Production Deployment Guide

Overview

This guide provides comprehensive instructions for deploying RUV-FANN (Recurrent Universal Value - Fractal Artificial Neural Network) in production environments, focusing on enterprise-grade scalability, reliability, and performance.

Deployment Architecture
Container Orchestration
Load Balancing
High Availability
Disaster Recovery
Performance Tuning
Security Considerations
Monitoring and Observability
Scaling Strategies
Troubleshooting

Deployment Architecture

Multi-Tier Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        Load Balancer Layer                       │
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐ │
│  │   ALB/NLB       │  │   CloudFlare    │  │   HAProxy       │ │
│  └─────────────────┘  └─────────────────┘  └─────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
                                 │
┌─────────────────────────────────────────────────────────────────┐
│                      Application Layer                           │
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐ │
│  │  RUV-FANN API   │  │  RUV-FANN API   │  │  RUV-FANN API   │ │
│  │   Instance 1    │  │   Instance 2    │  │   Instance N    │ │
│  └─────────────────┘  └─────────────────┘  └─────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
                                 │
┌─────────────────────────────────────────────────────────────────┐
│                      Data Layer                                  │
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐ │
│  │    PostgreSQL   │  │      Redis      │  │   S3/MinIO      │ │
│  │    Cluster      │  │     Cache       │  │  Object Store   │ │
│  └─────────────────┘  └─────────────────┘  └─────────────────┘ │
└─────────────────────────────────────────────────────────────────┘

Infrastructure Components

Core Services

API Gateway: Route requests, rate limiting, authentication
Application Servers: RUV-FANN compute instances
Database Cluster: Primary/replica PostgreSQL setup
Cache Layer: Redis cluster for session and computational caching
Object Storage: Model artifacts, training data, backups

Supporting Services

Message Queue: RabbitMQ/Apache Kafka for async processing
Monitoring Stack: Prometheus, Grafana, Jaeger
Log Aggregation: ELK Stack (Elasticsearch, Logstash, Kibana)
Secret Management: HashiCorp Vault or AWS Secrets Manager

Container Orchestration

Kubernetes Deployment

Namespace Configuration

apiVersion: v1
kind: Namespace
metadata:
  name: ruv-fann-prod
  labels:
    env: production
    app: ruv-fann

Application Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ruv-fann-api
  namespace: ruv-fann-prod
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  selector:
    matchLabels:
      app: ruv-fann-api
  template:
    metadata:
      labels:
        app: ruv-fann-api
        version: "1.0.0"
    spec:
      containers:
      - name: ruv-fann
        image: ruv-fann:latest
        ports:
        - containerPort: 8080
          name: http
        - containerPort: 9090
          name: metrics
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: ruv-fann-secrets
              key: database-url
        - name: REDIS_URL
          valueFrom:
            secretKeyRef:
              name: ruv-fann-secrets
              key: redis-url
        resources:
          requests:
            memory: "2Gi"
            cpu: "1000m"
          limits:
            memory: "4Gi"
            cpu: "2000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 5

Service Configuration

apiVersion: v1
kind: Service
metadata:
  name: ruv-fann-service
  namespace: ruv-fann-prod
spec:
  selector:
    app: ruv-fann-api
  ports:
  - name: http
    port: 80
    targetPort: 8080
  - name: metrics
    port: 9090
    targetPort: 9090
  type: ClusterIP

HorizontalPodAutoscaler

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: ruv-fann-hpa
  namespace: ruv-fann-prod
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: ruv-fann-api
  minReplicas: 3
  maxReplicas: 50
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

Docker Configuration

Multi-stage Dockerfile

# Build stage
FROM rust:1.75 AS builder
WORKDIR /app
COPY . .
RUN cargo build --release --bin ruv-fann

# Runtime stage
FROM debian:bookworm-slim
RUN apt-get update && apt-get install -y \
    ca-certificates \
    libssl3 \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app
COPY --from=builder /app/target/release/ruv-fann /usr/local/bin/
COPY --from=builder /app/config /app/config

EXPOSE 8080 9090
USER 1000:1000

CMD ["ruv-fann"]

