Avg sessions per user in 30 days: SELECT user_id, COUNT(DISTINCT session_id)/30.0 AS avg_sessions_per_day FROM events WHERE event_date >= CURRENT_DATE - 30 GROUP BY user_id. For 30-day window per user: use date range in WHERE. If sessions span dates: COUNT(DISTINCT session_id) /...
This medium-level SQL question appears frequently in data engineering interviews at companies like Meesho. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (sql, window) 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.
Avg sessions per user in 30 days: SELECT user_id, COUNT(DISTINCT session_id)/30.0 AS avg_sessions_per_day FROM events WHERE event_date >= CURRENT_DATE - 30 GROUP BY user_id. For 30-day window per user: use date range in WHERE. If sessions span dates: COUNT(DISTINCT session_id) / 30. Or use date diff for actual active days. 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.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.