diff --git a/cmake/cmake.platform b/cmake/cmake.platform
index 887fbd86d5..3aa1ffc07e 100644
--- a/cmake/cmake.platform
+++ b/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 ${CMAKE_SYSTEM_PROCESSOR} MATCHES "x86_64")
- MESSAGE("Current system arch is arm64")
+ MESSAGE("Current system arch is 64")
SET(TD_DARWIN_64 TRUE)
ADD_DEFINITIONS("-D_TD_DARWIN_64")
ENDIF ()
diff --git a/cmake/taostools_CMakeLists.txt.in b/cmake/taostools_CMakeLists.txt.in
index 9547323acf..c0de75c6dd 100644
--- a/cmake/taostools_CMakeLists.txt.in
+++ b/cmake/taostools_CMakeLists.txt.in
@@ -2,7 +2,7 @@
# taos-tools
ExternalProject_Add(taos-tools
GIT_REPOSITORY https://github.com/taosdata/taos-tools.git
- GIT_TAG e8bfca6
+ GIT_TAG aa45ad4
SOURCE_DIR "${TD_SOURCE_DIR}/tools/taos-tools"
BINARY_DIR ""
#BUILD_IN_SOURCE TRUE
diff --git a/docs/en/01-index.md b/docs/en/01-index.md
index 22e62bc5e0..5265be42f8 100644
--- a/docs/en/01-index.md
+++ b/docs/en/01-index.md
@@ -4,25 +4,24 @@ 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 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, 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, and make a few changes to accommodate your application, and it will work.
-We live in the era of big data, and scale-up is unable to meet the growing needs of business. Any modern data system must have the ability to scale out, and clustering has become an indispensable feature of big data systems. Not only did the TDengine team develop the cluster feature, but also decided to open source this important feature. To learn how to deploy, manage and maintain a TDengine cluster please refer to ["cluster deployment"](../deployment).
+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).
-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.
+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.
-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.
-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!
diff --git a/docs/en/02-intro/index.md b/docs/en/02-intro/index.md
index 5d21fbaf90..d385845d7c 100644
--- a/docs/en/02-intro/index.md
+++ b/docs/en/02-intro/index.md
@@ -3,7 +3,7 @@ title: Introduction
toc_max_heading_level: 2
---
-TDengine is an open source, high-performance, cloud native [time-series database](https://tdengine.com/tsdb/) optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. Its code, including its cluster feature is open source under GNU AGPL v3.0. Besides the database engine, it provides [caching](/develop/cache), [stream processing](/develop/stream), [data subscription](/develop/tmq) and other functionalities to reduce the system complexity and cost of development and operation.
+TDengine is an open source, high-performance, cloud native [time-series database](https://tdengine.com/tsdb/) optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. Its code, including its cluster feature is open source under GNU AGPL v3.0. Besides the database engine, it provides [caching](../develop/cache), [stream processing](../develop/stream), [data subscription](../develop/tmq) and other functionalities to reduce the system complexity and cost of development and operation.
This section introduces the major features, competitive advantages, typical use-cases and benchmarks to help you get a high level overview of TDengine.
@@ -12,34 +12,34 @@ This section introduces the major features, competitive advantages, typical use-
The major features are listed below:
1. Insert data
- * supports [using SQL to insert](/develop/insert-data/sql-writing).
- * supports [schemaless writing](/reference/schemaless/) just like NoSQL databases. It also supports standard protocols like [InfluxDB LINE](/develop/insert-data/influxdb-line)，[OpenTSDB Telnet](/develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](/develop/insert-data/opentsdb-json) among others.
- * supports seamless integration with third-party tools like [Telegraf](/third-party/telegraf/), [Prometheus](/third-party/prometheus/), [collectd](/third-party/collectd/), [StatsD](/third-party/statsd/), [TCollector](/third-party/tcollector/) and [icinga2/](/third-party/icinga2/), they can write data into TDengine with simple configuration and without a single line of code.
+ - Supports [using SQL to insert](../develop/insert-data/sql-writing).
+ - Supports [schemaless writing](../reference/schemaless/) just like NoSQL databases. It also supports standard protocols like [InfluxDB Line](../develop/insert-data/influxdb-line), [OpenTSDB Telnet](../develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](../develop/insert-data/opentsdb-json) among others.
+ - Supports seamless integration with third-party tools like [Telegraf](../third-party/telegraf/), [Prometheus](../third-party/prometheus/), [collectd](../third-party/collectd/), [StatsD](../third-party/statsd/), [TCollector](../third-party/tcollector/), [EMQX](../third-party/emq-broker), [HiveMQ](../third-party/hive-mq-broker), and [Icinga2](../third-party/icinga2/), they can write data into TDengine with simple configuration and without a single line of code.
2. Query data
- * supports standard [SQL](/taos-sql/), including nested query.
- * supports [time series specific functions](/taos-sql/function/#time-series-extensions) and [time series specific queries](/taos-sql/distinguished), like downsampling, interpolation, cumulated sum, time weighted average, state window, session window and many others.
- * supports [user defined functions](/taos-sql/udf).
-3. [Caching](/develop/cache/): TDengine always saves the last data point in cache, so Redis is not needed for time-series data processing.
-4. [Stream Processing](/develop/stream/): not only is the continuous query is supported, but TDengine also supports even driven stream processing, so Flink or spark is not needed for time-series daata processing.
-5. [Data Dubscription](/develop/tmq/): application can subscribe a table or a set of tables. API is the same as Kafka, but you can specify filter conditions.
+ - Supports standard [SQL](../taos-sql/), including nested query.
+ - Supports [time series specific functions](../taos-sql/function/#time-series-extensions) and [time series specific queries](../taos-sql/distinguished), like downsampling, interpolation, cumulated sum, time weighted average, state window, session window and many others.
+ - Supports [User Defined Functions (UDF)](../taos-sql/udf).
+3. [Caching](../develop/cache/): TDengine always saves the last data point in cache, so Redis is not needed for time-series data processing.
+4. [Stream Processing](../develop/stream/): Not only is the continuous query is supported, but TDengine also supports event driven stream processing, so Flink or Spark is not needed for time-series data processing.
+5. [Data Subscription](../develop/tmq/): Application can subscribe a table or a set of tables. API is the same as Kafka, but you can specify filter conditions.
6. Visualization
- * supports seamless integration with [Grafana](/third-party/grafana/) for visualization.
- * supports seamless integration with Google Data Studio.
+ - Supports seamless integration with [Grafana](../third-party/grafana/) for visualization.
+ - Supports seamless integration with Google Data Studio.
7. Cluster
- * supports [cluster](/deployment/) with the capability of increasing processing power by adding more nodes.
- * supports [deployment on Kubernetes](/deployment/k8s/)
- * supports high availability via data replication.
+ - Supports [cluster](../deployment/) with the capability of increasing processing power by adding more nodes.
+ - Supports [deployment on Kubernetes](../deployment/k8s/).
+ - Supports high availability via data replication.
8. Administration
- * provides [monitoring](/operation/monitor) on running instances of TDengine.
- * provides many ways to [import](/operation/import) and [export](/operation/export) data.
