基于案例一节课贯通Spark Streaming流计算框架的运行源码



1. 在线动态计算分类最热门商品案例回顾与演示

2. 基于案例贯通Spark Streaming的运行源码

1. 使用Spark Streaming + Spark SQL来在线动态计算电商中不同类别中最热门的商品排名，例如手机这个类别下面最热门的三款手机。

   是用mysql数据库作为元数据库，使用Hive作为存储引擎，使用Spark SQL作为查询引擎。

   其中链接数据库代码如下：

    

package com.dt.spark.com.dt.spark.streaming;
import java.sql.Connection;
import java.sql.DriverManager;
import java.util.LinkedList;
public class ConnectionPool {
   private static LinkedList connectionQueue;
   static {
      try {
         Class.forName("com.mysql.jdbc.Driver");
      } catch (ClassNotFoundException e) {
         e.printStackTrace();
      }  
   }
   public synchronized static Connection getConnection() {
      try {
         if(connectionQueue == null) {
            connectionQueue = new LinkedList();
            for(int i = 0; i < 5; i++) {
               Connection conn = DriverManager.getConnection(
                     "jdbc:mysql://Master:3306/sparkstreaming",
                     "root","123456");
               connectionQueue.push(conn);  
            }
         }
      } catch (Exception e) {
         e.printStackTrace();
      }
      return connectionQueue.poll();
   }

   public static void returnConnection(Connection conn) {
      connectionQueue.push(conn);  
   }
}

 

 

操作代码如下：

 

package com.dt.spark.com.dt.spark.streaming
import org.apache.spark.SparkConf
import org.apache.spark.sql.Row
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
import org.apache.spark.streaming.{Seconds, StreamingContext}
/**
  * 使用Spark Streaming+Spark SQL来在线动态计算电商中不同类别中最热门的商品排名，例如手机这个类别下面最热门的三种手机、电视这个类别
  * 下最热门的三种电视，该实例在实际生产环境下具有非常重大的意义；
  *
  * @author DT大数据梦工厂
  * 新浪微博：http://weibo.com/ilovepains/

  *   实现技术：Spark Streaming+Spark SQL，之所以Spark Streaming能够使用ML、sql、graphx等功能是因为有foreachRDD和Transform
  * 等接口，这些接口中其实是基于RDD进行操作，所以以RDD为基石，就可以直接使用Spark其它所有的功能，就像直接调用API一样简单。
  *  假设说这里的数据的格式：user item category，例如Rocky Samsung Android
  */
object OnlineTheTop3ItemForEachCategory2DB {
  def main(args: Array[String]){
    /**
      * 第1步：创建Spark的配置对象SparkConf，设置Spark程序的运行时的配置信息，
      * 例如说通过setMaster来设置程序要链接的Spark集群的Master的URL,如果设置
      * 为local，则代表Spark程序在本地运行，特别适合于机器配置条件非常差（例如
      * 只有1G的内存）的初学者       *
      */
    val conf = new SparkConf() //创建SparkConf对象
    conf.setAppName("OnlineTheTop3ItemForEachCategory2DB") //设置应用程序的名称，在程序运行的监控界面可以看到名称
//    conf.setMaster("spark://Master:7077") //此时，程序在Spark集群
    conf.setMaster("local[6]")
    //设置batchDuration时间间隔来控制Job生成的频率并且创建Spark Streaming执行的入口
    val ssc = new StreamingContext(conf, Seconds(5))
    ssc.checkpoint("/root/resource/checkpoint/")
    val userClickLogsDStream = ssc.socketTextStream("Master", 9999)

    val formattedUserClickLogsDStream = userClickLogsDStream.map(clickLog =>
        (clickLog.split(" ")(2) + "_" + clickLog.split(" ")(1), 1))
    val categoryUserClickLogsDStream = formattedUserClickLogsDStream.reduceByKeyAndWindow(_+_,
      _-_, Seconds(60), Seconds(20))

    categoryUserClickLogsDStream.foreachRDD { rdd => {
      if (rdd.isEmpty()) {
        println("No data inputted!!!")
      } else {
        val categoryItemRow = rdd.map(reducedItem => {
          val category = reducedItem._1.split("_")(0)
          val item = reducedItem._1.split("_")(1)
          val click_count = reducedItem._2
          Row(category, item, click_count)
        })

        val structType = StructType(Array(
          StructField("category", StringType, true),
          StructField("item", StringType, true),
          StructField("click_count", IntegerType, true)
        ))

        val hiveContext = new HiveContext(rdd.context)
        val categoryItemDF = hiveContext.createDataFrame(categoryItemRow, structType)

