Merge branch '3.0' of github.com:taosdata/TDengine into 3.0

.clang-format

@@ -88,4 +88,3 @@ Standard:        Auto
 TabWidth:        8
 UseTab:          Never
 ...
-

.gitattributes

+*.py linguist-detectable=false

.gitignore

 build/
 compile_commands.json
+CMakeSettings.json
 .cache
 .ycm_extra_conf.py
 .tasks

CMakeLists.txt

@@ -34,7 +34,7 @@ endif(${BUILD_TEST})
 
 add_subdirectory(source)
 add_subdirectory(tools)
-add_subdirectory(tests)
+add_subdirectory(utils)
 add_subdirectory(examples/c)
 
 # docs 

CMakeSettings.json

-{
-  "configurations": [
-    {
-      "name": "WSL-GCC-Debug",
-      "generator": "Unix Makefiles",
-      "configurationType": "Debug",
-      "buildRoot": "${projectDir}\\build\\",
-      "installRoot": "${projectDir}\\build\\",
-      "cmakeExecutable": "/usr/bin/cmake",
-      "cmakeCommandArgs": "",
-      "buildCommandArgs": "",
-      "ctestCommandArgs": "",
-      "inheritEnvironments": [ "linux_x64" ],
-      "wslPath": "${defaultWSLPath}",
-      "addressSanitizerRuntimeFlags": "detect_leaks=0",
-      "variables": [
-        {
-          "name": "CMAKE_INSTALL_PREFIX",
-          "value": "/mnt/d/TDengine/TDengine/build",
-          "type": "PATH"
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

Jenkinsfile

@@ -79,7 +79,7 @@ def pre_test(){
     rm -rf debug
     mkdir debug
     cd debug
-    cmake .. > /dev/null
+    cmake .. -DBUILD_TEST=true > /dev/null
     make -j4> /dev/null
 
     '''

Jenkinsfile2

@@ -173,7 +173,7 @@ def pre_test_build_mac() {
     '''
     sh '''
         cd ${WK}/debug
-        cmake ..
+        cmake .. -DBUILD_TEST=true
         make -j8
     '''
     sh '''
@@ -218,12 +218,12 @@ def pre_test_win(){
         if (env.CHANGE_URL =~ /\/TDengine\//) {
             bat '''
                 cd %WIN_INTERNAL_ROOT%
-                git pull
+                git pull origin ''' + env.CHANGE_TARGET + '''
             '''
             bat '''
                 cd %WIN_COMMUNITY_ROOT%
                 git remote prune origin
-                git pull
+                git pull  origin ''' + env.CHANGE_TARGET + '''
             '''
             bat '''
                 cd %WIN_COMMUNITY_ROOT%
@@ -236,7 +236,7 @@ def pre_test_win(){
         } else if (env.CHANGE_URL =~ /\/TDinternal\//) {
             bat '''
                 cd %WIN_INTERNAL_ROOT%
-                git pull
+                git pull origin ''' + env.CHANGE_TARGET + '''
             '''
             bat '''
                 cd %WIN_INTERNAL_ROOT%
@@ -302,7 +302,7 @@ def pre_test_build_win() {
         set CL=/MP8
         echo ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> cmake"
         time /t
-        cmake .. -G "NMake Makefiles JOM" || exit 7
+        cmake .. -G "NMake Makefiles JOM" -DBUILD_TEST=true || exit 7
         echo ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> jom -j 6"
         time /t
         jom -j 6 || exit 8

README.md

@@ -15,24 +15,26 @@
 [![Coverage Status](https://coveralls.io/repos/github/taosdata/TDengine/badge.svg?branch=develop)](https://coveralls.io/github/taosdata/TDengine?branch=develop)
 [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/4201/badge)](https://bestpractices.coreinfrastructure.org/projects/4201)
 
-English | [简体中文](README-CN.md) | We are hiring, check [here](https://tdengine.com/careers)
+English | [简体中文](README-CN.md) | [Learn more about TSDB](https://tdengine.com/tsdb/)
 
 # What is TDengine？
 
-TDengine is an open source, high-performance, cloud native [time-series database](https://tdengine.com/tsdb/what-is-a-time-series-database/) optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors. TDengine differentiates itself from other time-seires databases with the following advantages:
+TDengine is an open source, high-performance, cloud native [time-series database](https://tdengine.com/tsdb/) optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors. TDengine differentiates itself from other time-seires databases with the following advantages:
 
-- **[High-Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression.
+- **[High Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression.
 
 - **[Simplified Solution](https://tdengine.com/tdengine/simplified-time-series-data-solution/)**: Through built-in caching, stream processing and data subscription features, TDengine provides a simplified solution for time-series data processing. It reduces system design complexity and operation costs significantly.
 
 - **[Cloud Native](https://tdengine.com/tdengine/cloud-native-time-series-database/)**: Through native distributed design, sharding and partitioning, separation of compute and storage, RAFT, support for kubernetes deployment and full observability, TDengine is a cloud native Time-Series Database and can be deployed on public, private or hybrid clouds.
 
-- **[Ease of Use](https://docs.tdengine.com/get-started/docker/)**: For administrators, TDengine significantly reduces the effort to deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access. 
+- **[Ease of Use](https://tdengine.com/tdengine/easy-time-series-data-platform/)**: For administrators, TDengine significantly reduces the effort to deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access. 
 
 - **[Easy Data Analytics](https://tdengine.com/tdengine/time-series-data-analytics-made-easy/)**: Through super tables, storage and compute separation, data partitioning by time interval, pre-computation and other means, TDengine makes it easy to explore, format, and get access to data in a highly efficient way. 
 
 - **[Open Source](https://tdengine.com/tdengine/open-source-time-series-database/)**: TDengine’s core modules, including cluster feature, are all available under open source licenses. It has gathered 18.8k stars on GitHub. There is an active developer community, and over 139k running instances worldwide.
 
+For a full list of TDengine competitive advantages, please [check here](https://tdengine.com/tdengine/)
+
 # Documentation
 
 For user manual, system design and architecture, please refer to [TDengine Documentation](https://docs.tdengine.com) ([TDengine 文档](https://docs.taosdata.com))
@@ -230,9 +232,9 @@ After building successfully, TDengine can be installed by
 sudo make install
 ```
 
-Users can find more information about directories installed on the system in the [directory and files](https://docs.taosdata.com/reference/directory/) section.
+Users can find more information about directories installed on the system in the [directory and files](https://docs.tdengine.com/reference/directory/) section.
 
-Installing from source code will also configure service management for TDengine.Users can also choose to [install from packages](https://docs.taosdata.com/get-started/package/) for it.
+Installing from source code will also configure service management for TDengine.Users can also choose to [install from packages](https://docs.tdengine.com/get-started/package/) for it.
 
 To start the service after installation, in a terminal, use:
 
@@ -319,6 +321,11 @@ TDengine provides abundant developing tools for users to develop on TDengine. Fo
 
 Please follow the [contribution guidelines](CONTRIBUTING.md) to contribute to the project.
 
-# Join TDengine WeChat Group
+# Join the TDengine Community
+
+For more information about TDengine, you can follow us on social media and join our Discord server:
 
-Add WeChat “tdengine” to join the group，you can communicate with other users.
+- [Discord](https://discord.com/invite/VZdSuUg4pS)
+- [Twitter](https://twitter.com/TaosData)
+- [LinkedIn](https://www.linkedin.com/company/tdengine/)
+- [YouTube](https://www.youtube.com/channel/UCmp-1U6GS_3V3hjir6Uq5DQ)

cmake/cmake.define

@@ -2,8 +2,6 @@ cmake_minimum_required(VERSION 3.0)
 
 set(CMAKE_VERBOSE_MAKEFILE OFF)
 
-SET(BUILD_SHARED_LIBS "OFF")
-
 #set output directory
 SET(LIBRARY_OUTPUT_PATH ${PROJECT_BINARY_DIR}/build/lib)
 SET(EXECUTABLE_OUTPUT_PATH ${PROJECT_BINARY_DIR}/build/bin)

cmake/cmake.install

+SET(PREPARE_ENV_CMD "prepare_env_cmd")
+SET(PREPARE_ENV_TARGET "prepare_env_target")
+ADD_CUSTOM_COMMAND(OUTPUT ${PREPARE_ENV_CMD}
+    POST_BUILD
+    COMMAND echo "make test directory"
+    DEPENDS taosd
+    COMMAND ${CMAKE_COMMAND} -E make_directory ${TD_TESTS_OUTPUT_DIR}/cfg/
+    COMMAND ${CMAKE_COMMAND} -E make_directory ${TD_TESTS_OUTPUT_DIR}/log/
+    COMMAND ${CMAKE_COMMAND} -E make_directory ${TD_TESTS_OUTPUT_DIR}/data/
+    COMMAND ${CMAKE_COMMAND} -E echo dataDir ${TD_TESTS_OUTPUT_DIR}/data > ${TD_TESTS_OUTPUT_DIR}/cfg/taos.cfg
+    COMMAND ${CMAKE_COMMAND} -E echo logDir ${TD_TESTS_OUTPUT_DIR}/log  >> ${TD_TESTS_OUTPUT_DIR}/cfg/taos.cfg
+    COMMAND ${CMAKE_COMMAND} -E echo charset UTF-8  >> ${TD_TESTS_OUTPUT_DIR}/cfg/taos.cfg
+    COMMAND ${CMAKE_COMMAND} -E echo monitor 0  >> ${TD_TESTS_OUTPUT_DIR}/cfg/taos.cfg
+    COMMENT "prepare taosd environment")
+ADD_CUSTOM_TARGET(${PREPARE_ENV_TARGET} ALL WORKING_DIRECTORY ${TD_EXECUTABLE_OUTPUT_PATH} DEPENDS ${PREPARE_ENV_CMD})
+
 IF (TD_LINUX)
   SET(TD_MAKE_INSTALL_SH "${TD_SOURCE_DIR}/packaging/tools/make_install.sh")
   INSTALL(CODE "MESSAGE(\"make install script: ${TD_MAKE_INSTALL_SH}\")")

cmake/cmake.options

@@ -2,6 +2,12 @@
 # Deps options
 # =========================================================
 
+option(
+    BUILD_TEST
+    "If build unit tests using googletest"
+    OFF
+)
+
 IF(${TD_WINDOWS})
 
     MESSAGE("build pthread Win32")
@@ -45,12 +51,6 @@ IF(${TD_WINDOWS})
                 "If build wingetopt on Windows"
             ON
         )
-
-    option(
-            BUILD_TEST 
-            "If build unit tests using googletest" 
-            ON
-        )
     
     option(
             TDENGINE_3
@@ -65,31 +65,17 @@ IF(${TD_WINDOWS})
         )
 
 ELSEIF (TD_DARWIN_64)
-    add_definitions(-DCOMPILER_SUPPORTS_CXX13)
-    option(
-        BUILD_TEST
-        "If build unit tests using googletest"
-        ON
-    )
-ELSE ()
-    include(CheckCXXCompilerFlag)
-    CHECK_CXX_COMPILER_FLAG("-std=c++13" COMPILER_SUPPORTS_CXX13)
-    IF(${COMPILER_SUPPORTS_CXX13})
+    IF(${BUILD_TEST})
         add_definitions(-DCOMPILER_SUPPORTS_CXX13)
-        option(
-            BUILD_TEST
-            "If build unit tests using googletest"
-            ON
-        )
-    ELSE ()
-        option(
-            BUILD_TEST
-            "If build unit tests using googletest"
-            OFF
-        )
     ENDIF ()
 ENDIF ()
 
+option(
+    BUILD_SHARED_LIBS
+    ""
+    OFF
+    )
+
 option(
     RUST_BINDINGS
     "If build with rust-bindings"

cmake/cmake.platform

@@ -46,7 +46,7 @@ IF (${CMAKE_SYSTEM_NAME} MATCHES "Linux" OR ${CMAKE_SYSTEM_NAME} MATCHES "Darwin
 
         MESSAGE("Current system processor is ${CMAKE_SYSTEM_PROCESSOR}.")
         IF (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm64" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES "x86_64")
-            MESSAGE("Current system arch is arm64")
+            MESSAGE("Current system arch is 64")
             SET(TD_DARWIN_64 TRUE)
             ADD_DEFINITIONS("-D_TD_DARWIN_64")
         ENDIF ()
@@ -87,7 +87,7 @@ IF ("${CPUTYPE}" STREQUAL "")
     SET(TD_ARM_32 TRUE)
     ADD_DEFINITIONS("-D_TD_ARM_")
     ADD_DEFINITIONS("-D_TD_ARM_32")
-  ELSEIF (CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64")
+  ELSEIF (CMAKE_SYSTEM_PROCESSOR MATCHES "(aarch64)|(arm64)")
     MESSAGE(STATUS "The current platform is aarch64")
     SET(PLATFORM_ARCH_STR "arm64")
     SET(TD_ARM_64 TRUE)

cmake/cmake.version

@@ -2,7 +2,7 @@
 IF (DEFINED VERNUMBER)
   SET(TD_VER_NUMBER ${VERNUMBER})
 ELSE ()
-  SET(TD_VER_NUMBER "3.0.0.1")
+  SET(TD_VER_NUMBER "3.0.1.4")
 ENDIF ()
 
 IF (DEFINED VERCOMPATIBLE)

cmake/taosadapter_CMakeLists.txt.in

@@ -2,7 +2,7 @@
 # taosadapter
 ExternalProject_Add(taosadapter
         GIT_REPOSITORY https://github.com/taosdata/taosadapter.git
-        GIT_TAG abed566
+        GIT_TAG be729ab
         SOURCE_DIR "${TD_SOURCE_DIR}/tools/taosadapter"
         BINARY_DIR ""
         #BUILD_IN_SOURCE TRUE

cmake/taostools_CMakeLists.txt.in

@@ -2,7 +2,7 @@
 # taos-tools
 ExternalProject_Add(taos-tools
         GIT_REPOSITORY https://github.com/taosdata/taos-tools.git
-        GIT_TAG 833b721
+        GIT_TAG 70f5a1c
         SOURCE_DIR "${TD_SOURCE_DIR}/tools/taos-tools"
         BINARY_DIR ""
         #BUILD_IN_SOURCE TRUE

cmake/taosws_CMakeLists.txt.in

@@ -2,7 +2,7 @@
 # taosws-rs
 ExternalProject_Add(taosws-rs
         GIT_REPOSITORY https://github.com/taosdata/taos-connector-rust.git
-        GIT_TAG 7a54d21
+        GIT_TAG 1bdfca3
         SOURCE_DIR "${TD_SOURCE_DIR}/tools/taosws-rs"
         BINARY_DIR ""
         #BUILD_IN_SOURCE TRUE

docs/en/01-index.md

@@ -4,25 +4,26 @@ sidebar_label: Documentation Home
 slug: /
 ---
 
-
-TDengine is an [open source](https://tdengine.com/tdengine/open-source-time-series-database/), [cloud native](https://tdengine.com/tdengine/cloud-native-time-series-database/) time-series database optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors.  This document is the TDengine user manual. It introduces the basic, as well as novel concepts, in TDengine, and also talks in detail about installation, features, SQL, APIs, operation, maintenance, kernel design and other topics. It’s written mainly for architects, developers and system administrators.
+TDengine is an [open-source](https://tdengine.com/tdengine/open-source-time-series-database/), [cloud-native](https://tdengine.com/tdengine/cloud-native-time-series-database/) [time-series database](https://tdengine.com/tsdb/) optimized for the Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors. This document is the TDengine user manual. It introduces the basic, as well as novel concepts, in TDengine, and also talks in detail about installation, features, SQL, APIs, operation, maintenance, kernel design, and other topics. It’s written mainly for architects, developers, and system administrators.
 
 To get an overview of TDengine, such as a feature list, benchmarks, and competitive advantages, please browse through the [Introduction](./intro) section.
 
-TDengine greatly improves the efficiency of data ingestion, querying and storage by exploiting the characteristics of time series data, introducing the novel concepts of "one table for one data collection point" and "super table", and designing an innovative storage engine. To understand the new concepts in TDengine and make full use of the features and capabilities of TDengine, please read [“Concepts”](./concept) thoroughly.
+TDengine greatly improves the efficiency of data ingestion, querying, and storage by exploiting the characteristics of time series data, introducing the novel concepts of "one table for one data collection point" and "super table", and designing an innovative storage engine. To understand the new concepts in TDengine and make full use of the features and capabilities of TDengine, please read [Concepts](./concept) thoroughly.
+
+If you are a developer, please read the [Developer Guide](./develop) carefully. This section introduces the database connection, data modeling, data ingestion, query, continuous query, cache, data subscription, user-defined functions, and other functionality in detail. Sample code is provided for a variety of programming languages. In most cases, you can just copy and paste the sample code, and make a few changes to accommodate your application, and it will work.
 
-If you are a developer, please read the [“Developer Guide”](./develop) carefully. This section introduces the database connection, data modeling, data ingestion, query, continuous query, cache, data subscription, user-defined functions, and other functionality in detail. Sample code is provided for a variety of programming languages. In most cases, you can just copy and paste the sample code, make a few changes to accommodate your application, and it will work.
+We live in the era of big data, and scale-up is unable to meet the growing needs of the business. Any modern data system must have the ability to scale out, and clustering has become an indispensable feature of big data systems. Not only did the TDengine team develop the cluster feature, but also decided to open source this important feature. To learn how to deploy, manage and maintain a TDengine cluster please refer to [Cluster Deployment](../deployment).
 
-We live in the era of big data, and scale-up is unable to meet the growing needs of business. Any modern data system must have the ability to scale out, and clustering has become an indispensable feature of big data systems. Not only did the TDengine team develop the cluster feature, but also decided to open source this important feature. To learn how to deploy, manage and maintain a TDengine cluster please refer to ["cluster deployment"](../deployment).
+TDengine uses ubiquitous SQL as its query language, which greatly reduces learning costs and migration costs. In addition to the standard SQL, TDengine has extensions to better support time series data analysis. These extensions include functions such as roll-up, interpolation, and time-weighted average, among many others. The [SQL Reference](./taos-sql) chapter describes the SQL syntax in detail and lists the various supported commands and functions.
 
-TDengine uses ubiquitious SQL as its query language, which greatly reduces learning costs and migration costs. In addition to the standard SQL, TDengine has extensions to better support time series data analysis. These extensions include functions such as roll up, interpolation and time weighted average, among many others. The ["SQL Reference"](./taos-sql) chapter describes the SQL syntax in detail, and lists the various supported commands and functions.
+If you are a system administrator who cares about installation, upgrade, fault tolerance, disaster recovery, data import, data export, system configuration, how to monitor whether TDengine is running healthily, and how to improve system performance, please refer to, and thoroughly read the [Administration](./operation) section.
 
-If you are a system administrator who cares about installation, upgrade, fault tolerance, disaster recovery, data import, data export, system configuration, how to monitor whether TDengine is running healthily, and how to improve system performance, please refer to, and thoroughly read the ["Administration"](./operation) section.
+If you want to know more about TDengine tools, the REST API, and connectors for various programming languages, please see the [Reference](./reference) chapter.
 
-If you want to know more about TDengine tools, the REST API, and connectors for various programming languages, please see the ["Reference"](./reference) chapter.
+If you are very interested in the internal design of TDengine, please read the chapter [Inside TDengine](./tdinternal), which introduces the cluster design, data partitioning, sharding, writing, and reading processes in detail. If you want to study TDengine code or even contribute code, please read this chapter carefully.
 
-If you are very interested in the internal design of TDengine, please read the chapter ["Inside TDengine”](./tdinternal), which introduces the cluster design, data partitioning, sharding, writing, and reading processes in detail. If you want to study TDengine code or even contribute code, please read this chapter carefully.
+To get more general introduction about time series database, please read through [a series of articles](https://tdengine.com/tsdb/). To lean more competitive advantages about TDengine, please read through [a series of blogs](https://tdengine.com/tdengine/). 
 
-TDengine is an open source database, and we would love for you to be a part of TDengine. If you find any errors in the documentation, or see parts where more clarity or elaboration is needed, please click "Edit this page" at the bottom of each page to edit it directly.
+TDengine is an open-source database, and we would love for you to be a part of TDengine. If you find any errors in the documentation or see parts where more clarity or elaboration is needed, please click "Edit this page" at the bottom of each page to edit it directly.
 
-Together, we make a difference.
+Together, we make a difference!

docs/en/04-concept/index.md

@@ -6,101 +6,100 @@ In order to explain the basic concepts and provide some sample code, the TDengin
 
 <div className="center-table">
 <table>
-<thead><tr>
-    <th>Device ID</th>
-    <th>Time Stamp</th>
-    <th colSpan="3">Collected Metrics</th>
-    <th colSpan="2">Tags</th>
+  <thead>
+    <tr>
+      <th rowSpan="2">Device ID</th>
+      <th rowSpan="2">Timestamp</th>
+      <th colSpan="3">Collected Metrics</th>
+      <th colSpan="2">Tags</th>
     </tr>
-<tr>
-<th>Device ID</th>
-<th>Time Stamp</th>
-<th>current</th>
-<th>voltage</th>
-<th>phase</th>
-<th>location</th>
-<th>groupId</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>d1001</td>
-<td>1538548685000</td>
-<td>10.3</td>
-<td>219</td>
-<td>0.31</td>
-<td>California.SanFrancisco</td>
-<td>2</td>
-</tr>
-<tr>
-<td>d1002</td>
-<td>1538548684000</td>
-<td>10.2</td>
-<td>220</td>
-<td>0.23</td>
-<td>California.SanFrancisco</td>
-<td>3</td>
-</tr>
-<tr>
-<td>d1003</td>
-<td>1538548686500</td>
-<td>11.5</td>
-<td>221</td>
-<td>0.35</td>
-<td>California.LosAngeles</td>
-<td>3</td>
-</tr>
-<tr>
-<td>d1004</td>
-<td>1538548685500</td>
-<td>13.4</td>
-<td>223</td>
-<td>0.29</td>
-<td>California.LosAngeles</td>
-<td>2</td>
-</tr>
-<tr>
-<td>d1001</td>
-<td>1538548695000</td>
-<td>12.6</td>
-<td>218</td>
-<td>0.33</td>
-<td>California.SanFrancisco</td>
-<td>2</td>
-</tr>
-<tr>
-<td>d1004</td>
-<td>1538548696600</td>
-<td>11.8</td>
-<td>221</td>
-<td>0.28</td>
-<td>California.LosAngeles</td>
-<td>2</td>
-</tr>
-<tr>
-<td>d1002</td>
-<td>1538548696650</td>
-<td>10.3</td>
-<td>218</td>
-<td>0.25</td>
-<td>California.SanFrancisco</td>
-<td>3</td>
-</tr>
-<tr>
-<td>d1001</td>
-<td>1538548696800</td>
-<td>12.3</td>
-<td>221</td>
-<td>0.31</td>
-<td>California.SanFrancisco</td>
-<td>2</td>
-</tr>
-</tbody>
+    <tr>
+      <th>current</th>
+      <th>voltage</th>
+      <th>phase</th>
+      <th>location</th>
+      <th>groupid</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>d1001</td>
+      <td>1538548685000</td>
+      <td>10.3</td>
+      <td>219</td>
+      <td>0.31</td>
+      <td>California.SanFrancisco</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <td>d1002</td>
+      <td>1538548684000</td>
+      <td>10.2</td>
+      <td>220</td>
+      <td>0.23</td>
+      <td>California.SanFrancisco</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <td>d1003</td>
+      <td>1538548686500</td>
+      <td>11.5</td>
+      <td>221</td>
+      <td>0.35</td>
+      <td>California.LosAngeles</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <td>d1004</td>
+      <td>1538548685500</td>
+      <td>13.4</td>
+      <td>223</td>
+      <td>0.29</td>
+      <td>California.LosAngeles</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <td>d1001</td>
+      <td>1538548695000</td>
+      <td>12.6</td>
+      <td>218</td>
+      <td>0.33</td>
+      <td>California.SanFrancisco</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <td>d1004</td>
+      <td>1538548696600</td>
+      <td>11.8</td>
+      <td>221</td>
+      <td>0.28</td>
+      <td>California.LosAngeles</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <td>d1002</td>
+      <td>1538548696650</td>
+      <td>10.3</td>
+      <td>218</td>
+      <td>0.25</td>
+      <td>California.SanFrancisco</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <td>d1001</td>
+      <td>1538548696800</td>
+      <td>12.3</td>
+      <td>221</td>
+      <td>0.31</td>
+      <td>California.SanFrancisco</td>
+      <td>2</td>
+    </tr>
+  </tbody>
 </table>
 <a href="#model_table1">Table 1: Smart meter example data</a>
 </div>
 
-Each row contains the device ID, time stamp, collected metrics (current, voltage, phase as above), and static tags (location and groupId in Table 1) associated with the devices. Each smart meter generates a row (measurement) in a pre-defined time interval or triggered by an external event. The device produces a sequence of measurements with associated time stamps.
+Each row contains the device ID, timestamp, collected metrics (`current`, `voltage`, `phase` as above), and static tags (`location` and `groupid` in Table 1) associated with the devices. Each smart meter generates a row (measurement) in a pre-defined time interval or triggered by an external event. The device produces a sequence of measurements with associated timestamps.
 
 ## Metric
 
@@ -112,22 +111,22 @@ Label/Tag refers to the static properties of sensors, equipment or other types o
 
 ## Data Collection Point
 
-Data Collection Point (DCP) refers to hardware or software that collects metrics based on preset time periods or triggered by events. A data collection point can collect one or multiple metrics, but these metrics are collected at the same time and have the same time stamp. For some complex equipment, there are often multiple data collection points, and the sampling rate of each collection point may be different, and fully independent. For example, for a car, there could be a data collection point to collect GPS position metrics, a data collection point to collect engine status metrics, and a data collection point to collect the environment metrics inside the car. So in this example the car would have three data collection points. In the smart meters example, d1001, d1002, d1003, and d1004 are the data collection points.
+Data Collection Point (DCP) refers to hardware or software that collects metrics based on preset time periods or triggered by events. A data collection point can collect one or multiple metrics, but these metrics are collected at the same time and have the same timestamp. For some complex equipment, there are often multiple data collection points, and the sampling rate of each collection point may be different, and fully independent. For example, for a car, there could be a data collection point to collect GPS position metrics, a data collection point to collect engine status metrics, and a data collection point to collect the environment metrics inside the car. So in this example the car would have three data collection points. In the smart meters example, d1001, d1002, d1003, and d1004 are the data collection points.
 
 ## Table
 
 Since time-series data is most likely to be structured data, TDengine adopts the traditional relational database model to process them with a short learning curve. You need to create a database, create tables, then insert data points and execute queries to explore the data.
 
-To make full use of time-series data characteristics, TDengine adopts a strategy of "**One Table for One Data Collection Point**". TDengine requires the user to create a table for each data collection point (DCP) to store collected time-series data. For example, if there are over 10 million smart meters, it means 10 million tables should be created. For the table above, 4 tables should be created for devices D1001, D1002, D1003, and D1004 to store the data collected. This design has several benefits:
+To make full use of time-series data characteristics, TDengine adopts a strategy of "**One Table for One Data Collection Point**". TDengine requires the user to create a table for each data collection point (DCP) to store collected time-series data. For example, if there are over 10 million smart meters, it means 10 million tables should be created. For the table above, 4 tables should be created for devices d1001, d1002, d1003, and d1004 to store the data collected. This design has several benefits:
 
 1. Since the metric data from different DCP are fully independent, the data source of each DCP is unique, and a table has only one writer. In this way, data points can be written in a lock-free manner, and the writing speed can be greatly improved.
 2. For a DCP, the metric data generated by DCP is ordered by timestamp, so the write operation can be implemented by simple appending, which further greatly improves the data writing speed.
 3. The metric data from a DCP is continuously stored, block by block. If you read data for a period of time, it can greatly reduce random read operations and improve read and query performance by orders of magnitude.
 4. Inside a data block for a DCP, columnar storage is used, and different compression algorithms are used for different data types. Metrics generally don't vary as significantly between themselves over a time range as compared to other metrics, which allows for a higher compression rate.
 
-If the metric data of multiple DCPs are traditionally written into a single table, due to uncontrollable network delays, the timing of the data from different DCPs arriving at the server cannot be guaranteed, write operations must be protected by locks, and metric data from one DCP cannot be guaranteed to be continuously stored together. ** One table for one data collection point can ensure the best performance of insert and query of a single data collection point to the greatest possible extent.**
+If the metric data of multiple DCPs are traditionally written into a single table, due to uncontrollable network delays, the timing of the data from different DCPs arriving at the server cannot be guaranteed, write operations must be protected by locks, and metric data from one DCP cannot be guaranteed to be continuously stored together. **One table for one data collection point can ensure the best performance of insert and query of a single data collection point to the greatest possible extent.**
 
-TDengine suggests using DCP ID as the table name (like D1001 in the above table). Each DCP may collect one or multiple metrics (like the current, voltage, phase as above). Each metric has a corresponding column in the table. The data type for a column can be int, float, string and others. In addition, the first column in the table must be a timestamp. TDengine uses the time stamp as the index, and won’t build the index on any metrics stored. Column wise storage is used.
+TDengine suggests using DCP ID as the table name (like d1001 in the above table). Each DCP may collect one or multiple metrics (like the `current`, `voltage`, `phase` as above). Each metric has a corresponding column in the table. The data type for a column can be int, float, string and others. In addition, the first column in the table must be a timestamp. TDengine uses the timestamp as the index, and won’t build the index on any metrics stored. Column wise storage is used.
 
 Complex devices, such as connected cars, may have multiple DCPs. In this case, multiple tables are created for a single device, one table per DCP.
 
@@ -156,13 +155,20 @@ The relationship between a STable and the subtables created based on this STable
 
 Queries can be executed on both a table (subtable) and a STable. For a query on a STable, TDengine will treat the data in all its subtables as a whole data set for processing. TDengine will first find the subtables that meet the tag filter conditions, then scan the time-series data of these subtables to perform aggregation operation, which reduces the number of data sets to be scanned which in turn greatly improves the performance of data aggregation across multiple DCPs. In essence, querying a supertable is a very efficient aggregate query on multiple DCPs of the same type.
 
-In TDengine, it is recommended to use a subtable instead of a regular table for a DCP. In the smart meters example, we can create subtables like d1001, d1002, d1003, and d1004 under super table meters.
+In TDengine, it is recommended to use a subtable instead of a regular table for a DCP. In the smart meters example, we can create subtables like d1001, d1002, d1003, and d1004 under super table `meters`.
+
+To better understand the data model using metrics, tags, super table and subtable, please refer to the diagram below which demonstrates the data model of the smart meters example.
+
+<figure>
+
+![Meters Data Model Diagram](./supertable.webp)
 
-To better understand the data model using metri, tags, super table and subtable, please refer to the diagram below which demonstrates the data model of the smart meters example. ![Meters Data Model Diagram](./supertable.webp)
+<center><figcaption>Figure 1. Meters Data Model Diagram</figcaption></center>
+</figure>
 
 ## Database
 
-A database is a collection of tables. TDengine allows a running instance to have multiple databases, and each database can be configured with different storage policies. The [characteristics of time-series data](https://www.taosdata.com/blog/2019/07/09/86.html) from different data collection points may be different. Characteristics include collection frequency, retention policy and others which determine how you create and configure the database. For e.g. days to keep, number of replicas, data block size, whether data updates are allowed and other configurable parameters would be determined by the characteristics of your data and your business requirements. In order for TDengine to work with maximum efficiency in various scenarios, TDengine recommends that STables with different data characteristics be created in different databases.
+A database is a collection of tables. TDengine allows a running instance to have multiple databases, and each database can be configured with different storage policies. The [characteristics of time-series data](https://tdengine.com/tsdb/characteristics-of-time-series-data/) from different data collection points may be different. Characteristics include collection frequency, retention policy and others which determine how you create and configure the database. For e.g. days to keep, number of replicas, data block size, whether data updates are allowed and other configurable parameters would be determined by the characteristics of your data and your business requirements. In order for TDengine to work with maximum efficiency in various scenarios, TDengine recommends that STables with different data characteristics be created in different databases.
 
 In a database, there can be one or more STables, but a STable belongs to only one database. All tables owned by a STable are stored in only one database.
 
@@ -172,4 +178,4 @@ FQDN (Fully Qualified Domain Name) is the full domain name of a specific compute
 
 Each node of a TDengine cluster is uniquely identified by an End Point, which consists of an FQDN and a Port, such as h1.tdengine.com:6030. In this way, when the IP changes, we can still use the FQDN to dynamically find the node without changing any configuration of the cluster. In addition, FQDN is used to facilitate unified access to the same cluster from the Intranet and the Internet.
 
-TDengine does not recommend using an IP address to access the cluster. FQDN is recommended for cluster management. 
+TDengine does not recommend using an IP address to access the cluster. FQDN is recommended for cluster management.

docs/en/05-get-started/01-docker.md

@@ -3,7 +3,11 @@ sidebar_label: Docker
 title: Quick Install on Docker
 ---
 
-This document describes how to install TDengine in a Docker container and perform queries and inserts. To get started with TDengine in a non-containerized environment, see [Quick Install](../../get-started/package). If you want to view the source code, build TDengine yourself, or contribute to the project, see the [TDengine GitHub repository](https://github.com/taosdata/TDengine).
+This document describes how to install TDengine in a Docker container and perform queries and inserts.
+
+- The easiest way to explore TDengine is through [TDengine Cloud](http://cloud.tdengine.com). 
+- To get started with TDengine in a non-containerized environment, see [Quick Install from Package](../../get-started/package).
+- If you want to view the source code, build TDengine yourself, or contribute to the project, see the [TDengine GitHub repository](https://github.com/taosdata/TDengine).
 
 ## Run TDengine
 
@@ -13,7 +17,7 @@ If Docker is already installed on your computer, run the following command:
 docker run -d -p 6030:6030 -p 6041:6041 -p 6043-6049:6043-6049 -p 6043-6049:6043-6049/udp tdengine/tdengine
 ```
 
