Prometheus

How Prometheus works

SoundCloud originally built Prometheus. It later joined the Cloud Native Computing Foundation and is now a core part of cloud-native observability.

1. Applications and exporters expose metrics on an HTTP endpoint.
2. Prometheus scrapes those endpoints on a set interval.
3. Metrics are stored as time series with labels.
4. Teams query the data with PromQL.
5. Alertmanager sends notifications when rules fire, and Grafana visualizes trends.

Key Prometheus components

• Prometheus server: scrapes and stores metrics
• Exporters: expose metrics from systems such as nodes or databases
• PromQL: the query language for metrics
• Alertmanager: routes and groups alerts
• Pushgateway: accepts metrics from short-lived jobs

Prometheus vs. Grafana

The two tools are often used together but do different jobs.

Why Prometheus matters in Kubernetes and testing

Prometheus is a key part of Kubernetes observability. In testing, it captures numeric signals from test runs and links them to system behavior.

• System health: CPU, memory, and resource metrics from nodes, pods, and services
• Reliability engineering: tracks SLIs and SLOs
• Real-time insight: immediate view of workload performance and failures
• Alerting: Alertmanager notifies teams of anomalies
• Visualization: a data source for Grafana dashboards
• Test analytics: measure test performance and stability over time

Consider a SaaS team that alerts on test failure rates in Prometheus. When failures jump after a deploy, on-call sees it with the same tools they use for production.

Common challenges with Prometheus

• High cardinality: too many unique labels slow queries and inflate storage
• Retention limits: long-term storage needs Thanos, Cortex, or similar
• Complex queries: advanced PromQL has a learning curve
• Scaling: multi-cluster setups need careful planning
• Alert fatigue: poorly designed alerts overwhelm teams
• Integration gaps: inconsistent metric names across tools

Good metric design and labeling discipline keep Prometheus healthy.

How Prometheus relates to Testkube

Testkube exposes metrics in Prometheus format. Testing data joins production observability.

• Test metrics: execution duration, success and failure counts, and run frequency
• Native endpoints: Prometheus scrapes metrics directly from Testkube
• Real-time updates: metrics refresh during and after runs
• Grafana dashboards: test metrics next to cluster and app data
• Alerting: Prometheus alerts on Testkube metric thresholds
• Multi-cluster view: test metrics aggregated across clusters

Real-world examples

• Pass rate dashboards: a QA team charts Testkube pass rates and durations in Grafana
• Failure alerts: a DevOps engineer alerts when test failure rates cross a threshold
• Environment issues: a platform team correlates test metrics with CPU and memory use
• Stability trends: an SRE tracks test stability across clusters
• Deployment views: a pipeline owner adds Testkube metrics to deployment dashboards

Frequently Asked Questions (FAQs)