Using Broadcast Variables

Scenario

Broadcast distributes data sets to each node. It allows data to be obtained locally when a data set is needed during a Spark task. If broadcast is not used, data serialization will be scheduled to tasks each time when a task requires data sets. It is time-consuming and makes the task get bigger.

1. If a data set will be used by each slice of a task, broadcast the data set to each node.
2. When small and big tables need to be joined, broadcast small tables to each node. This eliminates the shuffle operation, changing the join operation into a common operation.

Procedure

When developing an application, add the following code to broadcast the testArr data to each node:

def main(args: Array[String]) {
  ...
  val testArr: Array[Long] = new Array[Long](200)
  val testBroadcast: Broadcast[Array[Long]] = sc.broadcast(testArr)
  val resultRdd: RDD[Long] = inpputRdd.map(input => handleData(testBroadcast, input))
  ...
}

def handleData(broadcast: Broadcast[Array[Long]], input: String) {
  val value = broadcast.value
  ...
}