1645. Hopper Company Queries II

SQL架构

Table: Drivers

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| driver_id   | int     |
| join_date   | date    |
+-------------+---------+
driver_id is the primary key for this table.
Each row of this table contains the driver's ID and the date they joined the Hopper company.

Table: Rides

+--------------+---------+
| Column Name  | Type    |
+--------------+---------+
| ride_id      | int     |
| user_id      | int     |
| requested_at | date    |
+--------------+---------+
ride_id is the primary key for this table.
Each row of this table contains the ID of a ride, the user's ID that requested it, and the day they requested it.
There may be some ride requests in this table that were not accepted.

Table: AcceptedRides

+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| ride_id       | int     |
| driver_id     | int     |
| ride_distance | int     |
| ride_duration | int     |
+---------------+---------+
ride_id is the primary key for this table.
Each row of this table contains some information about an accepted ride.
It is guaranteed that each accepted ride exists in the Rides table.

Write an SQL query to report the percentage of working drivers (working_percentage) for each month of 2020 where:

Note that if the number of available drivers during a month is zero, we consider the working_percentage to be 0.

Return the result table ordered by month in ascending order, where month is the month's number (January is 1, February is 2, etc.). Round working_percentage to the nearest 2 decimal places.

The query result format is in the following example.

Example 1:

Input:
Drivers table:
+-----------+------------+
| driver_id | join_date  |
+-----------+------------+
| 10        | 2019-12-10 |
| 8         | 2020-1-13  |
| 5         | 2020-2-16  |
| 7         | 2020-3-8   |
| 4         | 2020-5-17  |
| 1         | 2020-10-24 |
| 6         | 2021-1-5   |
+-----------+------------+
Rides table:
+---------+---------+--------------+
| ride_id | user_id | requested_at |
+---------+---------+--------------+
| 6       | 75      | 2019-12-9    |
| 1       | 54      | 2020-2-9     |
| 10      | 63      | 2020-3-4     |
| 19      | 39      | 2020-4-6     |
| 3       | 41      | 2020-6-3     |
| 13      | 52      | 2020-6-22    |
| 7       | 69      | 2020-7-16    |
| 17      | 70      | 2020-8-25    |
| 20      | 81      | 2020-11-2    |
| 5       | 57      | 2020-11-9    |
| 2       | 42      | 2020-12-9    |
| 11      | 68      | 2021-1-11    |
| 15      | 32      | 2021-1-17    |
| 12      | 11      | 2021-1-19    |
| 14      | 18      | 2021-1-27    |
+---------+---------+--------------+
AcceptedRides table:
+---------+-----------+---------------+---------------+
| ride_id | driver_id | ride_distance | ride_duration |
+---------+-----------+---------------+---------------+
| 10      | 10        | 63            | 38            |
| 13      | 10        | 73            | 96            |
| 7       | 8         | 100           | 28            |
| 17      | 7         | 119           | 68            |
| 20      | 1         | 121           | 92            |
| 5       | 7         | 42            | 101           |
| 2       | 4         | 6             | 38            |
| 11      | 8         | 37            | 43            |
| 15      | 8         | 108           | 82            |
| 12      | 8         | 38            | 34            |
| 14      | 1         | 90            | 74            |
+---------+-----------+---------------+---------------+
Output:
+-------+--------------------+
| month | working_percentage |
+-------+--------------------+
| 1     | 0.00               |
| 2     | 0.00               |
| 3     | 25.00              |
| 4     | 0.00               |
| 5     | 0.00               |
| 6     | 20.00              |
| 7     | 20.00              |
| 8     | 20.00              |
| 9     | 0.00               |
| 10    | 0.00               |
| 11    | 33.33              |
| 12    | 16.67              |
+-------+--------------------+
Explanation:
By the end of January --> two active drivers (10, 8) and no accepted rides. The percentage is 0%.
By the end of February --> three active drivers (10, 8, 5) and no accepted rides. The percentage is 0%.
By the end of March --> four active drivers (10, 8, 5, 7) and one accepted ride by driver (10). The percentage is (1 / 4) * 100 = 25%.
By the end of April --> four active drivers (10, 8, 5, 7) and no accepted rides. The percentage is 0%.
By the end of May --> five active drivers (10, 8, 5, 7, 4) and no accepted rides. The percentage is 0%.
By the end of June --> five active drivers (10, 8, 5, 7, 4) and one accepted ride by driver (10). The percentage is (1 / 5) * 100 = 20%.
By the end of July --> five active drivers (10, 8, 5, 7, 4) and one accepted ride by driver (8). The percentage is (1 / 5) * 100 = 20%.
By the end of August --> five active drivers (10, 8, 5, 7, 4) and one accepted ride by driver (7). The percentage is (1 / 5) * 100 = 20%.
By the end of September --> five active drivers (10, 8, 5, 7, 4) and no accepted rides. The percentage is 0%.
By the end of October --> six active drivers (10, 8, 5, 7, 4, 1) and no accepted rides. The percentage is 0%.
By the end of November --> six active drivers (10, 8, 5, 7, 4, 1) and two accepted rides by two different drivers (1, 7). The percentage is (2 / 6) * 100 = 33.33%.
By the end of December --> six active drivers (10, 8, 5, 7, 4, 1) and one accepted ride by driver (4). The percentage is (1 / 6) * 100 = 16.67%.


with recursive t3 as (select '2020-01-01' n union all select date_add(n,interval 1 month) # 得到 (2020年的 1到12月 但是 会多一个 2021年1yue 不要紧后面会滤掉)from t3 where year(n) =2020),t2 as (select month(df) `month` , max(co) mcfrom(select  # 2020 每月 Hopper公司工作的驾驶员数量join_date df,count(driver_id) over(order by join_date rows between unbounded preceding and current row) cofrom  (selectdriver_id,join_datefromDriversunion allselectnull driver_id, n join_datefromt3where  date_format(n,'%Y-%m') not in (selectdate_format(join_date,'%Y-%m')fromDrivers))ss2)s2where year(df) = 2020group by month(df)
), t1 as (# 不是每个月接受的乘车次数 是每个月接受乘车的司机个数 (应去重)********************************selectcount(distinct driver_id) cdi,month(requested_at) `month` from(select a.driver_id,r.requested_atfromAcceptedRides a left join Rides  rusing(ride_id))s1where date_format(requested_at,'%Y') = '2020'group by date_format(requested_at,'%Y-%m'))
select t2.month `month`,round(if( t2.mc = 0,0, ifnull(t1.cdi,0) /t2.mc *100),2) working_percentage
from
t2 left join  t1  using(`month`)  # t2 left join t1 得出题中需求
order by `month`

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