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@ -14,11 +14,11 @@ is an easy to use, powerful big data pipeline system.
- Easy to use
- provide a WYSIWYG web interface to configure data flow
- monitor big data flow status
- check big data flow logs
- provide checkpoint
- Strong Scalability:
- Support for custom development data processing components
- 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
@ -31,8 +31,8 @@ is an easy to use, powerful big data pipeline system.
* JDK 1.8 or newer
* Apache Maven 3.1.0 or newer
* Git Client (used during build process by 'bower' plugin)
* spark-2.1.0
* hadoop-2.6.0
* Spark-2.1.0
* Hadoop-2.6.0
## Getting Started
@ -57,7 +57,18 @@ To Build:
[INFO] ------------------------------------------------------------------------
To Run Piflow Server
- configure config.properties
- `run piflow server on intellij`:
- edit config.properties
- build piflow to generate piflow-server.jar
- main class is cn.piflow.api.Main(remember to set SPARK_HOME)
- `run piflow server by release version`:
- download piflow_release:https://github.com/cas-bigdatalab/piflow_release
- copy the piflow-server.jar to the piflow_release folder
- edit config.properties
- run start.sh
- `how to configure config.properties`
#server ip and port
server.ip=10.0.86.191
@ -77,18 +88,23 @@ To Run Piflow Server
#hive config
hive.metastore.uris=thrift://10.0.86.191:9083
#piflow jar path
#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/
- you can run piflow server on intellij
- main class is cn.piflow.api.Main
- remember to set SPARK_HOME
- you can run piflow server as follows:
- download piflowServer:***
- edit config.properties
- run start.sh
#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
To Run Piflow Web
- todo