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    "info": {
        "author": "Alec Clowes",
        "author_email": "aclowes@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Topic :: Software Development :: Libraries :: Application Frameworks"
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        "description": "YAWN: Yet Another Workflow Engine\n=================================\n\nYAWN provides a framework for executing a set of shell commands with dependencies\nin a distributed manner and on a repeating schedule. Other tools do similar things and\nare inspirations for this one; particularly Celery_ and Airflow_.\n\nBrowse it live at https://yawn.live, deployed on GKE_.\n\n.. _Celery: http://www.celeryproject.org/\n.. _Airflow: https://airflow.incubator.apache.org/\n.. _GKE: https://github.com/aclowes/yawn-gke\n\n.. image:: https://circleci.com/gh/aclowes/yawn/tree/master.svg?style=svg\n  :target: https://circleci.com/gh/aclowes/yawn/tree/master\n.. image:: https://codecov.io/gh/aclowes/yawn/branch/master/graph/badge.svg\n  :target: https://codecov.io/gh/aclowes/yawn\n\nPrinciple Differences\n---------------------\n\nYAWN is inspired by, but different from Celery and Airflow because it:\n\n* Runs each task in a separate subprocess, like Airflow but unlike Celery, which avoids polution of\n  a shared python interpreter and makes memory usage easier to reason about.\n\n* Uses PostgreSQL as the message broker and database, alleviating the need for a separate broker like\n  Redis or RabbitMQ. This avoids the `visibility timeout`_ issue when using Redis as a Celery broker.\n  YAWN uses the new ``SELECT ... FOR UPDATE SKIP LOCKED`` statement to efficiently select from the\n  queue table.\n\n.. _visibility timeout: http://docs.celeryproject.org/en/latest/getting-started/brokers/redis.html#id1\n\n* Stores the command, environment variables, stdout and stderror for each task execution,\n  so its easier to see the logs and history of what happened. Re-running a task does not overwrite\n  the previous run.\n\n* Does not support inputs or outputs other than the command line and environment variables, with the\n  intention that client applications should handle state instead.\n\nComponents\n----------\n\nWeb Server\n  The website provides a user interface to view the workflows and tasks running within them.\n  It allows you to run an existing workflow or re-run a failed task. The web server also provides\n  a REST API to remotely create and run workflows.\n\nWorker\n  The worker schedules and executes tasks. The worker uses ``subprocess.Popen`` to run tasks and\n  capture stdout and stderr.\n\nConcepts\n--------\n\nWorkflow\n  A set of Tasks that can depend on each other, forming what is popularly known as a directed\n  acyclic graph (DAG). Workflows can be scheduled to run on a regular basis and they are versioned\n  so they can change over time.\n\nRun\n  An instance of a workflow, manually triggered or scheduled.\n\nTask\n  A shell command that specifies the upstream tasks it depends on, the number times to retry, and a\n  timeout. The task is given environment variables configured in the workflow and run.\n\nExecution\n  A single execution of a Task's command, capturing the exit code and standard output and error.\n\nQueue\n  A first-in, first-out list of Tasks to execute.\n\nWorker\n  A process that reads from a set of Queues and executes the associated Tasks, recording the\n  results in an Execution.\n\nInstallation\n------------\n\nTo get started using YAWN::\n\n    # install the package - someone has yawn :-(\n    pip install yawns\n\n    # install postgres and create the yawn database\n    # the default settings localhost and no password\n    createdb yawn\n\n    # setup the tables by running db migrations\n    yawn migrate\n\n    # create a user to login with\n    yawn createsuperuser\n\n    # create some sample workflows\n    yawn examples\n\n    # start the webserver on port 8000\n    yawn webserver\n\n    # open a new terminal and start the worker\n    yawn worker\n\nHere is a screenshot of the page for a single workflow:\n\n.. image:: https://cloud.githubusercontent.com/assets/910316/21969288/fe40baf0-db51-11e6-97f2-7e6875c1e575.png\n\nREST API\n--------\n\nBrowse the API by going to http://127.0.0.1:8000/api/ in a browser.\n\nWhen creating a workflow, the format is (shown as YAML for readability)::\n\n    name: Example\n    parameters:\n      ENVIRONMENT: production\n      CALCULATION_DATE: 2017-01-01\n    schedule: 0 0 *\n    schedule_active: True\n\n    tasks:\n    - name: task_1\n      queue: default\n      max_retries: 1\n      timeout: 30\n      command: python my_awesome_program.py $ENVIRONMENT\n    - name: task_2\n      queue: default\n      command: echo $CALCULATION_DATE | grep 2017\n      upstream:\n      - task_1\n\n``/api/workflows/``\n  GET a list of versions or a single workflow version. POST to create or update a workflow\n  using the schema show above. PATCH to change the ``schedule``, ``schedule_active``, or\n  ``parameters`` fields only.\n\n  * POST - use the schema shown above\n  * PATCH ``{\"schedule_active\": false}``\n\n``/api/runs/``\n  GET a list of runs, optionally filtering to a particular workflow using ``?workflow=<id>``.\n  POST to create a new run. PATCH to change the parameters.\n\n  * POST ``{\"workflow_id\": 1, \"parameters\": null}``\n  * PATCH ``{\"parameters\": {\"ENVIRONMENT\": \"test\"}}``\n\n``/api/tasks/<id>/``\n  GET a single task from a workflow run, and its executions with their status and logging\n  information. PATCH to enqueue a task or kill a running execution.\n\n  * PATCH ``{\"enqueue\": true}``\n  * PATCH ``{\"terminate\": <execution_id>}``\n\nPython API\n----------\n\nImport and use the Django models to create your workflow::\n\n    from yawn.workflow.models import WorkflowName\n    from yawn.task.models import Template\n\n    name, _ = WorkflowName.objects.get_or_create(name='Simple Workflow Example')\n    workflow = name.new_version(parameters={'MY_OBJECT_ID': '1', 'SOME_SETTING': 'false'})\n    task1 = Template.objects.create(workflow=workflow, name='start', command='echo Starting...')\n    task2 = Template.objects.create(workflow=workflow, name='task2', command='echo Working on $MY_OBJECT_ID')\n    task2.upstream.add(task1)\n    task3 = Template.objects.create(workflow=workflow, name='task3',\n                                    command='echo Another busy thing && sleep 20')\n    task3.upstream.add(task1)\n    task4 = Template.objects.create(workflow=workflow, name='done', command='echo Finished!')\n    task4.upstream.add(task2, task3)\n\n    workflow.submit_run(parameters={'child': 'true'})\n\nAlternatively, use the serializer to give tasks as a dictionary in the format used\nby the API. This method checks if a version of the Workflow exists with the same structure,\nand will return the existing version if so::\n\n    from yawn.workflow.serializers import WorkflowSerializer\n\n    serializer = WorkflowSerializer(data=test_views.data())\n    serializer.is_valid(raise_exception=True)\n    workflow = serializer.save()\n    workflow.submit_run()\n\nLinks\n-----\n\n* Contributing_\n* License_\n* `Deploying YAWN on Kubernetes via Google Container Engine`_\n\n.. _Contributing: CONTRIBUTING.rst\n.. _License: LICENSE.txt\n.. _Deploying YAWN on Kubernetes via Google Container Engine: https://github.com/aclowes/yawn-gke\n",
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