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How would you identify and resolve a shuffle spill in Spark UI?

Spark/Big Datahard0.5 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
optimizationpartitionspark
Expert AnswerPremium
108 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Identify shuffle spill in Spark UI: (1) Stages tab—look for 'Spill (Memory)' and 'Spill (Disk)' in task metrics. (2) Executors tab—high 'Shuffle Write' and 'Spill' indicate spill. (3) Cause: Not enough memory for shuffle buffers....
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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