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Jobs can fail due to exceptions occurring. When your RQ workers run in the background, how do you get notified of these exceptions?

Default: FailedJobRegistry

The default safety net for RQ is the FailedJobRegistry. Every job that doesn’t execute successfully is stored here, along with its exception information (type, value, traceback).

from redis import Redis
from rq import Queue
from rq.job import Job
from rq.registry import FailedJobRegistry

redis = Redis()
queue = Queue(connection=redis)
registry = FailedJobRegistry(queue=queue)

# Show all failed job IDs and the exceptions they caused during runtime
for job_id in registry.get_job_ids():
    job = Job.fetch(job_id, connection=redis)
    print(job_id, job.exc_info)

Retrying Failed Jobs

New in version 1.5.0

RQ lets you easily retry failed jobs. To configure retries, use RQ’s Retry object that accepts max and interval arguments. For example:

from redis import Redis
from rq import Retry, Queue

from somewhere import my_func

queue = Queue(connection=redis)
# Retry up to 3 times, failed job will be requeued immediately
queue.enqueue(my_func, retry=Retry(max=3))

# Retry up to 3 times, with 60 seconds interval in between executions
queue.enqueue(my_func, retry=Retry(max=3, interval=60))

# Retry up to 3 times, with longer interval in between retries
queue.enqueue(my_func, retry=Retry(max=3, interval=[10, 30, 60]))

If you use `interval` argument with `Retry`, don't forget to run your workers using the `--with-scheduler` argument.

Custom Exception Handlers

RQ supports registering custom exception handlers. This makes it possible to inject your own error handling logic to your workers.

This is how you register custom exception handler(s) to an RQ worker:

from exception_handlers import foo_handler, bar_handler

w = Worker([q], exception_handlers=[foo_handler, bar_handler])

The handler itself is a function that takes the following parameters: job, exc_type, exc_value and traceback:

def my_handler(job, exc_type, exc_value, traceback):
    # do custom things here
    # for example, write the exception info to a DB

You might also see the three exception arguments encoded as:

def my_handler(job, *exc_info):
    # do custom things here
from exception_handlers import foo_handler

w = Worker([q], exception_handlers=[foo_handler],

Chaining Exception Handlers

The handler itself is responsible for deciding whether or not the exception handling is done, or should fall through to the next handler on the stack. The handler can indicate this by returning a boolean. False means stop processing exceptions, True means continue and fall through to the next exception handler on the stack.

It’s important to know for implementers that, by default, when the handler doesn’t have an explicit return value (thus None), this will be interpreted as True (i.e. continue with the next handler).

To prevent the next exception handler in the handler chain from executing, use a custom exception handler that doesn’t fall through, for example:

def black_hole(job, *exc_info):
    return False