Docker Compose for Development

version: '3.8'
services:
  ruv-fann:
    build: .
    ports:
      - "8080:8080"
      - "9090:9090"
    environment:
      - DATABASE_URL=postgresql://user:pass@postgres:5432/ruv_fann
      - REDIS_URL=redis://redis:6379
    depends_on:
      - postgres
      - redis
    
  postgres:
    image: postgres:15
    environment:
      POSTGRES_DB: ruv_fann
      POSTGRES_USER: user
      POSTGRES_PASSWORD: pass
    volumes:
      - postgres_data:/var/lib/postgresql/data
    
  redis:
    image: redis:7-alpine
    volumes:
      - redis_data:/data

volumes:
  postgres_data:
  redis_data:

Load Balancing

Application Load Balancer (AWS)

apiVersion: v1
kind: Service
metadata:
  name: ruv-fann-alb
  namespace: ruv-fann-prod
  annotations:
    service.beta.kubernetes.io/aws-load-balancer-type: "nlb"
    service.beta.kubernetes.io/aws-load-balancer-backend-protocol: "tcp"
    service.beta.kubernetes.io/aws-load-balancer-cross-zone-load-balancing-enabled: "true"
spec:
  type: LoadBalancer
  selector:
    app: ruv-fann-api
  ports:
  - port: 80
    targetPort: 8080
    protocol: TCP

HAProxy Configuration

global
    daemon
    maxconn 4096
    log stdout local0

defaults
    mode http
    timeout connect 5000ms
    timeout client 50000ms
    timeout server 50000ms
    option httplog
    option dontlognull

frontend ruv_fann_frontend
    bind *:80
    bind *:443 ssl crt /etc/ssl/certs/ruv-fann.pem
    redirect scheme https if !{ ssl_fc }
    
    # Health check endpoint
    acl health_check path_beg /health
    use_backend health_backend if health_check
    
    default_backend ruv_fann_backend

backend ruv_fann_backend
    balance roundrobin
    option httpchk GET /health
    http-check expect status 200
    
    server app1 ruv-fann-1:8080 check
    server app2 ruv-fann-2:8080 check
    server app3 ruv-fann-3:8080 check

backend health_backend
    server health 127.0.0.1:8080 check

NGINX Configuration

upstream ruv_fann_backend {
    least_conn;
    server ruv-fann-1:8080 weight=1 max_fails=3 fail_timeout=30s;
    server ruv-fann-2:8080 weight=1 max_fails=3 fail_timeout=30s;
    server ruv-fann-3:8080 weight=1 max_fails=3 fail_timeout=30s;
}

server {
    listen 80;
    listen 443 ssl http2;
    server_name ruv-fann.example.com;
    
    ssl_certificate /etc/ssl/certs/ruv-fann.crt;
    ssl_certificate_key /etc/ssl/private/ruv-fann.key;
    
    # Security headers
    add_header X-Frame-Options DENY;
    add_header X-Content-Type-Options nosniff;
    add_header X-XSS-Protection "1; mode=block";
    
    location / {
        proxy_pass http://ruv_fann_backend;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
        
        # Timeouts
        proxy_connect_timeout 30s;
        proxy_send_timeout 30s;
        proxy_read_timeout 30s;
    }
    
    location /health {
        access_log off;
        proxy_pass http://ruv_fann_backend;
    }
}

High Availability

Multi-Region Deployment

Primary Region (us-east-1)

3 Availability Zones
Application instances across all AZs
Database primary in AZ-1, replicas in AZ-2 and AZ-3
Redis cluster with sharding

Secondary Region (us-west-2)

2 Availability Zones
Read replicas for database
Standby application instances (auto-scaling disabled)
Cross-region backup replication

Database High Availability

PostgreSQL Cluster Setup

apiVersion: postgresql.cnpg.io/v1
kind: Cluster
metadata:
  name: ruv-fann-postgres
  namespace: ruv-fann-prod
spec:
  instances: 3
  
  postgresql:
    parameters:
      max_connections: "200"
      shared_buffers: "256MB"
      effective_cache_size: "1GB"
      maintenance_work_mem: "64MB"
      checkpoint_completion_target: "0.9"
      wal_buffers: "16MB"
      default_statistics_target: "100"
      random_page_cost: "1.1"
      effective_io_concurrency: "200"
      
  bootstrap:
    initdb:
      database: ruv_fann
      owner: ruv_fann_user
      secret:
        name: ruv-fann-db-credentials
        
