/google-cloud-solution-n-tier-serverless-web-app

Google Cloud solution architecture: [Workload Name]

Note: Standalone text or instructions enclosed between [ and ] (e.g., [Workload Name] or [Paste code here]) represent placeholder instructions or sample text that are replaced by the agent when populating this template.

1. Executive summary and workload overview

[A brief description of the workload, its business goals, and the high-level N-Tier (multi-tier) serverless solution architecture proposed across your compute and data tiers.]

2. Requirements and current state

2.1. Functional requirements

• Business processes: [Details of the business processes supported]
• Activities and use cases: [Details of the key activities and use cases]

2.2. Non-functional requirements

• Security & Zero-Trust: [Details of security requirements including single-domain reverse proxy, WAF mitigation, VPC-internal ingress isolation (INGRESS_TRAFFIC_INTERNAL_ONLY), and optional VPC Service Controls]
• Reliability & Availability: [Details of reliability constraints, SLA targets, auto-scaling concurrency, and multi-zone regional high availability]
• Cost: [Details of cost constraints, serverless $0 idle cost preferences (ALL_TRAFFIC + PGA), and pricing models]
• Operations & Observability: [Details of monitoring, structured logging, database Query Insights, and continuous deployment requirements]
• Performance: [Details of latency, connection pooling, Cloud CDN caching, and Memorystore Redis caching requirements]
• Sustainability: [Details of serverless resource optimization and carbon footprint reduction strategies]

2.3. Current state

[If applicable, describe the current on-premises, legacy v1 Cloud Run, or other-cloud architecture.]

• Current infrastructure: [Details of existing setup]
• Pain points and drivers for migration/redesign: [Details of drivers for migration/redesign]

2.4. Dependencies

• Internal dependencies: [Details of internal dependencies including existing VPCs, domain names, and downstream APIs]
• External dependencies: [Details of third-party SaaS products and on-premises tools]

3. Technical decomposition of the workload

[Decompose the workload into distinct logical tiers across the request lifecycle, ensuring strict security boundary segregation:]

• Tier 1 presentation tier (frontend / reverse proxy): Public-facing UI rendering and gateway proxy service (google_cloud_run_v2_service.frontend). Ingress restricted strictly to Application Load Balancer (INGRESS_TRAFFIC_INTERNAL_LOAD_BALANCER).
• Tier 2..N application tier (internal microservices / business logic): 1 to N internal business logic services (google_cloud_run_v2_service.backend_application, orders_service, etc.). Ingress restricted strictly to VPC-internal (INGRESS_TRAFFIC_INTERNAL_ONLY).
• Data tier (persistent database and in-memory cache): Private relational database (Cloud SQL PostgreSQL 18 Enterprise Edition with database_version = "POSTGRES_18", Regional HA availability_type = "REGIONAL", and Private Service Connect configuration psc_enabled = true, ipv4_enabled = false) and cache (Memorystore Redis via Private Services Access).
• Shared security and lifecycle tiers: Container image management (Artifact Registry), secret credentials (Secret Manager), private internal DNS resolution (Cloud DNS Managed Private Zone for run.app.), and zero-trust Cloud NGFW network firewall policies (google_compute_network_firewall_policy, google_compute_network_firewall_policy_association, and google_compute_network_firewall_policy_rule).

4. Proposed solution architecture

4.1. Google Cloud products and features mapping

[Map your confirmed decomposition directly to Google Cloud products based on the mandatory product mapping specifications in SKILL.md. Justify every selection and note trade-offs:]

[Populate the following table with the appropriate content at runtime.]

4.2. Architecture diagram

[Mermaid architecture flowchart illustrating the request and data flow across public and private tiers:] [The following diagram is a sample and must be updated to reflect actual recommendation if necessary.]

flowchart TD
    User["User (Browser)"] --> LB["Application Load Balancer (Cloud Armor WAF)"]
    LB --> Frontend["Presentation tier (Cloud Run - Load Balancer Ingress)"]
    Frontend --> Backend["Application tier (Cloud Run - VPC-Internal Ingress)"]
    Backend --> Data["Data tier (Cloud SQL & Memorystore Redis via PSC/PSA)"]

4.3. Architecture description

• Data flow: [Describe the secure request and data payload lifecycle from edge entry point https://domain.com through tier 1 reverse proxying, internal microservice processing, and persistent database/cache storage.]
• Tasks/control flow: [Describe container startup authentication, least-privilege IAM service account execution (roles/run.invoker, roles/cloudsql.client), and Cloud SQL sidecar socket connections.]

5. Design and configuration recommendations

[Populate the following subsections with clear, parameter-dense recommendations adhering to Google Cloud Architecture Framework pillars. The included text is a sample and must be updated to reflect actual recommendation if necessary.]

