an article like this one will start out with a technical word
"scaling". Unfortunately like health care reform, everyone
can’t always agree on what they mean by it, or even what the goal is. So,
I deliberately chose not to use the word, and use the non-technical words that
we can all agree on. Typically, when our database is slowing down, we
want it to be faster, stronger, bigger and better!
that in mind I’m going to discuss some of the various ways to get there, and
hopefully put some of the technology options in perspective. This will
help you survey the landscape, and plan for your future needs. The first
part of the article discussed query tuning and hardware changes, while this
installment covers adding additional servers, and application changes to make
Bigger With More Boxes
more MySQL instances to the mix is one way to get a faster overall response to
your application. If you have a server with multiple CPUs a lot of memory
and fast disk, chances are good you’re not fully exploiting all that processing
power. So in that case, it may be that running multiple MySQL instances
can scale on that server itself. That’s because MySQL out of the box is a
single process with multiple threads for sessions, so there’s a limit to how
much hardware it can really make use of.
single server is maxed out, you may well benefit from using multiple
servers. But whether your multiple MySQL instances are on one server with
different ports, or multiple servers, you still need a method for the
application to decide where to send queries. Do they make changes?
In that case, they’ll need to go to your single master database. Are they
doing selects, then a fleet of read-only slaves will work for those queries.
Partitioning and Sharding
many web applications identify users by session, dividing hits to the different
slaves by session could make a lot of sense. A-G, H-O, P-Z for example
might work, or a hash of the username, or the userid might be other methods to
distribute users on different servers. This is called the partition key
and is an important decision as it affects how you build out your slaves, and
potentially how load is distributed across those servers. It might also
affect outages of data, if one of the slaves goes down.
doing this type of partitioning, you’ll need to decide at runtime which
database to hit. This can be done with a middle layer like MySQL
Proxy. Although still in alpha, the concept is good, and some are already
using it in production. It sits on a server responding to requests at
port 3306 then forwards those queries on to the appropriate server behind the
scenes based on some logic coded in a high-speed language called lua.
other option is to make the decision on where to send your query in the
application itself. This is the most flexible method as it provides you
with full control of the decision making process. You can check the
slaves to see if they are caught up or lagging and use master_pos_wait as
needed. Your particular language or web framework may provide some
support for this kind of logic already, so check your documentation. You
might also look into Continuent Tungsten, DBIx::DBCluster for Perl and SQLRelay,
which supports a lot of different languages and databases. Also, a CMS
like Drupal for instance, already has multi-readonly slave support built in, so
you just have to enable it.
consideration when using this type of architecture is deciding whether to hit
the primary or slave and when. The most basic split is based on the query, all
INSERT, UPDATE, DELETE go to master, and SELECT to slaves. If you hit
the slave directly after a user submits a comment on a blog, for instance, it
may not be on the slave yet, due to the lag inherent in MySQLs replication
architecture. That is what’s called an artifact.
for stale data is a better method. If you have reporting queries that run
at night, this method might work well. You just need to make sure
replication is caught up.
method would be to track database changes by a version number, and verify that
you have the latest "version" before reading your data.
MySQL provides a function called master_pos_wait, which makes sure the slave is
up to a certain point in the binary log before completing.
2. Functional Partitioning
are you’re probably already doing a bit of this. It involves creating a
copy of the production database for different functions, such as one for data
warehousing and reporting, another for text searching, and so on.
Better MySQL Through Load Balancing
of your slaves have the same read-only data, you may want to do some sort of load-balancing
to distribute read traffic evenly. You can choose from random, least
connections, fastest response round robin, or some sort of weighted
decision. Although some hardware load balancers may provide you with
functionality you need, they tend to be designed for web-traffic, and don’t
have database specific features.
there are a number of software solutions, which are appealing. The Linux
Virtual Server or LVS project is very mature, and provides something like DNS,
but at the IP level, and is very very fast. A couple of projects have
been built on top of LVS too, including wackamole, which is peer-based so you don’t
have a single point of failure, and ultramonkey.
provides a lot of sophisticated features but scaling remains a nebulous and
exotic technical term thrown about by a lot of different folks in different
circumstances to mean possibly different things. So we’ve endeavored to
cover the topic while using this word less, and talking more about what you
care about, namely making MySQL faster, stronger, bigger and better.
See All Articles by Columnist Sean Hull