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How to use asyncio.create_task

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by vinitha@openweaver.com dot icon Updated: Sep 19, 2023

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Asyncio library is a Python library that provides support for asynchronous programming. It becomes an essential tool for writing concurrent and asynchronous code in Python. 


Asyncio is often associated with asynchronous I/O operations. You can use it for concurrency and non-blocking operations. The Async function simplifies difficult tasks by doing them asynchronously. 


Here's a breakdown of the different types of tasks you can create using asyncio:   

  • IO-bound function (Making HTTP Requests, Reading/Writing Files)   
  • Networking Tasks   
  • Timers and Delays   
  • Parallel Processing   
  • Complex Data Processing   
  • Microservices   


Asyncio has different ways to do tasks, each with its advantages and objectives. These methods allow you to achieve concurrency and parallelism in your asyncio-based applications. 


Here are some different ways you can execute tasks in asyncio. 

  • Event Loop   
  • Single-Threaded Asynchronous Execution   
  • Thread Pool Execution   
  • Process Pool Execution   
  • Running Multiple Coroutines Concurrently   
  • Asyncio's Event Loop Policies   


Creating efficient asyncio tasks requires careful design and attention to various factors. Here are some tips to help you create high-quality asyncio tasks:   

  • Use the async and await keyword to define asynchronous functions.   
  • Use Context Managers   
  • Error Handling   
  • Cancellation request and Timeout arguments   
  • Performance Optimization   
  • Logging and Monitoring   
  • Documentation and Conventions   


In conclusion, asyncio is a flexible framework for processing large data sets. Its asynchronous and non-blocking nature, combined with fine-grained control over task execution. It makes a compelling choice for data-intensive applications. asyncio's concurrency model can handle large volumes of data and maintain high performance.   

Fig: Preview of the output that you will get on running this code from your IDE

Code

In this solution we are using asyncio library

When should I use asyncio.create_task?

PythondotLines of Code : 47dotLicense : Strong Copyleft (CC BY-SA 4.0)

from asyncio import create_task, gather, sleep, run


async def process(index: int):
    await sleep(.5)
    print('ok:', index)


async def main1():
    tasks = [create_task(process(item)) for item in range(5)]
    print("tasks scheduled")
    await sleep(2)
    print("now gathering tasks")
    await gather(*tasks)
    print("gathered in main1")


async def main2():
    coroutines = [process(item) for item in range(5)]
    print("coroutines created")
    await sleep(2)
    print("now gathering coroutines")
    await gather(*coroutines)
    print("gathered in main2")


run(main1())
run(main2())

tasks scheduled
ok: 0
ok: 1
ok: 2
ok: 3
ok: 4
now gathering tasks
gathered in main1

coroutines created
now gathering coroutines
ok: 0
ok: 1
ok: 2
ok: 3
ok: 4
gathered in main2

Instructions

Follow the steps carefully to get the output easily.


  1. Download and Install the PyCharm Community Edition on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install asyncio - pip install asyncio
  4. Create a new Python file on your IDE.
  5. Copy the snippet using the 'copy' button and paste it into your python file.
  6. Run the current file to generate the output.


I hope you found this useful.


I found this code snippet by searching for ' When should I use asyncio.create_task?' in Kandi. You can try any such use case!

Environment tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. PyCharm Community Edition 2023.1
  2. The solution is created in Python 3.11.1 Version
  3. asyncio 0.21.0 Version


Using this solution, we can able to use asyncio.create_task with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to use asyncio.create_task .

Dependency library

pytest-asyncioby pytest-dev

Python doticonstar image 1153 doticonVersion:v0.21.0doticon
License: Permissive (Apache-2.0)

Asyncio support for pytest

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            pytest-asyncioby pytest-dev

            Python doticon star image 1153 doticonVersion:v0.21.0doticon License: Permissive (Apache-2.0)

            Asyncio support for pytest
            Support
              Quality
                Security
                  License
                    Reuse

                      You can search for any dependent library on kandi like ' asyncio '.

                      FAQ:  

                      1. What is the asyncio Task implementation in Python?  

                      In Python, the asyncio.task class is a fundamental component of the asyncio library. It represents an asynchronous unit of work or computation. The event loop creates tasks to run coroutines concurrently. The users utilize scheduling and managing the execution of asynchronous code. 


                      Here's an overview of the asyncio Task implementation:  

                      • Creation of Tasks  
                      • Running Tasks  
                      • Status and Cancellation  
                      • Cancellation  
                      • Error Handling  
                      • Dependencies and Composition  


                      2. How does the asyncio module work to create tasks?  

                      The asyncio module in Python provides a framework for writing asynchronous, non-blocking code. It allows you to create tasks within a single-threaded event loop. 


                      Here's a high-level overview of how the asyncio module works to create tasks:  

                      • Event Loop  
                      • Coroutines  
                      • Task Creation  
                      • Event Loop Execution  
                      • Concurrency and Non-blocking I/O  


                      3. What is the purpose of using an "async def say" statement?  

                      We use the async def statement in Python to define an asynchronous function. When using async def, you can design the function to run asynchronously. It allows pause and resume its execution without blocking the event loop. This is helpful when working with tasks that need to wait for external resources. 

                        

                      4. What are context managers, and how do they apply to creating tasks with the asyncio module?  

                      • Python's context managers help manage resources by acquiring and releasing them correctly. They simplify resource management and help avoid common issues like resource leaks. In Python, context managers are typically used with the with statement.  
                      • When using the asyncio module, context managers help manage resources in asynchronous code. It ensures a predictable acquisition and release of those resources.  


                      5. Why are Python coroutines better than other ways to create tasks with an async loop?  

                      Python coroutines are better than other ways of creating tasks in an async loop. They provide the following benefits:  

                      • Readability  
                      • Simplicity  
                      • Structured Control Flow  
                      • Encapsulation  
                      • Exception Handling  
                      • Resource Management  

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