I’ve been thinking a lot about performance lately—how we can build systems that not only work correctly but scale gracefully under pressure. That’s what led me to explore this stack: NestJS for structure, TypeORM for data, GraphQL for flexible queries, and Redis for speed. It’s a combination that delivers serious results when implemented well.

Let me show you how I approach building a high-performance GraphQL API from the ground up. The setup begins with a solid foundation.

nest new blog-api
cd blog-api
npm install @nestjs/graphql typeorm pg redis ioredis dataloader

Configuration matters. Here’s how I structure the core module to tie everything together:

// app.module.ts
@Module({
  imports: [
    TypeOrmModule.forRoot({
      type: 'postgres',
      host: process.env.DB_HOST,
      // ... additional config
    }),
    CacheModule.register({
      store: redisStore,
      host: process.env.REDIS_HOST,
      ttl: 300,
    }),
    GraphQLModule.forRoot({
      autoSchemaFile: 'schema.gql',
      playground: true,
    }),
  ],
})
export class AppModule {}

Have you ever wondered how to prevent the N+1 query problem that plagues so many GraphQL implementations? DataLoader is your answer. It batches and caches requests automatically.

// users.loader.ts
@Injectable()
export class UsersLoader {
  constructor(private usersService: UsersService) {}

  createLoaders() {
    return {
      byId: new DataLoader(async (ids: string[]) => {
        const users = await this.usersService.findByIds(ids);
        return ids.map(id => users.find(user => user.id === id));
      }),
    };
  }
}

Now consider this: what happens when multiple users request the same data simultaneously? Without caching, your database gets hammered with identical queries. Redis solves this elegantly.

// posts.service.ts
@Injectable()
export class PostsService {
  constructor(
    @Inject(CACHE_MANAGER) private cacheManager: Cache,
    private postsRepository: Repository<Post>,
  ) {}

  async findOne(id: string): Promise<Post> {
    const cachedPost = await this.cacheManager.get<Post>(`post:${id}`);
    if (cachedPost) return cachedPost;

    const post = await this.postsRepository.findOne({ where: { id } });
    await this.cacheManager.set(`post:${id}`, post, 300);
    return post;
  }
}

Real-time updates transform user experience. GraphQL subscriptions make this straightforward:

// posts.resolver.ts
@Resolver(() => Post)
export class PostsResolver {
  @Subscription(() => Post)
  postPublished() {
    return pubSub.asyncIterator('POST_PUBLISHED');
  }

  @Mutation(() => Post)
  async publishPost(@Args('id') id: string) {
    const post = await this.postsService.publish(id);
    pubSub.publish('POST_PUBLISHED', { postPublished: post });
    return post;
  }
}

Performance optimization isn’t just about adding caching—it’s about smart resource management. Implementing query complexity analysis prevents abusive queries:

// complexity.plugin.ts
const complexityPlugin = {
  requestDidStart: () => ({
    didResolveOperation({ request, document }) {
      const complexity = getComplexity({
        query: request.operationName ? 
          getOperationAST(document, request.operationName) : 
          document,
        estimators: [
          fieldExtensionsEstimator(),
          simpleEstimator({ defaultComplexity: 1 }),
        ],
      });
      if (complexity > MAX_COMPLEXITY) {
        throw new Error(`Query too complex: ${complexity}`);
      }
    },
  }),
};

Error handling deserves careful attention. Structured logging helps diagnose issues quickly:

// global-exception.filter.ts
@Catch()
export class GlobalExceptionFilter implements ExceptionFilter {
  catch(exception: unknown, host: ArgumentsHost) {
    const ctx = host.switchToHttp();
    const response = ctx.getResponse();
    const request = ctx.getRequest();

    logger.error('Unhandled exception', {
      exception,
      url: request.url,
      timestamp: new Date().toISOString(),
    });

    // Return sanitized error response
  }
}

Testing might not be glamorous, but it’s essential. Here’s how I ensure data loaders work correctly:

// users.loader.spec.ts
describe('UsersLoader', () => {
  it('should batch multiple requests', async () => {
    const loaders = usersLoader.createLoaders();
    const [user1, user2] = await Promise.all([
      loaders.byId.load('1'),
      loaders.byId.load('2'),
    ]);
    expect(userService.findByIds).toHaveBeenCalledTimes(1);
  });
});

Deployment considerations often get overlooked. Environment-specific configuration ensures smooth transitions:

// config/configuration.ts
export default () => ({
  environment: process.env.NODE_ENV,
  database: {
    host: process.env.DB_HOST,
    port: parseInt(process.env.DB_PORT, 10),
  },
  redis: {
    host: process.env.REDIS_HOST,
    ttl: process.env.NODE_ENV === 'production' ? 600 : 300,
  },
});

Building something that performs well requires attention at every layer—from database queries to network calls. The patterns I’ve shared here have served me well in production environments, handling thousands of requests per second while maintaining response times under 100ms.

What aspects of your current API could benefit from these approaches? I’d love to hear about your experiences and challenges. If this resonated with you, please share it with others who might find it useful—and let me know in the comments what you’d like to see next.