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How does Adaptive Query Execution (AQE) work?

Spark/Big Datahard0.6 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Globant
Key Concepts Tested
joinoptimizationpartitionsparksql
Expert AnswerPremium
113 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Adaptive Query Execution (AQE) optimizes Spark queries at runtime. Introduced in Spark 3.x, it: (1) Coalesces partitions after shuffle based on actual data sizes (`spark.sql.adaptive.coalescePartitions.enabled`). (2) Converts Sort-Merge Join to Broadcast Join when runtime stats show a join side is small....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Globant. The answer also includes follow-up discussion points that interviewers commonly explore.

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