--- title: "Weight memory-heavy steps so build and test run in parallel on separate agents" description: "Give heavy operations a weight, so the scheduler never places build and test on the same agent." url: https://starsling.dev/ci-playbooks/techniques/weighted-build-and-test canonicalUrl: https://starsling.dev/ci-playbooks/techniques/weighted-build-and-test --- # Weight memory-heavy steps so build and test run in parallel on separate agents Give heavy operations a weight, so the scheduler never places build and test on the same agent. ## 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. - **[DoorDash](https://starsling.dev/ci-playbooks/doordash#weighted-build-and-test)**: Use it when Two heavy steps run one after the other because running them together on one runner would exhaust its memory. Check before adopting: Weights stand in for memory or CPU, so measure each step's peak memory before you set them, and re-measure as the suite grows. Adapted for GitHub Actions; compare the original system with yours. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Weight memory-heavy steps so build and test run in parallel on separate agents. Give heavy operations a weight, so the scheduler never places build and test on the same agent. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/weighted-build-and-test.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 DoorDash used this technique Read what DoorDash changed and follow the original sources. ### DoorDash: Weight memory-heavy steps so build and test run in parallel on separate agents **Translated to GitHub Actions** - What DoorDash built: DoorDash finished and extended weighted operations in Rush, so an agent starts an operation only when its memory weight fits. - Your GitHub Actions version: Run build and test as parallel jobs on separate runners, sized to the memory each needs. Once its Next.js build no longer needed a pre-compile step, DoorDash could start Jest tests alongside the build. The build needs 12 GB of memory and the tests need 20 GB, so the two could never share an agent. DoorDash finished and extended weighted operations in Rush upstream, building on earlier work: each operation declares how many concurrency units it takes, and an agent starts an operation only when its weight fits. Build and test then ran in parallel on different agents, halving CI time to 11 minutes. DoorDash's Kotlin build service schedules by memory the same way, with tokens sized from each project's past memory use. Evidence: Documented technique Use it when: Two heavy steps run one after the other because running them together on one runner would exhaust its memory. Before applying it: Weights stand in for memory or CPU, so measure each step's peak memory before you set them, and re-measure as the suite grows. GitHub Actions adaptation: Compare the source system with your own workflow before applying this translation. [Read more in the DoorDash playbook](https://starsling.dev/ci-playbooks/doordash#weighted-build-and-test) [Primary source](https://careersatdoordash.com/blog/how-doordash-slashed-web-developer-build-times/) ## 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 ### [Clean up independent test resources concurrently](https://starsling.dev/ci-playbooks/techniques/parallel-cleanup) Independent index deletions do not need a separate serial wait per index. 1 implementation · 1 company (Mastra) ### [Compress release artifacts in parallel](https://starsling.dev/ci-playbooks/techniques/parallel-packaging) Compress independent release binaries two at a time. 1 implementation · 1 company (OpenAI) ### [Let shared packages without a build step stay cacheable](https://starsling.dev/ci-playbooks/techniques/uncluster-no-op-projects) Treat a package with nothing to build as cacheable, so its dependents spread across every CI agent. 1 implementation · 1 company (DoorDash) ## 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. ### [Shard tests across parallel jobs](https://starsling.dev/best-practices/github-actions/shard-tests) Use native test sharding and a job matrix to divide a long suite. ### [Parallelize independent jobs](https://starsling.dev/github-actions/optimizations/parallelize-independent-jobs) Run independent validation lanes concurrently and keep genuine job dependencies.