A while ago I put some code online that makes using txamqp with twistd easy.
Recently Jason Williams has forked the code and created a module called txampq-helpers which is available via pypi. This new module is now linked from the Rabbitmq devtools page.
I’ve merged Jason’s changes back into my repository as well.
A friend at work recently pointed me to a Python tutorial called Learn Python the Hard Way. It’s very basic but the later part has a little opinion chapter titled Advice from an old programmer which is worth taking the time to read. Below is one quote from this chapter.
Programming as an intellectual activity is the only art form that allows you to create interactive art. You can create projects that other people can play with and you can talk to them indirectly. No other art form is quite this interactive. Movies go out to the audience. Paintings don’t move. Code goes both ways.
Programming as a profession is only moderately interesting. It can be a good job, but if you want to make about the same money and be happier you could actually just go run a fast food joint. You are much better off using code as your secret weapon in another profession.
The other day I found Gephi which was used to create these amazing graphs based on GitHub data. So I thought it might be fun to pull some data into Gephi and play with it. I decided on using the Twitter API to obtain all of the Tweets related to the upcoming Canada 3.0 conference in Stratford, ON, CA. I used the ‘can30′ hash tag as the search term but since the Twitter search only returns Tweets less than seven days old the history is limited.
I used Python and igraph to create the graph and exported it to GraphML which Gephi can import. Here’s the resulting GraphML file if you are interested.
I also used igraph to export PNG and SVG versions.
The nodes in the graph are Twitter users. The size of the node is relative to the number of new Tweets with the #can30 hashtag. By ‘new’ tweets I mean not re-Tweets. The edges represent re-Tweets and the width of the edges are relative to how many times the source user re-Tweeted the destination.
Based on the graph, Canada3Forum is the largest source of new Tweets followed by tobidh and there are lots of users re-Tweeting Canada3Forum’s messages.
Canada 3.0 on Twitter
In one of my pet projects I’ve been using Twisted and txamqp. I use Twisted’s twistd to launch the service but unfortunately txamqp doesn’t come with any examples of how to use it with twistd. So I wrote a factory and protocol which makes it trivial to use txamqp with twistd. I haven’t tested it extensively but it appears to survive losing the connection to the AMQP server and reconnecting. I’m far from a Twisted expert though so please let me know if this isn’t the way it supposed to be done.
You can find the code in my Git repository.
Recently I’ve been playing with Linux’s QoS features in order to make my home Internet service a little better. Since I’m primarily interested in latency I used ping to benchmark the various configurations. This works reasonably well but it quickly becomes hard to compare the results.
So I decided to build a tool to perform several ping experiments, store the results and graph them. The result of this work is ping-exp.
At present ping-exp can vary the destination host name as well as the TOS field. The interval between pings and total number of pings is globally configurable. The results can be written to a file to be loaded later, output to a PNG or both. Line and scatter plots are supported. When not writing the image to a file ping-exp displays the graph using Matplotlib’s default graph viewer. This allows zooming in on interesting parts of the graph. In the future I’d like to add the ability to specify the ping packet size.
As an aside, Python and Matplotlib make this kind of stuff so much fun.
Below are a few graphs created by ping-exp.
ping-exp example #1
ping-exp example #2
ping-exp example #3
