Reviewed by Aditya Kumar · 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. DAGs in Spark: directed acyclic graph of RDD/DataFrame lineage. Role: (1) Optimization—pipeline narrow transformations, prune; (2) Fault...
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
DAGs in Spark: directed acyclic graph of RDD/DataFrame lineage. Role: (1) Optimization—pipeline narrow transformations, prune; (2) Fault tolerance—recompute lost partitions; (3) Scheduling—define stages and tasks. No cycles; each node is deterministic. Example: read, filter, map, groupBy. Best practices: use DAG for debugging (Spark UI); avoid overly complex lineage; leverage lazy evaluation for plan optimization.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Ignoring DAG in Spark UI. Pro-Move: 'Visualize lineage; debug stages; pipeline narrow.'
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