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Home/Questions/Spark/Big Data/Adaptive Query Execution (AQE): Discuss how AQE optimizes query execution in Spark dynamically based on runtime stats.

Adaptive Query Execution (AQE): Discuss how AQE optimizes query execution in Spark dynamically based on runtime stats.

Spark/Big Datahard0.3 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-24

AQE (Spark 3.x): Reoptimizes at runtime. Coalesces partitions after shuffle; converts sort-merge to broadcast when small side discovered; handles skew. Set spark.sql.adaptive.enabled=true. **Why**: Plan-time stats can be wrong; runtime reveals actual sizes. **Scalability**:...

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Key Concepts Tested
joinoptimizationpartitionsparksql
Expert Answer
63 words

AQE (Spark 3.x): Reoptimizes at runtime. Coalesces partitions after shuffle; converts sort-merge to broadcast when small side discovered; handles skew. Set spark.sql.adaptive.enabled=true. Why: Plan-time stats can be wrong; runtime reveals actual sizes. Scalability: Reduces shuffle partitions; avoids broadcast of large tables. Cost: Less shuffle I/O; better resource use. Best practice: Enable in production; use for skew join optimization; ANALYZE TABLE for good stats.

⚡
Pro Tip

Red Flag: Disabling AQE without measuring. Pro-Move: 'AQE enabled; skew join optimization fixed 3hr job to 20min.'

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