redis-limpyd-jobs 2
A queue/jobs system based on redis-limpyd, a redis orm (sort of) in python
License: WTFPL
Requires Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*
Keywords: redis,orm,jobs,queue

|PyPI Version| |Build Status|

redis-limpyd-jobs
=================

A queue/jobs system based on
`redis-limpyd <https://github.com/limpyd/redis-limpyd>`__
(`redis <http://redis.io>`__ orm (sort of) in python)

Where to find it:

-  Github repository: https://github.com/limpyd/redis-limpyd-jobs
-  Pypi package: https://pypi.python.org/pypi/redis-limpyd-jobs
-  Documentation: http://documentup.com/limpyd/redis-limpyd-jobs

Install:

Python versions 2.7, and 3.5 to 3.8 are supported (CPython and PyPy).

Redis-server versions >= 3 are supported.

Redis-py versions >= 3 are supported.

Redis-limpyd versions >= 2 are supported.

You can still use limpyd-extensions versions < 2 if you need something older than the above requirements.

.. code:: bash

    pip install redis-limpyd-jobs

Note that you actually need the
`redis-limpyd-extensions <https://github.com/limpyd/redis-limpyd-extensions>`__ (min v1.0)
in addition to `redis-limpyd <https://github.com/limpyd/redis-limpyd>`__ (min v1.2)
(both are automatically installed via pypi)

How it works
------------

``redis-limpyd-jobs`` provides three ``limpyd`` models (``Queue``,
``Job``, ``Error``), and a ``Worker`` class.

These models implement the minimum stuff you need to run jobs
asynchronously:

-  Use the ``Job`` model to store things to do
-  The ``Queue`` model will store a list of jobs, with a priority system
-  The ``Error`` model will store all errors
-  The ``Worker`` class is to be used in a process to go through a queue
   and run jobs

Simple example
--------------

.. code:: python

        from limpyd_jobs import STATUSES, Queue, Job, Worker

        # The function to run when a job is called by the worker
        def do_stuff(job, queue):
            # here do stuff with your job
            pass

        # Create a first job, name 'job:1', in a queue named 'myqueue', with a
        # priority of 1. The higher the priority, the sooner the job will run
        job1 = Job.add_job(identifier='job:1', queue_name='myqueue', priority=1)

        # Add another job in the same queue, with a higher priority, and a different
        # identifier (if the same was used, no new job would be added, but the
        # existing job's priority would have been updated)
        job2 = Job.add_job(identifier='job:2', queue_name='myqueue', priority=2)

        # Create a worker for the queue used previously, asking to call the
        # "do_stuff" function for each job, and to stop after 2 jobs
        worker = Worker(queues='myqueue', callback=do_stuff, max_loops=2)

        # Now really run the jobs
        worker.run()

        # Here our jobs are done, our queue is empty
        queue1 = Queue.get_queue('myqueue', priority=1)
        queue2 = Queue.get_queue('myqueue', priority=2)
        # nothing waiting
        print queue1.waiting.lmembers(), queue2.waiting.lmembers()
        >> [] []
        # two jobs in success (show PKs of jobs)
        print queue1.success.lmembers(), queue2.success.lmembers()
        >> ['limpyd_jobs.models.Job:1', 'limpyd_jobs.models.Job:2']

        # Check our jobs statuses
        print job1.status.hget() == STATUSES.SUCCESS
        >> True
        print job2.status.hget() == STATUSES.SUCCESS
        >> True

You notice how it works:

-  ``Job.add_job`` to create a job
-  ``Worker()`` to create a worker, with ``callback`` argument to set
   which function to run for each job
-  ``worker.run`` to launch a worker.

Notice that you can run as much workers as you want, even on the same
queue name. Internally, we use the ``blpop`` redis command to get jobs
atomically.

But you can also run only one worker, having only one queue, doing
different stuff in the callback depending on the ``idenfitier``
attribute of the job.

Workers are able to catch SIGINT/SIGTERM signals, finishing executing
the current job before exiting. Useful if used, for example, with
supervisord.

If you want to store more information in a job, queue or error, or want
to have a different behavior in a worker, it's easy because you can
create subclasses of everything in ``limpyd-jobs``, the ``limpyd``
models or the ``Worker`` class.