5.1. Security, privacy, and compliance

• Edge WAF & Ingress Filtering: Attach Cloud Armor security policy (google_compute_security_policy with sqli-v33-stable preconfigured rule) to Application Load Balancer backend service. Tier 1 Frontend ingress is restricted to INGRESS_TRAFFIC_INTERNAL_LOAD_BALANCER to prevent direct *.run.app URL bypass.
• VPC-Internal Compute Ingress & Cloud DNS: Intermediate/backend microservices (T2..TN) are restricted strictly to INGRESS_TRAFFIC_INTERNAL_ONLY. Deploy a Cloud DNS Managed Private Zone (google_dns_managed_zone) for run.app. bound to vpc_network with google_dns_record_set mapping *.run.app to Private Google Access VIPs (199.36.153.4/30 / 199.36.153.8/30) so egress = "ALL_TRAFFIC" requests resolve internally without hitting default-deny firewalls or public IP routing.
• Cloud NGFW Egress Firewalls & Sidecar Cert Exchange (443): Enforce zero-trust Cloud NGFW network firewall policies (google_compute_network_firewall_policy_rule) with default-deny outbound (0.0.0.0/0, priority 65534). Allow frontend egress to backend / PGA VIPs (80, 443), and backend egress (allow_backend_db_egress) to Cloud SQL PSC IP (TCP 5432) and PGA VIPs (TCP 443 on 199.36.153.4/30, 199.36.153.8/30) so the Cloud SQL Auth Proxy sidecar can query sqladmin.googleapis.com on startup for IAM certificate exchange.
• Cloud SQL Auth Proxy & IAM Database Auth (DB_SOCKET_PATH): Mount Cloud SQL Auth Proxy sidecar via cloud_sql_instance volume (/cloudsql/project:region:instance Unix socket). Configure IAM DB authentication (cloudsql.iam_authentication = on, roles/cloudsql.client via google_sql_user). Recommend DB_SOCKET_PATH (/cloudsql/... Unix socket) as the primary/default path over direct TCP (DB_PSC_ENDPOINT).
• VPC Service Controls Perimeter: Document/support wrapping run.googleapis.com, sqladmin.googleapis.com, and secretmanager.googleapis.com in an Org-level VPC-SC perimeter when enable_vpc_sc = true.
• Regional Sovereignty (if applicable): For EU/regional data residency compliance, deploy Regional External ALB (google_compute_region_backend_service), omit Cloud CDN, provision a regional proxy-only subnet (purpose = "REGIONAL_MANAGED_PROXY"), and specify network on the regional forwarding rule (google_compute_forwarding_rule).

5.2. Reliability

• Cloud SQL PostgreSQL Regional HA & PSC: Provision Cloud SQL for PostgreSQL (POSTGRES_18) Enterprise Edition with Regional High Availability (availability_type = "REGIONAL", point-in-time recovery enabled), and connect via Private Service Connect (psc_enabled = true, ipv4_enabled = false, google_compute_forwarding_rule).
• Redundant Serverless Deployment: Cloud Run v2 automatically distributes instances across physical zones in var.region. Configure scaling.min_instance_count and max_instance_count to eliminate cold-start latency and protect downstream tiers.

5.3. Operational excellence

• Structured Logging & Error Reporting: Emit structured JSON logs (severity, message, trace) for automated ingestion in Logs Explorer and unhandled exception grouping in Cloud Error Reporting.
• VPC Flow Logs & Firewall Policy Logging: Enable VPC Flow Logs (flow_sampling = 0.1, aggregation_interval = "INTERVAL_1_MIN") on Cloud Run subnet and firewall logging (enable_logging = var.enable_monitoring) for network access auditing.
• Database Insights & Synthetic Probes: Enable Cloud SQL Query Insights (query_insights_enabled = true) for query execution and contention auditing. Configure Cloud Monitoring Uptime Checks (/healthz) and threshold alerts for Frontend HTTP 5xx error rates.
• Infrastructure as Code (IaC): Manage all infrastructure using modular Terraform (assets/main.tf) with stateful deletion protection (deletion_protection = true).

5.4. Cost optimization

• Serverless Idle Efficiency ($0 Baseline): Internal routing via Direct VPC Egress (ALL_TRAFFIC) + Private Google Access + Cloud DNS private zone operates at $0 idle cost without intermediate internal load balancers.
• Edge Caching: Enable Cloud CDN (enable_cdn = true) on Global ALB to cache static assets and reduce Cloud Run container activations and egress fees.

5.5. Performance efficiency

• In-Memory Caching: Deploy Memorystore for Redis via Private Services Access (connect_mode = "PRIVATE_SERVICE_ACCESS") to cache session state and frequent database read queries.
• Connection Pooling: Implement database connection pooling inside application containers to prevent PostgreSQL connection saturation during traffic spikes.

