本节课分成二部分讲解:

    一、Spark Streaming on Pulling from Flume实战

    二、Spark Streaming on Pulling from Flume源码解析


先简单介绍下Flume的两种模式:推模式(Flume push to Spark Streaming)和 拉模式(Spark Streaming pull from Flume )

采用推模式推模式的理解就是Flume作为缓存,存有数据。监听对应端口,如果服务可以连接,就将数据push过去。(简单,耦合要低),缺点是Spark Streaming程序没有启动的话,Flume端会报错,同时会导致Spark Streaming程序来不及消费的情况。

采用拉模式:拉模式就是自己定义一个sink,Spark Streaming自己去channel里面取数据,根据自身条件去获取数据,稳定性好。


Flume pull实战:

第一步:安装Flume,本节课不在说明,参考(第87课:Flume推送数据到SparkStreaming案例实战和内幕源码解密

第二步:配置Flume,首先参照官网(http://spark.apache.org/docs/latest/streaming-flume-integration.html)要求添加依赖或直接下载3个jar包,并将其放入Flume安装目录下的lib目录中

spark-streaming-flume-sink_2.10-1.6.0.jar、scala-library-2.10.5.jar、commons-lang3-3.3.2.jar

wKioL1c1jsTBrJf4AAAx69YuJpI846.png

第三步:配置Flume环境参数,修改flume-conf.properties,从flume-conf.properties.template复制一份进行修改

#Flume pull模式

agent0.sources = source1

agent0.channels = memoryChannel

agent0.sinks = sink1


#配置Source1

agent0.sources.source1.type = spooldir

agent0.sources.source1.spoolDir = /home/hadoop/flume/tmp/TestDir

agent0.sources.source1.channels = memoryChannel

agent0.sources.source1.fileHeader = false

agent0.sources.source1.interceptors = il

agent0.sources.source1.interceptors.il.type = timestamp


#配置Sink1

agent0.sinks.sink1.type = org.apache.spark.streaming.flume.sink.SparkSink

agent0.sinks.sink1.hostname = SparkMaster

agent0.sinks.sink1.port = 9999

agent0.sinks.sink1.channel = memoryChannel


#配置channel

agent0.channels.memoryChannel.type = file

agent0.channels.memoryChannel.checkpointDir = /home/hadoop/flume/tmp/checkpoint

agent0.channels.memoryChannel.dataDirs = /home/hadoop/flume/tmp/dataDir


启动flume命令:

root@SparkMaster:~/flume/flume-1.6.0/bin# ./flume-ng agent --conf ../conf/ --conf-file ../conf/flume-conf.properties --name agent0 -Dflume.root.logger=INFO,console

或者root@SparkMaster:~/flume/flume-1.6.0# flume-ng agent --conf ./conf/ --conf-file ./conf/flume-conf.properties --name agent0 -Dflume.root.logger=INFO,console


第四步:编写简单的业务代码(Java版)

package com.dt.spark.SparkApps.sparkstreaming;

import java.util.Arrays;

import org.apache.spark.SparkConf;

import org.apache.spark.api.java.function.FlatMapFunction;

import org.apache.spark.api.java.function.Function2;

import org.apache.spark.api.java.function.PairFunction;

import org.apache.spark.streaming.Durations;

import org.apache.spark.streaming.api.java.JavaDStream;

import org.apache.spark.streaming.api.java.JavaPairDStream;

import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;

import org.apache.spark.streaming.api.java.JavaStreamingContext;

import org.apache.spark.streaming.flume.FlumeUtils;

import org.apache.spark.streaming.flume.SparkFlumeEvent;

import scala.Tuple2;

public class SparkStreamingPullDataFromFlume {

    public static void main(String[] args) {

        SparkConf conf = new SparkConf().setMaster("spark://SparkMaster:7077");

        conf.setAppName("SparkStreamingPullDataFromFlume");

        JavaStreamingContext jsc = new JavaStreamingContext(conf, Durations.seconds(30));

        // 获取数据

        JavaReceiverInputDStream lines = FlumeUtils.createPollingStream(jsc, "SparkMaster", 9999);

