Обсуждение: Postgres scalability and performance on windows

Gopal wrote:

> Functions : Simple coordinate reprojection and intersection query +
> inner join of table1 and table2.
>
> I think I have all the right indexes defined and indeed the performance
> for  queries under low loads is fast.

Can you do a EXPLAIN ANALYZE on your queries, and send the results back
to the list just to be sure?

> SQL server caches all the data in memory which is making it faster(uses
> about 1.2GB memory- which is fine).
>
> But postgres has everything spread across 10-15 processes, with each
> process using about 10-30MB, not nearly enough to cache all the data and
> ends up doing a lot of disk reads.

I don't know Windows memory management very well, but let me just say
that it's not that simple.

> I've read that postgres depends on OS to cache the files, I wonder if
> this is not happenning on windows.

Using the Task Manager, or whatever it's called these days, you can see
how much memory is used for caching.

--
   Heikki Linnakangas
   EnterpriseDB   http://www.enterprisedb.com

Am 23.11.2006 um 23:37 schrieb Gopal:
> hared_buffers = 20000                            # min 16 or
> max_connections*2, 8KB each
If this is not a copy & paste error, you should add the "s" at the
beginning of the line.

Also you might want to set this to a higher number. You are setting
about 20000 * 8k = 160MB, this number might be a bit too small if you
do a lot of queries spread over the whole dataset. I don't know
whether the memory management on Windows handles this well, but you
can give it a try.
> effective_cache_size = 82728                  # typically 8KB each
Hmm. I don't know what the real effect of this might be as the doc
states:

"This parameter has no effect on the size of shared memory allocated
by PostgreSQL, nor does it reserve kernel disk cache; it is used only
for estimation purposes."

You should try optimizing your shared_buffers to cache more of the data.
> But postgres has everything spread across 10-15 processes, with
> each process using about 10-30MB, not nearly enough to cache all
> the data and ends up doing a lot of disk reads.
It's not soo easy. PostgreSQL maintains a shared_buffer which is
accessible by all processes for reading. On a Unix system you can see
this in the output of top - don't know how this works on Windows.
> In any case I cannot believe that having 15-20 processes running on
> windows helps. Why not spwan of threads instead of processes, which
> migh be far less expensive and more efficient. Is there any way of
> doing this?
Because it brings you a whole lot of other problems? And because
PostgreSQL is not "made for Windows". PostgreSQL runs very good on
Linux, BSD, Mac OS X and others. The Windows version is quite young.

But before you blame stuff on PostgreSQL you should give more
information about the query itself.
>  My question is, should I just accept the performance I am getting
> as the limit on windows or should I be looking at some other params
> that I might have missed?
Post the "explain analyse select <your query here>" output here. That
might help to understand, why you get such a high CPU load.

cug

Hi,

Thanks for your suggestions. Here’s an output of the explain analyse. I’ll change the shared_buffers and look at the behaviour again.

"Limit  (cost=59.53..59.53 rows=1 width=28) (actual time=15.681..15.681 rows=1 loops=1)"

"  ->  Sort  (cost=59.53..59.53 rows=1 width=28) (actual time=15.678..15.678 rows=1 loops=1)"

"        Sort Key: ceil(subq.percentcover), (1::double precision / ds.maxresolution)"

"        ->  Hash Join  (cost=58.19..59.52 rows=1 width=28) (actual time=15.630..15.663 rows=2 loops=1)"

"              Hash Cond: ("outer".datasetid = "inner".did)"

"              ->  Seq Scan on tbl_metadata_dataset ds  (cost=0.00..1.21 rows=21 width=24) (actual time=0.006..0.021 rows=21 loops=1)"

"              ->  Hash  (cost=58.18..58.18 rows=1 width=12) (actual time=15.591..15.591 rows=2 loops=1)"

"                    ->  Sort  (cost=58.17..58.17 rows=1 width=117) (actual time=15.585..15.586 rows=2 loops=1)"

