DoJobber: A Python repository from DerBeutlin

DoJobber

DoJobber is a python task orchestration framework based on writing small single-task idempotent classes (Jobs), defining interdependencies, and letting python do all the work of running them in the "right order".

DoJobber builds an internal graph of Jobs. It will run Jobs that have no unmet dependencies, working up the chain until it either reaches the root or cannot go further due to Job failures.

Each Job serves a single purpose, and must be idempotent, i.e. it will produce the same results if executed once or multiple times, without causing any unintended side effects. Because of this you can run your python script multiple times and it will get closer and closer to completion as any previously-failed Jobs succeed.

Here's an example of how one might break down the overall goal of inviting friends over to watch a movie - this is the result of the tests/dojobber_example.py script.

Rather than a yaml-based syntax with many plugins, DoJobber lets you write in native python, so anything you can code you can plumb into the DoJobber framework.

DoJobber is conceptually based on a Google program known as Masher that was built for automating service and datacenter spinups, but shares no code with it.

Job Structure

Each Job is is own class. Here's an example:

class FriendsArrive(Job):
    DEPS = (InviteFriends,)

    def Check(self, *dummy_args, **dummy_kwargs):
        # Do something to verify that everyone has arrived.
        pass

    def Run(self, *dummy_args, **dummy_kwargs):
        pass

Each Job has a DEPS variable, Check method, and Run method.

DEPS

DEPS defines which other Jobs it is dependent on. This is used for generating the internal graph.

Check

Check executes and, if it does not raise an Exception, is considered to have passed. If it passes then the Job passed and the next Job will run. It's purpose is to verify that we are in the desired state for this Job. For example if the job was to create a user, this may look up the user in /etc/passwd.

Run

Run executes if Check failed. Its job is to do something to achieve our goal. DoJobber doesn't care if it returns anything, throws an exception, or exits - all this is ignored.

An example might be creating a user account, or adding a database entry, or launching an ansible playbook.

Recheck

The Recheck phase simply executes the Check method again. Hopefully the Run method did the work that was necessary, so Check will verify all is now well. If so (i.e. Check does not raise an Exception) then we consider this Job a success, and any dependent Jobs are not blocked from running.

Job Features

Job Arguments

Jobs can take both positional and keyword arguments. These are set via the set_args method:

dojob = dojobber.DoJobber()
dojob.configure(RootJob, ......)
dojob.set_args('arg1', 'arg2', foo='foo', bar='bar', ...)

Because of this it is best to accept both in your Check and Run methods:

def Check(self, *args, **kwargs):
    ....

def Run(self, *args, **kwargs):
    ....

If you're generating your keyword arguments from argparse or optparse, then you can be even lazier - send it in as a dict:

myparser = argparse.ArgumentParser()
myparser.add_argument('--movie', dest='movie', help='Movie to watch.')
...
args = myparser.parse_args()
dojob.set_args(**args.__dict__)

An then in your Check/Run you can use them by name:

def Check(self, *args, **kwargs):
    if kwargs['movie'] == 'Zardoz':
        raise Error('Really?')

Local Job Storage

Local Storage allows you to share information between a Job's Check and Run methods. For example a Check may do an expensive lookup or initialization which the Run may then use to speed up its work.

To use Local Job Storage, simply use the self.storage dictionary from your Check and/or Run methods.

Local Storage is not available to any other Jobs. See Global Job Storage for how you can share information between Jobs.

Example:

class UselessExample(Job):
def Check(self, \*dummy_args, **dummy_kwargs):
    if not self.storage.get('sql_username'):
    self.storage['sql_username'] = (some expensive API call)
    (check something)

def Run(self, *dummy_args, **kwargs):
    subprocess.call(COMMAND + [self.storage['sql_username']])

Global Job Storage

Global Storage allows you to share information between Jobs. Naturally it is up to you to assure any Job that requires Global Storage is defined as dependent on the Job(s) that set Global Storage.

To use Global Job Storage, simply use the self.global_storage dictionary from your Check and/or Run methods.

Global Storage is available to all Jobs. It is up to you to avoid naming collisions.

