gcp-cloud-architect

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: "gcp-cloud-architect"
description: "Design GCP architectures for startups and enterprises. Use when asked to design Google Cloud infrastructure, deploy to GKE or Cloud Run, configure BigQuery pipelines, optimize GCP costs, or migrate to GCP. Covers Cloud Run, GKE, Cloud Functions, Cloud SQL, BigQuery, and cost optimization."
---

# GCP Cloud Architect

Design scalable, cost-effective Google Cloud architectures for startups and enterprises with infrastructure-as-code templates.

---

## Workflow

### Step 1: Gather Requirements

Collect application specifications:

```
- Application type (web app, mobile backend, data pipeline, SaaS)
- Expected users and requests per second
- Budget constraints (monthly spend limit)
- Team size and GCP experience level
- Compliance requirements (GDPR, HIPAA, SOC 2)
- Availability requirements (SLA, RPO/RTO)
```

### Step 2: Design Architecture

Run the architecture designer to get pattern recommendations:

```bash
python scripts/architecture_designer.py --input requirements.json
```

**Example output:**

```json
{
  "recommended_pattern": "serverless_web",
  "service_stack": ["Cloud Storage", "Cloud CDN", "Cloud Run", "Firestore", "Identity Platform"],
  "estimated_monthly_cost_usd": 30,
  "pros": ["Low ops overhead", "Pay-per-use", "Auto-scaling", "No cold starts on Cloud Run min instances"],
  "cons": ["Vendor lock-in", "Regional limitations", "Eventual consistency with Firestore"]
}
```

Select from recommended patterns:
- **Serverless Web**: Cloud Storage + Cloud CDN + Cloud Run + Firestore
- **Microservices on GKE**: GKE Autopilot + Cloud SQL + Memorystore + Cloud Pub/Sub
- **Serverless Data Pipeline**: Pub/Sub + Dataflow + BigQuery + Looker
- **ML Platform**: Vertex AI + Cloud Storage + BigQuery + Cloud Functions

See `references/architecture_patterns.md` for detailed pattern specifications.

**Validation checkpoint:** Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding to Step 3.

### Step 3: Estimate Cost

Analyze estimated costs and optimization opportunities:

```bash
python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000
```

**Example output:**

```json
{
  "current_monthly_usd": 2000,
  "recommendations": [
    { "action": "Right-size Cloud SQL db-custom-4-16384 to db-custom-2-8192", "savings_usd": 380, "priority": "high" },
    { "action": "Purchase 1-yr committed use discount for GKE nodes", "savings_usd": 290, "priority": "high" },
    { "action": "Move Cloud Storage objects >90 days to Nearline", "savings_usd": 75, "priority": "medium" }
  ],
  "total_potential_savings_usd": 745
}
```

Output includes:
- Monthly cost breakdown by service
- Right-sizing recommendations
- Committed use discount opportunities
- Sustained use discount analysis
- Potential monthly savings

Use the [GCP Pricing Calculator](https://cloud.google.com/products/calculator) for detailed estimates.

### Step 4: Generate IaC

Create infrastructure-as-code for the selected pattern:

```bash
python scripts/deployment_manager.py --app-name my-app --pattern serverless_web --region us-central1
```

**Example Terraform HCL output (Cloud Run + Firestore):**

```hcl
terraform {
  required_providers {
    google = {
      source  = "hashicorp/google"
      version = "~> 5.0"
    }
  }
}

provider "google" {
  project = var.project_id
  region  = var.region
}

variable "project_id" {
  description = "GCP project ID"
  type        = string
}

variable "region" {
  description = "GCP region"
  type        = string
  default     = "us-central1"
}

resource "google_cloud_run_v2_service" "api" {
  name     = "${var.environment}-${var.app_name}-api"
  location = var.region

  template {
    containers {
      image = "gcr.io/${var.project_id}/${var.app_name}:latest"
      resources {
        limits = {
          cpu    = "1000m"
          memory = "512Mi"
        }
      }
      env {
        name  = "FIRESTORE_PROJECT"