This guide covers deploying the Real-Time Data Processing System on various platforms, from local development to production environments.

• Deno: Version 1.40.0 or higher
• Git: For version control
• Node.js: Optional, for package management tools

git clone https://github.com/yourusername/real-time-data-processing-system.git
cd real-time-data-processing-system

# macOS/Linux
curl -fsSL https://deno.land/install.sh | sh

# Windows (PowerShell)
irm https://deno.land/install.ps1 | iex

# Development mode with auto-reload
deno run --allow-all --watch main.tsx

# Production mode
deno run --allow-all main.tsx

Open your browser and navigate to:

http://localhost:8000

Visit Val Town and create an account.

1. Click "New Val"
2. Choose "HTTP" trigger type
3. Copy the contents of main.tsx into the editor

Create the following file structure in Val Town:

/main.tsx                    # Entry point
/backend/index.ts           # Main server
/backend/ingestion/eventIngestion.ts
/backend/processing/streamProcessor.ts
/backend/storage/database.ts
/backend/monitoring/metricsCollector.ts
/backend/monitoring/healthChecker.ts
/frontend/index.html
/frontend/index.tsx
/shared/types.ts
/shared/utils.ts

Click "Save" to deploy your val. Val Town will provide a public URL.

FROM denoland/deno:1.40.0

WORKDIR /app

# Copy dependency files
COPY . .

# Cache dependencies
RUN deno cache main.tsx

# Expose port
EXPOSE 8000

# Run the application
CMD ["run", "--allow-all", "main.tsx"]

# Build the image
docker build -t real-time-processor .

# Run the container
docker run -p 8000:8000 real-time-processor

Create docker-compose.yml:

version: '3.8'

services:
  app:
    build: .
    ports:
      - "8000:8000"
    environment:
      - NODE_ENV=production
    volumes:
      - ./data:/app/data
    restart: unless-stopped

  nginx:
    image: nginx:alpine
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf
      - ./ssl:/etc/nginx/ssl
    depends_on:
      - app
    restart: unless-stopped

1. Install AWS CLI and configure credentials
2. Create deployment package:

   zip -r deployment.zip . -x "*.git*" "node_modules/*"

3. Deploy using AWS CLI:

   aws lambda create-function \
     --function-name real-time-processor \
     --runtime provided.al2 \
     --role arn:aws:iam::account:role/lambda-role \
     --handler main.handler \
     --zip-file fileb://deployment.zip

1. Push Docker image to ECR
2. Create ECS task definition
3. Deploy to ECS cluster

# Build and push to Container Registry
gcloud builds submit --tag gcr.io/PROJECT_ID/real-time-processor

# Deploy to Cloud Run
gcloud run deploy real-time-processor \
  --image gcr.io/PROJECT_ID/real-time-processor \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated

# Create resource group
az group create --name real-time-processor --location eastus

# Deploy container
az container create \
  --resource-group real-time-processor \
  --name real-time-processor \
  --image your-registry/real-time-processor \
  --ports 8000 \
  --dns-name-label real-time-processor

deployment.yaml:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: real-time-processor
spec:
  replicas: 3
  selector:
    matchLabels:
      app: real-time-processor
  template:
    metadata:
      labels:
        app: real-time-processor
    spec:
      containers:
      - name: app
        image: real-time-processor:latest
        ports:
        - containerPort: 8000
        env:
        - name: NODE_ENV
          value: "production"
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"

service.yaml:

apiVersion: v1
kind: Service
metadata:
  name: real-time-processor-service
spec:
  selector:
    app: real-time-processor
  ports:
  - port: 80
    targetPort: 8000
  type: LoadBalancer

kubectl apply -f deployment.yaml
kubectl apply -f service.yaml

# Server Configuration
PORT=8000
NODE_ENV=production

# Database Configuration
DATABASE_URL=sqlite:./data/events.db
DATABASE_POOL_SIZE=10

# Monitoring Configuration
METRICS_RETENTION_HOURS=24
HEALTH_CHECK_INTERVAL=30

# Security Configuration
RATE_LIMIT_REQUESTS_PER_SECOND=10000
CORS_ORIGIN=*

# Performance Configuration
PROCESSING_INSTANCES=5
BATCH_SIZE=100
WINDOW_SIZE_MS=10000

Create config.json:

