Comments (1)
In this step it gathers all login names of users who gave a star to the project. Later the algorithm shuffles array of found logins to get random distribution and takes only 500 of them. These 500 are used for analysis of their starred projects.
While it's tempting to get only 5 random pages of project's followers, I think it will not work. I noticed a project can get lots of stars during some sort of event (e.g. when project was featured in news, podcast, etc.) - this gives uneven distribution of followers.
But, my old implementation was sequential - it processed only one page of login names at once. I converted it into parallel now and overall processes does not take insane amount of time anymore.
It is still not perfect, and could be done better. For example, having a server side with cached list of followers for popular repositories would make this step very fast. Something to think about :)..
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