/group-by/ Recent content in GROUP BY on 老张开工了 Hugo zh-cn Fri, 07 Feb 2025 10:00:00 +0800 /posts/2025/02/aggregation-and-grouping/ Fri, 07 Feb 2025 10:00:00 +0800 /posts/2025/02/aggregation-and-grouping/ 聚合函数 聚合函数将多行数据汇总为单个结果，是数据分析的核心工具。 -- 创建销售数据表 CREATE TABLE sales ( id SERIAL PRIMARY KEY, product VARCHAR(100), category VARCHAR(50), amount NUMERIC(10,2), quantity INTEGER, sale_date DATE, salesperson VARCHAR(50) ); INSERT INTO sales (product, category, amount, quantity, sale_date, salesperson) VALUES ('笔记本电脑', '电子产品', 5999, 2, '2025-01-01', '小陈'), ('手机', '电子产品', 3999, 5, '2025-01-02', '小陈'), ('耳机', '电子产品', 299, 10, '2025-01-03', '小李'), ('运动鞋', '服饰', 599, 3, '2025-01-05', '小王'), ('T恤', '服饰', 99, 20, '2025-01-06', '小陈'), ('咖啡机', '家居', 1299, 2, '2025-01-07', '小李'), ('台灯', '家居', 199, 8, '2025-01-08', '小王'), ('手机', '电子产品', 3999, 3, '2025-01-10', '小李'), ('笔记本电脑', '电子产品', 5999, 1, '2025-01-12', '小王'), ('运动鞋', '服饰', 599, 5, '2025-01-15', '小陈'), ('耳机', '电子产品', 299, 15, '2025-01-18', '小李'), ('台灯', '家居', 199, 12, '2025-01-20', '小陈'), ('咖啡机', '家居', 1299, 1, '2025-01-22', '小王'), ('T恤', '服饰', 99, 30, '2025-01-25', '小李'), ('手机', '电子产品', 3999, 4, '2025-01-28', '小陈'); 五种基础聚合 SELECT COUNT(*) AS 总记录数, COUNT(DISTINCT product) AS 不同商品数, SUM(amount) AS 总金额, AVG(amount) AS 平均金额, MAX(amount) AS 最大金额, MIN(amount) AS 最小金额 FROM sales; COUNT 的细节差异 -- COUNT(*) 统计行数（包括 NULL） SELECT COUNT(*) FROM sales; -- COUNT(column) 统计非 NULL 值的数量 SELECT COUNT(product) FROM sales; -- COUNT(DISTINCT column) 统计不重复的非 NULL 值数量 SELECT COUNT(DISTINCT product) FROM sales; -- 三者的区别 SELECT COUNT(*) AS 总行数, COUNT(salesperson) AS 非空姓名数, COUNT(DISTINCT salesperson) AS 不同姓名数 FROM sales; GROUP BY 分组聚合 -- 按产品分组 SELECT product, COUNT(*) AS 订单数, SUM(amount) AS 总销售额, SUM(quantity) AS 总销量, ROUND(AVG(amount), 2) AS 平均金额 FROM sales GROUP BY product ORDER BY 总销售额 DESC; 多维度分组 -- 按类别和产品分组（SQL 中，SELECT 的非聚合列必须出现在 GROUP BY 中） SELECT category, product, SUM(amount) AS 销售额, COUNT(*) AS 订单数 FROM sales GROUP BY category, product ORDER BY category, 销售额 DESC; PostgreSQL 允许按 SELECT 中列的序号简化：