Table: drivers
+-------------+---------+ | Column Name | Type | +-------------+---------+ | driver_id | int | | driver_name | varchar | +-------------+---------+ driver_id is the unique identifier for this table. Each row contains information about a driver.
Table: trips
+---------------+---------+ | Column Name | Type | +---------------+---------+ | trip_id | int | | driver_id | int | | trip_date | date | | distance_km | decimal | | fuel_consumed | decimal | +---------------+---------+ trip_id is the unique identifier for this table. Each row represents a trip made by a driver, including the distance traveled and fuel consumed for that trip.
Write a solution to find drivers whose fuel efficiency has improved by comparing their average fuel efficiency in the first half of the year with the second half of the year.
distance_km / fuel_consumed for each tripsecond_half_avg - first_half_avg)2 decimal placesReturn the result table ordered by efficiency improvement in descending order, then by driver name in ascending order.
The result format is in the following example.
Example:
Input:
drivers table:
+-----------+---------------+ | driver_id | driver_name | +-----------+---------------+ | 1 | Alice Johnson | | 2 | Bob Smith | | 3 | Carol Davis | | 4 | David Wilson | | 5 | Emma Brown | +-----------+---------------+
trips table:
+---------+-----------+------------+-------------+---------------+ | trip_id | driver_id | trip_date | distance_km | fuel_consumed | +---------+-----------+------------+-------------+---------------+ | 1 | 1 | 2023-02-15 | 120.5 | 10.2 | | 2 | 1 | 2023-03-20 | 200.0 | 16.5 | | 3 | 1 | 2023-08-10 | 150.0 | 11.0 | | 4 | 1 | 2023-09-25 | 180.0 | 12.5 | | 5 | 2 | 2023-01-10 | 100.0 | 9.0 | | 6 | 2 | 2023-04-15 | 250.0 | 22.0 | | 7 | 2 | 2023-10-05 | 200.0 | 15.0 | | 8 | 3 | 2023-03-12 | 80.0 | 8.5 | | 9 | 3 | 2023-05-18 | 90.0 | 9.2 | | 10 | 4 | 2023-07-22 | 160.0 | 12.8 | | 11 | 4 | 2023-11-30 | 140.0 | 11.0 | | 12 | 5 | 2023-02-28 | 110.0 | 11.5 | +---------+-----------+------------+-------------+---------------+
Output:
+-----------+---------------+------------------+-------------------+------------------------+ | driver_id | driver_name | first_half_avg | second_half_avg | efficiency_improvement | +-----------+---------------+------------------+-------------------+------------------------+ | 2 | Bob Smith | 11.24 | 13.33 | 2.10 | | 1 | Alice Johnson | 11.97 | 14.02 | 2.05 | +-----------+---------------+------------------+-------------------+------------------------+
Explanation:
The output table is ordered by efficiency improvement in descending order then by name in ascending order.
{"headers":{"drivers":["driver_id","driver_name"],"trips":["trip_id","driver_id","trip_date","distance_km","fuel_consumed"]},"rows":{"drivers":[[1,"Alice Johnson"],[2,"Bob Smith"],[3,"Carol Davis"],[4,"David Wilson"],[5,"Emma Brown"]],"trips":[[1,1,"2023-02-15",120.5,10.2],[2,1,"2023-03-20",200.0,16.5],[3,1,"2023-08-10",150.0,11.0],[4,1,"2023-09-25",180.0,12.5],[5,2,"2023-01-10",100.0,9.0],[6,2,"2023-04-15",250.0,22.0],[7,2,"2023-10-05",200.0,15.0],[8,3,"2023-03-12",80.0,8.5],[9,3,"2023-05-18",90.0,9.2],[10,4,"2023-07-22",160.0,12.8],[11,4,"2023-11-30",140.0,11.0],[12,5,"2023-02-28",110.0,11.5]]}}