-Note that TDengine Server uses TCP port 6030. Port 6041 is used by taosAdapter for the REST API service. Ports 6043 through 6049 are used by taosAdapter for other connectors. You can open these ports as needed.
+Note that TDengine Server 3.0 uses TCP port 6030. Port 6041 is used by taosAdapter for the REST API service. Ports 6043 through 6049 are used by taosAdapter for other connectors. You can open these ports as needed.
 
 Run the following command to ensure that your container is running:
 
@@ -21,7 +25,7 @@ Run the following command to ensure that your container is running:
 docker ps
 ```
 
-Enter the container and open the bash shell:
+Enter the container and open the `bash` shell:
 
 ```shell
 docker exec -it <container name> bash
@@ -31,68 +35,68 @@ You can now access TDengine or run other Linux commands.
 
 Note: For information about installing docker, see the [official documentation](https://docs.docker.com/get-docker/).
 
-## Insert Data into TDengine
+## Open the TDengine CLI
 
-You can use the `taosBenchmark` tool included with TDengine to write test data into your deployment.
+On the container, run the following command to open the TDengine CLI:
 
-To do so, run the following command:
+```
+$ taos
 
-   ```bash
-   $ taosBenchmark
-   
-   ```
+taos>
 
-This command creates the `meters` supertable in the `test` database. In the `meters` supertable, it then creates 10,000 subtables named `d0` to `d9999`. Each table has 10,000 rows and each row has four columns: `ts`, `current`, `voltage`, and `phase`. The timestamps of the data in these columns range from 2017-07-14 10:40:00 000 to 2017-07-14 10:40:09 999. Each table is randomly assigned a `groupId` tag from 1 to 10 and a `location` tag of either `Campbell`, `Cupertino`, `Los Angeles`, `Mountain View`, `Palo Alto`, `San Diego`, `San Francisco`, `San Jose`, `Santa Clara` or `Sunnyvale`.
+```
 
-   The `taosBenchmark` command creates a deployment with 100 million data points that you can use for testing purposes. The time required depends on the hardware specifications of the local system.
+## Test data insert performance
 
-   You can customize the test deployment that taosBenchmark creates by specifying command-line parameters. For information about command-line parameters, run the `taosBenchmark --help` command. For more information about taosBenchmark, see [taosBenchmark](/reference/taosbenchmark).
+After your TDengine Server is running normally, you can run the taosBenchmark utility to test its performance:
 
-## Open the TDengine CLI
-
-On the container, run the following command to open the TDengine CLI: 
+Start TDengine service and execute `taosBenchmark` (formerly named `taosdemo`) in a Linux or Windows terminal.
 
+```bash
+taosBenchmark
 ```
-$ taos
 
-taos> 
+This command creates the `meters` supertable in the `test` database. In the `meters` supertable, it then creates 10,000 subtables named `d0` to `d9999`. Each table has 10,000 rows and each row has four columns: `ts`, `current`, `voltage`, and `phase`. The timestamps of the data in these columns range from 2017-07-14 10:40:00 000 to 2017-07-14 10:40:09 999. Each table is randomly assigned a `groupId` tag from 1 to 10 and a `location` tag of either `California.Campbell`, `California.Cupertino`, `California.LosAngeles`, `California.MountainView`, `California.PaloAlto`, `California.SanDiego`, `California.SanFrancisco`, `California.SanJose`, `California.SantaClara` or `California.Sunnyvale`.
 
-```
+The `taosBenchmark` command creates a deployment with 100 million data points that you can use for testing purposes. The time required to create the deployment depends on your hardware. On most modern servers, the deployment is created in ten to twenty seconds.
 
-## Query Data in TDengine
+You can customize the test deployment that taosBenchmark creates by specifying command-line parameters. For information about command-line parameters, run the `taosBenchmark --help` command. For more information about taosBenchmark, see [taosBenchmark](../../reference/taosbenchmark).
 
-After using taosBenchmark to create your test deployment, you can run queries in the TDengine CLI to test its performance. For example:
+## Test data query performance
 
-From the TDengine CLI query the number of rows in the `meters` supertable:
+After using `taosBenchmark` to create your test deployment, you can run queries in the TDengine CLI to test its performance:
+
+From the TDengine CLI (taos) query the number of rows in the `meters` supertable:
 
 ```sql
-select count(*) from test.meters;
+SELECT COUNT(*) FROM test.meters;
 ```
 
 Query the average, maximum, and minimum values of all 100 million rows of data:
 
 ```sql
-select avg(current), max(voltage), min(phase) from test.meters;
+SELECT AVG(current), MAX(voltage), MIN(phase) FROM test.meters;
 ```
 
-Query the number of rows whose `location` tag is `San Francisco`:
+Query the number of rows whose `location` tag is `California.SanFrancisco`:
 
 ```sql
-select count(*) from test.meters where location="San Francisco";
+SELECT COUNT(*) FROM test.meters WHERE location = "California.SanFrancisco";
 ```
 
 Query the average, maximum, and minimum values of all rows whose `groupId` tag is `10`:
 
 ```sql
-select avg(current), max(voltage), min(phase) from test.meters where groupId=10;
+SELECT AVG(current), MAX(voltage), MIN(phase) FROM test.meters WHERE groupId = 10;
 ```
 
-Query the average, maximum, and minimum values for table `d10` in 1 second intervals:
+Query the average, maximum, and minimum values for table `d10` in 10 second intervals:
 
 ```sql
-select first(ts), avg(current), max(voltage), min(phase) from test.d10 interval(1s);
+SELECT FIRST(ts), AVG(current), MAX(voltage), MIN(phase) FROM test.d10 INTERVAL(10s);
 ```
-In the query above you are selecting the first timestamp (ts) in the interval, another way of selecting this would be _wstart which will give the start of the time window. For more information about windowed queries, see [Time-Series Extensions](../../taos-sql/distinguished/).
+
+In the query above you are selecting the first timestamp (ts) in the interval, another way of selecting this would be `\_wstart` which will give the start of the time window. For more information about windowed queries, see [Time-Series Extensions](../../taos-sql/distinguished/).
 
 ## Additional Information
 

docs/en/05-get-started/03-package.md

@@ -7,25 +7,30 @@ import Tabs from "@theme/Tabs";
 import TabItem from "@theme/TabItem";
 import PkgListV3 from "/components/PkgListV3";
 
-For information about installing TDengine on Docker, see [Quick Install on Docker](../../get-started/docker). If you want to view the source code, build TDengine yourself, or contribute to the project, see the [TDengine GitHub repository](https://github.com/taosdata/TDengine).
+This document describes how to install TDengine on Linux and Windows and perform queries and inserts.
 
-The full package of TDengine includes the TDengine Server (`taosd`), TDengine Client (`taosc`), taosAdapter for connecting with third-party systems and providing a RESTful interface, a command-line interface, and some tools. Note that taosAdapter supports Linux only. In addition to connectors for multiple languages, TDengine also provides a [REST API](../../reference/rest-api) through [taosAdapter](../../reference/taosadapter).
+- The easiest way to explore TDengine is through [TDengine Cloud](http://cloud.tdengine.com). 
+- To get started with TDengine on Docker, see [Quick Install on Docker](../../get-started/docker).
+- If you want to view the source code, build TDengine yourself, or contribute to the project, see the [TDengine GitHub repository](https://github.com/taosdata/TDengine).
 
-The standard server installation package includes `taos`, `taosd`, `taosAdapter`, `taosBenchmark`, and sample code. You can also download a lite package that includes only `taosd` and the C/C++ connector.
+The full package of TDengine includes the TDengine Server (`taosd`), TDengine Client (`taosc`), taosAdapter for connecting with third-party systems and providing a RESTful interface, a command-line interface (CLI, taos), and some tools. Note that taosAdapter supports Linux only. In addition to connectors for multiple languages, TDengine also provides a [REST API](../../reference/rest-api) through [taosAdapter](../../reference/taosadapter).
 
-The TDengine Community Edition is released as .deb and .rpm packages. The .deb package can be installed on Debian, Ubuntu, and derivative systems. The .rpm package can be installed on CentOS, RHEL, SUSE, and derivative systems. A .tar.gz package is also provided for enterprise customers, and you can install TDengine over `apt-get` as well. The .tar.tz package includes `taosdump`  and the TDinsight installation script. If you want to use these utilities with the .deb or .rpm package, download and install taosTools separately. TDengine can also be installed on 64-bit Windows servers.
+The standard server installation package includes `taos`, `taosd`, `taosAdapter`, `taosBenchmark`, and sample code. You can also download the Lite package that includes only `taosd` and the C/C++ connector.
+
+The TDengine Community Edition is released as Deb and RPM packages. The Deb package can be installed on Debian, Ubuntu, and derivative systems. The RPM package can be installed on CentOS, RHEL, SUSE, and derivative systems. A .tar.gz package is also provided for enterprise customers, and you can install TDengine over `apt-get` as well. The .tar.tz package includes `taosdump` and the TDinsight installation script. If you want to use these utilities with the Deb or RPM package, download and install taosTools separately. TDengine can also be installed on 64-bit Windows.
 
 ## Installation
 
 <Tabs>
 <TabItem label=".deb" value="debinst">
 
-1. Download the .deb installation package.
-<PkgListV3 type={6}/>
+1. Download the Deb installation package.
+   <PkgListV3 type={6}/>
 2. In the directory where the package is located, use `dpkg` to install the package:
 
+> Please replace `<version>` with the corresponding version of the package downloaded
+
 ```bash
-# Enter the name of the package that you downloaded.
 sudo dpkg -i TDengine-server-<version>-Linux-x64.deb
 ```
 
@@ -34,11 +39,12 @@ sudo dpkg -i TDengine-server-<version>-Linux-x64.deb
 <TabItem label=".rpm" value="rpminst">
 
 1. Download the .rpm installation package.
-<PkgListV3 type={5}/>
+   <PkgListV3 type={5}/>
 2. In the directory where the package is located, use rpm to install the package:
 
+> Please replace `<version>` with the corresponding version of the package downloaded
+
 ```bash
-# Enter the name of the package that you downloaded.
 sudo rpm -ivh TDengine-server-<version>-Linux-x64.rpm
 ```
 
@@ -47,11 +53,12 @@ sudo rpm -ivh TDengine-server-<version>-Linux-x64.rpm
 <TabItem label=".tar.gz" value="tarinst">
 
 1. Download the .tar.gz installation package.
-<PkgListV3 type={0}/>
+   <PkgListV3 type={0}/>
 2. In the directory where the package is located, use `tar` to decompress the package:
 
+> Please replace `<version>` with the corresponding version of the package downloaded
+
 ```bash
-# Enter the name of the package that you downloaded.
 tar -zxvf TDengine-server-<version>-Linux-x64.tar.gz
 ```
 
@@ -96,23 +103,23 @@ sudo apt-get install tdengine
 This installation method is supported only for Debian and Ubuntu.
 ::::
 </TabItem>
-<TabItem label="Windows" value="windows">           
+<TabItem label="Windows" value="windows">
 
-Note: TDengine only supports Windows Server 2016/2019 and windows 10/11 system versions on the windows platform.
+Note: TDengine only supports Windows Server 2016/2019 and Windows 10/11 on the Windows platform.
 
 1. Download the Windows installation package.
-<PkgListV3 type={3}/>
+   <PkgListV3 type={3}/>
 2. Run the downloaded package to install TDengine.
 
 </TabItem>
 </Tabs>
 
 :::info
-For information about TDengine releases, see [Release History](../../releases). 
+For information about TDengine releases, see [Release History](../../releases/tdengine).
 :::
 
 :::note
-On the first node in your TDengine cluster, leave the `Enter FQDN:` prompt blank and press **Enter**. On subsequent nodes, you can enter the end point of the first dnode in the cluster. You can also configure this setting after you have finished installing TDengine.
+On the first node in your TDengine cluster, leave the `Enter FQDN:` prompt blank and press **Enter**. On subsequent nodes, you can enter the endpoint of the first dnode in the cluster. You can also configure this setting after you have finished installing TDengine.
 
 :::
 
@@ -147,7 +154,7 @@ Active: inactive (dead)
 
 After confirming that TDengine is running, run the `taos` command to access the TDengine CLI.
 
-The following `systemctl` commands can help you manage TDengine:
+The following `systemctl` commands can help you manage TDengine service:
 
 - Start TDengine Server: `systemctl start taosd`
 
@@ -159,7 +166,7 @@ The following `systemctl` commands can help you manage TDengine:
 
 :::info
 
-- The `systemctl` command requires _root_ privileges. If you are not logged in as the `root` user, use the `sudo` command.
+- The `systemctl` command requires _root_ privileges. If you are not logged in as the _root_ user, use the `sudo` command.
 - The `systemctl stop taosd` command does not instantly stop TDengine Server. The server is stopped only after all data in memory is flushed to disk. The time required depends on the cache size.
 - If your system does not include `systemd`, you can run `/usr/local/taos/bin/taosd` to start TDengine manually.
 
@@ -174,23 +181,9 @@ After the installation is complete, run `C:\TDengine\taosd.exe` to start TDengin
 </TabItem>
 </Tabs>
 
-## Test data insert performance
-
-After your TDengine Server is running normally, you can run the taosBenchmark utility to test its performance:
-
-```bash
-taosBenchmark
-```
-
-This command creates the `meters` supertable in the `test` database. In the `meters` supertable, it then creates 10,000 subtables named `d0` to `d9999`. Each table has 10,000 rows and each row has four columns: `ts`, `current`, `voltage`, and `phase`. The timestamps of the data in these columns range from 2017-07-14 10:40:00 000 to 2017-07-14 10:40:09 999. Each table is randomly assigned a `groupId` tag from 1 to 10 and a `location` tag of either `Campbell`, `Cupertino`, `Los Angeles`, `Mountain View`, `Palo Alto`, `San Diego`, `San Francisco`, `San Jose`, `Santa Clara` or `Sunnyvale`.
-
-The `taosBenchmark` command creates a deployment with 100 million data points that you can use for testing purposes. The time required to create the deployment depends on your hardware. On most modern servers, the deployment is created in less than a minute.
-
-You can customize the test deployment that taosBenchmark creates by specifying command-line parameters. For information about command-line parameters, run the `taosBenchmark --help` command. For more information about taosBenchmark, see [taosBenchmark](../../reference/taosbenchmark).
-
-## Command Line Interface
+## Command Line Interface (CLI)
 
-You can use the TDengine CLI to monitor your TDengine deployment and execute ad hoc queries. To open the CLI, run the following command:
+You can use the TDengine CLI to monitor your TDengine deployment and execute ad hoc queries. To open the CLI, you can execute `taos` in the Linux terminal where TDengine is installed, or you can run `taos.exe` in the `C:\TDengine` directory of the Windows terminal where TDengine is installed to start the TDengine command line.
 
