README_CN.md · ydxiang/PiFlow

PiFlow是一个简单易用，功能强大的大数据流水线系统。

特性

简单易用
- 可视化配置流水线
- 监控流水线
- 查看流水线日志
- 检查点功能
扩展性强:
- 支持自定义开发数据处理组件
性能优越：
- 基于分布式计算引擎Spark开发
功能强大：
- 提供100+的数据处理组件
- 包括Hadoop 、Spark、MLlib、Hive、Solr、Redis、MemCache、ElasticSearch、JDBC、MongoDB、HTTP、FTP、XML、CSV、JSON等
- 集成了微生物领域的相关算法

架构

要求

JDK 1.8
Spark-2.11.8
Apache Maven 3.1.0
Spark-2.1.0 及以上版本
Hadoop-2.6.0

开始

Build PiFlow:

install external package

    mvn install:install-file -Dfile=/../piflow/piflow-bundle/lib/spark-xml_2.11-0.4.2.jar -DgroupId=com.databricks -DartifactId=spark-xml_2.11 -Dversion=0.4.2 -Dpackaging=jar
    mvn install:install-file -Dfile=/../piflow/piflow-bundle/lib/java_memcached-release_2.6.6.jar -DgroupId=com.memcached -DartifactId=java_memcached-release -Dversion=2.6.6 -Dpackaging=jar
    mvn install:install-file -Dfile=/../piflow/piflow-bundle/lib/ojdbc6-11.2.0.3.jar -DgroupId=oracle -DartifactId=ojdbc6 -Dversion=11.2.0.3 -Dpackaging=jar
    mvn install:install-file -Dfile=/../piflow/piflow-bundle/lib/edtftpj.jar -DgroupId=ftpClient -DartifactId=edtftp -Dversion=1.0.0 -Dpackaging=jar

mvn clean package -Dmaven.test.skip=true

    [INFO] Replacing original artifact with shaded artifact.
    [INFO] Reactor Summary:
    [INFO]
    [INFO] piflow-project ..................................... SUCCESS [  4.369 s]
    [INFO] piflow-core ........................................ SUCCESS [01:23 min]
    [INFO] piflow-configure ................................... SUCCESS [ 12.418 s]
    [INFO] piflow-bundle ...................................... SUCCESS [02:15 min]
    [INFO] piflow-server ...................................... SUCCESS [02:05 min]
    [INFO] ------------------------------------------------------------------------
    [INFO] BUILD SUCCESS
    [INFO] ------------------------------------------------------------------------
    [INFO] Total time: 06:01 min
    [INFO] Finished at: 2020-05-21T15:22:58+08:00
    [INFO] Final Memory: 118M/691M
    [INFO] ------------------------------------------------------------------------

运行 Piflow Server：

Intellij上运行PiFlow Server:
- 下载 piflow: git clone https://github.com/cas-bigdatalab/piflow.git
- 将PiFlow导入到Intellij
- 编辑配置文件config.properties
- Build PiFlow jar包:
  - Run --> Edit Configurations --> Add New Configuration --> Maven
  - Name: package
  - Command line: clean package -Dmaven.test.skip=true -X
  - run 'package' (piflow jar file will be built in ../piflow/piflow-server/target/piflow-server-0.9.jar)
- 运行 HttpService:
  - Edit Configurations --> Add New Configuration --> Application
  - Name: HttpService
  - Main class : cn.piflow.api.Main
  - Environment Variable: SPARK_HOME=/opt/spark-2.2.0-bin-hadoop2.6(change the path to your spark home)
  - run 'HttpService'
- 测试 HttpService:
  - run /../piflow/piflow-server/src/main/scala/cn/piflow/api/HTTPClientStartMockDataFlow.scala
  - change the piflow server ip and port to your configure
通过Release版本运行PiFlow:
- 根据需求下载不同版本PiFlow（建议下载最新版本）:
  https://github.com/cas-bigdatalab/piflow/releases/download/v0.5/piflow.tar.gz
  https://github.com/cas-bigdatalab/piflow/releases/download/v0.6/piflow-server-v0.6.tar.gz
  https://github.com/cas-bigdatalab/piflow/releases/download/v0.7/piflow-server-v0.7.tar.gz
- 解压piflow-server-v0.7.tar.gz:
  tar -zxvf piflow-server-v0.7.tar.gz
- 编辑配置文件config.properties
- 运行、停止、重启PiFlow Server
  start.sh、stop.sh、 restart.sh、 status.sh
- 测试 PiFlow Server
  - 设置环境变量 PIFLOW_HOME
    - vim /etc/profile
      export PIFLOW_HOME=/yourPiflowPath/bin
      export PATH=$PATH:$PIFLOW_HOME/bin
    - 运行如下命令
      piflow flow start example/mockDataFlow.json
      piflow flow stop appID
      piflow flow info appID
      piflow flow log appID
      
