Nowadays, microservices are very popular and important for each application. Microservices provide scalability and reliability of product and are easy to maintain. Microservices is a great architecture for platforms and applications with multiple microservices. Now the question is, how microservices communicate with each other? These separate microservices can communicate with each other with several different communication protocols.
In this article, we will explore different ways microservices can communicate with each other. Understanding how microservices communicate with each other is critical to your success. Some of the most common methods of their communication are mentioned below.
Types of Communication in Microservices
When discussing how microservices communicate with each other, two primary approaches emerge. There are two main methods for microservices communication with each:
- Synchronous Communication
- Asynchronous Communication
Each method answers the question, “how do microservices communicate with each other”, based on system requirements and design principles.
1. Synchronous Communication Method.
Synchronous method is used to send a request to another microservice and wait for response. The connection will be timed out if a response is not received in a defined time span.
REST API Calls
REST API Calls are the most common method of microservices communicating with each other over HTTP or HTTPS protocols. REST API Calls allows microservices to send data to different microservices and get the response in real-time. Microservices are needed to know the endpoints of other microservices for communication. Any microservice can send a request to other microservices and get a response. This method allows microservices to work together seamlessly.
If you are a developer and wondering how to test this method, you can use tools like Postman, JMeter, Cucumber, and k6. In the mentioned tools, you can provide the endpoint and payload of other microservices where you want to send requests and start your testing.
Advantages:
- Simple and easy to implement for communication.
- Payload format is human-readable data transfer (JSON/XML).
- This method can be tested via multiple free tools like Postman, JMeter etc.
Disadvantages:
- Increases coupling between services.
- Vulnerable to network latency and timeouts.
- Blocking calls may degrade performance if a service fails to respond.
gRPC
gRPC also known as Google Remote Procedure Call, is a high-performance protocol that uses HTTP or HTTPS and protocol buffers for data serialization. It enables microservices to communicate with each other easily. The best feature of this protocol is that it supports bi-directional communication. In simple language, this protocol allows the client and server to send multiple messages asynchronously. This is beneficial for applications where real-time data is required.
gRPC is easy to integrate with container orchestration platforms like Kubernetes. This protocol can be used in any language, such as .Net, Java, Python, and more.
If you want to test this protocol communication, you can use tools like gRPCurl, gRPC GUI Clients, and Protobuf Inspector.
Advantages:
- Faster communication with binary data transfer.
- Strongly typed schemas reduce errors.
- Supports streaming for real-time use cases.
Disadvantages:
- More complex than REST APIs.
- Steeper learning curve for developers unfamiliar with gRPC.
Also read:
2. Asynchronous Communication
Asynchronous communication method is not real-time communication. In this, the sender doesn’t wait for the receiver’s immediate response. In simple words, if you send mail to anyone, the receiver will not respond immediately, receiver will respond after some time till the time you can finish your other pending work. This method is often preferred for loosely coupled systems.
Message Queues
Message Queues allow microservices to communicate asynchronously like RabbitMQ, Apache Kafka, or Amazon SQS. In this model, services send messages to a queue, where they are stored until recipient service consumes the request.
Advantages:
- Decoupling: Message queues decouple services, allowing each service to operate independently without waiting for responses.
- Load Balancing: They can distribute workloads efficiently among multiple service instances.
- Resilience: If a service is temporarily down, messages can be stored and processed later, enhancing fault tolerance.
Disadvantages:
- Complexity: Introducing message queues adds system complexity and requires management of additional infrastructure.
- Message Ordering: Ensuring message ordering can be challenging, especially in distributed systems.
Event Streaming
Event streaming platforms like Apache Kafka or Amazon Kinesis enable services to produce and consumer events in real-time. This method mostly used in cases where event needs to be processed asynchronously and distributed on multiple services.
Advantages:
- Real-Time Processing: Event streaming allows for real-time data processing and responsiveness to changes.
- Scalability: Easy to scale services independently by adding event producers and consumers.
- Data Flow Visibility: Improves visibility into the data flow and system interactions.
Disadvantages:
- Eventual Consistency: Systems may have transient states due to eventual consistency, complicating data integrity.
- Complexity: Implementing a robust event-driven system can require additional architectural considerations and tooling.
Why Is Understanding Microservices Communication Important?
For developers and architects, knowing how microservices communicate with each other is fundamental to designing scalable, efficient, and resilient systems. Each communication method has its strengths and weaknesses, and the choice depends on factors like system requirements, latency tolerance, and scalability needs.
For example, synchronous communication might be ideal for simple, small-scale applications, whereas asynchronous communication suits complex, high-traffic systems.
Which communication method is best for you?
Selecting the right communication method depends on your application’s requirements. Here are some key factors to consider:
- Performance Needs: For real-time systems, asynchronous methods like event streaming are often better suited.
- Coupling and Dependencies: If reducing interdependencies is a priority, asynchronous communication is more effective.
- Fault Tolerance: Systems requiring high fault tolerance should leverage message queues to store and process messages later.
- Scalability: For large-scale applications, event-driven architectures are more adaptable.
- Team Expertise: Consider the learning curve and familiarity of your development team with technologies like gRPC, Kafka, or RabbitMQ.
Comparing Communication Methods
| Method | Type | Pros | Cons |
| RESTful APIs | Synchronous | Simple, human-readable, easy testing | Increased coupling, potential latency |
| gRPC | Synchronous | High performance, streaming support | Complexity, steeper learning curve |
| Message Queues | Asynchronous | Decouples services, fault-tolerant | Infrastructure complexity |
| Event Streaming | Asynchronous | Real-time, scalable | Eventual consistency, added complexity |
Conclusion
Understanding how microservices communicate with each other is critical for building efficient systems. By leveraging the right communication strategies, you can design architectures that are both resilient and scalable. Whether you choose synchronous methods like RESTful APIs and gRPC or asynchronous solutions like message queues and event streams, the key is to align the communication model with your application’s specific needs.
For more insights on microservices and modern software development, explore other articles on TechieTrail!
