LeetCode-数据库题（二） (52-125题 到1565）
文章目录
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- [1075. 项目员工 I](https://leetcode-cn.com/problems/project-employees-i/)
- [1076. 项目员工II](https://leetcode-cn.com/problems/project-employees-ii/)
- [1077. 项目员工 III](https://leetcode-cn.com/problems/project-employees-iii/)
- [1082. 销售分析 I ](https://leetcode-cn.com/problems/sales-analysis-i/)
- [1083. 销售分析 II](https://leetcode-cn.com/problems/sales-analysis-ii/)
- [1084. 销售分析III](https://leetcode-cn.com/problems/sales-analysis-iii/)
- [1097. 游戏玩法分析 V](https://leetcode-cn.com/problems/game-play-analysis-v/)
- [1098. 小众书籍](https://leetcode-cn.com/problems/unpopular-books/)
- [1107. 每日新用户统计](https://leetcode-cn.com/problems/new-users-daily-count/)
- [1112. 每位学生的最高成绩](https://leetcode-cn.com/problems/highest-grade-for-each-student/)
- [1113. 报告的记录](https://leetcode-cn.com/problems/reported-posts/)
- [1132. 报告的记录 II](https://leetcode-cn.com/problems/reported-posts-ii/)
- [1126. 查询活跃业务](https://leetcode-cn.com/problems/active-businesses/)
- [1127. 用户购买平台](https://leetcode-cn.com/problems/user-purchase-platform/)
- [1141. 查询近30天活跃用户数](https://leetcode-cn.com/problems/user-activity-for-the-past-30-days-i/)
- [1142. 过去30天的用户活动 II](https://leetcode-cn.com/problems/user-activity-for-the-past-30-days-ii/)
- [1148. 文章浏览 I](https://leetcode-cn.com/problems/article-views-i/)
- [1149. 文章浏览 II](https://leetcode-cn.com/problems/article-views-ii/)
- [1158. 市场分析 I](https://leetcode-cn.com/problems/market-analysis-i/)
- [1159. 市场分析 II](https://leetcode-cn.com/problems/market-analysis-ii/)
- [1164. 指定日期的产品价格](https://leetcode-cn.com/problems/product-price-at-a-given-date/)
- [1173. 即时食物配送 I](https://leetcode-cn.com/problems/immediate-food-delivery-i/)
- [1174. 即时食物配送 II](https://leetcode-cn.com/problems/immediate-food-delivery-ii/)
- [1179. 重新格式化部门表](https://leetcode-cn.com/problems/reformat-department-table/)
- [1193. 每月交易 I](https://leetcode-cn.com/problems/monthly-transactions-i/)
- [1205. 每月交易II](https://leetcode-cn.com/problems/monthly-transactions-ii/)
- [1194. 锦标赛优胜者](https://leetcode-cn.com/problems/tournament-winners/)
- [1204. 最后一个能进入电梯的人](https://leetcode-cn.com/problems/last-person-to-fit-in-the-elevator/)
- [1211. 查询结果的质量和占比](https://leetcode-cn.com/problems/queries-quality-and-percentage/)
- [1212. 查询球队积分](https://leetcode-cn.com/problems/team-scores-in-football-tournament/)
- [1225. 报告系统状态的连续日期](https://leetcode-cn.com/problems/report-contiguous-dates/)
- [1241. 每个帖子的评论数](https://leetcode-cn.com/problems/number-of-comments-per-post/)
- [1251. 平均售价](https://leetcode-cn.com/problems/average-selling-price/)
- [1264. 页面推荐](https://leetcode-cn.com/problems/page-recommendations/)
- [1270. 向公司CEO汇报工作的所有人](https://leetcode-cn.com/problems/all-people-report-to-the-given-manager/)
- [1280. 学生们参加各科测试的次数](https://leetcode-cn.com/problems/students-and-examinations/)
- [1285. 找到连续区间的开始和结束数字](https://leetcode-cn.com/problems/find-the-start-and-end-number-of-continuous-ranges/)
- [1294. 不同国家的天气类型](https://leetcode-cn.com/problems/weather-type-in-each-country/)
- [1303. 求团队人数](https://leetcode-cn.com/problems/find-the-team-size/)
- [1308. 不同性别每日分数总计](https://leetcode-cn.com/problems/running-total-for-different-genders/)
- [1321. 餐馆营业额变化增长](https://leetcode-cn.com/problems/restaurant-growth/)
- [1322. 广告效果](https://leetcode-cn.com/problems/ads-performance/)
- [1327. 列出指定时间段内所有的下单产品](https://leetcode-cn.com/problems/list-the-products-ordered-in-a-period/)
- [1336. 每次访问的交易次数](https://leetcode-cn.com/problems/number-of-transactions-per-visit/)
- [1341. 电影评分](https://leetcode-cn.com/problems/movie-rating/)
- [1350. 院系无效的学生](https://leetcode-cn.com/problems/students-with-invalid-departments/)
- [1355. 活动参与者](https://leetcode-cn.com/problems/activity-participants/)
- [1364. 顾客的可信联系人数量](https://leetcode-cn.com/problems/number-of-trusted-contacts-of-a-customer/)
- [1369. 获取最近第二次的活动](https://leetcode-cn.com/problems/get-the-second-most-recent-activity/)
- [1378. 使用唯一标识码替换员工ID](https://leetcode-cn.com/problems/replace-employee-id-with-the-unique-identifier/)
- [1384. 按年度列出销售总额](https://leetcode-cn.com/problems/total-sales-amount-by-year/)
- [1393. 股票的资本损益](https://leetcode-cn.com/problems/capital-gainloss/)
- [1398. 购买了产品A和产品B却没有购买产品C的顾客](https://leetcode-cn.com/problems/customers-who-bought-products-a-and-b-but-not-c/)
- [1407. 排名靠前的旅行者](https://leetcode-cn.com/problems/top-travellers/)
- [1412. 查找成绩处于中游的学生](https://leetcode-cn.com/problems/find-the-quiet-students-in-all-exams/)
- [1421. 净现值查询](https://leetcode-cn.com/problems/npv-queries/)
- [1435. 制作会话柱状图](https://leetcode-cn.com/problems/create-a-session-bar-chart/)
- [1440. 计算布尔表达式的值](https://leetcode-cn.com/problems/evaluate-boolean-expression/)
- [1445. 苹果和桔子](https://leetcode-cn.com/problems/apples-oranges/)
- [1454. 活跃用户](https://leetcode-cn.com/problems/active-users/)
- [1459. 矩形面积](https://leetcode-cn.com/problems/rectangles-area/)
- [1468. 计算税后工资](https://leetcode-cn.com/problems/calculate-salaries/)
- [1479. 周内每天的销售情况](https://leetcode-cn.com/problems/sales-by-day-of-the-week/)
- [1485. 按日期分组销售产品](https://leetcode-cn.com/problems/group-sold-products-by-the-date/)
- [1495. 上月播放的儿童适宜电影](https://leetcode-cn.com/problems/friendly-movies-streamed-last-month/)
- [1501. 可以放心投资的国家](https://leetcode-cn.com/problems/countries-you-can-safely-invest-in/)
- [1511. 消费者下单频率](https://leetcode-cn.com/problems/customer-order-frequency/)
- [1517. Find Users With Valid E-Mails](https://leetcode-cn.com/problems/find-users-with-valid-e-mails/)
- [1527. Patients With a Condition](https://leetcode-cn.com/problems/patients-with-a-condition/)
- [1532. The Most Recent Three Orders](https://leetcode-cn.com/problems/the-most-recent-three-orders/)
- [1543. Fix Product Name Format](https://leetcode-cn.com/problems/fix-product-name-format/)
- [1549. The Most Recent Orders for Each Product](https://leetcode-cn.com/problems/the-most-recent-orders-for-each-product/)
- [1555. 银行账户概要](https://leetcode-cn.com/problems/bank-account-summary/)
- [1565. Unique Orders and Customers Per Month](https://leetcode-cn.com/problems/unique-orders-and-customers-per-month/)
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1075. 项目员工 I
难度简单
SQL架构
项目表 Project：
+-------------+---------+
| Column Name | Type |
+-------------+---------+
| project_id | int |
| employee_id | int |
+-------------+---------+
主键为 (project_id, employee_id)。
employee_id 是员工表 Employee 表的外键。
员工表 Employee：
+------------------+---------+
| Column Name | Type |
+------------------+---------+
| employee_id | int |
| name | varchar |
| experience_years | int |
+------------------+---------+
主键是 employee_id。
请写一个 SQL 语句，查询每一个项目中员工的 平均 工作年限，精确到小数点后两位。
查询结果的格式如下：
Project 表：
+-------------+-------------+
| project_id | employee_id |
+-------------+-------------+
| 1 | 1 |
| 1 | 2 |
| 1 | 3 |
| 2 | 1 |
| 2 | 4 |
+-------------+-------------+
Employee 表：
+-------------+--------+------------------+
| employee_id | name | experience_years |
+-------------+--------+------------------+
| 1 | Khaled | 3 |
| 2 | Ali | 2 |
| 3 | John | 1 |
| 4 | Doe | 2 |
+-------------+--------+------------------+
Result 表：
+-------------+---------------+
| project_id | average_years |
+-------------+---------------+
| 1 | 2.00 |
| 2 | 2.50 |
+-------------+---------------+
第一个项目中，员工的平均工作年限是 (3 + 2 + 1) / 3 = 2.00；第二个项目中，员工的平均工作年限是 (3 + 2) / 2 = 2.50
select project_id,round(avg(experience_years),2) average_years
from Project p join Employee e
on p.employee_id=e.employee_id
group by project_id
1076. 项目员工II
难度简单
SQL架构
Table: Project
+-------------+---------+
| Column Name | Type |
+-------------+---------+
| project_id | int |
| employee_id | int |
+-------------+---------+
主键为 (project_id, employee_id)。
employee_id 是员工表 Employee 表的外键。
Table: Employee
+------------------+---------+
| Column Name | Type |
+------------------+---------+
| employee_id | int |
| name | varchar |
| experience_years | int |
+------------------+---------+
主键是 employee_id。
编写一个SQL查询，报告所有雇员最多的项目。
查询结果格式如下所示：
Project table:
+-------------+-------------+
| project_id | employee_id |
+-------------+-------------+
| 1 | 1 |
| 1 | 2 |
| 1 | 3 |
| 2 | 1 |
| 2 | 4 |
+-------------+-------------+
Employee table:
+-------------+--------+------------------+
| employee_id | name | experience_years |
+-------------+--------+------------------+
| 1 | Khaled | 3 |
| 2 | Ali | 2 |
| 3 | John | 1 |
| 4 | Doe | 2 |
+-------------+--------+------------------+
Result table:
+-------------+
| project_id |
+-------------+
| 1 |
+-------------+
第一个项目有3名员工，第二个项目有2名员工。
select project_id
from Project
group by project_id
having count(*) =
(select count(*) `num` from Project group by project_id order by count(*) desc limit 1);
开窗
select project_id from
(select project_id,rank()over(order by count(employee_id) desc) ranking from Project group by project_id) temp where ranking=1
1077. 项目员工 III
难度中等
SQL架构
项目表 Project：
+-------------+---------+
| Column Name | Type |
+-------------+---------+
| project_id | int |
| employee_id | int |
+-------------+---------+
(project_id, employee_id) 是这个表的主键
employee_id 是员工表 Employee 的外键
员工表 Employee：
+------------------+---------+
| Column Name | Type |
+------------------+---------+
| employee_id | int |
| name | varchar |
| experience_years | int |
+------------------+---------+
employee_id 是这个表的主键
写 一个 SQL 查询语句，报告在每一个项目中经验最丰富的雇员是谁。如果出现经验年数相同的情况，请报告所有具有最大经验年数的员工。
查询结果格式在以下示例中：
Project 表：
+-------------+-------------+
| project_id | employee_id |
+-------------+-------------+
| 1 | 1 |
| 1 | 2 |
| 1 | 3 |
| 2 | 1 |
| 2 | 4 |
+-------------+-------------+
Employee 表：
+-------------+--------+------------------+
| employee_id | name | experience_years |
+-------------+--------+------------------+
| 1 | Khaled | 3 |
| 2 | Ali | 2 |
| 3 | John | 3 |
| 4 | Doe | 2 |
+-------------+--------+------------------+
Result 表：
+-------------+---------------+
| project_id | employee_id |
+-------------+---------------+
| 1 | 1 |
| 1 | 3 |
| 2 | 1 |
+-------------+---------------+
employee_id 为 1 和 3 的员工在 project_id 为 1 的项目中拥有最丰富的经验。在 project_id 为 2 的项目中，employee_id 为 1 的员工拥有最丰富的经验。
select project_id ,employee_id
from(
select project_id,e.employee_id,
rank()over(partition by project_id order by experience_years desc) rk
from Project p join Employee e
on p.employee_id=e.employee_id
)t1
where rk=1
1082. 销售分析 I
难度简单22
SQL架构
产品表：Product
+--------------+---------+
| Column Name | Type |
+--------------+---------+
| product_id | int |
| product_name | varchar |
| unit_price | int |
+--------------+---------+
product_id 是这个表的主键.
销售表：Sales
+-------------+---------+
| Column Name | Type |
+-------------+---------+
| seller_id | int |
| product_id | int |
| buyer_id | int |
| sale_date | date |
| quantity | int |
| price | int |
+------ ------+---------+
这个表没有主键，它可以有重复的行.
product_id 是 Product 表的外键.
编写一个 SQL 查询，查询总销售额最高的销售者，如果有并列的，就都展示出来。
查询结果格式如下所示：
Product 表：
+------------+--------------+------------+
| product_id | product_name | unit_price |
+------------+--------------+------------+
| 1 | S8 | 1000 |
| 2 | G4 | 800 |
| 3 | iPhone | 1400 |
+------------+--------------+------------+
Sales 表：
+-----------+------------+----------+------------+----------+-------+
| seller_id | product_id | buyer_id | sale_date | quantity | price |
+-----------+------------+----------+------------+----------+-------+
| 1 | 1 | 1 | 2019-01-21 | 2 | 2000 |
| 1 | 2 | 2 | 2019-02-17 | 1 | 800 |
| 2 | 2 | 3 | 2019-06-02 | 1 | 800 |
| 3 | 3 | 4 | 2019-05-13 | 2 | 2800 |
+-----------+------------+----------+------------+----------+-------+
Result 表：
+-------------+
| seller_id |
+-------------+
| 1 |
| 3 |
+-------------+
Id 为 1 和 3 的销售者，销售总金额都为最高的 2800。
select seller_id
from (
select seller_id ,sum(price) tp,rank() over(order by sum(price) desc) rk
from Sales
group by seller_id
)t1
where rk =1
1083. 销售分析 II
难度简单13
SQL架构
Table: Product
+--------------+---------+
| Column Name | Type |
+--------------+---------+
| product_id | int |
| product_name | varchar |
| unit_price | int |
+--------------+---------+
product_id 是这张表的主键
Table: Sales
+-------------+---------+
| Column Name | Type |
+-------------+---------+
| seller_id | int |
| product_id | int |
| buyer_id | int |
| sale_date | date |
| quantity | int |
| price | int |
+------ ------+---------+
这个表没有主键，它可以有重复的行.
product_id 是 Product 表的外键.
编写一个 SQL 查询，查询购买了 S8 手机却没有购买 iPhone 的买家。注意这里 S8 和 iPhone 是 Product 表中的产品。
查询结果格式如下图表示：
Product table:
+------------+--------------+------------+
| product_id | product_name | unit_price |
+------------+--------------+------------+
| 1 | S8 | 1000 |
| 2 | G4 | 800 |
| 3 | iPhone | 1400 |
+------------+--------------+------------+
Sales table:
+-----------+------------+----------+------------+----------+-------+
| seller_id | product_id | buyer_id | sale_date | quantity | price |
+-----------+------------+----------+------------+----------+-------+
| 1 | 1 | 1 | 2019-01-21 | 2 | 2000 |
| 1 | 2 | 2 | 2019-02-17 | 1 | 800 |
| 2 | 1 | 3 | 2019-06-02 | 1 | 800 |
| 3 | 3 | 3 | 2019-05-13 | 2 | 2800 |
+-----------+------------+----------+------------+----------+-------+
Result table:
+-------------+
| buyer_id |
+-------------+
| 1 |
+-------------+
id 为 1 的买家购买了一部 S8，但是却没有购买 iPhone，而 id 为 3 的买家却同时购买了这 2 部手机。
select t.buyer_id from(
select s.buyer_id, p.product_name
from sales s
inner join
product p
on s.product_id=p.product_id and (p.product_name='S8' or p.product_name='iPhone')
group by s.buyer_id
having count(distinct p.product_name) = 1
) t
where t.product_name='S8'
效率低
select s.buyer_id
from sales as s left join product as p
on s.product_id=p.product_id
group by buyer_id
having sum(p.product_name='S8')>0 and sum(p.product_name='iPhone')=0
1084. 销售分析III
难度简单13
SQL架构
Table: Product
+--------------+---------+
| Column Name | Type |
+--------------+---------+
| product_id | int |
| product_name | varchar |
| unit_price | int |
+--------------+---------+
product_id 是这个表的主键
Table: Sales
+-------------+---------+
| Column Name | Type |
+-------------+---------+
| seller_id | int |
| product_id | int |
| buyer_id | int |
| sale_date | date |
| quantity | int |
| price | int |
+------ ------+---------+
这个表没有主键，它可以有重复的行.
product_id 是 Product 表的外键.
编写一个SQL查询，报告2019年春季才售出的产品。即仅在2019-01-01至2019-03-31（含）之间出售的商品。
查询结果格式如下所示：
Product table:
+------------+--------------+------------+
| product_id | product_name | unit_price |
+------------+--------------+------------+
| 1 | S8 | 1000 |
| 2 | G4 | 800 |
| 3 | iPhone | 1400 |
+------------+--------------+------------+
Sales table:
+-----------+------------+----------+------------+----------+-------+
| seller_id | product_id | buyer_id | sale_date | quantity | price |
+-----------+------------+----------+------------+----------+-------+
| 1 | 1 | 1 | 2019-01-21 | 2 | 2000 |
| 1 | 2 | 2 | 2019-02-17 | 1 | 800 |
| 2 | 2 | 3 | 2019-06-02 | 1 | 800 |
| 3 | 3 | 4 | 2019-05-13 | 2 | 2800 |
+-----------+------------+----------+------------+----------+-------+
Result table:
+-------------+--------------+
| product_id | product_name |
+-------------+--------------+
| 1 | S8 |
+-------------+--------------+
id为1的产品仅在2019年春季销售，其他两个产品在之后销售。
876ms
正常解法
select product_id, product_name
from product
where product_id not in (
select distinct product_id from sales
where sale_date > '2019-03-31' or sale_date < '2019-01-01')
867ms
sum = count
select product_id, product_name
from Sales join Product
using(product_id)
group by product_id
having sum(sale_date between "2019-01-01" and "2019-03-31") = count(sale_date)
987ms
sum 为0 类似于第一种解法，计算了sum效率就低了
select p.product_id, p.product_name
from sales s, product p
where s.product_id = p.product_id
group by s.product_id
having sum(s.sale_date> '2019-03-31')=0 and sum(s.sale_date < '2019-01-01') = 0
上一解法sum换成max
859ms
select p.product_id, p.product_name
from sales s, product p
where s.product_id = p.product_id
group by s.product_id
having min(s.sale_date)>='2019-01-01' and max(s.sale_date) <= '2019-03-31'
1097. 游戏玩法分析 V
难度困难
SQL架构
Activity 活动记录表
+--------------+---------+
| Column Name | Type |
+--------------+---------+
| player_id | int |
| device_id | int |
| event_date | date |
| games_played | int |
+--------------+---------+
（player_id，event_date）是此表的主键
这张表显示了某些游戏的玩家的活动情况
每一行是一个玩家的记录，他在某一天使用某个设备注销之前登录并玩了很多游戏（可能是 0）
我们将玩家的安装日期定义为该玩家的第一个登录日。
我们还将某个日期 X 的第 1 天留存时间定义为安装日期为 X 的玩家的数量，他们在 X 之后的一天重新登录，除以安装日期为 X 的玩家的数量，四舍五入到小数点后两位。
编写一个 SQL 查询，报告每个安装日期、当天安装游戏的玩家数量和第一天的留存时间。
查询结果格式如下所示：
Activity 表：
+-----------+-----------+------------+--------------+
| player_id | device_id | event_date | games_played |
+-----------+-----------+------------+--------------+
| 1 | 2 | 2016-03-01 | 5 |
| 1 | 2 | 2016-03-02 | 6 |
| 2 | 3 | 2017-06-25 | 1 |
| 3 | 1 | 2016-03-01 | 0 |
| 3 | 4 | 2016-07-03 | 5 |
+-----------+-----------+------------+--------------+
Result 表：
+------------+----------+----------------+
| install_dt | installs | Day1_retention |
+------------+----------+----------------+
| 2016-03-01 | 2 | 0.50 |
| 2017-06-25 | 1 | 0.00 |
+------------+----------+----------------+
玩家 1 和 3 在 2016-03-01 安装了游戏，但只有玩家 1 在 2016-03-02 重新登录，所以 2016-03-01 的第一天留存时间是 1/2=0.50
玩家 2 在 2017-06-25 安装了游戏，但在 2017-06-26 没有重新登录，因此 2017-06-25 的第一天留存时间为 0/1=0.00
select install_dt,count(distinct player_id)installs,
round(sum(if(datediff(event_date,install_dt)=1,1,0))/count(distinct player_id),2) Day1_retention
from
(
select *,min(event_date) over(partition by player_id) install_dt
from Activity
)t1
group by install_dt
1098. 小众书籍
难度中等
SQL架构
书籍表 Books：
+----------------+---------+
| Column Name | Type |
+----------------+---------+
| book_id | int |
| name | varchar |
| available_from | date |
+----------------+---------+
book_id 是这个表的主键。
订单表 Orders：
+----------------+---------+
| Column Name | Type |
+----------------+---------+
| order_id | int |
| book_id | int |
| quantity | int |
| dispatch_date | date |
+----------------+---------+
order_id 是这个表的主键。
book_id 是 Books 表的外键。
你需要写一段 SQL 命令，筛选出过去一年中订单总量 少于10本 的 书籍 。
注意：不考虑 上架（available from）距今 不满一个月 的书籍。并且 假设今天是 2019-06-23 。
Write an SQL query that reports the books that have sold less than 10 copies in the last year, excluding books that have been available for less than 1 month from today. Assume today is 2019-06-23.
