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How would you retrieve the first and last order for each customer from a sales table?

SQLmedium0.7 min read

To get first and last order per customer, use window functions MIN/MAX over order dates, or FIRST_VALUE/LAST_VALUE. Example: SELECT customer_id, order_id, order_date, FIRST_VALUE(order_id) OVER (PARTITION BY customer_id ORDER BY order_date) AS first_order_id,...

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Frequency
Low
Asked at 1 company
Category
487
questions in SQL
Difficulty Split
130E|271M|86H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Wipro
Key Concepts Tested
partitionwindow

Why This Question Matters

This medium-level SQL question appears frequently in data engineering interviews at companies like Wipro. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, window) will help you answer variations of this question confidently.

How to Approach This

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.

Expert Answer
134 words

To get first and last order per customer, use window functions MIN/MAX over order dates, or FIRST_VALUE/LAST_VALUE. Example: SELECT customer_id, order_id, order_date, FIRST_VALUE(order_id) OVER (PARTITION BY customer_id ORDER BY order_date) AS first_order_id, LAST_VALUE(order_id) OVER (PARTITION BY customer_id ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS last_order_id FROM sales. Simpler: SELECT customer_id, MIN(order_date) AS first_order, MAX(order_date) AS last_order FROM sales GROUP BY customer_id. To get full rows: WITH ranked AS (SELECT , ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date) rn_first, ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date DESC) rn_last FROM sales) SELECT FROM ranked WHERE rn_first=1 OR rn_last=1; 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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