Example:

# Store the number of CPUs on this machine for later
# Jobs to use for nefarious purposes.
class CountCPUs(Job):
    def Check(self, *dummy_args, **dummy_kwargs):
        self.global_storage['num_cpus'] = len(
            [x
             for x in open('/proc/cpuinfo').readlines()
             if 'vendor_id' in x])

# FixFanSpeed is dependent on CountCPUs
class FixFanSpeed(Job):
    DEPS = (CountCPUs,)

    def Check(self, *args, **kwargs):
        for cpu in range(self.global_storage['num_cpus']):
            ....

Cleanup

Jobs can have a Cleanup method. After checknrun is complete, the Cleanup method of each Job that ran (i.e. Run was executed) will be excuted. They are run in LIFO order, so Cleanups 'unwind' everything.

You can pass the cleanup=False option to DoJobber() to prevent Cleanup from happening and run it manually if you prefer:

dojob = dojobber.DoJobber()
dojob.configure(RootJob, cleanup=False, ......)
dojob.checknrun()
dojob.cleanup()

Creating Jobs Dynamically

You can dynamically create Jobs by making new Job classes and adding them to the DEPS of an existing class. This is useful if you need to create new Jobs based on commandline options. Dynamically creating many small single-purpose jobs is a better pattern than creating one large monolithic job that dynamically determines what it needs to do and check.

Here's an example of how you could create a new Job dynamically. We start with a base Job, SendInvite, which has uninitialized class valiables EMAIL and NAME:

# Base Job
class SendInvite(Job):
    EMAIL = None
    NAME = None

    def Check(self, *args, **kwargs):
        r = requests.get(
            'https://api.example.com/invited/' + self.EMAIL)
        assert(r.status_code == 200)

    def Run(self, *args, **kwargs):
        requests.post(
            'https://api.example.com/invite/' + self.EMAIL)

This example Job has Check/Run methods which use class variables EMAIL and NAME for their configuration.

So to get new Jobs based on this class, you create them and them to the DEPS of an existing Job such that they appear in the graph:

class InviteFriends(DummyJob):
    """Job that will become dynamically dependent on other Jobs."""
    DEPS = []


def invite_friends(people):
    """Add Invite Jobs for these people.

    People is a list of dictionaries with keys email and name.
    """
    for person in people:
        job = type('Invite {}'.format(person['name']),
                   (SendInvite,), {})
        job.EMAIL = person['email']
        job.NAME = person['name']
        InviteFriends.DEPS.append(job)

def main():
    # do a bunch of stuff
    ...

    # Dynamically add new Jobs to the InviteFriends
    invite_friends([
        {'name': 'Wendell Bagg', 'email': 'bagg@example.com'},
        {'name': 'Lawyer Cat', 'email': 'lawyercat@example.com'}
    ])

Job Types

There are several DoJobber Job types:

Job

Job requires a Check, Run, and may have optional Cleanup:

class CreateUser(Job):
    """Create our user's account."""

    def Check(self, *_, **kwargs):
        """Verify the user exists"""
        import pwd
        pwd.getpwnam(kwargs['username'])

    def Run(self, *_, **kwargs):
        """Create user given the commandline username/gecos arguments"""
        import subprocess
        subprocess.call([
            'sudo', '/usr/sbin/adduser',
            '--shell', '/bin/tcsh',
            '--gecos', kwargs['gecos'],
            kwargs['username'])

    ### Optional Cleanup method
    #def Cleanup(self):
    #   """Do something to clean up."""
    #   pass

DummyJob

DummyJob has no Check, Run, nor Cleanup. It is used simply to have a Job for grouping dependent or dynamically-created Jobs.

So a DummyJob may look as simple as this:

class PlaceHolder(DummyJob):
    DEPS = (Dependency1, Dependency2, ...)

RunonlyJob

A RunonlyJob has no check, just a Run, which will run every time.

If Run raises an exception then the Job is considered failed.

They cannot succeed in no_act mode, because in this mode the Run is never executed.

So an example Run may look like this:

class RemoveDangerously(RunonlyJob):
    DEPS = (UserAcceptedTheConsequences,)

    def Run(...):
        os.system('rm -rf /')

In general, avoid RunonlyJobs - it's better if you can understand if a change even needs making.

Examples

The tests/dojobber_example.py script in the source directory is fully-functioning suite of tests with numerous comments strewn throughout.

DerBeutlin/DoJobber