Metadata-Version: 1.0
Name: django-postgres-queue
Version: 0.4.4
Summary: UNKNOWN
Home-page: https://github.com/gavinwahl/django-postgres-queue
Author: Gavin Wahl
Author-email: gavinwahl@gmail.com
License: BSD
Description: django-postgres-queue
        =====================
        
        django-postgres-queue is a task queue system for Django backed by postgres.
        
        
        Why postgres?
        -------------
        
        I thought you were never supposed to use an RDBMS as a queue? Well, postgres
        has some features that make it not as bad as you might think, it has some
        compelling advantages.
        
        - Transactional behavior and reliability.
        
          Adding tasks is atomic with respect to other database work. There is no need
          to use ``transaction.on_commit`` hooks and there is no risk of a transaction
          being committed but the tasks it queued being lost.
        
          Processing tasks is atomic with respect to other database work. Database work
          done by a task will either be committed, or the task will not be marked as
          processed, no exceptions. If the task only does database work, you achieve
          true exactly-once message processing.
        
        - Operational simplicity
        
          By reusing the durable, transactional storage that we're already using
          anyway, there's no need to configure, monitor, and backup another stateful
          service. For small teams and light workloads, this is the right trade-off.
        
        - Easy introspection
        
          Since tasks are stored in a database table, it's easy to query and monitor
          the state of the queue.
        
        - Safety
        
          By using postgres transactions, there is no possibility of jobs being left in
          a locked or ambiguous state if a worker dies. Tasks immediately become
          available for another worker to pick up. You can even ``kill -9`` a worker
          and be sure your database and queue will be left in a consistent state.
        
        - Priority queues
        
          Since ordering is specified explicitly when selecting the next task to work
          on, it's easy to ensure high-priority tasks are processed first.
        
        
        Disadvantages
        -------------
        
        - Lower throughput than a dedicated queue server.
        - Harder to scale a relational database than a dedicated queue server.
        - Thundering herd. Postgres has no way to notify a single worker to wake up, so
          we can either wake every single worker up when a task is queued with
          LISTEN/NOTIFY, or workers have to short-poll.
        - With at-least-once delivery, a postgres transaction has to be held open for
          the duration of the task. For long running tasks, this can cause table bloat
          and performance problems.
        - When a task crashes or raises an exception under at-least-once delivery, it
          immediately becomes eligible to be retried. If you want to implement a retry
          delay, you must catch exceptions and requeue the task with a delay. If your
          task crashes without throwing an exception (eg SIGKILL), you could end up in
          an endless retry loop that prevents other tasks from being processed.
        
        
        How it works
        ------------
        
        django-postgres-queue is able to claim, process, and remove a task in a single
        query.
        
        .. code:: sql
        
            DELETE FROM dpq_job
            WHERE id = (
                SELECT id
                FROM dpq_job
                WHERE execute_at <= now()
                ORDER BY priority DESC, created_at
                FOR UPDATE SKIP LOCKED
                LIMIT 1
            )
            RETURNING *;
        
        As soon as this query runs, the task is unable to be claimed by other workers.
        When the transaction commits, the task will be deleted. If the transaction
        rolls back or the worker crashes, the task will immediately become available
        for another worker.
        
        To achieve at-least-once delivery, we begin a transaction, process the task,
        then commit the transaction. For at-most-once, we claim the task and
        immediately commit the transaction, then process the task. For tasks that don't
        have any external effects and only do database work, the at-least-once behavior
        is actually exactly-once (because both the claiming of the job and the database
        work will commit or rollback together).
        
        
        Comparison to Celery
        --------------------
        
        django-postgres-queue fills the same role as Celery. In addition to to using
        postgres as its backend, its intended to be simpler, without any of the funny
        business Celery does (metaprogramming, messing with logging, automatically
        importing modules). There is boilerplate to make up for the lack of
        metaprogramming, but I find that better than importing things by strings.
        
        Usage
        =====
        
        Requirements
        ------------
        
        django-postgres-queue requires Python 3, at least postgres 9.5 and at least
        Django 1.11.
        
        
        Installation
        ------------
        
        Install with pip::
        
          pip install django-postgres-queue
        
        Then add ``'dpq'`` to your ``INSTALLED_APPS``. Run ``manage.py migrate`` to
        create the jobs table.
        
        Instantiate a queue object. This can go wherever you like and be named whatever
        you like. For example, ``someapp/queue.py``:
        
        .. code:: python
        
            from dpq.queue import AtLeastOnceQueue
        
            queue = AtLeastOnceQueue(
                tasks={
                    # ...
                },
                notify_channel='my-queue',
            )
        
        
        You will need to import this queue instance to queue or process tasks. Use
        ``AtLeastOnceQueue`` for at-least-once delivery, or ``AtMostOnceQueue`` for
        at-most-once delivery.
        
        django-postgres-queue comes with a management command base class that you can
        use to consume your tasks. It can be called whatever you like, for example in a
        ``someapp/management/commands/worker.py``:
        
        .. code:: python
        
            from dpq.commands import Worker
        
            from someapp.queue import queue
        
            class Command(Worker):
                queue = queue
        
        Then you can run ``manage.py worker`` to start your worker.
        
