This article introduces Contentful (a cloud-hosted, headless, Content Management System (CMS)) and the graph database Neo4j. I have written a utility that allows data stored in Contentful to be imported into a Neo4j graph database. I’ll leave detailed explanations of what this can do until after I have explained the two systems. There are probably very few people who are familiar with both of these products, so I will start with an introduction to each.
This is how the editors enter the various fields that make up a content type.
However, Contentful does not provide a user interface for the “application”. Instead, it offers various APIs that allow the developer to use the data in the CMS however they like. This is very powerful in that you are not limited by the user interface provided by a traditional CMS – you get to use it however you like. Given that the data is served by a REST API (with GraphQL coming soon), you are not restricted by language.
Data in Contentful is partitioned into spaces, the equivalent of a distinct database. Spaces contain three kinds of things:
Entries and assets can be in preview or published states. There are two main API feeds – preview and publish.
Publish shows only published items, whereas
preview will also include more recent items that are in preview mode.
The Contentful space that I am using for my examples is “The example project” which you get by default when you sign up with Contentful. It is the content for a website that explains how to use Contentful with many languages.
Here are the content types within this space:
Creating a user interface for these is beyond the scope of this article, but you can find plenty of examples.
Neo4j is a graph database.
Unlike traditional relational databases, it works with nodes and relationships. Nodes are the entities of the system and may have a label and attributes. Nodes may be connected by relationships (which can be directional), and these relationships can also have attributes. You can query a Neo4j database using a query language called Cypher.
Neo4j also has an API that allows other systems to connect to it – these can be used to query and modify the graph.
Here, I have written one of the simplest queries possible in Neo4j:
This query will find me all nodes of the label category and return them. Here, I have shown the details of one of the nodes in the browser.
This is a more complex query:
This query will find me all nodes that are categories that have any relationship to another node and return these nodes. Note the visual nature of the query language. Nodes are contained in normal brackets but are displayed as circles in the browser!
There is far more that can be done with Neo4j and Cypher, but this is a good introduction.
Now that I have introduced the two platforms, I will explain how to move data from Contentful to Neo4j.
There are some warnings I should give you before you start to use it.
I typically use Neo4j for analytical databases that get dropped and recreated frequently. This may vary from other peoples usage and means that I don’t need to worry about database backups or migrations. I do need to worry about loading the database quickly.
My utility is available in the following GitHub repo:
Here is what you get when you ask for all data (note in a realistically-sized Contentful system, Neo4j would cap the displayed nodes at 100). This shows the relationships between the various nodes in the system. Here is the schema of the graph database:
This shows the relationships between the various content types. This is fun to play with and, with more detailed queries, can be informative. However there are more powerful queries such as:
This returns the asset nodes that are not linked to by any entry. These are orphan images that may have been published by mistake. This is something that the Contentful UI cannot do. You can also view the data in text form:
Hopefully, this has given you useful insight into what Contentful and Neo4j can be used for. Feel free to use my utility (at your own risk). It does make managing a Contentful system considerably easier – given that you can freely query the data.