rank,dense_rank,row_number区别
一：语法(用法)：
rank() over([partition by col1] order by col2)
dense_rank() over([partition by col1] order by col2)
row_number() over([partition by col1] order by col2)
其中[partition by col1]可省略。
二：区别
三个分析函数都是按照col1分组内从1开始排序
row_number() 是没有重复值的排序(即使两天记录相等也是不重复的)，可以利用它来实现分页
dense_rank() 是连续排序，两个第二名仍然跟着第三名
rank() 是跳跃拍学，两个第二名下来就是第四名
理论就不多讲了，看了案例，一下就明白了
SQL> create table t(
2 name varchar2(10),
3 score number(3));
Table created
SQL> insert into t(name,score)
2 select '语文',60 from dual union all
3 select '语文',90 from dual union all
4 select '语文',80 from dual union all
5 select '语文',80 from dual union all
6 select '数学',67 from dual union all
7 select '数学',77 from dual union all
8 select '数学',78 from dual union all
9 select '数学',88 from dual union all
10 select '数学',99 from dual union all
11 select '语文',70 from dual
12 /
10 rows inserted
SQL> select * from t;
NAME SCORE
---------- -----
语文 60
语文 90
语文 80
语文 80
数学 67
数学 77
数学 78
数学 88
数学 99
语文 70
10 rows selected
SQL> select name,score,rank() over(partition by name order by score) tt from t;
NAME SCORE TT
---------- ----- ----------
数学 67 1
数学 77 2
数学 78 3
数学 88 4
数学 99 5
语文 60 1
语文 70 2
语文 80 3 <----
语文 80 3 <----
语文 90 5
10 rows selected
SQL> select name,score,dense_rank() over(partition by name order by score) tt from t;
NAME SCORE TT
---------- ----- ----------
数学 67 1
数学 77 2
数学 78 3
数学 88 4
数学 99 5
语文 60 1
语文 70 2
语文 80 3 <----
语文 80 3 <----
语文 90 4
10 rows selected
SQL> select name,score,row_number() over(partition by name order by score) tt from t;
NAME SCORE TT
---------- ----- ----------
数学 67 1
数学 77 2
数学 78 3
数学 88 4
数学 99 5
语文 60 1
语文 70 2
语文 80 3 <----
语文 80 4 <----
语文 90 5
10 rows selected
SQL> select name,score,rank() over(order by score) tt from t;
NAME SCORE TT
---------- ----- ----------
语文 60 1
数学 67 2
语文 70 3
数学 77 4
数学 78 5
语文 80 6
语文 80 6
数学 88 8
语文 90 9
数学 99 10
10 rows selected
大家应该明白了吧！呵呵！接下来看应用
一：dense_rank------------------查询每门功课前三名
select name,score from (select name,score,dense_rank() over(partition by name order by score desc) tt from t) x where x.tt<=3
NAME SCORE
---------- -----
数学 99
数学 88
数学 78
语文 90
语文 80
语文 80
6 rows selected
二:rank------------------语文成绩70分的同学是排名第几。
select name,score,x.tt from (select name,score,rank() over(partition by name order by score desc) tt from t) x where x.name='语文' and x.score=70
NAME SCORE TT
---------- ----- ----------
语文 70 4
三：row_number——————分页查询
select xx.* from (select t.*,row_number() over(order by score desc) rowno from t) xx where xx.rowno between 1 and 3;
NAME SCORE ROWNO
---------- ----- ----------
数学 99 1
语文 90 2
数学 88 3
1.ROW_NUMBER()基本用法：
SELECT
SalesOrderID,
CustomerID,
ROW_NUMBER() OVER (ORDER BY SalesOrderID) AS RowNumber
FROM Sales.SalesOrderHeader
结果集：
SalesOrderID CustomerID RowNumber
--------------- ------------- ---------------
43659 676 1
43660 117 2
43661 442 3
43662 227 4
43663 510 5
43664 397 6
43665 146 7
43666 511 8
43667 646 9
:
2.RANK()基本用法:
SELECT
SalesOrderID,
CustomerID,
RANK() OVER (ORDER BY CustomerID) AS Rank
FROM Sales.SalesOrderHeader
结果集：
SalesOrderID CustomerID Rank
--------------- ------------- ----------------
43860 1 1
44501 1 1
45283 1 1
46042 1 1
46976 2 5
