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James RussoandClaude Opus 4.8
4da567df22
* feat(gcp-cloud-run): add Google Cloud Run + Workflows distributed render adapter Adds @hyperframes/gcp-cloud-run, the GCP counterpart to @hyperframes/aws-lambda (issue #932). The OSS distributed primitives (plan, renderChunk x N, assemble) are unchanged; this package is the storage/compute/orchestration glue. Package: Cloud Run handler (one image, three actions), runs under bun; GCS transport; in-image chrome-headless-shell resolver; client SDK (renderToCloudRun, getRenderProgress, deploySite, computeRenderCost); Dockerfile; Cloud Workflows definition; Terraform module; CLI cloudrun deploy|sites|render|render-batch|progress|destroy with --output-resolution and --strict-variables; 62 unit tests + docs + live smoke script. Shared extraction (removes ~640 lines of adapter duplication): move the cloud-agnostic config validator + content-hash into producer/distributed; both adapters import them. Validated end-to-end on GCP at 37.4 dB PSNR vs baseline. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(cli): resolve @hyperframes/gcp-cloud-run in the CLI build + root build The CLI bundle (esbuild) couldn't resolve `@hyperframes/gcp-cloud-run/sdk`, failing Build/Typecheck/CLI-smoke (and the perf/windows/regression jobs that build first). Mirror the aws-lambda handling: mark the gcp adapter + its /sdk subpath external in tsup.config.ts with a source alias, and add gcp-cloud-run to the root `build` filter so its dist exists for publish + runtime. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(ci): copy gcp-cloud-run manifest in Dockerfile.test for frozen install The regression test image runs `bun install --frozen-lockfile` after copying each workspace package.json individually. The CLI now depends on @hyperframes/gcp-cloud-run (workspace:*), so the frozen install fails to resolve it unless its manifest is present. Add the COPY line. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * feat(cli): add machine-sizing flags to `cloudrun deploy` Closes the parity gap with `lambda deploy` (which exposes --memory etc.). `cloudrun deploy` now threads --cpu, --memory, --max-instances, and --timeout into the Terraform apply; omitted flags keep the module defaults (4 vCPU / 16Gi / 100 instances / 3600s). For finer control, apply the module directly. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(gcp-cloud-run): address PR review (security, waste, limits, alerts) - server.ts: bucket-allowlist guard no longer fails open silently. Unset env logs a one-time WARNING; "*" is an explicit opt-out; otherwise it enforces. - server.ts: stop double-shipping audio.aac. It already rides in the plan tarball every consumer downloads, so drop the redundant standalone upload (plan) + re-download/overwrite (assemble); assemble reads it from the untar, falling back to a supplied AudioGcsUri for compat. - server.ts: chunk extension via path.extname() instead of slice(lastIndexOf). - workflow.yaml: clamp parallel concurrency_limit to math.min(chunkCount, 20) — Cloud Workflows hard-caps concurrent iterations at 20. - Dockerfile: pin bun (bun-v1.3.9) so an interop change can't silently break the image rebuild. - terraform: add min_instances var (default 0); add a workflow-failure alert (finished_execution_count status=FAILED) alongside the request-count one. - costAccounting: document that displayCost excludes GCS storage/egress. Verified against the actual APIs: @google-cloud/workflows@4.4.0 ICreateExecutionRequest has no executionId (so the idempotency-token suggestion isn't available in this client); Workflows concurrency cap is 20; failure metric is workflows.googleapis.com/finished_execution_count (status label). 174 adapter tests pass, fallow/oxlint/oxfmt/terraform clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(gcp-cloud-run): address round-2 review — error code + CFR forwarding - workflow.yaml: rename the zero-chunk failure code PLAN_TOO_LARGE → PLAN_PRODUCED_ZERO_CHUNKS. The old code implied a size-ceiling breach (the opposite cause), misleading anyone triaging the alert. - workflow.yaml: forward Config.cfr to the assemble step (`Cfr: ${("cfr" in config) and config.cfr}`). It was read by the handler but never sent, so exact-CFR was silently off for every Cloud Run render. Uses the same `in`-operator guard already proven in the retryable predicate. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(release): include gcp-cloud-run in set-version PACKAGES list set-version.ts (driven by release:prepare) bumps an explicit package list to the shared version on each release. gcp-cloud-run was wired into the build + publish.yml but missing here, so a release would leave it at a stale version and publish.yml would push the wrong version. Add it so the new package version-bumps + publishes in lockstep with the others. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
52 lines
1.8 KiB
Markdown
52 lines
1.8 KiB
Markdown
# Google Cloud Run example
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End-to-end deployment + smoke for [`@hyperframes/gcp-cloud-run`](../../packages/gcp-cloud-run) — the Cloud Run + Cloud Workflows adapter for HyperFrames distributed rendering.
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## Layout
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```
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scripts/smoke.sh Real-GCP smoke: build → deploy → render → PSNR → destroy
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sample-events/ Example request bodies for the Cloud Run handler
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(plan.json, render-chunk.json, assemble.json)
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```
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The Terraform module and the Cloud Workflows definition that the smoke deploys live with the package, at `packages/gcp-cloud-run/terraform/` (including `workflow.yaml`).
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## Prerequisites
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- `gcloud` authenticated, with a project that has **billing enabled**
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- `terraform` (≥ 1.5), `docker`, `ffmpeg`, `jq` on PATH
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## Run the smoke
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```bash
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# Renders the mp4-h264-sdr fixture through the workflow and PSNR-compares it
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# against the in-process baseline, then tears the stack down.
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./scripts/smoke.sh --project YOUR_GCP_PROJECT --region us-central1
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# Keep the stack up to poke at it:
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./scripts/smoke.sh --project YOUR_GCP_PROJECT --keep-stack
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# Render at several chunk sizes to see the fan-out scaling:
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./scripts/smoke.sh --project YOUR_GCP_PROJECT --chunk-sizes 30,15,10
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```
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Outputs land in `scripts/gcp-smoke-artifacts/`: `results.json`
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(`chunkSize × wallClockMs × psnrAvgDb`), the rendered MP4s, and each
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workflow execution's describe output.
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## Test the handler locally
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The sample events exercise the same body shape Cloud Workflows sends. With the
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container running locally (`PORT=8080`) and credentials that can reach a GCS
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bucket, you can drive a single action:
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```bash
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curl -sX POST localhost:8080/ \
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-H 'content-type: application/json' \
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--data @sample-events/plan.json | jq .
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```
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Replace the `PROJECT` placeholder bucket names and `REPLACE_WITH_PLAN_HASH`
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with real values from a prior `plan` response.
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