testing airflow and fixed the data type of the day when the data was …

[LuisJG8]

…extracted

[gemini-code-assist]

Summary of Changes

Hello @LuisJG8, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on establishing an initial Apache Airflow environment and refining existing data extraction logic. It sets up the foundational configuration and Dockerization for Airflow, introduces a sample DAG for testing, and addresses a critical data formatting issue by standardizing date representations. Additionally, it enhances the robustness of GitHub API interactions by implementing more sophisticated rate limit handling to prevent service interruptions.

Highlights

• Airflow Project Setup: Initialized the Airflow project with a new configuration file (.astro/config.yaml) and a Dockerfile specifying the base runtime image.
• New Airflow DAG: Introduced a basic Airflow DAG (my_dag.py) with a single task for testing purposes, scheduled to run daily.
• Date Format Standardization: Updated the todays_date variable in worker.py to use isoformat() for a more standardized and precise date representation, addressing a data type fix.
• Enhanced GitHub API Rate Limiting: Improved the get_github_data function to proactively manage GitHub API rate limits by stopping the worker when the batch size is reached or when remaining API calls fall below a critical threshold (100 calls).

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[gemini-code-assist]

Code Review

This pull request introduces an Airflow DAG for orchestration and updates the data extraction worker. The change to use isoformat() for dates is a good step towards standardization. However, there is a critical issue in worker.py where a module-level date variable is used, which will become stale in long-running processes and lead to incorrect data timestamps. This should be addressed by calculating the timestamp within the task execution. The other changes, including the new Airflow DAG and improved batching logic, are well-implemented.

[gemini-code-assist]

The todays_date variable is defined at the module level, meaning it's evaluated only once when the worker process starts. For a long-running process like a Celery worker, this timestamp will become stale and not reflect the actual time a task is executed. This will lead to incorrect got_data_in timestamps and incorrect fallback dates for other fields. To fix this, the timestamp should be generated inside the get_github_data task. This global variable should be removed, and datetime.now().isoformat() should be called directly where the current time is needed within the task.