Let’s look at some use cases that are suitable for containerization to get a fair understanding. The following types of applications are commonly deployed using containers:
- Microservices architecture: Applications that follow a microservices architecture, where the functionality is divided into small, independent services, are well-suited for containerization. Each microservice can be packaged as a separate container, enabling easier development, deployment, scaling, and management of the individual services.
- Web applications: Web applications, including frontend applications, backend APIs, and web services, can be containerized. Containers provide a consistent runtime environment, making it easier to package and deploy web applications across different environments, such as development, testing, and production.
- Stateful applications: Containers can also be used to run stateful applications that require persistent data storage. By leveraging container orchestration platforms’ features, such as persistent volumes or stateful sets, stateful applications such as databases, content management systems, or file servers can be containerized and managed effectively.
- Batch processing or scheduled jobs: Applications that perform batch processing tasks or scheduled jobs, such as data processing, periodic backups, or report generation, can benefit from containerization. Containers provide a controlled and isolated environment for running these jobs, ensuring consistent execution and reproducibility.
- CI/CD tools: Containerizing CI/CD tools such as Jenkins, GitLab CI/CD, or CircleCI allows for consistent and reproducible build, test, and deployment pipelines. Containers make it easier to manage dependencies, isolate build environments, and enable rapid deployment of CI/CD infrastructure.
- Development and testing environments: Containers are valuable for creating isolated and reproducible development and testing environments. Developers can use containers to package their applications along with the required dependencies, libraries, and development tools. This enables consistent development and testing experiences across different machines and team members.
- Internet of Things(IoT) applications: Containers can be used to deploy and manage applications in IoT scenarios. They provide lightweight and portable runtime environments for IoT applications, enabling easy deployment across edge devices, gateways, or cloud infrastructures.
- Machine learning and data analytics applications: Containerization is increasingly used to deploy machine learning models and data science applications. Containers encapsulate the necessary dependencies, libraries, and runtime environments, allowing for seamless deployment and scaling of data-intensive applications.
It’s important to note that not all applications are ideal candidates for containerization. Applications with heavy graphical interfaces, legacy monolithic architectures tightly coupled to the underlying infrastructure, or applications that require direct hardware access may not be suitable for containerization. Virtual machines or other deployment approaches may be more appropriate in such cases.
