""" Enhanced Mock ARF components for demo purposes In production, these would use the real agentic-reliability-framework package """ import time import json import hashlib from typing import Dict, Any, List, Optional import random import logging from datetime import datetime, timedelta logger = logging.getLogger(__name__) class MockARFSimulator: """Enhanced mock ARF simulator with realistic patterns""" def __init__(self, seed: Optional[int] = None): self.seed = seed or int(time.time()) random.seed(self.seed) self._incident_patterns = self._initialize_patterns() self._healing_actions = self._initialize_healing_actions() def _initialize_patterns(self) -> Dict[str, Dict[str, Any]]: """Initialize realistic incident patterns""" return { "cache_miss_storm": { "pattern": "exponential_miss_increase", "indicators": ["cache_hit_rate < 30%", "database_load > 80%", "response_time > 1500ms"], "typical_causes": ["key_eviction", "cold_cache", "traffic_spike"], "resolution_patterns": ["scale_out", "cache_warming", "ttl_optimization"] }, "db_connection_exhaustion": { "pattern": "connection_pool_saturation", "indicators": ["active_connections > 95%", "connection_wait > 30s", "query_timeout_rate > 10%"], "typical_causes": ["connection_leak", "slow_queries", "connection_pool_misconfig"], "resolution_patterns": ["pool_tuning", "query_optimization", "circuit_breaker"] }, "memory_leak": { "pattern": "gradual_memory_increase", "indicators": ["memory_usage > 90%", "gc_frequency_high", "restart_count_increasing"], "typical_causes": ["object_retention", "resource_leak", "cache_growth"], "resolution_patterns": ["heap_analysis", "restart", "memory_limit"] }, "api_rate_limit": { "pattern": "rate_limit_cascade", "indicators": ["429_rate > 40%", "retry_storm", "cascade_failures"], "typical_causes": ["burst_traffic", "misconfigured_limits", "retry_logic"], "resolution_patterns": ["backoff_strategy", "circuit_breaker", "cache_responses"] } } def _initialize_healing_actions(self) -> Dict[str, Dict[str, Any]]: """Initialize healing actions with success rates""" return { "scale_out": { "action": "increase_capacity", "success_rate": 0.87, "typical_recovery_time": "5-15 minutes", "risk_level": "low", "prerequisites": ["capacity_available", "auto_scaling_enabled"] }, "cache_warming": { "action": "preload_cache", "success_rate": 0.72, "typical_recovery_time": "2-10 minutes", "risk_level": "very_low", "prerequisites": ["predictive_model", "cache_pattern_known"] }, "restart_container": { "action": "graceful_restart", "success_rate": 0.95, "typical_recovery_time": "1-3 minutes", "risk_level": "medium", "prerequisites": ["health_checks", "load_balancer", "redundancy"] }, "circuit_breaker": { "action": "fail_fast_protection", "success_rate": 0.89, "typical_recovery_time": "instant", "risk_level": "low", "prerequisites": ["dependency_awareness", "fallback_strategy"] } } def simulate_arf_analysis(self, scenario: Dict[str, Any]) -> Dict[str, Any]: """Simulate ARF analysis pipeline with enhanced realism""" component = scenario.get('component', 'unknown') pattern_name = self._detect_pattern(component, scenario) return { "analysis_complete": True, "anomaly_detected": True, "severity": self._determine_severity(scenario), "root_cause": scenario.get('root_cause', 'resource_constraint'), "pattern_detected": True, "pattern_name": pattern_name, "pattern_confidence": self._calculate_pattern_confidence(pattern_name), "detection_method": "ensemble_ml_model", "detection_time_ms": random.randint(150, 350), "analysis_timestamp": time.time(), "processing_time_ms": random.randint(200, 500), "model_version": "arf-ml-v3.3.6", "features_analyzed": self._extract_features(scenario) } def run_rag_similarity_search(self, scenario: Dict[str, Any]) -> List[Dict[str, Any]]: """Simulate RAG similarity search with realistic data""" component = scenario.get('component', 'redis_cache') pattern_name = self._detect_pattern(component, scenario) # Generate realistic similar incidents similar_incidents = [] base_time = time.time() for i in range(random.randint(3, 5)): days_ago = random.randint(1, 90) incident_time = base_time - (days_ago * 86400) similarity = random.uniform(0.75, 0.95) success = similarity > 0.82 incident = { "incident_id": f"inc_{int(incident_time)}_{i}", "component": component, "pattern": pattern_name, "similarity_score": similarity, "cosine_similarity": similarity, "success": success, "resolution": self._get_recommended_action(component), "actions_taken": self._get_action_sequence(component, success), "resolution_time_minutes": random.uniform(3.5, 18.5), "timestamp": incident_time, "occurred_at": datetime.fromtimestamp(incident_time).isoformat(), "engineers_involved": random.randint(1, 3), "blast_radius": f"{random.randint(1, 5)} services", "root_cause_analysis": self._generate_root_cause(component) } if success: cost_saved = random.randint(1500, 