Rewrite update query to improve performance - sql attached

Question

Hi ,
The following query(pasted below) is taking 4 hours to compelete, table ef is having 10 crore records, and I have 3 binary indexes defined on ef on columns as_at_date, cc and fid (these are individual indexes), just want to know if there is any better way to write the query?
Many thanks
Rahul
SQL :
update ef emp
set emp.b2r = (
select e.b2r
from ef e
where e.as_at_date  = (select max(as_at_date) from ef where b2r is not null)
and e.b2r is not null
and emp.fid = e.fid
and emp.cc = e.cc
where substr(emp.as_at_date,1,6) >= (select substr(max(fk_reporting_date_id),1,6) from f_c_f)
and exists (
select e.b2r
from ef e
where e.as_at_date = (select max(as_at_date) from ef where b2r is not null)
and e.b2r is not null
and emp.fid=e.fid
and emp.cc=e.cc

Answer 1

> which column uniquely identifies every row : UNKNOW to me(really sorry)
This is a very critical piece of information. UNKNOWN is a very wrong answer. You need to know such things before keeping your hands on keyboard.
Any way based on your update statement you can convert it into merge statement like this
merge into ef a
using (
         select b2r
              , fid
              , cc
           from (
                   select b2r
                        , fid
                        , cc
                        , row_number() over
                                         partition by fid
                                                    , cc
                                             order by decode(b2r, null, null, as_at_date)
                                                 desc nulls last
                                       ) rno
                     from ef
          where rno = 1
      ) b
   on (
          a.fid = b.fid and
          a.cc  = b.cc  and
          ubstr(a.as_at_date,1,6) >= (select substr(max(fk_reporting_date_id),1,6) from f_c_f)
when matched then
update set a.b2r = b.b2r;
Note: The code is untested
This scans your EF table lesser number of time. As you are in 11g you can try using DBMS_PARALLEL_EXECUTE to speed up your SQL.