 ```bash
 taos
@@ -205,52 +198,71 @@ taos>
 For example, you can create and delete databases and tables and run all types of queries. Each SQL command must be end with a semicolon (;). For example:
 
 ```sql
-create database demo;
-use demo;
-create table t (ts timestamp, speed int);
-insert into t values ('2019-07-15 00:00:00', 10);
-insert into t values ('2019-07-15 01:00:00', 20);
-select * from t;
+CREATE DATABASE demo;
+USE demo;
+CREATE TABLE t (ts TIMESTAMP, speed INT);
+INSERT INTO t VALUES ('2019-07-15 00:00:00', 10);
+INSERT INTO t VALUES ('2019-07-15 01:00:00', 20);
+SELECT * FROM t;
+
            ts            |    speed    |
 ========================================
  2019-07-15 00:00:00.000 |          10 |
  2019-07-15 01:00:00.000 |          20 |
+
 Query OK, 2 row(s) in set (0.003128s)
 ```
 
 You can also can monitor the deployment status, add and remove user accounts, and manage running instances. You can run the TDengine CLI on either Linux or Windows machines. For more information, see [TDengine CLI](../../reference/taos-shell/).
-           
+
+## Test data insert performance
+
+After your TDengine Server is running normally, you can run the taosBenchmark utility to test its performance:
+
+Start TDengine service and execute `taosBenchmark` (formerly named `taosdemo`) in a Linux or Windows terminal.
+
+```bash
+taosBenchmark
+```
+
+This command creates the `meters` supertable in the `test` database. In the `meters` supertable, it then creates 10,000 subtables named `d0` to `d9999`. Each table has 10,000 rows and each row has four columns: `ts`, `current`, `voltage`, and `phase`. The timestamps of the data in these columns range from 2017-07-14 10:40:00 000 to 2017-07-14 10:40:09 999. Each table is randomly assigned a `groupId` tag from 1 to 10 and a `location` tag of either `California.Campbell`, `California.Cupertino`, `California.LosAngeles`, `California.MountainView`, `California.PaloAlto`, `California.SanDiego`, `California.SanFrancisco`, `California.SanJose`, `California.SantaClara` or `California.Sunnyvale`.
+
+The `taosBenchmark` command creates a deployment with 100 million data points that you can use for testing purposes. The time required to create the deployment depends on your hardware. On most modern servers, the deployment is created in ten to twenty seconds.
+
+You can customize the test deployment that taosBenchmark creates by specifying command-line parameters. For information about command-line parameters, run the `taosBenchmark --help` command. For more information about taosBenchmark, see [taosBenchmark](../../reference/taosbenchmark).
+
 ## Test data query performance
 
-After using taosBenchmark to create your test deployment, you can run queries in the TDengine CLI to test its performance:
+After using `taosBenchmark` to create your test deployment, you can run queries in the TDengine CLI to test its performance:
 
-From the TDengine CLI query the number of rows in the `meters` supertable:
+From the TDengine CLI (taos) query the number of rows in the `meters` supertable:
 
 ```sql
-select count(*) from test.meters;
+SELECT COUNT(*) FROM test.meters;
 ```
 
 Query the average, maximum, and minimum values of all 100 million rows of data:
 
 ```sql
-select avg(current), max(voltage), min(phase) from test.meters;
+SELECT AVG(current), MAX(voltage), MIN(phase) FROM test.meters;
 ```
 
-Query the number of rows whose `location` tag is `San Francisco`:
+Query the number of rows whose `location` tag is `California.SanFrancisco`:
 
 ```sql
-select count(*) from test.meters where location="San Francisco";
+SELECT COUNT(*) FROM test.meters WHERE location = "California.SanFrancisco";
 ```
 
 Query the average, maximum, and minimum values of all rows whose `groupId` tag is `10`:
 
 ```sql
-select avg(current), max(voltage), min(phase) from test.meters where groupId=10;
+SELECT AVG(current), MAX(voltage), MIN(phase) FROM test.meters WHERE groupId = 10;
 ```
 
-Query the average, maximum, and minimum values for table `d10` in 1 second intervals:
+Query the average, maximum, and minimum values for table `d10` in 10 second intervals:
 
 ```sql
-select first(ts), avg(current), max(voltage), min(phase) from test.d10 interval(1s);
+SELECT FIRST(ts), AVG(current), MAX(voltage), MIN(phase) FROM test.d10 INTERVAL(10s);
 ```
-In the query above you are selecting the first timestamp (ts) in the interval, another way of selecting this would be _wstart which will give the start of the time window. For more information about windowed queries, see [Time-Series Extensions](../../taos-sql/distinguished/).
+
+In the query above you are selecting the first timestamp (ts) in the interval, another way of selecting this would be `\_wstart` which will give the start of the time window. For more information about windowed queries, see [Time-Series Extensions](../../taos-sql/distinguished/).

docs/en/05-get-started/index.md

@@ -3,9 +3,9 @@ title: Get Started
 description: This article describes how to install TDengine and test its performance.
 ---
 
-The full package of TDengine includes the TDengine Server (`taosd`), TDengine Client (`taosc`), taosAdapter for connecting with third-party systems and providing a RESTful interface, a command-line interface, and some tools. In addition to connectors for multiple languages, TDengine also provides a [RESTful interface](/reference/rest-api) through [taosAdapter](/reference/taosadapter).
+You can install and run TDengine on Linux and Windows machines as well as Docker containers. You can also deploy TDengine as a managed service with TDengine Cloud.
 
-You can install and run TDengine on Linux and Windows machines as well as Docker containers.
+The full package of TDengine includes the TDengine Server (`taosd`), TDengine Client (`taosc`), taosAdapter for connecting with third-party systems and providing a RESTful interface, a command-line interface, and some tools. In addition to connectors for multiple languages, TDengine also provides a [RESTful interface](/reference/rest-api) through [taosAdapter](/reference/taosadapter).
 
 ```mdx-code-block
 import DocCardList from '@theme/DocCardList';

docs/en/07-develop/01-connect/_connect_cs.mdx

 ```csharp title="Native Connection"
-{{#include docs/examples/csharp/ConnectExample.cs}}
+{{#include docs/examples/csharp/connect/Program.cs}}
 ```
 
-:::info
-C# connector supports only native connection for now.
-
-:::
+```csharp title="WebSocket Connection"
+{{#include docs/examples/csharp/wsConnect/Program.cs}}
+```

docs/en/07-develop/01-connect/index.md

 ---
-title: Connect
-description: "This document explains how to establish connections to TDengine and how to install and use TDengine connectors."
+sidebar_label: Connect
+title: Connect to TDengine
+description: "How to establish connections to TDengine and how to install and use TDengine connectors."
 ---
 
 import Tabs from "@theme/Tabs";
@@ -279,6 +280,6 @@ Prior to establishing connection, please make sure TDengine is already running a
 </Tabs>
 
 :::tip
-If the connection fails, in most cases it's caused by improper configuration for FQDN or firewall. Please refer to the section "Unable to establish connection" in [FAQ](https://docs.taosdata.com/train-faq/faq).
+If the connection fails, in most cases it's caused by improper configuration for FQDN or firewall. Please refer to the section "Unable to establish connection" in [FAQ](https://docs.tdengine.com/train-faq/faq).
 
 :::

docs/en/07-develop/03-insert-data/02-influxdb-line.mdx

@@ -37,7 +37,8 @@ meters,location=California.LosAngeles,groupid=2 current=13.4,voltage=223,phase=0
 - All the data in `tag_set` will be converted to nchar type automatically .
 - Each data in `field_set` must be self-descriptive for its data type. For example 1.2f32 means a value 1.2 of float type. Without the "f" type suffix, it will be treated as type double.
 - Multiple kinds of precision can be used for the `timestamp` field. Time precision can be from nanosecond (ns) to hour (h).
-
+- You can configure smlChildTableName in taos.cfg to specify table names, for example, `smlChildTableName=tname`. You can insert `st,tname=cpul,t1=4 c1=3 1626006833639000000` and the cpu1 table will be automatically created. Note that if multiple rows have the same tname but different tag_set values, the tag_set of the first row is used to create the table and the others are ignored.
+- It is assumed that the order of field_set in a supertable is consistent, meaning that the first record contains all fields and subsequent records store fields in the same order. If the order is not consistent, set smlDataFormat in taos.cfg to false. Otherwise, data will be written out of order and a database error will occur.(smlDataFormat in taos.cfg default to false after version of 3.0.1.3)
 :::
 
 For more details please refer to [InfluxDB Line Protocol](https://docs.influxdata.com/influxdb/v2.0/reference/syntax/line-protocol/) and [TDengine Schemaless](/reference/schemaless/#Schemaless-Line-Protocol)
@@ -64,3 +65,7 @@ For more details please refer to [InfluxDB Line Protocol](https://docs.influxdat
     <CLine />
   </TabItem>
 </Tabs>
+
+## Query Examples
+If you want query the data of `location=California.LosAngeles,groupid=2`，here is the query sql：
+select * from `meters.voltage` where location="California.LosAngeles" and groupid=2

docs/en/07-develop/03-insert-data/03-opentsdb-telnet.mdx

@@ -31,7 +31,7 @@ For example:
 ```txt
 meters.current 1648432611250 11.3 location=California.LosAngeles groupid=3
 ```
-
+- The defult child table name is generated by rules.You can configure smlChildTableName in taos.cfg to specify chile table names, for example, `smlChildTableName=tname`. You can insert `meters.current 1648432611250 11.3 tname=cpu1 location=California.LosAngeles groupid=3` and the cpu1 table will be automatically created. Note that if multiple rows have the same tname but different tag_set values, the tag_set of the first row is used to create the table and the others are ignored.
 Please refer to [OpenTSDB Telnet API](http://opentsdb.net/docs/build/html/api_telnet/put.html) for more details.
 
 ## Examples
@@ -79,3 +79,6 @@ taos> select tbname, * from `meters.current`;
  t_7e7b26dd860280242c6492a16... | 2022-03-28 09:56:51.250 |              12.600000000 | 2       | California.SanFrancisco               |
 Query OK, 4 row(s) in set (0.005399s)
 ```
+## Query Examples
+If you want query the data of `location=California.LosAngeles groupid=3`，here is the query sql：
+select * from `meters.voltage` where location="California.LosAngeles" and groupid=3

docs/en/07-develop/03-insert-data/04-opentsdb-json.mdx

@@ -48,7 +48,7 @@ Please refer to [OpenTSDB HTTP API](http://opentsdb.net/docs/build/html/api_http
 
 - In JSON protocol, strings will be converted to nchar type and numeric values will be converted to double type.
 - Only data in array format is accepted and so an array must be used even if there is only one row.
-
+- The defult child table name is generated by rules.You can configure smlChildTableName in taos.cfg to specify chile table names, for example, `smlChildTableName=tname`. You can insert `"tags": { "host": "web02","dc": "lga","tname":"cpu1"}` and the cpu1 table will be automatically created. Note that if multiple rows have the same tname but different tag_set values, the tag_set of the first row is used to create the table and the others are ignored.
 :::
 
 ## Examples
@@ -94,3 +94,6 @@ taos> select * from `meters.current`;
  2022-03-28 09:56:51.250 |              12.600000000 |               2.000000000 | California.SanFrancisco               |
 Query OK, 2 row(s) in set (0.004076s)
 ```
+## Query Examples
+If you want query the data of "tags": {"location": "California.LosAngeles", "groupid": 1}，here is the query sql：
+select * from `meters.voltage` where location="California.LosAngeles" and groupid=1

docs/en/07-develop/03-insert-data/_cs_line.mdx

 ```csharp
-{{#include docs/examples/csharp/InfluxDBLineExample.cs}}
+{{#include docs/examples/csharp/influxdbLine/Program.cs}}
 ```

docs/en/07-develop/03-insert-data/_cs_opts_json.mdx

 ```csharp
-{{#include docs/examples/csharp/OptsJsonExample.cs}}
+{{#include docs/examples/csharp/optsJSON/Program.cs}}
 ```

docs/en/07-develop/03-insert-data/_cs_opts_telnet.mdx

 ```csharp
-{{#include docs/examples/csharp/OptsTelnetExample.cs}}
+{{#include docs/examples/csharp/optsTelnet/Program.cs}}
 ```

docs/en/07-develop/03-insert-data/_cs_sql.mdx

 ```csharp
-{{#include docs/examples/csharp/SQLInsertExample.cs}}
+{{#include docs/examples/csharp/sqlInsert/Program.cs}}
 ```

docs/en/07-develop/03-insert-data/_cs_stmt.mdx

 ```csharp
-{{#include docs/examples/csharp/StmtInsertExample.cs}}
+{{#include docs/examples/csharp/stmtInsert/Program.cs}}
 ```

docs/en/07-develop/04-query-data/_cs.mdx

 ```csharp
-{{#include docs/examples/csharp/QueryExample.cs}}
+{{#include docs/examples/csharp/query/Program.cs}}
 ```

docs/en/07-develop/04-query-data/_cs_async.mdx

 ```csharp
-{{#include docs/examples/csharp/AsyncQueryExample.cs}}
+{{#include docs/examples/csharp/asyncQuery/Program.cs}}
 ```

docs/en/07-develop/_sub_cs.mdx

 ```csharp
-{{#include docs/examples/csharp/SubscribeDemo.cs}}
+{{#include docs/examples/csharp/subscribe/Program.cs}}
 ```
\ No newline at end of file

docs/en/10-deployment/01-deploy.md

@@ -39,18 +39,18 @@ To get the hostname on any host, the command `hostname -f` can be executed.
 
 On the physical machine running the application, ping the dnode that is running taosd. If the dnode is not accessible, the application cannot connect to taosd. In this case, verify the DNS and hosts settings on the physical node running the application.
 
-The end point of each dnode is the output hostname and port, such as h1.taosdata.com:6030.
+The end point of each dnode is the output hostname and port, such as h1.tdengine.com:6030.
 
 ### Step 5
 
-Modify the TDengine configuration file `/etc/taos/taos.cfg` on each node. Assuming the first dnode of TDengine cluster is "h1.taosdata.com:6030", its `taos.cfg` is configured as following.
+Modify the TDengine configuration file `/etc/taos/taos.cfg` on each node. Assuming the first dnode of TDengine cluster is "h1.tdengine.com:6030", its `taos.cfg` is configured as following.
 
 ```c
 // firstEp is the end point to connect to when any dnode starts
-firstEp               h1.taosdata.com:6030
+firstEp               h1.tdengine.com:6030
 
 // must be configured to the FQDN of the host where the dnode is launched
-fqdn                  h1.taosdata.com
+fqdn                  h1.tdengine.com
 
 // the port used by the dnode, default is 6030
 serverPort            6030
@@ -76,13 +76,13 @@ The first dnode can be started following the instructions in [Get Started](/get-
 taos> show dnodes;
 id | endpoint | vnodes | support_vnodes | status | create_time | note |
 ============================================================================================================================================
-1 | h1.taosdata.com:6030 | 0 | 1024 | ready | 2022-07-16 10:50:42.673 | |
+1 | h1.tdengine.com:6030 | 0 | 1024 | ready | 2022-07-16 10:50:42.673 | |
 Query OK, 1 rows affected (0.007984s)
 
 
 ```
 
-From the above output, it is shown that the end point of the started dnode is "h1.taosdata.com:6030", which is the `firstEp` of the cluster.
+From the above output, it is shown that the end point of the started dnode is "h1.tdengine.com:6030", which is the `firstEp` of the cluster.
 
 ## Add DNODE
 
@@ -90,7 +90,7 @@ There are a few steps necessary to add other dnodes in the cluster.
 
 Second, we can start `taosd` as instructed in [Get Started](/get-started/).
 
-Then, on the first dnode i.e. h1.taosdata.com in our example, use TDengine CLI `taos` to execute the following command:
+Then, on the first dnode i.e. h1.tdengine.com in our example, use TDengine CLI `taos` to execute the following command:
 
 ```sql
 CREATE DNODE "h2.taos.com:6030";
@@ -98,7 +98,7 @@ CREATE DNODE "h2.taos.com:6030";
 
 This adds the end point of the new dnode (from Step 4) into the end point list of the cluster. In the command "fqdn:port" should be quoted using double quotes. Change `"h2.taos.com:6030"` to the end point of your new dnode. 
 
-Then on the first dnode h1.taosdata.com, execute `show dnodes` in `taos`
+Then on the first dnode h1.tdengine.com, execute `show dnodes` in `taos`
 
 ```sql
 SHOW DNODES;
@@ -114,7 +114,9 @@ The above process can be repeated to add more dnodes in the cluster.
 
 Any node that is in the cluster and online can be the firstEp of new nodes.
 Nodes use the firstEp parameter only when joining a cluster for the first time. After a node has joined the cluster, it stores the latest mnode in its end point list and no longer makes use of firstEp.
-However, firstEp is used by clients that connect to the cluster. For example, if you run `taos shell` without arguments, it connects to the firstEp by default.
+
+However, firstEp is used by clients that connect to the cluster. For example, if you run TDengine CLI `taos` without arguments, it connects to the firstEp by default.
+
 Two dnodes that are launched without a firstEp value operate independently of each other. It is not possible to add one dnode to the other dnode and form a cluster. It is also not possible to form two independent clusters into a new cluster.
 
 :::

docs/en/10-deployment/03-k8s.md

@@ -9,6 +9,7 @@ TDengine is a cloud-native time-series database that can be deployed on Kubernet
 
 Before deploying TDengine on Kubernetes, perform the following:
 
+* Current steps are compatible with Kubernetes v1.5 and later version.
 * Install and configure minikube, kubectl, and helm.
 * Install and deploy Kubernetes and ensure that it can be accessed and used normally. Update any container registries or other services as necessary.
 
@@ -100,7 +101,7 @@ spec:
             # Must set if you want a cluster.
             - name: TAOS_FIRST_EP
               value: "$(STS_NAME)-0.$(SERVICE_NAME).$(STS_NAMESPACE).svc.cluster.local:$(TAOS_SERVER_PORT)"
-            # TAOS_FQND should always be setted in k8s env.
+            # TAOS_FQDN should always be set in k8s env.
             - name: TAOS_FQDN
               value: "$(POD_NAME).$(SERVICE_NAME).$(STS_NAMESPACE).svc.cluster.local"
           volumeMounts:

docs/en/10-deployment/05-helm.md

@@ -152,7 +152,7 @@ clusterDomainSuffix: ""
 # converting an upper-snake-cased variable like `TAOS_DEBUG_FLAG`,
 # to a camelCase taos config variable `debugFlag`.
 #
-# See the variable list at https://www.taosdata.com/cn/documentation/administrator .
+# See the [Configuration Variables](../../reference/config)
 #
 # Note:
 # 1. firstEp/secondEp: should not be setted here, it's auto generated at scale-up.

docs/en/12-taos-sql/02-database.md

@@ -71,9 +71,9 @@ database_option: {
 - SINGLE_STABLE: specifies whether the database can contain more than one supertable.
   - 0: The database can contain multiple supertables.
   - 1: The database can contain only one supertable.
-- WAL_RETENTION_PERIOD: specifies the time after which WAL files are deleted. This parameter is used for data subscription. Enter a time in seconds. The default value is 0. A value of 0 indicates that each WAL file is deleted immediately after its contents are written to disk. -1: WAL files are never deleted.
-- WAL_RETENTION_SIZE: specifies the size at which WAL files are deleted. This parameter is used for data subscription. Enter a size in KB. The default value is 0. A value of 0 indicates that each WAL file is deleted immediately after its contents are written to disk. -1: WAL files are never deleted.
-- WAL_ROLL_PERIOD: specifies the time after which WAL files are rotated. After this period elapses, a new WAL file is created. The default value is 0. A value of 0 indicates that a new WAL file is created only after the previous WAL file was written to disk.
+- WAL_RETENTION_PERIOD: specifies the time after which WAL files are deleted. This parameter is used for data subscription. Enter a time in seconds. The default value of single copy is 0. A value of 0 indicates that each WAL file is deleted immediately after its contents are written to disk. -1: WAL files are never deleted. The default value of multiple copy is 4 days.
+- WAL_RETENTION_SIZE: specifies the size at which WAL files are deleted. This parameter is used for data subscription. Enter a size in KB. The default value of single copy is 0. A value of 0 indicates that each WAL file is deleted immediately after its contents are written to disk. -1: WAL files are never deleted. The default value of multiple copy is -1.
+- WAL_ROLL_PERIOD: specifies the time after which WAL files are rotated. After this period elapses, a new WAL file is created. The default value of single copy is 0. A value of 0 indicates that a new WAL file is created only after the previous WAL file was written to disk. The default values of multiple copy is 1 day.
 - WAL_SEGMENT_SIZE: specifies the maximum size of a WAL file. After the current WAL file reaches this size, a new WAL file is created. The default value is 0. A value of 0 indicates that a new WAL file is created only after the previous WAL file was written to disk.
 
 ### Example Statement

docs/en/12-taos-sql/03-table.md

@@ -57,7 +57,7 @@ table_option: {
 3. MAX_DELAY: specifies the maximum latency for pushing computation results. The default value is 15 minutes or the value of the INTERVAL parameter, whichever is smaller. Enter a value between 0 and 15 minutes in milliseconds, seconds, or minutes. You can enter multiple values separated by commas (,). Note: Retain the default value if possible. Configuring a small MAX_DELAY may cause results to be frequently pushed, affecting storage and query performance. This parameter applies only to supertables and takes effect only when the RETENTIONS parameter has been specified for the database.
 4. ROLLUP: specifies aggregate functions to roll up. Rolling up a function provides downsampled results based on multiple axes. This parameter applies only to supertables and takes effect only when the RETENTIONS parameter has been specified for the database. You can specify only one function to roll up. The rollup takes effect on all columns except TS. Enter one of the following values: avg, sum, min, max, last, or first.
 5. SMA: specifies functions on which to enable small materialized aggregates (SMA). SMA is user-defined precomputation of aggregates based on data blocks. Enter one of the following values: max, min, or sum This parameter can be used with supertables and standard tables.
-6. TTL: specifies the time to live (TTL) for the table. If the period specified by the TTL parameter elapses without any data being written to the table, TDengine will automatically delete the table. Note: The system may not delete the table at the exact moment that the TTL expires. Enter a value in days. The default value is 0. Note: The TTL parameter has a higher priority than the KEEP parameter. If a table is marked for deletion because the TTL has expired, it will be deleted even if the time specified by the KEEP parameter has not elapsed. This parameter can be used with standard tables and subtables.
+6. TTL: specifies the time to live (TTL) for the table. If TTL is specified when creatinga table, after the time period for which the table has been existing is over TTL, TDengine will automatically delete the table. Please be noted that the system may not delete the table at the exact moment that the TTL expires but guarantee there is such a system and finally the table will be deleted. The unit of TTL is in days. The default value is 0, i.e. never expire. 
 
 ## Create Subtables
 

docs/en/12-taos-sql/05-insert.md

@@ -16,6 +16,8 @@ INSERT INTO
         [(field1_name, ...)]
         VALUES (field1_value, ...) [(field1_value2, ...) ...] | FILE csv_file_path
     ...];
+
+INSERT INTO tb_name [(field1_name, ...)] subquery
 ```
 
 **Timestamps**
@@ -37,7 +39,7 @@ INSERT INTO
 
 4. The FILE clause inserts tags or data from a comma-separates values (CSV) file. Do not include headers in your CSV files.
 
-5. A single INSERT statement can write data to multiple tables.
+5. A single `INSERT ... VALUES` statement and `INSERT ... FILE` statement can write data to multiple tables.
 
 6. The INSERT statement is fully parsed before being executed, so that if any element of the statement fails, the entire statement will fail. For example, the following statement will not create a table because the latter part of the statement is invalid:
 
@@ -47,6 +49,8 @@ INSERT INTO
 
 7. However, an INSERT statement that writes data to multiple subtables can succeed for some tables and fail for others. This situation is caused because vnodes perform write operations independently of each other. One vnode failing to write data does not affect the ability of other vnodes to write successfully.
 
+8. Data from TDengine can be inserted into a specified table using the `INSERT ... subquery` statement. Arbitrary query statements are supported. This syntax can only be used for subtables and normal tables, and does not support automatic table creation.
+
 ## Insert a Record
 
 Single row or multiple rows specified with VALUES can be inserted into a specific table. A single row is inserted using the below statement.

docs/en/12-taos-sql/06-select.md

@@ -52,11 +52,6 @@ window_clause: {
   | STATE_WINDOW(col)
   | INTERVAL(interval_val [, interval_offset]) [SLIDING (sliding_val)] [WATERMARK(watermark_val)] [FILL(fill_mod_and_val)]
 
-changes_option: {
-    DURATION duration_val
-  | ROWS rows_val
-}
-
 group_by_clause:
     GROUP BY expr [, expr] ... HAVING condition
 
@@ -71,9 +66,9 @@ order_expr:
 
 A query can be performed on some or all columns. Data and tag columns can all be included in the SELECT list.
 
-## Wildcards
+### Wildcards
 
-You can use an asterisk (\*) as a wildcard character to indicate all columns. For standard tables, the asterisk indicates only data columns. For supertables and subtables, tag columns are also included.
+You can use an asterisk (\*) as a wildcard character to indicate all columns. For normal tables or sub-tables, the asterisk indicates only data columns. For supertables, tag columns are also included when using asterisk (\*).
 
 ```sql
 SELECT * FROM d1001;
@@ -126,7 +121,6 @@ SELECT DISTINCT col_name [, col_name ...] FROM tb_name;
 
 1. Configuration parameter `maxNumOfDistinctRes` in `taos.cfg` is used to control the number of rows to output. The minimum configurable value is 100,000, the maximum configurable value is 100,000,000, the default value is 1,000,000. If the actual number of rows exceeds the value of this parameter, only the number of rows specified by this parameter will be output.
 2. It can't be guaranteed that the results selected by using `DISTINCT` on columns of `FLOAT` or `DOUBLE` are exactly unique because of the precision errors in floating point numbers.
-3. `DISTINCT` can't be used in the sub-query of a nested query statement, and can't be used together with aggregate functions, `GROUP BY` or `JOIN` in the same SQL statement.
 
 :::
 
@@ -142,6 +136,8 @@ taos> SELECT ts, ts AS primary_key_ts FROM d1001;
 
 ### Pseudocolumns
 
+**Pseudocolumn:** A pseudo-column behaves like a table column but is not actually stored in the table. You can select from pseudo-columns, but you cannot insert, update, or delete their values. A pseudo-column is also similar to a function without arguments. This section describes these pseudo-columns:
+
 **TBNAME**
 The TBNAME pseudocolumn in a supertable contains the names of subtables within the supertable.
 
@@ -185,6 +181,14 @@ In TDengine, the first column of all tables must be a timestamp. This column is
 select _rowts, max(current) from meters;
 ```
 
+**\_IROWTS**
+
+The \_IROWTS pseudocolumn can only be used with INTERP function. This pseudocolumn can be used to retrieve the corresponding timestamp column associated with the interpolation results.
+
+```sql
+select _irowts, interp(current) from meters range('2020-01-01 10:00:00', '2020-01-01 10:30:00') every(1s) fill(linear);
+```
+
 ## Query Objects
 
 `FROM` can be followed by a number of tables or super tables, or can be followed by a sub-query.
@@ -354,19 +358,15 @@ SELECT ... FROM (SELECT ... FROM ...) ...;
 
 :::info
 
-- Only one layer of nesting is allowed, that means no sub query is allowed within a sub query
-- The result set returned by the inner query will be used as a "virtual table" by the outer query. The "virtual table" can be renamed using `AS` keyword for easy reference in the outer query.
-- Sub query is not allowed in continuous query.
+- The result of a nested query is returned as a virtual table used by the outer query. It's recommended to give an alias to this table for the convenience of using it in the outer query.
 - JOIN operation is allowed between tables/STables inside both inner and outer queries. Join operation can be performed on the result set of the inner query.
-- UNION operation is not allowed in either inner query or outer query.
-- The functions that can be used in the inner query are the same as those that can be used in a non-nested query.
+- The features that can be used in the inner query are the same as those that can be used in a non-nested query.
   - `ORDER BY` inside the inner query is unnecessary and will slow down the query performance significantly. It is best to avoid the use of `ORDER BY` inside the inner query.
 - Compared to the non-nested query, the functionality that can be used in the outer query has the following restrictions:
   - Functions
-    - If the result set returned by the inner query doesn't contain timestamp column, then functions relying on timestamp can't be used in the outer query, like `TOP`, `BOTTOM`, `FIRST`, `LAST`, `DIFF`.
-    - Functions that need to scan the data twice can't be used in the outer query, like `STDDEV`, `PERCENTILE`.
-  - `IN` operator is not allowed in the outer query but can be used in the inner query.
-  - `GROUP BY` is not supported in the outer query.
+    - If the result set returned by the inner query doesn't contain timestamp column, then functions relying on timestamp can't be used in the outer query, like INTERP,DERIVATIVE, IRATE, LAST_ROW, FIRST, LAST, TWA, STATEDURATION, TAIL, UNIQUE.
+    - If the result set returned by the inner query are not sorted in order by timestamp, then functions relying on data ordered by timestamp can't be used in the outer query, like LEASTSQUARES, ELAPSED, INTERP, DERIVATIVE, IRATE, TWA, DIFF, STATECOUNT, STATEDURATION, CSUM, MAVG, TAIL, UNIQUE. 
+    - Functions that need to scan the data twice can't be used in the outer query, like PERCENTILE.
 
