Prometheus

Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. It collects and stores metrics as time series data, i.e., streams of timestamped values. Its multi-dimensional data model, powerful query language (PromQL), and efficient storage make it a popular choice for modern cloud-native environments.

Key Features:

* Multi-dimensional Data Model: Metrics are identified by a metric name and a set of key-value pairs (labels). This allows for flexible and granular querying.
* PromQL: A powerful and flexible query language that allows users to select and aggregate time series data in real-time.
* Pull Model: Prometheus scrapes metrics from configured targets (called 'exporters') over HTTP at specified intervals. This simplifies configuration and management.
* Efficient Storage: Prometheus stores time series data locally on disk, optimized for its specific data model.
* Alerting: Integrated Alertmanager handles alerts, deduplicating, grouping, and routing them to various notification mechanisms (email, PagerDuty, Slack, etc.).
* Client Libraries: Provides client libraries in various languages (Go, Java, Python, Ruby, Rust, etc.) to instrument application code for metrics exposure.
* Service Discovery: Supports various service discovery mechanisms to automatically find scraping targets.

How it Works:

1. Instrumentation: Applications are instrumented with Prometheus client libraries to expose metrics (counters, gauges, histograms, summaries) via an HTTP endpoint (typically `/metrics`). These endpoints serve metrics in a specific text format.
2. Scraping: The Prometheus server is configured to periodically 'scrape' these `/metrics` endpoints from various targets.
3. Storage: Scraped metrics are stored locally on the Prometheus server's disk, indexed by their metric name and labels.
4. Querying & Alerting: Users can query the stored data using PromQL for dashboards (e.g., with Grafana) or to define alerting rules. When an alert condition is met, Prometheus sends the alert to the Alertmanager.
5. Visualization: While Prometheus has a basic UI, it's commonly integrated with Grafana for powerful and customizable dashboards.

Prometheus in Rust:

The `prometheus` crate in Rust provides a client library for instrumenting Rust applications. It allows developers to define and register various metric types (Counter, Gauge, Histogram, Summary) within their Rust code. These metrics can then be exposed through an HTTP endpoint, typically served by a web framework like `warp`, `actix-web`, `axum`, or `hyper`, making the Rust application a Prometheus 'exporter'.

Example Code

```rust
use prometheus::{Encoder, Gauge, Opts, Registry, TextEncoder, Counter};
use warp::Filter;
use std::time::Duration;
use tokio::time::sleep;

// A simple Rust application that exposes Prometheus metrics
#[tokio::main]
async fn main() {
    // 1. Create a Prometheus Registry (or use the default global one)
    // For this example, we'll use a global registry for simplicity.
    // In more complex scenarios, you might want to manage your own Registry.
    let registry = prometheus::default_registry();

    // 2. Define and register custom metrics
    // Counter: A metric that represents a single monotonically increasing counter.
    let request_counter = Counter::new(
        "http_requests_total",
        "Total number of HTTP requests received.",
    ).unwrap();
    registry.register(Box::new(request_counter.clone())).unwrap();

    // Gauge: A metric that represents a single numerical value that can arbitrarily go up and down.
    let current_connections = Gauge::new(
        "app_current_connections",
        "Current number of active connections.",
    ).unwrap();
    registry.register(Box::new(current_connections.clone())).unwrap();

    println!("Prometheus metrics exposed at http://127.0.0.1:8080/metrics");
    println!("Sample endpoint at http://127.00.1:8080/hello");

    // 3. Expose the /metrics endpoint
    let metrics_route = warp::path!("metrics")
        .and(warp::get()) // Only allow GET requests
        .map(move || {
            let mut buffer = vec![];
            let encoder = TextEncoder::new();
            let metric_families = registry.gather(); // Get all metrics from the registry
            encoder.encode(&metric_families, &mut buffer).unwrap();
            String::from_utf8(buffer).unwrap()
        });

    // 4. A sample application endpoint that increments the counter
    let hello_route = warp::path!("hello")
        .and(warp::get())
        .map(move || {
            request_counter.inc(); // Increment the request counter
            "Hello, Rust Prometheus! Request counted."
        });

    // 5. Simulate some background activity to update the gauge
    let gauge_updater = tokio::spawn(async move {
        let mut i = 0.0;
        loop {
            // Simulate connections fluctuating
            current_connections.set(10.0 + (i % 20.0)); // Value between 10 and 29
            i += 1.0;
            sleep(Duration::from_secs(5)).await;
        }
    });

    // 6. Combine routes and run the server
    let routes = metrics_route.or(hello_route);
    warp::serve(routes).run(([127, 0, 0, 1], 8080)).await;

    gauge_updater.abort(); // Stop the gauge updater task if server shuts down
}
```

To run this code:

1.  Create a new Rust project:
    ```bash
    cargo new prometheus_rust_example
    cd prometheus_rust_example
    ```
2.  Add dependencies to `Cargo.toml`:
    ```toml
    [package]
    name = "prometheus_rust_example"
    version = "0.1.0"
    edition = "2021"

    [dependencies]
    prometheus = "0.13"
    warp = "0.3"
    tokio = { version = "1", features = ["full"] }
    ```
3.  Replace `src/main.rs` with the code above.
4.  Run the application:
    ```bash
    cargo run
    ```

Now, you can open your browser or use `curl`:
*   Visit `http://127.0.0.1:8080/metrics` to see the exposed Prometheus metrics.
*   Visit `http://127.0.0.1:8080/hello` multiple times and then refresh `/metrics` to see the `http_requests_total` counter increment.

Example Code

Related Topics