[tinix]

shared memory only works on dedicated hardware.

if you're running in something like AWS fargate, there is no shared memory. have to use the network and file system which adds a lot of latency, way more than spawning a process.

copying processes through fork is a whole different problem.

green threads and an actor model will get you much further in my experience.

[bradleybuda]

Fargate is just a container runtime. You can fork processes and share memory like you can in any other Linux environment. You may not want to (because you are running many cheap / small containers) but if your Fargate containers are running 0.25 vCPUs then you probably don't want traditional multiprocessing or multithreading...

[tinix]

Go try it and report back.

Fargate isn't just ECS and plain containers.

You cannot use shared memory in fargate, there is literally no /dev/shm.

See "sharedMemorySize" here: https://docs.aws.amazon.com/AmazonECS/latest/developerguide/...

> If you're using tasks that use the Fargate launch type, the sharedMemorySize parameter isn't supported.

[sgarland]

Well don’t use Fargate, there’s your problem. Run programs on actual servers, not magical serverless bullshit.

[tinix]

> Well don’t use Fargate, there’s your problem. Run programs on actual servers, not magical serverless bullshit.

That kind of absolutism misses the point of why serverless architectures like Fargate exist. It might feel satisfying, but it closes the door on understanding why stateless and ephemeral workloads exist in the first place.

I get the frustration, but dismissing a production architecture outright ignores the constraints and trade-offs that lead teams to adopt it in the first place. It's worth asking: if so many teams are using this shit in production, at scale, with real stakes, what do they know that might be missing from my current mental model?

Serverless, like any abstraction, isn't magic. It's a tool with defined trade-offs, and resource/process isolation is one of them. If you're running containerized workloads at scale, optimizing for organizational velocity, security boundaries, and multi-tenant isolation, these constraints aren't bullshit, they're literally design parameters and intentional boundaries.

It's easy to throw shade from a distance, but the operational reality of running modern systems, especially in regulated or high-scale environments, looks very different from a home lab or startup sandbox.