Observability Concepts: Difference between revisions
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=Internal= | =Internal= | ||
* [[Observability#Subjects|Observability]] | * [[Observability#Subjects|Observability]] | ||
=Logs= | =Overview= | ||
=Metrics= | Observability is the art of understanding the dynamic state of your systems, including services 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. | ||
=Traces= | |||
=<span id='Log'></span>Logs= | |||
{{Internal|Logging#Overview|Logging}} | |||
=<span id='Metric'></span>Metrics= | |||
{{Internal|Prometheus_Concepts#Metrics|Prometheus Metrics}} | |||
==Aggregated Metrics== | |||
Not all metrics are stored. | |||
==Raw Metrics== | |||
All metrics are stored, so individual events can be identified. | |||
=<span id='Trace'></span>Traces= | |||
{{Internal|Tracing|Tracing}} | |||
=Language Support= | |||
{{Internal|Go Observability|Go Observability}} | |||
Latest revision as of 20:01, 6 June 2024
Internal
Overview
Observability is the art of understanding the dynamic state of your systems, including services 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
Aggregated Metrics
Not all metrics are stored.
Raw Metrics
All metrics are stored, so individual events can be identified.