ASAmol Shukla

18. Real life: e-commerce marketplace

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Lesson 18 of 21

Real life database practice: e-commerce marketplace (50 SQL problems)

Practise 50 SQL problems on a 2,000-order marketplace database — catalog, customers, revenue and returns, checked in your browser.

200 min 3 min read 50 problems · 0 solved Marketplace (2,000 orders)

In short

What does the marketplace practice database contain?

Five related tables: 50 sellers, 200 products, 500 customers, 2,000 orders and roughly 4,000 order lines. Revenue is quantity × unit_price minus discount; order_items holds the lines, so any revenue question has to join it to orders. The database is already selected in every problem.

By the end of this lesson you can

  • Read a five-table schema and pick the tables a question needs
  • Calculate payable revenue with quantity, price and discount
  • Aggregate order lines to orders without double counting
  • Rank products and sellers with window functions and CTEs

The database you are practising on

Marketplace is a fictional shopping site: 50 sellers list 200 products, 500 customers place 2,000 orders, and each order has one to three lines whose price and discount determine the final payable amount. This is the classic five-table schema behind almost every online store.

The whole set is fictional but shaped like the real thing: keys, foreign keys, dates, statuses and 2,000 rows in the busiest table. You do not import anything to start — practice mode loads the same rows into your browser. When you want the same data in MySQL Workbench, download the script (5 tables, 20 columns) and run it there.

What one row means

Read the grain before you write a join. Joining two tables without knowing what one row of each means is the single most common cause of doubled totals.

Table One row is Columns you will use
sellers one seller seller_id, seller_name, city, rating
products one listed product product_id, seller_id, product_name, category, price, stock
customers one customer customer_id, customer_name, city, signup_date
orders one order order_id, customer_id, order_date, status, payment_mode
order_items one product line in an order order_id, product_id, quantity, unit_price, discount

Your database is already selected

Every problem below runs against this database only. The code arena preselects it and does not show a database dropdown, so you never see MySQL's ERROR 1046: No database selected here.

You write one query, press Check answer, and it is compared row by row with the reference answer — same rows, same columns, any valid query wins. Use Hint first, Compare with the reference answer once you have tried, and Reset database if you changed rows while experimenting.

Common mistakes

Wrong

GROUP BY p.category while selecting p.product_name

Fix

Group by every non-aggregated column, or wrap the query in a CTE and rank inside it.

Why: MySQL with ONLY_FULL_GROUP_BY rejects columns that are neither grouped nor aggregated.

Wrong

Summing order totals after joining order_items and products to sellers

Fix

Aggregate at the grain of the answer (per seller) and join pre-aggregated CTEs, never raw lines twice.

Why: Every extra one-to-many join repeats rows and multiplies the total.

Wrong

Forgetting the discount column

Fix

Multiply by (100 - discount) / 100 as every reference answer does.

Why: Orders are paid after discount, so quantity × unit_price alone overstates revenue.

Check yourself

  1. Can you say in one sentence what one row of sellers means?
  2. Can you write a query that returns exactly one number from a 2,000-row table without scrolling the result?
  3. Can you explain why two of your joins could double a total, and how you avoided it?

Answer out loud or in your notebook. If one is hard, reread that section before practising.

Frequently asked questions

Do I need to install MySQL and import this data?

No. The code arena loads the marketplace database in your browser and preselects it for every problem. Download the .sql script to run the same queries in Workbench.

Are the answers checked automatically?

Yes. Your query and the reference answer run on fresh copies of the same rows and the results are compared row by row, so any correct query is accepted.

Is the marketplace data real?

No. It is fictional and generated from a fixed seed so every learner sees the same rows and the same expected answers.

Quick quiz — 3 questions

Quick Check: Real life: e-commerce marketplace

1 / 3
Where does an order's revenue actually live?

