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docs: read through and fix minor problems 02

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@ -12,32 +12,32 @@ 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/) 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).
- 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.
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 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](../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.
@ -52,7 +52,7 @@ By making full use of [characteristics of time series data](https://tdengine.com
- **[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.
@ -61,6 +61,7 @@ By making full use of [characteristics of time series data](https://tdengine.com
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:
![TDengine Database Technical Ecosystem ](eco_system.webp)
@ -76,15 +77,15 @@ 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.|
| ------------------------------------------------ | ------------------ | ----------------------- | ------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| 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** |
| ------------------------------------------------- | ------------------ | ----------------------- | ------------------- | ------------------------------------------------------------ |
| ----------------------------------------- | ------------------ | ----------------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 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. |
@ -92,25 +93,25 @@ As a high-performance, scalable and SQL supported time-series database, TDengine
### 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.|
| -------------------------------------------- | ------------------ | ----------------------- | ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 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 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.|
| 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

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@ -4,18 +4,18 @@ description: 简要介绍 TDengine 的主要功能
toc_max_heading_level: 2
---
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、车联网以及工业互联网进行了优化。TDengine 的代码，包括其集群功能，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。
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. 写入数据，支持
- [SQL 写入](../develop/insert-data/sql-writing)
- [Schemaless 写入](../reference/schemaless/)，支持多种标准写入协议
- [InfluxDB LINE 协议](../develop/insert-data/influxdb-line)
- [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
@ -23,34 +23,34 @@ TDengine的主要功能如下：
- [Prometheus](../third-party/prometheus)
- [StatsD](../third-party/statsd)
- [collectd](../third-party/collectd)
- [icinga2](../third-party/icinga2)
- [Icinga2](../third-party/icinga2)
- [TCollector](../third-party/tcollector)
- [EMQ](../third-party/emq-broker)
- [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
- [标准 SQL](../taos-sql)，含嵌套查询
- [时序数据特色函数](../taos-sql/function/#time-series-extensions)
- [时序顺序特色查询](../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](../taos-sql/udf)
- [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](../develop/stream)(Stream Processing)，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，API 与 Kafka 相同，而且可以指定过滤条件
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 [Kubernets 部署 TDengine](../deployment/k8s/)
- [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](../reference/taos-shell)，便于管理集群，检查系统状态，做即席查询
- 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): 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
- 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/)
更多细节功能，请阅读整个文档。
@ -63,9 +63,9 @@ TDengine的主要功能如下：
- **[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。
- **[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、Kubernets 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。
- **[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。
- **[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。
- **[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。
- **[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。
@ -73,13 +73,13 @@ TDengine的主要功能如下：
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
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：
<figure>
@ -88,11 +88,11 @@ TDengine的主要功能如下：
</figure>
<center>图 1. TDengine技术生态图</center>
上图中，左侧是各种数据采集或消息队列，包括 OPC-UA、MQTT、Telegraf、也包括 Kafka, 他们的数据将被源源不断的写入到 TDengine。右侧则是可视化、BI 工具、组态软件、应用程序。下侧则是 TDengine 自身提供的命令行程序 (CLI) 以及可视化管理管理。
上图中，左侧是各种数据采集或消息队列，包括 OPC-UA、MQTT、Telegraf、也包括 Kafka，他们的数据将被源源不断的写入到 TDengine。右侧则是可视化、BI 工具、组态软件、应用程序。下侧则是 TDengine 自身提供的命令行程序（CLI）以及可视化管理工具。
## 典型适用场景
作为一个高性能、分布式、支持 SQL 的时序数据库 （Database)，TDengine 的典型适用场景包括但不限于 IoT、工业互联网、车联网、IT 运维、能源、金融证券等领域。需要指出的是，TDengine 是针对时序数据场景设计的专用数据库和专用大数据处理工具，因充分利用了时序大数据的特点，它无法用来处理网络爬虫、微博、微信、电商、ERP、CRM 等通用型数据。本文对适用场景做更多详细的分析。
作为一个高性能、分布式、支持 SQL 的时序数据库（Database)，TDengine 的典型适用场景包括但不限于 IoT、工业互联网、车联网、IT 运维、能源、金融证券等领域。需要指出的是，TDengine 是针对时序数据场景设计的专用数据库和专用大数据处理工具，因其充分利用了时序大数据的特点，它无法用来处理网络爬虫、微博、微信、电商、ERP、CRM 等通用型数据。下面本文将对适用场景做更多详细的分析。
### 数据源特点和需求
@ -115,16 +115,16 @@ TDengine的主要功能如下：
### 系统功能需求
| 系统功能需求 | 不适用 | 可能适用 | 非常适用 | 简单说明 |
| -------------------------- | ------ | -------- | -------- | --------------------------------------------------------------------------------------------------------------------- |
| 要求完整的内置数据处理算法 | | √ | | TDengine 的实现了通用的数据处理算法，但是还没有做到妥善处理各行各业的所有要求，因此特殊类型的处理还需要应用层面处理。 |
| 需要大量的交叉查询处理 | | √ | | 这种类型的处理更多应该用关系型数据系统处理，或者应该考虑 TDengine 和关系型数据系统配合实现系统功能。 |
| -------------------------- | ------ | -------- | -------- | ------------------------------------------------------------------------------------------------------------------------- |
| 要求完整的内置数据处理算法 | | √ | | TDengine 实现了通用的数据处理算法，但是还没有做到妥善处理各行各业的所有需求，因此特殊类型的处理需求还需要在应用层面解决。 |
| 需要大量的交叉查询处理 | | √ | | 这种类型的处理更多应该用关系型数据库处理，或者应该考虑 TDengine 和关系型数据库配合实现系统功能。 |
### 系统性能需求
| 系统性能需求 | 不适用 | 可能适用 | 非常适用 | 简单说明 |
| ---------------------- | ------ | -------- | -------- | ------------------------------------------------------------------------------------------------------ |
| ---------------------- | ------ | -------- | -------- | -------------------------------------------------------------------------------------------------- |
| 要求较大的总体处理能力 | | | √ | TDengine 的集群功能可以轻松地让多服务器配合达成处理能力的提升。 |
| 要求高速处理数据 | | | √ | TDengine 的专门为 IoT 优化的存储和数据处理的设计，一般可以让系统得到超出同类产品多倍数的处理速度提升。 |
| 要求高速处理数据 | | | √ | TDengine 专门为 IoT 优化的存储和数据处理设计，一般可以让系统得到超出同类产品多倍数的处理速度提升。 |
| 要求快速处理小粒度数据 | | | √ | 这方面 TDengine 性能可以完全对标关系型和 NoSQL 型数据处理系统。 |
### 系统维护需求