Kubernetes is a powerful platform for managing containerized applications, but like any technology, it comes with its own set of challenges. Many teams fall into anti-patterns that can negatively impact scalability, security, and reliability. In this post, we’ll explore the top 4 Kubernetes anti-patterns, how they can harm your environment, and best practices to avoid them.

1. Over-Complicating Microservices

One of the most common Kubernetes anti-patterns is the over-complication of microservices. While breaking down applications into microservices is a core philosophy of Kubernetes, too much fragmentation can lead to an overly complex architecture.

Why It’s a Problem

  • Operational Complexity: Too many microservices make it difficult to manage, troubleshoot, and deploy.
  • Resource Overhead: Each microservice requires its own resources, which can lead to inefficient usage.
  • Increased Latency: With multiple services communicating over the network, you introduce additional latency, especially when services depend heavily on each other.

Best Practice

Keep services well-defined and purpose-driven. Only break down monoliths into microservices when necessary, and aim for loosely coupled services. If a microservice depends too much on others, it may be better off as part of a larger service.

2. Not Using Proper Resource Requests and Limits

Another common anti-pattern in Kubernetes is failing to set appropriate resource requests and limits for CPU and memory. While Kubernetes can dynamically allocate resources, not defining them properly leads to inefficient cluster utilization.

Why It’s a Problem

  • Over-provisioning: Without limits, containers can consume more resources than needed, leading to unnecessary costs.
  • Under-provisioning: Not specifying resource requests can cause Pods to be scheduled on nodes with insufficient resources, leading to throttling or even crashes.
  • Unpredictable Performance: If resource usage is not capped, applications may become unpredictable under load, causing instability in the cluster.

Best Practice

Set realistic resource requests and limits based on the actual performance requirements of your applications. Use monitoring tools like Prometheus or Grafana to analyze resource usage patterns and adjust configurations as needed.

Example configuration:

resources:
  requests:
    memory: "256Mi"
    cpu: "500m"
  limits:
    memory: "512Mi"
    cpu: "1"

3. Ignoring Health Checks (Liveness and Readiness Probes)

Kubernetes provides Liveness and Readiness Probes to help determine the state of your applications. Ignoring or misconfiguring these probes is another common anti-pattern.

Why It’s a Problem

  • Unhealthy Pods: Without proper health checks, unhealthy Pods may continue running, leading to degraded performance.
  • Delayed Failover: If the Readiness Probe isn’t configured, Kubernetes may continue sending traffic to Pods that aren’t ready to handle requests, causing user-facing issues.
  • Inefficient Scaling: Misconfigured probes can cause Pods to be terminated prematurely or fail to scale efficiently during traffic spikes.

Best Practice

Always configure Liveness and Readiness Probes to ensure Kubernetes can correctly manage the lifecycle of your Pods. For example, a Readiness Probe can be used to delay traffic until your application is fully initialized.

Example configuration:

livenessProbe:
  httpGet:
    path: /healthz
    port: 8080
  initialDelaySeconds: 5
  periodSeconds: 10

readinessProbe:
  httpGet:
    path: /ready
    port: 8080
  initialDelaySeconds: 5
  periodSeconds: 10

4. Not Using Namespaces for Isolation

By default, Kubernetes places all Pods in the default namespace if not specified. This leads to a common anti-pattern where environments and services are not properly isolated.

Why It’s a Problem

  • Lack of Segregation: Running multiple environments (e.g., dev, staging, prod) in the same namespace can lead to name collisions and accidental modifications of critical services.
  • Security Risks: Without namespace-level isolation, there’s a higher risk of accidental or malicious access to resources that should be restricted.
  • Harder to Manage: Mixing different teams’ workloads in the same namespace makes it difficult to manage and monitor resource usage and permissions.

Best Practice

Use namespaces to separate environments, teams, or services within the same cluster. This approach provides better security, isolation, and resource management.

For example:

kubectl create namespace dev
kubectl create namespace prod

You can also use Role-Based Access Control (RBAC) to limit access to namespaces based on user roles.

Conclusion

Avoiding these Kubernetes anti-patterns can help improve the overall stability, performance, and security of your cluster. By simplifying microservices, properly configuring resource limits, utilizing health checks, and organizing workloads with namespaces, you ensure that your Kubernetes environment runs smoothly and scales efficiently.