I’ve been using python a lot recently. A LOT! Mostly django applications… but there are exceptions. I’ve also signed up to every web-cloud-hosted-trial-service that I can find [heroku, openshift, dotcloud etc].
Through various projects at work and in my own time at home, I think I now have the basic (and the slightly advanced) skills to setup a full cloud application on virtual machines. don’t ask.
One of the niggles with working with lots of machines, I’ll explain why further down, is that you need to maintain subtly different settings and it isn’t the wisest of ideas to store all of that config data in source control.
12factor.net has 12 good recommendations for modern sw-dev and has some very persuasive points to make. Read it, read it now.
Now that you’ve read that, you’ll start to appreciate the number of environments that you small little application will be running on. And that’s just in your own deployment. In OSS-world, someone else might want to make a similar deployment too.
Counting explicitly there will be one checkout on my local developer desktop probably using a locally installed sqlite instance, no proxy/cache, using a development webserver and secured with my personal passwords. Then there will be a staging environment, used to prepare virtual machines with the app integrated into the image. These requires integrating into the operating system, probably with an apache/mysql with generic passwords. Finally there will be production deployments, which you let other people interact with. That is an environment that requires load balancing and performance monitoring. There may be multiple front ends that the application is deployed to, none of which maintain state for very long. These are probably backed up by a cluster of postgres databases, memcached proxies, DNS servers etc. Scaling just involved adding more machines, which means more deployments.
Of course, using the techniques outlined in 12factor, this model of cloudy development is a proven methodology.
So, that’s 3 or 4 different clones, just to deploy/develop one measly app. Add in other contributors, then that’s when I really start getting scared about storing any anything not application specific in the repo.
Anyway, I’ve come up with a solution, easily. It fits my needs well.
So, you have just started a new software project and decided that you’re going to do it in python. I get these niggling feelings all the time, I find writing them down in a (cloud backed up text file), then never looking at them ever again, helps.
Let’s start with
print "Hello World!"
But of course, that’s not very modular, so let’s make it a bit more interesting yet functionally the same.
HELLO_STRING="Hello World!" def main(): print HELLO_STRING if __name__ == '__main__': main()
Which is a little bit more useful, with the module level “constant”. Which means that we can now import this singleton module Let’s expand this a example further by turning this into a slightly more realistic module that is split into multiple files.
Place these files into a folder called hello
HELLO_STRING="Hello World!" def main(): print HELLO_STRING
Now, from the parent directory, you can zip the hello folder up and run it directly. Of course, there are a multitude of other ways to package modules [.egg].
This is great ‘n all, but it your code can’t run from anywhere else in your own filesystem without exporting PYTHONPATH, or some other shuffle.
Lets start by implementing the simplest api to distutils so that pip can take care of future deployments.
from distutils.core import setup setup( name = "hello", )
These file can get a lot more information in them, including versioning, dependency listing and controlling the setup/install process.
When a project starts to use words like “install” and “build process”, I start to get worried and panic about the mess that development/experimental builds have on my system as a whole. In the pythonic world, there is a work flow that hides this complexity away from your managed operating system. It acts like mini-chroots and isolates your work into lightweight virtual environments. Hence the name virtualenv.
You can create your own little virtual env like this.
1/. cd into the root of your project work. This is the one with the setup.py file in it.
2/. virtualenv –distribute –no-site-packages ENV
3/. source ENV/bin/activate
This plops you into a clean python environment (without python modules that you system has, but another system might not). The single most practical use this has is to keep track of dependencies that pip brings in. You don’t need root access to install extra packages, as the environment (including dependencies) are all stored under the ENV directory. You will need to activate the environment each time you want to hack around.
If we’re working in the OSS world, then consider uploading your modules to PyPi. Then others can get your module (and you can get other modules) with
pip install <module>
Now, when it comes to django application, if we follow Factor 3 all configuration must be in the environment. It doesn’t mean we can’t store them in files, just don’t store them in code repositories.
And now we come to the point of this posting. Entering the environment, including the config, is a concious step when using a virtualenv workflow. There is a way to coordinate this.
In Ruby-land, there is a de-facto standard location to store the environment in a file called
as KEY=value pairs, one per line. This file is respected by heroku, useful.
Support for this method of storing the environment can easily be add to your virtualenv environment.
# ... at or near the bottom if [ -f .env ]; then export $(cat .env) fi
And there you have it, a fully localized virtual environment, tailored for running where it is in the filesystem, and not sacrificing security secrets to the codebase. Just remember not to check in the .env, and force everyone to make their own.
To finish off, add this to source control and start distributing!
.env ENV *.pyc
git init git add . git commit -m 'initial commit' git remote add some host git push