Spark Data Skew: How to Detect and Fix It

Spark data skew occurs when shuffle operations distribute rows unevenly across partitions because a small number of hot keys, often nulls, default placeholders, or highly active entities, contain a disproportionate share of the data. It commonly affects joins, aggregations, and window functions, causing one oversized task to determine a stage’s completion time while other executors sit idle. Skew can be identified in the Spark UI when maximum task duration and shuffle-read size greatly exceed median values, then confirmed by counting records per key; high input size distinguishes skew from hardware stragglers or garbage-collection issues. Recommended remedies depend on the workload: Adaptive Query Execution can split oversized partitions for supported joins, broadcasting can eliminate shuffles when one join side is small, salting distributes extreme hot keys at the cost of replication and complexity, filtering or separately processing known hot keys can reduce concentrated work, and repartitioning by a suitable composite key may improve balance. The discussion also emphasizes measuring wasted executor capacity through stage metrics or tools such as Spark Analyzer, since skew can substantially increase both runtime and cluster costs.

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