Observability and Its Three Essential Pillars

What is Observability?

From an IT perspective, observability can be thought of as a property that facilitates the visualization and understanding of large systems (this is not a method—we will cover this topic later in the context of logs, metrics, and monitoring; however, I am using the keyword “property” here because observability provides the ability to examine outputs). When a system is observable, it allows developers to understand its internal state based on its external outputs. This enables IT personnel to identify the root cause of any performance issue by analyzing the data without the need for additional testing.

"In essence, observability provides end-to-end system visibility and insights, delivering the raw data needed for troubleshooting and debugging. As such, it helps you understand what the problem is, where it is, why it occurred, and how it might affect the performance of the entire system.

Observable architectures help engineering teams and IT administrators manage the complexity of today’s modern computing environments. In today’s world, there are highly dynamic computing environments featuring multi-cloud configurations and a wide variety of cloud-based applications, such as microservices-based systems and containerized workloads managed by Kubernetes. Various observability tools provide comprehensive visibility into these environments, helping to detect abnormal data patterns and performance bottlenecks before they impact end users.

What is an event? And what is its relationship to observability?

Events are distinct occurrences within the system that produce data at specific times; this data is collected as logs, metrics, and traces—the three pillars of observability.

In this context, since events can trigger distinct actions at specific times, observability tools rely on them.

What are metrics, logs, traces then?

Metrics provide insights into system performance by measuring various parameters. This helps teams understand the "what" of system issues. Examples of these metrics include CPU and disk usage for a host, latency for the network, and response time for an application. Additionally, metrics are typically aggregated to provide a summary view through dashboards, helping developers quickly assess the system's overall health, analyze data trends, and respond to system issues.

However, metrics are generally limited in providing context; therefore, they often need to be correlated with logs and traces to give developers a comprehensive understanding of system events.

Logs are immutable, exhaustive records of discrete events that occur within a system. They help teams understand the “why” of system issues.

Event logs can be binary, unstructured (as in plain text), or structured (as in JSON format). All log files are useful in the appropriate context, but structured logging approaches facilitate the parsing and analysis of text and metadata by organizing them. Observability tools aggregate log files from operating systems, network devices, in-house and third-party applications, and IoT devices — and when these logs are structured, teams can diagnose errors and understand system failures more efficiently. When an error, security breach, or other system issue occurs, logs provide the details needed to trace the root cause and understand what went wrong.

Logs offer valuable insights into system events and issues, but alone, they provide an incomplete picture. As is the case with metrics, observability tools must analyze and correlate log data with metrics and traces to maximize their combined value.

Traces capture mapping data—such as the path and lifetime of each component involved in processing a request—across distributed services to illustrate the request's workflow. In short, traces record the end-to-end "journey" of every request, from the user interface, through the entire architecture, and back to the user.

This tracing data may include information such as the duration of system events and operations, the order in which requests passed through distributed services, and so on.

Tracing—or, more precisely, distributed tracing—is useful in microservice architectures where requests can pass through multiple services before reaching their destinations. In this way, it provides information about the dependencies and interactions between different components and services.

Unlike metrics and logs, traces provide contextual information about how a request moves across services, helping enrich insights. However, tracing alone cannot reveal data trends or patterns. Setting up distributed tracing requires instrumentation across service deployments, which can make the process particularly complex and time-consuming.

Logs, metrics, and traces are the three fundamental methods used to achieve observability. Combining these elements enables operations teams to gain a holistic perspective and a detailed understanding of complex system behavior.