Circuit Breaker Pattern

Circuit Breaker Pattern

In this tutorial, we are going to discuss Design Patterns of Microservices Architecture which is the Circuit Breaker Pattern. We will use this pattern and practice when designing microservice architecture.

The Circuit Breaker pattern is a design pattern used in software development to detect and handle faults in distributed systems. It improves the system’s resilience by preventing an application from performing operations likely to fail, thereby avoiding unnecessary strain on the system and allowing it to recover more gracefully. The pattern is particularly useful in microservices architectures, where services depend on other services that may be temporarily unavailable or slow to respond.

Understanding Distributed Systems and Fault Tolerance

In today’s technology-driven world, distributed systems have become the norm. They power the applications we use daily, from social media platforms and streaming services to online marketplaces and cloud storage. But what makes these systems reliable, and how do they maintain smooth operation even in the face of potential failures?

Distributed systems are composed of multiple components, each executing its own tasks while communicating with others to collectively provide a service. They are designed to be resilient, but given the inherent complexities and the sheer number of components involved, the probability of encountering a failure, no matter how minor, is high.

This is where the concept of ‘fault tolerance’ comes in, a key aspect of designing robust distributed systems. Fault tolerance is the system’s ability to continue functioning correctly, possibly at a reduced level, rather than failing completely, when some part of it fails.

Implementing Fault Tolerance with the Circuit Breaker Pattern

Design patterns are solutions to common problems that occur repeatedly in a specific context. One such pattern that stands out for handling failures effectively in a distributed system is the Circuit Breaker pattern.

What is the Circuit Breaker Pattern?

Let’s start with a real-world example: In your home, circuit breakers prevent electrical fires by “tripping” and cutting off electricity when there’s a dangerous surge. Now, imagine this in the world of software.

In microservices architecture, the Circuit Breaker pattern acts like this safety mechanism. When a microservice (Service A) calls another (Service B), and if Service B is struggling (slow response or failures), the circuit breaker “trips” to prevent further strain. This way, Service A can either handle the issue gracefully or rely on a fallback mechanism, instead of continually waiting for Service B and potentially crashing itself.

Mechanism of the Circuit Breaker Pattern

This may seem straightforward, but how does the Circuit Breaker pattern handle different types of failures? How does it distinguish between a minor hiccup that might resolve itself in a few seconds and a major issue that could take minutes, or even hours, to fix?

  1. Closed State: Initially, the circuit breaker is in a Closed state, allowing requests through.
  2. Open State: If a certain number of requests fail (like timeouts or errors), the breaker “trips” to an Open state. This stops calls to the failing service, giving it time to recover.
  3. Half-Open State: After a cooldown period, the breaker enters a Half-Open state, allowing a limited number of test requests through. If these succeed, it goes back to Closed; if not, it returns to Open.

Example in Microservices

Imagine a microservice for processing customer orders. This service (Order Service) communicates with a Payment Service to process payments. If the Payment Service starts to fail or become slow, the Order Service will continue to make calls, waiting and potentially failing itself.

With a circuit breaker implemented:

  • After noticing a set number of failed attempts to the Payment Service, the circuit breaker trips.
  • The Order Service stops calling the Payment Service, returning a default response like “Payment processing is delayed” or it might queue the order for later processing.
  • After a cooldown period, the circuit breaker allows a few requests to check if the Payment Service is back to normal.
The Problem: The Struggles of Distributed Systems and Service Failures

To truly understand the value the Circuit Breaker pattern brings to the table, we first need to get a clear grasp of the problems it seeks to solve. Let’s take a step back and explore the reality of distributed systems.

When Systems Fail

As we’ve established, distributed systems are collections of independent components, or nodes, working together to provide a service. However, as with any system, the possibility of failure is always present. Nodes can go down due to network issues, hardware failures, or software bugs. Even a single node failure can significantly degrade the performance of the entire system, disrupting the service it provides.

It’s critical to remember that in distributed systems, failure is not an exception but a rule. Due to the intricate interplay of numerous components over a network, things can — and often do — go wrong. It could be a database timing out, an overloaded microservice, or an API taking longer than usual to respond. In worst-case scenarios, these minor issues can escalate into a catastrophic system-wide failure.

The Vicious Cycle of Failures

Imagine you have a distributed system with a variety of services. One of these services begins to experience increased latency due to an unexpected spike in user requests. This slow service now starts causing delays in other services that depend on it, creating a domino effect throughout the system. Now, these delays start piling up, and soon, your entire system is slowed down.

It gets worse. More requests keep coming in, but your struggling service is unable to process them efficiently. It becomes a vicious cycle – the more requests it receives, the slower it gets, and the slower it gets, the longer the request queue becomes. This situation is often described as a cascading failure — a failure that grows, spreading from one component to another, ultimately leading to a system-wide breakdown.

The Problem of Constant Retrials

In many systems, when a service fails to respond, the common practice is to retry the request. While this can be beneficial for handling transient issues that resolve themselves quickly, it often exacerbates the problem when dealing with a struggling or failing service.

Why is that? Well, let’s consider our scenario again. Your service is already overwhelmed with requests. If you add more retries into the mix, it only increases the load on the already struggling service. You’re basically trying to put out a fire with gasoline. This can lead to even more severe slowdowns or even a complete system shutdown.

The costs associated with such failures can be astronomical. They can lead to a loss in revenue, reduced customer trust, and potential damage to your brand’s reputation.

A Need for a Better Solution

So, what can we do? How can we protect our distributed systems from cascading failures, overloaded services, and the problem of constant retrials without giving the system any ‘cooling period’? How can we prevent the system from overloading a struggling service and give it a chance to recover?

Well, this is where the Circuit Breaker pattern comes in. It provides a mechanism that addresses these issues, helping to create a more resilient and stable system. As we delve deeper into the solution that the Circuit Breaker pattern provides, we’ll explore how this clever pattern can offer a protective layer around your service calls, preventing a single point of failure from bringing down your entire system.

At its core, the problem we’re trying to solve is about balance and management. We want our system to handle as many requests as it can, as efficiently as possible, without overloading any single part of it. This requires a level of coordination and error handling that can be challenging to implement.

Remember the earlier analogy of a fire? In our distributed system scenario, we need a firefighting squad that can detect the first signs of an overloaded service and act decisively to prevent the fire from spreading. This squad should have the ability to stop piling requests onto the struggling service, give it some room to breathe, and most importantly, enable the rest of the system to function as normally as possible.

In a nutshell, we want to prevent failures from propagating through the system, which is exactly what the Circuit Breaker pattern helps us achieve.

The Circuit Breaker Pattern: An Effective Shield Against Cascading Failures

Named after the electrical circuit breaker that we are all familiar with, the Circuit Breaker pattern in distributed systems serves a similar purpose – it ‘trips’ or ‘opens’ when it detects a certain number of failures, thereby protecting the system from further damage. In essence, it stops a failing system from being overwhelmed with requests and gives it time to recover, all while ensuring the broader system continues to function to the best of its ability.

But how does the Circuit Breaker pattern determine when to ‘trip’? What makes it decide that a service needs a ‘cooling period’? The answer lies in the unique way the Circuit Breaker pattern monitors and responds to the state of the service it is protecting.

Three States of Operation: Closed, Open, and Half-Open

The Circuit Breaker pattern operates in three states: Closed, Open, and Half-Open. The transitions between these states form the basis for the pattern’s operation and are driven by the outcomes of the service requests it handles.

  • Closed State: In the Closed state, the Circuit Breaker allows requests to reach the service as usual. It’s as if there’s no Circuit Breaker involved at all. However, behind the scenes, the Circuit Breaker is constantly monitoring the outcomes of the requests. If the number of failures exceeds a predetermined threshold within a defined time window, the Circuit Breaker ‘trips’ and transitions to the Open state.
  • Open State: Once the Circuit Breaker is in the Open state, it prevents any further requests from reaching the service. Instead of forwarding the requests and potentially overloading the already struggling service, it returns an error to the client immediately. This effectively gives the service a ‘cooling period’, time to recover and stabilize. After a predetermined timeout period, the Circuit Breaker moves into the Half-Open state.
  • Half-Open State: The Half-Open state is where the Circuit Breaker tests whether the service has recovered. It allows a limited number of requests to reach the service and closely monitors their outcomes. If these requests are successful, indicating that the service has recovered, the Circuit Breaker goes back to the Closed state. If the requests fail, however, it reverts to the Open state, providing the service with another ‘cooling period’.
Circuit Breaker Pattern

But what does this look like in a real-world context? Imagine you’re operating a popular online marketplace. Your customers are happily browsing and purchasing items. Behind the scenes, a multitude of services are working together to provide this seamless experience, one of which is your Payment Processing Service.

