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Testing Containerized Applications: Strategies, Best Practices and Challenges

According to a recent adoption survey on Kubernetes, many businesses use it to run their production workloads. Survey findings confirm that Kubernetes plays a pivotal role in powering mission-critical applications across various enterprises. It's safe to state that the development, deployment, and management of applications have been completely transformed by containerization. The many advantages of Kubernetes are its scalability, mobility, and resource efficiency. However, to guarantee their dependability and stability across a range of settings, containerized applications require strong testing techniques. The many testing approaches, best practices, and difficulties that come with testing containerized applications will all be covered in this article.

Understanding Containerized Applications

Understanding containerized applications is necessary before we talk about testing methodologies. You can build a docker image that creates a container containing all your application's dependencies. With this method, containers may be readily deployed across many environments and are isolated from the underlying infrastructure. Applications that are containerized usually consist of the runtime environment, libraries, dependencies, and application code, all packed into a single container image. For instance, a standard Java containerized application includes the code, config, Java runtime environment (JRE), and application-specific dependencies of a Java application, all contained within a container image.

The following image represents what goes inside a containerized application.

Screenshot 2024-06-11 at 16.03.39



Importance of Testing Containerized Applications

While containerization offers numerous benefits, such as isolation, portability, and scalability, it also introduces testing challenges. ⁤⁤Conventional testing methods may not be sufficient for dynamic containerized applications. The following points describe issues with a traditional testing approach for containerized applications.

  • Containerized applications are ephemeral and often scale dynamically in response to changing workloads. They might only last a while. Therefore, they may not be suitable for traditional testing methods that rely on reliable, static infrastructure.
  • Containerized applications are often run on distributed systems like Kubernetes, making replicating and identifying issues hard in a traditional testing setup.
  • Containerized applications often have numerous microservices that operate together for the system to function properly. Testing these components individually with a traditional testing approach may not ensure that the complete system functions properly.

Mitigating the risks and unknowns associated with testing containerized applications effectively and early in the development lifecycle is crucial for minimizing failures and downtimes in production. ⁤Early testing containerized applications act as a safety net, preventing bugs from plaguing the final product.

Let us understand the challenges you can face while testing containerized applications.

Challenges in Testing Containerized Applications

Data Management

By default, containers are transient and require certain configurations to enable data persistence. The problem lies in testing Containerized apps while handling data consistency and durability for numerous stateful container instances in a distributed environment such as Kubernetes.

Maintaining consistency and persistence throughout the events of the container lifecycle can be strenuous and time-consuming. For example, consider running a MySQL database in a container without mounting an external volume.

All database records saved in its filesystem are lost if the container is stopped or restarted. Testing schema modifications and database migrations in a containerized system can be challenging and require specific tools, i.e., Flyway and Liquibase.

Scaling Complexities

Containerized applications can be easily scaled up or down based on load and demand using orchestration platforms like Kubernetes, which provide built-in scalability features. However, specialized tools, such as Horizontal Pod Autoscaler and Vertical Pod Autoscaler, are needed to evaluate containerized apps for different scaling scenarios.

To evaluate the scalability of containerized applications, it is necessary to precisely supply and allocate resources such as memory, storage, and CPU. Estimating the resource needs for your application's scalability can be challenging due to unpredictable workloads.

Vulnerability Management

Container images could contain vulnerabilities and outdated dependencies. They must be regularly scanned for vulnerabilities and patched in case of major severities. Vulnerability testing or scanning of container images should be the highest priority, as outdated dependencies can tamper with your system.

While testing images for vulnerabilities seems like a good approach, automating vulnerability management using CI/CD takes a lot of effort.

Dependency Management

It’s a common perception that once an application is containerized, it will run the same regardless of its environment. Even though containers guarantee immutability, there are scenarios where the dependent library version differs between environments.

For example, your local build caches might store different library versions from those in a continuous integration pipeline, leading to discrepancies. As a result, something fails in the testing environment but not the production environment.

Let's look at an example of an e-commerce application where a Product catalog service interacts with a database. The catch is that there is a difference in the database driver version, and the service fails to connect with the database in a test environment.

The testing team struggles to test the entire flow because of this issue, which directly impacts deliverables. Such problems are often hard to debug and pose a risk to your overall testing strategy.

Environment Consistency

Although using Dockerfile with containers gives you a consistent environment, there is still room for errors that can impact the testing outcome.

There can be discrepancies such as:

  • Difference in environment variables
  • Difference in resource allocation
  • Difference in the base image
  • Difference in the network settings
  • Difference in configuration

These discrepancies can result in unwanted results while testing your containerized application and lower your confidence in your testing strategy.

Strategies for Testing Containerized Applications

Unit Testing

Unit testing is the process of testing separate modules or components of an application separately. Two methods exist:

  • Using unit tests to test your application outside of the container is the first strategy. You will use any unit testing framework, such as Junit, for this, and you will usually use mocks for any external dependencies that your application interacts with, such as databases.
  • Unit tests are executed within a container using the second method. Rather than simulating our dependencies, we may implement them using tools like Testcontainers, a testing framework for containerized apps. Therefore, you may use real database instances with the same configurations as production rather than using in-memory implementations or mocks of these external dependencies.

For example, the following code snippet shows how to set up a throwaway PostgreSQLContainer instance for unit testing.

