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Published November 8, 2022

Understanding the Three Pillars of Observability: Logs, Metrics and Traces

Understanding the Three Pillars of Observability: Logs, Metrics and Traces

Many people wonder what the difference is between monitoring vs. observability. While monitoring is simply watching a system, observability means truly understanding a system’s state. DevOps teams leverage observability to debug their applications or troubleshoot the root cause of system issues. Peak visibility is achieved by analyzing the three pillars of observability: Logs, metrics and traces.

Depending on who you ask, some use MELT as the four pillars of essential telemetry data (or metrics, events, logs and traces) but we’ll stick with the three core pillars for this piece.


Metrics are a numerical representation of data that are measured over a certain period of time, often leveraging a time-series database. DevOps teams can use predictions and mathematical modeling on their metrics to understand what is happening within their systems — in the past, currently and in the future.

The numbers within metrics are optimized to be stored for longer periods of time, and as a result, can be easily queried. Many teams build dashboards out of their metrics to visualize what is happening with their systems or use them to trigger real time alerts when something goes wrong.


Traces help DevOps teams get a picture of how applications are interacting with the resources they consume. Many teams that use microservices-based architectures rely heavily on distributed tracing to understand when failures or performance issues occur.

Software engineers sometimes set up request tracing by using instrumentation code to track and troubleshoot certain behaviors within their application’s code. In distributed software architectures like microservices-based environments, distributed tracing can follow requests through each isolated module or service.


Logs are perhaps the most critical and difficult to manage piece of the observability puzzle when you’re using traditional, one-size-fits-all observability tools. Also, logs are machine-generated data generated from the applications, cloud services, endpoint devices, and network infrastructure that make up modern enterprise IT environments.

While logs are simple to aggregate, storing and analyzing them using traditional tools like application performance monitoring (APM) can be a real challenge.

This article originally appeared on To read the full article, click here.