1 使用广播变量的使用步骤

（1）声明广播变量

val board = sc.boardcast(XX)

（2）使用 .value 获得广播变量的值

（3）变量只会被发到各个节点一次，且作为“只读变量”

以下是广播变量的源代码

向所有工作节点发送一个较大的只读值，以供一个或多个Spark操作使用。比如，如果你的应用需要向所有节点发送一个较大的只读查询表，广播变量用起来都很顺手。

  /**
   * 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.
   *
   * @param value value to broadcast to the Spark nodes
   * @return `Broadcast` object, a read-only variable cached on each machine
   */
  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
  }

2 广播变量的代码与理解

代码实现广播变量

object Spark_BoardCast {
  def main(args: Array[String]): Unit = {
    //1.创建SparkConf并设置App名称
    val conf: SparkConf = new SparkConf().setAppName("SparkCoreTest01").setMaster("local[*]")
    //2.创建SparkContext，该对象是提交Spark App的入口
    val sc: SparkContext = new SparkContext(conf)
    val rdd1 = sc.makeRDD(List( ("a",1), ("b", 2), ("c", 3), ("d", 4) ),4)
    val list = List( ("a",4), ("b", 5), ("c", 6), ("d", 7) )
    // 声明广播变量
    val broadcast: Broadcast[List[(String, Int)]] = sc.broadcast(list)

    val resultRDD: RDD[(String, (Int, Int))] = rdd1.map {
      case (key, num) => {
        var num2 = 0
        //for((k,v) <- list)
        // 使用广播变量
        for ((k, v) <- broadcast.value) {
          if (k == key) {
            num2 = v
          }
        }
        (key, (num, num2))
      }
    }
    resultRDD.foreach(println)

  }
}

代码的图形理解，相较于使用List方式，广播变量无需向每个task中都分发变量的值。