 :::
 

docs/en/12-taos-sql/12-distinguished.md

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

docs/en/12-taos-sql/14-stream.md

@@ -44,13 +44,13 @@ For example, the following SQL statement creates a stream and automatically crea
 
 ```sql
 CREATE STREAM avg_vol_s INTO avg_vol AS
-SELECT _wstartts, count(*), avg(voltage) FROM meters PARTITION BY tbname INTERVAL(1m) SLIDING(30s);
+SELECT _wstart, count(*), avg(voltage) FROM meters PARTITION BY tbname INTERVAL(1m) SLIDING(30s);
 ```
 
 ## Delete a Stream
 
 ```sql
-DROP STREAM [IF NOT EXISTS] stream_name
+DROP STREAM [IF EXISTS] stream_name
 ```
 
 This statement deletes the stream processing service only. The data generated by the stream is retained.

docs/en/12-taos-sql/19-limit.md

@@ -30,7 +30,7 @@ The following characters cannot occur in a password: single quotation marks ('),
 - Maximum number of columns is 4096. There must be at least 2 columns, and the first column must be timestamp.
 - The maximum length of a tag name is 64 bytes
 - Maximum number of tags is 128. There must be at least 1 tag. The total length of tag values cannot exceed 16 KB.
-- Maximum length of single SQL statement is 1 MB (1048576 bytes). It can be configured in the parameter `maxSQLLength` in the client side, the applicable range is [65480, 1048576].
+- Maximum length of single SQL statement is 1 MB (1048576 bytes). 
 - At most 4096 columns can be returned by `SELECT`. Functions in the query statement constitute columns. An error is returned if the limit is exceeded.
 - Maximum numbers of databases, STables, tables are dependent only on the system resources.
 - The number of replicas can only be 1 or 3.

docs/en/12-taos-sql/20-keywords.md

@@ -5,7 +5,9 @@ title: Reserved Keywords
 
 ## Keyword List
 
-There are about 200 keywords reserved by TDengine, they can't be used as the name of database, STable or table with either upper case, lower case or mixed case. The following list shows all reserved keywords:
+There are more than 200 keywords reserved by TDengine, they can't be used as the name of database, table, STable, subtable, column or tag with either upper case, lower case or mixed case. If you need to use these keywords, use the symbol `` ` `` to enclose the keywords, e.g. \`ADD\`.
+
+The following list shows all reserved keywords:
 
 ### A
 
@@ -14,15 +16,20 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
 - ACCOUNTS
 - ADD
 - AFTER
+- AGGREGATE
 - ALL
 - ALTER
+- ANALYZE
 - AND
+- APPS
 - AS
 - ASC
+- AT_ONCE
 - ATTACH
 
 ### B
 
+- BALANCE
 - BEFORE
 - BEGIN
 - BETWEEN
@@ -32,19 +39,27 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
 - BITNOT
 - BITOR
 - BLOCKS
+- BNODE
+- BNODES
 - BOOL
+- BUFFER
+- BUFSIZE
 - BY
 
 ### C
 
 - CACHE
-- CACHELAST
+- CACHEMODEL
+- CACHESIZE
 - CASCADE
+- CAST
 - CHANGE
+- CLIENT_VERSION
 - CLUSTER
 - COLON
 - COLUMN
 - COMMA
+- COMMENT
 - COMP
 - COMPACT
 - CONCAT
@@ -52,15 +67,18 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
 - CONNECTION
 - CONNECTIONS
 - CONNS
+- CONSUMER
+- CONSUMERS
+- CONTAINS
 - COPY
+- COUNT
 - CREATE
-- CTIME
+- CURRENT_USER
 
 ### D
 
 - DATABASE
 - DATABASES
-- DAYS
 - DBS
 - DEFERRED
 - DELETE
@@ -69,18 +87,23 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
 - DESCRIBE
 - DETACH
 - DISTINCT
+- DISTRIBUTED
 - DIVIDE
 - DNODE
 - DNODES
 - DOT
 - DOUBLE
 - DROP
+- DURATION
 
 ### E
 
+- EACH
+- ENABLE
 - END
-- EQ
+- EVERY
 - EXISTS
+- EXPIRED
 - EXPLAIN
 
 ### F
@@ -88,18 +111,20 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
 - FAIL
 - FILE
 - FILL
+- FIRST
 - FLOAT
+- FLUSH
 - FOR
 - FROM
-- FSYNC
+- FUNCTION
+- FUNCTIONS
 
 ### G
 
-- GE
 - GLOB
+- GRANT
 - GRANTS
 - GROUP
-- GT
 
 ### H
 
@@ -110,15 +135,18 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
 - ID
 - IF
 - IGNORE
-- IMMEDIA
+- IMMEDIATE
 - IMPORT
 - IN
-- INITIAL
+- INDEX
+- INDEXES
+- INITIALLY
+- INNER
 - INSERT
 - INSTEAD
 - INT
 - INTEGER
-- INTERVA
+- INTERVAL
 - INTO
 - IS
 - ISNULL
@@ -126,6 +154,7 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
 ### J
 
 - JOIN
+- JSON
 
 ### K
 
@@ -135,46 +164,57 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
 
 ### L
 
-- LE
+- LAST
+- LAST_ROW
+- LICENCES
 - LIKE
 - LIMIT
 - LINEAR
 - LOCAL
-- LP
-- LSHIFT
-- LT
 
 ### M
 
 - MATCH
+- MAX_DELAY
 - MAXROWS
+- MERGE
+- META
 - MINROWS
 - MINUS
+- MNODE
 - MNODES
 - MODIFY
 - MODULES
 
 ### N
 
-- NE
+- NCHAR
+- NEXT
+- NMATCH
 - NONE
 - NOT
 - NOTNULL
 - NOW
 - NULL
+- NULLS
 
 ### O
 
 - OF
 - OFFSET
+- ON
 - OR
 - ORDER
+- OUTPUTTYPE
 
 ### P
 
-- PARTITION
+- PAGES
+- PAGESIZE
+- PARTITIONS
 - PASS
 - PLUS
+- PORT
 - PPS
 - PRECISION
 - PREV
@@ -182,47 +222,63 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
 
 ### Q
 
+- QNODE
+- QNODES
 - QTIME
-- QUERIE
+- QUERIES
 - QUERY
-- QUORUM
 
 ### R
 
 - RAISE
-- REM
+- RANGE
+- RATIO
+- READ
+- REDISTRIBUTE
+- RENAME
 - REPLACE
 - REPLICA
 - RESET
-- RESTRIC
+- RESTRICT
+- RETENTIONS
+- REVOKE
+- ROLLUP
 - ROW
-- RP
-- RSHIFT
 
 ### S
 
+- SCHEMALESS
 - SCORES
 - SELECT
 - SEMI
+- SERVER_STATUS
+- SERVER_VERSION
 - SESSION
 - SET
 - SHOW
-- SLASH
+- SINGLE_STABLE
 - SLIDING
 - SLIMIT
-- SMALLIN
+- SMA
+- SMALLINT
+- SNODE
+- SNODES
 - SOFFSET
-- STable
-- STableS
+- SPLIT
+- STABLE
+- STABLES
 - STAR
 - STATE
-- STATEMEN
-- STATE_WI
+- STATE_WINDOW
+- STATEMENT
 - STORAGE
 - STREAM
 - STREAMS
+- STRICT
 - STRING
+- SUBSCRIPTIONS
 - SYNCDB
+- SYSINFO
 
 ### T
 
@@ -233,19 +289,24 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
 - TBNAME
 - TIMES
 - TIMESTAMP
+- TIMEZONE
 - TINYINT
+- TO
+- TODAY
 - TOPIC
 - TOPICS
+- TRANSACTION
+- TRANSACTIONS
 - TRIGGER
+- TRIM
 - TSERIES
+- TTL
 
 ### U
 
-- UMINUS
 - UNION
 - UNSIGNED
 - UPDATE
-- UPLUS
 - USE
 - USER
 - USERS
@@ -253,9 +314,13 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
 
 ### V
 
+- VALUE
 - VALUES
+- VARCHAR
 - VARIABLE
 - VARIABLES
+- VERBOSE
+- VGROUP
 - VGROUPS
 - VIEW
 - VNODES
@@ -263,14 +328,26 @@ There are about 200 keywords reserved by TDengine, they can't be used as the nam
 ### W
 
 - WAL
+- WAL_FSYNC_PERIOD
+- WAL_LEVEL
+- WAL_RETENTION_PERIOD
+- WAL_RETENTION_SIZE
+- WAL_ROLL_PERIOD
+- WAL_SEGMENT_SIZE
+- WATERMARK
 - WHERE
+- WINDOW_CLOSE
+- WITH
+- WRITE
 
 ### \_
 
 - \_C0
-- \_QSTART
-- \_QSTOP
+- \_IROWTS
 - \_QDURATION
-- \_WSTART
-- \_WSTOP
+- \_QEND
+- \_QSTART
+- \_ROWTS
 - \_WDURATION
+- \_WEND
+- \_WSTART

docs/en/12-taos-sql/22-meta.md

@@ -11,7 +11,15 @@ TDengine includes a built-in database named `INFORMATION_SCHEMA` to provide acce
 4. Future versions of TDengine can add new columns to INFORMATION_SCHEMA tables without affecting existing business systems.
 5. It is easier for users coming from other database management systems. For example, Oracle users can query data dictionary tables.
 
-Note: SHOW statements are still supported for the convenience of existing users.
+:::info
+
+- SHOW statements are still supported for the convenience of existing users.
+- Some columns in the system table may be keywords, and you need to use the escape character '\`' when querying, for example, to query the VGROUPS in the database `test`:
+```sql 
+   select `vgroups` from ins_databases where name = 'test';
+``` 
+
+:::
 
 This document introduces the tables of INFORMATION_SCHEMA and their structure.
 
@@ -102,7 +110,11 @@ Provides information about user-created databases. Similar to SHOW DATABASES.
 | 24  | wal_retention_period | INT              | WAL retention period                                   |
 | 25  |  wal_retention_size  | INT              | Maximum WAL size                                   |
 | 26  |   wal_roll_period    | INT              | WAL rotation period                                 |
-| 27  |   wal_segment_size   | WAL file size |
+| 27  |   wal_segment_size   | BIGINT | WAL file size |
+| 28  |   stt_trigger   | SMALLINT | The threshold for number of files to trigger file merging |
+| 29  |   table_prefix   | SMALLINT | The prefix length in the table name that is ignored when distributing table to vnode based on table name |
+| 30  |   table_suffix   | SMALLINT | The suffix length in the table name that is ignored when distributing table to vnode based on table name |
+| 31  |   tsdb_pagesize   | INT | The page size for internal storage engine, its unit is KB |
 
 ## INS_FUNCTIONS
 
@@ -245,3 +257,35 @@ Provides dnode configuration information.
 | 1   | dnode_id | INT          | Dnode ID |
 | 2   |   name   | BINARY(32)   | Parameter   |
 | 3   |  value   | BINARY(64)   | Value |
+
+## INS_TOPICS
+
+| #   |    **Column**    | **Data Type** | **Description**                  |
+| --- | :---------: | ------------ | ------------------------------ |
+| 1   | topic_name  | BINARY(192)  | Topic name                     |
+| 2   |   db_name   | BINARY(64)   | Database for the topic                |
+| 3   | create_time | TIMESTAMP    | Creation time              |
+| 4   |     sql     | BINARY(1024) | SQL statement used to create the topic |
+
+## INS_SUBSCRIPTIONS
+
+| #   |    **Column**    | **Data Type** | **Description**                  |
+| --- | :------------: | ------------ | ------------------------ |
+| 1   |   topic_name   | BINARY(204)  | Subscribed topic           |
+| 2   | consumer_group | BINARY(193)  | Subscribed consumer group         |
+| 3   |   vgroup_id    | INT          | Vgroup ID for the consumer |
+| 4   |  consumer_id   | BIGINT       | Consumer ID          |
+
+## INS_STREAMS
+
+| #   |    **Column**    | **Data Type** | **Description**                  |
+| --- | :----------: | ------------ | --------------------------------------- |
+| 1   | stream_name  | BINARY(64)   | Stream name                              |
+| 2   | create_time  | TIMESTAMP    | Creation time                                |
+| 3   |     sql      | BINARY(1024) | SQL statement used to create the stream             |
+| 4   |    status    | BIANRY(20)   | Current status                              |
+| 5   |  source_db   | BINARY(64)   | Source database                                |
+| 6   |  target_db   | BIANRY(64)   | Target database                              |
+| 7   | target_table | BINARY(192)  | Target table                      |
+| 8   |  watermark   | BIGINT       | Watermark (see stream processing documentation)        |
+| 9   |   trigger    | INT          | Method of triggering the result push (see stream processing documentation) |

docs/en/12-taos-sql/23-perf.md

@@ -61,15 +61,6 @@ Provides information about SQL queries currently running. Similar to SHOW QUERIE
 | 12  |  sub_status  | BINARY(1000) | Subquery status                   |
 | 13  |     sql      | BINARY(1024) | SQL statement                     |
 
-## PERF_TOPICS
-
-| #   |    **Column**    | **Data Type** | **Description**                  |
-| --- | :---------: | ------------ | ------------------------------ |
-| 1   | topic_name  | BINARY(192)  | Topic name                     |
-| 2   |   db_name   | BINARY(64)   | Database for the topic                |
-| 3   | create_time | TIMESTAMP    | Creation time              |
-| 4   |     sql     | BINARY(1024) | SQL statement used to create the topic |
-
 ## PERF_CONSUMERS
 
 | #   |    **Column**    | **Data Type** | **Description**                  |
@@ -83,15 +74,6 @@ Provides information about SQL queries currently running. Similar to SHOW QUERIE
 | 7   | subscribe_time | TIMESTAMP    | Time of first subscription                                        |
 | 8   | rebalance_time | TIMESTAMP    | Time of first rebalance triggering                                 |
 
-## PERF_SUBSCRIPTIONS
-
-| #   |    **Column**    | **Data Type** | **Description**                  |
-| --- | :------------: | ------------ | ------------------------ |
-| 1   |   topic_name   | BINARY(204)  | Subscribed topic           |
-| 2   | consumer_group | BINARY(193)  | Subscribed consumer group         |
-| 3   |   vgroup_id    | INT          | Vgroup ID for the consumer |
-| 4   |  consumer_id   | BIGINT       | Consumer ID          |
-
 ## PERF_TRANS
 
 | #   |    **Column**    | **Data Type** | **Description**                  |
@@ -113,17 +95,3 @@ Provides information about SQL queries currently running. Similar to SHOW QUERIE
 | 2   | create_time | TIMESTAMP    | Creation time                                |
 | 3   | stable_name | BINARY(192)  | Supertable name                        |
 | 4   |  vgroup_id  | INT          | Dedicated vgroup name                      |
-
-## PERF_STREAMS
-
-| #   |    **Column**    | **Data Type** | **Description**                  |
-| --- | :----------: | ------------ | --------------------------------------- |
-| 1   | stream_name  | BINARY(64)   | Stream name                              |
-| 2   | create_time  | TIMESTAMP    | Creation time                                |
-| 3   |     sql      | BINARY(1024) | SQL statement used to create the stream             |
-| 4   |    status    | BIANRY(20)   | Current status                              |
-| 5   |  source_db   | BINARY(64)   | Source database                                |
-| 6   |  target_db   | BIANRY(64)   | Target database                              |
-| 7   | target_table | BINARY(192)  | Target table                      |
-| 8   |  watermark   | BIGINT       | Watermark (see stream processing documentation)        |
-| 9   |   trigger    | INT          | Method of triggering the result push (see stream processing documentation) |

docs/en/12-taos-sql/24-show.md

@@ -5,16 +5,6 @@ title: SHOW Statement for Metadata
 
 `SHOW` command can be used to get brief system information. To get details about metatadata, information, and status in the system, please use `select` to query the tables in database `INFORMATION_SCHEMA`. 
 
-## SHOW ACCOUNTS
-
-```sql
-SHOW ACCOUNTS;
-```
-
-Shows information about tenants on the system.
-
-Note: TDengine Enterprise Edition only.
-
 ## SHOW APPS
 
 ```sql
@@ -194,7 +184,7 @@ Shows information about streams in the system.
 SHOW SUBSCRIPTIONS;
 ```
 
-Shows all subscriptions in the current database.
+Shows all subscriptions in the system.
 
 ## SHOW TABLES
 

docs/en/12-taos-sql/25-grant.md

 ---
-sidebar_label: Permissions Management
-title: Permissions Management
+sidebar_label: Access Control
+title: User and Access Control
+description: Manage user and user's permission
 ---
 
 This document describes how to manage permissions in TDengine.
@@ -8,15 +9,54 @@ This document describes how to manage permissions in TDengine.
 ## Create a User
 
 ```sql
-CREATE USER use_name PASS 'password';
+CREATE USER user_name PASS 'password' [SYSINFO {1|0}];
 ```
 
 This statement creates a user account.
 
-The maximum length of use_name is 23 bytes.
+The maximum length of user_name is 23 bytes.
 
 The maximum length of password is 128 bytes. The password can include leters, digits, and special characters excluding single quotation marks, double quotation marks, backticks, backslashes, and spaces. The password cannot be empty.
 
+`SYSINFO` indicates whether the user is allowed to view system information. `1` means allowed, `0` means not allowed. System information includes server configuration, dnode, vnode, storage. The default value is `1`.
+
+For example, we can create a user whose password is `123456` and is able to view system information.
+
+```sql
+taos> create user test pass '123456' sysinfo 1;
+Query OK, 0 of 0 rows affected (0.001254s)
+```
+
+## View Users
+
+To show the users in the system, please use 
+
+```sql
+SHOW USERS;
+```
+
+This is an example:
+
+```sql
+taos> show users;
+           name           | super | enable | sysinfo |       create_time       |
+================================================================================
+ test                     |     0 |      1 |       1 | 2022-08-29 15:10:27.315 |
+ root                     |     1 |      1 |       1 | 2022-08-29 15:03:34.710 |
+Query OK, 2 rows in database (0.001657s)
+```
+
+Alternatively, you can get the user information by querying a built-in table, INFORMATION_SCHEMA.INS_USERS. For example:
+
+```sql
+taos> select * from information_schema.ins_users;
+           name           | super | enable | sysinfo |       create_time       |
+================================================================================
+ test                     |     0 |      1 |       1 | 2022-08-29 15:10:27.315 |
+ root                     |     1 |      1 |       1 | 2022-08-29 15:03:34.710 |
+Query OK, 2 rows in database (0.001953s)
+```
+
 ## Delete a User
 
 ```sql
@@ -39,6 +79,13 @@ alter_user_clause: {
 - ENABLE: Specify whether the user is enabled or disabled. 1 indicates enabled and 0 indicates disabled.
 - SYSINFO: Specify whether the user can query system information. 1 indicates that the user can query system information and 0 indicates that the user cannot query system information.
 
+For example, you can use below command to disable user `test`:
+
+```sql
+taos> alter user test enable 0;
+Query OK, 0 of 0 rows affected (0.001160s)
+```
+
 
 ## Grant Permissions
 
@@ -61,7 +108,7 @@ priv_level : {
 }
 ```
 
-Grant permissions to a user.
+Grant permissions to a user, this feature is only available in enterprise edition.
 
 Permissions are granted on the database level. You can grant read or write permissions.
 
@@ -91,4 +138,4 @@ priv_level : {
 
 ```
 
-Revoke permissions from a user.
+Revoke permissions from a user, this feature is only available in enterprise edition.

docs/en/12-taos-sql/29-changes.md

@@ -11,12 +11,13 @@ description: "This document explains how TDengine SQL has changed in version 3.0
 | 1 | VARCHAR | Added | Alias of BINARY.
 | 2 | TIMESTAMP literal | Added | TIMESTAMP 'timestamp format' syntax now supported.
 | 3 | _ROWTS pseudocolumn | Added | Indicates the primary key. Alias of _C0.
-| 4 | INFORMATION_SCHEMA | Added |	Database for system metadata containing all schema definitions
-| 5 | PERFORMANCE_SCHEMA | Added | Database for system performance information.
-| 6 | Connection queries | Deprecated | Connection queries are no longer supported. The syntax and interfaces are deprecated.
-| 7 | Mixed operations | Enhanced | Mixing scalar and vector operations in queries has been enhanced and is supported in all SELECT clauses.
-| 8 | Tag operations | Added | Tag columns can be used in queries and clauses like data columns.
-| 9 | Timeline clauses and time functions in supertables | Enhanced | When PARTITION BY is not used, data in supertables is merged into a single timeline.
+| 4 | _IROWTS pseudocolumn | Added | Used to retrieve timestamps with INTERP function.
+| 5 | INFORMATION_SCHEMA | Added |	Database for system metadata containing all schema definitions
+| 6 | PERFORMANCE_SCHEMA | Added | Database for system performance information.
+| 7 | Connection queries | Deprecated | Connection queries are no longer supported. The syntax and interfaces are deprecated.
+| 8 | Mixed operations | Enhanced | Mixing scalar and vector operations in queries has been enhanced and is supported in all SELECT clauses.
+| 9 | Tag operations | Added | Tag columns can be used in queries and clauses like data columns.
+| 10 | Timeline clauses and time functions in supertables | Enhanced | When PARTITION BY is not used, data in supertables is merged into a single timeline.
 
 ## SQL Syntax
 

docs/en/12-taos-sql/index.md

 ---
 title: TDengine SQL
-description: "The syntax supported by TDengine SQL "
+description: 'The syntax supported by TDengine SQL '
 ---
 
 This section explains the syntax of SQL to perform operations on databases, tables and STables, insert data, select data and use functions. We also provide some tips that can be used in TDengine SQL. If you have previous experience with SQL this section will be fairly easy to understand. If you do not have previous experience with SQL, you'll come to appreciate the simplicity and power of SQL. TDengine SQL has been enhanced in version 3.0, and the query engine has been rearchitected. For information about how TDengine SQL has changed, see [Changes in TDengine 3.0](../taos-sql/changes).
@@ -15,7 +15,7 @@ Syntax Specifications used in this chapter:
 - | means one of a few options, excluding | itself.
 - … means the item prior to it can be repeated multiple times.
 
-To better demonstrate the syntax, usage and rules of TAOS SQL, hereinafter it's assumed that there is a data set of data from electric meters. Each meter collects 3 data measurements: current, voltage, phase. The data model is shown below:
+To better demonstrate the syntax, usage and rules of TDengine SQL, hereinafter it's assumed that there is a data set of data from electric meters. Each meter collects 3 data measurements: current, voltage, phase. The data model is shown below:
 
 ```
 taos> DESCRIBE meters;

docs/en/13-operation/01-pkg-install.md

 ---
-title: Install & Uninstall
+title: Install and Uninstall
 description: Install, Uninstall, Start, Stop and Upgrade
 ---
 
 import Tabs from "@theme/Tabs";
 import TabItem from "@theme/TabItem";
 
-TDengine community version provides deb and rpm packages for users to choose from, based on their system environment. The deb package supports Debian, Ubuntu and derivative systems. The rpm package supports CentOS, RHEL, SUSE and derivative systems. Furthermore, a tar.gz package is provided for TDengine Enterprise customers.
+This document gives more information about installing, uninstalling, and upgrading TDengine.
 
