scalability

a while ago i posted some performance benchmarks for drupal running on a variety of servers in amazon's elastic compute cloud.

amazon have just released ebs, the final piece of technology that makes their ec2 platform really viable for running lamp stacks stuck as drupal.

ebs, the "elastic block store", provides sophisticated storage for your database instance, with features including:

• high io throughput
• data replication
• large storage capacity
• hot backups using snapshots
• instance type portability e.g. quickly swapping your database hardware for a bigger machine.

recently i posted a couple of introductory articles on jmeter, a great apache open-source tool that allows you to measure the performance and scalability of a wide variety of services, especially web-applications.

i wrote these articles because although the online documentation provides reasonable reference material, it doesn't serve well as a jmeter introduction or tutorial.

things have changed a bit since then. the uk-based publishing house packt publishing were kind enough to send me a copy of emily halili's newly published book on jmeter, which is as far as i can tell, is the first book dedicated to the subject.

recently i posted some encouraging performance benchmarks for drupal running on a variety of servers in amazon's elastic compute cloud. while the performance was encouraging, the suitability of this environment for running lamp stacks was not. ec2 had some fundamental issues including a lack of static ip addresses and no viable persistent storage mechanism.

amazon are quickly rectifying these problems, and recently announced elasic ip addresses; a "static" ip address that you own and can dynamically point at any of your instances.

today amazon indicated that persistent storage will soon be available.

amazon's elastic compute cloud, "ec2", provides a flexible and scalable hosting option for applications. while ec2 is not inherently suited for running application stacks with relational databases such as lamp, it does provide many advantages over traditional hosting solutions.

in this article we get a sense of lamp performance on ec2 by running a series of benchmarks on the drupal cms system. these benchmarks establish read throughput numbers for logged-in and logged-out users, for each of amazon's hardware classes.

we also look at op-code caching, and gauge it's performance benefit in cpu-bound lamp deployments.

i recently posted an introductory article on using jmeter to load test your drupal application. if you've read this article and are curious about how to build a more sophisticated test that mimics realistic load on your site, read on.

the previous article showed you how to set up jmeter and create a basic test. to produce a more realistic test you should simulate "real world" use of your site. this typically involves simulating logged-in and logged-out users browsing and creating content. jmeter has some great functionality to help you do this.

there are many things that you can do to improve your drupal application's scalability, some of which we discussed in the recent scaling drupal - an open-source infrastructure for high-traffic drupal sites article.

when making scalability modifications to your system, it's important to quantify their effect, since some changes may have no effect or even decrease your scalability. the value of advertised scalability techniques often depends greatly on your particular application and network infrastructure, sometimes creating additional complexity with little benefit.

apache jmeter is a great tool to simulate load on your system and measure performance under that load. in this article, i demonstrate how to setup a testing environment, create a simple test and evaluate the results.

if you've setup a clustered drupal deployment (see scaling drupal step three - using heartbeat to implement a redundant load balancer), a good next-step, is to scale your database tier.

in this article i discuss scaling the database tier up and out. i compare database optimization and different database clustering techniques. i go on to explore the idea of database segmentation as a possibility for moderate drupal scaling. as usual, my examples are for apache2, mysql5 and drupal5 on debian etch. see the scalability overview for related articles.

i got some good feedback on my dedicated data server step towards scaling. kris buytaert in his everything is a freaking dns problem blog points out that nfs creates an unnecessary choke point. he may very well have a point.

having said that, i have run the suggested configuration in a multi-web-server, high-traffic production setting for 6 months without a glitch, and feedback on his blog shows gives example of other large sites doing the same thing. for even larger configurations, or if you just prefer, you might consider another method of synchronizing files between your web servers.

the authors of drupal have paid considerable attention to performance and scalability. consequently even a default install running on modest hardware can easily handle the demands of a small website. my four year old pc in my garage running a full lamp install, will happily serve up 50,000 page views in a day, providing solid end-user performance without breaking a sweat.

when the times comes for scalability. moving of of the garage

if you are lucky, eventually the time comes when you need to service more users than your system can handle. your initial steps should clearly focus on getting the most out of the built-in drupal optimization functionality, considering drupal performance modules, optimizing your php (including considering op-code caching) and working on database performance. John VanDyk and Matt Westgate have an excellent chapter on this subject in their new book, "pro drupal development"

once these steps are exhausted, inevitability you'll start looking at your hardware and network deployment.

if you've setup your drupal deployment with a separate database and web (drupal) server (see scaling drupal step one - a dedicated data server), a good next step, is to cluster your web servers. drupal generates a considerable load on the web server and can quickly become resource constrained there. having multiple web servers also increases the the redundancy of your deployment. as usual, my examples are for apache2, mysql5 and drupal5 on debian etch. see the scalability overview for related articles.