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Explain repartition vs. coalesce. Which one would you use to reduce shuffle operations?

Spark/Big Datahard0.5 min readPremium
Frequency
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Asked at 1 company
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questions in Spark/Big Data
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88E|81M|283H
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1,863
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Asked at these companies
American Express
Key Concepts Tested
optimizationpartition
Expert AnswerPremium
103 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. repartition(n) creates n partitions via full shuffle; coalesce(n) merges partitions without full shuffle (narrow). Use coalesce to reduce partitions (e.g., after filter)—avoids shuffle. Use repartition to increase partitions or change partition key. To reduce shuffle: coalesce. Example: df.filter(...).coalesce(10) to shrink after filter....
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 American Express. The answer also includes follow-up discussion points that interviewers commonly explore.

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