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Yet Another Workflow Engine, a subprocess-based DAG execution system

Project Description

YAWN provides a framework for executing a set of shell commands with dependencies in a distributed manner and on a repeating schedule. Other tools do similar things and are inspirations for this one; particularly Celery and Airflow.

Principles

What is different from existing tools and why?

Code separation
YAWN runs your commands as sub-processes, so there is no co-mingling of your python code with the YAWN code. You are responsible for how your code is released and what version of your code gets run.
State separation
Environment variables are the only state that can be passed to your commands. Your application should key off just a date or record ID and get more complex state from its data sources.
Versioning
The workflow configuration and every run is versioned, so you have a complete history.
Broker
YAWN uses Postgres to store internal state and as a message queue. Using only Postgres allows for simple setup and configuration. YAWN uses the new SELECT ... FOR UPDATE SKIP LOCKED statement to efficiently select from the queue table.

Components

Web Servers
The website provides a user interface to view the workflows and tasks running within them. It allows you to run an existing workflow or re-run a failed task. The web server also provides a REST API to programatically create and run workflows.
Workers
The worker schedules and executes tasks. The worker uses subprocess.Popen to run tasks and capture stdout and stderr.

Concepts

Workflow
A set of Tasks that can depend on each other, forming what is popularly known as a directed acyclic graph (DAG). Workflows can be scheduled to run on a regular basis and they are versioned so they can change over time.
Run
A manually triggered or scheduled run of a Workflow.
Task
A shell command that specifies the upstream tasks it depends on, the number times to retry, and a timeout. The task is given environment variables configured in the workflow and run.
Execution
A single execution of a Task’s command, capturing the exit code and standard output and error.
Queue
A first-in, first-out list of Tasks to execute.
Worker
A process that reads from a set of Queues and executes the associated Tasks, recording the results in an Execution.

Examples

Run yawn examples to populate two workflows into the database.

Here is a screenshot of the page for a single workflow:

REST API

Browse the API by going to http://127.0.0.1:8000/api/ in a browser.

When creating a workflow, the format is (shown as YAML for readability):

name: Example
parameters:
  ENVIRONMENT: production
  CALCULATION_DATE: 2017-01-01
schedule: 0 0 *
schedule_active: True

tasks:
- name: task_1
  queue: default
  max_retries: 1
  timeout: 30
  command: python my_awesome_program.py $ENVIRONMENT
- name: task_2
  queue: default
  command: echo $CALCULATION_DATE | grep 2017
  upstream:
  - task_1
/api/workflows/

GET a list of versions or a single workflow version. POST to create or update a workflow using the schema show above. PATCH to change the schedule, schedule_active, or parameters fields only.

  • POST - use the schema shown above
  • PATCH {"schedule_active": false}
/api/runs/

GET a list of runs, optionally filtering to a particular workflow using ?workflow=<id>. POST to create a new run. PATCH to change the parameters.

  • POST {"workflow_id": 1, "parameters": null}
  • PATCH {"parameters": {"ENVIRONMENT": "test"}}
/api/tasks/<id>/

GET a single task from a workflow run, and its executions with their status and logging information. PATCH to enqueue a task or kill a running execution.

  • PATCH {"enqueue": true}
  • PATCH {"terminate": <execution_id>}

Python API

Import and use the Django models to create your workflow:

from yawn.workflow.models import WorkflowName
from yawn.task.models import Template

name, _ = WorkflowName.objects.get_or_create(name='Simple Workflow Example')
workflow = name.new_version(parameters={'MY_OBJECT_ID': '1', 'SOME_SETTING': 'false'})
task1 = Template.objects.create(workflow=workflow, name='start', command='echo Starting...')
task2 = Template.objects.create(workflow=workflow, name='task2', command='echo Working on $MY_OBJECT_ID')
task2.upstream.add(task1)
task3 = Template.objects.create(workflow=workflow, name='task3',
                                command='echo Another busy thing && sleep 20')
task3.upstream.add(task1)
task4 = Template.objects.create(workflow=workflow, name='done', command='echo Finished!')
task4.upstream.add(task2, task3)

workflow.submit_run(parameters={'child': 'true'})

Alternatively, use the serializer to give tasks as a dictionary in the format used by the API. This method checks if a version of the Workflow exists with the same structure, and will return the existing version if so:

from yawn.workflow.serializers import WorkflowSerializer

serializer = WorkflowSerializer(data=test_views.data())
serializer.is_valid(raise_exception=True)
workflow = serializer.save()
workflow.submit_run()

Contributing

To develop on YAWN, fork the repository and checkout a local copy:

git clone https://github.com/<you>/yawn

Install the backend Django dependencies and run its server. Your database should be at postgres://localhost:5432/yawn by default. The yawn command is a wrapper on Django’s manage.py:

pip install -e .[test]
createdb yawn
yawn migrate
yawn runserver

Install the frontend create-react-app dependencies and run its server:

cd frontend
npm install
npm start

Run the tests:

pytest
npm test

Load some examples and run the worker to process them:

yawn examples
yawn worker
Release History

Release History

This version
History Node

0.1.0

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