""" QCrypt RNG - Monitoring and Analytics Comprehensive monitoring, metrics collection, and analytics """ import time import threading from datetime import datetime, timedelta from typing import Dict, List, Optional, Any from collections import defaultdict, deque import json import sqlite3 from contextlib import contextmanager from dataclasses import dataclass import statistics from app.config import settings @dataclass class MetricPoint: """Data class for metric points""" timestamp: datetime metric_name: str value: float labels: Dict[str, str] class MetricsCollector: """ Collects and stores application metrics """ def __init__(self): self.metrics_db_path = settings.usage_database_url.replace("sqlite:///", "") self._init_db() self._local_storage = threading.local() # In-memory metrics for real-time access self._realtime_metrics = defaultdict(list) self._max_points = 1000 # Max points to keep in memory def _init_db(self): """Initialize the metrics database""" with self._get_db_connection() as conn: conn.execute(''' CREATE TABLE IF NOT EXISTS metrics ( id INTEGER PRIMARY KEY AUTOINCREMENT, timestamp DATETIME DEFAULT CURRENT_TIMESTAMP, metric_name TEXT NOT NULL, value REAL NOT NULL, labels TEXT -- JSON string of labels ) ''') # Create indexes for faster queries conn.execute('CREATE INDEX IF NOT EXISTS idx_metric_name ON metrics(metric_name)') conn.execute('CREATE INDEX IF NOT EXISTS idx_timestamp ON metrics(timestamp)') conn.commit() @contextmanager def _get_db_connection(self): """Get a thread-safe database connection""" conn = sqlite3.connect(self.metrics_db_path, check_same_thread=False) try: yield conn finally: conn.close() def record_metric(self, metric_name: str, value: float, labels: Optional[Dict[str, str]] = None): """Record a metric point""" # Store in database with self._get_db_connection() as conn: conn.execute( "INSERT INTO metrics (metric_name, value, labels) VALUES (?, ?, ?)", (metric_name, value, json.dumps(labels) if labels else None) ) conn.commit() # Store in memory for real-time access metric_point = MetricPoint( timestamp=datetime.utcnow(), metric_name=metric_name, value=value, labels=labels or {} ) self._realtime_metrics[metric_name].append(metric_point) # Trim if too many points if len(self._realtime_metrics[metric_name]) > self._max_points: self._realtime_metrics[metric_name] = self._realtime_metrics[metric_name][-self._max_points:] def get_recent_metrics(self, metric_name: str, minutes: int = 60) -> List[MetricPoint]: """Get recent metrics for a specific metric name""" cutoff_time = datetime.utcnow() - timedelta(minutes=minutes) # First check in-memory cache recent_points = [ point for point in self._realtime_metrics[metric_name] if point.timestamp >= cutoff_time ] # If we don't have enough points in memory, query database if len(recent_points) < self._max_points: with self._get_db_connection() as conn: cursor = conn.execute( ''' SELECT timestamp, metric_name, value, labels FROM metrics WHERE metric_name = ? AND timestamp >= ? ORDER BY timestamp DESC LIMIT ? ''', (metric_name, cutoff_time.isoformat(), self._max_points) ) db_points = [] for row in cursor.fetchall(): timestamp = datetime.fromisoformat(row[0]) labels = json.loads(row[3]) if row[3] else {} db_points.append(MetricPoint( timestamp=timestamp, metric_name=row[1], value=row[2], labels=labels )) # Combine and sort all_points = recent_points + db_points all_points.sort(key=lambda x: x.timestamp, reverse=True) return all_points[:self._max_points] return recent_points def get_aggregated_metrics(self, metric_name: str, window_minutes: int = 60) -> Dict[str, float]: """Get aggregated metrics for a specific metric name""" recent_points = self.get_recent_metrics(metric_name, window_minutes) if not recent_points: return {} values = [point.value for point in recent_points] return { "count": len(values), "sum": sum(values), "avg": statistics.mean(values), "min": min(values), "max": max(values), "median": statistics.median(values) if values else 0, "std_dev": statistics.stdev(values) if len(values) > 1 else 0 } class AnalyticsService: """ Provides analytics and insights based on collected