* [R](/connections#r)：通过配置R的JDBC数据源访问保存在TDengine的数据
* [IDEA Database](https://www.taosdata.com/blog/2020/08/27/1767.html)：通过IDEA 数据库管理工具可视化使用 TDengine
......@@ -105,6 +106,7 @@ TDengine是一个高效的存储、查询、分析时序大数据的平台，专
* [数据导入](/administrator#import)：可按脚本文件导入，也可按数据文件导入
* [数据导出](/administrator#export)：从shell按表导出，也可用taosdump工具做各种导出
* [系统监控](/administrator#status)：检查系统现有的连接、查询、流式计算，日志和事件等
* [性能优化](/administrator#optimize)：对长期运行的系统进行维护优化，保障性能表现
* [文件目录结构](/administrator#directories)：TDengine数据文件、配置文件等所在目录
* [参数限制与保留关键字](/administrator#keywords)：TDengine的参数限制与保留关键字列表
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......@@ -9,8 +9,8 @@ TDengine的模块之一是时序数据库。但除此之外，为减少研发的
* __10倍以上的性能提升__：定义了创新的数据存储结构，单核每秒能处理至少2万次请求，插入数百万个数据点，读出一千万以上数据点，比现有通用数据库快十倍以上。
* __硬件或云服务成本降至1/5__：由于超强性能，计算资源不到通用大数据方案的1/5；通过列式存储和先进的压缩算法，存储空间不到通用数据库的1/10。
* __全栈时序数据处理引擎__：将数据库、消息队列、缓存、流式计算等功能融为一体，应用无需再集成Kafka/Redis/HBase/Spark/HDFS等软件，大幅降低应用开发和维护的复杂度成本。
* __强大的分析功能__：无论是十年前还是一秒钟前的数据，指定时间范围即可查询。数据可在时间轴上或多个设备上进行聚合。即席查询可通过Shell, Python, R, Matlab随时进行。
* __与第三方工具无缝连接__：不用一行代码，即可与Telegraf, Grafana, EMQ, HiveMQ, Prometheus, Matlab, R等集成。后续将支持OPC, Hadoop, Spark等, BI工具也将无缝连接。
* __强大的分析功能__：无论是十年前还是一秒钟前的数据，指定时间范围即可查询。数据可在时间轴上或多个设备上进行聚合。即席查询可通过Shell, Python, R, MATLAB随时进行。
* __与第三方工具无缝连接__：不用一行代码，即可与Telegraf, Grafana, EMQ, HiveMQ, Prometheus, MATLAB, R等集成。后续将支持OPC, Hadoop, Spark等, BI工具也将无缝连接。
* __零运维成本、零学习成本__：安装集群简单快捷，无需分库分表，实时备份。类似标准SQL，支持RESTful, 支持Python/Java/C/C++/C#/Go/Node.js, 与MySQL相似，零学习成本。
采用TDengine，可将典型的物联网、车联网、工业互联网大数据平台的总拥有成本大幅降低。但需要指出的是，因充分利用了物联网时序数据的特点，它无法用来处理网络爬虫、微博、微信、电商、ERP、CRM等通用型数据。
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......@@ -176,7 +176,7 @@ TDengine 分布式架构的逻辑结构图如下：
**通讯方式：**TDengine系统的各个数据节点之间，以及应用驱动与各数据节点之间的通讯是通过TCP/UDP进行的。因为考虑到物联网场景，数据写入的包一般不大，因此TDengine 除采用TCP做传输之外，还采用UDP方式，因为UDP 更加高效，而且不受连接数的限制。TDengine实现了自己的超时、重传、确认等机制，以确保UDP的可靠传输。对于数据量不到15K的数据包，采取UDP的方式进行传输，超过15K的，或者是查询类的操作，自动采取TCP的方式进行传输。同时，TDengine根据配置和数据包，会自动对数据进行压缩/解压缩，数字签名/认证等处理。对于数据节点之间的数据复制，只采用TCP方式进行数据传输。
**FQDN配置**：一个数据节点有一个或多个FQDN，可以在系统配置文件taos.cfg通过参数“fqdn"进行指定，如果没有指定，系统将自动获取计算机的hostname作为其FQDN。如果节点没有配置FQDN，可以直接将该节点的配置参数fqdn设置为它的IP地址。但不建议使用IP，因为IP地址可变，一旦变化，将让集群无法正常工作。一个数据节点的EP(End Point)由FQDN + Port组成。采用FQDN，需要保证DNS服务正常工作，或者在节点以及应用所在的节点配置好hosts文件。
**FQDN配置**：一个数据节点有一个或多个FQDN，可以在系统配置文件taos.cfg通过参数“fqdn"进行指定，如果没有指定，系统将自动获取计算机的hostname作为其FQDN。如果节点没有配置FQDN，可以直接将该节点的配置参数fqdn设置为它的IP地址。但不建议使用IP，因为IP地址可变，一旦变化，将让集群无法正常工作。一个数据节点的EP(End Point)由FQDN + Port组成。采用FQDN，需要保证DNS服务正常工作，或者在节点以及应用所在的节点配置好hosts文件。另外，这个参数值的长度需要控制在 96 个字符以内。
**端口配置：**一个数据节点对外的端口由TDengine的系统配置参数serverPort决定，对集群内部通讯的端口是serverPort+5。集群内数据节点之间的数据复制操作还占有一个TCP端口，是serverPort+10. 为支持多线程高效的处理UDP数据，每个对内和对外的UDP连接，都需要占用5个连续的端口。因此一个数据节点总的端口范围为serverPort到serverPort + 10，总共11个TCP/UDP端口。（另外还可能有 RESTful、Arbitrator 所使用的端口，那样的话就一共是 13 个。）使用时，需要确保防火墙将这些端口打开，以备使用。每个数据节点可以配置不同的serverPort。（详细的端口情况请参见 [TDengine 2.0 端口说明](https://www.taosdata.com/cn/documentation/faq#port)）
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......@@ -49,6 +49,7 @@ TDengine 的 JDBC 驱动实现尽可能与关系型数据库驱动保持一致
</tr>
</table>
注意：与 JNI 方式不同，RESTful 接口是无状态的，因此 `USE db_name` 指令没有效果，RESTful 下所有对表名、超级表名的引用都需要指定数据库名前缀。
## 如何获取 taos-jdbcdriver
......@@ -266,7 +267,9 @@ while(resultSet.next()){
> 查询和操作关系型数据库一致，使用下标获取返回字段内容时从 1 开始，建议使用字段名称获取。
### 处理异常
在报错后，通过SQLException可以获取到错误的信息和错误码：
```java
try (Statement statement = connection.createStatement()) {
// executeQuery
......@@ -279,11 +282,90 @@ try (Statement statement = connection.createStatement()) {
e.printStackTrace();
}
```
JDBC连接器可能报错的错误码包括3种：JDBC driver本身的报错（错误码在0x2301到0x2350之间），JNI方法的报错（错误码在0x2351到0x2400之间），TDengine其他功能模块的报错。
具体的错误码请参考：
* https://github.com/taosdata/TDengine/blob/develop/src/connector/jdbc/src/main/java/com/taosdata/jdbc/TSDBErrorNumbers.java
* https://github.com/taosdata/TDengine/blob/develop/src/inc/taoserror.h
### <a class="anchor" id="stmt-java"></a>通过参数绑定写入数据
从 2.1.2.0 版本开始，TDengine 的 **JDBC-JNI** 实现大幅改进了参数绑定方式对数据写入（INSERT）场景的支持。采用这种方式写入数据时，能避免 SQL 语法解析的资源消耗，从而在很多情况下显著提升写入性能。（注意：**JDBC-RESTful** 实现并不提供参数绑定这种使用方式。）
```java
Statement stmt = conn.createStatement();
Random r = new Random();
// INSERT 语句中，VALUES 部分允许指定具体的数据列；如果采取自动建表，则 TAGS 部分需要设定全部 TAGS 列的参数值：
TSDBPreparedStatement s = (TSDBPreparedStatement) conn.prepareStatement("insert into ? using weather_test tags (?, ?) (ts, c1, c2) values(?, ?, ?)");
// 设定数据表名：
s.setTableName("w1");
// 设定 TAGS 取值：
s.setTagInt(0, r.nextInt(10));
s.setTagString(1, "Beijing");
int numOfRows = 10;
// VALUES 部分以逐列的方式进行设置：
ArrayList<Long> ts = new ArrayList<>();
for (int i = 0; i < numOfRows; i++){
ts.add(System.currentTimeMillis() + i);
}
s.setTimestamp(0, ts);
ArrayList<Integer> s1 = new ArrayList<>();
for (int i = 0; i < numOfRows; i++){
s1.add(r.nextInt(100));
}
s.setInt(1, s1);
ArrayList<String> s2 = new ArrayList<>();
for (int i = 0; i < numOfRows; i++){
s2.add("test" + r.nextInt(100));
}
s.setString(2, s2, 10);
// AddBatch 之后，缓存并未清空。为避免混乱，并不推荐在 ExecuteBatch 之前再次绑定新一批的数据：
s.columnDataAddBatch();
// 执行绑定数据后的语句：
s.columnDataExecuteBatch();
// 执行语句后清空缓存。在清空之后，可以复用当前的对象，绑定新的一批数据（可以是新表名、新 TAGS 值、新 VALUES 值）：
s.columnDataClearBatch();
// 执行完毕，释放资源：
s.columnDataCloseBatch();
```
用于设定 TAGS 取值的方法总共有：
```java
public void setTagNull(int index, int type)
public void setTagBoolean(int index, boolean value)
public void setTagInt(int index, int value)
public void setTagByte(int index, byte value)
public void setTagShort(int index, short value)
public void setTagLong(int index, long value)
public void setTagTimestamp(int index, long value)
public void setTagFloat(int index, float value)
public void setTagDouble(int index, double value)
public void setTagString(int index, String value)
public void setTagNString(int index, String value)
```
用于设定 VALUES 数据列的取值的方法总共有：
```java
public void setInt(int columnIndex, ArrayList<Integer> list) throws SQLException
public void setFloat(int columnIndex, ArrayList<Float> list) throws SQLException
public void setTimestamp(int columnIndex, ArrayList<Long> list) throws SQLException
public void setLong(int columnIndex, ArrayList<Long> list) throws SQLException
public void setDouble(int columnIndex, ArrayList<Double> list) throws SQLException
public void setBoolean(int columnIndex, ArrayList<Boolean> list) throws SQLException
public void setByte(int columnIndex, ArrayList<Byte> list) throws SQLException
public void setShort(int columnIndex, ArrayList<Short> list) throws SQLException
public void setString(int columnIndex, ArrayList<String> list, int size) throws SQLException
public void setNString(int columnIndex, ArrayList<String> list, int size) throws SQLException
```
其中 setString 和 setNString 都要求用户在 size 参数里声明表定义中对应列的列宽。
### <a class="anchor" id="subscribe"></a>订阅
#### 创建
......@@ -450,8 +532,9 @@ Query OK, 1 row(s) in set (0.000141s)
| taos-jdbcdriver 版本 | TDengine 版本 | JDK 版本 |
| -------------------- | ----------------- | -------- |
| 2.0.22 | 2.0.18.0 及以上 | 1.8.x |
| 2.0.12 - 2.0.21 | 2.0.8.0 - 2.0.17.0 | 1.8.x |
| 2.0.31 | 2.1.3.0 及以上 | 1.8.x |
| 2.0.22 - 2.0.30 | 2.0.18.0 - 2.1.2.x | 1.8.x |
| 2.0.12 - 2.0.21 | 2.0.8.0 - 2.0.17.x | 1.8.x |
| 2.0.4 - 2.0.11 | 2.0.0.0 - 2.0.7.x | 1.8.x |
| 1.0.3 | 1.6.1.x 及以上 | 1.8.x |
| 1.0.2 | 1.6.1.x 及以上 | 1.8.x |
