ER for identity graphs is a great use case! We see teams do this a lot and with not-great tools. (Ex: users/IPs in splunk/elastic, which are better for simpler matches.) For one Graphistry project, we run a single node neo4j with 0.5b nodes/edges, so something in the description isn't adding up for me here wrt perf. Maybe an open benchmark would help? I do agree indexing matters, as that was night/day for our use cas…
Regarding the performance on neo4j: the challenge for an honest and fair test towards this would be about how to properly compare a server-based solution vs. a serverless solution. TiloDB automatically scales up and down without any further interaction due to using Lambdas for all calculations. So would you compare it with a relatively small neo4j instance or with a large cluster? I honestly don't know. When we start…
RE:extremes, we see graph DBs OK for small time series (ex: 2 nodes with a bunch of event multiedges), but not full blown time series... where we'd use a tsdb. Some vendors demo this, but always felt like wrong tool.
The many-hop case is interesting! We don't see 1K-hops typically, and I get nervous even at 10-20 on graph DBs we've used. I can imagine in logistics or sciences that happening more, or maybe even some rdf systems. Partition keys start mattering fast, whether a kvdb or a mpp, but I don't have an intuition here. Probably easier to differentiate on, but too niche?