metrics """ def __init__(self): self.collector = MetricsCollector() def track_api_call(self, endpoint: str, method: str, response_time: float, success: bool): """Track an API call""" # Record response time self.collector.record_metric( "api_response_time", response_time, {"endpoint": endpoint, "method": method, "success": str(success)} ) # Record success/failure count status = "success" if success else "failure" self.collector.record_metric( "api_calls_total", 1.0, {"endpoint": endpoint, "method": method, "status": status} ) def track_quantum_generation(self, algorithm: str, qubits_used: int, generation_time: float, entropy_bits: int): """Track quantum generation metrics""" self.collector.record_metric( "quantum_generation_time", generation_time, {"algorithm": algorithm, "qubits": str(qubits_used)} ) self.collector.record_metric( "entropy_bits_generated", entropy_bits, {"algorithm": algorithm} ) def track_pqc_operation(self, operation: str, algorithm: str, execution_time: float): """Track post-quantum cryptography operations""" self.collector.record_metric( "pqc_operation_time", execution_time, {"operation": operation, "algorithm": algorithm} ) def get_api_performance_summary(self, window_minutes: int = 60) -> Dict[str, Any]: """Get API performance summary""" # Get response time metrics response_time_metrics = self.collector.get_aggregated_metrics("api_response_time", window_minutes) # Get call volume with self.collector._get_db_connection() as conn: cursor = conn.execute( ''' SELECT labels, SUM(value) as count FROM metrics WHERE metric_name = 'api_calls_total' AND timestamp >= ? GROUP BY labels ''', ((datetime.utcnow() - timedelta(minutes=window_minutes)).isoformat(),) ) call_counts = {} for row in cursor.fetchall(): labels = json.loads(row[0]) if row[0] else {} label_key = f"{labels.get('method', 'unknown')}_{labels.get('status', 'unknown')}" call_counts[label_key] = row[1] return { "period_minutes": window_minutes, "response_time": response_time_metrics, "call_volume": call_counts, "summary": { "avg_response_time_ms": response_time_metrics.get("avg", 0) * 1000, "total_calls": sum(call_counts.values()), "success_rate": call_counts.get("GET_success", 0) + call_counts.get("POST_success", 0) / max(sum(call_counts.values()), 1) } } def get_quantum_performance_summary(self, window_minutes: int = 60) -> Dict[str, Any]: """Get quantum generation performance summary""" gen_time_metrics = self.collector.get_aggregated_metrics("quantum_generation_time", window_minutes) entropy_metrics = self.collector.get_aggregated_metrics("entropy_bits_generated", window_minutes) return { "period_minutes": window_minutes, "generation_time": gen_time_metrics, "entropy_bits": entropy_metrics, "summary": { "avg_generation_time_ms": gen_time_metrics.get("avg", 0) * 1000, "avg_entropy_bits": entropy_metrics.get("avg", 0), "total_generations": gen_time_metrics.get("count", 0) } } def get_pqc_performance_summary(self, window_minutes: int = 60) -> Dict[str, Any]: """Get post-quantum cryptography performance summary""" pqc_metrics = self.collector.get_aggregated_metrics("pqc_operation_time", window_minutes) return { "period_minutes": window_minutes, "operation_time": pqc_metrics, "summary": { "avg_operation_time_ms": pqc_metrics.get("avg", 0) * 1000, "total_operations": pqc_metrics.get("count", 0) } } # Global analytics service instance analytics_service = AnalyticsService() # Convenience functions for tracking common metrics def track_api_call(endpoint: str, method: str, response_time: float, success: bool): """Convenience function to track API calls""" analytics_service.track_api_call(endpoint, method, response_time, success) def track_quantum_generation(algorithm: str, qubits_used: int, generation_time: float, entropy_bits: int): """Convenience function to track quantum generation""" analytics_service.track_quantum_generation(algorithm, qubits_used, generation_time, entropy_bits) def track_pqc_operation(operation: str, algorithm: str, execution_time: float): """Convenience function to track PQC operations""" analytics_service.track_pqc_operation(operation, algorithm, execution_time)