Kubernetes Observability: A Challenge?

Kubernetes is a highly dynamic and complex environment, with numerous components and services running in containers across a distributed system. As a result, monitoring and gaining insights into the behavior and performance of the system can be challenging. This requires proper instrumenting to ensure that all components and applications running within the Kubernetes clusters emit the right data, that data is captured properly, and stored in a reliable manner for monitoring and analysis. This requires knowledge of the components and their behavior, as well as the right monitoring tools and practices to use them.

Some of the common challenges related to observability in Kubernetes can be summarized as:

  • Proper Instrumentation: Configuring proper monitoring for each component of Kubernetes is important to ensure a high level of observability, but setting up this proper instrumentation can be challenging for a team that is new to the ecosystem.
  • Integration with other tools and services: Integrating Kubernetes observability with other tools such as log aggregation systems, tracing systems, and alerting systems can be a challenge. For instance, the integration with Prometheus and Grafana.
  • Complex Distributed Applications: Deployment of complex distributed applications in k8s, makes it challenging to diagnose issues when they arise. Debugging and troubleshooting the issues may take considerable time.
  • Resource Utilization: With k8s pods/containers being created and destroyed frequently, it makes it ever more difficult to monitor resource utilization effectively. Not done right, can cause cluster downtime.
  • Large Data Volumes: Kubernetes generates a large amount of data, including logs, metrics, and events, which can make it challenging to collect, store, and analyze.
  • Multiple Clusters: You add multiple clusters to the above mix. The challenge is only X times larger.

In this blog, we will outline important components that must be setup to have a reliable Observability system in place.


Cloud Architects and engineers at High Plains Computing (HPC) have helped numerous clients across a wide range of industries overcome the challenges associated with Kubernetes observability. Below is the summary of how they have been tackling the challenges and setting up an environment that eliminates these challenges and provides companies with a crystal clear view into the inner workings of their Kubernetes ecosystem.


Gaining the information about the complete Kubernetes environment as it is setup at your company is of paramount importance. If the system under observation is not understood well, it can be detrimental to the performance of the system. For a system to be observable, it must produce many kinds of signals. Common signals in the context of Kubernetes are metrics such as CPU and memory usage, traces and logs, application performance data including time spans for code blocks and API/DB calls performance data.


Once the signal production is ensured, then Agents and Collectors can be deployed to collect, filter, and aggregate information from those signals, and forward it to monitoring services. Most modern agents and collectors are auto-instrumented and little is needed to tell them what to collect and what to ignore.


Once the signals are collected and cleaned by the Agents and Collectors, they must be stored in a reliable storage, such as a database


Monitoring services export end points for collection of signals and store them in proprietary storage so that end users can use various dashboards and query tools to visualize and slice/dice all system data and interpret system internal state.


Dashboards and other visualization tools can help users gain insights into the data being captured by the monitor agents and collectors. The visualizations can make the task of monitoring numerous Kubernetes clusters at the same time with very few eyes on the board.


Here are some tools that can be used by admin teams to help them setup proper observability into their Kubernetes clusters:

  • Tools like Grafana along with Loki or Elastic Stack can be used to do log aggregation for application log signals, which has been modernized and more flexible agents and collectors to replace traditional log signal aggregation using ELK (Elastic search , Logstash and Kibana) 
  • System performance metrics can be observed using Grafana + Prometheus
  • Application performance monitoring can be achieved by using Open Telemetry and Pixie
  • Distributed tracing can be done with help of Open Telemetry
  • In addition there are several observability related services within AWS that can be used to enhance Observability of cloud native applications. Some of these services are: AWS Open Search, Cloud Watch, AWS XRay, Managed Grafana, Managed Prometheus,etc.


In conclusion, the answer to the question posted in the title of this blog is YES

Setting up proper instrumentation in place can be challenging due to the complexity and dynamic nature of Kubernetes environments. This involves use of the right monitoring tools and practices, and integrate with other tools and services such as log aggregation and tracing systems.

Additionally, a centralized observability strategy can provide a unified view of the company’s Kubernetes infrastructure and applications, enabling teams to identify and troubleshoot issues more efficiently and optimize resource utilization.


The High Plains Computing (HPC) team has deep Kubernetes implementation and observability setup experience. Please reach out for any further information.

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