Tutorial 23: Process Telemetry & Prometheus Exporter 📡

Monitor process RSS memory, Tokio runtime tick latency, and active tasks using Rullst Radar (rullst::radar) and Prometheus (GET /metrics).

🛠️ Step 1: Mount Prometheus Exporter

In src/main.rs:

use axum::Router;
use rullst_core::radar::radar_metrics_router;
use rullst::Server;

#[tokio::main]
async fn main() -> Result<(), rullst_core::server::ServerError> {
    let app = Router::new()
        .merge(radar_metrics_router()); // Exposes GET /metrics

    Server::new(app.into()).run(3000).await
}

📊 Step 2: Prometheus Metrics Scrape Output

Query GET /metrics:

# HELP rullst_memory_rss_bytes Process RSS memory consumption
# TYPE rullst_memory_rss_bytes gauge
rullst_memory_rss_bytes 24510464

# HELP rullst_tokio_latency_microseconds Tokio runtime tick latency in microseconds
# TYPE rullst_tokio_latency_microseconds gauge
rullst_tokio_latency_microseconds 42

Visual dashboard available in Studio: http://localhost:5555/studio/radar.

/metrics is not authenticated by radar_metrics_router(). Restrict it with a private network, service-mesh policy, or reviewed authentication middleware; process and runtime measurements can disclose operational information.

💡 Key Takeaways

• The response is a point-in-time local snapshot and allocates its text body.
• Linux and Windows expose supported process RSS/CPU probes. Tokio task data is available only inside a Tokio runtime; unsupported probes are omitted.
• The scheduler-yield observation is not a universal request-latency target.