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. Spark memory: spark.executor.memory, spark.driver.memory; spark.memory.fraction (0.6) for execution vs cache;...
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Spark memory: spark.executor.memory, spark.driver.memory; spark.memory.fraction (0.6) for execution vs cache; spark.memory.storageFraction. Increase for larger partitions; reduce for many executors. Best practice: leave 20% for OS; avoid driver OOM (no large collects); tune based on workload; use memory overhead params if needed.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Driver = executor memory. Pro-Move: 'Leave 20% for OS; driver small; overhead params.'
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