SQL(面试实战05)
1. 工作日各时段叫车量、等待接单时间和调度时间
用户打车记录表tb_get_car_record
| id | uid | city | event_time | end_time | order_id |
|---|---|---|---|---|---|
| 1 | 107 | 北京 | 2021-09-20 11:00:00 | 2021-09-20 11:00:30 | 9017 |
| 2 | 108 | 北京 | 2021-09-20 21:00:00 | 2021-09-20 21:00:40 | 9008 |
| 3 | 108 | 北京 | 2021-09-20 18:59:30 | 2021-09-20 19:01:00 | 9018 |
| 4 | 102 | 北京 | 2021-09-21 08:59:00 | 2021-09-21 09:01:00 | 9002 |
| 5 | 106 | 北京 | 2021-09-21 17:58:00 | 2021-09-21 18:01:00 | 9006 |
| 6 | 103 | 北京 | 2021-09-22 07:58:00 | 2021-09-22 08:01:00 | 9003 |
| 7 | 104 | 北京 | 2021-09-23 07:59:00 | 2021-09-23 08:01:00 | 9004 |
| 8 | 103 | 北京 | 2021-09-24 19:59:20 | 2021-09-24 20:01:00 | 9019 |
| 9 | 101 | 北京 | 2021-09-24 08:28:10 | 2021-09-24 08:30:00 | 9011 |
（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 | 9017 | 107 | 213 | 2021-09-20 11:00:30 | 2021-09-20 11:02:10 | 2021-09-20 11:31:00 | 11 | 38 | 5 |
| 2 | 9008 | 108 | 204 | 2021-09-20 21:00:40 | 2021-09-20 21:03:00 | 2021-09-20 21:31:00 | 13.2 | 38 | 4 |
| 3 | 9018 | 108 | 214 | 2021-09-20 19:01:00 | 2021-09-20 19:04:50 | 2021-09-20 19:21:00 | 14 | 38 | 5 |
| 4 | 9002 | 102 | 202 | 2021-09-21 09:01:00 | 2021-09-21 09:06:00 | 2021-09-21 09:31:00 | 10 | 41.5 | 5 |
| 5 | 9006 | 106 | 203 | 2021-09-21 18:01:00 | 2021-09-21 18:09:00 | 2021-09-21 18:31:00 | 8 | 25.5 | 4 |
| 6 | 9007 | 107 | 203 | 2021-09-22 11:01:00 | 2021-09-22 11:07:00 | 2021-09-22 11:31:00 | 9.9 | 30 | 5 |
| 7 | 9003 | 103 | 202 | 2021-09-22 08:01:00 | 2021-10-22 08:15:00 | 2021-10-22 08:31:00 | 11 | 41.5 | 4 |
| 8 | 9004 | 104 | 202 | 2021-09-23 08:01:00 | 2021-09-23 08:13:00 | 2021-09-23 08:31:00 | 7.5 | 22 | 4 |
| 9 | 9005 | 105 | 202 | 2021-09-23 10:01:00 | 2021-09-23 10:13:00 | 2021-09-23 10:31:00 | 9 | 29 | 5 |
| 10 | 9019 | 103 | 202 | 2021-09-24 20:01:00 | 2021-09-24 20:11:00 | 2021-09-24 20:51:00 | 10 | 39 | 4 |
| 11 | 9011 | 101 | 211 | 2021-09-24 08:30:00 | 2021-09-24 08:31:00 | 2021-09-24 08:54:00 | 10 | 35 | 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，订单号-order_id设为null；
- 当有司机接单时，在打车订单表生成一条订单，填充接单时间-**order_time 及其左边的字段，上车时间-****start_time及其右边的字段全部为null**，并把订单号-****order_id和接单时间-*order_time*（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，则记录打车结束时间-**end_time。
- 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，填充订单表中该订单的**start_time-**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：统计周一到周五各时段的叫车量、平均等待接单时间和平均调度时间。全部以event_time-开始打车时间为时段划分依据，平均等待接单时间和平均调度时间均保留1位小数，平均调度时间仅计算完成了的订单，结果按叫车量升序排序。