Models
------

Job
~~~

A Job stores all needed informations about a task to run.

Note: If you want to subclass the Job model to add your own fields,
``run`` method, or whatever, note that the class must be at the first
level of a python module (ie not in a parent class or function) to work.

Job fields
^^^^^^^^^^

``identifier``
''''''''''''''

A string (``InstanceHashField``, indexed) to identify the job.

When using the (recommended) ``add_job`` class method, you can't have
many jobs with the same identifier in a waiting queue. If you create a
new job with an identifier while an other with the same is still in the
same waiting queue, what is done depends on the priority of the two
jobs: - if the new job has a lower (or equal) priority, it's discarded -
if the new job has a higher priority, the priority of the existing job
is updated to the higher.

In both cases the ``add_job`` class method returns the existing job,
discarding the new one.

A common way of using the identifier is to, at least, store a way to
identify the object on which we want the task to apply: - you can have
one or more queue for a unique task, and store only the ``id`` of an
object on the ``identifier`` field - you can have one or more queue each
doing many tasks, then you may want to store the task too in the
``identifier`` field: "task:id"

Note that by subclassing the ``Job`` model, you are able to add new
fields to a Job to store the task and other needed parameters, as
arguments (size for a photo to resize, a message to send...)

``status``
''''''''''

A string (``InstanceHashField``, indexed) to store the actual status of
the job.

It's a single letter but we provide a class to help using it verbosely:
``STATUSES``

.. code:: python

        from limpyd_jobs import STATUSES
        print STATUSES.SUCCESS
        >> "s"

When a job is created via the ``add_job`` class method, its status is
set to ``STATUSES.WAITING``, or ``STATUSES.DELAYED`` if it'is delayed by
setting ``delayed_until``. When it selected by the worker to execute it,
the status passes to ``STATUSES.RUNNING``. When finished, it's one of
``STATUSES.SUCCESS`` or ``STATUSES.ERROR``. An other available status is
``STATUSES.CANCELED``, useful if you want to cancel a job without
removing it from its queue.

You can also display the full string of a status:

.. code:: python

        print STATUSES.by_value(my_job.status.hget())
        >> "SUCCESS"

``priority``
''''''''''''

A string (``InstanceHashField``, indexed, default = 0) to store the
priority of the job.

The priority of a job determines in which Queue object it will be
stored. A worker listen for all queues with some names and different
priorities, but respecting the priority (reverse) order: the higher the
priority, the sooner the job will be executed.

We choose to use the "\`"higher priority is better" way of doing things
to give the possibility to always add a job in a higher priority than
any other ones.

Directly updating the priority of a job will not change the queue in
which it's stored. But when you add a job via the (recommended)
``add_job`` class method, if a job with the same identifier exists, its
priority will be updated (only if the new one is higher) and the job
will be moved to the higher priority queue.

``added``
'''''''''

A string (``InstanceHashField``) to store the date and time (a string
representation of ``datetime.utcnow()``) of the time the job was added
to its queue.

It's useful in combination of the ``end`` field to calculate the job
duration.

``start``
'''''''''

A string (``InstanceHashField``) to store the date and time (a string
representation of ``datetime.utcnow()``) of the time the job was fetched
from the queue, just before the callback is called.

It's useful in combination of the ``end`` field to calculate the job
duration.

``end``
'''''''

A string (``InstanceHashField``) to store the date and time (a string
representation of ``datetime.utcnow()``) of the moment the job was set
as finished or in error, just after the has finished.

It's useful in combination of the ``start`` field to calculate the job
duration.

``tries``
'''''''''

A integer saved as a string (``InstanceHashField``) to store the number
of times the job was executed. It can be more than one if it was
requeued after an error.

``delayed_until``
'''''''''''''''''

The string representation (``InstanceHashField``) of a ``datetime``
object until when the job may be in the ``delayed`` list (a redis
sorted-set) of the queue.

It can be set when calling ``add_job`` by passing either a
``delayed_until`` argument, which must be a ``datetime``, or a
``delayed_for`` argument, which must be a number of seconds (int or
float) or a ``timedelta`` object. The ``delayed_for`` argument will be
added to the current time (``datetime.utcnow()``) to compute
``delayed_until``.

If a job is in error after its execution and if the worker has a
positive ``requeue_delay_delta`` attribute, the ``delayed_until`` field
will be set accordingly, useful to retry a erroneous job after a certain
delay.

``queued``
''''''''''

This field is set to ``'1'`` when it's currently managed by a queue:
waiting, delayed, running. This flag is set when calling
``enqueue_or_delay``, and removed by the worker when the job is
canceled, is finished with success, or finished with error and not
requeued. It's this field that is checked to test if the same job
already exists when ``add_job`` is called.

``cancel_on_error``
'''''''''''''''''''

You must be set this field to a ``True`` value (don't forget that Redis
stores Strings, so ``0`` will be saved as ``"0"`` so it will be
``True``... so don't set it to ``False`` or ``0`` if you want a
``False`` value: yo can let it empty) if you don't want the job to be
requeued in case of error.

Note that if you want to do this for all jobs a a class, you may want to
set to ``True`` the ``always_cancel_on_error`` attribute of this class.

Job attributes
^^^^^^^^^^^^^^

``queue_model``
'''''''''''''''

When adding jobs via the ``add_job`` method, the model defined in this
attribute will be used to get or create a queue. It's set by default to
``Queue`` but if you want to update it to your own model, you must
subclass the ``Job`` model too, and update this attribute.

``queue_name``
''''''''''''''

``None`` by default, can be set when overriding the ``Job`` class to
avoid passing the ``queue_name`` argument to the job's methods
(especially ``add_job``)

Note that if you don't subclass the ``Job`` model, you can pass the
``queue_model`` argument to the ``add_job`` method.

``always_cancel_on_error``
''''''''''''''''''''''''''

Set this attribute to True if you want all your jobs of this class not
be be requeued in case of error. If you let it to its default value of
``False``, you can still do it job by job by setting their field
``cancel_on_error`` to a ``True`` value.

Job properties and methods
^^^^^^^^^^^^^^^^^^^^^^^^^^

``ident`` (property)
''''''''''''''''''''

The ``ident`` property is a string representation of the model + the
primary key of the job, saved in queues, allowing the retrieval of the
Job.

``must_be_cancelled_on_error`` (property)
'''''''''''''''''''''''''''''''''''''''''

The ``must_be_cancelled_on_error`` property returns a Boolean indicating
if, in case of error during its execution, the job must NOT be requeued.

By default it will be ``False``, but there is to way to change this
behavior:

-  setting the ``always_cancel_on_error`` of your job's class to
   ``True``.
-  setting the ``cancel_on_error`` field of your job to a ``True`` value

``duration`` (property)
'''''''''''''''''''''''

The ``duration`` property simply returns the time used to compute the
job. The return value is a ``datetime.timedelta`` object if the
``start`` and ``end`` fields are set, or ``None`` on the other case.

``run`` (method)
''''''''''''''''

It's the main method of the job, the only one you must override, to do
some tuff when the job is executed by the worker.

The return value of this method will be passed to the ``job_success`` of
the worker, then, if defined, to the ``on_success`` method of the job.

By default a ``NotImplemented`` error is raised.

Arguments:

-  ``queue``: The queue from which the job was fetched.

``requeue`` (method)
''''''''''''''''''''

The ``requeue`` method allow a job to be put back in the waiting (or
delayed) queue when its execution failed.

Arguments:

-  ``queue_name=None`` The queue name in which to save the job. If not
   defined, will use the job's class one. If both are undefined, an
   exception is raised.

-  ``priority=None`` The new priority of the new job. If not defined,
   the job will keep its actual priority.

-  ``delayed_until=None`` Set this to a ``datetime`` object to set the
   date on which the job will be really requeued. The real
   ``delayed_until`` can also be set by passing the ``delayed_for``
   argument.

-  ``delayed_for=None`` A number of seconds (as a int, float or a
   ``timedelta`` object) to wait before the job will be really requeued.
   It will compute the ``delayed_until`` field of the job.

-  ``queue_model=None`` The model to use to store queues. By default,
   it's set to ``Queue``, defined in the ``queue_model`` attribute of
   the ``Job`` model. If the argument is not set, the attribute will be
   used. Be careful to set it as attribute in your subclass, or as
   argument in ``requeue`` or the default ``Queue`` model will be used
   and jobs won't be saved in the expected queue model.

``enqueue_or_delay`` (method)
'''''''''''''''''''''''''''''

It's the method, called in ``add_job`` and ``requeue`` that will either
put the job in the waiting or delayed queue, depending of
``delayed_until``. If this argument is defined and in the future, the
job is delayed, else it's simply queued.

This method also set the ``queued`` flag of the job to ``'1'``.

Arguments:

-  ``queue_name=None`` The queue name in which to save the job. If not
   defined, will use the job's class one. If both are undefined, an
   exception is raised.

-  ``priority=None`` The new priority of the new job. Use the job's
   actual one if not defined.

-  ``delayed_until=None`` The date (must be either a ``datetime`` object
   of the string representation of one) until when the job will remain
   in the delayed queue. It will not be processed until this date.

-  ``prepend=False`` Set to ``True`` to add the job at the start of the
   waiting list, to be the first to be executed (only if not delayed)

-  ``queue_model=None`` The model to use to store queues. See
   ``add_job`` and ``requeue``.

``on_started`` (ghost method)
'''''''''''''''''''''''''''''

This method, if defined on your job model (it's not there by default, ie
"ghost") is called when the job is fetched by the worker and about to be
executed ("waiting" status)

Arguments:

-  ``queue``: The queue from which the job was fetched.

``on_success`` (ghost method)
'''''''''''''''''''''''''''''

This method, if defined on your job model (it's not there by default, ie
"ghost") is called by the worker when the job's execution was a success
(it did not raise any exception).

Arguments:

-  ``queue``: The queue from which the job was fetched.

-  ``result`` The data returned by the ``execute`` method of the worker,
   which call and return the result of the ``run`` method of the job (or
   the ``callback`` provided to the worker)

``on_error`` (ghost method)
'''''''''''''''''''''''''''

This method, if defined on your job model (it's not there by default, ie
"ghost") is called by the worker when the job's execution failed (an
exception was raised)

Arguments:

-  ``queue``: The queue from which the job was fetched.

-  ``exception``: The exception that was raised during the execution.

-  ``traceback``: The traceback at the time of the exception, if the
   ``save_tracebacks`` attribute of the worker was set to ``True``

``on_skipped`` (ghost method)
'''''''''''''''''''''''''''''

This method, if defined on your job model (it's not there by default, ie
"ghost") is called when the job, just fetched by the worker, could not
be executed because of its status, not "waiting". Another possible
reason is that the job was canceled during its execution (by settings
its status to ``STATUSES.CANCELED``)

-  ``queue``: The queue from which the job was fetched.

``on_requeued`` (ghost method)
''''''''''''''''''''''''''''''

This method, if defined on your job model (it's not there by default, ie
"ghost") is called by the worker when the job failed and has been
requeued by the worker.

-  ``queue``: The queue from which the job was fetched.

``on_delayed`` (ghost method)
'''''''''''''''''''''''''''''

This method, if defined on your job model (it's not there by default, ie
"ghost") is called by the worker when the job was delayed (by settings
its status to ``STATUSES.DELAYED``) during its execution (note that you
may also want to set the ``delayed_until`` of the job value to a correct
one datetime (a string represetation of an utc datetime), or the worker
will delay it for 60 seconds).

It can also be called if the job's status was set to
``STATUSES.DELAYED`` while still in the ``waiting`` list of the queue.

-  ``queue``: The queue from which the job was fetched.

Job class methods
^^^^^^^^^^^^^^^^^

``add_job``
'''''''''''

The ``add_job`` class method is the main (and recommended) way to create
a job. It will check if a job with the same identifier already exists in
a queue (not finished) and if one is found, update its priority (and
move it in the correct queue). If no existing job is found, a new one
will be created and added to a queue.

Arguments:

-  ``identifier`` The value for the ``identifier`` field.

-  ``queue_name=None`` The queue name in which to save the job. If not
   defined, will use the class one. If both are undefined, an exception
   is raised.

-  ``priority=0`` The priority of the new job, or the new priority of an
   already existing job, if this priority is higher of the existing one.

-  ``queue_model`` The model to use to store queues. By default, it's
   set to ``Queue``, defined in the ``queue_model`` attribute of the
   ``Job`` model. If the argument is not set, the attribute will be
   used. Be careful to set it as attribute in your subclass, or as
   argument in ``add_job`` or the default ``Queue`` model will be used
   and jobs won't be saved in the expected queue model.

-  ``prepend=False`` By default, all new jobs are added at the end of
   the waiting list (and taken from the start, it's a fifo list), but
   you can force jobs to be added at the beginning of the waiting list
   to be the first to be executed, simply by setting the ``prepend``
   argument to ``True``. If the job already exists, it will be moved at
   the beginning of the list.

-  ``delayed_until=None`` Set this to a ``datetime`` object to set the
   job to be executed in the future. If defined and in the future, the
   job will be added to the delayed list (a redis sorted-set) instead of
   the waiting one. The real ``delayed_until`` can also be set by
   passing the ``delayed_for`` argument.

-  ``delayed_for=None`` A number of seconds (as a int, float or a
   ``timedelta`` object) to wait before adding the job to the waiting
   list. It will compute the ``delayed_until`` field of the job.

If you use a subclass of the ``Job`` model, you can pass additional
arguments to the ``add_job`` method simply by passing them as named
arguments, they will be save if a new job is created (but not if an
existing job is found in a waiting queue)

``get_model_repr``
''''''''''''''''''

Returns the string representation of the model, used to compute the
``ident`` property of a job.

``get_from_ident``
''''''''''''''''''

Returns a job from a string previously got via the ``ident`` property of
a job.

Arguments:

-  ``ident`` A string including the modele representation of a job and
   it's primary key, as returned by the ``ident`` property.

Queue
~~~~~

