[mbuda]

Yes, memgrpah persists data on-disk, but there is not the support for larger than memory datasets yet (but a lot can fit on a single machine). In general, our primary focus at the moment is the scale out / proper graph sharding support. The progress on that side can be tracked under the following project -> https://github.com/orgs/memgraph/projects/5

How big is your graph and do you have specific queries in mind?

[grounder]

I have around 100GB of data in a relational database. Not sure how that would translate into nodes and edges. Is that amount feasible in Memgraph?

[mbuda]

It depends on a few things. If you want to migrate all the data that's heavier than just, e.g., the graph part and connections. It heavily depends on the final model, required data types and overall distribution of data. This guide -> https://memgraph.com/docs/memgraph/under-the-hood/storage can help you with calculating the amount of required RAM. Also, we plan to include a simple calculator into the next release of Memgraph Lab (https://memgraph.com/docs/memgraph-lab/) coming probably this wee :)

[grounder]

Thank you so much! One more question if you'll permit me - and then I'll leave you alone I promise. Social networks are often recommended as a use-case for a graph database. How well would Memgraph perform on generating an activity feed of all of the posts of the people I follow ordered by post date descending, for example (hopefully with pagination)?

[mbuda]

Hard to say without a particular query, and again it depends on the model, but from the explanation, that could be modeled as a 2-hop relationship. If you put in-place indexes and maybe some filtering, it should be fast.

Please join our Discord at https://discord.gg/memgraph, more people will be able to help you there :)