Merge pull request #16165 from taosdata/fix/release
release: merge from main to 3.0
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Shengliang Guan
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README-CN.md
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README-CN.md
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TDengine 是一款开源、高性能、云原生的时序数据库 (Time-Series Database, TSDB)。TDengine 能被广泛运用于物联网、工业互联网、车联网、IT 运维、金融等领域。除核心的时序数据库功能外，TDengine 还提供缓存、数据订阅、流式计算等功能，是一极简的时序数据处理平台，最大程度的减小系统设计的复杂度，降低研发和运营成本。与其他时序数据库相比，TDengine 的主要优势如下：
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- 高性能：通过创新的存储引擎设计，无论是数据写入还是查询，TDengine 的性能比通用数据库快 10 倍以上，也远超其他时序数据库，存储空间不及通用数据库的1/10。
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- **高性能**：通过创新的存储引擎设计，无论是数据写入还是查询，TDengine 的性能比通用数据库快 10 倍以上，也远超其他时序数据库，存储空间不及通用数据库的1/10。
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- 云原生：通过原生分布式的设计，充分利用云平台的优势，TDengine 提供了水平扩展能力，具备弹性、韧性和可观测性，支持k8s部署，可运行在公有云、私有云和混合云上。
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- **云原生**：通过原生分布式的设计，充分利用云平台的优势，TDengine 提供了水平扩展能力，具备弹性、韧性和可观测性，支持k8s部署，可运行在公有云、私有云和混合云上。
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- 极简时序数据平台：TDengine 内建消息队列、缓存、流式计算等功能，应用无需再集成 Kafka/Redis/HBase/Spark 等软件，大幅降低系统的复杂度，降低应用开发和运营成本。
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- **极简时序数据平台**：TDengine 内建消息队列、缓存、流式计算等功能，应用无需再集成 Kafka/Redis/HBase/Spark 等软件，大幅降低系统的复杂度，降低应用开发和运营成本。
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- 分析能力：支持 SQL，同时为时序数据特有的分析提供SQL扩展。通过超级表、存储计算分离、分区分片、预计算、自定义函数等技术，TDengine 具备强大的分析能力。
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- **分析能力**：支持 SQL，同时为时序数据特有的分析提供SQL扩展。通过超级表、存储计算分离、分区分片、预计算、自定义函数等技术，TDengine 具备强大的分析能力。
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- 简单易用：无任何依赖，安装、集群几秒搞定；提供REST以及各种语言连接器，与众多第三方工具无缝集成；提供命令行程序，便于管理和即席查询；提供各种运维工具。
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- **简单易用**：无任何依赖，安装、集群几秒搞定；提供REST以及各种语言连接器，与众多第三方工具无缝集成；提供命令行程序，便于管理和即席查询；提供各种运维工具。
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- 核心开源：TDengine 的核心代码包括集群功能全部开源，截止到2022年8月1日，全球超过 135.9k 个运行实例，GitHub Star 18.7k，Fork 4.4k，社区活跃。
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- **核心开源**：TDengine 的核心代码包括集群功能全部开源，截止到2022年8月1日，全球超过 135.9k 个运行实例，GitHub Star 18.7k，Fork 4.4k，社区活跃。
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# 文档
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README.md
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README.md
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@ -20,23 +20,19 @@ English | [简体中文](README-CN.md) | We are hiring, check [here](https://tde
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# What is TDengine？
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TDengine is an open source, high-performance, cloud native time-series database optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors. TDengine differentiates itself from other time-seires databases with the following advantages:
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TDengine is an open source, high performance , cloud native time-series database (Time-Series Database, TSDB).
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- **High-Performance**: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression.
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TDengine can be optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT, IT operation and maintenance, finance and other fields. In addition to the core time series database functions, TDengine also provides functions such as caching, data subscription, and streaming computing. It is a minimalist time series data processing platform that minimizes the complexity of system design and reduces R&D and operating costs. Compared with other time series databases, the main advantages of TDengine are as follows:
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- **Simplified Solution**: Through built-in caching, stream processing and data subscription features, TDengine provides a simplified solution for time-series data processing. It reduces system design complexity and operation costs significantly.
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- **Cloud Native**: Through native distributed design, sharding and partitioning, separation of compute and storage, RAFT, support for kubernetes deployment and full observability, TDengine is a cloud native Time-Series Database and can be deployed on public, private or hybrid clouds.
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- High-Performance: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression.
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- **Ease of Use**: For administrators, TDengine significantly reduces the effort to deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access.
