How to test microservices
Microservice architectures are becoming increasingly popular among software engineers in IT startups. The industry’s market size is estimated to cross USD 8 billion by 2026 at a CAGR of 18.6%. Although microservices have become more manageable, the distributed nature of the network is yet fairly complex.
Whether you are looking to make the shift from a monolithic architecture or are already using microservices, you need a robust microservice testing strategy. Continuous testing at various stages of the development and production processes is key to a healthy microservices architecture. In this piece, we guide you through developing a testing strategy.
Why is testing microservices necessary?
Despite the advantages it offers, a microservices architecture comes with several risks. Its distributed nature makes its functioning susceptible to complexity. By breaking down what would otherwise be a monolith, we achieve a new level of granularity. This microscopic view exposes connections that are useful but hidden.
Consequently, you need to strike a balance to keep the microservice application running. Each microservice must work both, in isolation and in tandem with other services in the network. Software testing, then, is not the easiest of tasks. Despite being a protracted process, it is integral to the smooth functioning of your software for the team and end-users alike.
How microservice testing works
Understanding the relevance of various testing services to your process will help you allocate resources more efficiently. In a distributed architecture, each microservice performs a highly specific task. Usually, microservice testing is done with no prior knowledge of the internal mechanisms of a system. This is called black box testing.
However, to ensure that the services, as well as the entire architecture, run well, we recommend taking a more holistic and comprehensive testing approach. Your strategy should cover all aspects of the process: from building functioning APIs to managing service requests post-production.
Let’s discuss a few test cases you should include in each step of the software lifecycle.
Running regular tests is doing the bare minimum to get your application up and running. Start with basic types of testing like unit, component, and integration testing.
At the foundational level, the microservice should function on its own. Run a unit test on the smallest testable portion of the service. Usually, this is done on a class or a set of classes using a rest API. Keeping your test units as small as possible increases the reliability of a particular unit. Sometimes using test doubles like stubs as well as simulations of objects that behave like code from actual release intended software might yield faster results during unit test automation.
Once you are satisfied with a unit’s performance in isolation, you can use a component test to determine how well it works with other units in the service. Each microservice is a standalone actor. Although it may call other APIs to run how you expect it to, it is self-reliant in its basic processing functionality.
Finally, write an integration test to ensure that independently developed microservices cooperate with one another. Any request made to the application will rely on microservices acting jointly. It is important, then, to test the communication paths that facilitate the fulfillment of a request. Tools like VCR record HTTP interactions between services. This allows you to test the same requests again without requiring the dependencies to be live.
Contract testing is similar in that it uses a contract to codify the interactions between two services, i.e., the producer and the consumer. This contract is called a schema, and it specifies mutually agreed-upon standards or rules based on which future requests between the services will run. The teams that operate these services agree not to use or modify the API in a way that breaks the contract.
The microservice ecosystem has evolved to the point that you can perform test automation for most processes. This gives your team room to focus their time and energy on interpreting the results and debugging or making appropriate changes.
The bottom line is that your application should be able to do what it is supposed to do. If you are building an e-commerce platform, make sure that each individual component can stand on its own and effectively contribute to the functioning of the application. If the search engine is not displaying results, or the user’s cart does not lead to the appropriate payment gateway, fix those bugs before moving to subsequent development stages and tests.
In addition to their basic functioning, it is useful to test your microservice's behavior with external actors. The goal is to ensure that the microservices architecture is exposing to the end-users what it claims to.
In addition to its usefulness in the backend, API testing reveals how well a service responds to an external request. For the application to run smoothly, the quality of APIs must be maintained. In a microservices architecture, requests can trigger unique communication paths, and users can make requests from multiple entry points or services. As a result, it is important to test multiple APIs.
Backward compatibility checks are to be used when updates are introduced to the application. They maintain synchronicity between old and new software versions so that the services continue to operate smoothly and the end-users do not receive any surprises.
Let us take the example of two users running the same application on different mobile phones. A user makes a request for an existing version of the application’s interface. They use an older phone model that depends upon microservice X to display the interface. The software engineer has developed a new microservice to display a faster, more appealing user interface to replace the old one. However, this microservice has been made with newer phone models in mind. If the new service is incompatible with the old phone, the developer risks breaking access to the application for every user operating that phone.
Compatibility tests, then, must be performed frequently so that all versions of an application in use work smoothly. Versioning APIs and documenting related changes are good practices to follow in this respect.
Although end-to-end testing is made difficult by the nature of microservices architectures, it can be useful to run in the staging environment. Attempting to locate all permutations and combinations is a futile endeavor. Instead, charting and testing a few common paths in addition to running other tests at varying stages of the software lifecycle will suffice.
