description Prometheus Overview
Built around numerical measurements collected over time, Prometheus provides an open-source foundation for monitoring systems and applications. It commonly gathers data by scraping metrics endpoints, then stores those readings as time series identified by names and labels. That label-based approach helps distinguish individual services or instances within a larger deployment, including Kubernetes environments. PromQL, its query language, allows operators to select and combine measurements when investigating performance or defining alert conditions.
Prometheus can supply data to dashboard tools, while Alertmanager handles alert routing and related notification tasks. It suits technical teams that want control over their monitoring setup and can maintain the infrastructure themselves. The flexibility comes with responsibility for configuration and ongoing operation, rather than the managed experience of a hosted monitoring service.
help Prometheus FAQ
Why is Prometheus so common in Kubernetes monitoring?
Prometheus uses a pull-based model that scrapes metrics endpoints, which fits well with dynamic Kubernetes services and pods. It also works closely with Alertmanager and exporters such as node_exporter and kube-state-metrics.
What is PromQL used for in Prometheus?
PromQL is Prometheus's query language for time-series metrics. It lets teams calculate rates, percentiles, error ratios, saturation, and alert conditions from labels like job, instance, namespace, and pod.
Does Prometheus store logs or traces?
Prometheus is built for numeric time-series metrics, not full logs or distributed traces. It is often paired with Grafana Loki for logs and systems like Jaeger or Tempo for tracing.
How does Prometheus alerting work?
Prometheus evaluates alert rules against metric data and sends firing alerts to Alertmanager. Alertmanager then handles grouping, silencing, routing, and delivery to tools like Slack, PagerDuty, or email.
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