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Coroot + qryn: turn telemetry into answers

Coroot + qryn: turn telemetry into answers

turn telemetry data into answers

Lorenzo Mangani's photo
Lorenzo Mangani
ยทDec 3, 2022ยท

2 min read

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Coroot is not another observability platform with flashy dashboards and alert rules. It is an assistant, that not only detects issues in your applications but also provides you with a list of possible fixes. Each recommendation is equipped with all the relevant details to implement the fix.

Let's start with the really good news:

Since the storage backend is Prometheus remote_write, Coroot is natively compatible with qryn. There's more - Coroot Node-Agent uses eBPF and also requires zero modifications to your app stack. It's plug-and-play observability!

Let's spin up a quick tutorial to showcase this exciting combination in action!

Requirements

Usage

Clone the tutorial repository at https://github.com/metrico/qryn-coroot-tutorial

git clone https://github.com/metrico/qryn-coroot-tutorial

The provided docker-compose will spin up coroot, coroot-agent and vector scraper pointed at your qryn instance, acting as a Prometheus remote_write.

Define your QRYN_URL endpoint and let the data collection begin.

export QRYN_URL=http://qryn:3100/prom/remote/write
docker-compose up -d

Coroot

There's no Grafana this time. The Coroot interface highlights issues and weak spots in your infrastructure, providing a clear view of all components, and helping you profile, fix and prevent service outages.

Configuration

On your first usage, you will be required to create a new Project.

  • use your qryn or qryn.cloud endpoint as the Prometheus URL

Usage

Metrics are emitted by the Coroot-Node-Agent and scraped by Vector into qryn. Data will automatically be populated in the Coroot User Interface.

Happy times!

That's it! ๐ŸŽ‰

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