Skip to main content

Provides a powerful, Django-inspired class-based DAG syntax for Apache Airflow.

Project description


Workflows are a cleaner way of implementing DAGs using a Django-inspired class-based syntax.

Simple Example

Let's create a single Airflow DAG, whose name is a camelcased version of the class name, and whose operator dependencies are in the order they are defined.

There is an option to override the default dependencies method implementation to customise the dependency chain for your use case.

import workflows

class ExampleWorkflow(workflows.Workflow):
    class Meta:
        schedule_interval = '0 9 * * *'

    do_something_useful = workflows.PythonOperator(
        python_callable=lambda **kwargs: print('something useful'),
    something_else = workflows.PythonOperator(
        python_callable=lambda **kwargs: print('Something not useful'),

globals()[ExampleWorkflow.DAG.dag_id] = ExampleWorkflow.DAG

Dynamic DAG Example

Let's create (in this case three) DAGs, created dynamically and based on the ExampleWorkflow class as implemented above. In other words, they will share the same DAG metadata (so schedule in this case).

import workflows

workflow_names = [

for workflow in workflow_names:
    WorkflowClass = workflows.create_workflow(
    globals()[WorkflowClass.DAG.dag_id] = WorkflowClass.DAG

Project details

Download files

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

Source Distribution

airflow-workflows-0.1.4.tar.gz (3.6 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page