Metrics Fundamentals
Metrics are numeric observations about a system collected over time. They show rates, current levels, and distributions so you can understand behavior and decide where to investigate.
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Intro
Metrics Fundamentals
Metrics turn repeated observations into time series. Each observation answers a narrow question: how many requests completed, how much memory is in use, or how long a request took. A metrics system stores observations with timestamps so you can compare the present with the recent past.
A metric is evidence, not a verdict on system health. A low CPU reading does not prove that users can complete a request. Start with the behavior you need to understand, then choose the measurement that can expose it.
The metric model
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Sources
- https://prometheus.io/docs/concepts/metric_types/
Supports
- Counter, gauge, histogram, and summary definitions
- Counter and gauge examples and constraints
- https://prometheus.io/docs/practices/naming/
Supports
- Metric names, units, total suffixes, labels, and cardinality cautions
- Base-unit guidance and meaningful aggregation rule of thumb
- https://prometheus.io/docs/practices/histograms/
Supports
- Histogram and summary tradeoffs
- Aggregation limits for summary quantiles and bucketed distributions
- https://opentelemetry.io/docs/specs/otel/metrics/
Supports
- Metrics API and SDK responsibilities
- Instrumentation, aggregation, processors, and exporters
- https://opentelemetry.io/docs/specs/otel/metrics/data-model/
Supports
- Metric streams, point kinds, resource, scope, attributes, and temporality
- Metric transformation and aggregation concepts
- https://github.com/roaldnefs/awesome-prometheus
Supports
- Curated discovery of Grafana, node exporter, and Blackbox Exporter