mirror of
https://github.com/heygen-com/hyperframes.git
synced 2026-10-30 17:27:09 +00:00
Miguel Ángel
992df8b203
* perf(producer): distributed chunks download only the videos they show A project video under compiled/ that chunks extract their own frames from now goes only to the chunks that show it, like video-sources/ files. A chunk skips the readiness wait for a deferred video it does not show, so it no longer logs media_load_failed for a file it was never given. * fix(producer): cloud site uploads pack the files their id hashes, symlinks included * perf(aws-lambda): chunks read only the byte ranges of a video they need A chunk that extracts its own frames from a deferred project video now reads it from S3 by byte range through a short-lived pre-signed URL, instead of downloading and hashing the whole object first. S3 attests the object's stored SHA-256 before the URL is signed; every other artifact is still downloaded and verified as before. * perf(aws-lambda): upload project files once to a shared content-addressed area * chore(producer): keep planV2 comment share at the base level * fix(engine): verify the TLS certificate of https video inputs and never surface their signed query * fix(aws-lambda): renders re-upload a site's expired files instead of failing * perf(aws-lambda): the plan step probes project videos in place instead of downloading them * fix(engine): https video reads reconnect and time out, and redaction covers wrapped causes Signed-URL reads now pass -rw_timeout, -reconnect and -reconnect_delay_max (both Lambda builds accept them) and ffmpeg also -reconnect_on_network_error, so a transient S3 error or a stalled read fails fast or recovers instead of waiting out the process timeout. Query redaction now descends into error.cause. * fix(aws-lambda): share entry bundles and ratchet package budget * fix(aws-lambda): site ids uploaded before files.json still render A site id from an earlier sites create has only sites/<id>/project.tar.gz. renderToLambda now falls back to that tarball, which the plan step still reads, and when neither key exists it fails before starting with both keys named and the fix (run sites create again). * fix(aws-lambda): renders fail before upload on a stack too old to read sites A stack deployed before files.json sites untars the site file in its plan step and fails after four retries. renderToLambda now reads the state machine definition first and stops with STACK_TOO_OLD and the redeploy command when the plan task does not list SITE_MANIFEST_INVALID, which new SAM and CDK stacks now treat as terminal. The README's upgrade section drops the planProtocol v1 fallback, which hits the same failure. * docs(aws-lambda): document shared content storage and the upgrade path The deploy guide still described the tarball HeadObject short-circuit. It now covers the cas/sha256/ store and its 30-day lifecycle rule, the STACK_TOO_OLD, SITE_FILES_MISSING and SITE_NOT_FOUND errors, that old site ids keep working, and that every project uploads once more after the upgrade. A test keeps a troubleshooting entry for each pre-submit error. * fix(producer): chunks log each video they read by URL, without its query A chunk reading a video straight from the bucket left no trace in its log. It now logs one line per remote source with the path and the URL minus its query string, so signatures never reach the log. * fix(engine): ffmpeg errors never keep part of a signed URL's query A process's stderr tail was trimmed before its URLs were redacted, so a cut inside a signed URL left a query fragment the URL pattern no longer matched, and the fragment reached the error. The stderr tail is now redacted before every trim, and the redactor also strips a signing parameter cut off from its URL. * fix(aws-lambda): comments name the site manifest instead of a file path * fix(engine): stderr redaction survives chunk splits, and every ffmpeg spawn uses it The stderr tail was redacted on each chunk and then cut, so once a cut severed a long URL's scheme, the rest of its signature in the next chunk matched neither pattern. The tail is now kept raw with 2x slack and redacted only when the text is produced, then cut to size. The unused producer audioExtractor (its own spawn, unredacted stderr) is deleted and the parity harness's PCM decode goes through ManagedChildProcess; a test fails on any ffmpeg/ffprobe spawn in engine, producer or aws-lambda that does not. * chore(producer): record the audioExtractor deletion as intended * fix(engine): drop a signature fragment the stderr tail cut from its URL The raw tail is kept at twice the cap, but that cut can itself land inside a signature, and when redaction shrinks the rest below the cap the fragment survived. After a cut, the severed token is dropped before redaction. * refactor(engine): name the severed-token drop instead of commenting it * test(engine): a cut-off URL leaves nothing behind; aws-lambda size budget to measured * fix(producer): chunk URL-read tests use main's frame list; producer size budget to measured * chore(producer): size budget at the measured size, no headroom * chore(producer): size budget back to measured x1.05, the check's own rule
266 lines
17 KiB
Plaintext
266 lines
17 KiB
Plaintext
---
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title: AWS Lambda
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description: "Deploy distributed HyperFrames rendering to AWS Lambda and drive renders from a laptop or CI."