A Queue stores a list of waiting jobs with a given priority, and keep a
list of successful jobs and ones on error.

Queue fields
^^^^^^^^^^^^

``name``
''''''''

A string (``InstanceHashField``, indexed), used by the ``add_job``
method to find the queue in which to store it. Many queues can have the
same names, but different priorities.

This name is also used by a worker to find which queues it needs to wait
for.

``priority``
''''''''''''

A string (``InstanceHashField``, indexed, default = 0), to store the
priority of a queue's jobs. All jobs in a queue are considered having
this priority. It's why, as said for the ``property`` fields of the
``Job`` model, changing the property of a job doesn't change its real
property. But adding (via the ``add_job`` class method of the ``Job``
model) a new job with the same identifier for the same queue's name can
update the job's priority by moving it to another queue with the correct
priority.

As already said, the higher the priority, the sooner the jobs in a queue
will be executed. If a queue has a priority of 2, and another queue of
the same name has a priority of 0, or 1, *all* jobs in the one with the
priority of 2 will be executed (at least fetched) before the others,
regardless of the number of workers.

``waiting``
'''''''''''

A list (``ListField``) to store the primary keys of job in the waiting
status. It's a fifo list: jobs are appended to the right (via
``rpush``), and fetched from the left (via ``blpop``)

When fetched, a job from this list is executed, then pushed in the
``success`` or ``error`` list, depending if the callback raised an
exception or not. If a job in this waiting list is not in the waiting
status, it will be skipped by the worker.

``success``
'''''''''''

A list (``ListField``) to store the primary keys of jobs fetched from
the waiting list and successfully executed.

``error``
'''''''''

A list (``ListField``) to store the primary keys of jobs fetched from
the waiting list for which the execution failed.

``delayed``
'''''''''''

A sorted set (``SortedSetField``) to store delayed jobs, ones having a
``delayed_until`` datetime in the future. The timestamp representation
of the ``delayed_until`` field is used as the score for this sorted-set,
to ease the retrieval of jobs that are now ready.

Queue attributes
^^^^^^^^^^^^^^^^

The ``Queue`` model has no specific attributes.

Queue properties and methods
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

``first_delayed`` (property)
''''''''''''''''''''''''''''

Returns a tuple representing the first job to be ready in the delayed
queue. It's a tuple with the job's pk and the timestamp representation
of it's ``delayed_until`` value (it's the score of the sorted\_set).

Returns None if the delayed queue is empty.

``first_delayed_time`` (property)
'''''''''''''''''''''''''''''''''

Return the timestamp representation of the first delayed job to be
ready, or None if the delayed queue is empty.