12500) incident["cost_savings"] = cost_saved incident["mttr_reduction"] = f"{random.randint(60, 85)}%" incident["user_impact"] = f"{random.randint(85, 99)}% reduction" similar_incidents.append(incident) # Sort by similarity similar_incidents.sort(key=lambda x: x['similarity_score'], reverse=True) # Add RAG metadata rag_metadata = { "vector_db": "chroma_v0.4.0", "embedding_model": "all-MiniLM-L6-v2", "index_size": f"{random.randint(500, 5000)} incidents", "retrieval_time_ms": random.randint(45, 120), "top_k": len(similar_incidents) } for incident in similar_incidents: incident["rag_metadata"] = rag_metadata return similar_incidents def calculate_pattern_confidence(self, scenario: Dict[str, Any], similar_incidents: List[Dict[str, Any]]) -> float: """Calculate pattern detection confidence with enhanced logic""" if not similar_incidents: return 0.70 # Base confidence without similar incidents # Base confidence from pattern matching component = scenario.get('component', 'unknown') pattern_name = self._detect_pattern(component, scenario) base_confidence = self._calculate_pattern_confidence(pattern_name) # Boost based on number of similar incidents incident_count = len(similar_incidents) incident_boost = min(0.15, incident_count * 0.025) # Boost based on average similarity avg_similarity = sum(i['similarity_score'] for i in similar_incidents) / incident_count similarity_boost = avg_similarity * 0.12 # Boost based on success rate success_count = sum(1 for i in similar_incidents if i['success']) success_rate = success_count / incident_count success_boost = success_rate * 0.10 # Boost based on recency (weight recent incidents more) recency_boost = self._calculate_recency_boost(similar_incidents) total_confidence = ( base_confidence + incident_boost + similarity_boost + success_boost + recency_boost ) # Cap at 0.98 and ensure minimum return max(0.70, min(0.98, total_confidence)) def create_mock_healing_intent(self, scenario: Dict[str, Any], similar_incidents: List[Dict[str, Any]], confidence: float = 0.85) -> Dict[str, Any]: """Create a realistic mock HealingIntent object""" component = scenario.get('component', 'redis_cache') pattern_name = self._detect_pattern(component, scenario) # Determine action based on component and pattern action_info = self._determine_healing_action(component, pattern_name) # Generate deterministic ID params_hash = hashlib.md5( json.dumps(action_info['parameters'], sort_keys=True).encode() ).hexdigest()[:8] # Calculate RAG similarity metrics rag_metrics = self._calculate_rag_metrics(similar_incidents) # Create healing intent healing_intent = { "action": action_info['action'], "component": component, "pattern": pattern_name, "parameters": action_info['parameters'], "justification": action_info['justification'], "confidence": confidence, "incident_id": f"inc_{int(time.time())}", "detected_at": time.time(), "similar_incidents_count": len(similar_incidents), "rag_similarity_score": rag_metrics['avg_similarity'], "rag_metrics": rag_metrics, "source": "oss_analysis", "intent_id": f"intent_{int(time.time())}_{params_hash}", "created_at": time.time(), "status": "created", "edition": "community", "requires_enterprise": True, "execution_allowed": False, "safety_checks": { "blast_radius": f"{random.randint(1, 3)} services", "business_hours": "compliant", "rollback_plan": "available", "approval_required": True, "risk_assessment": "low", "compliance_check": "passed" }, "expected_outcome": { "recovery_time_minutes": action_info['recovery_time'], "success_probability": action_info['success_rate'], "cost_savings_estimate": self._estimate_savings(scenario), "user_impact_reduction": f"{random.randint(85, 99)}%" }, "deterministic_id": f"intent_{params_hash}" } return healing_intent # Helper methods def _detect_pattern(self, component: str, scenario: Dict[str, Any]) -> str: """Detect incident pattern based on component""" if 'cache' in component.lower(): return "cache_miss_storm" elif 'database' in component.lower() or 'postgres' in component.lower(): return "db_connection_exhaustion" elif 'memory' in component.lower() or 'java' in component.lower(): return "memory_leak" elif 'api' in component.lower() or 'rate' in component.lower(): return "api_rate_limit" else: return "unknown_pattern" def _determine_severity(self, scenario: Dict[str, Any]) -> str: """Determine incident severity""" metrics = scenario.get('metrics', {}) if 'error_rate' in metrics and metrics['error_rate'] > 30: return "critical" elif 'response_time_ms' in metrics and metrics['response_time_ms'] > 2000: return "critical" elif 'memory_usage' in metrics and metrics['memory_usage'] > 90: return "high" else: return random.choice(["high", "medium"]) def _calculate_pattern_confidence(self, pattern_name: str) -> float: """Calculate confidence for specific pattern""" confidence_map = { "cache_miss_storm": 