Python concurrency with asyncio

Python concurrency with asyncio

Python's asyncio library allows developers to create single-threaded concurrent code using coroutines, which can significantly improve the performance and responsiveness of your programs.

What is AsyncIO?

asyncio is a Python library that provides support for writing single-threaded concurrent code. It includes classes and functions that allow you to write asynchronous code that can run concurrently with other tasks.

Key Concepts:

Advantages:

Example:

Here's a simple example of using asyncio to create concurrent tasks:

import asyncio
async def task1():
print("Task 1 started")
await asyncio.sleep(2)
print("Task 1 finished")
async def task2():
print("Task 2 started")
await asyncio.sleep(3)
print("Task 2 finished")
async def main():
await asyncio.gather(task1(), task2())
if name == "main":
asyncio.run(main())

In this example, we define two tasks: task1 and task2. Each task prints a message, waits for a specified amount of time using the await asyncio.sleep() method, and then prints another message. The asyncio.gather() function is used to run both tasks concurrently.

Best Practices:

By following these guidelines and leveraging the features provided by asyncio, you can write efficient, concurrent Python code that takes full advantage of the capabilities offered by modern CPUs.

Python concurrent programming tutorial

Here's a comprehensive guide to Python concurrent programming:

Python provides several ways for implementing concurrency, which includes threads, processes, and asynchronous I/O.

Python has its own threading module that allows you to create multiple threads within the same program. However, due to the Global Interpreter Lock (GIL), true parallelism is not achieved as Python can only execute one instruction at a time.

To start with Python threading:

import threading
import time
def worker():
print('Starting worker')
time.sleep(5)
print('Worker done')
threads = []
for i in range(5):
t = threading.Thread(target=worker)
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()

Python has a multiprocessing module that can create multiple processes, which can run simultaneously to improve the overall performance of your program.

Here's how you would create multiple processes:

import multiprocessing
import time
def worker():
print('Starting worker')
time.sleep(5)
print('Worker done')
processes = []
for i in range(5):
p = multiprocessing.Process(target=worker)
processes.append(p)
for p in processes:
p.start()
for p in processes:
p.join()

Python has a library called asyncio that provides support for asynchronous I/O operations, which are useful when performing network requests or waiting for certain events to occur.

Here's an example of how you would use asyncio:

import asyncio
import time
async def worker():
print('Starting worker')
await asyncio.sleep(5)
print('Worker done')
tasks = []
for i in range(5):
task = asyncio.create_task(worker())
tasks.append(task)
wait_completion = asyncio.gather(*tasks)
result = await wait_completion

The event loop is the core of Python's asynchronous I/O operations. It allows your program to continue running while waiting for certain events to occur.

Here's an example of how you would use an event loop:

import asyncio
async def worker(event_loop):
print('Starting worker')
await asyncio.sleep(5)
print('Worker done')
event_loop = asyncio.get_event_loop()
task = asyncio.create_task(worker(event_loop))
wait_completion = event_loop.run_until_complete(task)
print(wait_completion.result())

In conclusion, Python provides several options for implementing concurrency, including threading, multiprocessing, and asynchronous I/O operations. Choosing the right approach depends on the nature of your program and what you want to achieve.

Note: This is not a comprehensive guide to all aspects of concurrent programming in Python.