How To Run A Background Task When Using Websockets In FastAPI/Starlette?

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Introduction

When building real-time applications using FastAPI and WebSockets, it's essential to handle background tasks efficiently. Background tasks are operations that run independently of the main application flow, often requiring asynchronous execution. In this article, we'll explore how to run background tasks when using WebSockets in FastAPI/Starlette.

Background Tasks in FastAPI/Starlette

FastAPI and Starlette provide built-in support for asynchronous tasks using the asyncio library. However, when dealing with WebSockets, it's crucial to ensure that background tasks don't block the main application thread. We'll discuss the best practices for running background tasks in FastAPI/Starlette.

Using asyncio.create_task()

One way to run background tasks in FastAPI/Starlette is by using the asyncio.create_task() function. This function creates a new task that runs concurrently with the main application flow.

import asyncio

async def background_task():
# Simulate a long-running task
await asyncio.sleep(5)
print("Background task completed")


async def main():
# Create a new task
task = asyncio.create_task(background_task())
# Continue with the main application flow
await asyncio.sleep(3)
print("Main application flow completed")


asyncio.run(main())

In this example, the background_task() function simulates a long-running task that runs concurrently with the main application flow.

Using Celery

Another popular approach for running background tasks is by using Celery, a distributed task queue. Celery allows you to run tasks asynchronously, making it an excellent choice for real-time applications.

from celery import Celery

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


@app.task
def background_task():
# Simulate a long-running task
import time
time.sleep(5)
print("Background task completed")

In this example, we define a Celery task that simulates a long-running task.

Running Background Tasks with WebSockets

When using WebSockets in FastAPI/Starlette, it's essential to ensure that background tasks don't block the main application thread. We'll discuss the best practices for running background tasks with WebSockets.

Using asyncio.create_task() with WebSockets

To run background tasks with WebSockets, you can use the asyncio.create_task() function to create a new task that runs concurrently with the main application flow.

from fastapi import FastAPI, WebSocket
import asyncio

app = FastAPI()


async def background_task(websocket: WebSocket):
# Simulate a long-running task
await asyncio.sleep(5)
print("Background task completed")


@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
# Create a new task
task = asyncio.create_task(background_task(websocket))
# Continue with the main application flow
await websocket.accept()
await websocket.send_text("Hello, client!")
await asyncio.sleep(3)
print("Main application flow completed")

In this example, we define a WebSocket endpoint that creates a new task using asyncio.create_task().

Using Celery with WebSockets

To run background tasks with Celery and WebSockets, you can use the @app.task decorator to define a Celery task that runs asynchronously.

from fastapi import FastAPI, WebSocket
from celery import Celery

app = FastAPI()
celery = Celery('tasks', broker='amqp://guest@localhost//')


@celery.task
def background_task():
# Simulate a long-running task
import time
time.sleep(5)
print("Background task completed")


@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
# Create a new task
task = celery.send_task('tasks.background_task')
# Continue with the main application flow
await websocket.accept()
await websocket.send_text("Hello, client!")
await asyncio.sleep(3)
print("Main application flow completed")

In this example, we define a Celery task that simulates a long-running task.

Conclusion

Running background tasks with WebSockets in FastAPI/Starlette requires careful consideration of asynchronous execution and task queuing. By using asyncio.create_task() or Celery, you can ensure that background tasks run efficiently without blocking the main application thread. In this article, we've explored the best practices for running background tasks with WebSockets in FastAPI/Starlette.

Best Practices

When running background tasks with WebSockets in FastAPI/Starlette, keep the following best practices in mind:

  • Use asyncio.create_task() or Celery to run background tasks asynchronously.
  • Ensure that background tasks don't block the main application thread.
  • Use task queuing to manage background tasks efficiently.
  • Monitor task execution and performance to optimize your application.

By following these best practices, you can build efficient and scalable real-time applications using FastAPI/Starlette and WebSockets.

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Introduction

In our previous article, we explored how to run background tasks with WebSockets in FastAPI/Starlette. However, we understand that you may have questions about implementing this in your own projects. In this Q&A article, we'll address some of the most common questions and provide additional guidance on running background tasks with WebSockets in FastAPI/Starlette.

Q: What is the difference between asyncio.create_task() and Celery?

A: asyncio.create_task() and Celery are both used to run background tasks asynchronously. However, they serve different purposes and have different use cases.

  • asyncio.create_task() is a built-in function in Python's asyncio library that allows you to create a new task that runs concurrently with the main application flow. It's a lightweight solution that's suitable for small-scale applications.
  • Celery, on the other hand, is a distributed task queue that allows you to run tasks asynchronously across multiple workers. It's a more robust solution that's suitable for large-scale applications with complex task workflows.

Q: How do I handle task failures with asyncio.create_task()?

A: When using asyncio.create_task(), you can handle task failures by using the try-except block to catch any exceptions that occur during task execution. You can also use the asyncio.wait() function to wait for multiple tasks to complete and handle any exceptions that occur.

import asyncio

async def background_task():
# Simulate a task failure
raise Exception("Task failed")


async def main():
try:
task = asyncio.create_task(background_task())
await task
except Exception as e:
print(f"Task failed: {e}")


asyncio.run(main())

Q: How do I handle task failures with Celery?

A: When using Celery, you can handle task failures by using the try-except block to catch any exceptions that occur during task execution. You can also use the celery.result.AsyncResult class to retrieve the result of a task and handle any exceptions that occur.

from celery import Celery

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


@app.task
def background_task():
# Simulate a task failure
raise Exception("Task failed")


try:
task = background_task.delay()
result = task.get()
except Exception as e:
print(f"Task failed: {e}")

Q: How do I monitor task execution and performance with Celery?

A: When using Celery, you can monitor task execution and performance by using the celery.result.AsyncResult class to retrieve the result of a task and the celery.app.control.inspect class to inspect the state of tasks.

from celery import Celery

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


@app.task
def background_task():
# Simulate a task execution
import time
time.sleep(5)
print("Task completed")


task = background_task.delay()
result = task.get()
print(f"Task result: {result}")


inspect = app.control.inspect()
print(f"Task state: {inspect.stats()}")

Q: How do I optimize task execution and performance with Celery?

A: When using Celery, you can optimize task execution and performance by using the following techniques:

  • Use the celery.result.AsyncResult class to retrieve the result of a task and handle any exceptions that occur.
  • Use the celery.app.control.inspect class to inspect the state of tasks and optimize task execution.
  • Use the celery.app.control.purge method to purge tasks that are no longer needed.
  • Use the celery.app.control.reset method to reset the state of tasks.
from celery import Celery

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


@app.task
def background_task():
# Simulate a task execution
import time
time.sleep(5)
print("Task completed")


task = background_task.delay()
result = task.get()
print(f"Task result: {result}")


inspect = app.control.inspect()
print(f"Task state: {inspect.stats()}")


app.control.purge()
app.control.reset()

Conclusion

In this Q&A article, we've addressed some of the most common questions and provided additional guidance on running background tasks with WebSockets in FastAPI/Starlette. By following the best practices and techniques outlined in this article, you can build efficient and scalable real-time applications using FastAPI/Starlette and WebSockets.