下面是样例输出结果：
Books 表：
+---------+--------------------+----------------+
| book_id | name | available_from |
+---------+--------------------+----------------+
| 1 | "Kalila And Demna" | 2010-01-01 |
| 2 | "28 Letters" | 2012-05-12 |
| 3 | "The Hobbit" | 2019-06-10 |
| 4 | "13 Reasons Why" | 2019-06-01 |
| 5 | "The Hunger Games" | 2008-09-21 |
+---------+--------------------+----------------+
Orders 表：
+----------+---------+----------+---------------+
| order_id | book_id | quantity | dispatch_date |
+----------+---------+----------+---------------+
| 1 | 1 | 2 | 2018-07-26 |
| 2 | 1 | 1 | 2018-11-05 |
| 3 | 3 | 8 | 2019-06-11 |
| 4 | 4 | 6 | 2019-06-05 |
| 5 | 4 | 5 | 2019-06-20 |
| 6 | 5 | 9 | 2009-02-02 |
| 7 | 5 | 8 | 2010-04-13 |
+----------+---------+----------+---------------+
Result 表：
+-----------+--------------------+
| book_id | name |
+-----------+--------------------+
| 1 | "Kalila And Demna" |
| 2 | "28 Letters" |
| 5 | "The Hunger Games" |
+-----------+--------------------+
这题中英文 都有歧义，看结果进行分析
SELECT a.book_id, a.name
FROM books a LEFT JOIN orders b ON a.book_id=b.book_id
AND dispatch_date BETWEEN DATE_ADD('2019-06-23', INTERVAL -1 YEAR) AND '2019-06-23'
WHERE a.available_from <= DATE_ADD('2019-06-23',INTERVAL -1 MONTH)
GROUP BY a.book_id, a.name
HAVING SUM(IFNULL(b.quantity,0)) < 10
ORDER BY a.book_id;
此题有坑，首先订单近一年，再者quantity为null，还有近一月出版，sum不是订单是本数
还有就是date_add这个函数在hive里和mysql语法上有点小区别
1107. 每日新用户统计
难度中等
SQL架构
Traffic 表：
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| user_id | int |
| activity | enum |
| activity_date | date |
+---------------+---------+
该表没有主键，它可能有重复的行。
activity 列是 ENUM 类型，可能取 ('login', 'logout', 'jobs', 'groups', 'homepage') 几个值之一。
编写一个 SQL 查询，以查询从今天起最多 90 天内，每个日期该日期首次登录的用户数。假设今天是 2019-06-30.
查询结果格式如下例所示：
Traffic 表：
+---------+----------+---------------+
| user_id | activity | activity_date |
+---------+----------+---------------+
| 1 | login | 2019-05-01 |
| 1 | homepage | 2019-05-01 |
| 1 | logout | 2019-05-01 |
| 2 | login | 2019-06-21 |
| 2 | logout | 2019-06-21 |
| 3 | login | 2019-01-01 |
| 3 | jobs | 2019-01-01 |
| 3 | logout | 2019-01-01 |
| 4 | login | 2019-06-21 |
| 4 | groups | 2019-06-21 |
| 4 | logout | 2019-06-21 |
| 5 | login | 2019-03-01 |
| 5 | logout | 2019-03-01 |
| 5 | login | 2019-06-21 |
| 5 | logout | 2019-06-21 |
+---------+----------+---------------+
Result 表：
+------------+-------------+
| login_date | user_count |
+------------+-------------+
| 2019-05-01 | 1 |
| 2019-06-21 | 2 |
+------------+-------------+
请注意，我们只关心用户数非零的日期.
ID 为 5 的用户第一次登陆于 2019-03-01，因此他不算在 2019-06-21 的的统计内。
select login_date,count(*) user_count
from(
select user_id,min(activity_date) login_date
from Traffic
where activity = 'login'
group by user_id
having login_date>= DATE_ADD('2019-06-30',INTERVAL -90 DAY)
)t1
group by login_date
1112. 每位学生的最高成绩
难度中等
SQL架构
表：Enrollments
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| student_id | int |
| course_id | int |
| grade | int |
+---------------+---------+
(student_id, course_id) 是该表的主键。
编写一个 SQL 查询，查询每位学生获得的最高成绩和它所对应的科目，若科目成绩并列，取 course_id 最小的一门。查询结果需按 student_id 增序进行排序。
查询结果格式如下所示：
Enrollments 表：
+------------+-------------------+
| student_id | course_id | grade |
+------------+-----------+-------+
| 2 | 2 | 95 |
| 2 | 3 | 95 |
| 1 | 1 | 90 |
| 1 | 2 | 99 |
| 3 | 1 | 80 |
| 3 | 2 | 75 |
| 3 | 3 | 82 |
+------------+-----------+-------+
Result 表：
+------------+-------------------+
| student_id | course_id | grade |
+------------+-----------+-------+
| 1 | 2 | 99 |
| 2 | 2 | 95 |
| 3 | 3 | 82 |
+------------+-----------+-------+
select student_id,course_id ,grade
from (
select student_id,course_id ,grade,
rank()over(partition by student_id order by grade desc,course_id) rK
from Enrollments
)t1
where rk =1
1113. 报告的记录
难度简单
SQL架构
动作表：Actions
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| user_id | int |
| post_id | int |
| action_date | date |
| action | enum |
| extra | varchar |
+---------------+---------+
此表没有主键，所以可能会有重复的行。
action 字段是 ENUM 类型的，包含:('view', 'like', 'reaction', 'comment', 'report', 'share')
extra 字段是可选的信息（可能为 null），其中的信息例如有：1.报告理由(a reason for report) 2.反应类型(a type of reaction)
编写一条SQL，查询每种 报告理由（report reason）在昨天的报告数量。假设今天是 2019-07-05。
查询及结果的格式示例：
Actions table:
+---------+---------+-------------+--------+--------+
| user_id | post_id | action_date | action | extra |
+---------+---------+-------------+--------+--------+
| 1 | 1 | 2019-07-01 | view | null |
| 1 | 1 | 2019-07-01 | like | null |
| 1 | 1 | 2019-07-01 | share | null |
| 2 | 4 | 2019-07-04 | view | null |
| 2 | 4 | 2019-07-04 | report | spam |
| 3 | 4 | 2019-07-04 | view | null |
| 3 | 4 | 2019-07-04 | report | spam |
| 4 | 3 | 2019-07-02 | view | null |
| 4 | 3 | 2019-07-02 | report | spam |
| 5 | 2 | 2019-07-04 | view | null |
| 5 | 2 | 2019-07-04 | report | racism |
| 5 | 5 | 2019-07-04 | view | null |
| 5 | 5 | 2019-07-04 | report | racism |
+---------+---------+-------------+--------+--------+
Result table:
+---------------+--------------+
| report_reason | report_count |
+---------------+--------------+
| spam | 1 |
| racism | 2 |
+---------------+--------------+
注意，我们只关心报告数量非零的结果。
SELECT
extra AS report_reason,
COUNT(distinct post_id) AS report_count
FROM
Actions
WHERE
`action` = 'report' AND action_date = date_add('2019-07-05',Interval -1 day)
GROUP BY
extra;
1132. 报告的记录 II
难度中等
SQL架构
动作表： Actions
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| user_id | int |
| post_id | int |
| action_date | date |
| action | enum |
| extra | varchar |
+---------------+---------+
这张表没有主键，并有可能存在重复的行。
action 列的类型是 ENUM，可能的值为 ('view', 'like', 'reaction', 'comment', 'report', 'share')。
extra 列拥有一些可选信息，例如：报告理由（a reason for report）或反应类型（a type of reaction）等。
移除表： Removals
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| post_id | int |
| remove_date | date |
+---------------+---------+
这张表的主键是 post_id。
这张表的每一行表示一个被移除的帖子，原因可能是由于被举报或被管理员审查。
编写一段 SQL 来查找：在被报告为垃圾广告的帖子中，被移除的帖子的每日平均占比，四舍五入到小数点后 2 位。
查询结果的格式如下：
Actions table:
+---------+---------+-------------+--------+--------+
| user_id | post_id | action_date | action | extra |
+---------+---------+-------------+--------+--------+
| 1 | 1 | 2019-07-01 | view | null |
| 1 | 1 | 2019-07-01 | like | null |
| 1 | 1 | 2019-07-01 | share | null |
| 2 | 2 | 2019-07-04 | view | null |
| 2 | 2 | 2019-07-04 | report | spam |
| 3 | 4 | 2019-07-04 | view | null |
| 3 | 4 | 2019-07-04 | report | spam |
| 4 | 3 | 2019-07-02 | view | null |
| 4 | 3 | 2019-07-02 | report | spam |
| 5 | 2 | 2019-07-03 | view | null |
| 5 | 2 | 2019-07-03 | report | racism |
| 5 | 5 | 2019-07-03 | view | null |
| 5 | 5 | 2019-07-03 | report | racism |
+---------+---------+-------------+--------+--------+
Removals table:
+---------+-------------+
| post_id | remove_date |
+---------+-------------+
| 2 | 2019-07-20 |
| 3 | 2019-07-18 |
+---------+-------------+
Result table:
+-----------------------+
| average_daily_percent |
+-----------------------+
| 75.00 |
+-----------------------+
2019-07-04 的垃圾广告移除率是 50%，因为有两张帖子被报告为垃圾广告，但只有一个得到移除。
2019-07-02 的垃圾广告移除率是 100%，因为有一张帖子被举报为垃圾广告并得到移除。
其余几天没有收到垃圾广告的举报，因此平均值为：(50 + 100) / 2 = 75%
注意，输出仅需要一个平均值即可，我们并不关注移除操作的日期。
SELECT ROUND(AVG(proportion) * 100, 2) AS average_daily_percent
FROM (
SELECT actions.action_date, COUNT(DISTINCT removals.post_id)/COUNT(DISTINCT actions.post_id) AS proportion
FROM actions
LEFT JOIN removals
ON actions.post_id = removals.post_id
WHERE extra = 'spam'
GROUP BY actions.action_date
) a
要理解spam的含义
1126. 查询活跃业务
难度中等
SQL架构
事件表：Events
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| business_id | int |
| event_type | varchar |
| occurences | int |
+---------------+---------+
此表的主键是 (business_id, event_type)。
表中的每一行记录了某种类型的事件在某些业务中多次发生的信息。
写一段 SQL 来查询所有活跃的业务。
如果一个业务的某个事件类型的发生次数大于此事件类型在所有业务中的平均发生次数，并且该业务至少有两个这样的事件类型，那么该业务就可被看做是活跃业务。
查询结果格式如下所示：
Events table:
+-------------+------------+------------+
| business_id | event_type | occurences |
+-------------+------------+------------+
| 1 | reviews | 7 |
| 3 | reviews | 3 |
| 1 | ads | 11 |
| 2 | ads | 7 |
| 3 | ads | 6 |
| 1 | page views | 3 |
| 2 | page views | 12 |
+-------------+------------+------------+
结果表
+-------------+
| business_id |
+-------------+
| 1 |
+-------------+
'reviews'、 'ads' 和 'page views' 的总平均发生次数分别是 (7+3)/2=5, (11+7+6)/3=8, (3+12)/2=7.5。
id 为 1 的业务有 7 个 'reviews' 事件（大于 5）和 11 个 'ads' 事件（大于 8），所以它是活跃业务。
select business_id
from (
select business_id,occurences,
avg(occurences) over(partition by event_type) avo
from Events
)t1
where occurences > avo
group by business_id
having count(*)>=2
1127. 用户购买平台
难度困难
SQL架构
支出表: Spending
+-------------+---------+
| Column Name | Type |
+-------------+---------+
| user_id | int |
| spend_date | date |
| platform | enum |
| amount | int |
+-------------+---------+
这张表记录了用户在一个在线购物网站的支出历史，该在线购物平台同时拥有桌面端（'desktop'）和手机端（'mobile'）的应用程序。
这张表的主键是 (user_id, spend_date, platform)。
平台列 platform 是一种 ENUM ，类型为（'desktop', 'mobile'）。
写一段 SQL 来查找每天 仅 使用手机端用户、仅 使用桌面端用户和 同时 使用桌面端和手机端的用户人数和总支出金额。
查询结果格式如下例所示：
Spending table:
+---------+------------+----------+--------+
| user_id | spend_date | platform | amount |
+---------+------------+----------+--------+
| 1 | 2019-07-01 | mobile | 100 |
| 1 | 2019-07-01 | desktop | 100 |
| 2 | 2019-07-01 | mobile | 100 |
| 2 | 2019-07-02 | mobile | 100 |
| 3 | 2019-07-01 | desktop | 100 |
| 3 | 2019-07-02 | desktop | 100 |
+---------+------------+----------+--------+
Result table:
+------------+----------+--------------+-------------+
| spend_date | platform | total_amount | total_users |
+------------+----------+--------------+-------------+
| 2019-07-01 | desktop | 100 | 1 |
| 2019-07-01 | mobile | 100 | 1 |
| 2019-07-01 | both | 200 | 1 |
| 2019-07-02 | desktop | 100 | 1 |
| 2019-07-02 | mobile | 100 | 1 |
| 2019-07-02 | both | 0 | 0 |
+------------+----------+--------------+-------------+
在 2019-07-01, 用户1 同时 使用桌面端和手机端购买, 用户2 仅 使用了手机端购买，而用户3 仅 使用了桌面端购买。
在 2019-07-02, 用户2 仅 使用了手机端购买, 用户3 仅 使用了桌面端购买，且没有用户 同时 使用桌面端和手机端购买。
select
spend_date, platform,
ifnull(sum(total_am),0) total_amount,
ifnull(sum(total_u),0) total_users
from
(
select p.spend_date, p.platform, t.total_am, t.total_u
from
(
select distinct spend_date, "desktop" platform from Spending
union
select distinct spend_date, "mobile" platform from Spending
union
select distinct spend_date, "both" platform from Spending
) p
left join
(
select spend_date,
if(count(distinct platform)=1, platform, 'both') plat,
sum(amount) total_am,
count(distinct user_id) total_u
from Spending
group by spend_date, user_id
) t
on p.platform = t.plat and p.spend_date = t.spend_date
) temp
group by spend_date, platform
必须保证 desktop mobile both的顺序 ，所以 先列出三个字段
1141. 查询近30天活跃用户数
难度简单
SQL架构
活动记录表：Activity
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| user_id | int |
| session_id | int |
| activity_date | date |
| activity_type | enum |
+---------------+---------+
该表是用户在社交网站的活动记录。
该表没有主键，可能包含重复数据。
activity_type 字段为以下四种值 ('open_session', 'end_session', 'scroll_down', 'send_message')。
每个 session_id 只属于一个用户。
请写SQL查询出截至 2019-07-27（包含2019-07-27），近 30天的每日活跃用户数（当天只要有一条活动记录，即为活跃用户）。
查询结果示例如下：
Activity table:
+---------+------------+---------------+---------------+
| user_id | session_id | activity_date | activity_type |
+---------+------------+---------------+---------------+
| 1 | 1 | 2019-07-20 | open_session |
| 1 | 1 | 2019-07-20 | scroll_down |
| 1 | 1 | 2019-07-20 | end_session |
| 2 | 4 | 2019-07-20 | open_session |
| 2 | 4 | 2019-07-21 | send_message |
| 2 | 4 | 2019-07-21 | end_session |
| 3 | 2 | 2019-07-21 | open_session |
| 3 | 2 | 2019-07-21 | send_message |
| 3 | 2 | 2019-07-21 | end_session |
| 4 | 3 | 2019-06-25 | open_session |
| 4 | 3 | 2019-06-25 | end_session |
+---------+------------+---------------+---------------+
Result table:
+------------+--------------+
| day | active_users |
+------------+--------------+
| 2019-07-20 | 2 |
| 2019-07-21 | 2 |
+------------+--------------+
非活跃用户的记录不需要展示。
select activity_date day,count(distinct user_id) active_users
from Activity
where activity_date > date_add('2019-07-27',INTERVAL -1 MONTH)
group by activity_date
1142. 过去30天的用户活动 II
难度简单7收藏分享切换为英文关注反馈
SQL架构
Table: Activity
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| user_id | int |
| session_id | int |
| activity_date | date |
| activity_type | enum |
+---------------+---------+
该表没有主键，它可能有重复的行。
activity_type列是一种类型的ENUM（“ open_session”，“ end_session”，“ scroll_down”，“ send_message”）。
该表显示了社交媒体网站的用户活动。
请注意，每个会话完全属于一个用户。
编写SQL查询以查找截至2019年7月27日（含）的30天内每个用户的平均会话数，四舍五入到小数点后两位。我们只统计那些会话期间用户至少进行一项活动的有效会话。
查询结果格式如下例所示：
Activity table:
+---------+------------+---------------+---------------+
| user_id | session_id | activity_date | activity_type |
+---------+------------+---------------+---------------+
| 1 | 1 | 2019-07-20 | open_session |
| 1 | 1 | 2019-07-20 | scroll_down |
| 1 | 1 | 2019-07-20 | end_session |
| 2 | 4 | 2019-07-20 | open_session |
| 2 | 4 | 2019-07-21 | send_message |
| 2 | 4 | 2019-07-21 | end_session |
| 3 | 2 | 2019-07-21 | open_session |
| 3 | 2 | 2019-07-21 | send_message |
| 3 | 2 | 2019-07-21 | end_session |
| 3 | 5 | 2019-07-21 | open_session |
| 3 | 5 | 2019-07-21 | scroll_down |
| 3 | 5 | 2019-07-21 | end_session |
| 4 | 3 | 2019-06-25 | open_session |
| 4 | 3 | 2019-06-25 | end_session |
+---------+------------+---------------+---------------+
Result table:
+---------------------------+
| average_sessions_per_user |
+---------------------------+
| 1.33 |
+---------------------------+
User 1 和 2 在过去30天内各自进行了1次会话，而用户3进行了2次会话，因此平均值为（1 +1 + 2）/ 3 = 1.33。
SELECT IFNULL(ROUND(COUNT(DISTINCT session_id) / COUNT(DISTINCT user_id), 2), 0) AS average_sessions_per_user
from Activity
where activity_date > date_add('2019-07-27',INTERVAL -1 MONTH)
1个session id 代表一次会话
1148. 文章浏览 I
难度简单3收藏分享切换为英文关注反馈
SQL架构
Views 表：
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| article_id | int |
| author_id | int |
| viewer_id | int |
| view_date | date |
+---------------+---------+
此表无主键，因此可能会存在重复行。
此表的每一行都表示某人在某天浏览了某位作者的某篇文章。
请注意，同一人的 author_id 和 viewer_id 是相同的。
请编写一条 SQL 查询以找出所有浏览过自己文章的作者，结果按照 id 升序排列。
查询结果的格式如下所示：
Views 表：
+------------+-----------+-----------+------------+
| article_id | author_id | viewer_id | view_date |
+------------+-----------+-----------+------------+
| 1 | 3 | 5 | 2019-08-01 |
| 1 | 3 | 6 | 2019-08-02 |
| 2 | 7 | 7 | 2019-08-01 |
| 2 | 7 | 6 | 2019-08-02 |
| 4 | 7 | 1 | 2019-07-22 |
| 3 | 4 | 4 | 2019-07-21 |
| 3 | 4 | 4 | 2019-07-21 |
+------------+-----------+-----------+------------+
结果表：
+------+
| id |
+------+
| 4 |
| 7 |
+------+
select distinct author_id id
from Views
where author_id= viewer_id
order by id
1149. 文章浏览 II
难度中等4收藏分享切换为英文关注反馈
SQL架构
Table: Views
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| article_id | int |
| author_id | int |
| viewer_id | int |
| view_date | date |
+---------------+---------+
此表无主键，因此可能会存在重复行。此表的每一行都表示某人在某天浏览了某位作者的某篇文章。 请注意，同一人的 author_id 和 viewer_id 是相同的。
编写一条 SQL 查询来找出在同一天阅读至少两篇文章的人，结果按照 id 升序排序。
查询结果的格式如下：
Views table:
+------------+-----------+-----------+------------+
| article_id | author_id | viewer_id | view_date |
+------------+-----------+-----------+------------+
| 1 | 3 | 5 | 2019-08-01 |
| 3 | 4 | 5 | 2019-08-01 |
| 1 | 3 | 6 | 2019-08-02 |
| 2 | 7 | 7 | 2019-08-01 |
| 2 | 7 | 6 | 2019-08-02 |
| 4 | 7 | 1 | 2019-07-22 |
| 3 | 4 | 4 | 2019-07-21 |
| 3 | 4 | 4 | 2019-07-21 |
+------------+-----------+-----------+------------+
Result table:
+------+
| id |
+------+
| 5 |
| 6 |
+------+
SELECT DISTINCT viewer_id AS id
FROM Views
GROUP BY view_date, viewer_id
HAVING COUNT(DISTINCT article_id) >= 2
ORDER BY viewer_id
1158. 市场分析 I
难度中等
SQL架构
Table: Users
+----------------+---------+
| Column Name | Type |
+----------------+---------+
| user_id | int |
| join_date | date |
| favorite_brand | varchar |
+----------------+---------+
此表主键是 user_id，表中描述了购物网站的用户信息，用户可以在此网站上进行商品买卖。
Table: Orders
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| order_id | int |
| order_date | date |
| item_id | int |
| buyer_id | int |
| seller_id | int |
+---------------+---------+
此表主键是 order_id，外键是 item_id 和（buyer_id，seller_id）。
Table: Item
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| item_id | int |
| item_brand | varchar |
+---------------+---------+
此表主键是 item_id。
请写出一条SQL语句以查询每个用户的注册日期和在 2019 年作为买家的订单总数。
查询结果格式如下：
Users table:
+---------+------------+----------------+
| user_id | join_date | favorite_brand |
+---------+------------+----------------+
| 1 | 2018-01-01 | Lenovo |
| 2 | 2018-02-09 | Samsung |
| 3 | 2018-01-19 | LG |
| 4 | 2018-05-21 | HP |
+---------+------------+----------------+
Orders table:
+----------+------------+---------+----------+-----------+
| order_id | order_date | item_id | buyer_id | seller_id |
+----------+------------+---------+----------+-----------+
| 1 | 2019-08-01 | 4 | 1 | 2 |
| 2 | 2018-08-02 | 2 | 1 | 3 |
| 3 | 2019-08-03 | 3 | 2 | 3 |
| 4 | 2018-08-04 | 1 | 4 | 2 |
| 5 | 2018-08-04 | 1 | 3 | 4 |
| 6 | 2019-08-05 | 2 | 2 | 4 |
+----------+------------+---------+----------+-----------+
Items table:
+---------+------------+
| item_id | item_brand |
+---------+------------+
| 1 | Samsung |
| 2 | Lenovo |
| 3 | LG |
| 4 | HP |
+---------+------------+
Result table:
+-----------+------------+----------------+
| buyer_id | join_date | orders_in_2019 |
+-----------+------------+----------------+
| 1 | 2018-01-01 | 1 |
| 2 | 2018-02-09 | 2 |
| 3 | 2018-01-19 | 0 |
| 4 | 2018-05-21 | 0 |
+-----------+------------+----------------+
select user_id buyer_id,join_date, ifnull(cnt,0)orders_in_2019
from Users u left join
(select buyer_id,count(*) cnt
from Orders
where year(order_date) = 2019
group by buyer_id
)t1
on u.user_id =t1.buyer_id
1159. 市场分析 II
难度困难
SQL架构
表: Users
+----------------+---------+
| Column Name | Type |
+----------------+---------+
| user_id | int |
| join_date | date |
| favorite_brand | varchar |
+----------------+---------+
user_id 是该表的主键
表中包含一位在线购物网站用户的个人信息，用户可以在该网站出售和购买商品。
表: Orders
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| order_id | int |
| order_date | date |
| item_id | int |
| buyer_id | int |
| seller_id | int |
+---------------+---------+
order_id 是该表的主键
item_id 是 Items 表的外键
buyer_id 和 seller_id 是 Users 表的外键
表: Items
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| item_id | int |
| item_brand | varchar |
+---------------+---------+
item_id 是该表的主键
写一个 SQL 查询确定每一个用户按日期顺序卖出的第二件商品的品牌是否是他们最喜爱的品牌。如果一个用户卖出少于两件商品，查询的结果是 no 。
题目保证没有一个用户在一天中卖出超过一件商品
下面是查询结果格式的例子：
Users table:
+---------+------------+----------------+
| user_id | join_date | favorite_brand |
+---------+------------+----------------+
| 1 | 2019-01-01 | Lenovo |
| 2 | 2019-02-09 | Samsung |
| 3 | 2019-01-19 | LG |
| 4 | 2019-05-21 | HP |
+---------+------------+----------------+
Orders table:
+----------+------------+---------+----------+-----------+
| order_id | order_date | item_id | buyer_id | seller_id |
+----------+------------+---------+----------+-----------+
| 1 | 2019-08-01 | 4 | 1 | 2 |
| 2 | 2019-08-02 | 2 | 1 | 3 |
| 3 | 2019-08-03 | 3 | 2 | 3 |
| 4 | 2019-08-04 | 1 | 4 | 2 |
| 5 | 2019-08-04 | 1 | 3 | 4 |
| 6 | 2019-08-05 | 2 | 2 | 4 |
+----------+------------+---------+----------+-----------+
Items table:
+---------+------------+
| item_id | item_brand |
+---------+------------+
| 1 | Samsung |