0.92, "db_connection_exhaustion": 0.88, "memory_leak": 0.85, "api_rate_limit": 0.90, "unknown_pattern": 0.70 } return confidence_map.get(pattern_name, 0.75) def _extract_features(self, scenario: Dict[str, Any]) -> List[str]: """Extract features for ML analysis""" features = [] metrics = scenario.get('metrics', {}) for key, value in metrics.items(): if isinstance(value, (int, float)): features.append(f"{key}:{value}") # Add derived features if 'cache_hit_rate' in metrics and metrics['cache_hit_rate'] < 30: features.append("cache_miss_critical") if 'error_rate' in metrics and metrics['error_rate'] > 10: features.append("error_rate_high") return features[:10] # Limit to 10 features def _get_recommended_action(self, component: str) -> str: """Get recommended healing action""" if 'cache' in component.lower(): return 'scale_out' elif 'database' in component.lower(): return 'optimize_connections' elif 'memory' in component.lower(): return 'restart_container' else: return 'circuit_breaker' def _get_action_sequence(self, component: str, success: bool) -> List[str]: """Get sequence of actions taken""" base_actions = [] if 'cache' in component.lower(): base_actions = ["scale_out", "adjust_cache_ttl", "implement_warming"] elif 'database' in component.lower(): base_actions = ["increase_pool_size", "add_timeout", "optimize_queries"] if success and random.random() > 0.5: base_actions.append("add_monitoring") return base_actions def _generate_root_cause(self, component: str) -> str: """Generate realistic root cause""" causes = { 'cache': ["key_eviction_policy", "cold_cache_after_deploy", "traffic_spike_2x"], 'database': ["connection_leak_in_pool", "slow_query_cascade", "max_connections_limit"], 'memory': ["object_retention_in_cache", "thread_local_leak", "off_heap_memory_growth"] } for key, cause_list in causes.items(): if key in component.lower(): return random.choice(cause_list) return "resource_constraint_under_load" def _calculate_recency_boost(self, incidents: List[Dict[str, Any]]) -> float: """Calculate boost based on incident recency""" if not incidents: return 0.0 now = time.time() recent_count = 0 for incident in incidents: incident_time = incident.get('timestamp', now) days_ago = (now - incident_time) / 86400 if days_ago < 7: # Within last week recent_count += 1 return min(0.08, recent_count * 0.02) def _determine_healing_action(self, component: str, pattern: str) -> Dict[str, Any]: """Determine healing action with parameters""" if 'cache' in component.lower(): return { "action": 'scale_out', "parameters": {'scale_factor': random.choice([2, 3]), 'cache_ttl': 300}, "justification": "Scale Redis cluster and adjust cache TTL based on historical pattern", "success_rate": 0.87, "recovery_time": "5-15 minutes" } elif 'database' in component.lower(): return { "action": 'optimize_connections', "parameters": {'max_connections': 200, 'connection_timeout': 30}, "justification": "Optimize database connection pool settings based on load patterns", "success_rate": 0.82, "recovery_time": "2-8 minutes" } else: return { "action": 'restart_container', "parameters": {'grace_period': 30, 'drain_connections': True}, "justification": "Restart container to resolve memory issues with graceful shutdown", "success_rate": 0.95, "recovery_time": "1-3 minutes" } def _calculate_rag_metrics(self, incidents: List[Dict[str, Any]]) -> Dict[str, Any]: """Calculate RAG metrics""" if not incidents: return { "avg_similarity": 0.0, "similarity_std": 0.0, "coverage_score": 0.0 } similarities = [i.get('similarity_score', 0) for i in incidents] return { "avg_similarity": sum(similarities) / len(similarities), "similarity_std": np.std(similarities) if len(similarities) > 1 else 0.0, "coverage_score": min(1.0, len(incidents) / 5), "diversity_score": random.uniform(0.6, 0.9) } def _estimate_savings(self, scenario: Dict[str, Any]) -> int: """Estimate cost savings""" impact = scenario.get('business_impact', {}) revenue_loss = impact.get('revenue_loss_per_hour', 5000) # 70-90% savings estimate savings_percentage = random.uniform(0.7, 0.9) return int(revenue_loss * savings_percentage) # Global simulator instance _simulator = MockARFSimulator() # Public API functions (backward compatibility) def simulate_arf_analysis(scenario: Dict[str, Any]) -> Dict[str, Any]: return _simulator.simulate_arf_analysis(scenario) def run_rag_similarity_search(scenario: Dict[str, Any]) -> List[Dict[str, Any]]: return _simulator.run_rag_similarity_search(scenario) def calculate_pattern_confidence(scenario: Dict[str, Any], similar_incidents: List[Dict[str, Any]]) -> float: return _simulator.calculate_pattern_confidence(scenario, similar_incidents) def create_mock_healing_intent(scenario: Dict[str, Any], similar_incidents: List[Dict[str, Any]], confidence: float = 0.85) -> Dict[str, Any]: return _simulator.create_mock_healing_intent(scenario, similar_incidents, confidence)