Simplifying GraphQL Server Design for Optimal Performance
The Quest for Balance
When building a GraphQL server, two essential features come to mind: simplicity and performance. Striking a balance between these two is crucial, as optimizing for one at the expense of the other can render our application useless. No developer wants to use software that is extremely fast but complex to use, or very simple to use but slow.
The Power of Simplicity
In my previous article, I demonstrated how a GraphQL server can avoid the N+1 problem by having resolvers return IDs instead of objects. This approach simplifies the code for resolvers, making them easier to implement and maintain. However, this alone does not make the overall application simpler. To achieve true simplicity, we need to rethink how we design our GraphQL server.
Rethinking Data Models
The GraphQL project emphasizes the importance of thinking in graphs, but what does this really mean? A graph is not just a data structure; it’s a mental model that helps us understand relationships between objects. On the server-side, we don’t need to represent data as a graph; instead, we can use a simpler data structure.
Components to the Rescue
One approach is to use components to represent our data model on the server-side. This allows us to consolidate different models into a single structure, making it easier to process and manipulate data. By thinking in components, we can break down complex data relationships into manageable parts.
A Visual Guide to Modeling Data
Let’s consider an example: building a “Featured Director” widget. We can identify the components involved: <FeaturedDirector>, <Film>, and <Actor>. Each component has its own data requirements, which we can use to build our GraphQL query. On the server-side, we can process the request by flattening the components into types, ordering them as they appeared in the component hierarchy, and dealing with them in iterations.
The Data Loading Algorithm
The server’s data loading engine must implement an algorithm to load the data efficiently. This involves preparing an empty queue to store object IDs, retrieving the ID of the featured director object, and looping until there are no more entries on the queue. By the end of the iterations, we will have loaded all object data for all types.
Analyzing Time Complexity
Let’s analyze the time complexity of the solution. The number of queries to the database grows linearly with the number of types involved in the query, making it very performant. This solution is much better than dealing with graphs or trees, which can lead to exponential or logarithmic complexity.
Implemented Code
The solution outlined in this article is used by the GraphQL server in PHP that I’ve implemented, Gato GraphQL. The code for its data loading engine can be found online.
Conclusion
In this article, I’ve demonstrated how using components to represent the data model in the server can simplify GraphQL server design while maintaining optimal performance. By adopting this approach, we can build faster, more efficient, and easier-to-maintain GraphQL servers.