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Deployment and server management utilities

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Octocat-spinner-32 buedafab
Octocat-spinner-32 example
Octocat-spinner-32 opsfab
Octocat-spinner-32 LICENSE
Octocat-spinner-32 README.md
Octocat-spinner-32 fab_shared.py
Octocat-spinner-32 fabfile.py
Octocat-spinner-32 pip-requirements.txt
README.md

buedafab -- a collection of Fabric utilities used at Bueda

Description

buedafab is a collection of methods for deploying Python apps using Fabric.

At Bueda, we need to deploy Django, web.py and Tornado applications to our servers in the Amazon EC2 cloud. A frequent topic of conversation in the Python web application community seems to be a lack of a "default" pattern for deploying applications to production servers. Our approach may not work for you, but we would love to compare methods and see if both can't be improved.

Read the "Contributing" section of this README if you've got something to add.

Related Projects

django-boilerplate tornado-boilerplate python-webapp-etc comrade

Installation

buedafab only need be in your PYTHONPATH - a typical installation method is to clone a copy of the repository somewhere:

$ git clone git@github.com:bueda/ops ~/projects

Then edit your .bashrc or .zshrc file so your shell adds this directory to your PYTHONPATH every time you open a new terminal:

$ echo "export PYTHONPATH=$PYTHONPATH:$HOME/projects/ops" >> ~/.bashrc

In the future, it could be packaged up and installed with pip.

If you will be using the AWS features of buedafab, add your AWS access key ID and secret access key to your shell environment:

$ echo "export AWS_ACCESS_KEY_ID=<your access key id>" >> ~/.bashrc
$ echo "export AWS_SECRET_ACCESS_KEY=<your secret key>" >> ~/.bashrc

Python Package Dependencies

  • fabric
  • prettyprint
  • boto (only if you are using the Amazon Web Services commands)
  • pinder (only if you are using Campfire notification)
  • hoppy (only if you are using Hoptoad notification)

You can install all of these with pip using the pip-requirements.txt file:

$ pip install -r pip-requirements.txt

If you already have Fabric, the same can be accomplished with:

$ fab setup

The recommended method is to install buedafab's dependencies in each of your project's virtualenvs.

In any project that you will be using buedafab, activate that project's virtualenv (using virtualenvwrapper here) and install the ops repo's pip requirements:

web/$ workon web
(web)web/$ pip install -r ~/projects/ops/pip-requirements.txt

An alternative is to install them system-wide, but depending the way Fabric is installed you may encounter problems with Python imports.

Usage

To use any of the utilities, your project must have a fabfile.py and buedafab must be in your PYTHONPATH. Some of the utilities are ready-to-go Fabric commands - the only requirement is that you import them at the top of your fabfile.py.

For example, the rollback method in buedafab/tasks.py is s a complete Fabric command. The method will roll back an app deployment to the previous version. To use it in your project, import it at the top of the fabfile.py:

from buedafab.tasks import rollback

and on the command line, run it like any other Fabric task:

$ fab rollback

Most of the utilities (rollback included) depend on certain keys being set in the Fabric env. Check the documentation of the method you'd like to use to see what keys it will be expecting and set them to the correct value at the top of your project's fabfile.py. For example, the rollback method expects the path key to point to the deploy target on the server:

env.path = "/var/webapps/myapp"

The default values for many keys are defined in beudafab/defaults.py and can be overridden as needed by re-defining them at the top of your fabfile.py. The defaults should be sufficient if you're using our deploy directory layout & methodology as defined in the next section.

Here are the commands we use most often:

fab test

Run the test suite for this project. There are currently test runners defined for Django, Tornado, and general nose test suites. Just set env.test_runner to the appropriate method (or write your own).

fab lint

Run pylint on the project, including the packages in apps/, lib/ and vendor/, and using the .pylintrc file in the project's root.

fab ...

Some of the commands require an app environment - buedafab defines four different environments: localhost, development, staging and production. These are defined in buedafab/environments.py and can be overridden or extended.

