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It really depends on your setup and your needs. But here are a couple of things to keep in mind: You can set up multiple clusters (so run multiple instances of If you want to go more specific, you could set up queues specifically for one or more tasks/schedules. With queues, you can have tasks/schedules run a specific server. Say you have a very resource intensive task, then you can define the cluster on which it should run (a server/container with more resources). Or if you have a high priority task/schedule, then you could also create a cluster for those, so they won't be blocked by lower priority tasks/schedules. Ref: https://django-q2.readthedocs.io/en/master/cluster.html#multiple-queues Further, if you are having issues with a high cpu/ram usage, then you could try to reduce the polling: https://django-q2.readthedocs.io/en/master/configure.html#poll. But if your are waiting for tasks to be completed, then I wouldn't recommend setting this to a high value. You will likely be waiting longer in those cases. If your tasks are not time sensitive (or run for a longer time), then you are free to change this value to make django-q2 a little bit lighter. Same thing with the |
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Hi team,
I'm keen to get the view on how we should scale Django Q in our applications to meet demands.
For example, I've been running a multi-tenanted application which leverages schedules for each customer to perform a series of tasks like alerts, reminders, checking if items have been completed.
Keen to understand how we should be calculating minimum performance and I guess creating a bit of a guide to help everyone scale their Django Q to get the best experience of this tooling.
Would keen to hear on people who might have been using the tooling longer than I.
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