On Sat, Mar 17, 2012 at 23:07, Scott Marlowe <scott.marlowe@xxxxxxxxx> wrote:
Yeah try setting [work_mem] to something absurd like 500MB and see if the plan changes.
Suweet! Sorting now runs in-memory, and that makes a big difference, even when groveling over 1M records (under 12 seconds rather than 7 hours). Results in
On Sat, Mar 17, 2012 at 23:09, Scott Marlowe <scott.marlowe@xxxxxxxxx> wrote:
Also it looks like you're still not using the index on this:
Subquery Scan u1 (cost=0.00..313.55 rows=50 width=4) (actual
time=0.030..147.136 rows=10000 loops=1)
Filter: ((u1.type)::text = 'User::Twitter'::text)
Are you sure you're using an indexable condition?
I know that users.type is indexed -- what would keep that from being honored? FWIW, I believe that all user.type fields are set to User::Twitter, but that will change in the future.
On Sat, Mar 17, 2012 at 23:12, Scott Marlowe <scott.marlowe@xxxxxxxxx> wrote:
Also also this looks like it's the most expensive operation:
Seq Scan on followings f2 (cost=0.00..93523.95 rows=5534395 width=8)
(actual time=0.041..19365.834 rows=5535964 loops=1)
I'm guessing the f2.follower_id isn't very selective?
Not 100% sure what you mean -- f2.follower_id is very sparse (compared to f1.follower_id), but that's the point of this particular query. But since upping work_mem makes it run really fast, I'm not overly concerned about this one. Thanks for your help!
One last thought: I could re-cast this as a subquery / query pair, each with a single join. Am I correct in thinking that could make it really easy on the planner (especially if the tables were properly indexed)?
Thanks again.
- r
