NestJS: Using Faker.js to populate database (for testing)

Updated: February 14, 2024 By: Guest Contributor Post a comment


When building applications, testing with realistic data is crucial for spotting bugs and understanding how your application behaves under different circumstances. However, manually generating test data can be tedious and time-consuming. If you’re using NestJS for your application, integrating Faker.js is an excellent way to automate the creation of fake, but realistic, data for your database, making the testing process both efficient and comprehensive.

Why Use Faker.js?

Faker.js is a versatile library that allows developers to generate massive amounts of fake data for various purposes, including but not limited to testing, placeholders, and more. It supports a wide range of data types, from simple names and addresses to complex data structures. Using Faker.js with NestJS makes testing more manageable, especially during the development phase when you’re rapidly changing features and functionality.

Setting Up Your NestJS Project

Before integrating Faker.js, ensure your NestJS project is set up. If you haven’t created one, you can do so by running:

  npx @nestjs/cli new your-project-name 

This command scaffolds a new NestJS project. After setting up, you can proceed to integrate Faker.js into your project.

Installing Faker.js

Add Faker.js to your project by running:

  npm install --save faker 

Now, Faker.js is ready for use within your NestJS application.

Creating a Data Seeder Service

To effectively use Faker.js, create a service dedicated to seeding your database with fake data. This approach encapsulates the data generation logic, making your codebase clean and maintainable.

Here’s how you can create a data seeder service:

  export class SeederService {
    constructor(private readonly yourDataService: YourDataService) {}

    async seedData() {
      // Use Faker.js here to generate data

Within the seedData method, use Faker.js to generate and populate your database with realistic testing data.

Generating and Inserting Data

Now, it’s time to generate some data. Image we’re populating a user table, which requires names, emails, and addresses. Faker.js makes this simple:

  const userData = {
    address: faker.address.streetAddress()


This example creates a single user’s data. However, for testing, you likely need hundreds or thousands of records. In such cases, use a loop to generate the amount of data required:

  async function generateUsers(userCount: number): Promise {
    for(let i = 0; i < userCount; i++) {
      const userData = {
        address: faker.address.streetAddress()

      await this.yourDataService.create(userData);

This function generates and inserts a specified number of users into the database, massively speeding up the testing setup process.

Automating Data Generation

To further automate the process, consider setting up a script or a custom NestJS command that you can run every time you need fresh data. This strategy enhances efficiency, especially when working in a team or when regression testing is needed frequently.

Tips for Effective Data Seeding

  • When generating unique fields (e.g., email addresses), make sure to check for duplicates within your generated data set to avoid insertion errors.
  • Vary the data significantly to mimic real-world scenarios as closely as possible.
  • If your application involves dates (e.g., birthdates, appointment dates), use Faker.js to generate past or future dates within a realistic range.
  • Finally, be mindful of the volume of data you generate. Too much data might slow down your development environment. Generate just enough data to ensure your tests are comprehensive and realistic.


Integrating Faker.js with NestJS for database population offers a fast, efficient, and reliable means to generate test data, enhancing your application testing and development process. With the strategies discussed in this tutorial, you’re well-equipped to implement automated data generation in your NestJS projects, ensuring your tests are both thorough and efficient.