Asyncio Task&Futures - Learn Data Science with Travis

Travis chat

Filters

Asyncio Task&Futures

An asyncio Task is an object that schedules and runs an asyncio coroutine independently. It provides a handle to interact with the scheduled coroutine, allowing you to query its status, cancel it, and retrieve results or exceptions later. The Task is created from a coroutine and is represented by an instance of the asyncio.Task class ².

Futures, on the other hand, are low-level constructs that represent a result that may not be available yet. They are used to bridge callback-based code with high-level async/await code. In asyncio, you can create a Task using the asyncio.create_task() method or the asyncio.ensure_future() function, which schedules the coroutine for execution ²³.

The life-cycle of an asyncio Task consists of four stages: created, scheduled, running, and done. Understanding this life-cycle is crucial for effectively managing tasks in your asyncio programs ².

If you have more specific questions about asyncio Tasks or Futures, feel free to ask!

What is an Asyncio Task

Super Fast Python

You can create Task objects from coroutines in asyncio programs. Tasks provide a handle on independently scheduled and running coroutines and allow the task to be queried, canceled, and results and ex...

Asyncio Task Life-Cycle

Super Fast Python

An asyncio task has a 4-part life-cycle that transitions from created, scheduled, running, and done. In this tutorial, you will discover the life-cycle of an asyncio Task in Python. Let’s get started....

concurrent.futures.Future and asyncio.Future Not Compatible

Super Fast Python

You can mix concurrent.futures.Future and asyncio.Future objects in your Python program because they are not compatible. This means that instances of the asyncio.Future class cannot be used in concurr...

Asyncio Concurrent Tasks

Super Fast Python

We can execute asyncio tasks and coroutines concurrently, a main benefit of using asyncio. There are four main ways that we can achieve this, including issuing coroutines as independent tasks and awai...

Why Asyncio Task Never Runs and Completes

Super Fast Python

You can develop an asyncio program that schedules background tasks, but then never gives them an opportunity to run or complete. We can allow background tasks the opportunity to start running after th...

How to Run a Follow-Up Task in Asyncio

Super Fast Python

You can schedule follow-up tasks in asyncio either directly from the primary task, from the caller of the primary task, or automatically from a done callback function. In this tutorial, you will disco...

How to Create Asyncio Tasks in Python

Super Fast Python

You can create a task from a coroutine using the asyncio.create_task() function, or via low-level API functions such as asyncio.ensure_future() and loop.create_task(). In this tutorial, you will disco...

When Does Asyncio Switch Between Tasks

Super Fast Python

You may be wondering how asyncio chooses which task to run and how it switches between tasks. This is an important question and highlights how asyncio tasks are different from typical Python functions...

How to use asyncio.TaskGroup

Super Fast Python

You can manage a collection of asyncio.Task objects as a group using the asyncio.TaskGroup class. The asyncio.TaskGroup will allow tasks to be created, keep track of issued tasks, cancel all tasks if ...

concurrent.futures.Future vs asyncio.Future

Super Fast Python

The Python standard library provides two Future classes. The first is in the concurrent.futures module and the second is in the asyncio module: This raises the question: What is the difference between...

How to Get Asyncio Task Results

Super Fast Python

You can get the return value result from an asyncio task by calling the result() method on the Task object. In this tutorial, you will discover how to get a result from an asyncio task in Python. Let’...

Use asyncio.timeout_at() to Run Tasks With Deadlines

Super Fast Python

Last Updated on December 13, 2023 You can wait for asyncio tasks with a deadline using the asyncio.timeout_at() context manager. This asynchronous context manager will cancel the task if it takes too ...