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/*
分析函数的作用总结
1.可以减少表或索引的访问次数。
2.实现复杂的行间计算，聚合等，使sql的结构清晰。
3.缺点是很多分析函数要求排序，需要对排序的字段建立索引以达到优化的效果。
章节如下
1.分析函数简单示例
2.排序分析函数(RANK,DENSE_RANK,ROW_NUMBER)
3.分析函数(Top/Bottom N,First/Last,NTILE)
4.分析函数之窗口函数
5.分析函数之报表函数
*/
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--1.分析函数简单示例
--生成测试数据
DROP TABLE ORDERS_TMP;
CREATE TABLE ORDERS_TMP
(
CUST_NBR NUMBER(5) NOT NULL,
REGION_ID NUMBER(5) NOT NULL,
SALESPERSON_ID NUMBER(5) NOT NULL,
YEAR NUMBER(4) NOT NULL,
MONTH NUMBER(2) NOT NULL,
TOT_ORDERS NUMBER(7) NOT NULL,
TOT_SALES NUMBER(11,2) NOT NULL
);
INSERT INTO ORDERS_TMP VALUES(11,7,11,2001,7,2,12204);
INSERT INTO ORDERS_TMP VALUES(4,5,4,2001,10,2,37802);
INSERT INTO ORDERS_TMP VALUES(7,6,7,2001,2,3,3705);
INSERT INTO ORDERS_TMP VALUES(10,6,8,2001,1,2,21691);
INSERT INTO ORDERS_TMP VALUES(10,6,7,2001,2,3,42624);
COMMIT;
SELECT * FROM ORDERS_TMP A;
CUST_NBR REGION_ID SALESPERSON_ID YEAR MONTH TOT_ORDERS TOT_SALES
--------- ---------- -------------- ---------- ---------- ---------- ----------
11 7 11 2001 7 2 12204
4 5 4 2001 10 2 37802
7 6 7 2001 2 3 3705
10 6 8 2001 1 2 21691
10 6 7 2001 2 3 42624
--统计销售额大于%20以及所在区域的销售额,及各个客户销售比例
SELECT ALL_SALES.*, 100*ROUND(CUST_SALES/REGION_SALES,2)||'%' PERCENT
FROM (SELECT A.CUST_NBR CUSTOMER,
A.REGION_ID REGION,
SUM(A.TOT_SALES) CUST_SALES,
SUM(SUM(A.TOT_SALES)) OVER(PARTITION BY A.REGION_ID) REGION_SALES,
SUM(SUM(A.TOT_SALES)) OVER() TOTAL_SALES
FROM ORDERS_TMP A
GROUP BY A.REGION_ID, A.CUST_NBR) ALL_SALES
WHERE ALL_SALES.CUST_SALES > ALL_SALES.REGION_SALES * 0.2;
CUSTOMER REGION CUST_SALES REGION_SALES TOTAL_SALES PERCENT
---------- ---------- ---------- ------------ ----------- --------
4 5 37802 37802 118026 100%
10 6 64315 68020 118026 95%
11 7 12204 12204 118026 100%
--Over函数的作用是告诉SQL引擎：按照区域对数据进行分组，然后累积每个区域下所有客户的订单总额
--PARTITION 子句，将相同的行聚合在一起，若没有partition子句，标示统计所有的行
----------------------------------------------------------------------------------------------------------------------------------------
--二.分析函数(RANK,DENSE_RANK,ROW_NUMBER)
--生成测试数据
CREATE TABLE USER_ORDER
(
REGION_ID NUMBER(2),
CUSTOMER_ID NUMBER(2),
CUSTOMER_SALES NUMBER
);
INSERT INTO USER_ORDER VALUES(7,11,1590421);
INSERT INTO USER_ORDER VALUES(6,10,1696748);
INSERT INTO USER_ORDER VALUES(6,9,1108959);
INSERT INTO USER_ORDER VALUES(5,2,1224992);
INSERT INTO USER_ORDER VALUES(9,24,1224992);
INSERT INTO USER_ORDER VALUES(9,23,1224992);
COMMIT;
SELECT * FROM USER_ORDER;
REGION_ID CUSTOMER_ID CUSTOMER_SALES
---------- ----------- --------------
7 11 1590421
6 10 1696748
