SQL窗口函数之聚合窗口函数的使用(count,max,min,sum)

来源：脚本之家时间：2022-04-21 12:56:34

案例分析

案例使用的示例表

下面的查询中会用到两个表，其中sales_monthly表中存储了不同产品（苹果、香蕉、橘子）每个月的销售额情况。以下是该表中的部分数据：

transfer_log表中记录了一些银行账户的交易日志。以下是该表中的部分数据：

该表中的字段分别表示交易日志编号、交易时间、交易发起账户、交易接收账户、交易类型以及交易金额。这两个表的初始化脚本可以在文章底部获取。

1.移动平均值

AVG函数在作为窗口函数使用时，可以用于计算随着当前行移动的窗口内数据行的平均值。

例如，以下语句用于查找不同产品每个月以及截至当前月最近3个月的平均销售额

SELECT m.product,m.ym,m.amount,
AVG(m.amount) OVER(
 PARTITION BY m.product
 ORDER BY m.ym
 ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
)
FROM sales_monthly m
ORDER BY m.product,m.ym

AVG函数OVER子句中的PARTITION BY选项表示按照产品进行分区。

ORDER BY选项表示按照月份进行排序；ROWS BETWEEN 2 PRECEDING AND CURRENT ROW表示窗口从当前行的前2行开始，直到当前行结束。该查询返回的结果如下：

对于“橘子”：

第一个月的分析窗口只有1行数据，因此平均销售额为“10154”。

第二个月的分析窗口为第1行和第2行数据，因此平均销售额为“10168.5”（（10154+10183）/2）。

第三个月的分析窗口为第1行到第3行数据，因此平均销售额为“10194”（（10154+10183+10245）/3）。

依此类推，直到计算完“橘子”所有月份的平均销售额，然后开始计算其他产品的平均销售额。

2.累计求和（ROW）

SUM函数作为窗口函数时，可以用于统计指定窗口内的累计值。

例如，以下语句用于查找不同产品截至当前月份的累计销售额：

SELECT m.product,m.ym,m.amount,
SUM(m.amount) OVER(
 PARTITION BY m.product
 ORDER BY m.ym
 ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
)
FROM sales_monthly m
ORDER BY m.product,m.ym

SUM函数OVER子句中的PARTITION BY选项表示按照产品进行分区。

ORDER BY选项表示按照月份进行排序。

ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW表示窗口从当前分区第1行开始，直到当前行结束。

该查询返回的结果如下：

对于“橘子”：

第一个月的分析窗口只有1行数据，因此累计销售额为“10154”。

第二个月的分析窗口为第1行和第2行数据，因此累计销售额为“20337”（10154+10183）。

第三个月的分析窗口为第1行到第3行数据，因此累计销售额为“30582”（10154+10183+10245）。

依此类推，直到计算完“橘子”所有月份的累计销售额，然后开始计算其他产品的累计销售额。

提示：对于聚合窗口函数，如果我们没有指定ORDER BY选项，默认的窗口大小就是整个分区。

如果我们指定了ORDER BY选项，默认的窗口大小就是分区的第一行到当前行。

因此，以上示例语句中的ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW选项可以省略。省略后的语句：

执行结果与上面相同。

如果去掉ORDER BY选项，查询的窗口大小就是整个分区，如下图所示：

这时，合计值就变成了分区内所有记录的合计。

3.累计求和（RANGE）

除使用ROWS关键字以数据行为单位指定窗口的偏移量外，我们也可以使用RANGE关键字以数值为单位指定窗口的偏移量。

例如，以下语句用于查找短期之内（5天）累计转账超过100万元的账户：

SELECT log_ts,from_user,total_amount FROM (
    SELECT to_char(t.log_ts,"yyyy-mm-dd hh24:mi:ss") log_ts,t.from_user,t.amount,
      SUM(t.amount) OVER(
        PARTITION BY t.from_user
        ORDER BY t.log_ts
        RANGE INTERVAL "5" DAY PRECEDING
      ) AS total_amount
    FROM transfer_log t 
    WHERE t.type = "转账"
)
WHERE total_amount >= 1000000;

