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    "info": {
        "author": "Malthe Borch",
        "author_email": "mborch@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "Intended Audience :: System Administrators",
            "License :: OSI Approved :: BSD License",
            "Operating System :: POSIX",
            "Programming Language :: Python",
            "Topic :: Internet",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: System :: Distributed Computing",
            "Topic :: System :: Systems Administration"
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        "description": "PQ\n**\n\nA transactional queue system for PostgreSQL written in Python.\n\n.. figure:: https://pq.readthedocs.org/en/latest/_static/intro.svg\n   :alt: PQ does the job!\n\nIt allows you to push and pop items in and out of a queue in various\nways and also provides two scheduling options: delayed processing and\nprioritization.\n\nThe system uses a single table that holds all jobs across queues; the\nspecifics are easy to customize.\n\nThe system currently supports only the `psycopg2\n<https://pypi.python.org/pypi/psycopg2>`_ database driver - or\n`psycopg2cffi <https://pypi.python.org/pypi/psycopg2cffi>`_ for PyPy.\n\nThe basic queue implementation is similar to Ryan Smith's\n`queue_classic <https://github.com/ryandotsmith/queue_classic>`_\nlibrary written in Ruby, but uses `SKIP LOCKED\n<https://www.2ndquadrant.com/en/blog/what-is-select-skip-locked-for-in-postgresql-9-5/>`_\nfor concurrency control.\n\nIn terms of performance, the implementation clock in at about 1,000\noperations per second. Using the `PyPy <http://pypy.org/>`_\ninterpreter, this scales linearly with the number of cores available.\n\n\nGetting started\n===============\n\nAll functionality is encapsulated in a single class ``PQ``.\n\n     ``class PQ(conn=None, pool=None, table='queue', debug=False)``\n\nExample usage:\n\n.. code-block:: python\n\n    from psycopg2 import connect\n    from pq import PQ\n\n    conn = connect('dbname=example user=postgres')\n    pq = PQ(conn)\n\nFor multi-threaded operation, use a connection pool such as\n``psycopg2.pool.ThreadedConnectionPool``.\n\nYou probably want to make sure your database is created with the\n``utf-8`` encoding.\n\nTo create and configure the queue table, call the ``create()`` method.\n\n.. code-block:: python\n\n    pq.create()\n\nThe table name defaults to ``'queue'``. To use a different name, pass\nit as a string value as the ``table`` argument for the ``PQ`` class\n(illustrated above).\n\n\nQueues\n======\n\nThe ``pq`` object exposes queues through Python's dictionary\ninterface:\n\n.. code-block:: python\n\n    queue = pq['apples']\n\nThe ``queue`` object provides ``get`` and ``put`` methods as explained\nbelow, and in addition, it also works as a context manager where it\nmanages a transaction:\n\n.. code-block:: python\n\n    with queue as cursor:\n        ...\n\nThe statements inside the context manager are either committed as a\ntransaction or rejected, atomically. This is useful when a queue is\nused to manage jobs because it allows you to retrieve a job from the\nqueue, perform a job and write a result, with transactional\nsemantics.\n\nMethods\n=======\n\nUse the ``put(data)`` method to insert an item into the queue. It\ntakes a JSON-compatible object such as a Python dictionary:\n\n.. code-block:: python\n\n    queue.put({'kind': 'Cox'})\n    queue.put({'kind': 'Arthur Turner'})\n    queue.put({'kind': 'Golden Delicious'})\n\nItems are pulled out of the queue using ``get(block=True)``. The\ndefault behavior is to block until an item is available with a default\ntimeout of one second after which a value of ``None`` is returned.\n\n.. code-block:: python\n\n    def eat(kind):\n        print 'umm, %s apples taste good.' % kind\n\n    job = queue.get()\n    eat(**job.data)\n\nThe ``job`` object provides additional metadata in addition to the\n``data`` attribute as illustrated by the string representation:\n\n    >>> job\n    <pq.Job id=77709 size=1 enqueued_at=\"2014-02-21T16:22:06Z\" schedule_at=None>\n\nThe ``get`` operation is also available through iteration:\n\n.. code-block:: python\n\n    for job in queue:\n        if job is None:\n            break\n\n        eat(**job.data)\n\nThe iterator blocks if no item is available. Again, there is a default\ntimeout of one second, after which the iterator yields a value of\n``None``.\n\nAn application can then choose to break out of the loop, or wait again\nfor an item to be ready.\n\n.. code-block:: python\n\n    for job in queue:\n        if job is not None:\n            eat(**job.data)\n\n        # This is an infinite loop!