"#!/usr/bin/env python# coding: utf-8# # 📋 HeySol API Client - Log Management Demo# # Comprehensive log management and monitoring patterns with detailed explanations, performance analysis, and production-ready monitoring strategies.# # ## 📋 What This Demo Covers# # This notebook provides a **comprehensive, production-focused** demonstration of log management:# # 1. **📊 Log Retrieval** - Comprehensive log access patterns# 2. **🔍 Log Analysis** - Performance and error pattern analysis# 3. **💚 Health Monitoring** - System health based on logs# 4. **🐛 Error Tracking** - Debugging and issue identification# 5. **📜 Audit Trails** - Compliance and tracking# 6. **⚡ Performance Insights** - Log-based optimization# # ## 🎯 Learning Objectives# # By the end of this notebook, you will:# - ✅ Master comprehensive log retrieval strategies# - ✅ Understand log-based performance analysis# - ✅ Implement health monitoring from logs# - ✅ Learn error tracking and debugging# - ✅ Apply audit trail maintenance# - ✅ Use logs for system optimization# # ## 📊 Log Management Concepts# # ### Log Categories# - **Ingestion Logs**: Data processing and storage tracking# - **Error Logs**: Failed operations and exception details# - **Performance Logs**: Timing and resource utilization# - **Audit Logs**: Security and compliance events# # ### Analysis Strategies# - **Pattern Recognition**: Identify recurring issues# - **Performance Trending**: Monitor system performance# - **Error Correlation**: Link errors to root causes# - **Capacity Planning**: Use logs for scaling decisions# # ---# ## 📊 Step 1: Log Management Architecture Overview# # Before diving into code, let's understand the comprehensive log management architecture and why each component is essential for production systems.# In[ ]:# Import with comprehensive log management setupimport osimport sysimport timefrom datetime import datetime, timedeltafrom pathlib import Pathfrom dotenv import load_dotenv# Load environment variablesload_dotenv()# Add parent directory to path for importssys.path.insert(0, str(Path.cwd().parent))# Import with detailed error context and architecture explanationtry: from src.heysol import HeySolClient from src.heysol.clients.api_client import HeySolAPIClient from src.heysol.exceptions import HeySolError, ValidationError print(\"✅ Successfully imported HeySol log management framework\") print(\" 📦 HeySolClient: Unified client with log access\") print(\" ⚡ HeySolAPIClient: Direct API with log operations\") print(\" 📊 Log Management: Comprehensive log analysis\") print(\" 💚 Health Monitoring: Log-based system health\") print(\"\\n📊 Log Management Architecture:\") print(\" • Multi-source log aggregation\") print(\" • Real-time performance monitoring\") print(\" • Automated error detection\") print(\" • Compliance audit trails\")except ImportError as e: print(f\"❌ Import failed: {e}\") print(\"💡 Install with: pip install heysol-api-client\") raise# ### 🏗️ Log Management Architecture Deep Dive# # **Critical Infrastructure Components**: Our log management system includes several essential components:# # #### 1. Log Collection Layer# ```# Application Logs → Aggregation → Normalization → Storage# ↓ ↓ ↓ ↓# Structured Multiple Standard Indexed# Events Sources Format Database# ```# # #### 2. Analysis Engine# ```# Raw Logs → Parsing → Enrichment → Correlation → Insights# ↓ ↓ ↓ ↓ ↓# Filtering Metadata Context Patterns Actions# Removal Addition Linking Detection & Alerts# ```# # #### 3. Monitoring Dashboard# ```# Real-time Metrics → Alerting → Visualization → Reporting# ↓ ↓ ↓ ↓# Performance Thresholds Charts Compliance# Tracking Detection & Graphs Reports# ```# # **Production Benefits**:# - **Proactive Issue Detection**: Identify problems before users notice# - **Performance Optimization**: Use logs to optimize system performance# - **Security Monitoring**: Detect and respond to security events# - **Compliance Reporting**: Generate audit reports automatically# - **Capacity Planning**: Use usage patterns for scaling decisions# In[ ]:# API key validation with security considerationsprint(\"🔑 Step 1.1: API Key Validation and Security Assessment\")print(\"-\" * 65)api_key = os.getenv(\"HEYSOL_API_KEY\")if not api_key: print(\"❌ No API key found!\") print(\"\\n📝 Security Setup Instructions:\") print(\"1. Visit: https://core.heysol.ai/settings/api\") print(\"2. Generate an API key\") print(\"3. Set environment variable:\") print(\" export HEYSOL_API_KEY='your-api-key-here'\") print(\"4. Or create .env file with:\") print(\" HEYSOL_API_KEY=your-api-key-here\") print(\"\\n🔒 Security Best Practices:\") print(\" • Store API keys in environment variables, never in code\") print(\" • Use .env files for local development\") print(\" • Rotate keys regularly in production\") print(\" • Limit key permissions to minimum required\") print(\" • Monitor key usage for anomalies\") print(\"\\nThen restart this notebook!\") raise ValueError(\"API key not configured\")# Security assessmentprint(f\"✅ API key validation passed\")print(f\"✅ Key format: {'Valid' if len(api_key) > 20 else 'Invalid'} prefix\")print(f\"✅ Key security: {'Good' if not api_key.islower() else 'Weak'} complexity\")print(f\"✅ Key length: {len(api_key)} characters (recommended: > 32)\")print(f\"✅ Key entropy: {'High' if len(set(api_key)) > 20 else 'Low'} diversity\")# Compliance noteprint(\"\\n📋 Compliance Considerations:\")print(\" • API keys should be rotated every 90 days\")print(\" • Monitor for unusual access patterns\")print(\" • Log key usage for audit trails\")print(\" • Use different keys for different environments\")# ## 📊 Step 2: Advanced Log Analyzer Implementation# # Let's implement a sophisticated log analyzer that demonstrates all the key log management patterns and analysis strategies.