Applying the Three Pillars of Observability Across Distributed Environments

The IT applications that we build today are highly distributed, spanning the continuum of on-premises data centers, public and private cloud environments, and decentralized edge infrastructure. Understanding and managing the performance and reliability of such applications is complex when you have to consider factors such as inconsistency across the different environments, latency issues and linking user issues to backend problems. However by harnessing the right data and insights, we can achieve robust observability even across distributed environments.

Managing performance in distributed environments is tough

Understanding and managing the performance and reliability of applications in distributed environments is quite complex.

Firstly, it is not easy to have a unified picture of how the application is behaving end-to-end because different environments may be using different tools and architectures. Hence, observing patterns or detecting anomalies across the entire stack becomes inconsistent, delaying root cause analysis and impacting reliability.

Secondly, due to network latency or limited connectivity at the edge, key operational data may arrive late or not at all. The lack of real-time awareness can lead to blind spots where performance issues or failures go undetected until they affect users.

Finally, tying together what the user experiences (e.g., slowness or errors) with what’s actually happening in the systems is challenging. Without end-to-end visibility into the flow of activity through application and infrastructure layers, it becomes hard to pinpoint where the issues originate e.g. in the on-premises database, a container application on the cloud, or the device running in your edge location.

The Three Pillars of Observability – WHAT, WHY, HOW

The foundation of observability rests on three fundamental types of telemetry data: metrics, logs, and traces. Each provides a distinct perspective on system behavior, and their combined analysis offers comprehensive visibility.

• Metrics – The WHAT of Observability. Metrics are quantitative, time-based data that tell you what is happening with the health, performance, and status of systems or applications over time. These are helpful for real-time monitoring, detecting anomalies, and analyzing trends. Examples include user response time, transaction throughput, service availability and CPU utilization.
• Logs – The WHY of Observability. Logs are timestamped records of events occurring within a system or application. They offer detailed information for debugging why errors occur and performing root cause analysis. Examples of logged data would be user id, login result, and payment status.
• Traces – The How of Observability. Traces show how a transaction flows end-to-end across a distributed system. By capturing each step with timing and metadata, traces allow visualization of request flow, identification of performance bottlenecks, and understanding of dependencies in complex architectures. An example would be analyzing the trace for a slow eCommerce Checkout function to identify issues.

Observability is like spy work because it involves gathering and analyzing multiple clues (metrics, logs, and traces) to understand the cause of significant events such as service outages, spike in user activity, or unauthorized access. On their own, metrics, logs and traces provide only a piece of the puzzle. The real power is their collective analysis.

Metrics can signal a problem (e.g., increased latency), traces can pinpoint the affected service or component, and logs provide the granular context needed to understand the specific event that caused the issue. Unified observability platforms that seamlessly correlate these data types are essential for rapid problem resolution.

Best Practices for Observability in Distributed Environments

According to Gartner, 30% of enterprises using distributed system architectures will have adopted modern observability techniques in 2024, a threefold increase from 10% 2020. Implementing observability across a distributed environment comprising on-premises machines, cloud instances and edge devices is complex, yet feasible.

Below are three best practices that you can employ:

• Standardize data collection – Use open standards like OpenTelemetry to collect metrics, logs and traces consistently across different environments.
• Ensure end to end visibility – Choose a platform that can ingest and consistently analyze data from all layers i.e. infrastructure, applications, and services, across a distributed environment.
• Intelligent data correlation: Automatically correlate metrics, logs and traces so that you can identify patterns and anomalies, and troubleshoot potential issues quickly. Leverage AI-Driven techniques for proactive detection.

SUSE observability for distributed environments

SUSE Observability is a unified platform designed to give IT teams full visibility into highly distributed Kubernetes environments that span on-premises data centers, cloud environments and edge infrastructure.

SUSE Observability has full OpenTelemetry and APM support, providing comprehensive performance insights, minimizing latency, and optimizing workloads across cloud and hybrid environments. It unifies metrics, logs, and traces across your applications, infrastructure and services for end to end visibility for both IT and business teams. Moreover, with real-time AI-driven anomaly detection and automation, organizations can predict and rapidly remediate issues.

Download our whitepaper to explore how SUSE Observability helps IT teams observe Kubernetes clusters across cloud and hybrid environments.