A job is a Python object, representing a function that is invoked asynchronously in a worker (background) process. Any Python function can be invoked asynchronously, by simply pushing a reference to the function and its arguments onto a queue. This is called enqueueing.
To put jobs on queues, first declare a function:
import requests def count_words_at_url(url): resp = requests.get(url) return len(resp.text.split())
Noticed anything? There's nothing special about this function! Any Python function call can be put on an RQ queue.
To put this potentially expensive word count for a given URL in the background, simply do this:
from rq import Queue from redis import Redis from somewhere import count_words_at_url # Tell RQ what Redis connection to use redis_conn = Redis() q = Queue(connection=redis_conn) # no args implies the default queue # Delay execution of count_words_at_url('http://nvie.com') job = q.enqueue(count_words_at_url, 'http://nvie.com') print job.result # => None # Now, wait a while, until the worker is finished time.sleep(2) print job.result # => 889
If you want to put the work on a specific queue, simply specify its name:
q = Queue('low', connection=redis_conn) q.enqueue(count_words_at_url, 'http://nvie.com')
Queue('low') in the example above? You can use any queue name, so
you can quite flexibly distribute work to your own desire. A common naming
pattern is to name your queues after priorities (e.g.
In addition, you can add a few options to modify the behaviour of the queued job. By default, these are popped out of the kwargs that will be passed to the job function.
timeoutspecifies the maximum runtime of the job before it'll be considered 'lost'
result_ttlspecifies the expiry time of the key where the job result will be stored
ttlspecifies the maximum queued time of the job before it'll be cancelled
depends_onspecifies another job (or job id) that must complete before this job will be queued
job_idallows you to manually specify this jobs job_id
at_frontwill place the job at the front of the queue, instead of the back
argslets you bypass the auto-pop of these arguments, ie: specify a
timeoutargument for the underlying job function.
In the last case, it may be advantageous to instead use the explicit version of
q = Queue('low', connection=redis_conn) q.enqueue_call(func=count_words_at_url, args=('http://nvie.com',), timeout=30)
For cases where the web process doesn't have access to the source code running in the worker (i.e. code base X invokes a delayed function from code base Y), you can pass the function as a string reference, too.
q = Queue('low', connection=redis_conn) q.enqueue('my_package.my_module.my_func', 3, 4)
Besides enqueuing jobs, Queues have a few useful methods:
from rq import Queue from redis import Redis redis_conn = Redis() q = Queue(connection=redis_conn) # Getting the number of jobs in the queue print len(q) # Retrieving jobs queued_job_ids = q.job_ids # Gets a list of job IDs from the queue queued_jobs = q.jobs # Gets a list of enqueued job instances job = q.fetch_job('my_id') # Returns job having ID "my_id"
With RQ, you don't have to set up any queues upfront, and you don't have to specify any channels, exchanges, routing rules, or whatnot. You can just put jobs onto any queue you want. As soon as you enqueue a job to a queue that does not exist yet, it is created on the fly.
RQ does not use an advanced broker to do the message routing for you. You may consider this an awesome advantage or a handicap, depending on the problem you're solving.
Lastly, it does not speak a portable protocol, since it depends on pickle to serialize the jobs, so it's a Python-only system.
When jobs get enqueued, the
queue.enqueue() method returns a
This is nothing more than a proxy object that can be used to check the outcome
of the actual job.
For this purpose, it has a convenience
result accessor property, that
None when the job is not yet finished, or a non-
None value when
the job has finished (assuming the job has a return value in the first place,
If you're familiar with Celery, you might be used to its
Starting from RQ >= 0.3, there exists a similar decorator:
from rq.decorators import job @job('low', connection=my_redis_conn, timeout=5) def add(x, y): return x + y job = add.delay(3, 4) time.sleep(1) print job.result
For testing purposes, you can enqueue jobs without delegating the actual
execution to a worker (available since version 0.3.1). To do this, pass the
async=False argument into the Queue constructor:
>>> q = Queue('low', async=False) >>> job = q.enqueue(fib, 8) >>> job.result 21
The above code runs without an active worker and executes
synchronously within the same process. You may know this behaviour from Celery
New in RQ 0.4.0 is the ability to chain the execution of multiple jobs.
To execute a job that depends on another job, use the
q = Queue('low', async=False) report_job = q.enqueue(generate_report) q.enqueue(send_report, depends_on=report_job)
The ability to handle job dependencies allows you to split a big job into several smaller ones. A job that is dependent on another is enqueued only when it's dependency finishes successfully.
To learn about workers, see the workers documentation.
Technically, you can put any Python function call on a queue, but that does not mean it's always wise to do so. Some things to consider before putting a job on a queue:
__module__is importable by the worker. In particular, this means that you cannot enqueue functions that are declared in the
RQ workers will only run on systems that implement
fork(). Most notably,
this means it is not possible to run the workers on Windows.