| 2 | Lenovo |
| 3 | LG |
| 4 | HP |
+---------+------------+
Result table:
+-----------+--------------------+
| seller_id | 2nd_item_fav_brand |
+-----------+--------------------+
| 1 | no |
| 2 | yes |
| 3 | yes |
| 4 | no |
+-----------+--------------------+
id 为 1 的用户的查询结果是 no，因为他什么也没有卖出
id为 2 和 3 的用户的查询结果是 yes，因为他们卖出的第二件商品的品牌是他们自己最喜爱的品牌
id为 4 的用户的查询结果是 no，因为他卖出的第二件商品的品牌不是他最喜爱的品牌
select user_id seller_id, if(item_brand=favorite_brand,'yes','no') 2nd_item_fav_brand
from Users u
left join
(
select i.item_id,seller_id,item_brand
from Items i
join
(
select seller_id,item_id,rank() over(partition by seller_id order by order_date ) rk
from Orders
)t1
on i.item_id = t1.item_id
where rk =2
)t2
on u.user_id = t2.seller_id
1164. 指定日期的产品价格
难度中等
SQL架构
产品数据表: Products
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| product_id | int |
| new_price | int |
| change_date | date |
+---------------+---------+
这张表的主键是 (product_id, change_date)。
这张表的每一行分别记录了 某产品 在某个日期 更改后 的新价格。
写一段 SQL来查找在 2019-08-16 时全部产品的价格，假设所有产品在修改前的价格都是 10。
查询结果格式如下例所示：
Products table:
+------------+-----------+-------------+
| product_id | new_price | change_date |
+------------+-----------+-------------+
| 1 | 20 | 2019-08-14 |
| 2 | 50 | 2019-08-14 |
| 1 | 30 | 2019-08-15 |
| 1 | 35 | 2019-08-16 |
| 2 | 65 | 2019-08-17 |
| 3 | 20 | 2019-08-18 |
+------------+-----------+-------------+
Result table:
+------------+-------+
| product_id | price |
+------------+-------+
| 2 | 50 |
| 1 | 35 |
| 3 | 10 |
+------------+-------+
select distinct p.product_id,ifnull(t1.new_price,10) price
from Products p
left join
(
select product_id,new_price
from (
select product_id,new_price,change_date,Max(change_date) over(partition by product_id ) md
from Products
where change_date<='2019-08-16'
)tmp
where change_date = md
)t1
on p. product_id = t1.product_id
order by price desc
1173. 即时食物配送 I
难度简单
SQL架构
配送表: Delivery
+-----------------------------+---------+
| Column Name | Type |
+-----------------------------+---------+
| delivery_id | int |
| customer_id | int |
| order_date | date |
| customer_pref_delivery_date | date |
+-----------------------------+---------+
delivery_id 是表的主键。
该表保存着顾客的食物配送信息，顾客在某个日期下了订单，并指定了一个期望的配送日期（和下单日期相同或者在那之后）。
如果顾客期望的配送日期和下单日期相同，则该订单称为 「即时订单」，否则称为「计划订单」。
写一条 SQL 查询语句获取即时订单所占的百分比， 保留两位小数。
查询结果如下所示：
Delivery 表:
+-------------+-------------+------------+-----------------------------+
| delivery_id | customer_id | order_date | customer_pref_delivery_date |
+-------------+-------------+------------+-----------------------------+
| 1 | 1 | 2019-08-01 | 2019-08-02 |
| 2 | 5 | 2019-08-02 | 2019-08-02 |
| 3 | 1 | 2019-08-11 | 2019-08-11 |
| 4 | 3 | 2019-08-24 | 2019-08-26 |
| 5 | 4 | 2019-08-21 | 2019-08-22 |
| 6 | 2 | 2019-08-11 | 2019-08-13 |
+-------------+-------------+------------+-----------------------------+
Result 表:
+----------------------+
| immediate_percentage |
+----------------------+
| 33.33 |
+----------------------+
2 和 3 号订单为即时订单，其他的为计划订单。
select round(sum(if(order_date=customer_pref_delivery_date,1,0))/count(*)*100,2) immediate_percentage
from Delivery
1174. 即时食物配送 II
难度中等
SQL架构
配送表: Delivery
+-----------------------------+---------+
| Column Name | Type |
+-----------------------------+---------+
| delivery_id | int |
| customer_id | int |
| order_date | date |
| customer_pref_delivery_date | date |
+-----------------------------+---------+
delivery_id 是表的主键。
该表保存着顾客的食物配送信息，顾客在某个日期下了订单，并指定了一个期望的配送日期（和下单日期相同或者在那之后）。
如果顾客期望的配送日期和下单日期相同，则该订单称为 「即时订单」，否则称为「计划订单」。
「首次订单」是顾客最早创建的订单。我们保证一个顾客只会有一个「首次订单」。
写一条 SQL 查询语句获取即时订单在所有用户的首次订单中的比例。保留两位小数。
查询结果如下所示：
Delivery 表：
+-------------+-------------+------------+-----------------------------+
| delivery_id | customer_id | order_date | customer_pref_delivery_date |
+-------------+-------------+------------+-----------------------------+
| 1 | 1 | 2019-08-01 | 2019-08-02 |
| 2 | 2 | 2019-08-02 | 2019-08-02 |
| 3 | 1 | 2019-08-11 | 2019-08-12 |
| 4 | 3 | 2019-08-24 | 2019-08-24 |
| 5 | 3 | 2019-08-21 | 2019-08-22 |
| 6 | 2 | 2019-08-11 | 2019-08-13 |
| 7 | 4 | 2019-08-09 | 2019-08-09 |
+-------------+-------------+------------+-----------------------------+
Result 表：
+----------------------+
| immediate_percentage |
+----------------------+
| 50.00 |
+----------------------+
1 号顾客的 1 号订单是首次订单，并且是计划订单。
2 号顾客的 2 号订单是首次订单，并且是即时订单。
3 号顾客的 5 号订单是首次订单，并且是计划订单。
4 号顾客的 7 号订单是首次订单，并且是即时订单。
因此，一半顾客的首次订单是即时的。
开窗
select round(sum(if(order_date = fo && fo=d,1,0))/count(distinct customer_id)*100,2) immediate_percentage
from
(
select customer_id,order_date,min(order_date)over ( partition by customer_id) fo,
if(order_date=customer_pref_delivery_date,order_date ,null) d
from Delivery
)t1
另一种思路
select round (
sum(order_date = customer_pref_delivery_date) * 100 /
count(*),
2
) as immediate_percentage
from Delivery
where (customer_id, order_date) in (
select customer_id, min(order_date)
from delivery
group by customer_id
)
1179. 重新格式化部门表
难度
SQL架构
部门表 Department：
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| revenue | int |
| month | varchar |
+---------------+---------+
(id, month) 是表的联合主键。
这个表格有关于每个部门每月收入的信息。
月份（month）可以取下列值 ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]。
编写一个 SQL 查询来重新格式化表，使得新的表中有一个部门 id 列和一些对应 每个月 的收入（revenue）列。
查询结果格式如下面的示例所示：
Department 表：
+------+---------+-------+
| id | revenue | month |
+------+---------+-------+
| 1 | 8000 | Jan |
| 2 | 9000 | Jan |
| 3 | 10000 | Feb |
| 1 | 7000 | Feb |
| 1 | 6000 | Mar |
+------+---------+-------+
查询得到的结果表：
+------+-------------+-------------+-------------+-----+-------------+
| id | Jan_Revenue | Feb_Revenue | Mar_Revenue | ... | Dec_Revenue |
+------+-------------+-------------+-------------+-----+-------------+
| 1 | 8000 | 7000 | 6000 | ... | null |
| 2 | 9000 | null | null | ... | null |
| 3 | null | 10000 | null | ... | null |
+------+-------------+-------------+-------------+-----+-------------+
注意，结果表有 13 列 (1个部门 id 列 + 12个月份的收入列)。
SELECT id,
SUM(CASE `month` WHEN 'Jan' THEN revenue END) Jan_Revenue,
SUM(CASE `month` WHEN 'Feb' THEN revenue END) Feb_Revenue,
SUM(CASE `month` WHEN 'Mar' THEN revenue END) Mar_Revenue,
SUM(CASE `month` WHEN 'Apr' THEN revenue END) Apr_Revenue,
SUM(CASE `month` WHEN 'May' THEN revenue END) May_Revenue,
SUM(CASE `month` WHEN 'Jun' THEN revenue END) Jun_Revenue,
SUM(CASE `month` WHEN 'Jul' THEN revenue END) Jul_Revenue,
SUM(CASE `month` WHEN 'Aug' THEN revenue END) Aug_Revenue,
SUM(CASE `month` WHEN 'Sep' THEN revenue END) Sep_Revenue,
SUM(CASE `month` WHEN 'Oct' THEN revenue END) Oct_Revenue,
SUM(CASE `month` WHEN 'Nov' THEN revenue END) Nov_Revenue,
SUM(CASE `month` WHEN 'Dec' THEN revenue END) Dec_Revenue
FROM Department
GROUP BY id;
1193. 每月交易 I
SQL架构
Table: Transactions
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| country | varchar |
| state | enum |
| amount | int |
| trans_date | date |
+---------------+---------+
id 是这个表的主键。
该表包含有关传入事务的信息。
state 列类型为 “[”批准“，”拒绝“] 之一。
编写一个 sql 查询来查找每个月和每个国家/地区的事务数及其总金额、已批准的事务数及其总金额。
查询结果格式如下所示：
Transactions table:
+------+---------+----------+--------+------------+
| id | country | state | amount | trans_date |
+------+---------+----------+--------+------------+
| 121 | US | approved | 1000 | 2018-12-18 |
| 122 | US | declined | 2000 | 2018-12-19 |
| 123 | US | approved | 2000 | 2019-01-01 |
| 124 | DE | approved | 2000 | 2019-01-07 |
+------+---------+----------+--------+------------+
Result table:
+----------+---------+-------------+----------------+--------------------+-----------------------+
| month | country | trans_count | approved_count | trans_total_amount | approved_total_amount |
+----------+---------+-------------+----------------+--------------------+-----------------------+
| 2018-12 | US | 2 | 1 | 3000 | 1000 |
| 2019-01 | US | 1 | 1 | 2000 | 2000 |
| 2019-01 | DE | 1 | 1 | 2000 | 2000 |
+----------+---------+-------------+----------------+--------------------+-----------------------+
SELECT DATE_FORMAT(trans_date, '%Y-%m') AS month,
country,
COUNT(*) AS trans_count,
COUNT(IF(state = 'approved', 1, NULL)) AS approved_count,
SUM(amount) AS trans_total_amount,
SUM(IF(state = 'approved', amount, 0)) AS approved_total_amount
FROM Transactions
GROUP BY month, country
1193. 每月交易 I
难度中等
SQL架构
Table: Transactions
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| country | varchar |
| state | enum |
| amount | int |
| trans_date | date |
+---------------+---------+
id 是这个表的主键。
该表包含有关传入事务的信息。
state 列类型为 “[”批准“，”拒绝“] 之一。
编写一个 sql 查询来查找每个月和每个国家/地区的事务数及其总金额、已批准的事务数及其总金额。
查询结果格式如下所示：
Transactions table:
+------+---------+----------+--------+------------+
| id | country | state | amount | trans_date |
+------+---------+----------+--------+------------+
| 121 | US | approved | 1000 | 2018-12-18 |
| 122 | US | declined | 2000 | 2018-12-19 |
| 123 | US | approved | 2000 | 2019-01-01 |
| 124 | DE | approved | 2000 | 2019-01-07 |
+------+---------+----------+--------+------------+
Result table:
+----------+---------+-------------+----------------+--------------------+-----------------------+
| month | country | trans_count | approved_count | trans_total_amount | approved_total_amount |
+----------+---------+-------------+----------------+--------------------+-----------------------+
| 2018-12 | US | 2 | 1 | 3000 | 1000 |
| 2019-01 | US | 1 | 1 | 2000 | 2000 |
| 2019-01 | DE | 1 | 1 | 2000 | 2000 |
+----------+---------+-------------+----------------+--------------------+-----------------------+
SELECT DATE_FORMAT(trans_date, '%Y-%m') AS month,
country,
COUNT(*) AS trans_count,
COUNT(IF(state = 'approved', 1, NULL)) AS approved_count,
SUM(amount) AS trans_total_amount,
SUM(IF(state = 'approved', amount, 0)) AS approved_total_amount
FROM Transactions
GROUP BY month, country
1205. 每月交易II
难度中等
SQL架构
Transactions 记录表
+----------------+---------+
| Column Name | Type |
+----------------+---------+
| id | int |
| country | varchar |
| state | enum |
| amount | int |
| trans_date | date |
+----------------+---------+
id 是这个表的主键。
该表包含有关传入事务的信息。
状态列是类型为 [approved（已批准）、declined（已拒绝）] 的枚举。
Chargebacks 表
+----------------+---------+
| Column Name | Type |
+----------------+---------+
| trans_id | int |
| charge_date | date |
+----------------+---------+
退单包含有关放置在事务表中的某些事务的传入退单的基本信息。
trans_id 是 transactions 表的 id 列的外键。
每项退单都对应于之前进行的交易，即使未经批准。
编写一个 SQL 查询，以查找每个月和每个国家/地区的已批准交易的数量及其总金额、退单的数量及其总金额。
注意：在您的查询中，给定月份和国家，忽略所有为零的行。
查询结果格式如下所示：
Transactions 表：
+------+---------+----------+--------+------------+
| id | country | state | amount | trans_date |
+------+---------+----------+--------+------------+
| 101 | US | approved | 1000 | 2019-05-18 |
| 102 | US | declined | 2000 | 2019-05-19 |
| 103 | US | approved | 3000 | 2019-06-10 |
| 104 | US | declined | 4000 | 2019-06-13 |
| 105 | US | approved | 5000 | 2019-06-15 |
+------+---------+----------+--------+------------+
Chargebacks 表：
+------------+------------+
| trans_id | trans_date |
+------------+------------+
| 102 | 2019-05-29 |
| 101 | 2019-06-30 |
| 105 | 2019-09-18 |
+------------+------------+
Result 表：
+----------+---------+----------------+-----------------+-------------------+--------------------+
| month | country | approved_count | approved_amount | chargeback_count | chargeback_amount |
+----------+---------+----------------+-----------------+-------------------+--------------------+
| 2019-05 | US | 1 | 1000 | 1 | 2000 |
| 2019-06 | US | 2 | 8000 | 1 | 1000 |
| 2019-09 | US | 0 | 0 | 1 | 5000 |
+----------+---------+----------------+-----------------+-------------------+--------------------+
select
date_format(trans_date, '%Y-%m') month,
country,
sum(state = 'approved') approved_count,
sum(if(state = 'approved', amount, 0)) approved_amount,
sum(state = 'chargeback') chargeback_count,
sum(if(state = 'chargeback', amount, 0)) chargeback_amount
from (
select * from transactions
union all
select id, country, 'chargeback' state, amount, c.trans_date
from chargebacks c left join transactions t
on c.trans_id = t.id
) tmp
group by month, country
having approved_amount or chargeback_amount
1194. 锦标赛优胜者
难度困难
SQL架构
Players 玩家表
+-------------+-------+
| Column Name | Type |
+-------------+-------+
| player_id | int |
| group_id | int |
+-------------+-------+
玩家 ID 是此表的主键。
此表的每一行表示每个玩家的组。
Matches 赛事表
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| match_id | int |
| first_player | int |
| second_player | int |
| first_score | int |
| second_score | int |
+---------------+---------+
match_id 是此表的主键。
每一行是一场比赛的记录，第一名和第二名球员包含每场比赛的球员 ID。
第一个玩家和第二个玩家的分数分别包含第一个玩家和第二个玩家的分数。
你可以假设，在每一场比赛中，球员都属于同一组。
每组的获胜者是在组内得分最高的选手。如果平局，player_id 最小 的选手获胜。
编写一个 SQL 查询来查找每组中的获胜者。
查询结果格式如下所示
Players 表:
+-----------+------------+
| player_id | group_id |
+-----------+------------+
| 15 | 1 |
| 25 | 1 |
| 30 | 1 |
| 45 | 1 |
| 10 | 2 |
| 35 | 2 |
| 50 | 2 |
| 20 | 3 |
| 40 | 3 |
+-----------+------------+
Matches 表:
+------------+--------------+---------------+-------------+--------------+
| match_id | first_player | second_player | first_score | second_score |
+------------+--------------+---------------+-------------+--------------+
| 1 | 15 | 45 | 3 | 0 |
| 2 | 30 | 25 | 1 | 2 |
| 3 | 30 | 15 | 2 | 0 |
| 4 | 40 | 20 | 5 | 2 |
| 5 | 35 | 50 | 1 | 1 |
+------------+--------------+---------------+-------------+--------------+
Result 表:
+-----------+------------+
| group_id | player_id |
+-----------+------------+
| 1 | 15 |
| 2 | 35 |
| 3 | 40 |
+-----------+------------+
union all
SELECT group_id, player_id
FROM (
SELECT group_id, player_id, SUM(score) AS score
FROM (
-- 每个用户总的 first_score
SELECT Players.group_id, Players.player_id, SUM(Matches.first_score) AS score
FROM Players JOIN Matches ON Players.player_id = Matches.first_player
GROUP BY Players.player_id
UNION ALL
-- 每个用户总的 second_score
SELECT Players.group_id, Players.player_id, SUM(Matches.second_score) AS score
FROM Players JOIN Matches ON Players.player_id = Matches.second_player
GROUP BY Players.player_id
) s
GROUP BY player_id
ORDER BY score DESC, player_id
) result
GROUP BY group_id
select group_id, player_id
from (
select players.*, sum(if(player_id = first_player, first_score, second_score)) score
from players join matches
on player_id = first_player or player_id = second_player
group by player_id
order by score desc, player_id
) tmp
group by group_id
1204. 最后一个能进入电梯的人
难度中等
SQL架构
表: Queue
+-------------+---------+
| Column Name | Type |
+-------------+---------+
| person_id | int |
| person_name | varchar |
| weight | int |
| turn | int |
+-------------+---------+
person_id 是这个表的主键。
该表展示了所有等待电梯的人的信息。
表中 person_id 和 turn 列将包含从 1 到 n 的所有数字，其中 n 是表中的行数。
电梯最大载重量为 1000。
写一条 SQL 查询语句查找最后一个能进入电梯且不超过重量限制的 person_name 。题目确保队列中第一位的人可以进入电梯 。
查询结果如下所示 :
Queue 表
+-----------+-------------------+--------+------+
| person_id | person_name | weight | turn |
+-----------+-------------------+--------+------+
| 5 | George Washington | 250 | 1 |
| 3 | John Adams | 350 | 2 |
| 6 | Thomas Jefferson | 400 | 3 |
| 2 | Will Johnliams | 200 | 4 |
| 4 | Thomas Jefferson | 175 | 5 |
| 1 | James Elephant | 500 | 6 |
+-----------+-------------------+--------+------+
Result 表
+-------------------+
| person_name |
+-------------------+
| Thomas Jefferson |
+-------------------+
为了简化，Queue 表按 turn 列由小到大排序。
上例中 George Washington(id 5), John Adams(id 3) 和 Thomas Jefferson(id 6) 将可以进入电梯,因为他们的体重和为 250 + 350 + 400 = 1000。
Thomas Jefferson(id 6) 是最后一个体重合适并进入电梯的人。
select person_name
from(
select person_name ,sum(weight) over(order by turn) t
from Queue
)t1
where t<=1000
order by t desc
limit 1
1211. 查询结果的质量和占比
难度简单
SQL架构
查询表 Queries：
+-------------+---------+
| Column Name | Type |
+-------------+---------+
| query_name | varchar |
| result | varchar |
| position | int |
| rating | int |
+-------------+---------+
此表没有主键，并可能有重复的行。
此表包含了一些从数据库中收集的查询信息。
“位置”（position）列的值为 1 到 500 。
“评分”（rating）列的值为 1 到 5 。评分小于 3 的查询被定义为质量很差的查询。
将查询结果的质量 quality 定义为：
各查询结果的评分与其位置之间比率的平均值。
将劣质查询百分比 poor_query_percentage 为：
评分小于 3 的查询结果占全部查询结果的百分比。
编写一组 SQL 来查找每次查询的名称(query_name)、质量(quality) 和 劣质查询百分比(poor_query_percentage)。
质量(quality) 和劣质查询百分比(poor_query_percentage) 都应四舍五入到小数点后两位。
查询结果格式如下所示：
Queries table:
+------------+-------------------+----------+--------+
| query_name | result | position | rating |
+------------+-------------------+----------+--------+
| Dog | Golden Retriever | 1 | 5 |
| Dog | German Shepherd | 2 | 5 |
| Dog | Mule | 200 | 1 |
| Cat | Shirazi | 5 | 2 |
| Cat | Siamese | 3 | 3 |
| Cat | Sphynx | 7 | 4 |
+------------+-------------------+----------+--------+
Result table:
+------------+---------+-----------------------+
| query_name | quality | poor_query_percentage |
+------------+---------+-----------------------+
| Dog | 2.50 | 33.33 |
| Cat | 0.66 | 33.33 |
+------------+---------+-----------------------+
Dog 查询结果的质量为 ((5 / 1) + (5 / 2) + (1 / 200)) / 3 = 2.50
Dog 查询结果的劣质查询百分比为 (1 / 3) * 100 = 33.33
Cat 查询结果的质量为 ((2 / 5) + (3 / 3) + (4 / 7)) / 3 = 0.66
Cat 查询结果的劣质查询百分比为 (1 / 3) * 100 = 33.33
select query_name,
round(avg(rating/position),2) quality,
round(sum(if(rating<3,1,0))/count(*)*100,2) poor_query_percentage
from Queries
group by query_name
1212. 查询球队积分
难度中等
SQL架构
Table: Teams
+---------------+----------+
| Column Name | Type |
+---------------+----------+
| team_id | int |
| team_name | varchar |
+---------------+----------+
此表的主键是 team_id，表中的每一行都代表一支独立足球队。
Table: Matches
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| match_id | int |
| host_team | int |
| guest_team | int |
| host_goals | int |
| guest_goals | int |
+---------------+---------+
此表的主键是 match_id，表中的每一行都代表一场已结束的比赛，比赛的主客队分别由它们自己的 id 表示，他们的进球由 host_goals 和 guest_goals 分别表示。
积分规则如下：
- 赢一场得三分；
- 平一场得一分；
- 输一场不得分。
写出一条SQL语句以查询每个队的 team_id，team_name 和 num_points。结果根据 num_points 降序排序，如果有两队积分相同，那么这两队按 team_id 升序排序。
查询结果格式如下：
Teams table:
+-----------+--------------+
| team_id | team_name |
+-----------+--------------+
| 10 | Leetcode FC |
| 20 | NewYork FC |
| 30 | Atlanta FC |
| 40 | Chicago FC |
| 50 | Toronto FC |
+-----------+--------------+
Matches table:
+------------+--------------+---------------+-------------+--------------+
| match_id | host_team | guest_team | host_goals | guest_goals |
+------------+--------------+---------------+-------------+--------------+
| 1 | 10 | 20 | 3 | 0 |
| 2 | 30 | 10 | 2 | 2 |
| 3 | 10 | 50 | 5 | 1 |
| 4 | 20 | 30 | 1 | 0 |
| 5 | 50 | 30 | 1 | 0 |
+------------+--------------+---------------+-------------+--------------+
Result table:
+------------+--------------+---------------+
| team_id | team_name | num_points |
+------------+--------------+---------------+
| 10 | Leetcode FC | 7 |
| 20 | NewYork FC | 3 |
| 50 | Toronto FC | 3 |
| 30 | Atlanta FC | 1 |
| 40 | Chicago FC | 0 |
+------------+--------------+---------------+
select team_id , team_name ,sum(
if(team_id = host_team && host_goals>guest_goals ,3,0)+
if(team_id = host_team && host_goals=guest_goals,1,0)+
if(team_id = guest_team && host_goals=guest_goals,1,0)+
if(team_id = guest_team && host_goals<guest_goals ,3,0)
)num_points
from Teams t left join Matches m
on t.team_id =m.host_team or t.team_id =m.guest_team
group by team_id ,team_name
order by num_points desc,team_id
1225. 报告系统状态的连续日期
难度困难
SQL架构
Table: Failed
+--------------+---------+
| Column Name | Type |
+--------------+---------+
| fail_date | date |
+--------------+---------+
该表主键为 fail_date。
该表包含失败任务的天数.
