Field-Level Resolvers: The Key to Consistent and Resilient GraphQL APIs

GraphQL has revolutionised how we fetch and shape data, providing a clean abstraction layer between clients and servers. One of its core features, resolvers, allows us to define how each field in our schema gets its data. In some cases, developers may unintentionally diminish the benefits of GraphQL by relying on utility methods in resolvers. This practice not only defeats the purpose of GraphQL's design but also introduces unnecessary complexity and potential bugs.

Let’s dive into why this is problematic, how to do better, and how to improve error handling with field-level resolvers.

The Power of Resolvers

In GraphQL, resolvers are invoked for every instance of a type, regardless of where that type appears in your schema. This abstraction ensures that the logic for resolving data stays consistent across the board. For example:

schema {
  query: Query
}

type Query {
  project(id: ID!): Project
  user(id: ID!): User
}

type Project {
  id: ID!
  name: String!
  owner: User!
}

type User {
  id: ID!
  name: String!
  email: String!
}

Here, the User type is used in two places: directly in the Query for fetching users and nested within the Project type as the owner. Thanks to GraphQL's resolver system, we can define a single User resolver to handle how User fields are resolved, ensuring consistent behaviour everywhere User appears.

The Problem with utils

When you introduce utility methods to shape data outside of your resolvers, you break this abstraction. Consider this example:

// utils.ts
function mapToUser(userData: User) {
  return {
    id: userData.id,
    name: userData.full_name,
    email: userData.contact_email,
  };
}

// resolvers.ts
const resolvers: Resolvers<Context> = {
  Query: {
    project: async (_, { id }, { dataSources }) => {
      const project = await dataSources.projectAPI.getProject(id);
      return {
        ...project,
        owner: mapToUser(project.owner), // Utility method called here
      };
    },
    user: async (_, { id }, { dataSources }) => {
      const user = await dataSources.userAPI.getUser(id);
      return mapToUser(user); // Utility method called here
    },
  },
};

At first glance, this might seem fine. But here’s why it’s problematic:

1. Duplicated Logic

You’re forced to call mapToUser in every resolver where a User type appears. Forgetting to call it or calling it incorrectly can lead to inconsistent behavior across your API.

2. Breaking Abstraction

GraphQL's resolver system is designed to centralize how each type is resolved. By using a utility method, you’re sidestepping this feature and making your code less intuitive.

3. Loss of Flexibility

If you ever need to modify how a User type is resolved (e.g., adding new fields or handling errors), you’ll have to hunt down every place where mapToUser is called instead of updating a single resolver.

The Better Approach: Leverage Type Resolvers

Instead of using utility methods, define resolvers for your GraphQL types. Here’s how you can rewrite the above example:

const resolvers: Resolvers<Context> = {
  Query: {
    project: async (_, { id }, { dataSources }) => {
      return dataSources.projectAPI.getProject(id);
    },
    user: async (_, { id }, { dataSources }) => {
      return dataSources.userAPI.getUser(id);
    },
  },
  User: {
    id: (user) => user.id,
    name: (user) => user.full_name,
    email: (user) => user.contact_email,
  },
  Project: {
    owner: (project, _, { dataSources }) => {
      return dataSources.userAPI.getUser(project.ownerId);
    },
  },
};

Why This is Better

1. Consistency: The User resolver ensures that all User instances are resolved the same way, no matter where they appear in the schema.
2. Centralized Logic: Changes to how a User is resolved only need to be made in one place.
3. Harnessing GraphQL’s Strengths: By embracing the resolver system, you’re aligning with GraphQL’s core design principles and leveraging its full potential.

Enhanced Error Handling with Field-Level Resolvers

Another benefit of splitting logic into field-level resolvers is improved error handling. If you rely on a single resolver for an entire object, any error will propagate to the entire object, making all its fields unavailable. Field-level resolvers mitigate this by isolating errors to specific fields, allowing other data to still be returned.

Example: Single Object Resolver with Errors

Query: {
  project: async (_, { id }, { dataSources }) => {
    const project = await dataSources.projectAPI.getProject(id);

    return {
        id: project.id,
        title: project.title,
        owner: await dataSources.userAPI.getProjectOwner(project.owner.id) // async call that could error
  },
}

If getProjectOwner() fails, the client receives no data for project, even if some fields could have been resolved independently.

Example: Field-Level Resolvers for Better Resilience

Query: {
  project: async (_, { id }, { dataSources }) => {
    return dataSources.projectAPI.getProject(id);
  }
},
Project: {
  owner: async (project) => {
    return dataSources.userAPI.getProjectOwner(project.owner.id) // async call that could error
  }
}

In this case:

• If getProjectOwner() fails, GraphQL can still resolve other parts of the query if they don’t depend on project.
• Errors are isolated to specific fields, making it easier to debug and allowing partial data to be returned to the client.

Benefits of Field-Level Resolvers for Error Handling

1. Graceful Degradation: If one field fails to resolve, other fields can still return data.
2. Enhanced Debugging: Errors are isolated to specific fields, making them easier to trace.
3. Improved Resilience: Clients receive as much data as possible, even if some parts fail.

Considerations

While field-level resolvers improve error handling, they may introduce performance overhead if each field fetches its data separately. Tools like DataLoader can batch and cache requests to optimise performance.

Conclusion

Using utility methods in your resolvers may seem like a shortcut, but it ultimately undermines the power and elegance of GraphQL. By defining resolvers for your types and leveraging field-level resolvers, you can maintain a clean, consistent, and scalable API while improving error resilience. Embrace the abstraction that GraphQL provides—your future self and your users will thank you!