``delay_job`` (method)
''''''''''''''''''''''

Put a job in the delayed queue.

Arguments:

-  ``job`` The job to delay.

-  ``delayed_until`` A ``datetime`` object specifying when the job
   should be put back in the waiting queue. It will be converted into a
   timestamp used as the score of the delayed list, which is a redis
   sorted-set.

``enqueue_job`` (method)
''''''''''''''''''''''''

Put a job in the waiting list.

Arguments:

-  ``job`` The job to enqueue.

-  ``prepend=False`` Set to ``True`` to add the job at the start of the
   waiting list, to be the first to be executed.

``requeue_delayed_jobs`` (method)
'''''''''''''''''''''''''''''''''

This method will check for all jobs in the delayed queue that are now
ready to be executed and put them back in the waiting list.

This method will return the list of failures, each failure being a tuple
with the value returned by the ``ident`` property of a job, and the
message of the raised exception causing the failure.

Not that the status of the jobs is changed only if their status was
``STATUSES.DELAYED``. It allows to cancel a delayed job before.

Queue class methods
^^^^^^^^^^^^^^^^^^^

``get_queue``
'''''''''''''

The ``get_queue`` class method is the recommended way to get a ``Queue``
object. Given a name and a priority, it will return the found queue or
create a queue if no matching one exist.

Arguments:

-  ``name`` The name of the queue to get or create.

-  ``priority`` The priority of the queue to get or create.

If you use a subclass of the ``Queue`` model, you can pass additional
arguments to the ``get_queue`` method simply by passing them as named
arguments, they will be saved if a new queue is created (but not if an
existing queue is found)

``get_waiting_keys``
''''''''''''''''''''

The ``get_waiting_keys`` class method returns all the existing (waiting)
queues with the given names, sorted by priority (reverse order: the
highest priorities come first), then by names. The returned value is a
list of redis keys for each ``waiting`` lists of matching queues. It's
used internally by the workers as argument to the ``blpop`` redis
command.

Arguments:

-  ``names`` The names of the queues to take into accounts (can be a
   string if a single name, or a list of strings)

``count_waiting_jobs``
''''''''''''''''''''''

The ``count_waiting_jobs`` class method returns the number of jobs still
waiting for the given queue names, combining all priorities.

Arguments:

-  ``names`` The names of the queues to take into accounts (can be a
   string if a single name, or a list of strings)

``count_delayed_jobs``
''''''''''''''''''''''

The ``count_delayed_jobs`` class method returns the number of jobs still
delayed for the given queue names, combining all priorities.

Arguments:

-  ``names`` The names of the queues to take into accounts (can be a
   string if a single name, or a list of strings)

``get_all``
'''''''''''

The ``get_all`` class method returns a list of queues for the given
names.

Arguments:

-  ``name`` The names of the queues to take into accounts (can be a
   string if a single name, or a list of strings)

``get_all_by_priority``
'''''''''''''''''''''''

The ``get_all_by_priority`` class method returns a list of queues for
the given names, ordered by priorities (the highest priority first),
then names.

Arguments:

-  ``name`` The names of the queues to take into accounts (can be a
   string if a single name, or a list of strings)

Error
~~~~~

The ``Error`` model is used to store errors from the jobs that are not
successfully executed by a worker.

Its main purpose is to be able to filter errors, by queue name, job
model, job identifier, date, exception class name or code. You can use
your own subclass of the ``Error`` model and then store additional
fields, and filter on them.

Error fields
^^^^^^^^^^^^

``job_model_repr``
''''''''''''''''''

A string (``InstanceHashField``, indexed) to store the string
representation of the job's model.

``job_pk``
''''''''''

A string (``InstanceHashField``, indexed) to store the primary key of
the job which generated the error.

``idenfitier``
''''''''''''''

A string (``InstanceHashField``, indexed) to store the identifier of the
job that failed.

``queue_name``
''''''''''''''

A string (``InstanceHashField``, indexed) to store the name of the queue
the job was in when it failed.

``date_time``
'''''''''''''

A string (``InstanceHashField``, indexed with ``SimpleDateTimeIndex``) to
store the date and time (to the second) of the error (a string representation of
``datetime.utcnow()``). This field is indexed so you can filter
errors by date and time (string mode, not by parts of date and time, ie
``date_time__gt='2017-01-01'``), useful to graph errors.


``date``
''''''''

DEPRECATED: this is replaced by ``date_time`` but kept for now for compatibility

A string (``InstanceHashField``, indexed) to store the date (only the
date, not the time) of the error (a string representation of
``datetime.utcnow().date()``). This field is indexed so you can filter
errors by date, useful to graph errors.

``time``
''''''''

DEPRECATED: this is replaced by ``date_time`` but kept for now for compatibility

A string (``InstanceHashField``) to store the time (only the time, not
the date) of the error (a string representation of
``datetime.utcnow().time()``).

``type``
''''''''

A string (``InstanceHashField``, indexed) to store the type of error.
It's the class' name of the originally raised exception.

``code``
''''''''

A string (``InstanceHashField``, indexed) to store the value of the
``code`` attribute of the originally raised exception. Nothing is stored
here if there is no such attribute.

``message``
'''''''''''

A string (``InstanceHashField``) to store the string representation of
the originally raised exception.

``traceback``
'''''''''''''

A string (``InstanceHashField``) to store the string representation of
the traceback of the originally raised exception (the worker may not
have filled it)

Error properties and methods
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

``datetime``
''''''''''''

This property returns a ``datetime`` object based on the content of the
``date_time`` field of an ``Error`` object.

Error class methods
^^^^^^^^^^^^^^^^^^^

``add_error``
'''''''''''''

The ``add_error`` class method is the main (and recommended) way to add
an entry on the ``Error`` model, by accepting simple arguments that will
be break down (``job`` becomes ``identifier`` and ``job_pk``, ``when``
becomes ``date`` and ``time``, ``error`` becomes ``code`` and
``message``)

Arguments:

-  ``queue_name`` The name of the queue the job came from.

-  ``job`` The job which generated the error, from which we'll extract
   ``job_pk`` and ``identifier``

-  ``error`` An exception from which we'll extract the code and the
   message.

-  ``when=None`` A ``datetime`` object from which we'll extract the date
   and time.

   If not filled, ``datetime.utcnow()`` will be used.

-  ``trace=None`` The traceback, stringyfied, to store.

If you use a subclass of the ``Error`` model, you can pass additional
arguments to the ``add_error`` method simply by passing them as named
arguments, they will be save in the object to be created.

``collection_for_job``
''''''''''''''''''''''

The ``collection_for_job`` is a helper to retrieve the errors assiated
with a given job, more precisely for all the instances of this job with
the same identifier.

The result is a ``limpyd`` collection, to you can use ``filter``,
``instances``... on it.

Arguments:

-  ``job`` The job for which we want errors

The worker(s)
-------------

The Worker class
~~~~~~~~~~~~~~~~

The ``Worker`` class does all the logic, working with ``Queue`` and
``Job`` models.

