Personally I think child processes are the right approach for this. Launch a new process* for each query and it can (if you choose to go that route) dynamically load in compiled code. Exit when you’re done, and the dynamically loaded code is gone. A side benefit of that is memory leaks are contained, since all memory you allocate is about to be removed anyway.

I’d probably be fine with hundreds or thousands of these hanging in memory. I suspect the generated code for a single query would be in hundreds of kilobytes, maybe a megabyte. But yeah, this is one of those technical details I’d worry about.

Honestly, I wonder if you could just use an actual HTTP server for this? They can handle hundreds or even thousands of simultaneous requests. They can handle requests that complete in a fraction of a millisecond or ones that run for several hours. And they have good tools to catch/deal with code that segfaults, hits an endless loop, attempts to allocate terabytes of swap, etc. HTTP also has wonderful tools to load balance across multiple servers if you do need to scale to massive numbers of requests.

Not sure how a HTTP server would solve the CPU bottleneck of scanning terabytes of data per query?

I somehow didn’t think a regular JIT solution might be applicable here, but it is. Thank you! There seems to be a number of projects doing JIT for C++, will look at them.

Plenty of them on various sites, like this one I found yesterday.

This plea for help is specifically for non-coding, but still deeply technical work.

I’m pretty sure just like transport containers were standardized by ISO to make transport easier, game boxes should be standardized to fit in Kallax.

Last job, we started writing mixing bits of Kotlin in an otherwise mostly-Java in a monolithic Spring-based service. Good experience.

I found it crazy useful to study old, established, mature technologies, like relational databases, storage, low-level networking stack, optimizing compilers, etc. Much more valuable than learning the fad of the year. For example, consider studying internals of Postgresql if you’re using it.