Building Fast Websites

Complex websites have a tendency to be slow, and can give a horrible user-experience leaving your visitors waiting for pages to load.

To prevent this you need to look at all aspects of a website, and how it all hangs together, from the web-server, down to the very last pixel of an image.

Building Fast Websites

Let's break this down starting at the very front-end of any website.


Before making any optimisations to a website or page, you'll want to benchmark the site to get some stats on how fast or slow the page is to start with.

In the Apache software-suite you'll find ab (Apache Benchmark) to send requests to a web-server and produce some interesting stats. To send 50 requests, 5 at a time, use...

[rob@rob ~]$ ab -n50 -c5

I also find timing each page is useful, so you can always see at a glance if a page is taking a bit too long. Time::HiRes is ideal for this, just put the time taken in the footer of the every page, like this one.

Consolidate CSS and JavaScript

When a browser requests a web-page, it makes extra individual requests for all the CSS and JavaScript on the page. Most browsers have limits on how many concurrent connections/requests they can make, so imagine a worse-case scenario of a browser only able to make 2 requests at once.

If your page uses 5 CSS files and 10 JavaScript files, that's another 15 individual requests to the web-server required to load a single page.

Once quick win is to combine all your CSS into a single file, and the same for your JavaScript files, now instead of 15 requests, you're down to 2.

CSS Image Sprites

If a page uses several images, they are all downloaded as individual requests.

Firstly make sure you've optimised each image for use on the web, don't save JPGs with a quality of 12 unless you really need to, and check for profiles in your image-editor to "save-for-web".

With CSS Image Sprites, you build up a single image containing all the images for the page, and use CSS to specify where on the image each sub-image is located, with it's width and height.

Read more about CSS Image Sprites at W3Schools.


A really simple quick-win can be to simply enable compression on the web-server, since most browsers support compressed data being sent to them, reducing the size of data being transferred.


Caching is often used to speed up a website by keeping pages of a website in memory to re-serve quicker the next time they're requested, rather than going back to disk to load them. Squid is a common caching-proxy used for this purpose.

If you are building an application, you can also use caching to keep various objects/data-sets in memory. Maybe you have a large database query that takes over a second each time it's called, instead of doing that query everytime, you could cache the results for a set time period.

Memcached is a great tool for this type of caching, and can be configured across many servers in a cluster. It runs as a daemon and access is typically done over TCP, you can even query the cache over Telnet to inspect it.

Don't cache everything, you only really get benefits from caching large complex data-sets, if it's just a list of countries to use in a drop-down for example, a database hit each time will probably be faster since the database engine is no doubt caching queries too.

Indexing Database Tables

If your application makes use of a database, you can usually get huge speed improvements by putting an index on a table on the commonly used column used for looking up data.

Under MySQL you can take a query and ask the engine to explain how it will get the data. This becomes increasingly useful with joins, and should point you in the direction of which table/column to apply an index to.

mysql> explain select * from merchant where id=120;
| id | select_type | table    | type  | possible_keys | key     | key_len | ref   | rows | Extra |
|  1 | SIMPLE      | merchant | const | PRIMARY       | PRIMARY | 4       | const |    1 |       |
1 row in set (0.02 sec)

Depending on the table size, adding an index can take a very long time, so make sure you test in a development environment before modifying a live database, and possibly look at doing the change out-of-hours and take the website offline temporarily.

If your data is extremely large and your app is simply too slow when searching it, such as free text search operations, consider a NoSQL solution such as Solr or ElasticSearch. Think of them as a single table database, you lose some structure from a standard database, but you get a blisteringly-fast search component.


If you're familiar with a simple CGI script, each time a request is made, the Perl (or PHP, Bash, Python, etc) interpreter is loaded, along with all necessary modules. Your script then runs, sends back a response to the browser, and then exits, unloading the interpreter and all modules.

When a second request comes in, the whole process is repeated.

When you begin to use larger modules, such as Moose and the Catalyst framework, this load-time can easily reach a second or two, making your website awfully slow.

Persistence is the technique of loading the Perl interpreter and required modules once at web-server startup, with each request taking advantage of the modules in-memory.

