**Course Title:** Mastering Flask Framework: Building Modern Web Applications **Section Title:** Asynchronous Programming and Background Tasks **Topic:** Create a background task using Celery to send notifications or process data asynchronously.(Lab topic) **Objective:** By the end of this topic, you will be able to create a background task using Celery to send notifications or process data asynchronously in your Flask application. **What is Celery?** Celery is a distributed task queue that allows you to run tasks asynchronously in the background. It is a popular choice for building scalable and fault-tolerant systems. Celery is designed to be used with Python, but it can be used with other languages as well. **Why use Celery?** Celery provides several benefits, including: * **Asynchronous processing**: Celery allows you to run tasks asynchronously, which means that your application can continue to process requests while the task is being executed in the background. * **Scalability**: Celery is designed to be scalable, which means that you can easily add more workers to handle a large number of tasks. * **Fault tolerance**: Celery provides fault tolerance, which means that if a task fails, it can be retried or ignored. **Setting up Celery** To use Celery with your Flask application, you need to install the Celery library and set up a broker. A broker is a message broker that handles the communication between the Celery client and the Celery worker. Here are the steps to set up Celery: 1. **Install Celery**: You can install Celery using pip: `pip install celery` 2. **Set up a broker**: You can use a message broker like RabbitMQ or Redis to handle the communication between the Celery client and the Celery worker. You can install RabbitMQ using `pip install pika` and Redis using `pip install redis`. 3. **Create a Celery instance**: You can create a Celery instance by importing the `celery` module and creating a new instance: `celery = Celery('tasks', broker='amqp://guest@localhost//')` **Creating a background task** To create a background task using Celery, you need to define a function that will be executed by the Celery worker. Here is an example of a function that sends a notification: ```python from celery import Celery celery = Celery('tasks', broker='amqp://guest@localhost//') @celery.task def send_notification(name, message): print(f'Sending notification to {name}: {message}') ``` You can then call the `send_notification` function using the `delay` method: ```python send_notification.delay('John Doe', 'Hello, world!') ``` This will execute the `send_notification` function in the background, and the `delay` method will return immediately. **Example Use Case** Here is an example use case for using Celery to send notifications: ```python from celery = Celery('tasks', broker='amqp://guest@localhost//') @celery.task def send_notification(name, message): print(f'Sending notification to {name}: {message}') def send_notification_to_user(name, message): send_notification.delay(name, message) send_notification_to_user('John Doe', 'Hello, world!') ``` In this example, the `send_notification_to_user` function calls the `send_notification` function using the `delay` method, which executes the `send_notification` function in the background. **Conclusion** In this topic, we covered how to create a background task using Celery to send notifications or process data asynchronously in your Flask application. We discussed the benefits of using Celery, set up a Celery instance, and created a background task using the `delay` method. We also provided an example use case for using Celery to send notifications. **Exercise** Create a background task using Celery to process a list of numbers and return the sum of the numbers. **Solution** ```python from celery import Celery celery = Celery('tasks', broker='amqp://guest@localhost//') @celery.task def process_numbers(numbers): return sum(numbers) def process_numbers_in_background(numbers): result = process_numbers.delay(numbers) return result.get() numbers = [1, 2, 3, 4, 5] result = process_numbers_in_background(numbers) print(result) ``` **Note**: This is just a basic example and you should consider error handling and other edge cases when using Celery in a real-world application. **Leave a comment or ask for help if you have any questions or need further clarification on any of the concepts covered in this topic.**