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Explain the configuration of a Spark cluster for optimal performance

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
Morgan Stanley
Key Concepts Tested
optimizationpartitionsparksql
Expert AnswerPremium
100 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Spark cluster tuning: (1) Executor: 4–5 cores, 10–20GB RAM; (2) spark.executor.memory, spark.executor.cores; (3) Partitions: 2–4 times total cores; (4) spark.default.parallelism, spark.sql.shuffle.partitions; (5) Memory: spark.memory.fraction 0.6; (6) Dynamic allocation for variable load. Example: 10 nodes times 4 cores yields 200 shuffle partitions....
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 Morgan Stanley. The answer also includes follow-up discussion points that interviewers commonly explore.

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