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How does Spark execute a job? Explain the DAG and stages.

Spark/Big Datahard0.6 min readPremium
Frequency
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Asked at 1 company
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questions in Spark/Big Data
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in this category
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1,863
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Asked at these companies
Fragma Data Systems
Key Concepts Tested
optimizationpartitionspark
Expert AnswerPremium
119 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Spark execution: (1) Driver parses user code, builds Logical Plan. (2) Catalyst optimizes to Physical Plan. (3) DAG Scheduler creates DAG of stages—stages are bounded by shuffle (wide) dependencies. (4) Each stage has tasks; Task Scheduler launches tasks on executors. (5) Tasks in a stage run in parallel; stages run sequentially. Shuffle = new stage....
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 Fragma Data Systems. The answer also includes follow-up discussion points that interviewers commonly explore.

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