Earlier quoted context omitted.
ConceptNet [1] started out as an academic project that I was responsible for for a while. Since then I've left to start a company but I still maintain ConceptNet and build lots of stuff on it. [1] http://conceptnet5.media.mit.edu Here's a list of databases, some of them graph databases, some of them barely databases, where I've tried to store and look up edges of ConceptNet: - SQLite - PostgreSQL - MongoDB - Some awf…
It's interesting. I suppose most graph db operations could be easily enough broken down into a series of steps that could be performed in a more scalable but slower way. Did your hand-rolled hashtable have any characteristics that would make its performance characteristics difficult for a smarter optimizer (if such a thing existed in Neo4j)? Can you psudocode an example slow query/operation and indicate how many edge…
Here's what I have to be able to do in the database:
1. Import millions of edges from a flat file (time limit: 24 hours)
2. Query any node to return up to 100 edges connected to it (time limit: 100 milliseconds)
3. (nice to have) Find the maximal core of nodes that all have degree at least n to each other (time limit: a few hours)
4. Iterate all the edges between the nodes in a specified subset, such as the degree-3 core, which may still be millions of edges (time limit: a few hours)
#3 is optional, and the alternative is to export all the edges and compute it outside the database. But it's the only thing here that's actually a graph algorithm. However, every open-source graph database I've tried is orders of magnitude too slow at one of the other steps. They either fail at importing, fail at iterating, or fail to respond to trivial queries in a timely manner.
I forgot to mention one other non-graph-database system that met my requirements, which is Kyoto Cabinet. The main downside of it is the GPLv3 license.