 ## Install
 
@@ -35,12 +35,28 @@ TDengine is removed successfully!
 
 ```
 
+Apt-get package of taosTools can be uninstalled as below:
+
+```
+$ sudo apt remove taostools
+Reading package lists... Done
+Building dependency tree
+Reading state information... Done
+The following packages will be REMOVED:
+  taostools
+0 upgraded, 0 newly installed, 1 to remove and 0 not upgraded.
+After this operation, 68.3 MB disk space will be freed.
+Do you want to continue? [Y/n]
+(Reading database ... 147973 files and directories currently installed.)
+Removing taostools (2.1.2) ...
+```
+
 </TabItem>
 <TabItem label="Uninstall Deb" value="debuninst">
 
 Deb package of TDengine can be uninstalled as below:
 
-```bash
+```
 $ sudo dpkg -r tdengine
 (Reading database ... 137504 files and directories currently installed.)
 Removing tdengine (3.0.0.0) ...
@@ -48,6 +64,14 @@ TDengine is removed successfully!
 
 ```
 
+Deb package of taosTools can be uninstalled as below:
+
+```
+$ sudo dpkg -r taostools
+(Reading database ... 147973 files and directories currently installed.)
+Removing taostools (2.1.2) ...
+```
+
 </TabItem>
 
 <TabItem label="Uninstall RPM" value="rpmuninst">
@@ -59,6 +83,13 @@ $ sudo rpm -e tdengine
 TDengine is removed successfully!
 ```
 
+RPM package of taosTools can be uninstalled as below:
+
+```
+sudo rpm -e taostools
+taosToole is removed successfully!
+```
+
 </TabItem>
 
 <TabItem label="Uninstall tar.gz" value="taruninst">
@@ -67,115 +98,69 @@ tar.gz package of TDengine can be uninstalled as below:
 
 ```
 $ rmtaos
-Nginx for TDengine is running, stopping it...
 TDengine is removed successfully!
-
-taosKeeper is removed successfully!
-```
-
-</TabItem>
-</Tabs>
-
-:::note
-
-- We strongly recommend not to use multiple kinds of installation packages on a single host TDengine. 
-- After deb package is installed, if the installation directory is removed manually, uninstall or reinstall will not work. This issue can be resolved by using the command below which cleans up TDengine package information. You can then reinstall if needed.
-
-```bash
-   $ sudo rm -f /var/lib/dpkg/info/tdengine*
 ```
 
-- After rpm package is installed, if the installation directory is removed manually, uninstall or reinstall will not work. This issue can be resolved by using the command below which cleans up TDengine package information. You can then reinstall if needed.
+tar.gz package of taosTools can be uninstalled as below:
 
-```bash
-   $ sudo rpm -e --noscripts tdengine
 ```
+$ rmtaostools
+Start to uninstall taos tools ...
 
-:::
-
-## Installation Directory
-
-TDengine is installed at /usr/local/taos if successful.
-
-```bash
-$ cd /usr/local/taos
-$ ll
-$ ll
-total 28
-drwxr-xr-x  7 root root 4096 Feb 22 09:34 ./
-drwxr-xr-x 12 root root 4096 Feb 22 09:34 ../
-drwxr-xr-x  2 root root 4096 Feb 22 09:34 bin/
-drwxr-xr-x  2 root root 4096 Feb 22 09:34 cfg/
-lrwxrwxrwx  1 root root   13 Feb 22 09:34 data -> /var/lib/taos/
-drwxr-xr-x  2 root root 4096 Feb 22 09:34 driver/
-drwxr-xr-x 10 root root 4096 Feb 22 09:34 examples/
-drwxr-xr-x  2 root root 4096 Feb 22 09:34 include/
-lrwxrwxrwx  1 root root   13 Feb 22 09:34 log -> /var/log/taos/
+taos tools is uninstalled successfully!
 ```
 
-During the installation process:
+</TabItem>
+<TabItem label="Windows uninstall" value="windows">
+Run C:\TDengine\unins000.exe to uninstall TDengine on a Windows system.
+</TabItem>
+</Tabs>
 
-- Configuration directory, data directory, and log directory are created automatically if they don't exist
-- The default configuration file is located at /etc/taos/taos.cfg, which is a copy of /usr/local/taos/cfg/taos.cfg
-- The default data directory is /var/lib/taos, which is a soft link to /usr/local/taos/data
-- The default log directory is /var/log/taos, which is a soft link to /usr/local/taos/log
-- The executables at /usr/local/taos/bin are linked to /usr/bin
-- The DLL files at /usr/local/taos/driver are linked to /usr/lib
-- The header files at /usr/local/taos/include are linked to /usr/include
+:::info
 
-:::note
+- We strongly recommend not to use multiple kinds of installation packages on a single host TDengine. The packages may affect each other and cause errors.
 
-- When TDengine is uninstalled, the configuration /etc/taos/taos.cfg, data directory /var/lib/taos, log directory /var/log/taos are kept. They can be deleted manually with caution, because data can't be recovered. Please follow data integrity, security, backup or relevant SOPs before deleting any data.
-- When reinstalling TDengine, if the default configuration file /etc/taos/taos.cfg exists, it will be kept and the configuration file in the installation package will be renamed to taos.cfg.orig and stored at /usr/local/taos/cfg to be used as configuration sample. Otherwise the configuration file in the installation package will be installed to /etc/taos/taos.cfg and used.
+- After deb package is installed, if the installation directory is removed manually, uninstall or reinstall will not work. This issue can be resolved by using the command below which cleans up TDengine package information.
 
-## Start and Stop
+  ```
+  $ sudo rm -f /var/lib/dpkg/info/tdengine*
+  ```
 
-Linux system services `systemd`, `systemctl` or `service` are used to start, stop and restart TDengine. The server process of TDengine is `taosd`, which is started automatically after the Linux system is started. System operators can use `systemd`, `systemctl` or `service` to start, stop or restart TDengine server.
+You can then reinstall if needed.
 
-For example, if using `systemctl` , the commands to start, stop, restart and check TDengine server are below:
+- After rpm package is installed, if the installation directory is removed manually, uninstall or reinstall will not work. This issue can be resolved by using the command below which cleans up TDengine package information.
 
-- Start server：`systemctl start taosd`
+  ```
+  $ sudo rpm -e --noscripts tdengine
+  ```
 
-- Stop server：`systemctl stop taosd`
+You can then reinstall if needed.
 
-- Restart server：`systemctl restart taosd`
-
-- Check server status：`systemctl status taosd`
-
-Another component named as `taosAdapter` is to provide HTTP service for TDengine, it should be started and stopped using `systemctl`.
+:::
 
-If the server process is OK, the output of `systemctl status` is like below:
+Uninstalling and Modifying Files
 
-```
-Active: active (running)
-```
+- When TDengine is uninstalled, the configuration /etc/taos/taos.cfg, data directory /var/lib/taos, log directory /var/log/taos are kept. They can be deleted manually with caution, because data can't be recovered. Please follow data integrity, security, backup or relevant SOPs before deleting any data.
 
-Otherwise, the output is as below:
+- When reinstalling TDengine, if the default configuration file /etc/taos/taos.cfg exists, it will be kept and the configuration file in the installation package will be renamed to taos.cfg.orig and stored at /usr/local/taos/cfg to be used as configuration sample. Otherwise the configuration file in the installation package will be installed to /etc/taos/taos.cfg and used.
 
-```
-Active: inactive (dead)
-```
 
 ## Upgrade
-
 There are two aspects in upgrade operation: upgrade installation package and upgrade a running server.
 
 To upgrade a package, follow the steps mentioned previously to first uninstall the old version then install the new version.
 
 Upgrading a running server is much more complex. First please check the version number of the old version and the new version. The version number of TDengine consists of 4 sections, only if the first 3 sections match can the old version be upgraded to the new version. The steps of upgrading a running server are as below:
-
 - Stop inserting data
 - Make sure all data is persisted to disk
-- Make some simple queries (Such as total rows in stables, tables and so on. Note down the values. Follow best practices and relevant SOPs.)
 - Stop the cluster of TDengine
 - Uninstall old version and install new version
 - Start the cluster of TDengine
-- Execute simple queries, such as the ones executed prior to installing the new package, to make sure there is no data loss
+- Execute simple queries, such as the ones executed prior to installing the new package, to make sure there is no data loss 
 - Run some simple data insertion statements to make sure the cluster works well
 - Restore business services
 
 :::warning
-
 TDengine doesn't guarantee any lower version is compatible with the data generated by a higher version, so it's never recommended to downgrade the version.
 
 :::

docs/en/13-operation/02-planning.mdx

 ---
+sidebar_label: Resource Planning
 title: Resource Planning
 ---
 
 It is important to plan computing and storage resources if using TDengine to build an IoT, time-series or Big Data platform. How to plan the CPU, memory and disk resources required, will be described in this chapter.
 
-## Memory Requirement of Server Side
+## Server Memory Requirements
 
-By default, the number of vgroups created for each database is the same as the number of CPU cores. This can be configured by the parameter `maxVgroupsPerDb`. Each vnode in a vgroup stores one replica. Each vnode consumes a fixed amount of memory, i.e. `blocks` \* `cache`. In addition, some memory is required for tag values associated with each table. A fixed amount of memory is required for each cluster. So, the memory required for each DB can be calculated using the formula below:
+Each database creates a fixed number of vgroups. This number is 2 by default and can be configured with the `vgroups` parameter. The number of replicas can be controlled with the `replica` parameter. Each replica requires one vnode per vgroup. Altogether, the memory required by each database depends on the following configuration options:
 
-```
-Database Memory Size = maxVgroupsPerDb * replica * (blocks * cache + 10MB) + numOfTables * (tagSizePerTable + 0.5KB)
-```
+- vgroups
+- replica
+- buffer
+- pages
+- pagesize
+- cachesize
 
-For example, assuming the default value of `maxVgroupPerDB` is 64, the default value of `cache` is 16M, the default value of `blocks` is 6, there are 100,000 tables in a DB, the replica number is 1, total length of tag values is 256 bytes, the total memory required for this DB is: 64 \* 1 \* (16 \* 6 + 10) + 100000 \* (0.25 + 0.5) / 1000 = 6792M.
+For more information, see [Database](../../taos-sql/database).
 
-In the real operation of TDengine, we are more concerned about the memory used by each TDengine server process `taosd`.
+The memory required by a database is therefore greater than or equal to:
 
 ```
-    taosd_memory = vnode_memory + mnode_memory + query_memory
+vgroups * replica * (buffer + pages * pagesize + cachesize)
 ```
 
-In the above formula:
-
-1. "vnode_memory" of a `taosd` process is the memory used by all vnodes hosted by this `taosd` process. It can be roughly calculated by firstly adding up the total memory of all DBs whose memory usage can be derived according to the formula for Database Memory Size, mentioned above, then dividing by number of dnodes and multiplying the number of replicas.
-
-```
-    vnode_memory = (sum(Database Memory Size) / number_of_dnodes) * replica
-```
-
-2. "mnode_memory" of a `taosd` process is the memory consumed by a mnode. If there is one (and only one) mnode hosted in a `taosd` process, the memory consumed by "mnode" is "0.2KB \* the total number of tables in the cluster".
-
-3. "query_memory" is the memory used when processing query requests. Each ongoing query consumes at least "0.2 KB \* total number of involved tables".
-
-Please note that the above formulas can only be used to estimate the minimum memory requirement, instead of maximum memory usage. In a real production environment, it's better to reserve some redundance beyond the estimated minimum memory requirement. If memory is abundant, it's suggested to increase the value of parameter `blocks` to speed up data insertion and data query.
+However, note that this requirement is spread over all dnodes in the cluster, not on a single physical machine. The physical servers that run dnodes meet the requirement together. If a cluster has multiple databases, the memory required increases accordingly. In complex environments where dnodes were added after initial deployment in response to increasing resource requirements, load may not be balanced among the original dnodes and newer dnodes. In this situation, the actual status of your dnodes is more important than theoretical calculations.
 
-## Memory Requirement of Client Side
+## Client Memory Requirements
 
 For the client programs using TDengine client driver `taosc` to connect to the server side there is a memory requirement as well.
 
@@ -56,10 +48,10 @@ So, at least 3GB needs to be reserved for such a client.
 
 The CPU resources required depend on two aspects:
 
-- **Data Insertion** Each dnode of TDengine can process at least 10,000 insertion requests in one second, while each insertion request can have multiple rows. The difference in computing resource consumed, between inserting 1 row at a time, and inserting 10 rows at a time is very small. So, the more the number of rows that can be inserted one time, the higher the efficiency. Inserting in batch also imposes requirements on the client side which needs to cache rows to insert in batch once the number of cached rows reaches a threshold.
+- **Data Insertion** Each dnode of TDengine can process at least 10,000 insertion requests in one second, while each insertion request can have multiple rows. The difference in computing resource consumed, between inserting 1 row at a time, and inserting 10 rows at a time is very small. So, the more the number of rows that can be inserted one time, the higher the efficiency. If each insert request contains more than 200 records, a single core can process more than 1 million records per second. Inserting in batch also imposes requirements on the client side which needs to cache rows to insert in batch once the number of cached rows reaches a threshold.
 - **Data Query** High efficiency query is provided in TDengine, but it's hard to estimate the CPU resource required because the queries used in different use cases and the frequency of queries vary significantly. It can only be verified with the query statements, query frequency, data size to be queried, and other requirements provided by users.
 
-In short, the CPU resource required for data insertion can be estimated but it's hard to do so for query use cases. In real operation, it's suggested to control CPU usage below 50%. If this threshold is exceeded, it's a reminder for system operator to add more nodes in the cluster to expand resources.
+In short, the CPU resource required for data insertion can be estimated but it's hard to do so for query use cases. If possible, ensure that CPU usage remains below 50%. If this threshold is exceeded, it's a reminder for system operator to add more nodes in the cluster to expand resources.
 
 ## Disk Requirement
 
@@ -77,6 +69,6 @@ To increase performance, multiple disks can be setup for parallel data reading o
 
 ## Number of Hosts
 
-A host can be either physical or virtual. The total memory, total CPU, total disk required can be estimated according to the formulae mentioned previously. Then, according to the system resources that a single host can provide, assuming all hosts have the same resources, the number of hosts can be derived easily.
+A host can be either physical or virtual. The total memory, total CPU, total disk required can be estimated according to the formulae mentioned previously. If the number of data replicas is not 1, the required resources are multiplied by the number of replicas.
 
-**Quick Estimation for CPU, Memory and Disk** Please refer to [Resource Estimate](https://www.taosdata.com/config/config.html).
+Then, according to the system resources that a single host can provide, assuming all hosts have the same resources, the number of hosts can be derived easily.

docs/en/13-operation/03-tolerance.md

 ---
-sidebar_label: Fault Tolerance
-title: Fault Tolerance & Disaster Recovery
+title: Fault Tolerance and Disaster Recovery
 ---
 
 ## Fault Tolerance
@@ -11,22 +10,21 @@ When a data block is received by TDengine, the original data block is first writ
 
 There are 2 configuration parameters related to WAL:
 
-- walLevel：
-  - 0：wal is disabled 
-  - 1：wal is enabled without fsync 
-  - 2：wal is enabled with fsync
-- fsync：This parameter is only valid when walLevel is set to 2. It specifies the interval, in milliseconds, of invoking fsync. If set to 0, it means fsync is invoked immediately once WAL is written.
+- wal_level: Specifies the WAL level. 1 indicates that WAL is enabled but fsync is disabled. 2 indicates that WAL and fsync are both enabled. The default value is 1.
+- wal_fsync_period: This parameter is only valid when wal_level is set to 2. It specifies the interval, in milliseconds, of invoking fsync. If set to 0, it means fsync is invoked immediately once WAL is written.
 
-To achieve absolutely no data loss, walLevel should be set to 2 and fsync should be set to 1. There is a performance penalty to the data ingestion rate. However, if the concurrent data insertion threads on the client side can reach a big enough number, for example 50, the data ingestion performance will be still good enough. Our verification shows that the drop is only 30% when fsync is set to 3,000 milliseconds.
+To achieve absolutely no data loss, set wal_level to 2 and wal_fsync_period to 0. There is a performance penalty to the data ingestion rate. However, if the concurrent data insertion threads on the client side can reach a big enough number, for example 50, the data ingestion performance will be still good enough. Our verification shows that the drop is only 30% when wal_fsync_period is set to 3000 milliseconds.
 
 ## Disaster Recovery
 
-TDengine uses replication to provide high availability and disaster recovery capability.
+TDengine uses replication to provide high availability.
 
-A TDengine cluster is managed by mnode. To ensure the high availability of mnode, multiple replicas can be configured by the system parameter `numOfMnodes`. The data replication between mnode replicas is performed in a synchronous way to guarantee metadata consistency.
+A TDengine cluster is managed by mnodes. You can configure up to three mnodes to ensure high availability. The data replication between mnode replicas is performed in a synchronous way to guarantee metadata consistency.
 
-The number of replicas for time series data in TDengine is associated with each database. There can be many databases in a cluster and each database can be configured with a different number of replicas. When creating a database, parameter `replica` is used to configure the number of replications. To achieve high availability, `replica` needs to be higher than 1.
+The number of replicas for time series data in TDengine is associated with each database. There can be many databases in a cluster and each database can be configured with a different number of replicas. When creating a database, the parameter `replica` is used to specify the number of replicas. To achieve high availability, set `replica` to 3.
 
 The number of dnodes in a TDengine cluster must NOT be lower than the number of replicas for any database, otherwise it would fail when trying to create a table.
 
 As long as the dnodes of a TDengine cluster are deployed on different physical machines and the replica number is higher than 1, high availability can be achieved without any other assistance. For disaster recovery, dnodes of a TDengine cluster should be deployed in geographically different data centers.
+
+Alternatively, you can use taosX to synchronize the data from one TDengine cluster to another cluster in a remote location. However, taosX is only available in TDengine enterprise version, for more information please contact tdengine.com. 

docs/en/13-operation/10-monitor.md

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

docs/en/13-operation/17-diagnose.md

@@ -13,110 +13,59 @@ Diagnostic steps：
 1. If the port range to be diagnosed is being occupied by a `taosd` server process, please first stop `taosd.
 2. On the server side, execute command `taos -n server -P <port> -l <pktlen>` to monitor the port range starting from the port specified by `-P` parameter with the role of "server".
 3. On the client side, execute command `taos -n client -h <fqdn of server> -P <port> -l <pktlen>` to send a testing package to the specified server and port.
- 
--l <pktlen\>： The size of the testing package, in bytes. The value range is [11, 64,000] and default value is 1,000. Please note that the package length must be same in the above 2 commands executed on server side and client side respectively.
+
+-l <pktlen\>： The size of the testing package, in bytes. The value range is [11, 64,000] and default value is 1,000.
+Please note that the package length must be same in the above 2 commands executed on server side and client side respectively.
 
 Output of the server side for the example is below:
 
 ```bash
-# taos -n server -P 6000
-12/21 14:50:13.522509 0x7f536f455200 UTL work as server, host:172.27.0.7 startPort:6000 endPort:6011 pkgLen:1000
-
-12/21 14:50:13.522659 0x7f5352242700 UTL TCP server at port:6000 is listening
-12/21 14:50:13.522727 0x7f5351240700 UTL TCP server at port:6001 is listening
-...
-...
+# taos -n server -P 6030 -l 1000
+network test server is initialized, port:6030
+request is received, size:1000
+request is received, size:1000
 ...
-12/21 14:50:13.523954 0x7f5342fed700 UTL TCP server at port:6011 is listening
-12/21 14:50:13.523989 0x7f53437ee700 UTL UDP server at port:6010 is listening
-12/21 14:50:13.524019 0x7f53427ec700 UTL UDP server at port:6011 is listening
-12/21 14:50:22.192849 0x7f5352242700 UTL TCP: read:1000 bytes from 172.27.0.8 at 6000
-12/21 14:50:22.192993 0x7f5352242700 UTL TCP: write:1000 bytes to 172.27.0.8 at 6000
-12/21 14:50:22.237082 0x7f5351a41700 UTL UDP: recv:1000 bytes from 172.27.0.8 at 6000
-12/21 14:50:22.237203 0x7f5351a41700 UTL UDP: send:1000 bytes to 172.27.0.8 at 6000
-12/21 14:50:22.237450 0x7f5351240700 UTL TCP: read:1000 bytes from 172.27.0.8 at 6001
-12/21 14:50:22.237576 0x7f5351240700 UTL TCP: write:1000 bytes to 172.27.0.8 at 6001
-12/21 14:50:22.281038 0x7f5350a3f700 UTL UDP: recv:1000 bytes from 172.27.0.8 at 6001
-12/21 14:50:22.281141 0x7f5350a3f700 UTL UDP: send:1000 bytes to 172.27.0.8 at 6001
 ...
 ...
-...
-12/21 14:50:22.677443 0x7f5342fed700 UTL TCP: read:1000 bytes from 172.27.0.8 at 6011
-12/21 14:50:22.677576 0x7f5342fed700 UTL TCP: write:1000 bytes to 172.27.0.8 at 6011
-12/21 14:50:22.721144 0x7f53427ec700 UTL UDP: recv:1000 bytes from 172.27.0.8 at 6011
-12/21 14:50:22.721261 0x7f53427ec700 UTL UDP: send:1000 bytes to 172.27.0.8 at 6011
+request is received, size:1000
+request is received, size:1000
 ```
 
 Output of the client side for the example is below:
 
 ```bash
 # taos -n client -h 172.27.0.7 -P 6000
-12/21 14:50:22.192434 0x7fc95d859200 UTL work as client, host:172.27.0.7 startPort:6000 endPort:6011 pkgLen:1000
-
-12/21 14:50:22.192472 0x7fc95d859200 UTL server ip:172.27.0.7 is resolved from host:172.27.0.7
-12/21 14:50:22.236869 0x7fc95d859200 UTL successed to test TCP port:6000
-12/21 14:50:22.237215 0x7fc95d859200 UTL successed to test UDP port:6000
+taos -n client -h v3s2 -P 6030 -l 1000
+network test client is initialized, the server is v3s2:6030
+request is sent, size:1000
+response is received, size:1000
+request is sent, size:1000
+response is received, size:1000
 ...
 ...
 ...
-12/21 14:50:22.676891 0x7fc95d859200 UTL successed to test TCP port:6010
-12/21 14:50:22.677240 0x7fc95d859200 UTL successed to test UDP port:6010
-12/21 14:50:22.720893 0x7fc95d859200 UTL successed to test TCP port:6011
-12/21 14:50:22.721274 0x7fc95d859200 UTL successed to test UDP port:6011
-```
-
-The output needs to be checked carefully for the system operator to find the root cause and resolve the problem.
-
-## Startup Status and RPC Diagnostic
-
-`taos -n startup -h <fqdn of server>` can be used to check the startup status of a `taosd` process. This is a common task which should be performed by a system operator, especially in the case of a cluster, to determine whether `taosd` has been started successfully.
-
-`taos -n rpc -h <fqdn of server>` can be used to check whether the port of a started `taosd` can be accessed or not. If `taosd` process doesn't respond or is working abnormally, this command can be used to initiate a rpc communication with the specified fqdn to determine whether it's a network problem or whether `taosd` is abnormal.
-
-## Sync and Arbitrator Diagnostic
+request is sent, size:1000
+response is received, size:1000
+request is sent, size:1000
+response is received, size:1000
 
-```bash
-taos -n sync -P 6040 -h <fqdn of server>
-taos -n sync -P 6042 -h <fqdn of server>
+total succ:  100/100	cost:   16.23 ms	speed:    5.87 MB/s
 ```
 
-The above commands can be executed in a Linux shell to check whether the port for sync is working well and whether the sync module on the server side is working well. Additionally, `-P 6042` is used to check whether the arbitrator is configured properly and is working well.
-
-## Network Speed Diagnostic
-
-`taos -n speed -h <fqdn of server> -P 6030 -N 10 -l 10000000 -S TCP`
-
-From version 2.2.0.0 onwards, the above command can be executed in a Linux shell to test network speed. The command sends uncompressed packages to a running `taosd` server process or a simulated server process started by `taos -n server` to test the network speed. Parameters can be used when testing network speed are as below:
-
--n：When set to "speed", it means testing network speed. 
--h：The FQDN or IP of the server process to be connected to; if not set, the FQDN configured in `taos.cfg` is used. 
--P：The port of the server process to connect to, the default value is 6030. 
--N：The number of packages that will be sent in the test, range is [1,10000], default value is 100. 
--l：The size of each package in bytes, range is [1024, 1024 \* 1024 \* 1024], default value is 1024. 
--S：The type of network packages to send, can be either TCP or UDP, default value is TCP.  
-
-## FQDN Resolution Diagnostic
-
-`taos -n fqdn -h <fqdn of server>`
-
-From version 2.2.0.0 onward, the above command can be executed in a Linux shell to test the resolution speed of FQDN. It can be used to try to resolve a FQDN to an IP address and record the time spent in this process. The parameters that can be used for this purpose are as below:
-
--n：When set to "fqdn", it means testing the speed of resolving FQDN. 
--h：The FQDN to be resolved. If not set, the `FQDN` parameter in `taos.cfg` is used by default.  
+The output needs to be checked carefully for the system operator to find the root cause and resolve the problem.
 
 ## Server Log
 
-The parameter `debugFlag` is used to control the log level of the `taosd` server process. The default value is 131. For debugging and tracing, it needs to be set to either 135 or 143 respectively. 
-
-Once this parameter is set to 135 or 143, the log file grows very quickly especially when there is a huge volume of data insertion and data query requests. If all the logs are stored together, some important information may be missed very easily and so on the server side, important information is stored in a different place from other logs.
+The parameter `debugFlag` is used to control the log level of the `taosd` server process. The default value is 131. For debugging and tracing, it needs to be set to either 135 or 143 respectively.
 
-- The log at level of INFO, WARNING and ERROR is stored in `taosinfo` so that it is easy to find important information 
-- The log at level of DEBUG (135) and TRACE (143) and other information not handled by `taosinfo` are stored in `taosdlog`
+Once this parameter is set to 135 or 143, the log file grows very quickly especially when there is a huge volume of data insertion and data query requests. Ensure that the disk drive on which logs are stored has sufficient space.
 
 ## Client Log
 
-An independent log file, named as "taoslog+<seq num\>" is generated for each client program, i.e. a client process. The default value of `debugFlag` is also 131 and only logs at level of INFO/ERROR/WARNING are recorded. As stated above, for debugging and tracing, it needs to be changed to 135 or 143 respectively, so that logs at DEBUG or TRACE level can be recorded.
+An independent log file, named as "taoslog+<seq num\>" is generated for each client program, i.e. a client process. The parameter `debugFlag` is used to control the log level. The default value is 131. For debugging and tracing, it needs to be set to either 135 or 143 respectively.
+
+The default value of `debugFlag` is also 131 and only logs at level of INFO/ERROR/WARNING are recorded. As stated above, for debugging and tracing, it needs to be changed to 135 or 143 respectively, so that logs at DEBUG or TRACE level can be recorded.
 
 The maximum length of a single log file is controlled by parameter `numOfLogLines` and only 2 log files are kept for each `taosd` server process.
 
-Log files are written in an async way to minimize the workload on disk, but the trade off for performance is that a few log lines may be lost in some extreme conditions. 
+Log files are written in an async way to minimize the workload on disk, but the trade off for performance is that a few log lines may be lost in some extreme conditions. You can configure asynclog to 0 when needed for troubleshooting purposes to ensure that no log information is lost.

docs/en/14-reference/02-rest-api/02-rest-api.mdx

@@ -18,12 +18,12 @@ If the TDengine server is already installed, it can be verified as follows:
 
 The following example is in an Ubuntu environment and uses the `curl` tool to verify that the REST interface is working. Note that the `curl` tool may need to be installed in your environment.
 
-The following example lists all databases on the host h1.taosdata.com. To use it in your environment, replace `h1.taosdata.com` and `6041` (the default port) with the actual running TDengine service FQDN and port number.
+The following example lists all databases on the host h1.tdengine.com. To use it in your environment, replace `h1.tdengine.com` and `6041` (the default port) with the actual running TDengine service FQDN and port number.
 
