Observability is a core concept in DevOps that helps you understand and monitor the health and performance of your systems. To achieve true observability, you rely on three main types of data, often called the three pillars of observability: metrics, logs, and traces.

• Metrics: numerical values that show how your system is performing over time, such as CPU usage, memory consumption, or request rates;
• Logs: detailed records of events that happen within your system, like error messages, warnings, or informational outputs from applications;
• Traces: data that follows the journey of a single request as it moves through different parts of your system, helping you pinpoint slowdowns or failures.

By collecting and analyzing these three types of data, you can quickly detect issues, understand their causes, and ensure your applications run smoothly.

Understanding the Three Pillars of Observability

The three pillars of observability—metrics, logs, and traces—give you a comprehensive view of your systems. Each pillar provides a unique perspective, and together they help you quickly detect, investigate, and resolve issues in complex DevOps environments.

Metrics: Quantitative Health Indicators

• Provide numerical data about system performance, such as CPU usage, memory consumption, or request rates;
• Allow you to set alerts and thresholds for critical values;
• Enable you to spot trends and anomalies over time.

Logs: Detailed Event Records

• Capture discrete events and messages generated by applications or infrastructure;
• Offer context and details about what happened at a specific point in time;
• Help you diagnose root causes by showing errors, warnings, and informational events.

Traces: End-to-End Request Journeys

• Track the full path of a request as it moves through distributed systems;
• Reveal bottlenecks and latency issues by showing where time is spent;
• Allow you to correlate related events across services for deeper understanding.

Working Together for Complete Observability

By combining metrics, logs, and traces, you gain:

• A high-level overview of system health and performance;
• The ability to drill down into specific events for troubleshooting;
• Clear visibility into how requests flow across services, making it easier to identify and resolve issues.

Relying on all three pillars helps you proactively monitor, quickly investigate incidents, and maintain reliable, resilient systems in your DevOps practice.

Using Metrics, Logs, and Traces Together: A Practical Example

Suppose you manage an online store, and you notice that the average response time for the checkout page has spiked.

• Metrics: You monitor the checkout_response_time metric and see it has doubled in the last hour;
• Logs: You search the application logs for recent errors and find repeated PaymentServiceTimeout errors during checkout requests;
• Traces: You use distributed tracing to follow a slow checkout request. The trace shows that the delay happens when the application calls the external payment API.

By combining these insights, you quickly identify that the payment service is causing the slowdown. You contact the payment provider or reroute traffic to a backup service, resolving the issue and restoring normal checkout speeds.