11–20 of 71 posts
I can't understand how to use the query language. It all seems so magical! I tried building something with Cayley once but couldn't fetch all the data I wanted in a single query, or didn't know how to, then got frustrated and deleted everything.
Being on HN while doc is lorem ipsum ( https://cayley.io/1-getting-started/ ), damned !
Very nice! any plans to use it as a backend for google's badwolf[1] (a temporal graph store)? https://github.com/google/badwolf
The topic of 'reification' found throughout our recent discussion is how we can generally add metadata to links, thereby making it a lot easier to fit the two models together.
How matured is it? Has someone used it for big datasets? Last time I tried it wasn't ready to cope with a 250 GB N-Triples RDF. (two years ago)
I see Cayley now supports other backends beside LevelDB. "PostgreSQL and MongoDB for distributed stores" - that's good to read.
Maintainer here, good to see Cayley on HN again :) We've got a lot of new features on master, (GraphQL support, Gephi interfaces, Recursive iterators, etc) and are cutting a release next week. Active work in the coming releases on tightening down the indexing and really bringing it into prod. EDIT: Feel free to join the new Slack or the Discourse mailing list/discussion board!
Anyone use Cayley in prod? An old job used Neo4j, and the graph concept was great for specific use cases. As a lightweight graph store, Cayley was really exciting when it came out, but I haven't had a need for it since I left that job. It strikes me as really well made, and I'd love to hear any war stories.
- There were a couple of issue loading the data that we fixed and contributed back the patch
- Loading the data was really slow, and it got slower every time a new entry was added (Loading the full freebase dump required 1 week on a very beefy machine with SSD. Used LevelDB)
- Then the queries were relatively slow. Without going too much into details, we were using the data to analyze texts and extract entities, and the relationship between them, and even parallelizing the queries, they were relatively slow (depending on complexity between 0.1 and 1 sec on average). We solved the issue implementing a robust caching layer in front of it and carefully planning the queries.
- In general, it was stable and performant enough for a backend service. But we were pushing really the envelope of what it could do.
All in all, I would say that I was happy with it. In comparison, I tried a year earlier to use Neo4J in a similar role and I give up after 2 weeks because I wasn't even able to get it loading part of the dataset without crashing on a similar hardware.
How matured is it? Has someone used it for big datasets? Last time I tried it wasn't ready to cope with a 250 GB N-Triples RDF. (two years ago)