"                          Sort Key: tbl_metadata_chunks.datasetid"

"                          ->  HashAggregate  (cost=58.13..58.16 rows=1 width=117) (actual time=15.572..15.573 rows=2 loops=1)"

"                                ->  Hash IN Join  (cost=3.34..58.10 rows=7 width=117) (actual time=0.261..0.544 rows=50 loops=1)"

"                                      Hash Cond: ("outer".datasetid = "inner".datasetid)"

"                                      ->  Bitmap Heap Scan on tbl_metadata_chunks  (cost=2.05..56.67 rows=14 width=117) (actual time=0.204..0.384 rows=60 loops=1)"

"                                            Filter: (chunkgeometry && '0103000020E6100000010000000500000058631EDF87ECC1BF608F3D1911694940A0958A8763C9C1BF535069BA846C494026B5F1284FABB8BFAB1577356E6C494094E1170D33F3B8BF7700CC99FA68494058631EDF87ECC1BF608F3D1 (..)"

"                                            ->  Bitmap Index Scan on tbl_metadata_chunks_idx3  (cost=0.00..2.05 rows=14 width=0) (actual time=0.192..0.192 rows=60 loops=1)"

"                                                  Index Cond: (chunkgeometry && '0103000020E6100000010000000500000058631EDF87ECC1BF608F3D1911694940A0958A8763C9C1BF535069BA846C494026B5F1284FABB8BFAB1577356E6C494094E1170D33F3B8BF7700CC99FA68494058631EDF87ECC (..)"

"                                      ->  Hash  (cost=1.26..1.26 rows=10 width=4) (actual time=0.037..0.037 rows=10 loops=1)"

"                                            ->  Seq Scan on tbl_metadata_dataset  (cost=0.00..1.26 rows=10 width=4) (actual time=0.005..0.024 rows=10 loops=1)"

"                                                  Filter: (typeofdataid = 1)"

"Total runtime: 15.871 ms"

Gopal

On Fri, 24 Nov 2006 09:22:45 +0100
Guido Neitzer <lists@event-s.net> wrote:

> > effective_cache_size = 82728                  # typically 8KB each
> Hmm. I don't know what the real effect of this might be as the doc
> states:
>
> "This parameter has no effect on the size of shared memory allocated
> by PostgreSQL, nor does it reserve kernel disk cache; it is used
> only for estimation purposes."

   This is a hint to the optimizer about how much of the database may
   be in the OS level cache.

 ---------------------------------
   Frank Wiles <frank@wiles.org>
   http://www.wiles.org
 ---------------------------------

"Gopal" <gopal@getmapping.com> writes:
> Thanks for your suggestions. Here's an output of the explain analyse.

What's the query exactly, and what are the schemas of the tables it
uses (psql \d descriptions would do)?

The actual runtime seems to be almost all spent in the hash aggregation
step:

>          ->  HashAggregate  (cost=58.13..58.16 rows=1 width=117) (actual time=15.572..15.573 rows=2 loops=1)
>              ->  Hash IN Join  (cost=3.34..58.10 rows=7 width=117) (actual time=0.261..0.544 rows=50 loops=1)

15 msec seems like a long time to aggregate only 50 rows, so I'm
wondering what aggregates are being calculated and over what
datatypes...

            regards, tom lane

Tom,

This is the query and the schema....

Query is :
SELECT  subq.percentCover, ds.datasetname, ds.maxresolution
                FROM
                (
                        select
sum(area(intersection(snaptogrid(chunkgeometry,0.00000001),
                        GeometryFromText('POLYGON((-0.140030845589332
50.8208343077265,-0.138958398039148 50.8478005422809,-0.0963639712296823
50.8471133071392,-0.0974609286275892 50.8201477285483,-0.140030845589332
50.8208343077265))',4326))) * 100/ (0.00114901195862628)) as
percentCover,
                        datasetid as did from
                        tbl_metadata_chunks
                        where chunkgeometry &&
GeometryFromText('POLYGON((-0.140030845589332
50.8208343077265,-0.138958398039148 50.8478005422809,-0.0963639712296823
50.8471133071392,-0.0974609286275892 50.8201477285483,-0.140030845589332
50.8208343077265))',4326)
                        and datasetid in (select datasetid from
tbl_metadata_dataset where typeofdataid=1)
                        group by did
                        order by did desc
                )
                AS subq INNER JOIN tbl_metadata_dataset AS
                ds ON subq.did = ds.datasetid
                ORDER by ceil(subq.percentCover),1/ds.maxresolution
DESC;


