Observability Concepts: Difference between revisions
Jump to navigation
Jump to search
No edit summary |
|||
| Line 4: | Line 4: | ||
Observability is the art of understanding the dynamic state of your systems, service and infrastructure. Observability data consists of [[#Metric|metrics]], [[#Trace|traces]], [[#Logs|logs]], metadata, exceptions and even real-time events. Observability analysis leverages machine learning and heuristic techniques to generate insights from data. | Observability is the art of understanding the dynamic state of your systems, service and infrastructure. Observability data consists of [[#Metric|metrics]], [[#Trace|traces]], [[#Logs|logs]], metadata, exceptions and even real-time events. Observability analysis leverages machine learning and heuristic techniques to generate insights from data. | ||
=Logs= | =<span id='Log'></span>Logs= | ||
=Metrics= | =<span id='Metric'></span>Metrics= | ||
=Traces= | =<span id='Trace'></span>Traces= | ||
Revision as of 01:34, 6 September 2023
Internal
Overview
Observability is the art of understanding the dynamic state of your systems, service and infrastructure. Observability data consists of metrics, traces, logs, metadata, exceptions and even real-time events. Observability analysis leverages machine learning and heuristic techniques to generate insights from data.
Logs
Metrics
Traces
TO PROCESS:
- https://docs.honeycomb.io/getting-started/learning-about-observability/
- https://www.infoq.com/news/2018/06/observability-microservices
- https://blog.takipi.com/system-observability-making-your-production-environment-great-again/
- Tracing
Observability
- metrics-based monitoring
- logging (great for debugging)
- tracing