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What is the most common performance bottleneck in Spark jobs, and how would you resolve it?

Spark/Big Datamedium0.4 min readPremium
Frequency
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Asked at 1 company
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452
questions in Spark/Big Data
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88E|81M|283H
in this category
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1,863
across 7 categories
Asked at these companies
Bristol Myers Squibb
Key Concepts Tested
joinpartitionspark
Expert AnswerPremium
78 wordsInterview-ready
**#1 Bottleneck: Shuffle**. Network I/O for join, groupBy. Resolve: (1) Broadcast small tables. (2) Column pruning. (3) Partition by key. (4) Fix skew (salting). (5) Tune shuffle partitions. (6) AQE coalesce. **Other Bottlenecks**: GC (reduce executor memory, G1), data skew, too few executors, source slow. **Why Shuffle Dominates**: Moves data across network; serialization; often 70–90% of time. **Scalability Trade-offs**: Each fix has limits. Combine....
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 Bristol Myers Squibb. The answer also includes follow-up discussion points that interviewers commonly explore.

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