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---
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HyperFrames ships a first-class AWS Lambda deployment: one Lambda function fronts a Step Functions standard workflow that fans renders out across many parallel chunk workers, with intermediate artifacts in S3. End-to-end is three commands once your AWS credentials are configured.
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```bash
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hyperframes lambda deploy
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hyperframes lambda render ./my-project --width 1920 --height 1080 --wait
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hyperframes lambda destroy
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```
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Templates with [variables](/concepts/variables) work on the same Lambda stack — declare `data-composition-variables` on the composition, then pass values per render with `--variables` or fan a whole batch out with `lambda render-batch`. See the [Templates on Lambda](/deploy/templates-on-lambda) guide for the personalised-render pipeline (single render, batch from JSONL, programmatic SDK) and the 256 KiB Step Functions execution-input cap.
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## Architecture
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```
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┌──────────────────────────────────────────────────────────────────┐
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│ Step Functions state machine │
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│ Plan → Map(N) RenderChunk → Assemble │
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└──────────────────────────────────────────────────────────────────┘
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│ dispatches by event.Action
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▼
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┌──────────────────────────────────────────────────────────────────┐
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│ One Lambda function (packages/aws-lambda/dist/handler.zip) │
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│ handler.mjs │
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│ ├─ Action="plan" → @hyperframes/producer/distributed │
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│ ├─ Action="renderChunk" → @hyperframes/producer/distributed │
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│ └─ Action="assemble" → @hyperframes/producer/distributed │
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│ bin/ffmpeg — ffmpeg-static │
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│ node_modules/@sparticuz/chromium/ — Lambda-optimised Chromium │
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└──────────────────────────────────────────────────────────────────┘
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│ pure functions over local paths
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▼
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┌──────────────────────────────────────────────────────────────────┐
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│ S3 bucket — plan tarball + per-chunk outputs + final mp4 │
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└──────────────────────────────────────────────────────────────────┘
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```
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The Lambda handler is a thin dispatch: parse the Step Functions event, download inputs from S3 into `/tmp`, call the OSS primitive from `@hyperframes/producer/distributed`, upload outputs back, return a small JSON result. Everything heavy — capture, encode, audio mix — happens inside the OSS primitives.
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## Prerequisites
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| Tool | Why | Install |
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|------|-----|---------|
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| AWS credentials | The CLI and the deploy step both call AWS APIs. | Env vars, `~/.aws/credentials`, SSO, or IMDS — any chain the AWS SDK for JavaScript v3 would resolve. |
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| AWS SAM CLI | `hyperframes lambda deploy/destroy` shells out to `sam deploy`/`sam delete`. | [Install guide](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/install-sam-cli.html) |
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| `bun` | Used to build `packages/aws-lambda/dist/handler.zip` at deploy time. | `npm install -g bun` or [bun.sh](https://bun.sh) |
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| Lambda adapter | The published CLI loads the AWS adapter only for Lambda commands. | `npm install -g @hyperframes/aws-lambda` alongside the CLI |
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| HyperFrames repo checkout | `lambda deploy` builds the Lambda handler ZIP from source. Adopters who deploy outside a checkout can set `HYPERFRAMES_REPO_ROOT` to point at one. | `git clone https://github.com/heygen-com/hyperframes` |
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## Three deployment paths
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### Path 1 — `hyperframes lambda` CLI (recommended)
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The CLI is a thin wrapper around the SAM template + the `@hyperframes/aws-lambda` SDK. For most adopters this is the right starting point.