  storage:
    size: 100Gi
    storageClass: fast-ssd
    
  monitoring:
    enabled: true
    prometheusRule:
      enabled: true

Redis Cluster Configuration

apiVersion: v1
kind: ConfigMap
metadata:
  name: redis-cluster-config
  namespace: ruv-fann-prod
data:
  redis.conf: |
    cluster-enabled yes
    cluster-config-file nodes.conf
    cluster-node-timeout 5000
    appendonly yes
    appendfsync everysec
    save 900 1
    save 300 10
    save 60 10000
---
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: redis-cluster
  namespace: ruv-fann-prod
spec:
  serviceName: redis-cluster
  replicas: 6
  selector:
    matchLabels:
      app: redis-cluster
  template:
    metadata:
      labels:
        app: redis-cluster
    spec:
      containers:
      - name: redis
        image: redis:7-alpine
        command:
        - redis-server
        - /conf/redis.conf
        ports:
        - containerPort: 6379
          name: client
        - containerPort: 16379
          name: gossip
        volumeMounts:
        - name: conf
          mountPath: /conf
        - name: data
          mountPath: /data
      volumes:
      - name: conf
        configMap:
          name: redis-cluster-config
  volumeClaimTemplates:
  - metadata:
      name: data
    spec:
      accessModes: [ "ReadWriteOnce" ]
      resources:
        requests:
          storage: 10Gi

Circuit Breaker Pattern

use circuit_breaker::CircuitBreaker;
use std::time::Duration;

pub struct ResilientDatabase {
    pool: DatabasePool,
    circuit_breaker: CircuitBreaker,
}

impl ResilientDatabase {
    pub fn new(pool: DatabasePool) -> Self {
        let circuit_breaker = CircuitBreaker::builder()
            .failure_threshold(5)
            .timeout(Duration::from_secs(10))
            .reset_timeout(Duration::from_secs(30))
            .build();
            
        Self { pool, circuit_breaker }
    }
    
    pub async fn execute_query(&self, query: &str) -> Result<QueryResult, DatabaseError> {
        self.circuit_breaker.call(|| async {
            self.pool.execute(query).await
        }).await
    }
}

Disaster Recovery

Backup Strategy

Automated Backup Schedule

apiVersion: batch/v1
kind: CronJob
metadata:
  name: database-backup
  namespace: ruv-fann-prod
spec:
  schedule: "0 2 * * *"  # Daily at 2 AM
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: postgres-backup
            image: postgres:15
            command:
            - /bin/bash
            - -c
            - |
              TIMESTAMP=$(date +%Y%m%d_%H%M%S)
              pg_dump $DATABASE_URL | gzip > /backup/ruv_fann_backup_$TIMESTAMP.sql.gz
              aws s3 cp /backup/ruv_fann_backup_$TIMESTAMP.sql.gz s3://ruv-fann-backups/
              find /backup -name "*.sql.gz" -mtime +7 -delete
            env:
            - name: DATABASE_URL
              valueFrom:
                secretKeyRef:
                  name: ruv-fann-secrets
                  key: database-url
            volumeMounts:
            - name: backup-storage
              mountPath: /backup
          volumes:
          - name: backup-storage
            emptyDir: {}
          restartPolicy: OnFailure

Cross-Region Replication

apiVersion: v1
kind: ConfigMap
metadata:
  name: backup-replication-config
data:
  backup-script.sh: |
    #!/bin/bash
    set -e
    
    # Primary region backup
    BACKUP_FILE="ruv_fann_$(date +%Y%m%d_%H%M%S).sql.gz"
    pg_dump $DATABASE_URL | gzip > /tmp/$BACKUP_FILE
    
    # Upload to primary region
    aws s3 cp /tmp/$BACKUP_FILE s3://ruv-fann-backups-primary/
    
    # Replicate to secondary region
    aws s3 cp s3://ruv-fann-backups-primary/$BACKUP_FILE s3://ruv-fann-backups-secondary/
    
    # Verify backup integrity
    aws s3api head-object --bucket ruv-fann-backups-secondary --key $BACKUP_FILE
    
    # Cleanup local file
    rm /tmp/$BACKUP_FILE
    
    echo "Backup $BACKUP_FILE replicated successfully"

