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Cluster Resource Allocation in Spark

Spark/Big Dataeasy0.4 min readPremium
Frequency
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Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
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1,863
across 7 categories
Asked at these companies
Walmart
Key Concepts Tested
spark
Expert AnswerPremium
87 wordsInterview-ready
**Why allocation matters**: Wrong config = OOM, stragglers, or wasted spend. **Typical allocation**: Driver: 4–8GB, 2–4 cores (scheduling, collect). Executors: 4–8 cores, 8–16GB each. Rule of thumb: 3–5 tasks per core. **Scalability trade-offs**: Too many small executors = overhead; too few = underutilization. Avoid >1 executor per node for HDFS locality. **Cost implications**: Over-provisioning = 20–50% waste; under = no savings if jobs retry. Use dynamic allocation for variable load....
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