I recently opted to use Celery for a similar-ish task to the author's update_metrics job. Having generally been OK with Python's single-threaded performance and

I recently opted to use Celery for a similar-ish task to the author's update_metrics job. Having generally been OK with Python's single-threaded performance and simplicity, I was not particularly pleased with a solution that involved an additional backend service (RabbitMQ), separate logfiles, fighting with the Python module system during setup, and ultimately a lot less available documentation than for items which live in the standard library. All of this just to kick off a background process. I haven't had my coffee yet and am feeling a bit grumpy this morning, but I'm really not sure I would be willing to use it again for a project where I had to manage the code and the deployment. Regardless of my mood, celery definitely isn't a panacea for these problems -- it just happens to ameliorate ones where there's already a larger deployment to manage.