[suprememoocow]

Gitter co-founder here: we use neo4j for suggesting rooms.

So, if you're in room A and room B, and most people in those two rooms are also in room C, then we suggest that you join room C.

As I mentioned elsewhere, we also use some non-neo4j based methods for suggestions (including your GitHub graph). One of the problems we've had with neo4j is that we haven't been able to make it scale. It frequently burns up from being overloaded.

This is almost certainly down to the way we use neo4j but at some point I'd like to ditch it for a clustered suggestion algo that uses batch processes to cluster rooms together.

[scaryclam]

Sounds like you're going to be doing a lot of writes, which neo4j isn't designed for. While I get why you're using it, I'd suggest that it's not really the right tool for that job, which is possibly why you're having problems with it.

Do you really need persistance at all? If the application crashes everyone would be kicked out of the channels anyway no? I think making an in memory graph and ditching the graph DB entirely would be more helpful for that kind of suggestion tool.

[suprememoocow]

We update on a batch[1] fairly infrequently (daily iirc). We don't need exact room membership since the results are aggregated.

> If the application crashes everyone would be kicked out of the channels anyway no

No, Gitter's room membership is persistent (between restarts, crashes and sessions)

1: https://gitlab.com/gitlab-org/gitter/webapp/blob/master/scri...

[dsala]

Neo4j Powers a 1.2 Terabytes Size Data with Nearly Three Billion Nodes and Nine Billion Relationships to Create the World’s Largest Consumer Identity Graph. Neo4j’s native graph clustering architecture is ideally suited to deliver real-time query performance at scale, across massive customer graphs. Please see: https://neo4j.com/news/qualia-media-customers-experience-gre...