stateroom VS falcon

In the case of containers it gets tricky because of how it interacts with the scheduler (e.g. if a node is idle but has a bunch of paused containers that could be unpaused at any time, the scheduler has to decide how to proceed), but I love the concept. It's something I've thought a bit about in a world where the server can be compiled to WebAssembly, because it's imaginable to suspend it and serialize the memory state so that it can be sent off to storage somewhere and pulled out when the next request comes in. This was actually part of the motivation behind a library I wrote called Stateroom (https://github.com/drifting-in-space/stateroom), which creates a stateful WebSocket server as a WebAssembly module, but I haven't yet implemented the ability to freeze the state of the module between requests.

> "just drop it in and it just works" for self hosted websocket systems

I was also been underwhelmed by options in this area, so I've been working on a Rust library called Jamsocket[1]. The idea is that all you need to do is implement a trait, overload the functions for the events you want to handle (new connection, message, etc.) and deploy it.

I still need to work on production aspects of it, but it's in a state where you can play with it for local development, so I figured I'd share it here in case any Rust developers want to tinker with it.

[1] https://github.com/jamsocket/jamsocket

Good questions!

> Why do you need one process per user? / Wouldn't this "event loop" actually be more efficient that one user/process, as there would be less context switching cost from the OS?

We're particularly interested in apps that are often CPU-bound, so a traditional event-loop would be blocked for long periods of time. A typical solution is to put the work into a thread, so there would still be a context switch, albeit a smaller one.

The process-per-user approach makes the most sense when a significant amount of the data used by each user does not overlap with other users. VS Code (in client/server mode) is a good example of this -- the overhead of siloing each process is relatively low compared to the benefits it gives. We think more data-heavy apps will make the same trade-offs.

> Can I just keep a map of (connection, thread_id) on my server, and spawn one thread per user on my own server?

If you don't have to scale beyond one server, this approach works fine, but it makes scaling horizontally complicated because you suddenly can't just use a plain old load balancer. It's not just about routing requests to the right server; deciding which server to run the threads on becomes complicated because you ideally want to decide based on the server load of each. We started going down this path, realized we'd end up re-inventing Kubernetes, so decided to embrace it instead.

> Could I just load up my server with many cores, and give each user a SQLite database which runs each query in its own thread? This way a multi GB database would not be loaded into RAM, the query would filter it down to a result set.

If, for a particular use case, it's economical to keep the data ready in a database that supports the query pattern users will make, it's probably not a good fit for a session-lived backend. In database terms, where our architecture makes sense is when you need to create an index on a dataset (or subset of a dataset) during the runtime of an application. For example, if you have thousands of large parquet files in blob storage and you want a user to be able to load one and run [Falcon](https://github.com/vega/falcon)-type analysis on it.