6.job触发流程原理剖析与源码分析

从我们编写的一个小的spark  demo程序开始 :

val lines = sparkContext.textFile("")
val words = lines.flatMap(line => line.split("\t"))
val pairs = words.map(word => (word,1))
val counts = pairs.reduceByKey(_+_)
counts.foreach(count +. println(count_1+"_"+count_2))

在SparkContext中textFile方法源码如下:

/**
   * Read a text file from HDFS, a local file system (available on all nodes), or any
   * Hadoop-supported file system URI, and return it as an RDD of Strings.
   * 
   * 首先HadoopFile方法的调用会创建一个HadoopRDD , 该RDD中的元素就是一个一个的(key,value),其中key就是文本行的行号,value就是一行的文本值
   * 然后调用HadoopRDD的map()方法 , 该方法就会将HadoopRDD中的key移除掉 , 只保留value 
   * 最后就形成一个只包含文本行的MapPartitionRDD
   */
  def textFile(path: String, minPartitions: Int = defaultMinPartitions): RDD[String] = {
    assertNotStopped()
    hadoopFile(path, classOf[TextInputFormat], classOf[LongWritable], classOf[Text],
      minPartitions).map(pair => pair._2.toString).setName(path)
  }

这其中的原因已经在代码中说明 , 这其中会调用hadoopFile方法 , 源码如下 :

/** Get an RDD for a Hadoop file with an arbitrary InputFormat
   *
   * '''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each
   * record, directly caching the returned RDD or directly passing it to an aggregation or shuffle
   * operation will create many references to the same object.
   * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first
   * copy them using a `map` function.
   */
  def hadoopFile[K, V](
      path: String,
      inputFormatClass: Class[_ <: InputFormat[K, V]],
      keyClass: Class[K],
      valueClass: Class[V],
      minPartitions: Int = defaultMinPartitions
      ): RDD[(K, V)] = {
    assertNotStopped()
    // A Hadoop configuration can be about 10 KB, which is pretty big, so broadcast it.
    
    // 将Hadoop的配置信息作为广播变量 , 方便在每个节点上都可以读取到相同的Hadoop配置信息
    val confBroadcast = broadcast(new SerializableWritable(hadoopConfiguration))
    val setInputPathsFunc = (jobConf: JobConf) => FileInputFormat.setInputPaths(jobConf, path)
    // 创建HadoopRDD对象
    new HadoopRDD(
      this,
      confBroadcast,
      Some(setInputPathsFunc),
      inputFormatClass,
      keyClass,
      valueClass,
      minPartitions).setName(path)
  }

对于创建好的HadoopRDD并经过flatMap算子操作之后形成的算子最后调用reduceByKey的时候会经过一到隐式转换 , 因为在RDD中是没有reduceByKey方法的,

因此在调用reduceByKey时候其实是调用如下的方法 : 

/**
   * 其实在RDD里面是没有reduceByKey的 , 因此对RDD调用reduceByKey()方法时会触发scala的隐式转换;此时就会在作用域内寻找隐式转换
   * 因此就会在RDD中找到rddToPairRDDFunction()方法,然后调用RDD转换为PairRDDFunction , 在PairRDDFunction中就会调用reduceByKey方法
   */
  implicit def rddToPairRDDFunctions[K, V](rdd: RDD[(K, V)])
    (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null): PairRDDFunctions[K, V] = {
    new PairRDDFunctions(rdd)
  }

从上面的源码中可以看出最后真正的reduceBykey的方法在PairRDDFunctions中 , 源码如下 :

/**
   * Merge the values for each key using an associative reduce function. This will also perform
   * the merging locally on each mapper before sending results to a reducer, similarly to a
   * "combiner" in MapReduce.
   */
  def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = {
    combineByKey[V]((v: V) => v, func, func, partitioner)
  }
  /**
   * Merge the values for each key using an associative reduce function. This will also perform
   * the merging locally on each mapper before sending results to a reducer, similarly to a
   * "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions.
   */
  def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = {
    reduceByKey(new HashPartitioner(numPartitions), func)
  }
  /**
   * Merge the values for each key using an associative reduce function. This will also perform
   * the merging locally on each mapper before sending results to a reducer, similarly to a
   * "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/
   * parallelism level.
   */
  def reduceByKey(func: (V, V) => V): RDD[(K, V)] = {
    reduceByKey(defaultPartitioner(self), func)
  }

最后 , 不管经过多少transformation算子操作 , 若是没有action的算子操作的话, 那么是不会运行transformation算子的 , 因此foreach方法其实就是触发上面所有算子的操作 , 

而RDD的fereach的源码如下 :

/**
   * Applies a function f to all elements of this RDD.
   * 若是一个RDD调用了action的算子 , 比如foreach方法 , 那么其实就会触发job的运行 , 最后其实就会调用SparkContext的runjob方法
   */
  def foreach(f: T => Unit) {
    val cleanF = sc.clean(f)
    // 调用SparkContext的runjob方法
    sc.runJob(this, (iter: Iterator[T]) => iter.foreach(cleanF))
  }

最后真正调用的就是SparkContext的runjob方法 进行真正的任务运行 :

/**
   * Run a function on a given set of partitions in an RDD and pass the results to the given
   * handler function. This is the main entry point for all actions in Spark. The allowLocal
   * flag specifies whether the scheduler can run the computation on the driver rather than
   * shipping it out to the cluster, for short actions like first().
   */
  def runJob[T, U: ClassTag](
      rdd: RDD[T],
      func: (TaskContext, Iterator[T]) => U,
      partitions: Seq[Int],
      allowLocal: Boolean,
      resultHandler: (Int, U) => Unit) {
    if (stopped) {
      throw new IllegalStateException("SparkContext has been shutdown")
    }
    val callSite = getCallSite
    val cleanedFunc = clean(func)
    logInfo("Starting job: " + callSite.shortForm)
    if (conf.getBoolean("spark.logLineage", false)) {
      logInfo("RDD's recursive dependencies:\n" + rdd.toDebugString)
    }
    
    //最重要的就是这一行代码了 , 调用SparkContext中的DAGScheduler组件运行一个job
    dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, allowLocal,
      resultHandler, localProperties.get)
    progressBar.foreach(_.finishAll())
    rdd.doCheckpoint()
  }

其实SparkContext中运行job的组件就是之前讲到的DAGScheduler了 , 运行一个job就需要stage的划分了 ,  下面就是stage算法划分了