 ```bash
 curl -L -H "Authorization: Basic cm9vdDp0YW9zZGF0YQ==" \
   -d "select name, ntables, status from information_schema.ins_databases;" \
-  h1.taosdata.com:6041/rest/sql
+  h1.tdengine.com:6041/rest/sql
 ```
 
 The following return value results indicate that the verification passed.
@@ -123,7 +123,7 @@ where `TOKEN` is the string after Base64 encoding of `{username}:{password}`, e.
 
 ### HTTP body structure
 
-#### Successful Operation
+#### Successful Insert Operation
 
 Example:
 
@@ -143,7 +143,7 @@ Description:
 - rows: (`int`) Only returns `1`.
 - data: (`[][]any`) Returns the number of rows affected.
 
-#### Successful Query
+#### Successful Query Operation
 
 Example:
 

docs/en/14-reference/03-connector/04-java.mdx

@@ -133,8 +133,6 @@ The configuration parameters in the URL are as follows:
 - batchfetch: true: pulls result sets in batches when executing queries; false: pulls result sets row by row. The default value is true. Enabling batch pulling and obtaining a batch of data can improve query performance when the query data volume is large.
 - batchErrorIgnore:true: When executing statement executeBatch, if there is a SQL execution failure in the middle, the following SQL will continue to be executed. false: No more statements after the failed SQL are executed. The default value is: false.
 
-For more information about JDBC native connections, see [Video Tutorial](https://www.taosdata.com/blog/2020/11/11/1955.html).
-
 **Connect using the TDengine client-driven configuration file **
 
 When you use a JDBC native connection to connect to a TDengine cluster, you can use the TDengine client driver configuration file to specify parameters such as `firstEp` and `secondEp` of the cluster in the configuration file as below:

docs/en/14-reference/03-connector/05-go.mdx

@@ -7,7 +7,7 @@ title: TDengine Go Connector
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-import Preparition from "./_preparition.mdx"
+import Preparition from "./_preparation.mdx"
 import GoInsert from "../../07-develop/03-insert-data/_go_sql.mdx"
 import GoInfluxLine from "../../07-develop/03-insert-data/_go_line.mdx"
 import GoOpenTSDBTelnet from "../../07-develop/03-insert-data/_go_opts_telnet.mdx"

docs/en/14-reference/03-connector/06-rust.mdx

@@ -7,7 +7,7 @@ title: TDengine Rust Connector
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-import Preparition from "./_preparition.mdx"
+import Preparition from "./_preparation.mdx"
 import RustInsert from "../../07-develop/03-insert-data/_rust_sql.mdx"
 import RustBind from "../../07-develop/03-insert-data/_rust_stmt.mdx"
 import RustQuery from "../../07-develop/04-query-data/_rust.mdx"
@@ -118,7 +118,7 @@ The parameters are described as follows:
 - **protocol**: Specify which connection method to use. For example, `taos+ws://localhost:6041` uses Websocket to establish connections.
 - **username/password**: Username and password used to create connections.
 - **host/port**: Specifies the server and port to establish a connection. If you do not specify a hostname or port, native connections default to `localhost:6030` and Websocket connections default to `localhost:6041`.
-- **database**: Specify the default database to connect to.
+- **database**: Specify the default database to connect to. It's optional.
 - **params**：Optional parameters.
 
 A sample DSN description string is as follows:
@@ -157,15 +157,15 @@ async fn demo(taos: &Taos, db: &str) -> Result<(), Error> {
     let inserted = taos.exec_many([
         // create super table
         "CREATE TABLE `meters` (`ts` TIMESTAMP, `current` FLOAT, `voltage` INT, `phase` FLOAT) \
-         TAGS (`groupid` INT, `location` BINARY(16))",
+         TAGS (`groupid` INT, `location` BINARY(24))",
         // create child table
-        "CREATE TABLE `d0` USING `meters` TAGS(0, 'Los Angles')",
+        "CREATE TABLE `d0` USING `meters` TAGS(0, 'California.LosAngles')",
         // insert into child table
         "INSERT INTO `d0` values(now - 10s, 10, 116, 0.32)",
         // insert with NULL values
         "INSERT INTO `d0` values(now - 8s, NULL, NULL, NULL)",
         // insert and automatically create table with tags if not exists
-        "INSERT INTO `d1` USING `meters` TAGS(1, 'San Francisco') values(now - 9s, 10.1, 119, 0.33)",
+        "INSERT INTO `d1` USING `meters` TAGS(1, 'California.SanFrancisco') values(now - 9s, 10.1, 119, 0.33)",
         // insert many records in a single sql
         "INSERT INTO `d1` values (now-8s, 10, 120, 0.33) (now - 6s, 10, 119, 0.34) (now - 4s, 11.2, 118, 0.322)",
     ]).await?;

docs/en/14-reference/03-connector/07-python.mdx

@@ -7,7 +7,7 @@ description: "taospy is the official Python connector for TDengine. taospy provi
 import Tabs from "@theme/Tabs";
 import TabItem from "@theme/TabItem";
 
-`taospy is the official Python connector for TDengine. taospy provides a rich API that makes it easy for Python applications to use TDengine. `taospy` wraps both the [native interface](/reference/connector/cpp) and [REST interface](/reference/rest-api) of TDengine, which correspond to the `taos` and `taosrest` modules of the `taospy` package, respectively.
+`taospy` is the official Python connector for TDengine. taospy provides a rich API that makes it easy for Python applications to use TDengine. `taospy` wraps both the [native interface](/reference/connector/cpp) and [REST interface](/reference/rest-api) of TDengine, which correspond to the `taos` and `taosrest` modules of the `taospy` package, respectively.
 In addition to wrapping the native and REST interfaces, `taospy` also provides a set of programming interfaces that conforms to the [Python Data Access Specification (PEP 249)](https://peps.python.org/pep-0249/). It is easy to integrate `taospy` with many third-party tools, such as [SQLAlchemy](https://www.sqlalchemy.org/) and [pandas](https://pandas.pydata.org/).
 
 The direct connection to the server using the native interface provided by the client driver is referred to hereinafter as a "native connection"; the connection to the server using the REST interface provided by taosAdapter is referred to hereinafter as a "REST connection".

docs/en/14-reference/03-connector/08-node.mdx

@@ -7,7 +7,7 @@ title: TDengine Node.js Connector
 import Tabs from "@theme/Tabs";
 import TabItem from "@theme/TabItem";
 
-import Preparition from "./_preparition.mdx";
+import Preparition from "./_preparation.mdx";
 import NodeInsert from "../../07-develop/03-insert-data/_js_sql.mdx";
 import NodeInfluxLine from "../../07-develop/03-insert-data/_js_line.mdx";
 import NodeOpenTSDBTelnet from "../../07-develop/03-insert-data/_js_opts_telnet.mdx";

docs/en/14-reference/03-connector/09-csharp.mdx

@@ -7,7 +7,7 @@ title: C# Connector
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-import Preparition from "./_preparition.mdx"
+import Preparition from "./_preparation.mdx"
 import CSInsert from "../../07-develop/03-insert-data/_cs_sql.mdx"
 import CSInfluxLine from "../../07-develop/03-insert-data/_cs_line.mdx"
 import CSOpenTSDBTelnet from "../../07-develop/03-insert-data/_cs_opts_telnet.mdx"
@@ -17,7 +17,7 @@ import CSAsyncQuery from "../../07-develop/04-query-data/_cs_async.mdx"
 
 `TDengine.Connector` is a C# language connector provided by TDengine that allows C# developers to develop C# applications that access TDengine cluster data.
 
-The `TDengine.Connector` connector supports connect to TDengine instances via the TDengine client driver (taosc), providing data writing, querying, subscription, schemaless writing, bind interface, etc. The `TDengine.Connector` currently does not provide a REST connection interface. Developers can write their RESTful application by referring to the [REST API](/reference/rest-api/) documentation.
+The `TDengine.Connector` connector supports connect to TDengine instances via the TDengine client driver (taosc), providing data writing, querying, subscription, schemaless writing, bind interface, etc.The `TDengine.Connector` also supports WebSocket and developers can build connection through DSN, which supports data writing, querying, and parameter binding, etc.
 
 This article describes how to install `TDengine.Connector` in a Linux or Windows environment and connect to TDengine clusters via `TDengine.Connector` to perform basic operations such as data writing and querying.
 
@@ -35,6 +35,10 @@ Please refer to [version support list](/reference/connector#version-support)
 
 ## Supported features
 
+<Tabs defaultValue="native">
+
+<TabItem value="native" label="Native Connection">
+
 1. Connection Management
 2. General Query
 3. Continuous Query
@@ -42,6 +46,18 @@ Please refer to [version support list](/reference/connector#version-support)
 5. Subscription
 6. Schemaless
 
+</TabItem>
+
+<TabItem value="rest" label="WebSocket Connection">
+
+1. Connection Management
+2. General Query
+3. Continuous Query
+4. Parameter Binding
+
+</TabItem>
+</Tabs>
+
 ## Installation Steps
 
 ### Pre-installation preparation
@@ -74,12 +90,18 @@ cp -r src/ myProject
 cd myProject
 dotnet add exmaple.csproj reference src/TDengine.csproj
 ```
+
 </TabItem>
 </Tabs>
 
 ## Establish a Connection
 
-``` C#
+
+<Tabs defaultValue="native">
+
+<TabItem value="native" label="Native Connection">
+
+``` csharp
 using TDengineDriver;
 
 namespace TDengineExample
@@ -112,14 +134,62 @@ namespace TDengineExample
 
 ```
 
+</TabItem>
+
+<TabItem value="rest" label="WebSocket Connection">
+
+The structure of the DSN description string is as follows:
+
+```text
+[<protocol>]://[[<username>:<password>@]<host>:<port>][/<database>][?<p1>=<v1>[&<p2>=<v2>]]
+|------------|---|-----------|-----------|------|------|------------|-----------------------|
+|   protocol |   | username  | password  | host | port |  database  |  params               |
+```
+
+The parameters are described as follows:
+
+* **protocol**: Specify which connection method to use (support http/ws). For example, `ws://localhost:6041` uses Websocket to establish connections. 
+* **username/password**: Username and password used to create connections.
+* **host/port**: Specifies the server and port to establish a connection. Websocket connections default to `localhost:6041`.
+* **database**: Specify the default database to connect to. It's optional.
+* **params**：Optional parameters.
+
+A sample DSN description string is as follows:
+
+```text
+ws://localhost:6041/test
+```
+
+``` csharp
+{{#include docs/examples/csharp/wsConnect/Program.cs}}
+```
+
+</TabItem>
+</Tabs>
+
 ## Usage examples
 
 ### Write data
 
 #### SQL Write
 
+<Tabs defaultValue="native">
+
+<TabItem value="native" label="Native Connection">
+
 <CSInsert />
 
+</TabItem>
+
+<TabItem value="rest" label="WebSocket Connection">
+
+```csharp
+{{#include docs/examples/csharp/wsInsert/Program.cs}}
+```
+
+</TabItem>
+</Tabs>
+
 #### InfluxDB line protocol write
 
 <CSInfluxLine />
@@ -132,12 +202,48 @@ namespace TDengineExample
 
 <CSOpenTSDBJson />
 
+#### Parameter Binding
+
+<Tabs defaultValue="native">
+
+<TabItem value="native" label="Native Connection">
+
+``` csharp
+{{#include docs/examples/csharp/stmtInsert/Program.cs}}
+```
+
+</TabItem>
+
+<TabItem value="rest" label="WebSocket Connection">
+
+```csharp
+{{#include docs/examples/csharp/wsStmt/Program.cs}}
+```
+
+</TabItem>
+</Tabs>
+
 ### Query data
 
 #### Synchronous Query
 
+<Tabs defaultValue="native">
+
+<TabItem value="native" label="Native Connection">
+
 <CSQuery />
 
+</TabItem>
+
+<TabItem value="rest" label="WebSocket Connection">
+
+```csharp
+{{#include docs/examples/csharp/wsQuery/Program.cs}}
+```
+
+</TabItem>
+</Tabs>
+
 #### Asynchronous query
 
 <CSAsyncQuery />
@@ -145,18 +251,21 @@ namespace TDengineExample
 ### More sample programs
 
 |Sample program |Sample program description |
-|--------------------------------------------------------------------------------------------------------------------|------------ --------------------------------|
+|--------------------------------------------------------------------------------------------------------------------|--------------------------------------------|
 | [CURD](https://github.com/taosdata/taos-connector-dotnet/blob/3.0/examples/Query/Query.cs)                         | Table creation, data insertion, and query examples with TDengine.Connector    |
 | [JSON Tag](https://github.com/taosdata/taos-connector-dotnet/blob/3.0/examples/JSONTag)                             | Writing and querying JSON tag data with TDengine Connector |
 | [stmt](https://github.com/taosdata/taos-connector-dotnet/tree/3.0/examples/Stmt)                                   | Parameter binding with TDengine Connector |
 | [schemaless](https://github.com/taosdata/taos-connector-dotnet/blob/3.0/examples/schemaless)                       | Schemaless writes with TDengine Connector |
 | [async query](https://github.com/taosdata/taos-connector-dotnet/blob/3.0/examples/AsyncQuery/QueryAsync.cs) | Asynchronous queries with TDengine Connector |
-| [TMQ](https://github.com/taosdata/taos-connector-dotnet/blob/3.0/examples/TMQ/TMQ.cs)      | Data subscription with TDengine Connector |
+| [Subscription](https://github.com/taosdata/taos-connector-dotnet/blob/3.0/examples/TMQ/TMQ.cs)      | Subscription example with TDengine Connector |
+| [Basic WebSocket Usage](https://github.com/taosdata/taos-connector-dotnet/blob/5a4a7cd0dbcda114447cdc6d0c6dedd8e84a52da/examples/WS/WebSocketSample.cs)      | WebSocket basic data in and out with TDengine connector  |
+| [WebSocket Parameter Binding](https://github.com/taosdata/taos-connector-dotnet/blob/5a4a7cd0dbcda114447cdc6d0c6dedd8e84a52da/examples/WS/WebSocketSTMT.cs)      | WebSocket parameter binding example |
 
 ## Important update records
 
 | TDengine.Connector | Description |
 |--------------------|--------------------------------|
+|        3.0.1       | Support WebSocket and Cloud，With function query, insert, and parameter binding|
 |        3.0.0       | Supports TDengine 3.0.0.0. TDengine 2.x is not supported. Added `TDengine.Impl.GetData()` interface to deserialize query results. |
 |        1.0.7       | Fixed TDengine.Query() memory leak. |
 | 1.0.6 | Fix schemaless bug in 1.0.4 and 1.0.5. |
@@ -172,7 +281,6 @@ namespace TDengineExample
 `Taos` is an ADO.NET connector for TDengine, supporting Linux and Windows platforms. Community contributor `Maikebing@@maikebing contributes the connector`. Please refer to:
 
 * Interface download:<https://github.com/maikebing/Maikebing.EntityFrameworkCore.Taos>
-* Usage notes:<https://www.taosdata.com/blog/2020/11/02/1901.html>
 
 ## Frequently Asked Questions
 

docs/en/14-reference/03-connector/_linux_install.mdx

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

docs/en/14-reference/03-connector/_preparation.mdx

@@ -2,7 +2,7 @@
 
 :::info
 
-Since the TDengine client driver is written in C, using the native connection requires loading the client driver shared library file, which is usually included in the TDengine installer. You can install either standard TDengine server installation package or [TDengine client installation package](/get-started/). For Windows development, you need to install the corresponding [Windows client](https://www.taosdata.com/cn/all-downloads/#TDengine-Windows-Client) for TDengine.
+Since the TDengine client driver is written in C, using the native connection requires loading the client driver shared library file, which is usually included in the TDengine installer. You can install either standard TDengine server installation package or [TDengine client installation package](/get-started/). For Windows development, you need to install the corresponding Windows client, please refer to [Install TDengine](../../get-started/package).
 
 - libtaos.so: After successful installation of TDengine on a Linux system, the dependent Linux version of the client driver `libtaos.so` file will be automatically linked to `/usr/lib/libtaos.so`, which is included in the Linux scannable path and does not need to be specified separately.
 - taos.dll: After installing the client on Windows, the dependent Windows version of the client driver taos.dll file will be automatically copied to the system default search path C:/Windows/System32, again without the need to specify it separately.

docs/en/14-reference/03-connector/_preparition.mdx

-- 已安装客户端驱动（使用原生连接必须安装，使用 REST 连接无需安装）
-
-:::info
-
-由于 TDengine 的客户端驱动使用 C 语言编写，使用原生连接时需要加载系统对应安装在本地的客户端驱动共享库文件，通常包含在 TDengine 安装包。TDengine Linux 服务端安装包附带了 TDengine 客户端，也可以单独安装 [Linux 客户端](/get-started/) 。在 Windows 环境开发时需要安装 TDengine 对应的 [Windows 客户端](https://www.taosdata.com/cn/all-downloads/#TDengine-Windows-Client) 。
-
-- libtaos.so: 在 Linux 系统中成功安装 TDengine 后，依赖的 Linux 版客户端驱动 libtaos.so 文件会被自动拷贝至 /usr/lib/libtaos.so，该目录包含在 Linux 自动扫描路径上，无需单独指定。
-- taos.dll: 在 Windows 系统中安装完客户端之后，依赖的 Windows 版客户端驱动 taos.dll 文件会自动拷贝到系统默认搜索路径 C:/Windows/System32 下，同样无需要单独指定。
-
-:::

docs/en/14-reference/03-connector/_windows_install.mdx

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

docs/en/14-reference/03-connector/index.mdx

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

docs/en/14-reference/04-taosadapter.md

@@ -30,7 +30,7 @@ taosAdapter provides the following features.
 
 ### Install taosAdapter
 
-If you use the TDengine server, you don't need additional steps to install taosAdapter. You can download taosAdapter from [TDengine 3.0 released versions](../../releases) to download the TDengine server installation package. If you need to deploy taosAdapter separately on another server other than the TDengine server, you should install the full TDengine server package on that server to install taosAdapter. If you need to build taosAdapter from source code, you can refer to the [Building taosAdapter]( https://github.com/taosdata/taosadapter/blob/3.0/BUILD.md) documentation.
+If you use the TDengine server, you don't need additional steps to install taosAdapter. You can download taosAdapter from [TDengine 3.0 released versions](../../releases/tdengine) to download the TDengine server installation package. If you need to deploy taosAdapter separately on another server other than the TDengine server, you should install the full TDengine server package on that server to install taosAdapter. If you need to build taosAdapter from source code, you can refer to the [Building taosAdapter]( https://github.com/taosdata/taosadapter/blob/3.0/BUILD.md) documentation.
 
 ### Start/Stop taosAdapter
 

docs/en/14-reference/06-taosdump.md

@@ -116,5 +116,4 @@ Usage: taosdump [OPTION...] dbname [tbname ...]
 Mandatory or optional arguments to long options are also mandatory or optional
 for any corresponding short options.
 
-Report bugs to <support@taosdata.com>.
 ```

docs/en/14-reference/07-tdinsight/assets/15146-tdengine-monitor-dashboard.json

@@ -211,7 +211,7 @@
       ],
       "timeFrom": null,
       "timeShift": null,
-      "title": "Leader MNode",
+      "title": "Master MNode",
       "transformations": [
         {
           "id": "filterByValue",
@@ -221,7 +221,7 @@
                 "config": {
                   "id": "regex",
                   "options": {
-                    "value": "leader"
+                    "value": "master"
                   }
                 },
                 "fieldName": "role"
@@ -300,7 +300,7 @@
       ],
       "timeFrom": null,
       "timeShift": null,
-      "title": "Leader MNode Create Time",
+      "title": "Master MNode Create Time",
       "transformations": [
         {
           "id": "filterByValue",
@@ -310,7 +310,7 @@
                 "config": {
                   "id": "regex",
                   "options": {
-                    "value": "leader"
+                    "value": "master"
                   }
                 },
                 "fieldName": "role"

docs/en/14-reference/07-tdinsight/assets/tdengine-grafana-7.x.json

@@ -153,7 +153,7 @@
       ],
       "timeFrom": null,
       "timeShift": null,
-      "title": "Leader MNode",
+      "title": "Master MNode",
       "transformations": [
         {
           "id": "filterByValue",
@@ -163,7 +163,7 @@
                 "config": {
                   "id": "regex",
                   "options": {
-                    "value": "leader"
+                    "value": "master"
                   }
                 },
                 "fieldName": "role"
@@ -246,7 +246,7 @@
       ],
       "timeFrom": null,
       "timeShift": null,
-      "title": "Leader MNode Create Time",
+      "title": "Master MNode Create Time",
       "transformations": [
         {
           "id": "filterByValue",
@@ -256,7 +256,7 @@
                 "config": {
                   "id": "regex",
                   "options": {
-                    "value": "leader"
+                    "value": "master"
                   }
                 },
                 "fieldName": "role"

docs/en/14-reference/07-tdinsight/index.md

@@ -5,15 +5,23 @@ sidebar_label: TDinsight
 
 TDinsight is a solution for monitoring TDengine using the builtin native monitoring database and [Grafana].
 
-After TDengine starts, it will automatically create a monitoring database `log`. TDengine will automatically write many metrics in specific intervals into the `log` database. The metrics may include the server's CPU, memory, hard disk space, network bandwidth, number of requests, disk read/write speed, slow queries, other information like important system operations (user login, database creation, database deletion, etc.), and error alarms. With [Grafana] and [TDengine Data Source Plugin](https://github.com/taosdata/grafanaplugin/releases), TDinsight can visualize cluster status, node information, insertion and query requests, resource usage, vnode, dnode, and mnode status, exception alerts and many other metrics. This is very convenient for developers who want to monitor TDengine cluster status in real-time. This article will guide users to install the Grafana server, automatically install the TDengine data source plug-in, and deploy the TDinsight visualization panel using the `TDinsight.sh` installation script.
+After TDengine starts, it automatically writes many metrics in specific intervals into a designated database. The metrics may include the server's CPU, memory, hard disk space, network bandwidth, number of requests, disk read/write speed, slow queries, other information like important system operations (user login, database creation, database deletion, etc.), and error alarms. With [Grafana] and [TDengine Data Source Plugin](https://github.com/taosdata/grafanaplugin/releases), TDinsight can visualize cluster status, node information, insertion and query requests, resource usage, vnode, dnode, and mnode status, exception alerts and many other metrics. This is very convenient for developers who want to monitor TDengine cluster status in real-time. This article will guide users to install the Grafana server, automatically install the TDengine data source plug-in, and deploy the TDinsight visualization panel using the `TDinsight.sh` installation script.
 
