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这篇CFSDN的博客文章SQL Server 排序函数 ROW_NUMBER和RANK 用法总结由作者收集整理，如果你对这篇文章有兴趣，记得点赞哟.

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 。

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