Welcome back! In the last lesson, you learned how to use Zod to validate data in your Next.js backend. Now, let’s talk about another important part of building reliable backend systems: how you store and access your data.

When you build an app, you often need to save, update, or delete data. If you mix this data storage code directly with your business logic (the code that handles your app’s main features), things can get messy and hard to change later. For example, if you decide to switch from storing data in memory to using a database, you might have to rewrite a lot of your code.

The Repository Pattern is a way to solve this problem. It helps you separate the code that deals with data storage from the rest of your app. This makes your code easier to manage, test, and update in the future.

Before implementing repositories, let’s look at what we're abstracting:

This type defines the shape of every task in our app. As your project grows, you'll often want to add filtering, search, or persistence rules. The repository will give you a way to define those rules without repeating logic or polluting your route and service files.

Instead of scattering logic about how to create, fetch, or update tasks all over your codebase, we’ll centralize it in a single, reusable abstraction — the TaskRepository.

The Repository Pattern is a design pattern that acts as a middleman between your app and your data storage. Instead of your app talking directly to a database or in-memory store, it talks to a repository. The repository knows how to get, save, update, and delete data.

Real-world analogy:
Think of a repository like a library’s front desk. If you want a book, you don’t go into the storage room yourself. You ask the librarian (the repository), and they know how to find the book for you. If the library changes how it stores books, you don’t need to learn the new system — the librarian handles it.

Benefits of the Repository Pattern:

• Keeps your business logic clean and focused
• Makes it easy to swap out how and where you store data
• Helps with testing, since you can use fake data stores

Let’s see how we can use the Repository Pattern in our task app.

First, we define an interface for our repository. This is like a contract that specifies what methods any task repository must have:

Explanation:

• getAll() returns all tasks.
• getById(id) gets a single task by its ID.
• create(data) adds a new task (without an ID, since the repository will assign one).
• update(id, updates) changes an existing task.
• delete(id) removes a task.
• filterByCompletion(status) finds tasks by their completion status.

This abstraction allows you to swap out different implementations — such as a file-based repository, a database-backed one, or even a mock in-memory version for testing — without changing the consuming code. As long as the new repository adheres to this interface, everything else in the app (like the service layer) will work without modification. This is a key software engineering principle called programming to an interface, which promotes loose coupling and high flexibility.

Now, let’s implement this interface using an in-memory store (a simple Map). This is useful for development and testing:

Now, let’s see how our app uses the repository. Instead of working with the data store directly, our service functions call the repository methods:

Explanation:

• The service functions act as a bridge between your app’s business logic and the repository.
• If you ever want to change how tasks are stored (for example, switch to a database), you only need to update the repository, not the service functions or the rest of your app.

To visualize how this works, imagine this call chain:

Here’s a simplified example:

That ultimately calls:

Which calls:

So even though your route never touches the Map, it gets all the benefits.

This layered structure mirrors a clean architecture approach. Each layer has a clear responsibility:

• Route Handlers: handle requests and responses
• Service Layer: hold business logic
• Repository Layer: abstract data persistence

Keeping these responsibilities isolated helps your code scale as your app grows more complex.

💡 Coming soon: Later in this course, you’ll replace mapTaskRepository with a real file-based implementation. Thanks to the Repository Pattern, this will be a one-line change in your service layer — everything else will keep working as-is.

In this lesson, you learned how to use the Repository Pattern to keep your data storage code separate from your business logic. You saw how to define a repository interface, implement it with an in-memory store, and connect it to your service layer. This makes your code easier to manage and ready for future changes, like switching to a real database.

Next, you’ll get to practice these concepts by working with repositories in your own code. This hands-on experience will help you see the benefits of the Repository Pattern in action. Good luck, and let’s keep building!