LeetCode 1321. Restaurant Growth | SQL Rolling Window Average Explained #leetcodesql #LeetCode

LeetCode 1321. Restaurant Growth | SQL Rolling Window Average Explained
In this video, we solve LeetCode 1321. Restaurant Growth, a SQL problem that teaches you how to compute a 7-day rolling revenue and average amount spent by customers. Using powerful subqueries and aggregation techniques, we build a complete solution to understand daily restaurant performance over time. This problem is perfect for those who want to strengthen their understanding of date filtering, aggregation, and rolling window logic in SQL.

🔍 Problem Focus: LeetCode 1321. Restaurant Growth
The goal of LeetCode 1321. Restaurant Growth is to compute the total amount spent by customers over the past 7 days (including the current day) for each date, and also calculate the average amount spent over those days. The final result excludes the first 6 days to ensure there’s a full 7-day window for each entry.

🧠 SQL Concepts Covered
In this video, you’ll learn:

How to use subqueries to calculate dynamic date ranges

Using DATE_SUB() and DATE_ADD() to work with intervals

Filtering dates to include only those with a full 7-day history

Calculating rolling sums and averages in a non-window-function SQL environment

Applying GROUP BY to maintain date-level granularity

GROUP BY visited_on;
🎓 Why This Problem Is Important
LeetCode 1321. Restaurant Growth teaches you how to simulate sliding window analysis without using window functions like LAG() or OVER(). This is useful when working in SQL environments that don’t support advanced functions (like some versions of MySQL).

By practicing LeetCode 1321. Restaurant Growth, you improve your ability to:

Build real-world analytics queries

Understand customer behavior trends

Calculate moving averages for dashboards and KPIs

Master SQL logic for date-based data

👤 Who Should Watch
This video is ideal for:

Beginners looking to deepen SQL skills

Data analysts and engineers preparing for interviews

Professionals working with sales, revenue, or time-series data

Anyone wanting to crack LeetCode 1321. Restaurant Growth

🧪 Real-world Use Case
This solution mirrors common business use cases like:

Calculating weekly sales or active users

Monitoring 7-day performance trends

Performing rolling analysis on marketing or financial datasets

With LeetCode 1321. Restaurant Growth, you gain the confidence to write similar queries in business intelligence tools like Power BI, Tableau, or even Excel (via SQL-based backends).

📌 Final Thoughts
In this walkthrough of LeetCode 1321. Restaurant Growth, we tackled the problem using nested subqueries, date filtering, and grouping techniques. This elegant solution does not rely on window functions, making it a must-learn method for broader compatibility across SQL engines.

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