Introduction
The ai_disaster_recovery.py module ensures the resilience and continuity of the G.O.D Framework in the
event of system failures, unexpected disasters, or data breaches. This script focuses on implementing fail-safes
to prevent data loss, operational interruptions, and complete system failures when adverse events occur.
With robust mechanisms for real-time backups, recovery orchestration, and failure detection, this module integrates tightly with other G.O.D. systems, ensuring sustained performance and availability.
Purpose
- Backup Creation: Automates the process of creating real-time and scheduled snapshots of critical data.
- System Recovery: Reconstructs and restores damaged components using the latest backup or replication.
- Resilience Testing: Performs simulated failure scenarios to validate disaster recovery plans.
- Monitoring and Alerting: Tracks system health and alerts administrators about potential risks.
Key Features
- Redundancy Systems: Maintains multiple data copies across distributed storage nodes.
- Automatic Failover: Switches to backup systems or alternate regions during service interruptions.
- Data Encryption: Safeguards backups with strong encryption mechanisms, ensuring data privacy.
- Error Resilience: Integrates with
ai_error_tracker.pyto identify and fix recurrent failure points. - Test and Validation Framework: Validates recovery mechanisms using sandbox environments.
Logic and Implementation
The ai_disaster_recovery.py script coordinates disaster recovery procedures through real-time system monitoring,
data snapshot management, and failover mechanisms. It ensures the system's state is continuously preserved, and recovery procedures
are followed based on configuration rules and event triggers.
An outline of its implementation is presented below:
import os
import time
import shutil
import logging
class DisasterRecovery:
"""
Handles disaster recovery through backups, real-time monitoring,
and failover mechanisms.
"""
def __init__(self, backup_dir="/backups", retention_policy=5):
"""
Initialize the Disaster Recovery Manager.
:param backup_dir: Directory where backups are stored.
:param retention_policy: Number of recent backups to retain.
"""
self.backup_dir = backup_dir
self.retention_policy = retention_policy
def create_backup(self, source_dir):
"""
Creates a backup of the specified source directory.
:param source_dir: Directory to snapshot.
"""
timestamp = time.strftime("%Y%m%d_%H%M%S")
backup_path = os.path.join(self.backup_dir, f"backup_{timestamp}")
try:
shutil.copytree(source_dir, backup_path)
logging.info(f"Backup created at {backup_path}")
self._enforce_retention_policy()
except Exception as e:
logging.error(f"Failed to create backup: {e}")
def restore_backup(self, backup_name, target_dir):
"""
Restores the specified backup to the target directory.
:param backup_name: Name of the backup to restore.
:param target_dir: Directory to restore to.
"""
backup_path = os.path.join(self.backup_dir, backup_name)
if not os.path.exists(backup_path):
logging.error(f"Backup {backup_name} not found.")
return False
try:
shutil.copytree(backup_path, target_dir, dirs_exist_ok=True)
logging.info(f"Backup {backup_name} restored to {target_dir}")
return True
except Exception as e:
logging.error(f"Failed to restore backup: {e}")
return False
def _enforce_retention_policy(self):
"""
Removes oldest backups to ensure only the most recent ones are retained.
"""
backups = sorted(os.listdir(self.backup_dir))
while len(backups) > self.retention_policy:
oldest_backup = backups.pop(0)
shutil.rmtree(os.path.join(self.backup_dir, oldest_backup))
logging.info(f"Removed old backup: {oldest_backup}")
if __name__ == "__main__":
recovery = DisasterRecovery(retention_policy=3)
recovery.create_backup("./data")
recovery.restore_backup("backup_20231101_120000", "./restored_data")
Dependencies
The module uses the following dependencies:
os: For file and directory handling.shutil: To copy, move, delete, and archive files and directories.logging: For event logging and error reporting.time: For generating timestamps for backup naming.
Usage
The ai_disaster_recovery.py script can be used as follows:
- Set up a directory path for backups and define a retention policy.
- Invoke the
create_backupmethod, providing the source directory for snapshots. - Use the
restore_backupmethod to restore from a specific snapshot. - Ensure the script runs periodically or under specific triggers.
recovery = DisasterRecovery(backup_dir="/ai_system_backups", retention_policy=7)
recovery.create_backup("/ai_system_data")
recovery.restore_backup("backup_20231101_123000", "/ai_recovery_data")
System Integration
- Backup Manager: Coordinates with
backup_manager.pyfor large-scale backup strategies. - Monitoring: Interfaces with
ai_monitoring.pyto track system health and predict failures. - Error Handling: Collaborates with
ai_error_tracker.pyto identify and fix vulnerabilities.
Future Enhancements
- Cloud Integration: Enable backup synchronization with cloud storage systems for distributed redundancy.
- Incremental Backups: Introduce differential snapshots to improve backup performance and reduce storage use.
- Disaster Simulations: Add automated failure drills to test recovery systems periodically.