[Python] Comprehensive Guide to Using Proxies in Python: HTTP, SOCKS, and Asynchronou

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❌ Why Python is terrible at this (Proxies and threads):

Python’s "threads" don’t actually run in parallel.

Python has something called the Global Interpreter Lock (GIL). It means:

• Even if you create 100 threads in Python, only one thread runs at a time in a single process.
  
• This is terrible for network-heavy or I/O-heavy tasks, like using proxies, scraping, or concurrent HTTP requests.
  

To work around this, you must:

• Use
  
  Code:

  [color=#e74c3c][b]asyncio[/b][/color]
  (which adds complexity).
  
• Or use multiprocessing, which uses more memory and is slower to start.
  
• Or rely on third-party libraries like
  
  Code:

  [color=#e74c3c]requests[/color]
  ,
  
  Code:

  [color=#e74c3c]aiohttp[/color]
  ,
  
  Code:

  [color=#e74c3c]threading[/color]
  , or
  
  Code:

  [color=#e74c3c]concurrent.futures[/color]
  — all of which are slower and messier than Go's simple
  
  Code:

  [color=#e74c3c]go func()[/color]
  .
  

✅ Why Go is better:

1. Built-in concurrency via goroutines:
   • Go has lightweight, fast, and memory-efficient threads called goroutines.
     
   • You can launch thousands of goroutines using almost no memory.
     
   • Go handles scheduling for you with a powerful built-in scheduler, making concurrency easy and efficient.
     
2. Channels and sync tools built-in:
   • Go’s concurrency model is simple and powerful: channels let you safely share data between goroutines with no external libraries.
     
3. Standard library is excellent for HTTP and proxies:
   •  
     Code:

     [color=#2ecc71]http.Client[/color]
     in Go has built-in support for proxy settings and connection reuse.
     
   • You can control timeouts, redirects, and proxies with very little code — and it's fast.
     

 If you're writing a script that uses many proxies or connections, Go will:

• Use less RAM
  
• Run faster
  
• Be simpler to maintain
  

Python will:

• Choke under high concurrency
  
• Require async or multiprocessing
  
• Be more error-prone and complex
  

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