作为运维工程师，熟练掌握聚合函数是进行数据分析和报表生成的基础技能。本文将系统讲解MySQL聚合函数的使用方法，包含大量实用示例和运维场景中的应用技巧。
1 常用聚合函数详解
1.1 基础聚合函数
1.1.1 SUM()：求和计算
# 计算所有员工的工资总和
select sum(salary) from emp;
# 计算销售部门的加班费总额
select sum(overtime_pay) from test where department = 'sales';
1.1.2 AVG()：平均值
# 计算公司平均薪资
select avg(salary) from epm;
# 计算技术部平均工龄（保留2位小数）
select round(avg(years_of_service), 2) from epm where department = 'technology';
1.1.3 MAX()/MIN()：极值查询
# 查询最高和最低工资
select max(salary), min(salary) from epm;
# 找出最近和最早上架的商品
select max(create_time), min(create_time) from products;
1.1.4 COUNT()：计数统计
# 统计员工总数
select count(*) from epm;
# 统计有邮箱的员工数量（不统计NULL）
select count(email) from epm;
# 统计不同部门的数量
select count(distinct department) from epm;
1.2 运维场景应用案例
示例：监控表空间使用
# 统计各数据库占用空间（MB）
select table_schema, round(sum(data_length)/1024/1024, 2) as size_mb
from information_schema.tables
group by table_schema;
2 聚合函数与GROUP BY分组
2.1 基础分组统计
# 按部门统计员工
select department, count(*) as employee_count,avg(salary) as avg_salary
from epm
group by department;# 商品分类统计
select category,count(*) as product_count,min(price) as min_price,max(price) as max_price,avg(price) as avg_price
from products
group by category;
2.2 多列分组
# 按部门和职位统计
select department, job_title,count(*) as headcount,sum(salary) as total_salary
from epm
group by department, job_title;
2.3. 运维实战案例
# 分析慢查询日志，按查询类型统计平均执行时间
select query_type,count(*) as query_count,avg(execution_time) as avg_time_ms
from slow_query_log
where log_date = curdate()
group by query_type;
3 HAVING子句过滤聚合结果
3.1 基础用法
# 筛选销售额大于1000的部门
select department, sum(sales) as total_sales
from sales_records
group by department
having sum(sales) > 1000;
3.2 复杂过滤条件
# 筛选平均工资高于公司平均的部门
select department,avg(salary) as dept_avg_salary
from epm
group by department
having avg(salary) > (select avg(salary) from epm);
3.3 运维监控应用
# 找出表空间超过1GB的数据库
select table_schema,round(sum(data_length)/1024/1024, 2) as size_mb
from information_schema.tables
group by table_schema
having size_mb > 1024
order by size_mb desc;
4 高级技巧与性能优化
4.1 WITH ROLLUP小计功能
select department,job_title,count(*) as headcount
from epm
group by department, job_title with rollup;
4.2 聚合函数性能优化
alter table epm add index idx_department (department);
# 错误写法
select department, avg(salary)
from epm
where avg(salary) > 5000 # 这里会报错
group by department;# 正确写法
select department, avg(salary)
from epm
group by department
having avg(salary) > 5000;
4.3 聚合函数执行计划分析
explain
select department, count(*)
from epm
group by department;
5 常见问题解决方案
5.1 问题1：GROUP BY与SELECT列不一致
# 错误示例
select employee_name, department, count(*)
from epm
group by department;# 正确写法
select department, count(*)
from epm
group by department;# 或者包含所有非聚合列
select employee_name, department, count(*) over (partition by department)
from epm;
5.2 问题2：NULL值处理
# COUNT(列名)会忽略NULL，COUNT(*)不会
select count(commission), count(*)
from epm
where department = 'sales';# 使用IFNULL处理
select department, avg(ifnull(commission, 0))
from epm
group by department;
6 总结
希望这篇指南能帮助您更好了解聚合函数以及更好地在MySQL运维工作中运用聚合函数！
