Mocking gRPC Clients in C#: Fake It Till You Make It

About problem

In the process of developing any software product, one of the key stages is creating tests, as they help ensure the system operates correctly. An integral part of this process is dependency mocking, which allows for isolating the components being tested and minimizing the influence of external factors.

Mocking is an approach that involves creating simulations of system objects or components used during testing. Essentially, mocks are fake implementations of real objects. They are employed to isolate the code under test from its dependencies, whether these are databases, external APIs, or other services. Using mocks helps focus on testing the
logic of a specific component without being reliant on external systems, which might be unavailable, unstable, or complex to emulate.

With the advancement of technology and the shift towards microservice architectures, developers increasingly choose gRPC as a means of service-to-service communication due to its performance, scalability, and сonvenience.

gRPC (Google Remote Procedure Call) is a powerful framework for remote procedure calls (RPC) developed by Google. It enables efficient and fast connections between applications written in different programming languages by leveraging the HTTP/2 protocol and compact binary serialization through Protocol Buffers. This approach makes gRPC an ideal solution for modern distributed systems.

However, with the widespread adoption of gRPC, developers face a new challenge: integrating gRPC clients into tests. Since a gRPC client inevitably becomes one of the core dependencies of an application, it also becomes a critical part of the testing process—and, unfortunately, often introduces additional complexities. Simulating gRPC requests and their
handling requires the use of mocks or specialized tools, which may feel unfamiliar to those accustomed to more traditional REST APIs.

Therefore, a well-thought-out approach to mocking gRPC dependencies is a crucial step towards achieving comprehensive test coverage and ensuring the stability of the system as a whole.

In this article, we will focus on mocking gRPC clients, as the process of mocking the server-side (ServiceBase) does not require any specific actions. Typically, testing the server-side is similar to testing controllers in web applications and is already familiar to many developers.

gRPC clients, on the other hand, are more complex to test due to the nature of their interactions with the server. This process requires modeling various call scenarios, each differing in how they handle data streams. Let's review the main scenarios:

1. UnaryCall - A standard request-response call. The client sends a request to the server and receives a single response. This scenario is the most common and resembles traditional REST calls.
2. ClientSideStreaming - A call where the client sends a stream of data to the server. The stream is built progressively, and data is not sent all at once, as is the case with UnaryCall.
3. ServiceSideStreaming - A call where the client sends a single request to the server and receives a stream of data in response. The server's response is delivered in parts rather than as a single package, as in UnaryCall.
4. DuplexStreaming - The most complex scenario, where the client and server simultaneously exchange streams of data. This requires implementing bidirectional communication, making both development and testing more challenging.

Each of these scenarios introduces unique challenges to the process of mocking and testing, making the gRPC client a more complex object to work with compared to traditional REST clients.

Options for Mocking gRPC Clients

1. Moq - One of the most popular mocking libraries in the .NET ecosystem. It allows you to emulate gRPC client methods, define return values, and verify method calls.
2. Grpc.Core.Testing - A library specifically designed for testing gRPC clients. It provides helper classes like MockCall to simplify mock setup.
3. Grpc.Net.Testing.Moq - An extension of the Moq library optimized for Grpc.Net.Client. It is a convenient tool that helps minimize the amount of test code required.
4. Grpc.Net.Testing.NSubstitute - An alternative to the previous option, built on the NSubstitute library. It is ideal for those who prefer its syntax for mocking.

Each of these approaches has its strengths and unique features. Depending on the tools and requirements of your project, you can select the most suitable method for creating gRPC client mocks. In the following sections, we will delve deeper into each option to help you decide which one best meets your needs.

In our testing, we will not consider cases where values are precomputed outside the delegate passed to the Returns<> method.

Let's prepare a sample client

First, let's create a small sample client that will serve as the foundation for the mocking process. This client will be simple yet functional enough to demonstrate the key principles and approaches to testing with mocks.

