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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.

EasyTechnical
0 practiced
Given orders(order_id int, customer_id int, order_date date, total_amount numeric), write a SQL query to compute a running total of total_amount per customer ordered by order_date. Explain the role of frame specification and show the version using an explicit frame.
MediumTechnical
0 practiced
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.
MediumTechnical
0 practiced
Explain options to compute median per group when your SQL dialect does not have a built-in median aggregate. Show how to compute exact median using window functions and discuss performance vs using approximate functions like percentile_approx.
MediumTechnical
0 practiced
Provide a safe, production-ready SQL pattern to delete duplicate records in large tables using window functions in batches. Table: events(id bigserial primary key, event_key text, created_at timestamp). Explain batching, transaction sizing, and how to resume on failure.
HardTechnical
0 practiced
You must delete duplicates from a high-volume production table while minimizing write downtime and avoiding long locks. Describe a step-by-step safe procedure using window functions, batching, temporary tables, unique indexes (created concurrently), and backout plan. Consider implications for concurrent writes.

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