6 9 1108959
5 2 1224992
9 24 1224992
9 23 1224992
--不使用分析函数，查找出订单总额前三名的客户，请注意有金额相同的记录
SELECT ROWNUM, T.*
FROM (SELECT * FROM USER_ORDER ORDER BY CUSTOMER_SALES DESC) T
WHERE ROWNUM <= 3
ORDER BY CUSTOMER_SALES DESC;
ROWNUM REGION_ID CUSTOMER_ID CUSTOMER_SALES
---------- ---------- ----------- --------------
1 6 10 1696748
2 7 11 1590421
3 5 2 1224992
--很明显，若是简单的按照rownum排序的话，我们漏掉了两条
--采用分析函数
SELECT REGION_ID,
CUSTOMER_ID,
SUM(CUSTOMER_SALES) TOTAL,
RANK() OVER(ORDER BY SUM(CUSTOMER_SALES) DESC) RANK,
DENSE_RANK() OVER(ORDER BY SUM(CUSTOMER_SALES) DESC) DENSE_RANK,
ROW_NUMBER() OVER(ORDER BY SUM(CUSTOMER_SALES) DESC) ROWNUMBER
FROM USER_ORDER
GROUP BY REGION_ID, CUSTOMER_ID
REGION_ID CUSTOMER_ID TOTAL RANK DENSE_RANK ROWNUMBER
---------- ----------- ---------- ---------- ---------- ----------
6 10 1696748 1 1 1
7 11 1590421 2 2 2
9 23 1224992 3 3 3
5 2 1224992 3 3 4
9 24 1224992 3 3 5
6 9 1108959 6 4 6
--从上面的结果可以看出三种排序策略的不同
--1.RANK 函数对于相同的结果，排名是一样的，但会在最后一条相同的记录和下一条记录之间空出排名
--2.DENSE_RANK策略与RANK差不多，但不会空出排名，排名是连续的。
--3.ROW_NUMBER返回一个唯一值，若遇到相同的数据，排名依次递增
--使用分析函数对各个地区内的订单总额排序
SELECT REGION_ID,
CUSTOMER_ID,
SUM(CUSTOMER_SALES) TOTAL,
RANK() OVER(PARTITION BY REGION_ID ORDER BY SUM(CUSTOMER_SALES) DESC) RANK,
DENSE_RANK() OVER(PARTITION BY REGION_ID ORDER BY SUM(CUSTOMER_SALES) DESC) DENSE_RANK,
ROW_NUMBER() OVER(PARTITION BY REGION_ID ORDER BY SUM(CUSTOMER_SALES) DESC) ROW_NUMBER
FROM USER_ORDER
GROUP BY REGION_ID, CUSTOMER_ID;
REGION_ID CUSTOMER_ID TOTAL RANK DENSE_RANK ROW_NUMBER
--------- ----------- ---------- ---------- ---------- ----------
5 2 1224992 1 1 1
6 10 1696748 1 1 1
6 9 1108959 2 2 2
7 11 1590421 1 1 1
9 23 1224992 1 1 1
9 24 1224992 1 1 2
--Partition by 子句在排列函数中的作用是将一个结果集划分成几个部分，这样排列函数就能够应用于这各个子集。
UPDATE USER_ORDER SET CUSTOMER_SALES = NULL WHERE CUSTOMER_ID = 23;
COMMIT;
--修改用户订单表其中一条为空值，然后再来看排序结果
SELECT REGION_ID,
CUSTOMER_ID,
SUM(CUSTOMER_SALES) TOTAL,
RANK() OVER(PARTITION BY REGION_ID ORDER BY SUM(CUSTOMER_SALES) DESC) RANK,
DENSE_RANK() OVER(PARTITION BY REGION_ID ORDER BY SUM(CUSTOMER_SALES) DESC) DENSE_RANK,
ROW_NUMBER() OVER(PARTITION BY REGION_ID ORDER BY SUM(CUSTOMER_SALES) DESC) ROW_NUMBER
FROM USER_ORDER
GROUP BY REGION_ID, CUSTOMER_ID;
REGION_ID CUSTOMER_ID TOTAL RANK DENSE_RANK ROW_NUMBER
--------- ----------- ---------- ---------- ---------- ----------
5 2 1224992 1 1 1
6 10 1696748 1 1 1
6 9 1108959 2 2 2
7 11 1590421 1 1 1
9 23 1 1 1
9 24 1224992 2 2 2
--我们会发现空值会排第一位，需要调整下,在分析函数的结尾加上NULLS LAST(NULLS LAST/FIRST告诉Oracle空值排名)
SELECT REGION_ID,
CUSTOMER_ID,
SUM(CUSTOMER_SALES) TOTAL,