PostgreSQLContainer postgres = new PostgreSQLContainer("postgres:15");
postgres.start();
var username = postgres.getUsername();
var password = postgres.getPassword();
var jdbcUrl = postgres.getJdbcUrl();
//perform db operations
postgres.stop();

Integration Testing

Integration testing verifies that different components or services within an application are working together smoothly. It validates that crucial functionality between modules is working as expected.

To understand the concept of Integration testing, let's consider an e-commerce application scenario where an Order processing module interacts with a Payment gateway module. This integration test verifies that the order processing module sends correct order details, i.e., total order amount, etc., to the payment gateway.

The test also asserts that the mocked payment gateway will accept or decline this order's payment request. The order processing module will update the order status based on the payment gateway's response.

To summarize, we are testing integration between two modules to ensure the application behaves as expected.

When discussing integration testing of containerized applications, we must understand that the modern software stack is complex, and an application typically consists of various tools and technologies. Testing your containerized workloads with such complexity is daunting.

For example, if there is a distributed transaction involving Order processing and a Payment gateway module, then at a minimum, they will talk to a database, a messaging broker, and many other third-party dependencies. Having all these components working together seamlessly takes work.

An old-fashioned way of running integration tests is to have the environment running before you run your test. You can also try in-memory implementations of these tools, but they often don't have features compatible with your production use cases. For example, you might be using H2 database for your local database testing, but in production, you are using some advanced Postgres features that are unavailable with H2.

To fix this, you can leverage Testcontainers so with Testcontainers, instead of relying on the H2 database, you can use Postgres Docker container and point your tests to run against Postgres. So with Testcontainers for integration testing, you have lightweight throw-away instances of Docker containers.

They will run exactly how you would run them in production. The only prerequisite is that you will need a Docker-like container runtime to make it work. But that should be easy since you already run your applications as Docker containers.

End-to-End Testing

Unlike integration testing, end-to-end testing verifies your entire application workflow, not just interactions with certain modules. This type of testing is crucial for microservices that consists of multiple independent modules. Microservices architecture style has largely influenced use cases for containerization.

Let's not generalize, but you use more than one tech stack for all these microservices in some cases. Each microservice will define its goals and scope, and tools will be leveraged according to the requirements.

For example, Microservice A might be written in Java and talk to cache and databases like Redis and Postgres. At the same time, Microservice B might be written in Python and rely on a messaging system like Kafka. Now sprinkle some containers on this; voila, you have a use case where various containers run to complete the entire flow. Doing end-to-end testing in such a multi-container setup can be challenging.

Enter Docker Compose. We can use Docker Compose to have a single file, docker-compose.yaml to define all the dependent services required to run our containerized application. Docker Compose even bonds well with Testcontainers. The following is a simple docker compose file where the services backend and db are defined.


services:
backend:
build: backend
ports:
- 8080:8080
db:
image: postgres
...

 

This single configuration file alone brings up your entire tech stack for end-to-end testing, including the network and volume defined for your containerized application.

This might work well for local testing, but we must understand that Docker Compose has limitations and cannot be used in production use cases. For instance, Docker Compose runs on a single host and doesn't have self-healing and auto-scaling capabilities.

Enter Kubernetes. Kubernetes is the de facto standard for deploying containers at scale. It also offers an E2E framework to test components running in the Kubernetes cluster.

There are also Kubernetes native tools like Testkube that can effectively be used for E2E testing of containerized applications. It integrates with tools like Playwright, Cypress, or Postman to automate end-to-end tests.

Best Practices for Testing Containerized Applications

The following are some of the best practices for improving the testing of containerized applications. It's important to note that these guidelines are general recommendations and may need to be adapted or tailored to your specific use case or project requirements.

Use of Lightweight Test Images

Using lightweight base images for your tests is one strategy you can employ. It is even possible to have a specific Dockerfile for your tests. By using this method, you have reduced the number of dependencies in the Docker image used by your application. You don't need extra weight or complexity for your tests.

For example, for Java applications, you can use smaller JRE images rather than an image containing a full-blown JDK. Your tests will run more quickly and with less resource overhead as a result.

You can also create efficient test images suited to particular testing requirements with Alpine Linux-based images and Docker multi-stage builds.

Automation of Testing Processes

Automated testing is another tactic you should use to cut down on manual effort, favors consistency, and quicken the feedback loop. You may run your tests frequently and more efficiently with CI/CD platforms like Jenkins, GitLab CI, or CircleCI. This will help you find errors early in the process.

Shift Left Testing

In this approach, testing is done early and often during software development. In the legacy waterfall model approach, the previous phase should be completed before moving to the next phase.

So before we begin testing, we must close the development phase, which is relatively inefficient and counterproductive. Shift-left testing becomes more critical for containerized applications because of their distributed nature.

The following image represents the concept of shift left testing.

Screenshot 2024-06-11 at 16.08.09

Conclusion

To conclude, effective test strategies can mitigate data management, scaling issues, vulnerability management, dependency management, and environment consistency challenges.

We can improve the testing process by employing tools like Docker Compose and Kubernetes, as well as methodologies like end-to-end, integration, and unit testing, if necessary. Implementing best practices, such as employing lightweight test images, automating testing methods, and adopting a shift-left testing method, can also speed testing efforts and improve overall product quality.

Organizations can fully utilize containerized applications and provide trustworthy and resilient software solutions by following these tips and consistently improving testing procedures.

Ashish Choudhary

Written by Ashish Choudhary