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. Salting for skew: Add random salt to join key to distribute hot keys. Example: `df.withColumn('salt', (rand() * 10).cast('int'))`;...
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Salting for skew: Add random salt to join key to distribute hot keys. Example: df.withColumn('salt', (rand() * 10).cast('int')); replicate small table with same salt range; join on (key, salt). Best practice: Use when one key >> others; tune salt range.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Salting without tuning. Pro-Move: 'Salt range; replicate small; tune.'
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