        // 进行单词切分

        JavaDStream<String> words = lines.flatMap(new FlatMapFunction<SparkFlumeEvent, String>() {

            public Iterable<String> call(SparkFlumeEvent event) throws Exception {

                String line = new String(event.event().getBody().toString());

                return Arrays.asList(line.split(" "));

            }

        });

        // 进行map操作,转换成(key,value)格式

        JavaPairDStream<String, Integer> pairs = words.mapToPair(new PairFunction<String, String, Integer>() {

            public Tuple2<String, Integer> call(String word) throws Exception {

                return new Tuple2<String, Integer>(word, 1);

            }

        });

        // 进行reduceByKey动作,将key相同的value值进行合并

        JavaPairDStream<String, Integer> wordsCount = pairs.reduceByKey(new Function2<Integer, Integer, Integer>() {

            public Integer call(Integer v1, Integer v2) throws Exception {

                return v1 + v2;

            }

        });

        wordsCount.print();

        jsc.start();

        jsc.awaitTermination();

        jsc.close();

    }

}

将程序打包成jar文件上传到Spark集群中


第五步:启动HDFS、Spark集群和Flume

启动Flume:root@SparkMaster:~/flume/flume-1.6.0/bin# ./flume-ng agent --conf ../conf/ --conf-file ../conf/flume-conf.properties --name agent0 -Dflume.root.logger=INFO,console

第六步:往/home/hadoop/flume/tmp/TestDir目录中上传测试文件,查看Flume的日志变化

第七步:通过spark-submit命令运行程序:

./spark-submit --class com.dt.spark.SparkApps.SparkStreamingPullDataFromFlume --name SparkStreamingPullDataFromFlume /home/hadoop/spark/SparkStreamingPullDataFromFlume.jar

每隔30秒查看运行结果


第二部分:源码分析

1、创建createPollingStream (FlumeUtils.scala )

注意:默认的存储方式是MEMORY_AND_DISK_SER_2

/**

 * Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.

 * This stream will poll the sink for data and will pull events as they are available.

 * This stream will use a batch size of 1000 events and run 5 threads to pull data.

 * @param hostname Address of the host on which the Spark Sink is running

 * @param port Port of the host at which the Spark Sink is listening

 * @param storageLevel Storage level to use for storing the received objects

*/

def createPollingStream(

    ssc: StreamingContext,

    hostname: String,

    port: Int,

    storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2

  ): ReceiverInputDStream[SparkFlumeEvent] = {

  createPollingStream(ssc, Seq(new InetSocketAddress(hostname, port)), storageLevel)

}

2、参数配置:默认的全局参数,private 级别配置无法修改

private val DEFAULT_POLLING_PARALLELISM = 5

private val DEFAULT_POLLING_BATCH_SIZE = 1000

/**

 * Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.

 * This stream will poll the sink for data and will pull events as they are available.

 * This stream will use a batch size of 1000 events and run 5 threads to pull data.

 * @param addresses List of InetSocketAddresses representing the hosts to connect to.

 * @param storageLevel Storage level to use for storing the received objects

 */

def createPollingStream(

    ssc: StreamingContext,

    addresses: Seq[InetSocketAddress],

    storageLevel: StorageLevel

  ): ReceiverInputDStream[SparkFlumeEvent] = {

  createPollingStream(ssc, addresses, storageLevel,

    DEFAULT_POLLING_BATCH_SIZE, DEFAULT_POLLING_PARALLELISM)

}

3、创建FlumePollingInputDstream对象

/**

 * Creates an input stream that is to be used with the Spark Sink deployed on a Flume agent.

 * This stream will poll the sink for data and will pull events as they are available.

 * @param addresses List of InetSocketAddresses representing the hosts to connect to.

