Spark repartition vs coalesce: When to Use Each

Spark’s repartition() and coalesce() both alter DataFrame partition counts, but repartition performs a full shuffle that can increase or decrease partitions, rebalance data, or group rows by keys, while coalesce merges existing partitions without shuffling and can only reduce their number. Coalesce is generally efficient for modest reductions before writing balanced data because it avoids network and disk costs, but drastic reductions such as coalesce(1) can make jobs much slower by forcing all upstream scans and transformations into too few tasks. Repartition is preferable after heavy processing, when increasing parallelism, correcting uneven partition sizes, producing a single output file from large data, or aligning records by a partitioning column before a partitioned write. Output partition counts largely determine file counts and sizes, while partitioned writes may require repartitioning by the output key to prevent many small files. Adaptive Query Execution can automatically merge small shuffle partitions, but it differs from explicit coalesce(), and Spark UI plans and task metrics can help identify whether a partitioning choice introduces a shuffle, reduces parallelism, or exposes skew.

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