@Vinay Emmadi : In Spark, a hash shuffle join is a type of join that is used when joining two data sets on a common key. The data is first partitioned based on the join key, and then each partition is shuffled and sent to a node in the cluster. The shuffle data is then sorted and merged with the other data sets with the same join key.
Here's a step-by-step explanation of how hash shuffle join works in Spark:
- Partitioning: The two data sets that are being joined are partitioned based on their join key using the HashPartitioner. Each partition will have all the records with the same join key.
- Shuffle: The partitions are shuffled across the nodes in the cluster using the network. Each node receives one or more partitions.
- Local join: Each node performs a local join on the received partitions. The join operation compares the join keys of the two data sets and produces a new data set that includes the combined data from both sets that have the same join key.
- Merge: The local join results are merged together by a reduce-like operation called a shuffle reduce. The shuffle reduce operation merges the local join results for each join key into a single result.
- Output: The final result of the join is written to disk or returned to the application.
Hope this helps you!