Increasing work_mem and shared_buffers on Postgres 9.2 significantly slows down queries
First of all, keep in mind that work_mem is per operation and so it can get excessive pretty quickly. In general if you are not having trouble with sorts being slow I would leave work_mem alone until you need it.
Looking at your query plans, one thing that strikes me is that the buffer hits are very different looking at the two plans, and that even the sequential scans are slower. I suspect that the issue has to do with read-ahead caching and having less space for that. What this means is you are biasing memory for re-use of indexes and against reading tables on disk.
My understanding is that PostgreSQL will look to the cache for a page before reading it from disk because it doesn't know really whether the OS cache will contain that page. Because the pages are then staying in the cache and because that cache is slower than the OS cache, this changes the sorts of queries which are fast vs the sorts that are slow. In fact reading the plans, aside from work_mem issues, it looks like all of your query info comes from the cache but it is a question of which cache.
work_mem: how much memory we can allocate for a sort or related join operation. This is per operation, not per statement or per back-end, so a single complex query can use many times this amount of memory. It isn't clear you are hitting this limit but it is worth noting and being aware of. if you increase this too far, you lose memory that might be available for the read cache and the shared buffers.
shared_buffers: how much memory to allocate to the actual PostgreSQL page queue. Now, ideally the interesting set of your database will stay in memory cached here and in the read buffers. However, what this does is ensure that the most frequently used information across all backends gets cached and not flushed to disk. On Linux this cache is significantly slower than the OS disk cache, but it offers guarantees that the OS disk cache dos not and is transparent to PostgreSQL. This is pretty clearly where your problem is.
So what happens is that when we have a request, we check the shared buffers first since PostgreSQL has deep knowledge of this cache, and look for the pages. If they are not there we ask the OS to open them from the file, and if the OS has cached the result it returns the cached copy (this is faster than the shared buffers, but Pg can't tell whether it is cached or on disk, and disk is much slower so PostgreSQL typically will not take that chance). Keep in mind this affects random vs sequential page access as well. So you may get better performance with lower shared_buffers settings.
My gut sense is that you probably get better, or at least more consistent, performance in high concurrency environments with larger shared_buffer settings. Also keep in mind that PostgreSQL grabs this memory and holds it so if you have other things running on the system, the read buffers will hold files read by other processes. It's a very large and complex topic. Larger shared buffer settings provide better guarantees of performance but may deliver less performance in some cases.
Apart from the seemingly paradoxical effect that increasing work_mem decreases performance (@Chris might have an explanation), you can improve your function in at least two ways.
- Rewrite two fake
LEFT JOIN's withJOIN. That might confuse the query planner and lead to inferior plans.
SELECT count(*) AS ct
FROM contest c
JOIN contestparticipant cp ON cp.contestId = c.id
JOIN personinfo pi ON pi.id = cp.personinfoid
LEFT JOIN teammember tm ON tm.contestparticipantid = cp.id
LEFT JOIN staffmember sm ON sm.contestparticipantid = cp.id
LEFT JOIN person p ON p.id = cp.personid
WHERE (pi.firstname LIKE '%a%'
OR pi.lastname LIKE '%b%')
- Assuming that your actual search patterns are more selective, use trigram indexes on
pi.firstnameandpi.lastnameto support non-anchoredLIKEsearches. (Shorter patterns like'%a%'are supported as well but an index is not likely to help for non-selective predicates.):
CREATE INDEX personinfo_firstname_gin_idx ON personinfo USING gin (firstname gin_trgm_ops);
CREATE INDEX personinfo_lastname_gin_idx ON personinfo USING gin (lastname gin_trgm_ops);
Or one multicolumn index:
CREATE INDEX personinfo_name_gin_idx ON personinfo USING gin (firstname gin_trgm_ops, lastname gin_trgm_ops);
Should make your query quite a bit faster. You need to install the additional module pg_trgm for this. Details under these related questions:
- How is LIKE implemented?
- Pattern matching with LIKE, SIMILAR TO or regular expressions in PostgreSQL
Also, have you tried setting work_mem locally - for the current transaction only?
SET LOCAL work_mem = '96MB';
This keeps concurrent transactions from also eating more RAM, possibly starving each other.