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.

HardTechnical

58 practiced

Write a recursive CTE that aggregates balances up a hierarchical accounts table (accounts(account_id, parent_id)) for each date in balances(account_id, date, amount) so that parent accounts include the sum of their children for each date. Discuss complexity and how you would ensure performance for deep hierarchies.

EasyTechnical

65 practiced

A product manager asks for a report and also for row-level detail of the top performing items per category. Explain when you would use GROUP BY and when you'd use window functions to provide both aggregated summaries and retain row-level context. Provide a short example strategy combining both techniques for the same report.

EasyTechnical

64 practiced

Given orders(order_id, user_id, order_date, amount), write a SQL query that computes the cumulative spend per user ordered by order_date. Ensure that multiple orders on the same order_date are handled deterministically. Explain how the default window frame affects the result and show the correct frame to use.

MediumTechnical

70 practiced

Design a 4-week cohort retention query using window functions and CTEs. Given users(user_id, signup_date) and events(user_id, event_date), produce a table where each signup week (cohort) has columns for week0 (signup week), week1, week2, week3 retention percentages. Show the SQL approach and explain how window functions simplify the computation of per-user week offsets.

HardTechnical

69 practiced

Implement a 7-row rolling median of value for each user in purchases(user_id, purchase_ts, amount) using techniques available in Postgres or another dialect of your choice. If your dialect lacks an ordered-set aggregate, show an approach using windowing and row numbers or arrays. Describe performance and correctness trade-offs.

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