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FastAPI Backend Development Bootcamp · Lección

Repaso de async/await en Python

Repase los conceptos básicos de la programación asíncrona en Python, incluidos los bucles de eventos y las corrutinas.

Repaso de async/await en Python es una lección gratuita de FastAPI Backend Development Bootcamp en CoddyKit. Esta es la lección 1 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de FastAPI Backend Development Bootcamp, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de FastAPI Backend Development Bootcamp incluye 4 lecciones en total.

Partes de esta lección aún no han sido traducidas y se muestran en inglés.

Intro to Asynchronous Python

Welcome to the world of asynchronous programming in Python! This allows your programs to do multiple things without waiting for each task to finish before starting the next.

It's super useful for tasks that involve waiting, like network requests or reading/writing files, where your program would otherwise just sit idle.

Sync vs. Async: The Wait Game

Imagine cooking dinner:

  • Synchronous: You chop vegetables, then wait for them to cook, then wash dishes. Only one task happens at a time.
  • Asynchronous: You chop vegetables, put them on the stove, and while they're cooking (waiting), you start washing dishes. You're doing multiple things 'concurrently' by switching tasks when one is waiting.

Asynchronous programming helps your program stay busy instead of waiting!

The `async` Keyword: Coroutines

In Python, we use the async keyword to define a special type of function called a coroutine. A coroutine is a function that can be paused and resumed.

It doesn't run immediately when called; instead, it returns a 'coroutine object' that needs to be scheduled by an event loop to run.

import asyncio

async def hello_world():
    print("Hello, async world!")

# Calling it directly doesn't run it!
# It returns a coroutine object.
coro_obj = hello_world()
print(f"Type of coro_obj: {type(coro_obj)}")

# To actually run it, you need an event loop.
# We'll see how in a moment!

`await`: Pausing Execution

The await keyword is used inside an async def function (a coroutine) to pause its execution until another awaitable (like another coroutine or a Future) completes.

When a coroutine awaits something, it temporarily gives control back to the event loop, allowing other tasks to run. This is key to non-blocking behavior.

import asyncio

async def cook_rice():
    print("Starting to cook rice...")
    await asyncio.sleep(2) # Simulate 2 seconds of cooking
    print("Rice is cooked!")

async def chop_veg():
    print("Chopping vegetables...")
    await asyncio.sleep(1) # Simulate 1 second of chopping
    print("Vegetables chopped!")

async def main_meal():
    await chop_veg() # Wait for chopping to finish
    await cook_rice() # Then wait for rice to cook
    print("Dinner is ready!")

# This will run sequentially for now.
# We'll make it concurrent soon!
asyncio.run(main_meal())

The Event Loop: The Orchestrator

Think of the event loop as the conductor of an orchestra. It's responsible for:

  • Scheduling when coroutines run.
  • Handling I/O events (like network data arriving).
  • Switching between tasks when one is waiting (e.g., due to await).

Python's asyncio module provides the infrastructure for the event loop.

Running Async Code with `asyncio.run()`

To execute an asynchronous program, you typically use asyncio.run(). This function:

  • Gets an event loop for the current thread.
  • Runs the provided coroutine until it completes.
  • Manages the event loop's lifecycle.

It's the simplest way to start your top-level async function.

import asyncio

async def say_hello():
    print("Hello from coroutine!")
    await asyncio.sleep(0.5) # Wait for 0.5 seconds
    print("Goodbye from coroutine!")

async def main():
    print("Starting async program...")
    await say_hello()
    print("Async program finished.")

# This is the entry point for your async application
asyncio.run(main())

Simulating Non-Blocking Operations

asyncio.sleep() is an 'awaitable' that pauses the current coroutine for a given time. Crucially, it does NOT block the entire program. While one coroutine is sleeping, the event loop can switch to and run other coroutines.

This is how asynchronous programming achieves concurrency without needing multiple threads.

import asyncio
import time

async def task_one():
    print(f"Task One started at {time.strftime('%X')}")
    await asyncio.sleep(2)
    print(f"Task One finished at {time.strftime('%X')}")

async def task_two():
    print(f"Task Two started at {time.strftime('%X')}")
    await asyncio.sleep(1)
    print(f"Task Two finished at {time.strftime('%X')}")

async def main():
    start_time = time.monotonic()
    await task_one()
    await task_two()
    end_time = time.monotonic()
    print(f"Total time: {end_time - start_time:.2f} seconds")

asyncio.run(main())

Concurrent Execution with `asyncio.gather`

To truly run multiple coroutines concurrently (meaning they can interleave their execution when one awaits), we use asyncio.gather().

asyncio.gather() takes multiple awaitables and schedules them to run 'in parallel' on the event loop, waiting for all of them to complete.

import asyncio
import time

async def fetch_data(delay, name):
    print(f"Fetching {name} data... (starts at {time.strftime('%X')})")
    await asyncio.sleep(delay) # Simulate network request
    print(f"Finished {name} data. (ends at {time.strftime('%X')})")
    return f"Data from {name}"

async def main():
    start_time = time.monotonic()
    # Run fetch_data for 'users' and 'products' concurrently
    user_data, product_data = await asyncio.gather(
        fetch_data(2, "users"),
        fetch_data(1, "products")
    )
    end_time = time.monotonic()
    print(f"\nReceived: {user_data}, {product_data}")
    print(f"Total time: {end_time - start_time:.2f} seconds")

asyncio.run(main())

Async/Await Concepts Check

Which of the following statements about Python's async and await keywords are TRUE?

Refresher Recap & Next Steps

Fantastic! You've refreshed your understanding of Python's asynchronous fundamentals:

  • Asynchronous programming helps manage I/O-bound tasks efficiently.
  • async def defines coroutines, functions that can be paused.
  • await pauses a coroutine, yielding control to the event loop.
  • The event loop orchestrates coroutine execution.
  • asyncio.run() starts your async application.
  • asyncio.gather() allows running multiple coroutines concurrently.

Next, we'll see how FastAPI leverages these powerful concepts to build high-performance web APIs!

Preguntas frecuentes

¿La lección «Repaso de async/await en Python» es gratis?

Sí — el texto completo de «Repaso de async/await en Python» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de FastAPI Backend Development Bootcamp, actualiza a CoddyKit PRO. El curso de FastAPI Backend Development Bootcamp incluye 4 lecciones en total.

¿Qué aprenderé en «Repaso de async/await en Python»?

Repase los conceptos básicos de la programación asíncrona en Python, incluidos los bucles de eventos y las corrutinas. Practicas FastAPI Backend Development Bootcamp con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.

¿Necesito experiencia previa para empezar FastAPI Backend Development Bootcamp?

No se requiere experiencia previa. FastAPI Backend Development Bootcamp en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 1 de 4.

¿Cuánto tiempo toma la lección «Repaso de async/await en Python»?

La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.

¿Puedo escribir y ejecutar código en esta lección de FastAPI Backend Development Bootcamp?

Sí. Cada lección de FastAPI Backend Development Bootcamp incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.

Todas las lecciones de este curso

  1. Repaso de async/await en Python
  2. FastAPI y operaciones asíncronas
  3. Ejecución de tareas en segundo plano
  4. WebSockets para comunicación en tiempo real
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