Terminal.skills
Skills/gcp-cloud-run
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gcp-cloud-run

Deploy serverless containers on Google Cloud Run. Build and push container images, configure auto-scaling from zero, split traffic between revisions for canary deployments, and set up custom domains with managed TLS.

#gcp#cloud-run#serverless#containers
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed gcp-cloud-run v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Deploy the latest build to the staging environment and run smoke tests"
  • "Check the CI pipeline status and summarize any recent failures"

Information

Version
1.0.0
Author
terminal-skills
Category
DevOps
License
Apache-2.0

Documentation

Google Cloud Run is a fully managed serverless platform for running containers. It automatically scales from zero to thousands of instances, charges only for actual usage, and supports any language or binary that can run in a container.

Core Concepts

  • Service — a long-running container that auto-scales based on traffic
  • Revision — an immutable snapshot of a service's configuration and code
  • Traffic splitting — route percentages of traffic to different revisions
  • Job — run a container to completion (batch, cron, one-off tasks)
  • Concurrency — max simultaneous requests per container instance

Deploying a Service

bash
# Deploy from source code (Cloud Build + Cloud Run)
gcloud run deploy web-app \
  --source . \
  --region us-central1 \
  --allow-unauthenticated \
  --port 8080 \
  --memory 512Mi \
  --cpu 1 \
  --min-instances 0 \
  --max-instances 100 \
  --concurrency 80 \
  --set-env-vars "NODE_ENV=production,LOG_LEVEL=info"
bash
# Deploy from a pre-built container image
gcloud run deploy web-app \
  --image us-central1-docker.pkg.dev/my-project/repo/web-app:v1.2.0 \
  --region us-central1 \
  --allow-unauthenticated \
  --port 8080 \
  --memory 1Gi \
  --cpu 2 \
  --timeout 300 \
  --set-secrets "DATABASE_URL=db-url:latest,API_KEY=api-key:latest"

Building and Pushing Images

bash
# Build with Cloud Build and push to Artifact Registry
gcloud builds submit \
  --tag us-central1-docker.pkg.dev/my-project/repo/web-app:v1.2.0
dockerfile
# Dockerfile — optimized multi-stage build for Cloud Run
FROM node:20-slim AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --production=false
COPY . .
RUN npm run build

FROM node:20-slim
WORKDIR /app
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/node_modules ./node_modules
COPY package*.json ./
ENV PORT=8080
EXPOSE 8080
CMD ["node", "dist/server.js"]

Traffic Splitting

bash
# Deploy a new revision without sending traffic
gcloud run deploy web-app \
  --image us-central1-docker.pkg.dev/my-project/repo/web-app:v1.3.0 \
  --region us-central1 \
  --no-traffic
bash
# Split traffic: 90% to current, 10% to new revision (canary)
gcloud run services update-traffic web-app \
  --region us-central1 \
  --to-revisions web-app-00002=10,web-app-00001=90
bash
# Promote canary to 100%
gcloud run services update-traffic web-app \
  --region us-central1 \
  --to-latest
bash
# Rollback by routing all traffic to a previous revision
gcloud run services update-traffic web-app \
  --region us-central1 \
  --to-revisions web-app-00001=100

Cloud Run Jobs

bash
# Create a batch job
gcloud run jobs create data-export \
  --image us-central1-docker.pkg.dev/my-project/repo/data-export:latest \
  --region us-central1 \
  --memory 2Gi \
  --cpu 2 \
  --task-timeout 3600 \
  --max-retries 3 \
  --set-env-vars "EXPORT_FORMAT=csv"
bash
# Execute the job
gcloud run jobs execute data-export --region us-central1
bash
# Schedule a job with Cloud Scheduler
gcloud scheduler jobs create http data-export-daily \
  --location us-central1 \
  --schedule "0 2 * * *" \
  --uri "https://us-central1-run.googleapis.com/apis/run.googleapis.com/v1/namespaces/my-project/jobs/data-export:run" \
  --http-method POST \
  --oauth-service-account-email my-sa@my-project.iam.gserviceaccount.com

Custom Domains

bash
# Map a custom domain
gcloud run domain-mappings create \
  --service web-app \
  --domain app.example.com \
  --region us-central1
bash
# Get DNS records to configure
gcloud run domain-mappings describe \
  --domain app.example.com \
  --region us-central1

Service Configuration

bash
# Update environment variables and secrets
gcloud run services update web-app \
  --region us-central1 \
  --update-env-vars "FEATURE_FLAG=true" \
  --set-secrets "DB_PASS=db-password:latest" \
  --min-instances 1 \
  --cpu-boost
bash
# Set IAM policy (authenticated access only)
gcloud run services add-iam-policy-binding web-app \
  --region us-central1 \
  --member="serviceAccount:frontend@my-project.iam.gserviceaccount.com" \
  --role="roles/run.invoker"

Monitoring

bash
# View service details and URL
gcloud run services describe web-app --region us-central1 --format='value(status.url)'
bash
# List revisions
gcloud run revisions list --service web-app --region us-central1
bash
# Stream logs
gcloud run services logs tail web-app --region us-central1

Best Practices

  • Set min-instances=1 for latency-sensitive services to avoid cold starts
  • Use concurrency settings matching your app's thread model (default 80)
  • Store secrets in Secret Manager, not environment variables
  • Use multi-stage Docker builds for smaller images and faster deploys
  • Enable CPU boost for faster cold start initialization
  • Use traffic splitting for safe canary deployments
  • Set appropriate request timeouts (default 300s, max 3600s)
  • Use Cloud Run Jobs for batch work instead of long-running services