{
  "server": {
    "port": 8000,
    "host": "0.0.0.0"
  },
  "database": {
    "type": "sqlite",
    "path": "./data/events.db",
    "poolSize": 10
  },
  "processing": {
    "instances": 5,
    "windowSizeMs": 10000,
    "batchSize": 100
  },
  "monitoring": {
    "metricsRetentionHours": 24,
    "healthCheckIntervalSeconds": 30
  }
}

-- Create indexes for better query performance
CREATE INDEX idx_events_timestamp ON events(timestamp DESC);
CREATE INDEX idx_events_type_timestamp ON events(type, timestamp DESC);
CREATE INDEX idx_metrics_timestamp ON metrics(timestamp DESC);

-- Configure SQLite for better performance
PRAGMA journal_mode = WAL;
PRAGMA synchronous = NORMAL;
PRAGMA cache_size = 10000;
PRAGMA temp_store = memory;

# Increase file descriptor limits
ulimit -n 65536

# Optimize network settings
echo 'net.core.somaxconn = 65536' >> /etc/sysctl.conf
echo 'net.ipv4.tcp_max_syn_backlog = 65536' >> /etc/sysctl.conf

# Apply changes
sysctl -p

The system provides several health check endpoints:

# Basic health check
curl http://localhost:8000/api/health

# Detailed metrics
curl http://localhost:8000/api/metrics

# System status
curl http://localhost:8000/api/system/status

// Configure structured logging
const logger = {
  level: process.env.LOG_LEVEL || 'info',
  format: 'json',
  transports: [
    { type: 'console' },
    { type: 'file', filename: 'app.log' }
  ]
};

Add Prometheus endpoint:

app.get('/metrics', (c) => {
  // Return Prometheus-formatted metrics
  return c.text(prometheusMetrics);
});

Import the provided Grafana dashboard configuration from monitoring/grafana-dashboard.json.

server {
    listen 443 ssl http2;
    server_name your-domain.com;
    
    ssl_certificate /path/to/certificate.crt;
    ssl_certificate_key /path/to/private.key;
    
    ssl_protocols TLSv1.2 TLSv1.3;
    ssl_ciphers ECDHE-RSA-AES256-GCM-SHA512:DHE-RSA-AES256-GCM-SHA512;
    
    location / {
        proxy_pass http://localhost:8000;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
    }
}

# Allow only necessary ports
ufw allow 22/tcp   # SSH
ufw allow 80/tcp   # HTTP
ufw allow 443/tcp  # HTTPS
ufw enable

# Create backup script
#!/bin/bash
DATE=$(date +%Y%m%d_%H%M%S)
sqlite3 ./data/events.db ".backup ./backups/events_$DATE.db"

# Schedule with cron
0 2 * * * /path/to/backup-script.sh

1. Regular backups: Automated daily backups
2. Multi-region deployment: Deploy across multiple regions
3. Data replication: Real-time data replication
4. Recovery procedures: Documented recovery steps

1. High Memory Usage

   • Check for memory leaks in processing instances
   • Adjust batch sizes and window configurations
   • Monitor garbage collection

2. High Latency

   • Check database query performance
   • Monitor network connectivity
   • Review processing instance load

3. Connection Issues

   • Verify WebSocket configuration
   • Check firewall settings
   • Monitor connection pool usage

# Run with debug logging
DEBUG=* deno run --allow-all main.tsx

# Enable performance profiling
deno run --allow-all --inspect main.tsx

# Monitor real-time logs
tail -f app.log | grep ERROR

# Analyze performance metrics
grep "processing_time" app.log | awk '{sum+=$3; count++} END {print "Average:", sum/count}'