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What's the difference between narrow and wide transformations?

Spark/Big Datamedium0.3 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
Microsoft
Key Concepts Tested
joinpartition
Expert AnswerPremium
68 wordsInterview-ready
**Narrow**: No shuffle. Each partition processed independently. map, filter, flatMap. Can stay in single partition. **Wide**: Requires shuffle. Data from multiple partitions combined. join, groupBy, distinct, repartition. **Why It Matters**: Narrow = cheap, parallel. Wide = expensive; stage boundary. DAG split at wide. **Examples**: filter (narrow), reduceByKey (wide). **Scalability Trade-offs**: Minimize wide. Push filters early (narrow)....
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