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Re: Data split -- Creating a copy of database without outage

From: "Kevin Grittner" <Kevin(dot)Grittner(at)wicourts(dot)gov>
To: "Igor Shmain" <igor(dot)shmain(at)gmail(dot)com>,<pgsql-admin(at)postgresql(dot)org>
Subject: Re: Data split -- Creating a copy of database without outage
Date: 2012-06-06 16:38:35
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Lists: pgsql-admin
"Igor Shmain" <igor(dot)shmain(at)gmail(dot)com> wrote:
> Would it be possible for you to mention what hardware (cpu, ram,
> disks, etc.) and software your system uses to support this db size
> and number of transactions?
We have 4 Intel Xeon  X7350 @ 2.93GHz for 16 cores with 128GB RAM. 
We've got a pair of drives in RAID 1 for OS on its own controller,
four drives in RAID 10 for xlog directories on its own controller,
and a couple RAID 5 arrays, each about 40 drives, for our two
databases (3TB and 2TB).  I'm not exactly clear on the controller
configuration there except that I understand there are separate
paths from two controllers to each drive.  All controllers are using
battery-backed cache configured for write-back.
A machine like that is still capable of handling our current load;
but the load is always increasing so we step up the hardware each
time we replace a machine.  The new server (able to handle about
twice the load of the one I just described for our normal
transaction mix) has 4 Intel Xeon X7560 @ 2.27GHz for 32 cores with
256GB RAM.
We are replicating to each of the databases on these boxes using a
pool of 6 database connections to process data from 72 circuit court
databases and on the 2TB from other sources, like Supreme Court and
Court of Appeals, Board of Bar Examiners, etc.  For the read-only
web load we have a pool of 30 database connections.  Checking the
monitoring system for the read-only web application, at the moment
we are showing:
Active Requests: 3
Requests Per Second: 148.66
Active Sessions: 9081
This is running through a firewall to an apache web server in our
DMZ which just redirects through another firewall to a an apache web
server which just functions as a load balancer which sends the
requests to renderers (well, currently just one, since on the latest
hardware one renderer handles the load) which runs Tomcat connecting
to our custom Java middle tier on the database server machine which
provides the connection pooling and manages each database
transaction.  Requests for "boilerplate" content are served before
it gets to this point where it would show in this monitoring; this
is just requests which require database content.  One "request"
above may run up to about 15 queries, many of which contain a large
number of joins. 

While the load I show above would amount to about 13 million web
requests if it went on 24 hours per day, load does drop at night. 
Last I heard, we had about 5 million requests per day, but that was
a couple years ago and it seems to grow pretty steadily.

Last I checked, the replication consisted of about two million
database transactions per day, many of which have dozens (or
hundreds) of statements modifying data.  When idle time is detected
on a replication source, it is used to compare source data to
target, apply fixes to the target, and log the fixes for review. 
(These are infrequent, but I'm not comfortable running multi-master
replication without such automated review.)
> Buying a "super" computer, hoping that one day it will run at full
> throttle is not for startups. Getting such a powerful computer
> quickly and moving the database there is unrealistic. It makes
> more sense to design the system in a way so it can be easily and
> quickly distributed across many relatively inexpensive servers.
> That is why the sharding is needed. 
I understand the scaling need, and certainly don't want to discount
that.  Cloud resources might be an alternative to sharding in that
fashion, at least to a point.
Before we moved to PostgreSQL we were using a commercial database
which could not keep up with demand using just one box, so we load
balanced between identical servers.  Since the replication is
asynchronous and we didn't want people potentially jumping around in
time, we used session affinity from the renderers to particular
database servers to keep a consistent timeline for each user
session.  This sort of approach is a viable alternative to sharding
in some cases.
I hope that helps.

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