Introduction to Concurrency in Python

What is Concurrency?

Concurrency refers to the ability of a system to handle multiple tasks simultaneously. In the context of programming, it allows for efficient execution of code by enabling tasks to make progress without waiting for other tasks to complete.

Why Concurrency?

Utilizing concurrency can lead to:

• Improved performance for I/O-bound tasks
• Better resource utilization
• Increased responsiveness in applications

Threading in Python

Python's threading module allows for the creation of threads, enabling concurrent execution of code. Here's a simple example:

import threading
import time

def task():
    print("Task started")
    time.sleep(2)
    print("Task completed")

thread = threading.Thread(target=task)
thread.start()
print("Thread started, main program continues...")

In this example, the main program continues to run while the thread executes the task in parallel.

Asyncio Module

The asyncio module provides a way to write asynchronous code using the async/await syntax. Below is an example:

import asyncio

async def main():
    print("Main started")
    await asyncio.sleep(2)
    print("Main completed")

asyncio.run(main())

This code allows for non-blocking execution, making it ideal for I/O-bound tasks.

Best Practices for Concurrency

To effectively utilize concurrency in Python, consider the following best practices:

• Identify I/O-bound vs CPU-bound tasks.
• Use threading for I/O-bound tasks and multiprocessing for CPU-bound tasks.
• Leverage asyncio for handling multiple I/O operations.
• Be mindful of race conditions and use locks when necessary.

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