HardWindow Functions High Frequency

3-Day Rolling Sales Moving Average

Frequently Asked By:AmazonShopifyUber

Interactive In-Browser Workspace

Practice this problem on live schema tables in PostgreSQL 16 WASM with instant row verification and visual EXPLAIN plans.

Solve in Workbench

Problem Statement

For each sales date in `daily_sales`, compute a 3-day rolling average (the current day plus the preceding 2 days). Return: 1. `sale_date` 2. `daily_amount` 3. `rolling_3day_avg`: Average of `daily_amount` across `ROWS BETWEEN 2 PRECEDING AND CURRENT ROW`, rounded to 2 decimal places Order by `sale_date` ascending.

Why Technical Interviewers Ask This

Demand planning requires smoothing out day-of-week sales fluctuations. Supply chain analysts use a 3-day trailing moving average to forecast warehouse fulfillment staffing.

Common Pitfalls & Interview Traps

Forgetting required sorting and deterministic tie-breaking.

Not accounting for NULL or zero-value denominators during calculations.

Using inefficient quadratic subqueries instead of single-pass window functions constructs.

Canonical Modern SQL Solution

PostgreSQL 16 / ANSI SQL

SELECT 
        sale_date,
        daily_amount,
        ROUND(AVG(daily_amount) OVER (
          ORDER BY sale_date 
          ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
        ), 2) AS rolling_3day_avg
      FROM daily_sales
      ORDER BY sale_date ASC;

Approach Breakdown

This query addresses the problem requirements by filtering and structuring data in Window Functions. It meets the target criteria: Daily sales alongside 3-day rolling window average.

Edge Cases to Verbally Clarify with Your Interviewer

The dataset contains zero matching records (must return valid empty result without crashing).

Multiple records tie on sorting criteria (must evaluate tie-breaking order deterministically).

Foreign keys or secondary attributes have NULL values.

Frequently Asked Follow-Up Questions

What makes this question a favorite among Amazon and Shopify interviewers?

Interviewers use this problem to test whether candidates write defensive, scalable Window Functions queries that run efficiently on multi-million row production databases without excessive memory usage.

How does the query optimizer execute this query?

Using single-pass table scans with index lookups or sort buffers. Running EXPLAIN (ANALYZE, BUFFERS) in the DB Exercises workbench visualizes the execution nodes and buffer hit cache.