GraphQL Can Compose Headless Systems to Boost User Experience
With the rise of APIs, businesses can streamline their operations and automate workflows like never before. Two API technologies that have been gaining a lot of attention are headless (API-first) and GraphQL.
Today’s Software Is Headless
Headless services have become increasingly popular, especially in web development. A “headless” or “API first” service separates the frontend and backend applications, and offers an API instead of a web interface. Building on top of a headless service will improve the frontend user experience as developers can optimize the design and performance of their applications without the constraints of an all-in-one application.
The software of a headless service is decoupled, allowing developers to use the pieces interdependently. For instance, for a headless content management system (CMS) context, companies can use the built-in publishing features but deliver the data to their website developed in-house rather than using a prebuilt and possibly inferior website that comes with a traditional CMS.
This approach has become increasingly popular, allowing companies to create a consistent and seamless customer experience across all channels. For example, retail companies can create a consistent omnichannel experience using different services. But there are also some flaws to working with headless services.
Headless Data Needs to Be Composed
Usually, companies that adopt a headless service in their stack don’t stick to just one. Imagine storing your product data in one headless service and your content (like blog posts in the CMS example) in another. What happens if you want to combine this data? Relying on headless services could increase technical complexity, as developers often have to “glue” code to connect data stored in different places.
Not only do you have to glue data together, but the data could also be delivered in a different format by the various headless systems you’re using. If these data models don’t align, developers must understand how to connect the data and normalize the data into one structured format. This process can be time-consuming and prone to errors if not done carefully. Also, many developers write this “glue” code directly in the frontend application.
One solution to this problem is using a middleware like GraphQL to consolidate all your headless systems into one unified API.
GraphQL Can Compose Headless Systems
GraphQL is a query language for APIs that Facebook developed. It allows developers to request only the data they need, making API responses faster and more efficient. GraphQL also provides a powerful tool for developers to build APIs and offers much flexibility regarding data structures and relationships. It has become popular among companies that want to unify data from various backend data sources, such as a headless service.
As mentioned before, connecting different layers requires developers to write “glue code,” which can result in technical debt, increased workload and decreased flexibility. But when GraphQL is used as a middleware, the developer can easily manipulate and transform data before it reaches the client application, providing a more efficient and streamlined process. On top of this, GraphQL can be used to “stitch” or “merge” data — often called GraphQL federation.
GraphQL federation allows companies to connect their various headless services while still keeping these services decoupled. Both services don’t have to know the other’s existence, as all relations are defined in the GraphQL middleware. This approach especially shines when both your headless services and GraphQL middleware are delivered as a SaaS, meaning you don’t get the additional overhead of managing your own services.
More companies are building their stack on headless services, allowing them to move quicker than the competition and provide a better (and faster) user experience. When you rely on many headless services, using a GraphQL middleware lets you leverage the strength of having decoupled services without the hassle of “gluing” their data together.
