Thread: Large table performance

Hello -

I have a fairly large table (3 million records), and am fetching 10,000 non-contigous records doing a simple select on an indexed column ie

select grades from large_table where teacher_id = X

This is a test database, so the number of records is always 10,000 and i have 300 different teacher ids.

The problem is, sometimes fetching un-cached records takes 0.5 secs and sometimes (more often) is takes more like 10.0 seconds

(fetching the same records for a given teacher_id a second time takes about 0.25 secs)

Has anyone seen similar behavior or know what the solution might be?

any help much appreciated,
Mark



ps. My shared_buffers is set at 5000 (kernal max), and work_mem=8192

On 12-Jan-07, at 7:31 PM, Mark Dobbrow wrote:

> Hello -
>
> I have a fairly large table (3 million records), and am fetching
> 10,000 non-contigous records doing a simple select on an indexed
> column ie
>
> select grades from large_table where teacher_id = X
>
> This is a test database, so the number of records is always 10,000
> and i have 300 different teacher ids.
>
> The problem is, sometimes fetching un-cached records takes 0.5 secs
> and sometimes (more often) is takes more like 10.0 seconds
>
> (fetching the same records for a given teacher_id a second time
> takes about 0.25 secs)
>
> Has anyone seen similar behavior or know what the solution might be?
>
> any help much appreciated,
> Mark
>
>
>
> ps. My shared_buffers is set at 5000 (kernal max), and work_mem=8192

5000 is pretty low, you need at least 1/4 of memory for an 8.1.x or
newer server.
effective cache should be 3/4 of available memory

Dave
>
>

On Fri, Jan 12, 2007 at 07:40:25PM -0500, Dave Cramer wrote:
> 5000 is pretty low, you need at least 1/4 of memory for an 8.1.x or
> newer server.

Is this the new "common wisdom"? It looks like at some point, someone here
said "oh, and it looks like you're better off using large values here for
8.1.x and newer", and now everybody seems to repeat it as if it was always
well-known.

Are there any real benchmarks out there that we can point to? And, if you set
shared_buffers to half of the available memory, won't the kernel cache
duplicate more or less exactly the same data? (At least that's what people
used to say around here, but I guess the kernel cache gets adapted to the
fact that Postgres won't ask for the most common stuff, ie. the one in the
shared buffer cache.)

/* Steinar */
--
Homepage: http://www.sesse.net/

What if we start a project where we define tests for PostgreSQL
overall performance and individual points with any database structure?
It could be done, throught a SQL logger and statistics, where we can
see complete processess and measure then after. We have many things to
measure, and something that would help here is pg_buffercache (contrib
module). We could define many other tests.

I was thinking about something like that, where an aplication reads
information (from catalog too) about an production database, and use
this information to build a data set of any size, respecting anything
measured before.

Is it too complicated? I'm trying to make programs with C++ and
libpqxx, and successfully used Python with PostgreSQL before (was a
database structure comparer). Python could make it easyer, C++ could
be a chalenge for someone like me.

Someone would like to contribute? When we start the project? :)

On 1/12/07, Steinar H. Gunderson <sgunderson@bigfoot.com> wrote:
> On Fri, Jan 12, 2007 at 07:40:25PM -0500, Dave Cramer wrote:
> > 5000 is pretty low, you need at least 1/4 of memory for an 8.1.x or
> > newer server.
>
> Is this the new "common wisdom"? It looks like at some point, someone here
> said "oh, and it looks like you're better off using large values here for
> 8.1.x and newer", and now everybody seems to repeat it as if it was always
> well-known.
>
> Are there any real benchmarks out there that we can point to? And, if you set
> shared_buffers to half of the available memory, won't the kernel cache
> duplicate more or less exactly the same data? (At least that's what people
> used to say around here, but I guess the kernel cache gets adapted to the
> fact that Postgres won't ask for the most common stuff, ie. the one in the
> shared buffer cache.)
>
> /* Steinar */
> --
> Homepage: http://www.sesse.net/
>
> ---------------------------(end of broadcast)---------------------------
> TIP 7: You can help support the PostgreSQL project by donating at
>
>                http://www.postgresql.org/about/donate
>


--
Daniel Cristian Cruz
Analista de Sistemas
Especialista postgreSQL e Linux
Instrutor Certificado Mandriva

-----Original Message-----
From: pgsql-performance-owner@postgresql.org [mailto:pgsql-performance-owner@postgresql.org] On Behalf Of Mark Dobbrow
Sent: Friday, January 12, 2007 6:31 PM
To: pgsql-performance@postgresql.org
Subject: [PERFORM] Large table performance

Hello -

I have a fairly large table (3 million records), and am fetching 10,000 non-contigous records doing a simple select on an indexed column ie

select grades from large_table where teacher_id = X

This is a test database, so the number of records is always 10,000 and i have 300 different teacher ids.

