--- title: "Make unordered results deterministic before comparing" description: "Sort values whose order was never guaranteed, so an equivalent result passes on the first run." url: https://starsling.dev/ci-playbooks/techniques/normalize-unordered-results canonicalUrl: https://starsling.dev/ci-playbooks/techniques/normalize-unordered-results --- # Make unordered results deterministic before comparing Sort values whose order was never guaranteed, so an equivalent result passes on the first run. ## 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#normalize-unordered-results)**: Use it when A test compares ordering the producer never guaranteed, and deterministic ordering suits the public output. Check before adopting: Decide first whether ordering is part of the output contract. Sorting in place mutates the caller's slice, so sort a copy where callers share it, and keep membership and error-field assertions. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Make unordered results deterministic before comparing. Sort values whose order was never guaranteed, so an equivalent result passes on the first run. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/normalize-unordered-results.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: Make unordered results deterministic before comparing Cadence sorted the active-cluster list before formatting a domain error. A test expected [A B] while an equivalent result arrived as [B A]. OPT48 is adapted to one order-dependent test, and the change sorts the production error output. Evidence: Source PR/commit evidence The PR shows the expected and actual output from the flaky run and a two-line sort. ([Source](https://github.com/cadence-workflow/cadence/pull/6831)) Use it when: A test compares ordering the producer never guaranteed, and deterministic ordering suits the public output. Before applying it: Decide first whether ordering is part of the output contract. Sorting in place mutates the caller's slice, so sort a copy where callers share it, and keep membership and error-field assertions. [Read more in the Uber playbook](https://starsling.dev/ci-playbooks/uber#normalize-unordered-results) [Primary source](https://github.com/cadence-workflow/cadence/pull/6831) ## 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 ### [Check for leaks after this test cleans up](https://starsling.dev/ci-playbooks/techniques/check-cleanup-at-test-end) Run the leak check at the end of the test, where it inspects this test's own resources. 1 implementation · 1 company (Uber) ### [Prove a new test is stable before it joins the blocking suite](https://starsling.dev/ci-playbooks/techniques/flakiness-gate-before-blocking) Run each new end-to-end test many times in a dedicated pipeline, and admit it to the blocking checks only if it stays under a failure threshold. 1 implementation · 1 company (Shopify) ### [Wait for the state transition before asserting](https://starsling.dev/ci-playbooks/techniques/synchronize-assertions) Synchronize with the event that makes the assertion true, so the test stops needing reruns. 1 implementation · 1 company (Uber) ## 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