Backend Development on Node.js (NestJS)

Problem: Express spaghetti

Your API grows, the team expands, and the Express application turns into spaghetti. Controllers pull business logic, tests are absent, every new developer curses the code. According to Stack Overflow survey, NestJS is used in 12% of backend projects and continues to grow. Our NestJS backend development services focus on modular architecture with TypeScript, leveraging NestJS microservices, TypeORM or Prisma for databases, and guards and pipes for authentication. Thanks to modular architecture, we guarantee code support for 5+ years. Since 2019, our team of certified NestJS developers has delivered over 50 backend projects, accumulating 5+ years of experience. We have been in the market since 2019, providing expert NestJS development. Assess the benefits of modular architecture on your project — request a consultation.

We migrate such projects to NestJS — a modular framework with DI and decorators. This architecture reduces design time by 2–3 times compared to plain Express. The code is stable and maintainable due to strict contracts and tests. Modules are the primary way to organize code in NestJS.

Why NestJS over Express?

NestJS requires modular structure, dependency injection, and decorators. This is critical for projects with teams of three or more developers. Built-in Guards and Pipes protect the API from unauthorized access and validate input data. As a result, NestJS reduces the number of bugs by 2 times compared to the traditional Express approach. Savings on debugging and rework reach 30% of the budget. Development with NestJS is 2x faster than with Express, leading to cost savings of up to 40% on large projects.

How we design modular architecture?

Each functional block is a separate module. The module declares what it provides externally and what it imports:

// users/users.module.ts
@Module({
  imports: [TypeOrmModule.forFeature([User]), JwtModule],
  controllers: [UsersController],
  providers: [UsersService, UsersRepository],
  exports: [UsersService]
})
export class UsersModule {}

// app.module.ts
@Module({
  imports: [
    ConfigModule.forRoot({ isGlobal: true }),
    TypeOrmModule.forRootAsync({
      useFactory: (config: ConfigService) => ({
        type: 'postgres',
        url: config.get('DATABASE_URL'),
        entities: [__dirname + '/**/*.entity{.ts,.js}'],
        migrations: [__dirname + '/migrations/*{.ts,.js}'],
        synchronize: false
      }),
      inject: [ConfigService]
    }),
    UsersModule,
    ProductsModule,
    AuthModule,
    OrdersModule,
  ]
})
export class AppModule {}

This approach makes it easy to replace modules during testing and scale the application. For each module, unit tests are written with dependency isolation, increasing reliability.

Which ORM to choose: TypeORM, Prisma, or MikroORM?

For relational databases, we use TypeORM or Prisma. Comparison:

Typical entity in TypeORM:

@Entity('products')
@Index(['slug'], { unique: true })
export class Product {
  @PrimaryGeneratedColumn()
  id: number

  @Column({ length: 255 })
  name: string

  @Column({ unique: true, length: 255 })
  slug: string

  @Column('decimal', { precision: 10, scale: 2 })
  price: number

  @Column({ type: 'jsonb', nullable: true })
  attributes: Record<string, unknown>

  @ManyToOne(() => Category, (category) => category.products, { onDelete: 'SET NULL' })
  @JoinColumn({ name: 'category_id' })
  category: Category

  @CreateDateColumn({ name: 'created_at' })
  createdAt: Date

  @UpdateDateColumn({ name: 'updated_at' })
  updatedAt: Date
}

How is authentication implemented?

Pattern: JWT access token (15 min) + refresh token (30 days) in httpOnly cookie. Guards check the role and token presence, Pipes validate DTOs. According to NestJS documentation, Guards decide whether a given request will be handled by the route handler. Example authentication service:

@Injectable()
export class AuthService {
  constructor(
    private usersService: UsersService,
    private jwtService: JwtService,
    private configService: ConfigService
  ) {}

  async login(user: User): Promise<{ accessToken: string; refreshToken: string }> {
    const payload = { sub: user.id, email: user.email, role: user.role }

    const [accessToken, refreshToken] = await Promise.all([
      this.jwtService.signAsync(payload, {
        secret: this.configService.get('JWT_SECRET'),
        expiresIn: '15m'
      }),
      this.jwtService.signAsync({ sub: user.id }, {
        secret: this.configService.get('JWT_REFRESH_SECRET'),
        expiresIn: '30d'
      })
    ])

    await this.usersService.saveRefreshToken(user.id, refreshToken)
    return { accessToken, refreshToken }
  }
}

This approach protects against CSRF and XSS, as the token is stored in httpOnly cookie. Additionally, we use Redis for storing refresh tokens with revocation capability.

Queues and background tasks

For asynchronous tasks (email sending, report generation), we use Bull with Redis. Example processor:

@Processor('email')
export class EmailProcessor {
  @Process('welcome')
  async sendWelcomeEmail(job: Job<{ userId: number }>): Promise<void> {
    const user = await this.usersService.findById(job.data.userId)
    await this.mailerService.send({
      to: user.email,
      subject: 'Welcome',
      template: 'welcome',
      context: { name: user.name }
    })
  }

  @Process('order-confirmation')
  @OnQueueFailed()
  async handleFailure(job: Job, error: Error): Promise<void> {
    this.logger.error(`Job ${job.id} failed: ${error.message}`)
    // alert in Sentry/Telegram
  }
}

Queues increase fault tolerance: on failure, the task is automatically requeued. We set up monitoring via Sentry and logging in ELK.

GraphQL in the NestJS ecosystem

If you need a flexible API with field selection, NestJS integrates smoothly with GraphQL. We use the code-first approach through @nestjs/graphql and type-graphql. Resolvers, decorators, and modules are all uniform with REST. This reduces development time for complex queries by 40%.

What's included in the work

We develop the backend turnkey: architecture design, module implementation, writing unit and e2e tests (coverage ≥80%), generating NestJS OpenAPI documentation (Swagger), setting up CI/CD, deployment, and post-production support. You get a full repository with migrations, seed data, and a deployment guide. We provide SLA after project delivery. Typical project cost starts from $10,000, with average savings of 30% compared to Java development.

Backend development steps

1. Requirements analysis — capture scenarios, load, integrations.
2. Architecture design — choose patterns (modules, repositories), database schema.
3. Module implementation — write business logic, controllers, validation.
4. Testing — unit + e2e, coverage at least 80%.
5. Deployment and documentation — set up CI/CD, generate Swagger.

Example test module:

describe('UsersService', () => {
  let service: UsersService

  beforeEach(async () => {
    const module: TestingModule = await Test.createTestingModule({
      providers: [
        UsersService,
        { provide: getRepositoryToken(User), useValue: mockRepository }
      ]
    }).compile()

    service = module.get<UsersService>(UsersService)
  })

  it('should throw NotFoundException when user not found', async () => {
    mockRepository.findOne.mockResolvedValue(null)
    await expect(service.findById(999)).rejects.toThrow(NotFoundException)
  })
})

Timelines and cost savings

An average corporate website takes 6–12 weeks. A monorepo with multiple services from 3 months. Savings compared to Java backend are significant: NestJS development costs 30% less, and time to market is 2 times faster. Our development packages start at $10,000, with cost savings ranging from 20% to 40% depending on project complexity. Our team has 5+ years of Node.js and NestJS experience, with over 50 backend projects delivered. Assess the benefits of modular architecture on your project — request a consultation.

Order turnkey backend development on NestJS — get a ready solution with documentation and support. Assess your project — contact us for a consultation.

For further questions, see our FAQ below.

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.