Python multithreading is suitable for I/O-intensive tasks. 1. It is suitable for scenarios such as network requests, file reading and writing, user input waiting, etc., such as multi-threaded crawlers can save request waiting time; 2. It is not suitable for computing-intensive tasks such as image processing and mathematical operations, and cannot operate in parallel due to global interpreter lock (GIL). Implementation method: You can create and start threads through the threading module, and use join() to ensure that the main thread waits for the child thread to complete, and use Lock to avoid data conflicts, but it is not recommended to enable too many threads to avoid affecting performance. In addition, the ThreadPoolExecutor of the concurrent.futures module provides a simpler usage, supports automatic management of thread pools, asynchronous results acquisition, and map-based functions. Common problems include misunderstanding the role of GIL, unlocked shared variables, forgetting the join and too many threads lead to resource competition, which needs to be checked through logs or debugging tools.

Python's multi-threading implementation is actually a bit special. Many people initially thought it could make full use of multi-core CPUs like other languages, but in fact, it was not ideal. The main reason is that Python has a global interpreter lock (GIL), which restricts only one thread to execute Python bytecode at the same time. However, this does not mean that multithreading is useless in Python, there are still many scenarios that can be used.

What scenarios is multithreading suitable for?

Python's threading module is suitable for I/O-intensive tasks , such as network requests, file reading and writing, waiting for user input, etc. This kind of task is waiting for external resources most of the time. At this time, the switching thread will not be affected by GIL, but can improve overall efficiency.

For example: If you want to crawl data from multiple websites and each request has to wait for a few seconds to respond, then opening several threads to send requests at the same time can save a lot of time.

• Web crawler
• Log collection and processing
• Prevent interface stuttering in GUI applications

It is not recommended for calculation-intensive tasks (such as image processing, large amounts of math operations), because these tasks will be stuck with GIL and cannot be truly parallel.

How to use threading to implement multi-threading?

Using threading is the most straightforward way. The basic process is to create a thread object, specify the objective function, and then start the thread.

 import threading

def worker():
    print("Worker is running")

threads = []
for i in range(5):
    t = threading.Thread(target=worker)
    threads.append(t)
    t.start()

A few points to note:

• If you want all child threads such as the main thread to complete, you can add t.join()
• When sharing data, use locks ( threading.Lock() ) to avoid conflicts.
• It is not recommended to open too many threads, as dozens of them are almost the same, too many will slow down performance.

Is there a simpler way to write it? Try concurrent.futures

If you don't want to manually manage thread lifecycles, consider ThreadPoolExecutor in the concurrent.futures module, which is more concise and easier to control the number of concurrency.

 from concurrent.futures import ThreadPoolExecutor

def fetch_url(url):
    # Simulate a network request return f"Response from {url}"

urls = ["https://example.com/1", "https://example.com/2"]

with ThreadPoolExecutor(max_workers=5) as executor:
    results = list(executor.map(fetch_url, urls))

This method has several advantages:

• Automatically manage thread pool size
• Supports asynchronous results (via Future objects)
• It is easier to use with map, as_completed functions and other functions

Multi-threading pit

Although it is simple to use, there are some places that are prone to problems, especially for beginners.

• GIL's misunderstanding : I thought that I could run the CPU by opening multiple threads, but in fact, it was not very helpful to CPU-intensive tasks.
• Shared variables are not locked : multiple threads modify a variable at the same time may cause data confusion.
• Forgot join : The main thread ends too fast and the child thread is killed before it has been executed.
• Too many threads : Excessive concurrency can lead to resource competition and context switching overhead.

When encountering these problems, you might as well print the log first to see the thread execution order, or use debugging tools to observe the thread status.

Basically that's it. Python's multi-threading is not omnipotent, but it is still very practical in suitable scenarios. Mastering its scope of application and usage can make your program handle some tasks more efficiently.

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