Grafana Alloy eBPF profiling via auto-instrumentation in Kubernetes

This repository provides a practical demonstration of leveraging Grafana Alloy for continuous application profiling using eBPF and Pyroscope in Kubernetes. It illustrates a seamless approach to profiling Golang and Python processes, aiding in performance optimization.

Overview

eBPF profiling via Grafana Alloy is based on a few components:

• discovery.kubernetes for discovering Kubernetes pods
• discovery.relabel for detecting and filtering target processes and setting up labels
• pyroscope.ebpf for enabling eBPF profiling for specific applications
• pyroscope.write for writing the profiles data to a remote endpoint

Refer to the official documentation for an in-depth understanding and additional configuration options for eBPF with Grafana Alloy. Also, check the Grafana Alloy Components reference for more details on each used component.

Getting started

To use this example:

1. Set up a local kubernetes cluster using Kind or a similar tool.
2. Clone this repository and navigate to this example's directory.
3. Deploy the manifests:
   shellCopy

      kubectl apply -f alloy.yaml -f grafana.yaml -f pyroscope.yaml -f python-fast-slow.yaml
   

4. Port-forward the Grafana service to access the Explore Profiles app:
   shellCopy

      kubectl port-forward -n pyroscope-ebpf service/grafana  3000:3000
   

5. Explore profiles http://localhost:3000/a/grafana-pyroscope-app/profiles-explore

After the deployment is operational, the Grafana Alloy will profile the Go and Python applications using pyroscope.ebpf component.

Documentation

Refer to the official documentation for an in-depth understanding and additional configuration options for eBPF profiling with Grafana Alloy.