Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Even Kafka partition distribution: (1) Use meaningful keys—hash(key) % num_partitions; avoid hot keys (e.g., few keys dominate). (2) Use composite keys—e.g., customer_id + timestamp. (3) Add random component for high-cardinality keys. (4) Monitor partition lag and rebalance. (5)...
This medium-level SQL question appears frequently in data engineering interviews at companies like BCG. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition) 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.
Even Kafka partition distribution: (1) Use meaningful keys—hash(key) % num_partitions; avoid hot keys (e.g., few keys dominate). (2) Use composite keys—e.g., customer_id + timestamp. (3) Add random component for high-cardinality keys. (4) Monitor partition lag and rebalance. (5) Increase partitions if needed (creates new partitions, doesn't rebalance existing data). (6) Use custom partitioner when default hash is skewed. (7) Consider key salting for hot partitions. Example: key = userId + "-" + UUID.random() for anonymized load spread. Why it matters: Design choices compound at scale—wrong approach can cause 100× overhead. Scalability trade-offs: Profile before optimizing; validate on sample then full. Cost implications: Suboptimal choices multiply at billion-row scale.
Red Flag: Ignoring consumer lag until outage. Pro-Move: 'Lag alerts at 10K; autoscale consumers; rebalance when partition hit 3× avg.'
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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.