        A task function takes two arguments -- the queue instance in use, and the Job
        instance for this task. The function can be defined anywhere and called
        whatever you like. Here's an example:
        
        .. code:: python
        
            def debug_task(queue, job):
                print(job.args)
        
        To register it as a task, add it to your queue instance:
        
        .. code:: python
        
            queue = AtLeastOnceQueue(tasks={
                'debug_task': debug_task,
            })
        
        The key is the task name, used to queue the task. It doesn't have to match the
        function name.
        
        To queue the task, use ``enqueue`` method on your queue instance:
        
        .. code:: python
        
            queue.enqueue('debug_task', {'some_args': 0})
        
        Assuming you have a worker running for this queue, the task will be run
        immediately. The second argument must be a single json-serializeable value and
        will be available to the task as ``job.args``.
        
        
        Monitoring
        ----------
        
        Tasks are just database rows stored in the ``dpq_job`` table, so you can
        monitor the system with SQL.
        
        To get a count of current tasks:
        
        .. code:: sql
        
            SELECT count(*) FROM dpq_job WHERE execute_at <= now()
        
        
        This will include both tasks ready to process and tasks currently being
        processed. To see tasks currently being processed, we need visibility into
        postgres row locks. This can be provided by the `pgrowlocks extension
        <https://www.postgresql.org/docs/9.6/static/pgrowlocks.html>`_.  Once
        installed, this query will count currently-running tasks:
        
        .. code:: sql
        
            SELECT count(*)
            FROM pgrowlocks('dpq_job')
            WHERE 'For Update' = ANY(modes);
        
        You could join the results of ``pgrowlocks`` with ``dpq_job`` to get the full
        list of tasks in progress if you want.
        
        Logging
        -------
        
        django-postgres-queue logs through Python's logging framework, so can be
        configured with the ``LOGGING`` dict in your Django settings. It will not log
        anything under the default config, so be sure to configure some form of
        logging. Everything is logged under the ``dpq`` namespace. Here is an example
        configuration that will log INFO level messages to stdout:
        
        .. code:: python
        
            LOGGING = {
                'version': 1,
                'root': {
                    'level': 'DEBUG',
                    'handlers': ['console'],
                },
                'formatters': {
                    'verbose': {
                        'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s',
                    },
                },
                'handlers': {
                    'console': {
                        'level': 'INFO',
                        'class': 'logging.StreamHandler',
                        'formatter': 'verbose',
                    },
                },
                'loggers': {
                    'dpq': {
                        'handlers': ['console'],
                        'level': 'INFO',
                        'propagate': False,
                    },
                }
            }
        
        It would also be sensible to log WARNING and higher messages to something like
        Sentry:
        
        .. code:: python
        
            LOGGING = {
                'version': 1,
                'root': {
                    'level': 'INFO',
                    'handlers': ['sentry', 'console'],
                },
                'formatters': {
                    'verbose': {
                        'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s',
                    },
                },
                'handlers': {
                    'console': {
                        'level': 'INFO',
                        'class': 'logging.StreamHandler',
                        'formatter': 'verbose',
                    },
                    'sentry': {
                        'level': 'WARNING',
                        'class': 'raven.contrib.django.handlers.SentryHandler',
                    },
                },
                'loggers': {
                    'dpq': {
                        'level': 'INFO',
                        'handlers': ['console', 'sentry'],
                        'propagate': False,
                    },
                },
            }
        
        You could also log to a file by using the built-in ``logging.FileHandler``.
        
        Useful Recipes
        ==============
        These recipes aren't officially supported features of `django-postgres-queue`.
        We provide them so that you can mimic some of the common features in other
        task queues.
        
        Running tasks in tests
        ----------------------
        When testing code that queues tasks, it can be useful to explicitly run all the
        pending tasks from your test. To do this, you can use:
        
        .. code:: python
        
            while queue.run_once(): pass
        
        This will run all the tasks that have been queued so far, and you can now
        assert that they did the right thing.
        
        
        `CELERY_ALWAYS_EAGER`
        ---------------------
        Celery uses the `CELERY_ALWAYS_EAGER` setting to run a task immediately,
        without queueing it for a worker. It could be used during tests, and while
        debugging in a development environment with any workers turned off.
        
        .. code:: python
        
            class EagerAtLeastOnceQueue(AtLeastOnceQueue):
                def enqueue(self, *args, **kwargs):
                    job = super().enqueue(*args, **kwargs)
                    if settings.QUEUE_ALWAYS_EAGER:
                        self.run_job(job)
                    return job
        
Platform: UNKNOWN