......@@ -470,7 +553,7 @@ TDengine 目前支持时间戳、数字、字符、布尔类型，与 Java 对
| BIGINT | java.lang.Long |
| FLOAT | java.lang.Float |
| DOUBLE | java.lang.Double |
| SMALLINT | java.lang.Short |
| SMALLINT | java.lang.Short |
| TINYINT | java.lang.Byte |
| BOOL | java.lang.Boolean |
| BINARY | byte array |
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......@@ -56,7 +56,7 @@ TDengine提供了丰富的应用程序开发接口，其中包括C/C++、Java、
​ *taos.tar.gz*：应用驱动安装包
​ *driver*：TDengine应用驱动driver
​ *connector*: 各种编程语言连接器（go/grafanaplugin/nodejs/python/JDBC）
​ *examples*: 各种编程语言的示例程序(c/C#/go/JDBC/matlab/python/R)
​ *examples*: 各种编程语言的示例程序(c/C#/go/JDBC/MATLAB/python/R)
运行install_client.sh进行安装
......@@ -259,7 +259,7 @@ typedef struct taosField {
获取最近一次API调用失败的原因,返回值为字符串。
- `char *taos_errno(TAOS_RES *res)`
- `int taos_errno(TAOS_RES *res)`
获取最近一次API调用失败的原因，返回值为错误代码。
......@@ -291,9 +291,27 @@ typedef struct taosField {
TDengine的异步API均采用非阻塞调用模式。应用程序可以用多线程同时打开多张表，并可以同时对每张打开的表进行查询或者插入操作。需要指出的是，**客户端应用必须确保对同一张表的操作完全串行化**，即对同一个表的插入或查询操作未完成时（未返回时），不能够执行第二个插入或查询操作。
### 参数绑定API
<a class="anchor" id="stmt"></a>
### 参数绑定 API
除了直接调用 `taos_query` 进行查询，TDengine也提供了支持参数绑定的Prepare API，与 MySQL 一样，这些API目前也仅支持用问号`?`来代表待绑定的参数，具体如下：
除了直接调用 `taos_query` 进行查询，TDengine 也提供了支持参数绑定的 Prepare API，与 MySQL 一样，这些 API 目前也仅支持用问号 `?` 来代表待绑定的参数。
从 2.1.1.0 和 2.1.2.0 版本开始，TDengine 大幅改进了参数绑定接口对数据写入（INSERT）场景的支持。这样在通过参数绑定接口写入数据时，就避免了 SQL 语法解析的资源消耗，从而在绝大多数情况下显著提升写入性能。此时的典型操作步骤如下：
1. 调用 `taos_stmt_init` 创建参数绑定对象；
2. 调用 `taos_stmt_prepare` 解析 INSERT 语句；
3. 如果 INSERT 语句中预留了表名但没有预留 TAGS，那么调用 `taos_stmt_set_tbname` 来设置表名；
4. 如果 INSERT 语句中既预留了表名又预留了 TAGS（例如 INSERT 语句采取的是自动建表的方式），那么调用 `taos_stmt_set_tbname_tags` 来设置表名和 TAGS 的值；
5. 调用 `taos_stmt_bind_param_batch` 以多列的方式设置 VALUES 的值，或者调用 `taos_stmt_bind_param` 以单行的方式设置 VALUES 的值；
6. 调用 `taos_stmt_add_batch` 把当前绑定的参数加入批处理；
7. 可以重复第 3～6 步，为批处理加入更多的数据行；
8. 调用 `taos_stmt_execute` 执行已经准备好的批处理指令；
9. 执行完毕，调用 `taos_stmt_close` 释放所有资源。
说明：如果 `taos_stmt_execute` 执行成功，假如不需要改变 SQL 语句的话，那么是可以复用 `taos_stmt_prepare` 的解析结果，直接进行第 3～6 步绑定新数据的。但如果执行出错，那么并不建议继续在当前的环境上下文下继续工作，而是建议释放资源，然后从 `taos_stmt_init` 步骤重新开始。
除 C/C++ 语言外，TDengine 的 Java 语言 JNI Connector 也提供参数绑定接口支持，具体请另外参见：[参数绑定接口的 Java 用法](https://www.taosdata.com/cn/documentation/connector/java#stmt-java)。
接口相关的具体函数如下（也可以参考 [apitest.c](https://github.com/taosdata/TDengine/blob/develop/tests/examples/c/apitest.c) 文件中使用对应函数的方式）：
- `TAOS_STMT* taos_stmt_init(TAOS *taos)`
......@@ -301,11 +319,12 @@ TDengine的异步API均采用非阻塞调用模式。应用程序可以用多线
- `int taos_stmt_prepare(TAOS_STMT *stmt, const char *sql, unsigned long length)`
解析一条sql语句，将解析结果和参数信息绑定到stmt上，如果参数length大于0，将使用此参数作为sql语句的长度，如等于0，将自动判断sql语句的长度。
解析一条 SQL 语句，将解析结果和参数信息绑定到 stmt 上，如果参数 length 大于 0，将使用此参数作为 SQL 语句的长度，如等于 0，将自动判断 SQL 语句的长度。
- `int taos_stmt_bind_param(TAOS_STMT *stmt, TAOS_BIND *bind)`
进行参数绑定，bind指向一个数组，需保证此数组的元素数量和顺序与sql语句中的参数完全一致。TAOS_BIND 的使用方法与 MySQL中的 MYSQL_BIND 一致，具体定义如下：
不如 `taos_stmt_bind_param_batch` 效率高，但可以支持非 INSERT 类型的 SQL 语句。
进行参数绑定，bind 指向一个数组（代表所要绑定的一行数据），需保证此数组中的元素数量和顺序与 SQL 语句中的参数完全一致。TAOS_BIND 的使用方法与 MySQL 中的 MYSQL_BIND 一致，具体定义如下：
```c
typedef struct TAOS_BIND {
......@@ -319,9 +338,35 @@ typedef struct TAOS_BIND {
} TAOS_BIND;
```
- `int taos_stmt_set_tbname(TAOS_STMT* stmt, const char* name)`
（2.1.1.0 版本新增，仅支持用于替换 INSERT 语句中的参数值）
当 SQL 语句中的表名使用了 `?` 占位时，可以使用此函数绑定一个具体的表名。
- `int taos_stmt_set_tbname_tags(TAOS_STMT* stmt, const char* name, TAOS_BIND* tags)`
（2.1.2.0 版本新增，仅支持用于替换 INSERT 语句中的参数值）
当 SQL 语句中的表名和 TAGS 都使用了 `?` 占位时，可以使用此函数绑定具体的表名和具体的 TAGS 取值。最典型的使用场景是使用了自动建表功能的 INSERT 语句（目前版本不支持指定具体的 TAGS 列）。tags 参数中的列数量需要与 SQL 语句中要求的 TAGS 数量完全一致。
- `int taos_stmt_bind_param_batch(TAOS_STMT* stmt, TAOS_MULTI_BIND* bind)`
（2.1.1.0 版本新增，仅支持用于替换 INSERT 语句中的参数值）
以多列的方式传递待绑定的数据，需要保证这里传递的数据列的顺序、列的数量与 SQL 语句中的 VALUES 参数完全一致。TAOS_MULTI_BIND 的具体定义如下：
```c
typedef struct TAOS_MULTI_BIND {
int buffer_type;
void * buffer;
uintptr_t buffer_length;
int32_t * length;
char * is_null;
int num; // 列的个数，即 buffer 中的参数个数
} TAOS_MULTI_BIND;
```
- `int taos_stmt_add_batch(TAOS_STMT *stmt)`
将当前绑定的参数加入批处理中，调用此函数后，可以再次调用`taos_stmt_bind_param`绑定新的参数。需要注意，此函数仅支持 insert/import 语句，如果是select等其他SQL语句，将返回错误。
将当前绑定的参数加入批处理中，调用此函数后，可以再次调用 `taos_stmt_bind_param` 或 `taos_stmt_bind_param_batch` 绑定新的参数。需要注意，此函数仅支持 INSERT/IMPORT 语句，如果是 SELECT 等其他 SQL 语句，将返回错误。
- `int taos_stmt_execute(TAOS_STMT *stmt)`
......@@ -329,12 +374,17 @@ typedef struct TAOS_BIND {
- `TAOS_RES* taos_stmt_use_result(TAOS_STMT *stmt)`
获取语句的结果集。结果集的使用方式与非参数化调用时一致，使用完成后，应对此结果集调用 `taos_free_result`以释放资源。
获取语句的结果集。结果集的使用方式与非参数化调用时一致，使用完成后，应对此结果集调用 `taos_free_result` 以释放资源。
- `int taos_stmt_close(TAOS_STMT *stmt)`
执行完毕，释放所有资源。
- `char * taos_stmt_errstr(TAOS_STMT *stmt)`
（2.1.3.0 版本新增）
用于在其他 stmt API 返回错误（返回错误码或空指针）时获取错误信息。
### 连续查询接口
TDengine提供时间驱动的实时流式计算API。可以每隔一指定的时间段，对一张或多张数据库的表(数据流)进行各种实时聚合计算操作。操作简单，仅有打开、关闭流的API。具体如下：
......@@ -377,12 +427,15 @@ TDengine提供时间驱动的实时流式计算API。可以每隔一指定的时
* res：查询结果集，注意结果集中可能没有记录
* param：调用 `taos_subscribe`时客户程序提供的附加参数
* code：错误码
**注意**：在这个回调函数里不可以做耗时过长的处理，尤其是对于返回的结果集中数据较多的情况，否则有可能导致客户端阻塞等异常状态。如果必须进行复杂计算，则建议在另外的线程中进行处理。
* `TAOS_RES *taos_consume(TAOS_SUB *tsub)`
同步模式下，该函数用来获取订阅的结果。 用户应用程序将其置于一个循环之中。 如两次调用`taos_consume`的间隔小于订阅的轮询周期，API将会阻塞，直到时间间隔超过此周期。 如果数据库有新记录到达，该API将返回该最新的记录，否则返回一个没有记录的空结果集。 如果返回值为 `NULL`，说明系统出错。 异步模式下，用户程序不应调用此API。
**注意**：在调用 `taos_consume()` 之后，用户应用应确保尽快调用 `taos_fetch_row()` 或 `taos_fetch_block()` 来处理订阅结果，否则服务端会持续缓存查询结果数据等待客户端读取，极端情况下会导致服务端内存消耗殆尽，影响服务稳定性。
* `void taos_unsubscribe(TAOS_SUB *tsub, int keepProgress)`
取消订阅。 如参数 `keepProgress` 不为0，API会保留订阅的进度信息，后续调用 `taos_subscribe` 时可以基于此进度继续；否则将删除进度信息，后续只能重新开始读取数据。
......@@ -504,6 +557,13 @@ c1.close()
conn.close()
```
#### 关于纳秒 (nanosecond) 在 Python 连接器中的说明
由于目前 Python 对 nanosecond 支持的不完善(参见链接 1. 2. )，目前的实现方式是在 nanosecond 精度时返回整数，而不是 ms 和 us 返回的 datetime 类型，应用开发者需要自行处理，建议使用 pandas 的 to_datetime()。未来如果 Python 正式完整支持了纳秒，涛思数据可能会修改相关接口。
1. https://stackoverflow.com/questions/10611328/parsing-datetime-strings-containing-nanoseconds
2. https://www.python.org/dev/peps/pep-0564/
#### 帮助信息
用户可通过python的帮助信息直接查看模块的使用信息，或者参考tests/examples/python中的示例程序。以下为部分常用类和方法：
......@@ -535,7 +595,9 @@ conn.close()
## <a class="anchor" id="restful"></a>RESTful Connector
为支持各种不同类型平台的开发，TDengine提供符合REST设计标准的API，即RESTful API。为最大程度降低学习成本，不同于其他数据库RESTful API的设计方法，TDengine直接通过HTTP POST 请求BODY中包含的SQL语句来操作数据库，仅需要一个URL。RESTful连接器的使用参见[视频教程](https://www.taosdata.com/blog/2020/11/11/1965.html)。
为支持各种不同类型平台的开发，TDengine 提供符合 REST 设计标准的 API，即 RESTful API。为最大程度降低学习成本，不同于其他数据库 RESTful API 的设计方法，TDengine 直接通过 HTTP POST 请求 BODY 中包含的 SQL 语句来操作数据库，仅需要一个 URL。RESTful 连接器的使用参见[视频教程](https://www.taosdata.com/blog/2020/11/11/1965.html)。
注意：与标准连接器的一个区别是，RESTful 接口是无状态的，因此 `USE db_name` 指令没有效果，所有对表名、超级表名的引用都需要指定数据库名前缀。
### HTTP请求格式
......@@ -753,7 +815,7 @@ C#连接器支持的系统有：Linux 64/Windows x64/Windows x86
* 应用驱动安装请参考[安装连接器驱动步骤](https://www.taosdata.com/cn/documentation/connector#driver)。
* .NET接口文件TDengineDrivercs.cs和参考程序示例TDengineTest.cs均位于Windows客户端install_directory/examples/C#目录下。
* 在Windows系统上，C#应用程序可以使用TDengine的原生C接口来执行所有数据库操作，后续版本将提供ORM（dapper）框架驱动。
* 在Windows系统上，C#应用程序可以使用TDengine的原生C接口来执行所有数据库操作，后续版本将提供ORM（Dapper）框架驱动。
### 安装验证
......@@ -845,11 +907,15 @@ go env -w GOPROXY=https://goproxy.io,direct
sql.Open内置的方法，Close closes the statement.
