{ "data": { "question": { "questionId": "4091", "questionFrontendId": "3705", "categoryTitle": "Database", "boundTopicId": 3798940, "title": "Find Golden Hour Customers", "titleSlug": "find-golden-hour-customers", "content": "

Table: restaurant_orders

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\n+------------------+----------+\n| Column Name      | Type     | \n+------------------+----------+\n| order_id         | int      |\n| customer_id      | int      |\n| order_timestamp  | datetime |\n| order_amount     | decimal  |\n| payment_method   | varchar  |\n| order_rating     | int      |\n+------------------+----------+\norder_id is the unique identifier for this table.\npayment_method can be cash, card, or app.\norder_rating is between 1 and 5, where 5 is the best (NULL if not rated).\norder_timestamp contains both date and time information.\n
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Write a solution to find golden hour customers - customers who consistently order during peak hours and provide high satisfaction. A customer is a golden hour customer if they meet ALL the following criteria:

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Return the result table ordered by average_rating in descending order, then by customer_id​​​​​​​ in descending order.

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The result format is in the following example.

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Example:

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Input:

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restaurant_orders table:

\n\n
\n+----------+-------------+---------------------+--------------+----------------+--------------+\n| order_id | customer_id | order_timestamp     | order_amount | payment_method | order_rating |\n+----------+-------------+---------------------+--------------+----------------+--------------+\n| 1        | 101         | 2024-03-01 12:30:00 | 25.50        | card           | 5            |\n| 2        | 101         | 2024-03-02 19:15:00 | 32.00        | app            | 4            |\n| 3        | 101         | 2024-03-03 13:45:00 | 28.75        | card           | 5            |\n| 4        | 101         | 2024-03-04 20:30:00 | 41.00        | app            | NULL         |\n| 5        | 102         | 2024-03-01 11:30:00 | 18.50        | cash           | 4            |\n| 6        | 102         | 2024-03-02 12:00:00 | 22.00        | card           | 3            |\n| 7        | 102         | 2024-03-03 15:30:00 | 19.75        | cash           | NULL         |\n| 8        | 103         | 2024-03-01 19:00:00 | 55.00        | app            | 5            |\n| 9        | 103         | 2024-03-02 20:45:00 | 48.50        | app            | 4            |\n| 10       | 103         | 2024-03-03 18:30:00 | 62.00        | card           | 5            |\n| 11       | 104         | 2024-03-01 10:00:00 | 15.00        | cash           | 3            |\n| 12       | 104         | 2024-03-02 09:30:00 | 18.00        | cash           | 2            |\n| 13       | 104         | 2024-03-03 16:00:00 | 20.00        | card           | 3            |\n| 14       | 105         | 2024-03-01 12:15:00 | 30.00        | app            | 4            |\n| 15       | 105         | 2024-03-02 13:00:00 | 35.50        | app            | 5            |\n| 16       | 105         | 2024-03-03 11:45:00 | 28.00        | card           | 4            |\n+----------+-------------+---------------------+--------------+----------------+--------------+\n
\n\n

Output:

\n\n
\n+-------------+--------------+----------------------+----------------+\n| customer_id | total_orders | peak_hour_percentage | average_rating |\n+-------------+--------------+----------------------+----------------+\n| 103         | 3            | 100                  | 4.67           |\n| 101         | 4            | 75                   | 4.67           |\n| 105         | 3            | 100                  | 4.33           |\n+-------------+--------------+----------------------+----------------+\n
\n\n

Explanation:

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The results table is ordered by average_rating DESC, then customer_id DESC.

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\n", "translatedTitle": "寻找黄金时段客户", "translatedContent": "

表：restaurant_orders

\n\n
\n+------------------+----------+\n| Column Name      | Type     | \n+------------------+----------+\n| order_id         | int      |\n| customer_id      | int      |\n| order_timestamp  | datetime |\n| order_amount     | decimal  |\n| payment_method   | varchar  |\n| order_rating     | int      |\n+------------------+----------+\norder_id 是这张表的唯一主键。\npayment_method 可以是 cash，card 或 app。\norder_rating 在 1 到 5 之间，其中 5 是最佳（如果没有评分则是 NULL）。\norder_timestamp 同时包含日期和时间信息。\n
\n\n

编写一个解决方案来寻找 黄金时间客户 - 高峰时段持续订购且满意度高的客户。客户若满足以下所有条件，则被视为 黄金时段客户：

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返回结果表按 average_rating 降序 排序，然后按 customer_id 降序 排序。

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结果格式如下所示。

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示例：

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\n

输入：

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restaurant_orders 表：

\n\n
\n+----------+-------------+---------------------+--------------+----------------+--------------+\n| order_id | customer_id | order_timestamp     | order_amount | payment_method | order_rating |\n+----------+-------------+---------------------+--------------+----------------+--------------+\n| 1        | 101         | 2024-03-01 12:30:00 | 25.50        | card           | 5            |\n| 2        | 101         | 2024-03-02 19:15:00 | 32.00        | app            | 4            |\n| 3        | 101         | 2024-03-03 13:45:00 | 28.75        | card           | 5            |\n| 4        | 101         | 2024-03-04 20:30:00 | 41.00        | app            | NULL         |\n| 5        | 102         | 2024-03-01 11:30:00 | 18.50        | cash           | 4            |\n| 6        | 102         | 2024-03-02 12:00:00 | 22.00        | card           | 3            |\n| 7        | 102         | 2024-03-03 15:30:00 | 19.75        | cash           | NULL         |\n| 8        | 103         | 2024-03-01 19:00:00 | 55.00        | app            | 5            |\n| 9        | 103         | 2024-03-02 20:45:00 | 48.50        | app            | 4            |\n| 10       | 103         | 2024-03-03 18:30:00 | 62.00        | card           | 5            |\n| 11       | 104         | 2024-03-01 10:00:00 | 15.00        | cash           | 3            |\n| 12       | 104         | 2024-03-02 09:30:00 | 18.00        | cash           | 2            |\n| 13       | 104         | 2024-03-03 16:00:00 | 20.00        | card           | 3            |\n| 14       | 105         | 2024-03-01 12:15:00 | 30.00        | app            | 4            |\n| 15       | 105         | 2024-03-02 13:00:00 | 35.50        | app            | 5            |\n| 16       | 105         | 2024-03-03 11:45:00 | 28.00        | card           | 4            |\n+----------+-------------+---------------------+--------------+----------------+--------------+\n
\n\n

输出：

\n\n
\n+-------------+--------------+----------------------+----------------+\n| customer_id | total_orders | peak_hour_percentage | average_rating |\n+-------------+--------------+----------------------+----------------+\n| 103         | 3            | 100                  | 4.67           |\n| 101         | 4            | 75                   | 4.67           |\n| 105         | 3            | 100                  | 4.33           |\n+-------------+--------------+----------------------+----------------+\n
\n\n

解释：

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结果表按 average_rating 降序排序，然后按 customer_id 降序排序。

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