This guide will look at what Microservices are, how to test them, best practices for debugging Microservices, and some challenges that come with Microservices.
Microservices have evolved significantly. They started as a brand-new, hip toy and developed into a respectable architecture that revolutionizes how we create contemporary applications.
Microservices come with various benefits, like being independently deployable and scalable. They also allow for a very diversified technology stack.
However, there are plenty of reasons why Microservices can be challenging and require advanced skill sets.
How to Test Microservices
A testing pyramid is a tool that will help you test automated software. The pyramid typically employs three different kinds of tests:
- Unit tests
- Integrity checks
- End-to-end testing
Components and contract tests are two additional categories in the Microservice pyramid. Let’s dive into each one of them in detail –
Unit Tests for Microservices
One of the most thorough and common types of testing is the unit test. A unit is a class, method, or function you can test independently.
Unit testing is a crucial aspect of development methods, whether test-driven or development-driven.
Any time two services connect over an interface, a contract is created. All potential inputs, outputs, data structures, and side effects are listed in the contract.
For communication to be feasible, the service user and the provider must adhere to the terms outlined in the contract. Contract tests make sure Microservices follow their terms of service.
In simple terms, they verify that the inputs and outputs have the required features and that the service operates up to quality and performance standards. However, they do not entirely evaluate a service’s behavior.
Contract tests may be carried out by either the producer or the customer, depending on the connection between the services.
Microservice integration tests operate significantly differently from those in conventional architectures. The main idea behind making Microservices communicate is to find interface flaws. It can help find Integration tests that employ actual services instead of contract tests, which always use a faked version of one side.
Integration tests are not concerned with analyzing a service’s operations or business logic. As an alternative, we want to ensure that the Microservices can talk to one another and their databases.
We’re searching for mismatched request/response pairs and missing HTTP headers. Any of these significant components returning red indicates that the indicated aspect needs attention.
Integration tests are frequently implemented at the interface level and are key to the testing pyramid. Perfect, now let’s jump straight into component testing.
A microservice or group of Microservices that perform a specific function for the larger system is referred to as a ‘component.’
When performing component testing, we replace services with simulated resources or use mocking to investigate the component’s behavior in isolation.
Because they follow both positive and negative pathways, component tests are more thorough than integration tests.
For instance, they examine how the component handles simulated network failures or improperly formatted requests. Similar to acceptance or end-to-end testing, we want to know if the component satisfies the requirements of its intended user.
This makes component tests fundamentally different from unit tests but just as necessary as part of the testing pyramid. Finally, let’s look at end-to-end testing.
We have only tested the system fragmentarily so far. A microservice’s component components were tested using unit tests, API compliance was examined using contract tests, network calls were examined using integration tests, and the behavior of a subsystem was examined using component tests. The system is only tested at the very top of the automated testing pyramid.
End-to-end testing (E2E) ensures the system satisfies user demands and accomplishes user-set business goals. With a combination of UI and API tests, the E2E suite should test every microservice in the application using the same interfaces that customers would use.
The application should function in a setting as similar to production as feasible. The test environment should ideally have all the external services the application typically requires.
Challenges of Debugging Microservices
There are multiple challenges you might run into while debugging Microservices, but there are tools and strategies you can implement to tackle them.
Let’s discuss some of those challenges and what you can do to overcome them –
Recreating State in a Complex Environment
It might be challenging to gain a clear picture of the system’s state since the application’s state is dispersed over several Microservices rather than being aggregated in a single monolithic system.
The complexity of microservice systems tends to increase, which makes the situation worse. Cloud Infrastructure-as-a-Code tools can solve this problem.
It allows enterprises to simply develop, deploy, and manage AWS resource stacks by utilizing a template or a text file that serves as a single source of truth. CloudFormation works with YAML or JSON. The best part is that you don’t have to worry about how it keeps the infrastructure settings because it operates on AWS infrastructure.
This is coupled with complicated web forms as more components are added, with each module functioning independently, which could fail without impacting the others. Each service can also be fully independent of other services, log in a different way, and be built in a separate language.