Answers — Real life: e-commerce marketplace

  1. Easy. List Electronics products with their price. Target: 28 rows · product_id, product_name, price · (1, Headphones, 800); (2, Keyboard v2, 2230) …

    SELECT product_id, product_name, price FROM products WHERE category = 'Electronics' ORDER BY product_id;

  2. Easy. Show the 20 most recent delivered orders. Target: 20 rows · order_id, order_date, status · (835, 2025-12-29, delivered); (1955, 2025-12-28, delivered) …

    SELECT order_id, order_date, status FROM orders WHERE status = 'delivered' ORDER BY order_date DESC, order_id DESC LIMIT 20;

  3. Easy. List customers who live in Delhi. Target: 20 rows · customer_id, customer_name · (26, Kabir Gill); (27, Rahul Das) …

    SELECT customer_id, customer_name FROM customers WHERE city = 'Delhi' ORDER BY customer_id LIMIT 20;

  4. Easy. List sellers rated 4.5 or higher. Target: 14 rows · seller_id, seller_name, rating · (1, Traders Kaur, 4.99); (12, Store Mehta, 4.98) …

    SELECT seller_id, seller_name, rating FROM sellers WHERE rating >= 4.5 ORDER BY rating DESC, seller_id;

  5. Easy. List products that are out of stock. Target: 3 rows · product_id, product_name, stock · (2, Keyboard v2, 0); (9, Mug v3, 0) …

    SELECT product_id, product_name, stock FROM products WHERE stock = 0 ORDER BY product_id LIMIT 20;

  6. Easy. Show the first 20 orders paid by UPI. Target: 20 rows · order_id, order_date, payment_mode · (5, 2024-05-25, upi); (8, 2024-10-25, upi) …

    SELECT order_id, order_date, payment_mode FROM orders WHERE payment_mode = 'upi' ORDER BY order_id LIMIT 20;

  7. Easy. List products priced above 2,000. Target: 20 rows · product_id, product_name, price · (21, Marker set v2, 3000); (30, Design basics v3, 3000) …

    SELECT product_id, product_name, price FROM products WHERE price > 2000 ORDER BY price DESC, product_id LIMIT 20;

  8. Easy. List every product category once. Target: 6 rows · category · (Books); (Electronics) …

    SELECT DISTINCT category FROM products ORDER BY category;

  9. Easy. Count all orders. Target: 1 row · orders · (2000)

    SELECT COUNT(*) AS orders FROM orders;

  10. Easy. Show the line items of order 1. Target: 1 row · order_id, product_id, quantity, unit_price · (1, 135, 2, 1390)

    SELECT order_id, product_id, quantity, unit_price FROM order_items WHERE order_id = 1 ORDER BY product_id;

  11. Medium. List products priced between 500 and 1,500. Target: 20 rows · product_name, price · (Photo frame 137, 500); (Backpack 181, 500) …

    SELECT product_name, price FROM products WHERE price BETWEEN 500 AND 1500 ORDER BY price, product_id LIMIT 20;

  12. Medium. List cancelled and returned orders. Target: 20 rows · order_id, status, order_date · (1, returned, 2025-10-14); (7, cancelled, 2025-07-29) …

    SELECT order_id, status, order_date FROM orders WHERE status IN ('cancelled', 'returned') ORDER BY order_id LIMIT 20;

  13. Medium. List customers whose name starts with K. Target: 20 rows · customer_id, customer_name · (25, Kabir Sharma); (26, Kabir Gill) …

    SELECT customer_id, customer_name FROM customers WHERE customer_name LIKE 'K%' ORDER BY customer_id LIMIT 20;

  14. Medium. List orders placed in the first quarter of 2025. Target: 20 rows · order_id, order_date · (311, 2025-01-01); (417, 2025-01-02) …

    SELECT order_id, order_date FROM orders WHERE order_date >= '2025-01-01' AND order_date < '2025-04-01' ORDER BY order_date, order_id LIMIT 20;

  15. Medium. Show the payable amount for the first 20 order lines. Target: 20 rows · order_id, product_id, payable · (1, 135, 2529.8); (2, 5, 1981.2) …

    SELECT order_id, product_id, ROUND(quantity * unit_price * (100 - discount) / 100, 2) AS payable FROM order_items ORDER BY order_id, product_id LIMIT 20;

  16. Medium. Label every order with a fulfilment stage. Target: 20 rows · order_id, status, stage · (1, returned, Needs review); (2, delivered, Closed) …

