Unlocking the Power of Principled GraphQL: A Path to Rapid Iteration and Collaboration
As part of our ongoing series on conceptualizing, designing, and implementing a GraphQL server, we’re diving into the world of Principled GraphQL, a set of best practices for creating, maintaining, and operating a data graph. In this article, we’ll explore three key principles from the Integrity Principles section, which define how a graph should be created, managed, and exposed.
The Unity of One Graph
Principled GraphQL advocates for a single, unified graph across an organization, rather than multiple graphs created by individual teams. This approach promotes collaboration and consistency, ensuring that everyone is working towards a common goal.
Federated Implementation: Empowering Teams
The second principle, Federated Implementation, emphasizes the importance of decentralized ownership and responsibility. Each team should be accountable for maintaining the portion of the schema that exposes their data and services. This approach encourages autonomy, flexibility, and rapid iteration.
A Single Source of Truth: Tracking the Schema
The third principle, Track the Schema in a Registry, highlights the need for a single source of truth for registering and tracking the graph. This centralized registry ensures that everyone has access to the same information, reducing confusion and errors.
The Challenges of Collaboration
When implementing a GraphQL service, teams must navigate logistical and technical decisions. For instance, if the sales team owns the Product type, how can they accommodate requests from other teams, such as the tutorials team, to add new fields? There are several approaches to resolve this issue, including autonomous, delegated, and cross-boundary implementations. Each option has its trade-offs, and we’ll explore these in more detail.
Designing for Rapid Iteration
To enable rapid iteration and collaboration, we need to decentralize schema creation and empower teams to own their implementations. This requires a dynamic approach, where different teams can contribute to the same schema without overriding each other’s work or creating bureaucratic barriers. We’ll explore three iterations of architectural design, each building upon the previous one, to achieve this goal.
Iteration 1: Resolvers and Ownership
In our first iteration, we’ll create a resolver function that resolves differently for different types of products. Each team will own its own resolver implementation, and the product will be passed as an argument to the resolver.
Iteration 2: Combining Resolvers
In the second iteration, we’ll introduce a combineResolvers function, which combines resolvers from different teams. Each team provides its own resolver implementation, and these are combined into a single resolver.
Iteration 3: Subscribing Resolvers
In our final iteration, we’ll combine two design patterns: the publish-subscribe design pattern and the chain-of-responsibility design pattern. This approach decouples the resolver delegator from the actual resolvers, allowing teams to subscribe their resolvers to the chain and prioritize their position.
Rapid Iteration and Collaboration
The beauty of this strategy lies in its ability to enable rapid iteration and collaboration. Teams can subscribe new resolvers to handle special situations, and remove them when they’re no longer needed. This approach minimizes interaction across teams, allowing each team to own its portion of the schema and work autonomously.
Conclusion
By embracing Principled GraphQL and implementing a decentralized, dynamic approach to schema creation, teams can collaborate effectively, iterate rapidly, and create a robust and scalable GraphQL service. With this architecture, frontend developers can enjoy the autonomy and flexibility they need to write queries that fetch the data they require, without depending on others to provide required endpoints.