Diagnosing and fixing data skew in Spark
WHAT IT TESTS: distributed processing skew. OUTLINE: this is data skew, caused by uneven key distribution concentrating rows on few partitions; mitigate with salting, broadcast joins, repartitioning, or adaptive execution. RED FLAG: just adding more executors.
WHAT IT TESTS: whether you recognize and remedy uneven work distribution in distributed jobs. ANSWER OUTLINE: the problem is data skew, where a few partition keys hold disproportionately many rows so a handful of tasks dominate runtime while others idle, often from hot keys like nulls or a popular customer during joins or aggregations. Mitigate with salting hot keys, broadcast joins for small tables, repartitioning, isolating skewed keys, or enabling Adaptive Query Execution skew handling.
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- #spark
- #data-skew
- #distributed-systems
- #performance
- #data-engineering
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