Microservices architecture has emerged as the go-to method for creating scalable and maintainable systems as contemporary applications continue to expand in size and complexity. Teams now divide functionality into smaller, independently deployable services rather than implementing a single, huge application. Method calls within the same process facilitate quick and easy communication in a monolithic program. Because microservices communicate over a network, there are issues with latency, partial failures, versioning, and security.
In this post, I'll examine the most popular microservices communication techniques, discuss when and why to utilize them, and show how to implement them in.NET 10.
Recognizing Communication Styles in Microservices
Two basic communication types are commonly used by microservices:
1. Concurrent Communication
The calling service awaits a prompt answer from another provider. Examples include gRPC and REST APIs.
2. Communication That Is Asynchronous
After sending a message or event, the calling service proceeds with processing without waiting for a reply. Examples include message queues and platforms for streaming events.
Depending on performance requirements and business needs, the majority of production systems combine the two.
1. RESTful APIs (HTTP-Based Communication)
REST remains the most common and accessible communication mechanism in microservices. It relies on standard HTTP methods and usually exchanges data in JSON format.
Despite newer alternatives, REST continues to be relevant due to its simplicity, tooling support, and compatibility with browsers and external clients.
When REST Is the Right Choice
- Client-facing or public APIs
- Simple request–response workflows
- Scenarios where readability and debuggability matter
- Integration with third-party systems
RestAPI
Example: Product Microservice Using ASP.NET Core (.NET 10)
[ApiController]
[Route("api/products")]
public class ProductController : ControllerBase
{
[HttpGet]
public IActionResult GetProducts()
{
return Ok(new[] { "Laptop", "Tablet", "Mobile" });
}
[HttpPost]
public IActionResult CreateProduct(Product product)
{
return Ok(product);
}
}
Why REST Still Works Well?
- Easy to understand and maintain
- Mature ecosystem (Swagger, OpenAPI, Postman)
- Language and platform independent
Limitations to Be Aware Of
- JSON serialization increases payload size
- Higher latency for internal service-to-service calls
- Tight coupling due to synchronous request–response flow
2. gRPC – High-Performance Internal Communication
gRPC is designed for efficient, low-latency communication between internal services. It uses Protocol Buffers (Protobuf) for binary serialization and runs over HTTP/2, making it significantly faster than REST.
With .NET 10, gRPC continues to be a first-class citizen, especially for service-to-service communication inside a controlled environment.
Ideal Use Cases for gRPC
- Internal microservice communication
- High-throughput systems
- Real-time or streaming scenarios
- Strict API contract enforcement
Define the Service Contract (greet.proto)
syntax = "proto3";
option csharp_namespace = "GrpcService1";
package greet;
// The greeting service definition.
service Greeter {
// Sends a greeting
rpc SayHello (HelloRequest) returns (HelloReply);
}
Implement gRPC Service in .NET 10
namespace GrpcService1.Services
{
public class GreeterService(ILogger<GreeterService> logger) : Greeter.GreeterBase
{
public override Task<HelloReply> SayHello(HelloRequest request, ServerCallContext context)
{
logger.LogInformation("The message is received from {Name}", request.Name);
return Task.FromResult(new HelloReply
{
Message = "Hello " + request.Name
});
}
}
}
Key Benefits
- Very fast serialization and transport
- Strongly typed, contract-first design
- Built-in support for streaming
Trade-Offs
- Payloads are not human-readable
- Requires tooling for debugging
- Limited direct browser support
3. Message Queues – Asynchronous and Event-Driven Communication
Message queues enable asynchronous communication, allowing services to exchange messages without knowing about each other’s availability or location. This approach is fundamental to event-driven architectures.
Technologies like RabbitMQ, Apache Kafka, and cloud-based queues are commonly used with .NET microservices.
When Message Queues Are the Best Fit?
- Background processing
- Event publishing (e.g., OrderCreated)
- Loose coupling between services
- High resilience and fault tolerance
Example: RabbitMQ Producer in .NET 10
var factory = new ConnectionFactory { HostName = "localhost" };
using var connection = factory.CreateConnection();
using var channel = connection.CreateModel();
channel.QueueDeclare("productQueue", false, false, false);
var body = Encoding.UTF8.GetBytes("ProductCreated");
channel.BasicPublish("", "productQueue", null, body);
Why Teams Choose Message Queues
- Services are loosely coupled
- Improved system stability
- Easy horizontal scaling
Challenges
- Increased infrastructure complexity
- Harder end-to-end tracing
- Requires idempotent message handling
4. Apache Kafka – Event Streaming at Enterprise Scale
Apache Kafka is a distributed event streaming platform built for massive scale. Unlike traditional queues, Kafka stores events durably and allows multiple consumers to read them independently.
Kafka is often used when events are core to the business domain.
Common Kafka Use Cases
- Event sourcing
- Audit logs
- Real-time analytics
- Data pipelines across systems
Kafka works best when teams embrace event-driven thinking rather than request–response models
Conclusions
Choosing how microservices communicate is a long-term architectural decision, not just a technology choice.
- REST prioritizes simplicity and accessibility
- gRPC delivers speed and contract safety
- Message queues enable resilience and scalability
Modern systems built with .NET 10 often combine all three approaches to meet evolving business and technical demands.
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