exploring-rust-ecosystem VS rust-prometheus

In this article, I’ll show you how to set up monitoring of a Rust web application. The application will expose Prometheus metrics that will be visualized using Grafana. The monitored application is mongodb-redis demo described in detail here. Finally, the following architecture will be obtained:

The project is implemented with MongoDB Rust driver and redis-rs crate.

gRPC server

Source code

I used the prometheus library because it was the most popular and easiest to find. Some unusual and unexpected aspects were that, to delete metrics, all labels must be specified at once, and to record metrics, I always needed the full list of labels that were used during creation. However, these issues were manageable.

[dependencies] prometheus = { git = "https://github.com/tikv/rust-prometheus", rev = "ac86a264223c8d918a43e739ca3c48bb4aaedb90", features = ["process"] }

Metrics exposition is implemented with prometheus crate.

The new Elasticsearch exporter is written in the Rust programming language and is open-sourced on GitHub: github.com/vinted/elasticsearch-exporter-rs. The exporter uses asynchronous Tokio runtime, Rust Prometheus instrumentation library and the official Elasticsearch client library. Metrics collection is decoupled from the serving /metrics endpoint. In addition, Elasticsearch time-based metrics in milliseconds are converted into seconds to comply with Prometheus best practices (metrics ending in “millis” are replaced by “seconds”, “_bytes” and “_seconds” and postfixes are added where appropriate).

Rust has a popular binding: https://github.com/tikv/rust-prometheus