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Home/Questions/Spark/Big Data/Design an anti-skew strategy for a join on a high-cardinality key with a long-tail distribution (e.g., a few keys hold 80% of rows). Cover salting, split-skew, AQE, and cost/operational trade-offs.

Design an anti-skew strategy for a join on a high-cardinality key with a long-tail distribution (e.g., a few keys hold 80% of rows). Cover salting, split-skew, AQE, and cost/operational trade-offs.

Spark/Big Datahard3 min readPremium
Frequency
Low
Asked at 2 companies
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
Fragma Data SystemsMatrix
Interview Pro Tip

**Pro-Move**: 'We salt with 64 buckets for user_id; reduced p99 from 45min to 8min.' **Red Flag**: Joining without checking key distribution.

Key Concepts Tested
joinoptimizationpartitionsparksql
Expert AnswerPremium
605 wordsIncludes code examplesInterview-ready
**Section 1 — The Context (The 'Why')** Joining on a high-cardinality key (e.g., user_id across 100M+ rows) causes severe data skew when a few keys dominate. One partition gets 80% of rows; others are nearly empty....
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 Fragma Data Systems, Matrix. The answer also includes follow-up discussion points that interviewers commonly explore.

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