What is the best solution to this problem?

Question

I have so many solutions in mind right now but i am looking for the best solution if possible. i have the following query
SELECT one_query.date_required as Month_id,
           nvl(one_query.amount_used, 0) as overalluserhours_A,
              nvl(second_query.amount_used_b, 0) as overalluserhours_B,
              nvl((trunc(((second_query.amount_used_b/one_query.amount_used) * 100), 2)), 0) as p_change
from
             (select to_char(b1.needed_date,'YYYY-MM') as date_required,
                SUM(b1.amount_used) as amount_used,
                  b1.type_id as type_id
                    from table_one b1
                     where b1.zone_type like 'NEWYORK%'
                     and b1.type_id = 'CARS'
                    and trunc(b1.needed_date) between to_date('2009-01-01', 'YYYY-MM-DD') and to_date('2009-12-31', 'YYYY-MM-DD')
                          group by to_char(b1.needed_date,'YYYY-MM'), b1.type_id) one_query,
            (select to_char(b2.needed_date, 'YYYY-MM') as date_required,
                            SUM(b2.amount_used) as amount_used_b,
                                   b2.type_id as type_id
                                from table_one b2
                                   where b2.zone_type like
                                   'CHICAGO%'
                              and b2.type_id = 'BIKES'
                               and trunc(b2.needed_date) between to_date('2009-01-01', 'YYYY-MM-DD') and to_date('2009-12-31', 'YYYY-MM-DD')
                             group by to_char(b2.needed_date, 'YYYY-MM'), b2.type_id)second_query
where one_query.date_required = second_query.date_required(+);the above query is being used on table_one. The current problem I am having is based on the fact that table_one might sometimes contain data for only chicago and not for newyork. in this case, table_one would look like this
identification_id         needed_date                   zone_type             type_id                  
2                             3/22/2006 12:00:00          CHICAGO                BIKES
3                              2/12/2006 12:00:00         CHICAGO                BIKEShowever though, in other case, it could be the other way around. in this case, table_one will look like this
identification_id         needed_date                   zone_type             type_id                  
4                            4/21/2007 12:00:00          NEWYORK                CARS
5                             1/12/2007 12:00:00         NEWYORK                CARS
and finally table_one could contain information for both cases. hence, we could have the following situation
identification_id         needed_date                   zone_type             type_id                  
6                            6/21/2008 12:00:00          NEWYORK             BIKES
7                            8/12/2008 12:00:00         CHICAGO               CARSKindly note, my above query is currently being used inside a function. I know I can write so many if statement to handle but the main issue is regarding the fact, i am also using that query in another query which performs so many union all.

Answer 1

I'm not sure how you're going to parameterize it, how those filters change from call to call, but an idea would be something like this:
select date_required month_id,
       max(amt_used_chicago_bikes) overalluserhours_A,
       max(amt_used_newyork_cars) overalluserhours_B,
       (max(amt_used_chicago_bikes) / max(amt_used_newyork_cars)) * 100 p_change
  from (select zone_type,
               to_char(b1.needed_date, 'YYYY-MM') as date_required,
               b1.type_id as type_id,
               SUM(case when zone_type like 'CHICAGO%' and type_id = 'BIKES'
                             then b1.amount_used end) as amt_used_chicago_bikes,
               SUM(case when zone_type like 'NEWYORK%' and type_id = 'CARS'
                             then b1.amount_used end) as amt_used_newyork_cars
          from table_one b1
         where trunc(b1.needed_date) between
               to_date('2009-01-01', 'YYYY-MM-DD') and
               to_date('2009-12-31', 'YYYY-MM-DD')
         group by b1.zone_type,
                  to_char(b1.needed_date, 'YYYY-MM'),
                  b1.type_id)
where amt_used_chicago_bikes is not null or amt_used_newyork_cars is not null
group by date_required;Again, this is not the biggest concern regarding performance and certainly not the best way of doing it. Cracking those 250 lines of SQL and making it optimized would probably be the best way to approach the issue here.