The repository pattern is one of the most frequently misunderstood concepts in software design. Developers often implement it as a thin wrapper around their ORM, adding layers of abstraction that provide no real value while obscuring important details about how data flows through their systems.

The confusion stems from treating repositories as a way to hide the database. This misses the pattern's actual purpose entirely. A well-designed repository doesn't pretend the database doesn't exist—it models your domain's relationship with persistence in a way that keeps business logic clean and testable.

Understanding what repositories should and shouldn't abstract is the difference between a pattern that genuinely improves your codebase and one that just adds complexity. Let's examine how to apply this pattern in ways that deliver real architectural benefits.

The fundamental insight behind the repository pattern is deceptively simple: a repository represents a collection of domain objects, not a data access layer. This distinction changes everything about how you design the interface.

When you think of a repository as database abstraction, you end up with methods like ExecuteQuery or GetBySqlPredicate. These leak implementation details directly into your domain layer. Instead, a repository should feel like working with an in-memory collection that happens to be persisted somewhere.

Consider a CustomerRepository. Its interface should expose operations like Add, Remove, and FindById—operations you'd perform on any collection. The fact that these operations translate to SQL statements or API calls is an implementation detail hidden behind the interface.

This mental model has profound implications for your domain layer. Your business logic can treat persistence as a solved problem, focusing entirely on rules and behaviors. The repository contract guarantees that when you add a customer to the collection, they'll be there when you ask for them later. How that happens is none of the domain's concern.

Takeaway

Design repository interfaces as if you were modeling an in-memory collection of domain objects. If a method name references database concepts like queries, connections, or transactions, you're exposing implementation details that don't belong in the contract.

One of the fastest ways to ruin a repository is adding methods for every query your application needs. You start with FindById and FindByEmail, then add FindActiveByRegion, then FindInactiveCreatedBefore, and suddenly your repository has thirty methods and grows with every new feature.

This method explosion signals a design problem. The repository is trying to anticipate every possible way clients might want to filter the collection. Instead, consider separating what you're looking for from how the repository retrieves it.

The Specification pattern offers one solution. Rather than encoding query criteria in method names, you pass specification objects that describe the desired subset. The repository's job is simply to return objects matching that specification. This keeps the repository interface stable while allowing unlimited query flexibility.

Alternatively, some architectures benefit from exposing query capabilities through a separate read model entirely. Your repository handles writes and simple lookups for business operations, while a dedicated query service handles complex reporting and filtering needs. This separation acknowledges that read and write operations often have fundamentally different requirements.

Takeaway

When you find yourself adding multiple Find methods with specific filtering criteria, stop and consider specifications or query objects instead. A repository with more than a handful of methods is usually trying to do too much.

The ultimate test of your repository design is whether it enables fast, isolated unit tests for your domain logic. If testing business rules requires spinning up a database container or mocking dozens of ORM methods, your abstraction isn't providing the value it should.

A properly designed repository interface is trivially simple to fake. You can implement it with an in-memory dictionary that stores objects by ID, and your domain tests run in milliseconds without any infrastructure dependencies. This isn't about avoiding integration tests—it's about enabling rapid feedback during development.

Watch for repositories that force you to mock complex behavior in tests. If your fake repository needs to simulate transactions, lazy loading, or query translation, the interface is exposing too much of the underlying persistence mechanism. These details should be encapsulated in the real implementation, invisible to consumers.

The testing benefit extends beyond speed. When your domain tests work against simple in-memory fakes, they serve as living documentation of how your business logic behaves. Readers can understand the tests without knowing anything about your database schema or ORM configuration. This clarity is one of the most underappreciated advantages of clean repository design.

Takeaway

If you can't implement a working test fake for your repository using a simple dictionary or list, your interface is too complex. Simple interfaces enable simple tests, and simple tests enable confident refactoring.

The repository pattern succeeds when it creates a clear boundary between your domain's persistence needs and the technical details of how persistence actually works. It fails when it becomes either a leaky abstraction exposing database concepts or an ever-growing list of specialized query methods.

Focus on modeling collections of domain objects with stable, minimal interfaces. Push query complexity into specifications or separate read models. Let testing be your guide—if your tests are simple and fast, your design is probably sound.

A well-implemented repository makes your domain code more expressive, your tests more reliable, and your architecture more adaptable to change. That's the value this pattern offers when applied with intention rather than habit.