SQL入門-進階教程

小張過河V發表於2020-09-27

前言

接上一次課程《SQL入門-基礎教程》，本次課程是對上一次課程的深入，主要包括分組、聚合、連線、分析&視窗函式等方面，目的是通過講解常用的語法和函式，爭取用最短的時間對SQL有個整體的認識，每一塊內容的詳細講解會在後面出一個系列的文章進行詳細的詳解，敬請關注…

好了，我們開始吧

00、預備知識

先講幾個資料倉儲中的概念，目的是將口徑拉齊，大家有個初步的瞭解即可。

1、事實表

發生在現實世界中的操作型事件，其所產生的可度量數值，比如，門店交易資料、倉庫出庫量資料、供應鏈進銷存資料等

2、維度表

維度表就是你觀察該事物的角度（維度)，比如商品資訊表、門店資訊表、物流商品表等

3、大寬表

指業務主題相關的指標、維度、屬性關聯在一起的一張表，一般是由N張事件表和N張維度表關聯生成

一、資料準備

1、執行環境

本次課程的執行環境為開源的Hue系統：HUE連結
使用者名稱：demo 密碼：demo
PS：如果有公司的Hive SQL環境也可以，本次課程中的SQL都可以在Hive下面執行

2、資料表準備

本次使用with函式來代替資料表，在學習過程中with函式中的資料不需要改變，只需要在下面寫自己的邏輯即可。

with dw_wms_outbound_info_v100 as(
  select '10700001' as order_no, '0011' as shop_no, '5501' as product_code, '2020-07-31' as order_date, 10 as payment_amount, '20200901' as dt union all
  select '10700001' as order_no, '0011' as shop_no, '5502' as product_code, '2020-08-01' as order_date, 90 as payment_amount, '20200901' as dt union all
  select '10700002' as order_no, '0011' as shop_no, '5501' as product_code, '2020-08-02' as order_date, 20 as payment_amount, '20200901' as dt union all
  select '10700002' as order_no, '0011' as shop_no, '5503' as product_code, '2020-08-02' as order_date, 30 as payment_amount, '20200901' as dt union all
  select '10700003' as order_no, '0011' as shop_no, '5501' as product_code, '2020-08-02' as order_date, 20 as payment_amount, '20200901' as dt union all
  select '10700004' as order_no, '0012' as shop_no, '6601' as product_code, '2020-07-31' as order_date, 40 as payment_amount, '20200901' as dt union all
  select '10700004' as order_no, '0012' as shop_no, '6601' as product_code, '2020-08-01' as order_date, 50 as payment_amount, '20200901' as dt
)
  
, dim_store_info_v100 as (
  select '0011' as shop_no, '北京一店' as shop_name, '20200901' as dt  union all
  select '0012' as shop_no, '北京二店' as shop_name, '20200901' as dt
)
  
, dim_sku_info_v100 as (
  select '5501' as product_code, '可樂350ml' as product_name, '20200901' as dt  union all
  select '5502' as product_code, '雪碧500ml' as product_name, '20200901' as dt  union all
  select '5504' as product_code, '芬達243ml' as product_name, '20200901' as dt  union all
  select '6601' as product_code, '巧克力'    as product_name, '20200901' as dt
)
 
---------------------上面的SQL不要動-----在下面寫自己的SQL--------------------
 
select
  t1.dt,
  t1.order_no,
  t1.shop_no,
  t2.shop_name,
  t1.product_code,
  t3.product_name,
  t1.order_date,
  t1.payment_amount
from dw_wms_outbound_info_v100 t1
left join dim_store_info_v100 t2 on t2.dt = '20200901' and t1.shop_no = t2.shop_no
left join dim_sku_info_v100 t3 on t3.dt = '20200901' and t1.product_code = t3.product_code
where t1.dt = '20200901'

如下圖所示：
在這裡插入圖片描述

3、資料表的關係

如下圖所示：dw_wms_outbound_info_v100表為事實表，dim_store_info_v100和dim_sku_info_v100表為維度表，分別通過shop_no和product_code關聯起來。
在這裡插入圖片描述

二、進階語法

1、 with … as 語法

當我們在寫一些結構相對複雜的SQL語句時，可能某個子查詢在多個層級多個地方存在重複使用的情況，這個時候我們可以使用 with as 語句將其獨立出來，極大提高SQL可讀性，簡化SQL~

樣例：參考本次課程的[資料表準備SQL]

2、分組&聚合

2.0 語法結構

基本語法如下：
在這裡插入圖片描述

2.1 分組(group by)

分組顧名思義，即將原來的一整塊資料按某種維度分成幾小塊，語法關鍵字為 group by，對比excel的透視表功能。
語法：group by col1, col2, …

例如：按門店和商品對資料進行分組

select
    shop_no,
    product_code
from dw_wms_outbound_info_v100
where dt = '20200901'
group by
    shop_no,
    product_code

查詢結果：

shop_no	product_code
0011	5501
0011	5502
0012	6601
0011	5503

2.2 聚合

聚合是在每個分組內進行一些統計，如在分組內的最大值(max)、最小值(min)、平均值(avg)、資料條數(count)等聚合函式

例：統計每個分組內的資料條數？（使用count函式來統計）

select
    shop_no,
    product_code,
    count(*) as record_cnt
from dw_wms_outbound_info_v100
where dt = '20200901'
group by
    shop_no,
    product_code

查詢結果：

shop_no	product_code	record_cnt
0011	5501	3
0011	5502	1
0012	6601	2
0011	5503	1

注：count(*)代表的是統計組內所有的行，詳細使用參考下面的聚合函式使用

2.2 分組過濾(having)

當在gropu by 子句中使用having 子句時，查詢結果中只返回滿足having條件的組

例：統計每筆訂單支付的金額大於50的訂單號？（使用count函式來統計）

select
  order_no,
  sum(payment_amount) as payment_amount
from dw_wms_outbound_info_v100
group by order_no
having payment_amount > 50

查詢結果：

order_no	payment_amount
10700004	90
10700001	100

注：sum(payment_amount)代表的是統計組內所有的行的支付金額加總

3、表連線

3.0 語法結構

在這裡插入圖片描述

需要從多個資料表中讀取資料，這時我們就可以使用SQL語句中的連線（JOIN），基本格式為 join ... on...and，如果不顯示指定join型別，預設為inner，
在兩個或多個資料表中查詢資料。大家可以想象excel中的vlook使用過程。
</br>

PS：
1. 如下圖中的A和B為兩個表名
2. 下面圖來源於網上部落格

3.1 內連線(Inner join)

產生的結果是A和B的交集（相同列裡面的相同值）
語法：[inner] join … on 條件1 and 條件2 and …

如下圖所示：
在這裡插入圖片描述

例：查詢商品表dim_sku_info_v100中所有商品在門店有銷售的記錄

SQL:

select
  t1.dt,
  t1.order_no,
  t1.shop_no,
  t1.product_code,
  t3.product_name,
  t1.order_date,
  t1.payment_amount
from dw_wms_outbound_info_v100 t1
inner join dim_sku_info_v100 t3 on t1.product_code = t3.product_code
 
 
結果：
 
+--------+--------+-------+------------+-------------+------------+----------+--------------+
|dt      |order_no|shop_no|product_code|product_code2|product_name|order_date|payment_amount|
+--------+--------+-------+------------+-------------+------------+----------+--------------+
|20200901|10700001|0011   |5501        |5501         |可樂350ml   |2020-07-31|10            |
|20200901|10700001|0011   |5502        |5502         |雪碧500ml   |2020-08-01|90            |
|20200901|10700002|0011   |5501        |5501         |可樂350ml   |2020-08-02|20            |
|20200901|10700003|0011   |5501        |5501         |可樂350ml   |2020-08-02|20            |
|20200901|10700004|0012   |6601        |6601         |巧克力      |2020-07-31|40            |
|20200901|10700004|0012   |6601        |6601         |巧克力      |2020-08-01|50            |
+--------+--------+-------+------------+-------------+------------+----------+--------------+
 
說明：連線條件為商品編碼，商品5503和5504沒有顯示出來

3.2 外連線(Full Outer Join)

產生的結果是A和B的並集（如果沒有相同的值會用null作為值）
語法：full join … on 條件1 and 條件2 and …

在這裡插入圖片描述

例：查詢商品表dim_sku_info_v100中所有商品在門店的記錄，如果銷售記錄中的商品編碼不在dim_sku_info_v100表中也要顯示

SQL:
 
select
  t1.dt,
  t1.order_no,
  t1.shop_no,
  t1.product_code,
  t3.product_code as product_code2,
  t3.product_name,
  t1.order_date,
  t1.payment_amount
from dw_wms_outbound_info_v100 t1
full join dim_sku_info_v100 t3 on t1.product_code = t3.product_code
 
結果：
 
+--------+--------+-------+------------+-------------+------------+----------+--------------+
|dt      |order_no|shop_no|product_code|product_code2|product_name|order_date|payment_amount|
+--------+--------+-------+------------+-------------+------------+----------+--------------+
|20200901|10700002|0011   |5501        |5501         |可樂350ml   |2020-08-02|20            |
|20200901|10700003|0011   |5501        |5501         |可樂350ml   |2020-08-02|20            |
|20200901|10700001|0011   |5501        |5501         |可樂350ml   |2020-07-31|10            |
|20200901|10700001|0011   |5502        |5502         |雪碧500ml   |2020-08-01|90            |
|20200901|10700004|0012   |6601        |6601         |巧克力      |2020-07-31|40            |
|20200901|10700004|0012   |6601        |6601         |巧克力      |2020-08-01|50            |
|NULL    |NULL    |NULL   |NULL        |5504         |芬達243ml   |NULL      |NULL          |
|20200901|10700002|0011   |5503        |NULL         |NULL        |2020-08-02|30            |
+--------+--------+-------+------------+-------------+------------+----------+--------------+

3.3 左連線(Left outer join)

產生表A的完全集，根據on條件，B表中匹配的則有值（沒有匹配的則以null值取代）
語法：left join … on 條件1 and 條件2 and …

在這裡插入圖片描述

例：查詢門店所有銷售記錄的商品名稱

SQL:
 
select
  t1.dt,
  t1.order_no,
  t1.shop_no,
  t1.product_code,
  t3.product_code as product_code2,
  t3.product_name,
  t1.order_date,
  t1.payment_amount
from dw_wms_outbound_info_v100 t1
left join dim_sku_info_v100 t3 on t1.product_code = t3.product_code
 
結果：
 
+--------+--------+-------+------------+-------------+------------+----------+--------------+
|dt      |order_no|shop_no|product_code|product_code2|product_name|order_date|payment_amount|
+--------+--------+-------+------------+-------------+------------+----------+--------------+
|20200901|10700001|0011   |5501        |5501         |可樂350ml   |2020-07-31|10            |
|20200901|10700001|0011   |5502        |5502         |雪碧500ml   |2020-08-01|90            |
|20200901|10700002|0011   |5501        |5501         |可樂350ml   |2020-08-02|20            |
|20200901|10700002|0011   |5503        |NULL         |NULL        |2020-08-02|30            |
|20200901|10700003|0011   |5501        |5501         |可樂350ml   |2020-08-02|20            |
|20200901|10700004|0012   |6601        |6601         |巧克力      |2020-07-31|40            |
|20200901|10700004|0012   |6601        |6601         |巧克力      |2020-08-01|50            |
+--------+--------+-------+------------+-------------+------------+----------+--------------+

3.4 右連線(Right Outer Join)

產生表B的完全集，根據on條件，A表中匹配的則有值（沒有匹配的則以null值取代）,與Left Join相反
語法：right join … on 條件1 and 條件2 and …

在這裡插入圖片描述

例：查詢在商品表中所有商品在門店的銷售記錄

SQL:
 
select
  t1.dt,
  t1.order_no,
  t1.shop_no,
  t1.product_code,
  t3.product_code as product_code2,
  t3.product_name,
  t1.order_date,
  t1.payment_amount
from dw_wms_outbound_info_v100 t1
right join dim_sku_info_v100 t3 on t1.product_code = t3.product_code
 
結果：
 
+--------+--------+-------+------------+-------------+------------+----------+--------------+
|dt      |order_no|shop_no|product_code|product_code2|product_name|order_date|payment_amount|
+--------+--------+-------+------------+-------------+------------+----------+--------------+
|20200901|10700003|0011   |5501        |5501         |可樂350ml   |2020-08-02|20            |
|20200901|10700002|0011   |5501        |5501         |可樂350ml   |2020-08-02|20            |
|20200901|10700001|0011   |5501        |5501         |可樂350ml   |2020-07-31|10            |
|20200901|10700001|0011   |5502        |5502         |雪碧500ml   |2020-08-01|90            |
|NULL    |NULL    |NULL   |NULL        |5504         |芬達243ml   |NULL      |NULL          |
|20200901|10700004|0012   |6601        |6601         |巧克力      |2020-08-01|50            |
|20200901|10700004|0012   |6601        |6601         |巧克力      |2020-07-31|40            |
+--------+--------+-------+------------+-------------+------------+----------+--------------+

3.5 笛卡爾積(cross join)

cross對兩個表執行笛卡爾乘積（可以理解為沒有連線條件）。它為左錶行和右錶行的每種可能的組合返回一行，一般出現笛卡爾積時大概率現出了邏輯錯誤，比如連線條件寫錯表名，導致兩個連線時沒有連線條件。除非特殊情況下需要使用笛卡爾積

例：查詢門店所有銷售記錄的商品名稱

ps:注意下圖中的連線條件

SQL:
 
select
  t1.dt,
  t1.order_no,
  t1.shop_no,
  t1.product_code,
  t3.product_code as product_code2,
  t3.product_name,
  t1.order_date,
  t1.payment_amount
from dw_wms_outbound_info_v100 t1
left join dim_sku_info_v100 t3 on t1.product_code = t1.product_code
 
結果：如下
 
+--------+--------+-------+------------+-------------+------------+----------+--------------+
|dt      |order_no|shop_no|product_code|product_code2|product_name|order_date|payment_amount|
+--------+--------+-------+------------+-------------+------------+----------+--------------+
|20200901|10700001|0011   |5501        |5501         |可樂350ml   |2020-07-31|10            |
|20200901|10700001|0011   |5501        |5502         |雪碧500ml   |2020-07-31|10            |
|20200901|10700001|0011   |5501        |5504         |芬達243ml   |2020-07-31|10            |
|20200901|10700001|0011   |5501        |6601         |巧克力      |2020-07-31|10            |
|20200901|10700001|0011   |5502        |5501         |可樂350ml   |2020-08-01|90            |
|20200901|10700001|0011   |5502        |5502         |雪碧500ml   |2020-08-01|90            |
|20200901|10700001|0011   |5502        |5504         |芬達243ml   |2020-08-01|90            |
|20200901|10700001|0011   |5502        |6601         |巧克力      |2020-08-01|90            |
|20200901|10700002|0011   |5501        |5501         |可樂350ml   |2020-08-02|20            |
|20200901|10700002|0011   |5501        |5502         |雪碧500ml   |2020-08-02|20            |
|20200901|10700002|0011   |5501        |5504         |芬達243ml   |2020-08-02|20            |
|20200901|10700002|0011   |5501        |6601         |巧克力      |2020-08-02|20            |
|20200901|10700002|0011   |5503        |5501         |可樂350ml   |2020-08-02|30            |
|20200901|10700002|0011   |5503        |5502         |雪碧500ml   |2020-08-02|30            |
|20200901|10700002|0011   |5503        |5504         |芬達243ml   |2020-08-02|30            |
|20200901|10700002|0011   |5503        |6601         |巧克力      |2020-08-02|30            |
|20200901|10700003|0011   |5501        |5501         |可樂350ml   |2020-08-02|20            |
|20200901|10700003|0011   |5501        |5502         |雪碧500ml   |2020-08-02|20            |
|20200901|10700003|0011   |5501        |5504         |芬達243ml   |2020-08-02|20            |
|20200901|10700003|0011   |5501        |6601         |巧克力      |2020-08-02|20            |
|20200901|10700004|0012   |6601        |5501         |可樂350ml   |2020-07-31|40            |
|20200901|10700004|0012   |6601        |5502         |雪碧500ml   |2020-07-31|40            |
|20200901|10700004|0012   |6601        |5504         |芬達243ml   |2020-07-31|40            |
|20200901|10700004|0012   |6601        |6601         |巧克力      |2020-07-31|40            |
|20200901|10700004|0012   |6601        |5501         |可樂350ml   |2020-08-01|50            |
|20200901|10700004|0012   |6601        |5502         |雪碧500ml   |2020-08-01|50            |
|20200901|10700004|0012   |6601        |5504         |芬達243ml   |2020-08-01|50            |
|20200901|10700004|0012   |6601        |6601         |巧克力      |2020-08-01|50            |
+--------+--------+-------+------------+-------------+------------+----------+--------------+
 
 
Time taken: 0.815 seconds, Fetched 28 row(s)

可見，由於出現了笛卡爾積，由於寫錯將條件寫為（t1.product_code = t1.product_code），正確為t1.product_code = t2.product_code，最終資料最從7條變為了28條，資料量增長了4倍，

問題：如果A表有10萬條資料，B表有10萬條資料，如果發生邏輯錯誤，產生了笛卡爾積的操作，結果會是多少條資料？
10萬？ 100萬？ 10億？ 100億？

4、連線後聚合

實際操作中常會對多表連線後的結果執行聚合操作，語法格式為：
在這裡插入圖片描述
SQL的執行順序為 from -> join -> group -> having -> select -> order -> limit 在後面的課程中會專門對SQL的執行順序進行詳細講解

例：查詢門店每天商品的銷售額，按日期+商品名稱聚合

SQL:

select
  t1.order_date,
  t3.product_name,
  sum(t1.payment_amount) as payment_amount
from dw_wms_outbound_info_v100 t1
left join dim_sku_info_v100 t3 on t1.product_code = t3.product_code
group by
  t1.order_date,
  t3.product_name
 
結果：如下
 
+----------+------------+--------------+
|order_date|product_name|payment_amount|
+----------+------------+--------------+
|2020-07-31|可樂350ml   |10            |
|2020-08-02|NULL        |30            |
|2020-08-01|雪碧500ml   |90            |
|2020-08-02|可樂350ml   |40            |
|2020-08-01|巧克力      |50            |
|2020-07-31|巧克力      |40            |
+----------+------------+--------------+

5、union[distinct | all]

UNION將多個SELECT語句的結果集合併為一個獨立的結果集，distinct會對結果進行去重（預設為 distinct，去重操作會影響效能）,all保留所有

語法：
select_statement UNION [ALL | DISTINCT] 
select_statement UNION [ALL | DISTINCT] 
...
select_statement

例：使用union 來對0011門店和0012門店的有銷售商品進行彙總，

SQL:
 
select
  shop_no,product_code
from dw_wms_outbound_info_v100
where shop_no = '0011'
 
 
union
 
select
  shop_no,product_code
from dw_wms_outbound_info_v100
where shop_no = '0012'
 
結果：去重
 
+-------+------------+
|shop_no|product_code|
+-------+------------+
|0011   |5501        |
|0011   |5502        |
|0012   |6601        |
|0011   |5503        |
+-------+------------+

四、進階函式

1、聚合函式

函式名	用途
count([DISTINCT] col)	統計行數
avg(col)	平均數
min(col)	最小值
max(col)	最大值
sum(col)	求和
percentile_approx(col, p)	返回組中列的精確第p個百分點 p必須介於0和1之間
stddev(col)	標準差
collect_set(col)	欄位去重後的集合
collect_list(col)	欄位不去重的集合
corr(col1, col2)	返回兩列的皮爾遜相關係數

解釋：

count(統計行數)：對分組內的資料條數進行統計，有如下幾種方式：
count(1)：該種方式會對分組所有的記錄進行統計，沒有進行任何的過濾（1可以換成任何整數，習慣使用1）
count(*)：該方式同count(1)
count(col)：根據某列進行統計，如果某一條記錄該欄位為null，那麼此記錄不會統計
count(distinct col)：與count(col)相比，該方法會對欄位的值進行去重後統計

例如，首先查詢門店所有銷售記錄的商品名稱，並根據商品名稱欄位進行統計資料條數

SQL:
 
select
  order_date as od,
  count(1) as count1,
  count(*) as count2,
  count(product_code) as count3,
  count(product_name) as count4,
  round(avg(payment_amount),1) as avg,
  min(payment_amount) as min,
  max(payment_amount) as max,
  sum(payment_amount) as sum,
  round(percentile_approx(payment_amount, 0.5),1) as percentile,
  round(stddev(payment_amount),1) as stddev,
  collect_set(shop_no) as set,
  collect_list(shop_no) as list
from (
  select
    t1.dt,
    t1.order_no,
    t1.shop_no,
    t1.product_code,
    t3.product_code as product_code2,
    t3.product_name,
    t1.order_date,
    t1.payment_amount
  from dw_wms_outbound_info_v100 t1
  left join dim_sku_info_v100 t3 on t1.product_code = t3.product_code
) t1
group by order_date
 
