First published: 24 Jul 2024
Last modified:
Author CKA: Vincenzo Tagliavia (CKA, CKAD, CKS)
As Kubernetes continues to dominate the landscape of container orchestration, ensuring effective Kubernetes Monitoring & Observability is crucial for organisations of all sizes. For CTOs and CIOs in startups and large corporations alike, robust observability can significantly enhance security, and overall system reliability and robustness.
The question is, how do we approach Observability and what are the most common challenges organisations face with it in modern times?
Let’s define some terminology that may not be immediately obvious:
Monitoring and Observability are not the same thing.
Monitoring provides insights into what is happening in the system. Observability helps explain why the system behaves in a particular way.
This guide explores common challenges in Kubernetes Monitoring & Observability and offers practical, cost-effective solutions with a focus on open-source software and simplicity.
Additionally, we delve into the golden signals of observability to help you identify the critical metrics to monitor.
Metrics, Logs and Traces, allow your DevOps, or DevSecOps teams, to focus on the most important factors that make up Monitoring and Observability.
Metrics: Numeric values representing system performance as percentages over a set time period. Metrics help measure performance and resource utilisation. For example, if a metric shows “Average CPU Utilization was 40% over the last 7 days,” it means that, on average, the system used 40% of its CPU resources for activities like computations, dependency updates, or virtualization during that week.
Traces: Detailed logs that track a request’s path through the system, from start to finish. For example, if you submit a search query on a website, traces would show each step the request takes – from the user interface to the database and back – helping to identify bottlenecks and understand the request’s journey.
Logs: Records of discrete events that occur at specific times within the system. Logs come in various formats (plain text, structured, binary) and provide insights into system behaviour and issues.
To effectively implement observability in Kubernetes, you need to deploy monitoring tools that collect, store, and analyse these data types.
Kubernetes introduces unique challenges for observability. Unlike traditional applications or virtual machines, Kubernetes captures data in “snapshots” or specific points in time (IBM Developers).
Additionally, Kubernetes does not centralise logs by default.
Since logs are recorded in their respective environments, as the CTO/CIO of your organisation you may want to centralise the logging architecture of your systems. Log centralisation is a common strategy that would allow you to unify scattered data points from various sources across different environments.
At the implementation stage, DevOps or DevSecOps teams must enable Kubernetes auditing and possibly use a log processor – like fluentd or fluentbit for example – to aggregate, process, and route logs to centralised external systems.
When designing your observability infrastructure, a bottom-up approach could be to focus on the golden signals of observability – We suggest the ‘USE’ acronym to make it memorable:
Utilisation: measures how much of a resource (e.g., CPU, memory) is used relative to its capacity.
Saturation: refers to the degree to which resources are being used relative to their maximum capacity.
Errors: measures the individual errors and also the frequency of failed requests or operations.
In one of their most popular handbooks on System Reliability Engineering (SRE), Google mentions an additional Golden Signal – Latency.
Latency measures the time it takes for a request to travel from the client to the server and back.
Low latency is crucial for end-user applications because it has a direct impact on how your clients interact with your brand. Whether your clients interact with web services, APIs or real-time data streaming applications, the recommended latency metric must be below the 150-200ms mark – anything above that is a serious red flag your DevOps team must urgently look into.
Utilise Helm Charts to streamline the deployment of observability tools.
This approach reduces manual configuration and ensures consistency.
Helm Charts simplify the deployment process and are accessible even to those who aren’t Kubernetes experts. For organisations looking to simplify and accelerate development, Helm Charts offer a straightforward lifecycle with just four commands: helm install, helm upgrade, helm rollback, and helm uninstall.
These commands cover most of the needs of your DevSecOps team, making Helm Charts a practical choice.
If you’re operating in the cloud, consider using managed DevOps services such as Datadog or New Relic.
These services offer extensive features with minimal setup. However, it’s important to carefully evaluate the costs and benefits, as these integrated solutions may not be cost-effective for smaller organisations. Despite the expense, they deliver significant value by consolidating distributed metrics and tracing into a single, unified view.
Periodically review your observability setup to ensure it continues to meet the evolving needs of your Kubernetes environment.
Effective Kubernetes observability does not have to be complex or costly. By leveraging open-source tools like Prometheus, Grafana, Fluentd, Loki, Jaeger, and Zipkin, you can achieve efficient monitoring and troubleshooting while keeping resource investments minimal. Emphasising simplicity and cost-effectiveness will empower your team to focus on innovation and growth.
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