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How do you monitor Spark jobs?

Spark/Big Datahard0.4 min read

Reviewed by Aditya Kumar Ā· Last reviewed 2026-03-25

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Monitor Spark jobs: (1) Spark UIā€”stages, tasks, storage; (2) Spark History Server for completed; (3) Logsā€”driver, executor; (4)...

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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
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Key Concepts Tested
optimizationpartitionspark
Expert Answer
79 words

Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

Monitor Spark jobs: (1) Spark UIā€”stages, tasks, storage; (2) Spark History Server for completed; (3) Logsā€”driver, executor; (4) Metricsā€”Ganglia, Prometheus. Best practice: enable event logging; set up history server; monitor duration and failures; use Spark listener for custom metrics.

Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.

āš”
Pro Tip

Red Flag: No history server. Pro-Move: 'Event log S3; History Server; dashboards.'

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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 an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.

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