The main behavior is: - reading queue keys for the given names - waiting
for a job available in the queues - executing the job - manage success
or error - exit after a defined number of jobs or a maximum duration (if
defined), or when a ``SIGINT``/``SIGTERM`` signal is caught

The class is split in many short methods so that you can subclass it to
change/add/remove whatever you want.

Constructor arguments and worker's attributes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Each of the following worker's attributes can be set by an argument in
the constructor, using the exact same name. It's why the two are
described here together.

``queues``
''''''''''

Names of the queues to work with. It can be a list/tuple of strings, or
a string with names separated by a comma (no spaces), or without comma
for a single queue.

Note that all queues must be from the same ``queue_model``.

Default to ``None``, but if not set and not defined in a subclass, will
raise an ``LimpydJobsException``.

``queue_model``
'''''''''''''''

The model to use for queues. By default it's the ``Queue`` model
included in ``limpyd_jobs``, but you can use a subclass of the default
model to add fields, methods...

``error_model``
'''''''''''''''

The model to use for saving errors. By default it's the ``Error`` model
included in ``limpyd_jobs``, but you can use a subclass of the default
model to add fields, methods...

``logger_name``
'''''''''''''''

``limpyd_jobs`` uses the python ``logging`` module, so this is the name
to use for the logger created for the worker. The default value is
``LOGGER_NAME``, with ``LOGGER_NAME`` defined in ``limpyd_jobs.workers``
with a value of "limpyd-jobs".

``logger_level``
''''''''''''''''

It's the level set for the logger created with the name defined in
``logger_name``, default to ``logging.INFO``.

``save_errors``
'''''''''''''''

A boolean, default to ``True``, to indicate if we have to save errors in
the ``Error`` model (or the one defined in ``error_model``) when the
execution of the job is not successful.

``save_tracebacks``
'''''''''''''''''''

A boolean, default to ``True``, to indicate if we have to save the
tracebacks of exceptions in the ``Error`` model (or the one defined in
``error_model``) when the execution of the job is not successful (and
only if ``save_errors`` is ``True``)

``max_loops``
'''''''''''''

The max number of loops (fetching + executing a job) to do in the worker
lifetime, default to 1000. Note that after this number of loop, the
worker ends (the ``run`` method cannot be executed again)

The aim is to avoid memory leaks become too important.

``max_duration``
''''''''''''''''

If defined, the worker will end when its ``run`` method was called for
at least this number of seconds. By default it's set to ``None``, saying
there is no maximum duration.

``terminate_gracefully``
''''''''''''''''''''''''

To avoid interrupting the execution of a job, if
``terminate_gracefully`` is set to ``True`` (the default), the
``SIGINT`` and ``SIGTERM`` signals are caught, asking the worker to exit
when the current jog is done.

``callback``
''''''''''''

The callback is the function to run when a job is fetched. By default
it's the ``execute`` method of the worker (which calls the ``run``
method of jobs, which, if not overridden, raises a ``NotImplemented``
error) , but you can pass any function that accept a job and a queue as
argument.

Using the queue's name, and the job's identifier+model (via
``job.ident``), you can manage many actions depending on the queue if
needed.

If this callback (or the ``execute`` method) raises an exception, the
job is considered in error. In the other case, it's considered
successful and the return value is passed to the ``job_success`` method,
to let you do what you want with it.

``timeout``
'''''''''''

The timeout is used as parameter to the ``blpop`` redis command we use
to fetch jobs from waiting lists. It's 30 seconds by default but you can
change it to any positive number (in seconds). You can set it to ``0``
if you don't want any timeout be applied to the ``blpop`` command.

It's better to always set a timeout, to reenter the main loop and call
the ``must_stop`` method to see if the worker must exit. Note that the
number of loops is not updated in the case of the timeout occurred, so a
little ``timeout`` won't alter the number of loops defined by
``max_loops``.

``fetch_priorities_delay``
''''''''''''''''''''''''''

The ``fetch_priorities_delay`` is the delay between two fetches of the
list of priorities for the current worker.

If a job was added with a priority that did not exist when the worker
run was started, it will not be taken into account until this delay
expires.

Note that if this delay is, say, 5 seconds (it's 25 by default), and the
``timeout`` parameter is 30, you may wait 30 seconds before the new
priority fetch because if there is no jobs in the priority queues
actually managed by the worker, the time is in the redis hands.

``fetch_delayed_delay``
'''''''''''''''''''''''

The ``fetch_delayed_delay`` is the delay between two fetches of the
delayed jobs that are now ready in the queues managed by the worker.

Note that if this delay is, say, 5 seconds (it's 25 by default), and the
``timeout`` parameter is 30, you may wait 30 seconds before the new
delayed fetch because if there is no jobs in the priority queues
actually managed by the worker, the time is in the redis hands.

``requeue_times``
'''''''''''''''''

It's the number of times a job will be requeued when its execution
results in a failure. It will then be put back in the same queue.

This attribute is 0 by default so by default a job won't be requeued.

``requeue_priority_delta``
''''''''''''''''''''''''''

This number will be added to the current priority of the job that will
be requeued. By default it's set to -1 to decrease the priority at each
requeue.

``requeue_delay_delta``
'''''''''''''''''''''''

It's a number of seconds to wait before adding back an erroneous job in
the waiting queue, set by default to 30: when a job failed to execute,
it's put in the delayed queue for 30 seconds then it'll be put back in
the waiting queue (depending on the ``fetch_delayed_delay`` attribute)

Other worker's attributes
^^^^^^^^^^^^^^^^^^^^^^^^^

In case on subclassing, you can need these attributes, created and
defined during the use of the worker:

``keys``
''''''''

A list of keys of queues waiting lists, which are listened by the worker
for new jobs. Filled by the ``update_keys`` method.

``status``
''''''''''

The current status of the worker. ``None`` by default until the ``run``
method is called, after what it's set to ``"starting"`` while getting
for an available queue. Then it's set to ``"waiting"`` while the worker
waits for new jobs. When a job is fetched, the status is set to
``"running"``. And finally, when the loop is over, it's set to
``"terminated"``.

If the status is not ``None``, the ``run`` method cannot be called.

``logger``
''''''''''

The logger (from the ``logging`` python module) defined by the
``set_logger`` method.

``num_loops``
'''''''''''''

The number of loops done by the worker, incremented each time a job is
fetched from a waiting list, even if the job is skipped (bad status...),
or in error. When this number equals the ``max_loops`` attribute, the
worker ends.

``end_forced``
''''''''''''''

When ``True``, ask for the worker to terminate itself after executing
the current job. It can be set to ``True`` manually, or when a
SIGINT/SIGTERM signal is caught.

``end_signal_caught``
'''''''''''''''''''''

This boolean is set to ``True`` when a SIGINT/SIGTERM is caught (only if
the ``terminate_gracefully`` is ``True``)

``start_date``
''''''''''''''

``None`` by default, set to ``datetime.utcnow()`` when the ``run``
method starts.

