I don’t feel that way at all. I’ve been maintaining open source storage systems for few years. I love it. Absolutely love it. I maintain TidesDB it’s a storage engine. I also have back pain but that doesn’t mean you can’t do what you love.
TidesDB is a storage engine, you use it to build databases. It's persistent and built on a log-structured-merge tree (LSM) with modern research incorporated such as
I work on https://github.com/xtdb/xtdb which is broadly Postgres-compatible with a few key SQL extensions (SQL:2011 bitemporal tables + immutability, first-class nested data, pipeline syntax, etc). Built on Arrow and the JVM but is otherwise mostly from scratch.
XTDB is perhaps not directly relevant to the topic at hand, but I am a firm believer that ML workflows can benefit from robust temporal modelling.
I thought the docs were pretty good just going through them to see what the product was. For me I just don't see the use-case but I'm not well versed in their industry.
I think the docs are great to read, but implementing was a completely different story for me, ie, the Ask AI recommended solution for implementing Claude just didn’t work for me.
They do have GitHub discussions where you can raise things, but I also encountered some issues with installation that just made me want to roll the dice on another provider.
They do have a new release coming in a few weeks so I’ll try it again then for sure.
Edit: I think I’m coming across as negative and do want to recommend that it is worth trying out langfuse for sure if you’re looking at observability!
The internals document looks pretty great too. It actually talks of internals and goes pretty wide and deep. Saved for reading later once the coffee kicks in!
