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Home/Questions/Spark/Big Data/Describe the cluster configuration used in your project, including memory allocation, number of nodes, and executor/driver settings.

Describe the cluster configuration used in your project, including memory allocation, number of nodes, and executor/driver settings.

Spark/Big Dataeasy0.3 min read

**Why config matters**: Right-sizing = cost and performance. **Typical**: Driver 4–8GB, 2–4 cores. Executors: 8–16GB, 4–8 cores each. `spark.executor.memory`, `spark.executor.cores`. Dynamic allocation. YARN memory overhead ~10%. **Scalability trade-offs**: 4–5 tasks per core;...

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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
Capgemini
Key Concepts Tested
spark

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Expert Answer
56 words

Why config matters: Right-sizing = cost and performance. Typical: Driver 4–8GB, 2–4 cores. Executors: 8–16GB, 4–8 cores each. spark.executor.memory, spark.executor.cores. Dynamic allocation. YARN memory overhead ~10%. Scalability trade-offs: 4–5 tasks per core; too many small executors = overhead. Cost implications: Over-provision = waste; under = OOM. Best practice: Profile; tune per workload; leave headroom for overhead.

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