PUBLIC MARKS from holyver with tag performance

So slow pages can have a very real impact on your business. These are some very real incentives for whipping your site into shape, so let’s move on from the why to the how.

At Pantheon, when we look at challenges Drupal projects face, we don't only do what's worked well-enough in existing deployments. We ask ourselves how we can transcend some of the challenges entirely. How Drupal stores files is no exception. So, when we started building the next generation of Pantheon (now launched), we looked at our options -- and then built a solution entirely focused on the needs of Drupal developers.

The folks at Evernote were kind enough to write up an overview of their architecture in a post titled Architectural Digest. Dave Engberg describes their approach to networking, sharding, user storage, search, and some other custom services.

In this talk, Khalid of 2bits.com, Inc., Inc will talk about a how to scale a Drupal web site with the following statistics. 3.4 million pages per day peak 92 million page views per month 189,650 page views per hour peak 840,000 visits on peak day 22.96 million visits per month 52,747 visits per hour peak So far, this is the highest traffic a Drupal site gets that we heard of. What is amazing is that this web site runs on a single mid range server ...

Here are five rules to help you optimize your Drupal installation, as well as some examples to illustrate how to apply them. Server optimization is a huge field, and it is constantly evolving, but we can to study simple cases to understand more complex ones.

Actually usually any web server can handle a normal day of work, but what happens when the server under your administration gets, stumbled, or appears in Slashdot, or digg front pages, now a days even twitter may drive a lot of traffic to a webpage.

This post isn't meant to be an end all to Drupal and CDNs, but rather just some insight into the way I have tackled this issue for the time being. There are a number of options to choose from and a lot of different ways to go about it. You could get a new server locally and load balance your stuff, you could get a new server locally and use it as a static file server, you could team up with a big time CDN like Akamai or Limelight and go that route, or you could go the less expensive CDN route with something like Amazon S3.

APC (Alternative PHP Cache) is a PHP opcode cache. It stores PHP pages in RAM and reduces hard disk activity. This makes a real difference to the performance in Moodle, and you should be able to achieve a 50% increase in system capacity and your CPU will be 50% less busy. This guide is for installation of APC on a Windows server. The instructions have been tested on a Windows 2003 server, and they should also be applicable if you are using Windows XP as a test server.

in this article, i outline a step-by-step process for incrementally scaling your deployment, from a simple single-node drupal install running all components of the system, all the way to a load balanced, multi node system with database level optimization and clustering. since you almost certainly don't want to jump straight from your single node system to the mother of all redundant clustered systems in one step, i've broken this down into 5 incremental steps, each one building on the last. each step along the way is a perfectly viable deployment.

Obtaining Optimal Performance From Drupal And The LAMP Stack

Improving Drupal's Performance with the Boost Module for the UN's Millennium Campaign. How the Boost Module Helped a Very High Traffic Drupal Site Stay Online

A Web cache sits between one or more Web servers (also known as origin servers) and a client or many clients, and watches requests come by, saving copies of the responses — like HTML pages, images and files (collectively known as representations) — for itself. Then, if there is another request for the same URL, it can use the response that it has, instead of asking the origin server for it again.

Caching is a great example of the ubiquitous time-space tradeoff in programming. You can save time by using space to store results. In the case of websites, the browser can save a copy of images, stylesheets, javascript or the entire page. The next time the user needs that resource (such as a script or logo that appears on every page), the browser doesn’t have to download it again. Fewer downloads means a faster, happier site.

Vey useful links for cache control and browser behaviors * Explain of cache control in detail (very nice article! ) http://betterexplained.com/articles/how-to-optimize-your-site-with-http-caching/ * The State of Browser Caching: http://www.mnot.net/blog/2006/05/11/browser_caching * XMLHttpRequest caching: http://www.mnot.net/javascript/xmlhttprequest/cache.html

When tuning a site's performance, a system administrator needs certain tools to measure and monitor how the site is doing under increasing load, as well as identify where bottlenecks may be. Linux system performance monitoring tools On Linux, there are several tools available to you to do this. We describe what we have found as the most helpful in this section.

Apache prefork, StartServers, MaxSpareServers and MinSpareServers In the most common case, you will be using Apache in the prefork mode, meaning one process per connection, with a pool of processes pre-forked to standby for connections. The number of spare processes is defined by the values MaxSpareServers, MinSpareServers, while the number to start is defined by StartServers.

CSS Sprite Generator

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.

Maintenant que nous avons exécuter son scénario de tir de charges par paliers, il faut s’occuper des résultats. Nous allons utiliser Microsoft Access pour ce travail.

Pour ce tutorial, nous allons voir comment variabiliser des données saisies par formulaire pour effectuer un tir de charges avec JMeter avec des données changeantes à chaque itération du tir. La variabilisation des données permet de mieux simuler la diversité de requêtes que l’on peut avoir sur un environnement réel. Notamment en évitant les effets de cache (navigateur, persistance, etc). Pour ce tutorial, on suppose que vous avez déjà effectué le tutorial sur l’enregistrement d’un scénario fonctionnel dans JMeter.

HTML Link Parser est un composant de Apache JMeter qui mérite à être connu. En effet ce petit composant permet d’accélérer la variabilisation d’un scénario de test. Avant de vous montrer comment cela est possible, voici une utilisation du composant HTML Link Parser : un spider (un aspirateur de site). Cet exemple est récupéré de la documentation officielle du JMeter, et adapté pour avoir simplement un appel à tous les liens d’une page sans descendre à un niveau inférieur.

JMeter – Test de charges sur un webservice (SOAP) JMeter permet de faire des tirs de charges sur des webservices via SOAP. Je vous propose une petite démonstration. Pour ce test de charge (simple) sur un webservice, il faut un webservice… pour ma part, j’ai suivi ce tutorial du site developerWorks d’IBM pour avoir un webservice tout simple de type « hello world ». Develop and execute WS-BPEL V2.0 business processes using the Eclipse BPEL plug-in

JMeter est un logiciel permettant de faire des tests de charges orientés performances ou orientés métiers sur différentes protocoles ou technologies. Il est développé par la Fondation Apache, via son projet Jakarta. C’est une application Java graphique utilisant l’API Swing, pouvant fonctionner sur tout environnement / poste de travail acceptant une machine virtuelle Java, par exemple, Windows, Linux, etc.

Vous utilisez JMeter pour faire un tir de charges (ou autres), et vous devez avoir une requête qui s’exécute à un intervalle fixe quelque soit le temps de réponse de la requête. Par exemple, vous devez exécuter une requête chaque 20 secondes, le temps de réponse de la dite requête est 4,5 sec. Donc si on démarre à T0, on a T0 4,5 sec la requête, puis on demande à JMeter de faire une pause pendant (20 sec – 4,5 sec = 15,5 sec), puis une nouvelle requête, etc.