Table of Contents
✅ Example: Concurrently download multiple web pages
✅ Method 2: Use concurrent.futures (more concise)
? Explanation and suggestions
✅ Running effect (expected output clip)
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python multithreading example

Jul 30, 2025 am 03:27 AM

Python's multi-threading is suitable for I/O-intensive tasks. 1. Use threading.Thread to manually create threads and control execution; 2. Use ThreadPoolExecutor to manage thread pools more concisely and improve code readability; 3. Although GIL limits the parallelism of CPU-intensive tasks, it can still significantly reduce the total time-consuming in I/O operations such as network requests; 4. Exception processing should be added to enhance robustness in practical applications; 5. This mechanism is widely used in crawlers, API calls and other scenarios, which can effectively improve concurrency efficiency, and the total time-consuming is much lower than serial execution.

python multithreading example

Python's multithreading is suitable for handling I/O-intensive tasks, such as network requests, file reading and writing, etc. Although Python's multithreading is not suitable for CPU-intensive tasks due to the existence of GIL (Global Interpreter Lock), it is still very useful in concurrent I/O operations.

python multithreading example

Here is a simple multithreading example: use the threading module to download multiple web pages simultaneously.


✅ Example: Concurrently download multiple web pages

 import threading
import requests
import time

# List of URLs to request urls = [
    'https://httpbin.org/delay/1',
    'https://httpbin.org/delay/2',
    'https://httpbin.org/delay/1',
    'https://httpbin.org/delay/3',
]

def fetch_url(url):
    print(f"Start request: {url}")
    response = requests.get(url)
    print(f"Complete request: {url}, status code: {response.status_code}")

# Method 1: Use threading.Thread to create threads one by one def run_with_threads():
    threads = []
    start_time = time.time()

    for url in urls:
        thread = threading.Thread(target=fetch_url, args=(url,))
        threads.append(thread)
        thread.start()

    # Wait for all threads to complete for thread in threads:
        thread.join()

    print(f"Total time taken: {time.time() - start_time:.2f} seconds")

✅ Method 2: Use concurrent.futures (more concise)

 from concurrent.futures import ThreadPoolExecutor
import requests
import time

def fetch_url(url):
    print(f"Start request: {url}")
    response = requests.get(url)
    print(f"Complete request: {url}, status code: {response.status_code}")
    return response.status_code

def run_with_pool():
    start_time = time.time()
    # Create a thread pool with up to 4 threads with ThreadPoolExecutor(max_workers=4) as executor:
        results = list(executor.map(fetch_url, urls))

    print(f"All requests are completed, status code: {results}")
    print(f"Total time taken: {time.time() - start_time:.2f} seconds")

? Explanation and suggestions

  • threading.Thread : Suitable for manual control of threads, flexible but slightly more code.
  • ThreadPoolExecutor : More modern and concise, recommended for most scenarios.
  • Impact of GIL : Multithreading cannot truly perform CPU computing in parallel, but it can still significantly improve efficiency for I/O operations such as networks and files.
  • Exception handling : In actual projects, try-except should be added to fetch_url to prevent a request from failing, causing the entire program to crash.

For example:

python multithreading example
 def fetch_url(url):
    try:
        response = requests.get(url, timeout=5)
        print(f"Success: {url} -> {response.status_code}")
    except Exception as e:
        print(f"Failed: {url} -> {e}")

✅ Running effect (expected output clip)

 Start request: https://httpbin.org/delay/1
Start request: https://httpbin.org/delay/2
...
Complete request: https://httpbin.org/delay/1, status code: 200
Complete request: https://httpbin.org/delay/1, status code: 200
Total time: 3.12 seconds# instead of 1 2 1 3=7 seconds, which means it is concurrent execution

Basically that's it. Multithreading is very practical in scenarios such as crawlers, API calls, log writing, etc. The key is to understand that it is suitable for I/O scenarios, rather than used to accelerate computing.

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