296 lines
11 KiB
Markdown
296 lines
11 KiB
Markdown
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PiFlow是一个简单易用，功能强大的大数据流水线系统。
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## 目录
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- [特性](#特性)
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- [架构](#架构)
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- [要求](#要求)
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- [开始](#开始)
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## 特性
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- 简单易用
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- 可视化配置流水线
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- 监控流水线
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- 查看流水线日志
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- 检查点功能
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- 扩展性强:
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- 支持自定义开发数据处理组件
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- 性能优越：
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- 基于分布式计算引擎Spark开发
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- 功能强大：
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- 提供100+的数据处理组件
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- 包括Hadoop 、Spark、MLlib、Hive、Solr、Redis、MemCache、ElasticSearch、JDBC、MongoDB、HTTP、FTP、XML、CSV、JSON等
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- 集成了微生物领域的相关算法
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## 架构
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## 要求
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* JDK 1.8 及以上版本
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* Apache Maven 3.1.0 及以上版本
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* Git Client
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* Spark-2.1.0 及以上版本
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* Hadoop-2.6.0 及以上版本
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## 开始
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如何Build:
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`mvn clean package -Dmaven.test.skip=true`
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[INFO] Replacing original artifact with shaded artifact.
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[INFO] Replacing /opt/project/piflow/piflow-server/target/piflow-server-0.9.jar with /opt/project/piflow/piflow-server/target/piflow-server-0.9-shaded.jar
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[INFO] ------------------------------------------------------------------------
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[INFO] Reactor Summary:
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[INFO]
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[INFO] piflow-project ..................................... SUCCESS [ 4.602 s]
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[INFO] piflow-core ........................................ SUCCESS [ 56.533 s]
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[INFO] piflow-bundle ...................................... SUCCESS [02:15 min]
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[INFO] piflow-server ...................................... SUCCESS [03:01 min]
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[INFO] ------------------------------------------------------------------------
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[INFO] BUILD SUCCESS
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[INFO] ------------------------------------------------------------------------
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[INFO] Total time: 06:18 min
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[INFO] Finished at: 2018-12-24T16:54:16+08:00
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[INFO] Final Memory: 41M/812M
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[INFO] ------------------------------------------------------------------------
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如何运行Piflow Server：
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- `使用Intellij Idea`:
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- 编辑config.properties文件
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- build piflow工程，生成piflow-server.jar
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- 运行cn.piflow.api.Main
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- 切记设置SPARK_HOME
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- `直接运行release版本`:
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- 下载release版本，地址：https://github.com/cas-bigdatalab/piflow/releases
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- 将build好的piflow-server.jar拷贝到piflow_release文件夹（由于git不能上传超过1G大文件，故需自行build piflow-server.jar）
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- 编辑config.properties文件
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- 运行start.sh 或者后台运行 nohup ./start.sh > piflow.log 2>&1 &
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- `如何配置config.properties`
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#server ip and port
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server.ip=10.0.86.191
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server.port=8002
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h2.port=50002
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#spark and yarn config
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spark.master=yarn
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spark.deploy.mode=cluster
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yarn.resourcemanager.hostname=10.0.86.191
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yarn.resourcemanager.address=10.0.86.191:8032
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yarn.access.namenode=hdfs://10.0.86.191:9000
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yarn.stagingDir=hdfs://10.0.86.191:9000/tmp/
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yarn.jars=hdfs://10.0.86.191:9000/user/spark/share/lib/*.jar
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yarn.url=http://10.0.86.191:8088/ws/v1/cluster/apps/
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#hive config
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hive.metastore.uris=thrift://10.0.86.191:9083
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#piflow-server.jar path
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piflow.bundle=/opt/piflowServer/piflow-server-0.9.jar
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#checkpoint hdfs path
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checkpoint.path=hdfs://10.0.86.89:9000/piflow/checkpoints/
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#debug path
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debug.path=hdfs://10.0.88.191:9000/piflow/debug/
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#yarn url
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yarn.url=http://10.0.86.191:8088/ws/v1/cluster/apps/
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#the count of data shown in log
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data.show=10
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#h2 db port
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h2.port=50002
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如何运行Piflow Web：
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- https://github.com/cas-bigdatalab/piflow-web
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如何使用：
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- 命令行方式
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- 流水线样例配置
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{
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"flow":{
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"name":"test",
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"uuid":"1234",
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"checkpoint":"Merge",
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"stops":[
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{
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"uuid":"1111",
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"name":"XmlParser",
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"bundle":"cn.piflow.bundle.xml.XmlParser",
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"properties":{
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"xmlpath":"hdfs://10.0.86.89:9000/xjzhu/dblp.mini.xml",
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"rowTag":"phdthesis"
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}
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},
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{
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"uuid":"2222",
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"name":"SelectField",
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"bundle":"cn.piflow.bundle.common.SelectField",
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"properties":{
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"schema":"title,author,pages"
