{
    "info": {
        "author": "Luca Sbardella",
        "author_email": "luca@quantmind.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Web Environment",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Utilities"
        ],
        "description": "|pulsar-queue|\n\n:Badges: |license|  |pyversions| |status| |pypiversion|\n:Master CI: |master-build| |coverage-master|\n:Downloads: http://pypi.python.org/pypi/pulsar-queue\n:Source: https://github.com/quantmind/pulsar-queue\n:Mailing list: `google user group`_\n:Design by: `Quantmind`_ and `Luca Sbardella`_\n:Platforms: Linux, OSX, Windows. Python 3.5 and above\n:Keywords: server, asynchronous, concurrency, actor, process, queue, tasks, redis\n\n\n.. |pypiversion| image:: https://badge.fury.io/py/pulsar-queue.svg\n  :target: https://pypi.python.org/pypi/pulsar-queue\n.. |pyversions| image:: https://img.shields.io/pypi/pyversions/pulsar-queue.svg\n  :target: https://pypi.python.org/pypi/pulsar-queue\n.. |license| image:: https://img.shields.io/pypi/l/pulsar-queue.svg\n  :target: https://pypi.python.org/pypi/pulsar-queue\n.. |status| image:: https://img.shields.io/pypi/status/pulsar-queue.svg\n  :target: https://pypi.python.org/pypi/pulsar-queue\n.. |downloads| image:: https://img.shields.io/pypi/dd/pulsar-queue.svg\n  :target: https://pypi.python.org/pypi/pulsar-queue\n.. |master-build| image:: https://img.shields.io/travis/quantmind/pulsar-queue/master.svg\n  :target: https://travis-ci.org/quantmind/pulsar-queue\n.. |dev-build| image:: https://img.shields.io/travis/quantmind/pulsar-queue/dev.svg\n  :target: https://travis-ci.org/quantmind/pulsar-queue?branch=dev\n.. |coverage-master| image:: https://coveralls.io/repos/github/quantmind/pulsar-queue/badge.svg?branch=master\n  :target: https://coveralls.io/github/quantmind/pulsar-queue?branch=master\n.. |coverage-dev| image:: https://coveralls.io/repos/github/quantmind/pulsar-queue/badge.svg?branch=dev\n  :target: https://coveralls.io/github/quantmind/pulsar-queue?branch=dev\n\n\nAsynchronous server for consuming asynchronous IO tasks, green IO tasks,\nblocking IO tasks and long running CPU bound tasks.\n\n* Fully configurable\n* Consumers poll tasks from distributed message brokers (redis broker implemented)\n* Publish/subscribe for real-time event and logging (redis pub/sub backend)\n* Can schedule tasks when run as a scheduler (``--schedule-periodic`` flag)\n* Build on top of pulsar_ and asyncio_\n\n\nTL;DR\n========\n\nClone the repository::\n\n    git clone git@github.com:quantmind/pulsar-queue.git\n\n\nMove to the ``tests/example`` directory and run the server::\n\n    python manage.py\n\n\n\n.. contents:: **CONTENTS**\n\n\nFour steps tutorial\n========================\n\n1 - Create a script\n----------------------\n\nA simple python file which runs your application:\n\n.. code::\n\n    vim manage.py\n\n\n.. code:: python\n\n    from pq.api import PusarQueue\n\n\n    task_paths = ['sampletasks.*', 'pq.jobs']\n\n\n    def app():\n        return PusarQueue(config=__file__)\n\n    if __name__ == '__main__':\n        app().start()\n\n\n2 - Implement Jobs\n---------------------\n\nCreate the modules where Jobs_ are implemented.\nIt can be a directory containing several submodules.\n\n.. code::\n\n    mkdir sampletasks\n    cd sampletasks\n    vim mytasks.py\n\n.. code:: python\n\n    import asyncio\n    import time\n\n    from pq import api\n\n\n    @api.job()\n    def addition(self, a=0, b=0):\n        return a + b\n\n\n    @api.job()\n    async def asynchronous(self, lag=1):\n        start = time.time()\n        await asyncio.sleep(lag)\n        return time.time() - start\n\n\n3 - Run the server\n---------------------\n\nRun the server with two task consumers (pulsar actors).\n\n**NOTE**: Make sure you have Redis server up and running before you start the server.