Metrics Validation

Goal: Run chaos and automatically evaluate Prometheus metrics — getting a clear pass or fail without manual inspection.

This journey is well suited to CI/CD pipelines where you cannot watch the cluster in real time.

What you need

• Everything from the Getting Started guide (install krknctl, create a test workload, and run a first scenario)
• A Prometheus instance accessible from where Krkn runs (auto-detected on OpenShift; set via scenario flags on Kubernetes) — need to set one up? See installing Prometheus on a kind cluster
• krknctl installed

Steps

1. Install krknctl — follow the installation guide.

2. Create your alerts profile at config/alerts.yaml. This defines the PromQL expressions Krkn evaluates after each scenario:

   - expr: avg_over_time(histogram_quantile(0.99, rate(etcd_disk_wal_fsync_duration_seconds_bucket[2m]))[5m:]) > 0.01
     description: "etcd fsync latency too high: {{$value}}"
     severity: error
   
   - expr: sum(kube_pod_status_phase{phase="Failed"}) > 5
     description: "Too many failed pods: {{$value}}"
     severity: error
   

   Queries with severity: error cause Krkn to exit with a non-zero code. Queries with severity: info are logged only.

3. Run a scenario with the alerts profile mounted:

   krknctl run pod-scenarios --alerts-profile config/alerts.yaml
   

   Krkn evaluates the alert profile at the end of each scenario and reports pass or fail.

Reference docs

• SLO Validation — full details on alert profiles and PromQL configuration
• krknctl usage — full flag reference for run
• Installing Prometheus on a kind cluster — Helm-based setup for local testing

Next steps

To persist metrics long-term for regression analysis across releases, continue to Long-Term Storage.