Skip to main content
Join the official Python Developers Survey 2018 and win valuable prizes: Start the survey!

A simple database driven workflow engine

Project description

This project aim is to provide a simple database driven workflow engine that you can use to configure and automate complex operations. It is based on configurable state machines that are defined at runtime through the admin interface.

Workflow Definition

A workflow is defined by the following objects:

Workflow:main object, it must have a unique name
State:objects which represent the nodes of the graph they are workflow specific
Transition:Transitions can be manual or automatic, automatic transitions are executed asynchrnously as soon as an object reaches their intial state. Of course only one automatic transition can be executed at the time and it will normally change the state so that other transitions which were defined for the intial state cannot be executed anymore. Which transition is executed depends on the priority and on the related conditions
Condition:objects which limit the execution of transitions based on the properties of the object which is undergoing the process, of the user or on generic queries. Conditions are hierarchical and can be combined using special boolean conditions
Function:For every condition C of type function there must be a function which has value C for the condition. The function specifies the python function which will be called to check if the condition is fulfilled.
FunctionParameter:
 Each function parameter is passed as kwarg to its function
Callback:A callback defines a python function which should be called when a transition occurs, usually for required side effect. The callback will be called either within the same transaction and before the update of the object state, if execute_async is False or after the after the status has been updated in an independent thread
CallbackParameter:
 Each callback parameter is passed as kwarg to its callback

Below you can find a simple example of a 3 states workflow with a mix of manual and automatic transitions

from django.contrib.auth.models import User
from django_workflow import workflow
from django_workflow.models import Workflow, State, Transition, Condition, Function, FunctionParameter, Callback, CallbackParameter

# create the main workflow object
wf = Workflow.objects.create(name="Test_Workflow", object_type="django.contrib.auth.models.User")
# create 3 states
s1 = State.objects.create(name="state 1", workflow=wf, active=True, initial=True)
s2 = State.objects.create(name="state 2", workflow=wf, active=True)
#the final state is defined as inactive so that its skipped when scanning for automatic transitions
s3 = State.objects.create(name="state 3", workflow=wf, active=False)
# create the transitions, we have 2 automatic transitions from state 1 to state 2,
# the first is going to be executed despite t4 having a better priority because
# t1 has a lower automatic_delay
t1 = Transition.objects.create(name="auto_fast", initial_state=s1, final_state=s2, automatic=True, automatic_delay=1.0/24.0/3600.0, priority=2)
t4 = Transition.objects.create(name="auto_slow", initial_state=s1, final_state=s3, automatic=True,
    automatic_delay=1.0 / 24.0, priority=1)
t2 = Transition.objects.create(initial_state=s1, final_state=s3, automatic=False)
t3 = Transition.objects.create(initial_state=s2, final_state=s3, automatic=False)
# we set t3 to be executed only by superusers this can be done with a object_attribute_value conditon
c1 = Condition.objects.create(condition_type="function", transition=t3)
f1 = Function.objects.create(
    function_name="object_attribute_value",
    function_module="django_workflow.conditions",
    condition=c1
)
p11 = FunctionParameter.objects.create(function=f1, name="attribute_name", value="is_superuser")
p12 = FunctionParameter.objects.create(function=f1, name="attribute_value", value="True")
# we want to print out if transition 1 was executed, this can be done with a callback
cb1 = Callback.objects.create(transition=t1, function_name="_print", function_module="django_workflow.tests", order=1)
cp11 = CallbackParameter.objects.create(callback=cb1, name="text", value="Transition 1 Executed")

States and Transitions

Once the workflow is defined one can add objects to the workflow

obj = MyModelObject.objects.get(name="MyObjectName")
wf = workflow.get_workflow("Test_Workflow")
wf.add_object(obj.id)

The method add_object beside starting the tracking of the object in the state machine, it also triggers any automatic transition available in the initial state

To check the state of an object one can use:

workflow.get_object_state("Test_Workflow", object_id)

and to check for available transition, e.g. to know which buttons you can show in the UI, you can call

workflow.get_available_transitions("Test_Workflow", user, object_id)

where user should be the Django user that wishes to perform the action. The user is passed to each condition and callback so it is useful to check for authorization as well as to perform specific tasks, e.g. notifications

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
django_wf-0.9.1-py2.7.egg (32.0 kB) Copy SHA256 hash SHA256 Egg 2.7 Aug 29, 2017
django-wf-0.9.1.tar.gz (14.0 kB) Copy SHA256 hash SHA256 Source None Aug 29, 2017

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page