These commands don't do anything on their own, but are meant to prefix other commands, such as...

fab deploy

Deploy this app to the environment using the git-backed deploy strategy defined at the end of this README. Some example use cases:

Deploy a new tagged release from HEAD to all production machines (will prompt for the tag to use):

~/web$ fab production deploy

Tag a specific commit as a new release, then deploy to all production machines:

~/web$ fab production deploy:commit=6b4943c

Deploy HEAD to the development machine without making a tagged release:

~/web$ fab development deploy

Deploy a specific commit to the development machine without making a tagged release:

~/web$ fab development deploy:commit=6b4943c

Deploy a tag that already exists to all production machines:

~/web$ fab production deploy:release=v0.1.1

fab setup

A shortcut to bootstrap or update a virtualenv with the dependencies for this project. Installs the pip requirements files listed in env.pip_requirements and env.pip_requirements_dev and initializes/updates any git submodules.

It also supports the concept of "private packages" - i.e. Python packages that are not available on PyPi but require some local compilation and thus don't work well as git submodules. It can either download a tar file of the package from S3 or clone a git repository, build and install the package. The private package methods could use a little work - it's probably a better idea to clone a git submodule and just run python setup.py build.

Lastly, you can provide arbitrary Python functions that will run at the end of setup (env.extra_setup_tasks). We use this to download data files to developer machines (e.g. Lucene indices.

fab restart_webserver

Assuming your remote server has an init script for this application's process at /etc/init.d/%(unit)s, this command simple bounces the web server.

fab rollback

Roll back the deployed version to the previously deployed version - i.e. swap the current symlink from the a to the b directory (see below for the methodology this uses).

fab maintenancemode

If your Django project uses the maintenancemode app, this command will toggle that on and off. It finds the MAINTENANCE_MODE variable in your settings.py on the remote server, toggles its value and restarts the web server.

Deploy Directories & Methodology

After a few iterations, Bueda settled on a certain method for organizing deployed code. Here's what we've come up with.

Version Control

All projects are versioned with git in a remote repository accessible to all developers. It need not be GitHub, and you need not have a special deploy key on the remote server. You will need ssh-agent installed and running if you are using git submodules in your app.

Motive

Developers should only be able to deploy from a remote repository to make sure that deployed code (no matter if it's to a development, staging or production environment) is always available to other developers. If a developer deploys from a local repository and forgets to push their commits, it can wreak havoc if that developer becomes unavailable.

Additionally, deployment should always be a manually initiated push operation, not an automated pull one. A developer or operations person should always be at the helm of a deployment, no matter how automated the actual process is. This person should ideally be able to triage and resolve a bad deploy manually if the need arises.

Finally, servers shouldn't require any special authentication to deploy. Since we've always got a live person leading the deploy, they can use their own personal SSH authentication keys to clone or update a remote repository.

Application Environments

We maintain four application environments across at least two servers at Bueda - solo, development, staging and production. We use GitHub, and our commit and deploy workflow goes something like this:

As a developer...

  1. Create your own fork of the master repo on GitHub
  2. Clone your fork and make changes locally, test at localhost:8000 with built-in Django server
  3. Commit changes into a feature branch and push to your fork
  4. Send a pull request on GitHub to the repo's owner

As the repository's integration master:

  1. When someone sends a pull request, evaluate the difference and if it's good, merge it into the development branch of the main repo
  2. Deploy to development server: ~/web$ fab development deploy
  3. Test again on live development server, make sure everything works (including database access, since that is the primary difference)
  4. If all is good, merge the development branch into the master branch: ~/web$ git checkout master && git merge development master
  5. Deploy to to staging with Fabric to test the live production environment minus the public URL: ~/web$ fab staging deploy
  6. Again, if everyone looks good, tag and push to production with Fabric: ~/web$ fab production deploy
  7. If somehow after all of your diligent testing the update broke production, rollback to the last version: ~/web$ fab production rollback

Motive

Beyond developing and testing locally, developers should have an easy way to get their code onto a remote server somewhat similar to the production environment. This is our development environment - it is backed by a completely isolated database with no live user data, on a separate physical server (well, as separate as a virtual machine can get). Any developer can deploy here anytime to make collaborating with a distributed team as easy as possible. The development environment is usually running the HEAD of the development branch from git.

