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How would you optimize Spark jobs for better performance?

Spark/Big Datahard0.4 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
BCG
Key Concepts Tested
joinoptimizationpartitionsparksql
Expert AnswerPremium
88 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Spark job optimization: (1) Partitioning—partition by filter columns; avoid too many partitions. (2) Broadcast joins—for small tables (<10MB). (3) Coalesce/repartition—balance task sizes. (4) Caching—when reuse > 2x. (5) Predicate pushdown—use Parquet, filters early. (6) AQE—enable `spark.sql.adaptive.enabled`. (7) Avoid UDFs—use built-ins....
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 BCG. The answer also includes follow-up discussion points that interviewers commonly explore.

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