Suddenly, this Payment Processing Service starts experiencing delays due to a database issue. Customers trying to make purchases receive error messages, causing frustration and potentially leading to lost sales. If your system was equipped with a Circuit Breaker, however, this situation could have been handled much more gracefully.

With a Circuit Breaker in place, after a predetermined number of payment processing failures, it would have moved into the Open state. The Circuit Breaker would then instantly return a more user-friendly error message to your customers, perhaps suggesting they try again in a few minutes. In the meantime, the Payment Processing Service would have the breathing space it needs to resolve the database issue and recover.

After a certain timeout period, the Circuit Breaker would enter the Half-Open state, allowing a few payment requests through to check if the Payment Processing Service has recovered. If these transactions are processed successfully, it would be a sign that the service is back to normal operation, and the Circuit Breaker would transition back to the Closed state, allowing all transactions to go through as usual. However, if these requests fail, it would revert to the Open state, providing the service with more time to resolve the issue.

This way, the Circuit Breaker helps to prevent a small issue with one service from escalating into a major problem affecting the entire system. It mitigates the impact of a failing service on the end-user experience and prevents the problem from propagating further into the system.

Integrating the Circuit Breaker Pattern

Integrating the Circuit Breaker pattern into your system does not necessarily require a complete system overhaul. In fact, it can be added as a protective layer around your existing service calls without disrupting the underlying service logic.

Essentially, every time a request is made to a service, it is made through the Circuit Breaker. It is the Circuit Breaker that determines whether to forward the request to the service based on its current state and the outcome of recent requests.

This way, the Circuit Breaker pattern can be introduced gradually into a system, starting with critical services that could have a significant impact on the system’s functionality if they were to fail. Over time, as the benefits of the pattern become evident, it can be expanded to protect other services as well.

Circuit Breaker Settings: Finding the Right Balance

When implementing the Circuit Breaker pattern, it’s crucial to focus on the configuration of its parameters. These include:

  • Failure Threshold: Determines when the circuit breaker switches from Closed to Open state. It’s the limit of failed requests needed before activating the circuit breaker.
  • Timeout Period: The duration the Circuit Breaker remains in the Open state before moving to Half-Open. This is the recovery time given to the service.
  • Number of Requests in Half-Open State: This is how many requests are allowed through when the Circuit Breaker is in the Half-Open state, testing if the service has recovered.

The values for these parameters are vital. They must strike a balance between protecting the service, allowing it time to recover, and keeping the service as available as possible for handling requests. The ideal settings depend on several factors:

  • The nature of the service and the system.
  • The expected load on the service.
  • The criticality of the service to the system’s overall functionality.

It’s important to remember that these parameters shouldn’t be static. They can and should be adjusted dynamically in response to the system’s state and performance. For example, in times of high demand, adjustments might include:

  • Increasing the failure threshold, allowing the service to endure more failed requests before the Circuit Breaker activates.
  • Reducing the timeout period, enabling quicker recovery attempts, thus allowing the service to handle more requests during peak times.

These dynamic adjustments help in maintaining optimal service availability and performance, even under varying system conditions.

Benefits of the Circuit Breaker Pattern
  1. Improved Resilience:
    • Circuit Breaker Pattern prevents cascading failures in a distributed system by isolating faulty services.
    • Helps maintain the overall system stability by blocking calls to services that are down.
  2. Faster Failure Recovery:
    • Allows the system to recover more quickly by reducing the load on a failing service and giving it time to recover.
    • Automatically retries connections to see if the service has recovered.
  3. Better Resource Utilization:
    • Circuit Breaker Pattern reduces the waste of resources on failed requests by stopping attempts that are likely to fail.
    • Helps in managing system resources more efficiently by not overwhelming them with repeated failed requests.

The Circuit Breaker pattern is a powerful tool for improving the resilience and stability of distributed systems. By preventing excessive failures and managing the recovery process, it helps maintain the overall health of the system. Properly configured circuit breakers can prevent minor issues from escalating into major outages, making them a critical component of robust microservices architectures.

That’s all about the Circuit Breaker Pattern. If you have any queries or feedback, please write us email at Enjoy learning, Enjoy Microservices..!!

Circuit Breaker Pattern
Scroll to top