1) Accumulators:
Accumulators are used to implement counters and sum in Spark applications.
Accumulators allow you to aggregate values from tasks running on worker nodes back to the driver program. They provide a way for tasks to incrementally update a shared variable (the accumulator) in a way that is safe for distributed computation. The driver program can then access the final value of the accumulator after all tasks have completed. (we have single copy on drive machine)
Conclusion : Accumulators are an important feature of Apache Spark that allows us to perform distributed calculations on large datasets. They provide a simple and efficient way of accumulating data across multiple tasks in a distributed system. By using accumulators in our Spark applications, we can perform complex calculations on large datasets with ease.
2) Broadcast :
The name suggest are โbroadacastโ to the nodes of the spark cluster to avoid shuffle operations. It allow you to efficiently distribute read-only data to all worker nodes in the cluster. This data is cached in memory on each worker node, so tasks can access it without having to transfer the data over the network repeatedly. Broadcast variables are particularly useful when you have large datasets or other read-only data that needs to be shared across tasks.
(we have separte copy on each machine)
Conclusion : The primary purpose of broadcast variables is to address the challenge of data replication and distribution in distributed systems. Instead of replicating large datasets across multiple nodes, which can be both time-consuming and resource-intensive, broadcast variables enable the efficient transfer of data to all the machines in the cluster. By doing so, broadcast variables eliminate the need for repetitive data transfers and improve the performance of distributed computations.
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