How to Gain In-Depth Observability of Your Kubernetes Clusters

How to Gain In-Depth Observability of Your Kubernetes Clusters

How to Gain In-Depth Observability of Your Kubernetes Clusters

By Rox Williams | Last modified on 2023.06.07

In the breakneck world of modern software development, it’s hard to find teams that don’t use microservices, containers, and Kubernetes to get code out the door faster. But with the speed of deployment and delivery comes a very high degree of complexity. Here’s a look at the role Kubernetes clusters play in software development and how teams can leverage observability for the ultimate win-win: faster time to market, and the ability to find incidents before customers do.

What is Kubernetes?

Kubernetes (or K8s) is an open-source container orchestration system that streamlines application management by automating operational tasks, including deployments. Kubernetes is also very scalable. For these reasons, Kubernetes adoption has soared—it is the most popular container management platform and Cloud Native Computing Foundation (CNCF) project.

Is Kubernetes a container or framework?

Kubernetes is a container management platform or framework; it manages a team’s containers and containerized applications but is not a container.

How does Kubernetes work?

An implementation of Kubernetes is known as a cluster, and each cluster is made up of two parts: a control plane and the compute machines or nodes. The Kubernetes control plane ensures that all necessary APIs are available, and scheduling happens on the compute nodes as necessary. It’s essentially the heart of Kubernetes. The data needed by the control plane is typically stored in etcd, which is a high-availability key store for all cluster data. The nodes are where your Kubernetes resources actually run. When you spin up a new Kubernetes resource, the scheduler will ensure that one of the nodes in the cluster runs the workload.

Some of the key features one can glean from a Kubernetes implementation are:

  • Automated rollbacks and canary deployments
  • Streamlined—and declarative—deployment of various resources
  • Controlled storage and service discovery
  • Secrets and configurations management
  • Self-healing application environments
  • Self-service deployment of application environments

Benefits of Kubernetes

Cloud-native software development is complicated, but Kubernetes is a powerful tool that can help bring order to the (near) chaos. There are a number of concrete benefits.

  1. Container management with less effort (and no cost overruns): Market research firm IDC predicts almost 60% of development teams will be using containers and microservices by 2024 and those containers need to be managed, which is where Kubernetes comes in. Kubernetes makes it easy to keep track of everything, and because teams set limits on the number of containers and cloud instances, money and time aren’t wasted.
  2. Better DevOps and time to market: Adding Kubernetes into the mix frees up engineers to create rather than manage a long list of services.
  3. Support for multi-cloud: Kubernetes works with any cloud provider, which is ideal for organizations embracing multi or hybrid clouds.
  4. Portability: Kubernetes-managed containers can move seamlessly from one operating system to another with no downtime required
  5. Scale and automate deployments: Being able to add cycles as demand increases (or remove them when demand drops) is one of the genius benefits of containers—and it’s made even more impactful when coupled with container orchestration. Teams can scale with minimal effort
  6. Open source: No vendor lock-in, tons of community support, and add-on tools.

Why people are making the switch from monoliths to microservices

Microservices came into being because software development teams had a need for speed. The old ways of developing code on hardware or a virtual machine simply took too long. By breaking down the process into small bits of code that could all run at the same time and be tested, deployed, patched, or scaled easily, teams found it became possible to deploy monthly, weekly, daily, or even multiple times a day, giving them the ability to deliver fixes and innovation at a speed and scale that was previously out of reach.

What is observability?

In the context of software development, observability engineering helps you see “what looks weird.” In other words, it is the ability to notice strange application behavior and quickly identify the root causes of incidents. Observability is a proactive solution that lets you know why something is happening, rather than simply alerting you to the fact that something happened. Observability is often tied to speed: how quickly can a problem be identified and remedied? The more complex a system, the more need there is for true observability.

Observability vs. the old ways of logs and metrics

Back in the day, logs and metrics were enough and manageable. Teams would pore through logs to try and find what was causing a service disruption, but as applications became more complex, logs got exponentially larger and thus harder to wade through. Logs are useful tools, but are time-consuming.

While there is nothing inherently wrong with metrics, they are limited in what they can deliver because they’re only looking at specific areas of the system. What happens if an incident occurs somewhere that’s not monitored? True observability means teams can quickly look anywhere, follow a hunch, and ask questions on the fly—without rearchitecting the system. If you’re asking questions you’ve never even thought to ask, you’re on the road to observability.

Observability and the power of distributed tracing

Distributed tracing is a way of tracking and understanding the flow of requests through distributed systems. Due to the nature of their infrastructure, distributed systems are typically complex, with multiple services running on different servers or even in different data centers. This is where traditional logging methods in particular, will fall short.

