Implementation Guide

Step-by-step analysis and deployment checklist for all platforms

This guide provides a comprehensive analysis of Arivu’s architecture and a detailed checklist for deploying across different platforms.

Architecture Analysis (A-Z)

Backend Components

API Server

Technology: FastAPI + Uvicorn Port: 8000 (configurable) Requirements:

• Python 3.10+
• ~512 MB RAM minimum
• Async-capable runtime

Key Endpoints:

• POST /api/chat - Execute queries
• GET /api/health - Health check
• WebSocket /ws/live - Real-time updates
• GET /api/traces - Pipeline traces
• GET /api/sessions - Session history

Database Connectors

Supported Databases:

• PostgreSQL (primary)
• MySQL
• SQLite
• Snowflake
• Databricks

Connection Pool: SQLAlchemy with psycopg2/pymysql Requirements:

• Network access to database
• Proper credentials/auth
• ~10-50 MS latency to DB is acceptable

LLM Integration

Supported Providers:

• OpenAI (GPT-4, GPT-4o)
• Anthropic (Claude)
• Groq (Mixtral, Llama)
• DeepSeek
• HuggingFace (via Replicate)
• Ollama (local)

Requirements:

• API keys stored in secrets manager
• Internet access to LLM endpoints
• Rate limiting awareness
• Cost monitoring

Memory Backend

Options:

SQLite: Local file-based

• File: arivu.db
• Storage: Local filesystem or mounted volume
• Performance: Excellent for <100 concurrent users
• Scaling: Poor for distributed deployment

Redis: Distributed in-memory

• URL: redis://host:port/db
• Storage: In-memory with optional persistence
• Performance: Excellent for high concurrency
• Scaling: Excellent with Redis Cluster

Cache Layer

Caching Strategy:

• HTTP response caching (ETag, Last-Modified)
• Redis cache for query results (300s TTL)
• Frontend browser cache (manifest caching)
• CDN caching for static assets

Cache Keys:

• Semantic similarity on queries
• Session ID + query hash
• LLM model version

Background Jobs

Current Implementation:

• Synchronous within request
• Pipeline execution inline
• No message queue (yet)

For Large-Scale:

• Celery + Redis for async tasks
• Schema refresh as background job
• Report generation queue

Security & Auth

API Key Authentication:

• Header: X-API-Key
• Validation on all endpoints
• Environment: ARIVU_API_KEY

User Context:

• Session-per-user isolation
• User ID in logs for audit
• No build-in RBAC (extensible)

Database Credentials:

• Never in code
• Secrets manager (AWS/Azure/etc)
• Rotation ready

Frontend Components

Next.js Application

Technology: Next.js 15.5.9 + React 19 Build Output: Static HTML/JS/CSS Port: 3000 (dev), served on prod port Build Size: ~2-3 MB (gzipped)

Key Pages:

• / - Dashboard home
• /connections - Database connections
• /llms - LLM provider config
• /traces - Pipeline tracing
• /chat - Interactive query builder

Component Library

UI Framework: Radix UI + Tailwind CSS Icons: Lucide React Charts: Chart.js (via CrayonAI) Tables: TanStack React Table Notifications: Sonner toasts

Asset Size:

• HTML: ~150 KB
• JavaScript: ~1.5 MB (bundled)
• CSS: ~300 KB
• Custom fonts: ~500 KB

API Communication

Base URL:

• Dev: http://localhost:8000
• Prod: /api (same origin)

Integration Points:

• /api/chat - Query execution
• /api/connections - DB management
• /api/llms - LLM settings
• /api/traces - Execution traces
• /api/export - Data export

Request Pattern:

• JSON request/response
• CORS handling for cross-origin
• Error propagation to UI

State Management

Approach: React hooks + Context API Page State:

• Session ID persisted
• User preferences in localStorage
• Query results in React state
• Real-time updates via WebSocket

Session Persistence:

• LocalStorage: Settings, preferences
• URL params: Query context
• Cookies: Authentication (if needed)

Performance Optimization

Code Splitting:

• Dynamic imports for route pages
• Lazy loading for modals/dialogs

Image Optimization:

• Next.js Image component
• Responsive srcset
• WebP format support

Caching Strategy:

• Static pages: 30 days
• Dynamic pages: 1 hour
• Scripts: 1 year (with hash)

Supporting Infrastructure

Load Balancing

For Backend:

• HTTP Health checks every 30s
• Sticky sessions for WebSocket
• Round-robin for stateless APIs

For Frontend:

• DNS-level (Route 53, Azure DNS)
• CloudFront/CDN for static
• Geo-routing if multi-region

Monitoring & Logging

What to Track:

• API latency (p50, p95, p99)
• Error rates by endpoint
• Database query performance
• LLM token usage and cost
• Memory usage growth
• Cache hit ratios

Log Aggregation:

• Centralized logging (CloudWatch, Datadog)
• Structured JSON logs
• Request tracing across services

Backup & Disaster Recovery

Database Backups:

• Automated daily backups (7-30 day retention)
• Point-in-time recovery capability
• Cross-region replication (optional)
• Test restores weekly

Configuration Backups:

• Git version control for IaC
• Secrets in encrypted storage
• Database connection strings in vault

Networking

VPC/Network Setup:

• Public subnets for ALB/frontend
• Private subnets for backend/database
• NAT Gateway for outbound traffic
• Security groups (least privilege)

DNS Configuration:

• Apex domain → CDN/LB
• API subdomain → Backend
• WebSocket endpoint → Backend (sticky)

Deployment Scenario Comparison

Scenario 1: Startup (0-1000 MAU)

