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Continuing to learn k8s ecosystem and to achieve acceptable level
With trying eventually Helm, Argo CD and even trying to use not managed setup for k8s.
Going though books to find out theory about being SRE.
And about data intensive apps.
Learning and trying Kafka
Learning and trying FastAPI and diving in generally to async python ecosystem
Learning Go.
Learning few more books to increase code quality and its compositioning.
Getting more practice in monitoring and logging systems with applicating them to k8s.3 -
IBM job
12 plus experience in Kubernetes.
irony: Kubernetes is a 6 year old project.
https://tech.slashdot.org/story/20/...
actual listing:
https://intellijobs.ai/job/...6 -
Once I tried to apply for every job opening I could and found that the job requires 5 years of experience on fastapi. First I thought that, I need some more years to work on it then.
But plot twisted, all those jobs were from the future itself. -
Python muses me sometimes.
Gunicorn has a preload mode. It enables forking...
So Gunicorn starts, when Gunicorn loaded it forks the workers (Uvicorn / FastAPI in my case).
https://github.com/tiangolo/...
So if we add a function that creates the app... this function will be executed before forking, thus the memory at the state of creating the app will be duplicated.
You can thus spawn 40 workers, they would all have the same ML models.
Or in my case a client who does some things that should only be run by a single thread (with locking).
So the client has a cache, as long as I load the cache during the create_app phase, the cache will be shared between all instances and not created per instance.
It's ... Such a small detail. So simple.
Yet completely fucks my brain.
It's logical, yes. I understand what it does, yes.
But it still makes my brain fart.
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