Multithreading in Python

A detailed guide on handling multithreading in Python using the `threading` and `concurrent.futures` libraries. This article helps you understand how to use multithreading to improve concurrent processing efficiency.

In this article, we will explore how to handle multithreading in Python to enhance the performance of programs with concurrent tasks. We will use the threading and concurrent.futures libraries to implement illustrative examples.

Python code

1. Using the threading library

import threading
import time

def print_numbers():
    for i in range(1, 6):
        print(f"Number: {i}")
        time.sleep(1)

def print_letters():
    for letter in ['A', 'B', 'C', 'D', 'E']:
        print(f"Letter: {letter}")
        time.sleep(1)

# Create two threads
thread1 = threading.Thread(target=print_numbers)
thread2 = threading.Thread(target=print_letters)

# Start the threads
thread1.start()
thread2.start()

# Wait for both threads to complete
thread1.join()
thread2.join()

print("Multithreading complete")

2. Using the concurrent.futures library

import concurrent.futures
import time

def process_task(task_name, duration):
    print(f"Starting {task_name}")
    time.sleep(duration)
    return f"Completed {task_name}"

with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
    futures = [executor.submit(process_task, f"Task {i+1}", 2) for i in range(2)]
    
    for future in concurrent.futures.as_completed(futures):
        print(future.result())

print("Multithreading complete")

Detailed explanation

Code 1 - threading:

• threading.Thread(target=...): Creates a new thread that executes the specified function.
• start(): Begins execution of the thread.
• join(): Waits for the thread to complete before continuing with the next code segment.

Code 2 - concurrent.futures:

• ThreadPoolExecutor(max_workers=2): Creates a Thread Pool with 2 threads.
• executor.submit(...): Submits a task to the Thread Pool for processing.
• concurrent.futures.as_completed(futures): Iterates over tasks that have been completed.

System Requirements:

• Python 3.6 or later

How to install the libraries needed to run the Python code above:

Both threading and concurrent.futures are standard libraries and do not require additional installation.

Tips:

• Multithreading is useful for I/O-bound tasks but may not be effective for CPU-bound tasks due to the Global Interpreter Lock (GIL).
• Use concurrent.futures for more complex multithreading situations to maintain more readable and maintainable code.

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