    SELECT order_id, status, CASE WHEN status = 'delivered' THEN 'Closed' WHEN status = 'shipped' THEN 'In transit' ELSE 'Needs review' END AS stage FROM orders ORDER BY order_id LIMIT 20;

  17. Medium. Show product names in capitals with their category. Target: 20 rows · product, category · (HEADPHONES, Electronics); (KEYBOARD V2, Electronics) …

    SELECT UPPER(product_name) AS product, category FROM products ORDER BY product_id LIMIT 20;

  18. Medium. Label each seller by rating band. Target: 20 rows · seller_id, seller_name, rating, band · (1, Traders Kaur, 4.99, Top); (12, Store Mehta, 4.98, Top) …

    SELECT seller_id, seller_name, rating, CASE WHEN rating >= 4.5 THEN 'Top' WHEN rating >= 4 THEN 'Good' ELSE 'Watch' END AS band FROM sellers ORDER BY rating DESC, seller_id LIMIT 20;

  19. Medium. List customers who signed up in 2025. Target: 20 rows · customer_id, customer_name, signup_date · (2, Tanvi Nair, 2025-03-09); (3, Harpreet Khan, 2025-03-03) …

    SELECT customer_id, customer_name, signup_date FROM customers WHERE signup_date >= '2025-01-01' AND signup_date < '2026-01-01' ORDER BY customer_id LIMIT 20;

  20. Medium. Count how many different cities customers come from. Target: 1 row · cities · (10)

    SELECT COUNT(DISTINCT city) AS cities FROM customers;

  21. Medium. Show each product name and its character length. Target: 20 rows · product_name, name_length · (Headphones, 10); (Keyboard v2, 11) …

    SELECT product_name, LENGTH(product_name) AS name_length FROM products ORDER BY product_id LIMIT 20;

  22. Medium. List cash-on-delivery orders in 2024. Target: 20 rows · order_id, order_date · (4, 2024-09-17); (11, 2024-02-08) …

    SELECT order_id, order_date FROM orders WHERE payment_mode = 'cod' AND order_date >= '2024-01-01' AND order_date < '2025-01-01' ORDER BY order_id LIMIT 20;

  23. Hard. Count orders by status. Target: 4 rows · status, orders · (delivered, 1548); (shipped, 201) …

    SELECT status, COUNT(*) AS orders FROM orders GROUP BY status ORDER BY orders DESC, status;

  24. Hard. Show delivered revenue per category. Target: 6 rows · category, revenue · (Stationery, 2502814); (Fashion, 2477187.3) …

    SELECT p.category, SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) AS revenue FROM order_items i JOIN orders o ON o.order_id = i.order_id JOIN products p ON p.product_id = i.product_id WHERE o.status = 'delivered' GROUP BY p.category ORDER BY revenue DESC, p.category;

  25. Hard. Show the average product price per category. Target: 6 rows · category, avg_price · (Home, 1711.39); (Fashion, 1576.1) …

    SELECT category, ROUND(AVG(price), 2) AS avg_price FROM products GROUP BY category ORDER BY avg_price DESC, category;

  26. Hard. List customers with more than 5 orders. Target: 20 rows · customer_id, customer_name, orders · (1, Gurleen Bose, 28); (2, Tanvi Nair, 18) …

    SELECT c.customer_id, c.customer_name, COUNT(*) AS orders FROM customers c JOIN orders o ON o.customer_id = c.customer_id GROUP BY c.customer_id, c.customer_name HAVING COUNT(*) > 5 ORDER BY orders DESC, c.customer_id LIMIT 20;

  27. Hard. List sellers who list more than 3 products. Target: 20 rows · seller_id, seller_name, products · (7, Traders Gill, 9); (17, Store Rao, 8) …

    SELECT s.seller_id, s.seller_name, COUNT(*) AS products FROM sellers s JOIN products p ON p.seller_id = s.seller_id GROUP BY s.seller_id, s.seller_name HAVING COUNT(*) > 3 ORDER BY products DESC, s.seller_id LIMIT 20;

  28. Hard. Show delivered revenue per month of 2025. Target: 12 rows · month, revenue · (2025-01, 499711.8); (2025-02, 462268.2) …

