--- title: "Use build profiles to remove expensive work" description: "Measure where build time goes, then remove or replace the compiler work or dependencies responsible for the bottleneck." url: https://starsling.dev/ci-playbooks/techniques/profile-build-hotspots canonicalUrl: https://starsling.dev/ci-playbooks/techniques/profile-build-hotspots --- # Use build profiles to remove expensive work Measure where build time goes, then remove or replace the compiler work or dependencies responsible for the bottleneck. ## How to choose an approach Compare the examples by repository setup, prerequisites, and risks before adopting one. Each company implemented this in its own environment; measure each change against your own baseline. - **[OpenAI](https://starsling.dev/ci-playbooks/openai#profile-compiler-hotspots)**: Use it when Compiler profiles point to one macro, generic construct or module. Check before adopting: Compiler phases overlap, so their timings do not add up. Source refactors need behavior, API, runtime and target checks. - **[Stripe](https://starsling.dev/ci-playbooks/stripe#profile-build-dependencies)**: Use it when One target repeatedly compiles a large dependency that an equivalent implementation can avoid. Check before adopting: Keep public behavior, serialization formats and every required test, and keep a graph check so the dependency stays out. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Use build profiles to remove expensive work. Measure where build time goes, then remove or replace the compiler work or dependencies responsible for the bottleneck. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/profile-build-hotspots.md Inspect this repository’s CI workflows, build and test commands, dependencies, and recent run timings. Identify where this technique applies and explain the expected benefit using this repository’s evidence. Measure a baseline from recent comparable runs: elapsed time, queue time, runner minutes and failures where available. If data is unavailable, say what cannot be measured. Compare relevant company approaches, checking prerequisites, platform differences and tradeoffs. Rank applicable options by evidence, fit and measurable opportunity; do not present company results as predictions for this repo. Recommend the smallest safe change, show what checks and fallback paths remain, and ASK ME WHICH CHANGE TO APPROVE BEFORE EDITING ANY FILES. Only after approval, work on a new feature branch, make one reversible change, and open a reviewable pull request. Never merge or push to a default or protected branch. Keep required checks, test coverage, deployment and release behavior intact. Validate the change and compare equivalent runs where possible; report limitations and what should be monitored after merge. ```` ## How companies used this technique Read what each company changed and follow the original sources. ### OpenAI: Remove the compiler work a profile identifies Codex removed async-trait wrappers from its concrete tool handlers and kept boxing only at the object-safe registry adapter, after a compiler profile showed where rustc spent its time. Evidence: Source PR/commit evidence Clean codex-core rebuild: rustc total 187.15s to 68.98s ([Source](https://github.com/openai/codex/pull/16630)) Use it when: Compiler profiles point to one macro, generic construct or module. Before applying it: Compiler phases overlap, so their timings do not add up. Source refactors need behavior, API, runtime and target checks. [Read more in the OpenAI playbook](https://starsling.dev/ci-playbooks/openai#profile-compiler-hotspots) [Primary source](https://github.com/openai/codex/pull/16630) ### Stripe: Remove expensive dependencies from the hot build graph Sorbet replaced a Protobuf-based serialization path with JSON, removed protoc from its main target’s dependency graph, and added a Bazel query to its linters that flags any new path back to protoc. Evidence: Source PR/commit evidence On a Linux aarch64 developer machine, a debug build of the main target took 3:57.62 before and 2:55.61 after, per the author. ([Source](https://github.com/sorbet/sorbet/pull/10124)) Use it when: One target repeatedly compiles a large dependency that an equivalent implementation can avoid. Before applying it: Keep public behavior, serialization formats and every required test, and keep a graph check so the dependency stays out. [Read more in the Stripe playbook](https://starsling.dev/ci-playbooks/stripe#profile-build-dependencies) [Primary source](https://github.com/sorbet/sorbet/pull/10124) ## CI optimization is continuous work. Apply this technique yourself, or use StarSling Runners for faster GitHub Actions and optional agent-driven optimization PRs, which teams on eligible paid plans can turn on. [Get self-improving CI](https://starsling.dev/products/runners) ## Related techniques ### [Let an agent iterate against one CI metric](https://starsling.dev/ci-playbooks/techniques/agent-metric-loop) Pick one measurable CI number, such as a build time, and let an agent keep each change that beats the baseline and discard each one that crashes or runs slower. 1 implementation · 1 company (Shopify) ### [Link release builds with a faster linker](https://starsling.dev/ci-playbooks/techniques/faster-release-linker) When linking dominates the build, switch to a faster linker. 1 implementation · 1 company (OpenAI) ### [Measure the cost of the release build profile](https://starsling.dev/ci-playbooks/techniques/profile-release-linking) Measure what the release build profile costs before keeping it. 1 implementation · 1 company (OpenAI) ## Company Playbooks ### [Anthropic](https://starsling.dev/ci-playbooks/anthropic) Run only affected tests ### [DoorDash](https://starsling.dev/ci-playbooks/doordash) Run build and test in parallel, sharded across CI agents ### [Linear](https://starsling.dev/ci-playbooks/linear) Benchmark the same job on faster hardware ### [Mastra](https://starsling.dev/ci-playbooks/mastra) StarSling runners for the slowest test workflow ### [OpenAI](https://starsling.dev/ci-playbooks/openai) Rank CI fixes by the minutes they save ### [Ramp](https://starsling.dev/ci-playbooks/ramp) Move a CPU-bound test suite to a faster CPU ### [Shopify](https://starsling.dev/ci-playbooks/shopify) Let an agent iterate against one CI metric ### [Spotify](https://starsling.dev/ci-playbooks/spotify) Build arm64 on native arm64 runners ### [Stripe](https://starsling.dev/ci-playbooks/stripe) Run only the tests a change can reach ### [Uber](https://starsling.dev/ci-playbooks/uber) Build the merge-queue speculations most likely to be needed