[core] Prepare replay payloads as event frames arrive - #3548
[core] Prepare replay payloads as event frames arrive#3548NathanColosimo wants to merge 7 commits into
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🦋 Changeset detectedLatest commit: dd3ed77 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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🧪 E2E Test Results❌ Some tests failed ❌ Failed E2E Tests🪟 Windows (1 failed)nextjs-turbopack-quickjs (1 failed):
E2E Test SummarySummary
Details by Category✅ ▲ Vercel Production
✅ 💻 Local Development
✅ 📦 Local Production
✅ 🐘 Local Postgres
❌ 🪟 Windows
✅ vercel-multi-region
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Sim WorldSimulated world deterministic testing for races. Traces 🟠 Mint-ordered log — 3 fail of 41 total
Full trace: 🟢 Append-only log — 0 fail of 41 total
Full trace: |
📊 Workflow Benchmarks❌ The benchmark run for Partial results from the failed run: commit Backend:
📈 STSO distribution vs main (inline / queue-hop histograms)1020 steps (inline) Cumulative STSO time: main 440013ms → this run 369226ms (Δ -70787ms, -16%) 📜 Previous results (4)85a9860Fri, 14 Aug 2026 21:51:31 GMT · run logs
184d506Fri, 14 Aug 2026 20:27:25 GMT · run logs
7471e77Fri, 14 Aug 2026 07:24:27 GMT · run logs
294d4b7Fri, 14 Aug 2026 04:20:32 GMT · run logs
ℹ️ Metric definitions & methodologyThe 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). 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) 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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Summary
Performance signal
The latest benchmark and previous behavior run were recorded on the original cumulative branch before this review split. Across those runs, inline STSO improved 4–20% at p75, 10–24% at p90, and 39–51% at p99; total 1020-step workflow overhead improved 7–23%.
Cold TTFS regressed in both runs, including the turbo control that does not preload or prepare replay events, so that signal is dominated by deployment cold-start variability. The benchmarked branch also contained the small tracing layer in #3523.
Stack
Validation
@workflow/worldand@workflow/core