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 config for large jobs: `spark.executor.memory`, `spark.executor.cores`, `spark.sql.shuffle.partitions`,...
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Spark config for large jobs: spark.executor.memory, spark.executor.cores, spark.sql.shuffle.partitions, spark.dynamicAllocation.enabled, spark.sql.adaptive.enabled. Increase executor count, memory. Best practice: Benchmark; document; use config profiles.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Config without benchmark. Pro-Move: 'Benchmark; document; config profiles.'
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