        categoryItemDF.registerTempTable("categoryItemTable")

        val reseltDataFram = hiveContext.sql("SELECT category,item,click_count FROM (SELECT category,item,click_count,row_number()" +
          " OVER (PARTITION BY category ORDER BY click_count DESC) rank FROM categoryItemTable) subquery " +
          " WHERE rank <= 3")
        reseltDataFram.show()

        val resultRowRDD = reseltDataFram.rdd

        resultRowRDD.foreachPartition { partitionOfRecords => {

          if (partitionOfRecords.isEmpty){
            println("This RDD is not null but partition is null")
          } else {
            // ConnectionPool is a static, lazily initialized pool of connections
            val connection = ConnectionPool.getConnection()
            partitionOfRecords.foreach(record => {
              val sql = "insert into categorytop3(category,item,client_count) values('" + record.getAs("category") + "','" +
                record.getAs("item") + "'," + record.getAs("click_count") + ")"
              val stmt = connection.createStatement();
              stmt.executeUpdate(sql);

            })
            ConnectionPool.returnConnection(connection) // return to the pool for future reuse
          }
        }
        }
      }
    }
    }
    /**
      * 在StreamingContext调用start方法的内部其实是会启动JobScheduler的Start方法，进行消息循环，在JobScheduler
      * 的start内部会构造JobGenerator和ReceiverTacker，并且调用JobGenerator和ReceiverTacker的start方法：
      *   1，JobGenerator启动后会不断的根据batchDuration生成一个个的Job
      *   2，ReceiverTracker启动后首先在Spark Cluster中启动Receiver（其实是在Executor中先启动ReceiverSupervisor），在Receiver收到
      *   数据后会通过ReceiverSupervisor存储到Executor并且把数据的Metadata信息发送给Driver中的ReceiverTracker，在ReceiverTracker
      *   内部会通过ReceivedBlockTracker来管理接受到的元数据信息
      * 每个BatchInterval会产生一个具体的Job，其实这里的Job不是Spark Core中所指的Job，它只是基于DStreamGraph而生成的RDD
      * 的DAG而已，从Java角度讲，相当于Runnable接口实例，此时要想运行Job需要提交给JobScheduler，在JobScheduler中通过线程池的方式找到一个
      * 单独的线程来提交Job到集群运行（其实是在线程中基于RDD的Action触发真正的作业的运行），为什么使用线程池呢？
      *   1，作业不断生成，所以为了提升效率，我们需要线程池；这和在Executor中通过线程池执行Task有异曲同工之妙；
      *   2，有可能设置了Job的FAIR公平调度的方式，这个时候也需要多线程的支持；
      *
      */
    ssc.start()
    ssc.awaitTermination()

  }
}

 

 */
def this(
    master: String,
    appName: String,
    batchDuration: Duration,
    sparkHome: String = null,
    jars: Seq[String] = Nil,
    environment: Map[String, String] = Map()) = {
  this(StreamingContext.createNewSparkContext(master, appName, sparkHome, jars, environment),
       null, batchDuration)
}

StreamingContext在创建时会通过sparkConf在内部会构建SparkContext，所以StreamingContext是建立在一个SparkContext实例上，从这点也可以说明Spark Streaming是运行在Spark Core之上的。

 

def socketStream[T: ClassTag](
    hostname: String,
    port: Int,
    converter: (InputStream) => Iterator[T],
    storageLevel: StorageLevel
  ): ReceiverInputDStream[T] = {
  new SocketInputDStream[T](this, hostname, port, converter, storageLevel)
}

SocketStream底层调用的是SocketInputDStream实例，继承自ReceiverInputDStream通过SocketInputDstream然后基于Receiver方法接受数据

private[streaming]
class SocketInputDStream[T: ClassTag](
    ssc_ : StreamingContext,
    host: String,
    port: Int,
    bytesToObjects: InputStream => Iterator[T],
    storageLevel: StorageLevel
  ) extends ReceiverInputDStream[T](ssc_) {

  def getReceiver(): Receiver[T] = {
    new SocketReceiver(host, port, bytesToObjects, storageLevel)
  }
}

在ReceiverInputDstream构建的时候会初始化一个ReceiverRateController

override protected[streaming] val rateController: Option[RateController] = {
  if (RateController.isBackPressureEnabled(ssc.conf)) {
    Some(new ReceiverRateController(id, RateEstimator.create(ssc.conf, ssc.graph.batchDuration)))
  } else {
    None
  }
}

private[streaming] class ReceiverRateController(id: Int, estimator: RateEstimator)
    extends RateController(id, estimator) {
  override def publish(rate: Long): Unit =
    ssc.scheduler.receiverTracker.sendRateUpdate(id, rate)
}

