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Walkthrough Spark's architecture, focusing on driver, executors, and DAGs

Spark/Big Datahard2.5 min readPremium
Frequency
Low
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
KPMG
Key Concepts Tested
optimizationpartitionspark
Expert AnswerPremium
508 wordsIncludes code examplesInterview-ready
**Section 1 — The Context (The 'Why')** Spark DAG, stages, tasks. collect() to driver causes OOM. AQE coalesce reduces shuffle. A naive developer enables cache everywhere. **Section 2 — The Diagram** ``` [Driver] DAG | Stages | Tasks | +-> [Exec 1..N] Cache | Shuffle | v [Cluster Mgr] ``` **Section 3 — Component Logic** **Driver** constructs DAG. **Executors** run tasks; cache; shuffle. AQE coalesce. collect() = driver OOM. take() or write to S3....
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 KPMG. The answer also includes follow-up discussion points that interviewers commonly explore.

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