Recovery Procedures

Database Recovery

#!/bin/bash
# Database recovery procedure

# 1. Stop application instances
kubectl scale deployment ruv-fann-api --replicas=0 -n ruv-fann-prod

# 2. Create recovery database
kubectl exec -it postgres-primary-0 -n ruv-fann-prod -- createdb ruv_fann_recovery

# 3. Download latest backup
LATEST_BACKUP=$(aws s3 ls s3://ruv-fann-backups/ --recursive | sort | tail -n 1 | awk '{print $4}')
aws s3 cp s3://ruv-fann-backups/$LATEST_BACKUP /tmp/

# 4. Restore database
gunzip -c /tmp/$LATEST_BACKUP | kubectl exec -i postgres-primary-0 -n ruv-fann-prod -- psql ruv_fann_recovery

# 5. Validate data integrity
kubectl exec -it postgres-primary-0 -n ruv-fann-prod -- psql ruv_fann_recovery -c "SELECT COUNT(*) FROM neural_networks;"

# 6. Switch to recovered database
kubectl patch secret ruv-fann-secrets -n ruv-fann-prod -p '{"data":{"database-url":"'$(echo "postgresql://user:pass@postgres:5432/ruv_fann_recovery" | base64)'"}}'

# 7. Restart application
kubectl scale deployment ruv-fann-api --replicas=3 -n ruv-fann-prod

Failover to Secondary Region

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: disaster-recovery-rollout
spec:
  strategy:
    canary:
      steps:
      - setWeight: 0
      - pause: {}  # Manual approval required
      - setWeight: 100
      trafficRouting:
        nginx:
          stableIngress: ruv-fann-primary
          annotationPrefix: nginx.ingress.kubernetes.io
          additionalIngressAnnotations:
            canary-by-header: X-Disaster-Recovery

Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO)

Component	RTO	RPO	Strategy
Application	5 minutes	0	Rolling deployment
Database	15 minutes	5 minutes	Point-in-time recovery
Cache	2 minutes	15 minutes	Cluster failover
File Storage	1 minute	1 hour	Cross-region replication

Performance Tuning

Application Performance

Connection Pooling

use sqlx::{Pool, Postgres};
use std::time::Duration;

pub async fn create_database_pool(database_url: &str) -> Pool<Postgres> {
    sqlx::postgres::PgPoolOptions::new()
        .max_connections(20)
        .min_connections(5)
        .acquire_timeout(Duration::from_secs(30))
        .idle_timeout(Duration::from_secs(600))
        .max_lifetime(Duration::from_secs(1800))
        .connect(database_url)
        .await
        .expect("Failed to create database pool")
}

Caching Strategy

use redis::aio::ConnectionManager;
use serde::{Deserialize, Serialize};
use std::time::Duration;

#[derive(Serialize, Deserialize)]
pub struct CacheConfig {
    pub ttl: Duration,
    pub max_size: usize,
}

pub struct CacheManager {
    redis: ConnectionManager,
    config: CacheConfig,
}

impl CacheManager {
    pub async fn get_or_compute<T, F, Fut>(&self, key: &str, compute: F) -> Result<T, CacheError>
    where
        T: Serialize + for<'de> Deserialize<'de>,
        F: FnOnce() -> Fut,
        Fut: Future<Output = Result<T, CacheError>>,
    {
        // Try cache first
        if let Ok(cached) = self.get(key).await {
            return Ok(cached);
        }
        
        // Compute and cache
        let result = compute().await?;
        self.set(key, &result, self.config.ttl).await?;
        Ok(result)
    }
}

Database Performance

Index Optimization

-- Neural network lookup indexes
CREATE INDEX CONCURRENTLY idx_neural_networks_user_id ON neural_networks(user_id);
CREATE INDEX CONCURRENTLY idx_neural_networks_created_at ON neural_networks(created_at DESC);
CREATE INDEX CONCURRENTLY idx_neural_networks_status ON neural_networks(status) WHERE status IN ('training', 'ready');

-- Training data indexes
CREATE INDEX CONCURRENTLY idx_training_data_network_id ON training_data(network_id);
CREATE INDEX CONCURRENTLY idx_training_data_timestamp ON training_data(timestamp DESC);

-- Composite indexes for common queries
CREATE INDEX CONCURRENTLY idx_nn_user_status ON neural_networks(user_id, status) INCLUDE (name, created_at);

