I’ve been building APIs for years, and recently, I hit a wall with traditional REST services in a microservices architecture. The complexity of managing multiple endpoints, handling data consistency, and ensuring type safety across services became overwhelming. That’s when I discovered the powerful combination of NestJS, Prisma, and Apollo Federation. This stack has completely transformed how I approach API development, especially for complex systems like e-commerce platforms.
Why did this particular combination stand out? NestJS provides a solid foundation with its modular architecture and dependency injection. Prisma brings type-safe database operations to the table. Apollo Federation allows us to stitch multiple GraphQL services into a single cohesive API. Together, they create a development experience where your IDE becomes your best friend, catching errors before they reach production.
Let me show you how this works in practice. Imagine we’re building an e-commerce system. We start by defining our database schema with Prisma. The beauty of Prisma is how it generates TypeScript types automatically from your schema.
model User {
id String @id @default(cuid())
email String @unique
orders Order[]
}
model Product {
id String @id @default(cuid())
name String
price Decimal @db.Decimal(10, 2)
category Category @relation(fields: [categoryId], references: [id])
}
model Order {
id String @id @default(cuid())
user User @relation(fields: [userId], references: [id])
items OrderItem[]
}
Have you ever wondered how to keep your database queries type-safe across multiple services? Prisma Client gives you autocompletion and type checking for all database operations. This means you can’t accidentally query a field that doesn’t exist – the TypeScript compiler will catch it immediately.
Now, let’s talk about the federated architecture. Instead of one monolithic GraphQL server, we break our system into subgraphs. Each subgraph handles a specific domain, like users, products, or orders. The gateway service combines these into a single GraphQL endpoint. This approach gives us the best of both worlds: the separation of concerns from microservices and the unified interface of GraphQL.
Here’s how you might set up a users service in NestJS:
@ObjectType()
@Directive('@key(fields: "id")')
export class User {
@Field(() => ID)
id: string;
@Field()
email: string;
}
@Resolver(() => User)
export class UsersResolver {
constructor(private prisma: PrismaService) {}
@Query(() => User)
async user(@Args('id') id: string) {
return this.prisma.user.findUnique({ where: { id } });
}
}
Notice the @key directive? That’s Apollo Federation’s way of defining entity keys that other services can reference. This enables cross-service data resolution without tight coupling between your microservices.
But what happens when you need to query user data from the orders service? This is where reference resolvers come in. They allow services to resolve fields that belong to other services.
@Resolver(() => User)
export class UserReferenceResolver {
@ResolveReference()
async resolveReference(reference: { __typename: string; id: string }) {
return this.prisma.user.findUnique({ where: { id: reference.id } });
}
}
Have you encountered the N+1 query problem in GraphQL? It’s a common performance issue where a single query triggers multiple database calls. DataLoader solves this by batching and caching requests. Here’s how you might implement it:
@Injectable()
export class UsersLoader {
constructor(private prisma: PrismaService) {}
createUsersLoader() {
return new DataLoader<string, User>(async (userIds) => {
const users = await this.prisma.user.findMany({
where: { id: { in: userIds } },
});
const userMap = new Map(users.map(user => [user.id, user]));
return userIds.map(id => userMap.get(id));
});
}
}
Authentication and authorization in a federated system require careful planning. We typically handle authentication at the gateway level and pass user context to subgraphs. Each service can then make authorization decisions based on this context.
@Injectable()
export class JwtAuthGuard extends AuthGuard('jwt') {
canActivate(context: ExecutionContext) {
return super.canActivate(context);
}
}
@Resolver(() => Order)
export class OrdersResolver {
@UseGuards(JwtAuthGuard)
@Query(() => [Order])
async myOrders(@Context() context) {
const userId = context.req.user.id;
return this.prisma.order.findMany({ where: { userId } });
}
}
Testing becomes straightforward with this architecture. You can test each service independently, mocking dependencies as needed. For resolver testing, I often use a combination of unit tests for business logic and integration tests for database operations.
Deployment-wise, you can deploy each service separately, scaling them based on load. Monitoring becomes crucial – I recommend setting up distributed tracing to track requests across services.
What about error handling? In a federated system, errors need to be propagated correctly. Apollo Federation provides mechanisms for handling partial failures, ensuring that one failing service doesn’t break the entire query.
The development experience with this stack is exceptional. Hot reloading, automatic type generation, and excellent IDE support make iterative development smooth. I’ve found that teams can work on different services simultaneously without stepping on each other’s toes.
Building type-safe GraphQL APIs with NestJS, Prisma, and Apollo Federation has changed how I think about API development. The type safety catches errors early, the federated architecture scales beautifully, and the developer experience is second to none. It’s not just about writing code – it’s about creating maintainable, scalable systems that can evolve with your business needs.
I’d love to hear about your experiences with GraphQL and microservices. What challenges have you faced? What patterns have worked well for you? If you found this helpful, please share it with your network and leave a comment below – let’s continue the conversation!