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SQL176. 每个城市中评分最高的司机信息

描述

用户打车记录表tb_get_car_record

id uid city event_time end_time order_id
1 101 北京 2021-10-01 07:00:00 2021-10-01 07:02:00
NULL
2 102 北京
2021-10-01 09:00:30
2021-10-01 09:01:00
9001
3 101 北京
2021-10-01 08:28:10
2021-10-01 08:30:00
9002
4 103 北京
2021-10-02 07:59:00
2021-10-02 08:01:00
9003
5 104 北京
2021-10-03 07:59:20
2021-10-03 08:01:00
9004
6 105 北京
2021-10-01 08:00:00
2021-10-01 08:02:10
9005
7 106 北京
2021-10-01 17:58:00
2021-10-01 18:01:00
9006
8 107 北京
2021-10-02 11:00:00
2021-10-02 11:01:00
9007
9 108 北京
2021-10-02 21:00:00
2021-10-02 21:01:00
9008
10 109 北京
2021-10-08 18:00:00
2021-10-08 18:01:00
9009
(uid-用户ID, city-城市, event_time-打车时间, end_time-打车结束时间, order_id-订单号)


打车订单表tb_get_car_order

id order_id uid driver_id order_time start_time finish_time mileage fare grade
1 9002 101 202 2021-10-01 08:30:00
NULL
2021-10-01 08:31:00
NULL
NULL
NULL
2 9001 102 202 2021-10-01 09:01:00
2021-10-01 09:06:00
2021-10-01 09:31:00
10 41.5 5
3 9003
103 202 2021-10-02 08:01:00
2021-10-02 08:15:00
2021-10-02 08:31:00
11 41.5 4
4 9004
104 202 2021-10-03 08:01:00
2021-10-03 08:13:00
2021-10-03 08:31:00
7.5 22 4
5 9005
105 203 2021-10-01 08:02:10
NULL
2021-10-01 08:31:00
NULL NULL NULL
6 9006
106 203 2021-10-01 18:01:00
2021-10-01 18:09:00
2021-10-01 18:31:00
8 25.5 5
7 9007
107 203 2021-10-02 11:01:00
2021-10-02 11:07:00
2021-10-02 11:31:00
9.9 30 5
8 9008
108 203 2021-10-02 21:01:00
2021-10-02 21:10:00
2021-10-02 21:31:00
13.2 38 4
9 9009 109 203 2021-10-08 18:01:00 2021-10-08 18:11:50
2021-10-08 18:51:00
13 40 5
(order_id-订单号, uid-用户ID, driver_id-司机ID, order_time-接单时间, start_time-开始计费的上车时间,  finish_time-订单完成时间, mileage-行驶里程数, fare-费用, grade-评分)


场景逻辑说明
  • 用户提交打车请求后,在用户打车记录表生成一条打车记录,order_id-订单号设为null

  • 当有司机接单时,在打车订单表生成一条订单,填充order_time-接单时间及其左边的字段,start_time-开始计费的上车时间及其右边的字段全部为null,并把order_id-订单号order_time-接单时间end_time-打车结束时间)写入打车记录表;若一直无司机接单,超时或中途用户主动取消打车,则记录end_time-打车结束时间

  • 若乘客上车前,乘客或司机点击取消订单,会将打车订单表对应订单的finish_time-订单完成时间填充为取消时间,其余字段设为null

  • 当司机接上乘客时,填充订单表中该start_time-开始计费的上车时间
  • 当订单完成时填充订单完成时间、里程数、费用;评分设为null,在用户给司机打1~5星评价后填充。

问题:请统计每个城市中评分最高的司机平均评分、日均接单量和日均行驶里程数。

:有多个司机评分并列最高时,都输出。
平均评分和日均接单量保留1位小数,
日均行驶里程数保留3位小数,按日均接单数升序排序。

2285068
示例数据的输出结果如下
city driver_id avg_grade avg_order_num avg_mileage
北京 203 4.8 1.7 14.700
解释:
示例数据中,在北京市,共有2个司机接单,202的平均评分为4.3,203的平均评分为4.8,因此北京的最高评分的司机为203;203的共在3天里接单过,一共接单5次(包含1次接单后未完成),因此日均接单数为1.7;总行驶里程数为44.1,因此日均行驶里程数为14.700

示例1

输入:

