What is the meaning of a shared variable in Apache Spark? What is the use of it?

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Amit Raj

TECHNICAL ARCHIECT & LEAD ENGINEER – GEN AI & DATABRICKS, CLOUD (AZURE, AWS), DEVOPS, DBT

Spark Shared Variables help in reducing data transfer.There two types for shared variables-Broadcast variable and Accumulators. Broadcast variable: If we have a large dataset, instead of transferring a copy of data set for each task, we can use a broadcast variable which can be copied to each node...
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Spark Shared Variables help in reducing data transfer.There two types for shared variables-Broadcast variable and Accumulators. Broadcast variable: If we have a large dataset, instead of transferring a copy of data set for each task, we can use a broadcast variable which can be copied to each node at one time and share the same data for each task in that node. Broadcast variable help to give a large data set to each node. First, we need to create a broadcast variable using SparkContext.broadcast and then broadcast the same to all nodes from driver program. Value method can be used to access the shared value. The broadcast variable will be used only if tasks for multiple stages use the same data. Accumulator: Spark functions used variables defined in the driver program and local copies of variables will be generated. Accumulators are shared variables which help to update variables in parallel during execution and share the results from workers to the driver. read less
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Normally, when a function passed to a Spark operation (such as map or reduce) is executed on a remote cluster node, it works on separate copies of all the variables used in the function. These variables are copied to each machine, and no updates to the variables on the remote machine are propagated back...
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Normally, when a function passed to a Spark operation (such as map or reduce) is executed on a remote cluster node, it works on separate copies of all the variables used in the function. These variables are copied to each machine, and no updates to the variables on the remote machine are propagated back to the driver program. Supporting general, read-write shared variables across tasks would be inefficient. However, Spark does provide two limited types of shared variables for two common usage patterns: broadcast variables and accumulators. 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. Spark also attempts to distribute broadcast variables using efficient broadcast algorithms to reduce communication cost. Accumulators are variables that are only “added” to through an associative operation and can therefore be efficiently supported in parallel. They can be used to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of numeric types, and programmers can add support for new types. If accumulators are created with a name, they will be displayed in Spark’s UI. This can be useful for understanding the progress of running stages (NOTE: this is not yet supported in Python). read less
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