How to use Go or Rust to call Python scripts to achieve true parallel execution?

Break through Python GIL limits: Go/Rust calls Python scripts in parallel

Python project performance bottleneck? don’t worry! This article introduces how to use Go or Rust to call Python scripts to bypass the limitations of global interpreter locks (GIL), realize true parallel execution, and improve project efficiency.

Both Go and Rust can realize multi-process parallelism by calling external Python scripts, effectively avoiding GIL problems.

Go language implementation:

os/exec package of Go language can easily start a new process and run Python scripts to achieve parallelism. Examples are as follows:

 package main

import (
    "fmt"
    "os/exec"
)

func main() {
    cmd := exec.Command("python", "your_script.py")
    output, err := cmd.CombinedOutput()
    if err != nil {
        fmt.Println("Error:", err)
    }
    fmt.Println(string(output))
}

This code starts a new process to execute your_script.py and gets its output. Each Python script runs independently and does not interfere with each other, thus realizing true parallel processing.

Rust language implementation:

Rust's std::process::Command can also implement similar functions:

 use std::process::Command;

fn main() {
    let output = Command::new("python")
        .arg("your_script.py")
        .output()
        .expect("Failed to execute command");

    if output.status.success() {
        println!("{}", String::from_utf8_lossy(&output.stdout));
    } else {
        println!("Error: {}", String::from_utf8_lossy(&output.stderr));
    }
}

This code also starts a new process and executes your_script.py and processes the output. In this way, Rust can also make full use of multi-process parallelism to break through GIL restrictions.

Inter-process communication (IPC):

Whether it is Go or Rust, data interaction with Python processes can be performed through the inter-process communication (IPC) mechanism. This allows you to take advantage of the efficient parallelism capabilities of Go/Rust while maintaining the availability of Python code.

Through the above methods, you can significantly improve project performance, achieve true parallel execution, and solve the performance bottlenecks brought by Python GIL.

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