A nightly aggregation shuffles about 6 GB of data. The plan submitted to the cluster ended with 200 post-shuffle partitions, yet the Spark UI shows the final stage ran only 12 tasks, each processing a reasonably sized slice — and nobody edited the query. Which mechanism changed the task count while the query was running?
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Correct answer: Adaptive Query Execution, which coalesces small post-shuffle partitions into fewer, larger tasks using runtime statistics
Reference: https://docs.databricks.com/aws/en/optimizations/aqe
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02 — The Myelin Layer · Canonical Discussion
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Official Answer (B): Adaptive Query Execution, which coalesces small post-shuffle partitions into fewer, larger tasks using runtime statistics
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