The window function angle is the key insight here.
Start by clarifying the problem: each customer buys as many products as possible starting from the cheapest, so we need to compute a running total of prices per customer and select products where the running total does not exceed the budget. Use a window function like SUM() OVER (ORDER BY price) to calculate the cumulative cost, then filter and aggregate product IDs.
Pro tip: Mention that this is a classic 'greedy knapsack' problem and that the window function approach is efficient, but also discuss edge cases like ties in price (order by price, product_id for determinism) and customers with budget less than the cheapest product.
Confirm that customers can buy multiple units of the same product? Typically no, each product is unique. Also confirm that 'as many products as possible' means maximizing the count, which is achieved by buying cheapest first.
Use a window function: SUM(price) OVER (PARTITION BY customer_id ORDER BY price, product_id) to get the running total of prices for each customer as they buy products from cheapest to most expensive.
Select only rows where the cumulative sum is less than or equal to the customer's budget. This ensures each customer buys the maximum number of products possible.
Group by customer_id and use STRING_AGG or ARRAY_AGG to collect the product IDs into a list. Ensure ordering matches the purchase order (cheapest first).
Consider customers with no affordable products (return empty list or NULL), ties in price (order by product_id for consistency), and performance implications for large datasets (indexes on price, partitioning).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.