Celery Logging with Python Logging Handlers
Logging in Celery
We use Celery as our backend messaging abstraction at work, and have lots of disparate nodes (and across different development, test, and production deployments). As each system deployment now contains a large (and growing) number of nodes, we have been making a heavy push towards consolidated logging through a central logging server sink, using syslog (specifically rsyslog).
So, ideally, we would just use Celery’s configuration to specify a syslog handler (or maybe use a pipe). Unfortunately, it seems there is just not a simple way of doing this straight from Celery, as the only logging configuration parameters (with possible values) are:
Hints and guidance on the web in terms of getting arbitrary logging handlers (or syslog specifically) is sparse, as well as noisy. A lot of the code and discussion I found used Celery as the backend for a generic logging framework or library. But, I found very little in terms of getting Celery tasks and processes to actually log to syslog.
Bringing Arbitrary Logging to Celery
As nothing magically appeared as the “right” solution, I considered a couple of different ways to hook things up:
- Watched Files: Have Celery log to a file per usual, then add extra configuration and scripts to watch the file for changes and submit the changes to syslog directly. I didn’t go with this approach, as I really prefer to have configuration for our project within generic Python settings, and not need extra, system-specific scripts and setup.
- Patch Celery: The Celery logging hooks and code are fairly straightforward. I could have added the patch and submitted upstream. Unfortunately, for the project at work, we are moving to fast to wait for changes, and as we are looking forward to some upstream Celery releases, I’d rather not maintain a private custom patch set through all of that.
- Monkey Patch Celery: In the same vein as the former option, the same hooks and changes could be applied as a monkey patch instead. This is what I eventually went with.
Patching Logging Handlers into Celery
Monkey patching is oft-controversial and generally discouraged practice, as getting things wrong is easy, and interactions within the patched library / code can get really messy. In our case, I reluctantly chose monkey patching, as it was a short patch, easy to disable, and I had no good other solutions.
Now that I’ve given the standard disclaimer, let’s get to the patch! The relevant Celery function we want to patch in celery.log._setup_logger():
As we can see, there is only one shot at a handler, and it comes from
celery.log._detect_handler(), which either returns a stream or file
handler. What I would ideally like is to define be able to define a Django
settings variable (we use Django and not native Celery settings at work):
and define a new
I would have preferred a pure “
*args, **kwargs” version of the patched
function, but unfortunately, we need to dig out the formatter parameter if
set, so I copied the method signature of the original. Also, I used lambda’s
PATCH_CELERYD_LOG_HANDLERS and called the function in the new setup
logger so as to create a new handler object each time for loggers. It is
important to have different handler objects because formats attach to handler
objects, and Celery uses different formats for general/default/process logging
vs. task-specific logging. If a handler has last been set to a task logger
format, that includes a format for among other things “task_name”, then you
will get a KeyError on trying to issue a logging statement.
Other than that, the patched method is very easy to read – it iterates the custom handler function list and creates new handlers for each different logging object. Putting this in to a patched version, I also added a marker attribute to the logger object (to tell the patching is done), as well as the patching code to lock, switch and mark the methods:
With the patch applied, any number of Python standard logging handlers can now be added to the celery task / process loggers.
Applying the Patch at Runtime
So we’ve defined the patch, but how do we apply it? Part of the problem is celery configuration. If you apply the patch too early, you get the following exception:
So, we have to find a proper place in the initialization process, after Celery settings. The best solution I found for this is to create a faux Django “application” , that contains only the patch code. Let’s call the application “patch_celery”:
Copy and paste all of the patch code above into the init.py. And then, we simply add the patch application to the Django settings.py installed apps:
The best place to put the patch is right after the djcelery application.
Now, add your patch variable,
PATCH_CELERYD_LOG_HANDLERS, to settings.py
with any designated handlers (per previous example) and you’re off with any
logging handlers for celery tasks that you want! You can disable the patch by
simply removing the “patch_celery” application from installed applications in
settings, which enables easily toggling per appropriate context without major
The monkey patch to Celery certainly gets the job done for now. However, the
fact that the full method signature for
celery.log._setup_logger() is used
makes the patch very brittle – if a parameter changes in the underlying
method, the patched method may cease to work, or worse, give a wrong parameter
to the underlying method.