sqldatejoinunion-all

Add date based on two column values and transpose other columns


I have the next two tables:

Table A

Product Plant ProductionWeek1 ... ProductionWeek11
10011 ABC32 6742 ... 3645
10011 DEF10 3579 ... 8761
45891 GHI22 8945 ... 1556

Table B

Product Plant SalesWeek1 ... SalesWeek11
10011 ABC32 425 ... 364
10011 DEF10 879 ... 501
45891 GHI22 904 ... 395

I would want a table like this, when the first week start in 01/01/2001, the second week would be 08/01/2001 ans so on, it could be any date the important thing is that the difference between dates have to be one week:

Product Plant Production Sales Date
10011 ABC32 6742 425 01/01/2001
10011 ABC32 ... ... ...
10011 ABC32 3645 364 18/06/2001
10011 DEF10 3579 879 01/01/2001
10011 DEF10 ... ... ...
10011 DEF10 8761 501 18/06/2001
45891 GHI22 8945 904 01/01/2001
45891 GHI22 ... ... ...
45891 GHI22 1556 395 18/06/2001

I try the next code to transpose the columns for the two columns:

CREATE TABLE TABLE_A1 AS (SELECT P.PRODUCT AS PRODUCT_1, 
                              P.PLANT AS PLANT_1, 
                              P.PRODUCTION AS PRODUCTION_1
                              FROM (SELECT PRODUCT, PLANT, ProductionWeek1 FROM TableA 
                                    UNION 
                                    SELECT PRODUCT, PLANT, ProductionWeek2 FROM TableA
                                    UNION 
                                    ...
                                    SELECT PRODUCT, PLANT, ProductionWeek11 FROM TableA) p;

CREATE TABLE TABLE_B1 AS (SELECT s.PRODUCT AS PRODUCT_2, 
                              s.PLANT AS PLANT_2, 
                              s.SALES AS SALES_2
                              FROM (SELECT PRODUCT, PLANT, SalesWeek1 FROM TableB 
                                    UNION 
                                    SELECT PRODUCT, PLANT, SalesWeek2 FROM TableB
                                    UNION 
                                    ...
                                    SELECT PRODUCT, PLANT, SalesWeek11 FROM TableB) s;

Then try to put all together

CREATE TABLE FINAL_TABLE AS ( 
                            SELECT PRODUCT_1 AS PRODUCT,
                            PLANT_1_CONSUMOS AS PLANT, 
                            PRODUCTION_1,
                            SALES_2
                            FROM TABLE_A1
                            INNER JOIN TABLE_B1 
                            ON TABLE_B1.PRODUCT_2=TABLE_A1.PRODUCT_1
                            );

But I have a table like this:

Product Plant Production Sales
10011 ABC32 6742 425
10011 ABC32 3645 425
10011 ABC32 3645 364

The table is in disorder due to in the creation from TABLE_A1 and TABLE_B1 the union was in disorder too, so for that reason is important the date, for example I expect an union like this:

Product Plant Production Sales
10011 ABC32 6742 425
10011 ABC32 ... ...
10011 ABC32 3645 364

But I got the next table

Product Plant Production Sales
10011 ABC32 3645 364
10011 ABC32 ... ...
10011 ABC32 6742 425

where the value for the last week is in other row (not necesarry the first). Any help?


Solution

  • The current tables can be described as being "pivoted" because you have data representing a single week per column. To make sense of this you first need to "unpivot" that data and while you can use unions to do this, it is more efficient to simply cross join the source table to a set of rows representing each week (i.e. 11 rows). Then once cross joined use case expressions to move the data into the needed number of columns. e.g. the unpivoted result should look like this (ignoring weeks 2 to 10):

    product plant   week    type    value
    10011   ABC32   1   production  6742
    10011   DEF10   1   production  3579
    45891   GHI22   1   production  8945
    10011   ABC32   11  production  3645
    10011   DEF10   11  production  8761
    45891   GHI22   11  production  1556
    10011   ABC32   1   sales   425
    10011   DEF10   1   sales   879
    45891   GHI22   1   sales   904
    10011   ABC32   11  sales   364
    10011   DEF10   11  sales   501
    45891   GHI22   11  sales   395
    

    Once the data is available in this form it is a relatively simple task to produce a combination of the source columns by week. Note also that in the 11 weekly rows to be used it is also possible to supply the needed dates.

    nb as you have not specified which rdbms is being used the following is in Postgres syntax but aside from the ::date syntax used the rest is generic SQL.

    with w as (
              select 1 as week, '2001-01-01'::date as week_date union all
              /*   other weeks */
              select 11 as week, '2001-06-18'::date as week_date
            )
    , unpiv as (
            select t.Product, t.Plant, w.week, w.week_date, 'production' as type
                , case
                   when w.week = 1 then t.ProductionWeek1
                   /* Other weeks */
                   when w.week = 11 then t.ProductionWeek11
                end as value
            from Production t
            cross join w
            union all
            select t.Product, t.Plant, w.week, w.week_dat, 'sales' as type
                , case
                   when w.week = 1 then t.SalesWeek1
                   /* Other weeks */
                   when w.week = 11 then t.SalesWeek11
                end as value
            from Sales t
            cross join w
            )
    select
          u.Product, u.Plant
        , max(case when type = 'production' then value end) as production
        , max(case when type = 'sales' then value end) as sales
        , week, week_date
    from unpiv u
    group by
          u.Product, u.Plant
        , week, week_date
    order by
          u.Product, u.Plant
        , week, week_date
    
    product plant production sales week week_date
    10011 ABC32 6742 425 1 2001-01-01
    10011 ABC32 3645 364 11 2001-06-18
    10011 DEF10 3579 879 1 2001-01-01
    10011 DEF10 8761 501 11 2001-06-18
    45891 GHI22 8945 904 1 2001-01-01
    45891 GHI22 1556 395 11 2001-06-18

    fiddle

    and here is a second (only slightly different) approach: https://dbfiddle.uk/pC0wmedZ