Unlocking the power of Kubernetes through observability

The growth in microservices and cloud-native applications is making operating environments more complicated, and developer teams are looking for new ways to better understand the tech landscape. This is where Kubernetes has a significant impact.

As an open source container-orchestration system, Kubernetes has become a must-have for engineering teams because of its ability to effectively scale and automate complex processes. But while Kubernetes solves old problems, it can also create new ones if not implemented correctly.

Adopting containers and container orchestration requires dev teams to rethink their monitoring strategies to account for the new infrastructure layers introduced in a distributed Kubernetes environment.

To make sense of the data created by the Kubernetes platform, engineering teams must implement observability tools.

Creating greater oversight

New Relic's 2021 Observability Forecast found that 52% of tech leaders in ANZ and 57% in APAC said that it was 'very important' that observability is achieved for their organisation.

Observability goes beyond basic monitoring. It involves having real-time visibility across systems by collecting telemetry data, applying intelligence to contextualise this data, and visualising it to create actionable insights. It's not just about collecting logs, metrics and traces. Full observability is about relating data together.

The most valuable benefits of Kubernetes are only accessible by those who have great visibility across their systems. When developers can analyse their telemetry data, they can gain significant insights into the behaviour of their applications and can connect engineering decisions to business metrics, such as end-user latency.

Observability changes the way developers engage with their technology. It provides them with an understanding of events in their stack, and a greater understanding of the wider impact of errors.

Making sense of performance

Kubernetes observability allows teams to monitor new deployments, health checks and autoscaling. Kernel technologies such as Extended Berkeley Packet Filter (eBPF) can automatically collect metrics, events, traces and logs. eBPF can also cover each layer of a technology stack — including Kubernetes infrastructure.

The metadata created by deploying eBPF can help define the performance of applications and provide insights into metrics such as transaction times and error rates.

Tracking these kinds of dynamic events is vital to making sense of real-world performance. By having a graphical overview — a 'single pane of glass' — teams can see what is happening on a cluster and what pods are doing. In addition, devs can see how their applications are performing in a containerised manner.

Fix problems quickly

Poor observability can cause engineers to work for hours, blindly grappling with downtime, alerts and errors. As a result, issues are more likely to develop into critical failures that alienate end users.

With true observability, developer teams can instead address errors and anomalies from a distance via an easy-to-use interface. Over half (56%) of tech leaders in Australia and New Zealand and just under half (48%) in APAC indicated in New Relic's survey they will achieve and maintain end-to-end observability in the next year — so integrating Kubernetes should be key to this process.

Creating Kubernetes observability enables developers to form links between their monitoring tools and logged data, allowing them to review anomalies in a wider context. Developers can be self-sufficient in diagnosing problems — in production code and in speeding up the development process.

For the most part, the tech industry sees the value and potential of using Kubernetes, but its full potential is being held back by a lack of observability. Implementing observability provides greater clarity for developers and is crucial to the successful operation of all Kubernetes environments.

The clarity it provides makes it a vital tool for tech teams seeking to create greater oversight of their stack while understanding system performance and addressing issues quickly. Once teams improve on this, they'll be able to reap the rewards of improved business outcomes and create a more enjoyable user experience.