Python’s async and await are powerful features that enable you to write asynchronous code to improve the performance of your applications, particularly when dealing with I/O-bound and high-level structured network code.
Async and await are used with coroutines, which are special functions that can pause and resume their execution at specific points.
This allows multiple coroutines to run concurrently without the need for threads or complex synchronization.
To use the async and await keywords, you’ll first need to import the asyncio library, which provides the necessary framework for managing coroutines and the event loop.
Let’s Start With a Fun Example: Asynchronous Coffee Shop
Imagine walking into a bustling coffee shop. Baristas are working at full speed, brewing multiple coffees simultaneously, while new customers keep walking in. This is a perfect scenario to understand the power of Python’s async and await.
In our virtual coffee shop:
- Multiple customers can be served concurrently.
- Each coffee takes a random amount of time to prepare, between 1 to 5 seconds.
- Customers arrive at random intervals, every 1 to 3 seconds.
Let’s break down the code:
import asyncio
import random
async def make_coffee(order_number):
"""Simulate making a coffee."""
brew_time = random.randint(1, 5)
print(f"Order {order_number}: Brewing coffee... (takes {brew_time} seconds)")
await asyncio.sleep(brew_time)
print(f"Order {order_number}: Coffee is ready!")
async def customer(order_number):
"""Simulate a customer ordering a coffee."""
print(f"Order {order_number}: Customer arrived!")
await make_coffee(order_number)
async def coffee_shop():
"""Simulate the coffee shop serving customers."""
order_number = 1
while True:
await asyncio.gather(
customer(order_number),
asyncio.sleep(random.randint(1, 3))
)
order_number += 1
# Kickstart the simulation
asyncio.run(coffee_shop())
make_coffee Function: This simulates the brewing process. The await asyncio.sleep(brew_time) line mimics the time taken to brew a coffee. It’s asynchronous, so other tasks (like serving another customer) can run during this wait.
customer Function: Represents a customer’s arrival and their wait for coffee. The await make_coffee(order_number) line ensures that the customer waits for their coffee to be ready.
coffee_shop Function: This is the heart of our simulation. It uses asyncio.gather to handle both the arrival of a new customer and the brewing of coffee concurrently.
The beauty of this example is that it showcases how async and await can be used to handle multiple tasks concurrently, just like a real coffee shop. The baristas (our asynchronous functions) can brew multiple coffees at once, and new customers can arrive even if the previous ones haven’t been served yet.
Understanding Coroutines
Basics of Coroutines
Coroutines are a key concept in Python’s asynchronous programming. They can help you write concurrent and non-blocking code, making your programs more efficient.
A coroutine is a special kind of function, similar to a generator, which can be paused and resumed during its execution.
In Python, coroutines are created with the async def syntax. When a coroutine is called, it returns a coroutine object, but it doesn’t start the execution of the function immediately. You can run the coroutine using the await keyword, which schedules it for execution in an event loop. When the coroutine is paused, it allows other coroutines to run in the meantime, creating a non-blocking, concurrent execution environment.
Here’s an example of a simple coroutine:
async def my_coroutine():
print("Starting coroutine...")
await asyncio.sleep(1) # Pause the function for 1 second.
print("Resuming coroutine...")
Notice the async def syntax for defining the coroutine and the use of the await keyword to pause the function’s execution.
Coroutine with Async/Await Keywords
With Python’s async/await syntax, you can write more readable and maintainable asynchronous code. The async keyword is used to define a coroutine function, while the await keyword is used to pause the execution of the coroutine and wait for another coroutine to complete. This syntax is preferred over the older yield from approach used with generators.
Here’s a comparison of using generators and the newer async/await syntax:
Generators:
@asyncio.coroutine
def my_generator():
yield from asyncio.sleep(1)
Async/Await:
async def my_coroutine():
await asyncio.sleep(1)
As you can see, the async/await syntax is more concise and easier to understand. Remember that you can only use the await keyword inside an async def function. If you try to use it in a regular function, you’ll get a syntax error.
Event Loops and Tasks
In this section, we’ll discuss event loops and tasks in Python’s asyncio library, focusing on asynchronous tasks, scheduling and resuming tasks, and timeouts in tasks.
Asynchronous Tasks
Asynchronous tasks, or coroutines, are the building blocks of concurrent programming in Python and are managed by an event loop. The event loop runs asynchronous tasks and callbacks, performs network I/O operations, and manages subprocesses, all within a single thread. To create a task, you’ll need to use the asyncio.create_task() function, like so:
import asyncio
async def my_coroutine():
# Your async code here
task = asyncio.create_task(my_coroutine())
This creates a Task object that represents the asynchronous execution of your coroutine.
