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[core] Sort imports flagged by Biome's organizeImports - #3241
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🦋 Changeset detectedLatest commit: eb342ae The changes in this PR will be included in the next version bump. This PR includes changesets to release 0 packagesWhen changesets are added to this PR, you'll see the packages that this PR includes changesets for and the associated semver types Not sure what this means? Click here to learn what changesets are. Click here if you're a maintainer who wants to add another changeset to this PR |
🧪 E2E Test Results✅ All tests passed E2E Test SummarySummary
Details by Category✅ ▲ Vercel Production
✅ 💻 Local Development
✅ 📦 Local Production
✅ 🐘 Local Postgres
✅ 🪟 Windows
✅ 📋 Other
✅ vercel-multi-region
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📊 Workflow Benchmarkscommit Backend:
📈 STSO distribution vs main (inline / queue-hop histograms)1020 steps (inline) Cumulative STSO time: main 440353ms → this run 370247ms (Δ -70106ms, -16%) 1020 steps (queue-hop) Cumulative STSO time: main 9664ms → this run 8429ms (Δ -1235ms, -13%) ℹ️ Metric definitions & methodologyThe collapsed STSO distribution section above buckets every step gap of the sequential-steps run (not a sampled window), split by whether the step ending the gap ran inline — in the same warm process as the step before it, so the gap is pure framework overhead — or after a queue-hop — the first step of a fresh process, which pays queue dispatch, client reinit and event-log replay. Bars overlay the two runs: Best/P75/P90/P99 deltas compare against the most recent benchmark run on Metrics — TTFS: time to first step body (in-deployment start() → first step body, deployment clocks) · STSO: step-to-step overhead (gap between consecutive step bodies) · WO: workflow overhead (whole-run time outside step bodies, in-deployment anchored) · SL: stream latency (in-deployment write → read propagation, readAt - writtenAt) · SO: stream overhead (end-to-end write+consume time beyond the modelled generation window) Scenarios — step: one trivial no-op step, no stream; no hooks, so the run stays in turbo mode (in-process fast path) · stream: one streaming step; no hooks, so the run stays in turbo mode (in-process fast path) · hook + stream: registers a hook before one step, which exits turbo mode (dispatch path) · 1020 steps: 1020 trivial sequential steps; STSO is measured between consecutive steps in the given step ranges, and WO is the whole-run overhead outside step bodies · stream latency: parallel reader/writer steps on a dedicated stream; SL is the in-deployment write->read propagation (readAt - writtenAt) · stream overhead (text): writer streams 300 variable-length text token deltas paced at 100/s for 3s (a haiku-size LLM's token throughput) while a parallel reader drains the whole stream; SO is the end-to-end write+consume time beyond the 3s generation window (overhead/backpressure) · stream overhead (structured): same workload as stream overhead (text), but each delta is an AI-SDK-style structured object ({ type: 'text-delta', id, text }) instead of a raw string, so the SO gap vs the text scenario is the added serialization cost 🔴 marks a percentile over its target (within target is left unmarked). Targets (p75/p90/p99, ms) — TTFS 200/300/600 · SL 50/60/125 · SO 250/500/1000 All metrics are measured from deployment-side timestamps only. Runs are triggered by an in-deployment route that stamps the anchor ( Cold starts are kept in the numbers on purpose — they are part of real bursty-workload latency. The workbench deployment cold-starts the |
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No backport to This is a Biome To override, re-run the Backport to stable workflow manually via |
…_ENV For a deployment in a Vercel custom environment, the OIDC token's environment claim is the custom environment's slug (the platform mints `customEnvironment?.slug ?? envTarget`) while VERCEL_ENV reports 'preview' — so keying the cross-environment guard on VERCEL_ENV could false-refuse a legitimate delivery, e.g. a CLI client attributed to 'staging' starting a run on the staging deployment. VERCEL_TARGET_ENV is populated from exactly the same slug-or-target pair as the claim, so prefer it, keeping VERCEL_ENV as the fallback for contexts that don't inject it. Also sorts runtime.ts imports per the Biome rule that landed on main in #3241. Signed-off-by: Pranay Prakash <pranay.gp@gmail.com>
…environment queue deliveries client-side (#3244) * feat(core): stamp creator environment into runInput and reject cross-environment queue deliveries client-side `start()` makes two writes that have to land in the same tenant: the `run_created` event, attributed to whatever environment the caller authenticates as, and the queue message, pinned to a deployment. A misconfigured caller can split them — writing the run to one environment while addressing the message to a deployment in another. The consumer finds no run under its own tenant, the backend's resilient start (`run_started` creates the run when `run_created` was never seen) mints a second copy of the same run id in the consumer's environment, and both copies are real: the creator's sits pending forever while the other executes. The deployment id is not the discriminator — it matched end to end in the incident that motivated this. The environment is. So carry it: add an optional `World.getEnvironment()`, implement it in world-vercel from the same resolution that produces the `x-vercel-environment` header, and stamp it into the queue message's `runInput`. The consuming deployment already knows its own environment, so it can refuse the delivery itself with no server coordination — and refuse before `run_started`, the write that would create the fork. The refusal acks the message instead of throwing: the mismatch is baked into the message, so every redelivery would reach the same verdict and throwing would hot-loop until MAX_QUEUE_DELIVERIES. Both sides must be known for the check to run, so worlds with a single tenant (local, Postgres) and runs started by an older SDK behave exactly as before. A companion diagnostic logs a deployment-id mismatch without refusing, since deployment ids differ for benign reasons too. Signed-off-by: Pranay Prakash <pranay.gp@gmail.com> * fix(world-vercel): resolve the runtime environment from VERCEL_TARGET_ENV For a deployment in a Vercel custom environment, the OIDC token's environment claim is the custom environment's slug (the platform mints `customEnvironment?.slug ?? envTarget`) while VERCEL_ENV reports 'preview' — so keying the cross-environment guard on VERCEL_ENV could false-refuse a legitimate delivery, e.g. a CLI client attributed to 'staging' starting a run on the staging deployment. VERCEL_TARGET_ENV is populated from exactly the same slug-or-target pair as the claim, so prefer it, keeping VERCEL_ENV as the fallback for contexts that don't inject it. Also sorts runtime.ts imports per the Biome rule that landed on main in #3241. Signed-off-by: Pranay Prakash <pranay.gp@gmail.com> --------- Signed-off-by: Pranay Prakash <pranay.gp@gmail.com>
mainis red on the Biome check:assist/source/organizeImportsreportspackages/core/src/runtime.tsandpackages/core/src/runtime/step-executor.ts(run 30587485140). BothCOMPUTE_INSTANCE_IDimports landed out of order just before #3222 turned the check on, so every open PR inherits the failure through its merge commit.This is Biome's own autofix, no behavior change.
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