Best practices for concurrent programming in Python: avoid common pitfalls

Concurrent programming is a powerful technique that allows multiple tasks to be executed simultaneously, thereby improving the performance and responsiveness of the application. python provides a rich concurrency features, including multi-threading, multi-process and thread pool. However, in practice, concurrent programming may encounter some common pitfalls, which if not paid attention to may lead to performance issues, deadlocks, or even program crashes.

Avoid excessive concurrency

Creating too many threads or processes may exhaust system resources, causing the program to slow down or even crash. To avoid excessive concurrency, the number of threads or processes should be chosen carefully based on system resources and application requirements. System metrics such as CPU usage and memory usage can be monitored regularly to evaluate whether the concurrency level is appropriate.

Demo code:

import threading
import time

def task(i):
time.sleep(1)# 模拟一个耗时的任务
print(f"Task {i} completed")

# 创建过多线程
num_threads = 100
threads = [threading.Thread(target=task, args=(i,)) for i in range(num_threads)]

# 启动所有线程
for thread in threads:
thread.start()

Use thread pool

The thread pool is a queue that manages threads. It can automatically create and destroy threads to avoid creating too many threads. Thread pools provide a more efficient way to manage concurrency because threads do not need to be explicitly created and destroyed.

Demo code:

from concurrent.futures import ThreadPoolExecutor

# 创建线程池
executor = ThreadPoolExecutor(max_workers=5)

# 向线程池提交任务
for i in range(10):
executor.submit(task, i)

# 关闭线程池，等待所有任务完成
executor.shutdown(wait=True)

Avoid deadlock

Deadlock refers to two or more threads or processes waiting for each other to release the lock, causing the program to never continue executing. To avoid deadlocks, you should carefully consider the order in which locks are acquired and released, and use a lock hierarchy to avoid cyclic dependencies.

Demo code:

import threading
import time

lock1 = threading.Lock()
lock2 = threading.Lock()

def task1():
lock1.acquire()
time.sleep(1)# 模拟一个耗时的任务
lock2.acquire()
# 释放锁的顺序与获取锁的顺序相同，避免死锁
lock2.release()
lock1.release()

def task2():
lock2.acquire()
time.sleep(1)# 模拟一个耗时的任务
lock1.acquire()
# 释放锁的顺序与获取锁的顺序相同，避免死锁
lock1.release()
lock2.release()

# 创建两个线程并启动它们
thread1 = threading.Thread(target=task1)
thread2 = threading.Thread(target=task2)
thread1.start()
thread2.start()

Avoid race conditions

Race conditions refer to multiple threads or processes accessing shared data at the same time, resulting in uncertain results. To avoid race conditions, locks should be used to protect shared data, ensuring that only one thread or process accesses that data at any given moment.

Demo code:

import threading

shared_data = 0

def increment_shared_data():
global shared_data
shared_data += 1

# 创建两个线程，同时递增共享数据
thread1 = threading.Thread(target=increment_shared_data)
thread2 = threading.Thread(target=increment_shared_data)
thread1.start()
thread2.start()
thread1.join()
thread2.join()

# 输出共享数据，可能会得到一个意想不到的值，因为线程可能同时递增它
print(shared_data)

Other Best Practices

• Use the appropriate concurrency mode: Choose the appropriate concurrency mode, such as multi-threading, multi-process or coroutine, based on the specific requirements of the application.
• Consider memory management: Concurrent applications may create and destroy large numbers of objects, so it is important to manage memory carefully.
• Use debugging tools: Leverage Python's debugging tools such as pdb and logging to help identify and resolve concurrency issues.
• Regular testing: It is important to conduct thorough testing to verify the correctness and robustness of concurrent applications.

By following these best practices, developers can avoid common pitfalls in concurrent programming and build concurrent applications that are performant, scalable, and robust.

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