Improve efficiency: Master the key skills of Python multi-threaded concurrent programming
Abstract: In today's information age, efficiency has become the goal pursued by all walks of life. For program developers, improving programming efficiency is undoubtedly crucial. Python is an easy-to-learn and powerful programming language. Multi-threaded concurrent programming is one of the important means to improve efficiency. This article will introduce some key techniques and examples to help readers better master multi-threaded concurrent programming in Python.
import threading def print_numbers(): for i in range(1, 11): print(i) def print_letters(): for letter in 'abcdefghij': print(letter) if __name__ == '__main__': t1 = threading.Thread(target=print_numbers) t2 = threading.Thread(target=print_letters) t1.start() t2.start() t1.join() t2.join() print("Done")
In the above example, we created two threads, one thread is responsible for printing numbers, and the other thread is responsible for printing letters. Use the start() method to start the thread, and the join() method is used to wait for the thread execution to complete.
import concurrent.futures def calculate_square(number): return number * number if __name__ == '__main__': numbers = [1, 2, 3, 4, 5] with concurrent.futures.ThreadPoolExecutor() as executor: results = executor.map(calculate_square, numbers) for result in results: print(result)
In the above example, we use ThreadPoolExecutor to create a thread pool and distribute tasks to threads in the thread pool for execution through the map() method.
import threading count = 0 lock = threading.Lock() def increment(): global count with lock: count += 1 if __name__ == '__main__': threads = [] for _ in range(100): t = threading.Thread(target=increment) t.start() threads.append(t) for t in threads: t.join() print(count)
In the above example, we use the Lock class to ensure the atomic operation of count and avoid problems caused by multiple threads modifying count at the same time.
Conclusion:
By mastering the key skills of Python multi-threaded concurrent programming, we can better improve the efficiency of the program. In practical applications, multi-threading or single-threading should be appropriately selected based on the characteristics of the task to avoid concurrency problems. At the same time, attention should be paid to using locks to protect shared resources and avoid problems such as data competition.
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