The client will act as a starting point for developing and testing various interaction scenarios.

syntax = "proto3";

package tests;

service TestService {
  rpc Simple(TestRequest) returns(TestResponse);
  rpc SimpleClientStream(stream TestRequest) returns(TestResponse);
  rpc SimpleServerStream(TestRequest) returns(stream TestResponse);
  rpc SimpleClientServerStream(stream TestRequest) returns(stream TestResponse);
}

message TestRequest {
  int32 val = 1;
}
message TestResponse {
  int32 val = 1;
}

The created service includes four methods, each demonstrating different interaction scenarios typical for gRPC. These methods serve as examples for exploring key service operation models and testing their functionality. Here's a description of each:

1. Simple - UnaryCall
   
   This method implements a standard request-response interaction. It accepts data from the client and returns an identical response. It's a straightforward example, perfect for demonstrating the basic principles of gRPC.

2. SimpleClientStream - ClientSideStreaming
   
   This method is designed to handle a stream of data sent by the client. Its task is to calculate the sum of all transmitted values and return it as the result. This illustrates a model where the client sends data incrementally, and the server responds with an aggregated result.

3. SimpleServerStream - ServiceSideStreaming
   
   This method demonstrates a model where the server returns a stream of data in response to a single client request. The method generates and sends N messages to the client based on the parameters of the request. This approach is often used for transmitting large volumes of data.

4. SimpleClientServerStream - DuplexStreaming
   
   The most complex and powerful interaction scenario. This method accepts a stream of data from the client, sums it up, and sends the result back as N messages. This example showcases bidirectional interaction, where the client and server exchange data simultaneously.

Simply use Moq

For this implementation, we will exclusively use the Moq library, which is one of the most popular and powerful tools in the .NET ecosystem for creating mocks.

UnaryCall

One of the simplest and most intuitive methods for mocking is the UnaryCall scenario. Its essence lies in the classic "request-response" interaction model, where the client sends a request to the server and expects a single response.

This approach forms the foundation for many systems and scenarios, making it an excellent starting point for learning the mocking process. Its simplicity allows you to focus on the key aspects of testing without having to deal with the complexities of stream-based interactions.

In this example, we will demonstrate how to use Moq to emulate the behavior of a UnaryCall method and configure it to correctly process requests and return the expected results.

[Fact]
public void SimpleTest()
{
    // Arrange
    var mock = new Mock<TestService.TestServiceClient>(MockBehavior.Strict);

    mock
        .Setup(c => c.Simple(It.IsAny<TestRequest>(), It.IsAny<Metadata>(), It.IsAny<DateTime?>(), It.IsAny<CancellationToken>()))
        .Returns<TestRequest, Metadata, DateTime?, CancellationToken>((r, _, _, _) => new TestResponse { Val = r.Val });

    var client = mock.Object;

    var testRequest = new TestRequest { Val = 42 };

    // Act
    var response = client.Simple(testRequest);

    // Assert
    response.Val.Should().Be(testRequest.Val);
}

[Fact]
public async Task SimpleAsyncTest()
{
    // Arrange
    var mock = new Mock<TestService.TestServiceClient>(MockBehavior.Strict);

    mock
        .Setup(c => c.SimpleAsync(It.IsAny<TestRequest>(), It.IsAny<Metadata>(), It.IsAny<DateTime?>(), It.IsAny<CancellationToken>()))
        .Returns<TestRequest, Metadata, DateTime?, CancellationToken>(
            (r, _, _, _) => new AsyncUnaryCall<TestResponse>(
                responseAsync: Task.FromResult(new TestResponse { Val = r.Val }),
                responseHeadersAsync: Task.FromResult(new Metadata()),
                getStatusFunc: () => Status.DefaultSuccess,
                getTrailersFunc: () => [],
                disposeAction: () => { }));

    var client = mock.Object;

    // Act
    var request = new TestRequest { Val = 42 };
    var response = await client.SimpleAsync(request);

    // Assert
    response.Val.Should().Be(request.Val);
}

Even a seemingly simple scenario, like mocking a UnaryCall in its asynchronous version, can feel somewhat challenging due to the need to work with AsyncUnaryCall. This class requires specifying multiple parameters, such as tasks for the response, headers, status, and metadata, which significantly increases the workload.

