Start by clarifying the schema and the requirement to filter orders to the full year 2024. Then write a SQL query that groups by customer_id, computes SUM(amount) and COUNT(order_id), and orders the results by total spend descending. Explain each clause and consider edge cases like date boundaries and customers with no orders.
Pro tip: Mention that using COUNT(*) is fine for counting orders, but if order_id can be NULL, COUNT(order_id) is safer; also highlight that filtering with order_date >= '2024-01-01' AND order_date < '2025-01-01' avoids timezone and inclusivity issues.
Confirm the table structure, data types, and that 'full year 2024' means orders from Jan 1 to Dec 31, 2024. Ask if customers with zero orders should be included.
Use a WHERE clause to restrict to orders in 2024, preferably with a half-open interval to handle timestamps correctly.
Group by customer_id and compute SUM(amount) as total_spend and COUNT(order_id) as order_count.
Order the results by total_spend descending, and optionally include customer_id in the output.
Mention handling of NULLs, customers with no orders, and potential indexing on order_date or customer_id for performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, generate a complete date-user grid by cross joining distinct users with a date series covering the dataset's range. Then left join transactions to this grid, aggregate daily sums (coalescing nulls to 0), and compute the 7-day rolling sum using a window function partitioned by user and ordered by date with a ROWS BETWEEN 6 PRECEDING AND CURRENT ROW frame.
Pro tip: Clarify whether the rolling window should include only days with transactions or all calendar days; the latter requires the date spine. Also, mention that using a window frame of 6 preceding rows assumes one row per date per user, which the grid ensures.
Create a date series covering the min to max created_at, then cross join with distinct user_ids to get every user-date combination.
Group the original transactions by user_id and date to get daily sums, then left join to the grid and replace nulls with 0.
Use a window function: SUM(amount) OVER (PARTITION BY user_id ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW).
Ensure dates with no transactions show 0, and verify that the rolling sum correctly includes the current day and the previous 6 days.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the schema and definitions of 'inactive' and 'session activity'. Then, use a join between Sessions and Memberships on user_id, filter for sessions that fall outside any active membership period, and sum the overlapping minutes per user.
Pro tip: Watch for overlapping membership periods and sessions that span active/inactive boundaries; compute the intersection of session intervals with inactive periods to avoid double-counting.
Ask about the table structures, how active membership is defined (e.g., start_date, end_date), and whether sessions can span multiple days or overlap with multiple memberships.
For each user, determine the time intervals when they had no active membership. This may involve finding gaps between membership periods or using a calendar table.
For each session, calculate the portion of its duration that falls within inactive periods. Sum these durations per user.
Consider overlapping memberships, sessions that start before and end after an active period, and users with no memberships. Validate with sample data.
Use SQL with interval logic (e.g., generate_series, window functions, or self-joins) to compute the total inactive session minutes per user. Discuss indexing and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.