Profiling your Go applications is made easy by the Go ecosystem. In this blog post, we will explore how to use the github.com/pkg/profile package by Dave Cheney to easily debug and profile your programs.
To get started, follow these simple steps:
CPU Profiling
Step 1: Download github.com/pkg/profile by running the following command:
go get github.com/pkg/profile
Step 2: Add profiling to the main() function of your command:
package main
import (
//...
"github.com/pkg/profile"
)
func main() {
// CPU profiling by default
defer profile.Start().Stop()
//...
}
Step 3: Build and run your program. This will generate a *.pprof file in a temporary folder and display its location:
2017/08/03 14:26:28 profile: cpu profiling enabled, /var/...../cpu.pprof
Step 4: Install graphviz if you haven’t already. On a Mac, you can simply run brew install graphviz. For other platforms, refer to https://www.graphviz.org.
Step 5: Run go tool pprof with your binary location and the location of the cpu.pprof file:
To generate a PDF analysis:
go tool pprof --pdf ~/go/bin/yourbinary /var/path/to/cpu.pprof > file.pdf
You can also generate other types of visualizations, such as txt:
go tool pprof --txt ~/go/bin/yourbinary /var/path/to/cpu.pprof > file.txt
Memory Profiling
Memory profiling is similar to CPU profiling, but with a slight modification. Instead of using the default configuration for profile.Start(), we pass a profile.MemProfile flag:
defer profile.Start(profile.MemProfile).Stop()
Therefore, the updated code looks like this:
package main
import (
//...
"github.com/pkg/profile"
)
func main() {
// Memory profiling
defer profile.Start(profile.MemProfile).Stop()
//...
}
When running the program, it will generate a mem.pprof file instead of cpu.pprof.
For more information on profiling Go applications, you can refer to the following resources: