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121 lines
3.4 KiB
Markdown
121 lines
3.4 KiB
Markdown
---
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title: "RQ: Exceptions & Retries"
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layout: docs
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---
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Jobs can fail due to exceptions occurring. When your RQ workers run in the
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background, how do you get notified of these exceptions?
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## Default: FailedJobRegistry
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The default safety net for RQ is the `FailedJobRegistry`. Every job that doesn't
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execute successfully is stored here, along with its exception information (type,
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value, traceback).
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```python
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from redis import Redis
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from rq import Queue
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from rq.registry import FailedJobRegistry
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redis = Redis()
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queue = Queue(connection=redis)
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registry = FailedJobRegistry(queue=queue)
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# Show all failed job IDs and the exceptions they caused during runtime
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for job_id in registry.get_job_ids():
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job = Job.fetch(job_id, connection=redis)
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print(job_id, job.exc_info)
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```
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## Retrying Failed Jobs
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_New in version 1.5.0_
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RQ lets you easily retry failed jobs. To configure retries, use RQ's
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`Retry` object that accepts `max` and `interval` arguments. For example:
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```python
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from redis import Redis
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from rq import Retry, Queue
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from somewhere import my_func
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queue = Queue(connection=redis)
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# Retry up to 3 times, failed job will be requeued immediately
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queue.enqueue(my_func, retry=Retry(max=3))
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# Retry up to 3 times, with 60 seconds interval in between executions
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queue.enqueue(my_func, retry=Retry(max=3, interval=60))
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# Retry up to 3 times, with longer interval in between retries
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queue.enqueue(my_func, retry=Retry(max=3, interval=[10, 30, 60]))
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```
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<div class="warning">
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<img style="float: right; margin-right: -60px; margin-top: -38px" src="/img/warning.png" />
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<strong>Note:</strong>
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<p>
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If you use `interval` argument with `Retry`, don't forget to run your workers using
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the `--with-scheduler` argument.
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</p>
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</div>
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## Custom Exception Handlers
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RQ supports registering custom exception handlers. This makes it possible to
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inject your own error handling logic to your workers.
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This is how you register custom exception handler(s) to an RQ worker:
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```python
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from exception_handlers import foo_handler, bar_handler
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w = Worker([q], exception_handlers=[foo_handler, bar_handler])
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```
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The handler itself is a function that takes the following parameters: `job`,
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`exc_type`, `exc_value` and `traceback`:
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```python
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def my_handler(job, exc_type, exc_value, traceback):
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# do custom things here
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# for example, write the exception info to a DB
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```
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You might also see the three exception arguments encoded as:
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```python
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def my_handler(job, *exc_info):
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# do custom things here
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```
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{% highlight python %}
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from exception_handlers import foo_handler
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w = Worker([q], exception_handlers=[foo_handler],
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disable_default_exception_handler=True)
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{% endhighlight %}
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## Chaining Exception Handlers
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The handler itself is responsible for deciding whether or not the exception
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handling is done, or should fall through to the next handler on the stack.
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The handler can indicate this by returning a boolean. `False` means stop
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processing exceptions, `True` means continue and fall through to the next
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exception handler on the stack.
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It's important to know for implementors that, by default, when the handler
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doesn't have an explicit return value (thus `None`), this will be interpreted
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as `True` (i.e. continue with the next handler).
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To prevent the next exception handler in the handler chain from executing,
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use a custom exception handler that doesn't fall through, for example:
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```python
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def black_hole(job, *exc_info):
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return False
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```
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