Microservices is the most popular app development approach today. Rather than having a monolithic app that is tightly coupled, microservices apps are loosely coupled, with each service performing a certain function independently.
These services employ APIs to communicate. It is an excellent approach to app development, offering benefits like agility, shorter release cycles, and improving CI/CD.
The world’s leading companies use microservices architecture and credit them for their IT success. Using a microservices approach, you can scale each service as needed, thus improving fault tolerance and resource utilization.
Moreover, since each service functions independently, you can utilize different programming languages and technologies for them. But what is the significance of fault tolerance in microservices?
This article will give you the guiding principles for building fault-tolerant microservices applications.
What is Fault Tolerance in Microservices?
Fault tolerance is a critical concept that software developers should always prioritize when building a reliable system.
A textbook definition of fault tolerance is that a system continues to operate despite some problems. It refers to a system’s ability to continue operating properly in the event of the failure of some of its components.
Problems often occur and they could be for several reasons, like human error, hardware issues, network or software glitches, etc.
Fault tolerance is so important in microservices-based apps because if one service fails, it might create a domino effect and bring the whole system down.
Therefore, the primary goal of microservices fault tolerance is to prevent the system from failing due to faults or errors.
Two main types of errors or failures can occur:
Temporary
Temporary failures are issues or faults in a system that are expected to be short-lived and can be resolved with time. These failures are often caused by transient conditions, such as network glitches, brief power outages, or minor software bugs.
Systems that are designed to handle temporary failures implement mechanisms like retry strategies or temporary fallbacks to wait for the issue to resolve itself. You don’t need any major intervention to resolve such failures.
Permanent
Permanent failures are more serious compared to temporary ones. These errors are usually caused by human error, hardware problems, or software bugs and require you to manually intervene to fix them.
You can counter these types of failures by having robust redundancy mechanisms and data backup options. However, keep in mind that dealing with permanent failures takes longer than dealing with temporary failures.
High Availability vs Fault Tolerance: What’s The Difference
High availability and fault tolerance both aim to keep systems running with minimal downtime. However, they take different approaches for this goal, here is how:
- High availability aims to minimize downtime as much as possible, but never completely eliminates it. On the other hand, microservices fault tolerance ensures there is zero downtime at any moment.
- Fault tolerance has no well-defined metrics which is why it is not easily measurable. Unlike high availability, which takes percentage of uptime as criteria of measuring accessibility.
- High availability may involve short interruptions while switching to a backup system, while there is no noticeable impact on performance in microservices fault tolerance.
- High availability is implemented with load balancing and failover mechanisms. Fault tolerance relies on redundancy and replication.
- High availability is cheaper to implement and moderate in complexity, but since fault tolerance doesn’t tolerate downtime at all, it is expensive and more complex to implement.
Why Microservices Fault Tolerance is Important?
Fault tolerance in microservices ensures that app remains functional at all times, including when some glitches and errors occur. Additionally, here is why microservices fault tolerance matters:
Simpler Debugging
Fault tolerance systems can precisely point out the pain points and sources of failures. This expedites debugging and allows developers to identify and resolve issues quickly.
Minimum Downtime
Service failures can happen in any app, but they don’t have to be detrimental. Effective error handling can mitigate their impact to a great degree.
Microservices fault tolerance ensures that the overall app functionality remains undisturbed even if one particular aspect fails.
Users may experience some temporary inconvenience in such case, but the system keeps functioning.
Quicker Recovery
Fault tolerance in microservices has an additional benefit by enabling systems to recover quickly after any failure. The duration of issues is significantly reduced and failure mechanisms immediately bring the system back on track.
Fault Tolerance Principles for Microservices
Design for Failure
No matter how good a developer you are, system failures are something you cannot avoid, especially when you’re building distributed systems like microservices design patterns.
This is where designing for failure comes into play – it is a proactive approach to system engineering that involves intentionally planning and building systems with the expectation that failures will occur.
The goal is to create systems that can gracefully handle failures and continue to operate or recover with minimal impact.
Instead of trying to eliminate the possibility of failure, the focus is on mitigating the consequences of failures. As a result, you are always prepared to deal with failures, and they don’t catch you unawares.
Here are some software design techniques you can use to ensure a more fault-tolerant microservices architecture:
Partition
You can use partitioning to isolate crucial services from non-crucial ones. This will ensure that the failure of one service does not affect other services. You can implement a bulkhead to limit the error’s blast radius, thus keeping it from spreading to other parts of the system.
Circuit breaker
This is a software development services pattern aimed at enhancing a system’s resilience to failures. It is designed to prevent continuous calls to a potentially failing service, component, or operation from degrading the overall system’s performance.
The circuit breaker monitors the health of the operation, and if it detects a certain threshold of failures, it “opens,” preventing further calls to the failing component.
Using the circuit breaker design pattern helps in isolating the failure and allows the system to gracefully handle the issue, offering a form of fault tolerance.
After a predefined period or under specific conditions, the circuit breaker may “close” again, allowing normal operation to resume.
