nodejs-polars VS falcon

Polars: A blazingly fast DataFrame library written in Rust for data manipulation and analysis

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.