Query Optimization

-- Optimized query with proper joins and filtering
EXPLAIN (ANALYZE, BUFFERS) 
SELECT 
    nn.id,
    nn.name,
    nn.status,
    COUNT(td.id) as training_samples
FROM neural_networks nn
LEFT JOIN training_data td ON nn.id = td.network_id
WHERE nn.user_id = $1 
  AND nn.status = 'ready'
  AND nn.created_at >= $2
GROUP BY nn.id, nn.name, nn.status
ORDER BY nn.created_at DESC
LIMIT 10;

Connection Configuration

# PostgreSQL configuration
[database]
max_connections = 200
shared_buffers = "512MB"
effective_cache_size = "2GB"
maintenance_work_mem = "128MB"
checkpoint_completion_target = 0.9
wal_buffers = "32MB"
default_statistics_target = 100
random_page_cost = 1.1
effective_io_concurrency = 300
work_mem = "8MB"
huge_pages = "try"

Memory Management

Rust Memory Optimization

use std::sync::Arc;
use tokio::sync::RwLock;
use lru::LruCache;

pub struct OptimizedNeuralNetwork {
    // Use Arc for shared ownership
    weights: Arc<Vec<f32>>,
    
    // Use RwLock for concurrent read access
    cache: Arc<RwLock<LruCache<String, Vec<f32>>>>,
    
    // Pool connections
    pool: Arc<DatabasePool>,
}

impl OptimizedNeuralNetwork {
    pub fn new(capacity: usize, pool: Arc<DatabasePool>) -> Self {
        Self {
            weights: Arc::new(Vec::with_capacity(capacity)),
            cache: Arc::new(RwLock::new(LruCache::new(1000))),
            pool,
        }
    }
    
    pub async fn predict(&self, input: &[f32]) -> Result<Vec<f32>, PredictionError> {
        let cache_key = format!("prediction_{}", hash_input(input));
        
        // Check cache first
        {
            let cache = self.cache.read().await;
            if let Some(result) = cache.peek(&cache_key) {
                return Ok(result.clone());
            }
        }
        
        // Compute prediction
        let result = self.compute_prediction(input).await?;
        
        // Update cache
        {
            let mut cache = self.cache.write().await;
            cache.put(cache_key, result.clone());
        }
        
        Ok(result)
    }
}

CPU Optimization

Multi-threading Configuration

use rayon::prelude::*;
use std::sync::Arc;

pub struct ParallelProcessor {
    thread_pool: rayon::ThreadPool,
}

impl ParallelProcessor {
    pub fn new(threads: usize) -> Self {
        let thread_pool = rayon::ThreadPoolBuilder::new()
            .num_threads(threads)
            .thread_name(|i| format!("ruv-fann-worker-{}", i))
            .build()
            .expect("Failed to create thread pool");
            
        Self { thread_pool }
    }
    
    pub fn process_batch(&self, data: Vec<InputData>) -> Vec<OutputData> {
        self.thread_pool.install(|| {
            data.par_iter()
                .map(|input| self.process_single(input))
                .collect()
        })
    }
}

Security Considerations

Network Security

Network Policies

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: ruv-fann-network-policy
  namespace: ruv-fann-prod
spec:
  podSelector:
    matchLabels:
      app: ruv-fann-api
  policyTypes:
  - Ingress
  - Egress
  ingress:
  - from:
    - namespaceSelector:
        matchLabels:
          name: ingress-nginx
    ports:
    - protocol: TCP
      port: 8080
  egress:
  - to:
    - namespaceSelector:
        matchLabels:
          name: postgres
    ports:
    - protocol: TCP
      port: 5432
  - to:
    - namespaceSelector:
        matchLabels:
          name: redis
    ports:
    - protocol: TCP
      port: 6379

Secrets Management

Sealed Secrets

apiVersion: bitnami.com/v1alpha1
kind: SealedSecret
metadata:
  name: ruv-fann-secrets
  namespace: ruv-fann-prod
spec:
  encryptedData:
    database-url: AgBy3i4OJSWK+PiTySYZZA9rO43cGDEQAx...
    redis-url: AgBy3i4OJSWK+PiTySYZZA9rO43cGDEQAx...
    jwt-secret: AgBy3i4OJSWK+PiTySYZZA9rO43cGDEQAx...
    api-key: AgBy3i4OJSWK+PiTySYZZA9rO43cGDEQAx...
  template:
    metadata:
      name: ruv-fann-secrets
      namespace: ruv-fann-prod