RANK() OVER(PARTITION BY REGION_ID ORDER BY SUM(CUSTOMER_SALES) DESC NULLS LAST) RANK,
DENSE_RANK() OVER(PARTITION BY REGION_ID ORDER BY SUM(CUSTOMER_SALES) DESC NULLS LAST) DENSE_RANK,
ROW_NUMBER() OVER(PARTITION BY REGION_ID ORDER BY SUM(CUSTOMER_SALES) DESC NULLS LAST) ROW_NUMBER
FROM USER_ORDER
GROUP BY REGION_ID, CUSTOMER_ID;
REGION_ID CUSTOMER_ID TOTAL RANK DENSE_RANK ROW_NUMBER
---------- ----------- ---------- ---------- ---------- ----------
5 2 1224992 1 1 1
6 10 1696748 1 1 1
6 9 1108959 2 2 2
7 11 1590421 1 1 1
9 24 1224992 1 1 1
9 23 2 2 2
-------------------------------------------------------------------------------------------------------------------------------------
--三 分析函数(Top/Bottom N,First/Last,NTILE)
--删除掉空值及重复值
DELETE USER_ORDER WHERE CUSTOMER_ID IN （23，24);
COMMIT;
--查找出订单总额最多和最少的用户，若按照签名的排名分析函数，至少需要两个查询，下面使用First/last来解决这个问题
admin@ORCL> SELECT * FROM USER_ORDER;
REGION_ID CUSTOMER_ID CUSTOMER_SALES
---------- ----------- --------------
7 11 1590421
6 10 1696748
6 9 1108959
5 2 1224992
SELECT MIN(CUSTOMER_ID) KEEP(DENSE_RANK FIRST ORDER BY SUM(CUSTOMER_SALES) DESC) FIRST,
MIN(CUSTOMER_ID) KEEP(DENSE_RANK LAST ORDER BY SUM(CUSTOMER_SALES) DESC) LAST
FROM USER_ORDER
GROUP BY CUSTOMER_ID;
FIRST LAST
--------- ----------
10 9
--MIN中的函数用于保证当存在多个Frist或Last的情况，返回唯一条记录(注意)
--DENSE_RANK是必须的，RANK与ROW_NUMBER无法代替
--利用分析函数找出排名1/2的客户
SELECT REGION_ID,
CUSTOMER_ID,
SUM(CUSTOMER_SALES) AS CUSTOMER_SALES,
NTILE(2) OVER(ORDER BY SUM(CUSTOMER_SALES) DESC) TIL
FROM USER_ORDER
GROUP BY REGION_ID, CUSTOMER_ID;
--NTIL为各个记录在记录集中的排名计算比例，我们看到所有的记录被分为2个等级，那么我们只要取tile=1的记录值即可。
------------------------------------------------------------------------------------------------------------------------------------
--四 分析函数之窗口函数
CREATE TABLE ORDERS(
MONTH NUMBER(2),
TOT_SALES NUMBER
);
INSERT INTO ORDERS VALUES(1,610697);
INSERT INTO ORDERS VALUES(2,428676);
INSERT INTO ORDERS VALUES(3,637031);
INSERT INTO ORDERS VALUES(4,541146);
INSERT INTO ORDERS VALUES(5,592935);
INSERT INTO ORDERS VALUES(6,501485);
INSERT INTO ORDERS VALUES(7,606914);
INSERT INTO ORDERS VALUES(8,460520);
INSERT INTO ORDERS VALUES(9,392898);
INSERT INTO ORDERS VALUES(10,510117);
INSERT INTO ORDERS VALUES(11,532889);
INSERT INTO ORDERS VALUES(12,492458);
COMMIT;
SELECT * FROM ORDERS;
MONTH TOT_SALES
---------- ----------
1 610697
2 428676
3 637031
4 541146
5 592935
6 501485
7 606914
8 460520
9 392898
10 510117
11 532889
12 492458
--ROWS BETWEEN ..PRECEDING AND ...FOLLOWING 意思是：在xxx之前和xxx之后
SELECT MONTH,
SUM(TOT_SALES) MONTH_SALES,
