--- title: "Cache the directories the build actually uses" description: "Align restored and saved cache paths with the tool’s real output directories so the next run can use the cached work." url: https://starsling.dev/ci-playbooks/techniques/cache-real-output-paths canonicalUrl: https://starsling.dev/ci-playbooks/techniques/cache-real-output-paths --- # Cache the directories the build actually uses Align restored and saved cache paths with the tool’s real output directories so the next run can use the cached work. ## 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#align-cache-paths)**: Use it when A restore reports success but the tool still downloads or rebuilds. Check before adopting: Path conventions differ across runners. A cache-service error falls back to a real build and leaves test and lint results untouched. - **[Uber](https://starsling.dev/ci-playbooks/uber#repair-cache-contract)**: Use it when Cache steps report success but warm runs still download dependencies or rebuild unchanged targets. Check before adopting: Confirm the paths hold reusable inputs and outputs. The run-specific save key suits a content-addressed cache with stable restore prefixes. Measure restore and upload time, eviction and cold misses, and restrict cache writes across trust boundaries. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Cache the directories the build actually uses. Align restored and saved cache paths with the tool’s real output directories so the next run can use the cached work. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/cache-real-output-paths.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: Cache the directory the build actually uses Codex restored a home-directory cache while Windows Bazel could write to a different drive. The change exports one path, uses it for both restore and save, removes the conflicting Bazel setting, and falls back to a cold build when the cache service errors. Evidence: Source PR/commit evidence Restore, build and save share one cache path on Windows ([Source](https://github.com/openai/codex/pull/16740)) Use it when: A restore reports success but the tool still downloads or rebuilds. Before applying it: Path conventions differ across runners. A cache-service error falls back to a real build and leaves test and lint results untouched. [Read more in the OpenAI playbook](https://starsling.dev/ci-playbooks/openai#align-cache-paths) [Primary source](https://github.com/openai/codex/pull/16740) ### Uber: Cache the paths the build actually writes SubmitQueue originally cached ~/.cache/bazel while its build wrote separate disk and repository caches. The correction saves those actual paths, gives each job its own cache entries, and adds a per-run suffix with stable restore prefixes so the content-addressed cache can grow. OPT61 is adapted to repairing an existing cache. Evidence: Source PR/commit evidence The author links a first run of about 10m and a second run of 1m24s; the same PR also changes application and test code. ([Source](https://github.com/uber/submitqueue/pull/59)) Use it when: Cache steps report success but warm runs still download dependencies or rebuild unchanged targets. Before applying it: Confirm the paths hold reusable inputs and outputs. The run-specific save key suits a content-addressed cache with stable restore prefixes. Measure restore and upload time, eviction and cold misses, and restrict cache writes across trust boundaries. [Read more in the Uber playbook](https://starsling.dev/ci-playbooks/uber#repair-cache-contract) [Primary source](https://github.com/uber/submitqueue/pull/59) ## 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 ### [Bake stable dependencies into runner images](https://starsling.dev/ci-playbooks/techniques/prebuild-runner-images) Prepare versioned system dependencies and build downloads once in the runner image instead of installing them on every job. 2 implementations · 2 companies (Anthropic, Linear) ### [Cache package downloads and dependency stores](https://starsling.dev/ci-playbooks/techniques/reuse-package-downloads) Restore package-manager downloads or dependency stores while keeping a locked install that validates the requested dependency graph. 7 implementations · 6 companies (DoorDash, Linear, Shopify, Spotify, Stripe, Uber) ### [Persist compiler output between runs](https://starsling.dev/ci-playbooks/techniques/persist-compiler-cache) Restore compatible compiler outputs and intermediate objects instead of recompiling unchanged inputs. 2 implementations · 2 companies (OpenAI, Ramp) ## 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 ## Related guides Implementation guides for this technique. ### [Cache workflow dependencies](https://starsling.dev/best-practices/github-actions/cache-dependencies) Choose dependency cache paths and keys for your package manager. ### [Cut repeated test setup](https://starsling.dev/best-practices/github-actions/cut-repeated-test-setup) Share expensive setup at the scope your tests need. ### [Share setup steps across jobs](https://starsling.dev/github-actions/optimizations/avoid-duplicated-setup) Centralize repeated setup definitions and remove duplicate or unused steps. ### [Optimize npm installs](https://starsling.dev/github-actions/optimizations/optimize-npm-install) Cache npm dependencies and large downloads and remove unused tool installs. ### [Optimize pnpm installs](https://starsling.dev/github-actions/optimizations/optimize-pnpm-install) Set up the pnpm store cache in the right order and scope recursive commands.