Schema is

Table 1
CREATE TABLE public.tbl_metadata_dataset
(
datasetname varchar(70) NOT NULL,
maxresolution real,
typeofdataid integer NOT NULL,
datasetid serial NOT NULL,
CONSTRAINT "PK_Dataset" PRIMARY KEY (datasetid)
);
-- Indexes
CREATE INDEX dsnameindex ON tbl_metadata_dataset USING btree
(datasetname);-- Owner
ALTER TABLE public.tbl_metadata_dataset OWNER TO postgres;
-- Triggers
CREATE CONSTRAINT TRIGGER "RI_ConstraintTrigger_2196039" AFTER DELETE ON
tbl_metadata_dataset FROM tbl_metadata_chunks NOT DEFERRABLE INITIALLY
IMMEDIATE FOR EACH ROW EXECUTE PROCEDURE "RI_FKey_noaction_del"('dsid',
'tbl_metadata_chunks', 'tbl_metadata_dataset', 'UNSPECIFIED',
'datasetid', 'datasetid');
CREATE CONSTRAINT TRIGGER "RI_ConstraintTrigger_2196040" AFTER UPDATE ON
tbl_metadata_dataset FROM tbl_metadata_chunks NOT DEFERRABLE INITIALLY
IMMEDIATE FOR EACH ROW EXECUTE PROCEDURE "RI_FKey_noaction_upd"('dsid',
'tbl_metadata_chunks', 'tbl_metadata_dataset', 'UNSPECIFIED',
'datasetid', 'datasetid');


Table 2

CREATE TABLE public.tbl_metadata_chunks
(
chunkid serial NOT NULL,
chunkgeometry geometry NOT NULL,
datasetid integer NOT NULL,
CONSTRAINT tbl_metadata_chunks_pkey PRIMARY KEY (chunkid),
CONSTRAINT dsid FOREIGN KEY (datasetid) REFERENCES
tbl_metadata_dataset(datasetid)
);
-- Indexes
CREATE INDEX idx_dsid ON tbl_metadata_chunks USING btree (datasetid);
CREATE UNIQUE INDEX tbl_metadata_chunks_idx2 ON tbl_metadata_chunks
USING btree (nativetlx, nativetly, datasetid);
CREATE INDEX tbl_metadata_chunks_idx3 ON tbl_metadata_chunks USING gist
(chunkgeometry);-- Owner
ALTER TABLE public.tbl_metadata_chunks OWNER TO postgres;
-- Triggers
CREATE CONSTRAINT TRIGGER "RI_ConstraintTrigger_2194515" AFTER DELETE ON
tbl_metadata_chunks FROM tbl_metadata_chunkinfo NOT DEFERRABLE INITIALLY
IMMEDIATE FOR EACH ROW EXECUTE PROCEDURE "RI_FKey_restrict_del"('fk',
'tbl_metadata_chunkinfo', 'tbl_metadata_chunks', 'UNSPECIFIED',
'chunkid', 'chunkid');
CREATE CONSTRAINT TRIGGER "RI_ConstraintTrigger_2194516" AFTER UPDATE ON
tbl_metadata_chunks FROM tbl_metadata_chunkinfo NOT DEFERRABLE INITIALLY
IMMEDIATE FOR EACH ROW EXECUTE PROCEDURE "RI_FKey_restrict_upd"('fk',
'tbl_metadata_chunkinfo', 'tbl_metadata_chunks', 'UNSPECIFIED',
'chunkid', 'chunkid');
CREATE CONSTRAINT TRIGGER "RI_ConstraintTrigger_2196037" AFTER INSERT ON
tbl_metadata_chunks FROM tbl_metadata_dataset NOT DEFERRABLE INITIALLY
IMMEDIATE FOR EACH ROW EXECUTE PROCEDURE "RI_FKey_check_ins"('dsid',
'tbl_metadata_chunks', 'tbl_metadata_dataset', 'UNSPECIFIED',
'datasetid', 'datasetid');
CREATE CONSTRAINT TRIGGER "RI_ConstraintTrigger_2196038" AFTER UPDATE ON
tbl_metadata_chunks FROM tbl_metadata_dataset NOT DEFERRABLE INITIALLY
IMMEDIATE FOR EACH ROW EXECUTE PROCEDURE "RI_FKey_check_upd"('dsid',
'tbl_metadata_chunks', 'tbl_metadata_dataset', 'UNSPECIFIED',
'datasetid', 'datasetid');



