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A pyramid plugin that allows asynchronous and deferred task scheduling and management.

Project Description

The pyramid-scheduler package is a pyramid plugin that allows asynchronous and deferred task scheduling and management. It uses APScheduler for actual task management and Kombu for messaging between processes.

IMPORTANT: the pyramid-scheduler is very new, but fully functional. The following features are considered “beta”, and should probably not be used in production:

  • Multiple queues. For now, just use a single queue (and thus you are limited to a single background process).
  • Intuitive sequence of job events. Currently, the events that can be listened to (from pyramid_scheduler.api.Event, such as JOB_CREATED, JOB_EXECUTED, JOB_CANCELED, and JOB_REMOVED) do not reliably fire in an intuitive order. For example, you may get a JOB_REMOVED event before the JOB_EXECUTED event for a deferred job.



$ pip install pyramid-scheduler


# ini file settings:
#   [app:main]
#   scheduler.combined   = true | false   ## should execution be in-process?
#   scheduler.queues     = jobs           ## space-separated list of queues
# = %(dburl)s      ## the URL used for kombu messaging
#   ## other optional settings:
#   ##   scheduler.housekeeping
#   ##   scheduler.housekeeping.append
#   ##   scheduler.jobstore.default.class
#   ##   scheduler.misfire_grace_time

# enabling the plugin adds a `scheduler` attribute to the registry
def main(global_config, **settings):
  # ... (the usual pyramid startup calls) ...

# create an asynchronous task
def slow_process(name, id):
  # ...a slow asynchronous job...
def handle_request_quickly(request):
  request.registry.scheduler.add_async_job(slow_process, args=('my-first-arg', 2))

# schedule a deferred task for one hour from now
def delayed_process():
  # ...something that should happen later...
def handle_request_now(request):
  import time
  request.registry.scheduler.add_date_job(delayed_process, time.time() + 3600)

# do something every 10 minutes
def interval_process(reason=None):
  # ...gets executed every 10 minutes with an optional reason...
def handle_request_often(request):
  request.registry.scheduler.add_date_job(interval_process, minutes=10)


The pyramid-scheduler package refers to asynchronous or deferred tasks to be managed as “jobs” and these fall into the following categories:

  • Asynchronous jobs:

    These jobs are executed immediately, but asynchronously.

  • Deferred jobs, i.e. “date” jobs:

    Jobs that are scheduled to be performed at a specific time.

  • Interval jobs:

    Similar to deferred jobs, but that are then re-executed on an interval.

  • Cron jobs:

    Similar to interval jobs, but they use a scheduling definition that is similar to a unix “cron” definition.

Conceptually, there are two activities: the activity of creating the jobs and the activity of executing the jobs. These can be performed by the same process (with “scheduler.combined=true”), or they can be performed by different processes (with “scheduler.combined=false”).

If there are multiple processes that are creating jobs (for example, if your are running multiple servers or your WSGI configuration uses multiple processes), then you CANNOT run pyramid-scheduler in combined mode since then there will be multiple processes executing the jobs leading to multiple executions.

Typically, combined mode is used during development where a single pserve instance will be used. Then, in production mode, you will have multiple servers and WSGI processes that generate jobs, that are then executed by a single background process (managed via the pscheduler daemon).

Pyramid-scheduler supports multiple “queues”. The main reason to use separate queues is that the pscheduler can be configured to only process jobs for specific queues, which means that multiple pschedulers can work in parallel as long as they are listening to different queues. (A later enhancement is planned to allow multiple pschedulers to handle a single queue.)

IMPORTANT: currently, the following limitations exist:

  • The callback handler provided as the first argument to the scheduler add_*_job() methods must be a normal module-level defined function. It cannot be a lambda function, an internal function, a method, or otherwise function that is not globally resolvable using standard dot-notation.
  • The args and kwargs parameters must all be completely pickle-able.
  • Deferred jobs that are scheduled to occur further in the past than misfire_grace_time will be silently dropped.
  • Jobs that take a date or start_date parameter can specify those values either as an epoch int or float or as a datetime object. If a datetime is provided, it must be timezone “naive” (see the documentation of datetime).

