**Course Title:** Mastering Flask Framework: Building Modern Web Applications **Section Title:** Asynchronous Programming and Background Tasks **Topic:** Using Celery for background task management **Overview** In this topic, we will explore how to use Celery, a distributed task queue, to manage background tasks in our Flask application. Celery allows us to run tasks asynchronously, improving the performance and scalability of our application. **What is Celery?** Celery is a distributed task queue that allows us to run tasks asynchronously. It is a Python library that provides a simple way to run tasks in the background, without blocking the main thread of our application. **Why use Celery?** Celery provides several benefits, including: * **Improved performance**: By running tasks asynchronously, we can improve the performance of our application, as it is not blocked by long-running tasks. * **Scalability**: Celery allows us to scale our application horizontally, by adding more workers to handle tasks. * **Reliability**: Celery provides a way to retry failed tasks, ensuring that they are executed correctly. **Setting up Celery** To use Celery with Flask, we need to install the following packages: * `celery` * `Flask-CeleryExt` We can install them using pip: ```bash pip install celery Flask-CeleryExt ``` Next, we need to create a Celery instance and configure it to use a message broker (e.g., RabbitMQ or Redis). We will cover message brokers in the next topic. **Configuring Celery** To configure Celery, we need to create a `celeryconfig.py` file with the following content: ```python CELERY_BROKER_URL = 'amqp://guest@localhost//' CELERY_RESULT_BACKEND = 'amqp://guest@localhost//' CELERY_ACCEPT_CONTENT = ['json'] CELERY_TASK_SERIALIZER = 'json' CELERY_RESULT_SERIALIZER = 'json' ``` This configuration tells Celery to use RabbitMQ as the message broker and to store task results in the message broker. **Defining tasks** To define a task, we need to create a function that takes a single argument, `self`, and returns a result. We can use the `@app.task` decorator to define a task: ```python from celery import Celery app = Celery('tasks', broker='amqp://guest@localhost//') @app.task def add(x, y): return x + y ``` This defines a task called `add` that takes two arguments, `x` and `y`, and returns their sum. **Running tasks** To run a task, we can use the `delay` method: ```python result = add.delay(2, 3) print(result.get()) ``` This runs the `add` task with arguments `2` and `3` and prints the result. **Conclusion** In this topic, we covered how to use Celery to manage background tasks in our Flask application. We learned how to set up Celery, configure it to use a message broker, define tasks, and run tasks. In the next topic, we will cover setting up message brokers (RabbitMQ, Redis). **Exercise** Try running the `add` task with different arguments and observe the results. **Additional Resources** * Celery documentation: <https://docs.celeryproject.org/en/latest/> * Flask-CeleryExt documentation: <https://flask-celeryext.readthedocs.io/en/latest/> **Leave a comment or ask for help if you have any questions or need further clarification.**