    SELECT DATE_FORMAT(o.order_date, '%Y-%m') AS month, SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) AS revenue FROM order_items i JOIN orders o ON o.order_id = i.order_id WHERE o.status = 'delivered' AND o.order_date >= '2025-01-01' AND o.order_date < '2026-01-01' GROUP BY month ORDER BY month;

  29. Hard. Show categories that list more than 25 products. Target: 5 rows · category, products · (Fashion, 41); (Stationery, 40) …

    SELECT category, COUNT(*) AS products FROM products GROUP BY category HAVING COUNT(*) > 25 ORDER BY products DESC, category;

  30. Hard. Show the 5 products that sold the most units in delivered orders. Target: 5 rows · product_id, product_name, units · (17, Notebook, 83); (49, Water bottle 49, 82) …

    SELECT p.product_id, p.product_name, SUM(i.quantity) AS units FROM order_items i JOIN orders o ON o.order_id = i.order_id JOIN products p ON p.product_id = i.product_id WHERE o.status = 'delivered' GROUP BY p.product_id, p.product_name ORDER BY units DESC, p.product_id LIMIT 5;

  31. Hard. List sellers whose average product price is above 1,500. Target: 24 rows · seller_id, seller_name, avg_price · (26, Mart Nair, 2555); (48, Mart Reddy, 2490) …

    SELECT s.seller_id, s.seller_name, ROUND(AVG(p.price), 2) AS avg_price FROM sellers s JOIN products p ON p.seller_id = s.seller_id GROUP BY s.seller_id, s.seller_name HAVING AVG(p.price) > 1500 ORDER BY avg_price DESC, s.seller_id;

  32. Hard. Show the average delivered order value per payment mode. Target: 4 rows · payment_mode, avg_order_value · (netbanking, 8068.74); (upi, 8030.89) …

    WITH per_order AS (SELECT o.order_id, o.payment_mode, SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) AS order_value FROM order_items i JOIN orders o ON o.order_id = i.order_id WHERE o.status = 'delivered' GROUP BY o.order_id, o.payment_mode) SELECT payment_mode, ROUND(AVG(order_value), 2) AS avg_order_value FROM per_order GROUP BY payment_mode ORDER BY avg_order_value DESC, payment_mode;

  33. Hard. Show the return rate per category, highest first. Target: 6 rows · category, return_pct · (Electronics, 5.2); (Fashion, 5) …

    SELECT p.category, ROUND(100 * SUM(CASE WHEN o.status = 'returned' THEN 1 ELSE 0 END) / COUNT(*), 1) AS return_pct FROM order_items i JOIN orders o ON o.order_id = i.order_id JOIN products p ON p.product_id = i.product_id WHERE o.status IN ('delivered', 'returned') GROUP BY p.category ORDER BY return_pct DESC, p.category;

  34. Hard. List orders with more than 2 line items. Target: 20 rows · order_id, lines · (5, 3); (8, 3) …

    SELECT order_id, COUNT(*) AS lines FROM order_items GROUP BY order_id HAVING COUNT(*) > 2 ORDER BY lines DESC, order_id LIMIT 20;

  35. Hard. List products that have never been ordered. Target: 0 rows · product_id, product_name · 

    SELECT p.product_id, p.product_name FROM products p WHERE NOT EXISTS (SELECT 1 FROM order_items i WHERE i.product_id = p.product_id) ORDER BY p.product_id LIMIT 20;

  36. Hard. Show cities with more than 150 customers. Target: 0 rows · city, customers · 

    SELECT city, COUNT(*) AS customers FROM customers GROUP BY city HAVING COUNT(*) > 150 ORDER BY customers DESC, city;

  37. Professional. Show the 10 customers with the highest delivered spend. Target: 10 rows · customer_id, customer_name, spend · (12, Pooja Verma, 117648.3); (1, Gurleen Bose, 108337.8) …

    SELECT c.customer_id, c.customer_name, SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) AS spend FROM order_items i JOIN orders o ON o.order_id = i.order_id JOIN customers c ON c.customer_id = o.customer_id WHERE o.status = 'delivered' GROUP BY c.customer_id, c.customer_name ORDER BY spend DESC, c.customer_id LIMIT 10;