注：
输出示例：
示例数据的输出结果如下：
| period | get_car_num | avg_wait_time | avg_dispatch_time |
|---|---|---|---|
| 工作时间 | 1 | 0.5 | 1.7 |
| 休息时间 | 1 | 0.7 | 2.3 |
| 晚高峰 | 3 | 2.1 | 7.3 |
| 早高峰 | 4 | 2.2 | 8.0 |
解释：订单9017打车开始于11点整，属于工作时间，等待时间30秒，调度时间为1分40秒，示例数据中工作时间打车订单就一个，平均等待时间0.5分钟，平均调度时间1.7分钟。
示例1
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
(107, '北京', '2021-09-20 11:00:00', '2021-09-20 11:00:30', 9017),
(108, '北京', '2021-09-20 21:00:00', '2021-09-20 21:00:40', 9008),
(108, '北京', '2021-09-20 18:59:30', '2021-09-20 19:01:00', 9018),
(102, '北京', '2021-09-21 08:59:00', '2021-09-21 09:01:00', 9002),
(106, '北京', '2021-09-21 17:58:00', '2021-09-21 18:01:00', 9006),
(103, '北京', '2021-09-22 07:58:00', '2021-09-22 08:01:00', 9003),
(104, '北京', '2021-09-23 07:59:00', '2021-09-23 08:01:00', 9004),
(103, '北京', '2021-09-24 19:59:20', '2021-09-24 20:01:00', 9019),
(101, '北京', '2021-09-24 08:28:10', '2021-09-24 08:30:00', 9011);
INSERT INTO tb_get_car_order(order_id, uid, driver_id, order_time, start_time, finish_time, mileage, fare, grade) VALUES
(9017, 107, 213, '2021-09-20 11:00:30', '2021-09-20 11:02:10', '2021-09-20 11:31:00', 11, 38, 5),
(9008, 108, 204, '2021-09-20 21:00:40', '2021-09-20 21:03:00', '2021-09-20 21:31:00', 13.2, 38, 4),
(9018, 108, 214, '2021-09-20 19:01:00', '2021-09-20 19:04:50', '2021-09-20 19:21:00', 14, 38, 5),
(9002, 102, 202, '2021-09-21 09:01:00', '2021-09-21 09:06:00', '2021-09-21 09:31:00', 10.0, 41.5, 5),
(9006, 106, 203, '2021-09-21 18:01:00', '2021-09-21 18:09:00', '2021-09-21 18:31:00', 8.0, 25.5, 4),
(9007, 107, 203, '2021-09-22 11:01:00', '2021-09-22 11:07:00', '2021-09-22 11:31:00', 9.9, 30, 5),
(9003, 103, 202, '2021-09-22 08:01:00', '2021-09-22 08:15:00', '2021-09-22 08:31:00', 11.0, 41.5, 4),
(9004, 104, 202, '2021-09-23 08:01:00', '2021-09-23 08:13:00', '2021-09-23 08:31:00', 7.5, 22, 4),
(9005, 105, 202, '2021-09-23 10:01:00', '2021-09-23 10:13:00', '2021-09-23 10:31:00', 9, 29, 5),
(9019, 103, 202, '2021-09-24 20:01:00', '2021-09-24 20:11:00', '2021-09-24 20:51:00', 10, 39, 4),
(9011, 101, 211, '2021-09-24 08:30:00', '2021-09-24 08:31:00', '2021-09-24 08:54:00', 10, 35, 5);
输出：
工作时间|1|0.5|1.7
休息时间|1|0.7|2.3
晚高峰|3|2.1|7.3
早高峰|4|2.2|8.0
思路
统计周一到周五各时段的叫车量、平均等待接单时间和平均调度时间
(开始打车到司机接单为等待接单时间，从司机接单到上车为调度时间)
1.用户打车记录表tb_get_car_record 连接 打车订单表tb_get_car_order
from tb_get_car_order join tb_get_car_record using(order_id)
2.获取出车时间分类，订单id，等待接单时间、调度时间，
select
case