Table: Succeeded
+--------------+---------+
| Column Name | Type |
+--------------+---------+
| success_date | date |
+--------------+---------+
该表主键为 success_date。
该表包含成功任务的天数.
系统 每天 运行一个任务。每个任务都独立于先前的任务。任务的状态可以是失败或是成功。
编写一个 SQL 查询 2019-01-01 到 2019-12-31 期间任务连续同状态 period_state 的起止日期（start_date 和 end_date）。即如果任务失败了，就是失败状态的起止日期，如果任务成功了，就是成功状态的起止日期。
最后结果按照起始日期 start_date 排序
查询结果样例如下所示:
Failed table:
+-------------------+
| fail_date |
+-------------------+
| 2018-12-28 |
| 2018-12-29 |
| 2019-01-04 |
| 2019-01-05 |
+-------------------+
Succeeded table:
+-------------------+
| success_date |
+-------------------+
| 2018-12-30 |
| 2018-12-31 |
| 2019-01-01 |
| 2019-01-02 |
| 2019-01-03 |
| 2019-01-06 |
+-------------------+
Result table:
+--------------+--------------+--------------+
| period_state | start_date | end_date |
+--------------+--------------+--------------+
| succeeded | 2019-01-01 | 2019-01-03 |
| failed | 2019-01-04 | 2019-01-05 |
| succeeded | 2019-01-06 | 2019-01-06 |
+--------------+--------------+--------------+
结果忽略了 2018 年的记录，因为我们只关心从 2019-01-01 到 2019-12-31 的记录
从 2019-01-01 到 2019-01-03 所有任务成功，系统状态为 "succeeded"。
从 2019-01-04 到 2019-01-05 所有任务失败，系统状态为 "failed"。
从 2019-01-06 到 2019-01-06 所有任务成功，系统状态为 "succeeded"。
开窗函数
select type period_state, min(date) start_date, max(date) as end_date
from
(
select type, date, subdate(date,row_number()over(partition by type order by date)) as diff
from
(
select 'failed' as type, fail_date as date from Failed
where fail_date between '2019-01-01' and '2019-12-31'
union all
select 'succeeded' as type, success_date as date from Succeeded
where success_date between '2019-01-01' and '2019-12-31'
) a
)b
group by type,diff
order by start_date
1241. 每个帖子的评论数
难度简单
SQL架构
表 Submissions 结构如下：
+---------------+----------+
| 列名 | 类型 |
+---------------+----------+
| sub_id | int |
| parent_id | int |
+---------------+----------+
上表没有主键, 所以可能会出现重复的行。
每行可以是一个帖子或对该帖子的评论。
如果是帖子的话，parent_id 就是 null。
对于评论来说，parent_id 就是表中对应帖子的 sub_id。
编写 SQL 语句以查找每个帖子的评论数。
结果表应包含帖子的 post_id 和对应的评论数 number_of_comments 并且按 post_id 升序排列。
Submissions 可能包含重复的评论。您应该计算每个帖子的唯一评论数。
Submissions 可能包含重复的帖子。您应该将它们视为一个帖子。
查询结果格式如下例所示：
Submissions table:
+---------+------------+
| sub_id | parent_id |
+---------+------------+
| 1 | Null |
| 2 | Null |
| 1 | Null |
| 12 | Null |
| 3 | 1 |
| 5 | 2 |
| 3 | 1 |
| 4 | 1 |
| 9 | 1 |
| 10 | 2 |
| 6 | 7 |
+---------+------------+
结果表：
+---------+--------------------+
| post_id | number_of_comments |
+---------+--------------------+
| 1 | 3 |
| 2 | 2 |
| 12 | 0 |
+---------+--------------------+
表中 ID 为 1 的帖子有 ID 为 3、4 和 9 的三个评论。表中 ID 为 3 的评论重复出现了，所以我们只对它进行了一次计数。
表中 ID 为 2 的帖子有 ID 为 5 和 10 的两个评论。
ID 为 12 的帖子在表中没有评论。
表中 ID 为 6 的评论是对 ID 为 7 的已删除帖子的评论，因此我们将其忽略。
后join
select s.sub_id post_id,ifnull(number_of_comments,0) number_of_comments
from Submissions s
left join (
select parent_id ,count(distinct sub_id) number_of_comments
from Submissions
group by parent_id
) t1
on s.sub_id = t1.parent_id
where s.parent_id is null
group by post_id,number_of_comments
order by sub_id
先join
SELECT post_id, COUNT(sub_id) AS number_of_comments
FROM (
SELECT DISTINCT post.sub_id AS post_id, sub.sub_id AS sub_id
FROM Submissions post
LEFT JOIN Submissions sub
ON post.sub_id = sub.parent_id
WHERE post.parent_id is null
) T
GROUP BY post_id
ORDER BY post_id ASC
1251. 平均售价
难度简单
SQL架构
Table: Prices
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| product_id | int |
| start_date | date |
| end_date | date |
| price | int |
+---------------+---------+
(product_id，start_date，end_date) 是 Prices 表的主键。
Prices 表的每一行表示的是某个产品在一段时期内的价格。
每个产品的对应时间段是不会重叠的，这也意味着同一个产品的价格时段不会出现交叉。
Table: UnitsSold
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| product_id | int |
| purchase_date | date |
| units | int |
+---------------+---------+
UnitsSold 表没有主键，它可能包含重复项。
UnitsSold 表的每一行表示的是每种产品的出售日期，单位和产品 id。
编写SQL查询以查找每种产品的平均售价。
average_price 应该四舍五入到小数点后两位。
查询结果格式如下例所示：
Prices table:
+------------+------------+------------+--------+
| product_id | start_date | end_date | price |
+------------+------------+------------+--------+
| 1 | 2019-02-17 | 2019-02-28 | 5 |
| 1 | 2019-03-01 | 2019-03-22 | 20 |
| 2 | 2019-02-01 | 2019-02-20 | 15 |
| 2 | 2019-02-21 | 2019-03-31 | 30 |
+------------+------------+------------+--------+
UnitsSold table:
+------------+---------------+-------+
| product_id | purchase_date | units |
+------------+---------------+-------+
| 1 | 2019-02-25 | 100 |
| 1 | 2019-03-01 | 15 |
| 2 | 2019-02-10 | 200 |
| 2 | 2019-03-22 | 30 |
+------------+---------------+-------+
Result table:
+------------+---------------+
| product_id | average_price |
+------------+---------------+
| 1 | 6.96 |
| 2 | 16.96 |
+------------+---------------+
平均售价 = 产品总价 / 销售的产品数量。
产品 1 的平均售价 = ((100 * 5)+(15 * 20) )/ 115 = 6.96
产品 2 的平均售价 = ((200 * 15)+(30 * 30) )/ 230 = 16.96
SELECT
product_id,
Round(SUM(sales) / SUM(units), 2) AS average_price
FROM (
SELECT
Prices.product_id AS product_id,
Prices.price * UnitsSold.units AS sales,
UnitsSold.units AS units
FROM Prices
JOIN UnitsSold ON Prices.product_id = UnitsSold.product_id
WHERE UnitsSold.purchase_date BETWEEN Prices.start_date AND Prices.end_date
) T
GROUP BY product_id
2表关联的时候直接把日期做过滤
1264. 页面推荐
难度中等
SQL架构
朋友关系列表： Friendship
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| user1_id | int |
| user2_id | int |
+---------------+---------+
这张表的主键是 (user1_id, user2_id)。
这张表的每一行代表着 user1_id 和 user2_id 之间存在着朋友关系。
喜欢列表： Likes
+-------------+---------+
| Column Name | Type |
+-------------+---------+
| user_id | int |
| page_id | int |
+-------------+---------+
这张表的主键是 (user_id, page_id)。
这张表的每一行代表着 user_id 喜欢 page_id。
写一段 SQL 向user_id = 1 的用户，推荐其朋友们喜欢的页面。不要推荐该用户已经喜欢的页面。
你返回的结果中不应当包含重复项。
返回结果的格式如下例所示：
Friendship table:
+----------+----------+
| user1_id | user2_id |
+----------+----------+
| 1 | 2 |
| 1 | 3 |
| 1 | 4 |
| 2 | 3 |
| 2 | 4 |
| 2 | 5 |
| 6 | 1 |
+----------+----------+
Likes table:
+---------+---------+
| user_id | page_id |
+---------+---------+
| 1 | 88 |
| 2 | 23 |
| 3 | 24 |
| 4 | 56 |
| 5 | 11 |
| 6 | 33 |
| 2 | 77 |
| 3 | 77 |
| 6 | 88 |
+---------+---------+
Result table:
+------------------+
| recommended_page |
+------------------+
| 23 |
| 24 |
| 56 |
| 33 |
| 77 |
+------------------+
用户1 同 用户2, 3, 4, 6 是朋友关系。
推荐页面为： 页面23 来自于 用户2, 页面24 来自于 用户3, 页面56 来自于 用户3 以及 页面33 来自于 用户6。
页面77 同时被 用户2 和 用户3 推荐。
页面88 没有被推荐，因为 用户1 已经喜欢了它。
select distinct page_id recommended_page
from Likes
where user_id in(
select if(user1_id=1,user2_id,user1_id) user_id
from Friendship
where user1_id =1 or user2_id =1
)
and page_id not in (select page_id from Likes where user_id=1)
1270. 向公司CEO汇报工作的所有人
难度中等
SQL架构
员工表：Employees
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| employee_id | int |
| employee_name | varchar |
| manager_id | int |
+---------------+---------+
employee_id 是这个表的主键。
这个表中每一行中，employee_id 表示职工的 ID，employee_name 表示职工的名字，manager_id 表示该职工汇报工作的直线经理。
这个公司 CEO 是 employee_id = 1 的人。
用 SQL 查询出所有直接或间接向公司 CEO 汇报工作的职工的 employee_id 。
由于公司规模较小，经理之间的间接关系不超过 3 个经理。
可以以任何顺序返回的结果，不需要去重。
查询结果示例如下：
Employees table:
+-------------+---------------+------------+
| employee_id | employee_name | manager_id |
+-------------+---------------+------------+
| 1 | Boss | 1 |
| 3 | Alice | 3 |
| 2 | Bob | 1 |
| 4 | Daniel | 2 |
| 7 | Luis | 4 |
| 8 | Jhon | 3 |
| 9 | Angela | 8 |
| 77 | Robert | 1 |
+-------------+---------------+------------+
Result table:
+-------------+
| employee_id |
+-------------+
| 2 |
| 77 |
| 4 |
| 7 |
+-------------+
公司 CEO 的 employee_id 是 1.
