306 lines
7.0 KiB
Markdown
306 lines
7.0 KiB
Markdown
# 窗口函数速查
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## 基本语法
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```sql
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函数名() OVER (
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PARTITION BY 分组字段 -- 可选：分组
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ORDER BY 排序字段 -- 可选：排序
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ROWS/RANGE 窗口范围 -- 可选：窗口范围
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)
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```
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---
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## 排序函数
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| 函数 | 说明 | 特点 | 适用场景 |
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|------|------|------|----------|
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| ROW_NUMBER() | 连续排名 | 不跳号，相同值不同排名 | 每组取前N条、去重 |
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| RANK() | 排名 | 跳号，相同值相同排名 | 成绩排名 |
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| DENSE_RANK() | 紧密排名 | 不跳号，相同值相同排名 | 连续名次 |
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| NTILE(n) | 分桶 | 分成n组 | 数据分片、抽样 |
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### ROW_NUMBER 示例
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```sql
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-- 每个部门薪资最高的员工（取第一名）
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SELECT *
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FROM (
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SELECT
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name,
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department,
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salary,
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ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rn
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FROM employees
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) t
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WHERE rn = 1
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-- 去重：每个用户取最新订单
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SELECT *
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FROM (
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SELECT
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*,
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ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY created_at DESC) AS rn
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FROM orders
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) t
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WHERE rn = 1
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```
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### RANK 与 DENSE_RANK 示例
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```sql
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-- 成绩排名（跳号）
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SELECT
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name,
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score,
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RANK() OVER (ORDER BY score DESC) AS rank, -- 1,2,2,4,5...
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DENSE_RANK() OVER (ORDER BY score DESC) AS dense -- 1,2,2,3,4...
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FROM students
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```
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### NTILE 示例
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```sql
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-- 将用户分成4组（业绩分位）
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SELECT
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name,
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sales,
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NTILE(4) OVER (ORDER BY sales DESC) AS quartile -- 1(最高)到4(最低)
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FROM sales_data
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```
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---
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## 聚合函数（窗口内）
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| 函数 | 说明 |
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|------|------|
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| SUM(col) OVER | 累计求和 |
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| AVG(col) OVER | 累计平均 |
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| COUNT(col) OVER | 窗口内计数 |
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| MAX(col) OVER | 窗口内最大值 |
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| MIN(col) OVER | 窗口内最小值 |
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### 累计求和示例
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```sql
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-- 累计销售额（从开始到当前）
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SELECT
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date,
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amount,
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SUM(amount) OVER (ORDER BY date) AS cumulative_amount
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FROM daily_sales
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-- 分组累计（每个部门累计）
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SELECT
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date,
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department,
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amount,
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SUM(amount) OVER (PARTITION BY department ORDER BY date) AS dept_cumulative
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FROM sales_data
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```
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### 移动平均示例
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```sql
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-- 7天移动平均
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SELECT
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date,
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amount,
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AVG(amount) OVER (
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ORDER BY date
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ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
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) AS moving_avg_7d
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FROM daily_sales
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```
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---
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## 偏移函数
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| 函数 | 说明 | 适用场景 |
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|------|------|----------|
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| LAG(col, n) | 取前n行的值 | 环比、查看历史值 |
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| LAG(col, n, default) | 取前n行，无值返回default | 防止NULL |
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| LEAD(col, n) | 取后n行的值 | 查看未来值 |
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| LEAD(col, n, default) | 取后n行，无值返回default | 防止NULL |
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| FIRST_VALUE(col) | 窗口第一个值 | 组内首个值 |
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| LAST_VALUE(col) | 窗口最后一个值 | 组内末尾值 |
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### LAG 示例（环比计算）
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```sql
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-- 日环比增长
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SELECT
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date,
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amount,
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LAG(amount, 1) OVER (ORDER BY date) AS prev_day,
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amount - LAG(amount, 1) OVER (ORDER BY date) AS daily_growth,
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ROUND((amount - LAG(amount, 1) OVER (ORDER BY date))
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/ LAG(amount, 1) OVER (ORDER BY date) * 100, 2) AS growth_rate_pct
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FROM daily_sales
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-- 月度同比（取去年同期）
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SELECT
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month,
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revenue,
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LAG(revenue, 12) OVER (ORDER BY month) AS prev_year_revenue,
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ROUND((revenue - LAG(revenue, 12) OVER (ORDER BY month))
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/ LAG(revenue, 12) OVER (ORDER BY month) * 100, 2) AS yoy_growth_pct
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FROM monthly_revenue
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```