If you aim to implement more complex scenarios, for example, configuring the method's return value based on the parameters of the incoming Request, the task becomes even more complicated. In such cases, you need to dynamically create instances of AsyncUnaryCall<TestResponse>, which adds more code and makes the testing infrastructure more complex.

This can lead to additional time and resource costs for maintaining the tests, especially as the number of such methods grows or if they involve intricate data processing logic.

ClientSideStreaming

This scenario is more complex as it requires handling a stream of incoming messages. Unlike a simple "request-response" call, you need to account for the sequence of data sent by the client. This inevitably makes the mocking code more extensive and harder to comprehend.

In this example, we will emulate a service behavior where all incoming parameters are processed and summed, with the resulting sum returned as the response. This approach requires configuring the mocks to handle each message in the stream correctly, which adds an extra layer of complexity to the implementation of the tests.

To execute this task successfully, it is crucial not only to set up the mock properly but also to ensure that it accurately models the real service's behavior, particularly in the context of streaming data. This will help not only to validate the application's logic but also to uncover potential issues in data processing early in the development cycle.

[Fact]
public async Task SimpleClientStreamTest()
{
    // Arrange
    var mock = new Mock<TestService.TestServiceClient>(MockBehavior.Strict);

    var sum = 0;

    var taskCompletionSource = new TaskCompletionSource<TestResponse>();

    var streamWriter = new Mock<IClientStreamWriter<TestRequest>>(MockBehavior.Strict);
    streamWriter
        .Setup(c => c.WriteAsync(It.IsAny<TestRequest>()))
        .Callback<TestRequest>(m => sum += m.Val)
        .Returns(Task.CompletedTask);
    streamWriter
        .Setup(c => c.CompleteAsync())
        .Callback(() => taskCompletionSource.SetResult(new TestResponse { Val = sum }))
        .Returns(Task.CompletedTask);

    var asyncClientStreamingCall = new AsyncClientStreamingCall<TestRequest, TestResponse>(
        requestStream: streamWriter.Object,
        responseAsync: taskCompletionSource.Task,
        responseHeadersAsync: Task.FromResult(new Metadata()),
        getStatusFunc: () => Status.DefaultSuccess,
        getTrailersFunc: () => [],
        disposeAction: () => { });

    mock
        .Setup(c => c.SimpleClientStream(It.IsAny<Metadata>(), It.IsAny<DateTime?>(), It.IsAny<CancellationToken>()))
        .Returns(asyncClientStreamingCall);

    var client = mock.Object;

    var requests = Enumerable
        .Range(1, 10)
        .Select(i => new TestRequest { Val = i })
        .ToArray();

    // Act
    var call = client.SimpleClientStream();
    await call.RequestStream.WriteAllAsync(requests);

    // Assert
    var response = await call.ResponseAsync;

    var expected = requests.Sum(c => c.Val);

    expected.Should().Be(response.Val);
}

In this scenario, there arises a need to explicitly define a TaskCompletionSource, which allows deferring the execution of the response calculation until a specific moment. This approach enables delivering the result to the client only after explicitly calling the CompleteAsync method, making it useful for modeling asynchronous behavior.

However, such an implementation significantly complicates the code and increases its size, which can be problematic for large projects or when frequent test updates are required. These additional steps demand more time and resources and also increase the likelihood of errors in the test logic.

ServiceSideStreaming

This example examines a scenario where the client sends a single request containing a number, based on which the server generates and returns a stream of N responses, each including that number.

This approach is commonly used to implement server-side streaming, where the server gradually sends data to the client in response to a single request. It can be useful in cases where large amounts of data need to be transmitted, but not all at once — for instance, when delivering data in batches, updates, or processing results.

When mocking this scenario, it is essential to properly configure the method's behavior to return a data stream that accurately models the real service's functionality. This involves generating a sequence of responses based on the input value and setting up asynchronous behavior to simulate incremental data delivery.

