Large datasets in the Neo4j world refer to those that are substantially larger compared to the main memory of the system. In scenarios with such datasets, it is not possible for the Neo4j system to cache the entire database in memory, thereby providing blazingly fast traversals on the graph, because it will eventually lead to disk operations. Earlier, it was recommended to scale the system vertically using more RAM or solid state drives that have much lower seek times for the data on the disk compared to spinning drives. While SSDs considerably increase performance, even the fastest SSDs cannot replace the RAM, which in the end is the limiting factor.

In order to service huge workloads and manage large sets of data in Neo4j, partitioning graphs across multiple physical instances seem complex way to scale graph data. In versions up to 1.2, scaling seemed to be the con of this graph data store, but with the introduction of Neo4j High Availability (HA), there has been...