For the use case you are describing, none of my points are important really - an HTTP request that hits a database, then pushes something onto a queue for background processing doesn't exhibit any problems from a process bottleneck point of view on that end of things. You still need to have some logic to deal with backpressure from the queue, but that is a language agnostic concern.

Where you could hit a bottleneck might be in the background processing though, take for example the following scenario:

- A pool of N background job worker processes each pull an item off a queue, and spawn a process to perform the task in isolation
- A singleton process S provides exclusive access to some resource
- Each task calls some code which needs to interact with the resource controlled by S.

The problem with the above is that all of that concurrency/parallelism is nullified by the fact that the tasks are all going to block on S to do their work, the bottleneck of the design.

To be clear, you should always gather telemetry first, but lets assume that you've gathered that and you can clearly see that this bottleneck is an issue (the process mailbox has frequently got many messages waiting to be received, the average time to completion for jobs is increasing). To solve this depends on why the resource is held by S in the first place.

If its because the resource is not thread safe and requires exclusive access, then unless you can find a way to avoid needing the resource in every task, there isn't much you can do, but this should be fairly uncommon in practice.

If S exists because you needed to store some shared state somewhere, and someone told you that an Agent or GenServer was the way to go, then you could move that data to ETS and make it publically accessible so that functions which operate on that data read it from ETS directly rather than call the process. Now you've removed that bottleneck entirely.

If S exists because it needs to protect access to some data, but not all of it, and most tasks don't need to access the protected data, then you can move the parts that do not need to be protected into ETS, and keep the rest in the process. This might reduce the amount of contention on that singleton process by a huge amount, but if even half the processes no longer need to block on accessing it, then you've regained at least that much concurrency in the task processing code.

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The example above is something I've seen numerous times, but the important pattern to note is that you have some task that you've tried to parallelize by spawning multiple processes, but that task itself depends on something that is not, or cannot be done concurrently/in parallel.

Any time this pattern arises, you need to either find a way to enable concurrency in that dependency, or you should avoid doing the task in parallel in the first place. This is ultimately true of any parallelizable task - its only parallelizable if all of the tasks dependencies are themselves parallelizable, otherwise you end up bottlenecked on those dependencies and you've gained little to no benefit.

Where it becomes a bigger problem is when you consider the system at a higher level. Bottlenecks reduce throughput, which may end up, via backpressure, causing errors on the client due to overload, or depending on the domain, data being dropped because it can't be handled in time (e.g. soft real-time systems).

I don't have any code examples that really encompass all of this in one place, if you are interested in something specific, I can try to throw something together for you. Or if you have specific questions I can point you to some resources I've used to help understand some of these concepts.