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```bash
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hyperframes lambda deploy \
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--stack-name=hyperframes-prod \
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--region=us-east-1 \
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--memory=10240
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```
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By default the stack reserves no concurrency. Each render reads the account's
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unreserved Lambda concurrency when it plans, and runs up to 90% of it at once
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(at most 256 chunks), leaving the rest for other functions in the account. The
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plan logs the number it chose. Pass `--concurrency=N` to reserve `N` executions
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for the function instead; every render then runs at most `N` chunks at once.
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Upgrading: redeploying an existing stack without `--concurrency` keeps the
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reservation it already has, and `deploy` prints it. Stacks deployed by earlier
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CLI versions reserved 8 by default, so they stay at 8 until you pass
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`--concurrency=N`. Pass `--concurrency=none` to remove the reservation so each
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render sizes itself to the account.
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After `deploy`, render anything with:
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```bash
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hyperframes lambda render ./my-project --width 1920 --height 1080 --wait
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```
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The `--wait` flag blocks and streams per-chunk progress + accrued cost; drop it to fire-and-forget, then poll with `hyperframes lambda progress <renderId>` on your own cadence.
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See the [CLI reference](/packages/cli#hyperframes-lambda) for full flag documentation.
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#### Pre-staging a project with `sites create`
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Every `lambda render` call without `--site-id` hashes the project tree and checks each file against the bucket. For tight inner loops (CI smoke jobs, prompt iteration in a demo flow), pre-stage the project once and reuse the upload:
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```bash
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hyperframes lambda sites create ./my-project
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# → Site ID: a1b2c3d4e5f6g7h8 (content-addressed)
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hyperframes lambda render ./my-project --site-id=a1b2c3d4e5f6g7h8 \
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--width 1920 --height 1080 --wait
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```
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The `siteId` is content-addressed via a SHA-256 of the project tree. A site is a small `sites/<siteId>/files.json` that lists each file's path and SHA-256. The files themselves live once per bucket under `cas/sha256/`, shared by every site and render, so a file another project already uploaded is never uploaded again. Pass the same `--site-id` to as many `lambda render` calls as you like.
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The stack's bucket expires objects under `cas/sha256/` 30 days after they were last written (the `ExpireSharedContent` lifecycle rule). Each render or `sites create` that reuses a file at least 7 days old rewrites it in place, so files in use never expire. If a site's files did expire, `lambda render --site-id` re-uploads them from the project directory. `renderToLambda` given a `siteHandle` but no `projectDir` stops with `SITE_FILES_MISSING` before starting; run `sites create` again to restore them.
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#### Upgrading from a version before shared content storage
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- **Redeploy the stack before upgrading the CLI or SDK that starts renders.** A stack from an earlier version cannot read `files.json` sites. The CLI and `renderToLambda` check the state machine first and stop with `STACK_TOO_OLD` before uploading anything; run `hyperframes lambda deploy` (or redeploy your SAM template or CDK construct from the same version) and render again.
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- **Existing site ids keep working.** When `sites/<siteId>/files.json` is missing, the render uses that site's `project.tar.gz` from the earlier version. If neither exists the render stops with `SITE_NOT_FOUND`, naming both keys; run `sites create` again.
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- **Every project uploads once more.** The site id scheme changed (it now also covers symlinked files), so `sites create` or the first `lambda render` of each project after the upgrade returns a new site id and uploads its files once.
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### Path 2 — Direct SAM deploy
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If you want to read the CloudFormation before you deploy, or you need to customise the topology (extra alarms, SNS subscribers, KMS keys, …), invoke SAM directly against the template at `examples/aws-lambda/template.yaml`:
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```bash
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cd packages/aws-lambda
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bun run build:zip # produces dist/handler.zip
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cd ../../examples/aws-lambda
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sam deploy \
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--stack-name=hyperframes-prod \
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--region=us-east-1 \
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--resolve-s3 \
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--capabilities CAPABILITY_IAM \
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--no-confirm-changeset \
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--parameter-overrides ChromeSource=sparticuz ReservedConcurrency=8
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```
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The template emits three CloudFormation outputs you'll need to invoke renders:
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- `RenderBucketName` — S3 bucket for plan tarballs + per-chunk outputs + final renders.