-----Original Message-----
From: Frank Wiles [mailto:frank@wiles.org]
Sent: 24 November 2006 17:05
To: Guido Neitzer
Cc: Gopal; pgsql-performance@postgresql.org
Subject: Re: [PERFORM] Postgres scalability and performance on windows

On Fri, 24 Nov 2006 09:22:45 +0100
Guido Neitzer <lists@event-s.net> wrote:

> > effective_cache_size = 82728                  # typically 8KB each
> Hmm. I don't know what the real effect of this might be as the doc
> states:
>
> "This parameter has no effect on the size of shared memory allocated
> by PostgreSQL, nor does it reserve kernel disk cache; it is used
> only for estimation purposes."

   This is a hint to the optimizer about how much of the database may
   be in the OS level cache.

 ---------------------------------
   Frank Wiles <frank@wiles.org>
   http://www.wiles.org
 ---------------------------------


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"Gopal" <gopal@getmapping.com> writes:
> This is the query and the schema....
> ...
>                         select
> sum(area(intersection(snaptogrid(chunkgeometry,0.00000001),
>                         GeometryFromText('POLYGON((-0.140030845589332
> 50.8208343077265,-0.138958398039148 50.8478005422809,-0.0963639712296823
> 50.8471133071392,-0.0974609286275892 50.8201477285483,-0.140030845589332
> 50.8208343077265))',4326))) * 100/ (0.00114901195862628)) as
> percentCover,

So evidently area(intersection(snaptogrid(...))) takes about 300
microsec per row.  The PostGIS hackers would have to comment on whether
that seems out-of-line or not, and whether you can make it faster.

            regards, tom lane

On Nov 28, 2006, at 8:24 AM, Tom Lane wrote:
> "Gopal" <gopal@getmapping.com> writes:
>> This is the query and the schema....
>> ...
>>                         select
>> sum(area(intersection(snaptogrid(chunkgeometry,0.00000001),
>>                         GeometryFromText('POLYGON((-0.140030845589332
>> 50.8208343077265,-0.138958398039148
>> 50.8478005422809,-0.0963639712296823
>> 50.8471133071392,-0.0974609286275892
>> 50.8201477285483,-0.140030845589332
>> 50.8208343077265))',4326))) * 100/ (0.00114901195862628)) as
>> percentCover,
>
> So evidently area(intersection(snaptogrid(...))) takes about 300
> microsec per row.  The PostGIS hackers would have to comment on
> whether
> that seems out-of-line or not, and whether you can make it faster.


This is consistent with the typical cost for GIS geometry ops -- they
are relatively expensive.  When running queries against PostGIS
fields for our apps, about half the CPU time will be spent inside the
geometry ops.  Fortunately, there is significant opportunity for
improvement in the performance of the underlying code if anyone found
the time to optimize (and uglify) it for raw speed.


Cheers,

J. Andrew Rogers