\n\n.. code::\n\n    python manage.py -w 2\n\n4 - Queue tasks\n---------------------\n\nLaunch a python shell and play with the api\n\n.. code:: python\n\n    >>> from manage import app\n    >>> api = app().api()\n    >>> task = api.tasks.queue('addition', a=4, b=6)\n    >>> task\n    <TaskFuture pending ID=i26ad5c14c5bb422e87b0f7ccbce5ba06>\n    >>> task = task.wait()\n    task.addition<i24ab99ddf2744902a375e039790dcbc4><SUCCESS>\n    >>> task.result\n    10\n    >>> task.status_string\n    'SUCCESS'\n\nYou can also queue tasks with a ``delay``\n\n.. code:: python\n\n    >>> task = api.tasks.queue('addition', a=4, b=6, callback=False, delay=2).wait()\n    >>> task.status_string\n    'QUEUED'\n    >>> task.time_queued    # timestamp\n    >>> task = task.done_callback.wait()\n    >>> task.status_string\n    'SUCCESS'\n    >>> task.time_started - task.time_queued\n    2.00\n\n**NOTE**: The ``wait`` method in a task future can only be used on the shell\nor when the event loop is not running. In all other cases one should ``await``\nfor the task future in a coroutine.\n\nAPI\n=============\n\nThe producer API is obtained from the Task application ``api`` method:\n\n.. code:: python\n\n    from pq.api import PusarQueue\n\n    api = PusarQueue(...).api()\n\n\nAPI methods\n---------------\n\n*api*.start()\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nStart listening to events. This method return a coroutine which resolve in the api:\n\n.. code:: python\n\n    api = await api.start()\n\nThe start method is used when the api is used by application to queue messages/tasks\nand listen for events published by distributed consumers.\n\n*api*.on_events(*message_type*, *event_re*, *callback*)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nAdd a callback invoked every time an event matching the regular expression ``event_re``\noccurs on the ``message_type`` channel. The *callback* has the following signature:\n\n.. code:: python\n\n    def event_callback(channel, event, message):\n        # event is string, the event matched\n        # message is of type message_type\n\nIf the event is a task event (see events_) the message is a Task_ object.\n\nThis method is useful when creating applications which needs to respond to the\nqueue server events in real time::\n\n    api.on_events('task', 'queued', callback)\n    api.on_events('task', 'started', callback)\n    api.on_events('task', 'done', callback)\n\n\n*api*.remove_event_callback(*message_type*, *event_re*, *callback*)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nRemove a previously added event callback. This method is safe.\n\n*api*.queue(*message*, *callback=True*)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nQueue a message in the message queue, equivalent to:\n\n.. code:: python\n\n    api.broker.queue(message, callback)\n\nThis method returns a ``MessageFuture``, a subclass of asyncio Future_ which\nresolves in a ``message`` object.\nIf ``callback`` is True (default) the Future is resolved once the message\nis delivered (out of the queue), otherwise is is resolved once the message\nis queued (entered the queue).\n\n*api*.execute(*message*)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nExecute a message without queueing. This is only supported by messages with\na message consumer which execute them (the ``tasks`` consumer for example).\nIf *message* is a Task_, this method is equivalent to:\n\n.. code:: python\n\n    api.tasks.execute(task)\n\nThis method returns a ``MessageFuture``, a subclass of asyncio Future_ which\nresolve in a ``message`` object.\n\n*api*.consumers\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nList of consumers registered with the api.\n\nTasks API\n-----------------\n\nThe tasks producer is obtained vua the ``tasks`` property from the producer API instance\n\n.. code:: python\n\n    tasks = api.tasks\n\nThe following methods are available for the tasks producer:\n\n\n*tasks*.queue(*jobname*, *\\*\\*kwargs*)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nQueue a task and return a **TaskFuture** which is resolved once the task has finished.