Logs tell us where the problems happened, but do not provide any context as to why. Distributed tracing, on the other hand, creates a unique identifier (or trace ID) to track telemetry events through the system.

Why is Kubernetes observability important?

When an incident occurs, MTTR is of paramount importance. But Kubernetes-managed containers are literally packed with code pieces, layers, and interdependencies at a level never seen before in software development. Teams trying to troubleshoot what’s going on inside a container find themselves looking for a needle in a haystack, and those haystacks are getting bigger.

There is so much to look through that teams may need to context-switch constantly, jumping from logs to metrics to see if they can find the problem. For K8s not to be a victim of its own success (the ability to develop quickly being offset by the difficulty in troubleshooting), observability is key. To quickly find and resolve issues teams need access to high-cardinality data as well as the ability to analyze it.

A Kubernetes observability strategy will give teams better performance, less downtime, and potentially even cost reductions. Also, observable Kubernetes lets organizations find issues before customers do. In today’s environment, this is priceless. Teams can move rapidly with the confidence that they can easily find and fix problems despite a complicated, modern software development practice.

The challenges of gaining in-depth insight into your clusters

Increased complexity and dependencies

Kubernetes clusters are very busy places. The code in a Kubernetes cluster is interdependent, but it also operates in a very complex environment with a large number of moving parts. The parts, like services, nodes, pods, and deployments, may be spread across different clouds or networks but they can also run many instances at the same time.

All this to say: it’s easy to make one small change and cause lots of issues in different places, and they can often be difficult to discover. Add in the fact that containers can be scaled up or down and it’s clear why granular observability is critical to keeping containers a development force for good.

Using OpenTelemetry to gain more insights in K8s

Gaining in-depth insights into your Kubernetes clusters can feel overwhelming, but there is a secret weapon nearly every team can leverage: OpenTelemetry. This CNCF open source standard is an increasingly popular way to transfer metrics, logs, and traces to an observability platform.

Auto-instrumentation is available, meaning teams can get rolling immediately. While OTel, as it’s also known, isn’t an observability solution itself, it does have a K8s operator, which streamlines the transportation of complex telemetry data to nearly any observability platform. OpenTelemetry has a standard API and SDKs for 11 languages, and no doubt more support in the works.

OpenTelemetry is an ideal option for any team needing to debug containers, but it’s particularly attractive for those just starting an observability effort because auto-instrumentation makes it simple to get started. We’re big fans of OTel, but we also believe the vast majority of software development efforts will benefit from taking further steps to add manual instrumentation to the process. Manual instrumentation will ensure teams wring every last ounce of value out of high-cardinality data.

4 Kubernetes best practices

Kubernetes-orchestrated container use is unlikely to decrease in the coming years because the benefits to modern software development teams are simply too great. So it’s incumbent on organizations to have a plan to make K8s as observability-friendly as possible. Here are some best practices to consider:

1. Start with the culture. We believe every team should build a culture of observability, because incidents happen and customers aren’t necessarily patient or forgiving. But for cutting-edge teams using Kubernetes, multiple clouds, and complex frameworks, a culture of observability allows a team to move fast. Without it, progress will be stop-start-stop at best.

2.Observability during development. Security is not the only thing that needs to shift left. The most successful development teams understand the importance of distributed tracing, instrumenting code as it’s being written, and owning what you’re creating.

3. Embrace service-level objectives. SLOs set reliability targets for service performance and give teams a way to supercharge their observability efforts. SLOs can alert on things monitoring alone might miss, and they can support a closer alignment between development and the business side. We think thoughtfully-created SLOs can be an observability game-changer.

4. Observe before deploys (and during and after). With practice, buy-in, and encouragement, every part of the team can learn to add observability into the process, particularly during those moments where it matters, like before deployments (meaning before customers can be impacted). Treat observability like teams treat DevSecOps: add it into every step of the way until it becomes automatic.


Kubernetes is a ground-breaking solution for modern software teams that need to move quickly. But in this case, speed equals complexity—so when problems arise, it can be difficult (and time-consuming) to troubleshoot them. To get the most out of Kubernetes, it is critical for teams to make it truly observable, meaning it’s easy to have access to all the data including logs, metrics, and traces. 

While organizations work on a “shift left” observability culture, they can also jumpstart the process using OpenTelemetry. Containers are here to stay, so it makes sense to get ahead of this issue sooner rather than later.

How does Honeycomb work with Kubernetes?

Additional resources


How Honeycomb Monitors Kubernetes

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Case Study

Kubernetes in the real world: How Jack Henry uses Honeycomb

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How we (begrudgingly) moved some of our services to Kubernetes

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Send a Test Span Through an OpenTelemetry Collector

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