Comment by deepGem | Hacker News Reader

Comment by deepGem | Hacker News Reader

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I first started with neo4j as a primary data store for our semantic graph but there are some limitations that are forcing us to look for alternatives.

1. Adding edges to a neo4j graph is a painfully slow process. For a large graph with a few million nodes - it'll take days. 2. Scaling neo4j on a cluster is either not possible or it's a painful process. I'm yet to discover this.

However, the greatest advantage that neo4j offers is the ability to query a path. So far, no other graph databases that I know have this ability (including Apache spark and giraph).

It's quite possible to build a directed graph database as an adjaceny list in redis. We tried this and it's super fast and scalable. However, querying is very painful.

1. Adding nodes and relationships in Neo4j does not have to be slow. It really depends on how you are loading that data in. Neo4j provides many options for data import and a transactional endpoint over HTTP for batching transactions and decreasing disk write overhead.

2. The reason Neo4j is the only database that allows you to query a path is the same reason that setting up clustering or sharding is difficult. If your graph is complex then the problem is "How do I split up these subgraphs into shards so that traversals don't have to traverse across shards?" -- Building a giant adjacency list and using that as a traversal index is a clever idea, I must admit. :)

Openlink Virtuoso (and any other RDF store that supports them) does with the Property Paths feature of SPARQL 1.1.

Right but you have to use SPAERQL and from my limited experience with SPARQL it's not very fast either.

You should try again, depending on the software (i.e. which SPARQL database) it can be much faster than neo4j.

As someone else said, very much dependent on database engine. Some are faster than others, some scale better than others - it's about picking whats right for your requirements.

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