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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How do NULL values in columns used by PARTITION BY affect window function behavior? For instance, with PARTITION BY region where some rows have region=NULL, explain how those rows are partitioned and potential pitfalls when computing per-region aggregates.

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Using employees(emp_id, dept_id, salary), write a SQL query that returns the 3rd highest salary per department using NTH_VALUE. Explain frame requirements so that NTH_VALUE returns NULL when fewer than 3 distinct salaries exist, and how to get the 3rd distinct salary ignoring duplicates.

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You need the average of the last 5 distinct event types per user (by most recent occurrence). Propose an SQL approach using window functions or CTEs to select the last 5 distinct event types per user and compute the average of an associated metric for those events.

MediumTechnical

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Explain how ORDER BY inside an OVER clause can trigger sorts that spill to disk in some databases. As a data scientist running large analytics, what configuration knobs or rewrite strategies would you use to reduce sort memory usage and avoid spill?

HardTechnical

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A query uses SUM() OVER(PARTITION BY user_id ORDER BY ts) and AVG() OVER(PARTITION BY product_id ORDER BY ts) causing the optimizer to sort twice and heavy I/O. Propose techniques (materialized intermediates, temp tables, refactoring) to avoid redundant sorts and reduce I/O, and explain trade-offs.

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