Apache provides mod_perl to enable persistence between requests. Modules are only loaded when the web-server is started or restarted. This can be a pain at times if you're modifying modules and manually testing, needing to restart the web-server every time for the changes to take effect (or you could look at Apache2::Reload).

FastCGI is another approach which is web-server independent. FastCGI is actually a process which wraps your application in a daemon that either listens on a TCP port or via a UNIX socket file. When a request comes into the web-server, it in-turn sends a FastCGI request to your FastCGI application which is always running, waiting for requests.

It's important to note that FastCGI is a protocol itself, much like HTTP, but different. Your FastCGI applications(s) can also reside on a different server to the web-server, so scaling your application is quite straight-forward. FastCGI processes can be restarted independently of the web-server, and vice-versa.


For larger web-applications it's a good idea to investigate what frameworks are available to make your life easier. One common framework in the Perl-space is Catalyst, which has been developed by the Perl community to solve many common problems, so you don't waste time re-inventing the wheel, authentication and sessions are just 2 examples of features that are all ready for you to switch on in your app.

For database applications it's also worth taking a look at DBIx::Class which will speed up the majority of your database interactions and provides a clean way to work with your data - no more writing SQL statements.

Benchmark Results

Here we have a real-world example which unfortunately (for the owner) went the wrong way. An e-commerce website originally developed using Catalyst and running under FastCGI with Lighttpd, using Memcached for session caching.

The website was re-developed recently using the open-source PHP e-commerce platform, Magento. By using the web developer tools in Chrome, it was noted that page load times went from around 0.5s to 1.5s.

Server Software:        lighttpd/1.4.31
Server Hostname:
Server Port:            80

Document Path:          /
Document Length:        6185 bytes

Concurrency Level:      5
Time taken for tests:   2.123 seconds
Complete requests:      50
Failed requests:        0
Write errors:           0
Total transferred:      331194 bytes
HTML transferred:       315435 bytes
Requests per second:    23.55 [#/sec] (mean)
Time per request:       212.335 [ms] (mean)
Time per request:       42.467 [ms] (mean, concurrent)
Transfer rate:          152.32 [Kbytes/sec] received

Connection Times (ms)
              min  mean[+/-sd] median   max
Connect:       22   32   8.9     31      65
Processing:    83  177  98.0    126     390
Waiting:       52  146  97.3    103     354
Total:        116  209  96.3    166     413

Percentage of the requests served within ms
  50%    166
  66%    217
  75%    275
  80%    306
  90%    395
  95%    412
  98%    413
  99%    413
 100%    413 (longest request)
Server Software:        Apache/2.2.23
Server Hostname:
Server Port:            80

Document Path:          /
Document Length:        15311 bytes

Concurrency Level:      5
Time taken for tests:   11.700 seconds
Complete requests:      50
Failed requests:        0
Write errors:           0
Total transferred:      793050 bytes
HTML transferred:       765550 bytes
Requests per second:    4.27 [#/sec] (mean)
Time per request:       1169.984 [ms] (mean)
Time per request:       233.997 [ms] (mean, concurrent)
Transfer rate:          66.19 [Kbytes/sec] received

Connection Times (ms)
              min  mean[+/-sd] median   max
Connect:       12   18   5.5     16      39
Processing:   866 1113  89.4   1124    1304
Waiting:      839 1073  88.2   1088    1262
Total:        906 1131  88.7   1138    1336

Percentage of the requests served within ms
  50%   1138
  66%   1170
  75%   1173
  80%   1207
  90%   1234
  95%   1245
  98%   1336
  99%   1336
 100%   1336 (longest request)

The above tests were ran using Apache Benchmark, a total of 50 requests, 5 concurrently. Even with twice the amount of HTML, this shouldn't happen, we can deduce that the backend system needs to be optmised, probably database queries and application-level caching.

1 Comments. Leave new

Paul Gillespie
April 02, 2014 09:09

As well as reducing the number of requests to stylesheets and javascript files, it's also good practice to "minify" them too. This reduces the size of the files.

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