In product analytics at Splice, you often need both summarized metrics and row-level context. Interviewers ask this question to check whether you understand when SQL should collapse rows versus when it should preserve them.
Explain the difference between a window function and a GROUP BY in SQL. In your answer, cover:
Keep the answer practical rather than purely theoretical. The interviewer is looking for a clear explanation of behavior, a few concrete examples, and awareness of common mistakes such as using GROUP BY when you still need row-level detail.
GROUP BY combines multiple rows into one row per group. You use it when you want a summarized result, such as total sample downloads per user or average sessions per day.
SELECT creator_id, COUNT(*) AS download_count
FROM sample_downloads
GROUP BY creator_id;
A window function computes a value across a set of related rows without reducing the number of rows returned. This is useful when each original event still matters, such as showing each Studio session alongside the user's cumulative session count.
SELECT
user_id,
session_started_at,
COUNT(*) OVER (PARTITION BY user_id ORDER BY session_started_at) AS running_session_count
FROM studio_sessions;
Use GROUP BY for final summaries and rollups. Use window functions for ranking, running totals, comparisons to group averages, and identifying previous or next events while keeping each row visible.
SELECT
pack_id,
downloaded_at,
RANK() OVER (PARTITION BY genre ORDER BY download_count DESC) AS genre_rank
FROM pack_performance;
These features are not mutually exclusive. A common pattern is to aggregate first with GROUP BY, then apply a window function to rank or compare those aggregated results.
SELECT
month,
total_downloads,
RANK() OVER (ORDER BY total_downloads DESC) AS month_rank
FROM (
SELECT DATE_TRUNC('month', downloaded_at) AS month, COUNT(*) AS total_downloads
FROM sample_downloads
GROUP BY DATE_TRUNC('month', downloaded_at)
) monthly_downloads;