 ## System Requirements
 
-To deploy TDinsight, a single-node TDengine server or a multi-node TDengine cluster and a [Grafana] server are required. This dashboard requires TDengine 2.3.3.0 and above, with the `log` database enabled (`monitor = 1`).
+To deploy TDinsight, we need
+- a single-node TDengine server or a multi-node TDengine cluster and a [Grafana] server are required. This dashboard requires TDengine 3.0.1.0 and above, with the monitoring feature enabled. For detailed configuration, please refer to [TDengine monitoring configuration](../config/#monitoring-parameters).
+- taosAdapter has been instaleld and running, please refer to [taosAdapter](../taosadapter).
+- taosKeeper has been installed and running, please refer to  [taosKeeper](../taoskeeper).
+
+Please record
+- The endpoint of taosAdapter REST service, for example `http://tdengine.local:6041`
+- Authentication of taosAdapter, e.g. user name and password
+- The database name used by taosKeeper to store monitoring data
 
 ## Installing Grafana
 
-We recommend using the latest [Grafana] version 7 or 8 here. You can install Grafana on any [supported operating system](https://grafana.com/docs/grafana/latest/installation/requirements/#supported-operating-systems) by following the [official Grafana documentation Instructions](https://grafana.com/docs/grafana/latest/installation/) to install [Grafana].
+We recommend using the latest [Grafana] version 8 or 9 here. You can install Grafana on any [supported operating system](https://grafana.com/docs/grafana/latest/installation/requirements/#supported-operating-systems) by following the [official Grafana documentation Instructions](https://grafana.com/docs/grafana/latest/installation/) to install [Grafana].
 
 ### Installing Grafana on Debian or Ubuntu
 
@@ -71,7 +79,7 @@ chmod +x TDinsight.sh
 ./TDinsight.sh
 ```
 
-This script will automatically download the latest [Grafana TDengine data source plugin](https://github.com/taosdata/grafanaplugin/releases/latest) and [TDinsight dashboard](https://grafana.com/grafana/dashboards/15167) with configurable parameters for command-line options to the [Grafana Provisioning](https://grafana.com/docs/grafana/latest/administration/provisioning/) configuration file to automate deployment and updates, etc. With the alert setting options provided by this script, you can also get built-in support for AliCloud SMS alert notifications.
+This script will automatically download the latest [Grafana TDengine data source plugin](https://github.com/taosdata/grafanaplugin/releases/latest) and [TDinsight dashboard](https://github.com/taosdata/grafanaplugin/blob/master/dashboards/TDinsightV3.json) with configurable parameters for command-line options to the [Grafana Provisioning](https://grafana.com/docs/grafana/latest/administration/provisioning/) configuration file to automate deployment and updates, etc. With the alert setting options provided by this script, you can also get built-in support for AliCloud SMS alert notifications.
 
 Assume you use TDengine and Grafana's default services on the same host. Run `. /TDinsight.sh` and open the Grafana browser window to see the TDinsight dashboard.
 
@@ -106,18 +114,6 @@ Install and configure TDinsight dashboard in Grafana on Ubuntu 18.04/20.04 syste
 
 -E, --external-notifier <string>            Apply external notifier uid to TDinsight dashboard.
 
-Alibaba Cloud SMS as Notifier:
--s, --sms-enabled                           To enable tdengine-datasource plugin builtin Alibaba Cloud SMS webhook.
--N, --sms-notifier-name <string>            Provisioning notifier name.[default: TDinsight Builtin SMS]
--U, --sms-notifier-uid <string>             Provisioning notifier uid, use lowercase notifier name by default.
--D, --sms-notifier-is-default               Set notifier as default.
--I, --sms-access-key-id <string>            Alibaba Cloud SMS access key id
--K, --sms-access-key-secret <string>        Alibaba Cloud SMS access key secret
--S, --sms-sign-name <string>                Sign name
--C, --sms-template-code <string>            Template code
--T, --sms-template-param <string>           Template param, a escaped JSON string like '{"alarm_level":"%s","time":"%s","name":"%s","content":"%s"}'
--B, --sms-phone-numbers <string>            Comma-separated numbers list, eg "189xxxxxxxx,132xxxxxxxx"
--L, --sms-listen-addr <string>              [default: 127.0.0.1:9100]
 ```
 
 Most command-line options can take effect the same as environment variables.
@@ -136,17 +132,6 @@ Most command-line options can take effect the same as environment variables.
 | -t | --tdinsight-title | TDINSIGHT_DASHBOARD_TITLE | TDinsight dashboard title. [Default: TDinsight] | -e | -tdinsight-title
 | -e | --tdinsight-editable | TDINSIGHT_DASHBOARD_EDITABLE | If the dashboard is configured to be editable. [Default: false] | -e | --external
 | -E | --external-notifier | EXTERNAL_NOTIFIER | Apply the external notifier uid to the TDinsight dashboard.                                | -s
-| -s | --sms-enabled | SMS_ENABLED | Enable the tdengine-datasource plugin built into Alibaba Cloud SMS webhook.                    | -s
-| -N | --sms-notifier-name | SMS_NOTIFIER_NAME | The name of the provisioning notifier. [Default: `TDinsight Builtin SMS`] | -U
-| -U | --sms-notifier-uid | SMS_NOTIFIER_UID | "Notification Channel" `uid`, lowercase of the program name is used by default, other characters are replaced by "-". |-sms
-| -D | --sms-notifier-is-default | SMS_NOTIFIER_IS_DEFAULT | Set built-in SMS notification to default value.                                                |-sms-notifier-is-default
-| -I | --sms-access-key-id | SMS_ACCESS_KEY_ID | Alibaba Cloud SMS access key id |
-| -K | --sms-access-key-secret | SMS_ACCESS_KEY_SECRET | AliCloud SMS-access-secret-key |
-| -S | --sms-sign-name | SMS_SIGN_NAME | Signature |
-| -C | --sms-template-code | SMS_TEMPLATE_CODE | Template code |
-| -T | --sms-template-param | SMS_TEMPLATE_PARAM | JSON template for template parameters |
-| -B | --sms-phone-numbers | SMS_PHONE_NUMBERS | A comma-separated list of phone numbers, e.g. `"189xxxxxxxx,132xxxxxxxx"` |
-| -L | --sms-listen-addr | SMS_LISTEN_ADDR | Built-in SMS webhook listener address, default is `127.0.0.1:9100` |
 
 Suppose you start a TDengine database on host `tdengine` with HTTP API port `6041`, user `root1`, and password `pass5ord`. Execute the script.
 
@@ -166,24 +151,10 @@ Use the `uid` value obtained above as `-E` input.
 sudo ./TDinsight.sh -a http://tdengine:6041 -u root1 -p pass5ord -E existing-notifier
 ```
 
-If you want to use the [Alibaba Cloud SMS](https://www.aliyun.com/product/sms) service as a notification channel, you should enable it with the `-s` flag add the following parameters.
-
-- `-N`: Notification Channel name, default is `TDinsight Builtin SMS`.
-- `-U`: Channel uid, default is lowercase of `name`, any other character is replaced with -, for the default `-N`, its uid is `tdinsight-builtin-sms`.
-- `-I`: Alibaba Cloud SMS access key id.
-- `-K`: Alibaba Cloud SMS access secret key.
-- `-S`: Alibaba Cloud SMS signature.
-- `-C`: Alibaba Cloud SMS template id.
-- `-T`: Alibaba Cloud SMS template parameters, for JSON format template, example is as follows `'{"alarm_level":"%s", "time":"%s", "name":"%s", "content":"%s"}'`. There are four parameters: alarm level, time, name and alarm content.
-- `-B`: a list of phone numbers, separated by a comma `,`.
-
 If you want to monitor multiple TDengine clusters, you need to set up numerous TDinsight dashboards. Setting up non-default TDinsight requires some changes: the `-n` `-i` `-t` options need to be changed to non-default names, and `-N` and `-L` should also be changed if using the built-in SMS alerting feature.
 
 ```bash
 sudo . /TDengine.sh -n TDengine-Env1 -a http://another:6041 -u root -p taosdata -i tdinsight-env1 -t 'TDinsight Env1'
-# If using built-in SMS notifications
-sudo . /TDengine.sh -n TDengine-Env1 -a http://another:6041 -u root -p taosdata -i tdinsight-env1 -t 'TDinsight Env1' \
-  -s -N 'Env1 SMS' -I xx -K xx -S xx -C SMS_XX -T '' -B 00000000000 -L 127.0.0.01:10611
 ```
 
 Please note that the configuration data source, notification channel, and dashboard are not changeable on the front end. You should update the configuration again via this script or manually change the configuration file in the `/etc/grafana/provisioning` directory (this is the default directory for Grafana, use the `-P` option to change it as needed).
@@ -249,21 +220,23 @@ Save and test. It will report 'TDengine Data source is working' under normal cir
 
 ### Importing dashboards
 
-Point to **+** / **Create** - **import** (or `/dashboard/import` url).
+In the page of configuring data source, click **Dashboards** tab.
 
 ![TDengine Database TDinsight Import Dashboard and Configuration](./assets/import_dashboard.webp)
 
-Type the dashboard ID `15167` in the **Import via grafana.com** location and **Load**.
+Choose `TDengine for 3.x` and click `import`.
+
+After the importing is done, `TDinsight for 3.x` dashboard is available on the page of `search dashboards by name`.
 
-![TDengine Database TDinsight Import via grafana.com](./assets/import-dashboard-15167.webp)
+![TDengine Database TDinsight Import via grafana.com](./assets/import_dashboard_view.webp)
 
-Once the import is complete, the full page view of TDinsight is shown below.
+In the `TDinsight for 3.x` dashboard, choose the database used by taosKeeper to store monitoring data, you can see the monitoring result.
 
-![TDengine Database TDinsight show](./assets/TDinsight-full.webp)
+![TDengine Database TDinsight 选择数据库](./assets/select_dashboard_db.webp)
 
 ## TDinsight dashboard details
 
-The TDinsight dashboard is designed to provide the usage and status of TDengine-related resources [dnodes, mnodes, vnodes](https://www.taosdata.com/cn/documentation/architecture#cluster) or databases.
+The TDinsight dashboard is designed to provide the usage and status of TDengine-related resources, e.g. dnodes, mnodes, vnodes and databases.
 
 Details of the metrics are as follows.
 
@@ -285,7 +258,6 @@ This section contains the current information and status of the cluster, the ale
 - **Measuring Points Used**: The number of measuring points used to enable the alert rule (no data available in the community version, healthy by default).
 - **Grants Expire Time**: the expiration time of the enterprise version of the enabled alert rule (no data available for the community version, healthy by default).
 - **Error Rate**: Aggregate error rate (average number of errors per second) for alert-enabled clusters.
-- **Variables**: `show variables` table display.
 
 ### DNodes Status
 
@@ -294,7 +266,6 @@ This section contains the current information and status of the cluster, the ale
 - **DNodes Status**: simple table view of `show dnodes`.
 - **DNodes Lifetime**: the time elapsed since the dnode was created.
 - **DNodes Number**: the number of DNodes changes.
-- **Offline Reason**: if any dnode status is offline, the reason for offline is shown as a pie chart.
 
 ### MNode Overview
 
@@ -309,7 +280,6 @@ This section contains the current information and status of the cluster, the ale
 
 1. **Requests Rate(Inserts per Second)**: average number of inserts per second.
 2. **Requests (Selects)**: number of query requests and change rate (count of second).
-3. **Requests (HTTP)**: number of HTTP requests and request rate (count of second).
 
 ### Database
 
@@ -319,9 +289,8 @@ Database usage, repeated for each value of the variable `$database` i.e. multipl
 
 1. **STables**: number of super tables.
 2. **Total Tables**: number of all tables.
-3. **Sub Tables**: the number of all super table subtables.
-4. **Tables**: graph of all normal table numbers over time.
-5. **Tables Number Foreach VGroups**: The number of tables contained in each VGroups.
+3. **Tables**: number of normal tables.
+4. **Table number for each vgroup**: number of tables per vgroup.
 
 ### DNode Resource Usage
 
@@ -356,12 +325,11 @@ Currently, only the number of logins per minute is reported.
 
 Support monitoring taosAdapter request statistics and status details. Includes.
 
-1. **http_request**: contains the total number of requests, the number of failed requests, and the number of requests being processed
-2. **top 3 request endpoint**: data of the top 3 requests by endpoint group
-3. **Memory Used**: taosAdapter memory usage
-4. **latency_quantile(ms)**: quantile of (1, 2, 5, 9, 99) stages
-5. **top 3 failed request endpoint**: data of the top 3 failed requests by endpoint grouping
-6. **CPU Used**: taosAdapter CPU usage
+1. **http_request_inflight**: number of real-time requests.
+2. **http_request_total**: number of total requests.
+3. **http_request_fail**: number of failed requets.
+4. **CPU Used**: CPU usage of taosAdapter.
+5. **Memory Used**: Memory usage of taosAdapter.
 
 ## Upgrade
 
@@ -403,13 +371,6 @@ services:
       TDENGINE_API: ${TDENGINE_API}
       TDENGINE_USER: ${TDENGINE_USER}
       TDENGINE_PASS: ${TDENGINE_PASS}
-      SMS_ACCESS_KEY_ID: ${SMS_ACCESS_KEY_ID}
-      SMS_ACCESS_KEY_SECRET: ${SMS_ACCESS_KEY_SECRET}
-      SMS_SIGN_NAME: ${SMS_SIGN_NAME}
-      SMS_TEMPLATE_CODE: ${SMS_TEMPLATE_CODE}
-      SMS_TEMPLATE_PARAM: '${SMS_TEMPLATE_PARAM}'
-      SMS_PHONE_NUMBERS: $SMS_PHONE_NUMBERS
-      SMS_LISTEN_ADDR: ${SMS_LISTEN_ADDR}
     ports:
       - 3000:3000
 volumes:

docs/en/14-reference/11-docker/index.md

@@ -72,7 +72,7 @@ Next, ensure the hostname "tdengine" is resolvable in `/etc/hosts`.
 echo 127.0.0.1 tdengine |sudo tee -a /etc/hosts
 ```
 
-Finally, the TDengine service can be accessed from the taos shell or any connector with "tdengine" as the server address.
+Finally, the TDengine service can be accessed from the TDengine CLI or any connector with "tdengine" as the server address.
 
 ```shell
 taos -h tdengine -P 6030
@@ -116,7 +116,7 @@ If you want to start your application in a container, you need to add the corres
 FROM ubuntu:20.04
 RUN apt-get update && apt-get install -y wget
 ENV TDENGINE_VERSION=3.0.0.0
-RUN wget -c https://www.taosdata.com/assets-download/TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
+RUN wget -c https://www.taosdata.com/assets-download/3.0/TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
    && tar xvf TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
    && cd TDengine-client-${TDENGINE_VERSION} \
    && ./install_client.sh \
@@ -217,7 +217,7 @@ Here is the full Dockerfile:
 ```docker
 FROM golang:1.17.6-buster as builder
 ENV TDENGINE_VERSION=3.0.0.0
-RUN wget -c https://www.taosdata.com/assets-download/TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
+RUN wget -c https://www.taosdata.com/assets-download/3.0/TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
    && tar xvf TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
    && cd TDengine-client-${TDENGINE_VERSION} \
    && ./install_client.sh \
@@ -233,7 +233,7 @@ RUN go build
 FROM ubuntu:20.04
 RUN apt-get update && apt-get install -y wget
 ENV TDENGINE_VERSION=3.0.0.0
-RUN wget -c https://www.taosdata.com/assets-download/TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
+RUN wget -c https://www.taosdata.com/assets-download/3.0/TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
    && tar xvf TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
    && cd TDengine-client-${TDENGINE_VERSION} \
    && ./install_client.sh \

docs/en/14-reference/13-schemaless/13-schemaless.md

 ---
 title: Schemaless Writing
-description: "The Schemaless write method eliminates the need to create super tables/sub tables in advance and automatically creates the storage structure corresponding to the data, as it is written to the interface."
+description: 'The Schemaless write method eliminates the need to create super tables/sub tables in advance and automatically creates the storage structure corresponding to the data, as it is written to the interface.'
 ---
 
-In IoT applications, data is collected for many purposes such as intelligent control, business analysis, device monitoring and so on. Due to changes in business or functional requirements or changes in device hardware, the application logic and even the data collected may change. To provide the flexibility needed in such cases and in a rapidly changing IoT landscape, TDengine provides a series of interfaces for the schemaless writing method. These interfaces eliminate the need to create super tables and subtables in advance by automatically creating the storage structure corresponding to the data as the data is written to the interface. When necessary, schemaless writing will automatically add the required columns to ensure that the data written by the user is stored correctly.
+In IoT applications, data is collected for many purposes such as intelligent control, business analysis, device monitoring and so on. Due to changes in business or functional requirements or changes in device hardware, the application logic and even the data collected may change. Schemaless writing automatically creates storage structures for your data as it is being written to TDengine, so that you do not need to create supertables in advance. When necessary, schemaless writing
+will automatically add the required columns to ensure that the data written by the user is stored correctly.
 
 The schemaless writing method creates super tables and their corresponding subtables. These are completely indistinguishable from the super tables and subtables created directly via SQL. You can write data directly to them via SQL statements. Note that the names of tables created by schemaless writing are based on fixed mapping rules for tag values, so they are not explicitly ideographic and they lack readability.
 
@@ -19,12 +20,12 @@ With the following formatting conventions, schemaless writing uses a single stri
 measurement,tag_set field_set timestamp
 ```
 
-where :
+where:
 
 - measurement will be used as the data table name. It will be separated from tag_set by a comma.
-- tag_set will be used as tag data in the format `<tag_key>=<tag_value>,<tag_key>=<tag_value>`, i.e. multiple tags' data can be separated by a comma. It is separated from field_set by space.
-- field_set will be used as normal column data in the format of `<field_key>=<field_value>,<field_key>=<field_value>`, again using a comma to separate multiple normal columns of data. It is separated from the timestamp by a space.
-- The timestamp is the primary key corresponding to the data in this row.
+- `tag_set` will be used as tags, with format like `<tag_key>=<tag_value>,<tag_key>=<tag_value>` Enter a space between `tag_set` and `field_set`.
+- `field_set`will be used as data columns, with format like `<field_key>=<field_value>,<field_key>=<field_value>` Enter a space between `field_set` and `timestamp`.
+- `timestamp` is the primary key timestamp corresponding to this row of data
 
 All data in tag_set is automatically converted to the NCHAR data type and does not require double quotes (").
 
@@ -35,18 +36,19 @@ In the schemaless writing data line protocol, each data item in the field_set ne
 - Spaces, equal signs (=), commas (,), and double quotes (") need to be escaped with a backslash (\\) in front. (All refer to the ASCII character)
 - Numeric types will be distinguished from data types by the suffix.
 
-| **Serial number** | **Postfix** | **Mapping type** | **Size (bytes)** |
-| -------- | -------- | ------------ | -------------- |
-| 1 | none or f64 | double | 8 |
-| 2 | f32 | float | 4 |
-| 3 | i8/u8 | TinyInt/UTinyInt | 1 |
-| 4 | i16/u16 | SmallInt/USmallInt | 2 |
-| 5 | i32/u32 | Int/UInt | 4 |
-| 6 | i64/i/u64/u | Bigint/Bigint/UBigint/UBigint | 8 |
+| **Serial number** | **Postfix** | **Mapping type**              | **Size (bytes)** |
+| ----------------- | ----------- | ----------------------------- | ---------------- |
+| 1                 | None or f64 | double                        | 8                |
+| 2                 | f32         | float                         | 4                |
+| 3                 | i8/u8       | TinyInt/UTinyInt              | 1                |
+| 4                 | i16/u16     | SmallInt/USmallInt            | 2                |
+| 5                 | i32/u32     | Int/UInt                      | 4                |
+| 6                 | i64/i/u64/u | BigInt/BigInt/UBigInt/UBigInt | 8                |
 
 - `t`, `T`, `true`, `True`, `TRUE`, `f`, `F`, `false`, and `False` will be handled directly as BOOL types.
 
-For example, the following data rows indicate that the t1 label is "3" (NCHAR), the t2 label is "4" (NCHAR), and the t3 label is "t3" to the super table named `st` labeled "t3" (NCHAR), write c1 column as 3 (BIGINT), c2 column as false (BOOL), c3 column is "passit" (BINARY), c4 column is 4 (DOUBLE), and the primary key timestamp is 1626006833639000000 in one row.
+For example, the following data rows write c1 column as 3 (BIGINT), c2 column as false (BOOL), c3 column
+as "passit" (BINARY), c4 column as 4 (DOUBLE), and the primary key timestamp as 1626006833639000000 to child table with the t1 label as "3" (NCHAR), the t2 label as "4" (NCHAR), and the t3 label as "t3" (NCHAR) and the super table named `st`.
 
 ```json
 st,t1=3,t2=4,t3=t3 c1=3i64,c3="passit",c2=false,c4=4f64 1626006833639000000
@@ -58,102 +60,105 @@ Note that if the wrong case is used when describing the data type suffix, or if
 
 Schemaless writes process row data according to the following principles.
 
-1. You can use the following rules to generate the subtable names: first, combine the measurement name and the key and value of the label  into the next string:
+1. You can use the following rules to generate the subtable names: first, combine the measurement name and the key and value of the label into the next string:
 
 ```json
 "measurement,tag_key1=tag_value1,tag_key2=tag_value2"
 ```
 
 Note that tag_key1, tag_key2 are not the original order of the tags entered by the user but the result of using the tag names in ascending order of the strings. Therefore, tag_key1 is not the first tag entered in the line protocol.
-The string's MD5 hash value "md5_val" is calculated after the ranking is completed. The calculation result is then combined with the string to generate the table name: "t_md5_val". "t*" is a fixed prefix that every table generated by this mapping relationship has.
+The string's MD5 hash value "md5_val" is calculated after the ranking is completed. The calculation result is then combined with the string to generate the table name: "t_md5_val". "t_" is a fixed prefix that every table generated by this mapping relationship has.
+You can configure smlChildTableName in taos.cfg to specify table names, for example, `smlChildTableName=tname`. You can insert `st,tname=cpul,t1=4 c1=3 1626006833639000000` and the cpu1 table will be automatically created. Note that if multiple rows have the same tname but different tag_set values, the tag_set of the first row is used to create the table and the others are ignored.
 
 2. If the super table obtained by parsing the line protocol does not exist, this super table is created.
-If the subtable obtained by the parse line protocol does not exist, Schemaless creates the sub-table according to the subtable name determined in steps 1 or 2.
+3. If the subtable obtained by the parse line protocol does not exist, Schemaless creates the sub-table according to the subtable name determined in steps 1 or 2.
 4. If the specified tag or regular column in the data row does not exist, the corresponding tag or regular column is added to the super table (only incremental).
-5. If there are some tag columns or regular columns in the super table that are not specified to take values in a data row, then the values of these columns are set to NULL.
+5. If there are some tag columns or regular columns in the super table that are not specified to take values in a data row, then the values of these columns are set to
+   NULL.
 6. For BINARY or NCHAR columns, if the length of the value provided in a data row exceeds the column type limit, the maximum length of characters allowed to be stored in the column is automatically increased (only incremented and not decremented) to ensure complete preservation of the data.
 7. Errors encountered throughout the processing will interrupt the writing process and return an error code.
-8. In order to improve the efficiency of writing, it is assumed by default that the order of the fields in the same Super is the same (the first data contains all fields, and the following data is in this order). If the order is different, the parameter smlDataFormat needs to be configured to be false. Otherwise, the data is written in the same order, and the data in the library will be abnormal.
+8. It is assumed that the order of field_set in a supertable is consistent, meaning that the first record contains all fields and subsequent records store fields in the same order. If the order is not consistent, set smlDataFormat in taos.cfg to false. Otherwise, data will be written out of order and a database error will occur.(smlDataFormat in taos.cfg default to false after version of 3.0.1.3)
 
 :::tip
-All processing logic of schemaless will still follow TDengine's underlying restrictions on data structures, such as the total length of each row of data cannot exceed 16k bytes. See [TAOS SQL Boundary Limits](/taos-sql/limit) for specific constraints in this area.
+All processing logic of schemaless will still follow TDengine's underlying restrictions on data structures, such as the total length of each row of data cannot exceed
+16KB. See [TDengine SQL Boundary Limits](/taos-sql/limit) for specific constraints in this area.
+
 :::
 
 ## Time resolution recognition
 
 Three specified modes are supported in the schemaless writing process, as follows:
 
-| **Serial** | **Value** | **Description** |
-| -------- | ------------------- | ------------------------------- |
-| 1 | SML_LINE_PROTOCOL | InfluxDB Line Protocol |
-| 2 | SML_TELNET_PROTOCOL | OpenTSDB Text Line Protocol |
-| 3 | SML_JSON_PROTOCOL | JSON protocol format |
-
-In the SML_LINE_PROTOCOL parsing mode, the user is required to specify the time resolution of the input timestamp. The available time resolutions are shown in the following table.
+| **Serial** | **Value**           | **Description**        |
+| ---------- | ------------------- | ---------------------- |
+| 1          | SML_LINE_PROTOCOL   | InfluxDB Line Protocol |
+| 2          | SML_TELNET_PROTOCOL | OpenTSDB file protocol |
+| 3          | SML_JSON_PROTOCOL   | OpenTSDB JSON protocol |
 
-| **Serial Number** | **Time Resolution Definition** | **Meaning** |
-| -------- | --------------------------------- | -------------- |
-| 1 | TSDB_SML_TIMESTAMP_NOT_CONFIGURED | Not defined (invalid) |
-| 2 | TSDB_SML_TIMESTAMP_HOURS | hour |
-| 3 | TSDB_SML_TIMESTAMP_MINUTES | MINUTES
-| 4 | TSDB_SML_TIMESTAMP_SECONDS | SECONDS
-| 5 | TSDB_SML_TIMESTAMP_MILLI_SECONDS | milliseconds
-| 6 | TSDB_SML_TIMESTAMP_MICRO_SECONDS | microseconds
-| 7 | TSDB_SML_TIMESTAMP_NANO_SECONDS | nanoseconds |
+In InfluxDB line protocol mode, you must specify the precision of the input timestamp. Valid precisions are described in the following table.
 