when date_format(event_time,"%H:%i:%s") between "07:00:00" and "08:59:59" then "早高峰"
when date_format(event_time,"%H:%i:%s") between "09:00:00" and "16:59:59" then "工作时间"
when date_format(event_time,"%H:%i:%s") between "17:00:00" and "19:59:59" then "晚高峰"
else "休息时间"
end
period,
order_id,
timestampdiff(second,event_time,end_time) wait_time,
timestampdiff(second,order_time,start_time) dispatch_time
3.按照不同时段分组
group by period
4.获取 不同时段，叫车量，平均等待接单时间和平均调度时间
select
period,
count(order_id) get_car_num,
round(avg(wait_time)/60,1) avg_wait_time,
round(avg(dispatch_time)/60,1) avg_dispatch_time
题解
select
period,
count(order_id) get_car_num,
round(avg(wait_time)/60,1) avg_wait_time,
round(avg(dispatch_time)/60,1) avg_dispatch_time
from
(
select
case
when date_format(event_time,"%H:%i:%s")
between "07:00:00" and "08:59:59" then "早高峰"
when date_format(event_time,"%H:%i:%s")
between "09:00:00" and "16:59:59" then "工作时间"
when date_format(event_time,"%H:%i:%s")
between "17:00:00" and "19:59:59" then "晚高峰"
else "休息时间"
end
period,
order_id,
timestampdiff(second,event_time,end_time) wait_time,
timestampdiff(second,order_time,start_time) dispatch_time
from tb_get_car_order
join tb_get_car_record using(order_id)
where dayofweek(order_time) between 2 and 6
)t
group by period
order by get_car_num
2. 各城市最大同时等车人数
用户打车记录表tb_get_car_record
| id | uid | city | event_time | end_time | order_id |
|---|---|---|---|---|---|
| 1 | 108 | 北京 | 2021-10-20 08:00:00 | 2021-10-20 08:00:40 | 9008 |
| 2 | 118 | 北京 | 2021-10-20 08:00:10 | 2021-10-20 08:00:45 | 9018 |
| 3 | 102 | 北京 | 2021-10-20 08:00:30 | 2021-10-20 08:00:50 | 9002 |
| 4 | 106 | 北京 | 2021-10-20 08:05:41 | 2021-10-20 08:06:00 | 9006 |
| 5 | 103 | 北京 | 2021-10-20 08:05:50 | 2021-10-20 08:07:10 | 9003 |
| 6 | 104 | 北京 | 2021-10-20 08:01:01 | 2021-10-20 08:01:20 | 9004 |
| 7 | 105 | 北京 | 2021-10-20 08:01:15 | 2021-10-20 08:01:30 | 9019 |
| 8 | 101 | 北京 | 2021-10-20 08:28:10 | 2021-10-20 08:30:00 | 9011 |
（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 | 9008 | 108 | 204 | 2021-10-20 08:00:40 | 2021-10-20 08:03:00 | 2021-10-20 08:31:00 | 13.2 | 38 | 4 |
| 2 | 9018 | 108 | 214 | 2021-10-20 08:00:45 | 2021-10-20 08:04:50 | 2021-10-20 08:21:00 | 14 | 38 | 5 |
| 3 | 9002 | 102 | 202 | 2021-10-20 08:00:50 | 2021-10-20 08:06:00 | 2021-10-20 08:31:00 | 10 | 41.5 | 5 |
| 4 | 9006 | 106 | 206 | 2021-10-20 08:06:00 | 2021-10-20 08:09:00 | 2021-10-20 08:31:00 | 8 | 25.5 | 4 |
| 5 | 9003 | 103 | 203 | 2021-10-20 08:07:10 | 2021-10-20 08:15:00 | 2021-10-20 08:31:00 | 11 | 41.5 | 4 |