查詢結果:
 
+----------+------+------+------+------+----+---+---+---+----------+------+---------------+----------------------+
|od        |count1|count2|count3|count4|avg |min|max|sum|percentile|stddev|set            |list                  |
+----------+------+------+------+------+----+---+---+---+----------+------+---------------+----------------------+
|2020-08-02|3     |3     |3     |2     |23.3|20 |30 |70 |20        |5.8   |["0011"]       |["0011","0011","0011"]|
|2020-08-01|2     |2     |2     |2     |70.0|50 |90 |140|50        |28.3  |["0011","0012"]|["0011","0012"]       |
|2020-07-31|2     |2     |2     |2     |25.0|10 |40 |50 |10        |21.2  |["0011","0012"]|["0012","0011"]       |
+----------+------+------+------+------+----+---+---+---+----------+------+---------------+----------------------+

2、分析&視窗函式

常用的函式：

函式名	用途
row_number() OVER([partition_by_clause] order_by_clause)	分組排序，同一組內相同值會給不同序號
rank() OVER([partition_by_clause] order_by_clause)	組內從1開始按order by排序後，將相同的值輸出為相同的值，其它值不變（好亂，看下面樣例吧）
dense_rank() OVER([partition_by_clause] order_by_clause)	返回從1開始的遞增整數序列。輸出序列為ORDER BY表示式的重複值生成重複整數
lag(expr [, offset] [, default]) OVER ([partition_by_clause] order_by_clause)	該函式使用前一行的列值返回表示式的值。您可以指定一個整數偏移量，它指定一個行位置，位於當前行之前的某些行數
lead	與lag對應，記住一個就可以

2.1 row_number | rank | dense_rank

例如 ：根據支付金額排序

SQL:
 
select
    order_date,
    order_no,
    shop_no,
    payment_amount,
    row_number() over(order by payment_amount) as row_number,
    rank() over(order by payment_amount) as rank,
    dense_rank() over(order by payment_amount) as dense_rank
from dw_wms_outbound_info_v100
 
 
結果：
+----------+--------+-------+--------------+----------+----+----------+
|order_date|order_no|shop_no|payment_amount|row_number|rank|dense_rank|
+----------+--------+-------+--------------+----------+----+----------+
|2020-07-31|10700001|0011   |10            |1         |1   |1         |
|2020-08-02|10700002|0011   |20            |2         |2   |2         |
|2020-08-02|10700003|0011   |20            |3         |2   |2         |
|2020-08-02|10700002|0011   |30            |4         |4   |3         |
|2020-07-31|10700004|0012   |40            |5         |5   |4         |
|2020-08-01|10700004|0012   |50            |6         |6   |5         |
|2020-08-01|10700001|0011   |90            |7         |7   |6         |
+----------+--------+-------+--------------+----------+----+----------+

2.2 lag | lead

例如 ：計算每家門店每天相對於昨天的銷售額的變化？


SQL:
 
select
  t1.order_date,
  t1.shop_no,
  t1.payment_amount,
  -- 以下為昨天的銷售額
  lag(payment_amount) over(partition by shop_no order by t1.order_date) as yest_payment_amount,
  lag(payment_amount,1, 0) over(partition by shop_no order by t1.order_date) as yest_payment_amount2,
  lag(payment_amount,2, 0) over(partition by shop_no order by t1.order_date) as yest_payment_amount3,
  lead(payment_amount,1, 0) over(partition by shop_no order by t1.order_date) as yest_payment_amount4,
  lag(payment_amount,1, 0) over(partition by shop_no order by t1.order_date desc) as yest_payment_amount5
from (
  select
    order_date,
    shop_no,
    sum(payment_amount) as payment_amount --當天銷售額
  from dw_wms_outbound_info_v100
  group by
    order_date,
    shop_no
) t1
 
