Start by clarifying the schema and business definitions (e.g., what constitutes a customer, how to handle returns/refunds, and the time window). Then write a query that aggregates total spend per customer, orders descending, and limits to the top 3, using window functions or ORDER BY with LIMIT depending on SQL dialect and tie-handling requirements.
Pro tip: Mention how you would handle ties (e.g., using DENSE_RANK to include all customers with the same total) and discuss performance considerations like indexing on customer_id and order_date, which shows you think beyond just writing a query.
Ask about the tables involved (e.g., customers, orders, order_items), the definition of 'total amount spent' (e.g., sum of order totals, excluding returns), and the time period. Confirm whether ties should be included or broken arbitrarily.
Determine the necessary joins (e.g., customers to orders) and the aggregation: SUM(amount) grouped by customer. Consider whether to filter out cancelled orders or returns.
Use a window function like RANK() or DENSE_RANK() over the total spend, or simply ORDER BY total_spend DESC LIMIT 3. Choose based on tie-handling requirements and SQL dialect.
Check for edge cases (e.g., customers with no orders, nulls) and discuss performance optimizations like indexing or using CTEs for readability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the exact date truncation syntax.
Start by clarifying the table schema and the definition of 'orders per month' (e.g., count of distinct orders). Then write a SQL query that filters for 2023, groups by month, counts orders, and orders the results chronologically. Use DATE_TRUNC or EXTRACT to extract the month and ensure proper sorting.
Pro tip: Mention that you would validate the query by checking edge cases like months with zero orders and ensuring the date range is correctly bounded. Also, discuss performance considerations such as indexing on the order date column.
Ask about the table structure, column names, and whether 'orders per month' means count of orders or distinct customers. Confirm the date range and time zone.
Use a WHERE clause to restrict the data to orders placed in 2023, ensuring the date column is properly formatted.
Use DATE_TRUNC('month', order_date) or EXTRACT(MONTH FROM order_date) to group orders by month. Include the year if necessary.
Apply COUNT(*) or COUNT(DISTINCT order_id) to get the number of orders per month, then ORDER BY the month in ascending order.
Check for missing months (e.g., using a calendar table or generate_series) and consider indexing for performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.