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- `RenderStateMachineArn` — the Step Functions standard workflow that orchestrates Plan → Map → Assemble.
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- `RenderFunctionArn` — the single Lambda function the state machine dispatches against.
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<Warning>
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The SAM template's default for `ReservedConcurrency` is `-1` (unreserved), the same as the Path 1 CLI: each render runs up to 90% of the account's unreserved concurrency. Set it to bound how many chunks run at once.
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</Warning>
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### Path 3 — CDK construct
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For users already running CDK, the `@hyperframes/aws-lambda` package exports a `HyperframesRenderStack` L2 construct that emits the same topology as the SAM template:
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```ts
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import { App, CfnOutput, Stack } from "aws-cdk-lib";
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import { HyperframesRenderStack } from "@hyperframes/aws-lambda/cdk";
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const app = new App();
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const stack = new Stack(app, "MyApp");
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const render = new HyperframesRenderStack(stack, "Render", {
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projectName: "hyperframes",
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lambdaMemoryMb: 10240,
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reservedConcurrency: 8,
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chromeSource: "sparticuz",
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});
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new CfnOutput(stack, "RenderBucketName", { value: render.bucket.bucketName });
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new CfnOutput(stack, "StateMachineArn", { value: render.stateMachine.stateMachineArn });
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```
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`aws-cdk-lib` and `constructs` are declared as **optional peer dependencies** of `@hyperframes/aws-lambda`, so consumers who only need the SDK don't pay the CDK import cost.
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The construct exposes `.bucket`, `.renderFunction`, and `.stateMachine` so you can wire dashboards, SNS topics, or other AWS resources alongside it without re-deriving ARNs.
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## IAM permissions
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The CLI ships a built-in IAM bootstrap to avoid the "User is not authorized to perform iam:CreateRole" first-deploy trap:
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```bash
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# Print an inline policy doc to attach to the IAM user that runs the CLI.
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hyperframes lambda policies user
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# Print { TrustRelationship, InlinePolicy } for a CloudFormation service role.
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hyperframes lambda policies role
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# Validate a checked-in policy still covers the CLI's needs (exit non-zero on missing).
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hyperframes lambda policies validate ./infra/iam/hyperframes-deploy.json
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```
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Code that calls the SDK's `renderToLambda` with its own role needs `states:DescribeStateMachine` on the state machine besides `states:StartExecution` and S3 read/write on the bucket: each render reads the definition to confirm the stack is new enough.
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The generated documents grant `Resource: "*"` for the CLI's required action set. After your first successful deploy you can narrow `Resource` to the deployed ARNs — predictable per the CloudFormation outputs above. Adopters running the CLI in CI typically check the policy doc into source control and run `policies validate` as a pre-deploy step to catch drift.
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## Cost shape
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Lambda renders are billed by GB-seconds (Lambda billed duration × configured memory) plus a tiny per-state-transition fee for Step Functions standard workflows. `hyperframes lambda progress` exposes the running tally:
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```bash
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hyperframes lambda progress my-render-id
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# Status: SUCCEEDED
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# Progress: 100%
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# Frames: 480 / 480
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# Lambdas: 5
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# Cost: $0.0214 (Lambda $0.0210 + SFN $0.0004)
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# Output: s3://hyperframes-renders/.../output.mp4
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```
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The cost number is an estimate: Lambda duration comes from the handler result,
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and S3 transfer is not included. Use AWS billing data for actual spend.
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## Troubleshooting
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### `sam deploy` fails with "Stack already exists"
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Pass the same `--stack-name` you used the first time. SAM is idempotent — re-running on an existing stack resolves to a no-op or an in-place update.
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### `User is not authorized to perform iam:CreateRole`
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The IAM credential running `lambda deploy` doesn't have permission to create the service role CloudFormation needs. Run `hyperframes lambda policies user` and attach the printed policy to your IAM user (or take the `policies role` output and have your admin create a deploy role).
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### `Lambda function failed: PLAN_HASH_MISMATCH`
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Step Functions invoked a `renderChunk` with a plan hash that didn't match the planDir on S3. Almost always means the producer version differs between the local `plan()` build and the deployed Lambda ZIP. Re-run `hyperframes lambda deploy` (which rebuilds the ZIP) and re-render.
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### `Lambda function failed: BROWSER_GPU_NOT_SOFTWARE`
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The handler launched Chromium but the runtime probe found a non-SwiftShader GL backend. Hardware GL is non-deterministic across chunk boundaries, so distributed renders refuse it at the runtime-image / launch-flags layer (not at the composition layer). Rebuild the handler ZIP and redeploy:
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```bash
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bun run --cwd packages/aws-lambda build:zip
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hyperframes lambda deploy --stack-name=<your-stack>
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```
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The build pipeline pins `@sparticuz/chromium` + the Chrome flags (`--use-gl=swiftshader --use-angle=swiftshader`) so a fresh deploy almost always resolves this. If it persists, your stack's Lambda function is pointing at a stale handler ZIP from a previous deploy — `lambda deploy` always rebuilds, so re-running unsticks it.
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### Render seems stuck at `RUNNING`
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Most often a Lambda cold-start chain on a many-chunk render. The Map state's reserved concurrency caps how many chunks can run in parallel — if you set `--concurrency=4` and your render has 16 chunks, the state machine processes them in batches of 4. `hyperframes lambda progress <id>` shows how many invocations are in flight.
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If progress doesn't advance for >10 minutes, check the Step Functions execution in the AWS console — failed Lambda invocations include the typed error name (`FONT_FETCH_FAILED`, `FFMPEG_VERSION_MISMATCH`, etc.) which short-circuits the state machine.
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### Tearing down doesn't reclaim S3 storage
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The render bucket is created with CloudFormation `Retain` on delete — `hyperframes lambda destroy` (or `sam delete`) tears the function + state machine down but the bucket survives. This is intentional: it protects final-rendered MP4s from being lost when you re-deploy. To fully reclaim storage, empty + delete the bucket via the AWS console / `aws s3 rb`.
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### `STACK_TOO_OLD`
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The stack was deployed by an earlier version that cannot read this version's sites. Redeploy it (`hyperframes lambda deploy`), then render again. See [Upgrading](#upgrading-from-a-version-before-shared-content-storage).
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### `SITE_FILES_MISSING` or `SITE_NOT_FOUND`
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`SITE_FILES_MISSING`: some of the site's files expired from `cas/sha256/` after 30 days unused. Pass the project directory to `lambda render`, or run `sites create` again. `SITE_NOT_FOUND`: the bucket has no site under that id in either format; run `sites create` again and use the id it prints.
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## Current limits
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- There is no completion webhook. Poll with `hyperframes lambda progress` or
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watch the Step Functions execution.
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- There is no Lambda-specific composition-discovery verb. Point
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`lambda render` at the project directory containing `index.html`.
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- Each `--region` is an independent stack. Cross-region failover is not built
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in.
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- The distributed AWS path is SDR-only.
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- Each render chunk extracts only the video frames it shows, so the plan step
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no longer grows with video length. On a 10,240 MB function, a 26-minute
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576×1024 24 fps H.264 clip plans in about 20 seconds. Each chunk reads
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only the byte ranges of each video it shows, straight from the bucket. Variable-frame-rate and HDR
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sources are the exception: the plan step still extracts every frame of those,
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inside Lambda's 15-minute limit, in 2-minute segments, so
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`FFMPEG_PROCESS_TIMEOUT_MS` applies to each segment, not to the whole clip.
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- A chunk must also finish inside 15 minutes. For long renders, raise
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`--max-parallel-chunks` (`maxParallelChunks` in the SDK) so each chunk covers
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fewer frames.
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## Related topics
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- [Render variable-driven templates on Lambda](/deploy/templates-on-lambda)
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- [Use the AWS Lambda package](/packages/aws-lambda)
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- [Choose another rendering path](/deploy/overview)
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