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What is the difference between hash-based and sort-based shuffling in Apache Spark?

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On a high level, in hash-based shuffle each mapper M writes R shuffle files, one for each reducer where R is the number of reduce partitions. This results in M * R shuffle files. Since it is not uncommon for M and R to be O(1000), this quickly becomes expensive. An optimization with hash-based shuffle...
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On a high level, in hash-based shuffle each mapper M writes R shuffle files, one for each reducer where R is the number of reduce partitions. This results in M * R shuffle files. Since it is not uncommon for M and R to be O(1000), this quickly becomes expensive. An optimization with hash-based shuffle is consolidation, where all mappers run in the same core C write one file per reducer, resulting in C * R files. This is a strict improvement, but it is still relatively expensive. Instead, in sort-based shuffle each mapper writes a single partitioned file. This allows a particular reducer to request a specific portion of each mapper's single output file. In more detail, the mapper first fills up an internal buffer in memory and continually spills the contents of the buffer to disk, then finally merges all the spilled files together to form one final output file. This places much less stress on the file system and requires much fewer I/O operations especially on the read side. read less
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