Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Shuffle**: Redistribution of data across partitions so that rows with same key land on same partition. Required for groupBy, join, distinct. **Why Important**: Enables global aggregations and joins. But **expensive**—network I/O, serialization, disk (if spill). Often the...
Shuffle: Redistribution of data across partitions so that rows with same key land on same partition. Required for groupBy, join, distinct.
Why Important: Enables global aggregations and joins. But expensive—network I/O, serialization, disk (if spill). Often the bottleneck.
Minimize: (1) Broadcast small tables. (2) Column pruning before shuffle. (3) Partition by key when writing. (4) Filter before shuffle. (5) Coalesce after filter. (6) Salting for skew.
Scalability Trade-offs: Shuffle scales with data volume and partition count. Network bandwidth limits throughput.
Cost Implications: Shuffle-heavy jobs = long runtime, high cost. Reducing shuffle bytes = proportional savings.
Pro-Move: 'We monitor shuffle read/write in Spark UI; target <10% of input size.' Red Flag: Joining without broadcast consideration—default shuffle join.
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