Recommended Platform

Railway or Render

Estimated Cost

$10-20/month

Frontend: Vercel (free tier)
Backend: Railway/Render ($5-10)
Database: Railway PostgreSQL ($5-10)
Memory: SQLite (local) or Redis add-on

Scenario 2: Small Business (1000-10000 MAU)

Recommended Platform

DigitalOcean App Platform

Estimated Cost

$40-60/month

Frontend: Vercel Pro or DigitalOcean App
Backend: DigitalOcean App ($12)
Database: DigitalOcean Managed DB ($15)
Memory: DigitalOcean Redis ($15)
CDN: Included

Scenario 3: Mid-Market (10000-100000 MAU)

Recommended Platform

AWS ECS or Azure App Service

Estimated Cost

$100-200/month

Frontend: CloudFront + S3 ($10-15)
Backend: ECS Fargate 2x tasks ($50)
Database: RDS PostgreSQL ($40-60)
Memory: ElastiCache Redis ($20)
Monitoring: CloudWatch logs ($20)

Scenario 4: Enterprise (100000+ MAU)

Recommended Platform

AWS EKS or Azure AKS

Estimated Cost

$300-500/month

Frontend: Multi-CDN, Route 53 geo-routing
Backend: EKS auto-scaling ($100-150)
Database: RDS Aurora Multi-AZ ($80-100)
Memory: ElastiCache Redis Cluster ($50)
Monitoring: Datadog/NewRelic ($50-100)
DR: Multi-region hot standby ($100+)

Platform Decision Matrix

Quick Decision Tree

1. Budget?

   • < $20/month → Railway, Render
   • $20-100/month → DigitalOcean, Heroku

   • $100/month → AWS, Azure

2. Complexity tolerance?

   • Very low → Vercel + Railway
   • Low → DigitalOcean, Render
   • Medium → AWS ECS
   • High → AWS EKS, Azure AKS

3. Existing cloud provider?

   • AWS user → ECS or EKS
   • Azure user → App Service or AKS
   • None → DigitalOcean, Railway

Matrix

Factor	Railway	DigitalOcean	AWS	Azure
Price	$	$$	$$$	$$
Ease	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐	⭐⭐⭐
Scalability	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Control	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐

Universal Deployment Checklist

1. Pre-Deployment

   • Code review and testing complete
   • Environment variables documented
   • Database migration tested locally
   • Docker image builds successfully
   • All dependencies in requirements.txt
   • API keys secured (no hardcoded values)
   • Health check endpoints configured
   • Monitoring/logging configured
2. Infrastructure Setup

   • VPC/networking created
   • Database provisioned and tested
   • Redis/cache provisioned
   • Secrets manager configured
   • SSL certificates ready
   • DNS records configured
   • Load balancer configured
   • Auto-scaling policies defined
3. Deployment

   • Docker images built and pushed
   • Task definitions/manifests created
   • Environment variables set
   • Database migrations run
   • Initial deployment (canary or blue-green)
   • Smoke tests passing
   • Monitoring alerts triggered
   • Team notified
4. Post-Deployment

   • Health checks all green
   • Log aggregation working
   • Metrics dashboards visible
   • Alerts tested
   • Backup verified
   • Performance baseline established
   • Documentation updated
   • Incident response plan ready
5. Optimization

   • Monitor CPU/memory usage
   • Identify bottlenecks
   • Adjust auto-scaling thresholds
   • Optimize database queries
   • Tune cache TTLs
   • Review error logs
   • Plan capacity for next quarter

Platform-Specific Checklists

AWS ECS

• VPC created with public/private subnets
• RDS database created and tested
• ElastiCache Redis cluster created
• ECR repositories created
• IAM roles and policies configured
• ECS cluster created
• CloudWatch log groups created
• ALB created and configured
• Auto Scaling group configured
• Route 53 DNS configured
• CloudFront distribution created (optional)
• S3 buckets for logs/backups created

Azure App Service

• Resource Group created
• App Service Plan sized appropriately
• Web App created
• Azure SQL Database created
• Azure Cache for Redis created
• Container Registry created
• Container image uploaded
• Key Vault created
• Managed Identity enabled
• Application Gateway configured
• Auto-scaling rules defined
• Application Insights enabled

DigitalOcean

• Droplet created (if VPS) or App Platform app
• PostgreSQL database created
• Redis cluster created
• Container Registry created
• Image built and pushed
• Domain configured
• SSL certificate activated
• Firewall configured
• Monitoring alerts setup
• Backups configured
• App Platform connected to GitHub

Heroku/Railway

• Project created
• GitHub connected
• PostgreSQL add-on provisioned
• Redis add-on provisioned
• Environment variables set
• Procfile configured
• Buildpack selected
• Deploy triggers configured
• Custom domain configured
• SSL enabled
• Logs configured
• Auto-scaling enabled (if available)

Success Criteria

After deployment, verify:

Performance

• API p99 latency < 2s
• Frontend load < 3s
• 99.9% uptime

Reliability

• Health checks 100% green
• Automatic failover working
• Zero data loss
• Backups tested

Security

• No hardcoded secrets
• HTTPS everywhere
• Access logs present
• Encryption enabled

Next Steps

1. Choose Your Platform: Review the options and select based on your scenario
2. Follow Platform Guide: Go to AWS, Azure, or Other Platforms section
3. Test Locally First: Use Docker Compose to test the full stack
4. Use This Checklist: Mark off items as you complete them
5. Set Up Monitoring: Don’t skip logging and alerting
6. Plan Scaling: Know your growth strategy beforehand