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Home/Questions/Spark/Big Data/What is Adaptive Query Execution (AQE) in Spark 3.x, and how does it improve performance?

What is Adaptive Query Execution (AQE) in Spark 3.x, and how does it improve performance?

Spark/Big Datamedium0.6 min readPremium
Frequency
Low
Asked at 2 companies
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
HashedInSnowflake
Key Concepts Tested
joinpartitionsparksql
Expert AnswerPremium
126 wordsInterview-ready
AQE re-optimizes at runtime using actual statistics at stage boundaries, addressing the planning-time blind spot (e.g., wrong size estimates, skew). **Three features**: (1) **Coalesce shuffle partitions**—merges small partitions after shuffle to reduce task overhead; avoids 10K tiny tasks. (2) **Switch join strategy**—if one side is smaller than expected, converts sort-merge to broadcast; avoids unnecessary shuffle....
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 HashedIn, Snowflake. The answer also includes follow-up discussion points that interviewers commonly explore.

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