47997 2 5
49054 2 5
50216 2 5
51728 2 5
57044 2 5
63198 2 5
69488 2 5
44124 3 13
:
3.利用CTE来过滤ROW_NUMBER()的用法：
WITH NumberedRows AS
(
SELECT
SalesOrderID,
CustomerID,
ROW_NUMBER() OVER (ORDER BY SalesOrderID) AS RowNumber
FROM Sales.SalesOrderHeader
)
SELECT * FROM NumberedRows
WHERE RowNumber BETWEEN 100 AND 200
结果集：
SalesOrderID CustomerID RowNumber
--------------- ------------- --------------
43759 13257 100
43760 16352 101
43761 16493 102
:
43857 533 199
43858 36 200
4.带Group by的ROW_NUMBER()用法：
WITH CustomerSum
AS
(
SELECT CustomerID, SUM(TotalDue) AS TotalAmt
FROM Sales.SalesOrderHeader
GROUP BY CustomerID
)
SELECT
*,
ROW_NUMBER() OVER (ORDER BY TotalAmt DESC) AS RowNumber
FROM CustomerSum
结果集：
CustomerID TotalAmt RowNumber
------------- --------------- ---------------
678 1179857.4657 1
697 1179475.8399 2
170 1134747.4413 3
328 1084439.0265 4
514 1074154.3035 5
155 1045197.0498 6
72 1005539.7181 7
:
5.ROW_NUMBER()或是RANK()聚合用法：
WITH CustomerSum AS
(
SELECT CustomerID, SUM(TotalDue) AS TotalAmt
FROM Sales.SalesOrderHeader
GROUP BY CustomerID
)
SELECT *,
RANK() OVER (ORDER BY TotalAmt DESC) AS Rank
--或者是ROW_NUMBER() OVER (ORDER BY TotalAmt DESC) AS Row_Number
FROM CustomerSum
RANK()的结果集：
CustomerID TotalAmt Rank
----------- --------------------- --------------------
678 1179857.4657 1
697 1179475.8399 2
170 1134747.4413 3
328 1084439.0265 4
514 1074154.3035 5
:
6.DENSE_RANK()基本用法：
SELECT
SalesOrderID,
CustomerID,
DENSE_RANK() OVER (ORDER BY CustomerID) AS DenseRank
FROM Sales.SalesOrderHeader
WHERE CustomerID > 100
结果集：
SalesOrderID CustomerID DenseRank
------------ ----------- --------------------
46950 101 1
47979 101 1
49048 101 1
50200 101 1
51700 101 1
57022 101 1
63138 101 1
69400 101 1
43855 102 2
44498 102 2
45280 102 2
46038 102 2
46951 102 2
47978 102 2
49103 102 2
50199 102 2
51733 103 3
57058 103 3
:
7.RANK()与DENSE_RANK()的比较：
WITH CustomerSum AS
(
SELECT
CustomerID,
ROUND(CONVERT(int, SUM(TotalDue)) / 100, 8) * 100 AS TotalAmt
FROM Sales.SalesOrderHeader
GROUP BY CustomerID
)
SELECT *,
RANK() OVER (ORDER BY TotalAmt DESC) AS Rank,
DENSE_RANK() OVER (ORDER BY TotalAmt DESC) AS DenseRank
FROM CustomerSum
结果集：
CustomerID TotalAmt Rank DenseRank
----------- ----------- ------- --------------------
697 1272500 1 1
678 1179800 2 2
170 1134700 3 3
328 1084400 4 4
:
87 213300 170 170
667 210600 171 171
196 207700 172 172
451 206100 173 173
672 206100 173 173
27 205200 175 174
687 205200 175 174
163 204000 177 175
102 203900 178 176
:
8.NTILE()基本用法：
SELECT
SalesOrderID,
CustomerID,
NTILE(10000) OVER (ORDER BY CustomerID) AS NTile
FROM Sales.SalesOrderHeader
结果集：
SalesOrderID CustomerID NTile
--------------- ------------- ---------------
43860 1 1
44501 1 1
45283 1 1
46042 1 1
46976 2 2
47997 2 2
49054 2 2
50216 2 2
51728 2 3
57044 2 3
63198 2 3
69488 2 3
44124 3 4
:
45024 29475 9998
45199 29476 9998
60449 29477 9998
60955 29478 9999
49617 29479 9999
62341 29480 9999
45427 29481 10000
49746 29482 10000
49665 29483 10000
9.所有排序方法对比：
SELECT
SalesOrderID AS OrderID,
CustomerID,
ROW_NUMBER() OVER (ORDER BY CustomerID) AS RowNumber,
RANK() OVER (ORDER BY CustomerID) AS Rank,
DENSE_RANK() OVER (ORDER BY CustomerID) AS DenseRank,
NTILE(10000) OVER (ORDER BY CustomerID) AS NTile
FROM Sales.SalesOrderHeader
结果集：
OrderID CustomerID RowNumber Rank DenseRank NTile
-------- ------------- --------- ------- --------- --------
43860 1 1 1 1 1
44501 1 2 1 1 1
45283 1 3 1 1 1
46042 1 4 1 1 1
46976 2 5 5 2 2
47997 2 6 5 2 2
49054 2 7 5 2 2
50216 2 8 5 2 2
51728 2 9 5 2 3
57044 2 10 5 2 3
63198 2 11 5 2 3
69488 2 12 5 2 3
44124 3 13 13 3 4
44791 3 14 13 3 4
:
10.PARTITION BY基本使用方法：
SELECT
SalesOrderID,
SalesPersonID,
OrderDate,
ROW_NUMBER() OVER (PARTITION BY SalesPersonID ORDER BY OrderDate) AS OrderRank
FROM Sales.SalesOrderHeader
WHERE SalesPersonID IS NOT NULL
结果集：
SalesOrderID SalesPersonID OrderDate OrderRank
--------------- ---------------- ------------ --------------
:
43659 279 2001-07-01 00:00:00.000 1
43660 279 2001-07-01 00:00:00.000 2
43681 279 2001-07-01 00:00:00.000 3
43684 279 2001-07-01 00:00:00.000 4
43685 279 2001-07-01 00:00:00.000 5
43694 279 2001-07-01 00:00:00.000 6
43695 279 2001-07-01 00:00:00.000 7
43696 279 2001-07-01 00:00:00.000 8
43845 279 2001-08-01 00:00:00.000 9
43861 279 2001-08-01 00:00:00.000 10
:
48079 287 2002-11-01 00:00:00.000 1
48064 287 2002-11-01 00:00:00.000 2
48057 287 2002-11-01 00:00:00.000 3
47998 287 2002-11-01 00:00:00.000 4
48001 287 2002-11-01 00:00:00.000 5
48014 287 2002-11-01 00:00:00.000 6
47982 287 2002-11-01 00:00:00.000 7
47992 287 2002-11-01 00:00:00.000 8
48390 287 2002-12-01 00:00:00.000 9
48308 287 2002-12-01 00:00:00.000 10
:
11.PARTITION BY聚合使用方法：
WITH CTETerritory AS
(
SELECT
cr.Name AS CountryName,
CustomerID,
SUM(TotalDue) AS TotalAmt
FROM
Sales.SalesOrderHeader AS soh
INNER JOIN Sales.SalesTerritory AS ter ON soh.TerritoryID = ter.TerritoryID
INNER JOIN Person.CountryRegion AS cr ON cr.CountryRegionCode = ter.
CountryRegionCode
GROUP BY
cr.Name, CustomerID
)
SELECT
*,
RANK() OVER(PARTITION BY CountryName ORDER BY TotalAmt, CustomerID DESC) AS Rank
FROM CTETerritory
结果集：
CountryName CustomerID TotalAmt Rank
-------------- ------------- ----------- --------------
Australia 29083 4.409 1
Australia 29061 4.409 2
Australia 29290 5.514 3
Australia 29287 5.514 4
Australia 28924 5.514 5
:
Canada 29267 5.514 1
Canada 29230 5.514 2
Canada 28248 5.514 3
Canada 27628 5.514 4
Canada 27414 5.514 5
:
France 24538 4.409 1
France 24535 4.409 2
France 23623 4.409 3
France 23611 4.409 4
France 20961 4.409 5
:
12.PARTITION BY求平均数使用方法：
WITH CTETerritory AS
(
SELECT
cr.Name AS CountryName,
CustomerID,
SUM(TotalDue) AS TotalAmt
FROM
Sales.SalesOrderHeader AS soh
INNER JOIN Sales.SalesTerritory AS ter ON soh.TerritoryID = ter.TerritoryID
INNER JOIN Person.CountryRegion AS cr ON cr.CountryRegionCode = ter.
CountryRegionCode
GROUP BY
cr.Name, CustomerID
)
SELECT
*,
RANK() OVER (PARTITION BY CountryName ORDER BY TotalAmt, CustomerID DESC) AS Rank,
AVG(TotalAmt) OVER(PARTITION BY CountryName) AS Average
FROM CTETerritory
结果集：
CountryName CustomerID TotalAmt Rank Average
-------------- ------------- ----------- ------- ------------------
Australia 29083 4.409 1 3364.8318
Australia 29061 4.409 2 3364.8318
Australia 29290 5.514 3 3364.8318
:
Canada 29267 5.514 1 12824.756
Canada 29230 5.514 2 12824.756
Canada 28248 5.514 3 12824.756