Reviewed by Aditya Kumar · Last reviewed 2026-03-24
LAG: access previous row. SELECT id, amount, LAG(amount) OVER (ORDER BY date) AS prev_amount, amount - LAG(amount) OVER (ORDER BY date) AS diff FROM sales. Default: LAG(amount, 1, 0) for 0 when no previous. PARTITION BY for per-group: LAG(amount) OVER (PARTITION BY customer_id...
LAG: access previous row. SELECT id, amount, LAG(amount) OVER (ORDER BY date) AS prev_amount, amount - LAG(amount) OVER (ORDER BY date) AS diff FROM sales. Default: LAG(amount, 1, 0) for 0 when no previous. PARTITION BY for per-group: LAG(amount) OVER (PARTITION BY customer_id ORDER BY date). Use for: period-over-period, running differences. 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: Generic textbook answers. Pro-Move: 'At scale we measured X, implemented Y, achieved Z%—validated and iterated.'
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