Overview and Tutorial¶
Welcome to Fabric!
This document is a whirlwind tour of Fabric’s features and a quick guide to its use. Additional documentation (which is linked to throughout) can be found in the usage documentation – please make sure to check it out.
What is Fabric?¶
As the README says:
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.
More specifically, Fabric is:
- A tool that lets you execute arbitrary Python functions via the command line;
- A library of subroutines (built on top of a lower-level library) to make executing shell commands over SSH easy and Pythonic.
Naturally, most users combine these two things, using Fabric to write and execute Python functions, or tasks, to automate interactions with remote servers. Let’s take a look.
This wouldn’t be a proper tutorial without “the usual”:
def hello(): print("Hello world!")
Placed in a Python module file named fabfile.py, that function can be executed with the fab tool (installed as part of Fabric) and does just what you’d expect:
$ fab hello Hello world! Done.
That’s all there is to it. This functionality allows Fabric to be used as a (very) basic build tool even without importing any of its API.
The fab tool simply imports your fabfile and executes the function or functions you instruct it to. There’s nothing magic about it – anything you can do in a normal Python script can be done in a fabfile!
It’s often useful to pass runtime parameters into your tasks, just as you might during regular Python programming. Fabric has basic support for this using a shell-compatible notation: <task name>:<arg>,<kwarg>=<value>,.... It’s contrived, but let’s extend the above example to say hello to you personally:
def hello(name="world"): print("Hello %s!" % name)
By default, calling fab hello will still behave as it did before; but now we can personalize it:
$ fab hello:name=Jeff Hello Jeff! Done.
Those already used to programming in Python might have guessed that this invocation behaves exactly the same way:
$ fab hello:Jeff Hello Jeff! Done.
For the time being, your argument values will always show up in Python as strings and may require a bit of string manipulation for complex types such as lists. Future versions may add a typecasting system to make this easier.
As used above, fab only really saves a couple lines of if __name__ == "__main__" boilerplate. It’s mostly designed for use with Fabric’s API, which contains functions (or operations) for executing shell commands, transferring files, and so forth.
Let’s build a hypothetical Web application fabfile. Fabfiles usually work best at the root of a project:
. |-- __init__.py |-- app.wsgi |-- fabfile.py <-- our fabfile! |-- manage.py `-- my_app |-- __init__.py |-- models.py |-- templates | `-- index.html |-- tests.py |-- urls.py `-- views.py
We’re using a Django application here, but only as an example – Fabric is not tied to any external codebase, save for its SSH library.
For starters, perhaps we want to run our tests and commit to our VCS so we’re ready for a deploy:
from fabric.api import local def prepare_deploy(): local("./manage.py test my_app") local("git add -p && git commit")
The output of which might look a bit like this:
$ fab prepare_deploy [localhost] run: ./manage.py test my_app Creating test database... Creating tables Creating indexes .......................................... ---------------------------------------------------------------------- Ran 42 tests in 9.138s OK Destroying test database... [localhost] run: git add -p && git commit <interactive Git add / git commit edit message session> Done.
The code itself is straightforward: import a Fabric API function, local, and use it to run and interact with local shell commands. The rest of Fabric’s API is similar – it’s all just Python.
Organize it your way¶
Because Fabric is “just Python” you’re free to organize your fabfile any way you want. For example, it’s often useful to start splitting things up into subtasks:
from fabric.api import local def test(): local("./manage.py test my_app") def commit(): local("git add -p && git commit") def prepare_deploy(): test() commit()
The prepare_deploy task can be called just as before, but now you can make a more granular call to one of the sub-tasks, if desired.
Our base case works fine now, but what happens if our tests fail? Chances are we want to put on the brakes and fix them before deploying.
Fabric checks the return value of programs called via operations and will abort if they didn’t exit cleanly. Let’s see what happens if one of our tests encounters an error:
$ fab prepare_deploy [localhost] run: ./manage.py test my_app Creating test database... Creating tables Creating indexes .............E............................ ====================================================================== ERROR: testSomething (my_project.my_app.tests.MainTests) ---------------------------------------------------------------------- Traceback (most recent call last): [...] ---------------------------------------------------------------------- Ran 42 tests in 9.138s FAILED (errors=1) Destroying test database... Fatal error: local() encountered an error (return code 2) while executing './manage.py test my_app' Aborting.
Great! We didn’t have to do anything ourselves: Fabric detected the failure and aborted, never running the commit task.
But what if we wanted to be flexible and give the user a choice? A setting (or environment variable, usually shortened to env var) called warn_only lets you turn aborts into warnings, allowing flexible error handling to occur.
Let’s flip this setting on for our test function, and then inspect the result of the local call ourselves:
from __future__ import with_statement from fabric.api import local, settings, abort from fabric.contrib.console import confirm def test(): with settings(warn_only=True): result = local('./manage.py test my_app', capture=True) if result.failed and not confirm("Tests failed. Continue anyway?"): abort("Aborting at user request.") [...]
In adding this new feature we’ve introduced a number of new things:
- The __future__ import required to use with: in Python 2.5;
- Fabric’s contrib.console submodule, containing the confirm function, used for simple yes/no prompts;
- The settings context manager, used to apply settings to a specific block of code;
- Command-running operations like local can return objects containing info about their result (such as .failed, or .return_code);
- And the abort function, used to manually abort execution.