Lack of Observability and Tracing
Infrastructure is getting harder to grasp as serverless models and Microservices gain popularity. The inner workings of each module, serverless call, and cloud component are further difficult to explore.
Achieving observability in a microservices architecture is difficult because microservices operate independently and follow user requests between asynchronous modules. Additionally, it might be challenging to identify which services interact because each request may follow a different path. These characteristics collectively make it difficult for developers to identify an error’s primary cause.
Due to this, observability and tracing are challenging. Especially since developers frequently cannot determine the internal state of a microservice system simply on outputs. One of the best solutions to this is using infrastructure monitoring tools; they can provide you with the insight that you need in depth.
Multiple Microservices are continually running and communicating among themselves, creating their logs along the way.
When one or more services fail, the team must determine which service was affected and why. It is also tough to understand the entire request flow in Microservices. For example, which services have been requested? And what is the name and frequency of the service?
This can be remedied by making use of logging tools. Logging tools help you visualize log data and store them securely for future development.
Lack of Familiarity
With the pace at which Microservices are expanding, there are always new techniques and strategies that you can implement to make yourself more familiar with debugging Microservices.
The best solution is to grow your skills and talents in this rapidly evolving space.
Making use of free and paid online MOOCs, short courses, and certificate programs are excellent ways to quickly gain the knowledge and expertise that can propel your team forward in terms of performance and also future-proof your skillset.
Best Practices for Debugging Microservices
Externalize and Centralize the Storage of Your Logs
The first thing you should do is ensure the integrity of your logs’ data since it’s not possible to rely on later information being retrieved from a real machine, especially inside an auto-scaled virtual environment.
You wouldn’t approve a design that housed critical customer data on a single virtual machine without any backup copy, so you don’t want to do it with logs. If you’re using AWS, you can always send your output to CloudWatch. Also, you could consider utilizing Application Insights on Azure.
While there is no definitive right or wrong way to store log data, whether it’s in a database, on a disc, or in an S3 bucket, it’s still a best practice to ensure that the storage is durable and available when you are debugging and troubleshooting Microservices.
Create and Pass a Correlation Identifier Through All Requests
When you get an initial request that initiates some processing, you should generate an identification that can be traced from the first request to all subsequent processing.
If you reach out to other components or Microservices to continue your processing, you should always supply the same correlation identifier.
You must use this identification to log all subsequent components and Microservices so that you can compile a comprehensive history of all work done to process a request.
Return Transactional References Back to the Client
If you can follow a request’s path through your system, it’s all very interesting, but it’s not much fun if you receive a support call and are unsure of which request to look at.
When your API client makes a request and begins a process, you should include a transaction reference in the response headers.
When your Ops team wants to examine the issue, they should be able to submit the transaction reference via a support call, and the appropriate information should be available in the logs.
Invest in Setting Up a Logging Framework
Developers should arrange log data in an application to make processing easier and to log only important information to help with debugging and troubleshooting Microservices.
This organized logging aids in the creation of simple and adaptable Microservices logs, and the format of the recorded data is critical for tracing, debugging, and troubleshooting.
For many of the logging components, you might receive JSON documents rather than flat rows in a text file for each log.
This is because log processing and collation tools can quickly process each record. Since each record might include more detailed information, this sort of data structure is extremely important.
Consider Monitoring Tools
Monitoring is the Microservices’ control system. As Microservices get increasingly complicated, it becomes more difficult for you to analyze their performance and fix issues.
Given the dramatic changes in software delivery, the service must be monitored to help while troubleshooting Microservices.
For this reason, you should consider monitoring tools. Among other things, they can help you learn about the application’s overall health.
Learn about the performance of each service that makes up an application. Check that the API transactions are available and functioning properly.
If the logs from every system your code runs on are not easily accessible, debugging Microservices in production may be challenging. Because you cannot return to a machine instance later to examine what has changed, the issue is made worse by virtualized cloud instances, which can vanish at any time.
The management of logs and the fault-finding process must be planned during the design phase and carried out using the proper tooling and approach. Once you have done this, it dramatically improves the ease of managing your systems.