Scaling Teams with GraphQL - Why Relationships Matter

GraphQL has been a topic of discussion in the engineering community for some time now, and its approach to data fetching and API design continues to influence how we build software. This post expands on a talk I gave at Credit Karma's headquarters, exploring how GraphQL can impact not just our technical architectures, but also team collaboration and software development lifecycles. At the core of this discussion is a simple but powerful idea: relationships matter—both the relationships within your data and, crucially, the relationships within your organization.

Whether you're an experienced API architect, a frontend developer seeking more efficient data fetching, or an engineering leader considering new approaches for your teams, understanding the principles behind GraphQL can offer valuable insights.

So, What Exactly IS GraphQL? (And What It Isn't)

You've likely encountered GraphQL. Perhaps your teams are already using it, or maybe you're evaluating its fit for your systems. To start, let's clarify its fundamental nature, drawing from the official GraphQL specification:

GraphQL is, at its heart, an agreement—a contract, if you will—between the client and the server. It's a query language for your API, and a server-side runtime for executing those queries by using a type system you define for your data.

It's also important to understand what GraphQL isn't:

It's not a database query language. You don't write SQL (or NoSQL queries) directly in GraphQL.
It doesn't dictate data storage. GraphQL remains agnostic about where or how your data lives.
It doesn't specify data retrieval logic within your services. While "resolvers" (functions that fetch data for a field) are a key part of a GraphQL server, how those resolvers retrieve data from your microservices, databases, or third-party APIs is an implementation detail you control.

Think of GraphQL as a well-defined communication protocol that empowers clients to ask for precisely the data they need.

Refactoring Your Mind from REST

Many developers have a strong background in REST, which provides a familiar mental model for API design. When first approaching GraphQL, it can be helpful to map its concepts to RESTful principles.

Essentially:

If you want to GET data in REST, in GraphQL it's a Query.
If you want to change data (akin to POST, PUT, PATCH, DELETE), in GraphQL it's a Mutation.
If you want to subscribe to real-time data changes, GraphQL offers Subscriptions.

Consider a typical REST API where you might have endpoints like /projects, /projects/{name}, /contributors, and /contributors/{id}. Each resource often corresponds to a distinct endpoint.

With GraphQL, you typically interact with a single endpoint (e.g., /graphql). Clients send GET requests with the query in a parameter, or more commonly, POST requests containing the query, an optional operation name, and variables in the request body.

Entities, Types, and Defining Relationships

How do clients request specific "entities" in GraphQL? Through types. You, as the API designer, define the structure of your data. For instance, a Project type might look like this:

type Project {
  name: String
  tagline: String
  contributors: [User] # This defines a relationship
}

That contributors: [User] line is significant—it explicitly defines a relationship between a Project and a list of User types within the schema itself.

Now, if a client needs the tagline for a project named "GraphQL":

Request:

{
  project(name: "GraphQL") {
    tagline
  }
}

Response (JSON):

{
  "data": {
    "project": {
      "tagline": "A query language for APIs"
    }
  }
}

If the client also needs the usernames of the contributors to that project, they simply modify their query:

Request:

{
  project(name: "GraphQL") {
    tagline
    contributors {
      username
    }
  }
}

Response (JSON):

{
  "data": {
    "project": {
      "tagline": "A query language for APIs",
      "contributors": [
        { "username": "someuser" },
        { "username": "anotheruser" }
      ]
    }
  }
}

The client dictates the precise shape and content of the data returned. No more, no less.

How Does This Approach Help?

This client-specified data fetching model can alleviate several common API challenges:

Request Waterfalls: Clients can often retrieve all necessary data for a view in a single request, avoiding sequential, dependent calls.
Overfetching: Clients request only the fields they need, reducing the transfer of unnecessary data. This can lead to significant bandwidth savings and improved client performance.
Underfetching: Clients can fetch comprehensive data sets spanning multiple related entities in one go, rather than piecing together information from various endpoints.
Type-safety: The schema acts as a strong contract. Both clients and servers have a clear understanding of the data's shape and types.

