Kubernetes in the Cloud: A Guide to Observability

As per the saying “If you don’t measure it, you can’t manage it” by Deming, observability and monitoring is our way to measure our services.

Kubernetes is pretty revolutionary when it comes to the way it handles deployments and scales. But the way containers are continuously created and destroyed can sometimes present challenges with monitoring. This is where observability comes into play, offering critical insights into how your system is performing and why issues occur.

Want to revisit Kubernetes terminology? Read Demystifying Kubernetes in 5 Minutes.

What Is Observability in Kubernetes?

People like to use Observability as an umbrella term. But typically, it would mean metrics, logs, and traces. It’s like having a lens into the heart of your applications and infrastructure. By collecting and analyzing these outputs, observability helps you spot potential issues before they disrupt service and optimize overall system performance.

Three things that come to mind are:

Metrics

These are numbers, and they provide data about resource usage, error rates, and performance. A few popular metrics are CPU usage and memory usage in percentage, along with additional metadata about the metrics themselves (sometimes called dimensions).

Logs

Logs provide a detailed history of events within your system, such as errors or user actions. They offer context for troubleshooting and understanding application behavior. I am sure you have seen a "log" before: 

[2025-01-01 12:30:00] ERROR: Failed to connect to database on attempt 3, retrying...

Traces

Tracing gives an end-to-end view of requests as they pass through services, helping identify bottlenecks or latency issues. By following requests across multiple microservices, you can pinpoint where performance problems arise.

Logs and traces might sound similar, but they are different. Think of logs as a snapshot of what happened, whereas traces tell you how and why it happened across the entire system.

Observability is not really limited to one role in an organization, in itself is a piece of critical information passed around among different roles. For example, as a software engineer, you instrument the application code with metrics, logs, and traces. Now, you need something to collect, store, and analyze this data, using tools like Prometheus for metrics and Jaeger for traces.

If you are not already sold on Observability, I will summarize:

1. It makes sure everything runs smoothly and efficiently by identifying performance bottlenecks.
2. Improves system resilience and helps apps recover from failures (hopefully) quickly.
3. Continuous monitoring allows teams to detect anomalies early, preventing security breaches and ensuring sensitive data is protected.
4. You can build a wonderful-looking dashboard, which helps give you better insights on system performance. It may even help you save significant infrastructure costs (looking at you, AWS!).

Wait, I also mentioned Monitoring above. So what is that and how is THAT different?

While observability and monitoring are related, they serve different purposes. Monitoring involves setting up predefined checks/alerts to ensure that a system is functioning within acceptable parameters, your SLAs/SLOs. Observability, on the other hand, goes further by providing a comprehensive understanding of system behavior. It’s not just about knowing when something breaks; it’s about understanding why and how it happened. Both monitoring and observability are essential to effective system management.

Call Out: OpenTelemetry

OpenTelemetry (aka OTel) is a leading open-source collection of APIs, SDKs, and tools. Use it to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) to help you analyze your software’s performance and behavior. OpenTelemetry integrates with many popular libraries and frameworks, and supports code-based and zero-code instrumentation across diverse Kubernetes environments.

Conclusion

To conclude, Observability is more than a technical requirement — it's a strategic imperative for organizations looking to stay ahead in today’s competitive market. By leveraging the right tools and strategies, such as OTel for unified data collection, organizations can monitor, troubleshoot, and continuously optimize their Kubernetes applications. Through better visibility into system performance, organizations can make data-driven decisions, enhance application reliability, and meet business goals more effectively.

I don’t know who said that, but I love this quote: Stop guessing, start knowing!