``end_date``
''''''''''''

``None`` by default, set to ``datetime.utcnow()`` when the ``run``
method ends.

``wanted_end_date``
'''''''''''''''''''

None by default, it's computed to know when the worker must stop based
on the ``start_date`` and ``max_duration``. It will always be ``None``
if no ``max_duration`` is defined.

``connection``
''''''''''''''

It's a property, not an attribute, to get the current connection to the
redis server.

``parameters``
''''''''''''''

It's a tuple holding all parameters accepted by the worker's constructor

.. code:: python

        parameters = ('queues', 'callback', 'queue_model', 'error_model',
                      'logger_name', 'logger_level', 'save_errors',
                      'save_tracebacks', 'max_loops', 'max_duration',
                      'terminate_gracefuly', 'timeout', 'fetch_priorities_delay',
                      'fetch_delayed_delay', 'requeue_times',
                      'requeue_priority_delta', 'requeue_delay_delta')

Worker's methods
^^^^^^^^^^^^^^^^

As said before, the ``Worker`` class in spit in many little methods, to
ease subclassing. Here is the list of public methods:

``__init__``
''''''''''''

Signature:

.. code:: python

        def __init__(self, queues=None, **kwargs):

Returns nothing.

It's the constructor (you guessed it ;) ) of the ``Worker`` class,
expecting all arguments (defined in ``parameters``) that can also be
defined as class attributes.

It validates these arguments, prepares the logging and initializes other
attributes.

You can override it to add, validate, initialize other arguments or
attributes.

``handle_end_signal``
'''''''''''''''''''''

Signature:

.. code:: python

        def handle_end_signal(self):

Returns nothing.

It's called in the constructor if ``terminate_gracefully`` is ``True``.
It plugs the SIGINT and SIGTERM signal to the ``catch_end_signal``
method.

You can override it to catch more signals or do some checked before
plugging them to the ``catch_end_signal`` method.

``stop_handling_end_signal``
''''''''''''''''''''''''''''

Signature:

.. code:: python

        def stop_handling_end_signal(self):

Returns nothing.

It's called at the end of the ``run`` method, as we don't need to catch
the SIGINT and SIGTERM signals anymore. It's useful when launching a
worker in a python shell to finally let the shell handle these signals.
Useless in a script because the script is finished when the ``run``
method exits.

``set_logger``
''''''''''''''

Signature:

.. code:: python

    def set_logger(self):

Returns nothing.

It's called in the constructor to initialize the logger, using
``logger_name`` and ``logger_level``, saving it in ``self.logger``.

``must_stop``
'''''''''''''

Signature:

.. code:: python

    def must_stop(self):

Returns boolean.

It's called on the main loop, to exit it on some conditions: an end
signal was caught, the ``max_loops`` number was reached, or
``end_forced`` was set to ``True``.

``wait_for_job``
''''''''''''''''

Signature:

.. code:: python

    def wait_for_job(self):

Returns a tuple with a queue and a job

This method is called during the loop, to wait for an available job in
the waiting lists. When one job is fetched, returns the queue (an
instance of the model defined by ``queue_model``) on which the job was
found, and the job itself.

``get_job``
'''''''''''

Signature:

.. code:: python

    def get_job(self, job_ident):

Returns a job.

Called during ``wait_for_job`` to get a real job object based on the
job's ``ident`` (model + pk) fetched from the waiting lists.

``get_queue``
'''''''''''''

Signature:

.. code:: python

    def get_queue(self, queue_redis_key):

Returns a Queue.

Called during ``wait_for_job`` to get a real queue object (an instance
of the model defined by ``queue_model``) based on the key returned by
redis telling us in which list the job was found. This key is not the
primary key of the queue, but the redis key of it's waiting field.

``catch_end_signal``
''''''''''''''''''''

Signature:

.. code:: python

    def catch_end_signal(self, signum, frame):

Returns nothing.

It's called when a SIGINT/SIGTERM signal is caught. It's simply set
``end_signal_caught`` and ``end_forced`` to ``True``, to tell the worker
to terminate as soon as possible.

``execute``
'''''''''''

Signature:

.. code:: python

    def execute(self, job, queue):

Returns nothing by default.

This method is called if no ``callback`` argument is provided when
initiating the worker and call the ``run`` method of the job, which
raises a ``NotImplementedError`` by default.

If the execution is successful, no return value is attended, but if any,
it will be passed to the ``job_success`` method. And if an error
occurred, an exception must be raised, which will be passed to the
``job_error`` method.

``update_keys``
'''''''''''''''

Signature:

.. code:: python

    def update_keys(self):

Returns nothing.

Calling this method updates the internal ``keys`` attributes, which
contains redis keys of the waiting lists of all queues listened by the
worker.

It's actually called at the beginning of the ``run`` method, and at
intervals depending on ``fetch_priorities_delay``. Note that if a queue
with a specific priority doesn't exist when this method is called, but
later, by adding a job with ``add_job``, the worker will ignore it
unless this ``update_keys`` method was called again (programmatically or
by waiting at least ``fetch_priorities_delay`` seconds)

``run``
'''''''

Signature:

.. code:: python

    def run(self):

Returns nothing.

It's the main method of the worker, with all the logic: while we don't
have to stop (result of the ``must_stop`` method), fetch a job from
redis, and if this job is really in waiting state, execute it, and do
something depending of the status of the execution (success, error...).

In addition to the methods that do real stuff (``update_keys``,
``wait_for_job``), some other methods are called during the execution:
``run_started``, ``run_ended``, about the run, and ``job_skipped``,
``job_started``, ``job_success`` and ``job_error`` about jobs. You can
override these methods in subclasses to adapt the behavior depending on
your needs.

``run_started``
'''''''''''''''

Signature:

.. code:: python

    def run_started(self):

Returns nothing.

This method is called in the ``run`` method after the keys are computed
using ``update_keys``, just before starting the loop. By default it does
nothing but a log.info.

``run_ended``
'''''''''''''

Signature:

.. code:: python

    def run_ended(self):

Returns nothing.

This method is called just before exiting the ``run`` method. By default
it does nothing but a log.info.

``job_skipped``
'''''''''''''''

Signature:

.. code:: python

    def job_skipped(self, job, queue):

Returns nothing.

When a job is fetched in the ``run`` method, its status is checked. If
it's not ``STATUSES.WAITING``, this ``job_skipped`` method is called,
with two main arguments: the job and the queue in which it was found.

This method is also called when the job is canceled during its execution
(ie if, when the execution is done, the job's status is
``STATUSES.CANCELED``).

This method remove the ``queued`` flag of the job, logs the message
returned by the ``job_skipped_message`` method, then call, if defined,
the ``on_skipped`` method of the job.

``job_skipped_message``
'''''''''''''''''''''''

Signature:

.. code:: python

    def job_skipped_message(self, job, queue):

Returns a string to be logged in ``job_skipped``.

``job_started``
'''''''''''''''

Signature:

.. code:: python

    def job_started(self, job, queue):

Returns nothing.