+ - Provides [monitoring](../operation/monitor) on running instances of TDengine.
+ - Provides many ways to [import](../operation/import) and [export](../operation/export) data.
9. Tools
- * provides an interactive [command-line interface](/reference/taos-shell) for management, maintenance and ad-hoc queries.
- * provides a tool [taosBenchmark](/reference/taosbenchmark/) for testing the performance of TDengine.
+ - Provides an interactive [Command-line Interface (CLI)](../reference/taos-shell) for management, maintenance and ad-hoc queries.
+ - Provides a tool [taosBenchmark](../reference/taosbenchmark/) for testing the performance of TDengine.
10. Programming
- * provides [connectors](/reference/connector/) for [C/C++](/reference/connector/cpp), [Java](/reference/connector/java), [Python](/reference/connector/python), [Go](/reference/connector/go), [Rust](/reference/connector/rust), [Node.js](/reference/connector/node) and other programming languages.
- * provides a [REST API](/reference/rest-api/).
+ - Provides [connectors](../reference/connector/) for [C/C++](../reference/connector/cpp), [Java](../reference/connector/java), [Python](../reference/connector/python), [Go](../reference/connector/go), [Rust](../reference/connector/rust), [Node.js](../reference/connector/node) and other programming languages.
+ - Provides a [REST API](../reference/rest-api/).
-For more details on features, please read through the entire documentation.
+For more details on features, please read through the entire documentation.
## Competitive Advantages
@@ -49,23 +49,31 @@ By making full use of [characteristics of time series data](https://tdengine.com
- **[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.
+- **[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://tdengine.com/tdengine/easy-time-series-data-platform/)**: For administrators, TDengine significantly reduces the effort to[
-](https://tdengine.com/tdengine/easy-time-series-data-platform/) 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.
+ ](https://tdengine.com/tdengine/easy-time-series-data-platform/) 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.
+- **[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 over 19k stars on GitHub. There is an active developer community, and over 140k running instances worldwide.
-With TDengine, the total cost of ownership of your time-series data platform can be greatly reduced. 1: With its superior performance, the computing and storage resources are reduced significantly；2: With SQL support, it can be seamlessly integrated with many third party tools, and learning costs/migration costs are reduced significantly；3: With its simplified solution and nearly zero management, the operation and maintenance costs are reduced significantly.
+With TDengine, the total cost of ownership of your time-series data platform can be greatly reduced.
+
+1. With its superior performance, the computing and storage resources are reduced significantly.
+2. With SQL support, it can be seamlessly integrated with many third party tools, and learning costs/migration costs are reduced significantly.
+3. With its simplified solution and nearly zero management, the operation and maintenance costs are reduced significantly.
## Technical Ecosystem
+
This is how TDengine would be situated, in a typical time-series data processing platform:
+
+
-
Figure 1. TDengine Technical Ecosystem
+
Figure 1. TDengine Technical Ecosystem
+
On the left-hand side, there are data collection agents like OPC-UA, MQTT, Telegraf and Kafka. On the right-hand side, visualization/BI tools, HMI, Python/R, and IoT Apps can be connected. TDengine itself provides an interactive command-line interface and a web interface for management and maintenance.
@@ -75,42 +83,42 @@ As a high-performance, scalable and SQL supported time-series database, TDengine
### Characteristics and Requirements of Data Sources
-| **Data Source Characteristics and Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
-| -------------------------------------------------------- | ------------------ | ----------------------- | ------------------- | :----------------------------------------------------------- |
-| A massive amount of total data | | | √ | TDengine provides excellent scale-out functions in terms of capacity, and has a storage structure with matching high compression ratio to achieve the best storage efficiency in the industry.|
-| Data input velocity is extremely high | | | √ | TDengine's performance is much higher than that of other similar products. It can continuously process larger amounts of input data in the same hardware environment, and provides a performance evaluation tool that can easily run in the user environment. |
-| A huge number of data sources | | | √ | TDengine is optimized specifically for a huge number of data sources. It is especially suitable for efficiently ingesting, writing and querying data from billions of data sources. |
+| **Data Source Characteristics and Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
+| ------------------------------------------------ | ------------------ | ----------------------- | ------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| A massive amount of total data | | | √ | TDengine provides excellent scale-out functions in terms of capacity, and has a storage structure with matching high compression ratio to achieve the best storage efficiency in the industry. |
+| Data input velocity is extremely high | | | √ | TDengine's performance is much higher than that of other similar products. It can continuously process larger amounts of input data in the same hardware environment, and provides a performance evaluation tool that can easily run in the user environment. |
+| A huge number of data sources | | | √ | TDengine is optimized specifically for a huge number of data sources. It is especially suitable for efficiently ingesting, writing and querying data from billions of data sources. |
### System Architecture Requirements
-| **System Architecture Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
-| ------------------------------------------------- | ------------------ | ----------------------- | ------------------- | ------------------------------------------------------------ |
+| **System Architecture Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
+| ----------------------------------------- | ------------------ | ----------------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| A simple and reliable system architecture | | | √ | TDengine's system architecture is very simple and reliable, with its own message queue, cache, stream computing, monitoring and other functions. There is no need to integrate any additional third-party products. |
-| Fault-tolerance and high-reliability | | | √ | TDengine has cluster functions to automatically provide high-reliability and high-availability functions such as fault tolerance and disaster recovery. |
-| Standardization support | | | √ | TDengine supports standard SQL and provides SQL extensions for time-series data analysis. |
+| Fault-tolerance and high-reliability | | | √ | TDengine has cluster functions to automatically provide high-reliability and high-availability functions such as fault tolerance and disaster recovery. |
+| Standardization support | | | √ | TDengine supports standard SQL and provides SQL extensions for time-series data analysis. |
### System Function Requirements
-| **System Function Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
-| ------------------------------------------------- | ------------------ | ----------------------- | ------------------- | ------------------------------------------------------------ |
-| Complete data processing algorithms built-in | | √ | | While TDengine implements various general data processing algorithms, industry specific algorithms and special types of processing will need to be implemented at the application level.|
-| A large number of crosstab queries | | √ | | This type of processing is better handled by general purpose relational database systems but TDengine can work in concert with relational database systems to provide more complete solutions. |
+| **System Function Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
+| -------------------------------------------- | ------------------ | ----------------------- | ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Complete data processing algorithms built-in | | √ | | While TDengine implements various general data processing algorithms, industry specific algorithms and special types of processing will need to be implemented at the application level. |
