Python数据库爬存任务：多进程与多线程该如何选择？

Hey there! Let’s break this down specifically for your scenario—no confusing jargon, just straight-up practical advice.

First, let’s quickly recap the GIL stuff since that’s the core of the confusion: Python’s Global Interpreter Lock (GIL) means only one thread can execute Python bytecode at a time. This kills true parallelism for CPU-heavy tasks with threads, but it doesn’t matter much for tasks where your code is waiting around (aka IO-bound work).

Your Scenario: crawl_and_save_them_in_db per item #

Your task is 100% IO-bound, and here’s why:

• Crawling data means waiting for network responses (your code sits idle while the server sends data back)
• Saving to a database means waiting for disk/network IO (again, CPU does nothing while the database processes the write)

For IO-bound work like this, threads are the way to go—here’s the breakdown:

Why Multithreading is Better for Your Case #

• Low overhead: Threads are lightweight compared to processes. Each process spins up a whole new Python interpreter and copies your data into its own memory space; threads share the same memory space, so they’re faster to start and use less resources.
• GIL isn’t a problem: When a thread hits an IO wait (like waiting for a crawl or database write), it releases the GIL automatically. That lets another thread jump in and do its work while the first one waits. You get true concurrency without the hassle of processes.
• Easy to implement: Use concurrent.futures.ThreadPoolExecutor for clean, simple code. Here’s a quick example:

  from concurrent.futures import ThreadPoolExecutor
  import your_script  # Where your crawl_and_save_them_in_db lives
  
  # List of items you need to process
  items_to_process = ["item1", "item2", "item3", ...]
  
  # Adjust max_workers based on your target site's tolerance and database limits
  with ThreadPoolExecutor(max_workers=8) as executor:
      executor.map(your_script.crawl_and_save_them_in_db, items_to_process)
  

  Pro tip: Make sure each thread uses its own database connection—don’t share a single connection across threads, that’ll cause race conditions and errors.

When Would You Use Multiprocessing Instead? #

Only if your crawl_and_save_them_in_db includes heavy CPU-bound work on top of crawling/saving. For example:

• If you’re scraping raw HTML and then running complex data transformations, image processing, or mathematical calculations on the data before saving.

In that case, the GIL would block threads from parallelizing the CPU work, so multiprocessing (which bypasses the GIL by using separate interpreters) makes sense. But for pure crawl-and-save, it’s overkill—you’ll waste memory and startup time for no gain.

Quick Final Checklist #

• ✅ Use multithreading if your task is crawl + save (IO-bound)
• ✅ Use multiprocessing only if you add heavy CPU work to the pipeline
• ✅ Always limit concurrency (max_workers) to avoid overwhelming the target site or database

内容的提问来源于stack exchange，提问作者user3595632