  38. Professional. Show each category's share of delivered revenue. Target: 6 rows · category, revenue, pct · (Stationery, 2502814, 20.4); (Fashion, 2477187.3, 20.2) …

    SELECT p.category, SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) AS revenue, ROUND(100 * SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) / (SELECT SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) FROM order_items i JOIN orders o ON o.order_id = i.order_id WHERE o.status = 'delivered'), 1) AS pct FROM order_items i JOIN orders o ON o.order_id = i.order_id JOIN products p ON p.product_id = i.product_id WHERE o.status = 'delivered' GROUP BY p.category ORDER BY revenue DESC, p.category;

  39. Professional. Rank products by delivered revenue inside their category. Target: 20 rows · category, product_name, revenue, rnk · (Books, Design basics 54, 146191.5, 1); (Books, Data stories, 140389.4, 2) …

    WITH r AS (SELECT p.category, p.product_name, SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) AS revenue, RANK() OVER (PARTITION BY p.category ORDER BY SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) DESC) AS rnk FROM order_items i JOIN orders o ON o.order_id = i.order_id JOIN products p ON p.product_id = i.product_id WHERE o.status = 'delivered' GROUP BY p.category, p.product_id, p.product_name) SELECT category, product_name, revenue, rnk FROM r ORDER BY category, rnk LIMIT 20;

  40. Professional. Show 2025 delivered revenue by month with a running total. Target: 12 rows · month, revenue, running_total · (2025-01, 499711.8, 499711.8); (2025-02, 462268.2, 961980) …

    WITH m AS (SELECT DATE_FORMAT(o.order_date, '%Y-%m') AS month, SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) AS revenue FROM order_items i JOIN orders o ON o.order_id = i.order_id WHERE o.status = 'delivered' AND o.order_date >= '2025-01-01' AND o.order_date < '2026-01-01' GROUP BY month) SELECT month, revenue, SUM(revenue) OVER (ORDER BY month) AS running_total FROM m ORDER BY month;

  41. Professional. Show the month-over-month change in delivered orders for 2025. Target: 12 rows · month, orders, prev_orders, change · (2025-01, 68, NULL, NULL); (2025-02, 59, 68, -9) …

    WITH m AS (SELECT DATE_FORMAT(order_date, '%Y-%m') AS month, COUNT(*) AS orders FROM orders WHERE status = 'delivered' AND order_date >= '2025-01-01' AND order_date < '2026-01-01' GROUP BY month) SELECT month, orders, LAG(orders) OVER (ORDER BY month) AS prev_orders, orders - LAG(orders) OVER (ORDER BY month) AS change FROM m ORDER BY month;

  42. Professional. List customers with 3 or more delivered orders. Target: 20 rows · customer_id, customer_name, delivered_orders · (1, Gurleen Bose, 18); (3, Harpreet Khan, 14) …

    SELECT c.customer_id, c.customer_name, COUNT(*) AS delivered_orders FROM customers c JOIN orders o ON o.customer_id = c.customer_id WHERE o.status = 'delivered' GROUP BY c.customer_id, c.customer_name HAVING COUNT(*) >= 3 ORDER BY delivered_orders DESC, c.customer_id LIMIT 20;

  43. Professional. Show the best-selling product in each category by units. Target: 6 rows · category, product_name, units · (Books, Design basics 54, 71); (Electronics, Power bank 57, 82) …

    WITH r AS (SELECT p.category, p.product_name, SUM(i.quantity) AS units, ROW_NUMBER() OVER (PARTITION BY p.category ORDER BY SUM(i.quantity) DESC, p.product_id) AS rn FROM order_items i JOIN orders o ON o.order_id = i.order_id JOIN products p ON p.product_id = i.product_id WHERE o.status = 'delivered' GROUP BY p.category, p.product_id, p.product_name) SELECT category, product_name, units FROM r WHERE rn = 1 ORDER BY category;

  44. Professional. List customers who have never placed an order. Target: 14 rows · customer_id, customer_name · (66, Anaya Kaur); (149, Meera Sharma) …

    SELECT c.customer_id, c.customer_name FROM customers c WHERE NOT EXISTS (SELECT 1 FROM orders o WHERE o.customer_id = c.customer_id) ORDER BY c.customer_id LIMIT 20;

