django-river | Django workflow library that supports on the fly changes | BPM library

As others have mentioned in the comments, the problem is that the functions that are getting called via the post_save are taking a long time. (Remember that signals are not async!! - this is a common misconception).

I'm not familiar with django-river but taking a quick look at the functions that will get called post-save (see here and here) we can see that they involve additional calls to the database.

Whilst you save a lot of individual db hits by using bulk_create you are still doing calling the database again multiple times for each post_save signal.

In short. Not much!! For the vast majority of django requests, the slow part will be calling the database. This is why we try and minimise the number of calls to the db (using things like bulk_create).

Reading through the first few paragraphs of django-river the whole idea is to move things that would normally be in code to the database. The big advantage here is that you don't need to re-write code and re-deploy so often. But the disadvantage is that you're inevitably going to have to refer to the database more, which is going to slow things down. This will be fine for some use-cases, but not all.

There are two things I can think of which might help:

• Does all of this currently happen as part of the request/response cycle. And if it is, does it need to be? If the answers to these two questions are 'yes' and 'no' respectively, then you could move this work to a separate task queue. This will still be slow, but at least it won't slow down your site.
• Depending on exactly what your workflows are and the nature of the data you are creating, it might be the case that you can do everything that the post_save signals are doing in your own function, and do it more efficiently. But this will definitely depend upon your data, and your app, and will move away from the philosophy of django-river.