Beyond efficient data fetching, GraphQL's schema-first approach offers several developer experience benefits:

Intrinsic Documentation: The schema itself serves as a reliable source of documentation. Tools can introspect it to generate interactive API explorers.
Code Generation: The strongly-typed nature of GraphQL facilitates the generation of boilerplate code for client-side data fetching, type definitions, and even server-side resolver stubs.
Simplified Mocking: A well-defined schema makes it straightforward to create mock API responses for testing or UI development.
Early Query Validation: Tooling can validate client queries against the schema at development time, catching errors before they reach the server.

What GraphQL Doesn't Natively Handle

It's equally important to recognize the concerns that GraphQL, by design, does not directly address within its specification:

Caching strategies
Rate limiting and throttling
Fine-grained authorization logic (beyond basic field visibility)
Load balancing
CORS policies
Distributed tracing and analytics integration
File uploads (though common patterns and extensions like the graphql-multipart-request-spec exist)

Why are these omitted? As Dan Schafer, one of GraphQL's co-creators, noted:

"There are a lot of questions that [GraphQL] very specifically does not answer… that’s because [these questions] were already solved by FB in 2012."

GraphQL was designed to compose with existing solutions for these cross-cutting concerns. It adheres to a philosophy of doing one thing well: describing data requirements and fulfilling them.

GraphQL is intended to be a thin layer in your application stack. You will still need to architect solutions for these important operational aspects, and the broader ecosystem offers considerable guidance and tooling.

Architectural Considerations: Where Does GraphQL Fit?

For a relatively simple architecture, perhaps involving clients and a single database, GraphQL can be placed directly in front of the database. Resolvers would then interact with an ORM or execute raw database queries.

However, in more complex, distributed systems, GraphQL often serves as a gateway or data aggregation layer. It sits in front of various backend systems—microservices, legacy REST APIs, third-party services, and databases—providing a unified data graph to clients. This is where GraphQL can be particularly effective in defining the relationships between disparate data sources and abstracting backend complexity.

Building Your GraphQL API: Guiding Principles

When developing GraphQL APIs, especially at scale, adhering to a set of guiding principles can be invaluable. The Principled GraphQL initiative, largely informed by Apollo's extensive experience, outlines several such ideals:

Integrity

One Graph: Strive for a single, unified graph for your organization, rather than multiple, siloed graphs per team. This promotes consistency, discoverability, and reuse.
Federated Implementation: While there's one logical graph, its underlying implementation can and often should be federated. Different teams can own and manage distinct parts of the schema and their corresponding services. (Apollo Federation is a prominent technology enabling this pattern).
Track the Schema in a Registry: Employ a schema registry as the single source of truth for your graph's definition, version history, and operational health. (e.g., Apollo Studio Schema Registry).

Agility

Abstract, Demand-Oriented Schema: Design the schema to meet client needs and use cases, abstracting away the complexities of backend service implementations. Avoid merely mirroring your database structures.
Agile Schema Development: Evolve the schema incrementally based on actual client requirements and feedback.
Iteratively Improve Performance: Treat performance management as a continuous, data-driven process, adapting to changing query patterns and service implementations.
Use Graph Metadata to Empower Developers: Equip developers with rich, easily accessible information about the graph, including documentation and robust tooling.

Operations

Access and Demand Control: Implement mechanisms to manage which clients can access specific parts of the graph and control the complexity or volume of their requests.
Structured Logging: Capture comprehensive, structured logs of all graph operations. This data is vital for understanding usage patterns, debugging issues, and optimizing performance.
Separate the GraphQL Layer from the Service Layer: Maintain the GraphQL API as a distinct architectural layer, rather than deeply embedding its functionality within every backend service.

The Heart of GraphQL: The Schema

The schema is arguably the most critical component of a GraphQL API. It's the contract, the source of truth, and the enabler of GraphQL's powerful features and rich tooling ecosystem.

Even with numerous data stores and backend services, the ideal is to present one unified graph to your clients.

When embracing a federated implementation (Principle #2), the challenge becomes managing a schema whose authorship and ownership are distributed across multiple teams and services. This can introduce complexity. You'll encounter various terms and patterns: schema stitching (an older approach, generally superseded for new projects by federation), Apollo Federation (the modern approach), GraphQL gateways, schema composition, and delegation.