\nIt is possible to obtain a task future resolved when the task has been queued, rather than finished, by passing the **callback=False** parameter:\n\n.. code:: python\n\n    task = await tasks.queue(..., callback=False)\n    task.status_string  # QUEUED\n\nThe ``kwargs`` parameters are used as input parameters for the Job_ callable with the exception of:\n\n* ``callback``: discussed above\n* ``delay``: delay execution by a given number of seconds\n* ``queue``: overrides the Job_ [default_queue](#job-default-queue)\n* [timeout](#job-timeout)\n* ``meta_params``: dictionary of parameters used by the Job_ callable to override default values of:\n  * [max_retries](#job-max-retries)\n  * [retry_delay](#job-retry-delay)\n  * [max_concurrency](#job-max-concurrency)\n\n*tasks*.queue_local(*jobname*, *\\*\\*kwargs*)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nQueue a job in the local task queue. The local task queue is processed by the same server instance. It is equivalent to execute:\n\n.. code:: python\n\n    task = await tasks.queue(..., queue=tasks.node_name)\n    task.queue  # tasks.node_name\n\n\n*tasks*.execute(*jobname*, *\\*args*, *\\*\\*kwargs*)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nExecute a task immediately, it does not put the task in the task queue.\nThis method is useful for debugging and testing. It is equivalent to execute:\n\n.. code:: python\n\n    task = await tasks.queue(..., queue=False)\n    task.queue          # None\n    task.status_string  # SUCCESS\n\n\n*tasks*.queues()\n~~~~~~~~~~~~~~~~~~~~~~~~\n\nReturn the list of queue names the backend is subscribed. This list is not empty when the backend is a task consumer.\n\n*tasks*.job_list(*jobname=None*)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nReturns a list of ``job_name``, ``job_description`` tuples. The ``job_name`` is a string which must be used as the **jobname** parameter when executing or queing tasks. The ``job_description`` is a dictionary containing metadata and documentation for the job. Example:\n\n.. code:: python\n\n    jobs = dict(tasks.job_lits())\n    jobs['execute.python']\n    # {\n    #   'type': 'regular',\n    #   'concurrency': 'asyncio',\n    #   'doc_syntax': 'markdown',\n    #   'doc': 'Execute arbitrary python code on a subprocess ... '\n    # }\n\n\nThe Job class\n-----------------\n\nThe **Job** class is how task factories are implemented and added to the\ntasks backend registry. When writing a new **Job** one can either subclass:\n\n.. code:: python\n\n    import asyncio\n\n    class AsyncSleep(api.Job):\n\n        async def __call__(self, lag=1):\n            await asyncio.sleep(lag)\n\n\nor use the less verbose **job** decorator:\n\n.. code:: python\n\n    @api.job()\n    async def asyncsleep(self, lag=1):\n        await asyncio.sleep(lag)\n\n\nIn either cases the ``self`` parameter is an instance of a **Job** class and\nit has the following useful attributes and methods:\n\n*job*.backend\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe tasks backend that is processing this Task_ run\n\n*job*.default_queue\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe default queue name where tasks for this job are queued. By default it is ``None``\nin which case, if a ``queue`` is not given when queueing a task, the first queue\nfrom the `queues <#tasks_queues>`_ list taken.\n\n*job*.http\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nBest possible HTTP session handler for the job concurrency mode.\n\n*job*.logger\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nPython logging handler for this job. The name of this handler\nis ``<app_name>.<job.name>``.\n\n*job*.max_retries\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nOptional positive integer which specify the maximum number of retries when a\ntask fails or is revoked. If not available failing tasks are not re-queued.\nIt can be specified as a class attribute or during initialisation from the task\nmeta parameters.\n\n*job*.retry_delay\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nOptional positive integer which specifies the number of seconds to delay a task\nretry.