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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.

Browse it live at https://yawn.live, deployed on GKE.

https://circleci.com/gh/aclowes/yawn/tree/master.svg?style=svg https://codecov.io/gh/aclowes/yawn/branch/master/graph/badge.svg

Principle Differences

YAWN is inspired by, but different from Celery and Airflow because it:

  • Runs each task in a separate subprocess, like Airflow but unlike Celery, which avoids polution of a shared python interpreter and makes memory usage easier to reason about.
  • Uses PostgreSQL as the message broker and database, alleviating the need for a separate broker like Redis or RabbitMQ. This avoids the visibility timeout issue when using Redis as a Celery broker. YAWN uses the new SELECT ... FOR UPDATE SKIP LOCKED statement to efficiently select from the queue table.
  • Stores the command, environment variables, stdout and stderror for each task execution, so its easier to see the logs and history of what happened. Re-running a task does not overwrite the previous run.
  • Does not support inputs or outputs other than the command line and environment variables, with the intention that client applications should handle state instead.

Components

Web Server
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 remotely create and run workflows.
Worker
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
An instance of a workflow, manually triggered or scheduled.
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.

Installation

To get started using YAWN:

# install the package - someone has yawn :-(
pip install yawns

# install postgres and create the yawn database
# the default settings localhost and no password
createdb yawn

# setup the tables by running db migrations
yawn migrate

# create a user to login with
yawn createsuperuser

# create some sample workflows
yawn examples

# start the webserver on port 8000
yawn webserver

# open a new terminal and start the worker
yawn worker

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

https://cloud.githubusercontent.com/assets/910316/21969288/fe40baf0-db51-11e6-97f2-7e6875c1e575.png

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()

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