Re: large tables and simple "= constant" queries using indexes

Hi John,

You don't use the same 'gene_ref ='-value, so its not a perfect
comparison. And obviously, there is the fact that the data can be in the
disk cache, the second time you run it, which would explain the almost
instantaneous result for the second query.

If repeating the query a few times with 200 still makes it do its work
in 15 seconds and with 800 in less than 100ms, than you might have found
a bug, or it is at least something I don't know how to fix.

I doubt upping the default for all tables to 1000 is a good idea. The
data collected is used in the query-planning-stage, where more data
means more processing time. Obviously there is a tradeoff somewhere
between having more statistics and thus being able to plan the query
better versus requiring more time to process those statistics.

Best regards,

Arjen

On 10-4-2008 0:24 John Beaver wrote:
> Perfect - thanks Arjen. Using your value of 200 decreased the time to 15
> seconds, and using a value of 800 makes it almost instantaneous. I'm
> really not concerned about space usage; if having more statistics
> increases performance this much, maybe I'll just default it to 1000?
>
> Strangely, the steps taken in the explain analyze are all the same. The
> only differences are the predicted costs (and execution times).
>
> explain analyze for a statistics of 200:
> "Aggregate (cost=8831.27..8831.28 rows=1 width=0) (actual
> time=15198.407..15198.408 rows=1 loops=1)"
> " -> Bitmap Heap Scan on gene_prediction_view (cost=44.16..8825.29
> rows=2392 width=0) (actual time=19.719..15191.875 rows=2455 loops=1)"
> " Recheck Cond: (gene_ref = 500)"
> " -> Bitmap Index Scan on ix_gene_prediction_view_gene_ref
> (cost=0.00..43.56 rows=2392 width=0) (actual time=18.871..18.871
> rows=2455 loops=1)"
> " Index Cond: (gene_ref = 500)"
> "Total runtime: 15198.651 ms"
>
> explain analyze for a statistics of 800:
> "Aggregate (cost=8873.75..8873.76 rows=1 width=0) (actual
> time=94.473..94.473 rows=1 loops=1)"
> " -> Bitmap Heap Scan on gene_prediction_view (cost=44.25..8867.74
> rows=2404 width=0) (actual time=39.358..93.733 rows=2455 loops=1)"
> " Recheck Cond: (gene_ref = 301)"
> " -> Bitmap Index Scan on ix_gene_prediction_view_gene_ref
> (cost=0.00..43.65 rows=2404 width=0) (actual time=38.472..38.472
> rows=2455 loops=1)"
> " Index Cond: (gene_ref = 301)"
> "Total runtime: 94.622 ms"
>
>
>
>
> Arjen van der Meijden wrote:
>> First of all, there is the 'explain analyze' output, which is pretty
>> helpful in postgresql.
>>
>> My guess is, postgresql decides to do a table scan for some reason. It
>> might not have enough statistics for this particular table or column,
>> to make a sound decision. What you can try is to increase the
>> statistics target, which works pretty easy:
>> ALTER TABLE gene_prediction_view ALTER gene_ref SET STATISTICS 200;
>>
>> Valid ranges are from 1(0?) - 1000, the default is 10, the default on
>> my systems is usually 100. For such a large table, I'd go with 200.
>>
>> After that, you'll need to re-analyze your table and you can try again.
>>
>> Perhaps analyze should try to establish its own best guess to how many
>> samples it should take? The default of 10 is rather limited for large
>> tables.
>>
>> Best regards,
>>
>> Arjen
>>
>> On 9-4-2008 22:58 John Beaver wrote:
>>> Hi, I've started my first project with Postgres (after several years
>>> of using Mysql), and I'm having an odd performance problem that I was
>>> hoping someone might be able to explain the cause of.
>>>
>>> ----My query----
>>> - select count(*) from gene_prediction_view where gene_ref = 523
>>> - takes 26 seconds to execute, and returns 2400 (out of a total of
>>> 15 million records in the table)
>>>
>>> ---My problem---
>>> Using a single-column index to count 2400 records which are
>>> exactly one constant value doesn't sound like something that would
>>> take 26 seconds. What's the slowdown? Any silver bullets that might
>>> fix this?
>>>
>>> ----Steps I've taken----
>>> - I ran vacuum and analyze
>>> - I upped the shared_buffers to 58384, and I upped some of the
>>> other postgresql.conf values as well. Nothing seemed to help
>>> significantly, but maybe I missed something that would help
>>> specifically for this query type?
>>> - I tried to create a hash index, but gave up after more than 4
>>> hours of waiting for it to finish indexing
>>>
>>> ----Table stats----
>>> - 15 million rows; I'm expecting to have four or five times this
>>> number eventually.
>>> - 1.5 gigs of hard drive usage
>>>
>>> ----My development environment---
>>> - 2.6ghz dual-core MacBook Pro with 4 gigs of ram and a 7200 rpm
>>> hard drive
>>> - OS X 10.5.2
>>> - Postgres 8.3 (installed via MacPorts)
>>>
>>> ----My table----
>>>
>>> CREATE TABLE gene_prediction_view
>>> (
>>> id serial NOT NULL,
>>> gene_ref integer NOT NULL,
>>> go_id integer NOT NULL,
>>> go_description character varying(200) NOT NULL,
>>> go_category character varying(50) NOT NULL,
>>> function_verified_exactly boolean NOT NULL,
>>> function_verified_with_parent_go boolean NOT NULL,
>>> function_verified_with_child_go boolean NOT NULL,
>>> score numeric(10,2) NOT NULL,
>>> precision_score numeric(10,2) NOT NULL,
>>> CONSTRAINT gene_prediction_view_pkey PRIMARY KEY (id),
>>> CONSTRAINT gene_prediction_view_gene_ref_fkey FOREIGN KEY (gene_ref)
>>> REFERENCES sgd_annotations (id) MATCH SIMPLE
>>> ON UPDATE NO ACTION ON DELETE NO ACTION,
>>> CONSTRAINT gene_prediction_view_go_id_fkey FOREIGN KEY (go_id)
>>> REFERENCES go_terms (term) MATCH SIMPLE
>>> ON UPDATE NO ACTION ON DELETE NO ACTION,
>>> CONSTRAINT gene_prediction_view_gene_ref_key UNIQUE (gene_ref, go_id)
>>> )
>>> WITH (OIDS=FALSE);
>>> ALTER TABLE gene_prediction_view OWNER TO postgres;
>>>
>>> CREATE INDEX ix_gene_prediction_view_gene_ref
>>> ON gene_prediction_view
>>> USING btree
>>> (gene_ref);
>>>
>>>
>>>
>>>
>>
>