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### LEAD 示例
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```sql
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-- 查看下一行数据
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SELECT
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date,
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amount,
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LEAD(amount, 1) OVER (ORDER BY date) AS next_day_amount
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FROM daily_sales
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```
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### FIRST_VALUE / LAST_VALUE 示例
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```sql
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-- 每个部门薪资最高和最低的人
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SELECT
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name,
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department,
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salary,
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FIRST_VALUE(name) OVER (PARTITION BY department ORDER BY salary DESC) AS highest_paid,
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LAST_VALUE(name) OVER (
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PARTITION BY department
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ORDER BY salary DESC
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ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
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) AS lowest_paid
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FROM employees
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```
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**注意**：LAST_VALUE 默认窗口是 `ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW`，需要显式指定全窗口才能取到最后值。
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---
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## 窗口范围定义
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### ROWS（基于行数）
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```sql
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ROWS BETWEEN 3 PRECEDING AND CURRENT ROW -- 前3行到当前行
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ROWS BETWEEN 6 PRECEDING AND 1 FOLLOWING -- 前6行到后1行
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ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW -- 从开始到当前
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ROWS BETWEEN CURRENT ROW AND 3 FOLLOWING -- 当前行到后3行
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ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING -- 全窗口
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ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING -- 当前行及前后各1行（共3行）
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```
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### RANGE（基于值范围）
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```sql
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RANGE BETWEEN 100 PRECEDING AND CURRENT ROW -- 值差100以内
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RANGE BETWEEN INTERVAL '7' DAY PRECEDING AND CURRENT ROW -- 7天内
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```
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**ROWS vs RANGE**：
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- ROWS：固定行数，不受值影响
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- RANGE：根据排序字段的值计算范围
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---
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## 实战案例
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### 案例1：分组取Top N
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```sql
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-- 每个部门薪资前3名
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SELECT *
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FROM (
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SELECT
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name,
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department,
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salary,
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ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rn
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FROM employees
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) t
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WHERE rn <= 3
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```
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### 案例2：连续问题判断
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```sql
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-- 判断是否连续增长（连续3天增长）
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SELECT
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date,
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amount,
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CASE
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WHEN amount > LAG(amount, 1) OVER (ORDER BY date)
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AND LAG(amount, 1) OVER (ORDER BY date) > LAG(amount, 2) OVER (ORDER BY date)
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THEN '连续增长'
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ELSE '非连续增长'
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END AS trend
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FROM daily_sales
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```
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### 案例3：组内占比
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```sql
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-- 每个部门各员工薪资占比
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SELECT
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name,
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department,
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salary,
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ROUND(salary / SUM(salary) OVER (PARTITION BY department) * 100, 2) AS salary_pct
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FROM employees
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```
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### 案例4：累计百分比（帕累托分析）
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```sql
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-- 80/20分析：哪些客户贡献了80%销售额
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SELECT
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customer_id,
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sales_amount,
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ROUND(SUM(sales_amount) OVER (ORDER BY sales_amount DESC)
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/ SUM(sales_amount) OVER () * 100, 2) AS cumulative_pct
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FROM customer_sales
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ORDER BY sales_amount DESC
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```
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### 案例5：缺失值填充
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```sql
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-- 用前一个有效值填充NULL
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SELECT
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date,
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amount,
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LAST_VALUE(amount IGNORE NULLS) OVER (ORDER BY date) AS filled_amount
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FROM sales_data
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```
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---
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## 性能优化建议
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1. **减少 PARTITION BY 分组数量**：分组越多，计算越慢
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2. **合理使用窗口范围**：避免全窗口扫描
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3. **先过滤再窗口**：WHERE 条件前置，减少数据量
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4. **避免嵌套窗口函数**：多次调用会重复计算
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```sql
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-- 推荐：先过滤
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SELECT
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*,
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ROW_NUMBER() OVER (PARTITION BY dept ORDER BY salary DESC) AS rn
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FROM employees
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WHERE hire_date >= '2024-01-01' -- 先过滤，减少数据量
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-- 不推荐：先窗口再过滤（窗口函数在全部数据上执行）
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SELECT *
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FROM (
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SELECT *, ROW_NUMBER() OVER (...) AS rn
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FROM employees
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) t
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WHERE hire_date >= '2024-01-01'
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``` |