+| A large number of crosstab queries | | √ | | This type of processing is better handled by general purpose relational database systems but TDengine can work in concert with relational database systems to provide more complete solutions. |
### System Performance Requirements
-| **System Performance Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
-| ------------------------------------------------- | ------------------ | ----------------------- | ------------------- | ------------------------------------------------------------ |
-| Very large total processing capacity | | | √ | TDengine’s cluster functions can easily improve processing capacity via multi-server coordination. |
-| Extremely high-speed data processing | | | √ | TDengine’s storage and data processing are optimized for IoT, and can process data many times faster than similar products.|
-| Extremely fast processing of high resolution data | | | √ | TDengine has achieved the same or better performance than other relational and NoSQL data processing systems. |
+| **System Performance Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
+| ------------------------------------------------- | ------------------ | ----------------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------- |
+| Very large total processing capacity | | | √ | TDengine’s cluster functions can easily improve processing capacity via multi-server coordination. |
+| Extremely high-speed data processing | | | √ | TDengine’s storage and data processing are optimized for IoT, and can process data many times faster than similar products. |
+| Extremely fast processing of high resolution data | | | √ | TDengine has achieved the same or better performance than other relational and NoSQL data processing systems. |
### System Maintenance Requirements
-| **System Maintenance Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
-| ------------------------------------------------- | ------------------ | ----------------------- | ------------------- | ------------------------------------------------------------ |
-| Native high-reliability | | | √ | TDengine has a very robust, reliable and easily configurable system architecture to simplify routine operation. Human errors and accidents are eliminated to the greatest extent, with a streamlined experience for operators. |
-| Minimize learning and maintenance costs | | | √ | In addition to being easily configurable, standard SQL support and the TDengine CLI for ad hoc queries makes maintenance simpler, allows reuse and reduces learning costs.|
-| Abundant talent supply | √ | | | Given the above, and given the extensive training and professional services provided by TDengine, it is easy to migrate from existing solutions or create a new and lasting solution based on TDengine.|
+| **System Maintenance Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
+| --------------------------------------- | ------------------ | ----------------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| Native high-reliability | | | √ | TDengine has a very robust, reliable and easily configurable system architecture to simplify routine operation. Human errors and accidents are eliminated to the greatest extent, with a streamlined experience for operators. |
+| Minimize learning and maintenance costs | | | √ | In addition to being easily configurable, standard SQL support and the TDengine CLI for ad hoc queries makes maintenance simpler, allows reuse and reduces learning costs. |
+| Abundant talent supply | √ | | | Given the above, and given the extensive training and professional services provided by TDengine, it is easy to migrate from existing solutions or create a new and lasting solution based on TDengine. |
## Comparison with other databases
diff --git a/docs/en/04-concept/index.md b/docs/en/04-concept/index.md
index 5a9c55fdd6..b0a0c25d85 100644
--- a/docs/en/04-concept/index.md
+++ b/docs/en/04-concept/index.md
@@ -162,7 +162,7 @@ To better understand the data model using metri, tags, super table and subtable,
## 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.
diff --git a/docs/en/07-develop/01-connect/index.md b/docs/en/07-develop/01-connect/index.md
index 2053706421..901fe69d24 100644
--- a/docs/en/07-develop/01-connect/index.md
+++ b/docs/en/07-develop/01-connect/index.md
@@ -279,6 +279,6 @@ Prior to establishing connection, please make sure TDengine is already running a
:::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).
:::
diff --git a/docs/en/07-develop/03-insert-data/05-high-volume.md b/docs/en/07-develop/03-insert-data/05-high-volume.md
index 8163ae03b2..9ea0c88447 100644
--- a/docs/en/07-develop/03-insert-data/05-high-volume.md
+++ b/docs/en/07-develop/03-insert-data/05-high-volume.md
@@ -16,7 +16,7 @@ To achieve high performance writing, there are a few aspects to consider. In the
From the perspective of application program, you need to consider:
-1. The data size of each single write, also known as batch size. Generally speaking, higher batch size generates better writing performance. However, once the batch size is over a specific value, you will not get any additional benefit anymore. When using SQL to write into TDengine, it's better to put as much as possible data in single SQL. The maximum SQL length supported by TDengine is 1,048,576 bytes, i.e. 1 MB. It can be configured by parameter `maxSQLLength` on client side, and the default value is 65,480.
+1. The data size of each single write, also known as batch size. Generally speaking, higher batch size generates better writing performance. However, once the batch size is over a specific value, you will not get any additional benefit anymore. When using SQL to write into TDengine, it's better to put as much as possible data in single SQL. The maximum SQL length supported by TDengine is 1,048,576 bytes, i.e. 1 MB.
2. The number of concurrent connections. Normally more connections can get better result. However, once the number of connections exceeds the processing ability of the server side, the performance may downgrade.
@@ -46,12 +46,9 @@ If the data source is Kafka, then the appication program is a consumer of Kafka,
### Tune TDengine
-TDengine is a distributed and high performance time series database, there are also some ways to tune TDengine to get better writing performance.
+On the server side, database configuration parameter `vgroups` needs to be set carefully to maximize the system performance. If it's set too low, the system capability can't be utilized fully; if it's set too big, unnecessary resource competition may be produced. A normal recommendation for `vgroups` parameter is 2 times of the number of CPU cores. However, depending on the actual system resources, it may still need to tuned.
-1. Set proper number of `vgroups` according to available CPU cores. Normally, we recommend 2 \* number_of_cores as a starting point. If the verification result shows this is not enough to utilize CPU resources, you can use a higher value.
-2. Set proper `minTablesPerVnode`, `tableIncStepPerVnode`, and `maxVgroupsPerDb` according to the number of tables so that tables are distributed even across vgroups. The purpose is to balance the workload among all vnodes so that system resources can be utilized better to get higher performance.
-
-For more performance tuning parameters, please refer to [Configuration Parameters](../../../reference/config).
+For more configuration parameters, please refer to [Database Configuration](../../../taos-sql/database) and [Server Configuration](../../../reference/config)。
## Sample Programs
@@ -359,7 +356,7 @@ Writing process tries to read as much as possible data from message queue and wr
-SQLWriter class encapsulates the logic of composing SQL and writing data. Please be noted that the tables have not been created before writing, but are created automatically when catching the exception of table doesn't exist. For other exceptions caught, the SQL which caused the exception are logged for you to debug. This class also checks the SQL length, if the SQL length is closed to `maxSQLLength` the SQL will be executed immediately. To improve writing efficiency, it's better to increase `maxSQLLength` properly.
+SQLWriter class encapsulates the logic of composing SQL and writing data. Please be noted that the tables have not been created before writing, but are created automatically when catching the exception of table doesn't exist. For other exceptions caught, the SQL which caused the exception are logged for you to debug. This class also checks the SQL length, and passes the maximum SQL length by parameter maxSQLLength according to actual TDengine limit.
SQLWriter
diff --git a/docs/en/10-deployment/01-deploy.md b/docs/en/10-deployment/01-deploy.md
index a445b684dc..5dfcd3108d 100644
--- a/docs/en/10-deployment/01-deploy.md
+++ b/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;
diff --git a/docs/en/10-deployment/05-helm.md b/docs/en/10-deployment/05-helm.md
index 302730f1b5..a4fa681000 100644
--- a/docs/en/10-deployment/05-helm.md
+++ b/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.
diff --git a/docs/en/12-taos-sql/25-grant.md b/docs/en/12-taos-sql/25-grant.md
index 37438ee780..b9a3fa2321 100644
--- a/docs/en/12-taos-sql/25-grant.md
+++ b/docs/en/12-taos-sql/25-grant.md
@@ -1,6 +1,7 @@
---
-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.
diff --git a/docs/en/12-taos-sql/index.md b/docs/en/12-taos-sql/index.md
index e243cd2318..a5ffc9dc8d 100644
--- a/docs/en/12-taos-sql/index.md
+++ b/docs/en/12-taos-sql/index.md
@@ -1,6 +1,6 @@
---
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;
diff --git a/docs/en/14-reference/02-rest-api/02-rest-api.mdx b/docs/en/14-reference/02-rest-api/02-rest-api.mdx
index 74ba78b7fc..ce28ee87d9 100644
--- a/docs/en/14-reference/02-rest-api/02-rest-api.mdx
+++ b/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.
diff --git a/docs/en/14-reference/03-connector/04-java.mdx b/docs/en/14-reference/03-connector/04-java.mdx
index 0f977393f1..129d90ea85 100644
--- a/docs/en/14-reference/03-connector/04-java.mdx
+++ b/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:
diff --git a/docs/en/14-reference/03-connector/09-csharp.mdx b/docs/en/14-reference/03-connector/09-csharp.mdx
index c745b8dd1a..823e907599 100644
--- a/docs/en/14-reference/03-connector/09-csharp.mdx
+++ b/docs/en/14-reference/03-connector/09-csharp.mdx
@@ -172,7 +172,6 @@ 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:
-* Usage notes:
## Frequently Asked Questions
diff --git a/docs/en/14-reference/03-connector/_preparation.mdx b/docs/en/14-reference/03-connector/_preparation.mdx
index 07ebdbca3d..c6e42ce023 100644
--- a/docs/en/14-reference/03-connector/_preparation.mdx
+++ b/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.
diff --git a/docs/en/14-reference/06-taosdump.md b/docs/en/14-reference/06-taosdump.md
index 2105ba83fa..e73441a96b 100644
--- a/docs/en/14-reference/06-taosdump.md
+++ b/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 .
```
diff --git a/docs/en/14-reference/07-tdinsight/index.md b/docs/en/14-reference/07-tdinsight/index.md
index e74c9de7b2..2e56203525 100644
--- a/docs/en/14-reference/07-tdinsight/index.md
+++ b/docs/en/14-reference/07-tdinsight/index.md
@@ -263,7 +263,7 @@ Once the import is complete, the full page view of TDinsight is shown below.
## 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 [dnodes, mnodes, vnodes](../../taos-sql/node/) or databases.
Details of the metrics are as follows.
diff --git a/docs/en/14-reference/11-docker/index.md b/docs/en/14-reference/11-docker/index.md
index be1d72ff9c..7cd1e810dc 100644
--- a/docs/en/14-reference/11-docker/index.md
+++ b/docs/en/14-reference/11-docker/index.md
@@ -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 \
diff --git a/docs/en/14-reference/13-schemaless/13-schemaless.md b/docs/en/14-reference/13-schemaless/13-schemaless.md
index 816ebe0047..4f50c38cbb 100644
--- a/docs/en/14-reference/13-schemaless/13-schemaless.md
+++ b/docs/en/14-reference/13-schemaless/13-schemaless.md
@@ -1,6 +1,6 @@
---
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. 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
@@ -25,7 +25,7 @@ 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 tags, with format like `=,=` Enter a space between `tag_set` and `field_set`.
- `field_set`will be used as data columns, with format like `=,=` Enter a space between `field_set` and `timestamp`.
-- `timestamp` is the primary key timestamp corresponding to this row of data
+- `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 (").
@@ -36,14 +36,14 @@ 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.
@@ -61,7 +61,7 @@ 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"
@@ -82,7 +82,7 @@ You can configure smlChildTableName to specify table names, for example, `smlChi
:::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
-16KB. See [TAOS SQL Boundary Limits](/taos-sql/limit) for specific constraints in this area.
+16KB. See [TDengine SQL Boundary Limits](/taos-sql/limit) for specific constraints in this area.
:::
@@ -90,23 +90,23 @@ All processing logic of schemaless will still follow TDengine's underlying restr
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 file protocol |
-| 3 | SML_JSON_PROTOCOL | OpenTSDB JSON protocol |
+| **Serial** | **Value** | **Description** |
+| ---------- | ------------------- | ---------------------- |
+| 1 | SML_LINE_PROTOCOL | InfluxDB Line Protocol |
+| 2 | SML_TELNET_PROTOCOL | OpenTSDB file protocol |
+| 3 | SML_JSON_PROTOCOL | OpenTSDB JSON protocol |
In InfluxDB line protocol mode, you must specify the precision of the input timestamp. Valid precisions are described in the following table.
-| **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 |
+| **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 |
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.
diff --git a/docs/en/14-reference/14-taosKeeper.md b/docs/en/14-reference/14-taosKeeper.md
index 476b5a1fd2..665bc75380 100644
--- a/docs/en/14-reference/14-taosKeeper.md
+++ b/docs/en/14-reference/14-taosKeeper.md
@@ -1,7 +1,7 @@
---
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. 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).
-
+### 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
+$ taoskeeper -c
```
**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):
diff --git a/docs/en/20-third-party/09-emq-broker.md b/docs/en/20-third-party/09-emq-broker.md
index 0900dd3d75..2ead1bbaf4 100644
--- a/docs/en/20-third-party/09-emq-broker.md
+++ b/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
-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.
diff --git a/docs/en/20-third-party/12-google-data-studio.md b/docs/en/20-third-party/12-google-data-studio.md
new file mode 100644
index 0000000000..fc94f98056
--- /dev/null
+++ b/docs/en/20-third-party/12-google-data-studio.md
@@ -0,0 +1,36 @@
+---
+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”.
+
+
+
+Select the TDengine connector and click Authorize.
+
+
+
+Then sign in to your Google Account and click Allow to enable the connection to TDengine.
+
+
+
+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.
+
+
+
+After the connection is established, you can use Data Studio to process your data and create reports.
+
+
+
+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.
+
+
+
+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.
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diff --git a/docs/en/21-tdinternal/01-arch.md b/docs/en/21-tdinternal/01-arch.md
index 44651c0496..2f876adffc 100644
--- a/docs/en/21-tdinternal/01-arch.md
+++ b/docs/en/21-tdinternal/01-arch.md
@@ -12,6 +12,7 @@ The design of TDengine is based on the assumption that any hardware or software
Logical structure diagram of TDengine's distributed architecture is as follows:
+
Figure 1: TDengine architecture diagram
A complete TDengine system runs on one or more physical nodes. Logically, it includes data node (dnode), TDengine client driver (TAOSC) and application (app). There are one or more data nodes in the system, which form a cluster. The application interacts with the TDengine cluster through TAOSC's API. The following is a brief introduction to each logical unit.
@@ -38,15 +39,16 @@ A complete TDengine system runs on one or more physical nodes. Logically, it inc
**Cluster external connection**: TDengine cluster can accommodate a single, multiple or even thousands of data nodes. The application only needs to initiate a connection to any data node in the cluster. The network parameter required for connection is the End Point (FQDN plus configured port number) of a data node. When starting the application taos through CLI, the FQDN of the data node can be specified through the option `-h`, and the configured port number can be specified through `-p`. If the port is not configured, the system configuration parameter “serverPort” of TDengine will be adopted.
