I just wanted to update that I've found evidence that fixing the planner row estimation may not actually influence it to use the more performant merge join instead of hash join. I have found instances where the row estimation is *overestimated* by a magnitude of 4x (estimates 2.4m rows) and still chooses hash join over merge join, where merge join is much faster (45s v.s. 12s).
Is there maybe a cost constant that I should be increasing for RDS to influence the planner to choose a different strategy? I'm on a db.m4.large instance. Any insight around optimizing postgresql for RDS on this kind of workload or what cost constant parameters would help this situation would be extremely helpful. Thanks again, Branden On Sat, Jul 30, 2016 at 11:21 AM, Branden Visser <mrvis...@gmail.com> wrote: > Hi all, I seem to be in a bit of a jam with the query planner choosing > a significantly less optimal route for a query. > > First, a common check list I've seen around when researching these > types of issues on list: > > 1. pgsql version is PostgreSQL 9.4.7 running on Amazon RDS > 2. Vacuum analyze was run > 3. default_statistics_target was increased to 10,000 for the whole > test database and re-analyzed > > Code and info referenced is in a github gist [1]. The files are: > > [Q-Report.sql] The actual SQL query I'm trying to optimize, runs a > report on a large set of data > [QP-Report-Normal.txt] The query plan of Q-Report.sql without any > modification of the query plan > [QP-Report-DisableHashJoinltxt] The query plan of Q-Report.sql after > `set enable_hashjoin = false` > [Q-CC-Underestimated.sql] An isolated query that shows 1 of > potentially 2 significant row count underestimations > [QP-CC-Understimated.txt] The query plan of Q-CC-Underestimated.sql > > Essentially, my report query has a hash join that is taking about 70% > of the query time (45s), whereas a merge join seems to take less than > a second. > > It looks like there are probably 2 significant row underestimations > which may be contributing to this (first of which is the Q-CC query), > but I haven't been able to validate that this is specifically the > reason the query planner picks a hash join. > > My usage on this schema are mostly queries that get very small slices > of data, however this particular query is a background workload that > generates denormalized reports. That said, any kind of global > configuration being added that would jeopardize the performance of the > small-slice queries is less desirable. > > My question is, what would be the right way to go about diagnosing and > optimizing this kind of issue? I'm trying to think of ways to > potentially alter the structure of the query to reduce impact of the > planning estimates, but I'm not coming up with any good ideas there. > As mentioned, stats target was increased to 10,000 for testing and > while it changed the row estimates, it was only by about 12 rows -- > did I maybe do something wrong there? > > With this particular kind of query, I feel that I'd be willing to > trade out for a vast "overestimation" scenario for the analyzer, but I > can't find anything that allows me to tweak this for a particular > query. I've come across pg_hint_plan but doesn't seem like it would > support my version of PSQL and I'd prefer to be able to stay on RDS if > possible. > > I came across a pending patch for multivariate statistics, but that > seems a little bit far out at this point. > > Any advice for workarounds or solutions would be greatly appreciated! > > Thanks in advance, > Branden > > [1] https://gist.github.com/mrvisser/a22dddbdaa64ae1aa3dbc9637617b70d -- Sent via pgsql-general mailing list (pgsql-general@postgresql.org) To make changes to your subscription: http://www.postgresql.org/mailpref/pgsql-general
