+----------+---------+--------------+---------------------+-------------------------+-----------------------+
| order_id | user_id | restaurant_id| created_at          | estimated_delivery_at   | actual_delivery_at    |
+----------+---------+--------------+---------------------+-------------------------+-----------------------+
| 1        | 101     | 15           | 2023-07-10 12:00    | 2023-07-10 12:30        | 2023-07-10 12:40      |
| 2        | 102     | 17           | 2023-07-10 13:10    | 2023-07-10 13:45        | 2023-07-10 13:43      |
| 3        | 103     | 15           | 2023-07-11 10:05    | 2023-07-11 10:35        | 2023-07-11 11:00      |
| 4        | 104     | 18           | 2023-07-12 09:00    | 2023-07-12 09:25        | 2023-07-12 09:20      |
+----------+---------+--------------+---------------------+-------------------------+-----------------------+

An on-demand food-delivery company wants to measure and monitor late deliveries.

Write a SQL query that, for the last 7 days, returns each day’s total orders and the percentage that were delivered more than 10 minutes after estimated_delivery_at. Extend it to list the top 5 restaurants with the highest average delivery delay in that period.

Use DATE_TRUNC / DATE() for grouping, TIMESTAMPDIFF or equivalent to compute delay, and ORDER BY with LIMIT for ranking.

Doordash Data Scientist interview question: Measure Late Deliveries and Identify Top Delayed Restaurants. {"blocks": [{"key": "00de075e", "text": "orders", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, ...