-**Inter-cluster communication**: Data nodes connect with each other through TCP/UDP. When a data node starts, it will obtain the EP information of the dnode where the mnode is located, and then establish a connection with the mnode in the system to exchange information. There are three steps to obtain EP information of the mnode:
+**Inter-cluster communication**: Data nodes connect with each other through TCP/UDP. When a data node starts, it will obtain the EP information of the dnode where the mnode is located, and then establish a connection with the mnode in the system to exchange information. There are three steps to obtain EP information of the mnode:
-1. Check whether the mnodeEpList file exists, if it does not exist or cannot be opened normally to obtain EP information of the mnode, skip to the second step;
+1. Check whether the mnodeEpList file exists, if it does not exist or cannot be opened normally to obtain EP information of the mnode, skip to the second step;
2. Check the system configuration file taos.cfg to obtain node configuration parameters “firstEp” and “secondEp” (the node specified by these two parameters can be a normal node without mnode, in this case, the node will try to redirect to the mnode node when connected). If these two configuration parameters do not exist or do not exist in taos.cfg, or are invalid, skip to the third step;
3. Set your own EP as a mnode EP and run it independently. After obtaining the mnode EP list, the data node initiates the connection. It will successfully join the working cluster after connection. If not successful, it will try the next item in the mnode EP list. If all attempts are made, but the connection still fails, sleep for a few seconds before trying again.
**The choice of MNODE**: TDengine logically has a management node, but there is no separate execution code. The server-side only has one set of execution code, taosd. So which data node will be the management node? This is determined automatically by the system without any manual intervention. The principle is as follows: when a data node starts, it will check its End Point and compare it with the obtained mnode EP List. If its EP exists in it, the data node shall start the mnode module and become a mnode. If your own EP is not in the mnode EP List, the mnode module will not start. During the system operation, due to load balancing, downtime and other reasons, mnode may migrate to the new dnode, totally transparently and without manual intervention. The modification of configuration parameters is the decision made by mnode itself according to resources usage.
-**Add new data nodes:** After the system has a data node, it has become a working system. There are two steps to add a new node into the cluster.
+**Add new data nodes:** After the system has a data node, it has become a working system. There are two steps to add a new node into the cluster.
+
- Step1: Connect to the existing working data node using TDengine CLI, and then add the End Point of the new data node with the command "create dnode"
- Step 2: In the system configuration parameter file taos.cfg of the new data node, set the “firstEp” and “secondEp” parameters to the EP of any two data nodes in the existing cluster. Please refer to the user tutorial for detailed steps. In this way, the cluster will be established step by step.
@@ -57,6 +59,7 @@ A complete TDengine system runs on one or more physical nodes. Logically, it inc
To explain the relationship between vnode, mnode, TAOSC and application and their respective roles, the following is an analysis of a typical data writing process.
+
Figure 2: Typical process of TDengine
1. Application initiates a request to insert data through JDBC, ODBC, or other APIs.
@@ -121,16 +124,17 @@ The load balancing process does not require any manual intervention, and it is t
If a database has N replicas, a virtual node group has N virtual nodes. But only one is the Leader and all others are slaves. When the application writes a new record to system, only the Leader vnode can accept the writing request. If a follower vnode receives a writing request, the system will notifies TAOSC to redirect.
-### Leader vnode Writing Process
+### Leader vnode Writing Process
Leader Vnode uses a writing process as follows:
+
Figure 3: TDengine Leader writing process
1. Leader vnode receives the application data insertion request, verifies, and moves to next step;
2. If the system configuration parameter `“walLevel”` is greater than 0, vnode will write the original request packet into database log file WAL. If walLevel is set to 2 and fsync is set to 0, TDengine will make WAL data written immediately to ensure that even system goes down, all data can be recovered from database log file;
-3. If there are multiple replicas, vnode will forward data packet to follower vnodes in the same virtual node group, and the forwarded packet has a version number with data;
+3. If there are multiple replicas, vnode will forward data packet to follower vnodes in the same virtual node group, and the forwarded packet has a version number with data;
4. Write into memory and add the record to “skip list”;
5. Leader vnode returns a confirmation message to the application, indicating a successful write.
6. If any of Step 2, 3 or 4 fails, the error will directly return to the application.
@@ -140,6 +144,7 @@ Leader Vnode uses a writing process as follows:
For a follower vnode, the write process as follows:
+
Figure 4: TDengine Follower Writing Process
1. Follower vnode receives a data insertion request forwarded by Leader vnode;
@@ -212,6 +217,7 @@ When data is written to disk, the system decideswhether to compress the data bas
By default, TDengine saves all data in /var/lib/taos directory, and the data files of each vnode are saved in a different directory under this directory. In order to expand the storage space, minimize the bottleneck of file reading and improve the data throughput rate, TDengine can configure the system parameter “dataDir” to allow multiple mounted hard disks to be used by system at the same time. In addition, TDengine also provides the function of tiered data storage, i.e. storage on different storage media according to the time stamps of data files. For example, the latest data is stored on SSD, the data older than a week is stored on local hard disk, and data older than four weeks is stored on network storage device. This reduces storage costs and ensures efficient data access. The movement of data on different storage media is automatically done by the system and is completely transparent to applications. Tiered storage of data is also configured through the system parameter “dataDir”.
dataDir format is as follows:
+
```
dataDir data_path [tier_level]
```
@@ -270,6 +276,7 @@ For the data collected by device D1001, the number of records per hour is counte
TDengine creates a separate table for each data collection point, but in practical applications, it is often necessary to aggregate data from different data collection points. In order to perform aggregation operations efficiently, TDengine introduces the concept of STable (super table). STable is used to represent a specific type of data collection point. It is a table set containing multiple tables. The schema of each table in the set is the same, but each table has its own static tag. There can be multiple tags which can be added, deleted and modified at any time. Applications can aggregate or statistically operate on all or a subset of tables under a STABLE by specifying tag filters. This greatly simplifies the development of applications. The process is shown in the following figure:
+
Figure 5: Diagram of multi-table aggregation query
1. Application sends a query condition to system;
@@ -279,9 +286,8 @@ TDengine creates a separate table for each data collection point, but in practic
5. Each vnode first finds the set of tables within its own node that meet the tag filters from memory, then scans the stored time-series data, completes corresponding aggregation calculations, and returns result to TAOSC;
6. TAOSC finally aggregates the results returned by multiple data nodes and send them back to application.
-Since TDengine stores tag data and time-series data separately in vnode, by filtering tag data in memory, the set of tables that need to participate in aggregation operation is first found, which reduces the volume of data to be scanned and improves aggregation speed. At the same time, because the data is distributed in multiple vnodes/dnodes, the aggregation operation is carried out concurrently in multiple vnodes, which further improves the aggregation speed. Aggregation functions for ordinary tables and most operations are applicable to STables. The syntax is exactly the same. Please see TAOS SQL for details.
+Since TDengine stores tag data and time-series data separately in vnode, by filtering tag data in memory, the set of tables that need to participate in aggregation operation is first found, which reduces the volume of data to be scanned and improves aggregation speed. At the same time, because the data is distributed in multiple vnodes/dnodes, the aggregation operation is carried out concurrently in multiple vnodes, which further improves the aggregation speed. Aggregation functions for ordinary tables and most operations are applicable to STables. The syntax is exactly the same. Please see TDengine SQL for details.
