As cloud-native applications and microservices become more complex, developer teams struggle to make the most out of their infrastructure. Observability can cut through cluttered architectures to connect key engineering decisions to business metrics.
Driven by a constant desire for faster performance, improved efficiency and enhanced business outcomes, developer tools change rapidly to meet the increased pressures on engineering teams. While these tools move in the direction of progress, the pace of change means that dev teams sometimes miss the forest for the trees; today, one of the industry’s most widely deployed tools is also the most misunderstood.
The rise of microservices and cloud-native applications has created an application infrastructure that is increasingly complex and difficult to understand. The most dramatic example of this challenge is Kubernetes; while the container orchestration system has been widely adopted by developers (including 91% of respondents to the Cloud Native Computing Foundation’s 2020 Cloud-Native Survey), a majority of developers lack the clarity and understanding needed to inform decision-making for site reliability engineering with data.
Data analytics on application infrastructure is an emerging, underleveraged area in software engineering, and tasks like CI/CD workload sizing, resource allocation and load balancing tend to be ad hoc decisions made without the benefit of contextual data. When developers are able to analyze their telemetry data, they are able to gain significant insights into the behavior of their applications, automate certain manual workloads and connect engineering decisions to business metrics like end-user latency. To make sense of an increasingly chaotic infrastructure, engineering teams should rely on Kubernetes observability.
Defining Kubernetes Observability
The term “observability” tends to mean different things to different engineers, but rather than viewing it as a single product or solution, it should be viewed more as an overarching metric: observability defines your team’s ability to identify and understand a problem. With exceptional observability, teams can recognize and address errors and anomalies at a glance from a user-friendly interface. With poor observability, engineers can be working for hours in the dark trying to make sense of a hailstorm of alerts, errors and downtime.
Containers and microservices increase both the surface area of a team’s infrastructure and the frequency of software changes. This complexity makes it more difficult for teams to achieve good observability and understand the performance of their cloud-native applications. Kubernetes has become a must-have for engineering teams because it is viewed as the most effective tool for scalability and simplifying complex processes. But the most valuable benefits of Kubernetes clusters are only available to those who maintain great observability.
Insight-Rich and Instrumentation-Free
To enjoy the reliability and efficiency of a well-organized Kubernetes cluster, dev teams must be able to recognize both when their tools are underperforming and when their resources are underutilized. This begins with establishing a baseline for the health and capacity of the Kubernetes cluster. Infrastructure monitoring is essential on Kubernetes, and keeping track of application metrics provides a valuable first step when pulling apart performance issues and unexpected anomalies.
Kubernetes observability requires teams to monitor relevant events like new deployments, health checks and autoscaling; tracking dynamic events is vital to make sense of real-world performance. Technologies like eBPF automatically collect metrics, events, traces and logs, covering every layer of the tech stack from individual applications to the operating system, Kubernetes infrastructure and network layers. In addition to events, good Kubernetes observability includes understanding how microservices communicate with each other. Kubernetes metadata can define the performance and distributed traces of applications, providing valuable performance insights into throughput, transaction times and error rates.
Good observability isn’t simply a matter of monitoring. It also changes the way developers interact with their tools by correlating events and placing errors in the context of their broader environment. Without observability, developers often bounce between tools, struggling to make sense of the situation as a whole. With good Kubernetes observability, developers can connect logging data to other monitoring tools and assess anomalies in context.
As an industry, we’ve recognized the potential for Kubernetes, yet we’ve struggled to realize that potential. Observability is an essential step that must be taken to maximize resources, limit errors and quickly address problems when they occur. As teams improve their Kubernetes observability, they’ll enjoy both a less stressful user experience and improved business outcomes.