Such tests help ensure that the client logic correctly processes streaming responses and can handle delays, sequential processing, and other aspects of working with data streams effectively.

[Fact]
public async Task SimpleServerStreamTest()
{
    // Arrange
    var mock = new Mock<TestService.TestServiceClient>(MockBehavior.Strict);

    mock
        .Setup(
            c => c.SimpleServerStream(
                It.IsAny<TestRequest>(),
                It.IsAny<Metadata>(),
                It.IsAny<DateTime?>(),
                It.IsAny<CancellationToken>()))
        .Returns<TestRequest, Metadata, DateTime?, CancellationToken>(
            (r, _, _, _) =>
            {
                var reader = new Mock<IAsyncStreamReader<TestResponse>>(MockBehavior.Strict);

                var current = reader.SetupSequence(c => c.Current);
                var moveNext = reader.SetupSequence(c => c.MoveNext(It.IsAny<CancellationToken>()));

                for (var i = 0; i < r.Val; i++)
                {
                    moveNext = moveNext.ReturnsAsync(true);
                    current = current.Returns(new TestResponse { Val = r.Val });
                }

                moveNext = moveNext.ReturnsAsync(false);

                return new AsyncServerStreamingCall<TestResponse>(
                    responseStream: reader.Object,
                    responseHeadersAsync: Task.FromResult(new Metadata()),
                    getStatusFunc: () => Status.DefaultSuccess,
                    getTrailersFunc: () => [],
                    disposeAction: () => { });
            });

    var client = mock.Object;

    var testRequest = new TestRequest { Val = 42 };

    // Act
    var call = client.SimpleServerStream(testRequest);

    // Assert
    var responses = await call.ResponseStream
        .ReadAllAsync()
        .ToArrayAsync();

    responses.Should()
        .HaveCount(testRequest.Val).And
        .AllSatisfy(c => c.Val.Should().Be(testRequest.Val));
}

As evident from this scenario, testing server-side streaming requires defining a complex delegate that involves detailed configuration of the Mock<IAsyncStreamReader<>> object. This object plays a crucial role as it handles the sequential delivery of stream data, making its correct configuration essential.

While it is possible to move the setup of Mock<IAsyncStreamReader<>> outside the delegate to improve code readability and structure, this also allows linking the delegate's logic to an external context, making it more manageable. However, even with such an approach, it is not feasible to completely avoid explicitly defining AsyncServerStreamingCall<TestResponse> and creating a mock for IAsyncStreamReader<TestResponse>. These steps are integral to mocking a server-side stream and enable the emulation of asynchronous behavior.

Thus, although this approach is complex to implement, it remains practically the only way to model intricate streaming scenarios in gRPC. That said, creating helper methods for mock configuration or leveraging specialized libraries can significantly simplify the process and reduce the amount of code required, especially when such scenarios frequently appear in tests.

DuplexStreaming

This example combines elements from the two previous scenarios and illustrates the process of mocking more complex logic. In this case, the server receives a stream of messages from the client, processes them, and then returns a stream of N messages, where each message contains the sum of all values sent by the client.

This scenario represents a combination of client-side streaming and server-side streaming, making it one of the most complex types of interactions in gRPC. It requires simultaneous handling of the asynchronous incoming data stream and the generation of an asynchronous outgoing response stream.

When mocking this interaction, it is crucial to address two key aspects:

1. Emulating the input stream: Configuring the behavior of the Mock<IClientStreamWriter<>> object or a similar structure to handle data sent by the client.
2. Generating the output stream: Using AsyncServerStreamingCall<> to create a sequence of responses that accurately models the real behavior of the service.

This example highlights the complexity of mocking streaming interactions in gRPC while also demonstrating the importance of precisely configuring mock behavior to simulate real-world scenarios effectively.

