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Update ch05: SQL高级处理.md

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2021-08-13 22:40:21 +08:00
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ch05: SQL高级处理.md
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@@ -35,7 +35,7 @@ SELECT product_name
得到的结果是:
![图片](https://github.com/datawhalechina/team-learning-sql/blob/main/img/ch05/ch0501.png)
![图片](./img/ch05/ch0501.png)
我们先忽略生成的新列 - [ranking]， 看下原始数据在PARTITION BY 和 ORDER BY 关键字的作用下发生了什么变化。
@@ -46,7 +46,7 @@ ORDER BY 能够指定按照哪一列、何种顺序进行排序。为了按照
升序进行排序。本例中就省略了上述关键字 。
![图片](https://github.com/datawhalechina/team-learning-sql/blob/main/img/ch05/ch0502.png)
![图片](./img/ch05/ch0502.png)
@@ -90,7 +90,7 @@ SELECT product_name
FROM product  
```
![图片](https://github.com/datawhalechina/team-learning-sql/blob/main/img/ch05/ch0503.png)
![图片](./img/ch05/ch0503.png)
## 5.2.2 聚合函数在窗口函数上的使用
@@ -108,9 +108,9 @@ SELECT product_id
FROM product;  
```
![图片](https://github.com/datawhalechina/team-learning-sql/blob/main/img/ch05/ch0504.png)
![图片](./img/ch05/ch0504.png)
![图片](https://github.com/datawhalechina/team-learning-sql/blob/main/img/ch05/ch0505.png)
![图片](./img/ch05/ch0505.png)
可以看出，聚合函数结果是，按我们指定的排序，这里是product_id，**当前所在行及之前所有的行**的合计或均值。即累计到当前行的聚合。
@@ -154,11 +154,11 @@ SELECT product_id
ROWS 2 PRECEDING：
![图片](https://github.com/datawhalechina/team-learning-sql/blob/main/img/ch05/ch0506.png)
![图片](./img/ch05/ch0506.png)
ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING：
![图片](https://github.com/datawhalechina/team-learning-sql/blob/main/img/ch05/ch0507.png)
![图片](./img/ch05/ch0507.png)
## 5.3.1 窗口函数适用范围和注意事项
@@ -179,15 +179,15 @@ SELECT product_type
```
得到的结果为：
![图片](https://github.com/datawhalechina/team-learning-sql/blob/main/img/ch05/ch0508.png)
![图片](./img/ch05/ch0508.png)
![图片](https://github.com/datawhalechina/team-learning-sql/blob/main/img/ch05/ch0509.png)
![图片](./img/ch05/ch0509.png)
这里ROLLUP 对product_type, regist_date两列进行合计汇总。结果实际上有三层聚合，如下图 模块3是常规的 GROUP BY 的结果，需要注意的是衣服 有个注册日期为空的，这是本来数据就存在日期为空的，不是对衣服类别的合计； 模块2和1是 ROLLUP 带来的合计，模块2是对产品种类的合计，模块1是对全部数据的总计。
ROLLUP 可以对多列进行汇总求小计和合计。
# ![图片](https://github.com/datawhalechina/team-learning-sql/blob/main/img/ch05/ch0510.png)
# ![图片](./img/ch05/ch0510.png)
# 练习题