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A worker is a Python process that typically runs in the background and exists solely as a work horse to perform lengthy or blocking tasks that you don’t want to perform inside web processes.

Starting workers

To start crunching work, simply start a worker from the root of your project directory:

$ rq worker high normal low
*** Listening for work on high, normal, low
Got send_newsletter('me@nvie.com') from default
Job ended normally without result
*** Listening for work on high, normal, low
...

Workers will read jobs from the given queues (the order is important) in an endless loop, waiting for new work to arrive when all jobs are done.

Each worker will process a single job at a time. Within a worker, there is no concurrent processing going on. If you want to perform jobs concurrently, simply start more workers.

Burst mode

By default, workers will start working immediately and will block and wait for new work when they run out of work. Workers can also be started in burst mode to finish all currently available work and quit as soon as all given queues are emptied.

$ rq worker --burst high normal low
*** Listening for work on high, normal, low
Got send_newsletter('me@nvie.com') from default
Job ended normally without result
No more work, burst finished.
Registering death.

This can be useful for batch work that needs to be processed periodically, or just to scale up your workers temporarily during peak periods.

Inside the worker

The worker life-cycle

The life-cycle of a worker consists of a few phases:

  1. Boot. Loading the Python environment.
  2. Birth registration. The worker registers itself to the system so it knows of this worker.
  3. Start listening. A job is popped from any of the given Redis queues. If all queues are empty and the worker is running in burst mode, quit now. Else, wait until jobs arrive.
  4. Prepare job execution. The worker tells the system that it will begin work by setting its status to busy and registers job in the StartedJobRegistry.
  5. Fork a child process. A child process (the “work horse”) is forked off to do the actual work in a fail-safe context.
  6. Process work. This performs the actual job work in the work horse.
  7. Cleanup job execution. The worker sets its status to idle and sets both the job and its result to expire based on result_ttl. Job is also removed from StartedJobRegistry and added to to FinishedJobRegistry in the case of successful execution, or FailedQueue in the case of failure.
  8. Loop. Repeat from step 3.

Performance notes

Basically the rq worker shell script is a simple fetch-fork-execute loop. When a lot of your jobs do lengthy setups, or they all depend on the same set of modules, you pay this overhead each time you run a job (since you’re doing the import after the moment of forking). This is clean, because RQ won’t ever leak memory this way, but also slow.

A pattern you can use to improve the throughput performance for these kind of jobs can be to import the necessary modules before the fork. There is no way of telling RQ workers to perform this set up for you, but you can do it yourself before starting the work loop.

To do this, provide your own worker script (instead of using rq worker). A simple implementation example:

#!/usr/bin/env python
import sys
from rq import Connection, Worker

# Preload libraries
import library_that_you_want_preloaded

# Provide queue names to listen to as arguments to this script,
# similar to rq worker
with Connection():
    qs = sys.argv[1:] or ['default']

    w = Worker(qs)
    w.work()

Worker names

Workers are registered to the system under their names, see monitoring. By default, the name of a worker is equal to the concatenation of the current hostname and the current PID. To override this default, specify the name when starting the worker, using the --name option.

Retrieving worker information

Worker instances store their runtime information in Redis. Here’s how to retrieve them:

from rq.worker import Worker
workers = Worker.all(redis_conn)

If you only want to retrieve a specific worker:

from rq.worker import Worker
worker = Worker.find_by_key('rq:worker:name')

print(worker.birth_date) # date when worker was instantiated

Worker statistics

New in version 0.9.0.

If you want to check the utilization of your queues, Worker instances store a few useful information:

from rq.worker import Worker
worker = Worker.find_by_key('rq:worker:name')

worker.successful_job_count  # Number of jobs finished successfully
worker.failed_job_count. # Number of failed jobs processed by this worker
worker.total_working_time  # Number of time spent executing jobs

Taking down workers

If, at any time, the worker receives SIGINT (via Ctrl+C) or SIGTERM (via kill), the worker wait until the currently running task is finished, stop the work loop and gracefully register its own death.

If, during this takedown phase, SIGINT or SIGTERM is received again, the worker will forcefully terminate the child process (sending it SIGKILL), but will still try to register its own death.

Using a config file

New in version 0.3.2.

If you’d like to configure rq worker via a configuration file instead of through command line arguments, you can do this by creating a Python file like settings.py:

REDIS_URL = 'redis://localhost:6379/1'

# You can also specify the Redis DB to use
# REDIS_HOST = 'redis.example.com'
# REDIS_PORT = 6380
# REDIS_DB = 3
# REDIS_PASSWORD = 'very secret'

# Queues to listen on
QUEUES = ['high', 'normal', 'low']

# If you're using Sentry to collect your runtime exceptions, you can use this
# to configure RQ for it in a single step
SENTRY_DSN = 'http://public:secret@example.com/1'

The example above shows all the options that are currently supported.

Note: The QUEUES and REDIS_PASSWORD settings are new since 0.3.3.

To specify which module to read settings from, use the -c option:

$ rq worker -c settings

Custom worker classes

New in version 0.4.0.

There are times when you want to customize the worker’s behavior. Some of the more common requests so far are:

  1. Managing database connectivity prior to running a job.
  2. Using a job execution model that does not require os.fork.
  3. The ability to use different concurrency models such as multiprocessing or gevent.

You can use the -w option to specify a different worker class to use:

$ rq worker -w 'path.to.GeventWorker'

Custom Job and Queue classes

Will be available in next release.

You can tell the worker to use a custom class for jobs and queues using --job-class and/or --queue-class.

$ rq worker --job-class 'custom.JobClass' --queue-class 'custom.QueueClass'

Don’t forget to use those same classes when enqueueing the jobs.

For example:

from rq import Queue
from rq.job import Job

class CustomJob(Job):
    pass

class CustomQueue(Queue):
    job_class = CustomJob

queue = CustomQueue('default', connection=redis_conn)
queue.enqueue(some_func)

Custom exception handlers

New in version 0.5.5.

If you need to handle errors differently for different types of jobs, or simply want to customize RQ’s default error handling behavior, run rq worker using the --exception-handler option:

$ rq worker --exception-handler 'path.to.my.ErrorHandler'

# Multiple exception handlers is also supported
$ rq worker --exception-handler 'path.to.my.ErrorHandler' --exception-handler 'another.ErrorHandler'