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RQ Scheduler

RQ Scheduler is a small package that adds job scheduling capabilities to RQ, a Redis based Python queuing library.

Requirements

Installation

You can install RQ Scheduler via pip:

pip install rq-scheduler

Or you can download the latest stable package from PyPI.

Usage

Schedule a job involves doing two different things:

  1. Putting a job in the scheduler
  2. Running a scheduler that will move scheduled jobs into queues when the time comes

Scheduling a Job

There are two ways you can schedule a job. The first is using RQ Scheduler's enqueue_at:

from redis import Redis
from rq_scheduler import Scheduler
from datetime import datetime

scheduler = Scheduler(connection=Redis()) # Get a scheduler for the "default" queue

# Puts a job into the scheduler. The API is similar to RQ except that it
# takes a datetime object as first argument. So for example to schedule a
# job to run on Jan 1st 2020 we do:
scheduler.enqueue_at(datetime(2020, 1, 1), func)

# Here's another example scheduling a job to run at a specific date and time (in UTC),
# complete with args and kwargs.
scheduler.enqueue_at(datetime(2020, 1, 1, 3, 4), func, foo, bar=baz)

The second way is using enqueue_in. Instead of taking a datetime object, this method expects a timedelta and schedules the job to run at X seconds/minutes/hours/days/weeks later. For example, if we want to monitor how popular a tweet is a few times during the course of the day, we could do something like:

from datetime import timedelta

# Schedule a job to run 10 minutes, 1 hour and 1 day later
scheduler.enqueue_in(timedelta(minutes=10), count_retweets, tweet_id)
scheduler.enqueue_in(timedelta(hours=1), count_retweets, tweet_id)
scheduler.enqueue_in(timedelta(days=1), count_retweets, tweet_id)

Periodic & Repeated Jobs

As of version 0.3, RQ Scheduler also supports creating periodic and repeated jobs. You can do this via the schedule method. Note that this feature needs RQ >= 0.3.1.

This is how you do it:

scheduler.schedule(
    scheduled_time=datetime.now(), # Time for first execution, in UTC timezone
    func=func,                     # Function to be queued
    args=[arg1, arg2],             # Arguments passed into function when executed
    kwargs={'foo': 'bar'},         # Keyword arguments passed into function when executed
    interval=60,                   # Time before the function is called again, in seconds
    repeat=10                      # Repeat this number of times (None means repeat forever)
)

Retrieving scheduled jobs

Sometimes you need to know which jobs have already been scheduled. You can get a list of enqueued jobs with the get_jobs method:

list_of_job_instances = scheduler.get_jobs()

In it's simplest form (as seen in the above example) this method returns a list of all job instances that are currently scheduled for execution.

Additionally the method takes two optional keyword arguments until and with_times. The first one specifies up to which point in time scheduled jobs should be returned. It can be given as either a datetime / timedelta instance or an integer denoting the number of seconds since epoch (1970-01-01 00:00:00). The second argument is a boolen that determines whether the scheduled execution time should be returned along with the job instances.

Example:

# get all jobs until 2012-11-30 10:00:00
list_of_job_instances = scheduler.get_jobs(until=datetime(2012, 10, 30, 10))

# get all jobs for the next hour
list_of_job_instances = scheduler.get_jobs(until=timedelta(hours=1))

# get all jobs with execution times
jobs_and_times = scheduler.get_jobs(with_times=True)
# returns a list of tuples:
# [(<rq.job.Job object at 0x123456789>, datetime.datetime(2012, 11, 25, 12, 30)), ...]

Checking if a job is scheduled

You can check whether a specific job instance or job id is scheduled for execution using the familiar python in operator:

if job_instance in scheduler:
    # Do something
# or
if job_id in scheduler:
    # Do something

Canceling a job

To cancel a job, simply do:

scheduler.cancel(job)

Running the scheduler

RQ Scheduler comes with a script rqscheduler that runs a scheduler process that polls Redis once every minute and move scheduled jobs to the relevant queues when they need to be executed:

# This runs a scheduler process using the default Redis connection
rqscheduler

If you want to use a different Redis server you could also do:

rqscheduler --host localhost --port 6379 --db 0

The script accepts these arguments:

  • -H or --host: Redis server to connect to
  • -p or --port: port to connect to
  • -d or --db: Redis db to use
  • -P or --password: password to connect to Redis

The arguments pull default values from environment variables with the same names but with a prefix of RQ_REDIS_.

Changelog

Version 0.5.0
  • IMPORTANT! Job timestamps are now stored and interpreted in UTC format. If you have existing scheduled jobs, you should probably change their timestamp to UTC before upgrading to 0.5.0. Thanks @michaelbrooks!
  • You can now configure Redis connection via environment variables. Thanks @malthe!
  • rqscheduler script now accepts --pid argument. Thanks @jsoncorwin!
Version 0.4.0
  • Supports Python 3!
  • Scheduler.schedule now allows job timeout to be specified
  • rqscheduler allows Redis connection to be specified via --url argument
  • rqscheduler now accepts --path argument
Version 0.3.6
  • Scheduler key is not set to expire a few seconds after the next scheduling operation. This solves the issue of rqscheduler refusing to start after an unexpected shut down.
Version 0.3.5
  • Support StrictRedis
Version 0.3.4
  • Scheduler related job attributes (interval and repeat) are now stored in job.meta introduced in RQ 0.3.4
Version 0.3.3
  • You can now check whether a job is scheduled for execution using job in scheduler syntax
  • Added scheduler.get_jobs method
  • scheduler.enqueue and scheduler.enqueue_periodic will now raise a DeprecationWarning, please use scheduler.schedule instead
Version 0.3.2
  • Periodic jobs now require RQ >= 0.3.1
Version 0.3
  • Added the capability to create periodic (cron) and repeated job using scheduler.enqueue