I’ve spent countless hours building APIs, and I’ve seen firsthand how type safety can make or break a project. That’s why I’m excited to share my approach to creating robust GraphQL APIs using NestJS and Prisma. This combination has transformed how I develop applications, reducing bugs and improving developer experience significantly. If you’re tired of runtime errors and want to build something that scales gracefully, you’re in the right place. Let’s get started.

Setting up the foundation is crucial. I begin by creating a new NestJS project and installing essential packages. The code-first approach with GraphQL means I define my schema using TypeScript classes, which automatically generates the GraphQL schema. This keeps everything in sync and eliminates manual schema updates.

nest new graphql-api
npm install @nestjs/graphql graphql apollo-server-express
npm install prisma @prisma/client
npx prisma init

Have you ever noticed how database inconsistencies can creep into your application? Prisma solves this by providing a type-safe database client. I design my database schema in Prisma, which then generates TypeScript types automatically. This means I get compile-time errors if I try to access a field that doesn’t exist.

model User {
  id    String @id @default(cuid())
  email String @unique
  posts Post[]
}

model Post {
  id       String @id @default(cuid())
  title    String
  author   User   @relation(fields: [authorId], references: [id])
  authorId String
}

After defining the models, I run migrations to keep the database in sync. Prisma’s migration system handles this seamlessly, and the generated client gives me full type safety for all database operations.

Configuring NestJS for GraphQL involves setting up the module with the code-first approach. I prefer using the Apollo driver for its performance and features. The autoSchemaFile option tells NestJS to generate the GraphQL schema from my TypeScript classes.

@Module({
  imports: [
    GraphQLModule.forRoot<ApolloDriverConfig>({
      driver: ApolloDriver,
      autoSchemaFile: join(process.cwd(), 'src/schema.gql'),
    }),
  ],
})
export class AppModule {}

Now, let’s create the GraphQL models. I use classes with decorators to define objects, inputs, and arguments. This is where the magic happens – these classes serve both as GraphQL types and validation DTOs.

@ObjectType()
class User {
  @Field()
  id: string;

  @Field()
  email: string;

  @Field(() => [Post])
  posts: Post[];
}

@InputType()
class CreateUserInput {
  @Field()
  email: string;
}

What happens when your business logic becomes complex? Resolvers handle this by containing the application logic. I inject services into resolvers to keep code organized and testable. Dependency injection in NestJS makes this straightforward.

@Resolver(() => User)
class UserResolver {
  constructor(private userService: UserService) {}

  @Query(() => User)
  async user(@Args('id') id: string) {
    return this.userService.findById(id);
  }
}

Advanced GraphQL features like custom scalars and unions add power to your API. I often create custom scalars for dates or other complex types. Unions allow returning different types from a single field, which is useful for search results or error handling.

Authentication is critical for any API. I implement it using guards in NestJS, which can protect individual resolvers or fields. JWT tokens work well with GraphQL, and I set up context to include the user in every request.

@Injectable()
class AuthGuard implements CanActivate {
  canActivate(context: ExecutionContext) {
    const gqlContext = GqlExecutionContext.create(context);
    const request = gqlContext.getContext().req;
    // Validate JWT token
  }
}

Have you encountered performance issues with nested queries? DataLoader batches and caches database requests, solving the N+1 problem. I create instances for different models and use them in resolvers to optimize queries.

Error handling should be consistent across the API. I use custom filters to format errors and include validation using class-validator. This ensures clients receive clear error messages.

Testing is non-negotiable. I write unit tests for resolvers and services, and integration tests for the GraphQL API. NestJS’s testing utilities make this efficient.

Deployment involves building the application and setting up environment variables. I use Docker for consistency and monitor the API with tools like Apollo Studio. Performance optimization includes query complexity analysis and caching.

Building type-safe GraphQL APIs has changed how I approach development. The confidence that comes from compile-time checks and automated schema generation is invaluable. I encourage you to try this stack on your next project.

If you found this guide helpful, please like and share it with your colleagues. I’d love to hear about your experiences in the comments – what challenges have you faced with GraphQL APIs?