      piflow flowGroup start example/mockDataGroup.json
      piflow flowGroup stop groupId
      piflow flowGroup info groupId

如何配置config.properties

#spark and yarn config
spark.master=yarn
spark.deploy.mode=cluster

#hdfs default file system
fs.defaultFS=hdfs://10.0.86.191:9000

#yarn resourcemanager.hostname
yarn.resourcemanager.hostname=10.0.86.191

#if you want to use hive, set hive metastore uris
#hive.metastore.uris=thrift://10.0.88.71:9083

#show data in log, set 0 if you do not want to show data in logs
data.show=10

#server port
server.port=8002

#h2db port
h2.port=50002

运行PiFlow Web请到如下链接，PiFlow Server 与 PiFlow Web版本要对应：

https://github.com/cas-bigdatalab/piflow-web

接口Restful API：

flow json（可查看piflow-bin/example文件夹下的流水线样例）

flow example


      {
"flow": {
  "name": "MockData",
  "executorMemory": "1g",
  "executorNumber": "1",
  "uuid": "8a80d63f720cdd2301723b7461d92600",
  "paths": [
    {
      "inport": "",
      "from": "MockData",
      "to": "ShowData",
      "outport": ""
    }
  ],
  "executorCores": "1",
  "driverMemory": "1g",
  "stops": [
    {
      "name": "MockData",
      "bundle": "cn.piflow.bundle.common.MockData",
      "uuid": "8a80d63f720cdd2301723b7461d92604",
      "properties": {
        "schema": "title:String, author:String, age:Int",
        "count": "10"
      },
      "customizedProperties": {
  }
},
{
  "name": "ShowData",
  "bundle": "cn.piflow.bundle.external.ShowData",
  "uuid": "8a80d63f720cdd2301723b7461d92602",
  "properties": {
    "showNumber": "5"
  },
  "customizedProperties": {

  }
}

]
}
}

CURL方式：
- curl -0 -X POST http://10.0.86.191:8002/flow/start -H "Content-type: application/json" -d 'this is your flow json'
命令行方式：
- set PIFLOW_HOME
  vim /etc/profile
  export PIFLOW_HOME=/yourPiflowPath/piflow-bin
  export PATH=$PATH:$PIFLOW_HOME/bin
- command example
  piflow flow start yourFlow.json
  piflow flow stop appID
  piflow flow info appID
  piflow flow log appID
  
  piflow flowGroup start yourFlowGroup.json
  piflow flowGroup stop groupId
  piflow flowGroup info groupId

Docker镜像

根据需求拉取不同版本PiFlow docker镜像
docker pull registry.cn-hangzhou.aliyuncs.com/cnic_piflow/piflow:v0.7.1
docker pull registry.cn-hangzhou.aliyuncs.com/cnic_piflow/piflow:v0.6.1
查看Docker镜像的信息
docker images
通过镜像Id运行一个Container，所有PiFlow服务会自动运行
docker run --name piflow-v0.6 -it [imageID]
访问 "containerip:6001/piflow-web", 启动时间可能有些慢，需要等待几分钟
如果发生错误，所有应用都放在了/opt文件夹下，可自行单独启动服务