 

在此对其做负载均衡

通过启动一条线程来接受Socket网络数据

  /** Create a socket connection and receive data until receiver is stopped */
  def receive() {
    var socket: Socket = null
    try {
      logInfo("Connecting to " + host + ":" + port)
      socket = new Socket(host, port)
      logInfo("Connected to " + host + ":" + port)
      val iterator = bytesToObjects(socket.getInputStream())
      while(!isStopped && iterator.hasNext) {
        store(iterator.next)
      }
      if (!isStopped()) {
        restart("Socket data stream had no more data")
      } else {
        logInfo("Stopped receiving")
      }
    } catch {
      case e: java.net.ConnectException =>
        restart("Error connecting to " + host + ":" + port, e)
      case NonFatal(e) =>
        logWarning("Error receiving data", e)
        restart("Error receiving data", e)
    } finally {
      if (socket != null) {
        socket.close()
        logInfo("Closed socket to " + host + ":" + port)
      }
    }
  }
}

 

ssc.start()

 */
def start(): Unit = synchronized {
  state match {
    case INITIALIZED =>
      startSite.set(DStream.getCreationSite())
      StreamingContext.ACTIVATION_LOCK.synchronized {
        StreamingContext.assertNoOtherContextIsActive()
        try {
          validate()

          // Start the streaming scheduler in a new thread, so that thread local properties
          // like call sites and job groups can be reset without affecting those of the
          // current thread.
          ThreadUtils.runInNewThread("streaming-start") {
            sparkContext.setCallSite(startSite.get)
            sparkContext.clearJobGroup()
            sparkContext.setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, "false")
            scheduler.start()
          }
          state = StreamingContextState.ACTIVE
        } catch {

 

 

调用JobScheduler的Start方法

class JobScheduler(val ssc: StreamingContext) extends Logging
  private val jobSets: java.util.Map[Time, JobSet] = new ConcurrentHashMap[Time, JobSet]

//时间和JobSet的对应
  private val numConcurrentJobs = ssc.conf.getInt("spark.streaming.concurrentJobs", 1)
  private val jobExecutor =
    ThreadUtils.newDaemonFixedThreadPool(numConcurrentJobs, "streaming-job-executor")
  private val jobGenerator = new JobGenerator(this)

//创建一个job的生成器
  val clock = jobGenerator.clock

//产生job的时间源
  val listenerBus = new StreamingListenerBus()
//创建Streaming流的监听器
  // These two are created only when scheduler starts.
  // eventLoop not being null means the scheduler has been started and not stopped
  var receiverTracker: ReceiverTracker = null
  // A tracker to track all the input stream information as well as processed record number
  var inputInfoTracker: InputInfoTracker = null
  private var eventLoop: EventLoop[JobSchedulerEvent] = null
  def start(): Unit = synchronized {
    if (eventLoop != null) return // scheduler has already been started
    logDebug("Starting JobScheduler")
    eventLoop = new EventLoop[JobSchedulerEvent]("JobScheduler") {
      override protected def onReceive(event: JobSchedulerEvent): Unit = processEvent(event)
      override protected def onError(e: Throwable): Unit = reportError("Error in job scheduler", e)
    }
    eventLoop.start()

//通过实践循环来处理例如接收数据事件，通过事件驱动方式来处理，底层应该是根据RPC框架来处理
    // attach rate controllers of input streams to receive batch completion updates
    for {
      inputDStream <- ssc.graph.getInputStreams
      rateController <- inputDStream.rateController
    } ssc.addStreamingListener(rateController)

    listenerBus.start(ssc.sparkContext)
    receiverTracker = new ReceiverTracker(ssc)
    inputInfoTracker = new InputInfoTracker(ssc)
    receiverTracker.start()
    jobGenerator.start()
    logInfo("Started JobScheduler")
  }

Event中进行事件的处理

 