### Precomputation
In order to effectively improve the performance of query processing, based-on the unchangeable feature of IoT data, statistical information of data stored in data block is recorded in the head of data block, including max value, min value, and sum. We call it a precomputing unit. If the query processing involves all the data of a whole data block, the pre-calculated results are directly used, and no need to read the data block contents at all. Since the amount of pre-calculated data is much smaller than the actual size of data block stored on disk, for query processing with disk IO as bottleneck, the use of pre-calculated results can greatly reduce the pressure of reading IO and accelerate the query process. The precomputation mechanism is similar to the BRIN (Block Range Index) of PostgreSQL.
-
diff --git a/docs/en/25-application/03-immigrate.md b/docs/en/25-application/03-immigrate.md
index 9614574c71..1aabaa43e7 100644
--- a/docs/en/25-application/03-immigrate.md
+++ b/docs/en/25-application/03-immigrate.md
@@ -41,7 +41,7 @@ The agents deployed in the application nodes are responsible for providing opera
- **TDengine installation and deployment**
-First of all, please install TDengine. Download the latest stable version of TDengine from the official website and install it. For help with using various installation packages, please refer to the blog ["Installation and Uninstallation of TDengine Multiple Installation Packages"](https://www.taosdata.com/blog/2019/08/09/566.html).
+First of all, please install TDengine. Download the latest stable version of TDengine from the official website and install it. For help with using various installation packages, please refer to [Install TDengine](../../get-started/package)
Note that once the installation is complete, do not start the `taosd` service before properly configuring the parameters.
@@ -51,7 +51,7 @@ TDengine version 2.4 and later version includes `taosAdapter`. taosAdapter is a
Users can flexibly deploy taosAdapter instances, based on their requirements, to improve data writing throughput and provide guarantees for data writes in different application scenarios.
-Through taosAdapter, users can directly write the data collected by `collectd` or `StatsD` to TDengine to achieve easy, convenient and seamless migration in application scenarios. taosAdapter also supports Telegraf, Icinga, TCollector, and node_exporter data. For more details, please refer to [taosAdapter](/reference/taosadapter/).
+Through taosAdapter, users can directly write the data collected by `collectd` or `StatsD` to TDengine to achieve easy, convenient and seamless migration in application scenarios. taosAdapter also supports Telegraf, Icinga, TCollector, and node_exporter data. For more details, please refer to [taosAdapter](../../reference/taosadapter/).
If using collectd, modify the configuration file in its default location `/etc/collectd/collectd.conf` to point to the IP address and port of the node where to deploy taosAdapter. For example, assuming the taosAdapter IP address is 192.168.1.130 and port 6046, configure it as follows.
@@ -411,7 +411,7 @@ TDengine provides a wealth of help documents to explain many aspects of cluster
### Cluster Deployment
-The first is TDengine installation. Download the latest stable version of TDengine from the official website, and install it. Please refer to the blog ["Installation and Uninstallation of Various Installation Packages of TDengine"](https://www.taosdata.com/blog/2019/08/09/566.html) for the various installation package formats.
+The first is TDengine installation. Download the latest stable version of TDengine from the official website, and install it. Please refer to [Install TDengine](../../get-started/package) for more details.
Note that once the installation is complete, do not immediately start the `taosd` service, but start it after correctly configuring the parameters.
diff --git a/docs/zh/01-index.md b/docs/zh/01-index.md
index 79d5424ac2..f9127121f3 100644
--- a/docs/zh/01-index.md
+++ b/docs/zh/01-index.md
@@ -4,22 +4,22 @@ sidebar_label: 文档首页
slug: /
---
-TDengine是一款[开源](https://www.taosdata.com/tdengine/open_source_time-series_database)、[高性能](https://www.taosdata.com/fast)、[云原生](https://www.taosdata.com/tdengine/cloud_native_time-series_database)的时序数据库（Time Series Database, TSDB）, 它专为物联网、工业互联网、金融等场景优化设计。同时它还带有内建的缓存、流式计算、数据订阅等系统功能，能大幅减少系统设计的复杂度，降低研发和运营成本，是一极简的时序数据处理平台。本文档是 TDengine 用户手册，主要是介绍 TDengine 的基本概念、安装、使用、功能、开发接口、运营维护、TDengine 内核设计等等，它主要是面向架构师、开发者与系统管理员的。
