Comparison of threads and processes in Python concurrent programming: when to use which

Threads and processes: concepts and differences

Thread is a lightweight execution unit that shares the same address space and resources with the process. They are created and destroyed quickly, which makes them very efficient when handling intensive tasks. However, threads cannot span multiple CPU cores because they are restricted by the Global Interpreter Lock (GIL).

Process is an independent execution unit with its own dedicated memory space and resources. They are heavier than threads and take longer to create and destroy. However, processes can span multiple CPU cores, allowing true parallelism.

Execute background tasks without blocking the main thread
Processing multiple small tasks in parallel
Share data without locks (protected via GIL)

Demo code:

import threading

def thread_function():
print("This is a thread.")

thread = threading.Thread(target=thread_function)
thread.start()
thread.join()# 等待线程完成

Requires parallel processing across multiple CPU cores
Need to isolate different memory spaces and resources
Handling intensive tasks or long-running tasks

Demo code:

import multiprocessing

def process_function():
print("This is a process.")

process = multiprocessing.Process(target=process_function)
process.start()
process.join()# 等待进程完成

Thread:

Limited by GIL, cannot span multiple CPU cores
Care needs to be taken when accessing shared data to avoid race conditions

process:

Heavier than threads, takes longer to create and destroy
The communication overhead between processes is large

python Concurrent programming , the choice between threads or processes depends on the needs of the specific application. Threads are great for processing intensive tasks, while processes are great for parallel processing across multiple CPU cores. By understanding their differences, you can choose the right tools to optimize your application performance.

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