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I am going through Spark Programming guide that says:
Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks.
Considering the above, what are the use cases of broadcast variables? What problems do broadcast variables solve?
When we create any broadcast variable like below, the variable reference, here it is broadcastVar available in all the nodes in the cluster?
val broadcastVar = sc.broadcast(Array(1, 2, 3)) How long these variables available in the memory of the nodes?
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A broadcast variable is any variable, other than the loop variable or a sliced variable, that does not change inside the loop. At the start of a parfor -loop, the values of any broadcast variables are sent to all workers. This type of variable can be useful or even essential for particular tasks.
A broadcast variable. Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. They can be used, for example, to give every node a copy of a large input dataset in an efficient manner.
Using broadcast variables can improve performance by reducing the amount of network traffic and data serialization required to execute your Spark application.
Sometimes, a variable needs to be shared across tasks, or between tasks and the driver program. Spark supports two types of shared variables: broadcast variables, which can be used to cache a value in memory on all nodes, and accumulators, which are variables that are only “added” to, such as counters and sums.
Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. Considering the above, what are the use cases of broadcast variables? What problems do broadcast variables solve?
apache spark - What are broadcast variables? What problems do they solve? - Stack Overflow What are broadcast variables? What problems do they solve? Bookmark this question. Show activity on this post. Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks.
– Hadoop In Real World What are broadcast variables in Spark and when to use them? What are broadcast variables in Spark and when to use them? Broadcast variables are variables which are available in all executors executing the Spark application. These variables are already cached and ready to be used by tasks executing as part of the application.
We can use Broadcast variable to deal with such scenarios. This is useful when tasks across multiple stages need the same data or when caching the data in de-serialized form is important. This will reduce the size of each serialized task, and the cost of launching a job over a cluster.
If you have huge array that is accessed from Spark Closures, for example some reference data, this array will be shipped to each spark node with closure. For example if you have 10 nodes cluster with 100 partitions (10 partitions per node), this Array will be distributed at least 100 times (10 times to each node).
If you use broadcast it will be distributed once per node using efficient p2p protocol.
val array: Array[Int] = ??? // some huge array val broadcasted = sc.broadcast(array) And some RDD
val rdd: RDD[Int] = ??? In this case array will be shipped with closure each time
rdd.map(i => array.contains(i)) and with broadcast you'll get huge performance benefit
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