Observability Overview
Explore how observability lets teams understand, correlate, and act on system data (logs, metrics, traces, events, etc) to diagnose issues, improve reliability and predict anomalies.
What is Observability?
Observability is a fundamental concept in modern software engineering and system monitoring.
It’s the process of understanding what is happening inside a system by analyzing the data it generates. This includes signals like logs, metrics, traces, events, and profiling data.
When collected and correlated, they give us the ability to detect issues, understand system behavior, health, bottlenecks, and performance, as well as prevent downtime.
At its core, observability helps us answer three crucial questions about a dynamic system: what went wrong, why, and how to fix it.
The Evolution of Observability in Software Engineering
Observability, while gaining prominence in recent years, has its roots in control theory.
It was initially used to describe the ability to deduce the internal state of a system from its outputs. In software engineering, however, observability has evolved beyond basic monitoring practices.
Traditional monitoring systems rely on predefined metrics and alerting thresholds. They are reactive, which means that they wait for something to go wrong before alerting operators.
Observability, on the other hand, is proactive. It equips teams with the ability to investigate unknowns and not just predefined failures. With observability, you can investigate ‘unknown unknowns,’ i.e., problems or situations that you hadn’t anticipated or instrumented in advance. At its core, observability answers three critical questions: What happened within the system? Why did it happen?
Observability vs. Monitoring: Understanding the Difference
Monitoring Is Reactive
Observability Is Proactive
With observability, you’re not just getting alerts; you have the data needed to investigate why a particular issue occurred. When an issue arises, observability provides the context needed to understand the complete picture.
The Three Pillars of Observability
- Logs are records of discrete events that have taken place within the system.
- They are usually timestamped and can provide detailed information about what happened at a specific time.
- Logs are particularly helpful in understanding past events and troubleshooting issues after they’ve occurred.
Logs
- Metrics are numerical values that represent the state of a system over time.
- These are often aggregated and provide an at-a-glance view of the system’s performance.
- Metrics are helpful in tracking trends, identifying resource bottlenecks, and ensuring that the system is behaving within acceptable parameters.
Metrics
- Traces are used to capture end-to-end request flows across various services.
- They help to pinpoint latency issues and understand how individual services contribute to the overall performance of the application.
- Traces allow teams to recreate a user’s journey through a system and spot where issues or slowdowns occur.
Traces
What an Observability Stack Looks Like
An observability stack is built to collect, process, store, and visualize telemetry data.
Each layer handles a specific function and supports how teams detect, analyze, and respond to system behavior. The core components of any observability stack include:
Signal Capture
Collect logs, metrics, traces, events, and profiling data from all layers of the system.
Ingestion Pipeline
Structure and enrich signals in transit to prepare them for indexing.
Storage Layer
Store data in a searchable format, optimized for real-time queries and high-volume indexing.
Dashboards & Alerts Present filtered views of system behavior and generate alerts when conditions meet known risk thresholds.
Why Observability is Crucial in Modern IT Systems
Distributed systems introduce complexity. Complexity introduces risk.
Modern IT systems are increasingly distributed and dynamic, particularly with the adoption of microservices architecture and cloud-native technologies.
Applications today run across cloud, on-prem systems, containers, and virtual machines, with dependencies that shift as workloads scale. In such environments, traditional monitoring techniques fall short because:
There are too many moving parts
Failures are harder to predict
The interdependencies between services are complex.
Observability offers a way to see what’s happening within these systems in real-time, improving response and resolution time. It provides insights into how systems interact, where bottlenecks occur, and what part of the system is underperforming.