Monitor Your Rust Web Service with Prometheus Metrics

Unlock the Power of Prometheus: Collecting Metrics in Rust Web Services

Are you tired of flying blind when it comes to your web service’s performance? Do you struggle to identify bottlenecks and optimize your application? Look no further than Prometheus, the industry-standard tool for collecting and analyzing metrics. In this tutorial, we’ll show you how to harness the power of Prometheus in your Rust web service, giving you the insights you need to take your application to the next level.

Getting Started

To follow along, you’ll need a recent Rust installation (1.39+) and a way to start a local Prometheus instance – we’ll use Docker for this example. Create a new Rust project and edit the Cargo.toml file to add the necessary dependencies, including the prometheus crate for recording metrics, the warp web framework, the tokio async runtime, and rand for generating random metrics data.

Defining Your Metrics

The first step in collecting metrics is to define what you want to measure. We’ll create a REGISTRY to record metrics throughout the program’s run, and define four key metrics:

• INCOMING_REQUESTS: count incoming requests to the /some route
• CONNECTED_CLIENTS: count clients currently connected via websockets
• RESPONSE_CODE_COLLECTOR: count different response codes of a series of randomly generated requests
• RESPONSE_TIME_COLLECTOR: collect response times of a series of randomly generated requests

Each metric has a specific data type, such as IntCounter or HistogramVec, which determines how the data is collected and analyzed.

Registering Your Metrics

With your metrics defined, it’s time to register them with the REGISTRY. We’ll create a registration function that calls the register method of REGISTRY with boxed versions of our metrics. This sets up the infrastructure for collecting metrics from anywhere in the application.

Tracking Metrics

Now it’s time to start collecting some metrics! We’ll implement a warp web service with three routes: one to track incoming requests, one to handle websocket connections, and one to collect random response data. We’ll also spawn a data_collector function to simulate collecting request data from another service.

Publishing Metrics to Prometheus

To make our metrics available to Prometheus, we’ll implement a metrics_handler that encodes the collected metrics into a buffer and returns them as a string. This allows Prometheus to collect the metrics from our service.

Testing with Prometheus

Let’s put it all together! We’ll create a local Prometheus instance using Docker, and configure it to poll our service for new data every five seconds. Start the Rust web service using cargo run, and navigate to http://localhost:9090/graph to explore the collected metrics.

Unleashing the Power of Prometheus

With our setup complete, we can start exploring the power of Prometheus. Try querying the 90th percentile of response times, or the sum of increase over time by response type. The possibilities are endless!

Take Your Application to the Next Level

LogRocket is here to help you make the most of your Rust application. Our platform provides full visibility into web frontends, allowing you to debug issues, track performance, and optimize your app for success. Try LogRocket today and start unlocking the full potential of your application!