[Fact]
public async Task SimpleClientServerStreamTest()
{
    // Arrange
    var mock = new Mock<TestService.TestServiceClient>(MockBehavior.Strict);

    mock
        .Setup(c => c.SimpleClientServerStream(It.IsAny<Metadata>(), It.IsAny<DateTime?>(), It.IsAny<CancellationToken>()))
        .Returns<Metadata, DateTime?, CancellationToken>(
            (_, _, _) =>
            {
                var requestStream = new Mock<IClientStreamWriter<TestRequest>>(MockBehavior.Strict);
                var responseStream = new Mock<IAsyncStreamReader<TestResponse>>(MockBehavior.Strict);

                var requests = new List<TestRequest>();
                var responses = new List<TestResponse>();

                // Request handling: Collect all incoming messages
                requestStream
                    .Setup(c => c.WriteAsync(It.IsAny<TestRequest>()))
                    .Callback<TestRequest>(r => requests.Add(r))
                    .Returns(Task.CompletedTask);

                requestStream
                    .Setup(c => c.CompleteAsync())
                    .Callback(
                        () =>
                        {
                            // Create responses based on the requests
                            var sum = requests.Sum(r => r.Val);
                            responses.AddRange(Enumerable.Repeat(new TestResponse { Val = sum }, requests.Count));
                        })
                    .Returns(Task.CompletedTask);

                // Response stream setup
                var index = -1;

                responseStream
                    .Setup(c => c.MoveNext(It.IsAny<CancellationToken>()))
                    .Returns(() => Task.FromResult(++index < responses.Count));

                responseStream
                    .SetupGet(c => c.Current)
                    .Returns(() => responses[index]);

                return new AsyncDuplexStreamingCall<TestRequest, TestResponse>(
                    requestStream: requestStream.Object,
                    responseStream: responseStream.Object,
                    responseHeadersAsync: Task.FromResult(new Metadata()),
                    getStatusFunc: () => Status.DefaultSuccess,
                    getTrailersFunc: () => [],
                    disposeAction: () => { });
            });

    var client = mock.Object;

    var testRequests = Enumerable
        .Range(start: 1, count: 42)
        .Select(_ => new TestRequest { Val = 1 })
        .ToArray();

    // Act
    var call = client.SimpleClientServerStream();

    await call.RequestStream.WriteAllAsync(testRequests);

    // Assert
    var responses = await call.ResponseStream
        .ReadAllAsync()
        .ToArrayAsync();

    var expected = testRequests.Sum(c => c.Val);

    responses.Should()
        .HaveCount(testRequests.Length).And
        .AllSatisfy(c => c.Val.Should().Be(expected));
}

As evident from the example, implementing bidirectional streaming requires writing a significant amount of auxiliary code. To fully emulate stream behavior, additional state variables must be introduced to manually implement a state machine that manages the sequential return of elements in the style of IEnumerable<>.

This approach significantly complicates the code, making maintenance more challenging, especially when the mock needs to be adapted for new scenarios. For instance, if instead of accumulating messages, sequential processing of each element is required, or if new stream processing logic needs to be added, the existing code would demand substantial modifications.

This complexity and the high maintenance cost highlight the need for more elegant solutions, such as specialized libraries or utility tools that can automate part of the work and simplify the implementation of complex streaming interactions.

Conclusion

In my opinion, calling this approach convenient is quite difficult. While it works well for mocking simple endpoints like "request-response" (UnaryCall), where everything looks relatively compact and maintainable, the situation becomes significantly more complicated when it comes to streaming endpoints—be it client-side, server-side, or bidirectional
streaming. Testing such scenarios requires creating and maintaining a considerable amount of auxiliary code, including classes implementing IAsyncStreamReader<> and complex state management logic.

This becomes particularly problematic in projects where mocking such endpoints is frequently needed or where diverse stream processing scenarios must be tested. In these cases, the amount of required code grows significantly, and its maintenance can become a real headache.

If using third-party libraries to simplify the mocking of gRPC clients is not an option, I strongly recommend encapsulating the client logic into a separate class, such as class MyClient : IClient. This approach allows you to create an abstract interface that is decoupled from the classes provided by the Grpc library.

