Latest revision as of 01:36, 6 September 2023

Internal

Observability Concepts

https://www.infoq.com/articles/monitoring-SRE-golden-signals

Rate, errors, latency, saturation and utilization.

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 =Internal=
 * [[Monitoring#Subjects|Monitoring]]
 * [[Monitoring Tools]]
 =Pooling=
 =Trapping=
 =Observability=
+{{Internal|Observability Concepts|Observability Concepts}}
-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.
-<font color=darkkhaki>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
-* https://github.com/OpenObservability/OpenMetrics
-* https://opentelemetry.io/
-</font>
 =Golden Signals=
 {{External|https://www.infoq.com/articles/monitoring-SRE-golden-signals}}
 Rate, errors, latency, saturation and utilization.
-=Metric=
-==Aggregated Metrics==
-Not all metrics are stored.
-==Raw Metrics==
-All metrics are stored, so individual events can be identified.
-==Examples==
-* [[Prometheus_Concepts#Metrics|Prometheus Metrics]]
-==Organizatorium==
-* https://www.dropwizard.io/en/latest/
-* [[Micrometer]]