python multithreading example
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'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.

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 tofetch_url
to prevent a request from failing, causing the entire program to crash.
For 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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