-In SML_TELNET_PROTOCOL and SML_JSON_PROTOCOL modes, the time precision is determined based on the length of the timestamp (in the same way as the OpenTSDB standard operation), and the user-specified time resolution is ignored at this point.
+| **No.** | **Precision**                     | **Description**       |
+| ------- | --------------------------------- | --------------------- |
+| 1       | TSDB_SML_TIMESTAMP_NOT_CONFIGURED | Not defined (invalid) |
+| 2       | TSDB_SML_TIMESTAMP_HOURS          | Hours                 |
+| 3       | TSDB_SML_TIMESTAMP_MINUTES        | Minutes               |
+| 4       | TSDB_SML_TIMESTAMP_SECONDS        | Seconds               |
+| 5       | TSDB_SML_TIMESTAMP_MILLI_SECONDS  | Milliseconds          |
+| 6       | TSDB_SML_TIMESTAMP_MICRO_SECONDS  | Microseconds          |
+| 7       | TSDB_SML_TIMESTAMP_NANO_SECONDS   | Nanoseconds           |
 
-## Data schema mapping rules
+In OpenTSDB file and JSON protocol modes, the precision of the timestamp is determined from its length in the standard OpenTSDB manner. User input is ignored.
 
-This section describes how data for line protocols are mapped to data with a schema. The data measurement in each line protocol is mapped as follows:
-- The tag name in tag_set is the name of the tag in the data schema
-- The name in field_set is the column's name. 
+## Data Model Mapping
 
-The following data is used as an example to illustrate the mapping rules.
+This section describes how data in line protocol is mapped to a schema. The data measurement in each line is mapped to a
+supertable name. The tag name in tag_set is the tag name in the schema, and the name in field_set is the column name in the schema. The following example shows how data is mapped:
 
 ```json
 st,t1=3,t2=4,t3=t3 c1=3i64,c3="passit",c2=false,c4=4f64 1626006833639000000
 ```
 
-The row data mapping generates a super table: `st`, which contains three labels of type NCHAR: t1, t2, t3. Five data columns are ts (timestamp), c1 (bigint), c3 (binary), c2 (bool), c4 (bigint). The mapping becomes the following SQL statement.
+This row is mapped to a supertable: `st` contains three NCHAR tags: t1, t2, and t3. Five columns are created: ts (timestamp), c1 (bigint), c3 (binary), c2 (bool), and c4 (bigint). The following SQL statement is generated:
 
 ```json
 create stable st (_ts timestamp, c1 bigint, c2 bool, c3 binary(6), c4 bigint) tags(t1 nchar(1), t2 nchar(1), t3 nchar(2))
 ```
 
-## Data schema change handling
+## Processing Schema Changes
 
-This section describes the impact on the data schema for different line protocol data writing cases.
+This section describes the impact on the schema caused by different data being written.
 
-When writing to an explicitly identified field type using the line protocol, subsequent changes to the field's type definition will result in an explicit data schema error, i.e., will trigger a write API report error. As shown below, the
+If you use line protocol to write to a specific tag field and then later change the field type, a schema error will ocur. This triggers an error on the write API. This is shown as follows:
 
 ```json
-st,t1=3,t2=4,t3=t3 c1=3i64,c3="passit",c2=false,c4=4 1626006833639000000
-st,t1=3,t2=4,t3=t3 c1=3i64,c3="passit",c2=false,c4=4i 1626006833640000000
+st,t1=3,t2=4,t3=t3 c1=3i64,c3="passit",c2=false,c4=4    1626006833639000000
+st,t1=3,t2=4,t3=t3 c1=3i64,c3="passit",c2=false,c4=4i   1626006833640000000
 ```
 
-The data type mapping in the first row defines column c4 as DOUBLE, but the data in the second row is declared as BIGINT by the numeric suffix, which triggers a parsing error with schemaless writing.
+The first row defines c4 as a double. However, in the second row, the suffix indicates that the value of c4 is a bigint. This causes schemaless writing to throw an error.
 
-If the line protocol before the column declares the data column as BINARY, the subsequent one requires a longer binary length, which triggers a super table schema change.
+An error also occurs if data input into a binary column exceeds the defined length of the column.
 
 ```json
-st,t1=3,t2=4,t3=t3 c1=3i64,c5="pass" 1626006833639000000
-st,t1=3,t2=4,t3=t3 c1=3i64,c5="passit" 1626006833640000000
+st,t1=3,t2=4,t3=t3 c1=3i64,c5="pass"     1626006833639000000
+st,t1=3,t2=4,t3=t3 c1=3i64,c5="passit"   1626006833640000000
 ```
 
-The first line of the line protocol parsing will declare column c5 is a BINARY(4) field. The second line data write will parse column c5 as a BINARY column. But in the second line, c5's width is 6 so you need to increase the width of the BINARY field to be able to accommodate the new string.
+The first row defines c5 as a binary(4). but the second row writes 6 bytes to it. This means that the length of the binary column must be expanded to contain the data.
 
 ```json
-st,t1=3,t2=4,t3=t3 c1=3i64 1626006833639000000
-st,t1=3,t2=4,t3=t3 c1=3i64,c6="passit" 1626006833640000000
+st,t1=3,t2=4,t3=t3 c1=3i64               1626006833639000000
+st,t1=3,t2=4,t3=t3 c1=3i64,c6="passit"   1626006833640000000
 ```
 
-The second line of data has an additional column c6 of type BINARY(6) compared to the first row. Then a column c6 of type BINARY(6) is automatically added at this point.
+The preceding data includes a new entry, c6, with type binary(6). When this occurs, a new column c6 with type binary(6) is added automatically.
 
-## Write integrity
+## Write Integrity
 
-TDengine provides idempotency guarantees for data writing, i.e., you can repeatedly call the API to write data with errors. However, it does not give atomicity guarantees for writing multiple rows of data. During the process of writing numerous rows of data in one batch, some data will be written successfully, and some data will fail.
+TDengine guarantees the idempotency of data writes. This means that you can repeatedly call the API to perform write operations with bad data. However, TDengine does not guarantee the atomicity of multi-row writes. In a multi-row write, some data may be written successfully and other data unsuccessfully.
 
-## Error code
+##: Error Codes
 
-If it is an error in the data itself during the schemaless writing process, the application will get `TSDB_CODE_TSC_LINE_SYNTAX_ERROR` error message, which indicates that the error occurred in writing. The other error codes are consistent with the TDengine and can be obtained via the `taos_errstr()` to get the specific cause of the error.
+The TSDB_CODE_TSC_LINE_SYNTAX_ERROR indicates an error in the schemaless writing component.
+This error occurs when writing text. For other errors, schemaless writing uses the standard TDengine error codes
+found in taos_errstr.

docs/en/14-reference/14-taosKeeper.md

 ---
 sidebar_label: taosKeeper
 title: taosKeeper
-description: Instructions and tips for using taosKeeper
+description: exports TDengine monitoring metrics.
 ---
 
 ## Introduction
@@ -22,26 +22,35 @@ You can compile taosKeeper separately and install it. Please refer to the [taosK
 
 ### Configuration and running methods
 
-<!-- taosKeeper needs to be executed on the terminal of the operating system, it supports two configuration methods: [Command-line arguments](#command-line-arguments-in-detail) and [configuration file](#configuration-file-parameters-in-detail). Command-line arguments take precedence over values in the configuration file. -->
-taosKeeper needs to be executed on the terminal of the operating system. To run taosKeeper, see [configuration file](#configuration-file-parameters-in-detail).
+taosKeeper needs to be executed on the terminal of the operating system, it supports three configuration methods: [Command-line arguments](#command-line-arguments-in-detail), [environment variable](#environment-variable-in-detail) and [configuration file](#configuration-file-parameters-in-detail). The precedence of those is Command-line, environment variable and configuration file.
 
 **Make sure that the TDengine cluster is running correctly before running taosKeeper. ** Ensure that the monitoring service in TDengine has been started. For more information, see [TDengine Monitoring Configuration](../config/#monitoring).
 
-<!--
 ### Command-Line Parameters
 
-You can use command-line parameters to run taosBenchmark and control its behavior:
+You can use command-line parameters to run taosKeeper and control its behavior:
 
 ```shell
-taosKeeper
+$ taosKeeper
 ```
--->
+### Environment variable
+
+You can use Environment variable to run taosKeeper and control its behavior:
+
+```shell
+$ export TAOS_KEEPER_TDENGINE_HOST=192.168.64.3
+ 
+$ taoskeeper
+```
+
+you can run `taoskeeper -h` for more detail.
+
 ### Configuration File
 
 You can quickly launch taosKeeper with the following commands. If you do not specify a configuration file, `/etc/taos/keeper.toml` is used by default. If this file does not specify configurations, the default values are used. 
 
 ```shell
-taoskeeper -c <keeper config file>
+$ taoskeeper -c <keeper config file>
 ```
 
 **Sample configuration files**
@@ -110,7 +119,7 @@ Query OK, 1 rows in database (0.036162s)
 #### Export Monitoring Metrics
 
 ```shell
-curl http://127.0.0.1:6043/metrics
+$ curl http://127.0.0.1:6043/metrics
 ```
 
 Sample result set (excerpt):

docs/en/14-reference/_telegraf.mdx

@@ -22,5 +22,4 @@ An example is as follows.
   username = "root"
   password = "taosdata"
   data_format = "influx"
-  influx_max_line_bytes = 250
 ```

docs/en/20-third-party/01-grafana.mdx

@@ -6,9 +6,7 @@ title: Grafana
 import Tabs from "@theme/Tabs";
 import TabItem from "@theme/TabItem";
 
-TDengine can be quickly integrated with the open-source data visualization system [Grafana](https://www.grafana.com/) to build a data monitoring and alerting system. The whole process does not require any code development. And you can visualize the contents of the data tables in TDengine on a dashboard.
-
-You can learn more about using the TDengine plugin on [GitHub](https://github.com/taosdata/grafanaplugin/blob/master/README.md).
+TDengine can be quickly integrated with the open-source data visualization system [Grafana](https://www.grafana.com/) to build a data monitoring and alerting system. The whole process does not require any code development. And you can visualize the contents of the data tables in TDengine on a dashboard. You can learn more about using the TDengine plugin on [GitHub](https://github.com/taosdata/grafanaplugin/blob/master/README.md).
 
 ## Prerequisites
 
@@ -65,7 +63,6 @@ Restart Grafana service and open Grafana in web-browser, usually <http://localho
 Save the script and type `./install.sh --help` for the full usage of the script.
 
 </TabItem>
-
 <TabItem value="manual" label="Install & Configure Manually">
 
 Follow the installation steps in [Grafana](https://grafana.com/grafana/plugins/tdengine-datasource/?tab=installation) with the [``grafana-cli`` command-line tool](https://grafana.com/docs/grafana/latest/administration/cli/) for plugin installation.
@@ -76,7 +73,7 @@ grafana-cli plugins install tdengine-datasource
 sudo -u grafana grafana-cli plugins install tdengine-datasource
 ```
 
-Alternatively, you can manually download the .zip file from [GitHub](https://github.com/taosdata/grafanaplugin/releases/tag/latest) or [Grafana](https://grafana.com/grafana/plugins/tdengine-datasource/?tab=installation) and unpack it into your grafana plugins directory.
+You can also download zip files from [GitHub](https://github.com/taosdata/grafanaplugin/releases/tag/latest) or [Grafana](https://grafana.com/grafana/plugins/tdengine-datasource/?tab=installation) and install manually. The commands are as follows:
 
 ```bash
 GF_VERSION=3.2.2
@@ -131,7 +128,7 @@ docker run -d \
   grafana/grafana
 ```
 
-You can setup a zero-configuration stack for TDengine + Grafana by [docker-compose](https://docs.docker.com/compose/) and [Grafana provisioning](https://grafana.com/docs/grafana/latest/administration/provisioning/) file：
+You can setup a zero-configuration stack for TDengine + Grafana by [docker-compose](https://docs.docker.com/compose/) and [Grafana provisioning](https://grafana.com/docs/grafana/latest/administration/provisioning/) file:
 
 1. Save the provisioning configuration file to `tdengine.yml`.
 
@@ -196,7 +193,7 @@ Go back to the main interface to create a dashboard and click Add Query to enter
 
 As shown above, select the `TDengine` data source in the `Query` and enter the corresponding SQL in the query box below for query.
 
-- INPUT SQL: enter the statement to be queried (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 plugin, indicating the range and time interval of queries fetched from the Grafana plugin panel. In addition to the built-in variables, custom template variables are also supported.
+- INPUT SQL: Enter the desired query (the results being two columns and multiple rows), such as `select _wstart, avg(mem_system) from log.dnodes_info where ts >= $from and ts < $to interval($interval)`. In this statement, $from, $to, and $interval are variables that Grafana replaces with the query time range and interval. In addition to the built-in variables, custom template variables are also supported.
 - ALIAS BY: This allows you to set the current query alias.
 - GENERATE SQL: Clicking this button will automatically replace the corresponding variables and generate the final executed statement.
 
@@ -208,7 +205,11 @@ Follow the default prompt to query the average system memory usage for the speci
 
 ### Importing the Dashboard
 
-You can install TDinsight dashboard in data source configuration page (like `http://localhost:3000/datasources/edit/1/dashboards`) as a monitoring visualization tool for TDengine cluster. The dashboard is published in Grafana as [Dashboard 15167 - TDinsight](https://grafana.com/grafana/dashboards/15167). Check the [TDinsight User Manual](/reference/tdinsight/) for the details.
+You can install TDinsight dashboard in data source configuration page (like `http://localhost:3000/datasources/edit/1/dashboards`) as a monitoring visualization tool for TDengine cluster. Ensure that you use TDinsight for 3.x.
+
+![TDengine Database Grafana plugine import dashboard](./import_dashboard.webp)
+
+A dashboard for TDengine 2.x has been published on Grafana: [Dashboard 15167 - TDinsight](https://grafana.com/grafana/dashboards/15167)) 。 Check the [TDinsight User Manual](/reference/tdinsight/) for the details.
 
 For more dashboards using TDengine data source, [search here in Grafana](https://grafana.com/grafana/dashboards/?dataSource=tdengine-datasource). Here is a sub list:
 

docs/en/20-third-party/03-telegraf.md

@@ -15,6 +15,7 @@ To write Telegraf data to TDengine requires the following preparations.
 - The TDengine cluster is deployed and functioning properly
 - taosAdapter is installed and running properly. Please refer to the [taosAdapter manual](/reference/taosadapter) for details.
 - Telegraf has been installed. Please refer to the [official documentation](https://docs.influxdata.com/telegraf/v1.22/install/) for Telegraf installation.
+- Telegraf collects the running status measurements of current system. You can enable [input plugins](https://docs.influxdata.com/telegraf/v1.22/plugins/) to insert [other formats](https://docs.influxdata.com/telegraf/v1.24/data_formats/input/) data to Telegraf then forward to TDengine.
 
 ## Configuration steps
 <Telegraf />
@@ -31,11 +32,12 @@ Use TDengine CLI to verify Telegraf correctly writing data to TDengine and read
 
 ```
 taos> show databases;
-              name              |      created_time       |   ntables   |   vgroups   | replica | quorum |  days  |           keep           |  cache(MB)  |   blocks    |   minrows   |   maxrows   | wallevel |    fsync    | comp | cachelast | precision | update |   status   |
-====================================================================================================================================================================================================================================================================================
- telegraf                       | 2022-04-20 08:47:53.488 |          22 |           1 |       1 |      1 |     10 | 3650                     |          16 |           6 |         100 |        4096 |        1 |        3000 |    2 |         0 | ns        |      2 | ready      |
- log                            | 2022-04-20 07:19:50.260 |           9 |           1 |       1 |      1 |     10 | 3650                     |          16 |           6 |         100 |        4096 |        1 |        3000 |    2 |         0 | ms        |      0 | ready      |
-Query OK, 2 row(s) in set (0.002401s)
+              name              |
+=================================
+ information_schema             |
+ performance_schema             |
+ telegraf                       |
+Query OK, 3 rows in database (0.010568s)
 
 taos> use telegraf;
 Database changed.
@@ -65,3 +67,11 @@ taos> select * from telegraf.system limit 10;
 |
 Query OK, 3 row(s) in set (0.013269s)
 ```
+
+:::note
+
+- TDengine take influxdb format data and create unique ID for table names by the rule.
+The user can configure `smlChildTableName` paramter to generate specified table names if he/she needs. And he/she also need to insert data with specified data format.
+For example, Add `smlChildTableName=tname` in the taos.cfg file. Insert data `st,tname=cpu1,t1=4 c1=3 1626006833639000000` then the table name will be cpu1. If there are multiple lines has same tname but different tag_set, the first line's tag_set will be used to automatically creating table and ignore other lines. Please refer to [TDengine Schemaless](/reference/schemaless/#Schemaless-Line-Protocol)
+:::
+

docs/en/20-third-party/06-statsd.md

 ---
 sidebar_label: StatsD
-title: StatsD writing
+title: StatsD Writing
 ---
 
 import StatsD from "../14-reference/_statsd.mdx"
@@ -12,8 +12,8 @@ You can write StatsD data to TDengine by simply modifying the configuration file
 ## Prerequisites
 
 To write StatsD data to TDengine requires the following preparations.
-- The TDengine cluster has been deployed and is working properly
-- taosAdapter is installed and running properly. Please refer to the [taosAdapter manual](/reference/taosadapter) for details.
+1. The TDengine cluster is deployed and functioning properly
+2. taosAdapter is installed and running properly. Please refer to the taosAdapter manual for details.
 - StatsD has been installed. To install StatsD, please refer to [official documentation](https://github.com/statsd/statsd)
 
 ## Configuration steps
@@ -39,8 +39,12 @@ $ echo "foo:1|c" | nc -u -w0 127.0.0.1 8125
 Use the TDengine CLI to verify that StatsD data is written to TDengine and can read out correctly.
 
 ```
-Welcome to the TDengine shell from Linux, Client Version:3.0.0.0
-Copyright (c) 2022 by TAOS Data, Inc. All rights reserved.
+taos> show databases;
+              name              |      created_time       |   ntables   |   vgroups   | replica | quorum |  days  |           keep           |  cache(MB)  |   blocks    |   minrows   |   maxrows   | wallevel |    fsync    | comp | cachelast | precision | update |   status   |
+====================================================================================================================================================================================================================================================================================
+ log                            | 2022-04-20 07:19:50.260 |          11 |           1 |       1 |      1 |     10 | 3650                     |          16 |           6 |         100 |        4096 |        1 |        3000 |    2 |         0 | ms        |      0 | ready      |
+ statsd                         | 2022-04-20 09:54:51.220 |           1 |           1 |       1 |      1 |     10 | 3650                     |          16 |           6 |         100 |        4096 |        1 |        3000 |    2 |         0 | ns        |      2 | ready      |
+Query OK, 2 row(s) in set (0.003142s)
 
 taos> use statsd;
 Database changed.

docs/en/20-third-party/09-emq-broker.md

@@ -9,7 +9,7 @@ MQTT is a popular IoT data transfer protocol. [EMQX](https://github.com/emqx/emq
 
 The following preparations are required for EMQX to add TDengine data sources correctly.
 - The TDengine cluster is deployed and working properly
-- taosAdapter is installed and running properly. Please refer to the [taosAdapter manual](/reference/taosadapter) for details.
+- taosAdapter is installed and running properly. Please refer to the [taosAdapter manual](../../reference/taosadapter) for details.
 - If you use the emulated writers described later, you need to install the appropriate version of Node.js. V12 is recommended.
 
 ## Install and start EMQX
@@ -28,8 +28,6 @@ USE test;
 CREATE TABLE sensor_data (ts TIMESTAMP, temperature FLOAT, humidity FLOAT, volume FLOAT, pm10 FLOAT, pm25 FLOAT, so2 FLOAT, no2 FLOAT, co FLOAT, sensor_id NCHAR(255), area TINYINT, coll_time TIMESTAMP);
 ```
 
-Note: The table schema is based on the blog [(In Chinese) Data Transfer, Storage, Presentation, EMQX + TDengine Build MQTT IoT Data Visualization Platform](https://www.taosdata.com/blog/2020/08/04/1722.html) as an example. Subsequent operations are carried out with this blog scenario too. Please modify it according to your actual application scenario.
-
 ## Configuring EMQX Rules
 
 Since the configuration interface of EMQX differs from version to version, here is v4.4.5 as an example. For other versions, please refer to the corresponding official documentation.
@@ -137,5 +135,5 @@ Use the TDengine CLI program to log in and query the appropriate databases and t
 
 ![TDengine Database EMQX result in taos](./emqx/check-result-in-taos.webp)
 
-Please refer to the [TDengine official documentation](https://docs.taosdata.com/) for more details on how to use TDengine.
+Please refer to the [TDengine official documentation](https://docs.tdengine.com/) for more details on how to use TDengine.
 EMQX Please refer to the [EMQX official documentation](https://www.emqx.io/docs/en/v4.4/rule/rule-engine.html) for details on how to use EMQX.

docs/en/20-third-party/10-hive-mq-broker.md

 ---
 sidebar_label: HiveMQ Broker
-title: HiveMQ Broker writing
+title: HiveMQ Broker Writing
 ---
 
-[HiveMQ](https://www.hivemq.com/) is an MQTT broker that provides community and enterprise editions. HiveMQ is mainly for enterprise emerging machine-to-machine M2M communication and internal transport, meeting scalability, ease of management, and security features. HiveMQ provides an open-source plug-in development kit. MQTT data can be saved to TDengine via TDengine extension for HiveMQ. Please refer to the [HiveMQ extension - TDengine documentation](https://github.com/huskar-t/hivemq-tdengine-extension/blob/b62a26ecc164a310104df57691691b237e091c89/README_EN.md) for details on how to use it.
+[HiveMQ](https://www.hivemq.com/) is an MQTT broker that provides community and enterprise editions. HiveMQ is mainly for enterprise emerging machine-to-machine M2M communication and internal transport, meeting scalability, ease of management, and security features. HiveMQ provides an open-source plug-in development kit. MQTT data can be saved to TDengine via TDengine extension for HiveMQ. For more information, see [HiveMQ TDengine Extension](https://github.com/huskar-t/hivemq-tdengine-extension/blob/b62a26ecc164a310104df57691691b237e091c89/README_EN.md).
\ No newline at end of file

docs/en/20-third-party/12-google-data-studio.md

+---
+sidebar_label: Google Data Studio
+title: Use Google Data Studio to access TDengine
+---
+
+Data Studio is a powerful tool for reporting and visualization, offering a wide variety of charts and connectors and making it easy to generate reports based on predefined templates. Its ease of use and robust ecosystem have made it one of the first choices for people working in data analysis.
+
+TDengine is a high-performance, scalable time-series database that supports SQL. Many businesses and developers in fields spanning from IoT and Industry Internet to IT and finance are using TDengine as their time-series database management solution.
+
+The TDengine team immediately saw the benefits of using TDengine to process time-series data with Data Studio to analyze it, and they got to work to create a connector for Data Studio.
+
+With the release of the TDengine connector in Data Studio, you can now get even more out of your data. To obtain the connector, first go to the Data Studio Connector Gallery, click Connect to Data, and search for “TDengine”.
+
+![02](gds/gds-02.png.webp)
+
+Select the TDengine connector and click Authorize.
+
+![03](gds/gds-03.png.webp)
+
+Then sign in to your Google Account and click Allow to enable the connection to TDengine.
+
+![04](gds/gds-04.png.webp)
+
+In the Enter URL field, type the hostname and port of the server running the TDengine REST service. In the following fields, type your username, password, database name, table name, and the start and end times of your query range. Then, click Connect.
+
+![05](gds/gds-05.png.webp)
+
+After the connection is established, you can use Data Studio to process your data and create reports.
+
+![06](gds/gds-06.png.webp)
+
+In Data Studio, TDengine timestamps and tags are considered dimensions, and all other items are considered metrics. You can create all kinds of custom charts with your data – some examples are shown below.
+
+![07](gds/gds-07.png.webp)
+
+With the ability to process petabytes of data per day and provide monitoring and alerting in real time, TDengine is a great solution for time-series data management. Now, with the Data Studio connector, we’re sure you’ll be able to gain new insights and obtain even more value from your data.