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How many stages are created in a Spark job, and how are they formed?

Spark/Big Datahard0.5 min readPremium
Frequency
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Asked at 1 company
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452
questions in Spark/Big Data
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88E|81M|283H
in this category
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1,863
across 7 categories
Asked at these companies
Coforge
Key Concepts Tested
joinoptimizationpartitionspark
Expert AnswerPremium
101 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Stage count = number of shuffle boundaries + 1. Shuffle (e.g., groupBy, join, distinct) creates a stage boundary. Example: `df.join(df2).groupBy('x').count()`—join causes shuffle (stage 1→2), groupBy causes shuffle (stage 2→3). So 3 stages. Stages with no shuffle (e.g., filter, map) merge with prior stage....
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