When the job is fetched and its status verified (it must be
``STATUSES.WAITING``), the ``job_started`` method is called, just before
the callback (or the ``execute`` method if no ``callback`` is defined),
with the job and the queue in which it was found.

This method updates the ``start`` and ``status`` fields of the job, then
log the message returned by ``job_started_message`` and finally call, if
defined, the ``on_started`` method of the job.

``job_started_message``
'''''''''''''''''''''''

Signature:

.. code:: python

    def job_started_message(self, job, queue):

Returns a string to be logged in ``job_started``.

``job_success``
'''''''''''''''

Signature:

.. code:: python

    def job_success(self, job, queue, job_result):

Returns nothing.

When the callback (or the ``execute`` method) is finished, without
having raised any exception, the job is considered successful, and the
``job_success`` method is called, with the job and the queue in which it
was found, and the return value of the callback method.

Note that this method is not called, and so the job not considered a
"success" if, when the execution is done, the status of the job is
either ``STATUS.CANCELED`` or ``STATUS.DELAYED``. In these cases, the
methods ``job_skipped`` and ``job_delayed`` are called respectively.

This method remove the ``queued`` flag of the job, updates its ``end``
and ``status`` fields, moves the job into the ``success`` list of the
queue, then log the message returned by ``job_success_message`` and
finally call, if defined, the ``on_success`` method of the job.

``job_success_message``
'''''''''''''''''''''''

Signature:

.. code:: python

    def job_success_message(self, job, queue, job_result):

Returns a string to be logged in ``job_success``.

``job_delayed``
'''''''''''''''

Signature:

.. code:: python

    def job_delayed(self, job, queue):

Returns nothing.

When the callback (or the ``execute`` method) is finished, without
having raised an exception, and the status of the job at this moment is
``STATUSES.DELAYED``, the job is not successful but not in error: it
will be delayed.

Another way to have this method called if its a job is in the
``waiting`` queue but its status was set to ``STATUSES.DELAYED``. In
this cas, the job is not executed, but delayed by calling this method.

This method check if the job has a ``delayed_until`` value, and if not,
or if an invalid one, it is set to 60 seconds in the future. You may
want to explicitly set this value, or at least clear the field because
if the job was initially delayed, the value may be set, but in the past,
and the job will be delayed to this date, so, not delayed but just
queued.

With this value, the method ``enqueue_or_delay`` of the queue is called,
to really delay the job.

Then, log the message returned by ``job_delayed_message`` and finally
call, if defined, the ``on_delayed`` method of the job.

``job_delayed_message``
'''''''''''''''''''''''

Signature:

.. code:: python

    def job_delayed_message(self, job, queue):

Returns a string to be logged in ``job_delayed``.

``job_error``
'''''''''''''

Signature:

.. code:: python

    def job_error(self, job, queue, exception, trace=None):

Returns nothing.

When the callback (or the ``execute`` method) is terminated by raising
an exception, the ``job_error`` method is called, with the job and the
queue in which it was found, and the raised exception and, if
``save_tracebacks`` is ``True``, the traceback.

This method remove the ``queued`` flag of the job if it is no to be
requeued, updates its ``end`` and ``status`` fields, moves the job into
the ``error`` list of the queue, adds a new error object (if
``save_errors`` is ``True``), then log the message returned by
``job_error_message`` and call the ``on_error`` method of the job is
called, if defined.

And finally, if the ``must_be_cancelled_on_error`` property of the job
is False, and the ``requeue_times`` worker attribute allows it
(considering the ``tries`` attribute of the job, too), the
``requeue_job`` method is called.

``job_error_message``
'''''''''''''''''''''

Signature:

.. code:: python

    def job_error_message(self, job, queue, to_be_requeued_exception, trace=None):

Returns a string to be logged in ``job_error``.

``job_requeue_message``
'''''''''''''''''''''''

Signature:

.. code:: python

    def job_requeue_message(self, job, queue):

Returns a string to be logged in ``job_error`` when the job was
requeued.

``additional_error_fields``
'''''''''''''''''''''''''''

Signature:

.. code:: python

    def additional_error_fields(self, job, queue, exception, trace=None):

Returns a dictionary of fields to add to the error object, empty by
default.

This method is called by ``job_error`` to let you define a dictionary of
fields/values to add to the error object which will be created, if you
use a subclass of the ``Error`` model, defined in ``error_model``.

To pass these additional fields to the error object, you have to
override this method in your own subclass.

``requeue_job``
'''''''''''''''

.. code:: python

    def requeue_job(self, job, queue, priority, delayed_for=None):

Returns nothing.

This method is called to requeue the job when its execution failed, and
will call the ``requeue`` method of the job, then its ``requeued`` one,
and finally will log the message returned by ``job_requeue_message``.

``id``
''''''

It's a property returning a string identifying the current worker, used
in logging to distinct log entries for each worker.

``elapsed``
'''''''''''

It's a property returning, when running the time elapsed since when the
``run`` started. When the ``run`` method ends, it's the time between
``start_date`` and ``end_date``.

If the ``run`` method is not called, it will be set to ``None``.

``log``
'''''''

Signature:

.. code:: python

    def log(self, message, level='info'):

Returns nothing.

``log`` is a simple wrapper around ``self.logger``, which automatically
add the ``id`` of the worker at the beginning. It can accepts a
``level`` argument which is ``info`` by default.

``set_status``
''''''''''''''

Signature:

.. code:: python

    def set_status(self, status):

Returns nothing.

``set_status`` simply update the worker's ``status`` field.

``count_waiting_jobs``
''''''''''''''''''''''

Signature:

.. code:: python

    def count_waiting_jobs(self):

Returns the number of jobs in waiting state that can be run by this
worker.

``count_delayed_jobs``
''''''''''''''''''''''

Signature:

.. code:: python

    def count_delayed_jobs(self):

Returns the number of jobs in the delayed queues managed by this worker.

