Breaking Free from Chaos: Kafka's Epic Quest to Save Microservices from Circuit Breaker

In the realm of microservices architecture, developers often encounter challenges when it comes to handling the resilience and fault tolerance of distributed systems. To address these challenges, the Circuit Breaker pattern emerged as a popular solution. Originally introduced as for managing faults in distributed systems, it aimed to prevent cascading failures and provide a fallback mechanism.

A common example of such kind of failure can be an e-commerce system with three microservices: Inventory, Payment, and Order.

1. The Inventory service experiences a sudden surge in traffic during a sale event, causing it to slow down or become unresponsive.
2. The Payment service, relying on the Inventory service to check product availability, starts experiencing delays in processing payments due to the slow response from Inventory.
3. The Order service, depending on both Inventory and Payment services, faces issues in processing customer orders, leading to delays and potential errors in the order fulfillment process.

By implementing the Circuit Breaker pattern, you can mitigate the impact of cascading failures. The Circuit Breaker would detect the failures in the Inventory Service and trip, temporarily isolating it. This allows the Payment Service and the Order Service to avoid unnecessary requests and quickly fail over to alternative mechanisms, such as using cached data or providing fallback responses. By preventing the propagation of failures, the Circuit Breaker pattern helps to maintain the stability and resilience of the system.

Why should you limit its usage?

First of all, a Microservices architecture is designed to provide autonomy and independent scalability for individual services. However, when the Circuit Breaker pattern is applied, it de-facto means a certain level of dependency and coupling between services. This interference with service autonomy can undermine the very principles of microservices architecture. This kind of design is what I use to name a distributed monolith instead of a microservice architecture implementing a proper autonomy principle. Therefore, a systematic use of the Circuit Breaker might be a clue that your architecture should be revisited.

Then it adds an additional layer of complexity to your microservices architecture. Each service needs to include circuit breaker logic, making the code more intricate and harder to maintain. As the number of services and interdependencies grow, managing circuit breakers becomes increasingly challenging, leading to a more convoluted system. Also, it raises a couple of questions like :

• What's the expected behavior when the circuit breaker trips?
• Fall back to a predefined value?
• Return a cached response from a previous call?
• Raise an error code?

All those points need to be discussed with the business and implemented carefully. Long story short it means that you need to implement infrastructure management related code in your application while you should focus on the business logic.

Event-Driven Architecture to the Rescue

An event-driven architecture can foster loose coupling and reduce the reliance on the Circuit Breaker pattern. Indeed, services can communicate through events rather than direct synchronous calls. Events are produced when significant actions or changes occur, and other services consume those events to react accordingly. This asynchronous nature of communication reduces tight coupling between services as they don't have direct dependencies on each other's interfaces. By decoupling services through events, the need for direct service-to-service interactions decreases. This means there are fewer points of failure and potential cascading failures, reducing the need for extensive usage of the Circuit Breaker pattern. This approach reduces the need for synchronous calls and minimizes the chances of failures propagating throughout the system. With looser coupling and the absence of tight dependencies, the Circuit Breaker pattern becomes less necessary for isolating failures.

That being said, it's important to bear in mind that in an event-driven architecture, services maintain eventual consistency rather than immediate consistency. They react to events and update their own state asynchronously, which allows them to operate independently without relying on immediate responses from other services.

Kafka

That kind of architecture is often paired with Kafka because it is built upon a distributed commit log design. It maintains an immutable, ordered sequence of records, or "log," allowing events to be written, stored, and consumed in the order they occurred. This inherent log-based architecture makes Kafka highly suitable for capturing, storing, and processing streams of events at scale.

In addition, Kafka's fault tolerance, scalability, event retention, stream processing capabilities, exactly-once semantics, and ecosystem make it uniquely suited as a foundational component for building robust and scalable event-driven architectures. While other messaging systems can fulfill some aspects of event-driven architectures, Kafka's specific design and features make it a powerful and instrumental choice for event streaming, data processing, and reliable event-driven communication at scale.

While the Circuit Breaker pattern can provide some benefits in terms of fault tolerance and resilience, it should be used judiciously in a microservices-oriented architecture. The increased complexity, operational overhead, delayed feedback, interference with service autonomy, and lack of granularity are important factors to consider when deciding whether to adopt this.

As a conclusion, I would say that this decision is obviously not binary, the rule of thumb should be avoiding the Circuit Breaker, however implementing an event driven architecture might not be possible for the whole information system, as you may have to deal with external APIs out of your control or legacy systems that may be too expensive to refactor to emit events.