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How to Pass Db:session Object to Celery Task In Fastapi?

6 minutes read

To pass a db:session object to a Celery task in FastAPI, you can create a custom dependency in FastAPI to inject the database session into your route functions. This dependency can then be used to retrieve the database session and pass it as an argument to your Celery task.


First, create a dependency that initializes the database session and passes it to the route function:

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from fastapi import Depends
from sqlalchemy.orm import Session
from app.db import get_db

async def get_db_session(db: Session = Depends(get_db)):
    return db


Next, define your Celery task and pass the db:session object as an argument:

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from celery import Celery

celery_app = Celery('tasks', broker='redis://localhost:6379/0')

@celery_app.task
def process_data(db_session):
    # Use the db_session object to interact with the database
    pass


Finally, in your route function, retrieve the db:session object using the custom dependency and pass it as an argument to the Celery task:

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from fastapi import FastAPI
from app.dependencies import get_db_session
from app.tasks import process_data

app = FastAPI()

@app.post('/process-data')
async def process_data_route(db_session: Session = Depends(get_db_session)):
    process_data.delay(db_session)
    return {'message': 'Data processing started'}



How to handle task dependencies and priorities in Celery with FastAPI?

In Celery, task dependencies and priorities can be handled using task chords and the Task.apply_async method.


Task dependencies can be set by creating a chord task that wraps all the dependent tasks. A chord task will only run once all the dependent tasks have completed successfully.


For example, in FastAPI, you can create a Celery task that specifies dependencies like this:

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from celery import chord
from celery import group

@celery.task
def dependent_task():
    # Do some work
    return 'dependent task result'

@celery.task
def independent_task():
    # Do some work
    return 'independent task result'

@celery.task
def run_dependent_tasks():
    # Specifying the dependent tasks
    dependent_tasks = group(dependent_task.s())
    job = chord(dependent_tasks)(independent_task.s())
    job.apply_async()


To set task priorities, you can use the Task.apply_async method and specify the priority argument:

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@celery.task
def high_priority_task():
    # Do some work
    return 'high priority task result'

high_priority_task.apply_async(priority=10)


By setting the priority of tasks, you can control the order in which they are executed by the Celery worker.


Overall, handling task dependencies and priorities in Celery with FastAPI involves creating task chords for dependencies and using the Task.apply_async method for setting priorities.


How to configure Celery to use a specific backend in FastAPI?

To configure Celery to use a specific backend in FastAPI, follow these steps:

  1. Install the necessary libraries:
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pip install celery redis


  1. Create a Celery instance in your FastAPI application file, typically main.py:
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from fastapi import FastAPI
from celery import Celery

app = FastAPI()

# Initialize Celery
celery = Celery(
    'tasks',
    broker='redis://localhost:6379/0',
    backend='redis://localhost:6379/0'
)

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


  1. In the above example, we have used Redis as the backend and broker. You can change the configuration based on your requirements.
  2. Run the Celery worker by executing the following command in your terminal: 
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celery -A main.celery worker --loglevel=info


  1. Now, you can use the Celery instance in your FastAPI routes by importing it and calling your tasks:
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from main import celery

@router.post("/add")
def create_task(x: int, y: int):
    task = celery.send_task('tasks.add', args=[x, y])
    return {"task_id": task.id}


  1. Make sure to start your FastAPI application along with the Celery worker to execute and monitor your Celery tasks.


By following these steps, you can configure Celery to use a specific backend in FastAPI.


How to use task chaining and group operations in Celery with FastAPI?

To use task chaining and group operations in Celery with FastAPI, you can follow these steps:

  1. Install Celery and FastAPI: pip install celery fastapi
  2. Create a Celery instance in your FastAPI application: from fastapi import FastAPI from celery import Celery app = FastAPI() # Configure Celery celery = Celery( 'tasks', broker='redis://localhost:6379/0', backend='redis://localhost:6379/0' )
  3. Define Celery tasks that will be chained or grouped: @celery.task def add(x, y): return x + y @celery.task def multiply(x, y): return x * y
  4. Use task chaining to execute tasks sequentially: from celery import chain @app.get('/task-chain') async def task_chain(): result = chain(add.s(2, 2), multiply.s(4)).apply_async() return {'result': result.get()}
  5. Use group operations to execute tasks in parallel: from celery import group @app.get('/task-group') async def task_group(): result = group(add.s(2, 2), multiply.s(4)).apply_async() return {'result': result.get()}
  6. Start the Celery worker: celery -A tasks worker
  7. Run the FastAPI application: uvicorn app:app --reload


Now you can trigger task chaining and group operations by making requests to the corresponding endpoint URLs (/task-chain and /task-group) in your FastAPI application.


How to test and debug a Celery task in a FastAPI project?

To test and debug a Celery task in a FastAPI project, you can follow these steps:

  1. Set up a testing environment: Create a separate testing environment for your FastAPI project where you can run your tests and debug your Celery tasks.
  2. Write unit tests: Write unit tests for your Celery tasks using a testing framework like pytest. You can create test cases to check the functionality and output of your Celery tasks.
  3. Mock Celery dependencies: In your unit tests, you can mock the dependencies of your Celery tasks using libraries like unittest.mock or MagicMock. This allows you to isolate and test your Celery tasks without relying on external dependencies.
  4. Use Celery's testing utilities: Celery provides testing utilities that you can use to simulate task execution and inspect the results. These utilities can help you debug issues and verify the behavior of your Celery tasks.
  5. Enable logging: Enable logging in your Celery tasks to track the execution flow and debug any issues that arise during task execution. You can use the logging module in Python to log information, warnings, errors, and debug messages.
  6. Use debugging tools: Use debugging tools like pdb or PyCharm's debugger to step through your Celery task code and identify any errors or issues. You can set breakpoints, inspect variables, and analyze the execution flow to troubleshoot problems.
  7. Monitor Celery workers: Use Celery's monitoring tools to monitor the performance and status of your Celery workers. You can check worker logs, task queues, and task results to diagnose any issues with task execution.


By following these steps, you can effectively test and debug your Celery tasks in a FastAPI project to ensure they work as expected and handle errors gracefully.


How to set up Celery in a FastAPI project?

To set up Celery in a FastAPI project, you can follow these steps:

  1. Install Celery and the Redis broker:
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pip install celery
pip install redis


  1. Create a Celery instance in your FastAPI project:
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from celery import Celery

celery = Celery(
    "tasks",
    broker="redis://localhost:6379/0",
    backend="redis://localhost:6379/0"
)


  1. Create a Celery task:
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@celery.task
def add(x, y):
    return x + y


  1. Start the Celery worker:
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celery -A main.celery worker --loglevel=info


  1. Use the Celery task in your FastAPI application:
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from fastapi import FastAPI
from main import add

app = FastAPI()

@app.get("/add")
async def add_route(x: int, y: int):
    result = add.delay(x, y)
    return {"task_id": result.id}


  1. Run your FastAPI application and call the /add endpoint to trigger the Celery task.


By following these steps, you can set up Celery in your FastAPI project to offload time-consuming tasks to background workers.

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