In the Spark UI, a stage's tasks report large Spill (Disk) values — tens of GB spilled across the stage — and the stage takes three times longer than its siblings. Executor garbage-collection time is elevated, but no single task is disproportionately larger than the others. What does the spill indicate, and what is a reasonable remedy?
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Correct answer: Shuffle data per task exceeds executor memory, so it spills to disk — raise memory per executor or shuffle partitions
Reference: https://docs.databricks.com/aws/en/optimizations/spark-ui-guide/
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Official Answer (C): Shuffle data per task exceeds executor memory, so it spills to disk — raise memory per executor or shuffle partitions
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