SUM(SUM(TOT_SALES)) OVER (ORDER BY MONTH ROWS BETWEEN 1 PRECEDING AND UNBOUNDED FOLLOWING)TOTAL_SALES1,
SUM(SUM(TOT_SALES)) OVER (ORDER BY MONTH ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) TOTAL_SALES2,
SUM(SUM(TOT_SALES)) OVER (ORDER BY MONTH ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)TOTAL_SALES3
FROM ORDERS
GROUP BY MONTH;
MONTH MONTH_SALES TOTAL_SALES1 TOTAL_SALES2 TOTAL_SALES3
---------- ----------- ------------ ------------ ------------
1 610697 6307766 1039373 6307766
2 428676 6307766 1676404 6307766
3 637031 5697069 1606853 6307766
4 541146 5268393 1771112 6307766
5 592935 4631362 1635566 6307766
6 501485 4090216 1701334 6307766
7 606914 3497281 1568919 6307766
8 460520 2995796 1460332 6307766
9 392898 2388882 1363535 6307766
10 510117 1928362 1435904 6307766
11 532889 1535464 1535464 6307766
12 492458 1025347 1025347 6307766
--从结果中可以看出。
--TOTAL_SALES1逻辑是统计当前行+前一条+随后的所有记录。
--TOTAL_SALES2逻辑是统计当前行+当前行的前一条+当前行的后一条。
--TOTAL_SALES3逻辑是统计所有行
--统计每月订单总额及截止到当前月的订单总额
SELECT MONTH,
SUM(TOT_SALES) MONTH_SALES,
SUM(SUM(TOT_SALES)) OVER(ORDER BY MONTH ASC ROWS BETWEEN UNBOUNDED PRECEDING AND 0 FOLLOWING) TOTAL_SALES1,
SUM(SUM(TOT_SALES)) OVER(ORDER BY MONTH ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) TOTAL_SALE2,
SUM(SUM(TOT_SALES)) OVER(ORDER BY MONTH) TOTAL_SALES3,
ROUND(AVG(SUM(TOT_SALES)) OVER(ORDER BY MONTH),2) AVG_SALES
FROM ORDERS
GROUP BY MONTH;
MONTH MONTH_SALES TOTAL_SALES1 TOTAL_SALE2 TOTAL_SALES3 AVG_SALES
---------- ----------- ------------ ----------- ------------ ----------
1 610697 610697 610697 610697 610697
2 428676 1039373 1039373 1039373 519686.5
3 637031 1676404 1676404 1676404 558801.33
4 541146 2217550 2217550 2217550 554387.5
5 592935 2810485 2810485 2810485 562097
6 501485 3311970 3311970 3311970 551995
7 606914 3918884 3918884 3918884 559840.57
8 460520 4379404 4379404 4379404 547425.5
9 392898 4772302 4772302 4772302 530255.78
10 510117 5282419 5282419 5282419 528241.9
11 532889 5815308 5815308 5815308 528664.36
12 492458 6307766 6307766 6307766 525647.17
--从结果来看，前三条分析函数语句结果是相同的。
--order by 默认数据范围是当前所需的partition的第一行到当前行
--AVG_SALES用于统计滚动的月平均销量
--我们还可以用First_value和Last_value用于在窗口记录集中查找第一条和最后一条记录
SELECT MONTH,
SUM(TOT_SALES) MONTH_SALES,
FIRST_VALUE(SUM(TOT_SALES)) OVER(ORDER BY MONTH ASC ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) PRE_VALUE,
LAST_VALUE(SUM(TOT_SALES)) OVER(ORDER BY MONTH ASC ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) NEXT_VALUE
FROM ORDERS
GROUP BY MONTH;
MONTH MONTH_SALES PRE_VALUE NEXT_VALUE
---------- ----------- ---------- ----------
1 610697 610697 428676
2 428676 610697 637031
