# AI-generated smart backup strategy example
backup_strategy:
  frequency: auto  # Automatically adjusted based on data change frequency
  retention:
    daily: 7       # Keep last 7 days
    weekly: 4      # Keep last 4 weeks
    monthly: 12    # Keep last 12 months
  priority:
    database: high      # High-frequency backup for databases
    static_assets: low  # Low-frequency backup for static assets
  encryption: true       # Automatic encryption
  compression: lz4       # Intelligent compression algorithm selection

# Install backup tools
apt update && apt install -y restic borgbackup encryption-tools

# Install AI components
pip install pandas numpy scikit-learn

# Initialize local backup repository
borg init --encryption=repokey /backup/borg-local

# Configure cloud storage (S3 compatible example)
export AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key
export BORG_REMOTE_PATH=/backup/s3-bucket

# Create encrypted key storage
borg key export /backup/borg-local /root/borg-key-$(date +%Y%m%d).key

#!/usr/bin/env python3
"""AI Smart Backup Script"""
import os
import json
import subprocess
import pandas as pd
from datetime import datetime, timedelta
from sklearn.linear_model import LinearRegression

class SmartBackup:
    def __init__(self, config_path='backup_config.json'):
        self.config = self.load_config(config_path)
        self.history = self.load_history()
        self.model = self.train_model()
    
    def load_config(self, path):
        with open(path) as f:
            return json.load(f)
    
    def load_history(self):
        """Load backup historical data"""
        try:
            df = pd.read_csv('/var/log/backup_history.csv')
            return df
        except:
            return pd.DataFrame()
    
    def train_model(self):
        """Train backup frequency prediction model"""
        if len(self.history) < 10:
            return None
        
        df = self.history.copy()
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        df = df.sort_values('timestamp')
        
        # Simple linear regression to predict next backup time
        if len(df) >= 7:
            X = df['backup_size'].values.reshape(-1, 1)
            y = df['interval_hours'].values
            
            model = LinearRegression()
            model.fit(X, y)
            return model
        return None
    
    def predict_backup_frequency(self):
        """Predict optimal backup frequency"""
        latest_size = self.history['backup_size'].iloc[-1] if len(self.history) > 0 else 0
        
        if self.model:
            predicted_interval = self.model.predict([[latest_size]])[0]
        else:
            # Default strategy
            predicted_interval = 24
        
        # Adjust based on business importance
        critical = self.config.get('critical_data', [])
        if critical:
            predicted_interval = min(predicted_interval, 6)  # Critical data at least every 6 hours
        
        return max(1, int(predicted_interval))
    
    def execute_backup(self, source, target):
        """Execute backup"""
        cmd = f"borg create {target}::{datetime.now().strftime('%Y%m%d-%H%M%S')} {source}"
        result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
        
        # Record history
        backup_size = len(result.stdout)
        self.save_history(backup_size)
        
        return result.returncode == 0
    
    def save_history(self, size):
        """Save backup history"""
        df = pd.DataFrame({
            'timestamp': [datetime.now().isoformat()],
            'backup_size': [size],
            'status': ['success']
        })
        
        if os.path.exists('/var/log/backup_history.csv'):
            existing = pd.read_csv('/var/log/backup_history.csv')
            df = pd.concat([existing, df], ignore_index=True)
        
        df.to_csv('/var/log/backup_history.csv', index=False)
    
    def validate_backup(self, target):
        """Validate backup integrity"""
        cmd = f"borg check {target}"
        result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
        return result.returncode == 0

if __name__ == '__main__':
    backup = SmartBackup()
    frequency = backup.predict_backup_frequency()
    print(f"Next backup time: {frequency} hours later")
    
    # Execute backup
    if backup.execute_backup('/data', '/backup/borg-local'):
        print("Backup successful")
    
    # Validate backup
    if backup.validate_backup('/backup/borg-local'):
        print("Backup validation passed")

# Edit crontab
crontab -e

# Add smart backup task (runs daily, adjusted by AI prediction)
0 2 * * * /usr/local/bin/ai-backup.py --check
*/30 * * * * /usr/local/bin/ai-backup.py --execute

# Automatic recovery test weekly
0 3 * * 0 /usr/local/bin/ai-backup.py --test-restore

# Recovery test script
#!/bin/bash
set -e
echo "Starting recovery test..."
borg extract /backup/borg-local::latest --dry-run
echo "Recovery test complete, backup is usable"

# One-click recovery script
#!/bin/bash
# disaster_recovery.sh

BACKUP_REPO="/backup/borg-local"
RESTORE_PATH="/data/restored"
LOG_FILE="/var/log/disaster_recovery.log"

echo "[$(date)] Starting disaster recovery..." | tee -a $LOG_FILE

# Step 1: List available backups
echo "Available backup list:" | tee -a $LOG_FILE
borg list $BACKUP_REPO | tee -a $LOG_FILE

# Step 2: Select recovery point (most recent healthy backup)
LATEST=$(borg list $BACKUP_REPO 2>/dev/null | grep -v "^Archive" | head -1 | awk '{print $1}')
echo "Selected recovery point: $LATEST" | tee -a $LOG_FILE

# Step 3: Validate backup integrity
echo "Validating backup integrity..." | tee -a $LOG_FILE
borg check $BACKUP_REPO
if [ $? -ne 0 ]; then
    echo "Backup validation failed, trying next" | tee -a $LOG_FILE
    LATEST=$(borg list $BACKUP_REPO | grep -v "^Archive" | head -2 | tail -1 | awk '{print $1}')
fi

# Step 4: Execute recovery
echo "Starting data recovery to $RESTORE_PATH..." | tee -a $LOG_FILE
mkdir -p $RESTORE_PATH
borg extract $BACKUP_REPO::$LATEST --output-dir $RESTORE_PATH

# Step 5: Verify recovery
echo "Verifying recovered data integrity..." | tee -a $LOG_FILE
if [ -f "$RESTORE_PATH/database.sql" ]; then
    echo "Database file recovered successfully" | tee -a $LOG_FILE
fi

echo "[$(date)] Disaster recovery complete" | tee -a $LOG_FILE

# Multi-site backup configuration
sites:
  primary:
    location: us-east-1
    backup_target: s3://primary-backup
  secondary:
    location: eu-west-1
    backup_target: s3://secondary-backup
  failover:
    enabled: true
    auto_switch: true
    health_check:
      interval: 30  # Check every 30 seconds
      threshold: 3  # Trigger switch after 3 consecutive failures

import json
from datetime import datetime

def generate_backup_report():
    """Generate backup health report"""
    report = {
        'timestamp': datetime.now().isoformat(),
        'metrics': {
            'last_backup': '2026-08-01T02:00:00Z',
            'backup_duration': '15m 32s',
            'data_size': '2.3 GB',
            'compression_ratio': '3.2:1',
            'success_rate': '99.8%',
            'next_scheduled': '2026-08-02T02:00:00Z'
        },
        'alerts': [],
        'recommendations': [
            'Consider adding off-site backup',
            '100% backup success rate in last 7 days',
            'Storage costs can be optimized'
        ]
    }
    return report