\n\n*job*.name\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe name of this job. Used to queue tasks\n\n*job*.task\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe Task_ instance associated with this task run\n\n*job*.queue(*jobname*, *\\*args*, *\\*\\*kwargs*)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nQueue a new job form a task run. It is equivalent to:\n\n.. code:: python\n\n    meta_params = {'from_task': self.task.id}\n    self.backend.tasks.queue(..., meta_params=meta_params)\n\n\n*job*.shell(*command*, *\\*\\*kwargs*)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nExecute a shell command and returns a coroutine:\n\n.. code:: python\n\n    await self.shell(\"...\")\n\n\nThe Task\n-----------\n\nA task contains the metadata information of a job run and it is exchanged between task producers and task consumers via a distributed task queue.\n\n\nTask States\n-----------------\n\nA Task_ can have one of the following ``task.status``:\n\n* ``QUEUED = 6`` a task queued but not yet executed.\n* ``STARTED = 5`` a task where execution has started.\n* ``RETRY = 4`` a task is retrying calculation.\n* ``REVOKED = 3`` the task execution has been revoked (or timed-out).\n* ``FAILURE = 2`` task execution has finished with failure.\n* ``SUCCESS = 1`` task execution has finished with success.\n\n\n**FULL_RUN_STATES**\n\nThe set of states for which a Task_ has run: ``FAILURE`` and ``SUCCESS``\n\n\n**READY_STATES**\n\nThe set of states for which a Task_ has finished: ``REVOKED``, ``FAILURE`` and ``SUCCESS``\n\nEvents\n-------------\n\nThe task queue broadcast several events during task execution and internal state:\n\n* ``task_queued``: a new Task_ has been queued, the message is a task instance\n* ``task_started``: a Task_ has started to be consumed by a task consumer, it is out of the task queue\n* ``task_done``: a Task_ is done, the message is a task in a **READY_STATES**\n\n\nConfiguration\n------------------\n\nThere are several parameters you can use to twick the way the task queue works.\nIn this list the name in bold is the entry point in the config file and **cfg**\ndictionary, while, the value between brackets shows the command line entry with default\nvalue.\n\n* **concurrent_tasks** (``--concurrent-tasks 5``)\n\n    The maximum number of concurrent tasks for a given worker in a task consumer server.\n\n* **data_store** (``--data-store redis://127.0.0.1:6379/7``)\n\n    Data store used for publishing and subscribing to messages (redis is the\n    only backend available at the moment)\n\n* **max_requests** (``--max-requests 0``)\n\n    The maximum number of tasks a worker will process before restarting.\n    A 0 value (the default) means no maximum number, workers will process\n    all tasks forever.\n\n* **message_broker** (``--message-broker ...``)\n\n    Data store used as distributed task queue. If not provided (default) the\n    ``data_store`` is used instead. Redis is the\n    only backend available at the moment.\n\n* **message_serializer** (``--message-serializer json``)\n\n    The decoder/encoder for messages and tasks. The default is **JSON** but **Message Pack**\n    is also available if msgpack_ is installed.\n\n* **schedule_periodic** (``--schedule-periodic``)\n\n    When ``True``, the task application can schedule periodic Jobs_.\n    Usually, only one running server is responsible for\n    scheduling tasks.\n\n* **task_pool_timeout** (``--task-pool-timeout 2``)\n\n    Timeout in seconds for asynchronously polling tasks from the queues. No need to change this parameter really.\n\n* **workers** (``--workers 4``)\n\n    Number of workers (processes) consuming tasks.\n\n\nTasks Concurrency\n======================\n\nA task can run in one of four ``concurrency`` modes.\nIf not specified by the ``Job``, the concurrency mode is ``ASYNC_IO``.\n\nASYNC_IO\n-----------\n\nThe asynchronous IO mode is associated with tasks which return\nan asyncio Future or a coroutine. These tasks run concurrently\nin the worker event loop.\nAn example can be a Job to scrape web pages and create new tasks to process the html\n\n.. code:: python\n\n    @api.job()\n    async def scrape(self, url=None):\n        assert url, \"url is required\"\n        request = await self.http.get(url)\n        html = request.text()\n        task = self.queue('process.html', html=html, callback=False)\n        return task.id\n\n\nTHREAD_IO\n-------------\n\nThis concurrency mode is best suited for tasks performing\n*blocking* IO operations.