Security Scanning

Container Security

apiVersion: v1
kind: Pod
metadata:
  name: security-scan
spec:
  containers:
  - name: trivy-scanner
    image: aquasec/trivy:latest
    command:
    - trivy
    - image
    - --exit-code
    - "1"
    - --severity
    - HIGH,CRITICAL
    - ruv-fann:latest

Monitoring and Observability

Metrics Collection

Prometheus Configuration

apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
      evaluation_interval: 15s
    
    rule_files:
      - "ruv_fann_rules.yml"
    
    scrape_configs:
      - job_name: 'ruv-fann'
        static_configs:
          - targets: ['ruv-fann-service:9090']
        metrics_path: /metrics
        scrape_interval: 10s
        
      - job_name: 'postgres-exporter'
        static_configs:
          - targets: ['postgres-exporter:9187']
          
      - job_name: 'redis-exporter'
        static_configs:
          - targets: ['redis-exporter:9121']

Custom Metrics

use prometheus::{Counter, Histogram, IntGauge, Registry};
use std::sync::Arc;

pub struct Metrics {
    pub requests_total: Counter,
    pub request_duration: Histogram,
    pub active_connections: IntGauge,
    pub neural_networks_total: IntGauge,
}

impl Metrics {
    pub fn new(registry: &Registry) -> Arc<Self> {
        let metrics = Arc::new(Self {
            requests_total: Counter::new(
                "ruv_fann_requests_total",
                "Total number of requests"
            ).unwrap(),
            
            request_duration: Histogram::with_opts(
                prometheus::HistogramOpts::new(
                    "ruv_fann_request_duration_seconds",
                    "Request duration in seconds"
                ).buckets(vec![0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0])
            ).unwrap(),
            
            active_connections: IntGauge::new(
                "ruv_fann_active_connections",
                "Number of active database connections"
            ).unwrap(),
            
            neural_networks_total: IntGauge::new(
                "ruv_fann_neural_networks_total",
                "Total number of neural networks"
            ).unwrap(),
        });
        
        registry.register(Box::new(metrics.requests_total.clone())).unwrap();
        registry.register(Box::new(metrics.request_duration.clone())).unwrap();
        registry.register(Box::new(metrics.active_connections.clone())).unwrap();
        registry.register(Box::new(metrics.neural_networks_total.clone())).unwrap();
        
        metrics
    }
}

Logging Strategy

Structured Logging

use tracing::{info, warn, error, instrument};
use tracing_subscriber::{layer::SubscriberExt, util::SubscriberInitExt};

pub fn init_logging() {
    tracing_subscriber::registry()
        .with(tracing_subscriber::EnvFilter::new(
            std::env::var("RUST_LOG")
                .unwrap_or_else(|_| "ruv_fann=info".into()),
        ))
        .with(tracing_subscriber::fmt::layer().json())
        .init();
}

#[instrument(fields(user_id = %user_id, model_id = %model_id))]
pub async fn train_model(user_id: u64, model_id: u64) -> Result<(), TrainingError> {
    info!("Starting model training");
    
    match perform_training(user_id, model_id).await {
        Ok(()) => {
            info!("Model training completed successfully");
            Ok(())
        }
        Err(e) => {
            error!("Model training failed: {}", e);
            Err(e)
        }
    }
}

Alerting Rules

Prometheus Alerting Rules

groups:
- name: ruv-fann.rules
  rules:
  - alert: HighErrorRate
    expr: (rate(ruv_fann_requests_total{status=~"5.."}[5m]) / rate(ruv_fann_requests_total[5m])) > 0.1
    for: 5m
    labels:
      severity: critical
    annotations:
      summary: "High error rate detected"
      description: "Error rate is {{ $value }} for the last 5 minutes"
      
  - alert: HighLatency
    expr: histogram_quantile(0.95, rate(ruv_fann_request_duration_seconds_bucket[5m])) > 1
    for: 5m
    labels:
      severity: warning
    annotations:
      summary: "High latency detected"
      description: "95th percentile latency is {{ $value }}s"
      
  - alert: DatabaseConnectionsHigh
    expr: ruv_fann_active_connections > 80
    for: 2m
    labels:
      severity: warning
    annotations:
      summary: "High number of database connections"
      description: "{{ $value }} active connections"

