Looking at the chart above, where the dotted red line is a reference point to show where we started the year, notice how site speed improvements tend to be significant and noticeable, as they are optimization-driven. Degradations, however, can generally be of any “amount,” as they happen for various reasons. LinkedIn’s page-serving pipeline has many moving parts. We deploy code multiple times per day, operate a micro-service architecture with hundreds of services, and infrastructure upgrades are frequent. A slowdown in any of these components can cause degradations.
While large degradations can be caught using A/B testing, canary analysis, or anomaly detection, small ones tend to leak to production. Thus, performance of a page has a tendency to always degrade over time.
This led to having the centralized Performance Team focus on identifying these leaks, called “site speed regressions,” and to craft tools and processes to fix them.
It’s an interesting principle. I could see this principle work for tracking database performance degradation as well.