Comment by youdont
2 days ago
Looks very interesting, but I've seen these kind of multi-paradigm databases like Gel, Helix and Surreal and I'm not sure that any of them quite hit the graph spot.
Does Helix support much of the graph algorithm world? For things like GrapgRAG.
Either way, I'd be all over it if there was a python SDK witch worked with the generated types!
Shameless plug: If you're exploring graph+vector databases, check out https://github.com/Pometry/Raphtory/ — with a full Python SDK and built-in support for most common graph algorithms.
It’s built in Rust with native vector support. The open-source version is in-memory, but the commercial version supports disk-based scaling (we tested it with a 3TB graph on an M1 MacBook + insert all 100x faster than existing GraphDBs).
Looking at your benchmarks you say for inserting 1k edges its around 500,000 ns/iteration. Is this 500,000 ns/per edge insertion or for all 1k of them?
We started as a graph database, so that's definitely the main thing we want to get right and we wan't to prioritise capturing all that functionality.
We have a python SDK already! What do you mean by generated types though?
I have been happily using Gel (formerly EdgeDB) for a few projects. I'm curious what you think it is missing in regards to hitting the "graph spot"?
gel is a relational database, have you been building with it under a graph type philosophy?