[BlooIt]

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!