3.3 KiB
Dashboards
Grafana dashboard exports, kept as JSON so they're versioned alongside the infrastructure they visualize instead of only existing inside Grafana's own database.
gitlab-application-runner-metrics.json
Custom-built dashboard for the self-hosted GitLab instance and its Runner — see GitLab CI/CD for the full write-up of why this is hand-built rather than an imported community dashboard.
Data sources required: Prometheus (metrics) and Loki (logs), both already provisioned by the monitoring stack.
Rows
| Row | Panels |
|---|---|
| GitLab Rails & Puma Overview | Request queue latency (p95 / avg), Puma memory watchdog limit, live nginx/workhorse logs (Loki) correlated against the same timeline |
| Database & Redis Performance | Postgres and Redis metrics |
| Cache & CI Metrics | Rails cache performance, CI pipeline metrics |
| GitLab Runner Metrics | Active jobs vs. concurrency limit, job execution duration, job errors/sec, Runner process memory footprint |
GitLab Rails & Puma Overview
Request queue latency sits directly above a live Loki panel streaming GitLab's own nginx/workhorse logs — no separate dashboard needed to go from "latency spiked" to "here's what was actually happening at that moment":
The logs panel queries Loki directly rather than linking out to Explore, so a real request line like a POST /api/v4/jobs/request from the Runner polling for work is visible in the same timeline as the latency graph above it — pulling live logs into the same row as request latency (rather than a separate Loki-only dashboard) was a deliberate choice, not an afterthought.
GitLab Runner Metrics
Runner Memory Footprint hovering around 27–30 MiB confirms the Docker-executor Runner itself is genuinely lightweight at idle — the actual resource cost only shows up in the job containers it spins up temporarily, not the Runner process itself. Active Jobs vs. Limit tracks against the concurrent = 1 cap set in config.toml, so a graph pinned at 1 during a running job (rather than climbing higher) confirms that RAM guardrail is actually being enforced, not just configured.
Importing
Grafana → Dashboards → New → Import → Upload JSON file, select gitlab-application-runner-metrics.json, map the DS_PROMETHEUS variable to your Prometheus data source when prompted. The Loki panel uses your default Loki data source directly — no variable mapping needed for that one, since there's only ever one Loki instance in this stack.
Metric naming note
Panels query GitLab's own /-/metrics endpoint directly (e.g. gitlab_rails_queue_duration_seconds_bucket, process_resident_memory_bytes{job="gitlab-runner"}) — these metric names come from an externally scraped GitLab instance with its bundled Omnibus monitoring disabled. If reusing this dashboard against a GitLab instance running its own internal Prometheus (Omnibus monitoring enabled), the metric names and job labels won't match and panels will show no data.