 * @param maxBatchSize Maximum number of events to be pulled from the Spark sink in a

 *                     single RPC call

 * @param parallelism Number of concurrent requests this stream should send to the sink. Note

 *                    that having a higher number of requests concurrently being pulled will

 *                    result in this stream using more threads

 * @param storageLevel Storage level to use for storing the received objects

 */

def createPollingStream(

    ssc: StreamingContext,

    addresses: Seq[InetSocketAddress],

    storageLevel: StorageLevel,

    maxBatchSize: Int,

    parallelism: Int

  ): ReceiverInputDStream[SparkFlumeEvent] = {

  new FlumePollingInputDStream[SparkFlumeEvent](ssc, addresses, maxBatchSize,

    parallelism, storageLevel)

}

4、继承自ReceiverInputDstream并覆写getReciver方法,调用FlumePollingReciver接口

private[streaming] class FlumePollingInputDStream[T: ClassTag](

    _ssc: StreamingContext,

    val addresses: Seq[InetSocketAddress],

    val maxBatchSize: Int,

    val parallelism: Int,

    storageLevel: StorageLevel

  ) extends ReceiverInputDStream[SparkFlumeEvent](_ssc) {

   override def getReceiver(): Receiver[SparkFlumeEvent] = {

    new FlumePollingReceiver(addresses, maxBatchSize, parallelism, storageLevel)

  }

}

5、ReceiverInputDstream 构建了一个线程池,设置为后台线程;并使用lazy和工厂方法创建线程和NioClientSocket(NioClientSocket底层使用NettyServer的方式)

lazy val channelFactoryExecutor =

  Executors.newCachedThreadPool(new ThreadFactoryBuilder().setDaemon(true).

    setNameFormat("Flume Receiver Channel Thread - %d").build())

lazy val channelFactory =

  new NioClientSocketChannelFactory(channelFactoryExecutor, channelFactoryExecutor)

6、receiverExecutor 内部也是线程池;connections是指链接分布式Flume集群的FlumeConnection实体句柄的个数,线程拿到实体句柄访问数据。

lazy val receiverExecutor = Executors.newFixedThreadPool(parallelism,

  new ThreadFactoryBuilder().setDaemon(true).setNameFormat("Flume Receiver Thread - %d").build())

private lazy val connections = new LinkedBlockingQueue[FlumeConnection]()

7、启动时创建NettyTransceiver,根据并行度(默认5个)循环提交FlumeBatchFetcher

override def onStart(): Unit = {

  // Create the connections to each Flume agent.

  addresses.foreach(host => {

    val transceiver = new NettyTransceiver(host, channelFactory)

    val client = SpecificRequestor.getClient(classOf[SparkFlumeProtocol.Callback], transceiver)

    connections.add(new FlumeConnection(transceiver, client))

  })

  for (i <- 0 until parallelism) {

    logInfo("Starting Flume Polling Receiver worker threads..")

    // Threads that pull data from Flume.

    receiverExecutor.submit(new FlumeBatchFetcher(this))

  }

}

8、FlumeBatchFetcher run方法中从Receiver中获取connection链接句柄ack跟消息确认有关

def run(): Unit = {

  while (!receiver.isStopped()) {

    val connection = receiver.getConnections.poll()

    val client = connection.client

    var batchReceived = false

    var seq: CharSequence = null

    try {

      getBatch(client) match {

        case Some(eventBatch) =>

          batchReceived = true

          seq = eventBatch.getSequenceNumber

          val events = toSparkFlumeEvents(eventBatch.getEvents)

          if (store(events)) {

            sendAck(client, seq)

          } else {

            sendNack(batchReceived, client, seq)

          }

        case None =>

      }

    } catch {

9、获取一批一批数据方法

/**

 * Gets a batch of events from the specified client. This method does not handle any exceptions

 * which will be propogated to the caller.

 * @param client Client to get events from

 * @return [[Some]] which contains the event batch if Flume sent any events back, else [[None]]

 */

private def getBatch(client: SparkFlumeProtocol.Callback): Option[EventBatch] = {

  val eventBatch = client.getEventBatch(receiver.getMaxBatchSize)

  if (!SparkSinkUtils.isErrorBatch(eventBatch)) {

    // No error, proceed with processing data

    logDebug(s"Received batch of ${eventBatch.getEvents.size} events with sequence " +

      s"number: ${eventBatch.getSequenceNumber}")

    Some(eventBatch)

  } else {

    logWarning("Did not receive events from Flume agent due to error on the Flume agent: " +

      eventBatch.getErrorMsg)

    None

  }

}


备注:

资料来源于:DT_大数据梦工厂

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