What is Asynchronous programming?

Asynchronous programming has been attaining much attention for a while and a good reason. Although it can be complicated than the outdated linear style, it is also much more efficient.

Asynchronous programming aims to solve the point that a shoe needs to tie up, but the person does not want to wait until they are done with their jog. The same is goes for a web page. Let us say resources are loading from various locations, and one of the pictures present on some server is not responding. With synchronous loading, it may hang the entire page. With asynchronous loading, other functions of the page load while the requestor waits for the different server’s response.

As a System system, the system may not wait for an execution step to complete before moving on to the next process. It will move on to future executions even though a forgoing step has not yet finished and is still running in the background. It means that the system recognizes what to do when an initial step does finish execution.

Asynchronous Programming in Python

Asynchronous Practices in Real Life

Asynchronous programs require thinking differently about coding. While this new way of philosophy can be hard to cope with User’s head around, it is also an exciting practice. That is for the reason that the real world is almost utterly asynchronous.

Imagine this: A parent is trying to do numerous things at once. They have to balance the checklist, and keep an eye on the kids, do laundry. One way or another, they can do all the tasks in parallel without even thinking about it! Let’s break it down:

  • Balancing the checklist is an asynchronous task. One-step follows another until the task completes.
  • However, it can halt away from the checklist to do laundry. Unloaded dryer, move clothes from the washer to the dryer, and start another the washer again.
  • Operating the washer and dryer is an asynchronous task, but the bulk job happens after the washers and dryers are started. Once they are operational, let them go, Operator can walk away and get back to the checklist tasks. At this very point, the dryer and washer tasks have become asynchronous. They will run unconventionally until the timer goes off (notifying the User that the task needs attention).

These examples can help to demonstrate the perceptions of blocking and non-blocking code. Let’s think about this in programming terms. In this example, the User is like a CPU. While the User is moving the laundry around, the User (the CPU) is busy and blocked from performing other work, like harmonizing the checklist, which is okay because the task is comparatively swift.

On the other hand, starting the dryer and washer does not stop Users from executing the different tasks. It is an asynchronous function because Users do not have to postpone for it to finish. Once it is underway, Users can perform other tasks, which refers to a context switch.
The context of what User is doing has transformed, and the machine’s beeper notifies User when the laundry task is complete.

This is how a human works all the time. They unsurprisingly juggle numerous things at once, often without even knowing about it. However, a developer trick is to interpret these behaviors into code that performs the same tasks.

Asynchronous Practices in Python


Asyncio() is a concurrency function that was provided in Python 3.4. This is designed to utilize coroutines and futures to streamline asynchronous programs. It makes it almost as readable as synchronous code as callbacks do not exist in it.
Asyncio() structures the code, so subtasks are distinct as coroutines permit Users to plan them as they please, including concurrently. Coroutines contain return points where the User describes possible points where a context shift can happen if other tasks are undecided. However, It will not if there is no task pending.


A coroutine() is a function with an async() explanation. It can also be an object that gets returned from a coroutine() function.

By marking a function as async(), it can be called await statement like await say_after(1, ‘hello’). Await means that the package will run up until the await statement, call the function, and suspend implementation until the function completes and other coroutines may run.

That interruption of execution means that the package returns to the event loop. When the User uses asyncio(), an event loop executes all the asynchronous tasks, completes network IO and runs sub-processes. For the most part, when a programmer writes coroutines, they will use tasks to run them.


Tasks enable the coder to run a coroutine in an event loop; that streamlines managing the implementation of several coroutines.

Event Loop

An event loop manages and allocates the accomplishment of different tasks. It records them and handles assigning the flow of control between them.


The most recognizable way is to use multiple processes. From the terminal, the User can start the script two, three, four…ten times (numerous times), and then all the scripts will run individually or simultaneously. The operating structure underneath will take responsibility for sharing the processing of CPU resources amongst all those instances. Alternatively, the User can use the Multi-Processing library, which supports spawning processes, as shown in the example below.

def print_func(continent='Asia'):
    print('The name of continent is : ', continent)
if __name__ == "__main__":  # confirms that the code is under main function
    names = ['America', 'Europe', 'Africa']
    procs = []
    proc = Process(target=print_func)  # instantiating without any argument

    # instantiating process with arguments
    for name in names:
        # print(name)
        proc = Process(target=print_func, args=(name,))

    # complete the processes
    for proc in procs:


The name of the continent is Asia
The name of the continent is America
The name of the continent is Europe
The name of the continent is Africa

Tasks and coroutines have their usages. Suppose there is a combination of I/O and computation or different computations. In that case, the implementer can favorably run them together and decrease processing time by running concurrently rather than consecutively.

However, this does not let the User run numerous similar tasks at the same time. For that User need multiprocessing—the focus of a future article.

Async() is a style of concurrency programming technique in which multiple tasks release the processing of CPU during waiting periods in order to accommodate other tasks. In Python, there are numerous ways to achieve concurrency. Based on the code flow, requirement, architecture design, data manipulation, and use cases, the User can select these methods.