### 其他代码示例
[Consume Messages from Kafka](https://github.com/taosdata/go-demo-kafka) 是一个通过 Go 语言实现消费 Kafka 队列写入 TDengine 的示例程序，也可以作为通过 Go 连接 TDengine 的写法参考。
## <a class="anchor" id="nodejs"></a>Node.js Connector
Node.js连接器支持的系统有：
| **CPU类型** | x64（64bit） | | | aarch64 | aarch32 |
|**CPU类型** | x64（64bit） | | | aarch64 | aarch32 |
| ------------ | ------------ | -------- | -------- | -------- | -------- |
| **OS类型** | Linux | Win64 | Win32 | Linux | Linux |
| **支持与否** | **支持** | **支持** | **支持** | **支持** | **支持** |
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![img](page://images/connections/import_dashboard2.jpg)
## <a class="anchor" id="matlab"></a>Matlab
## <a class="anchor" id="matlab"></a>MATLAB
MatLab可以通过安装包内提供的JDBC Driver直接连接到TDengine获取数据到本地工作空间。
MATLAB 可以通过安装包内提供的 JDBC Driver 直接连接到 TDengine 获取数据到本地工作空间。
### MatLab的JDBC接口适配
### MATLAB 的 JDBC 接口适配
MatLab的适配有下面几个步骤，下面以Windows10上适配MatLab2017a为例：
MATLAB 的适配有下面几个步骤，下面以 Windows 10 上适配 MATLAB2021a 为例：
- 将TDengine安装包内的驱动程序JDBCDriver-1.0.0-dist.jar拷贝到${matlab_root}\MATLAB\R2017a\java\jar\toolbox
- 将TDengine安装包内的taos.lib文件拷贝至${matlab_ root _dir}\MATLAB\R2017a\lib\win64
- 将新添加的驱动jar包加入MatLab的classpath。在${matlab_ root _dir}\MATLAB\R2017a\toolbox\local\classpath.txt文件中添加下面一行
​
- 将 TDengine 客户端安装路径下的 `\TDengine\connector\jdbc的驱动程序taos-jdbcdriver-2.0.25-dist.jar` 拷贝到 `${matlab_root}\MATLAB\R2021a\java\jar\toolbox`。
- 将 TDengine 安装包内的 `taos.lib` 文件拷贝至 `${matlab_root_dir}\MATLAB\R2021\lib\win64`。
- 将新添加的驱动 jar 包加入 MATLAB 的 classpath。在 `${matlab_root_dir}\MATLAB\R2021a\toolbox\local\classpath.txt` 文件中添加下面一行：
```
$matlabroot/java/jar/toolbox/JDBCDriver-1.0.0-dist.jar
$matlabroot/java/jar/toolbox/taos-jdbcdriver-2.0.25-dist.jar
```
- 在${user_home}\AppData\Roaming\MathWorks\MATLAB\R2017a\下添加一个文件javalibrarypath.txt, 并在该文件中添加taos.dll的路径，比如您的taos.dll是在安装时拷贝到了C:\Windows\System32下，那么就应该在javalibrarypath.txt中添加如下一行：
​
- 在 `${user_home}\AppData\Roaming\MathWorks\MATLAB\R2021a\` 下添加一个文件 `javalibrarypath.txt`，并在该文件中添加 taos.dll 的路径，比如您的 taos.dll 是在安装时拷贝到了 `C:\Windows\System32` 下，那么就应该在 `javalibrarypath.txt` 中添加如下一行：
```
C:\Windows\System32
```
### 在MatLab中连接TDengine获取数据
### 在 MATLAB 中连接 TDengine 获取数据
在成功进行了上述配置后，打开MatLab。
在成功进行了上述配置后，打开 MATLAB。
- 创建一个连接：
```matlab
conn = database(‘db’, ‘root’, ‘taosdata’, ‘com.taosdata.jdbc.TSDBDriver’, ‘jdbc:TSDB://127.0.0.1:0/’)
conn = database(‘test’, ‘root’, ‘taosdata’, ‘com.taosdata.jdbc.TSDBDriver’, ‘jdbc:TSDB://192.168.1.94:6030/’)
```
- 执行一次查询：
```matlab
sql0 = [‘select * from tb’]
data = select(conn, sql0);
```
- 插入一条记录:
```matlab
sql1 = [‘insert into tb values (now, 1)’]
exec(conn, sql1)
```
更多例子细节请参考安装包内examples\Matlab\TDengineDemo.m文件。
更多例子细节请参考安装包内 `examples\Matlab\TDengineDemo.m` 文件。
## <a class="anchor" id="r"></a>R
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......@@ -85,7 +85,7 @@ taos>
将后续的数据节点添加到现有集群，具体有以下几步：
1. 按照[《立即开始》](https://www.taosdata.com/cn/documentation/getting-started/)一章的方法在每个物理节点启动taosd；
1. 按照[《立即开始》](https://www.taosdata.com/cn/documentation/getting-started/)一章的方法在每个物理节点启动taosd；（注意：每个物理节点都需要在 taos.cfg 文件中将 firstEP 参数配置为新集群首个节点的 End Point——在本例中是 h1.taos.com:6030）
2. 在第一个数据节点，使用CLI程序taos, 登录进TDengine系统, 执行命令:
......
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......@@ -99,7 +99,7 @@ taosd -C
下面仅仅列出一些重要的配置参数，更多的参数请看配置文件里的说明。各个参数的详细介绍及作用请看前述章节，而且这些参数的缺省配置都是工作的，一般无需设置。**注意：配置修改后，需要重启*taosd*服务才能生效。**
- firstEp: taosd启动时，主动连接的集群中首个dnode的end point, 默认值为localhost:6030。
- fqdn：数据节点的FQDN，缺省为操作系统配置的第一个hostname。如果习惯IP地址访问，可设置为该节点的IP地址。
- fqdn：数据节点的FQDN，缺省为操作系统配置的第一个hostname。如果习惯IP地址访问，可设置为该节点的IP地址。这个参数值的长度需要控制在 96 个字符以内。
- serverPort：taosd启动后，对外服务的端口号，默认值为6030。（RESTful服务使用的端口号是在此基础上+11，即默认值为6041。）
- dataDir: 数据文件目录，所有的数据文件都将写入该目录。默认值：/var/lib/taos。
- logDir：日志文件目录，客户端和服务器的运行日志文件将写入该目录。默认值：/var/log/taos。
......@@ -116,20 +116,22 @@ taosd -C
**注意：**对于端口，TDengine会使用从serverPort起13个连续的TCP和UDP端口号，请务必在防火墙打开。因此如果是缺省配置，需要打开从6030到6042共13个端口，而且必须TCP和UDP都打开。（详细的端口情况请参见 [TDengine 2.0 端口说明](https://www.taosdata.com/cn/documentation/faq#port)）
不同应用场景的数据往往具有不同的数据特征，比如保留天数、副本数、采集频次、记录大小、采集点的数量、压缩等都可完全不同。为获得在存储上的最高效率，TDengine提供如下存储相关的系统配置参数：
不同应用场景的数据往往具有不同的数据特征，比如保留天数、副本数、采集频次、记录大小、采集点的数量、压缩等都可完全不同。为获得在存储上的最高效率，TDengine提供如下存储相关的系统配置参数（既可以作为 create database 指令的参数，也可以写在 taos.cfg 配置文件中用来设定创建新数据库时所采用的默认值）：
- days：一个数据文件存储数据的时间跨度，单位为天，默认值：10。
- keep：数据库中数据保留的天数，单位为天，默认值：3650。（可通过 alter database 修改）
- minRows：文件块中记录的最小条数，单位为条，默认值：100。
- maxRows：文件块中记录的最大条数，单位为条，默认值：4096。
- comp：文件压缩标志位，0：关闭；1：一阶段压缩；2：两阶段压缩。默认值：2。（可通过 alter database 修改）
- walLevel：WAL级别。1：写wal，但不执行fsync；2：写wal, 而且执行fsync。默认值：1。
- fsync：当wal设置为2时，执行fsync的周期。设置为0，表示每次写入，立即执行fsync。单位为毫秒，默认值：3000。
- cache：内存块的大小，单位为兆字节（MB），默认值：16。
- days：一个数据文件存储数据的时间跨度。单位为天，默认值：10。
- keep：数据库中数据保留的天数。单位为天，默认值：3650。（可通过 alter database 修改）
- minRows：文件块中记录的最小条数。单位为条，默认值：100。
- maxRows：文件块中记录的最大条数。单位为条，默认值：4096。
- comp：文件压缩标志位。0：关闭；1：一阶段压缩；2：两阶段压缩。默认值：2。（可通过 alter database 修改）
- wal：WAL级别。1：写wal，但不执行fsync；2：写wal, 而且执行fsync。默认值：1。（在 taos.cfg 中参数名需要写作 walLevel）（可通过 alter database 修改）
- fsync：当wal设置为2时，执行fsync的周期。设置为0，表示每次写入，立即执行fsync。单位为毫秒，默认值：3000。（可通过 alter database 修改）
- cache：内存块的大小。单位为兆字节（MB），默认值：16。
- blocks：每个VNODE（TSDB）中有多少cache大小的内存块。因此一个VNODE的用的内存大小粗略为（cache * blocks）。单位为块，默认值：4。（可通过 alter database 修改）
- replica：副本个数，取值范围：1-3。单位为个，默认值：1。（可通过 alter database 修改）
- precision：时间戳精度标识，ms表示毫秒，us表示微秒。默认值：ms。
- cacheLast：是否在内存中缓存子表的最近数据，0：关闭；1：缓存子表最近一行数据；2：缓存子表每一列的最近的非NULL值，3：同时打开缓存最近行和列功能，默认值：0。（可通过 alter database 修改）（从 2.0.11 版本开始支持此参数）
- replica：副本个数。取值范围：1-3，单位为个，默认值：1。（可通过 alter database 修改）
- quorum：多副本环境下指令执行的确认数要求。取值范围：1、2，单位为个，默认值：1。（可通过 alter database 修改）
- precision：时间戳精度标识。ms表示毫秒，us表示微秒，默认值：ms。（2.1.2.0 版本之前、2.0.20.7 版本之前在 taos.cfg 文件中不支持此参数。）
- cacheLast：是否在内存中缓存子表的最近数据。0：关闭；1：缓存子表最近一行数据；2：缓存子表每一列的最近的非NULL值；3：同时打开缓存最近行和列功能。默认值：0。（可通过 alter database 修改）（从 2.1.2.0 版本开始此参数支持 0～3 的取值范围，在此之前取值只能是 [0, 1]；而 2.0.11.0 之前的版本在 SQL 指令中不支持此参数。）（2.1.2.0 版本之前、2.0.20.7 版本之前在 taos.cfg 文件中不支持此参数。）
- update：是否允许更新。0：不允许；1：允许。默认值：0。
对于一个应用场景，可能有多种数据特征的数据并存，最佳的设计是将具有相同数据特征的表放在一个库里，这样一个应用有多个库，而每个库可以配置不同的存储参数，从而保证系统有最优的性能。TDengine允许应用在创建库时指定上述存储参数，如果指定，该参数就将覆盖对应的系统配置参数。举例，有下述SQL：
......@@ -142,7 +144,6 @@ taosd -C
TDengine集群中加入一个新的dnode时，涉及集群相关的一些参数必须与已有集群的配置相同，否则不能成功加入到集群中。会进行校验的参数如下：
- numOfMnodes：系统中管理节点个数。默认值：3。
- balance：是否启动负载均衡。0：否，1：是。默认值：1。
- mnodeEqualVnodeNum: 一个mnode等同于vnode消耗的个数。默认值：4。
- offlineThreshold: dnode离线阈值，超过该时间将导致该dnode从集群中删除。单位为秒，默认值：86400*10（即10天）。
- statusInterval: dnode向mnode报告状态时长。单位为秒，默认值：1。
......@@ -150,6 +151,10 @@ TDengine集群中加入一个新的dnode时，涉及集群相关的一些参数
- maxVgroupsPerDb: 每个数据库中能够使用的最大vgroup个数。
- arbitrator: 系统中裁决器的end point，缺省为空。
- timezone、locale、charset 的配置见客户端配置。（2.0.20.0 及以上的版本里，集群中加入新节点已不要求 locale 和 charset 参数取值一致）
- balance：是否启用负载均衡。0：否，1：是。默认值：1。
- flowctrl：是否启用非阻塞流控。0：否，1：是。默认值：1。
- slaveQuery：是否启用 slave vnode 参与查询。0：否，1：是。默认值：1。
- adjustMaster：是否启用 vnode master 负载均衡。0：否，1：是。默认值：1。
为方便调试，可通过SQL语句临时调整每个dnode的日志配置，系统重启后会失效：
......@@ -413,6 +418,19 @@ TDengine启动后，会自动创建一个监测数据库log，并自动将服务
这些监测信息的采集缺省是打开的，但可以修改配置文件里的选项enableMonitor将其关闭或打开。
<a class="anchor" id="optimize"></a>
## 性能优化
因数据行 [update](https://www.taosdata.com/cn/documentation/faq#update)、表删除、数据过期等原因，TDengine 的磁盘存储文件有可能出现数据碎片，影响查询操作的性能表现。从 2.1.3.0 版本开始，新增 SQL 指令 COMPACT 来启动碎片重整过程：
```mysql
COMPACT VNODES IN (vg_id1, vg_id2, ...)
