On a cluster with 4 workers, a silver aggregation over a 3 GB DataFrame shows 200 shuffle tasks in the Spark UI; most tasks process only a few megabytes, and scheduling overhead dominates the stage time. The team wants fewer, larger tasks. spark.sql.shuffle.partitions is at its default value. Which statement is accurate?
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Correct answer: spark.sql.shuffle.partitions (default 200) sets the shuffle partition count — lower it or rely on AQE coalescing
Reference: https://docs.databricks.com/aws/en/optimizations/aqe
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Official Answer (B): spark.sql.shuffle.partitions (default 200) sets the shuffle partition count — lower it or rely on AQE coalescing
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