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- Simplified Solution: Through built-in caching, stream processing and data subscription features, TDengine provides a simplified solution for time-series data processing. It reduces system design complexity and operation costs significantly.
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- **Easy Data Analytics**: Through super tables, storage and compute separation, data partitioning by time interval, pre-computation and other means, TDengine makes it easy to explore, format, and get access to data in a highly efficient way.
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- Cloud Native: Through native distributed design, sharding and partitioning, separation of compute and storage, RAFT, support for kubernetes deployment and full observability, TDengine is a cloud native Time-Series Database and can be deployed on public, private or hybrid clouds.
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- Ease of Use: For administrators, TDengine significantly reduces the effort to deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access.
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- Easy Data Analytics: Through super tables, storage and compute separation, data partitioning by time interval, pre-computation and other means, TDengine makes it easy to explore, format, and get access to data in a highly efficient way.
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- Open Source: TDengine’s core modules, including cluster feature, are all available under open source licenses. It has gathered 18.8k stars on GitHub, an active developer community, and over 137k running instances worldwide.
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- **Open Source**: TDengine’s core modules, including cluster feature, are all available under open source licenses. It has gathered 18.8k stars on GitHub. There is an active developer community, and over 139k running instances worldwide.
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# Documentation
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@ -44,15 +40,10 @@ For user manual, system design and architecture, please refer to [TDengine Docum
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# Building
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At the moment, TDengine server supports running on Linux, Windows systems.Any OS application can also choose the RESTful interface of taosAdapter to connect the taosd service . TDengine supports X64/ARM64 CPU , and it will support MIPS64, Alpha64, ARM32, RISC-V and other CPU architectures in the future.
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You can choose to install through source code according to your needs, [container](https://docs.taosdata.com/get-started/docker/), [installation package](https://docs.taosdata.com/get-started/package/) or [Kubenetes](https://docs.taosdata.com/deployment/k8s/) to install. This quick guide only applies to installing from source.
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TDengine provide a few useful tools such as taosBenchmark (was named taosdemo) and taosdump. They were part of TDengine. By default, TDengine compiling does not include taosTools. You can use `cmake .. -DBUILD_TOOLS=true` to make them be compiled with TDengine.
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To build TDengine, use [CMake](https://cmake.org/) 3.0.2 or higher versions in the project directory.
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```java
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{{#include docs/examples/java/src/main/java/com/taos/example/SubscribeDemo.java}}
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{{#include docs/examples/java/src/main/java/com/taos/example/MetersDeserializer.java}}
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{{#include docs/examples/java/src/main/java/com/taos/example/Meters.java}}
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```
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```java
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{{#include docs/examples/java/src/main/java/com/taos/example/MetersDeserializer.java}}
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```java
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{{#include docs/examples/java/src/main/java/com/taos/example/SubscribeDemo.java}}
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{{#include docs/examples/java/src/main/java/com/taos/example/MetersDeserializer.java}}
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{{#include docs/examples/java/src/main/java/com/taos/example/Meters.java}}
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```
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```java
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{{#include docs/examples/java/src/main/java/com/taos/example/MetersDeserializer.java}}
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<dependency>
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<groupId>org.apache.logging.log4j</groupId>
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<artifactId>log4j-core</artifactId>
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<version>2.14.1</version>
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<version>2.17.1</version>
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</dependency>
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<!-- proxool -->
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<dependency>
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<description>Demo project for TDengine</description>
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<properties>
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<spring.version>5.3.2</spring.version>
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<spring.version>5.3.20</spring.version>
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</properties>
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<dependencies>
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<dependency>
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<groupId>com.alibaba</groupId>
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<artifactId>fastjson</artifactId>
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<version>1.2.75</version>
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<version>1.2.83</version>
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</dependency>
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<!-- mysql: just for test -->
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<dependency>
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<groupId>mysql</groupId>
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<artifactId>mysql-connector-java</artifactId>
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<version>8.0.16</version>
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<version>8.0.28</version>
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<scope>test</scope>
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</dependency>
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<!-- log4j -->
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<dependency>
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<groupId>org.apache.logging.log4j</groupId>
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<artifactId>log4j-core</artifactId>
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<version>2.14.1</version>
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<version>2.17.1</version>
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</dependency>
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<!-- junit -->
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<dependency>
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pBlockInfo->window.skey, pBlockInfo->window.ekey, pBlockInfo->rows);
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pCost->skipBlocks += 1;
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*status = FUNC_DATA_REQUIRED_FILTEROUT;
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return TSDB_CODE_SUCCESS;
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}
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