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What metrics do you use to determine whether a Spark job is going well or not?

Spark/Big Dataeasy0.5 min read

**Good Signs**: Even task duration (no skew). Low GC time. No spill. High executor compute time vs. total. Input bytes processed. No task failures. **Bad Signs**: Skewed task duration (one 10x longer). High GC. Spill (memory/disk). Low compute ratio. Task failures. Growing...

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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
Asked at these companies
Delivery Hero
Key Concepts Tested
spark

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Expert Answer
91 words

Good Signs: Even task duration (no skew). Low GC time. No spill. High executor compute time vs. total. Input bytes processed. No task failures.

Bad Signs: Skewed task duration (one 10x longer). High GC. Spill (memory/disk). Low compute ratio. Task failures. Growing backlog.

Spark UI Metrics: Stage duration, shuffle read/write, GC time, spill. Task duration histogram. DAG.

SLA Metrics: End-to-end duration, success rate. Alert on p99 > baseline.

Scalability Trade-offs: Too many metrics = noise. Focus on duration, skew, spill, failures.

Cost Implications: Good metrics = fast triage. Prevent recurring failures.

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