zprofiller

README

ZProfiller

Overview

zprofiller is a Go package designed to facilitate the integration of Go's built-in pprof profiling into web applications using the Chi router. It offers a structured way to expose profiling endpoints to monitor and analyze the performance of Go applications in both development and production environments.

Features

• Easy Integration: Seamlessly integrates with the Chi router.
• Automatic Profiling Endpoints: Automatically registers standard pprof endpoints.
• Custom Configuration: Allows customization of server timeouts and logging.

Getting Started

Installation

To use zprofiller in your project, ensure you have Go installed and your workspace is set up, then add zprofiller to your dependencies:

go get -u github.com/zondax/zprofiller

Integration

Integrate zprofiller into your Go application:

1. Import the Package

   import (
       "github.com/zondax/zprofiller"
   )
   

2. Create a Config Object

   config := &zprofiller.Config{
       ReadTimeOut:  5 * time.Second,
       WriteTimeOut: 5 * time.Second,
       Logger:       logger.NewLogger(),
   }
   

3. Instantiate zprofiller

   profiler := zprofiller.New(nil, config)
   

4. Run the Profiler

   err := profiler.Run(":9999")
   if err != nil {
       log.Fatalf("Failed to start profiler: %v", err)
   }
   

Usage

Access the profiling endpoints at http://localhost:<port>/debug/pprof/, where <port> is the port you specified.

Viewing pprof Results on the Web

Accessing pprof via Web Browser

Navigate to:

http://localhost:<port>/debug/pprof/

This index page links to profiles like Heap, Goroutine, Threadcreate, Block, and Mutex.

Visualizing Profiles

Use tools like Go Tool Pprof or Graphviz for deeper analysis:

• Go Tool Pprof:

  go tool pprof -http=:8081 http://localhost:<port>/debug/pprof/profile
  

  This command downloads the CPU profile data from your application and opens it in an interactive web interface on http://localhost:8081.

• Graphviz:

  sudo apt-get install graphviz
  go tool pprof -http=:8081 --graph http://localhost:<port>/debug/pprof/profile
  

Online Tools and Extensions

Consider using online tools or browser extensions like pprof++ for Chrome for in-browser visualization of pprof data.

Performance Considerations

When integrating profiling tools such as pprof into your application, it is essential to consider the potential impact on performance:

• Resource Usage: Profiling operations can consume significant CPU and memory resources, particularly when capturing and analyzing high-frequency data such as CPU profiles.
• Production Use: While pprof can be invaluable for diagnosing issues in production, it should be enabled selectively. Consider using environment variables or configuration files to control access to profiling endpoints.
• Sampling Rate: Adjust the sampling rate of profiles according to the performance impact and the level of detail required. Lower rates can reduce overhead but may miss critical details.
• Security: Exposing profiling information can introduce security risks. Ensure that profiling endpoints are protected with authentication mechanisms and are only accessible by authorized personnel.
• Impact Measurement: Continuously monitor the impact of enabling profiling on your system’s response times and resource usage. Disable profiling when not needed to avoid unnecessary overhead.

Security Considerations

Ensure that access to profiling endpoints is secured, especially in production environments, to protect sensitive application data.

Documentation

Index

• type Config
• type ZProfiller
  • func New(_ metrics.TaskMetrics, config *Config) ZProfiller

Types

type Config

type Config struct {
	ReadTimeOut  time.Duration
	WriteTimeOut time.Duration
	Logger       *logger.Logger
}

type ZProfiller

type ZProfiller interface {
	Run(addr ...string) error
}

func New

func New(_ metrics.TaskMetrics, config *Config) ZProfiller