This not only simplifies testing but also makes the code more flexible and easier to maintain. Instead of directly working with gRPC clients, tests can operate on your custom abstraction, allowing it to be easily replaced with a mock or another implementation without embedding complex mocking logic directly into the tests. This approach also improves code readability and makes future extensions easier to implement.

Grpc.Core.Testing

This is a deprecated library that includes the TestCalls class, which provides helper methods for generating gRPC calls, with one method for each call type. In earlier versions, the library handled the proper initialization of additional parameters. However, in its current state, it serves only as a wrapper around constructors.

The Grpc.Core.Testing library is a deprecated tool that was initially created to simplify the testing of gRPC applications. It offers the TestCalls class, which includes a set of helper methods for generating various types of gRPC calls. Specifically, the library provides methods for the main call scenarios: UnaryCall, ClientStreamingCall, ServerStreamingCall, and DuplexStreamingCall.

In the library's earlier stages, it took responsibility for correctly initializing all the additional parameters required for gRPC operations. This significantly eased the developers' workload, reducing the complexity of writing tests and enabling them to focus on testing application logic more quickly.

However, as gRPC evolved and newer versions were introduced, the library's functionality gradually became limited to serving as a simple wrapper around gRPC object constructors. Instead of fully automating the initialization process, it now provides only basic templates that require manual adjustments. This has reduced its usefulness for modern projects,
where developers are still required to handle parameter configuration themselves.

For example, implementing a UnaryCall using the library would look like this:

[Fact]
public async Task SimpleAsyncDelegateTest()
{
    // Arrange
    var mock = new Mock<TestService.TestServiceClient>(MockBehavior.Strict);

    mock
        .Setup(c => c.SimpleAsync(It.IsAny<TestRequest>(), It.IsAny<Metadata>(), It.IsAny<DateTime?>(), It.IsAny<CancellationToken>()))
        .Returns<TestRequest, Metadata, DateTime?, CancellationToken>(
            (r, _, _, _) =>  TestCalls.AsyncUnaryCall(
                responseAsync: Task.FromResult(new TestResponse { Val = r.Val }),
                responseHeadersAsync: Task.FromResult(new Metadata()),
                getStatusFunc: () => Status.DefaultSuccess,
                getTrailersFunc: () => [],
                disposeAction: () => { }));

    var client = mock.Object;

    // Act
    var request = new TestRequest { Val = 42 };
    var response = await client.SimpleAsync(request);

    // Assert
    response.Val.Should().Be(request.Val);
}

In my opinion, using this library in modern projects is hardly justified. The main reason is its deprecated status and limited functionality, which no longer offers meaningful simplification for writing tests.

When the library first appeared, it was useful for automating the initialization of complex structures and parameters required for testing gRPC calls. However, over time, its capabilities have become restricted, and it is now essentially a simple wrapper around constructors, failing to address real development challenges.

For projects that require flexibility, convenience, and minimal boilerplate code, using this library tends to add unnecessary complexity rather than reduce it. Instead, I recommend focusing on more modern and relevant tools that provide a user-friendly API and significantly simplify the process of mocking gRPC calls.

Grpc.Net.Testing.Moq

Description

Grpc.Net.Testing.Moq is a library designed to eliminate the excessive boilerplate code that inevitably arises when using other tools for mocking gRPC. Its main advantage lies in its simplicity and ease of use, achieved through integration with the popular Moq library.

Instead of requiring manual setup of complex structures and classes characteristic of gRPC, Grpc.Net.Testing.Moq offers an intuitive approach to mocking. It abstracts away low-level details, allowing developers to focus on testing business logic while minimizing the time spent configuring mocks.

The library provides convenient tools for managing gRPC call mocking, simplifying their use even in complex scenarios, such as data streaming or bidirectional calls. As a result, developers can significantly reduce the amount of auxiliary code and improve the readability of their tests, making it an excellent choice for modern projects.

Using

Let’s review the same scenarios we discussed earlier for the Moq library. Using Grpc.Net.Testing.Moq, the mocking process becomes significantly simpler and requires less boilerplate code while maintaining flexibility and ease of configuration.

