#MySQL8.0之其他新特性
CREATE DATABASE dbtest18;
USE dbtest18;
#窗口函数
#1.1 演示窗口函数的效果
CREATE TABLE sales(
id INT PRIMARY KEY AUTO_INCREMENT,
city VARCHAR(15),
county VARCHAR(15),
sales_value DECIMAL
);
INSERT INTO sales(city,county,sales_value)
VALUES
('北京','海淀',10.00),
('北京','朝阳',20.00),
('上海','黄埔',30.00),
('上海','长宁',10.00);
SELECT * FROM sales;
/*
需求：现在计算这个网站在每个城市的销售总额、在全国的销售总额、每个区的销售额占所在城市销售
额中的比率，以及占总销售额中的比率。
如果用分组和聚合函数，就需要分好几步来计算
*/
CREATE TEMPORARY TABLE a
AS
SELECT SUM(sales_value) AS sum_val -- 全国总额
FROM sales;
SELECT * FROM a;
CREATE TEMPORARY TABLE b -- 城市总额
AS
SELECT city,SUM(sales_value) AS city_sum
FROM sales
GROUP BY city;
DROP TABLE b;
SELECT * FROM b;
SELECT * FROM sales;
SELECT s.city AS 城市,s.county AS 地区,s.sales_value AS 区销售额,
s.sales_value / b.city_sum AS 城市销售额, s.sales_value / a.sum_val AS 总销售额
FROM sales AS s JOIN b
ON s.city = b.city
JOIN a
ORDER BY s.city,s.county;
#方式二
SELECT city AS 城市,county AS 区,sales_value AS 区销售额,
SUM(sales_value) OVER(PARTITION BY city) AS 市销售额, -- 计算市销售额
sales_value/SUM(sales_value) OVER(PARTITION BY city) AS 市比率,
SUM(sales_value) OVER() AS 总销售额, -- 计算总销售额
sales_value/SUM(sales_value) OVER() AS 总比率
FROM sales
ORDER BY city,county;
#介绍窗口函数
CREATE TABLE employees
AS
SELECT * FROM atguigudb.`employees`;
SELECT * FROM employees;
#创建表
CREATE TABLE goods(
id INT PRIMARY KEY AUTO_INCREMENT,
category_id INT,
category VARCHAR(15),
NAME VARCHAR(30),
price DECIMAL(10,2),
stock INT,
upper_time DATETIME
);
INSERT INTO goods(category_id,category,NAME,price,stock,upper_time)
VALUES
(1, '女装/女士精品', 'T恤', 39.90, 1000, '2020-11-10 00:00:00'),
(1, '女装/女士精品', '连衣裙', 79.90, 2500, '2020-11-10 00:00:00'),
(1, '女装/女士精品', '卫衣', 89.90, 1500, '2020-11-10 00:00:00'),
(1, '女装/女士精品', '牛仔裤', 89.90, 3500, '2020-11-10 00:00:00'),
(1, '女装/女士精品', '百褶裙', 29.90, 500, '2020-11-10 00:00:00'),
(1, '女装/女士精品', '呢绒外套', 399.90, 1200, '2020-11-10 00:00:00'),
(2, '户外运动', '自行车', 399.90, 1000, '2020-11-10 00:00:00'),
(2, '户外运动', '山地自行车', 1399.90, 2500, '2020-11-10 00:00:00'),
(2, '户外运动', '登山杖', 59.90, 1500, '2020-11-10 00:00:00'),
(2, '户外运动', '骑行装备', 399.90, 3500, '2020-11-10 00:00:00'),
(2, '户外运动', '运动外套', 799.90, 500, '2020-11-10 00:00:00'),
(2, '户外运动', '滑板', 499.90, 1200, '2020-11-10 00:00:00');
SELECT * FROM goods;
#序号函数
#ROW_NUMBER()函数
#ROW_NUMBER()函数能够对数据中的序号进行顺序显示。
#查询 goods 数据表中每个商品分类下价格降序排列的各个商品信息。
SELECT ROW_NUMBER() OVER(PARTITION BY category_id ORDER BY price DESC) AS
row_num,
id, category_id, category, NAME, price, stock
FROM goods;
#举例：查询 goods 数据表中每个商品分类下价格最高的3种商品信息。
SELECT *
FROM (
SELECT ROW_NUMBER() OVER(PARTITION BY category_id ORDER BY price DESC) AS
row_num,
id, category_id, category, NAME, price, stock
FROM goods) t
WHERE row_num <= 3;
#RANK()函数
/*
使用RANK()函数能够对序号进行并列排序，并且会跳过重复的序号，比如序号为1、1、3。
