Celery background task processing
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What you'll learn:
Setting the Stage - **Description:** Begin with an overview of why background tasks are important (improving responsiveness, handling long-running processes). Learn about common use cases (sending emails, processing images, data analysis). Introduce the concept of Celery as a distributed task queue and discuss its architecture: the broker, the workers, and the task producer. - **Resources/Activities:** - **Expected Outcomes:** Understand the problem Celery solves and be familiar with the core components involved. Successfully set up your Python development environment and potentially the message broker.
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What you'll learn:
Getting Started - **Description:** Learn how to install Celery and a message broker (e.g., RabbitMQ or Redis) using pip. Explore the basic configuration of Celery, including setting up the broker URL and specifying the result backend. - **Resources/Activities:** - **Expected Outcomes:** Successfully install Celery and a message broker. Create a basic Celery configuration file. Be able to troubleshoot common installation issues.
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What you'll learn:
Task Definition and Execution - **Description:** Learn how to define a Celery task using the `@app.task` decorator. Understand how to call (enqueue) a task from your application code, and the difference between `delay()` and `apply_async()`. Learn how tasks are executed by the worker processes. - **Resources/Activities:** - **Expected Outcomes:** Define and execute basic Celery tasks. Understand the role of the Celery worker and see how tasks are processed.
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What you'll learn:
Monitoring and Debugging - **Description:** Learn how to retrieve the results of asynchronous tasks using `task.AsyncResult`. Understand how to handle exceptions within tasks and how to configure retry mechanisms. Introduce logging within Celery tasks. - **Resources/Activities:** - **Expected Outcomes:** Understand how to retrieve task results, handle task exceptions, and implement retries. Practice using logging for debugging.
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What you'll learn:
Automating Processes - **Description:** Learn how to schedule Celery tasks to run periodically using the Celery Beat scheduler. Explore different scheduling options (e.g., every minute, every hour, daily). - **Resources/Activities:** - **Expected Outcomes:** Understand how to schedule tasks using Celery Beat and configure periodic task execution.
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What you'll learn:
Passing and Processing Data - **Description:** Explore how to pass arguments to Celery tasks and how Celery serializes and deserializes data. Learn about different serialization formats (e.g., JSON, Pickle). Briefly explore the importance of security implications when using Pickle. - **Resources/Activities:** - **Expected Outcomes:** Understand how to pass data to tasks, and understand the role serialization plays.
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What you'll learn:
Advanced Topics and Next Steps - **Description:** Explore basic concepts of scaling Celery (e.g., using multiple workers). Discuss common best practices for writing Celery tasks (e.g., idempotency, data consistency, dealing with long-running tasks). Summarize what has been learned and suggest next steps for further exploration. - **Resources/Activities:** - **Expected Outcomes:** Have a basic understanding of scaling Celery. Be aware of best practices for writing Celery tasks. Have a plan for future learning and practice.
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