DROP TABLE IF EXISTS tb_get_car_record,tb_get_car_order;
CREATE TABLE tb_get_car_record (
    id INT PRIMARY KEY AUTO_INCREMENT COMMENT '自增ID',
    uid INT NOT NULL COMMENT '用户ID',
    city VARCHAR(10) NOT NULL COMMENT '城市',
    event_time datetime COMMENT '打车时间',
    end_time datetime COMMENT '打车结束时间',
    order_id INT COMMENT '订单号'
) CHARACTER SET utf8 COLLATE utf8_bin;

CREATE TABLE tb_get_car_order (
    id INT PRIMARY KEY AUTO_INCREMENT COMMENT '自增ID',
    order_id INT NOT NULL COMMENT '订单号',
    uid INT NOT NULL COMMENT '用户ID',
    driver_id INT NOT NULL COMMENT '司机ID',
    order_time datetime COMMENT '接单时间',
    start_time datetime COMMENT '开始计费的上车时间',
    finish_time datetime COMMENT '订单结束时间',
    mileage FLOAT COMMENT '行驶里程数',
    fare FLOAT COMMENT '费用',
    grade TINYINT COMMENT '评分'
) CHARACTER SET utf8 COLLATE utf8_bin;

INSERT INTO tb_get_car_record(uid, city, event_time, end_time, order_id) VALUES
 (101, '北京', '2021-10-01 07:00:00', '2021-10-01 07:02:00', null),
 (102, '北京', '2021-10-01 09:00:30', '2021-10-01 09:01:00', 9001),
 (101, '北京', '2021-10-01 08:28:10', '2021-10-01 08:30:00', 9002),
 (103, '北京', '2021-10-02 07:59:00', '2021-10-02 08:01:00', 9003),
 (104, '北京', '2021-10-03 07:59:20', '2021-10-03 08:01:00', 9004),
 (105, '北京', '2021-10-01 08:00:00', '2021-10-01 08:02:10', 9005),
 (106, '北京', '2021-10-01 17:58:00', '2021-10-01 18:01:00', 9006),
 (107, '北京', '2021-10-02 11:00:00', '2021-10-02 11:01:00', 9007),
 (108, '北京', '2021-10-02 21:00:00', '2021-10-02 21:01:00', 9008),
 (109, '北京', '2021-10-08 18:00:00', '2021-10-08 18:01:00', 9009);

INSERT INTO tb_get_car_order(order_id, uid, driver_id, order_time, start_time, finish_time, mileage, fare, grade) VALUES
 (9002, 101, 202, '2021-10-01 08:30:00', null, '2021-10-01 08:31:00', null, null, null),
 (9001, 102, 202, '2021-10-01 09:01:00', '2021-10-01 09:06:00', '2021-10-01 09:31:00', 10.0, 41.5, 5),
 (9003, 103, 202, '2021-10-02 08:01:00', '2021-10-02 08:15:00', '2021-10-02 08:31:00', 11.0, 41.5, 4),
 (9004, 104, 202, '2021-10-03 08:01:00', '2021-10-03 08:13:00', '2021-10-03 08:31:00', 7.5, 22, 4),
 (9005, 105, 203, '2021-10-01 08:02:10', null, '2021-10-01 08:31:00', null, null, null),
 (9006, 106, 203, '2021-10-01 18:01:00', '2021-10-01 18:09:00', '2021-10-01 18:31:00', 8.0, 25.5, 5),
 (9007, 107, 203, '2021-10-02 11:01:00', '2021-10-02 11:07:00', '2021-10-02 11:31:00', 9.9, 30, 5),
 (9008, 108, 203, '2021-10-02 21:01:00', '2021-10-02 21:10:00', '2021-10-02 21:31:00', 13.2, 38, 4),
 (9009, 109, 203, '2021-10-08 18:01:00', '2021-10-08 18:11:50', '2021-10-08 18:51:00', 13, 40, 5);

输出:

