Development technologies, like Kubernetes, are evolving rapidly. Driven by a quest to deliver faster performance, optimized efficiency, and better business results, organizations are under pressure to keep up with the changing needs of engineering teams, as well as their customers.
While development tools are geared towards progression, the speed of change means that they sometimes lose the forest for the trees. In today’s world, the most common tool within the industry is equally the most misunderstood. However, to thrive, developers must be able to understand Kubernetes clusters with observability.
Navigating complex infrastructures
The rise of microservices and cloud-native applications has created an application infrastructure that is increasingly complex and difficult to understand. Kubernetes is a clear example of this. According to the 2020 Cloud Native Computing Foundations Survey on Cloud Natives, a significant proportion of developers lack the clarity and understanding necessary to inform decision-making for data-driven site reliability engineering.
However, data analysis in the application infrastructure is an emerging area within software engineering. Generally, tasks such as CI / CD workload size, resource allocation, and load balancing are ad-hoc decisions and are defined by a lack of contextual awareness. In contrast, developers who have access to the analyzed data can better understand the behavior of their applications. They will also be able to automate manual workloads and connect engineering decisions with business-centric metrics such as end-user latency.
To navigate this increasingly complex landscape, engineering teams must employ Kubernetes observability. With this in mind, let’s explore what this means for developers, engineers, and businesses in general.
What is Kubernetes observability?
The definition of “observability” means different things to different people. However, despite its various interpretations, it should be viewed as a general metric, as opposed to a standalone product or solution. Ultimately, this is because observability has the potential to define your team’s ability to identify and respond to a problem. When used accurately, observability will allow developer teams to recognize and address bugs and anomalies. In contrast, teams that just don’t embrace observability, or get it wrong, will find themselves working tirelessly in the dark, trying to make sense of a tsunami of alerts, errors, and downtime.
In addition to the challenges, microservices and containers increase the footprint of a team’s infrastructure, as well as the recurrence of software changes. This complexity hampers the ability of engineering teams to achieve optimized observability as well as their understanding of the performance of their cloud-native applications. Implementing Kubernetes is therefore critical for engineering teams to effectively scale and simplify complex and siled processes. However, the most invaluable benefits of Kubernetes clusters can only be achieved by those that maintain good observability.
Enjoying the benefits of Kubernetes
To reap the benefits of organized Kubernetes clusters, such as increased application reliability and efficiency, developer teams must first be able to quickly identify and respond when their tools are performing poorly. To do this, they must establish a baseline for the health and capacity of the cluster. Therefore, infrastructure monitoring is essential and tracking application metrics provides an important first step in recognizing performance challenges as well as unexpected anomalies.
Using Kubernetes observability, developer teams must monitor for relevant events such as health checks, new deployments, and autoscaling. Technologies like the extended Berkeley Packet Filter (eBPF) automatically track metrics, logs and events, covering every layer of the technology stack from applications to operating systems. Additionally, properly implemented Kubernetes observability includes understanding how microservices communicate with each other. Kubernetes metadata can ultimately define performance and distributed traces of applications. As a result, developers have visibility into important performance data, specifically throughput, transaction times, and error rates. Consequently, they will also have access to the correct tools to help them troubleshoot and resolve problems, improving efficiency and productivity.
But accurate observability is not simply a case of monitoring. Actually, it is deeper. It fundamentally alters the ways developers interact with their applications by correlating events and contextualizing errors in the context of their larger environment. Without observability, teams juggle a number of tools, struggling to make sense of the bigger picture. Those who implement good Kubernetes observation capabilities will find that they can connect log data to other monitoring tools, allowing them to better assess anomalies and resolve issues quickly.
Leading from the front with visibility
As an industry, we have recognized the potential of Kubernetes, but we have struggled to realize that potential. Observability is an essential step to take to maximize resources, limit errors, and quickly address problems when they occur. As teams improve Kubernetes observability, they will enjoy both a less stressful user experience and better business results.
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