Spark学习笔记-Streaming集成Flume

Spark Streaming 与 Flume集成有两种模式：

1、基于推模式

Flume 配置文件如下：

a1.sources = r1
a1.channels = c1
a1.sinks = k1

a1.sources.r1.type = netcat
a1.sources.r1.bind = centos.host1
a1.sources.r1.port = 22222
a1.sources.r1.channels = c1

a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

a1.sinks.k1.type = avro
a1.sinks.k1.channel = c1
a1.sinks.k1.hostname = localhost
a1.sinks.k1.port = 11111

当然也可以配置其他source，比如exec、thrift、avro等

a1.sources.r1.type = avro
a1.sources.r1.bind = localhost
a1.sources.r1.port = 22222
a1.sources.r1.channels = c1 

Spark 代码如下：

import org.apache.spark.streaming.flume._
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.SparkContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.storage.StorageLevel

object SparkStreamingFlume1 {

  def main(args: Array[String]) {

    if (args.length < 2) {  
      print("please enter host and port")
      System.exit(1)
    }  

    val sc = new SparkContext("spark://centos.host1:7077", "Spark Streaming Flume Integration")

    //创建StreamingContext，20秒一个批次
    val ssc = new StreamingContext(sc, Seconds(20))

    val hostname = args(0)
    val port = args(1).toInt
    val storageLevel = StorageLevel.MEMORY_ONLY
    val flumeStream = FlumeUtils.createStream(ssc, hostname, port, storageLevel)

    flumeStream.count().map(cnt => "Received " + cnt + " flume events." ).print()

    //开始运行
    ssc.start()
    //计算完毕退出
    ssc.awaitTermination()

    sc.stop()
  }

}

提交Spark，这里需要注意的添加必要的jar包，可以在提交的时候加上--jars来指定相关的jar包，也可以在sc中调用addJar()添加

[hadoop@centos spark-1.1.0-bin-hadoop2.4]$bin/spark-submit --class org.project.modules.streaming.SparkStreamingFlume1 --jars lib/spark-streaming-flume-sink_2.10-1.1.0.jar,lib/spark-examples-1.1.0-hadoop2.4.0.jar --master spark://centos.host1:7077 /home/hadoop/temp/flume.jar centos.host1 11111

启动flume

[hadoop@centos flume-1.5.0.1]$bin/flume-ng agent --conf conf --conf-file conf/example5.properties --name a1 -Dflume.root.logger=INFO,console

source类型是netcat的话，可以通过telnet centos.host1 22222测试，如果是avro类型的话，可以通过flume内部提供了一个avro client测试，方式如下

[hadoop@centos flume-1.5.0.1]$bin/flume-ng avro-client --conf conf -H localhost -p 44444 -F a.xml -Dflume.root.logger=DEBUG,console 

2、基于拉模式

Flume 配置文件如下：

a1.sources = r1
a1.channels = c1
a1.sinks = k1

a1.sources.r1.type = netcat
a1.sources.r1.bind = centos.host1
a1.sources.r1.port = 22222
a1.sources.r1.channels = c1

a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

a1.sinks.k1.type = org.apache.spark.streaming.flume.sink.SparkSink
a1.sinks.k1.hostname = centos.host1
a1.sinks.k1.port = 11111
a1.sinks.k1.channel = c1

Spark代码如下：

import org.apache.spark.streaming.flume._
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.SparkContext
import org.apache.spark.streaming.Seconds
import org.apache.spark.storage.StorageLevel

object SparkStreamingFlume2 {

  def main(args: Array[String]) {

    if (args.length < 2) {  
      print("please enter host and port")
      System.exit(1)
    }  

    val sc = new SparkContext("spark://centos.host1:7077", "Spark Streaming Flume Integration")

    //创建StreamingContext，20秒一个批次
    val ssc = new StreamingContext(sc, Seconds(20))

    val hostname = args(0)
    val port = args(1).toInt
    val storageLevel = StorageLevel.MEMORY_ONLY
    val flumeStream = FlumeUtils.createPollingStream(ssc, hostname, port, storageLevel)

    flumeStream.count().map(cnt => "Received " + cnt + " flume events." ).print()

    //开始运行
    ssc.start()
    //计算完毕退出
    ssc.awaitTermination()

    sc.stop()
  }

}

启动Flume，这里主要需要添加 scala-library.jar 、spark-streaming-flume-sink_2.10-1.1.0.jar 到$FLUME_HOME/lib目录下

[hadoop@centos flume-1.5.0.1]$bin/flume-ng agent --conf conf --conf-file conf/example6.properties --name a1 -Dflume.root.logger=INFO,console

可以通过telnet centos.host1 22222测试

提交Spark，这里需要注意的和上面一样添加必要的jar包

[hadoop@centos spark-1.1.0-bin-hadoop2.4]$bin/spark-submit --class org.project.modules.streaming.SparkStreamingFlume2 --jars lib/spark-streaming-flume-sink_2.10-1.1.0.jar,lib/spark-examples-1.1.0-hadoop2.4.0.jar --master spark://centos.host1:7077 /home/hadoop/temp/flume.jar centos.host1 11111