Async/Await: Asynchronous Programming In Python
Tags: python, programming, tutorial
Published: 2023-05-08 12:00:57

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 delay 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 the 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.


![Synchronous & Asynchronous Diagram](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/xl29kn8zyuy64niaknue.png)

## `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).
    

**Synchronous approach:**

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 allows other tasks to run during that downtime.**

> The [Source](https://www.youtube.com/watch?v=iG6fr81xHKA&t=254s) 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.

```python
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:**

```python
import asyncio

async def func():
    print("Hey....")
    await asyncio.sleep(1)
    print("I am here...")

asyncio.run(func())
```

1. `async def func()` - **We used** `async` to make the function asynchronous.
    
2. `await asyncio.sleep(1)` - **Here, we used** `await` and used the `asyncio.sleep()` to delay the execution of `print` statement below it.
    
3. `asyncio.run(func())` - **Finally we are calling the function. You can see that we used** `asyncio` to call the function, if we try to call the function simply as usual then we get a `RuntimeError`.
    

* **Note: You must be on Python 3.7 or above.**
    

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**

```python
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")
```

1. We created two `async` functions -
    
2. First is the `write()` function that prints `Hey` then wait for 1 second and then again prints `there`.
    
3. Second is `main()` function that executes `write()` function 3 times using `asyncio.gather()`.
    
4. And then we wrote a code that calculates the time taken to execute the `async` functions.
    

***Here's the output:***


![Asynchronous output](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/gwn94rg5ev1nxqhwmxtf.png)

**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**

```python
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's the output:***


![Synchronous output](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/dktzta5a03261lc7gp1v.png)

**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.

---

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---

**That's all for now**

**Keep coding✌✌**