Observability is the ability to measure and infer a system’s current state based on the data it generates. This data is usually in the form of logs, metrics, and traces. As a brief example, you could observe the health of your microservices application by examining its metrics.
Observability provides developers with a holistic view of how a complex system is functioning. Through data collection, storage, and analysis, developers gain the ability to identify and troubleshoot issues in their systems.
Observability starts with collecting data in real-time. The collected data is then stored in a centralized location for analysis. This analysis can be done through a machine learning algorithm, visualization, or combination of statistical techniques.
The outcome of this analysis alerts developers to any anomalies within an application or system. Alerts can be automated and triggered by established thresholds, severity levels, or other criteria based on business or application needs. Once the anomaly is identified and located, developers can use the data to debug and resolve the issue.
As mentioned above, the primary types of observability data are logs, metrics, and traces.
Monitoring is the ability to observe and check the progress of processes happening within a system or application. Monitoring relies heavily on metrics. In short, it provides visualization of the environment and enables you test against known problems. Observability, on the other hand, supplies new and deeper data that allows you to infer that an issue may exist. You can then dive into the issue’s cause to gain insight into the future.
Monitoring and observability are not distinctly separate. Rather, they are data analysis options and visualization techniques that allow developers to reach insights faster.
With those definitions established, the table below takes a closer look at four subtle differences between monitoring and observability in software applications. These four differences are divided into scope, granularity, flexibility, and analysis.
| Monitoring | Observability | |
|---|---|---|
| Scope | Measures metrics (e.g., system uptime, CPU usage, error rates) | Understands the system’s mechanisms at work based on their outputs |
Granularity |
Aggregates or samples collected data at a regular cadence (based on predefined metrics) |
Collects and analyzes granular data to get deeper insight and understanding of system behavior |
Flexibility |
Implements predefined dashboards or alert thresholds that are difficult to modify once deployed |
Uses a flexible and adaptable approach with easy-to-change tools that accommodate evolving situations and requirements |
Analysis |
Identifies and reacts to specific events or anomalies |
Emphasizes proactive analysis and troubleshooting by giving developers the tools they need to identify a problem’s cause and implement solutions over time |
Telemetry in software observability refers to the practice of collecting and transmitting data about the performance and behavior of a software system in real-time. This data (response times, error rates, resource consumption, etc.) is used to monitor and understand the system’s current state, and help developers identify opportunities to improve performance.
In conversations on telemetry, OpenTelemetry (OTel) often becomes a talking point because it offers a simplified approach to make observability easier for developers. OTel is a set of open-source tools and libraries that standardize the collection of telemetry data (logs, metrics, and traces) from software systems.
You can learn more about OTel and ways it affects the cloud-native landscape in How OpenTelemetry Is Changing the Way We Trace and Design Apps.
Observability provides developers with a better understanding of their applications, which enables:
Observability does come with some drawbacks, and the most common include:
NGINX is proud to provide additional free educational resources on both observability and OTel: