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Key factors for Adopting Kubernetes Observability within Your Enterprise

Ensar Architecture Team

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Kubernetes: Container orchestration

Kubernetes is an open source platform for deploying and managing containers. It provides a container runtime, container orchestration, container-centric infrastructure orchestration, self-healing mechanisms, service discovery and load balancing. It’s used for the deployment, scaling, management, and composition of application containers across clusters of hosts.

Kubernetes Observability Overview

Kubernetes Observability provides a comprehensive view of your infrastructure, highlighting strengths, weaknesses, and areas for enhancement to foster a robust system. Logs, metrics, and traces enhance system observability, particularly in the Kubernetes environment.

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Observability, in its most basic sense, pertains to the ability to assess a system's current state using generated data such as logs, metrics, and traces.

Traditional observability tools, however, were designed to monitor the health and behavior of monolithic systems.

These tools struggle to accommodate distributed and complex architectures like Kubernetes, where the structure continuously evolves, and numerous pods are created and terminated within minutes. The dynamic and transient nature of Kubernetes means that predefined logs and metrics are insufficient for addressing troubleshooting issues.

Consequently, implementing a customized solution designed for distributed microservices architecture is recommended. This solution should align with the ever-changing nature of Kubernetes and gather data that enables your teams to identify and resolve errors in real-time.

Observability vs. Monitoring

While often used interchangeably, observability and monitoring have distinct applications. Monitoring Kubernetes involves verifying the performance of the Kubernetes environment, clusters, nodes, containers, pods, and application workloads. In contrast, observability offers a comprehensive view and crucial insights for enhancing performance, bolstering stability, and strengthening the resilience of applications, K8s components, and the entire infrastructure. For example, Prometheus – a popular Kubernetes monitoring tool – supports real-time monitoring and alerting, symptom detection, and anomaly notifications for your teams. Kubernetes observability tools generate extensive overviews and detailed summaries of application performance while connecting metric changes with Kubernetes events.

Kubernetes has emerged as the preferred choice for developing and deploying scalable, distributed applications. However, Kubernetes lacks awareness of an application's internal state. As a result, developers and SREs utilize telemetry data (logs, metrics, and traces) to comprehend their code's behavior during runtime. These constitute the three pillars of Kubernetes observability.

Components of Kubernetes Observability

Logs are files that record events, warnings, and errors within the software environment. They typically include contextual information, such as the event's specific time and the associated user or endpoint. Log messages present data in plain text, either structured or binary format. It is essential to consider storage strategies before generating log messages, as they require a significant amount of storage space.

Metrics are numerical representations of data collected over a specific time period. They help assess overall system health and gather insights using telemetry signals. Metrics are typically stored in time-series data stores like Prometheus and can be optimized for storage, compression, or processing as needed. Metrics outperform logs in one area – their easy retrieval and querying, which makes them ideal for alerting and developing dynamic dashboards.

Traces Traces represent sequential events that illustrate an end-to-end request path within a distributed system. Understanding the request flow simplifies troubleshooting performance bottlenecks and identifying dependencies between various services for your team.

Top 3 Strategies for Addressing Observability Challenges

As cloud-native environments help businesses evolve and drive customer success, maintaining sufficient visibility becomes increasingly challenging. Although Kubernetes is a valuable tool, its dynamic abstraction layer introduces new errors and complicates error identification, troubleshooting, and prevention. Here are three proven approaches for implementing observability in Kubernetes:

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Embrace data correlation

Kubernetes observability involves more than just collecting and analyzing logs, metrics, and traces from various cluster components; it requires contextualizing each event based on the cluster's functioning at the time of the event and the subsequent consequences.

Furthermore, considering the user's perspective is crucial when dealing with such vast amounts of data. Many organizations only grant teams access to backend information, neglecting to demonstrate the impact of Kubernetes performance on business outcomes. As a result, your teams must establish correlations between code pushed into production, backend operations, and frontend experiences. One way to accomplish this is by linking Kubernetes monitoring data with real-time metrics, such as insights on overall user experience and conversions.