| 6 | 9004 | 104 | 204 | 2021-10-20 08:01:20 | 2021-10-20 08:13:00 | 2021-10-20 08:31:00 | 7.5 | 22 | 4 |
| 7 | 9019 | 105 | 205 | 2021-10-20 08:01:30 | 2021-10-20 08:11:00 | 2021-10-20 08:51:00 | 10 | 39 | 4 |
| 8 | 9011 | 101 | 211 | 2021-10-20 08:30:00 | 2021-10-20 08:31:00 | 2021-10-20 08:54:00 | 10 | 35 | 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，订单号-order_id设为null；
- 当有司机接单时，在打车订单表生成一条订单，填充接单时间-order_time及其左边的字段，上车时间及其右边的字段全部为null，并把订单号和接单时间（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订单完成时间-finish_time填充为取消时间，其余字段设为null。
- 当司机接上乘客时，填充打车订单表中该订单的上车时间start_time。
- 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：请统计各个城市在2021年10月期间，单日中最大的同时等车人数。
注: 等车指从开始打车起，直到取消打车、取消等待或上车前的这段时间里用户的状态。
如果同一时刻有人停止等车，有人开始等车，等车人数记作先增加后减少。
结果按各城市最大等车人数升序排序，相同时按城市升序排序。
输出示例：
示例结果如下
| city | max_wait_uv |
|---|---|
| 北京 | 5 |
解释：由打车订单表可以得知北京2021年10月20日有8条打车记录，108号乘客从08:00:00等到08:03:00，118号乘客从08:00:10等到08:04:50…,由此得知08:02:00秒时刻，共有5人在等车。
示例1
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
(108, '北京', '2021-10-20 08:00:00', '2021-10-20 08:00:40', 9008),
(108, '北京', '2021-10-20 08:00:10', '2021-10-20 08:00:45', 9018),
(102, '北京', '2021-10-20 08:00:30', '2021-10-20 08:00:50', 9002),
(106, '北京', '2021-10-20 08:05:41', '2021-10-20 08:06:00', 9006),
(103, '北京', '2021-10-20 08:05:50', '2021-10-20 08:07:10', 9003),
(104, '北京', '2021-10-20 08:01:01', '2021-10-20 08:01:20', 9004),
(103, '北京', '2021-10-20 08:01:15', '2021-10-20 08:01:30', 9019),
(101, '北京', '2021-10-20 08:28:10', '2021-10-20 08:30:00', 9011);
INSERT INTO tb_get_car_order(order_id, uid, driver_id, order_time, start_time, finish_time, mileage, fare, grade) VALUES
(9008, 108, 204, '2021-10-20 08:00:40', '2021-10-20 08:03:00', '2021-10-20 08:31:00', 13.2, 38, 4),
(9018, 108, 214, '2021-10-20 08:00:45', '2021-10-20 08:04:50', '2021-10-20 08:21:00', 14, 38, 5),
(9002, 102, 202, '2021-10-20 08:00:50', '2021-10-20 08:06:00', '2021-10-20 08:31:00', 10.0, 41.5, 5),
(9006, 106, 203, '2021-10-20 08:06:00', '2021-10-20 08:09:00', '2021-10-20 08:31:00', 8.0, 25.5, 4),
(9003, 103, 202, '2021-10-20 08:07:10', '2021-10-20 08:15:00', '2021-10-20 08:31:00', 11.0, 41.5, 4),
(9004, 104, 202, '2021-10-20 08:01:20', '2021-10-20 08:13:00', '2021-10-20 08:31:00', 7.5, 22, 4),
(9019, 103, 202, '2021-10-20 08:01:30', '2021-10-20 08:11:00', '2021-10-20 08:51:00', 10, 39, 4),
(9011, 101, 211, '2021-10-20 08:30:00', '2021-10-20 08:31:00', '2021-10-20 08:54:00', 10, 35, 5);