// UnaryCall

mock
    .Setup(c => c.SimpleAsync(It.IsAny<TestRequest>(), It.IsAny<Metadata>(), It.IsAny<DateTime?>(), It.IsAny<CancellationToken>()))
    .ReturnsAsync<TestService.TestServiceClient, TestRequest, TestResponse>(r => new TestResponse { Val = r.Val });

// ClientSideStreaming

mock
    .Setup(c => c.SimpleClientStream(It.IsAny<Metadata>(), It.IsAny<DateTime?>(), It.IsAny<CancellationToken>()))
    .ReturnsAsync(r => new TestResponse { Val = r.Sum(c => c.Val) });

// ServerSideStreaming

mock
    .Setup(c => c.SimpleServerStream(It.IsAny<TestRequest>(), It.IsAny<Metadata>(), It.IsAny<DateTime?>(), It.IsAny<CancellationToken>()))
    .ReturnsAsync<TestService.TestServiceClient, TestRequest, TestResponse>(r => Enumerable.Range(1, r.Val).Select(_ => new TestResponse { Val = r.Val }));

// DuplexStreaming

mock
    .Setup(c => c.SimpleClientServerStream(It.IsAny<Metadata>(), It.IsAny<DateTime?>(), It.IsAny<CancellationToken>()))
    .ReturnsAsync(
        r =>
        {
            var sum = r.Sum(c => c.Val);
            return Enumerable.Range(1, sum).Select(_ => new TestResponse { Val = sum });
        });

From the provided code, the key advantage of Grpc.Net.Testing.Moq becomes immediately clear: the minimal amount of code required to configure client mocking. The library significantly simplifies the process and eliminates the need for cumbersome constructs typical of manual approaches or other tools.

Main advantages

1. Flexibility in mocking configuration - The library supports multiple ways to configure client behavior.
2. Particularly beneficial for streaming - The most significant improvement is felt when working with streaming methods. Thanks to the library, there is no need to create custom wrappers or implement complex logic to manage incoming and outgoing data streams. This allows developers to focus on testing business logic rather than low-level stream configuration.
3. Readability and ease of maintenance - Code written using Grpc.Net.Testing.Moq becomes noticeably simpler and easier to understand. This is especially important in large projects where tests can be complex and require frequent updates.

The Grpc.Net.Testing.Moq library clearly excels in convenience and ease of use, particularly in the context of streaming methods. It not only minimizes boilerplate code but also makes the mocking process more intuitive and efficient.

Useful links

1. Grpc.Net.Testing.NSubstitute - implementation for NSubstitute
2. Grpc.Net.Testing.Moq

Conclusion

Based on the review of various solutions for mocking gRPC clients, we can summarize the findings in a comparison table that highlights their features, advantages, and limitations. This format makes it easier to understand which solution is best suited for a specific project or task.

The comparison of solutions for mocking gRPC clients shows that the choice of tool depends on the specific requirements of the project, the complexity of the scenarios, and the need to support modern development standards.

1. If a project involves simple request-response scenarios (UnaryCall) and requires maximum control over mock configuration, the standard approach using Moq remains a reliable option. However, its complexity and the amount of boilerplate code make it less suitable for complex streaming scenarios.

2. For legacy systems or supporting projects on older versions of gRPC, the Grpc.Core.Testing library can be considered. Nevertheless, due to its deprecated status and limited capabilities, its use is justified only when no other alternatives are available.

3. For modern projects, especially those heavily utilizing streaming calls, the optimal solution is Grpc.Net.Testing.Moq or Grpc.Net.Testing.NSubstitute. These tools offer minimal boilerplate code, flexibility in mock configuration, and significant simplification of working with streams. They are particularly useful for accelerating development and improving test readability.

Mastering the mocking of gRPC clients is a key step toward building more robust and testable applications. I’ve outlined several approaches, highlighting the advantages and limitations of each tool. Try applying the methods described and choose the one that best aligns with your goals and requirements. Happy coding!