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What is data shuffling in Spark, and how do you minimize its impact on job performance?

Spark/Big Datahard0.4 min readPremium
Frequency
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Asked at 1 company
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questions in Spark/Big Data
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in this category
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Asked at these companies
Coforge
Key Concepts Tested
joinoptimizationpartitionspark
Expert AnswerPremium
85 wordsInterview-ready
**Shuffle**: Redistribution for join, groupBy, distinct. Wide transformation; network and serialization cost. **Minimize**: (1) Broadcast small tables. (2) Column pruning before shuffle. (3) Filter early. (4) Partition by key when writing. (5) Repartition by key before multiple shuffles. (6) Salting for skew. (7) AQE coalesce. (8) Increase shuffle partitions if partitions too large. **Why It Matters**: Shuffle often 70–90% of job time....
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 Coforge. The answer also includes follow-up discussion points that interviewers commonly explore.

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