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Kubernetes Application Deployment Best Practices

With Kubernetes approaching it's sixth year anniversary, our guest blogger, Eddie Segal, explains the best practices for deploying applications.

Written by Eddie Segal • Last Updated: • Cloud •

Kubernetes Application Deployment Best Practices

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Containerized applications have become a standard for software applications developers. Managing these containers can potentially be done manually in small deployments. However, you need an orchestration solution for larger deployments like those required by large enterprises. Kubernetes, which was created by Google engineers, is one such solution. 

What Is Kubernetes?

Kubernetes (K8s) is an open-source platform for scaling, management, and automatic deployment of containerized applications. Kubernetes is designed to group containers into logical units, also known as clusters. You can run K8s in on-premise, public and hybrid cloud environments.

Main Kubernetes features include:

  • Self-healing—disables containers that fail health checks, restart failed containers, and replaces containers after node failure.
  • Load balancing—automatically assigns DNS names and IP addresses to pods and balances the loads across them.
  • Automatic bin packing—assigns containers according to resource requirements to optimize resource use and ensures availability.
  • Horizontal scaling and batch execution—you can scale applications manually or automatically, and manage batches and Continuous Integration (CI) workloads. 

Kubernetes Deployment Options

Developers have several options to speed up the production of Kubernetes applications. This includes Kubernetes as a Service (KaaS) and Platform as a Service (PaaS) solutions. 

PaaS Kubernetes Deployment

PaaS enables developers to focus only on software development by abstracting away hardware and software deployment tasks. For instance, if you need a web server and a database, you need to enter configuration options and the PaaS will do the remaining work for you. 

Examples of PaaS systems for Kubernetes deployment include OpenShift, Cloud Foundry, Google App Engine.

KaaS Deployment

KaaS is a self-service option for Kubernetes management and deployment. KaaS solutions are responsible for the management and configuration of K8s. Some vendors also enable you to outsource the hosting of your deployment. 

Examples of KaaS solutions include Platform9, Amazon Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), and Azure Kubernetes Service (AKS).

Kubernetes Design Principles

Since K8s is cloud-native, it supports agile design principles like flexibility, automation, and portability.

Portability

Kubernetes can run across multiple environments, including on-premise, public cloud, private cloud, and personal devices. You can easily move your Kubernetes applications across different environments.

General-purpose

There is no restriction on what type of application you can deploy with Kubernetes. You can deploy any type of workload, including stateless or stateful services, batch jobs, and legacy monolithic applications. You can write Kubernetes apps in any language or framework without any restrictions.

Flexibility

Kubernetes is built as a collection of pluggable components and layers. As a result, you can replace many functionalities with custom, built-in solutions. This flexibility enables you to use a dedicated solution together with Kubernetes.

Extensibility

Kubernetes enables you to extend its capabilities with custom solutions. Extensibility enables you to develop numerous add-ons and third-party API extensions for Kubernetes. For example, go module support brings the recently added go feature to Kubernetes developers.

Automation

One of Kubernetes’s goals is to reduce the amount of manual operations. Applications can scale and heal themselves without any manual intervention when deployed with Kubernetes. You can also integrate Kubernetes into your CI pipeline. This integration enables developers to automatically commit code changes onto the test environment.

6 Kubernetes Application Deployment Best Practices

Deploying Kubernetes on an enterprise scale is a complex process that requires expertise. The following best practices can help you ease the process.

  1. Limit container operations to ensure security

You have to limit the number of operations to minimum when deploying containerized applications. You can achieve this by running non-root containers. Non-root containers are containers with a random user that is different from root. Non-root containers are an imperative requirement in some Kubernetes-based platforms like OpenShift.

  1. Monitor your deployments

You have to monitor your workloads if you want them to be ready for production. You can monitor your application status with tools like Prometheus and Wavefront. These tools support metrics exporters for most production-ready charts. 

In addition, ensure that your workloads integrate with logging tools like ELK. Logging tools can improve the monitoring of your containerized applications. The advantages of logging include auditing, early failure prevention, performance analysis, trend detection, debugging and many more.

  1. Implement a proper testing process

Updating a label in a StatefulSet can break the helm upgrade command. Therefore, you should include upgrade tests in your pipeline. You can do minor versions upgrades without any manual intervention. Hoewer, major version upgrades cannot work without manual intervention.

Another common issue is the addition of features to a chart. Chart features are disabled by default. As a result, a normal helm install test will not detect any issue. You can prevent this potential problem by increasing the test coverage of your helm charts with many different values.yaml files. 

  1. Use the builder pattern

The builder pattern enables you to include the compiler, the dependencies and unit tests inside the container. You can then run the code and output the build artifacts. Build artifacts are then combined with any static files, and bundles. The container can also include some monitoring or debugging tools. 

Eventually, your container file should only reference your base image and the runtime environment container. This pattern is more useful for static languages that compile like C++ or Go.

  1. Keep base images small

Build your packages on top of the leanest most viable base images. Smaller base images reduce overhead. For example, your app size may be only about 5 MB. However, it may include an extra 600 MB of unnecessary libraries if you take an off-the-shelf image with Node.js. Other advantages of smaller images include faster builds, less storage, faster image pulls, less attack surface.

  1. Use readiness and liveness probes

A flawed application can result in revenue loss. Fortunately, Kubernetes offers two key concepts to check the failure status of your applications:

  • Readiness probes—ensures that the service load balancer of the application does not route traffic to the app if the probe fails.
  • Liveness probes—disables the container your application is running on if the probe fails.

Using readiness and liveness probes help you control your application health before and after it starts up in a cluster. The service will send traffic to the application if your app passes the probe.

Conclusion

Kubernetes deployment can be challenging, especially if you do not have in-house experts to perform the configuration and ongoing management. However, you cannot sacrifice or ignore the benefits of K8s due to lack of expertise. A carefully constructed plan can help you roll out even the largest deployments. Implementing the tips covered in this article should help you build and implement such a plan.

If you've deployed applications, what are your best practices? Post your comments below and let's discuss?

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Picture of Eddie Segal

Eddie Segal is an electronics engineer with a Master’s Degree from Be’er Sheva University, a big data and web analytics specialist, and also a technology writer. In my writing, I cover subjects ranging from cloud computing to agile development to cybersecurity and deep learning.

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