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What happens when an executor fails during a task execution?

Spark/Big Dataeasy0.4 min readPremium

**Sequence**: (1) Spark marks executor lost. (2) Tasks on that executor rescheduled on others. (3) Cached RDD blocks recomputed (or from replicas). (4) Shuffle outputs from parent stage recomputed if needed. (5) After spark.task.maxFailures retries, job fails. **Why...

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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
PWC
Key Concepts Tested
spark

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Expert Answer
82 words

Sequence: (1) Spark marks executor lost. (2) Tasks on that executor rescheduled on others. (3) Cached RDD blocks recomputed (or from replicas). (4) Shuffle outputs from parent stage recomputed if needed. (5) After spark.task.maxFailures retries, job fails.

Why Resilient: RDD lineage enables recomputation. Shuffle is deterministic. No single point of failure for data.

Scalability Trade-offs: Recompute adds time. Frequent executor loss = investigate (OOM, bad node, network).

Cost Implications: Occasional failure = marginal. Chronic failure = cluster or code issue; wasted retries.

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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 a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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