πFlow is an easy to use, powerful big data pipeline system.

Table of Contents

• Features
• Architecture
• Requirements
• Getting Started
• PiFlow Docker
• Use Interface
• Principled Stand
• Contact Us

Features

• Easy to use
  • provide a WYSIWYG web interface to configure data flow
  • monitor data flow status
  • check the logs of data flow
  • provide checkpoints
• Strong scalability:
  • Support customized development of data processing components
• Superior performance
  • based on distributed computing engine Spark
• Powerful
  • 100+ data processing components available
  • include Spark、MLlib、Hadoop、Hive、HBase、TDengine、OceanBase、openLooKeng、TiDB、Solr、Redis、Memcache、Elasticsearch、JDBC、MongoDB、HTTP、FTP、XML、CSV、JSON，etc.

Architecture

Requirements

• JDK 1.8
• Scala-2.12.18
• Apache Maven 3.1.0 or newer
• Spark-3.4.0
• Hadoop-3.3.0

Compatible with X86 architecture and ARM architecture, Support CentOS and Kirin system deployment

Getting Started

To Build:

• 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] ------------------------------------------------------------------------
  

Run πFlow Server：

• run piflow server on Intellij:

  • download piflow: git clone https://github.com/cas-bigdatalab/piflow.git

  • import piflow into Intellij

  • edit config.properties file

  • build piflow to generate piflow jar:

    • 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)

  • run 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'

  • test HttpService:

    • run /../piflow/piflow-server/src/main/scala/cn/piflow/api/HTTPClientStartMockDataFlow.scala
    • change the piflow server ip and port to your configure

• run piflow server by release version:

  • download piflow.tar.gz:
    https://github.com/cas-bigdatalab/piflow/releases/download/v1.2/piflow-server-v1.5.tar.gz

  • unzip piflow.tar.gz:
    tar -zxvf piflow.tar.gz

  • edit config.properties

  • run start.sh、stop.sh、 restart.sh、 status.sh

  • test piflow server

    • set PIFLOW_HOME

      • vim /etc/profile
        export PIFLOW_HOME=/yourPiflowPath/bin
        export PATH=$PATH:$PIFLOW_HOME/bin

      • command
        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

• how to configure 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
  
  #If you want to upload python stop,please set hdfs configs
  #example hdfs.cluster=hostname:hostIP
  #hdfs.cluster=master:127.0.0.1
  #hdfs.web.url=master:50070
  

Run πFlow Web：

• Visit address, download the corresponding *.tar.gz file, and modify the corresponding configuration file（The version must be consistent with piflow-server）
  • https://github.com/cas-bigdatalab/piflow-web/releases/tag/v1.5
• If you want to upload python stops, please modify docker.service

  vim /usr/lib/systemd/system/docker.service
  ExecStart=/usr/bin/dockerd -H tcp://0.0.0.0:2375 -H unix://var/run/docker.sock
  systemctl daemon-reload
  systemctl restart docker

Restful API：

• flow json

  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 POST：

  • curl -0 -X POST http://10.0.86.191:8002/flow/start -H "Content-type: application/json" -d 'this is your flow json'

• Command line：

  • 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-started

• pull piflow images
  docker pull registry.cn-hangzhou.aliyuncs.com/cnic_piflow/piflow:v1.5

• show docker images
  docker images

• run a container with piflow imageID ， all services run automatically. Please Set HOST_IP and some docker configs.
  docker run -h master -itd --env HOST_IP=*.*.*.* --name piflow-v1.5 -p 6001:6001 -v /usr/bin/docker:/usr/bin/docker -v /var/run/docker.sock:/var/run/docker.sock --add-host docker.host:*.*.*.* [imageID]

• please visit "HOST_IP:6001", it may take a while

• if somethings goes wrong, all the application are in /opt folder

use-interface

• Login:

  

• Dashboard:

  

• Flow list:

  

• Create flow:

  

• Configure flow:

  

• Load flow:

  

• Monitor flow:

  

• Flow logs:

  

• Group list:

  

• Configure group:

  

• Monitor group:

  

• Process List:

  

• Template List:

  

• DataSource List:

  

• Schedule List:

  

• StopHub List:

  

Contact Us

• Name:Yang Gang, Tian Yao
• Mobile Phone：13253365393, 18501260806
• WeChat：13253365393, 18501260806
• Email: [email protected], [email protected]
• Private vulnerability contact information：[email protected]
• Wechat User Group

• Wechat Official Account