I have two tables:
I need to optimize the following select statement
select r.id_range, sum(t.delta) sum_deltas
from trips t,
ranges r
where t.id_object = r.id_object
and t.trip_date between r.date_since and r.date_until
group by r.id_range
according to the condition there is always exactly one matching row for trip in 'ranges'
Does anyone have an idea how to speed things up, is it even possible?
It's always possible to speed things up; it just may not be worth the time / effort / money / disk-space / additional overheads etc.
Firstly please use the explicit join syntax. It's been the SQL standard for a few decades now and it helps avoid a lot of potential errors. Your query would become:
select r.id_range, sum(t.delta) sum_deltas
from trips t
join ranges r
on t.id_object = r.id_object
and t.trip_date between r.date_since and r.date_until
group by r.id_range
This query would imply that you need two indexes - unique if possible. On ranges
you should have an index on id_object, date_since, date_until
. The index on trips
would be id_object, trip_date
. If trips
were smaller I might consider adding delta
on to the end of that index so you never enter the table at all but only do a index scan. As it stands you're going to have to do a table access by index rowid.
Having written all that your problem may be slightly different. You're going to be full-scanning both tables with this query. Your problem might be the indexes. If the optimizer is using the indexes then it's possible you're doing an index unique/range scan for each id_object
in trips
or ranges
and then, because of the use of columns not in the indexes you will be doing an table access by index rowid. This can be massively expensive.
Try adding a hint to force a full-scan of both tables:
select /*+ full(t) full(r) */ r.id_range, sum(t.delta) sum_deltas
from trips t
join ranges r
on t.id_object = r.id_object
and t.trip_date between r.date_since and r.date_until
group by r.id_range