3 637031 428676 541146
4 541146 637031 592935
5 592935 541146 501485
6 501485 592935 606914
7 606914 501485 460520
8 460520 606914 392898
9 392898 460520 510117
10 510117 392898 532889
11 532889 510117 492458
12 492458 532889 492458
--生成测试数据，测试窗口函数中RANGE的使用
CREATE TABLE ORDERS_TIME
(
ORDER_DATE DATE,
AMOUNT NUMBER
);
INSERT INTO ORDERS_TIME VALUES(TO_DATE('1999-01-01','YYYY-MM-DD'),100);
INSERT INTO ORDERS_TIME VALUES(TO_DATE('2000-01-01','YYYY-MM-DD'),50);
INSERT INTO ORDERS_TIME VALUES(TO_DATE('1999-01-01','YYYY-MM-DD'),30);
INSERT INTO ORDERS_TIME VALUES(TO_DATE('2000-01-03','YYYY-MM-DD'),20);
INSERT INTO ORDERS_TIME VALUES(TO_DATE('2000-01-02','YYYY-MM-DD'),200);
COMMIT;
SELECT * FROM ORDERS_TIME;
ORDER_DATE AMOUNT
-------------- ----------
1999/1/1 100
2000/1/1 50
1999/1/1 30
2000/1/3 20
2000/1/2 200
2000/2/2 200
SELECT ORDER_DATE,SUM(AMOUNT),
SUM(SUM(AMOUNT)) OVER(ORDER BY ORDER_DATE RANGE BETWEEN INTERVAL '1' DAY PRECEDING AND INTERVAL '1' DAY FOLLOWING) SALES
FROM ORDERS_TIME
GROUP BY ORDER_DATE;
ORDER_DATE SUM(AMOUNT) SALES
---------- -------- -----
1999/1/1 130 130
2000/1/1 50 250
2000/1/2 200 270
2000/1/3 20 220
2000/2/2 200 200
--RANGE及interval关键字用于统计当前日期的销售额+前一天+后一天
---窗口函数进阶--比较相邻
SELECT ORDER_DATE,SUM(AMOUNT),
LAG(SUM(AMOUNT),1,NULL) OVER(ORDER BY ORDER_DATE) PRE_SALES,
LEAD(SUM(AMOUNT),1,NULL) OVER(ORDER BY ORDER_DATE) NEXT_SALES
FROM ORDERS_TIME
GROUP BY ORDER_DATE;
--，数据的偏移量默认是1，若没有下一个值，用null来代替
--也可以加partition by进行分组，然后当寻找上一个/下一个值时，只在组内寻找
----------------------------------------------------------------------------------------------------------------------------------------
--五 分析函数之报表函数
--统计每个客户的销量占区域销量的比例，采用比较常规方法
SELECT CUST_NBR,
REGION_ID,
SUM(TOT_SALES) CUST_SALES,
100*ROUND(SUM(TOT_SALES)/SUM(SUM(TOT_SALES)) OVER(PARTITION BY REGION_ID),2)||'%' PERCENT
FROM ORDERS_TMP O
GROUP BY REGION_ID, CUST_NBR;
CUST_NBR REGION_ID CUST_SALES PERCENT
---------- ---------- ---------- -------------------------
4 5 37802 100%
7 6 3705 5%
10 6 64315 95%
11 7 12204 100%
--下面使用报表函数RATIO_TO_REPORT
SELECT CUST_NBR,
REGION_ID,
SUM(TOT_SALES) CUST_SALES,
100*ROUND(RATIO_TO_REPORT((SUM(TOT_SALES))) OVER(PARTITION BY REGION_ID),2)||'%' PERCENT
FROM ORDERS_TMP O
GROUP BY REGION_ID, CUST_NBR;
CUST_NBR REGION_ID CUST_SALES PERCENT
---------- ---------- ---------- ---------------------
4 5 37802 100%
7 6 3705 5%
10 6 64315 95%
11 7 12204 100%
--结果是一样的，Oracle提供的Ratio_to_report函数统计每条记录在其子集中所占的比例
--本文章参考
--http://wenku.baidu.com/view/6694a7225901020207409c5d.html
Oracle 分析函数详解
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