```
COMPACT 命令对指定的一个或多个 VGroup 启动碎片重整，系统会通过任务队列尽快安排重整操作的具体执行。COMPACT 指令所需的 VGroup id，可以通过 `SHOW VGROUPS;` 指令的输出结果获取；而且在 `SHOW VGROUPS;` 中会有一个 compacting 列，值为 1 时表示对应的 VGroup 正在进行碎片重整，为 0 时则表示并没有处于重整状态。
需要注意的是，碎片重整操作会大幅消耗磁盘 I/O。因此在重整进行期间，有可能会影响节点的写入和查询性能，甚至在极端情况下导致短时间的阻写。
## <a class="anchor" id="directories"></a>文件目录结构
安装TDengine后，默认会在操作系统中生成下列目录或文件：
......@@ -444,7 +462,7 @@ TDengine的所有可执行文件默认存放在 _/usr/local/taos/bin_ 目录下
- 数据库名：不能包含“.”以及特殊字符，不能超过 32 个字符
- 表名：不能包含“.”以及特殊字符，与所属数据库名一起，不能超过 192 个字符
- 表的列名：不能包含特殊字符，不能超过 64 个字符
- 数据库名、表名、列名，都不能以数字开头
- 数据库名、表名、列名，都不能以数字开头，合法的可用字符集是“英文字符、数字和下划线”
- 表的列数：不能超过 1024 列
- 记录的最大长度：包括时间戳 8 byte，不能超过 16KB（每个 BINARY/NCHAR 类型的列还会额外占用 2 个 byte 的存储位置）
- 单条 SQL 语句默认最大字符串长度：65480 byte
......@@ -460,43 +478,44 @@ TDengine的所有可执行文件默认存放在 _/usr/local/taos/bin_ 目录下
目前 TDengine 有将近 200 个内部保留关键字，这些关键字无论大小写均不可以用作库名、表名、STable 名、数据列名及标签列名等。这些关键字列表如下：
| 关键字列表 | | | | |
| ---------- | ----------- | ------------ | ---------- | --------- |
| ABLOCKS | CONNECTIONS | HAVING | MODULES | SMALLINT |
| ABORT | COPY | ID | NCHAR | SPREAD |
| ACCOUNT | COUNT | IF | NE | STABLE |
| ACCOUNTS | CREATE | IGNORE | NONE | STABLES |
| ADD | CTIME | IMMEDIATE | NOT | STAR |
| AFTER | DATABASE | IMPORT | NOTNULL | STATEMENT |
| ALL | DATABASES | IN | NOW | STDDEV |
| ALTER | DAYS | INITIALLY | OF | STREAM |
| AND | DEFERRED | INSERT | OFFSET | STREAMS |
| AS | DELIMITERS | INSTEAD | OR | STRING |
| ASC | DESC | INTEGER | ORDER | SUM |
| ATTACH | DESCRIBE | INTERVAL | PASS | TABLE |
| AVG | DETACH | INTO | PERCENTILE | TABLES |
| BEFORE | DIFF | IP | PLUS | TAG |
| BEGIN | DISTINCT | IS | PRAGMA | TAGS |
| BETWEEN | DIVIDE | ISNULL | PREV | TBLOCKS |
| BIGINT | DNODE | JOIN | PRIVILEGE | TBNAME |
| BINARY | DNODES | KEEP | QUERIES | TIMES |
| BITAND | DOT | KEY | QUERY | TIMESTAMP |
| BITNOT | DOUBLE | KILL | RAISE | TINYINT |
| BITOR | DROP | LAST | REM | TOP |
| BOOL | EACH | LE | REPLACE | TOPIC |
| BOTTOM | END | LEASTSQUARES | REPLICA | TRIGGER |
| BY | EQ | LIKE | RESET | UMINUS |
| CACHE | EXISTS | LIMIT | RESTRICT | UNION |
| CASCADE | EXPLAIN | LINEAR | ROW | UPLUS |
| CHANGE | FAIL | LOCAL | ROWS | USE |
| CLOG | FILL | LP | RP | USER |
| CLUSTER | FIRST | LSHIFT | RSHIFT | USERS |
| COLON | FLOAT | LT | SCORES | USING |
| COLUMN | FOR | MATCH | SELECT | VALUES |
| COMMA | FROM | MAX | SEMI | VARIABLE |
| COMP | GE | METRIC | SET | VGROUPS |
| CONCAT | GLOB | METRICS | SHOW | VIEW |
| CONFIGS | GRANTS | MIN | SLASH | WAVG |
| CONFLICT | GROUP | MINUS | SLIDING | WHERE |
| CONNECTION | GT | MNODES | SLIMIT | |
| 关键字列表 | | | | |
| ------------ | ------------ | ------------ | ------------ | ------------ |
| ABORT | CREATE | IGNORE | NULL | STAR |
| ACCOUNT | CTIME | IMMEDIATE | OF | STATE |
| ACCOUNTS | DATABASE | IMPORT | OFFSET | STATEMENT |
| ADD | DATABASES | IN | OR | STATE_WINDOW |
| AFTER | DAYS | INITIALLY | ORDER | STORAGE |
| ALL | DBS | INSERT | PARTITIONS | STREAM |
| ALTER | DEFERRED | INSTEAD | PASS | STREAMS |
| AND | DELIMITERS | INT | PLUS | STRING |
| AS | DESC | INTEGER | PPS | SYNCDB |
| ASC | DESCRIBE | INTERVAL | PRECISION | TABLE |
| ATTACH | DETACH | INTO | PREV | TABLES |
| BEFORE | DISTINCT | IS | PRIVILEGE | TAG |
| BEGIN | DIVIDE | ISNULL | QTIME | TAGS |
| BETWEEN | DNODE | JOIN | QUERIES | TBNAME |
| BIGINT | DNODES | KEEP | QUERY | TIMES |
| BINARY | DOT | KEY | QUORUM | TIMESTAMP |
| BITAND | DOUBLE | KILL | RAISE | TINYINT |
| BITNOT | DROP | LE | REM | TOPIC |
| BITOR | EACH | LIKE | REPLACE | TOPICS |
| BLOCKS | END | LIMIT | REPLICA | TRIGGER |
| BOOL | EQ | LINEAR | RESET | TSERIES |
| BY | EXISTS | LOCAL | RESTRICT | UMINUS |
| CACHE | EXPLAIN | LP | ROW | UNION |
| CACHELAST | FAIL | LSHIFT | RP | UNSIGNED |
| CASCADE | FILE | LT | RSHIFT | UPDATE |
| CHANGE | FILL | MATCH | SCORES | UPLUS |
| CLUSTER | FLOAT | MAXROWS | SELECT | USE |
| COLON | FOR | MINROWS | SEMI | USER |
| COLUMN | FROM | MINUS | SESSION | USERS |
| COMMA | FSYNC | MNODES | SET | USING |
| COMP | GE | MODIFY | SHOW | VALUES |
| COMPACT | GLOB | MODULES | SLASH | VARIABLE |
| CONCAT | GRANTS | NCHAR | SLIDING | VARIABLES |
| CONFLICT | GROUP | NE | SLIMIT | VGROUPS |
| CONNECTION | GT | NONE | SMALLINT | VIEW |
| CONNECTIONS | HAVING | NOT | SOFFSET | VNODES |
| CONNS | ID | NOTNULL | STABLE | WAL |
| COPY | IF | NOW | STABLES | WHERE |
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......@@ -87,6 +87,7 @@
TDengine还没有一组专用的validation queries。然而建议你使用系统监测的数据库”log"来做。
<a class="anchor" id="update"></a>
## 9. 我可以删除或更新一条记录吗？
TDengine 目前尚不支持删除功能，未来根据用户需求可能会支持。
......
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# TDengine Documentation
TDengine is a highly efficient platform to store, query, and analyze time-series data. It is specially designed and optimized for IoT, Internet of Vehicles, Industrial IoT, IT Infrastructure and Application Monitoring, etc. It works like a relational database, such as MySQL, but you are strongly encouraged to read through the following documentation before you experience it, especially the Data Model and Data Modeling sections. In addition to this document, you should also download and read our technology white paper. For the older TDengine version 1.6 documentation, please click here.
## [TDengine Introduction](/evaluation)
* [TDengine Introduction and Features](/evaluation#intro)
* [TDengine Use Scenes](/evaluation#scenes)
* [TDengine Performance Metrics and Verification]((/evaluation#))
## [Getting Started](/getting-started)
* [Quickly Install](/getting-started#install): install via source code/package / Docker within seconds
- [Easy to Launch](/getting-started#start): start / stop TDengine with systemctl
- [Command-line](/getting-started#console) : an easy way to access TDengine server
- [Experience Lightning Speed](/getting-started#demo): running a demo, inserting/querying data to experience faster speed
- [List of Supported Platforms](/getting-started#platforms): a list of platforms supported by TDengine server and client
- [Deploy to Kubernetes](https://taosdata.github.io/TDengine-Operator/en/index.html)：a detailed guide for TDengine deployment in Kubernetes environment
## [Overall Architecture](/architecture)
- [Data Model](/architecture#model): relational database model, but one table for one device with static tags
- [Cluster and Primary Logical Unit](/architecture#cluster): Take advantage of NoSQL, support scale-out and high-reliability
- [Storage Model and Data Partitioning/Sharding](/architecture#sharding): tag data will be separated from time-series data, segmented by vnode and time
- [Data Writing and Replication Process](/architecture#replication): records received are written to WAL, cached, with acknowledgement is sent back to client, while supporting multi-replicas
- [Caching and Persistence](/architecture#persistence): latest records are cached in memory, but are written in columnar format with an ultra-high compression ratio
- [Data Query](/architecture#query): support various functions, time-axis aggregation, interpolation, and multi-table aggregation
## [Data Modeling](/model)
- [Create a Database](/model#create-db): create a database for all data collection points with similar features
- [Create a Super Table(STable)](/model#create-stable): create a STable for all data collection points with the same type
- [Create a Table](/model#create-table): use STable as the template, to create a table for each data collecting point
## [TAOS SQL](/taos-sql)
- [Data Types](/taos-sql#data-type): support timestamp, int, float, nchar, bool, and other types
- [Database Management](/taos-sql#management): add, drop, check databases
- [Table Management](/taos-sql#table): add, drop, check, alter tables
- [STable Management](/taos-sql#super-table): add, drop, check, alter STables
- [Tag Management](/taos-sql#tags): add, drop, alter tags
- [Inserting Records](/taos-sql#insert): support to write single/multiple items per table, multiple items across tables, and support to write historical data
- [Data Query](/taos-sql#select): support time segment, value filtering, sorting, manual paging of query results, etc
- [SQL Function](/taos-sql#functions): support various aggregation functions, selection functions, and calculation functions, such as avg, min, diff, etc
- [Time Dimensions Aggregation](/taos-sql#aggregation): aggregate and reduce the dimension after cutting table data by time segment
- [Boundary Restrictions](/taos-sql#limitation): restrictions for the library, table, SQL, and others
- [Error Code](/taos-sql/error-code): TDengine 2.0 error codes and corresponding decimal codes
## [Efficient Data Ingestion](/insert)
- [SQL Ingestion](/insert#sql): write one or multiple records into one or multiple tables via SQL insert command
- [Prometheus Ingestion](/insert#prometheus): Configure Prometheus to write data directly without any code
- [Telegraf Ingestion](/insert#telegraf): Configure Telegraf to write collected data directly without any code
- [EMQ X Broker](/insert#emq): Configure EMQ X to write MQTT data directly without any code
- [HiveMQ Broker](/insert#hivemq): Configure HiveMQ to write MQTT data directly without any code
## [Efficient Data Querying](/queries)
- [Main Query Features](/queries#queries): support various standard functions, setting filter conditions, and querying per time segment
- [Multi-table Aggregation Query](/queries#aggregation): use STable and set tag filter conditions to perform efficient aggregation queries
- [Downsampling to Query Value](/queries#sampling): aggregate data in successive time windows, support interpolation
## [Advanced Features](/advanced-features)
- [Continuous Query](/advanced-features#continuous-query): Based on sliding windows, the data stream is automatically queried and calculated at regular intervals
- [Data Publisher/Subscriber](/advanced-features#subscribe): subscribe to the newly arrived data like a typical messaging system
- [Cache](/advanced-features#cache): the newly arrived data of each device/table will always be cached
- [Alarm Monitoring](/advanced-features#alert): automatically monitor out-of-threshold data, and actively push it based-on configuration rules
## [Connector](/connector)
- [C/C++ Connector](/connector#c-cpp): primary method to connect to TDengine server through libtaos client library
- [Java Connector(JDBC)]: driver for connecting to the server from Java applications using the JDBC API
- [Python Connector](/connector#python): driver for connecting to TDengine server from Python applications
- [RESTful Connector](/connector#restful): a simple way to interact with TDengine via HTTP
- [Go Connector](/connector#go): driver for connecting to TDengine server from Go applications
- [Node.js Connector](/connector#nodejs): driver for connecting to TDengine server from Node.js applications
- [C# Connector](/connector#csharp): driver for connecting to TDengine server from C# applications
- [Windows Client](https://www.taosdata.com/blog/2019/07/26/514.html): compile your own Windows client, which is required by various connectors on the Windows environment
## [Connections with Other Tools](/connections)
- [Grafana](/connections#grafana): query the data saved in TDengine and provide visualization
- [MATLAB](/connections#matlab): access data stored in TDengine server via JDBC configured within MATLAB
- [R](/connections#r): access data stored in TDengine server via JDBC configured within R
- [IDEA Database](https://www.taosdata.com/blog/2020/08/27/1767.html): use TDengine visually through IDEA Database Management Tool
## [Installation and Management of TDengine Cluster](/cluster)
- [Preparation](/cluster#prepare): important considerations before deploying TDengine for production usage
- [Create Your First Node](/cluster#node-one): simple to follow the quick setup
- [Create Subsequent Nodes](/cluster#node-other): configure taos.cfg for new nodes to add more to the existing cluster
- [Node Management](/cluster#management): add, delete, and check nodes in the cluster
- [High-availability of Vnode](/cluster#high-availability): implement high-availability of Vnode through multi-replicas
- [Mnode Management](/cluster#mnode): automatic system creation without any manual intervention
- [Load Balancing](/cluster#load-balancing): automatically performed once the number of nodes or load changes
- [Offline Node Processing](/cluster#offline): any node that offline for more than a certain period will be removed from the cluster
- [Arbitrator](/cluster#arbitrator): used in the case of an even number of replicas to prevent split-brain
## [TDengine Operation and Maintenance](/administrator)
- [Capacity Planning](/administrator#planning): Estimating hardware resources based on scenarios
- [Fault Tolerance and Disaster Recovery](/administrator#tolerance): set the correct WAL and number of data replicas
- [System Configuration](/administrator#config): port, cache size, file block size, and other system configurations
- [User Management](/administrator#user): add/delete TDengine users, modify user password
