Go Pprof Analyzer

🚀 Running a PProf Docker Container with Simplified Chinese

This project provides a Docker container for running PProf, enabling you to perform performance profiling on your Go projects.

🚀 Quick Start

Pull the PProf Image

docker pull dockerhub.schaefer.name/pprof:latest-amd64

Create a Configuration File

Create a pprof-config.yaml file with the following content:

address: ":8080"
pprofs:
  - name: "Heap profile"
    pattern: ".*\.heap"
    contentTypes: ["application/octet-stream"]
    subProcessProfile: true
    type: "heap"
  - name: "CPU profile"
    pattern: ".*\.cpu"
    contentTypes: ["text/plain"]
    type: "cpu"
  - name: "Goroutine profile"
    pattern: ".*\.goroutine"
    contentTypes: ["text/plain"]
    type: "goroutine"

Start the PProf Container

docker run -d --name pprof -p 8080:8080 -v $(pwd)/pprof-config.yaml:/etc/pprof/config.yaml dockerhub.schaefer.name/pprof:latest-amd64

Access the PProf Web Interface

# Access http://<your-docker-host>:8080 in your browser or http://localhost:8080 if running locally

💻 Usage Examples

Integrate Project Code with PProf

Add Profiling Code to Your Go Project

import (
	"fmt"
	"log"
	"net/http"
	
	"github.com/google/pprof/agent"
)

func main() {
	log.Println("Starting application on port 8081")
	http.HandleFunc("/", handler)
	http.ListenAndServe(":8081", nil)
}

func handler(w http.ResponseWriter, r *http.Request) {
	if r.URL.Path == "/debug/pprof" {
		w.Header().Set("Content-Type", "application/octet-stream")
		agent.Profile(r.Context(), w, agent.MainArgs{})
		return
	}
	fmt.Fprintf(w, "Hello, World!")
}

Start Profiling with curl

# Profile a CPU file
curl -X POST http://localhost:8080/pprof/upload -F "file=@./testdata/gobench.cpu"

# Start the interactive PProf UI (macOS only)
curl -X POST http://localhost:8080/pprof/debug \
  -H "Content-Type: application/json; charset=utf-8" \
  -d '{"address":"http://localhost:8081/debug/pprof/"}'

JSON Request Examples

Analyze a Local CPU Profile File

{
	"tool_name": "analyze_pprof",
	"arguments": {
		"profile_path": "./testdata/gobench.cpu",
		"type": "cpu"
	}
}

Generate a Flame Graph

{
	"tool_name": "generate_flamegraph",
	"arguments": {
		"profile_path": "./testdata/gobench.cpu",
		"output_path": "./flame-graph.svg"
	}
}

📚 Documentation

Release Notes

v0.1.2 (2023-05-26)

• Fixed a memory leak issue in flame graph generation.
• Improved the efficiency of profile file parsing.
• Supported the subprocess mode for goroutine profiling.
• Added a timestamp-based naming rule for profile files.

Known Issues

• Does not support analyzing very large profile files (over 1GB).
• May experience delays when processing certain types of profile data.

Usage Instructions

Please ensure you have pulled the latest image before running the PProf container and correctly configured your application to generate the corresponding profile files. If you encounter any issues, refer to the official documentation or contact technical support.

🔮 Future Improvement Plans

1. Support more types of profile files.
2. Provide a web-based interactive analysis interface.
3. Increase support for multi-threaded and asynchronous applications.
4. Optimize memory usage efficiency.
5. Add a result export function.