Before pushing it to production, we recommend replicating the application to the anticipated real environment to the greatest degree possible. The entire infrastructure of microservices, including the flows between endpoints, must be tested. Replication in an end-to-end test gives more reliable results. These will inform your understanding of your software’s performance and interaction with end-users. Substituting users with testers or dummy users is a viable option. While testing, the software, and the microservices that comprise it, must be exercised in a way that they will be used when deployed.
Why test in production? It offers a level of certainty impossible to achieve in development or staging environments. End-to-end tests only get you so far and cannot compare to testing on an actively running program.
Microservice software architectures may not crash entirely in one go as a monolithic application would. The decentralized network helps to isolate failures to subsections or specific microservices as the rest of the application keeps running. However, relying on cooperation between different services makes partial failure a possibility, and a noticeable one at that. Such failures on the communication paths can hamper the application’s regular functionality.
Running performance tests and load tests in the production environment is useful in this regard. It helps determine application resilience and continuous delivery. During these tests, you can observe how well the software performs in the face of high demand. Today, load testing tools are available on the cloud to simulate a high-traffic environment. This load can either be in the form of API requests or inter-service data transfer.
The flexibility of a microservice architecture also encourages variety in the communication paths that an end-user can trigger through their requests and behaviors. This diversity stems from the fact that microservices differ in their functions and capacities and their responses to requests and calls. These differences also make it easier for them to adapt to shifting demands, either by themselves or in conjunction with other services. Microservice networks also often rely on external services, and those have their own specific capacities and processes. Such flexibility necessitates testing the architecture in high-pressure contexts to maintain its functionality across different situations.
Changes or inadequate debugging, in addition to the independent-yet-connected structure, can make for fertile ground for failures. Tests cannot prevent failures from cropping up, but they help account for the errors. These errors are few and often not as severe. It is possible to isolate them in some use cases, as microservices are independently developed. The entire network does not, then, need to shut down. For example, a food delivery application experiences a GPS failure. End-users are not able to track their delivery partners’ movement. However, they can continue to place orders and contact customer service from within the mobile application. The other services can continue running even when one is down.
Failures can be managed with robust change management processes, load balancing, and rate limiters. Running smoke tests to account for common issues both in development and production is non-negotiable.
Testing your release as it goes out makes sure you are not promoting a service unless it is working the way it is supposed to. Canary or blue-green deployment is a test that deploys a release only to a fraction of its target audience. This strategy offers a real test environment without having to resort to dummies. Additionally, any issues faced are smaller than they would have been if the entire audience were interacting with the application.
It is also important to test feature toggles. If you intend to introduce a new feature, there is a small possibility that you will decide not to push it at the last moment. If this were to occur, your application must work without breaks induced by the presence or absence of the feature. Toggling the feature, therefore, should not affect the user experience of the rest of the application.
Users expect failures; the goal is not to avoid them (because that is impossible!) but to ensure that the experience is as smooth as it can be. In these situations, delivering prompt responses and error codes can go a long way.
Testing does not end there. After you release your application, verification in real-time is of utmost importance. Through alerting and monitoring, you can track metrics and relevant indicators for assessing performance. Observability is a valuable contributor to your testing strategy by way of providing feedback. It allows you to observe the output of your application to better understand and improve your microservices network.
You should track not only technical metrics like latency and data usage but also business product-level indicators. Technical test data presents only half the picture. Product-based metrics are valuable in understanding end-user behavior and feedback.
Receiving this feedback in real-time allows you to respond promptly, either manually or using the software. Increasingly, we are developing applications that anticipate the different needs of users and adapt by themselves. This is only possible with continuous monitoring and testing. Having solid response mechanisms, including failure and error codes, is indispensable in identifying and fixing a problem.
In the context of microservice architectures, a single API request by an end-user can trigger a cascading chain of commands across the entire network. It is impossible to capture the failures manually. Automated testing becomes useful in that it contributes relevant data to a centralized database where alerts or humans can access it. Usually, microservice monitoring systems place trackers on each microservice for that data.
Finally, testing is made more effective by putting robust standards in place. These standards outline bare minimum expectations for microservices and infuse certainty into the software lifecycle. This increases confidence amongst developers. The standards also establish accountability mechanisms for easier cooperation and collaboration between development teams.
Putting together your testing strategy
Testing is not a one-time process. To reap its benefits, we recommend actively incorporating it into your workflow every step of the way, from development to post-release. Now that you know the extent to which testing plays a role in microservice-based software development, it is time to put it into practice. Platforms like Microsoft Azure and Amazon Web Services (AWS) provide services like compute power, database and storage space, networking, and DevOps, facilitating microservice-based software development.
Cortex facilitates this process by helping you keep an eye on the microservices applications you are currently using, what each one does, and who owns it. Our tools are designed to simplify your management processes. When you are equipped with knowledge of best practices and the appropriate tools, microservices can elevate your development experience!