This is part 2. Part 1 is here about how I managed to make this site fast.

The web framework powering this site is Django and in front of that is Nginx which serves all the static content (once before Amazon CloudFront CDN takes over) and all non-static traffic is passed on to a uWSGI daemon which is running 6 worker processes. The database that stores the content is PostgreSQL and all caching is done in Redis. Actually another Redis database is used for other things such as maintaining a quick look-up index of keywords to primary keys so that I can quickly mesh together blog posts by keywords.

However, as we all know the deciding factor of a web sites server-side speed is effectively the speed of the database or any other disk-bound I/O device. To remedy this I've set up some practical caching strategies which I'm quite happy with.

So, how fast is it? Here's an ab stress test against home page with 10,000 requests spread across 10 concurrent users:

Document Path:          /
Document Length:        73272 bytes

Concurrency Level:      10
Time taken for tests:   4.426 seconds
Complete requests:      10000
Failed requests:        0
Write errors:           0
Total transferred:      734250000 bytes
HTML transferred:       732720000 bytes
Requests per second:    2259.59 [#/sec] (mean)
Time per request:       4.426 [ms] (mean)
Time per request:       0.443 [ms] (mean, across all concurrent requests)
Transfer rate:          162022.11 [Kbytes/sec] received

I could probably make that 2,300 requests/second to 3,000 or 4,000 if I just increase the number of workers. However, that costs memory and since I'm currently running 19 other uWSGI workers on this server that all (all 25) in total take up a steady 1.4 Gb I don't feel like increasing that number much more. Besides since this site doesn't really get any traffic, I'm not so concerned about massive throughput on concurrent benchmarks but more about serving each and every page as fast as possible the few times it's called.

Every single page on this site is behind some sort of internal cache. The only time the PostgreSQL is involved is in rendering a page is when it's first requested after a comment has been entered or I've added (or edited) a new post. Thing is, I don't want to be inconvenienced by a stupid cache that forces me to wait an hour every time I change something. No, instead lots of Django database model signals are put in place that fire off cache invalidation when certain pieces of data is changed. You can see the code for that here.

So, for the home page for example: For each request, a small piece of Python code checks the Redis for what the latest comment add-date is and based on that tells the Django page_cache decorator to either render the page as normal or to serve the whole HTML payload from Redis. In other words, on a successful cache "hit" it actually needs two Redis look-ups. Even that could be improved and blindly just spare these look-ups by serving from the workers allocated Python memory instead but that would make things fragile, hard to unit test and it would only make the benchmarks faster which is not necessary.

The most important thing to optimize on a web site is the static content. Well, there's little point in serving the static content fast if it takes 3 seconds to say what static content to serve. Also, a fast website is likely to appear more favorable on the Google bot which effectively makes the site appear higher on Google searches.

In the next part, I'll try to share more in-depth technical bits and pieces of what I actually did although they're no secrets I think some of them are best practice and even senior web developers sometimes get them wrong.



Thanks Peter! Helpful information.


Good article Peter!! I think choosing web server like Nginx (mostly for static website) plays a key role as well, we can tweak it to optimize the performance.

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