Microservices Testing Challenges are an integral aspect of microservices architecture. This software design approach involves breaking down an application into smaller, independent services that can communicate via APIs. Each service is tailored to execute a distinct business function and is developed and deployed independently.
In recent years, the trend of adopting microservices architecture has been increasing among organizations. This approach allows developers to build and deploy applications more quickly, enhance scalability, and promote flexibility.
Switching to this multi-repo system is a clear investment in agility. However, testing microservices can pose significant challenges due to the complexity of the system. Since each service has its own data storage and deployment, it creates more independent elements, which causes multiple points of failure.
From complexity and inter-service dependencies to limited testing tools, the microservices landscape can be complex and daunting.
Teams must test microservices individually and together to determine their stability and quality. In the absence of a good testing plan, you won't be able to get the most out of microservices. Moreover, you’ll end up regretting your decision to make the switch from monolith to microservice.
Implementing micro-services the right way is a lot of hard work, and testing adds to that challenge because of their sheer size and complexity. Let’s understand the testing challenges that comes with microservices architecture.
Key Challenges in Microservices Testing
When you make the switch from a monolithic design to a microservices-based design, you are setting up multiple points of failure. Those failure points become difficult to identify and fix in such an intricately dependent infrastructure.
As an application grows in size, the dependency, communication, and coordination between different individual services also increase, adding to the overall complexity of the design. The greater the number of such connections, the more difficult it becomes to prevent failure.
According to a DevOps survey, testing microservices is a challenge for 72% of engineering teams.
Inter-service Dependency
Each individual service is dependent on another for its proper functioning. The more services there are, the higher the number of inter-service communications that might fail. In this complex web of inter-service communications, a breakdown in any of the services has a cascading effect on all others dependent on it.
Calls between services can go through many layers, making it hard to understand how they depend on each other. If the nth dependency has a latency spike, it can cause a chain of problems further upstream.
Data Management
Managing data in a microservices architecture can be a complex task. With services operating independently, data may be stored in various databases, data lakes, or data warehouses. Managing data consistency across services can be challenging, and errors can occur, which can cause significant problems.
Customer data may be stored in several databases, and ensuring data consistency can be challenging. For example, if a customer updates their details, the change must reflect in all databases.
Communication and Coordination between services
The microservices architecture approach involves many services communicating with each other to provide the desired functionality. Services communicate with each other through APIs. Service coordination is essential to ensuring that the system works correctly. Testing communication and coordination between services can be challenging, especially when the number of services increases.
Finding the root cause of failure
When multiple services talk to each other, a failure can show up in any service, but the cause of that problem can originate from a different service deep down. Doing RCA for the failure becomes extremely tedious, time-consuming and high effort for teams of these distributed systems.
Uber has over 2200 microservices in its web of interconnected services; if one service fails, all upstream services suffer the consequences. The more services there are, the more difficult it is to find the one that originated the problem.
Unexpected Functional changes
Uber decided to move to a distributed code base to break down application logic into several small repositories that can be built and deployed with speed.
Though this gave teams the flexibility to make frequent changes, it also increased the speed at which new failures were introduced.
A study by Dimensional Research found that the average cost of an hour of downtime for an enterprise is $300,000, highlighting the importance of minimizing unexpected functionality changes in microservices.
So these rapid and continuous code changes, makes multi-repo systems more vulnerable to unintended breaking failures like latency, data manipulation etc.
Difficulty in localizing the issue
Each service is autonomous, but when it breaks, the failure it triggers can propagate far and wide, with damaging effects.
This means the failure can show up elsewhere, but the trigger could be several services upstream. Hence, identifying and localizing the issue is very tedious, sometimes impossible without the right tools.
How to overcome such challenges?
The inherent complexity and inter-service dependency in microservices pose significant challenges for testing. The traditional testing approach is not effective for testing these multi-repo systems, and a modified approach is necessary. A tailored approach to testing microservices is needed to match their intelligent architecture. The conventional method of unit testing, integration testing, and end-to-end testing is inadequate. Unit tests rely on mocks, which make them less reliable, while end-to-end testing requires the entire system to be up and running, making them tedious and costly.
In a consumer-provider model, one microservice depends on another microservice to perform a specific task or provide specific data. This means that if the downstream service changes its response on which the consumer depends, it could break irreversibly. Therefore, testing the contract schema between APIs to ensure the smooth functioning of services is crucial.
The easiest way to achieve this is to test every service independently for contracts [+data] by examining the API response of the service. In the upcoming post, we will discuss a modified test pyramid that is more suitable for microservices testing.
The HyperTest Way to Approach Microservices Testing
HyperTest is an API test automation platform that helps teams generate and run integration tests for their micro-services without ever writing a single line of code. It can use your application traffic to build integration tests in hours or days that can take teams months, if not years, to build.
Not just that this builds very high coverage without effort, it by design makes it impossible for teams to introduce a breaking change or failure in your apps that is not first reported by HyperTest.
HyperTest localizes the root cause of the breaking change to the right service very quickly, saving debugging time.
It can be triggered from the CI/CD pipelines, making it easy to execute.
Conclusion
Contract [+data] tests are-the optimal solution to test distributed systems. These service level contract tests are simple to build and easy to maintain, keep the microservices in a "releasable state".
Make use of this optimal approach with first-of-its-kind tool, HyperTest and never face the trouble of testing microservices again. Schedule a demo today!
FAQs
What Are Microservices?
Microservices are a software development approach where an application is divided into small, independent components that perform specific tasks and communicate with each other through APIs. This architecture improves agility, allowing for faster development and scaling. It simplifies testing and maintenance by isolating components. If one component fails, it doesn't impact the entire system. Microservices also align with cloud technologies, reducing costs and resource consumption.
What tool is used to test microservices?
HyperTest is a no-code test automation tool used for testing APIs. It works with an unique approach that can help developers automatically generate integration tests that test code with all its external components for every commit.
It works on Real-time traffic replication (RTR), which monitors real user activity from production using a SDK set-up in your repo and automatically converts real-world scenarios into testable cases. These can be run locally or via CI to catch first-cut regressions and errors before a merge request moves to production.
How do we test microservices?
Microservices testing requires an automated testing approach since the number of interaction surfaces keeps on increasing as the number of services grow. HyperTest has developed a unique approach that can help developers automatically generate integration tests that test code with all its external components for every commit. It works on Real-time traffic replication (RTR), which monitors real user activity from production using a SDK set-up in your repo and automatically converts real-world scenarios into testable cases.