Walking in Graph Database and the Meaning it Holds

Graph Databases in a Distributed Platform 

TitanDB is designed to scale out across a distributed cluster. With Cassandra, it has a distributed storage engine to scale the database as it adds nodes. In comparison, Neo4j, the leading graph database, scales up and has a master/slave architecture, which requires more powerful machines for scaling. Storage across clusters is what TitanDB has as its advantage.

Neo4j and Titan take a fundamentally different approach, said Emil Eifrem, founder of Neo4j. He said they made the decision to build a graph database from scratch for reasons of performance and reliability. Tradeoffs come when building a graph database on top of a database not built for graphs. Neo4j built their own relational database management system and a query language for it. For the record, the company announced Neo4j 2.2 this week with added read and write scalability.

TitanDB takes the other approach, building a non-native graph database on top of another database. Eifrem said databases like Cassandra are great at handling large volumes and ingesting lots of data. But he questions how fast queries run and thus how much they satisfy real-time business needs.

OrientDB, according to its web site, has a hybrid document-graph engine. It is built on SQL but adds extensions to enable tree and graph manipulation, while also offering a multi-master and shared architecture to overcome the master-slave bottleneck in write operations. Their site has a detailed comparative analysis of the differences between Orient and Neo4j.

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