From 2,500 requests to 24: Rebuilding star history without the stargazers
Curated from GitHub Engineering
If you rely on GitHub’s public APIs, you know the pain of hidden rate limits and opaque scaling constraints. This post details how the GitHub engineering team re-architected their star history endpoint to handle massive traffic spikes without leaking user data or overloading the database. They moved from a model that required fetching individual stargazer identities to an aggregated, pre-computed trend system. The result is a 99% reduction in downstream requests, a significant win for both platform stability and privacy. For SREs, this is a masterclass in decoupling read-heavy analytics from core identity data. Takeaway: When building public-facing metrics, never assume you can scale by simply adding more database connections; invest in pre-aggregation and data minimization to protect your backend from unbounded query costs.
Our new repo star history API now provides trends without returning individual identities or requiring unbounded database work. These are the engineering tradeoffs we made. The post From 2,500 requests to 24: Rebuilding star history without the stargazers appeared first on The GitHub Blog .
— GitHub Engineering