On 7/26/06, nuggets72@xxxxxxx <nuggets72@xxxxxxx> wrote:
Hello,
Sorry for my poor english,
My problem :
I meet some performance problem during load increase.
massive update of 50.000.000 records and 2.000.000 insert with a weekly
frequency in a huge table (+50.000.000 records, ten fields, 12 Go on hard disk)
current performance obtained : 120 records / s
At the beginning, I got a better speed : 1400 records/s
CPU : bi xeon 2.40GHz (cache de 512KB)
postgresql version : 8.1.4
OS : debian Linux sa 2.6.17-mm2
Hard disk scsi U320 with scsi card U160 on software RAID 1
Memory : only 1 Go at this time.
My database contains less than ten tables. But the main table takes more than 12
Go on harddisk. This table has got ten text records and two date records.
I use few connection on this database.
I try many ideas :
- put severals thousands operations into transaction (with BEGIN and COMMIT)
- modify parameters in postgres.conf like
shared_buffers (several tests with 30000 50000 75000)
fsync = off
checkpoint_segments = 10 (several tests with 20 - 30)
checkpoint_timeout = 1000 (30-1800)
stats_start_collector = off
unfortunately, I can't use another disk for pg_xlog file.
But I did not obtain a convincing result
My program does some resquest quite simple.
It does some
UPDATE table set dat_update=current_date where id=XXXX ;
And if not found
id does some
insert into table
My sysadmin tells me write/read on hard disk aren't the pb (see with iostat)
your sysadmin is probably wrong. random query across 50m table on
machine with 1gb memory is going to cause alot of seeking. take a
look at your pg data folder and you will see it is much larger than
1gb. a lookup of a cached tuple via a cached index might take 0.2ms,
and might take 200ms if it has to completely to disk on a 50m table.
normal reality is somehwere in between depending on various factors.
my guess is that as you add more memory, the time will drift from the
slow case (120/sec) to the fast case (1400/sec).
you may consider the following alternative:
1. bulk load your 2m update set into scratch table via copy interface
2. update table set dat_update=current_date where table.id=scratch.id
3. insert into table select [...], current_date where not exists
(select id from table where table.id = scratch.id);
you may experiment with boolean form of #3, using 'except' also.
while running these monster queries definately crank up work mem in
expesnse of shared buffers.
merlin
im am guessing you are bottlenecked at the lookup, not the update. so, if