 

private def processEvent(event: JobSchedulerEvent) {
    try {
      event match {
        case JobStarted(job, startTime) => handleJobStart(job, startTime)
        case JobCompleted(job, completedTime) => handleJobCompletion(job, completedTime)
        case ErrorReported(m, e) => handleError(m, e)
      }
    } catch {
      case e: Throwable =>
        reportError("Error in job scheduler", e)
    }
  }

  private def handleJobStart(job: Job, startTime: Long) {
    val jobSet = jobSets.get(job.time)
    val isFirstJobOfJobSet = !jobSet.hasStarted
    jobSet.handleJobStart(job)
    if (isFirstJobOfJobSet) {
      // "StreamingListenerBatchStarted" should be posted after calling "handleJobStart" to get the
      // correct "jobSet.processingStartTime".
      listenerBus.post(StreamingListenerBatchStarted(jobSet.toBatchInfo))
    }
    job.setStartTime(startTime)
    listenerBus.post(StreamingListenerOutputOperationStarted(job.toOutputOperationInfo))
    logInfo("Starting job " + job.id + " from job set of time " + jobSet.time)
  }

  private def handleJobCompletion(job: Job, completedTime: Long) {
    val jobSet = jobSets.get(job.time)
    jobSet.handleJobCompletion(job)
    job.setEndTime(completedTime)
    listenerBus.post(StreamingListenerOutputOperationCompleted(job.toOutputOperationInfo))
    logInfo("Finished job " + job.id + " from job set of time " + jobSet.time)
    if (jobSet.hasCompleted) {
      jobSets.remove(jobSet.time)
      jobGenerator.onBatchCompletion(jobSet.time)
      logInfo("Total delay: %.3f s for time %s (execution: %.3f s)".format(
        jobSet.totalDelay / 1000.0, jobSet.time.toString,
        jobSet.processingDelay / 1000.0
      ))
      listenerBus.post(StreamingListenerBatchCompleted(jobSet.toBatchInfo))
    }
    job.result match {
      case Failure(e) =>
        reportError("Error running job " + job, e)
      case _ =>
    }
  }

  private def handleError(msg: String, e: Throwable) {
    logError(msg, e)
    ssc.waiter.notifyError(e)
  }

  private class JobHandler(job: Job) extends Runnable with Logging {
    import JobScheduler._

    def run() {
      try {
        val formattedTime = UIUtils.formatBatchTime(
          job.time.milliseconds, ssc.graph.batchDuration.milliseconds, showYYYYMMSS = false)
        val batchUrl = s"/streaming/batch/?id=${job.time.milliseconds}"
        val batchLinkText = s"[output operation ${job.outputOpId}, batch time ${formattedTime}]"

        ssc.sc.setJobDescription(
          s"""Streaming job from $batchUrl">$batchLinkText""")
        ssc.sc.setLocalProperty(BATCH_TIME_PROPERTY_KEY, job.time.milliseconds.toString)
        ssc.sc.setLocalProperty(OUTPUT_OP_ID_PROPERTY_KEY, job.outputOpId.toString)

        // We need to assign `eventLoop` to a temp variable. Otherwise, because
        // `JobScheduler.stop(false)` may set `eventLoop` to null when this method is running, then
        // it's possible that when `post` is called, `eventLoop` happens to null.
        var _eventLoop = eventLoop
        if (_eventLoop != null) {
          _eventLoop.post(JobStarted(job, clock.getTimeMillis()))
          // Disable checks for existing output directories in jobs launched by the streaming
          // scheduler, since we may need to write output to an existing directory during checkpoint
          // recovery; see SPARK-4835 for more details.
          PairRDDFunctions.disableOutputSpecValidation.withValue(true) {
            job.run()
          }
          _eventLoop = eventLoop
          if (_eventLoop != null) {
            _eventLoop.post(JobCompleted(job, clock.getTimeMillis()))
          }
        } else {
          // JobScheduler has been stopped.
        }
      } finally {
        ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, null)
        ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, null)
      }
    }
  }
}

private[streaming] object JobScheduler {
  val BATCH_TIME_PROPERTY_KEY = "spark.streaming.internal.batchTime"
  val OUTPUT_OP_ID_PROPERTY_KEY = "spark.streaming.internal.outputOpId"
}

 

JobGernerated代码部分，提交JobSet将submitJob事件放在事件队列中

private def generateJobs(time: Time) {
  // Set the SparkEnv in this thread, so that job generation code can access the environment
  // Example: BlockRDDs are created in this thread, and it needs to access BlockManager
  // Update: This is probably redundant after threadlocal stuff in SparkEnv has been removed.
  SparkEnv.set(ssc.env)
  Try {
    jobScheduler.receiverTracker.allocateBlocksToBatch(time) // allocate received blocks to batch
    graph.generateJobs(time) // generate jobs using allocated block
  } match {
    case Success(jobs) =>
      val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time)
      jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToInputInfos))
    case Failure(e) =>
      jobScheduler.reportError("Error generating jobs for time " + time, e)
  }
  eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false))
}

获取Input输入信息，触发submitJobset分发job到每台机器，在jobScheduler中进行调用调用jobHandler，jobHandler继承自runnerbal接口

foreachRDD会将Dstream添加到DstreamGraph中

 