employee_id 是 2 和 77 的职员直接汇报给公司 CEO。
employee_id 是 4 的职员间接汇报给公司 CEO 4 --> 2 --> 1 。
employee_id 是 7 的职员间接汇报给公司 CEO 7 --> 4 --> 2 --> 1 。
employee_id 是 3, 8 ，9 的职员不会直接或间接的汇报给公司 CEO。
select employee_id
from
(
select a.employee_id
from Employees a
left join Employees b on a.manager_id = b.employee_id
left join Employees c on b.manager_id = c.employee_id
where a.manager_id=1 or b.manager_id=1 or c.manager_id=1
)t1
where employee_id!=1
1280. 学生们参加各科测试的次数
难度简单
SQL架构
学生表: Students
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| student_id | int |
| student_name | varchar |
+---------------+---------+
主键为 student_id（学生ID），该表内的每一行都记录有学校一名学生的信息。
科目表: Subjects
+--------------+---------+
| Column Name | Type |
+--------------+---------+
| subject_name | varchar |
+--------------+---------+
主键为 subject_name（科目名称），每一行记录学校的一门科目名称。
考试表: Examinations
+--------------+---------+
| Column Name | Type |
+--------------+---------+
| student_id | int |
| subject_name | varchar |
+--------------+---------+
这张表压根没有主键，可能会有重复行。
学生表里的一个学生修读科目表里的每一门科目，而这张考试表的每一行记录就表示学生表里的某个学生参加了一次科目表里某门科目的测试。
SELECT a.student_id, a.student_name, b.subject_name, COUNT(e.subject_name) AS attended_exams
FROM Students a CROSS JOIN Subjects b
LEFT JOIN Examinations e ON a.student_id = e.student_id AND b.subject_name = e.subject_name
GROUP BY a.student_id, b.subject_name
ORDER BY a.student_id, b.subject_name
CROSS JOIN Mysql中没有full outer join hive中可以用
1285. 找到连续区间的开始和结束数字
难度中等
SQL架构
表：Logs
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| log_id | int |
+---------------+---------+
id 是上表的主键。
上表的每一行包含日志表中的一个 ID。
后来一些 ID 从 Logs 表中删除。编写一个 SQL 查询得到 Logs 表中的连续区间的开始数字和结束数字。
将查询表按照 start_id 排序。
查询结果格式如下面的例子：
Logs 表：
+------------+
| log_id |
+------------+
| 1 |
| 2 |
| 3 |
| 7 |
| 8 |
| 10 |
+------------+
结果表：
+------------+--------------+
| start_id | end_id |
+------------+--------------+
| 1 | 3 |
| 7 | 8 |
| 10 | 10 |
+------------+--------------+
结果表应包含 Logs 表中的所有区间。
从 1 到 3 在表中。
从 4 到 6 不在表中。
从 7 到 8 在表中。
9 不在表中。
10 在表中。
SELECT
MIN(log_id) start_id,
MAX(log_id) end_id
FROM
(SELECT
log_id,
log_id - row_number() OVER(ORDER BY log_id) as diff
FROM Logs) t
GROUP BY diff
相似1225题
1294. 不同国家的天气类型
难度简单
SQL架构
国家表：Countries
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| country_id | int |
| country_name | varchar |
+---------------+---------+
country_id 是这张表的主键。
该表的每行有 country_id 和 country_name 两列。
天气表：Weather
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| country_id | int |
| weather_state | varchar |
| day | date |
+---------------+---------+
(country_id, day) 是该表的复合主键。
该表的每一行记录了某个国家某一天的天气情况。
写一段 SQL 来找到表中每个国家在 2019 年 11 月的天气类型。
天气类型的定义如下：当 weather_state 的平均值小于或等于15返回 Cold，当 weather_state 的平均值大于或等于 25 返回 Hot，否则返回 Warm。
你可以以任意顺序返回你的查询结果。
查询结果格式如下所示：
Countries table:
+------------+--------------+
| country_id | country_name |
+------------+--------------+
| 2 | USA |
| 3 | Australia |
| 7 | Peru |
| 5 | China |
| 8 | Morocco |
| 9 | Spain |
+------------+--------------+
Weather table:
+------------+---------------+------------+
| country_id | weather_state | day |
+------------+---------------+------------+
| 2 | 15 | 2019-11-01 |
| 2 | 12 | 2019-10-28 |
| 2 | 12 | 2019-10-27 |
| 3 | -2 | 2019-11-10 |
| 3 | 0 | 2019-11-11 |
| 3 | 3 | 2019-11-12 |
| 5 | 16 | 2019-11-07 |
| 5 | 18 | 2019-11-09 |
| 5 | 21 | 2019-11-23 |
| 7 | 25 | 2019-11-28 |
| 7 | 22 | 2019-12-01 |
| 7 | 20 | 2019-12-02 |
| 8 | 25 | 2019-11-05 |
| 8 | 27 | 2019-11-15 |
| 8 | 31 | 2019-11-25 |
| 9 | 7 | 2019-10-23 |
| 9 | 3 | 2019-12-23 |
+------------+---------------+------------+
Result table:
+--------------+--------------+
| country_name | weather_type |
+--------------+--------------+
| USA | Cold |
| Austraila | Cold |
| Peru | Hot |
| China | Warm |
| Morocco | Hot |
+--------------+--------------+
USA 11 月的平均 weather_state 为 (15) / 1 = 15 所以天气类型为 Cold。
Australia 11 月的平均 weather_state 为 (-2 + 0 + 3) / 3 = 0.333 所以天气类型为 Cold。
Peru 11 月的平均 weather_state 为 (25) / 1 = 25 所以天气类型为 Hot。
China 11 月的平均 weather_state 为 (16 + 18 + 21) / 3 = 18.333 所以天气类型为 Warm。
Morocco 11 月的平均 weather_state 为 (25 + 27 + 31) / 3 = 27.667 所以天气类型为 Hot。
我们并不知道 Spain 在 11 月的 weather_state 情况所以无需将他包含在结果中。
select country_name,( case when avg(weather_state)<=15 then 'Cold'
when avg(weather_state)>=25 then 'Hot'
else 'Warm' end ) weather_type
from Countries c join Weather w on c.country_id = w.country_id
where date_format(day,"%Y-%m")='2019-11'
group by country_name
1303. 求团队人数
难度简单
SQL架构
员工表：Employee
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| employee_id | int |
| team_id | int |
+---------------+---------+
employee_id 字段是这张表的主键，表中的每一行都包含每个员工的 ID 和他们所属的团队。
编写一个 SQL 查询，以求得每个员工所在团队的总人数。
查询结果中的顺序无特定要求。
查询结果格式示例如下：
Employee Table:
+-------------+------------+
| employee_id | team_id |
+-------------+------------+
| 1 | 8 |
| 2 | 8 |
| 3 | 8 |
| 4 | 7 |
| 5 | 9 |
| 6 | 9 |
+-------------+------------+
Result table:
+-------------+------------+
| employee_id | team_size |
+-------------+------------+
| 1 | 3 |
| 2 | 3 |
| 3 | 3 |
| 4 | 1 |
| 5 | 2 |
| 6 | 2 |
+-------------+------------+
ID 为 1、2、3 的员工是 team_id 为 8 的团队的成员，
ID 为 4 的员工是 team_id 为 7 的团队的成员，
ID 为 5、6 的员工是 team_id 为 9 的团队的成员。
select employee_id,count(*) over(partition by team_id) team_size
from Employee
1308. 不同性别每日分数总计
难度中等
SQL架构
表: Scores
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| player_name | varchar |
| gender | varchar |
| day | date |
| score_points | int |
+---------------+---------+
(gender, day)是该表的主键
一场比赛是在女队和男队之间举行的
该表的每一行表示一个名叫 (player_name) 性别为 (gender) 的参赛者在某一天获得了 (score_points) 的分数
如果参赛者是女性，那么 gender 列为 'F'，如果参赛者是男性，那么 gender 列为 'M'
写一条SQL语句查询每种性别在每一天的总分，并按性别和日期对查询结果排序
下面是查询结果格式的例子：
Scores表:
+-------------+--------+------------+--------------+
| player_name | gender | day | score_points |
+-------------+--------+------------+--------------+
| Aron | F | 2020-01-01 | 17 |
| Alice | F | 2020-01-07 | 23 |
| Bajrang | M | 2020-01-07 | 7 |
| Khali | M | 2019-12-25 | 11 |
| Slaman | M | 2019-12-30 | 13 |
| Joe | M | 2019-12-31 | 3 |
| Jose | M | 2019-12-18 | 2 |
| Priya | F | 2019-12-31 | 23 |
| Priyanka | F | 2019-12-30 | 17 |
+-------------+--------+------------+--------------+
结果表:
+--------+------------+-------+
| gender | day | total |
+--------+------------+-------+
| F | 2019-12-30 | 17 |
| F | 2019-12-31 | 40 |
| F | 2020-01-01 | 57 |
| F | 2020-01-07 | 80 |
| M | 2019-12-18 | 2 |
| M | 2019-12-25 | 13 |
| M | 2019-12-30 | 26 |
| M | 2019-12-31 | 29 |
| M | 2020-01-07 | 36 |
+--------+------------+-------+
女性队伍:
第一天是 2019-12-30，Priyanka 获得 17 分，队伍的总分是 17 分
第二天是 2019-12-31, Priya 获得 23 分，队伍的总分是 40 分
第三天是 2020-01-01, Aron 获得 17 分，队伍的总分是 57 分
第四天是 2020-01-07, Alice 获得 23 分，队伍的总分是 80 分
男性队伍：
第一天是 2019-12-18, Jose 获得 2 分，队伍的总分是 2 分
第二天是 2019-12-25, Khali 获得 11 分，队伍的总分是 13 分
第三天是 2019-12-30, Slaman 获得 13 分，队伍的总分是 26 分
第四天是 2019-12-31, Joe 获得 3 分，队伍的总分是 29 分
第五天是 2020-01-07, Bajrang 获得 7 分，队伍的总分是 36 分
select gender , day ,sum( score_points) over(partition by gender order by day) total
from Scores
1321. 餐馆营业额变化增长
难度中等
SQL架构
表: Customer
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| customer_id | int |
| name | varchar |
| visited_on | date |
| amount | int |
+---------------+---------+
(customer_id, visited_on) 是该表的主键
该表包含一家餐馆的顾客交易数据
visited_on 表示 (customer_id) 的顾客在 visited_on 那天访问了餐馆
amount 是一个顾客某一天的消费总额
你是餐馆的老板，现在你想分析一下可能的营业额变化增长（每天至少有一位顾客）
写一条 SQL 查询计算以 7 天（某日期 + 该日期前的 6 天）为一个时间段的顾客消费平均值
查询结果格式的例子如下：
- 查询结果按
visited_on排序 average_amount要 保留两位小数，日期数据的格式为 (‘YYYY-MM-DD’)
Customer 表:
+-------------+--------------+--------------+-------------+
| 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 |
+-------------+--------------+--------------+-------------+
结果表:
+--------------+--------------+----------------+
| 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 |
+--------------+--------------+----------------+
第一个七天消费平均值从 2019-01-01 到 2019-01-07 是 (100 + 110 + 120 + 130 + 110 + 140 + 150)/7 = 122.86
第二个七天消费平均值从 2019-01-02 到 2019-01-08 是 (110 + 120 + 130 + 110 + 140 + 150 + 80)/7 = 120
第三个七天消费平均值从 2019-01-03 到 2019-01-09 是 (120 + 130 + 110 + 140 + 150 + 80 + 110)/7 = 120
第四个七天消费平均值从 2019-01-04 到 2019-01-10 是 (130 + 110 + 140 + 150 + 80 + 110 + 130 + 150)/7 = 142.86
select visited_on,amount,round(amount/7,2) average_amount
from (
select visited_on,ant,lag(visited_on,6,null) over(order by visited_on) lg,
sum(ant) over(order by visited_on rows between 6 PRECEDING and current row) amount
from(
select visited_on ,sum(amount) ant
from Customer
group by visited_on
)t1
)t2
where lg is not null
1322. 广告效果
难度简单8收藏分享切换为英文关注反馈
SQL架构
表: Ads
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| ad_id | int |
| user_id | int |
| action | enum |
+---------------+---------+
(ad_id, user_id) 是该表的主键
该表的每一行包含一条广告的 ID(ad_id)，用户的 ID(user_id) 和用户对广告采取的行为 (action)
action 列是一个枚举类型 ('Clicked', 'Viewed', 'Ignored') 。
一家公司正在运营这些广告并想计算每条广告的效果。
广告效果用点击通过率（Click-Through Rate：CTR）来衡量，公式如下:
写一条SQL语句来查询每一条广告的 ctr ，
ctr 要保留两位小数。结果需要按 ctr 降序、按 ad_id 升序 进行排序。
查询结果示例如下：
Ads 表:
+-------+---------+---------+
| ad_id | user_id | action |
+-------+---------+---------+
| 1 | 1 | Clicked |
| 2 | 2 | Clicked |
| 3 | 3 | Viewed |
| 5 | 5 | Ignored |
| 1 | 7 | Ignored |
| 2 | 7 | Viewed |
| 3 | 5 | Clicked |
| 1 | 4 | Viewed |
| 2 | 11 | Viewed |
| 1 | 2 | Clicked |
+-------+---------+---------+
结果表:
+-------+-------+
| ad_id | ctr |
+-------+-------+
| 1 | 66.67 |
| 3 | 50.00 |
| 2 | 33.33 |
| 5 | 0.00 |
+-------+-------+
对于 ad_id = 1, ctr = (2/(2+1)) * 100 = 66.67
对于 ad_id = 2, ctr = (1/(1+2)) * 100 = 33.33
对于 ad_id = 3, ctr = (1/(1+1)) * 100 = 50.00
对于 ad_id = 5, ctr = 0.00, 注意 ad_id = 5 没有被点击 (Clicked) 或查看 (Viewed) 过
注意我们不关心 action 为 Ingnored 的广告
结果按 ctr（降序），ad_id（升序）排序
精简
SELECT ad_id,
ROUND(IFNULL(SUM(action = 'Clicked') /
(SUM(action = 'Clicked') + SUM(action = 'Viewed')) * 100, 0), 2) AS ctr
FROM Ads
GROUP BY ad_id
ORDER BY ctr DESC, ad_id ASC;
笨方法
select a.ad_id,ifnull(ctr,0) ctr
from Ads a
left join (
select ad_id,round(sum(if(action='Clicked',1,0))/count(*)*100,2) ctr
from Ads
where action !='Ignored'
group by ad_id
)t1
on a.ad_id= t1.ad_id
group by ad_id,ctr
order by ctr desc,ad_id
1327. 列出指定时间段内所有的下单产品
难度简单
SQL架构
表: Products
+------------------+---------+
| Column Name | Type |
+------------------+---------+
| product_id | int |
| product_name | varchar |
| product_category | varchar |
+------------------+---------+
product_id 是该表主键。
该表包含该公司产品的数据。
表: Orders
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| product_id | int |
| order_date | date |
| unit | int |
+---------------+---------+
该表无主键，可能包含重复行。
product_id 是表单 Products 的外键。
unit 是在日期 order_date 内下单产品的数目。
写一个 SQL 语句，要求获取在 2020 年 2 月份下单的数量不少于 100 的产品的名字和数目。
返回结果表单的顺序无要求。
查询结果的格式如下：
Products 表:
+-------------+-----------------------+------------------+
| product_id | product_name | product_category |
+-------------+-----------------------+------------------+
| 1 | Leetcode Solutions | Book |
| 2 | Jewels of Stringology | Book |
| 3 | HP | Laptop |
| 4 | Lenovo | Laptop |
| 5 | Leetcode Kit | T-shirt |
+-------------+-----------------------+------------------+
Orders 表:
+--------------+--------------+----------+
| product_id | order_date | unit |
+--------------+--------------+----------+
| 1 | 2020-02-05 | 60 |
| 1 | 2020-02-10 | 70 |
| 2 | 2020-01-18 | 30 |
| 2 | 2020-02-11 | 80 |
| 3 | 2020-02-17 | 2 |
| 3 | 2020-02-24 | 3 |
| 4 | 2020-03-01 | 20 |
| 4 | 2020-03-04 | 30 |
| 4 | 2020-03-04 | 60 |
| 5 | 2020-02-25 | 50 |
| 5 | 2020-02-27 | 50 |
| 5 | 2020-03-01 | 50 |
+--------------+--------------+----------+
Result 表:
+--------------------+---------+
| product_name | unit |
+--------------------+---------+
| Leetcode Solutions | 130 |
| Leetcode Kit | 100 |
+--------------------+---------+
2020 年 2 月份下单 product_id = 1 的产品的数目总和为 (60 + 70) = 130 。
2020 年 2 月份下单 product_id = 2 的产品的数目总和为 80 。
2020 年 2 月份下单 product_id = 3 的产品的数目总和为 (2 + 3) = 5 。
2020 年 2 月份 product_id = 4 的产品并没有下单。
2020 年 2 月份下单 product_id = 5 的产品的数目总和为 (50 + 50) = 100 。
select product_name,sum(unit) unit
from Orders o left join Products p
on o.product_id=p.product_id
where date_format(order_date,'%Y-%m')='2020-02'
group by product_name
having unit>=100
1336. 每次访问的交易次数
难度困难
SQL架构
表: Visits
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| user_id | int |
| visit_date | date |
+---------------+---------+
(user_id, visit_date) 是该表的主键
该表的每行表示 user_id 在 visit_date 访问了银行
表: Transactions
+------------------+---------+
| Column Name | Type |
+------------------+---------+
| user_id | int |
| transaction_date | date |
| amount | int |
+------------------+---------+
该表没有主键，所以可能有重复行
该表的每一行表示 user_id 在 transaction_date 完成了一笔 amount 数额的交易
可以保证用户 (user) 在 transaction_date 访问了银行 (也就是说 Visits 表包含 (user_id, transaction_date) 行)
银行想要得到银行客户在一次访问时的交易次数和相应的在一次访问时该交易次数的客户数量的图表
写一条 SQL 查询多少客户访问了银行但没有进行任何交易，多少客户访问了银行进行了一次交易等等
结果包含两列：
transactions_count：客户在一次访问中的交易次数visits_count：在transactions_count交易次数下相应的一次访问时的客户数量
transactions_count` 的值从 `0` 到所有用户一次访问中的 `max(transactions_count)
按 transactions_count 排序
下面是查询结果格式的例子：
Visits 表:
+---------+------------+
| user_id | visit_date |
+---------+------------+
| 1 | 2020-01-01 |
| 2 | 2020-01-02 |
| 12 | 2020-01-01 |
| 19 | 2020-01-03 |
| 1 | 2020-01-02 |
| 2 | 2020-01-03 |
| 1 | 2020-01-04 |
| 7 | 2020-01-11 |
| 9 | 2020-01-25 |
| 8 | 2020-01-28 |
+---------+------------+
Transactions 表:
+---------+------------------+--------+
| user_id | transaction_date | amount |
+---------+------------------+--------+
| 1 | 2020-01-02 | 120 |
| 2 | 2020-01-03 | 22 |
| 7 | 2020-01-11 | 232 |
| 1 | 2020-01-04 | 7 |
| 9 | 2020-01-25 | 33 |
| 9 | 2020-01-25 | 66 |
| 8 | 2020-01-28 | 1 |
| 9 | 2020-01-25 | 99 |
+---------+------------------+--------+
结果表:
+--------------------+--------------+
| transactions_count | visits_count |
+--------------------+--------------+
| 0 | 4 |
| 1 | 5 |
| 2 | 0 |
| 3 | 1 |
+--------------------+--------------+
* 对于 transactions_count = 0, visits 中 (1, "2020-01-01"), (2, "2020-01-02"), (12, "2020-01-01") 和 (19, "2020-01-03") 没有进行交易，所以 visits_count = 4 。
* 对于 transactions_count = 1, visits 中 (2, "2020-01-03"), (7, "2020-01-11"), (8, "2020-01-28"), (1, "2020-01-02") 和 (1, "2020-01-04") 进行了一次交易，所以 visits_count = 5 。
* 对于 transactions_count = 2, 没有客户访问银行进行了两次交易，所以 visits_count = 0 。
* 对于 transactions_count = 3, visits 中 (9, "2020-01-25") 进行了三次交易，所以 visits_count = 1 。
* 对于 transactions_count >= 4, 没有客户访问银行进行了超过3次交易，所以我们停止在 transactions_count = 3 。
如下是这个例子的图表：
SELECT *
FROM
(
SELECT t5.rnb AS transactions_count, IFNULL(visits_count, 0) AS visits_count
FROM
(
SELECT 0 AS rnb
UNION
SELECT ROW_NUMBER() OVER () AS rnb
FROM Transactions
) t5
LEFT JOIN
(
SELECT
cnt AS transactions_count
,COUNT(user_id) AS visits_count
FROM
(
SELECT t1.user_id, COUNT(t2.amount) AS cnt
FROM Visits t1
LEFT JOIN Transactions t2
ON t1.user_id = t2.user_id AND t1.visit_date = t2.transaction_date
GROUP BY user_id, visit_date
) t3
GROUP BY cnt
) t4
ON t5.rnb = t4.transactions_count
) t6
WHERE transactions_count <= (
SELECT COUNT(t2.amount) AS cnt
FROM Visits t1
LEFT JOIN Transactions t2
ON t1.user_id = t2.user_id AND t1.visit_date = t2.transaction_date
GROUP BY t1.user_id, visit_date
ORDER BY cnt DESC
LIMIT 1)
难点 从0自增序列，2交易的人数为0
select pcnt transactions_count,count(*) visits_count
from (
select visit_date,
sum(if(amount is null,0,1)) over(partition by transaction_date ) pcnt,
count(*) over(partition by visit_date ) tcnt
from Visits v left join Transactions t
on v.user_id= t.user_id and v.visit_date=t.transaction_date
)t1
group by pcnt
这个得出结果是[0, 4], [1, 5], [3, 3] 少了[2,0] 还没想到什么好办法能把[2，0]加进去。。。
1341. 电影评分
难度中等
SQL架构
表：Movies
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| movie_id | int |
| title | varchar |
+---------------+---------+
movie_id 是这个表的主键。
title 是电影的名字。
表：Users
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| user_id | int |
| name | varchar |
+---------------+---------+
user_id 是表的主键。
表：Movie_Rating
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| movie_id | int |
| user_id | int |
| rating | int |
| created_at | date |
+---------------+---------+
(movie_id, user_id) 是这个表的主键。
这个表包含用户在其评论中对电影的评分 rating 。
created_at 是用户的点评日期。
请你编写一组 SQL 查询：
-
查找评论电影数量最多的用户名。
如果出现平局，返回字典序较小的用户名。
-
查找在
2020 年 2 月 平均评分最高
的电影名称。
如果出现平局，返回字典序较小的电影名称。
查询分两行返回，查询结果格式如下例所示：
Movies 表：
+-------------+--------------+
| movie_id | title |
+-------------+--------------+
| 1 | Avengers |
| 2 | Frozen 2 |
| 3 | Joker |
+-------------+--------------+
Users 表：
+-------------+--------------+
| user_id | name |
+-------------+--------------+
| 1 | Daniel |
| 2 | Monica |
| 3 | Maria |
| 4 | James |
+-------------+--------------+
Movie_Rating 表：
+-------------+--------------+--------------+-------------+
| movie_id | user_id | rating | created_at |
+-------------+--------------+--------------+-------------+
| 1 | 1 | 3 | 2020-01-12 |
| 1 | 2 | 4 | 2020-02-11 |
| 1 | 3 | 2 | 2020-02-12 |
| 1 | 4 | 1 | 2020-01-01 |
| 2 | 1 | 5 | 2020-02-17 |
| 2 | 2 | 2 | 2020-02-01 |
| 2 | 3 | 2 | 2020-03-01 |
| 3 | 1 | 3 | 2020-02-22 |
| 3 | 2 | 4 | 2020-02-25 |
+-------------+--------------+--------------+-------------+
Result 表：
+--------------+
| results |
+--------------+
| Daniel |
| Frozen 2 |
+--------------+
Daniel 和 Monica 都点评了 3 部电影（"Avengers", "Frozen 2" 和 "Joker"） 但是 Daniel 字典序比较小。
Frozen 2 和 Joker 在 2 月的评分都是 3.5，但是 Frozen 2 的字典序比较小。
select name results
from
(
select m.user_id ,u.name
from Movie_Rating m left join Users u
on m.user_id = u.user_id
group by user_id
order by count(*) desc,name
limit 1
)t1
union
(
select title results
from Movie_Rating r left join Movies m
on r.movie_id =m.movie_id
where date_format(created_at,'%Y-%m')='2020-02'
group by r.movie_id
order by avg(rating) desc,title
limit 1
)
1350. 院系无效的学生
难度简单
SQL架构
院系表: Departments
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| name | varchar |
+---------------+---------+
id 是该表的主键
该表包含一所大学每个院系的 id 信息
学生表: Students
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| name | varchar |
| department_id | int |
+---------------+---------+
id 是该表的主键
该表包含一所大学每个学生的 id 和他/她就读的院系信息
写一条 SQL 语句以查询那些所在院系不存在的学生的 id 和姓名
可以以任何顺序返回结果
下面是返回结果格式的例子
Departments 表:
+------+--------------------------+
| id | name |
+------+--------------------------+
| 1 | Electrical Engineering |
| 7 | Computer Engineering |
| 13 | Bussiness Administration |
+------+--------------------------+
Students 表:
+------+----------+---------------+
| id | name | department_id |
+------+----------+---------------+
| 23 | Alice | 1 |
| 1 | Bob | 7 |
| 5 | Jennifer | 13 |
| 2 | John | 14 |
| 4 | Jasmine | 77 |
| 3 | Steve | 74 |
| 6 | Luis | 1 |
| 8 | Jonathan | 7 |
| 7 | Daiana | 33 |
| 11 | Madelynn | 1 |
+------+----------+---------------+
结果表:
+------+----------+
| id | name |
+------+----------+
| 2 | John |
| 7 | Daiana |
| 4 | Jasmine |
| 3 | Steve |
+------+----------+
John, Daiana, Steve 和 Jasmine 所在的院系分别是 14, 33, 74 和 77， 其中 14, 33, 74 和 77 并不存在于院系表
select id,name
from Students
where department_id not in
(
select id
from Departments
)
1355. 活动参与者
难度中等
SQL架构
表: Friends
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| name | varchar |
| activity | varchar |
+---------------+---------+
id 是朋友的 id 和该表的主键
name 是朋友的名字
activity 是朋友参加的活动的名字
表: Activities
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| name | varchar |
+---------------+---------+
id 是该表的主键
name 是活动的名字
写一条 SQL 查询那些既没有最多，也没有最少参与者的活动的名字
可以以任何顺序返回结果，Activities 表的每项活动的参与者都来自 Friends 表
下面是查询结果格式的例子：
Friends 表:
+------+--------------+---------------+
| id | name | activity |
+------+--------------+---------------+
| 1 | Jonathan D. | Eating |
| 2 | Jade W. | Singing |
| 3 | Victor J. | Singing |
| 4 | Elvis Q. | Eating |
| 5 | Daniel A. | Eating |
| 6 | Bob B. | Horse Riding |
+------+--------------+---------------+
Activities 表:
+------+--------------+
| id | name |
+------+--------------+
| 1 | Eating |
| 2 | Singing |
| 3 | Horse Riding |
+------+--------------+
Result 表:
+--------------+
| activity |
+--------------+
| Singing |
+--------------+
Eating 活动有三个人参加, 是最多人参加的活动 (Jonathan D. , Elvis Q. and Daniel A.)
Horse Riding 活动有一个人参加, 是最少人参加的活动 (Bob B.)
