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What role does executor memory and CPU configuration play in maximizing parallelism?

Spark/Big Datamedium0.5 min readPremium

**Parallelism**: Total tasks ≈ partition count. Concurrent tasks ≈ executors × cores per executor. More cores = more concurrent tasks. **Memory**: Per executor. Too large = GC pauses. Too small = spill. Typical: 4–8 cores, 8–16GB. Tasks per executor = cores (one task 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
TCS
Key Concepts Tested
partition

Why This Question Matters

This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like TCS. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition) will help you answer variations of this question confidently.

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Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.

Expert Answer
93 words

Parallelism: Total tasks ≈ partition count. Concurrent tasks ≈ executors × cores per executor. More cores = more concurrent tasks.

Memory: Per executor. Too large = GC pauses. Too small = spill. Typical: 4–8 cores, 8–16GB. Tasks per executor = cores (one task per core).

Formula: Parallelism = executors × cores. Bounded by partition count.

Why It Matters: Under-provision = idle cores. Over-provision = memory pressure, GC.

Scalability Trade-offs: 5–6 tasks per executor (cores) is sweet spot. Leave headroom for off-heap.

Cost Implications: Right size = best throughput per dollar. Oversized = waste.

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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