The problem is, sometimes fetching un-cached records takes 0.5 secs and sometimes (more often) is takes more like 10.0 seconds

(fetching the same records for a given teacher_id a second time takes about 0.25 secs)

Has anyone seen similar behavior or know what the solution might be?

any help much appreciated,
Mark



ps. My shared_buffers is set at 5000 (kernal max), and work_mem=8192

Depending on the available memory try increasing the shared buffers and work_mem and see if that changes the query execution time. Also make sure you have proper indices created and also if possible try doing partitions for the table.

Once you post the EXPLAIN ANALYZE output that will certainly help solving the problem...

-------------
Shoaib Mir
EnterpriseDB (www.enterprisedb.com)

Mark,

This behavior likely depends on how the data is loaded into the DBMS.  If the records you are fetching are distributed widely among the 3M records on disk, then


On 1/12/07 4:31 PM, "Mark Dobbrow" <mdobbrow@yahoo.com> wrote:

Hello -

I have a fairly large table (3 million records), and am fetching 10,000 non-contigous records doing a simple select on an indexed column ie

select grades from large_table where teacher_id = X

This is a test database, so the number of records is always 10,000 and i have 300 different teacher ids.

The problem is, sometimes fetching un-cached records takes 0.5 secs and sometimes (more often) is takes more like 10.0 seconds

(fetching the same records for a given teacher_id a second time takes about 0.25 secs)

Has anyone seen similar behavior or know what the solution might be?

any help much appreciated,
Mark



ps. My shared_buffers is set at 5000 (kernal max), and work_mem=8192

Mark,

Note that selecting an index column means that Postgres fetches the whole rows from disk.  I think your performance problem is either: 1) slow disk or 2) index access of distributed data.  If it’s (1), there are plenty of references from this list on how to check for that and fix it.  If it’s (2), see below.

The performance of index accessed data in Postgres depends on how the data is loaded into the DBMS.  If the records you are fetching are distributed widely among the 3M records on disk, then the select is going to “hop, skip and jump” across the disk to get the records you need.  If those records are stored more closely together, then the fetching from disk is going to be sequential.  A good example of the best situation for an index is an index on a date column when the data is loaded sequentially by date.  A query against a specific date range will result in an ordered fetch from the disk, which leverages fast sequential access.

The difference in performance between ordered and distributed access is similar to the difference between “random seek” and “sequential” performance of the disk subsystem.  The random seek performance of typical disk subsystems with one thread (for one user in postgres) is 120 seeks per second.  If your data was randomly distributed, you’d expect about 10,000/120 = 83 seconds to gather these records.  Since you’re getting 10 seconds, I expect that your data is lumped into groups and you are getting a mix of sequential reads and seeks.

Note that adding more disks into a RAID does not help the random seek performance within Postgres, but may linearly improve the ordered access speed.  So even with 44 disks in a RAID10 pool on a Sun X4500, the seek performance of Postgres (and other DBMS’s without async or threaded I/O) is that of a single disk – 120 seeks per second.  Adding more users allows the seeks to scale on such a machine as users are added, up to the number of disks in the RAID.  But for your one user example – no help.

If your problem is (2), you can re-order the data on disk by using a CREATE TABLE statement like this:
  CREATE TABLE fast_table AS SELECT * FROM slow_table ORDER BY teacher_id;
  CREATE INDEX teacher_id_ix ON fast_table;
  VACUUM ANALYZE fast_table;

You should then see ordered access when you do index scans on teacher_id.

- Luke


On 1/12/07 4:31 PM, "Mark Dobbrow" <mdobbrow@yahoo.com> wrote:

Hello -

I have a fairly large table (3 million records), and am fetching 10,000 non-contigous records doing a simple select on an indexed column ie

select grades from large_table where teacher_id = X

This is a test database, so the number of records is always 10,000 and i have 300 different teacher ids.

The problem is, sometimes fetching un-cached records takes 0.5 secs and sometimes (more often) is takes more like 10.0 seconds

(fetching the same records for a given teacher_id a second time takes about 0.25 secs)

Has anyone seen similar behavior or know what the solution might be?

any help much appreciated,
Mark



ps. My shared_buffers is set at 5000 (kernal max), and work_mem=8192