My solution to this task are added in the above task 1 and task 2 files

My task

Async Await in Python

NOTE: Use Python3.8 and above

This task, enlists the async await feature in Python.

This task contains three parts of subtasks that needs to be completed for implementing Async Await in Python.

Background:

Python provides parallel programming using threads and multiprocessing. However, due to Global Interpreter Lock, Python cannot provide parallelism similar to what multi-threading works in other languages, such as Java/C++.

Effectively, only one thread runs at a time, while others are waiting for GIL to be released, so ultimately, we are running in a synchronous manner for Python.

In order to tackle that, different languages have provided language constructs for running the different blocking process concurrently.

For example, in JavaScript we use concepts of Promise, and in Python, it provides Async/Await - Asyncio. So it uses the concurrency paradigm.

What do you need to do:

• Task 1 - Create a 3 coroutines for execution and see if you can parallelise the execution.

  Details about Task

  import asyncio
  import time
  
  async def sleep_coro(duration):
      await asyncio.sleep(duration)
  
  async def main():
      obj1 = sleep_coro(1)
      obj2 = sleep_coro(2)
      obj3 = sleep_coro(3)
      
      # See that the three object would execute synchronously, 
      # so it will take 1 + 2 + 3 seconds to execute.
      start = time.time()
  
      await obj1
      await obj2
      await obj3
  
      time_taken = time.time() - start
      print('Time Taken {0}'.format(time_taken))
  
  asyncio.run(main())

  Now google and find how you would parallelise the execution of task.

  Solution to Task 1

  import asyncio
  import time
  
  
  async def sleep_coro(duration):
      await asyncio.sleep(duration)
  
  
  async def main():
      obj1 = sleep_coro(1)
      obj2 = sleep_coro(2)
      obj3 = sleep_coro(3)
  
      # See that the three object would execute synchronously,
      # so it will take max(1, 2, 3) seconds to execute.
      start = time.time()
  
      await asyncio.gather(obj1, obj2, obj3)
  
      time_taken = time.time() - start
      print('Time Taken {0}'.format(time_taken))
  
  asyncio.run(main())

  Task for the above part - Now replace the sleep_coro with a function that would download a page from https://reqres.in/api/users?page{el}(this has been changed) where el is an element in arr = [1, 2, 3] *

  * -> Changed from - https://www.google.com/search?q={name_arr}

  Use this resource for more details for downloading a page - https://docs.aiohttp.org/en/stable/#client-example

• Task 2

  Task 2 - Write up a script to download the JSON file for the URL - https://xkcd.com/{comic_id}/info.0.json, where the comic_id is a natural number from 1 - 200. Download the json response and save it in a file. Do it in a synchronous manner, something like this -

  for i in range(1, 201):
  # pseudo code
  # download response
  # create a file with a unique name
  # paste the json contents in the file

  Now, using Async/Await, try to parallelise the downloading of files, so that you can accelerate the execution.

  Record the time taken, and compare the time difference of execution.

Materials:

• https://realpython.com/async-io-python/
• https://docs.python.org/3/library/asyncio.html
• https://docs.aiohttp.org/en/stable/#client-example

Tech Stack:

• Asyncio, Async/Await in Python, AioHttp

Learning from the Task:

• From this task, you will learn that how Asyncio achieves concurrency in Python.