Graceful Degradation
Graceful degradation is another very important design principle – it refers to a system that can maintain basic functionality in the wake of a failure. That means the system does not completely shut down when facing an issue; instead, it gracefully degrades the service.
This approach ensures that users or components experience minimal disruption during adverse conditions, thereby promoting the system’s overall resilience. This design principle is critical to building performant microservices apps.
Decentralization
Decentralization is part of microservices examples whereby you distribute services across numerous data centers or nodes to avoid single points of failure. It means that even if one service fails, the whole system will still not fail.
Here are some decentralization strategies you can use:
Service Replication
Service replication involves creating multiple instances or replicas of a service and distributing them across different nodes or locations in a network.
Service replication aims to bolster the system’s fault tolerance and performance by allowing multiple nodes to handle requests. If one instance fails, another replica can take over, ensuring continuous service availability.
Service Discovery
Service discovery is a mechanism that enables nodes in a distributed system, like microservice design patterns, to dynamically find and communicate with each other without prior knowledge of their locations.
It facilitates the scalability and flexibility of distributed systems by automating the process of identifying and connecting to microservices as you add or remove them.
Distributed Data Management
When you are working with distributed systems, you can increase their fault tolerance by distributing data across multiple nodes or locations in a decentralized manner. You will have to employ techniques like sharding to distribute data storage and processing tasks across multiple nodes.
Redundancy
Redundancy is the design principles in Flutter or in other development frameworks where you have duplicate components as a backup for the main components so that in case of a main component’s failure, the duplicate component can step in and take over.
This ensures that the system operates irrespective of component failure. Having redundant components is a vital aspect of building robust microservices applications, especially if your app has a mission-critical environment requiring uninterrupted operations.
Isolation
Isolation is another critical design principle that enables you to ensure and maintain a system’s stability. As the name suggests, isolation refers to containing a failure to keep it from impacting the system. It also helps keep bugs from proliferating in the microservices solutions system.
You can achieve isolation through various techniques, such as process sandboxing, virtualization, or containerization, where each component operates independently within its designated environment.
By isolating components, errors or faults in one part of the system are less likely to propagate to other areas, contributing to improved fault tolerance and system resilience.
Fail-Fast
High-quality software development is all about the speedy detection of bugs or errors. Fail-fast personifies this need. It is a programming and design philosophy that encourages a system or component to detect and respond to errors or anomalies as soon as they occur.
It does not allow failures or problems to propagate and potentially cause more extensive issues later in the process. You need to have a robust monitoring mechanism to follow the fail-fast principle.
Having such a warning system ensures that you can quickly detect problems as they arise in your microservices architecture and resolve them as quickly as possible. Rapid debugging, software testing services, and problem resolution are key to achieving software success.
Here are some of the things you should follow to effectively employ the fail-fast design principle:
- Ensure rigorous testing of your code, including end-to-end, unit, and integration testing
- Perform health checks
- Use metrics and logs
- Employ timeouts, retries, circuit breakers, and fallback mechanisms
- Use CI/CD Pipelines to update microservices
How to Test Fault Tolerance
If you want to be sure that your app is ready to move to production, you should test its fault tolerance. All testing strategies aim to identify a system’s weaknesses. They also aim to ensure that the system keeps operating and handles failures without collapsing.
The first testing to carry out is unit testing services. Here, you will test individual components of the system and see if there are any issues. The key here is to identify these issues so that they don’t spread to other components of the system.
Next, perform integration testing to verify and check how the different services in your microservices application are working with each other. You should test for both normal and abnormal scenarios.
Now implement chaos engineering, where you will intentionally inject control instances of failure or disruptions to observe how your microservices app reacts to it and handles it.
You can simulate a wide range of scenarios to identify weaknesses and then implement improvements to enhance system resilience. Next, perform load testing to ensure that your application can work under heavy loads.
Load testing is a very important aspect of ensuring fault tolerance in a system. You should test normal and peak load scenarios for efficient testing.
Lastly, run some disaster recovery tests to gauge how your app recovers from a failure. You should check data restoration and backup processes to make sure the app goes back to normal after suffering from a failure.
Conclusion
Learning how to build fault-tolerant microservices applications is a critical skill for app developers. Every microservices developer should learn these fault tolerance principles in addition to knowing about the best microservices design principles.
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FAQs
Fault tolerance in microservices refers to a system's capability to maintain proper operation even when some components fail. It involves using techniques such as circuit breakers, timeouts, and redundancy to keep the system functional and accessible despite disruptions in individual services.
Fault isolation in microservices is all about keeping problems from spreading. If one service runs into trouble, it shouldn’t bring down the whole system. This is done using techniques like bulkheads, process isolation, and dedicated thread pools to make sure failures stay contained and don’t cause a domino effect across other services.
Fault tolerance and resilience are similar but not quite the same in microservices. Fault tolerance is about making sure the system keeps working even if something fails, using things like retries, timeouts, and circuit breakers. Resilience, on the other hand, is more about how the system recovers, adapts to changes, and keeps running smoothly under pressure. It often includes self-healing and smart adjustments to handle issues on its own.