 
結果：
+----------+-------+--------------+-------------------+--------------------+--------------------+--------------------+--------------------+
|order_date|shop_no|payment_amount|yest_payment_amount|yest_payment_amount2|yest_payment_amount3|yest_payment_amount4|yest_payment_amount5|
+----------+-------+--------------+-------------------+--------------------+--------------------+--------------------+--------------------+
|2020-07-31|0012   |40            |NULL               |0                   |0                   |50                  |50                  |
|2020-08-01|0012   |50            |40                 |40                  |0                   |0                   |0                   |
|2020-07-31|0011   |10            |NULL               |0                   |0                   |90                  |90                  |
|2020-08-01|0011   |90            |10                 |10                  |0                   |70                  |70                  |
|2020-08-02|0011   |70            |90                 |90                  |10                  |0                   |0                   |
+----------+-------+--------------+-------------------+--------------------+--------------------+--------------------+--------------------+

2.3 over

與over 配置使用的聚合函式：

COUNT
SUM
MIN
MAX
AVG

使用樣例：

SQL:
 
select
  t1.order_date,
  t1.order_no,
  t1.shop_no,
  t1.product_code,
  t1.payment_amount,
  --統計每天門店每天的單量
  count(order_no) over(partition by order_date, shop_no) as shop_order_cnt,
  --統計每天門店每天的銷售額
  sum(payment_amount) over(partition by order_date, shop_no) as payment_amount_sum,
  --統計每天所有門店單品最高支付金額
  max(payment_amount) over(partition by order_date) as payment_amount_max
from dw_wms_outbound_info_v100 t1
 
結果：
 
+----------+--------+-------+------------+--------------+--------------+------------------+------------------+
|order_date|order_no|shop_no|product_code|payment_amount|shop_order_cnt|payment_amount_sum|payment_amount_max|
+----------+--------+-------+------------+--------------+--------------+------------------+------------------+
|2020-08-02|10700002|0011   |5503        |30            |3             |70                |30                |
|2020-08-02|10700002|0011   |5501        |20            |3             |70                |30                |
|2020-08-02|10700003|0011   |5501        |20            |3             |70                |30                |
|2020-08-01|10700004|0012   |6601        |50            |1             |50                |90                |
|2020-08-01|10700001|0011   |5502        |90            |1             |90                |90                |
|2020-07-31|10700004|0012   |6601        |40            |1             |40                |40                |
|2020-07-31|10700001|0011   |5501        |10            |1             |10                |40                |
+----------+--------+-------+------------+--------------+--------------+------------------+------------------+

3、複雜型別

函式名	說明	demo	結果
map(key1, value1, …)	根據給定的key-value生成map	select map(‘a’, ‘001’, ‘b’,‘002’) as res select map(‘a’, ‘001’, ‘b’,‘002’, ‘a’, ‘003’)[‘a’] as res	{“a”:“001”,“b”:“002”} 003
named_struct(name1, val1, name2, val2, …)	建立struct結構	select named_struct(‘expression’,‘1*1’,‘unit’,10).expression	1*1
array(val1, val2, …)	根據給定的元素生成array	生成陣列：select array(‘a’, ‘b’) as res 查詢元素：select array(‘a’, ‘b’)[0] as res	[“a”,“b”] a

4、集合函式

函式名	說明	demo	結果
array_contains	Returns TRUE if the array contains value.	select array_contains(array(‘a’, ‘b’), ‘b’) as res select array_contains(array(‘a’, ‘b’), ‘c’) as res	true false
size	返回陣列或map元素的個數	select size(array(‘a’, ‘b’)) as res select size(map(‘a’, ‘b’)) as res	2 1
sort_array	對陣列進行排序	select sort_array( array(‘b’, ‘c’,‘a’ ) ) as res	[“a”,“b”,“c”]

參考文件

1、Windowing and Analytics Functions
2、HUE–hive函式大全

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SQL入門-進階教程

文章目錄

前言

00、預備知識

1、事實表

2、維度表

3、大寬表

一、資料準備

1、執行環境

2、資料表準備

3、資料表的關係

二、進階語法

1、 with … as 語法

2、分組&聚合

2.0 語法結構

2.1 分組(group by)

2.2 聚合

2.2 分組過濾(having)

3、表連線

3.0 語法結構

3.1 內連線(Inner join)

3.2 外連線(Full Outer Join)

3.3 左連線(Left outer join)

3.4 右連線(Right Outer Join)

3.5 笛卡爾積(cross join)

4、連線後聚合

5、union[distinct | all]

四、進階函式

1、聚合函式

2、分析&視窗函式

2.1 row_number | rank | dense_rank

2.2 lag | lead

2.3 over

3、複雜型別

4、集合函式

參考文件

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