First, clarify the schema and definitions (e.g., what constitutes an active user, platform column, date range). Then, build a query that generates a complete date-platform grid, aggregates distinct active users per day, and computes a 7-day rolling sum of distinct users using window functions, ensuring zero counts for missing days.
Pro tip: Mention that distinct counts cannot be summed directly across days; you need to either use a self-join with distinct user counting over the 7-day window or pre-aggregate at the user-day level and then count distinct users within the window. Also, highlight the importance of handling time zones and date boundaries.
Ask about the table structure, definitions of 'active user' and 'platform', and the exact date range. Confirm whether the rolling window includes the current day and how to handle partial weeks at the start.
Create a cross join between a date series covering the range and all distinct platforms to ensure every platform appears for each day, even with zero activity.
Compute the number of distinct active users per platform per day from the activity table, then left join this to the date-platform grid and replace nulls with zeros.
For each platform and day, calculate the distinct count of users active in the trailing 7-day window. This can be done via a self-join or by using a window function with a distinct count workaround (e.g., pre-aggregating user-day pairs).
Check results for correctness, especially at the start of the range where fewer than 7 days are available. Discuss how to handle time zones and whether the rolling window should be based on calendar days or 24-hour periods.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty clean once you filter the orders correctly.
Start by clarifying the schema and business rules (e.g., revenue definition, cancelled order status, date range). Then outline a SQL query that filters orders, aggregates revenue per product, ranks within each category using a window function, and selects the top 3 with tie-breaking by product_id. Finally, discuss validation and edge cases.
Pro tip: Mention that you would confirm whether 'revenue' means gross or net (after discounts/refunds) and whether cancelled orders are identified by a status flag or a separate table. This shows attention to data quality and business context.
Ask about the orders table structure, how revenue is calculated, how cancelled orders are marked, and the exact date range (last 30 days from today or from a specific date).
Write a subquery to filter orders within the last 30 days and exclude cancelled orders, then group by category and product_id to sum revenue.
Use a window function like ROW_NUMBER() or RANK() with PARTITION BY category ORDER BY revenue DESC, product_id ASC to assign ranks.
Filter to ranks <= 3 and select the required columns: rank, revenue, category, product_id. Order the final result by category and rank.
Consider ties beyond top 3, products with zero revenue, timezone issues, and performance implications. Suggest testing with sample data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I love streak questions and also kind of hate them.
First, clarify the problem: define the reference date, the data schema (user_id, login_date), and confirm that a day counts if there is at least one login. Then, outline a solution that deduplicates logins per user per day, computes the difference between the reference date and each login date, and finds the longest consecutive sequence ending at the reference date. Finally, discuss how to implement this efficiently in SQL or Python, handling edge cases like missing days and users with no logins.
Pro tip: Mention that you would validate the streak calculation by manually checking a few users, and discuss how to handle time zones and the definition of a 'day' (e.g., UTC vs. local time) to avoid off-by-one errors.
Confirm the reference date, the definition of a login day (at least one login), and the expected output format. Ask about data volume and whether the solution should be in SQL or Python.
Deduplicate logins to get one row per user per day. Ensure dates are in a consistent format and handle time zones if necessary.
For each user, calculate the difference in days between the reference date and each login date. Identify consecutive sequences by checking if the difference between consecutive login dates is 1 day.
Determine the streak that ends on or before the reference date. This is the length of the consecutive sequence that includes the most recent login date if it is the reference date or the day before, otherwise 0.
Write the query or code, then test with edge cases: users with no logins, users with a gap before the reference date, and users with long streaks. Validate results manually for a few users.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the schema and definitions (e.g., what counts as a non-cancelled order, how to handle ties in order date). Then use a window function to rank orders per user, filter to the first two non-cancelled orders, and apply the 90-day and 1.5x value conditions. Finally, output the required fields and consider edge cases like missing signup dates or multiple orders on the same day.
Pro tip: Mention that you would validate the logic by checking a few users manually and also consider the business context: a 1.5x increase in second order value might indicate successful upselling or cross-selling, which is valuable for DoorDash. Also, be explicit about how you handle ties in order timestamps (e.g., using order ID as a tiebreaker).
Confirm what 'non-cancelled' means (e.g., status not in ('cancelled', 'refunded')), how to define 'second order' (by order date or timestamp), and how to handle ties. Also clarify if signup date is always present.
Filter out cancelled orders, then use ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY order_date, order_id) to assign a rank to each order per user.
Select users where rank = 1 (first order) and rank = 2 (second order). Join these two orders per user to compare their values and dates.
Filter to cases where the second order date is within 90 days of signup date and the second order's gross value >= 1.5 * first order's gross value. Compute days between orders (second order date - first order date).
Select user_id, first_order_id, first_order_amount, second_order_id, second_order_amount, and days_between. Validate results by checking a few users manually and consider edge cases like same-day orders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.