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Explain Spark's narrow vs. wide transformations and when to use each

Spark/Big Datahard0.5 min readPremium
Frequency
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Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
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1,863
across 7 categories
Asked at these companies
Morgan Stanley
Key Concepts Tested
joinoptimizationpartitionspark
Expert AnswerPremium
109 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Narrow transformations process each partition independently—no shuffle (map, filter, mapPartitions). Wide transformations require shuffling data across partitions (groupBy, join, distinct, repartition). Use narrow when possible for speed. Use wide when aggregation or joins are required....
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