The worker.py script
~~~~~~~~~~~~~~~~~~~~

To help using ``limpyd_jobs``, an executable python script is provided:
``scripts/worker.py`` (usable as ``limpyd-jobs-worker``, in your path,
when installed from the package)

This script is highly configurable to help you launching workers without
having to write a script or customize the one included.

With this script you don't have to write a custom worker too, because
all arguments attended by a worker can be passed as arguments to the
script.

The script is based on a ``WorkerConfig`` class defined in
``limpyd_jobs.workers``, that you can customize by subclassing it, and
you can tell the script to use your class instead of the default one.

You can even pass one or many python paths to add to ``sys.path``.

This script is designed to ease you as much as possible.

Instead of explaining all arguments, see below the result of the
``--help`` command for this script:

::

    $ limpyd-jobs-worker  --help
    Usage: worker.py [options]

    Run a worker using redis-limpyd-jobs

    Options:
      --pythonpath=PYTHONPATH
                            A directory to add to the Python path, e.g.
                            --pythonpath=/my/module
      --worker-config=WORKER_CONFIG
                            The worker config class to use, e.g. --worker-
                            config=my.module.MyWorkerConfig, default to
                            limpyd_jobs.workers.WorkerConfig
      --print-options       Print options used by the worker, e.g. --print-options
      --dry-run             Won't execute any job, just starts the worker and
                            finish it immediatly, e.g. --dry-run
      --queues=QUEUES       Name of the Queues to handle, comma separated e.g.
                            --queues=queue1,queue2
      --queue-model=QUEUE_MODEL
                            Name of the Queue model to use, e.g. --queue-
                            model=my.module.QueueModel
      --error-model=ERROR_MODEL
                            Name of the Error model to use, e.g. --queue-
                            model=my.module.ErrorModel
      --worker-class=WORKER_CLASS
                            Name of the Worker class to use, e.g. --worker-
                            class=my.module.WorkerClass
      --callback=CALLBACK   The callback to call for each job, e.g. --worker-
                            class=my.module.callback
      --logger-name=LOGGER_NAME
                            The base name to use for logging, e.g. --logger-base-
                            name="limpyd-jobs.%s"
      --logger-level=LOGGER_LEVEL
                            The level to use for logging, e.g. --worker-class=ERROR
      --save-errors         Save job errors in the Error model, e.g. --save-errors
      --no-save-errors      Do not save job errors in the Error model, e.g. --no-
                            save-errors
      --save-tracebacks     Save exception tracebacks on job error in the Error
                            model, e.g. --save-tracebacks
      --no-save-tracebacks  Do not save exception tracebacks on job error in the
                            Error model, e.g. --no-save-tracebacks
      --max-loops=MAX_LOOPS
                            Max number of jobs to run, e.g. --max-loops=100
      --max-duration=MAX_DURATION
                            Max duration of the worker, in seconds (None by
                            default), e.g. --max-duration=3600
      --terminate-gracefuly
                            Intercept SIGTERM and SIGINT signals to stop
                            gracefuly, e.g. --terminate-gracefuly
      --no-terminate-gracefuly
                            Do NOT intercept SIGTERM and SIGINT signals, so don't
                            stop gracefuly, e.g. --no-terminate-gracefuly
      --timeout=TIMEOUT     Max delay (seconds) to wait for a redis BLPOP call (0
                            for no timeout), e.g. --timeout=30
      --fetch-priorities-delay=FETCH_PRIORITIES_DELAY
                            Min delay (seconds) to wait before fetching new
                            priority queues, e.g. --fetch-priorities-delay=20
      --fetch-delayed-delay=FETCH_DELAYED_DELAY
                            Min delay (seconds) to wait before updating delayed
                            jobs, e.g. --fetch-delayed-delay=20
      --requeue-times=REQUEUE_TIMES
                            Number of time to requeue a failing job (default to
                            0), e.g. --requeue-times=5
      --requeue-priority-delta=REQUEUE_PRIORITY_DELTA
                            Delta to add to the actual priority of a failing job
                            to be requeued (default to -1, ie one level lower),
                            e.g. --requeue-priority-delta=-2
      --requeue-delay-delta=REQUEUE_DELAY_DELTA
                            How much time (seconds) to delay a job to be requeued
                            (default to 30), e.g. --requeue-delay-delta=15
      --database=DATABASE   Redis database to use (host:port:db), e.g.
                            --database=localhost:6379:15
      --no-title            Do not update the title of the worker's process, e.g.
                            --no-title
      --version             show program's version number and exit
      -h, --help            show this help message and exit

Except for ``--pythonpath``, ``--worker-config``,
``--print-options``,\ ``--dry-run``, ``--worker-class`` and
``--no-title``, all options will be passed to the worker.

So, if you use the default models, the default worker with its default
options, and to launch a worker to work on the queue "queue-name", all
you need to do is:

.. code:: bash

    limpyd-jobs-worker --queues=queue-name  --callback=python.path.to.callback

We use the ``setproctitle`` module to display useful informations in the
process name, to have stuff like this:

::

    limpyd-jobs-worker#1566090 [init] queues=foo,bar
    limpyd-jobs-worker#1566090 [starting] queues=foo,bar loop=0/1000 waiting=10 delayed=0
    limpyd-jobs-worker#1566090 [running] queues=foo,bar loop=1/1000 waiting=9 delayed=2 duration=0:00:15
    limpyd-jobs-worker#1566090 [terminated] queues=foo,bar loop=10/1000 waiting=0 delayed=0 duration=0:12:27

You can disable it by passing the ``--no-title`` argument.

Note that if no logging handler is set for the ``logger-name``, a
``StreamHandler`` formatter will be automatically added by the script,
given logs like:

::

    [19122] 2013-10-02 00:51:24,158 (limpyd-jobs) WARNING  [038480] [test|job:1] job skipped (current status: SUCCESS)

(the format used is
``"[%(process)d] %(asctime)s (%(name)s) %(levelname)-8s %(message)s"``)

Executing code before loading worker class
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Sometimes you may want to do some initialization work before even
loading the Worker class, for example, using django, to add
``django.setup()``

For this, simple override the ``WorkerConfig`` class:

.. code:: python

    import django

    from limpyd_jobs.workers import WorkerConfig


    class MyWorkerConfig(WorkerConfig):
        def __init__(self, argv=None):

            django.setup()

            super(MyWorkerConfig, self).__init__(argv)

And pass the python path to this class using the ``--worker-config``
option to the ``limpyd-jobs-worker`` script.

Tests
-----

The ``redis-limpyd-jobs`` package is fully tested (coverage: 100%).

To run the tests, which are not installed via the ``setup.py`` file, you
can do:

::

    $ python run_tests.py
    [...]
    Ran 136 tests in 19.353s

    OK

Or if you have ``nosetests`` installed:

::

    $ nosetests
    [...]
    Ran 136 tests in 20.471s

    OK

The ``nosetests`` configuration is provided in the ``setup.cfg`` file
and include the coverage, if ``nose-cov`` is installed.

Final words
-----------

-  you can see a full example in ``example.py`` (in the source, not in
   the installed package)
-  to use ``limpyd_jobs`` models on your own redis database instead of
   the default one (``localhost:6379:db=0``), simply use the
   ``use_database`` method of the main model:

   .. code:: python

       from limpyd.contrib.database import PipelineDatabase
       from limpyd_jobs.models import BaseJobsModel

       database = PipelineDatabase(host='localhost', port=6379, db=15)
       BaseJobsModel.use_database(database)

   or simply change the connection settings:

   .. code:: python

       from limpyd_jobs.models import BaseJobsModel

       BaseJobsModel.database.connect(host='localhost', port=6379, db=15)

The end.
--------

.. |PyPI Version| image:: https://img.shields.io/pypi/v/redis-limpyd-jobs.png
   :target: https://pypi.python.org/pypi/redis-limpyd-jobs
.. |Build Status| image:: https://travis-ci.org/limpyd/redis-limpyd-jobs.png?branch=master
   :target: https://travis-ci.org/limpyd/redis-limpyd-jobs