其中，SUM函数OVER子句中的RANGE选项指定了一个5天之内的时间窗口。该查询返回的结果如下：

截至2021年1月10日7时46分02秒，账户“62221234567890”在最近5天之内累计转账105万元。

示例表和脚本

-- 创建销量表sales_monthly
-- product表示产品名称，ym表示年月，amount表示销售金额（元）
CREATE TABLE sales_monthly(product VARCHAR(20), ym VARCHAR(10), amount NUMERIC(10, 2));
 
-- 生成测试数据
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201801",10159.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201802",10211.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201803",10247.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201804",10376.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201805",10400.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201806",10565.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201807",10613.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201808",10696.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201809",10751.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201810",10842.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201811",10900.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201812",10972.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201901",11155.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201902",11202.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201903",11260.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201904",11341.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201905",11459.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("苹果","201906",11560.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201801",10138.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201802",10194.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201803",10328.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201804",10322.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201805",10481.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201806",10502.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201807",10589.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201808",10681.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201809",10798.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201810",10829.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201811",10913.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201812",11056.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201901",11161.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201902",11173.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201903",11288.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201904",11408.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201905",11469.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("香蕉","201906",11528.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201801",10154.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201802",10183.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201803",10245.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201804",10325.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201805",10465.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201806",10505.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201807",10578.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201808",10680.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201809",10788.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201810",10838.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201811",10942.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201812",10988.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201901",11099.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201902",11181.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201903",11302.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201904",11327.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201905",11423.00);
INSERT INTO sales_monthly (product,ym,amount) VALUES ("桔子","201906",11524.00);
 
 
-- 创建银行交易日志表transfer_log
-- Oracle、MySQL、PostgreSQL以及SQLite
CREATE TABLE transfer_log
( log_id    INTEGER NOT NULL PRIMARY KEY, -- 交易日志编号
  log_ts    TIMESTAMP NOT NULL, -- 交易时间
  from_user VARCHAR(50) NOT NULL, -- 交易发起账号
  to_user   VARCHAR(50), -- 交易接收账号
  type      VARCHAR(10) NOT NULL, -- 交易类型
  amount    NUMERIC(10) NOT NULL -- 交易金额（元）
);
 
 
-- 生成测试数据
-- Oracle 需要执行以下ALTER语句
ALTER SESSION SET nls_timestamp_format = "YYYY-MM-DD HH24:MI:SS";
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (1,"2021-01-02 10:31:40","62221234567890",NULL,"存款",50000);
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (2,"2021-01-02 10:32:15","62221234567890",NULL,"存款",100000);
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (3,"2021-01-03 08:14:29","62221234567890","62226666666666","转账",200000);
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (4,"2021-01-05 13:55:38","62221234567890","62226666666666","转账",150000);
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (5,"2021-01-07 20:00:31","62221234567890","62227777777777","转账",300000);
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (6,"2021-01-09 17:28:07","62221234567890","62227777777777","转账",500000);
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (7,"2021-01-10 07:46:02","62221234567890","62227777777777","转账",100000);
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (8,"2021-01-11 09:36:53","62221234567890",NULL,"存款",40000);
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (9,"2021-01-12 07:10:01","62221234567890","62228888888881","转账",10000);
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (10,"2021-01-12 07:11:12","62221234567890","62228888888882","转账",8000);
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (11,"2021-01-12 07:12:36","62221234567890","62228888888883","转账",5000);
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (12,"2021-01-12 07:13:55","62221234567890","62228888888884","转账",6000);
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (13,"2021-01-12 07:14:24","62221234567890","62228888888885","转账",7000);
INSERT INTO transfer_log (log_id,log_ts,from_user,to_user,type,amount) VALUES (14,"2021-01-21 12:11:16","62221234567890","62228888888885","转账",70000);

到此这篇关于SQL窗口函数之聚合窗口函数的使用的文章就介绍到这了,更多相关SQL 聚合窗口函数内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家！

关键词：窗口函数窗口大小移动平均值测试数据依此类推

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