- [Import Data](/administrator#import): import data into TDengine from either script or CSV file
- [Export Data](/administrator#export): export data either from TDengine shell or from the taosdump tool
- [System Monitor](/administrator#status): monitor the system connections, queries, streaming calculation, logs, and events
- [File Directory Structure](/administrator#directories): directories where TDengine data files and configuration files located
- [Parameter Restrictions and Reserved Keywords](/administrator#keywords): TDengine’s list of parameter restrictions and reserved keywords
## TDengine Technical Design
- [System Module]: taosd functions and modules partitioning
- [Data Replication]: support real-time synchronous/asynchronous replication, to ensure high-availability of the system
- [Technical Blog](https://www.taosdata.com/cn/blog/?categories=3): More technical analysis and architecture design articles
## Common Tools
- [TDengine sample import tools](https://www.taosdata.com/blog/2020/01/18/1166.html)
- [TDengine performance comparison test tools](https://www.taosdata.com/blog/2020/01/18/1166.html)
- [Use TDengine visually through IDEA Database Management Tool](https://www.taosdata.com/blog/2020/08/27/1767.html)
## Performance: TDengine vs Others
- [Performance: TDengine vs InfluxDB with InfluxDB’s open-source performance testing tool](https://www.taosdata.com/blog/2020/01/13/1105.html)
- [Performance: TDengine vs OpenTSDB](https://www.taosdata.com/blog/2019/08/21/621.html)
- [Performance: TDengine vs Cassandra](https://www.taosdata.com/blog/2019/08/14/573.html)
- [Performance: TDengine vs InfluxDB](https://www.taosdata.com/blog/2019/07/19/419.html)
- [Performance Test Reports of TDengine vs InfluxDB/OpenTSDB/Cassandra/MySQL/ClickHouse](https://www.taosdata.com/downloads/TDengine_Testing_Report_cn.pdf)
## More on IoT Big Data
- [Characteristics of IoT and Industry Internet Big Data](https://www.taosdata.com/blog/2019/07/09/characteristics-of-iot-big-data/)
- [Features and Functions of IoT Big Data platforms](https://www.taosdata.com/blog/2019/07/29/542.html)
- [Why don’t General Big Data Platforms Fit IoT Scenarios?](https://www.taosdata.com/blog/2019/07/09/why-does-the-general-big-data-platform-not-fit-iot-data-processing/)
- [Why TDengine is the best choice for IoT, Internet of Vehicles, and Industry Internet Big Data platforms?](https://www.taosdata.com/blog/2019/07/09/why-tdengine-is-the-best-choice-for-iot-big-data-processing/)
## FAQ
- [FAQ: Common questions and answers](/faq)
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# TDengine Introduction
## <a class="anchor" id="intro"></a> About TDengine
TDengine is an innovative Big Data processing product launched by Taos Data in the face of the fast-growing Internet of Things (IoT) Big Data market and technical challenges. It does not rely on any third-party software, nor does it optimize or package any open-source database or stream computing product. Instead, it is a product independently developed after absorbing the advantages of many traditional relational databases, NoSQL databases, stream computing engines, message queues, and other software. TDengine has its own unique Big Data processing advantages in time-series space.
One of the modules of TDengine is the time-series database. However, in addition to this, to reduce the complexity of research and development and the difficulty of system operation, TDengine also provides functions such as caching, message queuing, subscription, stream computing, etc. TDengine provides a full-stack technical solution for the processing of IoT and Industrial Internet BigData. It is an efficient and easy-to-use IoT Big Data platform. Compared with typical Big Data platforms such as Hadoop, TDengine has the following distinct characteristics:
- **Performance improvement over 10 times**: An innovative data storage structure is defined, with each single core can process at least 20,000 requests per second, insert millions of data points, and read more than 10 million data points, which is more than 10 times faster than other existing general database.
- **Reduce the cost of hardware or cloud services to 1/5**: Due to its ultra-performance, TDengine’s computing resources consumption is less than 1/5 of other common Big Data solutions; through columnar storage and advanced compression algorithms, the storage consumption is less than 1/10 of other general databases.
- **Full-stack time-series data processing engine**: Integrate database, message queue, cache, stream computing, and other functions, and the applications do not need to integrate with software such as Kafka/Redis/HBase/Spark/HDFS, thus greatly reducing the complexity cost of application development and maintenance.
- **Powerful analysis functions**: Data from ten years ago or one second ago, can all be queried based on a specified time range. Data can be aggregated on a timeline or multiple devices. Ad-hoc queries can be made at any time through Shell, Python, R, and MATLAB.
- **Seamless connection with third-party tools**: Integration with Telegraf, Grafana, EMQ, HiveMQ, Prometheus, MATLAB, R, etc. without even one single line of code. OPC, Hadoop, Spark, etc. will be supported in the future, and more BI tools will be seamlessly connected to.
- **Zero operation cost & zero learning cost**: Installing clusters is simple and quick, with real-time backup built-in, and no need to split libraries or tables. Similar to standard SQL, TDengine can support RESTful, Python/Java/C/C + +/C#/Go/Node.js, and similar to MySQL with zero learning cost.
With TDengine, the total cost of ownership of typical IoT, Internet of Vehicles, and Industrial Internet Big Data platforms can be greatly reduced. However, it should be pointed out that due to making full use of the characteristics of IoT time-series data, TDengine cannot be used to process general data from web crawlers, microblogs, WeChat, e-commerce, ERP, CRM, and other sources.
![TDengine Technology Ecosystem](page://images/eco_system.png)
<center>Figure 1. TDengine Technology Ecosystem</center>
## <a class="anchor" id="scenes"></a>Overall Scenarios of TDengine
As an IoT Big Data platform, the typical application scenarios of TDengine are mainly presented in the IoT category, with users having a certain amount of data. The following sections of this document are mainly aimed at IoT-relevant systems. Other systems, such as CRM, ERP, etc., are beyond the scope of this article.
### Characteristics and Requirements of Data Sources
From the perspective of data sources, designers can analyze the applicability of TDengine in target application systems as following.
| **Data Source Characteristics and Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
| -------------------------------------------------------- | ------------------ | ----------------------- | ------------------- | :----------------------------------------------------------- |
| A huge amount of total data | | | √ | TDengine provides excellent scale-out functions in terms of capacity, and has a storage structure matching high compression ratio to achieve the best storage efficiency in the industry. |
| Data input velocity is occasionally or continuously huge | | | √ | TDengine's performance is much higher than other similar products. It can continuously process a large amount of input data in the same hardware environment, and provide a performance evaluation tool that can easily run in the user environment. |
| A huge amount of data sources | | | √ | TDengine is designed to include optimizations specifically for a huge amount of data sources, such as data writing and querying, which is especially suitable for efficiently processing massive (tens of millions or more) data sources. |
### System Architecture Requirements
| **System Architecture Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
| ------------------------------------------------- | ------------------ | ----------------------- | ------------------- | ------------------------------------------------------------ |
| Require a simple and reliable system architecture | | | √ | TDengine's system architecture is very simple and reliable, with its own message queue, cache, stream computing, monitoring and other functions, and no need to integrate any additional third-party products. |
| Require fault-tolerance and high-reliability | | | √ | TDengine has cluster functions to automatically provide high-reliability functions such as fault tolerance and disaster recovery. |
| Standardization specifications | | | √ | TDengine uses standard SQL language to provide main functions and follow standardization specifications. |
### System Function Requirements
| **System Architecture Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
| ------------------------------------------------- | ------------------ | ----------------------- | ------------------- | ------------------------------------------------------------ |
| Require completed data processing algorithms built-in | | √ | | TDengine implements various general data processing algorithms, but has not properly handled all requirements of different industries, so special types of processing shall be processed at the application level. |
| Require a huge amount of crosstab queries | | √ | | This type of processing should be handled more by relational database systems, or TDengine and relational database systems should fit together to implement system functions. |
### System Performance Requirements
| **System Architecture Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
| ------------------------------------------------- | ------------------ | ----------------------- | ------------------- | ------------------------------------------------------------ |
| Require larger total processing capacity | | | √ | TDengine’s cluster functions can easily improve processing capacity via multi-server-cooperating. |
| Require high-speed data processing | | | √ | TDengine’s storage and data processing are designed to be optimized for IoT, can generally improve the processing speed by multiple times than other similar products. |
| Require fast processing of fine-grained data | | | √ | TDengine has achieved the same level of performance with relational and NoSQL data processing systems. |
### System Maintenance Requirements
| **System Architecture Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
| ------------------------------------------------- | ------------------ | ----------------------- | ------------------- | ------------------------------------------------------------ |
| Require system with high-reliability | | | √ | TDengine has a very robust and reliable system architecture to implement simple and convenient daily operation with streamlined experiences for operators, thus human errors and accidents are eliminated to the greatest extent. |
| Require controllable operation learning cost | | | √ | As above. |
| Require abundant talent supply | √ | | | As a new-generation product, it’s still difficult to find talents with TDengine experiences from market. However, the learning cost is low. As the vendor, we also provide extensive operation training and counselling services. |
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# Quick Start
## <a class="anchor" id="install"></a>Quick Install
TDengine software consists of 3 parts: server, client, and alarm module. At the moment, TDengine server only runs on Linux (Windows, mac OS and more OS supports will come soon), but client can run on either Windows or Linux. TDengine client can be installed and run on Windows or Linux. Applications based-on any OSes can all connect to server taosd via a RESTful interface. About CPU, TDengine supports X64/ARM64/MIPS64/Alpha64, and ARM32、RISC-V, other more CPU architectures will be supported soon. You can set up and install TDengine server either from the [source code](https://www.taosdata.com/en/getting-started/#Install-from-Source) or the [packages](https://www.taosdata.com/en/getting-started/#Install-from-Package).
### <a class="anchor" id="source-install"></a>Install from Source
Please visit our [TDengine github page](https://github.com/taosdata/TDengine) for instructions on installation from the source code.
### Install from Docker Container
Please visit our [TDengine Official Docker Image: Distribution, Downloading, and Usage](https://www.taosdata.com/blog/2020/05/13/1509.html).
### <a class="anchor" id="package-install"></a>Install from Package
It’s extremely easy to install for TDengine, which takes only a few seconds from downloaded to successful installed. The server installation package includes clients and connectors. We provide 3 installation packages, which you can choose according to actual needs:
Click [here](https://www.taosdata.com/cn/getting-started/#%E9%80%9A%E8%BF%87%E5%AE%89%E8%A3%85%E5%8C%85%E5%AE%89%E8%A3%85) to download the install package.
