What is the purpose of broadcast variables in Spark?

Broadcast variables in Spark serve as a mechanism for efficiently distributing large datasets to all nodes in a cluster. Their primary purpose is to share read-only data between different nodes, thereby enhancing performance and reducing data transfer overhead in parallel operations.

In Spark, when a task needs to use a certain dataset (such as a large array or map), the dataset is copied and sent to each executor, which can result in excessive network transfer overhead. To avoid this, broadcast variables can be used to replicate the dataset on each worker node, reducing data transfer costs and improving performance.

Broadcast variables are used in the following scenarios:

1. Frequently used read-only data: If a task requires frequent access to a read-only data set, the data can be saved on all nodes using broadcast variables to avoid repetitive transmission.
2. Larger data sets: When dealing with larger data sets, using broadcast variables can prevent the repeated transmission of data in each task, thus improving efficiency.

You can complete the process by using broadcast variables.

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2. Access the broadcast data in the task through the value attribute of the broadcast variable.

Here is a simple example of using broadcast variables in Spark.

val data = sc.parallelize(Seq(1, 2, 3, 4, 5))
val broadcastData = sc.broadcast(data.collect())

val result = sc.parallelize(Seq(1, 2, 3))
  .map(x => x * broadcastData.value.sum())

In this example, the data set is broadcasted to each node and then the broadcast variable broadcastData is used in the map operation to compute the result, avoiding the need to repeatedly transmit data in each task.