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}
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},
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{
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"uuid":"3333",
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"name":"PutHiveStreaming",
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"bundle":"cn.piflow.bundle.hive.PutHiveStreaming",
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"properties":{
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"database":"sparktest",
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"table":"dblp_phdthesis"
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}
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},
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{
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"uuid":"4444",
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"name":"CsvParser",
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"bundle":"cn.piflow.bundle.csv.CsvParser",
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"properties":{
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"csvPath":"hdfs://10.0.86.89:9000/xjzhu/phdthesis.csv",
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"header":"false",
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"delimiter":",",
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"schema":"title,author,pages"
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}
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},
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{
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"uuid":"555",
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"name":"Merge",
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"bundle":"cn.piflow.bundle.common.Merge",
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"properties":{
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"inports":"data1,data2"
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}
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},
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{
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"uuid":"666",
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"name":"Fork",
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"bundle":"cn.piflow.bundle.common.Fork",
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"properties":{
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"outports":"out1,out2,out3"
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}
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},
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{
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"uuid":"777",
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"name":"JsonSave",
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"bundle":"cn.piflow.bundle.json.JsonSave",
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"properties":{
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"jsonSavePath":"hdfs://10.0.86.89:9000/xjzhu/phdthesis.json"
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}
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},
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{
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"uuid":"888",
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"name":"CsvSave",
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"bundle":"cn.piflow.bundle.csv.CsvSave",
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"properties":{
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"csvSavePath":"hdfs://10.0.86.89:9000/xjzhu/phdthesis_result.csv",
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"header":"true",
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"delimiter":","
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}
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}
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],
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"paths":[
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{
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"from":"XmlParser",
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"outport":"",
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"inport":"",
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"to":"SelectField"
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},
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{
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"from":"SelectField",
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"outport":"",
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"inport":"data1",
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"to":"Merge"
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},
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{
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"from":"CsvParser",
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"outport":"",
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"inport":"data2",
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"to":"Merge"
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},
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{
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"from":"Merge",
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"outport":"",
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"inport":"",
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"to":"Fork"
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},
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{
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"from":"Fork",
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"outport":"out1",
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"inport":"",
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"to":"PutHiveStreaming"
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},
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{
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"from":"Fork",
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"outport":"out2",
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"inport":"",
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"to":"JsonSave"
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},
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{
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"from":"Fork",
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"outport":"out3",
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"inport":"",
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"to":"CsvSave"
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}
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]
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}
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}
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- 运行命令
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- curl -0 -X POST http://serverIP:serverPort/flow/start -H "Content-type: application/json" -d '你的流水线json配置文件'
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- 访问piflow web: 试运行地址 "http://piflow.ml/piflow-web", user/password: admin/admin
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- 登录
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- 流水线列表
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- 流水线配置
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- 流水线资源配置
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- 运行流水线
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- 删除流水线
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- 流水线保存模板
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- 创建流水线:用户点击创建按钮,需要输入流水线名称及描述信息,同时可设置流水线需要的资源.
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- 配置流水线:用户可通过拖拽方式进行流水线的配置，方式类似visio
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- 搜索流水线组件：画布左边栏显示组件组和组件，可按关键字搜索,户选择好组件后可拖至画布中央
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- 流水线基本信息：画布右侧显示流水线基本信息，包括流水线名称及描述
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- 流水线配置：画布中央选择任一数据处理组件,右侧显示该数据处理组件的基本信息,包括名称描述,作者等信息.选择AttributeInfo tab,显示该数据处理组件的属性信息,用户可根据实际需求进行配置
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- 运行流水线：用户配置好流水线后,可点击运行按钮运行流水线
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- 流水线监控：进入流水线监控页面。监控页面会显示整条流水线的执行状况，包括运行状态、执行进度、执行时间等,击具体数据处理组件，显示该数据处理组件的运行状况，包括运行状态、执行时间。
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- 查看流水线日志
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- 运行中流水线列表: 已运行流水线会显示在Process List中，包括开始时间、结束时间、进度、状态等。同时可对已运行流水线进行查看，在运行，停止，和删除操作
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- 运行流水线检查点
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- 创建保存模板
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- 模板列表
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- 下载模板：模板会保存成xml文件存放到本地
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- 上传模板
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- 加载模板
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