For more about installation process, please refer [TDengine Installation Packages: Install and Uninstall](https://www.taosdata.com/blog/2019/08/09/566.html), and [Video Tutorials](https://www.taosdata.com/blog/2020/11/11/1941.html).
## <a class="anchor" id="start"></a>Quick Launch
After installation, you can start the TDengine service by the `systemctl` command.
```bash
$ systemctl start taosd
```
Then check if the service is working now.
```bash
$ systemctl status taosd
```
If the service is running successfully, you can play around through TDengine shell `taos`.
**Note:**
- The `systemctl` command needs the **root** privilege. Use **sudo** if you are not the **root** user.
- To get better product feedback and improve our solution, TDengine will collect basic usage information, but you can modify the configuration parameter **telemetryReporting** in the system configuration file taos.cfg, and set it to 0 to turn it off.
- TDengine uses FQDN (usually hostname) as the node ID. In order to ensure normal operation, you need to set hostname for the server running taosd, and configure DNS service or hosts file for the machine running client application, to ensure the FQDN can be resolved.
- TDengine supports installation on Linux systems with[ systemd ](https://en.wikipedia.org/wiki/Systemd)as the process service management, and uses `which systemctl` command to detect whether `systemd` packages exist in the system:
```bash
$ which systemctl
```
If `systemd` is not supported in the system, TDengine service can also be launched via `/usr/local/taos/bin/taosd` manually.
## <a class="anchor" id="console"></a>TDengine Shell Command Line
To launch TDengine shell, the command line interface, in a Linux terminal, type:
```bash
$ taos
```
The welcome message is printed if the shell connects to TDengine server successfully, otherwise, an error message will be printed (refer to our [FAQ](https://www.taosdata.com/en/faq) page for troubleshooting the connection error). The TDengine shell prompt is:
```cmd
taos>
```
In the TDengine shell, you can create databases, create tables and insert/query data with SQL. Each query command ends with a semicolon. It works like MySQL, for example:
```mysql
create database demo;
use demo;
create table t (ts timestamp, speed int);
insert into t values ('2019-07-15 00:00:00', 10);
insert into t values ('2019-07-15 01:00:00', 20);
select * from t;
ts | speed |
===================================
19-07-15 00:00:00.000| 10|
19-07-15 01:00:00.000| 20|
Query OK, 2 row(s) in set (0.001700s)
```
Besides the SQL commands, the system administrator can check system status, add or delete accounts, and manage the servers.
### Shell Command Line Parameters
You can configure command parameters to change how TDengine shell executes. Some frequently used options are listed below:
- -c, --config-dir: set the configuration directory. It is */etc/taos* by default.
- -h, --host: set the IP address of the server it will connect to. Default is localhost.
- -s, --commands: set the command to run without entering the shell.
- -u, -- user: user name to connect to server. Default is root.
- -p, --password: password. Default is 'taosdata'.
- -?, --help: get a full list of supported options.
Examples:
```bash
$ taos -h 192.168.0.1 -s "use db; show tables;"
```
### Run SQL Command Scripts
Inside TDengine shell, you can run SQL scripts in a file with source command.
```mysql
taos> source <filename>;
```
### Shell Tips
- Use up/down arrow key to check the command history
- To change the default password, use "alter user" command
- Use ctrl+c to interrupt any queries
- To clean the schema of local cached tables, execute command `RESET QUERY CACHE`
## <a class="anchor" id="demo"></a>Experience TDengine’s Lightning Speed
After starting the TDengine server, you can execute the command `taosdemo` in the Linux terminal.
```bash
$ taosdemo
```
Using this command, a STable named `meters` will be created in the database `test` There are 10k tables under this stable, named from `t0` to `t9999`. In each table there are 100k rows of records, each row with columns （`f1`, `f2` and `f3`. The timestamp is from "2017-07-14 10:40:00 000" to "2017-07-14 10:41:39 999". Each table also has tags `areaid` and `loc`: `areaid` is set from 1 to 10, `loc` is set to "beijing" or "shanghai".
It takes about 10 minutes to execute this command. Once finished, 1 billion rows of records will be inserted.
In the TDengine client, enter sql query commands and then experience our lightning query speed.
- query total rows of records：
```mysql
taos> select count(*) from test.meters;
```
- query average, max and min of the total 1 billion records：
```mysql
taos> select avg(f1), max(f2), min(f3) from test.meters;
```
- query the number of records where loc="beijing":
```mysql
taos> select count(*) from test.meters where loc="beijing";
```
- query the average, max and min of total records where areaid=10：
```mysql
taos> select avg(f1), max(f2), min(f3) from test.meters where areaid=10;
```
- query the average, max, min from table t10 when aggregating over every 10s:
```mysql
taos> select avg(f1), max(f2), min(f3) from test.t10 interval(10s);
```
**Note**: you can run command `taosdemo` with many options, like number of tables, rows of records and so on. To know more about these options, you can execute `taosdemo --help` and then take a try using different options.
## Client and Alarm Module
If your client and server running on different machines, please install the client separately. Linux and Windows packages are provided:
- TDengine-client-2.0.10.0-Linux-x64.tar.gz(3.0M)
- TDengine-client-2.0.10.0-Windows-x64.exe(2.8M)
- TDengine-client-2.0.10.0-Windows-x86.exe(2.8M)
Linux package of Alarm Module is as following (please refer [How to Use Alarm Module](https://github.com/taosdata/TDengine/blob/master/alert/README_cn.md)):
- TDengine-alert-2.0.10.0-Linux-x64.tar.gz (8.1M)
## <a class="anchor" id="platforms"></a>List of Supported Platforms
List of platforms supported by TDengine server
| | **CentOS 6/7/8** | **Ubuntu 16/18/20** | **Other Linux** | UnionTech UOS | NeoKylin | LINX V60/V80 |
| ------------------ | ---------------- | ------------------- | --------------- | ------------- | -------- | ------------ |
| X64 | ● | ● | | ○ | ● | ● |
| Raspberry ARM32 | | ● | ● | | | |
| Loongson MIPS64 | | | ● | | | |
| Kunpeng ARM64 | | ○ | ○ | | ● | |
| SWCPU Alpha64 | | | ○ | ● | | |
| FT ARM64 | | ○Ubuntu Kylin | | | | |
| Hygon X64 | ● | ● | ● | ○ | ● | ● |
| Rockchip ARM64/32 | | | ○ | | | |
| Allwinner ARM64/32 | | | ○ | | | |
| Actions ARM64/32 | | | ○ | | | |
| TI ARM32 | | | ○ | | | |
Note: ● has been verified by official tests; ○ has been verified by unofficial tests.
List of platforms supported by TDengine client and connectors
At the moment, TDengine connectors can support a wide range of platforms, including hardware platforms such as X64/X86/ARM64/ARM32/MIPS/Alpha, and development environments such as Linux/Win64/Win32.
Comparison matrix as following:
| **CPU** | **X64 64bit** | | | **X86 32bit** | **ARM64** | **ARM32** | **MIPS Godson** | **Alpha Shenwei** | **X64 TimecomTech** |
| ----------- | ------------- | --------- | --------- | ------------- | --------- | --------- | --------------- | ----------------- | ------------------- |
| **OS** | **Linux** | **Win64** | **Win32** | **Win32** | **Linux** | **Linux** | **Linux** | **Linux** | **Linux** |
| **C/C++** | ● | ● | ● | ○ | ● | ● | ● | ● | ● |
| **JDBC** | ● | ● | ● | ○ | ● | ● | ● | ● | ● |
| **Python** | ● | ● | ● | ○ | ● | ● | ● | -- | ● |
| **Go** | ● | ● | ● | ○ | ● | ● | ○ | -- | -- |
| **NodeJs** | ● | ● | ○ | ○ | ● | ● | ○ | -- | -- |
| **C#** | ○ | ● | ● | ○ | ○ | ○ | ○ | -- | -- |
| **RESTful** | ● | ● | ● | ● | ● | ● | ● | ● | ● |
Note: ● has been verified by official tests; ○ has been verified by unofficial tests.
Please visit [Connectors](https://www.taosdata.com/en/documentation/connector) section for more detailed information.
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# Data Modeling
TDengine adopts a relational data model, so we need to build the "database" and "table". Therefore, for a specific application scenario, it is necessary to consider the design of the database, STable and ordinary table. This section does not discuss detailed syntax rules, but only concepts.
Please watch the [video tutorial](https://www.taosdata.com/blog/2020/11/11/1945.html) for data modeling.
## <a class="anchor" id="create-db"></a> Create a Database
Different types of data collection points often have different data characteristics, including frequency of data collecting, length of data retention time, number of replicas, size of data blocks, whether to update data or not, and so on. To ensure TDengine working with great efficiency in various scenarios, TDengine suggests creating tables with different data characteristics in different databases, because each database can be configured with different storage strategies. When creating a database, in addition to SQL standard options, the application can also specify a variety of parameters such as retention duration, number of replicas, number of memory blocks, time accuracy, max and min number of records in a file block, whether it is compressed or not, and number of days a data file will be overwritten. For example:
```mysql
CREATE DATABASE power KEEP 365 DAYS 10 BLOCKS 4 UPDATE 1;
```
The above statement will create a database named “power”. The data of this database will be kept for 365 days (it will be automatically deleted 365 days later), one data file created per 10 days, and the number of memory blocks is 4 for data updating. For detailed syntax and parameters, please refer to [Data Management section of TAOS SQL](https://www.taosdata.com/en/documentation/taos-sql#management).
After the database created, please use SQL command USE to switch to the new database, for example:
```mysql
USE power;
```
Replace the database operating in the current connection with “power”, otherwise, before operating on a specific table, you need to use "database name. table name" to specify the name of database to use.
**Note:**
- Any table or STable belongs to a database. Before creating a table, a database must be created first.
- Tables in two different databases cannot be JOIN.
## <a class="anchor" id="create-stable"></a> Create a STable
An IoT system often has many types of devices, such as smart meters, transformers, buses, switches, etc. for power grids. In order to facilitate aggregation among multiple tables, using TDengine, it is necessary to create a STable for each type of data collection point. Taking the smart meter in Table 1 as an example, you can use the following SQL command to create a STable:
```mysql
CREATE STABLE meters (ts timestamp, current float, voltage int, phase float) TAGS (location binary(64), groupdId int);
```
**Note:** The STABLE keyword in this instruction needs to be written as TABLE in versions before 2.0.15.
Just like creating an ordinary table, you need to provide the table name (‘meters’ in the example) and the table structure Schema, that is, the definition of data columns. The first column must be a timestamp (‘ts’ in the example), the other columns are the physical metrics collected (current, volume, phase in the example), and the data types can be int, float, string, etc. In addition, you need to provide the schema of the tag (location, groupId in the example), and the data types of the tag can be int, float, string and so on. Static attributes of collection points can often be used as tags, such as geographic location of collection points, device model, device group ID, administrator ID, etc. The schema of the tag can be added, deleted and modified afterwards. Please refer to the [STable Management section of TAOS SQL](https://www.taosdata.com/cn/documentation/taos-sql#super-table) for specific definitions and details.
Each type of data collection point needs an established STable, so an IoT system often has multiple STables. For the power grid, we need to build a STable for smart meters, transformers, buses, switches, etc. For IoT, a device may have multiple data collection points (for example, a fan for wind-driven generator, some collection points capture parameters such as current and voltage, and some capture environmental parameters such as temperature, humidity and wind direction). In this case, multiple STables need to be established for corresponding types of devices. All collected physical metrics contained in one and the same STable must be collected at the same time (with a consistent timestamp).
A STable allows up to 1024 columns. If the number of physical metrics collected at a collection point exceeds 1024, multiple STables need to be built to process them. A system can have multiple DBs, and a DB can have one or more STables.
## <a class="anchor" id="create-table"></a> Create a Table
TDengine builds a table independently for each data collection point. Similar to standard relational data, one table has a table name, Schema, but in addition, it can also carry one or more tags. When creating, you need to use the STable as a template and specify the specific value of the tag. Taking the smart meter in Table 1 as an example, the following SQL command can be used to build the table:
```mysql
CREATE TABLE d1001 USING meters TAGS ("Beijing.Chaoyang", 2);
```
Where d1001 is the table name, meters is the name of the STable, followed by the specific tag value of tag Location as "Beijing.Chaoyang", and the specific tag value of tag groupId 2. Although the tag value needs to be specified when creating the table, it can be modified afterwards. Please refer to the [Table Management section of TAOS SQL](https://www.taosdata.com/en/documentation/taos-sql#table) for details.
**Note: ** At present, TDengine does not technically restrict the use of a STable of a database (dbA) as a template to create a sub-table of another database (dbB). This usage will be prohibited later, and it is not recommended to use this method to create a table.
