Question

Edit :- for any one with a similar problem, there’s a good article covering various solutions here

Given the following tables recs and audit, how would one in SQL transform into the resultant table.

A little background, the former table is a simplified example of an standard SQL table used in a CRUD application collecting data. On any update to a column a record is written to an audit table in EAV form. There is now a requirement to transform the recs table into a historical table with a copy of each row as it was at a point in time for reporting (the data will be stored in a star schema data warehouse ultimately.

It seems like this would be straightforward enough in a procedural language and manageable (if ugly) using cursors, but is there a set based approach that would work?

I’m using T-SQL right now, but I imagine that I could port any examples or ideas from any sufficiently rich SQL dialect.

Setup

create table recs
(
    ID int identity(1,1) not null primary key,
    Column1 nvarchar(30) not null,
    Column2 nvarchar(30) not null,
    sys_updated_on datetime not null
)

create table audit
(
    ID int identity(1,1) not null primary key,
    recs_id int not null,
    fieldname nvarchar(30) not null,
    old_value nvarchar(30) not null,
    new_value nvarchar(30) not null,
    sys_updated_on datetime not null
)

insert into recs (Column1, Column2, sys_updated_on)
values ('A', 'B', '2012-10-31 22:00')
    , ('C', 'D', '2012-10-31 22:30')

insert into audit (recs_id, fieldname, old_value, new_value, sys_updated_on)
values (1, 'Column1', 'Z', 'A', '2012-10-31 22:00')
    , (2, 'Column2','X', 'D', '2012-10-31 22:30')
    , (1, 'Column1', 'Y', 'Z', '2012-10-31 21:00')

Resultant Data

Recs            

ID  Column1 Column2 sys_updated_on
1         A       B 31/10/2012 22:00:00
2         C       D 31/10/2012 22:30:00

Audit                   

ID  recs_id fieldname   old_value   new_value   sys_updated_on
1         1   Column1           Z           A   31/10/2012 22:00:00
2         2   Column2           X           D   31/10/2012 22:30:00
3         1   Column1           Y           Z   31/10/2012 21:00:00

Desired result

           
recs_id              sys_updated_on       Column1   Column2
1                          null             Y             B
1           31/10/2012 21:00:00             Z             B
1           31/10/2012 22:00:00             A             B
2                          null             C             X
2           31/10/2012 22:30:00             C             D

Answer 1

Interesting….

Try this

;with cte as
(   
    select recs_id, sys_updated_on, column1, column2, 
        ROW_NUMBER() over (order by sys_updated_on) rn
    from audit a
    pivot 
        (max(old_value) for fieldname in (column1,column2)) p
)
select 
    recs_id,
    case when ud1>ud2 then ud1 else ud2 end as updateddate,
    coalesce(cte.column1,mc1,recs.column1),
    coalesce(cte.column2,mc2,recs.column2)
from cte
    outer apply 
        (
        select top 1 
            column1 as mc1, sys_updated_on as ud1 
        from cte prev1 
        where prev1.recs_id=cte.recs_id and prev1.rn<cte.rn 
        order by prev1.rn desc
        ) r1
    outer apply 
        (
        select top 1 
            column2 as mc2, sys_updated_on as ud2 
        from cte prev2 
        where prev2.recs_id=cte.recs_id and prev2.rn<cte.rn 
        order by prev2.rn desc
        ) r2
    inner join recs on cte.recs_id = recs.id
where cte.sys_updated_on is not null
union
    select id, sys_updated_on, Column1, Column2 from recs
order by recs_id, cte.updateddate