思路
请统计各个城市在2021年10月期间，单日中最大的同时等车人数。
方式一：
- 状态1：司机接单前取消，则没有生成order_id,这种情况 order_id IS NULL 记录end_time
- 状态2：司机接单后取消，则没有上车时间，start_time IS NULL 记录 finish_time
- 状态3：正常上车，记录start_time,start_time IS NOT NULL
SELECT city,event_time uv_time,1 AS uv
FROM tb_get_car_record #进入等车状态
UNION ALL
SELECT city,end_time uv_time,-1 AS uv
FROM tb_get_car_record
WHERE order_id IS NULL #接单前取消
UNION ALL
SELECT city,IFNULL(start_time,finish_time) uv_time,-1 AS uv
FROM tb_get_car_order
LEFT JOIN tb_get_car_record USING(order_id)#接单后取消或上车
方式二：
SELECT city, event_time as at_time, 1 as wait_uv
FROM tb_get_car_record
UNION ALL
SELECT city,
COALESCE(start_time, finish_time, end_time) as at_time, -1 as wait_uv
FROM tb_get_car_record
LEFT JOIN tb_get_car_order USING(order_id)
2.筛选时间窗：WHERE DATE_FORMAT(at_time, "%Y-%m") = "2021-10"
3.定义按城市和日期分区按时刻排序的窗口（先增加后减少，所以uv倒排）：
SUM(wait_uv) over(PARTITION BY city, DATE(at_time) ORDER BY at_time, wait_uv DESC) as current_max
4.计算当前最大等车人数：
SUM(wait_uv) over(wd_city_date) as current_max
5.按城市分组：
GROUP BY city
6.计算各城市最大等待人数：
MAX(current_max) as max_wait_uv
题解
方式一：
select
city,max(wait_uv)max_wait_uv
from
(
select city,date(time)days,sum(uv)over(partition by city,date(time) order by time,uv desc) wait_uv
from
(
#进入等车状态
SELECT city,event_time time,1 AS uv
FROM tb_get_car_record
UNION ALL
SELECT city,end_time time,-1 AS uv
FROM tb_get_car_record
WHERE order_id IS NULL #接单前取消
UNION ALL
SELECT city,IFNULL(start_time,finish_time) uv_time,-1 AS uv
FROM tb_get_car_order
LEFT JOIN tb_get_car_record USING(order_id)#接单后取消或上车
)t_city_wait
where date_format(time,"%Y-%m")="2021-10"
)t_max_city_wait
group by city
order by max_wait_uv,city
方式二：
SELECT city, MAX(current_max) as max_wait_uv
FROM (
SELECT city, SUM(wait_uv)
over(PARTITION BY city, DATE(at_time) ORDER BY at_time, wait_uv DESC) as current_max
FROM (
SELECT city, event_time as at_time, 1 as wait_uv
FROM tb_get_car_record
UNION ALL
SELECT city, COALESCE(start_time, finish_time, end_time) as at_time, -1 as wait_uv
FROM tb_get_car_record
LEFT JOIN tb_get_car_order USING(order_id)
) as t_uv_at_time
WHERE DATE_FORMAT(at_time, "%Y-%m") = "2021-10"
-- WINDOW wd_city_date as (PARTITION BY city, DATE(at_time) ORDER BY at_time, wait_uv DESC)
) as t_city_cur_max
GROUP BY city
ORDER BY max_wait_uv, city;
方式三：
WITH t1 AS(
#每个城市等车瞬时UV
SELECT city,
SUM(uv)OVER(PARTITION BY city ORDER BY uv_time,uv DESC) AS uv_cnt
FROM (
SELECT city,event_time uv_time,1 AS uv
