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How does improper partitioning affect Spark job performance?

SQLmedium0.3 min readPremium
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1,863
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PWC
Key Concepts Tested
partitionspark
Expert AnswerPremium
54 wordsInterview-ready
**Skew**: Few partitions have most data—stragglers; one task runs 10× longer. **Too many**: Scheduler overhead; small tasks inefficient; many small files. **Too few**: Underutilized cluster; OOM on large partitions. **Wrong key**: Full shuffle; no pruning. **Symptoms**: Uneven task progress; OOM; long tail....
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