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Show HN: Coroot – eBPF-based, open source observability with actionable insights

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Show HN: Coroot – eBPF-based, open source observability with actionable insights

#1

Post by openWrangler »

A common open source approach to observability will begin with databases and visualizations for telemetry - Grafana, Prometheus, Jaeger. But observability doesn’t begin and end here: these tools require configuration, dashboard customization, and may not actually pinpoint the data you need to mitigate system risks.

Coroot was designed to solve the problem of manual, time-consuming observability analysis: it handles the full observability journey — from collecting telemetry to turning it into actionable insights. We also strongly believe that simple observability should be an innovation everyone can benefit from: which is why our software is open source.

Features:

- Cost monitoring to track and minimise your cloud expenses (AWS, GCP, Azure.)

- SLO tracking with alerts to detect anomalies and compare them to your system’s baseline behaviour.

- 1-click application profiling: see the exact line of code that caused an anomaly.

- Mapped timeframes (stop digging through Grafana to find when the incident occurred.)

- eBPF automatically gathers logs, metrics, traces, and profiles for you.

- Service map to grasp a complete at-a-glance picture of your system.

- Automatic discovery and monitoring of every application deployment in your kubernetes cluster.

We welcome any feedback and hope the tool can improve your workflow!

Show HN: Coroot – eBPF-based, open source observability with actionable insights
github.com

Re: Show HN: Coroot – eBPF-based, open source observability with actionable insights

#2

Post by Conasg »

I took a cursory look and I like what I see – the service maps are really good, I love the level of detail. I will say, one thing I'm looking for with this kind of software, to maximise value, is structured logging support, and from what I could see, each log line just has the raw payload currently. Is that something you have on your roadmap?

Re: Show HN: Coroot – eBPF-based, open source observability with actionable insights

#4

Post by nikolay_sivko »

Conasg wrote: post #2

I took a cursory look and I like what I see – the service maps are really good, I love the level of detail. I will say, one thing I'm looking for with this kind of software, to maximise value, is structured logging support, and from what I could see, each log line just has the raw payload currently. Is that something you have on your roadmap?

In addition to raw logs, Coroot can extract recurring patterns to generate log-based metrics [1].

We also plan to convert structured logs into OpenTelemetry attributes [2].

[1] https://demo.coroot.com/p/tbuzvelk/applications/default:Depl... [2] https://github.com/coroot/coroot/issues/490

Re: Show HN: Coroot – eBPF-based, open source observability with actionable insights

#5

Post by nikolay_sivko »

toobulkeh wrote: post #3

We're on sentry today, but have been waiting for a fully OSS solution like this.

(I'm a co-founder). At Coroot, we're strong believers in open source, especially when it comes to observability. Agents often require significant privileges, and the cost of switching solutions is high, so being open source is the only way to provide real guarantees for businesses.

Re: Show HN: Coroot – eBPF-based, open source observability with actionable insights

#8

Post by nikolay_sivko »

IOT_Apprentice wrote: post #6

Can this also be used in a non-cloud environment? Or even say in promox based setup locally?

It only requires a modern Linux kernel. Note: The agent does not support Docker-in-Docker environments, such as KinD or Minikube (D-in-D plugin).

Re: Show HN: Coroot – eBPF-based, open source observability with actionable insights

#9

Post by nikolay_sivko »

esafak wrote: post #7

What's the data transformation story; for ML on metrics?

Coroot builds a model of each system, allowing it to traverse the dependency graph and identify correlations between metrics. On top of that, we're experimenting with LLMs for summarization — here are a few examples: https://oopsdb.coroot.com/failures/cpu-noisy-neighbor/
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