On 08/18/2011 05:55 PM, Midge Brown wrote:
> DB1 is 10GB and consists of multiple tables that I've spread out so
> that the 3 most used have their data and indexes on 6 separate RAID1
> drives, the 3 next busiest have data & index on 3 drives, and the
> remaining tables and indexes are on the RAID10 drive. The WAL for all
> is on a separate RAID1 drive.
> DB2 is 25GB with data, index, and WAL all on separate RAID1 drives.
> DB3 is 15GB with data, index, and WAL on separate RAID1 drives.
Anytime you have a set of disks and a set of databases/tables to lay out
onto them, there are two main options to consider:
-Put all of them into a single RAID10 array. Performance will be high
now matter what subset of the database is being used. But if one
particular part of a database is really busy, it can divert resources
away from the rest.
-Break the database into fine-grained pieces and carefully lay out each
of them on disk. Performance of any individual chunk will be steady
here. But if only a subset of the data is being used, server resources
will be idle. All of the disks that don't have data related to that
will be unused.
Consider two configurations following these ideas:
1) 12 disks are placed into a large RAID10 array. Peak transfer rate
will be about 600MB/s on sequential scans.
2) 6 RAID1 arrays are created and the database is manually laid out onto
those disks. Peak transfer rate from any one section will be closer to
Each of these is optimizing for a different use scenario. Here's the
best case for each:
-One user is active, and they're hitting one of the database sections.
In setup (1) they might get 600MB/s, the case where it shows the most
benefit. In setup (2), they'd only get 100MB/s.
-10 users are pounding one section of the database; 1 user is hitting a
different section. In setup (2), all 10 users will be fighting over
access to one section of the disk, each getting (at best) 10MB/s of its
transfers. The nature of random I/O means that it will likely be much
worse for them. Meanwhile, the user hitting the other database section
will still be merrily chugging away getting their 100MB/s. Had setup
(1) been used, you'd have 11 users fighting over 600MB/s, so at best
55MB/s for each. And with the random mix, it could be much worse.
Which of these is better? Well, (1) is guaranteed to use your hardware
to its fullest capability. There are some situations where contention
over the disk array will cause performance to be lower for some people,
compared to if they had an isolated environment split up more like (2).
But the rest of the time, (2) will have taken a large number of disks
and left them idle. The second example shows this really well. The
mere fact that you have such a huge aggregate speed available means that
the big array really doesn't necessarily suffer that badly from a heavy
load. It has 6X as much capacity to handle them. You really need to
have a >6:1 misbalance in access before the carefully laid out version
pulls ahead. In every other case, the big array wins.
You can defend (2) as the better choice if you have really compelling,
hard data proving use of the various parts of the data is split quite
evenly among the expected incoming workload. If you have response time
latency targets that require separating resources evenly among the
various types of users, it can also make sense there. I don't know if
the data you've been collecting from your older version is good enough
to know that for sure or not.
In every other case, you'd be better off just dumping the whole pile
into a single, large array, and letting the array and operating system
figure out how to schedule things best. That why this is the normal
practice for building PostgreSQL systems. The sole exception is that
splitting out the pg_xlog filesystem can usually be justified in a
larger array. The fact that it's always sequential I/O means that
mixing its work with the rest of the server doesn't work as well as
giving it a dedicated pair of drives to write to, where it doesn't ever
stop to seek somewhere else.
> wal_buffers = 32MB
This might as well drop to 16MB. And you've already gotten some
warnings about work_mem. Switching to a connection pooler would help
with that, too.
> autovacuum_analyze_threshold = 250
> autovacuum_naptime = 10min
> autovacuum_vacuum_threshold = 250
> vacuum_cost_delay = 10ms
This strikes me as more customization than you really should be doing to
autovacuum, if you haven't been running on a recent version of
PostgreSQL yet. You shouldn't ever need to touch the thresholds for
example. Those only matter on really small tables; once something gets
big enough to really matter, the threshold part is really small compared
to the scale factor one. And the defaults are picked partly so that
cleanup of the system catalog tables is done frequently enough. You're
slowing that cleanup by moving the thresholds upward so much, and that's
not a great idea.
For similar reasons, you really shouldn't be touching autovacuum_naptime
unless there's really good evidence it's necessary for your environment.
Changing things such that regular vacuums executed at the command line
happen with a cost delay like this should be fine though. Those will
happen using twice as many resources as the autovacuum ones, but not run
as fast as possible as in the normal case.
> deadlock_timeout = 3s
You probably don't want to increase this. When you reach the point
where you want to find slow lock issues by turning on log_lock_waits,
you're just going to put it right back to the default again--or lower it.
Greg Smith 2ndQuadrant US greg(at)2ndQuadrant(dot)com Baltimore, MD
PostgreSQL Training, Services, and 24x7 Support www.2ndQuadrant.us
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