PRD: Infrastructure & Deployment

Generated: 2025-07-23 00:00 UTC
Status: Complete
Verified:

Executive Summary

This PRD defines the infrastructure architecture, deployment strategies, and operational requirements for the Document Conversion Service. It establishes standards for cloud-native deployment across multiple providers, automated CI/CD pipelines, monitoring systems, and disaster recovery procedures to ensure a highly available, scalable, and resilient service.

Key Objectives

Design multi-cloud architecture for resilience and cost optimization
Implement automated deployment pipelines with zero-downtime updates
Establish comprehensive monitoring and alerting systems
Ensure disaster recovery with minimal data loss
Optimize infrastructure costs while maintaining performance

User Stories

As a DevOps Engineer

I want automated deployments with rollback capabilities
I want comprehensive monitoring dashboards
I want infrastructure as code for all resources
I want automated scaling based on demand

As a Site Reliability Engineer

I want 99.9% uptime for the service
I want automated incident response
I want disaster recovery procedures
I want performance optimization tools

As a Security Engineer

I want secure infrastructure configuration
I want automated security scanning
I want compliance monitoring
I want audit trails for all changes

As a Finance Manager

I want predictable infrastructure costs
I want cost optimization recommendations
I want resource utilization reports
I want budget alerts and controls

Functional Requirements

Cloud Architecture

1. Multi-Cloud Strategy

graph TB subgraph Primary - Google Cloud A[Cloud Functions] --> B[Cloud Storage] A --> C[Cloud SQL] A --> D[Memorystore] E[Cloud Load Balancer] --> A end subgraph Secondary - AWS F[Lambda Functions] --> G[S3] F --> H[RDS] F --> I[ElastiCache] J[ALB] --> F end K[Global Load Balancer] --> E K --> J L[CDN - Cloudflare] --> K

Primary Region: Google Cloud (europe-west2)

Cloud Functions for conversion services
Cloud Storage for temporary files
Cloud SQL for metadata
Memorystore for caching

Secondary Region: AWS (eu-west-1)

Lambda Functions for failover
S3 for backup storage
RDS for database replica
ElastiCache for cache replica

2. Microservices Architecture

Service Breakdown:

services:
  api-gateway:
    replicas: 3-10
    memory: 512MB
    cpu: 0.5
    
  xlsx-converter:
    replicas: 5-20
    memory: 2GB
    cpu: 2
    
  pdf-converter:
    replicas: 5-20
    memory: 4GB
    cpu: 2
    
  docx-converter:
    replicas: 5-20
    memory: 2GB
    cpu: 1
    
  webhook-service:
    replicas: 3-10
    memory: 1GB
    cpu: 1
    
  email-processor:
    replicas: 3-10
    memory: 1GB
    cpu: 1

3. Container Strategy

Google Cloud Run:

FROM node:20-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
EXPOSE 8080
CMD ["node", "server.js"]

Container Registry:

Primary: Google Container Registry
Mirror: AWS ECR
Vulnerability scanning enabled
Image signing required

Deployment Pipeline

1. CI/CD Architecture

graph LR A[Git Push] --> B[GitHub Actions] B --> C{Tests} C -->|Pass| D[Build Images] C -->|Fail| E[Notify] D --> F[Security Scan] F --> G[Stage Deploy] G --> H{Smoke Tests} H -->|Pass| I[Production Deploy] H -->|Fail| J[Rollback] I --> K[Health Checks]

2. Deployment Stages

Development:

Automatic deployment on push to dev branch
Isolated environment
Test data only
Relaxed rate limits

Staging:

Deployment on push to staging branch
Production-like environment
Synthetic testing
Performance validation

Production:

Manual approval required
Blue-green deployment
Canary releases (5% → 25% → 100%)
Automatic rollback on errors

3. Infrastructure as Code

Terraform Modules:

module "conversion_service" {
  source = "./modules/cloud-function"
  
  name          = "xlsx-converter"
  runtime       = "nodejs20"
  memory        = 2048
  timeout       = 540
  max_instances = 100
  
  environment_variables = {
    NODE_ENV = "production"
    REDIS_URL = module.cache.connection_string
  }
}

Resource Management:

Version controlled infrastructure
Automated state management
Drift detection
Cost estimation

Monitoring & Observability

1. Metrics Collection

Application Metrics:

Request rate and latency
Conversion success/failure rates
File size distribution
Processing time percentiles

Infrastructure Metrics:

CPU and memory utilization
Network throughput
Storage usage
Queue depths

Business Metrics:

Conversions per customer
Revenue per conversion
API usage by endpoint
Feature adoption rates

2. Logging Architecture

graph TD A[Application Logs] --> D[Log Aggregator] B[System Logs] --> D C[Audit Logs] --> D D --> E[ElasticSearch] E --> F[Kibana] D --> G[Long-term Storage] H[Log Analysis] --> I[Alerts] E --> H

Log Standards:

{
  "timestamp": "2025-07-23T10:00:00Z",
  "level": "INFO",
  "service": "xlsx-converter",
  "trace_id": "abc123",
  "user_id": "user-456",
  "message": "Conversion completed",
  "metadata": {
    "file_size": 1048576,
    "duration_ms": 2500,
    "sheets_processed": 3
  }
}

3. Alerting System

Alert Categories:

P1 - Critical: Service down, data loss risk
P2 - High: Performance degradation, high error rate
P3 - Medium: Scaling issues, cost anomalies
P4 - Low: Maintenance reminders, optimization suggestions

Alert Routing:

alerts:
  critical:
    - pagerduty
    - slack:#incidents
    - email:oncall@company.com
  
  high:
    - slack:#alerts
    - email:devops@company.com
  
  medium:
    - slack:#monitoring
    - jira:auto-create

Scaling & Performance

1. Auto-scaling Policies

Horizontal Scaling:

scaling:
  metrics:
    - type: cpu
      target: 70%
    - type: memory
      target: 80%
    - type: request_rate
      target: 1000
    - type: queue_depth
      target: 100
  
  policy:
    scale_up:
      increment: 2
      cooldown: 60s
    scale_down:
      decrement: 1
      cooldown: 300s

2. Performance Optimization

Caching Strategy:

CDN for static assets
Redis for session data
Application-level caching
Database query caching

Resource Optimization:

Right-sizing instances
Spot/preemptible instances
Reserved capacity discounts
Scheduled scaling

3. Load Testing

Testing Scenarios:

Baseline: 100 requests/second
Peak: 1,000 requests/second
Burst: 5,000 requests/second
Sustained: 500 requests/second for 24 hours

Disaster Recovery

1. Backup Strategy

Backup Schedule:

Database: Every 6 hours
File storage: Real-time replication
Configuration: Daily snapshots
Logs: Continuous streaming

Retention Policy:

Daily backups: 7 days
Weekly backups: 4 weeks
Monthly backups: 12 months
Annual backups: 7 years

2. Recovery Procedures

graph LR A[Incident Detection] --> B{Severity} B -->|Critical| C[Activate DR Site] B -->|High| D[Partial Failover] B -->|Medium| E[Scale Primary] C --> F[DNS Failover] F --> G[Verify Services] G --> H[Monitor Recovery]

Recovery Targets:

RTO (Recovery Time Objective): < 1 hour
RPO (Recovery Point Objective): < 15 minutes
Automated failover for critical services
Manual approval for full DR activation

3. Business Continuity

Redundancy Levels:

Geographic: Multi-region deployment
Provider: Multi-cloud capability
Service: No single points of failure
Data: Triple replication minimum

Non-Functional Requirements

Reliability Requirements

Service availability: 99.9% (43.8 minutes/month)
API availability: 99.95% (21.9 minutes/month)
Data durability: 99.999999999% (11 nines)
Mean time to recovery: < 30 minutes

Performance Requirements

API latency p50: < 100ms
API latency p99: < 1s
Conversion time p50: < 5s
Conversion time p99: < 30s

Security Requirements

Encryption in transit: TLS 1.3
Encryption at rest: AES-256
Key rotation: Monthly
Access logging: 100% coverage

Technical Specifications

Infrastructure Components

1. Compute Resources

Google Cloud Functions:

Gen 2 environment
Concurrency: 1000
Memory: 256MB - 8GB
Timeout: 60s - 3600s

AWS Lambda:

Runtime: Node.js 20.x
Memory: 128MB - 10GB
Timeout: 900s
Reserved concurrency

2. Storage Systems

Object Storage:

Primary: Google Cloud Storage
Secondary: AWS S3
Lifecycle policies enabled
Cross-region replication

Database:

Primary: Cloud SQL (PostgreSQL 14)
Read replicas: 2 per region
Automated backups
Point-in-time recovery

3. Networking

Load Balancing:

Global load balancer
Regional load balancers
Health checks every 5s
Connection draining

CDN Configuration:

Cloudflare Enterprise
100+ edge locations
Custom cache rules
DDoS protection

Deployment Automation

1. GitOps Workflow

name: Deploy to Production
on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Run tests
        run: npm test
      - name: Build and push
        run: |
          docker build -t converter:$GITHUB_SHA .
          docker push gcr.io/project/converter:$GITHUB_SHA
      - name: Deploy
        run: |
          gcloud run deploy converter \
            --image gcr.io/project/converter:$GITHUB_SHA \
            --region europe-west2

2. Configuration Management

Environment Variables:

Managed via Google Secret Manager / AWS Secrets Manager
Automatic rotation support
Audit logging enabled
Version control

Success Metrics

Infrastructure KPIs

Deployment frequency: > 10/week
Lead time to production: < 1 hour
Mean time to recovery: < 30 minutes
Change failure rate: < 5%

Cost Metrics

Cost per conversion: < $0.01
Infrastructure efficiency: > 80%
Reserved capacity utilization: > 90%
Spot instance usage: > 50%

Operational Metrics

Automation coverage: > 95%
Self-healing success: > 90%
Alert accuracy: > 95%
Runbook coverage: 100%

Dependencies

External Dependencies

Cloud provider APIs
Container registries
Monitoring services
Security scanning tools

Internal Dependencies

Source control system
CI/CD platform
Secret management
Documentation system

Timeline & Milestones

Phase 1: Foundation (Months 1-2)

Basic infrastructure setup
CI/CD pipeline
Monitoring basics
Manual deployments

Phase 2: Automation (Months 2-3)

Full IaC implementation
Automated deployments
Advanced monitoring
Auto-scaling setup

Phase 3: Multi-Cloud (Months 3-4)

AWS infrastructure
Cross-cloud replication
Failover procedures
Cost optimization

Phase 4: Advanced Ops (Months 4-5)

Chaos engineering
Advanced analytics
ML-based optimization
Full automation

Risk Mitigation

Infrastructure Risks

Provider outages: Multi-cloud deployment
Capacity limits: Pre-scaling and reservations
Cost overruns: Budget alerts and limits

Operational Risks

Deployment failures: Automated rollbacks
Configuration drift: GitOps enforcement
Security breaches: Defense in depth

Future Considerations

Infrastructure Evolution

Kubernetes migration
Service mesh adoption
Edge computing
Serverless containers

Operational Enhancements

AIOps implementation
Predictive scaling
Self-healing systems
Cost optimization AI