Singing 活动有两个人参加 (Victor J. and Jade W.)
select activity
from (
select activity,
rank()over(order by cnt) rk1,
rank()over(order by cnt desc) rk2
from
(
select activity ,count(*) cnt
from Friends
group by activity
)t1
)t2
where rk1 !=1 and rk2 != 1
不需要关联 Activities表，因为 至少有一人参加
1364. 顾客的可信联系人数量
难度中等
SQL架构
顾客表：Customers
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| customer_id | int |
| customer_name | varchar |
| email | varchar |
+---------------+---------+
customer_id 是这张表的主键。
此表的每一行包含了某在线商店顾客的姓名和电子邮件。
联系方式表：Contacts
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| user_id | id |
| contact_name | varchar |
| contact_email | varchar |
+---------------+---------+
(user_id, contact_email) 是这张表的主键。
此表的每一行表示编号为 user_id 的顾客的某位联系人的姓名和电子邮件。
此表包含每位顾客的联系人信息，但顾客的联系人不一定存在于顾客表中。
发票表：Invoices
+--------------+---------+
| Column Name | Type |
+--------------+---------+
| invoice_id | int |
| price | int |
| user_id | int |
+--------------+---------+
invoice_id 是这张表的主键。
此表的每一行分别表示编号为 user_id 的顾客拥有有一张编号为 invoice_id、价格为 price 的发票。
为每张发票 invoice_id 编写一个SQL查询以查找以下内容：
customer_name：与发票相关的顾客名称。price：发票的价格。contacts_cnt：该顾客的联系人数量。trusted_contacts_cnt：可信联系人的数量：既是该顾客的联系人又是商店顾客的联系人数量（即：可信联系人的电子邮件存在于客户表中）。
将查询的结果按照 invoice_id 排序。
查询结果的格式如下例所示：
Customers table:
+-------------+---------------+--------------------+
| customer_id | customer_name | email |
+-------------+---------------+--------------------+
| 1 | Alice | [email protected] |
| 2 | Bob | [email protected] |
| 13 | John | [email protected] |
| 6 | Alex | [email protected] |
+-------------+---------------+--------------------+
Contacts table:
+-------------+--------------+--------------------+
| user_id | contact_name | contact_email |
+-------------+--------------+--------------------+
| 1 | Bob | [email protected] |
| 1 | John | [email protected] |
| 1 | Jal | [email protected] |
| 2 | Omar | [email protected] |
| 2 | Meir | [email protected] |
| 6 | Alice | [email protected] |
+-------------+--------------+--------------------+
Invoices table:
+------------+-------+---------+
| invoice_id | price | user_id |
+------------+-------+---------+
| 77 | 100 | 1 |
| 88 | 200 | 1 |
| 99 | 300 | 2 |
| 66 | 400 | 2 |
| 55 | 500 | 13 |
| 44 | 60 | 6 |
+------------+-------+---------+
Result table:
+------------+---------------+-------+--------------+----------------------+
| invoice_id | customer_name | price | contacts_cnt | trusted_contacts_cnt |
+------------+---------------+-------+--------------+----------------------+
| 44 | Alex | 60 | 1 | 1 |
| 55 | John | 500 | 0 | 0 |
| 66 | Bob | 400 | 2 | 0 |
| 77 | Alice | 100 | 3 | 2 |
| 88 | Alice | 200 | 3 | 2 |
| 99 | Bob | 300 | 2 | 0 |
+------------+---------------+-------+--------------+----------------------+
Alice 有三位联系人，其中两位(Bob 和 John)是可信联系人。
Bob 有两位联系人, 他们中的任何一位都不是可信联系人。
Alex 只有一位联系人(Alice)，并是一位可信联系人。
John 没有任何联系人。
select invoice_id ,customer_name,price,ifnull(cnt,0) contacts_cnt,ifnull(bc,0) trusted_contacts_cnt
from Invoices i
left join (
select user_id ,count(*) cnt
from Contacts
group by user_id
) t1
on i.user_id=t1.user_id
left join (
select user_id ,count(*) bc
from Contacts
where contact_name in
(
select customer_name
from Customers
)
group by user_id
)t2
on i.user_id = t2.user_id
left join Customers c
on i.user_id= c.customer_id
order by invoice_id
就是麻烦点 各种join
1369. 获取最近第二次的活动
难度困难
SQL架构
表: UserActivity
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| username | varchar |
| activity | varchar |
| startDate | Date |
| endDate | Date |
+---------------+---------+
该表不包含主键
该表包含每个用户在一段时间内进行的活动的信息
名为 username 的用户在 startDate 到 endDate 日内有一次活动
写一条SQL查询展示每一位用户 最近第二次 的活动
如果用户仅有一次活动，返回该活动
一个用户不能同时进行超过一项活动，以 任意 顺序返回结果
下面是查询结果格式的例子：
UserActivity 表:
+------------+--------------+-------------+-------------+
| username | activity | startDate | endDate |
+------------+--------------+-------------+-------------+
| Alice | Travel | 2020-02-12 | 2020-02-20 |
| Alice | Dancing | 2020-02-21 | 2020-02-23 |
| Alice | Travel | 2020-02-24 | 2020-02-28 |
| Bob | Travel | 2020-02-11 | 2020-02-18 |
+------------+--------------+-------------+-------------+
Result 表:
+------------+--------------+-------------+-------------+
| username | activity | startDate | endDate |
+------------+--------------+-------------+-------------+
| Alice | Dancing | 2020-02-21 | 2020-02-23 |
| Bob | Travel | 2020-02-11 | 2020-02-18 |
+------------+--------------+-------------+-------------+
Alice 最近第二次的活动是从 2020-02-24 到 2020-02-28 的旅行, 在此之前的 2020-02-21 到 2020-02-23 她进行了舞蹈
Bob 只有一条记录，我们就取这条记录
select username, activity ,startDate,endDate
from
(
select username, activity ,startDate,endDate ,
rank()over(partition by username order by startDate desc) rk,
lag( startDate ,1,null)over(partition by username order by startDate ) lg
from UserActivity
)t1
where rk=2 or (rk = 1 && lg is null)
1378. 使用唯一标识码替换员工ID
难度简单
SQL架构
Employees 表：
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| name | varchar |
+---------------+---------+
id 是这张表的主键。
这张表的每一行分别代表了某公司其中一位员工的名字和 ID 。
EmployeeUNI 表：
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| unique_id | int |
+---------------+---------+
(id, unique_id) 是这张表的主键。
这张表的每一行包含了该公司某位员工的 ID 和他的唯一标识码（unique ID）。
写一段SQL查询来展示每位用户的 唯一标识码（unique ID ）；如果某位员工没有唯一标识码，使用 null 填充即可。
你可以以 任意 顺序返回结果表。
查询结果的格式如下例所示：
Employees table:
+----+----------+
| id | name |
+----+----------+
| 1 | Alice |
| 7 | Bob |
| 11 | Meir |
| 90 | Winston |
| 3 | Jonathan |
+----+----------+
EmployeeUNI table:
+----+-----------+
| id | unique_id |
+----+-----------+
| 3 | 1 |
| 11 | 2 |
| 90 | 3 |
+----+-----------+
EmployeeUNI table:
+-----------+----------+
| unique_id | name |
+-----------+----------+
| null | Alice |
| null | Bob |
| 2 | Meir |
| 3 | Winston |
| 1 | Jonathan |
+-----------+----------+
Alice and Bob 没有唯一标识码, 因此我们使用 null 替代。
Meir 的唯一标识码是 2 。
Winston 的唯一标识码是 3 。
Jonathan 唯一标识码是 1 。
select unique_id,e.name
from Employees e left join EmployeeUNI u
on e.id = u.id
1384. 按年度列出销售总额
难度困难
SQL架构
Product 表：
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| product_id | int |
| product_name | varchar |
+---------------+---------+
product_id 是这张表的主键。
product_name 是产品的名称。
Sales 表：
+---------------------+---------+
| Column Name | Type |
+---------------------+---------+
| product_id | int |
| period_start | varchar |
| period_end | date |
| average_daily_sales | int |
+---------------------+---------+
product_id 是这张表的主键。
period_start 和 period_end 是该产品销售期的起始日期和结束日期，且这两个日期包含在销售期内。
average_daily_sales 列存储销售期内该产品的日平均销售额。
编写一段SQL查询每个产品每年的总销售额，并包含 product_id, product_name 以及 report_year 等信息。
销售年份的日期介于 2018 年到 2020 年之间。你返回的结果需要按 product_id 和 report_year 排序。
查询结果格式如下例所示：
Product table:
+------------+--------------+
| product_id | product_name |
+------------+--------------+
| 1 | LC Phone |
| 2 | LC T-Shirt |
| 3 | LC Keychain |
+------------+--------------+
Sales table:
+------------+--------------+-------------+---------------------+
| product_id | period_start | period_end | average_daily_sales |
+------------+--------------+-------------+---------------------+
| 1 | 2019-01-25 | 2019-02-28 | 100 |
| 2 | 2018-12-01 | 2020-01-01 | 10 |
| 3 | 2019-12-01 | 2020-01-31 | 1 |
+------------+--------------+-------------+---------------------+
Result table:
+------------+--------------+-------------+--------------+
| product_id | product_name | report_year | total_amount |
+------------+--------------+-------------+--------------+
| 1 | LC Phone | 2019 | 3500 |
| 2 | LC T-Shirt | 2018 | 310 |
| 2 | LC T-Shirt | 2019 | 3650 |
| 2 | LC T-Shirt | 2020 | 10 |
| 3 | LC Keychain | 2019 | 31 |
| 3 | LC Keychain | 2020 | 31 |
+------------+--------------+-------------+--------------+
LC Phone 在 2019-01-25 至 2019-02-28 期间销售，该产品销售时间总计35天。销售总额 35*100 = 3500。
LC T-shirt 在 2018-12-01 至 2020-01-01 期间销售，该产品在2018年、2019年、2020年的销售时间分别是31天、365天、1天，2018年、2019年、2020年的销售总额分别是31*10=310、365*10=3650、1*10=10。
LC Keychain 在 2019-12-01 至 2020-01-31 期间销售，该产品在2019年、2020年的销售时间分别是：31天、31天，2019年、2020年的销售总额分别是31*1=31、31*1=31。
(
select Sales.product_id, product_name, '2018' as 'report_year', if(period_start<'2019-01-01', (datediff(if(period_end<'2019-01-01', period_end, date('2018-12-31')), if(period_start>='2018-01-01', period_start, date('2018-01-01')))+1)*average_daily_sales, 0) as total_amount
from Sales
join Product on Sales.product_id = Product.product_id
having total_amount>0
)
union(
select Sales.product_id, product_name, '2019' as 'report_year', if( period_start<'2020-01-01', (datediff(if(period_end<'2020-01-01', period_end, date('2019-12-31')), if(period_start>='2019-01-01', period_start, date('2019-01-01')))+1)*average_daily_sales , 0) as total_amount
from Sales
join Product on (Sales.product_id = Product.product_id )
having total_amount>0
)
union(
select Sales.product_id, product_name, '2020' as 'report_year', (datediff(if(period_end<'2021-01-01', period_end, date('2020-12-31')), if(period_start>='2020-01-01', period_start, date('2020-01-01')))+1)*average_daily_sales as total_amount
from Sales
join Product on (Sales.product_id = Product.product_id)
having total_amount>0
)
order by product_id, report_year
各个年份进行union,就是年份判断的时候麻烦些
1393. 股票的资本损益
难度中等
SQL架构
Stocks 表：
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| stock_name | varchar |
| operation | enum |
| operation_day | int |
| price | int |
+---------------+---------+
(stock_name, day) 是这张表的主键
operation 列使用的是一种枚举类型，包括：('Sell','Buy')
此表的每一行代表了名为 stock_name 的某支股票在 operation_day 这一天的操作价格。
保证股票的每次'Sell'操作前，都有相应的'Buy'操作。
编写一个SQL查询来报告每支股票的资本损益。
股票的资本损益是一次或多次买卖股票后的全部收益或损失。
以任意顺序返回结果即可。
SQL查询结果的格式如下例所示：
Stocks 表:
+---------------+-----------+---------------+--------+
| stock_name | operation | operation_day | price |
+---------------+-----------+---------------+--------+
| Leetcode | Buy | 1 | 1000 |
| Corona Masks | Buy | 2 | 10 |
| Leetcode | Sell | 5 | 9000 |
| Handbags | Buy | 17 | 30000 |
| Corona Masks | Sell | 3 | 1010 |
| Corona Masks | Buy | 4 | 1000 |
| Corona Masks | Sell | 5 | 500 |
| Corona Masks | Buy | 6 | 1000 |
| Handbags | Sell | 29 | 7000 |
| Corona Masks | Sell | 10 | 10000 |
+---------------+-----------+---------------+--------+
Result 表:
+---------------+-------------------+
| stock_name | capital_gain_loss |
+---------------+-------------------+
| Corona Masks | 9500 |
| Leetcode | 8000 |
| Handbags | -23000 |
+---------------+-------------------+
Leetcode 股票在第一天以1000美元的价格买入，在第五天以9000美元的价格卖出。资本收益=9000-1000=8000美元。
Handbags 股票在第17天以30000美元的价格买入，在第29天以7000美元的价格卖出。资本损失=7000-30000=-23000美元。
Corona Masks 股票在第1天以10美元的价格买入，在第3天以1010美元的价格卖出。在第4天以1000美元的价格再次购买，在第5天以500美元的价格出售。最后，它在第6天以1000美元的价格被买走，在第10天以10000美元的价格被卖掉。资本损益是每次（’Buy'->'Sell'）操作资本收益或损失的和=（1010-10）+（500-1000）+（10000-1000）=1000-500+9000=9500美元。
select stock_name,sell-buy capital_gain_loss
from(
select stock_name ,
sum(if(operation='Buy', price,0))over(partition by stock_name ) buy,
sum(if(operation='Sell',price,0))over(partition by stock_name) sell
from Stocks s
)t1
group by stock_name,buy,sell
1398. 购买了产品A和产品B却没有购买产品C的顾客
难度中等
SQL架构
Customers 表：
+---------------------+---------+
| Column Name | Type |
+---------------------+---------+
| customer_id | int |
| customer_name | varchar |
+---------------------+---------+
customer_id 是这张表的主键。
customer_name 是顾客的名称。
Orders 表：
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| order_id | int |
| customer_id | int |
| product_name | varchar |
+---------------+---------+
order_id 是这张表的主键。
customer_id 是购买了名为 "product_name" 产品顾客的id。
请你设计 SQL 查询来报告购买了产品 A 和产品 B 却没有购买产品 C 的顾客的 ID 和姓名（ customer_id 和 customer_name ），我们将基于此结果为他们推荐产品 C 。
您返回的查询结果需要按照 customer_id 排序。
查询结果如下例所示。
Customers table:
+-------------+---------------+
| customer_id | customer_name |
+-------------+---------------+
| 1 | Daniel |
| 2 | Diana |
| 3 | Elizabeth |
| 4 | Jhon |
+-------------+---------------+
Orders table:
+------------+--------------+---------------+
| order_id | customer_id | product_name |
+------------+--------------+---------------+
| 10 | 1 | A |
| 20 | 1 | B |
| 30 | 1 | D |
| 40 | 1 | C |
| 50 | 2 | A |
| 60 | 3 | A |
| 70 | 3 | B |
| 80 | 3 | D |
| 90 | 4 | C |
+------------+--------------+---------------+
Result table:
+-------------+---------------+
| customer_id | customer_name |
+-------------+---------------+
| 3 | Elizabeth |
+-------------+---------------+
只有 customer_id 为 3 的顾客购买了产品 A 和产品 B ，却没有购买产品 C 。
select o.customer_id, customer_name
from Orders o left join Customers c
on o.customer_id=c.customer_id
group by customer_id
having sum(product_name ='A')>=1 and sum(product_name='B')>=1 and sum(product_name='C')=0
1407. 排名靠前的旅行者
难度简单
SQL架构
表单: Users
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| name | varchar |
+---------------+---------+
id 是该表单主键.
name 是用户名字.
表单: Rides
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| user_id | int |
| distance | int |
+---------------+---------+
id 是该表单主键.
user_id 是本次行程的用户的 id, 而该用户此次行程距离为 distance.
写一段 SQL , 报告每个用户的旅行距离.
返回的结果表单, 以 travelled_distance 降序排列, 如果有两个或者更多的用户旅行了相同的距离, 那么再以 name 升序排列.
查询结果格式, 如下例所示.
Users 表单:
+------+-----------+
| id | name |
+------+-----------+
| 1 | Alice |
| 2 | Bob |
| 3 | Alex |
| 4 | Donald |
| 7 | Lee |
| 13 | Jonathan |
| 19 | Elvis |
+------+-----------+
Rides 表单:
+------+----------+----------+
| id | user_id | distance |
+------+----------+----------+
| 1 | 1 | 120 |
| 2 | 2 | 317 |
| 3 | 3 | 222 |
| 4 | 7 | 100 |
| 5 | 13 | 312 |
| 6 | 19 | 50 |
| 7 | 7 | 120 |
| 8 | 19 | 400 |
| 9 | 7 | 230 |
+------+----------+----------+
Result 表单:
+----------+--------------------+
| name | travelled_distance |
+----------+--------------------+
| Elvis | 450 |
| Lee | 450 |
| Bob | 317 |
| Jonathan | 312 |
| Alex | 222 |
| Alice | 120 |
| Donald | 0 |
+----------+--------------------+
Elvis 和 Lee 旅行了 450 英里, Elvis 是排名靠前的旅行者, 因为他的名字在字母表上的排序比 Lee 更小.
Bob, Jonathan, Alex 和 Alice 只有一次行程, 我们只按此次行程的全部距离对他们排序.
Donald 没有任何行程, 他的旅行距离为 0.
select name,sum(ifnull(distance,0)) travelled_distance
from Users u left join Rides r
on u.id = r.user_id
group by name
order by travelled_distance desc, name
1412. 查找成绩处于中游的学生
难度困难
SQL架构
表: Student
+---------------------+---------+
| Column Name | Type |
+---------------------+---------+
| student_id | int |
| student_name | varchar |
+---------------------+---------+
student_id 是该表主键.
student_name 学生名字.
表: Exam
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| exam_id | int |
| student_id | int |
| score | int |
+---------------+---------+
(exam_id, student_id) 是该表主键.
学生 student_id 在测验 exam_id 中得分为 score.
成绩处于中游的学生是指至少参加了一次测验, 且得分既不是最高分也不是最低分的学生。
写一个 SQL 语句，找出在所有测验中都处于中游的学生 (student_id, student_name)。
不要返回从来没有参加过测验的学生。返回结果表按照 student_id 排序。
查询结果格式如下。
Student 表：
+-------------+---------------+
| student_id | student_name |
+-------------+---------------+
| 1 | Daniel |
| 2 | Jade |
| 3 | Stella |
| 4 | Jonathan |
| 5 | Will |
+-------------+---------------+
Exam 表：
+------------+--------------+-----------+
| exam_id | student_id | score |
+------------+--------------+-----------+
| 10 | 1 | 70 |
| 10 | 2 | 80 |
| 10 | 3 | 90 |
| 20 | 1 | 80 |
| 30 | 1 | 70 |
| 30 | 3 | 80 |
| 30 | 4 | 90 |
| 40 | 1 | 60 |
| 40 | 2 | 70 |
| 40 | 4 | 80 |
+------------+--------------+-----------+
Result 表：
+-------------+---------------+
| student_id | student_name |
+-------------+---------------+
| 2 | Jade |
+-------------+---------------+
对于测验 1: 学生 1 和 3 分别获得了最低分和最高分。
对于测验 2: 学生 1 既获得了最高分, 也获得了最低分。
对于测验 3 和 4: 学生 1 和 4 分别获得了最低分和最高分。
学生 2 和 5 没有在任一场测验中获得了最高分或者最低分。
因为学生 5 从来没有参加过任何测验, 所以他被排除于结果表。
由此, 我们仅仅返回学生 2 的信息。
select e.student_id,student_name
from Exam e left join Student s
on e.student_id=s.student_id
where e.student_id not in(
select student_id
from(
select student_id,rank() over(partition by exam_id order by score desc) rkmax, rank() over(partition by exam_id order by score ) rkmin
from Exam
)t1
where rkmax = 1 or rkmin =1
)
group by e.student_id,student_name
order by e.student_id
1421. 净现值查询
难度中等
SQL架构
表: NPV
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| year | int |
| npv | int |
+---------------+---------+
(id, year) 是该表主键.
该表有每一笔存货的年份, id 和对应净现值的信息.
表: Queries
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| year | int |
+---------------+---------+
(id, year) 是该表主键.
该表有每一次查询所对应存货的 id 和年份的信息.
写一个 SQL, 找到 Queries 表中每一次查询的净现值.
结果表没有顺序要求.
查询结果的格式如下所示:
NPV 表:
+------+--------+--------+
| id | year | npv |
+------+--------+--------+
| 1 | 2018 | 100 |
| 7 | 2020 | 30 |
| 13 | 2019 | 40 |
| 1 | 2019 | 113 |
| 2 | 2008 | 121 |
| 3 | 2009 | 12 |
| 11 | 2020 | 99 |
| 7 | 2019 | 0 |
+------+--------+--------+
Queries 表:
+------+--------+
| id | year |
+------+--------+
| 1 | 2019 |
| 2 | 2008 |
| 3 | 2009 |
| 7 | 2018 |
| 7 | 2019 |
| 7 | 2020 |
| 13 | 2019 |
+------+--------+
结果表:
+------+--------+--------+
| id | year | npv |
+------+--------+--------+
| 1 | 2019 | 113 |
| 2 | 2008 | 121 |
| 3 | 2009 | 12 |
| 7 | 2018 | 0 |
| 7 | 2019 | 0 |
| 7 | 2020 | 30 |
| 13 | 2019 | 40 |
+------+--------+--------+
(7, 2018)的净现值不在 NPV 表中, 我们把它看作是 0.