北京|203|4.8|1.7|14.700

原站题解

上次编辑到这里,代码来自缓存 点击恢复默认模板

Mysql 解法, 执行用时: 37ms, 内存消耗: 6376KB, 提交时间: 2022-01-25

# 问题:请统计每个城市中评分最高的司机平均评分、日均接单量和日均行驶里程数。

# 注:有多个司机评分并列最高时,都输出。
#        平均评分和日均接单量保留1位小数,
#        日均行驶里程数保留3位小数,按日均接单数升序排序。
       
       
with c as(
SELECT driver_id
FROM(
    SELECT *,rank() over(partition by city order by avg_grade desc) as r
    FROM(
        SELECT city,driver_id,avg(grade) as avg_grade
        FROM tb_get_car_order tbgco
        JOIN tb_get_car_record tbgcr USING(order_id)
        GROUP BY city,driver_id) as a) as b
WHERE r = 1)

SELECT city,tbgco.driver_id,
       round(avg(grade),1) as avg_grade,
       round(count(order_time) / count(distinct DATE(order_time)),1) as avg_order_num,
       sum(mileage) / count(distinct DATE(order_time)) as avg_mileage
FROM tb_get_car_order tbgco
JOIN tb_get_car_record tbgcr USING(order_id)
WHERE tbgco.driver_id in (select * from c)
GROUP BY city,tbgco.driver_id
order by avg_order_num

Mysql 解法, 执行用时: 37ms, 内存消耗: 6384KB, 提交时间: 2021-12-27

select city,driver_id,avg_grade,avg_order_num,avg_mileage
from
(select city,driver_id,avg_grade,avg_order_num,avg_mileage,
dense_rank() over(partition by city order by avg_grade desc) as rank_grade
from
(select city,driver_id,round(avg(grade),1)avg_grade,
round(count(ord.order_id)/count(distinct date_format(order_time,"%Y-%m-%d")),1)avg_order_num,
round(sum(mileage)/count(distinct date_format(order_time,"%Y-%m-%d")),3)avg_mileage
from tb_get_car_order ord
left join tb_get_car_record re
on ord.order_id=re.order_id
group by city,driver_id)a )b
where rank_grade=1
order by avg_order_num;

Mysql 解法, 执行用时: 37ms, 内存消耗: 6384KB, 提交时间: 2021-12-06

select city,driver_id,avg_grade,avg_order_num,avg_mileage from
(select r.city,o.driver_id,
round(avg(o.grade),1) avg_grade,
round(count(o.order_id)/(count(distinct date(order_time))),1) avg_order_num,
round(sum(o.mileage)/(count(distinct date(order_time))),3) avg_mileage,
dense_rank() over (partition by r.city order by round(avg(o.grade),1) desc) t_rank
from tb_get_car_order o 
inner join tb_get_car_record r on r.order_id=o.order_id
group by r.city,o.driver_id) as a
where t_rank = 1
order by avg_order_num

Mysql 解法, 执行用时: 37ms, 内存消耗: 6408KB, 提交时间: 2022-01-22

SELECT city, driver_id, avg_grade, avg_order_num, avg_mileage
FROM (
    SELECT city, driver_id, ROUND(avg_grade, 1) as avg_grade,
        ROUND(order_num / work_days, 1) as avg_order_num,
        ROUND(toal_mileage / work_days, 3) as avg_mileage,
        RANK() over(PARTITION BY city ORDER BY avg_grade DESC) as rk
    FROM (
        SELECT driver_id, city, AVG(grade) as avg_grade,
            COUNT(DISTINCT DATE(order_time)) as work_days,
            COUNT(order_time) as order_num,
            SUM(mileage) as toal_mileage
        FROM tb_get_car_record
        JOIN tb_get_car_order USING(order_id)
        GROUP BY driver_id, city
    ) as t_driver_info
) as t_driver_rk
WHERE rk = 1
ORDER BY avg_order_num;

Mysql 解法, 执行用时: 37ms, 内存消耗: 6412KB, 提交时间: 2021-12-19

select 
t.city,
t.driver_id,
t.avg_grade,
t.avg_order_num,
t.avg_mileage
from
(select 
r.city as city,
o.driver_id as driver_id,
round(avg(o.grade) ,1) as avg_grade,
round(count(*)/count(distinct date(finish_time)),1)as avg_order_num,
round(sum(o.mileage)/count(distinct date(finish_time)),3)as avg_mileage,
rank() over (partition by r.city order by round(avg(o.grade) ,1) desc) as ranking
from tb_get_car_order o
join tb_get_car_record r using(order_id)
group by r.city, o.driver_id) as t
where t.ranking=1
order by t.avg_order_num

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