  45. Professional. List products priced above their category's average. Target: 20 rows · product_name, category, price · (Design basics v3, Books, 3000); (Python primer 163, Books, 2900) …

    SELECT p.product_name, p.category, p.price FROM products p WHERE p.price > (SELECT AVG(x.price) FROM products x WHERE x.category = p.category) ORDER BY p.category, p.price DESC, p.product_id LIMIT 20;

  46. Professional. Show the 10 sellers with the most delivered revenue. Target: 10 rows · seller_id, seller_name, revenue · (25, Store Sharma, 728459.4); (45, Bazaar Reddy, 635834.7) …

    SELECT s.seller_id, s.seller_name, SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) AS revenue FROM order_items i JOIN orders o ON o.order_id = i.order_id JOIN products p ON p.product_id = i.product_id JOIN sellers s ON s.seller_id = p.seller_id WHERE o.status = 'delivered' GROUP BY s.seller_id, s.seller_name ORDER BY revenue DESC, s.seller_id LIMIT 10;

  47. Professional. Show each customer's first order date. Target: 20 rows · customer_id, customer_name, first_order · (298, Vihaan Verma, 2025-12-26); (498, Ishaan Chopra, 2025-12-19) …

    SELECT c.customer_id, c.customer_name, MIN(o.order_date) AS first_order FROM customers c JOIN orders o ON o.customer_id = c.customer_id GROUP BY c.customer_id, c.customer_name ORDER BY first_order DESC, c.customer_id LIMIT 20;

  48. Professional. Build a 2025 monthly dashboard: delivered orders, revenue and average order value. Target: 12 rows · month, orders, revenue, avg_order_value · (2025-01, 68, 499711.8, 7348.7); (2025-02, 59, 462268.2, 7835.05) …

    WITH per_order AS (SELECT o.order_id, DATE_FORMAT(o.order_date, '%Y-%m') AS month, SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) AS order_value FROM order_items i JOIN orders o ON o.order_id = i.order_id WHERE o.status = 'delivered' AND o.order_date >= '2025-01-01' AND o.order_date < '2026-01-01' GROUP BY o.order_id, month) SELECT month, COUNT(*) AS orders, SUM(order_value) AS revenue, ROUND(AVG(order_value), 2) AS avg_order_value FROM per_order GROUP BY month ORDER BY month;

  49. Professional. Show each payment mode's share of delivered revenue in 2025. Target: 4 rows · payment_mode, revenue, pct · (card, 1786860.8, 29); (netbanking, 1487571, 24.2) …

    SELECT o.payment_mode, SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) AS revenue, ROUND(100 * SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) / (SELECT SUM(ROUND(i.quantity * i.unit_price * (100 - i.discount) / 100, 2)) FROM order_items i JOIN orders o2 ON o2.order_id = i.order_id WHERE o2.status = 'delivered' AND o2.order_date >= '2025-01-01' AND o2.order_date < '2026-01-01'), 1) AS pct FROM order_items i JOIN orders o ON o.order_id = i.order_id WHERE o.status = 'delivered' AND o.order_date >= '2025-01-01' AND o.order_date < '2026-01-01' GROUP BY o.payment_mode ORDER BY revenue DESC, o.payment_mode;

  50. Professional. Show each seller's best-selling product by delivered units. Target: 20 rows · seller_name, product_name, units · (Bazaar Gill, Notebook, 83); (Shop Mehta, Water bottle 49, 82) …

    WITH r AS (SELECT s.seller_name, p.product_name, SUM(i.quantity) AS units, ROW_NUMBER() OVER (PARTITION BY s.seller_id ORDER BY SUM(i.quantity) DESC, p.product_id) AS rn FROM order_items i JOIN orders o ON o.order_id = i.order_id JOIN products p ON p.product_id = i.product_id JOIN sellers s ON s.seller_id = p.seller_id WHERE o.status = 'delivered' GROUP BY s.seller_id, s.seller_name, p.product_id, p.product_name) SELECT seller_name, product_name, units FROM r WHERE rn = 1 ORDER BY units DESC, seller_name LIMIT 20;

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