rache 0.2

schedule_job

schedule_job('job id', schedule_in=<seconds>, **kwargs)

A given job ID is unique from the scheduler perspective. Scheduling it twice results in it being scheduled at the time decided in the last call.

**kwargs can be used to attach data to your jobs. For instance, if you have jobs to fetch URLs and want to attach a timeout to these jobs:

schedule_job('http://example.com/test', schedule_in=3600, timeout=10)

The job data is persistent. To remove a key from the data, call schedule_job() with that key set to None:

schedule_job('http://example.com/test', schedule_in=3600, timeout=None)

schedule_in is mandatory. This means you can’t update an existing job without rescheduling it.

pending_jobs

jobs = pending_jobs(reschedule_in=None, limit=None)

(the returned value is a generator)

Fetches the pending jobs and returns a list of jobs. Each job is a dictionnary with an id key and its additional data.

reschedule_in controls whether to auto-reschedule jobs in a given time. This is useful if you have periodic jobs but also want to special-case some jobs according to their results (enqueue is rq-style syntax):

jobs = pending_jobs(reschedule_in=3600)

for job in jobs:
    enqueue(do_something, kwargs=job)

def do_something(**kwargs):
    # … do some work

    if some_condition:
        # re-schedule in 30 days
        schedule_job(kwargs['id'], schedule_in=3600 * 24 * 30)

limit allows you to limit the number of jobs returned. Remaining jobs are left on schedule, even if they should have been scheduled right now.

delete_job

delete_job('<job id>')

Removes a job completely from the scheduler.

job_details

job_details('<job id>')

Returns a dictionnary with the job data. The job ID and scheduled time are set in the id and schedule_at keys of the returned value.

scheduled_jobs

scheduled_jobs(with_times=False)

(the returned value is a generator)

Fetches all the job IDs stored in the scheduler. This returns a list of IDs or a list of (job_id, timestamp) tuples if with_times is set to True.

This is useful for syncing jobs between the scheduler and a database, for instance.