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Explain the concept of checkpointing in Spark and why it is important.

Spark/Big Datahard0.7 min readPremium
Frequency
Low
Asked at 2 companies
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
CitiGlobant
Key Concepts Tested
spark
Expert AnswerPremium
140 wordsInterview-ready
Checkpointing breaks RDD lineage by materializing to storage, trading storage I/O for DAG truncation. **Why it exists**: Long lineage (e.g., 1000+ stages in iterative algorithms like PageRank or ML) causes stack overflow in the driver and makes fault recovery expensive—Spark would replay the entire DAG. **Scalability trade-off**: Checkpoint is a full write; at scale, this is costly (network, disk). For batch, prefer persist/cache when lineage is < ~50 stages....
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