**1. Salting**: Add random suffix to skewed keys to spread load; requires two-phase aggregation. **2. Two-phase aggregation**: Aggregate with salted key, then aggregate again without salt. **3. Broadcast**: For small dimension tables, broadcast to avoid shuffle. **4. Custom...
Red Flag: Suggesting "add more executors" as the main fix for skew. Pro-Move: Say you enable AQE and skew join, and fall back to salting for known hot keys (e.g., default user_id, null keys).
This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like BCG, Bitwise, Citi, and 1 others. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, partition, spark) will help you answer variations of this question confidently.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.
1. Salting: Add random suffix to skewed keys to spread load; requires two-phase aggregation. 2. Two-phase aggregation: Aggregate with salted key, then aggregate again without salt. 3. Broadcast: For small dimension tables, broadcast to avoid shuffle. 4. Custom partitioning: Pre-partition by known skewed keys. 5. Increase partitions: Spreads work but doesn't fix root cause. 6. AQE Skew Join (Spark 3.0+): Automatically splits skewed partitions. Why it matters: Skew causes stragglers; one task runs 10x longer, wasting cluster. Scalability: Salting adds shuffle; AQE is automatic but has overhead. Cost: Skew increases wall-clock time and thus cost; fixing it reduces job duration.
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