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Explain the difference between coalescing and repartitioning in Spark

Spark/Big Datahard0.5 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
Morgan Stanley
Key Concepts Tested
optimizationpartitionspark
Expert AnswerPremium
94 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Coalesce reduces partitions by merging (no full shuffle); repartition redistributes (full shuffle). Coalesce: df.coalesce(4)—can only decrease. Repartition: df.repartition(8) or df.repartition('key'). Use coalesce to reduce (e.g., before write); repartition to increase or change distribution....
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