7 Common Deployment Pitfalls While Using Kubernetes

 

In a team's DevOps journey, Kubernetes deployment can feel like a turning point. With the right setup and upkeep, it promises reliable deployment, scalability, and potent container orchestration. In actuality, a lot of teams fall into typical pitfalls that ultimately impede progress and raise operational risks.

The Voice of Kubernetes Experts Report 2024 indicates a strong industry shift towards cloud-native platforms, with 80% of new applications expected to be built on them within five years.

In the blog, we understand this relevance by discussing how common missteps directly affect deployment efficiency, team productivity, and service reliability.

1. Misconfigured Resource Requests and Limits

You can fine-tune the amount of CPU and memory that your pods can use with Kubernetes, but one of the most common and costly errors is to misconfigure these limits.

These two points frequently lead to misconfiguration:

• Waste of CPU and Memory

Underutilization or resource starvation are frequently the results of improperly defined requests and limits. In managed Kubernetes platforms like GKE or EKS, this not only impacts application performance but also drives up expenses.

• Errors in CrashLoopBack

Pods that use more resources than are available risk becoming stuck in a cycle of crashes. Instead of addressing the underlying problems caused by resource policies, teams frequently waste hours chasing symptoms.

2. Ignoring Health Checks and Probes

Consider readiness and liveness probes as the early warning systems of Kubernetes. Your cluster becomes blind to issues that could have been prevented if you had ignored them.

Overlooking these critical checks often leads to the following problems:

• Checks for Liveness and Readiness

These are essential components of Kubernetes resilience and are not optional add-ons. If readiness checks are skipped, a service that isn't yet ready may appear to be available. Pods that lack liveness probes remain active even when they are frozen.

• Downtime While Updates Are Being Made

Rolling updates may result in cascading failures if the right probes are not used. Too early or too late service exposure results in preventable user disruptions.

3. Over-Reliance on Defaults

Although Kubernetes comes with sensible defaults, they aren't customized to meet your networking, scaling, or security requirements. Overlooking the need for custom configurations can lead to significant vulnerabilities and operational challenges.

 This is particularly evident in these areas:

• Security and Permissions for Clusters

Excessive privileges may be provided by default roles and bindings. In multi-tenant settings, this creates security vulnerabilities that could be taken advantage of.

• Policies for Networks

Kubernetes permits unfettered communication between pods unless specifically configured. This can rapidly transform a single weak container into a lateral attack entry point.

4. Insufficient Logging and Monitoring

Teams cannot identify performance regressions or problems in real time in production without strong logging and monitoring. 

This absence of essential insights typically manifests in two ways:

• Absence of Centralized Logs

Debugging Kubernetes problems can become a laborious, manual procedure without tools like Fluentd, Loki, or ELK stacks.

• Trends in Missed Performance

Long before they affect output, bottlenecks can be identified by tracking CPU or memory usage over time. Teams may remain unaware of these trends if Prometheus and Grafana are not used.

5. Not Using Helm or Kustomize Properly

Helm and Kustomize are excellent in this situation because they assist teams in treating configuration as code, guaranteeing portability, consistency, and traceability across environments. However, without proper discipline and implementation, the very benefits it offers can be undermined. 

This often leads to two main problems:

• Absence of Templating Discipline

Helm charts and Kustomize overlays are frequently handled like static files by teams. The advantages of version control and reusable configurations are lost when they are not used as intended.

• Drift in Configuration

Environments become out of sync when modifications are made by hand instead of using templates. This drift disrupts CI/CD pipelines and results in inconsistencies.

6. Overcomplicating Initial Setup

It's tempting to build for scale before you even have scale when teams first implement Kubernetes. They might implement intricate patterns like service meshes, multi-cluster topologies, or layered ingress controllers.

 This approach usually causes two main problems:

• Deploying Before Understanding Workloads

Maintenance overhead results from implementing multi-cluster topologies, intricate ingress controllers, or service meshes without sufficient justification.

• Tool Sprawl

Sprawl Kubernetes functions best in an ecosystem that is narrowly focused. Overuse of tools, particularly those with overlapping features, can complicate troubleshooting.

7. Ignoring Security at Early Stages

Security is frequently put off until later, but with Kubernetes, a single mistake can expose your entire cluster. Insecure default settings allow dangerous configurations to go undetected in the absence of safeguards.

Two common early security oversights often leave clusters exposed:

• Containers Operating as Root

If such a container is compromised, attackers can escalate privileges and potentially affect the entire node or even the cluster. A compromised container has the potential to rapidly worsen.

• Missing Pod Security Standards (PSS): 

Dangerous setups such as privileged mode or host access are left unchecked. Adopt a secure-by-default mentality first. 

How to Stay on the Right Track?

It's important to realign with tried-and-true methods now that we've examined these pitfalls.

• Begin modestly and test non-production setups first.

• Make use of GitOps workflows and infrastructure as code.

• Prior to scaling, implement basic observability.

• Review network policies and RBAC on a regular basis.

Conclusion

DevOps enables a smooth transition to integration from monolith to microservices, but only if teams approach it with the proper mindset. In production settings, the cost of minor errors can mount quickly, ranging from neglecting resource limits to omitting health checks. However, the first step to better practices is awareness.

Having trouble with unforeseen Kubernetes problems? To avoid long-term technical debt, let knowledgeable engineers help you find the best solutions.