What are the main practices and tips for safe and efficient multithreaded application work?

For safe and efficient work with multithreaded applications in Python, consider the following practices:

• Use thread-safe data structures: For example, from the queue or collections modules.
• Minimize shared state: The less shared data between threads, the lower the chance of errors.
• Use locks (Lock, RLock): To protect critical sections where shared data is modified.
• Avoid deadlocks: Plan the order of lock acquisition and use timeouts.
• Use high-level synchronization primitives: Events (Event), semaphores (Semaphore), conditions (Condition).
• Understand GIL limitations: In CPython, the global interpreter lock limits thread parallelism for CPU-bound tasks; for these, it's better to use processes (multiprocessing).
• Testing and debugging: Use tools to detect race conditions and synchronization issues.

Example of using a lock:

import threading

counter = 0
lock = threading.Lock()

def increment():
    global counter
    with lock:
        temp = counter
        temp += 1
        counter = temp

threads = [threading.Thread(target=increment) for _ in range(1000)]
for t in threads:
    t.start()
for t in threads:
    t.join()

print(counter)  # Expected 1000

Following these recommendations will help avoid common errors and improve the reliability of multithreaded applications.