FROM tb_get_car_record #进入等车状态
UNION ALL
SELECT city,end_time uv_time,-1 AS uv
FROM tb_get_car_record WHERE order_id IS NULL #接单前取消
UNION ALL
SELECT city,IFNULL(start_time,finish_time) uv_time,-1 AS uv
FROM tb_get_car_order
LEFT JOIN tb_get_car_record USING(order_id)#接单后取消或上车
)AS t WHERE DATE_FORMAT(uv_time,'%Y%m')='202110' #2021年10月
)
SELECT city,MAX(uv_cnt) max_wait_uv FROM t1 GROUP BY citY ORDER BY max_wait_uv,citY;#排序先按照uv升序，uv一样按照城市升序
拓展
coalesce(expression_1, expression_2, ...,expression_n)函数
在学习sql时，发现coalesce函数，和三则运算表达式有些类似，不过它又好像跟三则表达式有些不同，它跟java 的泛型也有些类似，参数个数可以动态传递，类似这样的用法在mysql函数中是很少见的。
语法
coalesce(expression_1, expression_2, ...,expression_n)依次参考各参数表达式，遇到非null值即停止并返回该值。如果所有的表达式都是空值，最终将返回一个空值。使用coalesce在于大部分包含空值的表达式最终将返回空值
select
coalesce(null,null,1,2);
输出：
1
3. 某宝店铺的SPU数量
11月结束后，小牛同学需要对其在某宝的网店就11月份用户交易情况和产品情况进行分析以更好的经营小店。
已知产品情况表product_tb如下（其中，item_id指某款号的具体货号，style_id指款号，tag_price表示标签价格，inventory指库存量）：
| item_id | style_id | tag_price | inventory |
|---|---|---|---|
| A001 | A | 100 | 20 |
| A002 | A | 120 | 30 |
| A003 | A | 200 | 15 |
| B001 | B | 130 | 18 |
| B002 | B | 150 | 22 |
| B003 | B | 125 | 10 |
| B004 | B | 155 | 12 |
| C001 | C | 260 | 25 |
| C002 | C | 280 | 18 |
请你统计每款的SPU（货号）数量，并按SPU数量降序排序，以上例子的输出结果如下：
| style_id | SPU_num |
|---|---|
| B | 4 |
| A | 3 |
| C | 2 |
示例1
drop table if exists product_tb;
CREATE TABLE product_tb(
item_id char(10) NOT NULL,
style_id char(10) NOT NULL,
tag_price int(10) NOT NULL,
inventory int(10) NOT NULL
);
INSERT INTO product_tb VALUES('A001', 'A', 100, 20);
INSERT INTO product_tb VALUES('A002', 'A', 120, 30);
INSERT INTO product_tb VALUES('A003', 'A', 200, 15);
INSERT INTO product_tb VALUES('B001', 'B', 130, 18);
INSERT INTO product_tb VALUES('B002', 'B', 150, 22);
INSERT INTO product_tb VALUES('B003', 'B', 125, 10);
INSERT INTO product_tb VALUES('B004', 'B', 155, 12);
INSERT INTO product_tb VALUES('C001', 'C', 260, 25);
INSERT INTO product_tb VALUES('C002', 'C', 280, 18);
输出
B|4
A|3
C|2
题解
select style_id,count(*) SPU_num
from product_tb
group by style_id
order by SPU_num desc
4.某宝店铺的实际销售额与客单价
11月结束后，小牛同学需要对其在某宝的网店就11月份用户交易情况和产品情况进行分析以更好的经营小店。
已知11月份销售数据表sales_tb如下（其中，sales_date表示销售日期，user_id指用户编号，item_id指货号，sales_num表示销售数量，sales_price表示结算金额）：
| sales_date | user_id | item_id | sales_num | sales_price |
|---|---|---|---|---|
| 2021-11-01 | 1 | A001 | 1 | 90 |
| 2021-11-01 | 2 | A002 | 2 | 220 |
| 2021-11-01 | 2 | B001 | 1 | 120 |
| 2021-11-02 | 3 | C001 | 2 | 500 |
