This one took me longer than I'd like to admit to even parse.
Break the problem into stages: first aggregate total spend per customer per category, then rank categories within each customer using RANK() with the specified tiebreaker (earliest order date), filter for ranks 1 and 2, and finally left join back to the customer list to include customers with no orders as a null row. Use CTEs to keep the query modular and readable, and explicitly handle ties at rank 2 by relying on RANK() rather than ROW_NUMBER().
Pro tip: Explicitly state that RANK() is chosen over DENSE_RANK() or ROW_NUMBER() because the requirement is to include ties at rank 2, and that the tiebreaker (earliest order date) is applied within the ORDER BY of the window function to ensure deterministic ranking. Also mention that a LEFT JOIN from the customer table is essential to preserve customers with no orders.
Write a CTE that joins orders, order items, and products to compute total spend per customer per category, and also capture the earliest order date for each customer-category pair as the tiebreaker.
In a second CTE, apply RANK() OVER (PARTITION BY customer_id ORDER BY total_spend DESC, earliest_order_date ASC) to assign a rank to each category per customer, ensuring ties at rank 2 are preserved.
Select from the ranked CTE where rank <= 2, which includes all categories tied at rank 2.
LEFT JOIN the filtered results to the customers table so that customers with no orders appear with NULL values for category and spend.
Select customer_id, category, total_spend, and rank, ordering by customer_id and rank for clarity, and ensure the query is ANSI SQL compliant with no temp tables.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The percent_of_total column tripped me up because SUM(...) OVER (PARTITION BY customer_id) on a NULL spend row doesn't naturally give you 0, it gives you NULL.
Start by clarifying the existing query structure and the grain of the result set, then use a window function to compute each category's share of the customer's total spend. Ensure customers with no orders are handled via a LEFT JOIN and COALESCE to return 0 instead of NULL.
Pro tip: Mention that you would validate the denominator is the customer's total spend across all categories, not just the categories present, and that you'd test edge cases like customers with zero orders and single-category customers.
Ask about the current query, table structures, and how customers, orders, and categories relate. Confirm the grain of the result (e.g., one row per customer per category).
Use a window function like SUM(spend) OVER (PARTITION BY customer_id) to calculate each customer's total spend across all categories.
Divide each category's spend by the customer's total spend, using NULLIF to avoid division by zero, and multiply by 100 if a percentage is desired.
Use a LEFT JOIN from customers to orders so customers with no orders appear, and wrap the percent calculation in COALESCE(..., 0) to show 0 instead of NULL.
Check that percentages sum to 100% per customer (when orders exist), and test customers with zero orders, single orders, and multiple categories.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the cleanest part of the whole thing.
Start by clarifying the data model and the definition of 'top category' and 'percent_of_total'. Then outline a SQL query that computes each customer's top category and its percentage, and finally applies a filter to keep only those with percentage under 50%. Emphasize the use of window functions and proper aggregation to avoid errors.
Pro tip: Mention that you would validate the filter by checking edge cases, such as customers with exactly 50% or ties in top category, and ensure the query is efficient by using CTEs or subqueries appropriately.
Confirm the table structure, how categories are defined, and what 'percent_of_total' represents (e.g., spend per category divided by total spend).
Aggregate spend by customer and category, then calculate each category's percentage of the customer's total spend.
Use a window function like ROW_NUMBER() or RANK() partitioned by customer, ordered by percentage descending, to select the top category.
Apply a WHERE clause to keep only customers whose top category's percent_of_total is less than 50%.
Check results for correctness (e.g., ties, boundary cases) and consider performance implications, using CTEs or temporary tables if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.