TypeORM Setup for Your Web Application: Entities, Migrations, Repositories

When developing with Node.js and TypeScript, many face the same pain: manually writing SQL queries and synchronizing the schema with code. One mistake in an SQL query — and production goes down. TypeORM solves this problem, but its configuration requires accounting for a dozen nuances: from connection settings to migration generation. We will help you set up TypeORM for your project — from a simple server to a complex NestJS application. Our engineers have over 10 years of experience working with relational databases and guarantee the stability of your schema.

Why Standard TypeORM Setup Often Leads to Problems?

N+1 query — a classic problem when using ORM. Without optimization, loading 100 articles with authors will generate 101 queries. TypeORM makes it easy to set up eager loading via relations or use Query Builder for precise control. Another pain is synchronize: true in production: this leads to data loss when the schema changes. Proper migration configuration with diff file generation is the only safe way to update the database. The third issue is performance: unoptimized queries create extra load. TypeORM provides tools for monitoring and fine-tuning: subscribers for caching, connection pooling, slow query logging. Under a typical load of 10,000 requests per minute, a proper configuration reduces query count by 60%.

How We Configure TypeORM: A Case Study

From our practice: on a project with PostgreSQL and a load of 10,000 requests per minute, we faced performance degradation due to N+1 when loading related tags. Solution: we used Query Builder with leftJoinAndSelect and pagination via skip/take. Additionally, we configured a connection pool with max: 20 and idleTimeoutMillis: 30000, which reduced response time by 40%. Using Query Builder allowed executing a complex query 3 times faster compared to loading via relations. Proper connection pooling saved the client about 150,000 rubles per year on infrastructure. Order TypeORM setup — get a consultation from an engineer within an hour.

// db/data-source.ts
import 'reflect-metadata'
import { DataSource } from 'typeorm'
import { User } from './entities/User'
import { Post } from './entities/Post'

export const AppDataSource = new DataSource({
  type: 'postgres',
  url: process.env.DATABASE_URL,
  entities: [User, Post],
  migrations: ['dist/db/migrations/*.js'],
  migrationsTableName: 'migrations',
  synchronize: false,  // NEVER true in production
  logging: process.env.NODE_ENV === 'development' ? ['query', 'error'] : ['error'],
  ssl: process.env.NODE_ENV === 'production' ? { rejectUnauthorized: false } : false,
  extra: {
    max: 20,
    idleTimeoutMillis: 30000,
  }
})

// Initialization
await AppDataSource.initialize()

What Is Included in TypeORM Setup?

The work process consists of five stages:

1. Analysis — we examine the current database schema, load, and query profile.
2. Design — we choose a pattern (Active Record or Data Mapper), design entities and relationships.
3. Implementation — we configure the connection, write entities with decorators, create migrations and repositories.
4. Testing — we check performance, absence of N+1, and correctness of migrations.
5. Deployment and monitoring — we deploy to production, configure logging and alerts.

What is included in the result:

• DataSource configuration with pool and SSL (supports any database: PostgreSQL, MySQL, SQLite).
• Repositories and entities with indexes and relationships.
• Migrations (generation, running, rollback).
• Query Builder for typical queries.
• NestJS integration (module, services, controllers).
• Subscribers for caching and notifications.
• API and architecture documentation.
• 30-day warranty after delivery — we fix bugs for free.

Pattern Comparison and Entity Selection

Our engineers always recommend Data Mapper for projects with business logic, as it gives more control and simplifies testing. But if you need rapid prototyping — Active Record is also a working option. Proper entity selection is critical for performance. Use @PrimaryGeneratedColumn('uuid') for distributed systems, @Index() for frequently filtered fields. A repository is a layer between business logic and the database. Wrap all queries to an entity in a repository: this makes the code reusable and testable.

Example repository with pagination:

const postRepository = AppDataSource.getRepository(Post)

async function findPosts(opts: { page: number; limit: number; search?: string }) {
  const { page, limit, search } = opts
  const qb = postRepository.createQueryBuilder('post')
    .leftJoinAndSelect('post.author', 'author')
    .leftJoinAndSelect('post.tags', 'tag')
    .where('post.published = :published', { published: true })
    .orderBy('post.createdAt', 'DESC')
    .skip((page - 1) * limit)
    .take(limit)

  if (search) {
    qb.andWhere(
      'post.title ILIKE :search OR post.content ILIKE :search',
      { search: `%${search}%` }
    )
  }

  const [items, total] = await qb.getManyAndCount()
  return { items, total, pages: Math.ceil(total / limit) }
}

// Complex aggregates
const stats = await AppDataSource.query(`
  SELECT
    date_trunc('week', created_at) AS week,
    count(*) AS posts,
    count(*) FILTER (WHERE published = true) AS published
  FROM posts
  WHERE created_at >= now() - interval '90 days'
  GROUP BY 1
  ORDER BY 1
`)

Why Migrations Are Important for Production?

According to official TypeORM documentation, migrations are the only safe way to manage the schema in production. Migations allow versioning database schema changes and applying them sequentially. TypeORM can automatically generate migrations based on changes in entities. This saves up to 50% of time on developing migrations compared to writing SQL manually. Example of generation and application:

# Generate migration from schema diff
npx typeorm migration:generate -n AddUserProfile -d dist/db/data-source.js

# Create an empty migration manually
npx typeorm migration:create -n AddIndexes

# Apply
npx typeorm migration:run -d dist/db/data-source.js

# Revert the last one
npx typeorm migration:revert -d dist/db/data-source.js

// db/migrations/1234567890-AddUserProfile.ts
import { MigrationInterface, QueryRunner } from 'typeorm'

export class AddUserProfile1234567890 implements MigrationInterface {
  public async up(queryRunner: QueryRunner): Promise<void> {
    await queryRunner.query(`
      CREATE TABLE profiles (
        id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
        user_id UUID NOT NULL UNIQUE REFERENCES users(id) ON DELETE CASCADE,
        bio TEXT,
        avatar_url VARCHAR(500),
        updated_at TIMESTAMPTZ NOT NULL DEFAULT now()
      )
    `)
  }

  public async down(queryRunner: QueryRunner): Promise<void> {
    await queryRunner.query(`DROP TABLE profiles`)
  }
}

NestJS Integration and Event Subscribers

In NestJS, setting up TypeORM boils down to importing TypeOrmModule with configuration. Additionally, you can inject repositories via the @InjectRepository decorator. Subscribers allow reacting to entity changes: after insert, update search index; after publication, send a notification. This is a powerful tool if not overused. Example of NestJS setup:

// app.module.ts
import { TypeOrmModule } from '@nestjs/typeorm'

@Module({
  imports: [
    TypeOrmModule.forRootAsync({
      inject: [ConfigService],
      useFactory: (config: ConfigService) => ({
        type: 'postgres',
        url: config.get('DATABASE_URL'),
        entities: [__dirname + '/**/*.entity{.ts,.js}'],
        migrations: [__dirname + '/db/migrations/*{.ts,.js}'],
        migrationsRun: true,
        synchronize: false,
      })
    }),
    TypeOrmModule.forFeature([User, Post])
  ]
})
export class AppModule {}

Estimated Timeframes and Cost

Basic TypeORM setup with entities, migrations, and repositories: 1–2 days. Integration into a NestJS project with modules and tests: 2–3 days. Migrating an existing project from another ORM to TypeORM: 3–5 days. The cost of basic TypeORM configuration depends on project complexity. Contact us for a free estimate — it takes no more than an hour.

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.