Advanced SQL Window Functions Questions

Mastery of Structured Query Language window functions and advanced aggregation techniques for analytical queries. Core function families include ranking functions such as ROW_NUMBER, RANK, DENSE_RANK, and NTILE; offset functions such as LAG and LEAD; value functions such as FIRST_VALUE, LAST_VALUE, and NTH_VALUE; and aggregate window expressions such as SUM OVER and AVG OVER. Candidates should understand the OVER clause with PARTITION BY and ORDER BY, frame specifications using ROWS BETWEEN and RANGE BETWEEN, tie handling, null behavior, and how frame definitions affect results. Common application patterns include top N per group, deduplication using row numbering, running totals and cumulative aggregates, moving averages, percent rank and distribution calculations, event sequencing and period over period comparisons, gap and island analysis, cohort and retention analysis, and trend and growth calculations. The topic also covers structuring complex queries with Common Table Expressions including recursive Common Table Expressions to break multi step analytical pipelines and to handle hierarchical or iterative problems, and choosing between window functions, GROUP BY, joins, and subqueries for correctness and readability. Performance and correctness considerations are essential, including join and sort costs, index usage, memory and sort spill behavior, execution planning and query optimization techniques, and trade offs across different database dialects and large data volumes. Interview assessments typically ask candidates to write and explain queries that use these functions, reason about frame semantics for edge cases such as ties, nulls, and partition boundaries, and to rewrite or optimize expensive queries.

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Explain the pitfalls of using LAST_VALUE without adjusting the frame. Given sales(sale_date, store_id, amount), why does LAST_VALUE(amount) OVER (PARTITION BY store_id ORDER BY sale_date) often return the current row's amount? Show how to write a query that reliably returns the partition's last amount.

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Show how to use a LATERAL (or CROSS APPLY) subquery in Postgres to produce top-1 recent event per user, and compare it against a ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_time DESC) approach. When is LATERAL preferable?

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Find sessions (continuous activity periods) per user in the table user_events(user_id int, event_time timestamp). A session starts when the gap to the previous event is > 1 hour. Return user_id, session_start, session_end, event_count for each session using SQL window functions.

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Cohort retention with reactivation: Given users(user_id, signup_date) and events(user_id, event_date), compute a rolling 12-week retention table that treats reactivated users as 'retained' in the week they reappear but only counts unique users once per cohort-week. Use window functions and, if necessary, recursive CTEs to handle complex reactivation patterns. Explain your SQL and edge cases.

HardTechnical

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Advanced gap-and-island: In table events(user_id int, event_time timestamp, status text) identify islands where status = 'active' for contiguous periods and compute total active time per island, allowing a tolerance of 5 minutes between events to still count as contiguous. Provide a scalable SQL solution suitable for billions of rows.

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