所有其它查询的净现值都能在 NPV 表中找到.
select q.id,q.year,ifnull(npv,0) npv
from Queries q left join NPV n
on q.id = n.id and q.year = n.year
npv 净现值概念 了解下
1435. 制作会话柱状图
难度简单
SQL架构
表：Sessions
+---------------------+---------+
| Column Name | Type |
+---------------------+---------+
| session_id | int |
| duration | int |
+---------------------+---------+
session_id 是该表主键
duration 是用户访问应用的时间, 以秒为单位
你想知道用户在你的 app 上的访问时长情况。因此决定统计访问时长区间分别为 “[0-5>”, “[5-10>”, “[10-15>” 和 “15 or more” （单位：分钟）的会话数量，并以此绘制柱状图。
写一个SQL查询来报告（访问时长区间，会话总数）。结果可用任何顺序呈现。
下方为查询的输出格式：
Sessions 表：
+-------------+---------------+
| session_id | duration |
+-------------+---------------+
| 1 | 30 |
| 2 | 199 |
| 3 | 299 |
| 4 | 580 |
| 5 | 1000 |
+-------------+---------------+
Result 表：
+--------------+--------------+
| bin | total |
+--------------+--------------+
| [0-5> | 3 |
| [5-10> | 1 |
| [10-15> | 0 |
| 15 or more | 1 |
+--------------+--------------+
对于 session_id 1，2 和 3 ，它们的访问时间大于等于 0 分钟且小于 5 分钟。
对于 session_id 4，它的访问时间大于等于 5 分钟且小于 10 分钟。
没有会话的访问时间大于等于 10 分钟且小于 15 分钟。
对于 session_id 5, 它的访问时间大于等于 15 分钟。
Union
select '[0-5>' as bin, count(*) as total from Sessions where duration/60>=0 and duration/60<5
union
select '[5-10>' as bin, count(*) as total from Sessions where duration/60>=5 and duration/60<10
union
select '[10-15>' as bin, count(*) as total from Sessions where duration/60>=10 and duration/60<15
union
select '15 or more'as bin, count(*) as total from Sessions where duration/60>=15
还有很多其他解法
select a.bin, count(b.bin) as total
from
(
select '[0-5>' as bin union select '[5-10>' as bin union select '[10-15>' as bin union select '15 or more' as bin
)a
left join
(
select case
when duration < 300 then '[0-5>'
when duration >= 300 and duration < 600 then '[5-10>'
when duration >= 600 and duration < 900 then '[10-15>'
else '15 or more'
end bin
from Sessions
)b
on a.bin = b.bin
group by a.bin
1440. 计算布尔表达式的值
难度中等
SQL架构
表 Variables:
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| name | varchar |
| value | int |
+---------------+---------+
name 是该表主键.
该表包含了存储的变量及其对应的值.
表 Expressions:
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| left_operand | varchar |
| operator | enum |
| right_operand | varchar |
+---------------+---------+
(left_operand, operator, right_operand) 是该表主键.
该表包含了需要计算的布尔表达式.
operator 是枚举类型, 取值于('<', '>', '=')
left_operand 和 right_operand 的值保证存在于 Variables 表单中.
写一个 SQL 查询, 以计算表 Expressions 中的布尔表达式.
返回的结果表没有顺序要求.
查询结果格式如下例所示.
Variables 表:
+------+-------+
| name | value |
+------+-------+
| x | 66 |
| y | 77 |
+------+-------+
Expressions 表:
+--------------+----------+---------------+
| left_operand | operator | right_operand |
+--------------+----------+---------------+
| x | > | y |
| x | < | y |
| x | = | y |
| y | > | x |
| y | < | x |
| x | = | x |
+--------------+----------+---------------+
Result 表:
+--------------+----------+---------------+-------+
| left_operand | operator | right_operand | value |
+--------------+----------+---------------+-------+
| x | > | y | false |
| x | < | y | true |
| x | = | y | false |
| y | > | x | true |
| y | < | x | false |
| x | = | x | true |
+--------------+----------+---------------+-------+
如上所示, 你需要通过使用 Variables 表来找到 Expressions 表中的每一个布尔表达式的值.
select e.left_operand,e.operator,e.right_operand,
case e.operator
when '>' then if(v1.value>v2.value,'true','false')
when '<' then if(v1.value<v2.value,'true','false')
else if(v1.value=v2.value,'true','false')
end value
from Expressions e
left join Variables v1 on v1.name = e.left_operand
left join Variables v2 on v2.name = e.right_operand
1445. 苹果和桔子
难度中等
SQL架构
表: Sales
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| sale_date | date |
| fruit | enum |
| sold_num | int |
+---------------+---------+
(sale_date,fruit) 是该表主键.
该表包含了每一天中"苹果" 和 "桔子"的销售情况.
写一个 SQL 查询, 报告每一天 苹果 和 桔子 销售的数目的差异.
返回的结果表, 按照格式为 (‘YYYY-MM-DD’) 的 sale_date 排序.
查询结果表如下例所示:
Sales 表:
+------------+------------+-------------+
| sale_date | fruit | sold_num |
+------------+------------+-------------+
| 2020-05-01 | apples | 10 |
| 2020-05-01 | oranges | 8 |
| 2020-05-02 | apples | 15 |
| 2020-05-02 | oranges | 15 |
| 2020-05-03 | apples | 20 |
| 2020-05-03 | oranges | 0 |
| 2020-05-04 | apples | 15 |
| 2020-05-04 | oranges | 16 |
+------------+------------+-------------+
Result 表:
+------------+--------------+
| sale_date | diff |
+------------+--------------+
| 2020-05-01 | 2 |
| 2020-05-02 | 0 |
| 2020-05-03 | 20 |
| 2020-05-04 | -1 |
+------------+--------------+
在 2020-05-01, 卖了 10 个苹果 和 8 个桔子 (差异为 10 - 8 = 2).
在 2020-05-02, 卖了 15 个苹果 和 15 个桔子 (差异为 15 - 15 = 0).
在 2020-05-03, 卖了 20 个苹果 和 0 个桔子 (差异为 20 - 0 = 20).
在 2020-05-04, 卖了 15 个苹果 和 16 个桔子 (差异为 15 - 16 = -1).
select sale_date,sold_num-ld diff
from
(
select sale_date,sold_num , fruit ,lead(sold_num ,1,null) over(partition by sale_date ) ld
from Sales
)t1
where fruit='apples'
1454. 活跃用户
难度中等
SQL架构
表 Accounts:
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| name | varchar |
+---------------+---------+
id 是该表主键.
该表包含账户 id 和账户的用户名.
表 Logins:
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| login_date | date |
+---------------+---------+
该表无主键, 可能包含重复项.
该表包含登录用户的账户 id 和登录日期. 用户也许一天内登录多次.
写一个 SQL 查询, 找到活跃用户的 id 和 name.
活跃用户是指那些至少连续 5 天登录账户的用户.
返回的结果表按照 id 排序.
结果表格式如下例所示:
Accounts 表:
+----+----------+
| id | name |
+----+----------+
| 1 | Winston |
| 7 | Jonathan |
+----+----------+
Logins 表:
+----+------------+
| id | login_date |
+----+------------+
| 7 | 2020-05-30 |
| 1 | 2020-05-30 |
| 7 | 2020-05-31 |
| 7 | 2020-06-01 |
| 7 | 2020-06-02 |
| 7 | 2020-06-02 |
| 7 | 2020-06-03 |
| 1 | 2020-06-07 |
| 7 | 2020-06-10 |
+----+------------+
Result 表:
+----+----------+
| id | name |
+----+----------+
| 7 | Jonathan |
+----+----------+
id = 1 的用户 Winston 仅仅在不同的 2 天内登录了 2 次, 所以, Winston 不是活跃用户.
id = 7 的用户 Jonathon 在不同的 6 天内登录了 7 次, , 6 天中有 5 天是连续的, 所以, Jonathan 是活跃用户.
后续问题:
如果活跃用户是那些至少连续 n 天登录账户的用户, 你能否写出通用的解决方案?
select t3.id,name
from
(
select distinct id
from
(
select id,login_date,lead(login_date,4,null) over(partition by id order by login_date) ld
from
(
select id,login_date
from Logins
group by id,login_date
)t1
)t2
where datediff(ld,login_date)=4
)t3
left join Accounts a
on t3.id = a.id
注意用户当天重复登入
1459. 矩形面积
难度中等
SQL架构
表: Points
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| id | int |
| x_value | int |
| y_value | int |
+---------------+---------+
id 是该表主键.
每个点都表示为二维空间 (x_value, y_value).
写一个 SQL 语句, 报告由表中任意两点可以形成的所有可能的矩形.
结果表中的每一行包含三列 (p1, p2, area) 如下:
- p1 和 p2 是矩形两个对角的 id 且 p1 < p2.
- 矩形的面积由列 area 表示.
请按照面积大小降序排列，如果面积相同的话, 则按照 p1 和 p2 升序对结果表排序
Points 表:
+----------+-------------+-------------+
| id | x_value | y_value |
+----------+-------------+-------------+
| 1 | 2 | 8 |
| 2 | 4 | 7 |
| 3 | 2 | 10 |
+----------+-------------+-------------+
Result 表:
+----------+-------------+-------------+
| p1 | p2 | area |
+----------+-------------+-------------+
| 2 | 3 | 6 |
| 1 | 2 | 2 |
+----------+-------------+-------------+
p1 应该小于 p2 并且面积大于 0.
p1 = 1 且 p2 = 2 时, 面积等于 |2-4| * |8-7| = 2.
p1 = 2 且 p2 = 3 时, 面积等于 |4-2| * |7-10| = 6.
p1 = 1 且 p2 = 3 时, 是不可能为矩形的, 因为面积等于 0.
select a.id P1,b.id P2,abs(a.x_value-b.x_value)*abs(a.y_value-b.y_value) as area
from Points a,Points b
where a.id<b.id and a.x_value != b.x_value and a.y_value != b.y_value
order by area desc,P1 ,P2
1468. 计算税后工资
难度中等
SQL架构
Salaries 表：
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| company_id | int |
| employee_id | int |
| employee_name | varchar |
| salary | int |
+---------------+---------+
(company_id, employee_id) 是这个表的主键
这个表包括员工的company id, id, name 和 salary
写一条查询 SQL 来查找每个员工的税后工资
每个公司的税率计算依照以下规则
- 如果这个公司员工最高工资不到 1000 ，税率为 0%
- 如果这个公司员工最高工资在 1000 到 10000 之间，税率为 24%
- 如果这个公司员工最高工资大于 10000 ，税率为 49%
按任意顺序返回结果，税后工资结果取整
结果表格式如下例所示：
Salaries 表：
+------------+-------------+---------------+--------+
| company_id | employee_id | employee_name | salary |
+------------+-------------+---------------+--------+
| 1 | 1 | Tony | 2000 |
| 1 | 2 | Pronub | 21300 |
| 1 | 3 | Tyrrox | 10800 |
| 2 | 1 | Pam | 300 |
| 2 | 7 | Bassem | 450 |
| 2 | 9 | Hermione | 700 |
| 3 | 7 | Bocaben | 100 |
| 3 | 2 | Ognjen | 2200 |
| 3 | 13 | Nyancat | 3300 |
| 3 | 15 | Morninngcat | 1866 |
+------------+-------------+---------------+--------+
Result 表：
+------------+-------------+---------------+--------+
| company_id | employee_id | employee_name | salary |
+------------+-------------+---------------+--------+
| 1 | 1 | Tony | 1020 |
| 1 | 2 | Pronub | 10863 |
| 1 | 3 | Tyrrox | 5508 |
| 2 | 1 | Pam | 300 |
| 2 | 7 | Bassem | 450 |
| 2 | 9 | Hermione | 700 |
| 3 | 7 | Bocaben | 76 |
| 3 | 2 | Ognjen | 1672 |
| 3 | 13 | Nyancat | 2508 |
| 3 | 15 | Morninngcat | 5911 |
+------------+-------------+---------------+--------+
对于公司 1 ，最高工资是 21300 ，其每个员工的税率为 49%
对于公司 2 ，最高工资是 700 ，其每个员工税率为 0%
对于公司 3 ，最高工资是 7777 ，其每个员工税率是 24%
税后工资计算 = 工资 - ( 税率 / 100）*工资
对于上述案例，Morninngcat 的税后工资 = 7777 - 7777 * ( 24 / 100) = 7777 - 1866.48 = 5910.52 ，取整为 5911
select company_id,employee_id , employee_name,
round(case when maxsalary<1000 then salary
when maxsalary<10000 then salary*(1-0.24)
else salary*(1-0.49) end ,0)salary
from(
select *,max(salary) over(partition by company_id ) maxsalary
from Salaries
)t1
1479. 周内每天的销售情况
难度困难
SQL架构
表：Orders
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| order_id | int |
| customer_id | int |
| order_date | date |
| item_id | varchar |
| quantity | int |
+---------------+---------+
(order_id, item_id) 是该表主键
该表包含了订单信息
order_date 是id为 item_id 的商品被id为 customer_id 的消费者订购的日期.
表：Items
+---------------------+---------+
| Column Name | Type |
+---------------------+---------+
| item_id | varchar |
| item_name | varchar |
| item_category | varchar |
+---------------------+---------+
item_id 是该表主键
item_name 是商品的名字
item_category 是商品的类别
你是企业主，想要获得分类商品和周内每天的销售报告。
写一个SQL语句，报告 周内每天 每个商品类别下订购了多少单位。
返回结果表单 按商品类别排序 。
查询结果格式如下例所示：
Orders 表：
+------------+--------------+-------------+--------------+-------------+
| order_id | customer_id | order_date | item_id | quantity |
+------------+--------------+-------------+--------------+-------------+
| 1 | 1 | 2020-06-01 | 1 | 10 |
| 2 | 1 | 2020-06-08 | 2 | 10 |
| 3 | 2 | 2020-06-02 | 1 | 5 |
| 4 | 3 | 2020-06-03 | 3 | 5 |
| 5 | 4 | 2020-06-04 | 4 | 1 |
| 6 | 4 | 2020-06-05 | 5 | 5 |
| 7 | 5 | 2020-06-05 | 1 | 10 |
| 8 | 5 | 2020-06-14 | 4 | 5 |
| 9 | 5 | 2020-06-21 | 3 | 5 |
+------------+--------------+-------------+--------------+-------------+
Items 表：
+------------+----------------+---------------+
| item_id | item_name | item_category |
+------------+----------------+---------------+
| 1 | LC Alg. Book | Book |
| 2 | LC DB. Book | Book |
| 3 | LC SmarthPhone | Phone |
| 4 | LC Phone 2020 | Phone |
| 5 | LC SmartGlass | Glasses |
| 6 | LC T-Shirt XL | T-Shirt |
+------------+----------------+---------------+
Result 表：
+------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| Category | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday |
+------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| Book | 20 | 5 | 0 | 0 | 10 | 0 | 0 |
| Glasses | 0 | 0 | 0 | 0 | 5 | 0 | 0 |
| Phone | 0 | 0 | 5 | 1 | 0 | 0 | 10 |
| T-Shirt | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+------------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
在周一(2020-06-01, 2020-06-08)，Book分类(ids: 1, 2)下，总共销售了20个单位(10 + 10)
在周二(2020-06-02)，Book分类(ids: 1, 2)下，总共销售了5个单位
在周三(2020-06-03)，Phone分类(ids: 3, 4)下，总共销售了5个单位
在周四(2020-06-04)，Phone分类(ids: 3, 4)下，总共销售了1个单位
在周五(2020-06-05)，Book分类(ids: 1, 2)下，总共销售了10个单位，Glasses分类(ids: 5)下，总共销售了5个单位
在周六, 没有商品销售
在周天(2020-06-14, 2020-06-21)，Phone分类(ids: 3, 4)下，总共销售了10个单位(5 + 5)
没有销售 T-Shirt 类别的商品
select item_category as category,
sum(case when num = 2 then quantity else 0 end) as Monday,
sum(case when num = 3 then quantity else 0 end) as Tuesday,
sum(case when num = 4 then quantity else 0 end) as Wednesday,
sum(case when num = 5 then quantity else 0 end) as Thursday,
sum(case when num = 6 then quantity else 0 end) as Friday,
sum(case when num = 7 then quantity else 0 end) as Saturday,
sum(case when num = 1 then quantity else 0 end) as Sunday
from
(select item_category, quantity,dayofweek(order_date) as num from
items i left join orders o
on i.item_id=o.item_id) t
group by item_category
order by item_category
1485. 按日期分组销售产品
难度简单
SQL架构
表 Activities：
+-------------+---------+
| 列名 | 类型 |
+-------------+---------+
| sell_date | date |
| product | varchar |
+-------------+---------+
此表没有主键，它可能包含重复项。
此表的每一行都包含产品名称和在市场上销售的日期。
编写一个 SQL 查询来查找每个日期、销售的不同产品的数量及其名称。
每个日期的销售产品名称应按词典序排列。
返回按 sell_date 排序的结果表。
查询结果格式如下例所示。
Activities 表：
+------------+-------------+
| sell_date | product |
+------------+-------------+
| 2020-05-30 | Headphone |
| 2020-06-01 | Pencil |
| 2020-06-02 | Mask |
| 2020-05-30 | Basketball |
| 2020-06-01 | Bible |
| 2020-06-02 | Mask |
| 2020-05-30 | T-Shirt |
+------------+-------------+
Result 表：
+------------+----------+------------------------------+
| sell_date | num_sold | products |
+------------+----------+------------------------------+
| 2020-05-30 | 3 | Basketball,Headphone,T-shirt |
| 2020-06-01 | 2 | Bible,Pencil |
| 2020-06-02 | 1 | Mask |
+------------+----------+------------------------------+
对于2020-05-30，出售的物品是 (Headphone, Basketball, T-shirt)，按词典序排列，并用逗号 ',' 分隔。
对于2020-06-01，出售的物品是 (Pencil, Bible)，按词典序排列，并用逗号分隔。
对于2020-06-02，出售的物品是 (Mask)，只需返回该物品名。
select sell_date, count(distinct product) num_sold,
group_concat(distinct product order by product) products
from Activities
group by sell_date
行转列
1495. 上月播放的儿童适宜电影
难度简单
SQL架构
表: TVProgram
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| program_date | date |
| content_id | int |
| channel | varchar |
+---------------+---------+
(program_date, content_id) 是该表主键.
该表包含电视上的节目信息.
content_id 是电视一些频道上的节目的 id.
表: Content
+------------------+---------+
| Column Name | Type |
+------------------+---------+
| content_id | varchar |
| title | varchar |
| Kids_content | enum |
| content_type | varchar |
+------------------+---------+
content_id 是该表主键.
Kids_content 是枚举类型, 取值为('Y', 'N'), 其中:
'Y' 表示儿童适宜内容, 而'N'表示儿童不宜内容.
content_type 表示内容的类型, 比如电影, 电视剧等.
写一个 SQL 语句, 报告在 2020 年 6 月份播放的儿童适宜电影的去重电影名.
返回的结果表单没有顺序要求.
查询结果的格式如下例所示.
TVProgram 表:
+--------------------+--------------+-------------+
| program_date | content_id | channel |
+--------------------+--------------+-------------+
| 2020-06-10 08:00 | 1 | LC-Channel |
| 2020-05-11 12:00 | 2 | LC-Channel |
| 2020-05-12 12:00 | 3 | LC-Channel |
| 2020-05-13 14:00 | 4 | Disney Ch |
| 2020-06-18 14:00 | 4 | Disney Ch |
| 2020-07-15 16:00 | 5 | Disney Ch |
+--------------------+--------------+-------------+
Content 表:
+------------+----------------+---------------+---------------+
| content_id | title | Kids_content | content_type |
+------------+----------------+---------------+---------------+
| 1 | Leetcode Movie | N | Movies |
| 2 | Alg. for Kids | Y | Series |
| 3 | Database Sols | N | Series |
| 4 | Aladdin | Y | Movies |
| 5 | Cinderella | Y | Movies |
+------------+----------------+---------------+---------------+
Result 表:
+--------------+
| title |
+--------------+
| Aladdin |
+--------------+
"Leetcode Movie" 是儿童不宜的电影.
"Alg. for Kids" 不是电影.
"Database Sols" 不是电影
"Alladin" 是电影, 儿童适宜, 并且在 2020 年 6 月份播放.
"Cinderella" 不在 2020 年 6 月份播放.
select distinct title
from TVProgram t left join Content c
on t.content_id = c.content_id
where Kids_content ='Y'
and date_format(program_date ,'%Y-%m')='2020-06'
and content_type='Movies'
LEFT()函数参见：https://www.begtut.com/sql/func-mysql-left.html
REGEXP语法参见：
https://www.cnblogs.com/timssd/p/5882742.html
https://www.cnblogs.com/zhaopanpan/p/10133224.html
DATE_FORMAT()函数参见：https://www.w3school.com.cn/sql/func_date_format.asp
EXTRACT()函数参见：https://www.runoob.com/sql/func-extract.html
DATEDIFF()函数参见：https://www.runoob.com/sql/func-datediff-mysql.html
YEAR()函数参见：https://blog.csdn.net/moakun/article/details/82528829
MONTH()函数参见：https://www.yiibai.com/mysql/month.html
1501. 可以放心投资的国家
难度中等
SQL架构
表 Person:
+----------------+---------+
| Column Name | Type |
+----------------+---------+
| id | int |
| name | varchar |
| phone_number | varchar |
+----------------+---------+
id 是该表主键.
该表每一行包含一个人的名字和电话号码.
电话号码的格式是:'xxx-yyyyyyy', 其中xxx是国家码(3个字符), yyyyyyy是电话号码(7个字符), x和y都表示数字. 同时, 国家码和电话号码都可以包含前导0.
表 Country:
+----------------+---------+
| Column Name | Type |
+----------------+---------+
| name | varchar |
| country_code | varchar |
+----------------+---------+
country_code是该表主键.
该表每一行包含国家名和国家码. country_code的格式是'xxx', x是数字.
表 Calls:
+-------------+------+
| Column Name | Type |
+-------------+------+
| caller_id | int |
| callee_id | int |
| duration | int |
+-------------+------+
该表无主键, 可能包含重复行.
每一行包含呼叫方id, 被呼叫方id和以分钟为单位的通话时长. caller_id != callee_id
一家电信公司想要投资新的国家. 该公司想要投资的国家是: 该国的平均通话时长要严格地大于全球平均通话时长.
写一段 SQL, 找到所有该公司可以投资的国家.
返回的结果表没有顺序要求.
查询的结果格式如下例所示.