Scaling Strategies

Horizontal Scaling

Auto-scaling Configuration

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: ruv-fann-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: ruv-fann-api
  minReplicas: 3
  maxReplicas: 100
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
  - type: Pods
    pods:
      metric:
        name: requests_per_second
      target:
        type: AverageValue
        averageValue: "1000"
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Percent
        value: 10
        periodSeconds: 60
    scaleUp:
      stabilizationWindowSeconds: 60
      policies:
      - type: Percent
        value: 50
        periodSeconds: 60

Vertical Scaling

VPA Configuration

apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: ruv-fann-vpa
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: ruv-fann-api
  updatePolicy:
    updateMode: "Auto"
  resourcePolicy:
    containerPolicies:
    - containerName: ruv-fann
      maxAllowed:
        cpu: 4
        memory: 8Gi
      minAllowed:
        cpu: 100m
        memory: 512Mi
      controlledResources: ["cpu", "memory"]

Troubleshooting

Common Issues and Solutions

Database Connection Issues

# Check database connectivity
kubectl exec -it ruv-fann-api-xxx -- sh
nc -zv postgres-service 5432

# Check connection pool status
curl http://localhost:8080/debug/pool-status

# Reset connection pool
curl -X POST http://localhost:8080/debug/reset-pool

Memory Leaks

# Check memory usage
kubectl top pods -n ruv-fann-prod

# Generate heap dump
curl -X POST http://localhost:8080/debug/heap-dump > heap.dump

# Analyze with profiling tools
cargo flamegraph --bin ruv-fann

Performance Issues

# Check CPU usage
kubectl top pods -n ruv-fann-prod --sort-by=cpu

# Check database slow queries
kubectl exec -it postgres-primary-0 -- psql -c "
SELECT query, mean_time, calls 
FROM pg_stat_statements 
ORDER BY mean_time DESC 
LIMIT 10;"

# Check Redis performance
kubectl exec -it redis-0 -- redis-cli --latency-history

Health Checks

Comprehensive Health Check

use serde_json::json;
use axum::{Json, http::StatusCode};

pub async fn health_check(
    State(app_state): State<AppState>,
) -> Result<Json<Value>, (StatusCode, Json<Value>)> {
    let mut health_status = json!({
        "status": "healthy",
        "timestamp": chrono::Utc::now(),
        "version": env!("CARGO_PKG_VERSION")
    });
    
    // Check database
    match app_state.db.execute("SELECT 1").await {
        Ok(_) => {
            health_status["database"] = json!("healthy");
        }
        Err(e) => {
            health_status["database"] = json!(format!("unhealthy: {}", e));
            health_status["status"] = json!("unhealthy");
        }
    }
    
    // Check Redis
    match app_state.redis.ping().await {
        Ok(_) => {
            health_status["redis"] = json!("healthy");
        }
        Err(e) => {
            health_status["redis"] = json!(format!("unhealthy: {}", e));
            health_status["status"] = json!("unhealthy");
        }
    }
    
    // Check external services
    health_status["external_services"] = check_external_services().await;
    
    if health_status["status"] == "healthy" {
        Ok(Json(health_status))
    } else {
        Err((StatusCode::SERVICE_UNAVAILABLE, Json(health_status)))
    }
}

Emergency Procedures

Circuit Breaker Manual Override

# Disable specific service
kubectl patch deployment ruv-fann-api -p '{"spec":{"template":{"metadata":{"annotations":{"circuit-breaker/database":"open"}}}}}'

# Enable maintenance mode
kubectl patch configmap ruv-fann-config -p '{"data":{"maintenance_mode":"true"}}'

# Scale down to minimum
kubectl scale deployment ruv-fann-api --replicas=1

Database Emergency Procedures

# Switch to read-only mode
kubectl exec -it postgres-primary-0 -- psql -c "ALTER SYSTEM SET default_transaction_read_only = on;"
kubectl exec -it postgres-primary-0 -- psql -c "SELECT pg_reload_conf();"

# Promote read replica
kubectl patch postgresql postgres-cluster --type merge -p '{"spec":{"instances":1}}'

# Emergency backup
kubectl exec postgres-primary-0 -- pg_dump ruv_fann | gzip > emergency_backup_$(date +%Y%m%d_%H%M%S).sql.gz

This comprehensive production deployment guide provides enterprise-grade strategies for deploying, scaling, and maintaining RUV-FANN in production environments. Regular review and testing of these procedures is essential for maintaining system reliability and performance.