fastapi Schedule

FastAPI doesn't have built-in support for scheduling tasks, but it is possible to implement scheduling functionality using external libraries or tools.

One popular library for scheduling tasks in Python is Celery. Celery is a distributed task queue that allows you to run tasks asynchronously and schedule them for execution at specific times or intervals.

To use Celery with FastAPI, you would need to configure the Celery instance and define the tasks you want to schedule. Here's an example:

from celery import Celery

app = Celery('tasks', broker='pyamqp://guest@localhost//')

@app.task
def add(x, y):
    return x + y

@app.task
def multiply(x, y):
    return x * y

In this example, we define two tasks: `add` and `multiply`. These tasks can be scheduled for execution at specific times or intervals using Celery's scheduling features.

To start the Celery worker and scheduler, you can use the following commands:

celery -A tasks worker --loglevel=info
celery -A tasks beat --loglevel=info

The first command starts the worker that executes the tasks, while the second command starts the scheduler that schedules the tasks.

You can then use FastAPI to trigger the tasks by calling their corresponding functions. For example:

from fastapi import FastAPI
from tasks import add, multiply

app = FastAPI()

@app.post('/add')
def add_numbers(x: int, y: int):
    result = add.delay(x, y)
    return {'task_id': result.id}

@app.post('/multiply')
def multiply_numbers(x: int, y: int):
    result = multiply.delay(x, y)
    return {'task_id': result.id}

In this example, we define two endpoints `/add` and `/multiply` that trigger the `add` and `multiply` tasks, respectively. The `delay` method is used to schedule the tasks for execution asynchronously.

Note that this is just a simple example, and there are many other libraries and tools that can be used for scheduling tasks in Python. Celery is just one option that is commonly used in conjunction with FastAPI.