Cloud native technologies, such as containers or serverless computing, are essential for building highly-portable applications in the cloud. You can design more resilient, scalable, and adaptable applications for changing environments by leveraging these technologies. We can explain these three benefits in one word: portable.
Unlike monolithic models that become cumbersome and nearly impossible to manage, cloud native microservices architectures are modular. This approach gives you the freedom to pick the right tool for the job, a service that does one specific function and does it well. It’s here where a cloud native approach shines, as it provides an efficient process for updating and replacing individual components without affecting the entire workload. Developing with a cloud native mindset leads to a declarative approach to deployment: the application, the supporting software stacks, and system configurations.
Think of containers as super-lightweight virtual machines designed for one particular task. Containers also are ephemeral–here one minute, gone the next. There’s no persistence. Instead, persistence gets tied to block storage or other mounts within the host filesystem but not within the container itself.
Containerizing applications makes them portable! I can give you a container image, and you can deploy and run it across different operating systems and CPU architectures. Since containerized applications are self-contained units that get packaged with all necessary dependencies, libraries, and configuration files, code does not need to change between different cloud environments. As such, here’s how containers lead to portability in a cloud native design.
- Lightweight virtualization: Containers provide an isolated environment for running applications, sharing the host OS kernel but isolating processes, file systems, and network resources.
- Portable and consistent: Containers package applications and their dependencies together, ensuring they run consistently across different environments, from development to production.
- Resource-efficient: Containers consume fewer resources than virtual machines, as they isolate processes and share the host OS kernel; they do not require the overhead of running a separate “guest” OS on top of the host OS.
- Fast start-up and deployment: Containers start up quickly, as they do not need to boot a full OS, making them ideal for rapid deployment, scaling, and recovery scenarios.
- Immutable infrastructure: Containers are designed to be immutable, meaning they do not change once built, which simplifies deployment, versioning, and rollback processes, and helps ensure consistent behavior across environments.
When Should You Consider Containers?
Containers allow you to maintain consistency. Certain aspects of development will get omitted in staging and production; for instance, verbose debug outputs. But the code that ships from development will remain intact throughout proceeding testing and deployment cycles.
Containers are very resource efficient and super lightweight. While we mentioned that containers are akin to virtual machines, they could be tens of megabytes as opposed to the gigs we’re used to on giant (or even smaller but wastefully utilized) VMs. The lighter they get, the faster they start up, which is important for achieving elasticity and performant horizontal scale in dynamic cloud computing environments. Containers also are designed to be immutable. If something changes, you don’t embed the new changes within the container; you just tear it down and create a new container. With this in mind, here are other considerations when deciding if containers should be part of your cloud native model.
- Improved deployment consistency: Containers package applications and their dependencies together, ensuring consistent behavior across different environments, simplifying deployment, and reducing the risk of configuration-related issues.
- Enhanced scalability: Containers enable rapid scaling of applications by quickly spinning up new instances to handle increased demand, optimizing resource usage, and improving overall system performance.
- Cost-effective resource utilization: Containers consume fewer resources than traditional virtual machines, allowing businesses to run more instances on the same hardware, leading to cost savings on cloud infrastructure.
- Faster development and testing cycles: Containers facilitate a seamless transition between development, testing, and production environments, streamlining the development process and speeding up the release of new features and bug fixes.
- Simplified application management: Container orchestration platforms manage the deployment, scaling, and maintenance of containerized applications, automating many operational tasks and reducing the burden on IT teams.
Container Best Practices
There are many ways to run your containers, and they’re all interoperable. For instance, when migrating from AWS, you simply re-deploy your container images to the new environment, and away you and your workload go. There are different tools and engines you can use to run containers. All of them have different resource utilization and price points. If you’re hosting with Linode (Akamai’s cloud computing services), you can run your containers using our Linode Kubernetes Engine (LKE). You can also spin up Podman, HashiCorp Nomad, or Docker Swarm, or Compose on a virtual machine.
These open-standard tools allow you to quickly go through development and testing with the added value of simplified management when using a service like LKE. Kubernetes becomes your control plane with all the knobs and dials to orchestrate your containers with tools built on open standards. In addition, if you decide to use a platform-native offering like AWS Elastic Container Service (ECS), you’ll pay for a different sort of utilization.
Another important part of containers is understanding registries, which you use to store and access your container images. We often recommend using Harbor. A CNCF project, Harbor allows you to run your private container registry, allowing you to control the security around it.
Always be testing and have a very in-depth regression test suite to ensure your code is of the highest quality for performance and security. Containers should also have a plan for failure. If a container fails, what does that retry mechanism look like? How does it get restarted? What sort of impact is that going to have? How will my application recover? Does stateful data persist on the mapped volume or bind mount?
Here are some additional best practices for using containers as part of your cloud native development model.
- Use lightweight base images: Start with a lightweight base image, such as Alpine Linux or BusyBox, to reduce the overall size of the container and minimize the attack surface.
- Use container orchestration: Use container orchestration tools such as Kubernetes, HashiCorp Nomad, Docker Swarm, or Apache Mesos to manage and scale containers across multiple hosts.
- Use container registries: Use container registries such as Docker Hub, GitHub Packages registry, GitLab Container registry, Harbor, etc., to store and access container images. This makes sharing and deploying container images easier across multiple hosts and computing environments.
- Limit container privileges: Limit the privileges of containers to only those necessary for their intended purpose. Deploy rootless containers where possible to reduce the risk of exploitation if a container is compromised.
- Implement resource constraints: Set resource constraints such as CPU and memory limits to prevent containers from using too many resources and affecting the system’s overall performance.
- Keep containers up-to-date: Keep container images up-to-date with the latest security patches and updates to minimize the risk of vulnerabilities.
- Test containers thoroughly: Before deploying them to production, ensure that they work as expected and are free of vulnerabilities. Automate testing at every stage with CI pipelines to reduce human error.
- Implement container backup and recovery: Implement a backup and recovery strategy for persistent data that containers interact with to ensure that workloads can quickly recover in case of a failure or disaster.