Person 表:
+----+----------+--------------+
| id | name | phone_number |
+----+----------+--------------+
| 3 | Jonathan | 051-1234567 |
| 12 | Elvis | 051-7654321 |
| 1 | Moncef | 212-1234567 |
| 2 | Maroua | 212-6523651 |
| 7 | Meir | 972-1234567 |
| 9 | Rachel | 972-0011100 |
+----+----------+--------------+
Country 表:
+----------+--------------+
| name | country_code |
+----------+--------------+
| Peru | 051 |
| Israel | 972 |
| Morocco | 212 |
| Germany | 049 |
| Ethiopia | 251 |
+----------+--------------+
Calls 表:
+-----------+-----------+----------+
| caller_id | callee_id | duration |
+-----------+-----------+----------+
| 1 | 9 | 33 |
| 2 | 9 | 4 |
| 1 | 2 | 59 |
| 3 | 12 | 102 |
| 3 | 12 | 330 |
| 12 | 3 | 5 |
| 7 | 9 | 13 |
| 7 | 1 | 3 |
| 9 | 7 | 1 |
| 1 | 7 | 7 |
+-----------+-----------+----------+
Result 表:
+----------+
| country |
+----------+
| Peru |
+----------+
国家Peru的平均通话时长是 (102 + 102 + 330 + 330 + 5 + 5) / 6 = 145.666667
国家Israel的平均通话时长是 (33 + 4 + 13 + 13 + 3 + 1 + 1 + 7) / 8 = 9.37500
国家Morocco的平均通话时长是 (33 + 4 + 59 + 59 + 3 + 7) / 6 = 27.5000
全球平均通话时长 = (2 * (33 + 3 + 59 + 102 + 330 + 5 + 13 + 3 + 1 + 7)) / 20 = 55.70000
所以, Peru是唯一的平均通话时长大于全球平均通话时长的国家, 也是唯一的推荐投资的国家.
笛卡尔积
select c2.name as country
from Calls c1,Person p,Country c2
where (p.id=c1.caller_id or p.id=c1.callee_id) and c2.country_code=left(p.phone_number,3)
group by c2.name
having avg(duration)>(select avg(duration) from Calls)
思路更清晰
with people_country as
(
select id, c.name country
from Person p left join Country c
on left(p.phone_number,3) = c.country_code
)
select country
from
(
select country, avg(duration) avgtime
from
(
select caller_id id, duration
from Calls
union all
select callee_id, duration
from Calls
) t left join people_country
using(id)
group by country
) temp
where avgtime >
(
select avg(duration) avgtime
from
(
select caller_id, duration
from Calls
union all
select callee_id, duration
from Calls
) t
)
1511. 消费者下单频率
难度简单
SQL架构
表: Customers
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| customer_id | int |
| name | varchar |
| country | varchar |
+---------------+---------+
customer_id 是该表主键.
该表包含公司消费者的信息.
表: Product
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| product_id | int |
| description | varchar |
| price | int |
+---------------+---------+
product_id 是该表主键.
该表包含公司产品的信息.
price 是本产品的花销.
表: Orders
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| order_id | int |
| customer_id | int |
| product_id | int |
| order_date | date |
| quantity | int |
+---------------+---------+
order_id 是该表主键.
该表包含消费者下单的信息.
customer_id 是买了数量为"quantity", id为"product_id"产品的消费者的 id.
Order_date 是订单发货的日期, 格式为('YYYY-MM-DD').
写一个 SQL 语句, 报告消费者的 id 和名字, 其中消费者在 2020 年 6 月和 7 月, 每月至少花费了$100.
结果表无顺序要求.
查询结果格式如下例所示.
Customers
+--------------+-----------+-------------+
| customer_id | name | country |
+--------------+-----------+-------------+
| 1 | Winston | USA |
| 2 | Jonathan | Peru |
| 3 | Moustafa | Egypt |
+--------------+-----------+-------------+
Product
+--------------+-------------+-------------+
| product_id | description | price |
+--------------+-------------+-------------+
| 10 | LC Phone | 300 |
| 20 | LC T-Shirt | 10 |
| 30 | LC Book | 45 |
| 40 | LC Keychain | 2 |
+--------------+-------------+-------------+
Orders
+--------------+-------------+-------------+-------------+-----------+
| order_id | customer_id | product_id | order_date | quantity |
+--------------+-------------+-------------+-------------+-----------+
| 1 | 1 | 10 | 2020-06-10 | 1 |
| 2 | 1 | 20 | 2020-07-01 | 1 |
| 3 | 1 | 30 | 2020-07-08 | 2 |
| 4 | 2 | 10 | 2020-06-15 | 2 |
| 5 | 2 | 40 | 2020-07-01 | 10 |
| 6 | 3 | 20 | 2020-06-24 | 2 |
| 7 | 3 | 30 | 2020-06-25 | 2 |
| 9 | 3 | 30 | 2020-05-08 | 3 |
+--------------+-------------+-------------+-------------+-----------+
Result 表:
+--------------+------------+
| customer_id | name |
+--------------+------------+
| 1 | Winston |
+--------------+------------+
Winston 在2020年6月花费了$300(300 * 1), 在7月花费了$100(10 * 1 + 45 * 2).
Jonathan 在2020年6月花费了$600(300 * 2), 在7月花费了$20(2 * 10).
Moustafa 在2020年6月花费了$110 (10 * 2 + 45 * 2), 在7月花费了$0.
select customer_id,name
from Customers
where customer_id in
(select customer_id
from
(select customer_id, month(order_date) as month , sum(quantity*price) as total
from Orders o left join Product p on o.product_id = p.product_id
where month(order_date) = 6 or month(order_date)=7
group by customer_id,month(order_date)
) as t1
where total >=100
group by customer_id
having count(*)>=2
)
1517. Find Users With Valid E-Mails
难度简单
SQL架构
Table: Users
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| user_id | int |
| name | varchar |
| mail | varchar |
+---------------+---------+
user_id is the primary key for this table.
This table contains information of the users signed up in a website. Some e-mails are invalid.
Write an SQL query to find the users who have valid emails.
A valid e-mail has a prefix name and a domain where:
- The prefix name is a string that may contain letters (upper or lower case), digits, underscore
'_', period'.'and/or dash'-'. The prefix name must start with a letter. - The domain is
'@leetcode.com'.
Return the result table in any order.
The query result format is in the following example.
Users
+---------+-----------+-------------------------+
| user_id | name | mail |
+---------+-----------+-------------------------+
| 1 | Winston | [email protected] |
| 2 | Jonathan | jonathanisgreat |
| 3 | Annabelle | [email protected] |
| 4 | Sally | [email protected] |
| 5 | Marwan | quarz#[email protected] |
| 6 | David | [email protected] |
| 7 | Shapiro | [email protected] |
+---------+-----------+-------------------------+
Result table:
+---------+-----------+-------------------------+
| user_id | name | mail |
+---------+-----------+-------------------------+
| 1 | Winston | [email protected] |
| 3 | Annabelle | [email protected] |
| 4 | Sally | [email protected] |
+---------+-----------+-------------------------+
The mail of user 2 doesn't have a domain.
The mail of user 5 has # sign which is not allowed.
The mail of user 6 doesn't have leetcode domain.
The mail of user 7 starts with a period.
考察正则表达式的使用
SELECT *
FROM Users
WHERE mail REGEXP '^[a-zA-Z]+[\\w_\\.\\-]*@leetcode.com$'
ORDER BY user_id;
select * from Users
where mail regexp '^[a-zA-Z]+[a-zA-Z0-9_\\./\\-]{0,}@leetcode.com$'
order by user_id
坑点：
1、前缀可能是一个字母，比如“[email protected]”，所以匹配非首字母外的前缀字符数量要用{0,}或*，不能用+。
2、题意要求：underscore ‘’, period ‘.’ and/or dash ‘-’，/没加单引号，不留神可能写漏/。
3、后缀可能是“@leetcodeecom”，所以要对“.”加转义符号。
4、后缀可能是“@LEETCODE.COM”，默认是不区分大小写匹配，所以要加上“BINARY”区分大小写。
语法：
1、https://www.cnblogs.com/timssd/p/5882742.html
2、https://www.cnblogs.com/zhaopanpan/p/10133224.html
3、"双反斜杠+w"表示字母、数字、下划线，相对"a-zA-Z0-9"的写法更简洁。
1527. Patients With a Condition
难度简单
SQL架构
Table: Patients
+--------------+---------+
| Column Name | Type |
+--------------+---------+
| patient_id | int |
| patient_name | varchar |
| conditions | varchar |
+--------------+---------+
patient_id is the primary key for this table.
'conditions' contains 0 or more code separated by spaces.
This table contains information of the patients in the hospital.
Write an SQL query to report the patient_id, patient_name all conditions of patients who have Type I Diabetes. Type I Diabetes always starts with DIAB1 prefix
Return the result table in any order.
The query result format is in the following example.
Patients
+------------+--------------+--------------+
| patient_id | patient_name | conditions |
+------------+--------------+--------------+
| 1 | Daniel | YFEV COUGH |
| 2 | Alice | |
| 3 | Bob | DIAB100 MYOP |
| 4 | George | ACNE DIAB100 |
| 5 | Alain | DIAB201 |
+------------+--------------+--------------+
Result table:
+------------+--------------+--------------+
| patient_id | patient_name | conditions |
+------------+--------------+--------------+
| 3 | Bob | DIAB100 MYOP |
| 4 | George | ACNE DIAB100 |
+------------+--------------+--------------+
Bob and George both have a condition that starts with DIAB1.
select patient_id , patient_name ,conditions
from Patients
where conditions like '%DIAB1%'
1532. The Most Recent Three Orders
难度中等
SQL架构
Table: Customers
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| customer_id | int |
| name | varchar |
+---------------+---------+
customer_id is the primary key for this table.
This table contains information about customers.
Table: Orders
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| order_id | int |
| order_date | date |
| customer_id | int |
| cost | int |
+---------------+---------+
order_id is the primary key for this table.
This table contains information about the orders made by customer_id.
Each customer has one order per day.
Write an SQL query to find the most recent 3 orders of each user. If a user ordered less than 3 orders return all of their orders.
Return the result table sorted by customer_name in ascending order and in case of a tie by the customer_id in ascending order. If there still a tie, order them by the order_date in descending order.
The query result format is in the following example:
Customers
+-------------+-----------+
| customer_id | name |
+-------------+-----------+
| 1 | Winston |
| 2 | Jonathan |
| 3 | Annabelle |
| 4 | Marwan |
| 5 | Khaled |
+-------------+-----------+
Orders
+----------+------------+-------------+------+
| order_id | order_date | customer_id | cost |
+----------+------------+-------------+------+
| 1 | 2020-07-31 | 1 | 30 |
| 2 | 2020-07-30 | 2 | 40 |
| 3 | 2020-07-31 | 3 | 70 |
| 4 | 2020-07-29 | 4 | 100 |
| 5 | 2020-06-10 | 1 | 1010 |
| 6 | 2020-08-01 | 2 | 102 |
| 7 | 2020-08-01 | 3 | 111 |
| 8 | 2020-08-03 | 1 | 99 |
| 9 | 2020-08-07 | 2 | 32 |
| 10 | 2020-07-15 | 1 | 2 |
+----------+------------+-------------+------+
Result table:
+---------------+-------------+----------+------------+
| customer_name | customer_id | order_id | order_date |
+---------------+-------------+----------+------------+
| Annabelle | 3 | 7 | 2020-08-01 |
| Annabelle | 3 | 3 | 2020-07-31 |
| Jonathan | 2 | 9 | 2020-08-07 |
| Jonathan | 2 | 6 | 2020-08-01 |
| Jonathan | 2 | 2 | 2020-07-30 |
| Marwan | 4 | 4 | 2020-07-29 |
| Winston | 1 | 8 | 2020-08-03 |
| Winston | 1 | 1 | 2020-07-31 |
| Winston | 1 | 10 | 2020-07-15 |
+---------------+-------------+----------+------------+
Winston has 4 orders, we discard the order of "2020-06-10" because it is the oldest order.
Annabelle has only 2 orders, we return them.
Jonathan has exactly 3 orders.
Marwan ordered only one time.
We sort the result table by customer_name in ascending order, by customer_id in ascending order and by order_date in descending order in case of a tie.
Follow-up:
Can you write a general solution for the most recent n orders?
select name customer_name ,customer_id,order_id,order_date
from (
select name ,o.customer_id,order_id,order_date ,rank()over(partition by o.customer_id order by order_date desc) rk
from Orders o left join Customers c
on o.customer_id=c.customer_id
)t1
where rk <=3
order by customer_name ,customer_id,order_date desc
1543. Fix Product Name Format
难度简单
SQL架构
Table: Sales
+--------------+---------+
| Column Name | Type |
+--------------+---------+
| sale_id | int |
| product_name | varchar |
| sale_date | date |
+--------------+---------+
sale_id is the primary key for this table.
Each row of this table contains the product name and the date it was sold.
Since table Sales was filled manually in the year 2000, product_name may contain leading and/or trailing white spaces, also they are case-insensitive.
Write an SQL query to report
product_namein lowercase without leading or trailing white spaces.sale_datein the format('YYYY-MM')totalthe number of times the product was sold in this month.
Return the result table ordered by product_name in ascending order, in case of a tie order it by sale_date in ascending order.
The query result format is in the following example.
Sales
+------------+------------------+--------------+
| sale_id | product_name | sale_date |
+------------+------------------+--------------+
| 1 | LCPHONE | 2000-01-16 |
| 2 | LCPhone | 2000-01-17 |
| 3 | LcPhOnE | 2000-02-18 |
| 4 | LCKeyCHAiN | 2000-02-19 |
| 5 | LCKeyChain | 2000-02-28 |
| 6 | Matryoshka | 2000-03-31 |
+------------+------------------+--------------+
Result table:
+--------------+--------------+----------+
| product_name | sale_date | total |
+--------------+--------------+----------+
| lcphone | 2000-01 | 2 |
| lckeychain | 2000-02 | 2 |
| lcphone | 2000-02 | 1 |
| matryoshka | 2000-03 | 1 |
+--------------+--------------+----------+
In January, 2 LcPhones were sold, please note that the product names are not case sensitive and may contain spaces.
In Februery, 2 LCKeychains and 1 LCPhone were sold.
In March, 1 matryoshka was sold.
select trim(lower(product_name)) as product_name,
date_format(sale_date,'%Y-%m') as sale_date,
count(*) as total
from Sales
group by trim(lower(product_name)), date_format(sale_date,'%Y-%m')
order by product_name asc, sale_date asc
注意大小写、空格
1549. The Most Recent Orders for Each Product
难度中等
SQL架构
Table: Customers
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| customer_id | int |
| name | varchar |
+---------------+---------+
customer_id is the primary key for this table.
This table contains information about the customers.
Table: Orders
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| order_id | int |
| order_date | date |
| customer_id | int |
| product_id | int |
+---------------+---------+
order_id is the primary key for this table.
This table contains information about the orders made by customer_id.
There will be no product ordered by the same user more than once in one day.
Table: Products
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| product_id | int |
| product_name | varchar |
| price | int |
+---------------+---------+
product_id is the primary key for this table.
This table contains information about the Products.
Write an SQL query to find the most recent order(s) of each product.
Return the result table sorted by product_name in ascending order and in case of a tie by the product_id in ascending order. If there still a tie, order them by the order_id in ascending order.
The query result format is in the following example:
Customers
+-------------+-----------+
| customer_id | name |
+-------------+-----------+
| 1 | Winston |
| 2 | Jonathan |
| 3 | Annabelle |
| 4 | Marwan |
| 5 | Khaled |
+-------------+-----------+
Orders
+----------+------------+-------------+------------+
| order_id | order_date | customer_id | product_id |
+----------+------------+-------------+------------+
| 1 | 2020-07-31 | 1 | 1 |
| 2 | 2020-07-30 | 2 | 2 |
| 3 | 2020-08-29 | 3 | 3 |
| 4 | 2020-07-29 | 4 | 1 |
| 5 | 2020-06-10 | 1 | 2 |
| 6 | 2020-08-01 | 2 | 1 |
| 7 | 2020-08-01 | 3 | 1 |
| 8 | 2020-08-03 | 1 | 2 |
| 9 | 2020-08-07 | 2 | 3 |
| 10 | 2020-07-15 | 1 | 2 |
+----------+------------+-------------+------------+
Products
+------------+--------------+-------+
| product_id | product_name | price |
+------------+--------------+-------+
| 1 | keyboard | 120 |
| 2 | mouse | 80 |
| 3 | screen | 600 |
| 4 | hard disk | 450 |
+------------+--------------+-------+
Result table:
+--------------+------------+----------+------------+
| product_name | product_id | order_id | order_date |
+--------------+------------+----------+------------+
| keyboard | 1 | 6 | 2020-08-01 |
| keyboard | 1 | 7 | 2020-08-01 |
| mouse | 2 | 8 | 2020-08-03 |
| screen | 3 | 3 | 2020-08-29 |
+--------------+------------+----------+------------+
keyboard's most recent order is in 2020-08-01, it was ordered two times this day.
mouse's most recent order is in 2020-08-03, it was ordered only once this day.
screen's most recent order is in 2020-08-29, it was ordered only once this day.
The hard disk was never ordered and we don't include it in the result table.
select product_name,product_id,order_id,order_date
from
(
select product_name ,o.product_id ,order_id,order_date ,
rank() over(partition by o.product_id order by order_date desc) rk
from Orders o left join Products p
on o.product_id =p.product_id
)t1
where rk =1
order by product_name,product_id,order_id
1555. 银行账户概要
难度中等
SQL架构
用户表： Users
+--------------+---------+
| Column Name | Type |
+--------------+---------+
| user_id | int |
| user_name | varchar |
| credit | int |
+--------------+---------+
user_id 是这个表的主键。
表中的每一列包含每一个用户当前的额度信息。
交易表：Transaction
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| trans_id | int |
| paid_by | int |
| paid_to | int |
| amount | int |
| transacted_on | date |
+---------------+---------+
trans_id 是这个表的主键。
表中的每一列包含银行的交易信息。
ID 为 paid_by 的用户给 ID 为 paid_to 的用户转账。
力扣银行 (LCB) 帮助程序员们完成虚拟支付。我们的银行在表 Transaction 中记录每条交易信息，我们要查询每个用户的当前余额，并检查他们是否已透支（当前额度小于 0）。
写一条 SQL 语句，查询：
user_id用户 IDuser_name用户名credit完成交易后的余额credit_limit_breached检查是否透支 （“Yes” 或 “No”）
以任意顺序返回结果表。
查询格式见如下示例：
Users 表：
+------------+--------------+-------------+
| user_id | user_name | credit |
+------------+--------------+-------------+
| 1 | Moustafa | 100 |
| 2 | Jonathan | 200 |
| 3 | Winston | 10000 |
| 4 | Luis | 800 |
+------------+--------------+-------------+
Transaction 表：
+------------+------------+------------+----------+---------------+
| trans_id | paid_by | paid_to | amount | transacted_on |
+------------+------------+------------+----------+---------------+
| 1 | 1 | 3 | 400 | 2020-08-01 |
| 2 | 3 | 2 | 500 | 2020-08-02 |
| 3 | 2 | 1 | 200 | 2020-08-03 |
+------------+------------+------------+----------+---------------+
结果表：
+------------+------------+------------+-----------------------+
| user_id | user_name | credit | credit_limit_breached |
+------------+------------+------------+-----------------------+
| 1 | Moustafa | -100 | Yes |
| 2 | Jonathan | 500 | No |
| 3 | Winston | 9900 | No |
| 4 | Luis | 800 | No |
+------------+------------+------------+-----------------------+
Moustafa 在 "2020-08-01" 支付了 $400 并在 "2020-08-03" 收到了 $200 ，当前额度 (100 -400 +200) = -$100
Jonathan 在 "2020-08-02" 收到了 $500 并在 "2020-08-08" 支付了 $200 ，当前额度 (200 +500 -200) = $500
Winston 在 "2020-08-01" 收到了 $400 并在 "2020-08-03" 支付了 $500 ，当前额度 (10000 +400 -500) = $9900
Luis 未收到任何转账信息，额度 = $800
select *,if(credit<0,'Yes','No') credit_limit_breached
from (
select user_id ,user_name ,u.credit-ifnull(t1.amount,0)+ifnull(t2.amount,0) credit
from users u left join
(
select paid_by,sum(amount) amount
from transaction
group by paid_by
)t1
on u.user_id = t1.paid_by
left join
(
select paid_to,sum(amount) amount
from transaction
group by paid_to
)t2
on u.user_id =t2.paid_to
)tmp
1565. Unique Orders and Customers Per Month
难度简单
SQL架构
Table: Orders
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| order_id | int |
| order_date | date |
| customer_id | int |
| invoice | int |
+---------------+---------+
order_id is the primary key for this table.
This table contains information about the orders made by customer_id.
Write an SQL query to find the number of unique orders and the number of unique users with invoices > $20 for each different month.
Return the result table sorted in any order.
The query result format is in the following example:
Orders
+----------+------------+-------------+------------+
| order_id | order_date | customer_id | invoice |
+----------+------------+-------------+------------+
| 1 | 2020-09-15 | 1 | 30 |
| 2 | 2020-09-17 | 2 | 90 |
| 3 | 2020-10-06 | 3 | 20 |
| 4 | 2020-10-20 | 3 | 21 |
| 5 | 2020-11-10 | 1 | 10 |
| 6 | 2020-11-21 | 2 | 15 |
| 7 | 2020-12-01 | 4 | 55 |
| 8 | 2020-12-03 | 4 | 77 |
| 9 | 2021-01-07 | 3 | 31 |
| 10 | 2021-01-15 | 2 | 20 |
+----------+------------+-------------+------------+
Result table:
+---------+-------------+----------------+
| month | order_count | customer_count |
+---------+-------------+----------------+
| 2020-09 | 2 | 2 |
| 2020-10 | 1 | 1 |
| 2020-12 | 2 | 1 |
| 2021-01 | 1 | 1 |
+---------+-------------+----------------+
In September 2020 we have two orders from 2 different customers with invoices > $20.
In October 2020 we have two orders from 1 customer, and only one of the two orders has invoice > $20.
In November 2020 we have two orders from 2 different customers but invoices < $20, so we don't include that month.
In December 2020 we have two orders from 1 customer both with invoices > $20.
In January 2021 we have two orders from 2 different customers, but only one of them with invoice > $20.
select date_format(order_date,'%Y-%m') month,count(order_id) order_count,count(distinct customer_id) customer_count
from Orders
where invoice>20
group by month