使用RANK()函数能够对序号进行并列排序，并且会跳过重复的序号，比如序号为1、1、3。
举例：使用RANK()函数获取 goods 数据表中各类别的价格从高到低排序的各商品信息。
*/
SELECT RANK() OVER(PARTITION BY category_id ORDER BY price DESC) AS row_num,
id, category_id, category, NAME, price, stock
FROM goods;
#DENSE_RANK()函数
/*
DENSE_RANK()函数对序号进行并列排序，并且不会跳过重复的序号，比如序号为1、1、2。
举例：使用DENSE_RANK()函数获取 goods 数据表中各类别的价格从高到低排序的各商品信息。
*/
SELECT DENSE_RANK() OVER(PARTITION BY category_id ORDER BY price DESC) ASrow_num,
id, category_id, category, NAME, price, stock
FROM goods;
#分布函数
#PERCENT_RANK()函数
#PERCENT_RANK()函数是等级值百分比函数。按照如下方式进行计算。
#(rank - 1) / (rows - 1)
#举例：计算 goods 数据表中名称为“女装/女士精品”的类别下的商品的PERCENT_RANK值。
#方式一：
SELECT RANK() OVER w AS r,
PERCENT_RANK() OVER w AS pr,
id, category_id, category, NAME, price, stock
FROM goods
WHERE category_id = 1 WINDOW w AS (PARTITION BY category_id ORDER BY price DESC);
#方式二：
SELECT RANK() OVER (PARTITION BY category_id ORDER BY price DESC)AS r,
PERCENT_RANK() OVER (PARTITION BY category_id ORDER BY price DESC) AS pr,
id, category_id, category, NAME, price, stock
FROM goods
WHERE category_id = 1;
#CUME_DIST()函数
#CUME_DIST()函数主要用于查询小于或等于某个值的比例。
SELECT CUME_DIST() OVER(PARTITION BY category_id ORDER BY price ASC) AS cd,
id, category, NAME, price
FROM goods;
#前后函数
#LAG(expr,n)函数
#LAG(expr,n)函数返回当前行的前n行的expr的值
#查询goods数据表中前一个商品价格与当前商品价格的差值
SELECT id, category, NAME, price, pre_price, price - pre_price AS diff_price
FROM (
SELECT id, category, NAME, price,LAG(price,1) OVER w AS pre_price
FROM goods
WINDOW w AS (PARTITION BY category_id ORDER BY price)) t;
#LEAD(expr,n)函数
#LEAD(expr,n)函数返回当前行的后n行的expr的值
#举例：查询goods数据表中后一个商品价格与当前商品价格的差值。
SELECT id, category, NAME, behind_price, price,behind_price - price AS
diff_price
FROM(
SELECT id, category, NAME, price,LEAD(price, 1) OVER w AS behind_price
FROM goods WINDOW w AS (PARTITION BY category_id ORDER BY price)) t;
#首位函数
#FIRST_VALUE(expr)函数
#FIRST_VALUE(expr)函数返回第一个expr的值。
#按照价格排序，查询第1个商品的价格信息。
SELECT id, category, NAME, price, stock,FIRST_VALUE(price) OVER w AS
first_price
FROM goods WINDOW w AS (PARTITION BY category_id ORDER BY price);
#LAST_VALUE(expr)函数
/*
LAST_VALUE(expr)函数返回最后一个expr的值。
举例：按照价格排序，查询最后一个商品的价格信息。
*/
SELECT id, category, NAME, price, stock,LAST_VALUE(price) OVER w AS last_price
FROM goods WINDOW w AS (PARTITION BY category_id ORDER BY price );
#其他函数
#NTH_VALUE(expr,n)函数
/*
NTH_VALUE(expr,n)函数返回第n个expr的值。
举例：查询goods数据表中排名第2和第3的价格信息。
*/
SELECT id, category, NAME, price,NTH_VALUE(price,2) OVER w AS second_price,
NTH_VALUE(price,3) OVER w AS third_price
FROM goods WINDOW w AS (PARTITION BY category_id ORDER BY price);
#NTILE(n)函数
#NTILE(n)函数将分区中的有序数据分为n个桶，记录桶编号。
SELECT NTILE(3) OVER w AS nt,id, category, NAME, price
FROM goods WINDOW w AS (PARTITION BY category_id ORDER BY price);
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