7 Common Kubernetes Pitfalls (and How I Learned to Avoid Them)

7 Common Kubernetes Pitfalls (and How I Learned to Avoid Them) It’s no secret that Kubernetes can be both powerful and frustrating at times. When I first started dabbling with container orchestration, I made more than my fair share of mistakes enough to compile a whole list of pitfalls. In this post, I want to walk through seven big gotchas I’ve encountered (or seen others run into) and share some tips on how to avoid them. Whether you’re just kicking the tires on Kubernetes or already managing production clusters, I hope these insights help you steer clear of a little extra stress. 1. Skipping resource requests and limits The pitfall : Not specifying CPU and memory requirements in Pod specifications. This typically happens because Kubernetes does not require these fields, and workloads can often start and run without them-making the omission easy to overlook in early configurations or during rapid deployment cycles. Context : In Kubernetes, resource requests and limits are critical for efficient cluster management. Resource requests ensure that the scheduler reserves the appropriate amount of CPU and memory for each pod, guaranteeing that it has the necessary resources to operate. Resource limits cap the amount of CPU and memory a pod can use, preventing any single pod from consuming excessive resources and potentially starving other pods. When resource requests and limits are not set: Resource Starvation: Pods may get insufficient resources, leading to degraded performance or failures. This is because Kubernetes schedules pods based on these requests. Without them, the scheduler might place too many pods on a single node, leading to resource contention and performance bottlenecks. Resource Hoarding: Conversely, without limits, a pod might consume more than its fair share of resources, impacting the performance and stability of other pods on the same node. This can lead to issues such as other pods getting evicted or killed by the Out-Of-Memory (OOM) killer due to lack of available memory. How to avoid it: Start with modest requests (for example 100m CPU, 128Mi memory) and see how your app behaves. Monitor real-world usage and refine your values; the HorizontalPodAutoscaler can help automate scaling based on metrics. Keep an eye on kubectl top pods or your logging/monitoring tool to confirm you’re not over- or under-provisioning. My reality check : Early on, I never thought about memory limits. Things seemed fine on my local cluster. Then, on a larger environment, Pods got OOMKilled left and right. Lesson learned. For detailed instructions on configuring resource requests and limits for your containers, please refer to Assign Memory Resources to Containers and Pods (part of the official Kubernetes documentation). 2. Underestimating liveness and readiness probes The pitfall : Deploying containers without explicitly defining how Kubernetes should check their health or readiness. This tends to happen because Kubernetes will consider a container “running” as long as the process inside hasn’t exited. Without additional signals, Kubernetes assumes the workload is functioning-even if the application inside is unresponsive, initializing, or stuck. Context : Liveness, readiness, and startup...

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