On Apr 10, 2008, at 9:44 AM, John Beaver wrote:
Thanks a lot, all of you - this is excellent advice. With the data
clustered and statistics at a more reasonable value of 100, it now
reproducibly takes even less time - 20-57 ms per query.
After reading the section on "Statistics Used By the Planner" in the
manual, I was a little concerned that, while the statistics sped up
the queries that I tried immeasurably, that the most_common_vals
array was where the speedup was happening, and that the values which
wouldn't fit in this array wouldn't be sped up. Though I couldn't
offhand find an example where this occurred, the clustering approach
seems intuitively like a much more complete and scalable solution,
at least for a read-only table like this.
As to whether the entire index/table was getting into ram between my
statistics calls, I don't think this was the case. Here's the
behavior that I found:
- With statistics at 10, the query took 25 (or so) seconds no matter
how many times I tried different values. The query plan was the same
as for the 200 and 800 statistics below.
- Trying the same constant a second time gave an instantaneous
result, I'm guessing because of query/result caching.
- Immediately on increasing the statistics to 200, the query took a
reproducibly less amount of time. I tried about 10 different values
- Immediately on increasing the statistics to 800, the query
reproducibly took less than a second every time. I tried about 30
different values.
- Decreasing the statistics to 100 and running the cluster command
brought it to 57 ms per query.
- The Activity Monitor (OSX) lists the relevant postgres process as
taking a little less than 500 megs.
- I didn't try decreasing the statistics back to 10 before I ran the
cluster command, so I can't show the search times going up because
of that. But I tried killing the 500 meg process. The new process
uses less than 5 megs of ram, and still reproducibly returns a
result in less than 60 ms. Again, this is with a statistics value of
100 and the data clustered by gene_prediction_view_gene_ref_key.
And I'll consider the idea of using triggers with an ancillary table
for other purposes; seems like it could be a useful solution for
something.
FWIW, killing the backend process responsible for the query won't
necessarily clear the table's data from memory as that will be in the
shared_buffers. If you really want to flush the data from memory you
need to read in data from other tables of a size total size greater
than your shared_buffers setting.
Erik Jones
DBA | Emma®
[EMAIL PROTECTED]
800.595.4401 or 615.292.5888
615.292.0777 (fax)
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