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Compare Spark's lineage recovery with Hadoop's block replication mechanism.

Spark/Big Datamedium0.5 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-24

**Why this matters**: Different fault-tolerance philosophies—compute vs storage. **Spark**: Lineage-based recovery. Lost partition = recompute from RDD lineage (transformations). No data replication; trades storage for compute. **Hadoop**: Block replication (default 3x)....

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Key Concepts Tested

partitionspark

Expert Answer

92 words

Why this matters: Different fault-tolerance philosophies—compute vs storage. Spark: Lineage-based recovery. Lost partition = recompute from RDD lineage (transformations). No data replication; trades storage for compute. Hadoop: Block replication (default 3x). Data-centric; node failure = read from replica. Scalability trade-offs: Spark = less storage, more recompute on failure; Hadoop = more storage, fast recovery. Long lineage = slow recovery; checkpoint truncates. Cost implications: Spark = cheaper storage, recompute cost on failure; Hadoop = 3x storage cost. Best practice: Checkpoint long lineages; use reliable storage (S3) for shuffle; consider replication for hot data.

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Pro Tip

Red Flag: Not knowing Spark can recompute. Pro-Move: 'We checkpoint every 10 stages; S3 shuffle for durability—best of both.'

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