Why should you care?

Restaurant dashboard on Swiggy shows average delivery time per city — GROUP BY city, AVG(minutes).

See it live — copy this example

Run in MySQL Workbench or the mysql CLI.

SELECT c.city,
       COUNT(o.order_id) AS orders,
       SUM(o.total_inr) AS city_revenue
FROM customers c
JOIN orders o ON o.customer_id = c.customer_id
GROUP BY c.city
HAVING city_revenue >= 1000
ORDER BY city_revenue DESC;

What happened?

• Join attaches orders to customer cities.
• Aggregates compute per city.
• HAVING drops cities under ₹1000 total revenue.

Practice next

1. Seed multi-city customers and orders.
2. Run query; verify math on one city manually.
3. Move city filter to WHERE vs HAVING — note difference.
4. Add AVG(o.total_inr) per city.
5. GROUP BY city, DATE(o.placed_at) if date column exists.

Remember

GROUP BY + aggregates = per-bucket metrics. HAVING filters summarized rows. Mind JOIN grain to avoid inflated SUM.

DataFlow regional sales

Leadership sees city_revenue ranked for warehouse placement.

Outcome: Inventory moves to high-GMV cities.