Python Async Generator: Mastering Asyncio in Modern Applications

Asyncio Overview

Asyncio is a Python library that allows you to write asynchronous code, providing an event loop, coroutines, and tasks to help manage concurrency without the need for parallelism.With asyncio, you can develop high-performance applications that harness the power of asynchronous programming, without running into callback hell or dealing with the complexity of threads.

Async and Await Key Concepts

By incorporating the async and await keywords, Python’s asynchronous generators build upon the foundation of traditional generators, which make use of the yield keyword.

To work effectively with asyncio, there are two essential concepts you should understand: async and await.

async: The async keyword defines a function as a coroutine, making it possible to execute asynchronously. When you define a function with async def, you’re telling Python that the function is capable of asynchronous execution. This means that it can be scheduled to run concurrently without blocking other tasks.
await: The await keyword allows you to pause and resume the execution of a coroutine within your asynchronous code. Using await before calling another coroutine signifies that your current coroutine should wait for the completion of the called coroutine. While waiting, the asyncio event loop can perform other tasks concurrently.

Here’s a simple example incorporating these concepts:

import asyncio

async def my_coroutine():
    print("Starting the coroutine")
    # Simulate a blocking operation using asyncio.sleep
    await asyncio.sleep(2)
    print("Coroutine completed")

# Schedule the coroutine as a task
my_task = asyncio.create_task(my_coroutine())

# Run the event loop until the task is completed
asyncio.run(my_task)

Generators and Asyncio

Generators are a powerful feature in Python that allow you to create an iterator using a function. They enable you to loop over a large sequence of values without creating all the values in memory.

You can learn everything about generators in our Finxter tutorial here:

💡 Recommended: Understanding Generators In Python

Generators are particularly useful when working with asynchronous programming, like when using the asyncio library.

Yield Expressions and Statements

In Python, the yield keyword is used in generator functions to produce values one at a time. This enables you to pause the execution of the function, return the current value, and resume execution later.

There are two types of yield expressions you should be familiar with:

the yield expression and
the yield from statement.

A simple yield expression in a generator function might look like this:

def simple_generator():
    for i in range(5):
        yield i

This generator function produces values from 0 to 4, one at a time. You can use this generator in a for loop to print the generated values:

for value in simple_generator():
    print(value)

yield from is a statement used to delegate part of a generator’s operation to another generator. It can simplify your code when working with nested generators.

Here’s an example of how you might use yield from in a generator:

def nested_generator():
    yield "Start"
    yield from range(3)
    yield "End"

for value in nested_generator():
    print(value)

This code will output:

Start
0
1
2
End

Python Async Generators

Asynchronous generators were introduced in Python 3.6 with the PEP 525 proposal, enabling developers to handle asynchronous tasks more efficiently using the async def and yield keywords. In an async generator, you’ll need to define a function with the async def keyword, and the function body should contain the yield statement.

Here is an example of creating an asynchronous generator:

import asyncio

async def async_generator_example(start, stop):
    for number in range(start, stop):
        await asyncio.sleep(1)
        yield number

Using Async Generators

To consume values from an async generator, you’ll need to use the async for loop. The async for loop was introduced alongside async generators in Python 3.6 and makes it straightforward to iterate over the yielded values from the async generator.

Here’s an example of using async for to work with the async generator:

import asyncio

async def main():
    async for num in async_generator_example(1, 5):
        print(num)

# Run the main function using asyncio's event loop
if __name__ == "__main__":
    asyncio.run(main())

In this example, the main() function loops over the values yielded by the async_generator_example() async generator, printing them one by one.

Errors in Async Generators

Handling errors in async generators can be a bit different compared to regular generators. An important concept to understand is that when an exception occurs inside an async generator, it may propagate up the call stack and eventually reach the async for loop. To handle such situations gracefully, you should use try and except blocks within your async generator code.

Here’s an example that shows how to handle errors in async generators:

import asyncio

async def async_generator_example(start, stop):
    for number in range(start, stop):
        try:
            await asyncio.sleep(1)
            if number % 2 == 0:
                raise ValueError("Even numbers are not allowed.")
            yield number
        except ValueError as e:
            print(f"Error in generator: {e}")

async def main():
    async for num in async_generator_example(1, 5):
        print(num)

# Run the main function using asyncio's event loop
if __name__ == "__main__":
    asyncio.run(main())

In this example, when the async generator encounters an even number, it raises a ValueError. The exception is handled within the generator function, allowing the async generator to continue its execution and the async for loop to iterate over the remaining odd numbers.

