Build times are one of the most visible metrics in any engineering organization. When your builds take too long, developers lose focus, context switches multiply, and productivity suffers. At our company, we had reached a point where our build pipeline was taking nearly 45 minutes for a full test suite run. That's nearly an hour of waiting per developer per day, multiplied across our entire team.
We knew we had to act. But we also knew that a complete rewrite of our build system wasn't feasible. Instead, we took a methodical approach to understanding where the time was actually being spent, and made targeted optimizations that delivered real results.
Start by measuring, not guessing
The first step in any optimization effort is to understand the current state. We instrumented our build pipeline with detailed timing information, breaking down each stage into its component parts. What we discovered was surprising: nearly 60% of our build time was spent in just three areas.
"The biggest wins come from understanding where time is actually spent, not from making assumptions about where the bottlenecks are."
Armed with this data, we were able to prioritize our efforts. We focused on the areas that would deliver the most impact, rather than spreading ourselves thin across the entire pipeline. This data-driven approach meant we could make confident decisions about where to invest our engineering effort.
The results speak for themselves. By implementing caching strategies, parallelizing previously sequential tasks, and removing unnecessary dependencies, we cut our build times in half. More importantly, we did it without requiring developers to change how they work or adopt a new build system. The improvements were transparent and immediate.