| 2021-11-02 | 4 | B001 | 1 | 120 |
| 2021-11-03 | 5 | C001 | 1 | 240 |
| 2021-11-03 | 6 | C002 | 1 | 270 |
| 2021-11-04 | 7 | A003 | 1 | 180 |
| 2021-11-04 | 8 | B002 | 1 | 140 |
| 2021-11-04 | 9 | B001 | 1 | 125 |
| 2021-11-05 | 10 | B003 | 1 | 120 |
| 2021-11-05 | 10 | B004 | 1 | 150 |
| 2021-11-05 | 10 | A003 | 1 | 180 |
| 2021-11-06 | 11 | B003 | 1 | 120 |
| 2021-11-06 | 10 | B004 | 1 | 150 |
请你统计实际总销售额与客单价（人均付费，总收入/总用户数，结果保留两位小数），以上例子的输出结果如下：
| sales_total | per_trans |
|---|---|
| 2725 | 247.73 |
示例1
drop table if exists sales_tb;
CREATE TABLE sales_tb(
sales_date date NOT NULL,
user_id int(10) NOT NULL,
item_id char(10) NOT NULL,
sales_num int(10) NOT NULL,
sales_price int(10) NOT NULL
);
INSERT INTO sales_tb VALUES('2021-11-1', 1, 'A001', 1, 90);
INSERT INTO sales_tb VALUES('2021-11-1', 2, 'A002', 2, 220);
INSERT INTO sales_tb VALUES('2021-11-1', 2, 'B001', 1, 120);
INSERT INTO sales_tb VALUES('2021-11-2', 3, 'C001', 2, 500);
INSERT INTO sales_tb VALUES('2021-11-2', 4, 'B001', 1, 120);
INSERT INTO sales_tb VALUES('2021-11-3', 5, 'C001', 1, 240);
INSERT INTO sales_tb VALUES('2021-11-3', 6, 'C002', 1, 270);
INSERT INTO sales_tb VALUES('2021-11-4', 7, 'A003', 1, 180);
INSERT INTO sales_tb VALUES('2021-11-4', 8, 'B002', 1, 140);
INSERT INTO sales_tb VALUES('2021-11-4', 9, 'B001', 1, 125);
INSERT INTO sales_tb VALUES('2021-11-5', 10, 'B003', 1, 120);
INSERT INTO sales_tb VALUES('2021-11-5', 10, 'B004', 1, 150);
INSERT INTO sales_tb VALUES('2021-11-5', 10, 'A003', 1, 180);
INSERT INTO sales_tb VALUES('2021-11-6', 11, 'B003', 1, 120);
INSERT INTO sales_tb VALUES('2021-11-6', 10, 'B004', 1, 150);
输出
2725|247.73
题解
select
sum(sales_price)sales_total,
round(sum(sales_price)/count(distinct user_id),2)per_trans
from sales_tb
where month(sales_date)="11"
5. 某宝店铺折扣率
11月结束后，小牛同学需要对其在某宝的网店就11月份用户交易情况和产品情况进行分析以更好的经营小店。
已知产品情况表product_tb如下（其中，item_id指某款号的具体货号，style_id指款号，tag_price表示标签价格，inventory指库存量）：
| item_id | style_id | tag_price | inventory |
|---|---|---|---|
| A001 | A | 100 | 20 |
| A002 | A | 120 | 30 |
| A003 | A | 200 | 15 |
| B001 | B | 130 | 18 |
| B002 | B | 150 | 22 |
| B003 | B | 125 | 10 |
| B004 | B | 155 | 12 |
| C001 | C | 260 | 25 |
| C002 | C | 280 | 18 |
11月份销售数据表sales_tb如下（其中，sales_date表示销售日期，user_id指用户编号，item_id指货号，sales_num表示销售数量，sales_price表示结算金额）：
| sales_date | user_id | item_id | sales_num | sales_price |
|---|---|---|---|---|
| 2021-11-01 | 1 | A001 | 1 | 90 |
| 2021-11-01 | 2 | A002 | 2 | 220 |
