To keep a monorepo’s mainline trustworthy, define the checks that must pass, run them against the exact code state that will land, and control how concurrent changes enter the branch. Faster builds help, but speed is safe only when dependency information is accurate and build actions are reproducible. The right design depends on your languages, build systems, test requirements, and change volume.
What does “green” mean for a monorepo?
A green mainline is not simply a branch with a passing status badge. It means the required build steps have succeeded for each commit point in the repository history. In the 2025 paper CI at Scale: Lean, Green, and Fast, Dhruva Juloori, Zhongpeng Lin, Matthew Williams, Eddy Shin, and Sonal Mahajan define it this way: “A mainline is considered green if all build steps—compilation, unit tests, and UI tests—are successfully executed for every commit point in the repository history.” The paper
In practice, teams need to make that definition operational. Decide which checks are required for a change, how affected components are identified, and what state of the code those checks evaluate. A passing result on an earlier version of a change does not establish that a later merge or a newly combined set of changes will pass.
Define the trust contract
- List required checks by change type, including compilation, unit tests, integration or UI tests where relevant.
- Specify how the system determines which targets and tests are affected. Treat dependency-graph accuracy as a correctness requirement, not just a build optimization.
- Require checks to represent the code state that is actually eligible to land, including interactions with other queued changes.
- Make failures visible and actionable: identify the affected change, failed check, and whether the failure blocks landing.
How should concurrent changes reach the mainline?
When several changes are prepared at once, CI must solve both a scheduling problem and an integration problem. Changes that do not conflict may be checked concurrently. Changes that overlap, alter shared dependencies, or otherwise affect one another need explicit handling: order them, test them together, or revalidate after the combined state changes.
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A merge or submit queue can protect the landing point by controlling which proposed changes are tested and admitted. Uber’s SubmitQueue paper describes one implementation: it speculatively executes builds for possible combinations, uses conflict analysis to prune combinations that need not be tried, and lands changes only after required checks pass. This is a case study from Uber, not a requirement that every repository adopt speculative scheduling or machine learning. CI at Scale: Lean, Green, and Fast
Choose queue behavior for your contention level
- Low submission volume: A simpler serialized queue may be sufficient. It is easier to reason about, though contributors may wait while earlier changes are validated.
- Concurrent independent work: Run eligible validation in parallel, but preserve a clear rule for what happens when a preceding landing changes the base state.
- Frequent conflicts or high volume: Consider speculative validation of likely combinations, with conflict-aware pruning and a policy for retries or failed combinations. Complexity is worthwhile only if it improves throughput without weakening the landing guarantee.
Whichever design you choose, define what happens when a queued change fails, when a new change invalidates prior results, and when the queue is saturated. Otherwise, the queue can either become a bottleneck or admit a state that was never validated.
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How can builds get faster without skipping important tests?
Reduce duplicated work and avoid running checks that truly cannot be affected by a change, but only when the dependency and impact model is dependable. Incremental builds, test selection, caching, and distributed execution can shorten feedback loops; each can also produce false confidence if inputs are hidden or affected targets are missed.
Use the dependency graph as a correctness boundary
For a change-based test selector to be safe, it must know which targets depend on the changed files and which checks are mandatory regardless of impact. Validate the selector against full test runs and keep essential integration or end-to-end checks in the required path when local impact analysis cannot establish that they are unaffected.
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Make work reusable and actions reproducible
Caches and remote execution work best when a build action has explicit inputs, declared tools, and predictable outputs. If an action depends on a developer’s local environment, an undeclared file, or machine-specific state, a cached result may not correspond to the work CI needs to perform.
Bazel’s documentation describes remote execution as running actions on a separate execution platform and emphasizes isolated actions across varied environments. It recommends toolchain rules rather than assumptions about a local PATH or JAVA_HOME. It also warns that implicit dependencies and state retained by a local compiler can disappear when remote actions run separately. Bazel: Rules for remote execution
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Expose environment assumptions before scaling out
Sandboxing or remote execution can reveal dependencies that happen to exist on one workstation but not on a clean worker. Pay particular attention to host-dependent binaries, configure-style workspace rules, installed packages, and symlinks that point to local tools. Put platform-specific setup in suitable build rules or a controlled toolchain environment instead of relying on undocumented host state. These checks are especially important when one repository combines ecosystems with different compiler, package, or platform expectations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does Uber’s reported improvement show—and not show?
The authors of the 2025 Uber paper report that enhancements to SubmitQueue across Uber’s major Go, iOS, and Android monorepos were associated with approximately 53% lower CI resource usage, 44% lower CPU usage, and 37% lower P95 waiting times. These are rounded figures from the paper’s abstract for Uber’s evaluated system; they are not a general benchmark or a forecast for another organization. The paper’s abstract
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Those results make queue design a worthwhile area to investigate when contention is a real source of cost or delay. They do not establish that a particular queue architecture, build system, or optimization will deliver the same gains elsewhere.
How should teams compare implementation choices?
The available sources do not provide a neutral, controlled head-to-head ranking of Bazel, Buck, Pants, or CI vendors. Evaluate choices against your repository’s actual constraints rather than treating a single tool’s documentation or one organization’s results as a universal verdict.
Quick Recap
| Evaluation area | Questions to answer |
|---|---|
| Language and build-system coverage | Can the approach support every language and build system already in the repository? What custom toolchains or rules will teams need to build and maintain? |
| Dependency and impact accuracy | Does the graph correctly represent generated files, shared libraries, cross-language interfaces, and indirect dependencies? Can the team detect when selection omits affected checks? |
| Incremental build and test selection | Which work can be safely skipped for a change, and which checks remain mandatory? What evidence supports the selection logic? |
| Hermeticity | Do local machines and CI workers use declared, compatible inputs and toolchains, or do builds rely on hidden host state? |
| Queue policy | How are conflicts, failures, invalidated results, retries, and high submission volume handled? |
| Performance and feedback | Measure worker resource use, queue wait, and time to useful feedback separately. A faster compile does not necessarily fix a long queue. |
| Migration and ownership | How much migration work is required, who owns custom rules and infrastructure, and what is the continuing maintenance burden? |
A practical rollout sequence
- Write down the mainline contract. Identify required checks and the exact code state they must validate before landing.
- Map language and build boundaries. Record which tools, generated artifacts, shared components, and platform-specific steps connect parts of the repository.
- Measure the bottleneck. Separate build execution time from queue wait and feedback latency; determine whether duplicated work, contention, or environment inconsistency is the main problem.
- Harden actions and toolchains. Declare inputs and tools, reduce reliance on host state, and use sandboxing or controlled workers to find hidden assumptions.
- Improve selection cautiously. Introduce incremental build or test selection only where dependency data is reliable, and preserve required checks that cannot safely be excluded.
- Choose queue complexity deliberately. Start with the simplest policy that preserves the landing guarantee; add parallel or speculative validation where measured contention justifies the operational cost.
- Track trust as well as speed. Watch for missed dependencies, flaky checks, stale results, and failures after landing—not just a shorter build duration.
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