TDengine suggests to use the globally unique ID of data collection point as a table name (such as device serial number). However, in some scenarios, there is no unique ID, and multiple IDs can be combined into a unique ID. It is not recommended to use a unique ID as tag value.
**Automatic table creating** : In some special scenarios, user is not sure whether the table of a certain data collection point exists when writing data. In this case, the non-existent table can be created by using automatic table building syntax when writing data. If the table already exists, no new table will be created. For example:
```mysql
INSERT INTO d1001 USING METERS TAGS ("Beijng.Chaoyang", 2) VALUES (now, 10.2, 219, 0.32);
```
The SQL statement above inserts records (now, 10.2, 219, 0.32) into table d1001. If table d1001 has not been created yet, the STable meters is used as the template to automatically create it, and the tag value "Beijing.Chaoyang", 2 is marked at the same time.
For detailed syntax of automatic table building, please refer to the "[Automatic Table Creation When Inserting Records](https://www.taosdata.com/en/documentation/taos-sql#auto_create_table)" section.
## Multi-column Model vs Single-column Model
TDengine supports multi-column model. As long as physical metrics are collected simultaneously by a data collection point (with a consistent timestamp), these metrics can be placed in a STable as different columns. However, there is also an extreme design, a single-column model, in which each collected physical metric is set up separately, so each type of physical metrics is set up separately with a STable. For example, create 3 Stables, one each for current, voltage and phase.
TDengine recommends using multi-column model as much as possible because of higher insertion and storage efficiency. However, for some scenarios, types of collected metrics often change. In this case, if multi-column model is adopted, the structure definition of STable needs to be frequently modified so make the application complicated. To avoid that, single-column model is recommended.
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# Efficient Data Writing
TDengine supports multiple interfaces to write data, including SQL, Prometheus, Telegraf, EMQ MQTT Broker, HiveMQ Broker, CSV file, etc. Kafka, OPC and other interfaces will be provided in the future. Data can be inserted in a single piece or in batches, data from one or multiple data collection points can be inserted at the same time. TDengine supports multi-thread insertion, nonsequential data insertion, and also historical data insertion.
## <a class="anchor" id="sql"></a> SQL Writing
Applications insert data by executing SQL insert statements through C/C + +, JDBC, GO, or Python Connector, and users can manually enter SQL insert statements to insert data through TAOS Shell. For example, the following insert writes a record to table d1001:
```mysql
INSERT INTO d1001 VALUES (1538548685000, 10.3, 219, 0.31);
```
TDengine supports writing multiple records at a time. For example, the following command writes two records to table d1001:
```mysql
INSERT INTO d1001 VALUES (1538548684000, 10.2, 220, 0.23) (1538548696650, 10.3, 218, 0.25);
```
TDengine also supports writing data to multiple tables at a time. For example, the following command writes two records to d1001 and one record to d1002:
```mysql
INSERT INTO d1001 VALUES (1538548685000, 10.3, 219, 0.31) (1538548695000, 12.6, 218, 0.33) d1002 VALUES (1538548696800, 12.3, 221, 0.31);
```
For the SQL INSERT Grammar, please refer to [Taos SQL insert](https://www.taosdata.com/en/documentation/taos-sql#insert)。
**Tips:**
- To improve writing efficiency, batch writing is required. The more records written in a batch, the higher the insertion efficiency. However, a record cannot exceed 16K, and the total length of an SQL statement cannot exceed 64K (it can be configured by parameter maxSQLLength, and the maximum can be configured to 1M).
- TDengine supports multi-thread parallel writing. To further improve writing speed, a client needs to open more than 20 threads to write parallelly. However, after the number of threads reaches a certain threshold, it cannot be increased or even become decreased, because too much frequent thread switching brings extra overhead.
- For a same table, if the timestamp of a newly inserted record already exists, (no database was created using UPDATE 1) the new record will be discarded as default, that is, the timestamp must be unique in a table. If an application automatically generates records, it is very likely that the generated timestamps will be the same, so the number of records successfully inserted will be smaller than the number of records the application try to insert. If you use UPDATE 1 option when creating a database, inserting a new record with the same timestamp will overwrite the original record.
- The timestamp of written data must be greater than the current time minus the time of configuration parameter keep. If keep is configured for 3650 days, data older than 3650 days cannot be written. The timestamp for writing data cannot be greater than the current time plus configuration parameter days. If days is configured to 2, data 2 days later than the current time cannot be written.
## <a class="anchor" id="prometheus"></a> Direct Writing of Prometheus
As a graduate project of Cloud Native Computing Foundation, [Prometheus](https://www.prometheus.io/) is widely used in the field of performance monitoring and K8S performance monitoring. TDengine provides a simple tool [Bailongma](https://github.com/taosdata/Bailongma), which only needs to be simply configured in Prometheus without any code, and can directly write the data collected by Prometheus into TDengine, then automatically create databases and related table entries in TDengine according to rules. Blog post [Use Docker Container to Quickly Build a Devops Monitoring Demo](https://www.taosdata.com/blog/2020/02/03/1189.html), which is an example of using bailongma to write Prometheus and Telegraf data into TDengine.
### Compile blm_prometheus From Source
Users need to download the source code of [Bailongma](https://github.com/taosdata/Bailongma) from github, then compile and generate an executable file using Golang language compiler. Before you start compiling, you need to complete following prepares:
- A server running Linux OS
- Golang version 1.10 and higher installed
- An appropriated TDengine version. Because the client dynamic link library of TDengine is used, it is necessary to install the same version of TDengine as the server-side; for example, if the server version is TDengine 2.0. 0, ensure install the same version on the linux server where bailongma is located (can be on the same server as TDengine, or on a different server)
Bailongma project has a folder, blm_prometheus, which holds the prometheus writing API. The compiling process is as follows:
```bash
cd blm_prometheus
go build
```
If everything goes well, an executable of blm_prometheus will be generated in the corresponding directory.
### Install Prometheus
Download and install as the instruction of Prometheus official website. [Download Address](https://prometheus.io/download/)
### Configure Prometheus
Read the Prometheus [configuration document](https://prometheus.io/docs/prometheus/latest/configuration/configuration/) and add following configurations in the section of Prometheus configuration file
- url: The URL provided by bailongma API service, refer to the blm_prometheus startup example section below
After Prometheus launched, you can check whether data is written successfully through query taos client.
### Launch blm_prometheus
blm_prometheus has following options that you can configure when you launch blm_prometheus.
```sh
--tdengine-name
If TDengine is installed on a server with a domain name, you can also access the TDengine by configuring the domain name of it. In K8S environment, it can be configured as the service name that TDengine runs
--batch-size
blm_prometheus assembles the received prometheus data into a TDengine writing request. This parameter controls the number of data pieces carried in a writing request sent to TDengine at a time.
--dbname
Set a name for the database created in TDengine, blm_prometheus will automatically create a database named dbname in TDengine, and the default value is prometheus.
--dbuser
Set the user name to access TDengine, the default value is'root '
--dbpassword
Set the password to access TDengine, the default value is'taosdata '
--port
The port number blm_prometheus used to serve prometheus.
```
### Example
Launch an API service for blm_prometheus with the following command:
```bash
./blm_prometheus -port 8088
```
Assuming that the IP address of the server where blm_prometheus located is "10.1.2. 3", the URL shall be added to the configuration file of Prometheus as:
remote_write:
\- url: "http://10.1.2.3:8088/receive"
### Query written data of prometheus
The format of generated data by Prometheus is as follows:
```json
{
Timestamp: 1576466279341,
Value: 37.000000,
apiserver_request_latencies_bucket {
component="apiserver",
instance="192.168.99.116:8443",
job="kubernetes-apiservers",
le="125000",
resource="persistentvolumes", s
cope="cluster",
verb="LIST",
version=“v1"
}
}
```
Where apiserver_request_latencies_bucket is the name of the time-series data collected by prometheus, and the tag of the time-series data is in the following {}. blm_prometheus automatically creates a STable in TDengine with the name of the time series data, and converts the tag in {} into the tag value of TDengine, with Timestamp as the timestamp and value as the value of the time-series data. Therefore, in the client of TDEngine, you can check whether this data was successfully written through the following instruction.
```mysql
use prometheus;
select * from apiserver_request_latencies_bucket;
```
## <a class="anchor" id="telegraf"></a> Direct Writing of Telegraf
[Telegraf](https://www.influxdata.com/time-series-platform/telegraf/) is a popular open source tool for IT operation data collection. TDengine provides a simple tool [Bailongma](https://github.com/taosdata/Bailongma), which only needs to be simply configured in Telegraf without any code, and can directly write the data collected by Telegraf into TDengine, then automatically create databases and related table entries in TDengine according to rules. Blog post [Use Docker Container to Quickly Build a Devops Monitoring Demo](https://www.taosdata.com/blog/2020/02/03/1189.html), which is an example of using bailongma to write Prometheus and Telegraf data into TDengine.
### Compile blm_telegraf From Source Code
Users need to download the source code of [Bailongma](https://github.com/taosdata/Bailongma) from github, then compile and generate an executable file using Golang language compiler. Before you start compiling, you need to complete following prepares:
- A server running Linux OS
- Golang version 1.10 and higher installed
- An appropriated TDengine version. Because the client dynamic link library of TDengine is used, it is necessary to install the same version of TDengine as the server-side; for example, if the server version is TDengine 2.0. 0, ensure install the same version on the linux server where bailongma is located (can be on the same server as TDengine, or on a different server)
Bailongma project has a folder, blm_telegraf, which holds the Telegraf writing API. The compiling process is as follows:
```bash
cd blm_telegraf
go build
```
If everything goes well, an executable of blm_telegraf will be generated in the corresponding directory.
### Install Telegraf
At the moment, TDengine supports Telegraf version 1.7. 4 and above. Users can download the installation package on Telegraf's website according to your current operating system. The download address is as follows: https://portal.influxdata.com/downloads
### Configure Telegraf
Modify the TDengine-related configurations in the Telegraf configuration file /etc/telegraf/telegraf.conf.
In the output plugins section, add the [[outputs.http]] configuration:
- url: The URL provided by bailongma API service, please refer to the example section below
- data_format: "json"
- json_timestamp_units: "1ms"
In agent section:
- hostname: The machine name that distinguishes different collection devices, and it is necessary to ensure its uniqueness
- metric_batch_size: 100, which is the max number of records per batch wriiten by Telegraf allowed. Increasing the number can reduce the request sending frequency of Telegraf.
For information on how to use Telegraf to collect data and more about using Telegraf, please refer to the official [document](https://docs.influxdata.com/telegraf/v1.11/) of Telegraf.
### Launch blm_telegraf
blm_telegraf has following options, which can be set to tune configurations of blm_telegraf when launching.
```sh
--host
The ip address of TDengine server, default is null
--batch-size
blm_prometheus assembles the received telegraf data into a TDengine writing request. This parameter controls the number of data pieces carried in a writing request sent to TDengine at a time.
--dbname
Set a name for the database created in TDengine, blm_telegraf will automatically create a database named dbname in TDengine, and the default value is prometheus.
--dbuser
Set the user name to access TDengine, the default value is 'root '
--dbpassword
Set the password to access TDengine, the default value is'taosdata '
--port
The port number blm_telegraf used to serve Telegraf.
```
### Example
Launch an API service for blm_telegraf with the following command
```bash
./blm_telegraf -host 127.0.0.1 -port 8089
```
Assuming that the IP address of the server where blm_telegraf located is "10.1.2. 3", the URL shall be added to the configuration file of telegraf as:
```yaml
url = "http://10.1.2.3:8089/telegraf"
```
### Query written data of telegraf
The format of generated data by telegraf is as follows:
```json
{
"fields": {
"usage_guest": 0,
"usage_guest_nice": 0,
"usage_idle": 89.7897897897898,
"usage_iowait": 0,
"usage_irq": 0,
"usage_nice": 0,
"usage_softirq": 0,
"usage_steal": 0,
"usage_system": 5.405405405405405,
"usage_user": 4.804804804804805
},
"name": "cpu",
"tags": {
"cpu": "cpu2",
"host": "bogon"
},
"timestamp": 1576464360
}
```
Where the name field is the name of the time-series data collected by telegraf, and the tag field is the tag of the time-series data. blm_telegraf automatically creates a STable in TDengine with the name of the time series data, and converts the tag field into the tag value of TDengine, with Timestamp as the timestamp and fields values as the value of the time-series data. Therefore, in the client of TDEngine, you can check whether this data was successfully written through the following instruction.
```mysql
use telegraf;
select * from cpu;
```
MQTT is a popular data transmission protocol in the IoT. TDengine can easily access the data received by MQTT Broker and write it to TDengine.
