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README
Apache-2.0

ChunJun

npm version license npm downloads master coverage

EN doc CN doc

介绍

纯钧(ChunJun,原名FlinkX),是一款稳定、易用、高效、批流一体的数据集成框架,目前基于实时计算引擎Flink实现多种异构数据源之间的数据同步与计算,已在上千家公司部署且稳定运行。

官方网站:https://dtstack.github.io/chunjun/

特性

纯钧(ChunJun)将不同的数据库抽象成了reader/source 插件,writer/sink 插件和lookup 维表插件,其具有以下特点:

  • 基于实时计算引擎Flink,支持JSON模版配置任务,兼容Flink SQL语法;
  • 支持分布式运行,支持flink-standalone、yarn-session、yarn-per job等多种提交方式;
  • 支持Docker一键部署,支持K8S 部署运行;
  • 支持多种异构数据源,可支持MySQL、Oracle、SQLServer、Hive、Kudu等20多种数据源的同步与计算;
  • 易拓展,高灵活性,新拓展的数据源插件可以与现有数据源插件即时互通,插件开发者不需要关心其他插件的代码逻辑;
  • 不仅仅支持全量同步,还支持增量同步、间隔轮训;
  • 批流一体,不仅仅支持离线同步及计算,还兼容实时场景;
  • 支持脏数据存储,并提供指标监控等;
  • 配合checkpoint实现断点续传;
  • 不仅仅支持同步DML数据,还支持Schema变更同步;

源码编译

获取代码

使用git工具将纯钧项目代码下载在本地

git clone https://github.com/DTStack/chunjun.git

项目编译

在项目源码目录下执行

./mvnw clean package -DskipTests

或者执行

sh build/build.sh

多平台兼容

chunjun目前支持tdh和开源hadoop平台,对不同的平台有需要使用不同的maven命令打包

平台类型 含义
tdh mvn clean package -DskipTests -P default,tdh 打包出inceptor插件以及default支持的插件
default mvn clean package -DskipTests -P default 除了inceptor插件之外的所有插件

常见问题

1.编译找不到DB2、达梦、Gbase、Ojdbc8等驱动包

解决办法:在$CHUNJUN_HOME/jars目录下有这些驱动包,可以手动安装,也可以使用插件提供的脚本安装:

## windows平台
./$CHUNJUN_HOME/bin/install_jars.bat

## unix平台
./$CHUNJUN_HOME/bin/install_jars.sh

2. 关于编译ChunJun-core报错Failed to read artifact descriptor for com.google.errorprone:javac-shaded

报错信息:

[ERROR]Failed to execute goal com.diffplug.spotless:spotless-maven-plugin:2.4.2:check(spotless-check)on project chunjun-core:
        Execution spotless-check of goal com.diffplug.spotless:spotless-maven-plugin:2.4.2:check failed:Unable to resolve dependencies:
        Failed to collect dependencies at com.google.googlejavaformat:google-java-format:jar:1.7->com.google.errorprone:javac-shaded:jar:9+181-r4173-1:
        Failed to read artifact descriptor for com.google.errorprone:javac-shaded:jar:9+181-r4173-1:Could not transfer artifact
        com.google.errorprone:javac-shaded:pom:9+181-r4173-1 from/to aliyunmaven(https://maven.aliyun.com/repository/public): 
        Access denied to:https://maven.aliyun.com/repository/public/com/google/errorprone/javac-shaded/9+181-r4173-1/javac-shaded-9+181-r4173-1.pom -> [Help 1]

解决:

https://repo1.maven.org/maven2/com/google/errorprone/javac-shaded/9+181-r4173-1/javac-shaded-9+181-r4173-1.jar 从这个地址下载javac-shaded-9+181-r4173-1.jar, 临时放到chunjun根目录下jars目录里,然后在源码根目录下 执行安装依赖包命令如下:

mvn install:install-file -DgroupId=com.google.errorprone -DartifactId=javac-shaded -Dversion=9+181-r4173-1 -Dpackaging=jar -Dfile=./jars/javac-shaded-9+181-r4173-1.jar

快速开始

以下表格是分支与flink版本之间的对应关系,如果版本没有对齐,可能会导致任务出现序列化异常,类冲突等问题。

分支 flink 版本
master 1.12.7
1.12_release 1.12.7
1.10_release 1.10.1
1.8_release 1.8.3

纯钧支持多种模式运行任务,不同模式下,所依赖的环境和步骤有所不同,以下内容是不同模式下的提交步骤:

Local

Local 模式不依赖Flink环境和Hadoop环境,在本地环境启动一个JVM进程执行纯钧任务。

提交步骤

进入到chunjun-dist 目录,执行命令

sh bin/chunjun-local.sh  -job chunjun-examples/json/stream/stream.json

即可执行一个简单的 stream -> stream 同步任务

参考视频

Standalone

Standalone模式依赖Flink Standalone环境,不依赖Hadoop环境。

提交步骤

1. 添加chunjun依赖包
  1. 根据实际情况找到依赖文件: 通过maven编译的方式构建项目时,依赖文件目录为'chunjun-dist'; 通过官网下载压缩包解压使用时,依赖文件目录为解压后的目录,例如'chunjun-assembly-1.12-SNAPSHOT-chunjun-dist'

  2. 将依赖文件复制到Flink lib目录下,例如

cp -r chunjun-dist $FLINK_HOME/lib

注意: 这个复制操作需要在所有Flink cluster机器上执行,否则部分任务会出现类找不到的错误。

2. 启动Flink Standalone环境
sh $FLINK_HOME/bin/start-cluster.sh

启动成功后默认端口为8081,我们可以访问当前机器的8081端口进入standalone的flink web ui

3. 提交任务

进入到本地chunjun-dist目录,执行命令

sh bin/chunjun-standalone.sh -job chunjun-examples/json/stream/stream.json

提交成功之后,可以在flink web ui 上观察任务情况;

参考视频

Yarn Session

YarnSession 模式依赖Flink 和 Hadoop 环境,需要在任务提交之前启动相应的yarn session;

提交步骤

1. 启动Yarn Session环境

Yarn Session 模式依赖Flink 和 Hadoop 环境,需要在提交机器中提前设置好$HADOOP_HOME和$FLINK_HOME

我们需要使用yarn-session -t参数上传chunjun-dist

cd $FLINK_HOME/bin
./yarn-session -t $CHUNJUN_HOME -d
2. 提交任务

通过yarn web ui 查看session 对应的application $SESSION_APPLICATION_ID,进入到本地chunjun-dist目录,执行命令

sh ./bin/chunjun-yarn-session.sh -job chunjun-examples/json/stream/stream.json -confProp {\"yarn.application.id\":\"SESSION_APPLICATION_ID\"}

yarn.application.id 也可以在 flink-conf.yaml 中设置;提交成功之后,可以通过 yarn web ui 上观察任务情况。

参考视频

Yarn Per-Job

Yarn Per-Job 模式依赖Flink 和 Hadoop 环境,需要在提交机器中提前设置好$HADOOP_HOME和$FLINK_HOME。

提交步骤

Yarn Per-Job 提交任务配置正确即可提交。进入本地chunjun-dist目录,执行命令提交任务。

sh ./bin/chunjun-yarn-perjob.sh -job chunjun-examples/json/stream/stream.json

提交成功之后,可以通过 yarn web ui 上观察任务情况;

插件文档

详情请访问:https://dtstack.github.io/chunjun/documents/

贡献者

感谢所有的贡献者!

contributors

开源协议

纯钧遵循Apache 2.0 开源协议。

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简介

基于flink的分布式数据同步框架 展开 收起
Java
Apache-2.0
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