need help in grouping cyclic rows

  • Hello all,

    I have an interesting question.

    I have a table with following data.

    column1 column2 country

    'abc' 'def' USA

    'def' 'abc' USA

    'xxx' 'yyy' Canada

    'yyy' 'xxx' Canada

    I need the result set to be the distinct values with any combination of column1 and column2

    The result set should look as follows

    result1

    column1 column2 country

    'abc' 'def' USA

    'xxx' 'yyy' Canada

    or

    result2

    column1 column2 country

    'def' 'abc' USA

    'yyy' 'xxx' Canada

    it should display only unique values for the combination of column1 and column2

    Do we have any sql function that can get this resolved ?

    Bcoz i want to display the result in a view. so I am not concerned abt cursors.

    Thanks in advance.

  • If the results don't matter, then why do it? What if there is a 3rd entry for Canada? What are the results supposed to be?

    Here's a script to generate the data, just looking for some clarification

    declare @t table (column1 char(3), column2 Char(3), country varchar(10))

    insert into @t values('abc', 'def', 'USA'),

    ('def', 'abc', 'USA'),

    ('xxx', 'yyy', 'Canada'),

    ('yyy', 'xxx', 'Canada')

    What happens after these inserts?

    insert into @t values('abc', 'yyy', 'Canada'),

    ('xxx', 'def', 'Canada')

    For better, quicker answers, click on the following...
    http://www.sqlservercentral.com/articles/Best+Practices/61537/

    For better answers on performance questions, click on the following...
    http://www.sqlservercentral.com/articles/SQLServerCentral/66909/

  • Something like this?

    ; WITH CTE AS

    (

    SELECT RN = ROW_NUMBER() OVER ( PARTITION BY Country , ( CHECKSUM( column1 + column2 ) + CHECKSUM( column2 + column1 )) ORDER BY ( SELECT NULL ))

    ,Column1 , column2 , country

    from @t

    )

    SELECT column1 , column2 , country

    FROM CTE

    WHERE RN = 1

    Please be advised that CHECKSUM is unreliable though, for certain cases 🙂

  • OK, here is another solution, which is primarily for sets with more than 3 columns. For 2 columns, the other solution will scale up well ( imho )

    ; WITH UnPivotData AS

    (

    SELECT Vals ,

    RN = ROW_NUMBER() OVER (ORDER BY Vals)

    FROM @t SOURCE_TABLE

    UNPIVOT

    ( Vals FOR ColName IN ( [column1] , [column2]) ) UNPIVOT_HANDLE

    -- You'll 've to hardcode your column names above if the matching sets

    -- are scattered in more than 2 columns

    GROUP BY Vals

    ),

    GroupedData AS

    (

    SELECT SrcData.column1 , SrcData.column2 , SrcData.country,

    -- Add the columns here as well

    RowNum = ROW_NUMBER() OVER ( PARTITION BY Country ,SUM ( Unpvt.RN ) ORDER BY ( SELECT 0) )

    FROM UnPivotData Unpvt

    INNER JOIN @t SrcData

    ON ( SrcData.column1 = Unpvt.Vals OR SrcData.column2 = Unpvt.Vals )

    -- You'll 've to add the others columns to the OR clause

    -- if the matching sets are scattered in more than 2 columns

    GROUP BY

    SrcData.column1 , SrcData.column2 , SrcData.country

    -- Add the columns here as well

    )

    SELECT column1 , column2 , country

    FROM GroupedData

    WHERE RowNum = 1

  • CELKO (5/12/2011)


    CREATE VIEW Foobar (country_name, col_1, col_2)

    AS

    SELECT DISTINCT country_name,

    CASE WHEN col_1 <= col_2 THEN col_1 ELSE col_2 END AS col_1,

    CASE WHEN col_2 <= col_1 THEN col_1 ELSE col_2 END AS col_2

    FROM Foob;

    Ya just gotta love simple! 🙂

    --Jeff Moden


    RBAR is pronounced "ree-bar" and is a "Modenism" for Row-By-Agonizing-Row.
    First step towards the paradigm shift of writing Set Based code:
    ________Stop thinking about what you want to do to a ROW... think, instead, of what you want to do to a COLUMN.

    Change is inevitable... Change for the better is not.


    Helpful Links:
    How to post code problems
    How to Post Performance Problems
    Create a Tally Function (fnTally)

Viewing 5 posts - 1 through 4 (of 4 total)

You must be logged in to reply to this topic. Login to reply