In Neo4j, what level of specificity should be used when granularity level can be unlimited?

Question

The hardest thing to wrap my head around when using a graph database, is choosing level of granularity. Lets say I have a graph for things that occur at certain days of the

Answer 1

The level of granularity of your data model should be driven by your query requirements, not the other way around. That is: when modeling your database, you should ask yourself: "what kind of query I will do over my data?". Based on the answers of this question, you will get a good start point to make a good model with an appropriate granularity level.

In the book Learning Neo4j, by Rik Van Bruggen (you can download in this link) the author says about design graph databases for query-ability:

Like with any database management system, but perhaps even more so for a graph database management system such as Neo4j, your queries will drive your model. What we mean with this is that, exactly like it was with any type of database that you may have used in the past or would still be using today, you will need to make specific design decisions based on specific trade-offs. Therefore, it follows that there is no one perfect way to model in a graph database such as Neo4j. It will all depend on the questions that you want to ask of the data, and this will drive your design and your model.

So, based on this, the answer of your question "what level of specificity should be used when granularity level can be unlimited?" is: it depends on your query requirements. Think first in the queries you will do, and after in the data model.

My suggestion is: keep your model as simple as possible in the beginning and, when required, make gradual changes.