A key insight, often emphasized by Marc-André Giroux, is captured in his statement:

"When we distribute our GraphQL Schema across different services... what we're saying is 'I want to use GraphQL for my inter-service communication'."

This highlights a potential pitfall: designing your public-facing GraphQL schema based on your internal service architecture. API consumers shouldn't need to understand your backend's internal structure. A well-designed schema abstracts these details, allowing backend services to evolve independently without breaking client applications.

Adopting a client-focused approach, sometimes analogous to the "Backend for Frontend" (BFF) pattern, is often beneficial.

If you distribute schema authorship, the goal is to compose or federate these partial schemas into a single, coherent logical graph, ideally managed and validated by a schema registry. The modern emphasis is on distributing the execution of the graph, while maintaining a unified conceptual schema. This allows individual service teams to focus on their specific domains.

Tooling Up: Consuming GraphQL APIs

Once a schema is defined, how do client developers interact with it effectively? The GraphQL tooling ecosystem is extensive:

Command-Line Interfaces: Tools based on graphql-config and various CLIs, notably the one included with GraphQL Code Generator, can assist with schema management, type generation, and other development tasks.
IDE Extensions: Extensions for popular IDEs like VS Code (such as the official "GraphQL: Language Feature Support" by the GraphQL Foundation, or extensions from Apollo and Prisma) offer syntax highlighting, autocompletion, real-time validation against the schema, and go-to-definition capabilities. These significantly boost developer productivity.
Schema Visualization: Tools like GraphQL Voyager provide an interactive, visual way to explore your schema, making it easier to understand types and their relationships without parsing schema definition language (SDL) files.
GraphQL IDEs:
- In-browser IDEs such as GraphiQL (often enhanced with plugins like an explorer view).
- Standalone applications like Insomnia and Postman, which offer robust GraphQL support.
- Cloud-based solutions like Apollo Studio Explorer. These simplify query composition, documentation Browse, and testing of operations, including subscriptions.
Client Libraries: A variety of client libraries cater to different needs and frameworks:
- Apollo Client: A popular, feature-rich option offering caching, local state management, and robust support for patterns like Apollo Federation.
- Relay: Developed by Meta, Relay is a powerful client, particularly within the React ecosystem, known for its performance optimizations and opinionated data-handling patterns.
- Urql: A highly extensible and performant GraphQL client that has gained traction due to its flexibility and thoughtful design.
- graphql-request: A minimal, lightweight client well-suited for simple use cases or server-to-server GraphQL communication. Many other clients exist, often tailored to specific frontend frameworks (e.g., for Vue, Svelte, Angular).
Mocking & Documentation Generation: The schema enables straightforward mock data generation (using tools like graphql-tools or plugins for @graphql-codegen/typescript-mock-data) and the automated generation of API documentation.
Type-Safe Codebases: Combining GraphQL with statically-typed languages like TypeScript is a potent strategy. Code generation tools like GraphQL Code Generator can create TypeScript types directly from your GraphQL schema, facilitating end-to-end type safety across your application stack. This practice has become increasingly prevalent.

It Really Is All About Relationships

Lee Byron, reflecting on the evolution of web development, once spoke of the "steady march forward of better abstractions, better syntax, and better mental models." GraphQL, with its schema-driven approach, represents one such evolution in how we think about and interact with data.

But the impact extends beyond mere technical improvements. As Jon Wong from Coursera aptly put it:

"Coordination between Engineers happens with GraphQL schemas as our common language."

GraphQL establishes an agreement between client and server. Crucially, it also fosters an agreement and shared understanding between your development teams.

So, as you explore, adopt, or refine your use of GraphQL, consider it not just as a technology for data graphs, but as a catalyst for improving relationships:

The relationships you explicitly define within your data model.
The relationships and contracts between your various services.
And, most importantly, the working relationships and collaboration between the people building and consuming those services.

When teams operate with a shared language and common goals, they are better positioned to succeed. GraphQL, with its schema at the core, can serve as that powerful, unifying language.