However, despite the additional complexity, it’s still pretty easy to follow, and is now much more flexible.
Let’s start wrapping up our fabfile by putting in the keystone: a deploy task that ensures the code on our server is up to date:
def deploy(): code_dir = '/srv/django/myproject' with cd(code_dir): run("git pull") run("touch app.wsgi")
Here again, we introduce a handful of new concepts:
- Fabric is just Python – so we can make liberal use of regular Python code constructs such as variables and string interpolation;
- cd, an easy way of prefixing commands with a cd /to/some/directory call.
- run, which is similar to local but runs remotely instead of locally.
We also need to make sure we import the new functions at the top of our file:
from __future__ import with_statement from fabric.api import local, settings, abort, run, cd from fabric.contrib.console import confirm
With these changes in place, let’s deploy:
$ fab deploy No hosts found. Please specify (single) host string for connection: my_server [my_server] run: git pull [my_server] out: Already up-to-date. [my_server] out: [my_server] run: touch app.wsgi Done.
We never specified any connection info in our fabfile, so Fabric prompted us at runtime. Connection definitions use SSH-like “host strings” (e.g. user@host:port) and will use your local username as a default – so in this example, we just had to specify the hostname, my_server.
git pull works fine if you’ve already got a checkout of your source code – but what if this is the first deploy? It’d be nice to handle that case too and do the initial git clone:
def deploy(): code_dir = '/srv/django/myproject' with settings(warn_only=True): if run("test -d %s" % code_dir).failed: run("git clone [email protected]:/path/to/repo/.git %s" % code_dir) with cd(code_dir): run("git pull") run("touch app.wsgi")
As with our calls to local above, run also lets us construct clean Python-level logic based on executed shell commands. However, the interesting part here is the git clone call: since we’re using Git’s SSH method of accessing the repository on our Git server, this means our remote run call will need to authenticate itself.
Older versions of Fabric (and similar high level SSH libraries) run remote programs in limbo, unable to be touched from the local end. This is problematic when you have a serious need to enter passwords or otherwise interact with the remote program.
Fabric 1.0 and later breaks down this wall and ensures you can always talk to the other side. Let’s see what happens when we run our updated deploy task on a new server with no Git checkout:
$ fab deploy No hosts found. Please specify (single) host string for connection: my_server [my_server] run: test -d /srv/django/myproject Warning: run() encountered an error (return code 1) while executing 'test -d /srv/django/myproject' [my_server] run: git clone [email protected]:/path/to/repo/.git /srv/django/myproject [my_server] out: Cloning into /srv/django/myproject... [my_server] out: Password: <enter password> [my_server] out: remote: Counting objects: 6698, done. [my_server] out: remote: Compressing objects: 100% (2237/2237), done. [my_server] out: remote: Total 6698 (delta 4633), reused 6414 (delta 4412) [my_server] out: Receiving objects: 100% (6698/6698), 1.28 MiB, done. [my_server] out: Resolving deltas: 100% (4633/4633), done. [my_server] out: [my_server] run: git pull [my_server] out: Already up-to-date. [my_server] out: [my_server] run: touch app.wsgi Done.
Notice the Password: prompt – that was our remote git call on our Web server, asking for the password to the Git server. We were able to type it in and the clone continued normally.
Defining connections beforehand¶
Specifying connection info at runtime gets old real fast, so Fabric provides a handful of ways to do it in your fabfile or on the command line. We won’t cover all of them here, but we will show you the most common one: setting the global host list, env.hosts.
env is a global dictionary-like object driving many of Fabric’s settings, and can be written to with attributes as well (in fact, settings, seen above, is simply a wrapper for this.) Thus, we can modify it at module level near the top of our fabfile like so:
from __future__ import with_statement from fabric.api import * from fabric.contrib.console import confirm env.hosts = ['my_server'] def test(): do_test_stuff()
When fab loads up our fabfile, our modification of env will execute, storing our settings change. The end result is exactly as above: our deploy task will run against the my_server server.
This is also how you can tell Fabric to run on multiple remote systems at once: because env.hosts is a list, fab iterates over it, calling the given task once for each connection.
Our completed fabfile is still pretty short, as such things go. Here it is in its entirety:
from __future__ import with_statement from fabric.api import * from fabric.contrib.console import confirm env.hosts = ['my_server'] def test(): with settings(warn_only=True): result = local('./manage.py test my_app', capture=True) if result.failed and not confirm("Tests failed. Continue anyway?"): abort("Aborting at user request.") def pack(): local('tar czf /tmp/my_project.tgz .') def prepare_deploy(): test() pack() def deploy(): put('/tmp/my_project.tgz', '/tmp/') with cd('/srv/django/my_project/'): run('tar xzf /tmp/my_project.tgz') run('touch app.wsgi')
This fabfile makes use of a large portion of Fabric’s feature set:
- defining fabfile tasks and running them with fab;
- calling local shell commands with local;
- modifying env vars with settings;
- handling command failures, prompting the user, and manually aborting;
- and defining host lists and run-ning remote commands.
However, there’s still a lot more we haven’t covered here! Please make sure you follow the various “see also” links, and check out the documentation table of contents on the main index page.
Thanks for reading!