5.6. Sustainability

• Scale-to-Zero Compute: Fully serverless Cloud Run architecture scales compute instances to zero during idle periods, optimizing datacenter utilization and carbon efficiency.

6. Deployment guidance

[The following steps are samples only. Provide complete, actionable deployment instructions, your generated modular Terraform code, and your generated cross-platform verification scripts below.]

6.1. Step-by-step deployment instructions

• Zero-Install Environment Recommendation: For immediate deployment without local SDK installations, run all steps below inside Google Cloud Shell (https://shell.cloud.google.com), where terraform, python3, gcloud, and git are 100% pre-installed and authenticated out of the box.

1. Select Google Cloud Project and Enable APIs:

   gcloud config set project [YOUR_PROJECT_ID]
   gcloud services enable run.googleapis.com sqladmin.googleapis.com redis.googleapis.com \
       servicenetworking.googleapis.com secretmanager.googleapis.com monitoring.googleapis.com \
       dns.googleapis.com

2. Initialize and Apply Modular Terraform Configuration:

   terraform init
   # For production with a custom domain:
   terraform apply -var="domain_name=app.mycompany.com"
   # For sandbox/dev testing over IP with a self-signed certificate:
   terraform apply -var="use_self_signed_cert=true"

3. DNS Cutover & SSL Validation: If using a Google-managed certificate (use_self_signed_cert = false), create a DNS A record at your registrar pointing var.domain_name to the outputted load_balancer_ip. If using self-signed mode (use_self_signed_cert = true), you can test immediately over HTTPS via curl -k https://[LOAD_BALANCER_IP]/.

4. Optional VPC Service Controls (enable_vpc_sc) Configuration: If deploying with enable_vpc_sc = true, ensure your active identity is an Organization Access Context Manager Admin and update your service perimeter to include the newly deployed project and required APIs before starting containers.

6.2. Modular infrastructure as code (main.tf)

[Embed your complete, deploy-ready Terraform (main.tf) code generated from assets/main.tf below, containing Section 5.1 ("Tier 1 presentation tier: frontend reverse proxy") and replicated Section 5.2 ("Tier 2 application tier: private backend API") building blocks inside assets/main.tf tailored to the user's workload, ensuring database_version = "POSTGRES_18" is strictly preserved:]

# [Paste complete deploy-ready generated main.tf HCL code here]

6.3. Step-by-step gcloud CLI deployment commands

[Replace the following sample code with the complete sequence of gcloud CLI commands required to deploy this exact three-tier architecture manually from the terminal. Ensure the sequence executes in strict bottom-up deployment order (internal data tier -> internal microservices -> public presentation gateway -> external Application Load Balancer), enforces --database-version=POSTGRES_18 for Cloud SQL, and captures dynamic downstream URLs into shell variables to automatically pass them inside --update-env-vars:]

# 1. Create VPC network and private subnet with Private Google Access enabled
[gcloud command to create a VPC network]
[gcloud command to create a subnet]

# 2. Provision data tier (Private Cloud SQL instance with Public IP disabled & Private Redis)
[gcloud command to create a Cloud SQL instance with --database-version=POSTGRES_18 --no-assign-ip --enable-private-service-connect ...]
[gcloud command to create a Redis instance]

# 3. Deploy internal application tier service (strictly VPC-internal ingress & Direct VPC Egress)
[gcloud command to create an application tier service]

# 4. Extract generated internal application tier URL into shell variable for service wiring
[shell command to extract an application tier URL]

# 5. Deploy presentation tier frontend service with load balancer-only ingress, injecting BACKEND_URL right into environment variables
[gcloud command to deploy the presentation tier service]

# 6. Create Serverless NEG and attach to external Application Load Balancer with Cloud Armor WAF
[gcloud command to create a serverless NEG]
# [Paste remaining gcloud compute backend-services / url-maps / target-https-proxies commands here]

6.4. Solution verification guide and custom automated validation script

[Embed your custom automated validation script (e.g., self-contained cross-platform Python script using standard urllib / subprocess or cross-platform shell script) generated per Phase 4 validation steps across SSL provisioning, Tier 1 ingress blocking, Tier 2..N internal VPC ingress blocking, Application Load Balancer reachability, and Cloud Armor WAF SQLi interception:]

# [Paste custom generated verification script code here]

• Execution Commands:

python3 validate.py <custom_domain> <ssl_cert_name> <frontend_run_url> [internal_run_url_1 ...]

7. References

• Google Cloud Architecture Framework
• Cloud Run Direct VPC Egress
• Private Service Connect for Cloud SQL
• Google Cloud Terraform Best Practices