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.

MediumTechnical

78 practiced

Write a SQL query to return top 5 products by revenue per category but if the 5th rank is tied among multiple products include all products that tie for 5th place. Table: sales(product_id, category, revenue). Explain your choice of ranking function.

MediumTechnical

82 practiced

Design a SQL workflow to compute weekly retention for user cohorts. Tables:

users(user_id int, signup_date date)events(user_id int, event_date date)

Return a table with cohort_week (ISO week of signup), week_number (0 = signup week), cohort_size, retained_users, and retention_rate for weeks 0..12. Use window functions where helpful and explain how you compute cohort_size and retention per cohort.

MediumTechnical

59 practiced

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?

HardSystem Design

59 practiced

You have an ad-hoc analytical query that uses multiple window functions and takes minutes on a 1TB fact table. Design a solution to make these analytics interactive (sub-5 second) for analysts. Consider pre-aggregation, partitioning, materialized views, incremental refresh, and cost trade-offs.

EasyTechnical

68 practiced

Write a PostgreSQL query that returns the top 3 sales records by amount for each region from the table:

sales(region text, salesperson text, amount numeric, sale_date date, sale_id int)

Requirements:- Return up to 3 rows per region ordered by amount DESC and tie-broken deterministically by sale_date DESC then sale_id ASC- Include region, salesperson, amount, sale_date, and rank_in_region- Use window functions and ensure the query is performant and readable for reviewers

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