IT observability is the theory that a system’s internal state should be understood based on its external outputs. Historically, a technological failure could be attributed to a limited set of causes, making a problem easy to identify and correct with a straightforward solution. Modern technology, however, is more complex, and problems are therefore harder to identify, diagnose, and resolve. IT administrators—more specifically, software developers—must be able to monitor systems for all potential issues, and observability is what enables them to do so.
Observability is achieved by monitoring an aggregate of three types of telemetry data:
- Metrics, which are a measure of system performance (memory, processing power, etc.)
- Event logs, which are time-stamped records of all activity taking place within a system
- Traces, which are documented relationships among sequential events
All of this information is combined to illustrate a broader understanding of what the issue is, where it’s happening, what caused it, and what can be done to fix it.
Observability vs. monitoring
As referenced above, monitoring is the means by which observability is accomplished. When an IT administrator reviews metrics, logs, and traces, they are monitoring the status of their systems. Monitoring speaks to the what; if this information is inadequate or inaccurate, the why will be less clear and the observability will be diminished. Observability requires administrators to dive deeper into the data to build an accurate representation of the system’s internal status. In this way, monitoring is good for understanding a system’s broad health status, but observability provides insight on a more granular level.
Why is observability important?
Greater observability ultimately leads to a better understanding of the data a system contains. It allows DevOps teams to actively debug their systems instead of taking a reactionary approach and waiting for something to go wrong. Observability is also based on exploring properties and patterns not defined in advance, which makes it possible to identify patterns or problems that would otherwise go undetected.
With applications specifically, greater observability leads to fewer and shorter downtimes. Automated observability, then, assists with the continuous integration and continuous delivery principles of DevOps teams. Some application performance management (APM) tools can automate observability by performing code instrumentation and analyzing the results to generate actionable insights. These results can then be used to help with troubleshooting and optimization.
