fix: Better loading of SvelteKit routes directory - #3509
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Instead of accessing private files from SvelteKit, we use @sveltejs/load-config to load the Svelte config (that package also knows about checking Vite config). The deadlock is avoided by having a module-level Set to see if we're currently recursing or not. This is necessary for SvelteKit 3 since there the config lives exclusively in the vite config, and the previous logic did not handle that. This also uncovered that we're needlessly rebuilding the generated files in sub builds/workers (SvelteKit, at least below 3, starts off secondary builds; and some things are done in workers), which a new file cache now checks. Picked from stable branch PR #3474 Signed-off-by: Simon Holthausen <simon.holthausen@vercel.com>
🦋 Changeset detectedLatest commit: 806bc7e The changes in this PR will be included in the next version bump. This PR includes changesets to release 16 packages
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📊 Workflow Benchmarkscommit Backend:
Streams
📈 STSO distribution vs main (inline / queue-hop histograms)1020 steps (inline) Cumulative STSO time: main 277417ms → this run 260509ms (Δ -16908ms, -6%) 📈 CRTT drill-down vs main (RTT distributions & profiles)RTT over stream progress (avg per tenth of stream, bars scaled min→max): RTT by chunk size (avg per log size bin, ~160B → ~12KB serialized, bars scaled min→max): Delivery jitter over stream progress (avg positive CDV per tenth of stream, bars scaled min→max): 📜 Previous results (1)3f938d4Thu, 13 Aug 2026 07:50:33 GMT · run logs
ℹ️ Metric definitions & methodologyStreams: writer/reader sustained rates (steady window, 10% trimmed each side), first-chunk RTT (the stream-open path, before any buffering/backpressure), CRTT percentiles, and worst delivery stall (CDV max). Cells are medians across iterations; per-run values in the artifacts. No 🔴/🟢 marks until targets attach. The collapsed STSO distribution section above buckets every step gap, split inline (same warm process — pure framework overhead) vs queue-hop (fresh process — dispatch, reinit, replay). The collapsed CRTT drill-down: per-variant RTT histograms (fixed log bins, 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) · Fan-out TTFS: fan-out time to first step (in-deployment start() → first of the parallel step bodies to complete) · Fan-out TTLS: fan-out time to last step (in-deployment start() → last of the parallel step bodies to complete, i.e. when the Promise.all resolves) · STSO: step-to-step overhead (gap between consecutive step bodies) · WO: workflow overhead (whole-run time outside step bodies, in-deployment anchored) · CRTT: chunk round-trip time (per-chunk write → read latency, one clock domain: deployment → stream backend → same deployment) · CDV: chunk delay variation / delivery jitter (inter-arrival gap minus inter-write gap per seq-adjacent pair; skew-free; the row is each run's MAX positive value, so one stall moves it) 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 · Promise.all(100 steps): 100 trivial no-op steps started together in a single Promise.all; Fan-out TTFS is the first of them to complete and Fan-out TTLS the last, both from the in-deployment clientStart, so their gap is the spread the runtime adds across the fan-out · paced control (100/s, 60B): the control: 300 tiny (~60B) deltas metronome-paced at 100/s — zero workload structure, so it reads the transport floor and flush cadence, and disambiguates transport-wide vs workload-specific when a replay row moves · size sweep (100/s, 160B-12KB): same pacing as the control with deltas padded in rotation across seven log-spaced sizes (~160B–12KB) — rotation decouples size from stream position, so it isolates whether chunk size causes latency · replay gateway-gpt-5.4-nano-2000t (1x): raw provider SSE cadence captured at the AI gateway boundary (gpt-5.4-nano, the most popular gateway model; per-token deltas p50 208B = the modal production chunk size), replayed exactly as measured — the typical customer's workload; its CDV is the typical customer's real delivery jitter · replay eve-gpt-5.6-sol-2000t (1x): a captured eve turn (gpt-5.6-sol, the most-used demanding eve model; ~2000 output tokens = production p50 turn length) replayed exactly as measured — eve's envelope protocol re-ships the cumulative message so sizes ramp 142B→13KB; the demanding outlier tenant's reality · replay eve-gpt-5.6-sol-2000t (2x): the same eve capture at 2x — the headroom/stress row; real fast-tier models emit the same chunk sizes at proportionally higher rate, so time compression is a faithful speed model · first chunk (pooled): every run's seq-0 RTT pooled across all stream scenarios — the first chunk precedes any workload differentiation, so pooling samples one shared stream-open path with exact percentiles Replay cadences (semantic sha256) — eve-gpt-5.6-sol-2000t 🔴 marks a percentile over its target (within target is left unmarked). Targets (p75/p90/p99, ms) — TTFS 200/300/600 All timestamps are deployment-side; runs are triggered in-deployment, so the CI runner and api.vercel.com sit outside every measured window. TTFS = Cold starts stay in the numbers (real bursty-workload latency, inflates P75+); Best is the warm floor. |
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🧪 E2E Test Results✅ All tests passed 🛠 Infra Events (absorbed by the harness)Platform anomalies the e2e harness detected and worked around (e.g. a run the queue never picked up, replaced by a fresh run). Clustered timestamps indicate a backend blip; a steady drip indicates a platform issue worth escalating.
E2E Test SummarySummary
Details by Category✅ ▲ Vercel Production
✅ 💻 Local Development
✅ 📦 Local Production
✅ 🐘 Local Postgres
✅ 🪟 Windows
✅ 🌐 Cross-language Conformance
✅ vercel-multi-region
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Sim WorldSimulated world deterministic testing for races. Traces 🟠 world-sim scenario book — 1 fail of 41 total
Full trace: |
2 participants
Description
Instead of accessing private files from SvelteKit, we use @sveltejs/load-config to load the Svelte config (that package also knows about checking Vite config). The deadlock is avoided by having a module-level Set to see if we're currently recursing or not.
This is necessary for SvelteKit 3 since there the config lives exclusively in the vite config, and the previous logic did not handle that.
This also uncovered that we're needlessly rebuilding the generated files in sub builds/workers (SvelteKit, at least below 3, starts off secondary builds; and some things are done in workers), which a new file cache now checks.
Picked from stable branch PR #3474
How did you test your changes?
On a SvelteKit project
PR Checklist - Required to merge
pnpm changesetwas run to create a changelog for this PRpatchfor bug fixes,minorfor new features,majorfor breaking changes.pnpm changeset --emptyif you are changing documentation or workbench appsgit commit --signoffon your commits)@vercel/workflowin a comment once the PR is ready, and the above checklist is complete