Under the hood, pyramid-scheduler uses APScheduler to do the actual processing and scheduling. For messaging between the job creators and the pscheduler background process, it uses Kombu messaging, which supports a variety of transports including Redis and SQLAlchemy. This package was developed as an alternative to celery, due to severe limitations found in the celery API and shortcomings in the actual implementation.


You can manually install it by running:

$ pip install pyramid-scheduler

However, a better approach is to use standard python distribution utilities, and add pyramid-scheduler as a dependency in your project’s install_requires parameter in your Then run a python develop.

Then, enable the package either in your INI file via:

pyramid.includes = pyramid_scheduler

or in code in your package’s application initialization via:

def main(global_config, **settings):
  # ...
  # ...


The following configuration options (placed in the “[app:main]” section of your INI file):

TODO: add documentation

  • scheduler.combined
  • scheduler.queues
  • scheduler.housekeeping
  • scheduler.housekeeping.append
  • scheduler.jobstore.default.class
  • scheduler.misfire_grace_time


The first step in debugging a pyramid-scheduler instance is to elevate the logging, and that is easiest via the application configuration. Here, an example that increases logging to DEBUG level and sends the logs to STDERR:

keys               = scheduler, ...

keys               = console, ...

keys               = generic, ...

level              = DEBUG
handlers           = console
qualname           = pyramid_scheduler

class              = StreamHandler
args               = (sys.stderr,)
level              = NOTSET
formatter          = generic

format             = %(levelname)-5.5s [%(name)s] %(message)s

If that does not expose the source of the problem, you can take some of the following actions:

Confirm Communication

You can confirm that the task producers and consumers are communicating by sending a print-jobs message. First, check the configurations by sending the message from a fake producer by using the pscheduler --message feature as follows:

$ pscheduler --message 'print-jobs' {CONFIG}.ini

DEBUG [pyramid_scheduler.pscheduler] loading application from "{CONFIG}.ini"
DEBUG [] sending message <pyramid_scheduler.api.Event message={'message': 'print-jobs'}> to messenger

and you should see something like this in the pscheduler daemon logs (depending on what happens to STDOUT, you may only see the DEBUG messages, not the actual Jobstore messages):

DEBUG [] received message: <pyramid_scheduler.api.Event message={'message': 'print-jobs'}>
DEBUG [pyramid_scheduler.scheduler] received message event: print-jobs
Jobstore default:
    pyramid_scheduler_wrapper (trigger: cron[hour='0', minute='5'], next run at: 2014-12-03 00:05:00)
Jobstore internal.transient.8524480f-26b4-4a69-8bcd-3bb180d0cf9e:
    housekeeping (trigger: interval[1 day, 0:00:00], next run at: 2014-12-03 14:45:26.696818)

If that does not work, you need to check the application configurations, both on the consumer and producer sides. You may also need to debug the Kombu sub-system.

TODO: add documentation

Task Execution Process

There are several ways that the tasks in a queue can actually be executed. The preferred way, described here, is to use the pscheduler script which is intended to be run in daemon mode by a daemon-running service, such as DJB’s daemontools package.

Install daemontools (adjust for your package manager):

$ apt-get install daemontools

This should install & start the svscan monitoring against the /etc/service directory. You can do a ps to confirm this, and if it is not running, read the daemontools docs. If it is scanning a directory other than /etc/service, adjust the following examples appropriately.

Create & configure the logging subsystem by creating the following file in /etc/service/pscheduler/log/run (where pscheduler can be whatever you want). This example will store up to 100MiB of logs in the /var/log/pscheduler directory:

exec multilog t s10485760 n10 /var/log/pscheduler

Create & configure the pscheduler service by creating the following file in /etc/service/pscheduler/run (where pscheduler can be whatever you want). This example will run pscheduler as the www-data user (it is simplest if it runs as the same user as the appserver that is producing pyramid-scheduler tasks):

exec env -i PATH="/bin:/usr/bin:$PATH" \
setuidgid www-data \
/path/to/virtualenv/bin/pscheduler \
  /path/to/config.ini \

And ensure that both files are executable:

$ chmod 755 /etc/service/pscheduler/log/run
$ chmod 755 /etc/service/pscheduler/run
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