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Handling Skewness in Data - salting, broadcast join

Spark/Big Datahard0.4 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
Meesho
Key Concepts Tested
joinoptimizationpartitionspark
Expert AnswerPremium
88 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Skew handling: (1) Salting—add random suffix to key, process, then aggregate; (2) Broadcast join—when one side small; (3) Split—process hot keys separately. Example salting: df.withColumn('salt', (rand()*n).cast('int')).groupBy(col('key'), col('salt')).agg(...).groupBy('key').agg(...)....
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 Meesho. The answer also includes follow-up discussion points that interviewers commonly explore.

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