2. Underestimating Liveness...

# 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. … 1. 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. 2. 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. ... 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. … ## 4. Treating dev and prod exactly the same **The pitfall**: Deploying the same Kubernetes manifests with identical settings across development, staging, and production environments. This often occurs when teams aim for consistency and reuse, but overlook that environment-specific factors—such as traffic patterns, resource availability, scaling needs, or access control—can differ significantly. Without customization, configurations optimized for one environment may cause instability, poor performance, or security gaps in another. … ## 5. Leaving old stuff floating around **The pitfall**: Leaving unused or outdated resources—such as Deployments, Services, ConfigMaps, or PersistentVolumeClaims—running in the cluster. This often happens because Kubernetes does not automatically remove resources unless explicitly instructed, and there is no built-in mechanism to track ownership or expiration. Over time, these forgotten objects can accumulate, consuming cluster resources, increasing cloud costs, and creating operational confusion, especially when stale Services or LoadBalancers continue to route traffic. … ## 6. Diving too deep into networking too soon **The pitfall**: Introducing advanced networking solutions—such as service meshes, custom CNI plugins, or multi-cluster communication—before fully understanding Kubernetes' native networking primitives. This commonly occurs when teams implement features like traffic routing, observability, or mTLS using external tools without first mastering how core Kubernetes networking works: including Pod-to-Pod communication, ClusterIP Services, DNS resolution, and basic ingress traffic handling. As a result, network-related issues become harder to troubleshoot, especially when overlays introduce additional abstractions and failure points. … ## 7. Going too light on security and RBAC **The pitfall**: Deploying workloads with insecure configurations, such as running containers as the root user, using the `latest` image tag, disabling security contexts, or assigning overly broad RBAC roles like `cluster-admin`. These practices persist because Kubernetes does not enforce strict security defaults out of the box, and the platform is designed to be flexible rather than opinionated. Without explicit security policies in place, clusters can remain exposed to risks like container escape, unauthorized privilege escalation, or accidental production changes due to unpinned images.