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. Optimize slow production job: (1) Identify bottleneck—Spark UI, stage duration, skew. (2) Fix skew—salting, split hot keys. (3) Tune...
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Optimize slow production job: (1) Identify bottleneck—Spark UI, stage duration, skew. (2) Fix skew—salting, split hot keys. (3) Tune resources—executor memory/cores, parallelism. (4) Optimize logic—remove unnecessary shuffles, use broadcast. (5) Data—partition pruning, file compaction. (6) Consider incremental processing. Best practice: Set SLA; add monitoring; run optimization sprint; A/B test configs.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: 'We fixed it' without root cause. Pro-Move: 'SLA; bottleneck; skew; salting.'
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