**Course Title:** Modern Python Programming: Best Practices and Trends **Section Title:** Concurrency and Parallelism **Topic:** Build a program that handles multiple tasks concurrently using `asyncio` and threading.(Lab topic) Welcome to this lab topic, where we'll build a program that handles multiple tasks concurrently using `asyncio` and threading. In this module, you'll learn how to leverage the power of concurrency in Python to improve the performance and responsiveness of your applications. ### Objective By the end of this lab topic, you'll be able to: * Use `asyncio` to write single-threaded concurrent code * Use threading to write multi-threaded concurrent code * Combine `asyncio` and threading to handle multiple tasks concurrently * Understand the benefits and limitations of using `asyncio` and threading ### What is Concurrency? Concurrency refers to the ability of a program to perform multiple tasks simultaneously. This can be achieved through various techniques, including multi-threading, multi-processing, and asynchronous programming. ### What is Asyncio? Asyncio is a built-in Python library that allows you to write single-threaded concurrent code using coroutines, multiplexing I/O access over sockets and other resources, and implementing network clients and servers. ### What is Threading? Threading is a built-in Python library that allows you to write multi-threaded concurrent code. It's particularly useful when you need to perform blocking I/O operations, such as reading or writing to a file. ### Building a Program with `asyncio` Let's build a simple program that uses `asyncio` to perform multiple tasks concurrently. We'll create a program that fetches data from multiple websites and displays the results. ```python import asyncio import aiohttp async def fetch_data(session, url): async with session.get(url) as response: return await response.text() async def main(): urls = ["https://www.python.org", "https://www.github.com"] async with aiohttp.ClientSession() as session: tasks = [fetch_data(session, url) for url in urls] results = await asyncio.gather(*tasks) for result in results: print(result) asyncio.run(main()) ``` In this example, we define an `async` function `fetch_data` that takes a `session` object and a `url` string as parameters. We use the `aiohttp` library to send a GET request to the specified URL and return the response text. We then define an `async` function `main` that creates a list of tasks using the `fetch_data` function. We use the `asyncio.gather` function to wait for all the tasks to complete and return a list of results. Finally, we use the `asyncio.run` function to run the `main` function and print the results. ### Building a Program with Threading Let's build a simple program that uses threading to perform multiple tasks concurrently. We'll create a program that calculates the sum of numbers in multiple lists. ```python import threading import time def calculate_sum(numbers): total = sum(numbers) print(f"Sum of {numbers}: {total}") numbers_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] threads = [] for numbers in numbers_list: thread = threading.Thread(target=calculate_sum, args=(numbers,)) thread.start() threads.append(thread) for thread in threads: thread.join() ``` In this example, we define a function `calculate_sum` that takes a list of numbers and calculates their sum. We then create a list of threads using the `threading.Thread` class and start each thread using the `start` method. We then use the `join` method to wait for each thread to complete. ### Combining `asyncio` and Threading Let's build a program that combines `asyncio` and threading to handle multiple tasks concurrently. We'll create a program that fetches data from multiple websites and calculates the sum of numbers in multiple lists. ```python import asyncio import aiohttp import threading import time async def fetch_data(session, url): async with session.get(url) as response: return await response.text() def calculate_sum(numbers): total = sum(numbers) print(f"Sum of {numbers}: {total}") async def main(): urls = ["https://www.python.org", "https://www.github.com"] numbers_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] async with aiohttp.ClientSession() as session: tasks = [fetch_data(session, url) for url in urls] results = await asyncio.gather(*tasks) threads = [] for numbers in numbers_list: thread = threading.Thread(target=calculate_sum, args=(numbers,)) thread.start() threads.append(thread) for thread in threads: thread.join() asyncio.run(main()) ``` In this example, we define an `async` function `fetch_data` that takes a `session` object and a `url` string as parameters. We use the `aiohttp` library to send a GET request to the specified URL and return the response text. We then define a function `calculate_sum` that takes a list of numbers and calculates their sum. We define an `async` function `main` that uses `asyncio` to fetch data from multiple websites and threading to calculate the sum of numbers in multiple lists. ### Conclusion In this lab topic, you learned how to build a program that handles multiple tasks concurrently using `asyncio` and threading. You learned how to use `asyncio` to write single-threaded concurrent code, how to use threading to write multi-threaded concurrent code, and how to combine `asyncio` and threading to handle multiple tasks concurrently. You can leave a comment below or ask for help if you have any questions or need further clarification on any of the concepts. ### Additional Resources * [Asyncio documentation](https://docs.python.org/3/library/asyncio.html) * [Threading documentation](https://docs.python.org/3/library/threading.html) * [Aiohttp documentation](https://aiohttp.readthedocs.io/en/stable/) Please proceed to the next topic: **Introduction to NumPy for numerical computing** (from Data Science and Visualization with Python).