\n\n\nScheduling\n==========\n\nItems can be scheduled such that they're not pulled until a later\ntime:\n\n.. code-block:: python\n\n    queue.put({'kind': 'Cox'}, '5m')\n\nIn this example, the item is ready for work five minutes later. The\nmethod also accepts ``datetime`` and ``timedelta`` objects.\n\n\nPriority\n========\n\nIf some items are more important than others, a time expectation can\nbe expressed:\n\n.. code-block:: python\n\n    queue.put({'kind': 'Cox'}, expected_at='5m')\n\nThis tells the queue processor to give priority to this item over an\nitem expected at a later time, and conversely, to prefer an item with\nan earlier expected time. Note that items without a set priority are\npulled last.\n\nThe scheduling and priority options can be combined:\n\n.. code-block:: python\n\n    queue.put({'kind': 'Cox'}, '1h', '2h')\n\nThis item won't be pulled out until after one hour, and even then,\nit's only processed subject to it's priority of two hours.\n\n\nEncoding and decoding\n=====================\n\nThe task data is encoded and decoded into JSON using the built-in\n`json` module. If you want to use a different implementation or need\nto configure this, pass `encode` and/or `decode` arguments to the `PQ`\nconstructor.\n\n\nPickles\n=======\n\nIf a queue name is provided as ``<name>/pickle``\n(e.g. ``'jobs/pickle'``), items are automatically pickled and\nunpickled using Python's built-in ``cPickle`` module:\n\n.. code-block:: python\n\n    queue = pq['apples/pickle']\n\n    class Apple(object):\n        def __init__(self, kind):\n           self.kind = kind\n\n    queue.put(Apple('Cox'))\n\nThis allows you to store most objects without having to add any\nfurther serialization code.\n\nThe old pickle protocol ``0`` is used to ensure the pickled data is\nencoded as ``ascii`` which should be compatible with any database\nencoding. Note that the pickle data is still wrapped as a JSON string at the\ndatabase level.\n\nWhile using the pickle protocol is an easy way to serialize objects,\nfor advanced users t might be better to use JSON serialization\ndirectly on the objects, using for example the object hook mechanism\nin the built-in `json` module or subclassing\n`JSONEncoder <https://docs.python.org/2/library/json.html#json.JSONEncoder>`.\n\n\nTasks\n=====\n\n``pq`` comes with a higher level ``API`` that helps to manage ``tasks``.\n\n\n.. code-block:: python\n\n    from pq.tasks import PQ\n\n    pq = PQ(...)\n\n    queue = pq['default']\n\n    @queue.task(schedule_at='1h')\n    def eat(kind):\n        print 'umm, %s apples taste good.' % kind\n\n    eat('Cox')\n\n    queue.work()\n\n\n``tasks``'s ``jobs`` can optionally be re-scheduled on failure:\n\n.. code-block:: python\n\n    @queue.task(schedule_at='1h', max_retries=2, retry_in='10s')\n    def eat(kind):\n        # ...\n\n\nTime expectations can be overriden at ``task`` call:\n\n.. code-block:: python\n\n    eat('Cox', _expected_at='2m', _schedule_at='1m')\n\n\nThread-safety\n=============\n\nAll objects are thread-safe as long as a connection pool is provided\nwhere each thread receives its own database connection.\n\n\nChanges\n=======\n\n1.8.1 (2019-07-30)\n------------------\n\n- Added overridable `encode` and `decode` methods which are\n  responsible for turning task data into `JSON` and vice-versa.\n\n1.8.0 (2019-07-03)\n------------------\n\n- Change policy on task priority. Tasks with a null-value for\n  `expected_at` are now processed after items that have a value set.\n\n1.7.0 (2019-04-07)\n------------------\n\n- Use `SKIP LOCKED` instead of advisory lock mechanism (PostgreSQL 9.5+).\n\n1.6.1 (2018-11-14)\n------------------\n\n- Fix queue class factory pattern.\n\n1.6 (2018-11-12)\n----------------\n\n- Fix compatibility with `NamedTupleCursor`.\n\n- Fix duplicate column name issue.\n\n- Add option to specify own queue class.\n\n\n1.5 (2017-04-18)\n----------------\n\n- Fixed Python 2 compatibility.\n\n\n1.4 (2016-03-25)\n----------------\n\n- Added worker class and handler helper decorator.\n  [jeanphix]\n\n\n1.3 (2015-05-11)\n----------------\n\n- Python 3 compatibility.\n  [migurski]\n\n- Fix time zone issue.\n\n\n1.2 (2014-10-21)\n----------------\n\nImprovements:\n\n- Fixed concurrency issue where a large number of locks would be held\n  as a queue grows in size.\n\n- Fixed a database connection resource issue.\n\n\n1.1 (2014-02-27)\n----------------\n\nFeatures:\n\n- A queue is now also a context manager, providing transactional\n  semantics.\n\n- A queues now returns task objects which provide metadata and allows\n  reading and writing task data.\n\nImprovements:\n\n- The same connection pool can now be used with different queues.\n\nBugs:\n\n- The `Literal` string wrapper did not work correctly with `psycopg2`.\n\n- The transaction manager now correctly returns connections to the\n  pool.\n\n\n1.0 (2013-11-20)\n----------------\n\n- Initial public release.\n",
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