The Software Delivery Life Cycle (SDLC) is composed of a series of iterations guided by development best practices. This is because software development and delivery requires a great deal of trial and error, and confidence in the final product is built upon making continuous improvements through a process called Continuous Integration and Continuous Development (CI/CD). The emerging trend of the CI/CD pipeline is becoming a core part of the process of software iteration because it allows for systemic builds and tests before the actual artifact is completed and deployed. Unlike traditional SDLC processes, which do not focus on real-time testing as part of the process, CI/CD pipelines guarantee consistency since the application is deployable and able to interact with all relevant tools at every development milestone.
Testing is an essential part of the development process, especially when using CI/CD pipelines. With the rise of containerization, the demand for container-native integration test codebase coverage keeps increasing due to the need to run the same test on different operating systems. The different software modules combined in a container need to work seamlessly as a unit, which could be made easier by integration testing. The main challenge faced in integration testing is that it tends to consume a lot of resources due to the underlying application dependencies. This article will explore the effectiveness of integration testing in container-native applications while using minimal workload resources.
Containerization has changed the way applications are built and delivered, resulting in isolated, dependency-managed, and immutable software that can be deployed anywhere. All of these advancements, along with a smaller resource footprint, contribute to cutting operating costs and management overhead. A container is a standard unit of software that contains code and all of the application's dependencies, allowing it to function independently and efficiently in a single computing environment. With containerization, applications are isolated in a secure space with their dependencies installed, then stored as single container image files (locally or remotely) so that they can run independently. The primary benefits of containerization include application isolation, improved operational agility, and process consistency.
With the advent of Agile software methodology, IT industries are embracing DevOps technology, which prioritizes high efficiency through the widespread use of automation tools. Two of the main pillars of DevOps are continuous integration (CI) and continuous delivery (CD), and with the utility of the CI/CD pipeline, coupled with the rise of containerization, container pipelines are becoming more and more popular. A container pipeline is essentially an automated SDLC for containerization, wherein every stage is iteratively tested and improved upon continuously – from image creation to integration, testing, and production deployment. Some of the container pipeline options available today include Heroku, Azure DevOps, AWS Elastic Beanstalk, GitLab CI/CD, Jenkins, and Google Cloud Build.
Typically, the container pipeline consists of the following stages:
The CI/CD pipeline is based on automation wherein the building process vets the artifacts at every stage of continuous integration, deployment, and delivery. Since software engineering is an iterative process, the project goes through several quality control steps (such as finding bugs and identifying fixes) before engineers achieve a viable release candidate. The end product of the CI/CD pipeline (the viable candidate) is then packaged, distributed, and configured before deployment. In a nutshell, the goal of a CI/CD pipeline is to improve the quality of the release, reduce risk, and enable consistent collaboration between engineers, operations teams, and quality assurance teams.
One of the primary cornerstones of continuous integration (CI) is the ability to build consistently. As the team's CI procedures evolve, they become more consistent and efficient, which also allows for the possibility of having more builds available with more consistency. Combining containerization with automated pipelines using CI/CD tools offers more flexibility to software delivery teams while also speeding up the development process. Using container pipelines not only ensures consistency during the development process, but also makes the application more robust and available since it eliminates the potential for human error. This is because automated container pipelines prioritize repeatability in testing, which means that image containers become more and more user-friendly at every stage of the CI/CD software delivery process.
Testing starts once a container image has been built and deployed into the application staging environment. At this point, extensive testing is carried out to ensure that the application functions and performs as expected, and that the container is robust and secure. Some of the container pipeline tests that are integrated at this stage include:
One of the main challenges faced during container pipeline testing is the lack of standardized dependencies for clustered or dependent containers. The main goal of containerization is to avoid infrastructural complexity by ensuring that every container version is fault-tolerant and does not affect the underlying orchestration platform. This way, developers do not have to worry about having the correct dependencies since every container image version already contains the corresponding packages. To ensure seamless container pipeline testing, the initial container pipeline setup should be oriented toward achieving end-to-end container independence, proper configurations, and an automated pipeline while maintaining observability, security, and policy management.
As stated above, working with cloud-native containerized applications makes it easier to take advantage of the CI/CD framework with container pipelines. With technology like Kubernetes or Docker, you can set up containerized pipelines that control the complete life cycle of microservices and container cluster applications.Slim AI has created an end-to-end platform to help DevOps teams with their software delivery process through the creation of production-ready containers andoptimized images. Container pipelines solve integration challenges through testing and CI/CD delivery processes. To get started or to learn more about containerized pipelines, you can sign up for theSlim Developer Platform here.