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Explain how Spark handles data partitioning and the role of shuffles in performance tuning.

Spark/Big Datahard0.6 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
Uber
Key Concepts Tested
joinoptimizationpartitionspark
Expert AnswerPremium
121 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Spark partitioning: data is divided into partitions; each partition maps to a task. Default partition count from input or 200. Shuffle repartitions by key (hash or range). Shuffles are expensive—network and disk I/O....
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 Uber. The answer also includes follow-up discussion points that interviewers commonly explore.

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