- 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.
The Freebase Film Data has only 21M facts. Freebase 1.9 billion facts.
How long does it load? What's the avg query response for very simple searches (like who is the US president)?
Dgraph was 10X faster.
Can someone run the benchmark for the real Freebase (1.9 billion facts)?
Also LevelDB/Bolt is not suitable for this, better use MongoDB or Postgres or MySQL as Cayley data store.