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g1301_1400.s1321_restaurant_growth.readme.md Maven / Gradle / Ivy

1321\. Restaurant Growth

Medium

SQL Schema

Table: `Customer`

    +---------------+---------+ 
    | Column Name   | Type    | 
    +---------------+---------+ 
    | customer_id   | int     | 
    | name          | varchar | 
    | visited_on    | date    | 
    | amount        | int     | 
    +---------------+---------+ 

(customer_id, visited_on) is the primary key for this table. This table contains data about customer transactions in a restaurant. visited_on is the date on which the customer with ID (customer_id) has visited the restaurant. amount is the total paid by a customer.

You are the restaurant owner and you want to analyze a possible expansion (there will be at least one customer every day).

Write an SQL query to compute the moving average of how much the customer paid in a seven days window (i.e., current day + 6 days before). `average_amount` should be **rounded to two decimal places**.

Return result table ordered by `visited_on` **in ascending order**.

The query result format is in the following example.

**Example 1:**

**Input:** Customer table:

    +-------------+--------------+--------------+-------------+ 
    | customer_id | name         | visited_on   | amount      | 
    +-------------+--------------+--------------+-------------+ 
    | 1           | Jhon         | 2019-01-01   | 100         | 
    | 2           | Daniel       | 2019-01-02   | 110         | 
    | 3           | Jade         | 2019-01-03   | 120         | 
    | 4           | Khaled       | 2019-01-04   | 130         | 
    | 5           | Winston      | 2019-01-05   | 110         | 
    | 6           | Elvis        | 2019-01-06   | 140         | 
    | 7           | Anna         | 2019-01-07   | 150         | 
    | 8           | Maria        | 2019-01-08   | 80          | 
    | 9           | Jaze         | 2019-01-09   | 110         | 
    | 1           | Jhon         | 2019-01-10   | 130         | 
    | 3           | Jade         | 2019-01-10   | 150         | 
    +-------------+--------------+--------------+-------------+

**Output:**

    +--------------+--------------+----------------+ 
    | visited_on   | amount       | average_amount | 
    +--------------+--------------+----------------+ 
    | 2019-01-07   | 860          | 122.86         | 
    | 2019-01-08   | 840          | 120            | 
    | 2019-01-09   | 840          | 120            | 
    | 2019-01-10   | 1000         | 142.86         | 
    +--------------+--------------+----------------+

**Explanation:**

1st moving average from 2019-01-01 to 2019-01-07 has an average_amount of (100 + 110 + 120 + 130 + 110 + 140 + 150)/7 = 122.86

2nd moving average from 2019-01-02 to 2019-01-08 has an average_amount of (110 + 120 + 130 + 110 + 140 + 150 + 80)/7 = 120

3rd moving average from 2019-01-03 to 2019-01-09 has an average_amount of (120 + 130 + 110 + 140 + 150 + 80 + 110)/7 = 120

4th moving average from 2019-01-04 to 2019-01-10 has an average_amount of (130 + 110 + 140 + 150 + 80 + 110 + 130 + 150)/7 = 142.86