95852f1357
- FFmpeg: Enable CPU throttling to reduce idle costs - Whisper: Keep CPU throttling disabled (model loading needs full CPU) - Remove readinessProbe (requires BETA launch stage) - Both services scale to zero when idle for cost savings 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
117 lines
3.5 KiB
YAML
117 lines
3.5 KiB
YAML
# =============================================================================
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# Cloud Run Service: FFmpeg HTTP Service
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# =============================================================================
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# Autoscaling FFmpeg processing service for Cloud Run deployment.
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# This service handles CPU-intensive video encoding via HTTP endpoints.
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#
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# Key features:
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# - Scale to zero when idle (pay only for compute time used)
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# - Up to 20 instances for parallel video processing
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# - 8 vCPU / 32GB RAM for fast encoding
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# - Startup CPU boost for faster cold starts
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# - Faster startup than Whisper (no model loading)
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# =============================================================================
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apiVersion: serving.knative.dev/v1
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kind: Service
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metadata:
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name: ffmpeg-http-service
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annotations:
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run.googleapis.com/ingress: internal # Only accessible from within GCP
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run.googleapis.com/execution-environment: gen2 # Required for 8 vCPU
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spec:
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template:
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metadata:
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annotations:
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# Autoscaling configuration
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autoscaling.knative.dev/minScale: "0" # Scale to zero when idle
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autoscaling.knative.dev/maxScale: "20" # Max 20 concurrent instances
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# Cloud Run Gen2 features
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run.googleapis.com/execution-environment: gen2 # Required for 8 vCPU
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run.googleapis.com/cpu-throttling: "true" # Throttle CPU when idle to reduce costs
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run.googleapis.com/startup-cpu-boost: "true" # Faster cold start
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spec:
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# Only 1 FFmpeg operation at a time per instance (CPU-intensive)
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containerConcurrency: 1
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# 10-minute timeout for long encoding operations
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timeoutSeconds: 600
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serviceAccountName: accessible-video-worker@PROJECT_ID.iam.gserviceaccount.com
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containers:
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- image: gcr.io/PROJECT_ID/ffmpeg-http-service:latest
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ports:
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- containerPort: 8080
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env:
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- name: APP_ENV
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value: "prod"
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- name: PYTHONPATH
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value: "/app"
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- name: PYTHONUNBUFFERED
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value: "1"
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- name: PYTHONDONTWRITEBYTECODE
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value: "1"
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# GCP Configuration
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- name: GCP_PROJECT_ID
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value: "PROJECT_ID"
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- name: GCS_BUCKET
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valueFrom:
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secretKeyRef:
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name: gcs-bucket-name
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key: latest
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# MongoDB for job tracking (optional, for logging)
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- name: MONGODB_URL
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valueFrom:
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secretKeyRef:
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name: mongodb-url
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key: latest
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# OpenTelemetry configuration
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- name: OTEL_SERVICE_NAME
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value: "ffmpeg-http-service"
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- name: OTEL_SERVICE_VERSION
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value: "1.0.0"
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- name: OTEL_TRACES_EXPORTER
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value: "gcp_trace"
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# Sentry configuration (optional)
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- name: SENTRY_DSN
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valueFrom:
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secretKeyRef:
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name: sentry-dsn
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key: latest
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- name: SENTRY_ENVIRONMENT
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value: "production"
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resources:
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limits:
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memory: "32Gi"
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cpu: "8000m" # 8 vCPU
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requests:
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memory: "4Gi"
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cpu: "2000m" # 2 vCPU minimum
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# Health checks
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startupProbe:
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httpGet:
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path: /health
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port: 8080
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initialDelaySeconds: 5 # FFmpeg starts fast (no model to load)
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periodSeconds: 5
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timeoutSeconds: 5
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failureThreshold: 6
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livenessProbe:
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httpGet:
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path: /health
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port: 8080
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initialDelaySeconds: 15
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periodSeconds: 30
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timeoutSeconds: 10
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