Scheduling and Resuming Tasks
The event loop is responsible for scheduling and resuming tasks. When you create a task using asyncio.create_task(), the event loop schedules the task for execution. To allow other tasks to run concurrently, you should use the await keyword when calling other asynchronous functions. This will pause the current task and yield control back to the event loop, which then resumes the next available task.
For example, instead of using time.sleep() to block your code, you should use await asyncio.sleep(), which allows other tasks to execute while your coroutine is “sleeping”:
async def my_coroutine():
print("Starting task")
await asyncio.sleep(5)
print("Task complete")
If you need to run blocking code, you can use the loop.run_in_executor() function to run the code in a separate thread while allowing other async tasks to continue running:
loop = asyncio.get_running_loop() result = await loop.run_in_executor(None, blocking_function)
Asynchronous Programming with Asyncio
Understanding Asyncio.run() Function
Asyncio is a powerful library in Python that allows you to write asynchronous code using the async/await syntax. The asyncio.run() function is the main entry point to run a coroutine. This function wraps the given async function with a call to .run_until_complete() and takes care of initializing and closing the event loop. When you want to execute an async function, you can use asyncio.run() to start it.
For example:
import asyncio
async def main():
print("Hello, world!")
asyncio.run(main())
Asyncio Sleep Method
The asyncio.sleep() function is a convenient way to suspend the execution of a coroutine for a specified amount of time. This method takes a single argument, which represents the number of seconds to sleep. The await keyword is used to pause the coroutine while it’s waiting for the sleep to finish.
Here’s an example:
import asyncio
async def sleep_example():
print("Start")
await asyncio.sleep(1)
print("Finish after 1 second")
asyncio.run(sleep_example())
In this example, the await asyncio.sleep(1) line suspends the execution of the sleep_example() coroutine for one second.
Asyncio API Functions
The asyncio API provides several functions to manage and schedule coroutines.
One of the most used API functions is asyncio.gather(). This function takes coroutines as arguments and returns a single coroutine that gathers the results of all the given coroutines.
When using asyncio.gather(), you can run multiple coroutines concurrently, which helps in improving the performance of your asynchronous code.
For example:
import asyncio
async def task_one():
await asyncio.sleep(1)
return "Task one completed."
async def task_two():
await asyncio.sleep(2)
return "Task two completed."
async def main():
tasks = [task_one(), task_two()]
results = await asyncio.gather(*tasks)
print(results)
asyncio.run(main())
In this example, task_one() and task_two() run concurrently, thanks to the asyncio.gather() function. When both tasks are completed, the results are printed as a list.
Remember to use the async/await keywords and the various functions provided by the asyncio package to make your Python code more efficient and responsive through asynchronous programming.
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Asynchronous Code and Concurrency
The Role of the Event Loop
In asynchronous programming, the event loop plays a crucial role in managing concurrent tasks. It is essentially a scheduler that allows your code to execute non-blocking I/O operations. When your code has to perform an I/O operation (like an HTTP request), the event loop lets the code keep running while waiting for the result. Once the I/O operation is complete, the event loop resumes the task.
The event loop is an essential component of the asyncio library in Python. This library provides a straightforward way to write asynchronous code using the async/await syntax. It serves as a foundation for many asynchronous Python frameworks, such as web servers and database connectors.
Intro to Cooperative Multitasking
Cooperative multitasking is a technique that allows multiple tasks to be executed concurrently by voluntarily yielding control to each other. In this approach, it is up to each task to give up control and allow other tasks to run.
In Python, you make use of cooperative multitasking by using asynchronous functions called coroutines. Coroutines are declared using the async def syntax and await other coroutines using the await keyword. When a coroutine awaits another coroutine, it temporarily suspends its execution, allowing other tasks to run in the meantime.
Here’s a simple example using asyncio:
import asyncio
async def wait_and_print(message, delay):
await asyncio.sleep(delay)
print(message)
async def main():
await asyncio.gather(
wait_and_print("Hello, world!", 1),
wait_and_print("Asyncio is awesome!", 2)
)
asyncio.run(main())
Concurrency in Python
Python offers various approaches to dealing with concurrency, such as threading, multiprocessing, and asynchronous programming. Asynchronous programming with asyncio is particularly suitable for I/O-bound tasks, such as making HTTP requests or querying databases.
Through asynchronous programming, Python can manage concurrency without the need for parallelism. This is achieved by interleaving the execution of tasks using event loops and cooperative multitasking. While true parallelism requires multiple cores, concurrency through asynchronous programming can be achieved on single-core systems by efficiently utilizing the available resources.