+TDengine 是一款[开源](https://www.taosdata.com/tdengine/open_source_time-series_database)、[高性能](https://www.taosdata.com/fast)、[云原生](https://www.taosdata.com/tdengine/cloud_native_time-series_database)的时序数据库（Time Series Database, TSDB）, 它专为物联网、车联网、工业互联网、金融、IT 运维等场景优化设计。同时它还带有内建的缓存、流式计算、数据订阅等系统功能，能大幅减少系统设计的复杂度，降低研发和运营成本，是一款极简的时序数据处理平台。本文档是 TDengine 的用户手册，主要是介绍 TDengine 的基本概念、安装、使用、功能、开发接口、运营维护、TDengine 内核设计等等，它主要是面向架构师、开发工程师与系统管理员的。
-TDengine 充分利用了时序数据的特点，提出了“一个数据采集点一张表”与“超级表”的概念，设计了创新的存储引擎，让数据的写入、查询和存储效率都得到极大的提升。为正确理解并使用TDengine, 无论如何，请您仔细阅读[基本概念](./concept)一章。
+TDengine 充分利用了时序数据的特点，提出了“一个数据采集点一张表”与“超级表”的概念，设计了创新的存储引擎，让数据的写入、查询和存储效率都得到极大的提升。为正确理解并使用 TDengine，无论如何，请您仔细阅读[基本概念](./concept)一章。
-如果你是开发者，请一定仔细阅读[开发指南](./develop)一章，该部分对数据库连接、建模、插入数据、查询、流式计算、缓存、数据订阅、用户自定义函数等功能都做了详细介绍，并配有各种编程语言的示例代码。大部分情况下，你只要把示例代码拷贝粘贴，针对自己的应用稍作改动，就能跑起来。
+如果你是开发工程师，请一定仔细阅读[开发指南](./develop)一章，该部分对数据库连接、建模、插入数据、查询、流式计算、缓存、数据订阅、用户自定义函数等功能都做了详细介绍，并配有各种编程语言的示例代码。大部分情况下，你只要复制粘贴示例代码，针对自己的应用稍作改动，就能跑起来。
-我们已经生活在大数据的时代，纵向扩展已经无法满足日益增长的业务需求，任何系统都必须具有水平扩展的能力，集群成为大数据以及 database 系统的不可缺失功能。TDengine 团队不仅实现了集群功能，而且将这一重要核心功能开源。怎么部署、管理和维护 TDengine 集群，请参考[部署集群](./deployment)一章。
+我们已经生活在大数据时代，纵向扩展已经无法满足日益增长的业务需求，任何系统都必须具有水平扩展的能力，集群成为大数据以及 Database 系统的不可缺失功能。TDengine 团队不仅实现了集群功能，而且将这一重要核心功能开源。怎么部署、管理和维护 TDengine 集群，请仔细参考[部署集群](./deployment)一章。
-TDengine 采用 SQL 作为其查询语言，大大降低学习成本、降低迁移成本，但同时针对时序数据场景，又做了一些扩展，以支持插值、降采样、时间加权平均等操作。[SQL 手册](./taos-sql)一章详细描述了 SQL 语法、详细列出了各种支持的命令和函数。
+TDengine 采用 SQL 作为查询语言，大大降低学习成本、降低迁移成本，但同时针对时序数据场景，又做了一些扩展，以支持插值、降采样、时间加权平均等操作。[SQL 手册](./taos-sql)一章详细描述了 SQL 语法、详细列出了各种支持的命令和函数。
-如果你是系统管理员，关心安装、升级、容错灾备、关心数据导入、导出，配置参数，怎么监测 TDengine 是否健康运行，怎么提升系统运行的性能，那么请仔细参考[运维指南](./operation)一章。
+如果你是系统管理员，关心安装、升级、容错灾备、关心数据导入、导出、配置参数，如何监测 TDengine 是否健康运行，如何提升系统运行的性能，请仔细参考[运维指南](./operation)一章。
-如果你对 TDengine 外围工具，REST API, 各种编程语言的连接器想做更多详细了解，请看[参考指南](./reference)一章。
+如果你对 TDengine 的外围工具、REST API、各种编程语言的连接器（Connector）想做更多详细了解，请看[参考指南](./reference)一章。
-如果你对 TDengine 内部的架构设计很有兴趣，欢迎仔细阅读[技术内幕](./tdinternal)一章，里面对集群的设计、数据分区、分片、写入、读出、查询、聚合查询的流程都做了详细的介绍。如果你想研读 TDengine 代码甚至贡献代码，请一定仔细读完这一章。
+如果你对 TDengine 的内部架构设计很有兴趣，欢迎仔细阅读[技术内幕](./tdinternal)一章，里面对集群的设计、数据分区、分片、写入、读出、查询、聚合查询的流程都做了详细的介绍。如果你想研读 TDengine 代码甚至贡献代码，请一定仔细读完这一章。
-最后，作为一个开源软件，欢迎大家的参与。如果发现文档的任何错误，描述不清晰的地方，都请在每个页面的最下方，点击“编辑本文档“直接进行修改。
+最后，作为一个开源软件，欢迎大家的参与。如果发现文档有任何错误、描述不清晰的地方，请在每个页面的最下方，点击“编辑本文档”直接进行修改。
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diff --git a/docs/zh/02-intro.md b/docs/zh/02-intro.md
index f6779b8776..012c49d2c3 100644
--- a/docs/zh/02-intro.md
+++ b/docs/zh/02-intro.md
@@ -4,72 +4,95 @@ description: 简要介绍 TDengine 的主要功能
toc_max_heading_level: 2
---
-TDengine 是一款[开源](https://www.taosdata.com/tdengine/open_source_time-series_database)、[高性能](https://www.taosdata.com/tdengine/fast)、[云原生](https://www.taosdata.com/tdengine/cloud_native_time-series_database)的时序数据库（Time Series Database, TSDB）。TDengine 能被广泛运用于物联网、工业互联网、车联网、IT 运维、金融等领域。除核心的时序数据库功能外，TDengine 还提供[缓存](../develop/cache/)、[数据订阅](../develop/tmq)、[流式计算](../develop/stream)等功能，是一极简的时序数据处理平台，最大程度的减小系统设计的复杂度，降低研发和运营成本。
+TDengine 是一款开源、高性能、云原生的[时序数据库](https://tdengine.com/tsdb/)，且针对物联网、车联网、工业互联网、金融、IT 运维等场景进行了优化。TDengine 的代码，包括集群功能，都在 GNU AGPL v3.0 下开源。除核心的时序数据库功能外，TDengine 还提供[缓存](../develop/cache/)、[数据订阅](../develop/tmq)、[流式计算](../develop/stream)等其它功能以降低系统复杂度及研发和运维成本。
-本章节介绍TDengine的主要功能、竞争优势、适用场景、与其他数据库的对比测试等等，让大家对TDengine有个整体的了解。
+本章节介绍 TDengine 的主要功能、竞争优势、适用场景、与其他数据库的对比测试等等，让大家对 TDengine 有个整体的了解。
## 主要功能
-TDengine的主要功能如下：
+TDengine 的主要功能如下：
-1. 高速数据写入，除 [SQL 写入](../develop/insert-data/sql-writing)外，还支持 [Schemaless 写入](../reference/schemaless/)，支持 [InfluxDB LINE 协议](../develop/insert-data/influxdb-line)，[OpenTSDB Telnet](../develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](../develop/insert-data/opentsdb-json)等协议写入；
-2. 第三方数据采集工具 [Telegraf](../third-party/telegraf)，[Prometheus](../third-party/prometheus)，[StatsD](../third-party/statsd)，[collectd](../third-party/collectd)，[icinga2](../third-party/icinga2), [TCollector](../third-party/tcollector), [EMQ](../third-party/emq-broker), [HiveMQ](../third-party/hive-mq-broker) 等都可以进行配置后，不用任何代码，即可将数据写入；
-3. 支持[各种查询](../develop/query-data),包括聚合查询、嵌套查询、降采样查询、插值等
-4. 支持[用户自定义函数](../develop/udf)
-5. 支持[缓存](../develop/cache)，将每张表的最后一条记录缓存起来，这样无需 Redis
-6. 支持[流式计算](../develop/stream)(Stream Processing)
-7. 支持[数据订阅](../develop/tmq)，而且可以指定过滤条件
-8. 支持[集群](../deployment/)，可以通过多节点进行水平扩展，并通过多副本实现高可靠
-9. 提供[命令行程序](../reference/taos-shell)，便于管理集群，检查系统状态，做即席查询
-10. 提供多种数据的[导入](../operation/import)、[导出](../operation/export)
-11. 支持对[TDengine 集群本身的监控](../operation/monitor)
-12. 提供各种语言的[连接器](../connector): 如 C/C++, Java, Go, Node.JS, Rust, Python, C# 等
-13. 支持 [REST 接口](../connector/rest-api/)
-14. 支持与[ Grafana 无缝集成](../third-party/grafana)
-15. 支持与 Google Data Studio 无缝集成
-16. 支持 [Kubernetes 部署](../deployment/k8s)
+1. 写入数据，支持
+ - [SQL 写入](../develop/insert-data/sql-writing)
+ - [无模式（Schemaless）写入](../reference/schemaless/)，支持多种标准写入协议
+ - [InfluxDB Line 协议](../develop/insert-data/influxdb-line)
+ - [OpenTSDB Telnet 协议](../develop/insert-data/opentsdb-telnet)
+ - [OpenTSDB JSON 协议](../develop/insert-data/opentsdb-json)
+ - 与多种第三方工具的无缝集成，它们都可以仅通过配置而无需任何代码即可将数据写入 TDengine
+ - [Telegraf](../third-party/telegraf)
+ - [Prometheus](../third-party/prometheus)
+ - [StatsD](../third-party/statsd)
+ - [collectd](../third-party/collectd)
+ - [Icinga2](../third-party/icinga2)
+ - [TCollector](../third-party/tcollector)
+ - [EMQX](../third-party/emq-broker)
+ - [HiveMQ](../third-party/hive-mq-broker) ；
+2. 查询数据，支持
+ - [标准 SQL](../taos-sql)，含嵌套查询
+ - [时序数据特色函数](../taos-sql/function/#time-series-extensions)
+ - [时序数据特色查询](../taos-sql/distinguished)，例如降采样、插值、累加和、时间加权平均、状态窗口、会话窗口等
+ - [用户自定义函数（UDF）](../taos-sql/udf)
+3. [缓存](../develop/cache)，将每张表的最后一条记录缓存起来，这样无需 Redis 就能对时序数据进行高效处理
+4. [流式计算（Stream Processing）](../develop/stream)，TDengine 不仅支持连续查询，还支持事件驱动的流式计算，这样在处理时序数据时就无需 Flink 或 Spark 这样流式计算组件
+5. [数据订阅](../develop/tmq)，应用程序可以订阅一张表或一组表的数据，提供与 Kafka 相同的 API，而且可以指定过滤条件
+6. 可视化
+ - 支持与 [Grafana](../third-party/grafana/) 的无缝集成
+ - 支持与 Google Data Studio 的无缝集成
+7. 集群
+ - [集群部署](../deployment/)，可以通过增加节点进行水平扩展以提升处理能力
+ - 可以通过 [Kubernetes 部署 TDengine](../deployment/k8s/)
+ - 通过多副本提供高可用能力
+8. 管理
+ - [监控](../operation/monitor)运行中的 TDengine 实例
+ - 多种[数据导入](../operation/import)方式
+ - 多种[数据导出](../operation/export)方式
+9. 工具
+ - 提供[交互式命令行程序（CLI）](../reference/taos-shell)，便于管理集群，检查系统状态，做即席查询
+ - 提供压力测试工具[taosBenchmark](../reference/taosbenchmark)，用于测试 TDengine 的性能
+10. 编程
+ - 提供各种语言的[连接器（Connector）](../connector): 如 [C/C++](../connector/cpp)、[Java](../connector/java)、[Go](../connector/go)、[Node.js](../connector/node)、[Rust](../connector/rust)、[Python](../connector/python)、[C#](../connector/csharp) 等
+ - 支持 [REST 接口](../connector/rest-api/)
-更多细小的功能，请阅读整个文档。
+更多细节功能，请阅读整个文档。
## 竞争优势
-由于 TDengine 充分利用了[时序数据特点](https://www.taosdata.com/blog/2019/07/09/105.html)，比如结构化、无需事务、很少删除或更新、写多读少等等，设计了全新的针对时序数据的存储引擎和计算引擎，因此与其他时序数据库相比，TDengine 有以下特点：
+由于 TDengine 充分利用了[时序数据特点](https://www.taosdata.com/blog/2019/07/09/105.html)，比如结构化、无需事务、很少删除或更新、写多读少等等，因此与其他时序数据库相比，TDengine 有以下特点：
-- **[高性能](https://www.taosdata.com/tdengine/fast)**：通过创新的存储引擎设计，无论是数据写入还是查询，TDengine 的性能比通用数据库快 10 倍以上，也远超其他时序数据库，存储空间不及通用数据库的1/10。
+- **[高性能](https://www.taosdata.com/tdengine/fast)**：TDengine 是唯一一个解决了时序数据存储的高基数难题的时序数据库，支持上亿数据采集点，并在数据插入、查询和数据压缩上远胜其它时序数据库。
-- **[云原生](https://www.taosdata.com/tdengine/cloud_native_time-series_database)**：通过原生分布式的设计，充分利用云平台的优势，TDengine 提供了水平扩展能力，具备弹性、韧性和可观测性，支持k8s部署，可运行在公有云、私有云和混合云上。
+- **[极简时序数据平台](https://www.taosdata.com/tdengine/simplified_solution_for_time-series_data_processing)**：TDengine 内建缓存、流式计算和数据订阅等功能，为时序数据的处理提供了极简的解决方案，从而大幅降低了业务系统的设计复杂度和运维成本。
-- **[极简时序数据平台](https://www.taosdata.com/tdengine/simplified_solution_for_time-series_data_processing)**：TDengine 内建消息队列、缓存、流式计算等功能，应用无需再集成 Kafka/Redis/HBase/Spark 等软件，大幅降低系统的复杂度，降低应用开发和运营成本。
+- **[云原生](https://www.taosdata.com/tdengine/cloud_native_time-series_database)**：通过原生的分布式设计、数据分片和分区、存算分离、RAFT 协议、Kubernetes 部署和完整的可观测性，TDengine 是一款云原生时序数据库并且能够部署在公有云、私有云和混合云上。
-- **[分析能力](https://www.taosdata.com/tdengine/easy_data_analytics)**：支持 SQL，同时为时序数据特有的分析提供SQL扩展。通过超级表、存储计算分离、分区分片、预计算、自定义函数等技术，TDengine 具备强大的分析能力。
+- **[简单易用](https://www.taosdata.com/tdengine/ease_of_use)**：对系统管理员来说，TDengine 大幅降低了管理和维护的代价。对开发者来说， TDengine 提供了简单的接口、极简的解决方案和与第三方工具的无缝集成。对数据分析专家来说，TDengine 提供了便捷的数据访问能力。
-- **[简单易用](https://www.taosdata.com/tdengine/ease_of_use)**：无任何依赖，安装、集群几秒搞定；提供REST以及各种语言连接器，与众多第三方工具无缝集成；提供命令行程序，便于管理和即席查询；提供各种运维工具。
+- **[分析能力](https://www.taosdata.com/tdengine/easy_data_analytics)**：通过超级表、存储计算分离、分区分片、预计算和其它技术，TDengine 能够高效地浏览、格式化和访问数据。
-- **[核心开源](https://www.taosdata.com/tdengine/open_source_time-series_database)**：TDengine 的核心代码包括集群功能全部开源，截止到2022年8月1日，全球超过 135.9k 个运行实例，GitHub Star 18.7k，Fork 4.4k，社区活跃。
+- **[核心开源](https://www.taosdata.com/tdengine/open_source_time-series_database)**：TDengine 的核心代码包括集群功能全部在开源协议下公开。全球超过 140k 个运行实例，GitHub Star 19k，且拥有一个活跃的开发者社区。
采用 TDengine，可将典型的物联网、车联网、工业互联网大数据平台的总拥有成本大幅降低。表现在几个方面：
-1. 由于其超强性能，它能将系统需要的计算资源和存储资源大幅降低
+1. 由于其超强性能，它能将系统所需的计算资源和存储资源大幅降低
2. 因为支持 SQL，能与众多第三方软件无缝集成，学习迁移成本大幅下降
-3. 因为是一极简的时序数据平台，系统复杂度、研发和运营成本大幅降低
-4. 因为维护简单，运营维护成本能大幅降低
+3. 因为是一款极简的时序数据平台，系统复杂度、研发和运营成本大幅降低
## 技术生态
-在整个时序大数据平台中，TDengine 在其中扮演的角色如下：
+在整个时序大数据平台中，TDengine 扮演的角色如下：
+