在JobScheduler中会实例化receiverTracker和InputInforTracker的变量，在ReceiverTracker中会创建一个ReceiverTrackerEndpoint的变量

def start(): Unit = synchronized {
  if (isTrackerStarted) {
    throw new SparkException("ReceiverTracker already started")
  }

  if (!receiverInputStreams.isEmpty) {
    endpoint = ssc.env.rpcEnv.setupEndpoint(
      "ReceiverTracker", new ReceiverTrackerEndpoint(ssc.env.rpcEnv))
    if (!skipReceiverLaunch) launchReceivers()
    logInfo("ReceiverTracker started")
    trackerState = Started
  }
}

 

查看lauchReceivers中发现

 * worker nodes as a parallel collection, and runs them.
 */
private def launchReceivers(): Unit = {
  val receivers = receiverInputStreams.map(nis => {
    val rcvr = nis.getReceiver()
    rcvr.setReceiverId(nis.id)
    rcvr
  })

  runDummySparkJob()

  logInfo("Starting " + receivers.length + " receivers")
  endpoint.send(StartAllReceivers(receivers))
}

获取个个worker节点的Reciver，查看runDummySparkJob发现课程一中，MakRDD，作为负载均衡将信息均发到各个节点上去。

在ReceiverTrackerEndPoint接收到StartALLReceivers消息，并做如下处理

override def receive: PartialFunction[Any, Unit] = {
  // Local messages
  case StartAllReceivers(receivers) =>
    val scheduledLocations = schedulingPolicy.scheduleReceivers(receivers, getExecutors)
    for (receiver <- receivers) {
      val executors = scheduledLocations(receiver.streamId)
      updateReceiverScheduledExecutors(receiver.streamId, executors)
      receiverPreferredLocations(receiver.streamId) = receiver.preferredLocation
      startReceiver(receiver, executors)
    }

中调用StartReciver中发现new ReceiverSupervisorImpl

private def startFirstTime() {
  val startTime = new Time(timer.getStartTime())
  graph.start(startTime - graph.batchDuration)
  timer.start(startTime.milliseconds)
  logInfo("Started JobGenerator at " + startTime)
}

如果使用了checkpoint那么就是重新启动，没有启动就startFirstTime方法，在startFirstTime中发现graph.start,和time1.start

def start(time: Time) {

  this.synchronized {

    require(zeroTime == null, "DStream graph computation already started")

    zeroTime = time

    startTime = time

    outputStreams.foreach(_.initialize(zeroTime))

    outputStreams.foreach(_.remember(rememberDuration))

    outputStreams.foreach(_.validateAtStart)

    inputStreams.par.foreach(_.start())

  }

}

调用DstreamGraph的start方法

回到前面的submitJob中，submitJob的第二个参数，是一个函数，它的功能是Worker节点上启动Receiver

val supervisor = new ReceiverSupervisorImpl(receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption)

supervisor.start()

supervisor.awaitTermination()

这里要弄清ReceiverInputDstream和Recevier的区别。Receiver是具体接收数据的，而ReceiverInputDstream是对Receiver做了一层封装。

private[streaming] final def getOrCompute(time: Time): Option[RDD[T]] = {
  // If RDD was already generated, then retrieve it from HashMap,
  // or else compute the RDD
  generatedRDDs.get(time).orElse {
    // Compute the RDD if time is valid (e.g. correct time in a sliding window)
    // of RDD generation, else generate nothing.
    if (isTimeValid(time)) {

      val rddOption = createRDDWithLocalProperties(time, displayInnerRDDOps = false) {
        // Disable checks for existing output directories in jobs launched by the streaming
        // scheduler, since we may need to write output to an existing directory during checkpoint
        // recovery; see SPARK-4835 for more details. We need to have this call here because
        // compute() might cause Spark jobs to be launched.
        PairRDDFunctions.disableOutputSpecValidation.withValue(true) {
          compute(time)
        }
      }

      rddOption.foreach { case newRDD =>
        // Register the generated RDD for caching and checkpointing
        if (storageLevel != StorageLevel.NONE) {
          newRDD.persist(storageLevel)
          logDebug(s"Persisting RDD ${newRDD.id} for time $time to $storageLevel")
        }
        if (checkpointDuration != null && (time - zeroTime).isMultipleOf(checkpointDuration)) {
          newRDD.checkpoint()
          logInfo(s"Marking RDD ${newRDD.id} for time $time for checkpointing")
        }
        generatedRDDs.put(time, newRDD)
      }
      rddOption
    } else {
      None
    }
  }
}

是用getOrCompute方法，生成指定时间的RDD

备注：

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