Djangocon 2011 Day One

Real-time Django

I really enjoyed Ben Slavin’s talk on Real-time Django. He shed some good insight on what to cache and when. Essentially, I would summarize it as to cache many things at every level that makes sense. On top of perhaps view level caching, you should cache partial results or really anything that prevents you from hitting your database more than you need. I have been playing with an approach that uses this to cache data from multiple databases in one fast cache. I liked the concept of “continuous caching” where essentially some out-of-band process is caching views or data so that actual requests for views don’t hit the DB.


I chose to attend Alex Gaynor’s talk on Pypy at Quora rather than Frank Wiles talk on Postgres performance tuning but it was a tough choice. Alex thinks one big strength for Django (from his time at Quora not using it) was that picking up a foreign Django codebase is easy because of all the conventions that virtually all Django apps follow. If you know Django, you can easily tell all the URLs for any Django app ( or all the forms ( Unfortunately, the Django admin doesn’t use these conventions. In passing, he also mentioned a project called Johnny Cache which I have to try. I followed some live-blogging on Frank’s talk and it looked like there were some good tidbits.

I was interested by Eric Holscher’s talk on setting up Read the Docs and I really need to spend some time looking at their Chef recipes and learning Chef in general.

If you’re at Djangocon, say hi!

Updates on Piston

About a year ago, I wrote a little about why I’m not using Piston. Piston appears to be dead! There hasn’t been a commit since September which is almost a full year ago. This project was touted as “the way” to do REST APIs in Django and I’m sad that it doesn’t seem to be maintained. I saw some other forks of the project on Github, but there still doesn’t seem to be much work on it lately. Does anybody know what happened?

Sparklines in D3

A couple weeks ago, Protovis, a visualization library I’d been using was deprecated in favor of D3 and I thought I’d share some of the work I’d done porting visualizations from the old to the new.

One example that Protovis has for which there is no corresponding tutorial is sparklines. This sparkline shows the San Diego Padres’ first 100 games of the 2011 season. Up ticks are wins and down ticks are losses. Red ticks show shutouts. This is similar to the visualization in Tufte’s “Beautiful Evidence” p. 54.

I created another simple visualization for the National League West. This shows all five teams of the NL West on a single graphic. It is pretty easy to adapt this code to a single sparkline. So far I have been fairly pleased with D3’s performance and the ease of use.

Django logging and sentry

Django-sentry has been a fairly useful tool for me. The docs are good but they only talk about integrating with logging the old way.

Here’s how to use Sentry with logging dictionary config:

RPC4Django 0.1.9 is now available

Go get it!

Here are the changes:

  • Added a CookieTransport class with a lot of help from Douglas Peter Sculley.
  • RPC4Django’s logging now goes to the rpc4django logger.
  • Catches an ExpatError in xmlrpclib that was previously uncaught under certain conditions
  • Fixed error with scanning of INSTALLED_APPS for @rpcmethods. This was causing an issue when South was installed. (#807628)
  • Fixed bug #807653 related to scanning ServerProxy objects