\nA ``THREAD_IO`` job runs its tasks in the event loop executor.\nYou can use this model for most blocking operation unless\n\n* Long running CPU bound\n* The operation does not release the GIL\n\nExample of tasks suitable for thread IO are IO operations on files.\nFor example the test suite uses this Job for testing ``THREAD_IO``\nconcurrency (check the ``tests.example.jobs.standard`` module\nfor the full code):\n\n\n.. code:: python\n\n    @api.job(concurrency=api.THREAD_IO)\n    def extract_docx(self, input=None, output=None):\n        \"\"\"\n        Extract text from a docx document\n        \"\"\"\n        import docx\n        assert input and output, \"input and output must be given\"\n        document = docx.Document(input)\n        text = '\\n\\n'.join(_docx_text(document))\n        with open(output, 'w') as fp:\n            fp.write(text)\n        return {\n            'thread': threading.get_ident(),\n            'text': len(text)\n        }\n\nCPUBOUND\n------------\n\nIt assumes the task performs blocking CPU bound operations.\nJobs with this consurrency mode run their tasks on sub-processeses\nusing `asyncio subprocess`_ module.\n\nExtend\n=================\n\nIt is possible to enhance the task queue application by passing\na custom ``Manager`` during initialisation.\nFor example:\n\n.. code:: python\n\n    from pq import api\n\n    class Manager(api.Manager):\n\n        async def store_message(self, message):\n            \"\"\"This method is called when a message/task is queued,\n            started and finished\n            \"\"\"\n            if message.type == 'task':\n                # save this task into a db for example\n\n        def queues(self):\n            \"\"\"List of queue names for Task consumers\n            By default it returns the node name and the task_queues\n            in the config dictionary.\n            \"\"\"\n            queues = [self.backend.node_name]\n            queues.extend(self.cfg.task_queues)\n            return queues\n\n\n    tq = PulsarQueue(Manager, ...)\n\n\nThe ``Manager`` class is initialised when the backend handler is initialised\n(on each consumer and in the scheduler).\n\nChangelog\n==============\n\n* `Versions 0.5 <https://github.com/quantmind/pulsar-queue/blob/master/docs/history/0.5.md>`_\n* `Versions 0.4 <https://github.com/quantmind/pulsar-queue/blob/master/docs/history/0.4.md>`_\n* `Versions 0.3 <https://github.com/quantmind/pulsar-queue/blob/master/docs/history/0.3.md>`_\n* `Versions 0.2 <https://github.com/quantmind/pulsar-queue/blob/master/docs/history/0.2.md>`_\n* `Versions 0.1 <https://github.com/quantmind/pulsar-queue/blob/master/docs/history/0.1.md>`_\n\nLicense\n=============\nThis software is licensed under the BSD 3-clause License. See the LICENSE\nfile in the top distribution directory for the full license text. Logo designed by Ralf Holzemer,\n`creative common license`_.\n\n\n.. _`google user group`: https://groups.google.com/forum/?fromgroups#!forum/python-pulsar\n.. _`Luca Sbardella`: http://lucasbardella.com\n.. _`Quantmind`: http://quantmind.com\n.. _`creative common license`: http://creativecommons.org/licenses/by-nc/3.0/\n.. _pulsar: https://github.com/quantmind/pulsar\n.. _asyncio: https://docs.python.org/3/library/asyncio.html\n.. _greenlet: https://greenlet.readthedocs.io/en/latest/\n.. _msgpack: https://pypi.python.org/pypi/msgpack-python\n.. _`asyncio subprocess`: https://docs.python.org/3/library/asyncio-subprocess.html\n.. _Future: https://docs.python.org/3/library/asyncio-task.html#future\n.. _Job: #the-job-class\n.. _Jobs: #the-job-class\n.. _Task: #the-task\n.. _Events: #events\n.. _events: #events\n.. |pulsar-queue| image:: https://pulsar.fluidily.com/assets/queue/pulsar-queue-banner-400-width.png\n   :target: https://github.com/quantmind/pulsar-queue\n",
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