From json to pqt

[LuisJG8]

No description provided.

[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 refactors the task distribution mechanism within worker.py to enhance the efficiency and idempotency of data retrieval. It introduces parallel processing for the get_github_data task by leveraging Celery's group primitive, allowing a larger data fetching operation to be split into smaller, concurrently executable chunks. The title "From json to pqt" is not directly reflected in the provided patch, which focuses solely on Celery task management.

Highlights

• Celery Task Distribution: The worker.py file now utilizes Celery's group primitive to distribute get_github_data tasks, enabling parallel processing.
• Function Renaming and Refactoring: The get_more_data function has been renamed to distribute_tasks and now orchestrates the creation and execution of multiple get_github_data sub-tasks.
• Improved Data Retrieval Efficiency: Ten get_github_data tasks are now launched concurrently, each processing a chunk of 500 items, significantly improving the efficiency of data retrieval.

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[gemini-code-assist]

Code Review

This pull request introduces a new Celery task distribute_tasks to parallelize fetching data from GitHub by splitting it into a group of sub-tasks. This is a good approach for improving performance and idempotency.

However, I've found a critical issue in the implementation that will cause the tasks to fail at runtime. I've also noted a medium-severity issue regarding the use of magic numbers.

Key points for improvement:

• Critical Bug: The get_github_data task is called with incorrect arguments, passing an integer where a Github object is expected. This needs to be fixed.
• Magic Numbers: The range for distributing tasks uses hardcoded numbers, which should be extracted into constants for better readability and maintainability.
• Incomplete Change: The client.py file, which seems to be the entry point for starting the Celery job, has not been updated to call the new distribute_tasks function. It still calls the old get_github_data task directly, bypassing the new parallelization logic.
• Misleading Title: The PR title 'From json to pqt' does not seem to reflect the changes made, which are focused on refactoring task distribution rather than changing data formats.

Please see my detailed comments in the code.

[gemini-code-assist]

There's a critical issue in how the get_github_data task is being called. The task's signature is def get_github_data(self, start_in_repo_num: int = 0, github_instance: Github = gh). When you call get_github_data.s(start, 500), Celery passes start as start_in_repo_num and the integer 500 as github_instance. The github_instance parameter expects a Github object, not an integer. This will cause an AttributeError at runtime when the task tries to use this parameter (e.g., github_instance.get_repos(...)).

To fix this, you should remove the second argument. If 500 was intended as a limit, the get_github_data task signature and implementation would need to be updated to handle it.

Suggested change

	get_github_data.s(start, 500)
	get_github_data.s(start)

[gemini-code-assist]

The values 5000 and 500 are used here as magic numbers. For better readability and maintainability, consider defining them as named constants at the module level. For example:

TOTAL_REPOS_TO_PROCESS = 5000
CHUNK_SIZE = 500
# ...
for start in range(0, TOTAL_REPOS_TO_PROCESS, CHUNK_SIZE):

This makes the code's intent clearer and easier to modify in the future.