| 2021-11-01 | 2 | B001 | 1 | 120 |
| 2021-11-02 | 3 | C001 | 2 | 500 |
| 2021-11-02 | 4 | B001 | 1 | 120 |
| 2021-11-03 | 5 | C001 | 1 | 240 |
| 2021-11-03 | 6 | C002 | 1 | 270 |
| 2021-11-04 | 7 | A003 | 1 | 180 |
| 2021-11-04 | 8 | B002 | 1 | 140 |
| 2021-11-04 | 9 | B001 | 1 | 125 |
| 2021-11-05 | 10 | B003 | 1 | 120 |
| 2021-11-05 | 10 | B004 | 1 | 150 |
| 2021-11-05 | 10 | A003 | 1 | 180 |
| 2021-11-06 | 11 | B003 | 1 | 120 |
| 2021-11-06 | 10 | B004 | 1 | 150 |
请你统计折扣率（GMV/吊牌金额，GMV指的是成交金额），以上例子的输出结果如下（折扣率保留两位小数）：
| discount_rate(%) |
|---|
| 93.97 |
示例1
drop table if exists product_tb;
CREATE TABLE product_tb(
item_id char(10) NOT NULL,
style_id char(10) NOT NULL,
tag_price int(10) NOT NULL,
inventory int(10) NOT NULL
);
INSERT INTO product_tb VALUES('A001', 'A', 100, 20);
INSERT INTO product_tb VALUES('A002', 'A', 120, 30);
INSERT INTO product_tb VALUES('A003', 'A', 200, 15);
INSERT INTO product_tb VALUES('B001', 'B', 130, 18);
INSERT INTO product_tb VALUES('B002', 'B', 150, 22);
INSERT INTO product_tb VALUES('B003', 'B', 125, 10);
INSERT INTO product_tb VALUES('B004', 'B', 155, 12);
INSERT INTO product_tb VALUES('C001', 'C', 260, 25);
INSERT INTO product_tb VALUES('C002', 'C', 280, 18);
drop table if exists sales_tb;
CREATE TABLE sales_tb(
sales_date date NOT NULL,
user_id int(10) NOT NULL,
item_id char(10) NOT NULL,
sales_num int(10) NOT NULL,
sales_price int(10) NOT NULL
);
INSERT INTO sales_tb VALUES('2021-11-1', 1, 'A001', 1, 90);
INSERT INTO sales_tb VALUES('2021-11-1', 2, 'A002', 2, 220);
INSERT INTO sales_tb VALUES('2021-11-1', 2, 'B001', 1, 120);
INSERT INTO sales_tb VALUES('2021-11-2', 3, 'C001', 2, 500);
INSERT INTO sales_tb VALUES('2021-11-2', 4, 'B001', 1, 120);
INSERT INTO sales_tb VALUES('2021-11-3', 5, 'C001', 1, 240);
INSERT INTO sales_tb VALUES('2021-11-3', 6, 'C002', 1, 270);
INSERT INTO sales_tb VALUES('2021-11-4', 7, 'A003', 1, 180);
INSERT INTO sales_tb VALUES('2021-11-4', 8, 'B002', 1, 140);
INSERT INTO sales_tb VALUES('2021-11-4', 9, 'B001', 1, 125);
INSERT INTO sales_tb VALUES('2021-11-5', 10, 'B003', 1, 120);
INSERT INTO sales_tb VALUES('2021-11-5', 10, 'B004', 1, 150);
INSERT INTO sales_tb VALUES('2021-11-5', 10, 'A003', 1, 180);
INSERT INTO sales_tb VALUES('2021-11-6', 11, 'B003', 1, 120);
INSERT INTO sales_tb VALUES('2021-11-6', 10, 'B004', 1, 150);
输出
93.97
题解
select round(sum(sales_price)/sum(sales_num*tag_price)*100,2) 'discount_rate(%)'
from product_tb
join sales_tb
using(item_id)