asyncio — How to use Async/Await in Python.
In this article, we gonna discuss how we can use async/await in Python?
Now, if you are familiar with JavaScript then you might know that we use async
which makes a function return a promise, and await
which makes a function wait for a promise.
We use async
and await
when we need to fetch some data from the servers to make the execution of the function delayed until the promise is resolved and in meantime, the other functions get executed without blocking the other tasks.
This happens because the code is executed asynchronously.
Well, this is not a JavaScript tutorial so we don’t dive deep into the working of asynchronous
operations in JavaScript.
But I do explain the complete meaning of Asynchronous ahead in this article.
Introduction
Now we gonna discuss how we exactly use async/await in Python?
Just above we encountered the term Asynchronous. What does it mean?
For simple understanding, it just means that not occurring at the same time. It is theoretical meaning. We gonna learn the Practical definition of asynchronous ahead of this article with an example that lets you understand it easily.
asyncio
- Asynchronous IO :
We cannot use async
/ await
syntax directly in the function to make it asynchronous rather we can use a Python package asyncio
to achieve the goal.
Before diving into the asyncio module let’s understand Asynchronous IO first.
note: I refer to asynchronous IO as async IO and
asyncio
is a Python package.
Asynchronous IO :
async IO is designed to work on concurrent programming that has received support in Python, from Python 3.4 through Python 3.7 and beyond this.
Let’s understand it with the help of an example:
We gonna understand it by the example of a chess master Judit Polgár.
Judit Polgár, a chess master hosts a chess exhibition in which some amateur players took part and she plays with them. She has two ways of conducting the exhibition: synchronously and asynchronously.
Here are the assumptions:
- There are 24 opponents.
- Judit makes each chess move in 5 seconds.
- Opponents each take 55 seconds to make a move.
- Games average 30 pair moves (60 moves total).
Judit plays one game at a time, never moves to the next game until the current game is completed. Each game takes (55 + 5) 30 = 1800 seconds or 30 minutes. The entire exhibition takes 24*30 = 720 minutes or 12 hours.
Asynchronous approach:
In this approach, Judit moves table to table, making one move at each table. After making each move she leaves the table and lets the opponent make their next move during the wait time.
One move on all 24 games takes Judit Polgár 24*5 = 120 seconds or 2 minutes. So, the entire exhibition is now cut down to 120*30 = 3600 seconds or just 1 hour.
From the above example, it concludes that async IO tasks that take a long waiting period get blocked and allow other tasks to run during that downtime.
The Source of this example.
Let’s move to the next part of this article.
Python’s asyncio
module :
According to documentation, asyncio
is a type of library that helps us to write concurrent code using async
/await
syntax.
Since it’s a module we have to import it.
import asyncio
asyncio
works with Coroutines. Coroutines are nothing but a specialized version of Python generator functions.
A coroutine is a function that can suspend its execution before reaching the return and it can indirectly pass the control to another coroutine for some time.
Coroutines declared with the async/await syntax is the preferred way of writing asyncio applications.
Here’s the simple use case:
import asyncio async def func():
print("Hey....")
await asyncio.sleep(1)
print("I am here...") asyncio.run(func())
async def func()
- We usedasync
to make the function asynchronous.await asyncio.sleep(1)
- Here, we usedawait
and used theasyncio.sleep()
to delay the execution ofprint
statement below it.asyncio.run(func())
- Finally, we are calling the function. You can see that we usedasyncio
to call the function, if we try to call the function simply as usual then we get aRuntimeError
.
Until here, you surely get some idea on Asynchronous IO and asyncio use case.
Let’s look at the examples demonstrating the Asynchronous function:
Asynchronous
import asyncio
async def write():
print("Hey")
await asyncio.sleep(1)
print("there")
async def main():
await asyncio.gather(write(), write(), write())
if __name__ == "__main__":
import time
start = time.perf_counter()
asyncio.run(main())
elapsed = time.perf_counter() - start
print(f"File executed in {elapsed:0.2f} seconds")
- We created two
async
functions - - First is the
write()
function that printsHey
then wait for 1 second and then again printsthere
. - Second is
main()
function that executeswrite()
function 3 times usingasyncio.gather()
. - And then we wrote a code that calculates the time taken to execute the
async
functions.
We can clearly see that the whole code was executed in just 1 second using the asynchronous approach.
What if we execute the same code in a Synchronous way:
Synchronous
import time
def write():
print("Hey")
time.sleep(1)
print("there")
def main():
for _ in range(3):
write()
if __name__ == "__main__":
start = time.perf_counter()
main()
elapsed = time.perf_counter() - start
print(f"File executed in {elapsed:0.2f} seconds")
Here, this code snippet took 3 seconds to execute using the synchronous approach.
Now we are clearly able to see the differences in both the approaches and even understand how it’s happened.
Conclusion
We can use async
/ await
in Python to make the high-level structured code network for better efficiency.
asyncio
is used as a foundation for various Python asynchronous frameworks that provide high-performance networks and web servers, database connection libraries, distributed task queues, etc.
Keep coding✌✌
Originally published at https://geekpython.in.