zoukankan      html  css  js  c++  java
  • SQL Server 排序函数 ROW_NUMBER和RANK 用法总结

    下面的例子和SQL语句均在SQL Server 2008环境下运行通过,使用SQL Server自带的AdventureWorks数据库。

    转载请注明此文原创自

    CSDN TJVictor的专栏:http://blog.csdn.net/tjvictor/archive/2009/07/08/4331039.aspx

    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
     :

    转载请注明此文原创自CSDN TJVictor的专栏:http://blog.csdn.net/tjvictor/archive/2009/07/08/4331039.aspx

  • 相关阅读:
    Shader-另类实现
    Unity3d- 资源
    我不快乐
    陷入了一个NGUI自适应的一个坑
    ngui 自适应
    NGUI的数据绑定
    我已经很努力,但是依旧写给自己
    那些奇葩的叫法
    C#修改类模板
    开启Unity3D之旅
  • 原文地址:https://www.cnblogs.com/12taotie21/p/4821310.html
Copyright © 2011-2022 走看看