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 These benchmarks are a bit outdated, we’re currently updating them this sprint.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…
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…
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…
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…
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 These benchmarks are a bit outdated, we’re currently updating them this sprint.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…
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…
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…
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…