HN user
Hello. These benchmarks are a bit outdated, we’re currently updating them this sprint.
The open-source in-memory version loads around 3 million edges/second, while the on-disk version handles does about 2 million edges/second with a WAL batch size of 100, and 3m with no WAL.
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).
Good questions.
1) You can persist a graph to disk. By default, this uses protobuf (`save_to_file`), however we’re migrating to Parquet in next release for better performance because we noticed loading a 100m edge graph from scratch (CSV, Pandas, or raw Parquet) is actually faster (~1M rows/sec) than from persisted proto, which isn’t ideal. There’s also a private version that uses custom memory buffers for on-disk storage, handling updates and compaction automatically.
2) You can run a Raphtory instance either as a GraphQL server or an embedded library. For the server, multiple users can query the persisted graphs, which are stored in a simple folder structure with namespaces (for different graphs). For now, access control needs to be managed externally, however it's on our roadmap!
Checkout https://github.com/Pometry/Raphtory, it's written in Rust, embedded (the binaries are about 20mb) and you can use the Python APIs as a drop-in replacement for NetworkX. Disclaimer, I am one of the people behind it.