spark变量使用broadcast、accumulator

broadcast

官方文档描述：

Broadcast a read-only variable to the cluster, returning a [[org.apache.spark.broadcast.Broadcast]] object for reading it in distributed functions. The variable will be sent to each cluster only once.

源码分析：

这里使用告警方式代替异常，为了是避免用户进程中断；可能有用户创建了广播变量但是没有使用他们；

  /**
   * Broadcast a read-only variable to the cluster, returning a
   * [[org.apache.spark.broadcast.Broadcast]] object for reading it in distributed functions.
   * The variable will be sent to each cluster only once.
   */
  def broadcast[T: ClassTag](value: T): Broadcast[T] = {
    assertNotStopped()
    require(!classOf[RDD[_]].isAssignableFrom(classTag[T].runtimeClass),
      "Can not directly broadcast RDDs; instead, call collect() and broadcast the result.")
    val bc = env.broadcastManager.newBroadcast[T](value, isLocal)
    val callSite = getCallSite
    logInfo("Created broadcast " + bc.id + " from " + callSite.shortForm)
    cleaner.foreach(_.registerBroadcastForCleanup(bc))
    bc
  }

广播变量允许程序员将一个只读的变量缓存在每台机器上，而不用在任务之间传递变量。广播变量可被用于有效地给每个节点一个大输入数据集的副本。Spark还尝试使用高效地广播算法来分发变量，进而减少通信的开销。 Spark的动作通过一系列的步骤执行，这些步骤由分布式的洗牌操作分开。Spark自动地广播每个步骤每个任务需要的通用数据。这些广播数据被序列化地缓存，在运行任务之前被反序列化出来。这意味着当我们需要在多个阶段的任务之间使用相同的数据，或者以反序列化形式缓存数据是十分重要的时候，显式地创建广播变量才有用。

实例

List<Integer> data = Arrays.asList(5, 1, 1, 4, 4, 2, 2);
JavaRDD<Integer> javaRDD = javaSparkContext.parallelize(data,5);
final Broadcast<List<Integer>> broadcast = javaSparkContext.broadcast(data);
JavaRDD<Integer> result = javaRDD.map(new Function<Integer, Integer>() {    
  List<Integer> iList = broadcast.value();    
  @Override    
  public Integer call(Integer v1) throws Exception {        
    Integer isum = 0;        
    for(Integer i : iList)            
      isum += i;        
    return v1 + isum;    
  }
});
System.out.println(result.collect());

accumulator

源码分析：

// Methods for creating shared variables

  /**
   * Create an [[org.apache.spark.Accumulator]] variable of a given type, which tasks can "add"
   * values to using the `+=` method. Only the driver can access the accumulator's `value`.
   */
  @deprecated("use AccumulatorV2", "2.0.0")
  def accumulator[T](initialValue: T)(implicit param: AccumulatorParam[T]): Accumulator[T] = {
    val acc = new Accumulator(initialValue, param)
    cleaner.foreach(_.registerAccumulatorForCleanup(acc.newAcc))
    acc
  }

  /**
   * Create an [[org.apache.spark.Accumulator]] variable of a given type, with a name for display
   * in the Spark UI. Tasks can "add" values to the accumulator using the `+=` method. Only the
   * driver can access the accumulator's `value`.
   */
  @deprecated("use AccumulatorV2", "2.0.0")
  def accumulator[T](initialValue: T, name: String)(implicit param: AccumulatorParam[T])
    : Accumulator[T] = {
    val acc = new Accumulator(initialValue, param, Some(name))
    cleaner.foreach(_.registerAccumulatorForCleanup(acc.newAcc))
    acc
  }

累加器是仅仅被相关操作累加的变量，因此可以在并行中被有效地支持。它可以被用来实现计数器和sum。Spark原生地只支持数字类型的累加器，开发者可以添加新类型的支持。如果创建累加器时指定了名字，可以在Spark的UI界面看到。这有利于理解每个执行阶段的进程（对于Python还不支持）。
累加器通过对一个初始化了的变量v调用SparkContext.accumulator(v)来创建。在集群上运行的任务可以通过add或者”+=”方法在累加器上进行累加操作。但是，它们不能读取它的值。只有驱动程序能够读取它的值，通过累加器的value方法。

class VectorAccumulatorParam implements AccumulatorParam<Vector> {    
  @Override    
  //合并两个累加器的值。
  //参数r1是一个累加数据集合
  //参数r2是另一个累加数据集合
  public Vector addInPlace(Vector r1, Vector r2) {
    r1.addAll(r2);
    return r1;    
  }    
  @Override 
  //初始值   
  public Vector zero(Vector initialValue) {        
     return initialValue;    
  }    
  @Override
  //添加额外的数据到累加值中
  //参数t1是当前累加器的值
  //参数t2是被添加到累加器的值    
  public Vector addAccumulator(Vector t1, Vector t2) {        
      t1.addAll(t2);        
      return t1;    
  }
}
List<Integer> data = Arrays.asList(5, 1, 1, 4, 4, 2, 2);
JavaRDD<Integer> javaRDD = javaSparkContext.parallelize(data,5);

final Accumulator<Integer> accumulator = javaSparkContext.accumulator(0);
Vector initialValue = new Vector();
for(int i=6;i<9;i++)    
  initialValue.add(i);
//自定义累加器
final Accumulator accumulator1 = javaSparkContext.accumulator(initialValue,new VectorAccumulatorParam());
JavaRDD<Integer> result = javaRDD.map(new Function<Integer, Integer>() {    
  @Override    
  public Integer call(Integer v1) throws Exception {        
    accumulator.add(1);        
    Vector term = new Vector();        
    term.add(v1);        
    accumulator1.add(term);        
    return v1;    
  }
});
System.out.println(result.collect());
System.out.println("~~~~~~~~~~~~~~~~~~~~~" + accumulator.value());
System.out.println("~~~~~~~~~~~~~~~~~~~~~" + accumulator1.value());