OpenCredo, consulting partner in the Neo4j Partner Graph, discusses their experiences with using graph databases, Neo4j, and the potential of graph-based applications.

As part of our work, we often help our customers choose the right datastore for a project. There are usually a number of considerations involved in that process, such as performance, scalability, the expected size of the data set, and the suitability of the data model to the problem at hand.

This blog post is about my experience with graph database technologies, specifically Neo4j. I would like to share some thoughts on when Neo4j is a good fit but also what challenges Neo4j faces now and in the near future.

I would like to focus on the data model in this blog post, which for me is the crux of the matter. Why? Simply because if you don’t choose the appropriate data model, there are things you won’t be able to do efficiently and other things you won’t be able to do at all. Ultimately, all the considerations I mentioned earlier influence each other and it boils down to finding the most acceptable trade-off rather than picking a database technology for one specific feature one might fancy.

So when is a graph model suitable? In a nutshell when the domain consists of semi-structured, highly connected data. That being said, it is important to understand that semi-structured doesn’t imply an absence of structure; there needs to be some order in your data to make any domain model purposeful. What it actually means is that the database doesn’t enforce a schema explicitly at any given point in time. This makes it possible for entities of different types to cohabit – usually in different dimensions – in the same graph without the need to make them all fit into a single rigid structure. It also means that the domain can evolve and be enriched over time when new requirements are discovered, mostly with no fear of breaking the existing structure.

Effectively, you can start taking a more fluid view of your domain as a number of superimposed layers or dimensions, each one representing a slice of the domain, and each layer can potentially be connected to nodes in other layers.

More importantly, the graph becomes the single place where the full domain representation can be consolidated in a meaningful and coherent way. This is something I have experienced on several projects, because modeling for the graph gives developers the opportunity to think about the domain in a natural and holistic way. The alternative is often a data-centric approach, that usually results from integrating different data flows together into a rigidly structured form which is convenient for databases but not for the domain itself.

As an example to illustrate these points, let’s take a very simple Social Network model. Initially the graph simply consists of nodes representing users of the service together with relationships that link users who know each other. Those nodes and relationships represent one self-contained concept and therefore live in one “dimension” of the domain. Later, if the service evolves to allow users to express their preferences on TV shows, another dimension with the appropriate nodes and relationships can be added into the graph to capture this new concept. Now, whenever a user expresses a preference for a particular TV show, a relationship from the Social Network dimension through to the TV Shows dimension can be created.

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