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Advanced Concepts and Applications
Asynchronous Generators
Asynchronous generators are a powerful feature in async-await programming. They allow you to create generator functions which can produce a series of results while asynchronously waiting for other operations to complete. To create an asynchronous generator, you simply need to add the async def keyword before the function and use await when necessary.
For example:
async def async_generator():
for i in range(5):
await asyncio.sleep(1)
yield i
This generator would produce values from 0 to 4, with a 1-second delay between each yield. To consume the results, use the async for statement:
async for value in async_generator():
print(value)
Understanding Threading
In the context of async-await, understanding threading is crucial. Traditional threading can create issues with shared variables and resources, leading to difficult-to-debug problems. However, in async-await programming, there is generally only one thread, making it inherently safer.
You can think of async-await as a cooperative multitasking approach, where the tasks voluntarily yield control to one another when waiting for resources or operations to complete. This allows you to achieve concurrency without dealing with the complexities of thread management.
Dealing with Latency
Network latency can cause delays and slow down your application. Async-await is an effective solution to dealing with latency, as it allows you to keep the flow of your program moving even when certain tasks face delays due to network or resource constraints.
For example, assume you need to make several web requests to fetch data. Instead of waiting for each request to complete before starting the next, async-await enables you to initiate all requests simultaneously, significantly decreasing the total time spent waiting.
In a similar fashion, async-await can also be leveraged to process database queries, file I/O, and other high-latency operations.
Introspection in Asyncio
Introspection is the mechanism that allows you to examine current tasks, coroutines, and other aspects of the event loop, aiding in understanding and debugging your program. Asyncio, the Python library that provides asynchronous concurrency, offers several introspection tools.
For example, asyncio.all_tasks(loop=None) returns a set of all currently scheduled tasks in the specified loop (or the default loop if not specified). This allows you to track the progress of tasks, identify bottlenecks, and gain insight into the overall health of your program.
Additionally, other introspection methods include asyncio.current_task(loop=None) and asyncio.Task.get_stack(*, limit=None) allowing for detailed analysis of task states and traceback information, respectively.
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Frequently Asked Questions
How does async/await work with Python’s asyncio?
Async/await works in conjunction with Python’s asyncio library to handle concurrency in a more efficient and simpler way. They allow you to define asynchronous code as coroutines, which can pause their execution instead of blocking the entire program.
When a coroutine encounters an await expression, it yields control back to the event loop, allowing other tasks to run in the meantime. You can use asyncio.run() to run a coroutine or combine multiple coroutines with asyncio.gather().
Find more about it in this tutorial.
What are the benefits of async/await over threading in Python?
Async/await provides some advantages over threading, including lower memory overhead, better performance with large numbers of I/O-bound tasks, and easier debugging.
Additionally, async/await is more Pythonic while dealing with concurrency and offers a more readable syntax.
However, it’s important to note that async/await is most effective for I/O-bound workloads, whereas threading is more suitable for CPU-bound tasks.
Learn more about Async IO in Python.
How can I use Python’s requests library with async/await?
The requests library is not designed for async/await usage, but you can use the httpx library, which provides a similar API but with async/await support. To use httpx, install it via pip, then use the async and await keywords alongside httpx methods like httpx.get() or httpx.post().
💡 Don’t forget to wrap your code in async def functions and run them using the asyncio event loop.
What is the difference between a Python coroutine and an async function?
A Python coroutine is a function that can pause its execution and resume later, allowing other tasks to run in the meantime. They are defined using the async def syntax.
On the other hand, an async function is simply a function that has been declared with the async keyword. All async functions are coroutines, but not all coroutines are specifically async functions.
You can find more information about the Python coroutines and async functions here.
How can I run multiple async tasks concurrently in Python?
To run multiple async tasks concurrently, you can use the asyncio.gather() function. This function takes a list of coroutines as its arguments and returns an awaitable object that can be used with the await keyword.
When you await the result of asyncio.gather(), all the given coroutines will be scheduled to run concurrently, and the results will be returned in the same order as the input coroutines.
Remember that you need to use an asyncio event loop to run your async tasks, like asyncio.run().
What is the use of asynchronous context managers in Python?
Asynchronous context managers are useful for managing resources in an asynchronous environment, such as when you use async with statement.
They ensure proper acquisition and release of resources in a non-blocking manner, making your code more efficient, and preventing issues like resource leaks or deadlocks.
Examples of asynchronous context managers include opening and closing files, connecting to databases, or using an HTTP client.
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