## <a class="anchor" id="emq"></a> Direct Writing of EMQ Broker
[EMQ](https://github.com/emqx/emqx) is an open source MQTT Broker software, with no need of coding, only to use "rules" in EMQ Dashboard for simple configuration, and MQTT data can be directly written into TDengine. EMQ X supports storing data to the TDengine by sending it to a Web service, and also provides a native TDengine driver on Enterprise Edition for direct data store. Please refer to [EMQ official documents](https://docs.emqx.io/broker/latest/cn/rule/rule-example.html#%E4%BF%9D%E5%AD%98%E6%95%B0%E6%8D%AE%E5%88%B0-tdengine) for more details.
## <a class="anchor" id="hivemq"></a> Direct Writing of HiveMQ Broker
[HiveMQ](https://www.hivemq.com/) is an MQTT agent that provides Free Personal and Enterprise Edition versions. It is mainly used for enterprises, emerging machine-to-machine(M2M) communication and internal transmission to meet scalability, easy management and security features. HiveMQ provides an open source plug-in development kit. You can store data to TDengine via HiveMQ extension-TDengine. Refer to the [HiveMQ extension-TDengine documentation](https://github.com/huskar-t/hivemq-tdengine-extension/blob/b62a26ecc164a310104df57691691b237e091c89/README.md) for more details.
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# Efficient Data Querying
## <a class="anchor" id="queries"></a> Main Query Features
TDengine uses SQL as the query language. Applications can send SQL statements through C/C + +, Java, Go, Python connectors, and users can manually execute SQL Ad-Hoc Query through the Command Line Interface (CLI) tool TAOS Shell provided by TDengine. TDengine supports the following query functions:
- Single-column and multi-column data query
- Multiple filters for tags and numeric values: >, <, =, < >, like, etc
- Group by, Order by, Limit/Offset of aggregation results
- Four operations for numeric columns and aggregation results
- Time stamp aligned join query (implicit join) operations
- Multiple aggregation/calculation functions: count, max, min, avg, sum, twa, stddev, leastsquares, top, bottom, first, last, percentile, apercentile, last_row, spread, diff, etc
For example, in TAOS shell, the records with vlotage > 215 are queried from table d1001, sorted in descending order by timestamps, and only two records are outputted.
```mysql
taos> select * from d1001 where voltage > 215 order by ts desc limit 2;
ts | current | voltage | phase |
======================================================================================
2018-10-03 14:38:16.800 | 12.30000 | 221 | 0.31000 |
2018-10-03 14:38:15.000 | 12.60000 | 218 | 0.33000 |
Query OK, 2 row(s) in set (0.001100s)
```
In order to meet the needs of an IoT scenario, TDengine supports several special functions, such as twa (time weighted average), spread (difference between maximum and minimum), last_row (last record), etc. More functions related to IoT scenarios will be added. TDengine also supports continuous queries.
For specific query syntax, please see the [Data Query section of TAOS SQL](https://www.taosdata.com/cn/documentation/taos-sql#select).
## <a class="anchor" id="aggregation"></a> Multi-table Aggregation Query
In an IoT scenario, there are often multiple data collection points in a same type. TDengine uses the concept of STable to describe a certain type of data collection point, and an ordinary table to describe a specific data collection point. At the same time, TDengine uses tags to describe the statical attributes of data collection points. A given data collection point has a specific tag value. By specifying the filters of tags, TDengine provides an efficient method to aggregate and query the sub-tables of STables (data collection points of a certain type). Aggregation functions and most operations on ordinary tables are applicable to STables, and the syntax is exactly the same.
**Example 1**: In TAOS Shell, look up the average voltages collected by all smart meters in Beijing and group them by location
```mysql
taos> SELECT AVG(voltage) FROM meters GROUP BY location;
avg(voltage) | location |
=============================================================
222.000000000 | Beijing.Haidian |
219.200000000 | Beijing.Chaoyang |
Query OK, 2 row(s) in set (0.002136s)
```
**Example 2**: In TAOS Shell, look up the number of records with groupId 2 in the past 24 hours, check the maximum current of all smart meters
```mysql
taos> SELECT count(*), max(current) FROM meters where groupId = 2 and ts > now - 24h;
cunt(*) | max(current) |
==================================
5 | 13.4 |
Query OK, 1 row(s) in set (0.002136s)
```
TDengine only allows aggregation queries between tables belonging to a same STable, means aggregation queries between different STables are not supported. In the Data Query section of TAOS SQL, query class operations will all be indicated that whether STables are supported.
## <a class="anchor" id="sampling"></a> Down Sampling Query, Interpolation
In a scenario of IoT, it is often necessary to aggregate the collected data by intervals through down sampling. TDengine provides a simple keyword interval, which makes query operations according to time windows extremely simple. For example, the current values collected by smart meter d1001 are summed every 10 seconds.
```mysql
taos> SELECT sum(current) FROM d1001 INTERVAL(10s);
ts | 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)
```
The down sampling operation is also applicable to STables, such as summing the current values collected by all smart meters in Beijing every second.
```mysql
taos> SELECT SUM(current) FROM meters where location like "Beijing%" INTERVAL(1s);
ts | sum(current) |
======================================================
2018-10-03 14:38:04.000 | 10.199999809 |
2018-10-03 14:38:05.000 | 32.900000572 |
2018-10-03 14:38:06.000 | 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)
```
The down sampling operation also supports time offset, such as summing the current values collected by all smart meters every second, but requires each time window to start from 500 milliseconds.
```mysql
taos> SELECT SUM(current) FROM meters INTERVAL(1s, 500a);
ts | 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)
```
In a scenario of IoT, it is difficult to synchronize the time stamp of collected data at each point, but many analysis algorithms (such as FFT) need to align the collected data strictly at equal intervals of time. In many systems, it’s required to write their own programs to process, but the down sampling operation of TDengine can be easily solved. If there is no collected data in an interval, TDengine also provides interpolation calculation function.
For details of syntax rules, please refer to the [Time-dimension Aggregation section of TAOS SQL](https://www.taosdata.com/en/documentation/taos-sql#aggregation).
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# Connections with Other Tools
## <a class="anchor" id="grafana"></a> Grafana
TDengine can quickly integrate with [Grafana](https://www.grafana.com/), an open source data visualization system, to build a data monitoring and alarming system. The whole process does not require any code to write. The contents of the data table in TDengine can be visually showed on DashBoard.
### Install Grafana
TDengine currently supports Grafana 5.2.4 and above. You can download and install the package from Grafana website according to the current operating system. The download address is as follows:
https://grafana.com/grafana/download.
### Configure Grafana
TDengine Grafana plugin is in the /usr/local/taos/connector/grafanaplugin directory.
Taking Centos 7.2 as an example, just copy grafanaplugin directory to /var/lib/grafana/plugins directory and restart Grafana.
```bash
sudo cp -rf /usr/local/taos/connector/grafanaplugin /var/lib/grafana/plugins/tdengine
```
### Use Grafana
#### Configure data source
You can log in the Grafana server (username/password:admin/admin) through localhost:3000, and add data sources through `Configuration -> Data Sources` on the left panel, as shown in the following figure:
![img](page://images/connections/add_datasource1.jpg)
Click `Add data source` to enter the Add Data Source page, and enter TDengine in the query box to select Add, as shown in the following figure:
![img](page://images/connections/add_datasource2.jpg)
Enter the data source configuration page and modify the corresponding configuration according to the default prompt:
![img](page://images/connections/add_datasource3.jpg)
- Host: IP address of any server in TDengine cluster and port number of TDengine RESTful interface (6041), default [http://localhost:6041](http://localhost:6041/)
- User: TDengine username.
- Password: TDengine user password.
Click `Save & Test` to test. Success will be prompted as follows:
![img](page://images/connections/add_datasource4.jpg)
#### Create Dashboard
Go back to the home to create Dashboard, and click `Add Query` to enter the panel query page:
![img](page://images/connections/create_dashboard1.jpg)
As shown in the figure above, select the TDengine data source in Query, and enter the corresponding sql in the query box below to query. Details are as follows:
- INPUT SQL: Enter the statement to query (the result set of the SQL statement should be two columns and multiple rows), for example: `select avg(mem_system) from log.dn where ts >= $from and ts < $to interval($interval)` , where `from`, `to` and `interval` are built-in variables of the TDengine plug-in, representing the query range and time interval obtained from the Grafana plug-in panel. In addition to built-in variables, it is also supported to use custom template variables.
- ALIAS BY: You can set alias for the current queries.
- GENERATE SQL: Clicking this button will automatically replace the corresponding variable and generate the final statement to execute.
According to the default prompt, query the average system memory usage at the specified interval of the server where the current TDengine deployed in as follows:
![img](page://images/connections/create_dashboard2.jpg)
> Please refer to Grafana [documents](https://grafana.com/docs/) for how to use Grafana to create the corresponding monitoring interface and for more about Grafana usage.
#### Import Dashboard
A `tdengine-grafana.json` importable dashboard is provided under the Grafana plug-in directory/usr/local/taos/connector/grafana/tdengine/dashboard/.
Click the `Import` button on the left panel and upload the `tdengine-grafana.json` file:
![img](page://images/connections/import_dashboard1.jpg)
You can see as follows after Dashboard imported.
![img](page://images/connections/import_dashboard2.jpg)
## <a class="anchor" id="matlab"></a> MATLAB
MATLAB can access data to the local workspace by connecting directly to the TDengine via the JDBC Driver provided in the installation package.
### JDBC Interface Adaptation of MATLAB
Several steps are required to adapt MATLAB to TDengine. Taking adapting MATLAB2017a on Windows10 as an example:
- Copy the file JDBCDriver-1.0.0-dist.ja*r* in TDengine package to the directory ${matlab_root}\MATLAB\R2017a\java\jar\toolbox
- Copy the file taos.lib in TDengine package to ${matlab root dir}\MATLAB\R2017a\lib\win64
- Add the .jar package just copied to the MATLAB classpath. Append the line below as the end of the file of ${matlab root dir}\MATLAB\R2017a\toolbox\local\classpath.txt
- ```
$matlabroot/java/jar/toolbox/JDBCDriver-1.0.0-dist.jar
```
- Create a file called javalibrarypath.txt in directory ${user_home}\AppData\Roaming\MathWorks\MATLAB\R2017a_, and add the _taos.dll path in the file. For example, if the file taos.dll is in the directory of C:\Windows\System32，then add the following line in file javalibrarypath.txt:
- ```
C:\Windows\System32
```
- ### Connect to TDengine in MATLAB to get data
After the above configured successfully, open MATLAB.
- Create a connection:
```matlab
conn = database(‘db’, ‘root’, ‘taosdata’, ‘com.taosdata.jdbc.TSDBDriver’, ‘jdbc:TSDB://127.0.0.1:0/’)
```
* Make a query:
```matlab
sql0 = [‘select * from tb’]
data = select(conn, sql0);
```
* Insert a record:
```matlab
sql1 = [‘insert into tb values (now, 1)’]
exec(conn, sql1)
```
For more detailed examples, please refer to the examples\Matlab\TDEngineDemo.m file in the package.
## <a class="anchor" id="r"></a> R
R language supports connection to the TDengine database through the JDBC interface. First, you need to install the JDBC package of R language. Launch the R language environment, and then execute the following command to install the JDBC support library for R language:
```R
install.packages('RJDBC', repos='http://cran.us.r-project.org')
```
After installed, load the RJDBC package by executing `library('RJDBC')` command.
Then load the TDengine JDBC driver:
```R
drv<-JDBC("com.taosdata.jdbc.TSDBDriver","JDBCDriver-2.0.0-dist.jar", identifier.quote="\"")
```
If succeed, no error message will display. Then use the following command to try a database connection:
```R
conn<-dbConnect(drv,"jdbc:TSDB://192.168.0.1:0/?user=root&password=taosdata","root","taosdata")
```
Please replace the IP address in the command above to the correct one. If no error message is shown, then the connection is established successfully, otherwise the connection command needs to be adjusted according to the error prompt. TDengine supports below functions in *RJDBC* package:
- `dbWriteTable(conn, "test", iris, overwrite=FALSE, append=TRUE)`: Write the data in a data frame iris to the table test in the TDengine server. Parameter overwrite must be false. append must be TRUE and the schema of the data frame iris should be the same as the table test.
- `dbGetQuery(conn, "select count(*) from test")`: run a query command
- `dbSendUpdate (conn, "use db")`: Execute any non-query sql statement. For example, `dbSendUpdate (conn, "use db")`, write data `dbSendUpdate (conn, "insert into t1 values (now, 99)")`, and the like.
- `dbReadTable(conn, "test")`: read all the data in table test
- `dbDisconnect(conn)`: close a connection
- `dbRemoveTable(conn, "test")`: remove table test
The functions below are not supported currently:
- `dbExistsTable(conn, "test")`: if table test exists
- `dbListTables(conn)`: list all tables in the connection
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