Three Reasons Why Your Kubernetes-based Projects Fail and How You Can Avoid Them

February 18, 2022 | 4 minute read
Kumar Varun
Product Management - Logging Analytics
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In this blog, I’ll share three common pitfalls you should avoid when working with Kubernetes projects, with emphasis on monitoring and troubleshooting using logs and metrics. These pitfalls will help you steer clear of failure and succeed in your Kubernetes-based project - giving you peace of mind knowing that your team will have an easier time implementing native cloud projects in the future.

Reasons Why Kubernetes Projects Fail

Kubernetes is designed to provide a platform for data centers to run more efficiently with highly configurable and agile automation capabilities. With all this automation comes its own set of challenges - namely, challenges with monitoring and troubleshooting.

One common pitfall when working with Kubernetes projects is being unable to find failure points in your overall environment – which consist of infrastructure, Kubernetes platform, and the applications - due to a lack of relevant logs or metrics. To avoid this problem, you should enable log collection for all components, including the network.

Another common pitfall when working with Kubernetes is misconfiguration - when something goes wrong due to human error or not understanding how something works. To avoid this, you should use configuration management tools, like Helm to keep everyone on the same page.

Finally, another common pitfall when running Kubernetes projects is being unable to troubleshoot production issues because there's no way for your team to analyze the system without impacting the live environment. To avoid this issue, you should invest in an application that can provide comprehensive observability and management capabilities for all aspects of your environment.

Understand the importance of Kubernetes monitoring and troubleshooting with logs and metrics

Kubernetes provides a lot of value in both development and operations. One of the main advantages is that it eliminates the need for an administrator to manually control and configure individual containers. Instead, Kubernetes can create and manage these containers on developers’ behalf.

Along with this automation come challenges. In most cases, this includes monitoring what's going on with your application(s) running in a Kubernetes cluster, troubleshooting issues when they arise and being able to provide metrics from what you found so that the next person can avoid a similar problem.

Monitoring is important to understand what is happening inside your system at all times. Monitoring also helps identify potential problems before they happen, which gives you time to fix them before they have an impact on your system or business process.

Troubleshooting is all about identifying root causes for errors in your application when they happen - allowing for time to fix any problems before it has an impact on your system or business. Finally, metrics and topology-based exploration are helpful by providing information about the health of your application from a user or technical perspective - providing better insight into what needs fixing and helping you understand why something happened.

Figure 1:  OKE Cluster Summary


Figure 2:  Services dashboard

Tips for Avoiding Kubernetes-based project failure

  1. Make sure your monitoring is comprehensive and scalable
  2. Follow best practices for log collection
  3. Use logs, metrics, and object states together to troubleshoot issues

Logging Analytics Kubernetes Solution provides a comprehensive monitoring solution that is scalable using logs and metrics from all components and Kubernetes object details. Logging Analytics also provides several Out-of-the-box dashboards and saved searches to quickly start monitoring and troubleshooting issues.

Logging Analytics Kubernetes monitoring solution reference architecture
Figure 3:  Logging Analytics Kubernetes monitoring solution reference architecture


You can deploy Logging Analytics Kubernetes Solution in a few simple steps. Deployment of agent Daemon Sets and Replica Sets for collecting logs, metrics, and object information can be done using kubectl, helm charts, or terraform. Step-by-step instructions are available here on GitHub.

Once deployed you’ll have near real-time access to all the logs generated in your environment along with Kubernetes Objects info for analysis. Here are a few examples:

Figure 4: OKE Environment information


Figure 5:  Topology Based Exploration and Analysis in Log Explorer


If you are managing multiple clusters, you can use the custom filters in dashboards to select the scope to a specific cluster, namespace, node, or pod as needed and your selected scope will automatically be applied to all relevant dashboard's widgets.

Get started today and gain full control over your Kubernetes environment using Logging Analytics. Our "Monitor Kubernetes and OKE clusters with OCI Logging Analytics" reference architecture might be a good start.

If you want to see this in action, join us for the webinar on March 10th - "Troubleshoot and Monitor Cloud Native Apps with Logging Analytics" - Register here or watch it on-demand if you read this later.

Resources on Kubernetes and Logging Analytics:

Overview of Container Engine for Kubernetes

Logging Analytics Documentation

Logging Analytics Livelabs

Monitor Kubernetes and OKE clusters with OCI Logging Analytics




Kumar Varun

Product Management - Logging Analytics

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