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Job orchestration framework based on individual idempotent python classes and dependencies.

Project description


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/`` script.

.. image::
:alt: DoJobber example graph
:width: 90%
:align: center

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.

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

Each Job has a DEPS attribute, ``Check`` method, and ``Run`` method.


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


``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`` 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.


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``

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()

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
```` 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.


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

def Run(self, *dummy_args, **kwargs): + [['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.


# 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(
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']):


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'

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, ......)

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(
'' + self.EMAIL)
assert(r.status_code == 200)

def Run(self, *args, **kwargs):
'' + self.EMAIL)

This example Job has ``Check``/``Run`` methods which use class
attribute ``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']

def main():
# do a bunch of stuff

# Dynamically add new Jobs to the InviteFriends
{'name': 'Wendell Bagg', 'email': ''},
{'name': 'Lawyer Cat', 'email': ''}

Retry Logic

DoJobber is meant to be able to be retried over and over until
you achieve success. You may be tempted to write something like

retry = 5
while retry:
if dojob.success():
print('Trying again...')
retry -= 1

However this is not necessary, and in fact is a waste of computing
cycles. The above code would cause us to check even the already
successful nodes unnecessarily, slowing everything down.

Instead, you can use two class attribute to configure retry
parameters. ``TRIES`` specifies how many times your Job can
erun before we give up, and ``RETRY_DELAY`` specifies the
minimum amount of time between retries.

Retries are useful for those cases where an action in ``Run``
fails due to a temporary condition (maybe the remote server
is unavailable briefly), or where the activities triggered
in the ``Run`` take time to complete (maybe an API call
returns immediately, but background fullfillment takes 30

By relying on retry logic, instead of adding in arbirtary
``sleep`` cycles in your code, you can have a more robust
Job graph.

Storage Considerations

When a Job is retried, it will be created from scratch. This means
that ``storage`` **is not available between runs**, however ``global_storage``
is. This is done to keep things as pristine as possible between
Job executions.

TRIES Attribute
TRIES defines the number of tries (check/run/recheck cycles)
that the Job is allowed to do before giving up. It must be >= 1.

The TRIES default if unspecified is 3, which can be changed
in ``configure()`` via the ``default_tries=###`` argument, for

class Foo(Job):
TRIES = 10

class Bar(Job):
DEPS = (Foo,)
... # No TRIES attribute


dojob = dojobber.DoJobber()
dojob.configure(Foo, default_tries=1)

In the above case, Foo can be tried 10 times, while Bar can only be
tried 1 time, since it has no ``TRIES`` specified and ``default_tries``
in configure is 1.


RETRY_DELAY defines the minimum amount of time to wait between
tries (check/run/recheck cycles) of **this** Job before giving
up with permanent failure. It is measured in seconds, and may
be any non-negative numeric value, including 0 and fractional
seconds like 0.02.

The RETRY_DELAY default if unspecified is 1 , which can be
changed in ``configure()`` via the ``default_retry-delay=###`` argument,
for example::

class Foo(Job):
RETRY_DELAY = 10.5 # A long but strangely precise value...

class Bar(Job):
DEPS = (Foo,)
... # No RETRY_DELAY attribute


dojob = dojobber.DoJobber()
dojob.configure(Foo, default_retry_delay=0.5)

In the above case, Foo will never start unless at least 10.5 seconds
have passed since the previous Foo attempt, while Bar only required
0.5 seconds have passed since it has no ``RETRY_DELAY`` specified
and ``default_retry_delay`` in configure is 0.5.

Delay minimization

When a Job has a failure it is not immediately retried.
Instead we will hit all Jobs in the graph that are still
awaiting check/run/recheck. Once every reachable Job has
been hit we will 'start over' on the Jobs that failed.

In practice this means that you aren't wasting the full
RETRY_DELAY because other Jobs were likely doing work
between retries of this Job. (Unless your graph is
highly linear and there are no unblocked Jobs.)

You can see how Job retries are interleaved by looking
at the example code::

$ tests/ -v | grep 'recheck: fail'
TurnOnTV.recheck: fail "Remote batteries are dead."
SitOnCouch.recheck: fail "No space on couch."
PopcornBowl.recheck: fail "Dishwasher cycle not done yet."
Pizza.recheck: fail "Giordano's did not arrive yet."
TurnOnTV.recheck: fail "Remote batteries are dead."
SitOnCouch.recheck: fail "No space on couch."
PopcornBowl.recheck: fail "Dishwasher cycle not done yet."
Pizza.recheck: fail "Giordano's did not arrive yet."
TurnOnTV.recheck: fail "Remote batteries are dead."
SitOnCouch.recheck: fail "No space on couch."
PopcornBowl.recheck: fail "Dishwasher cycle not done yet."
PopcornBowl.recheck: fail "Dishwasher cycle not done yet."
PopcornBowl.recheck: fail "Dishwasher cycle not done yet."
Popcorn.recheck: fail "Still popping..."
Popcorn.recheck: fail "Still popping..."

Note initially we have several Jobs that fail on
distinct branches, and these can be retried in a round-robin
sort of fashion. Only once we end up at strict dependencies
of PopcornBowl and Popcorn do we see single Jobs being retried
without others getting their time.

Job Types

There are several DoJobber Job types:


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

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

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


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, ...)


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.

Debugging and Logging

There are two types of logging for DoJobber: runtime information
about Job success/failure for anyone wanting more details
about the processing of your Jobs, and developer DoJobber
debugging which is useful when writing your DoJobber code.

Runtime Debugging

To increase verbosity of Job success and failures you
pass `verbose` or `debug` keyword arguments to `configure`::

dojob = dojobber.DoJobber()
dojob.configure(RootJob, verbose=True, ....)
# or
dojob.configure(RootJob, debug=True, ....)

Setting `verbose` will show a line of check/run/recheck status
to stderr, as well as any failure output from rechecks, such as::

FindTVRemote.check: fail pass
FindTVRemote.recheck: pass
TurnOnTV.check: fail pass

Using `debug` will additionally show a full stacktrace of
any failure of check/run/recheck phases.

Development Debugging

When writing your DoJobber code you may want to turn on
the developer debugging capabilities. This is enabled
when DoJobber is initialized by passing the `dojobber_loglevel`
keyword argument::

import logging
dojob = DoJobber(dojobber_loglevel=logging.DEBUG)

DoJobber's default is to show `CRITICAL` errors only.
Acceptable levels are those defined in the logging module.

This can help identify problems when writing your code,
such as passing a non-iterable as a `DEPS` variable,
watching as your Job graph is created from the
classes, etc.


The ``tests/`` script in the source directory is
fully-functioning suite of tests with numerous comments strewn

See Also

`Bri Hatch <>`_ gave a talk
about DoJobber at LinuxFestNorthwest in 2018. You can find his
`presentation <>`_
on his website, and the
`presentation video <>`_ is
available on YouTube.

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