From latency to instant: Modernizing GitHub Issues navigation performance

Summary

GitHub Issues addressed navigation latency by shifting workloads to the client using a local-first, stale-while-revalidate architecture. To evaluate perceived delays, the team tracked user transitions through Highest Priority Content thresholds, aiming for instant rendering in under 200 milliseconds. The core implementation added a persistent client-side cache using IndexedDB, paired with a synchronous in-memory tier to serve hot issue payloads without asynchronous overhead. A selective preheating mechanism resolves missing cache entries for high-intent links without overburdening backend capacity with redundant requests. Following broad rollout, the proportion of instant React soft navigations increased from 4% to approximately 22%, yielding an overall cache-hit ratio of around 33%.

Context

GitHub Issues users experienced workflow-breaking latency caused by redundant data fetching, server rendering, network requests, and client boot overhead across navigation paths.

Approach / What changed

Adopting a local-first stale-while-revalidate architecture featuring IndexedDB persistent caching, a synchronous in-memory cache layer, and selective preheating for high-intent issue references.

Takeaways

• Tracking Highest Priority Content (HPC) shifted the team's focus from p90/p99 tail latency outliers to the distribution of sessions rendered within instant (<200 ms) and fast (<1000 ms) thresholds.
• Implementing stale-while-revalidate caching in IndexedDB increased instant React soft navigations from 4% to roughly 22%, yielding a 33% cache-hit ratio and an acceptable 4.7% server-cache divergence rate.
• Preheating avoids the capacity spikes of eager prefetching on high-fanout surfaces by triggering network fetches only when targeted data is entirely absent from the local cache.

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