What is observability?

Observability in software engineering context refers to how well you know and understand the state of your applications and the overall health of your system. To achieve this, various diagnostic information such as logs, metrics, and traces must be collected and analyzed.

As software grows increasingly complex, it is important to focus on observability to detect issues, resolve problems quicker, and maintain a performant, reliable system.

This is particularly true in modern, distributed systems where multiple components are interacting with one other for critical business functions. Without robust observability, it becomes difficult to track down the root cause of problems and address them accordingly.

Three pillars of observability

While there are many data sources for gaining better observability for your system, three types of data are often considered the three pillars of observability: logs, metrics, and traces.

  • Logs: records of events or messages in a system (e.g., HTTP request, user activity).
  • Metrics: quantitative data points that measure various aspects of a system (e.g., CPU/memory usage, API response times).
  • Traces: data that record the flow of requests through a system.

Observability vs. monitoring vs. telemetry

Observability is often confused with monitoring and telemetry. While closely related, these three topics touch on different aspects.

Telemetry is the process of collecting data, while monitoring is the process of collecting and analyzing that data for alerting purposes.

Observability goes beyond traditional monitoring as the latter simply focuses on detecting problems whereas observability aims to provide a comprehensive view of the system’s internal state.

Benefits of observability

Robust observability tooling helps engineers understand the state of their systems and allow them to troubleshoot issues efficiently. Other benefits of observability include:

  • Faster time to resolution. Observability tools and platforms can quickly surface information about their systems and pinpoint engineers to the root of the issue.
  • Improved performance and reliability. Engineers can use observability tooling to identify bottlenecks or systems that require more resources to deal with increased load. Observability also provides data needed to influence system topology or scaling behavior.
  • Better developer experience. By integrating robust observability tooling with other critical systems such as alerting and incident management, engineers can focus on building rather than spending more time on fighting fires.

Role of observability

Observability plays a vital role in various functions within engineering organizations. For example, it provides insights into the CI/CD pipeline and infrastructure for DevOps teams to manage and operate. Likewise, it helps SRE teams collect important metrics on the production environment to respond to problems.

In fact, observability can be valuable to everyone in the organization. Observability Driven Development (ODD) is a rising trend in successful organizations that push observability principles earlier into the development process. This can establish a healthy observability engineering culture for better reliability and performance.

Implementing observability

To implement observability effectively, it is critical to choose a modern and comprehensive observability platform that can respond to the needs of today’s engineering teams.

A great observability platform can efficiently ingest, process, and visualize important pieces of information for engineers to understand and respond to issues.

Popular platforms include Datadog, Splunk, New Relic, and Honeycomb. Lightrun is a notable developer observability platform.