11 KiB
11 KiB
目录
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 及以上版本
- Apache Maven 3.1.0 及以上版本
- Git Client
- Spark-2.1.0 及以上版本
- Hadoop-2.6.0 及以上版本
开始
如何Build:
mvn clean package -Dmaven.test.skip=true
[INFO] Replacing original artifact with shaded artifact.
[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
[INFO] ------------------------------------------------------------------------
[INFO] Reactor Summary:
[INFO]
[INFO] piflow-project ..................................... SUCCESS [ 4.602 s]
[INFO] piflow-core ........................................ SUCCESS [ 56.533 s]
[INFO] piflow-bundle ...................................... SUCCESS [02:15 min]
[INFO] piflow-server ...................................... SUCCESS [03:01 min]
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 06:18 min
[INFO] Finished at: 2018-12-24T16:54:16+08:00
[INFO] Final Memory: 41M/812M
[INFO] ------------------------------------------------------------------------
如何运行Piflow Server:
-
使用Intellij Idea
:- 编辑config.properties文件
- build piflow工程,生成piflow-server.jar
- 运行cn.piflow.api.Main
- 切记设置SPARK_HOME
-
直接运行release版本
:- 下载release版本,地址:https://github.com/cas-bigdatalab/piflow/releases
- 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)
- 编辑config.properties文件
- 运行start.sh 或者后台运行 nohup ./start.sh > piflow.log 2>&1 &
-
如何配置config.properties
#server ip and port server.ip=10.0.86.191 server.port=8002 h2.port=50002 #spark and yarn config spark.master=yarn spark.deploy.mode=cluster yarn.resourcemanager.hostname=10.0.86.191 yarn.resourcemanager.address=10.0.86.191:8032 yarn.access.namenode=hdfs://10.0.86.191:9000 yarn.stagingDir=hdfs://10.0.86.191:9000/tmp/ yarn.jars=hdfs://10.0.86.191:9000/user/spark/share/lib/*.jar yarn.url=http://10.0.86.191:8088/ws/v1/cluster/apps/ #hive config hive.metastore.uris=thrift://10.0.86.191:9083 #piflow-server.jar path piflow.bundle=/opt/piflowServer/piflow-server-0.9.jar #checkpoint hdfs path checkpoint.path=hdfs://10.0.86.89:9000/piflow/checkpoints/ #debug path debug.path=hdfs://10.0.88.191:9000/piflow/debug/ #yarn url yarn.url=http://10.0.86.191:8088/ws/v1/cluster/apps/ #the count of data shown in log data.show=10 #h2 db port h2.port=50002
如何运行Piflow Web:
如何使用:
- 命令行方式
-
流水线样例配置
{ "flow":{ "name":"test", "uuid":"1234", "checkpoint":"Merge", "stops":[ { "uuid":"1111", "name":"XmlParser", "bundle":"cn.piflow.bundle.xml.XmlParser", "properties":{ "xmlpath":"hdfs://10.0.86.89:9000/xjzhu/dblp.mini.xml", "rowTag":"phdthesis" } }, { "uuid":"2222", "name":"SelectField", "bundle":"cn.piflow.bundle.common.SelectField", "properties":{ "schema":"title,author,pages" } }, { "uuid":"3333", "name":"PutHiveStreaming", "bundle":"cn.piflow.bundle.hive.PutHiveStreaming", "properties":{ "database":"sparktest", "table":"dblp_phdthesis" } }, { "uuid":"4444", "name":"CsvParser", "bundle":"cn.piflow.bundle.csv.CsvParser", "properties":{ "csvPath":"hdfs://10.0.86.89:9000/xjzhu/phdthesis.csv", "header":"false", "delimiter":",", "schema":"title,author,pages" } }, { "uuid":"555", "name":"Merge", "bundle":"cn.piflow.bundle.common.Merge", "properties":{ "inports":"data1,data2" } }, { "uuid":"666", "name":"Fork", "bundle":"cn.piflow.bundle.common.Fork", "properties":{ "outports":"out1,out2,out3" } }, { "uuid":"777", "name":"JsonSave", "bundle":"cn.piflow.bundle.json.JsonSave", "properties":{ "jsonSavePath":"hdfs://10.0.86.89:9000/xjzhu/phdthesis.json" } }, { "uuid":"888", "name":"CsvSave", "bundle":"cn.piflow.bundle.csv.CsvSave", "properties":{ "csvSavePath":"hdfs://10.0.86.89:9000/xjzhu/phdthesis_result.csv", "header":"true", "delimiter":"," } } ], "paths":[ { "from":"XmlParser", "outport":"", "inport":"", "to":"SelectField" }, { "from":"SelectField", "outport":"", "inport":"data1", "to":"Merge" }, { "from":"CsvParser", "outport":"", "inport":"data2", "to":"Merge" }, { "from":"Merge", "outport":"", "inport":"", "to":"Fork" }, { "from":"Fork", "outport":"out1", "inport":"", "to":"PutHiveStreaming" }, { "from":"Fork", "outport":"out2", "inport":"", "to":"JsonSave" }, { "from":"Fork", "outport":"out3", "inport":"", "to":"CsvSave" } ]
} }
-
运行命令
- 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'
-
- 访问piflow web: 试运行地址 "http://piflow.ml/piflow-web", user/password: admin/admin
- 登录
- 流水线列表
- 流水线配置
- 流水线资源配置
- 运行流水线
- 删除流水线
- 流水线保存模板
- 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.
- 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
- 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
- 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
- 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
- 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
- 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。
- 查看流水线日志
- 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
- 运行流水线检查点
- 创建保存模板
- 模板列表
- 下载模板:模板会保存成xml文件存放到本地
- 上传模板
- 加载模板