--- title: "Serve checkouts from pre-warmed repository clones" description: "Keep repository clones warm on a few machines and run Git operations against them, so jobs skip the cold clone." url: https://starsling.dev/ci-playbooks/techniques/git-checkout-service canonicalUrl: https://starsling.dev/ci-playbooks/techniques/git-checkout-service --- # Serve checkouts from pre-warmed repository clones Keep repository clones warm on a few machines and run Git operations against them, so jobs skip the cold clone. ## How to choose an approach This technique currently has one documented company example. Validate its prerequisites, tradeoffs and original CI environment against your repository before adapting it. - **[Uber](https://starsling.dev/ci-playbooks/uber#git-checkout-service)**: Use it when Many jobs or services clone the same large repository, and each pays for the clone and adds load on the Git server. Check before adopting: Freshness is eventually consistent with the upstream repository, so let a caller ask for the latest state when a job needs it. Adapted for GitHub Actions; compare the original system with yours. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Serve checkouts from pre-warmed repository clones. Keep repository clones warm on a few machines and run Git operations against them, so jobs skip the cold clone. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/git-checkout-service.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 Uber used this technique Read what Uber changed and follow the original sources. ### Uber: Serve checkouts from pre-warmed repository clones **Translated to GitHub Actions** - What Uber built: Uber's GitFarm serves checkouts from pre-warmed bare clones, so clients get a full Git checkout in under 500 milliseconds. - Your GitHub Actions version: Check out from a pre-warmed repository clone or a shallow fetch, sized to what the job reads. Cloning Uber's Go monorepo takes roughly 15 minutes and consumes around 6 CPU cores, 32 GB of memory and over 40 GB of disk. GitFarm runs standard Git commands for other services inside ephemeral sandboxes backed by pre-warmed repository checkouts. Each backend keeps one bare clone per repository, updated by push-based events with a fetch every 5 minutes behind them. Clients get a full Git checkout in under 500 milliseconds and no local clone. Evidence: Company-reported result or observation Uber reports that clients get a full Git checkout in under 500 milliseconds, and services no longer suffer cold starts of 10-15 minutes due to initial clones on each host. ([Source](https://www.uber.com/us/en/blog/gitfarm-as-a-service/)) Uber reports a CODEOWNERS service's startup time going from 15-20 minutes to under 1 minute, and its CPU from over 70 cores to 16 cores, after switching to GitFarm API calls. ([Source](https://www.uber.com/us/en/blog/gitfarm-as-a-service/)) Use it when: Many jobs or services clone the same large repository, and each pays for the clone and adds load on the Git server. Before applying it: Freshness is eventually consistent with the upstream repository, so let a caller ask for the latest state when a job needs it. GitHub Actions adaptation: Compare the source system with your own workflow before applying this translation. [Read more in the Uber playbook](https://starsling.dev/ci-playbooks/uber#git-checkout-service) [Primary source](https://www.uber.com/us/en/blog/gitfarm-as-a-service/) ## 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 ### [Fetch only the repository history a job needs](https://starsling.dev/ci-playbooks/techniques/fetch-needed-history) Limit checkout depth and fetched refs to the comparisons or release history the job actually consumes. 4 implementations · 4 companies (Linear, Mastra, OpenAI, Stripe) ### [Keep full history and download old blobs on demand](https://starsling.dev/ci-playbooks/techniques/blobless-history) Change detection often needs commit history and filenames, not old file contents. 1 implementation · 1 company (Mastra) ### [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) ## 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. ### [Filter workflows by changed files](https://starsling.dev/best-practices/github-actions/path-filter-workflows) Use paths and job conditions while keeping required checks reporting. ### [Cancel superseded CI runs](https://starsling.dev/best-practices/github-actions/cancel-superseded-runs) Cancel obsolete PR runs with a correctly scoped concurrency group.