K8s Anti-Design Pattern Series - Deploying without memory and CPU limits

Introduction

Kubernetes is a powerful tool for deploying and managing applications in a cluster environment. However, without proper management of resources, applications can easily overwhelm the cluster and cause chaos. This can be especially problematic in production environments where even minor issues can lead to catastrophic consequences. In this blog, we will explore the importance of resource limits and automation in managing applications in a Kubernetes cluster.

Resource Limits:

Applications deployed in a Kubernetes cluster have no inherent constraints on the resources they can consume. This means that they have the potential to take over the entire cluster, leading to performance issues for other applications. To prevent this, it is crucial to set resource limits for each application, regardless of the cluster they are deployed to. It's not enough to simply check how much resources an application typically uses - peak traffic and load scenarios must also be considered to avoid unexpected resets or crashes. However, setting limits that are too high can lead to inefficiencies and wasted resources in the cluster.

Automation:

To streamline the management of resources in a Kubernetes cluster, automation can be leveraged. By using tools like vertical cluster auto-scalers, the entire resource game can be automated once Kubernetes and the application's resource needs have been mastered. It's also important to investigate the usage habits of programming languages and the underlying platform to ensure that legacy software with memory limit issues, such as Java apps written before JVM 1.5, are managed properly. With proper resource limits and automation, applications can be efficiently managed in a Kubernetes cluster, ensuring optimal performance and avoiding catastrophic consequences.