An online shop has two tables, customers and orders. Each order records which customer placed it (customer_id), the amount (amount), and the time it was placed (created_at). Write a single SELECT statement that counts how many orders each customer placed. The result has two columns: - customer_name : the customer's name - order_count : how many orders that customer placed Sort by customer name in ascending order. Write your query in solution.sql, and pressing 'Run test' runs it against a real database and shows the result table.
It's a real-world work ticket. Figure out what to build.
You don't have to write it yourself. Tell the AI in the terminal to build it.
Check the code actually meets the ticket, get issues fixed, then submit.
This challenge has you write a PostgreSQL SELECT statement that counts how many orders each customer placed. A table of customers and a table of orders come ready with data, and pressing 'Run test' runs your query against a real database and shows the result table. The result has two columns: one for the customer's name, one for their order count.
This problem is about "what quietly disappears when you pair two tables and count." A query that counts rows can look right on a few common rows, yet when one side has no match at all (a customer with no orders, say), the answer changes. Before trusting such a query, don't just skim a handful of common customers — run it for real and watch that every customer still has a row in the result.