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What do you understand by data shuffling in Spark? Why is it important?

Spark/Big Datamedium0.5 min readPremium
Frequency
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Asked at 1 company
Category
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questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Freecharge
Key Concepts Tested
joinpartitionspark
Expert AnswerPremium
95 wordsInterview-ready
**Shuffle**: Redistribution of data across partitions so that rows with same key land on same partition. Required for groupBy, join, distinct. **Why Important**: Enables global aggregations and joins. But **expensive**—network I/O, serialization, disk (if spill). Often the bottleneck. **Minimize**: (1) Broadcast small tables. (2) Column pruning before shuffle. (3) Partition by key when writing. (4) Filter before shuffle. (5) Coalesce after filter....
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