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How do you optimize Spark jobs for performance?

Spark/Big Datahard0.6 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
Fragma Data SystemsPresidio
Key Concepts Tested
joinoptimizationpartitionpythonsparksql
Expert AnswerPremium
128 wordsInterview-ready
Optimization is a hierarchy: (1) **Reduce data scanned**—partition pruning, predicate pushdown, column pruning; biggest lever. (2) **Reduce shuffle**—broadcast small tables, avoid unnecessary repartitions, co-locate joins. (3) **Right-size parallelism**—spark.sql.shuffle.partitions ~2–4× cores; too many = task overhead; too few = underutilization. (4) **Avoid serialization hot paths**—use built-in functions over UDFs; UDFs break Catalyst and force row-by-row Python or slower Java....
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 Fragma Data Systems, Presidio. The answer also includes follow-up discussion points that interviewers commonly explore.

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