The staging environment is one big step closer to production. This environment is usually running the HEAD of the master branch. This environment is on a separate physical server from production, but uses the production database.

Finally, the production environment exists on its own server (or cluster of load-balanced servers) and only ever runs a tagged commit from the master branch in git. This way anyone can tell which version is in production by looking in their git repository for the latest tag.

Directories

All apps are deployed to /var/webapps/appname - the actual path is arbitrary, this is just our convention. We bind this path to the EC2 instances ephemeral storage on /mnt, to avoid filling up the root drive. The path to an application named five would be /var/webapps/five.

Within the deploy directory (continuing with our example of /var/webapps/five) there is a releases subdirectory. This in turn contains two more subdirectories, a and b. Each of these contains a clone of the project's git repository. Why not put a and b at right at /var/webapps/five? There are some cases where you need to store logs or data files along side an app, and it's good not to mix those with releases.

There is also a current symlink that points to either a or b. Wherever current points, that's what's running in production.

The final directory structure looks like:

/var/
   webapps/
        app/
            releases/
                a/
                b/
                current -> a

Motive

Our Internet spelunking found four prevailing ways to organize deploy directories:

Separate deploy directories by timestamp

Every time you deploy, a new directory is created on the remote server with the current timestamp. The repository is either re-cloned from the remote, pushed from the local repo, or archived and uploaded from the deploying machine. A current symlink points to the deployed release. To rollback, find the timestamp before current.

Separate deploy directories by commit SHA or tag

Each commit SHA or tag (or output from git describe) gets a separate folder. The repository is either re-cloned from the remote, pushed from the local repo, or archived and uploaded from the deploying machine. A current symlink points to the deployed release, and previous to the last (for rollback purposes).

In both this scenario and the timestamp strategy, if the deploys are archived and uploaded, it is a good idea to keep the original app-VERSION.tar.gz in a packages/ directory alongside releases.

Single deploy directory, version selected with git

Use one deploy target folder per application, and rely on git to update and checkout the version you wish to deploy. No need for symlinks - the one folder is always the deployed version. There is no specific way to 'rollback' since there is no record of the 'previous' version except the git log.

GitHub uses this approach.

Two deploy directories, version selected with git

This method is similar to the single deploy directory approach in that it uses git to update to the latest version, but it keeps two directories - an a and a b (the names are arbitrary) and uses symlinks to bounce back and forth between them on alternate deploys.

This method doesn't require a git reset --hard and gives you a bit of a buffer between the currently deployed version and the one that's about to go live. Especially considering that this gets you separate virtualenvs (and a thus a buffer against funky egg installs or PyPi outages) it is the method we have settled on and what buedafab uses.

Virtualenv

Both the a and b directories have separate virtualenvs as a subdirectory named env. The web server must be careful to either add this directory to the PYTHONPATH or use the Python executable at env/bin/python.

buedafab relies on pip's requirements file support to define package dependencies in a project - see buedafab/deploy/packages.py for more details.

Motive

At one point in time, we wiped and rebuilt a virtualenv for the application from scratch each deploy. This is clearly the safest solution, as it will always get the correct versions of each package. The time spent, however, was not worth the added safety to us. Our applications have dependencies that typically take 5-10 minutes to install from scratch - by sharing the virtualenv from the previous deploy and using the --update flag to bump package versions as necessary, we save a lot of time.

The only tricky point at the moment is if your pip requirements files have paths to SCM repositories. Perhaps due to operator error, but we have found pip to be unreliable when it comes to checking out specific tags or commit IDs from SCM. It can often get in a bad state (e.g. a detached HEAD in a git repository that pip can't seem to handle) that requires the src/ directory inside the virtualenv to be wiped.

Example

The fabfile-example.py file in this repository has an example fabfile.py for a project that uses a wide range of the methods from buedfab, and sets a few extra env keys.

TODO

  • Document crontab support, and add the scripts directory to the boilerplate repository. We stopped using this in favor of celery scheduled tasks, but someone else may still want it (and the code works fine).

Contributing

If you have improvements or bug fixes:

  • Fork the repository on GitHub
  • File an issue for the bug fix/feature request in GitHub
  • Create a topic branch
  • Push your modifications to that branch
  • Send a pull request

Authors

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