Monitoring

LoadPilot exposes a Prometheus metrics endpoint and ships a pre-provisioned Grafana dashboard that updates in real time during a test run.

Grafana Dashboard

LoadPilot Grafana Dashboard

The dashboard has four sections:

Section	Panels
Throughput	RPS — Current vs Target, Active Workers
Latency	Latency Percentiles (p50 / p95 / p99 / max), p99 gauge
Errors	Error Rate %, Cumulative Requests & Errors
Per Task	Latency by Task (p50 / p99), Error Rate by Task %

The Per Task section is populated automatically when a scenario defines multiple named @task methods. A $task template variable lets you filter by individual endpoints. Tasks appear in the legend as soon as the first scrape completes.

Per Task Grafana panels

Prometheus metrics

The coordinator exposes metrics at :9090/metrics during a run:

Metric	Description
`loadpilot_current_rps`	Observed request rate
`loadpilot_target_rps`	Configured target RPS
`loadpilot_active_workers`	Active VUser threads
`loadpilot_latency_p50_ms`	p50 latency (ms)
`loadpilot_latency_p95_ms`	p95 latency (ms)
`loadpilot_latency_p99_ms`	p99 latency (ms)
`loadpilot_latency_max_ms`	Max latency (ms)
`loadpilot_requests_total`	Cumulative request count
`loadpilot_errors_total`	Cumulative error count

Per-task metrics

When a scenario has named tasks the coordinator also emits per-task metrics with a task label:

Metric	Description
`loadpilot_task_requests_total{task="..."}`	Cumulative requests for this task
`loadpilot_task_errors_total{task="..."}`	Cumulative errors for this task
`loadpilot_task_latency_p50_ms{task="..."}`	p50 latency for this task (ms)
`loadpilot_task_latency_p99_ms{task="..."}`	p99 latency for this task (ms)
`loadpilot_task_latency_mean_ms{task="..."}`	Mean latency for this task (ms)

Task names come from the method names of @task-decorated functions in your scenario class.

Local setup (single machine)

loadpilot run scenarios/checkout.py --target https://api.example.com

The coordinator starts automatically. Forward Prometheus and open Grafana:

# Grafana ships embedded in the HTML report — open after the run:
open results/report.html

# Or run Prometheus + Grafana separately and point them at :9090

Kubernetes (Helm)

The Helm chart deploys Prometheus and Grafana with the dashboard pre-provisioned. See Development → Helm Chart for install instructions.

# Forward Grafana to localhost
kubectl port-forward -n loadpilot svc/loadpilot-grafana 3000:3000

Then open http://localhost:3000 — login admin / <adminPassword>.

Coordinator in-cluster (recommended)

Deploy the coordinator as a persistent pod so Prometheus can scrape it from inside the cluster — no host networking hacks required:

# Build and load the coordinator image
docker build -f engine/Dockerfile.coordinator -t loadpilot-coordinator:local .
kind load docker-image loadpilot-coordinator:local --name <cluster-name>

# Deploy with coordinator enabled
helm upgrade loadpilot cli/loadpilot/charts/loadpilot \
  -n loadpilot --reuse-values \
  --set coordinator.enabled=true \
  --set coordinator.image=loadpilot-coordinator \
  --set coordinator.tag=local \
  --set coordinator.imagePullPolicy=Never

# Port-forward coordinator API and run via it
kubectl port-forward -n loadpilot svc/loadpilot-coordinator 8080:8080
loadpilot run scenarios/checkout.py \
  --target https://api.example.com \
  --coordinator-url http://localhost:8080

Prometheus scrapes loadpilot-coordinator:9090 in-cluster automatically when coordinator.enabled=true.

Coordinator running locally (alternative)

To scrape a coordinator running on the host machine:

helm upgrade loadpilot cli/loadpilot/charts/loadpilot \
  --set monitoring.coordinator.scrapeTarget=host.docker.internal:9090

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