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What are the performance trade-offs of using salting to mitigate data skewness?

Spark/Big Datamedium0.5 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
PWC
Key Concepts Tested
joinpartition
Expert AnswerPremium
99 wordsInterview-ready
**Salting**: Add random suffix to skewed keys (e.g., key -> key_1, key_2, ... key_N) to distribute load. **Trade-offs**: (1) **More partitions**—Nx keys; more shuffle output. (2) **Extra aggregation**—after join/agg, must merge salted groups. (3) **Memory**—expanded join keys. (4) **Benefit**—eliminates stragglers; job finishes instead of one task running 10x longer. **When to Use**: One/few keys dominate (e.g., null, default, popular tenant)....
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 PWC. The answer also includes follow-up discussion points that interviewers commonly explore.

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