Advanced Topics

Multiprocessing and Threading

When working with Python async generators, you can leverage the power of multiprocessing and threading to execute tasks concurrently.

The concurrent.futures module provides a high-level interface for asynchronously executing callables, enabling you to focus on your tasks rather than managing threads, processes, and synchronization.

Using ThreadPoolExecutor and ProcessPoolExecutor, you can manage multiple threads and processes, respectively.

For example, in asynchronous I/O operations, you can utilize asyncio and run synchronous functions in a separate thread using the run_in_executor() method to avoid blocking the main event loop:

import asyncio
from concurrent.futures import ThreadPoolExecutor

async def async_fetch(url):
    with ThreadPoolExecutor() as executor:
        loop = asyncio.get_event_loop()
        return await loop.run_in_executor(executor, requests.get, url)

Contextlib and Python Asyncio

contextlib is a useful Python library for context and resource management, and it readily integrates with asyncio.

The contextlib.asynccontextmanager is available for creating asynchronous context managers. This can be particularly helpful when working with file I/O, sockets, or other resources that require clean handling:

import asyncio
from contextlib import asynccontextmanager

@asynccontextmanager
async def async_open(filename, mode):
    file = await open_async(filename, mode)
    try:
        yield file
    finally:
        await file.close()

async for line in async_open('example.txt'):
    print(line)

Asyncio and Database Operations

Asynchronous I/O can significantly improve the performance of database-intensive applications. Many database libraries now support asyncio, allowing you to execute queries and manage transactions asynchronously.

Here’s an example using the aiomysql library for interacting with a MySQL database:

import asyncio
import aiomysql

async def query_database(query):
    pool = await aiomysql.create_pool(user='user', password='pass', db='mydb')
    async with pool.acquire() as conn:
        async with conn.cursor() as cur:
            await cur.execute(query)
            return await cur.fetchall()

Performance and Optimization Tips

To enhance the performance of your asyncio program, consider the following optimization tips:

Profile your code to identify performance bottlenecks
Use asyncio.gather(*coroutines) to schedule multiple coroutines concurrently, which minimizes the total execution time
Manage the creation and destruction of tasks using asyncio.create_task() and await task.cancel()
Limit concurrency when working with resources that might become overwhelmed by too many simultaneous connections

Keep in mind that while asyncio allows for concurrent execution of tasks, it’s not always faster than synchronous code, especially for CPU-bound operations. So, it’s essential to analyze your specific use case before deciding on an asynchronous approach.

🧑‍💻 Tip: In my view, asynchronous programming doesn’t improve performance in >90% of personal and small use cases. In many professional cases it also doesn’t outperform intelligent synchronous programming due to scheduling overhead and CPU context switches.

Frequently Asked Questions

How to create an async generator in Python?

To create an async generator in Python, you need to define a coroutine function that utilizes the yield expression. Use the async def keyword to declare the function, and then include the yield statement to produce values. For example:

async def my_async_generator():
    for i in range(3):
        await asyncio.sleep(1)
        yield i

What is the return type of an async generator?

The return type of an async generator is an asynchronous generator object. It’s an object that implements both __aiter__ and __anext__ methods, allowing you to iterate over it asynchronously using an async for loop.

How to use ‘send’ with an async generator?

Currently, Python does not support using send with async generators. You can only loop over the generator and make use of the yield statement.

Why is an async generator not iterable?

An async generator is not a regular iterable, meaning you can’t use a traditional for loop due to its asynchronous nature. Instead, async generators are asynchronous iterables that must be processed using an async for loop.

How to work with an async iterator?

To work with an async iterator, use an async for loop. This will allow you to iterate through the asynchronous generator and process its items concurrently. For example:

async def my_async_generator_consumer():
    async for value in my_async_generator():
        print("Received:", value)

Can I use ‘yield from’ with an async generator?

No, you cannot use yield from with an async generator. Instead, you should use the async for loop to asynchronously iterate through one generator and then yield the values inside another async generator. For instance:

async def another_async_generator():
    async for item in my_async_generator():
        yield item

This another_async_generator() function will asynchronously iterate over my_async_generator() and yield items produced by the original generator.

That’s enough for today. Let’s have some fun — check out this blog tutorial on creating a small fun game in Python: