I originally posted the question below back in Dec 2006, and many
helpful suggestions resulted. Unfortunately, since this was a closet
effort, my official duties pushed further exploration to the back
burner, then I lost my original test environment. So while I can no
longer compare to BigDBMS, I've just made some discoveries that I
thought others might find helpful.
The app (which I inherited) was implemented making exhaustive use of
stored procedures. All inserts and updates are done using procs. When
configuration changes produced no noticeable improvements in
performance, I turned to the application architecture. In a new
environment, I updated an insert/update intensive part of the app to use
embedded insert and update statements instead of invoking stored
procedures that did the same work. All the remaining code, database
implementation, hardware, etc remains the same.
The results were significant. Running a repeatable test set of data
produced the following results:
With stored procs: 2595 seconds
With embedded inserts/updates: 991 seconds
So at least in this one scenario, it looks like the extensive use of
stored procs is contributing significantly to long run times.
Guy Rouillier wrote:
I don't want to violate any license agreement by discussing performance,
so I'll refer to a large, commercial PostgreSQL-compatible DBMS only as
BigDBMS here.
I'm trying to convince my employer to replace BigDBMS with PostgreSQL
for at least some of our Java applications. As a proof of concept, I
started with a high-volume (but conceptually simple) network data
collection application. This application collects files of 5-minute
usage statistics from our network devices, and stores a raw form of
these stats into one table and a normalized form into a second table. We
are currently storing about 12 million rows a day in the normalized
table, and each month we start new tables. For the normalized data, the
app inserts rows initialized to zero for the entire current day first
thing in the morning, then throughout the day as stats are received,
executes updates against existing rows. So the app has very high update
activity.
In my test environment, I have a dual-x86 Linux platform running the
application, and an old 4-CPU Sun Enterprise 4500 running BigDBMS and
PostgreSQL 8.2.0 (only one at a time.) The Sun box has 4 disk arrays
attached, each with 12 SCSI hard disks (a D1000 and 3 A1000, for those
familiar with these devices.) The arrays are set up with RAID5. So I'm
working with a consistent hardware platform for this comparison. I'm
only processing a small subset of files (144.)
BigDBMS processed this set of data in 20000 seconds, with all foreign
keys in place. With all foreign keys in place, PG took 54000 seconds to
complete the same job. I've tried various approaches to autovacuum
(none, 30-seconds) and it doesn't seem to make much difference. What
does seem to make a difference is eliminating all the foreign keys; in
that configuration, PG takes about 30000 seconds. Better, but BigDBMS
still has it beat significantly.
I've got PG configured so that that the system database is on disk array
2, as are the transaction log files. The default table space for the
test database is disk array 3. I've got all the reference tables (the
tables to which the foreign keys in the stats tables refer) on this
array. I also store the stats tables on this array. Finally, I put the
indexes for the stats tables on disk array 4. I don't use disk array 1
because I believe it is a software array.
I'm out of ideas how to improve this picture any further. I'd
appreciate some suggestions. Thanks.
--
Guy Rouillier
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