Automatizing tasks with Fabric

We all know that good programmers are lazy. Fabric is another trick that you should have up your sleeve, if you want to be good:

Fabric is a Python (2.5 or higher) library and command-line tool for streamlining the use of SSH for application deployment or systems administration tasks.

Although Fabric is often mentioned in web development contexts for deploying server software to remote machines, it is general enough for all kinds of remote administration tasks. To give you an analogy: Fabric is for administrative tasks as to what a Makefile is for build tasks.

Defining Fabric tasks

Fabric tasks are defined in file called fabfile.py, which is read by the fab command-line tool. Each function defined in this file is accessible from the command line as a fab parameter. So, if you have something like

def hello():
    print("Hello, world")

defined in that file, you can execute it from the command line via

$ fab hello
hello

Done.

It’s a great way to make Python scripts easily accessible from the command line, but not very useful on its own. What makes Fabric shine are the local and run functions. You can think of these functions as local and remote wrappers around the subprocess module:

from fabric.api import local, run

def hello():
    cmd = 'echo $HOSTNAME'
    local(cmd, shell=True)
    run(cmd, shell=True)

will print this on my local machine:

$ fab hello
[localhost] local: echo $HOSTNAME
titan
No hosts found. Please specify (single) host string for connection: bloerg.net
[bloerg.net] run: echo $HOSTNAME
[bloerg.net] out: openSUSE-121-64-minimal
[bloerg.net] out:


Done.
Disconnecting from bloerg.net... done.

Because, I haven’t specified a host for the run command, Fabric will ask me for a connection string. When a connection is established, the echo command is executed on the bloerg.net host.

Now, that we can run shell code remotely, it’s pretty straightforward to fetch, build and install code. Let’s assume I have to build and install ROOT and VTK on a couple of machines from source. The following fabfile.py will take care of this in an easy-to-understand and composable way (security considerations set aside):

import os
from fabric.api import run, env, cd

VTK_SOURCE = 'http://www.vtk.org/files/release/5.10/vtk-5.10.1.tar.gz'
ROOT_SOURCE = 'ftp://root.cern.ch/root/root_v5.34.05.source.tar.gz'
ROOT_CFLAGS = ' '.join(['--enable-mathmore',
                        '--enable-minuit2',
                        '--enable-fftw3',
                        '--enable-xml',
                        '--prefix=/usr/local'])
BUILD_PATH = '/tmp'

def wget(url):
    run('wget %s' % url)

def untar(filename):
    run('tar xfz %s' % filename)

def base(path):
    return os.path.join(BUILD_PATH, path)

def fetch():
    with cd(_base('.')):
        wget(ROOT_SOURCE)
        untar(os.path.basename(ROOT_SOURCE))

        wget(VTK_SOURCE)
        untar(os.path.basename(VTK_SOURCE))

def install():
    sudo('zypper in gsl gsl-devel')
    fetch()

    n_cores = run('grep -c ^processor /proc/cpuinfo')
    make_cmd = 'make -j%s' % n_cores

    with cd(_base('root')):
        run('./configure %s' % ROOT_CFLAGS)
        run(make_cmd)
        sudo('make install')

    with cd(_base('VTK5.10.1')):
        run('cmake . -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release')
        run(make_cmd)
        sudo('make install')

    sudo('ldconfig')

To change into a certain directory for the time being, we have to use the cd context manager because each command will be executed in its own SSH session. As you can imagine, sudo will execute a command just like run but with super user rights.

Defining and executing tasks is dead simple and the excellent documentation helps mastering more advanced topics such as roles and parallel execution.

Alternatives

Now, you could implement the same procedure in a shell script, copy that to each machine and execute there. But here are some reasons why this is not the best approach:

  1. Compared to shell languages, Python has a relatively sane syntax and a huge standard library.
  2. This approach doesn’t scale beyond a couple of machines. With Fabric you have a single file in a central location.
  3. The strong declarative style of individual tasks documents your intents.

However, Fabric is not the end of the story. For massive infrastructure automatization Chef and Puppet are probably much better suited.


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