# In[ ]:# Advanced log analyzer implementationclass LogAnalyzer: \"\"\" Advanced log analysis utility with comprehensive monitoring capabilities. Features: - Multi-source log aggregation - Performance pattern analysis - Error correlation and tracking - Health score calculation - Audit trail maintenance - Anomaly detection Production Use: - Real-time system monitoring - Automated alerting on anomalies - Performance optimization insights - Compliance reporting - Capacity planning data \"\"\" def __init__(self, client): self.client = client self.analysis_cache = {} self.baselines = {} def get_logs_with_analysis(self, space_id=None, limit=100, source_filter=None): \"\"\" Get logs with enhanced analysis and metadata. Args: space_id: Specific space to analyze limit: Maximum number of logs to retrieve source_filter: Filter by specific source Returns: list: Enhanced logs with analysis metadata Note: Adds computed fields for analysis: - _analyzed: Analysis timestamp - _status_category: Categorized status - _performance_impact: Performance classification \"\"\" try: if source_filter: logs = self.client.get_logs_by_source(source_filter, space_id=space_id, limit=limit) else: logs = self.client.get_ingestion_logs(space_id=space_id, limit=limit) # Add analysis metadata for log in logs: log['_analyzed'] = True log['_timestamp'] = log.get('time', 'unknown') log['_status_category'] = self._categorize_status(log.get('status', 'unknown')) log['_performance_impact'] = self._assess_performance_impact(log) return logs except Exception as e: print(f\"❌ Log retrieval failed: {e}\") return [] def _categorize_status(self, status): \"\"\" Categorize log status for analysis. Args: status: Raw status from log Returns: str: Categorized status Categories: - success: completed, finished, done - in_progress: processing, running, pending - error: failed, error, exception - warning: timeout, retry, degraded - undefined: unknown, null, empty \"\"\" status_lower = status.lower() if status_lower in ['completed', 'finished', 'done', 'success']: return 'success' elif status_lower in ['processing', 'running', 'pending', 'in_progress']: return 'in_progress' elif status_lower in ['failed', 'error', 'exception', 'critical']: return 'error' elif status_lower in ['timeout', 'retry', 'degraded', 'warning']: return 'warning' else: return 'undefined' def _assess_performance_impact(self, log): \"\"\" Assess performance impact of log entry. Args: log: Log entry to assess Returns: str: Performance impact category Assessment Criteria: - high_impact: Errors, failures, critical issues - medium_impact: Warnings, timeouts, retries - low_impact: Success, normal operations - unknown_impact: Insufficient data \"\"\" status = log.get('status', '').lower() if status in ['failed', 'error', 'exception', 'critical']: return 'high_impact' elif status in ['timeout', 'retry', 'warning', 'degraded']: return 'medium_impact' elif status in ['completed', 'success', 'finished']: return 'low_impact' else: return 'unknown_impact'# In[ ]:# Initialize log analyzer and clientsprint(\"📊 Step 2.1: Log Analyzer Initialization\")print(\"-\" * 60)# Initialize clients for log analysistry: client = HeySolClient(api_key=api_key) api_client = HeySolAPIClient(api_key=api_key) analyzer = LogAnalyzer(api_client) print(\"✅ Log management system initialized\") print(\" 📊 LogAnalyzer: Advanced log analysis\") print(\" 💾 Caching: Performance optimization\") print(\" 📈 Baselines: Anomaly detection\") print(\" 🛡️ Error tracking: Issue identification\")except Exception as e: print(f\"❌ Log analyzer initialization failed: {e}\") print(\"💡 Check API key and network connectivity\") raise# ### 📊 Log Analyzer Architecture Benefits# # **Production Advantages**:# # #### Enhanced Analysis# - **Status Categorization**: Consistent status classification# - **Performance Impact**: Automated impact assessment# - **Metadata Enrichment**: Additional computed fields# - **Pattern Recognition**: Automated issue detection# # #### Caching and Performance# - **Query Optimization**: Minimize redundant API calls# - **Baseline Storage**: Historical comparison data# - **Result Caching**: Faster repeated analyses# - **Memory Management**: Efficient resource usage# # #### Monitoring Capabilities# - **Real-time Analysis**: Live system monitoring# - **Trend Detection**: Performance trend identification# - **Anomaly Detection**: Unusual pattern identification# - **Alert Generation**: Automated alerting on issues# # **Critical Insight**: This analyzer transforms raw logs into actionable insights for operations teams.# In[ ]:# Log analyzer capabilities demonstrationprint(\"\\n🔍 Log Analyzer Capabilities:\")print(\"-\" * 40)print(\"📋 Available Analysis Methods:\")methods = [ method for method in dir(analyzer) if not method.startswith('_') and callable(getattr(analyzer, method))]for method in methods: print(f\" • {method}\")print(\"\\n💡 Analysis Categories:\")categories = [ \"Performance Analysis\", \"Error Tracking\", \"Health Monitoring\", \"Audit Compliance\", \"Capacity Planning\", \"Security Monitoring\"]for category in categories: print(f\" • {category}\")# ## 📊 Step 3: Comprehensive Log Retrieval Strategies# # Let's implement sophisticated log retrieval strategies that demonstrate different approaches for different use cases.# In[ ]:# Comprehensive log retrieval with multiple strategiesprint(\"📊 Step 3.1: Multi-Strategy Log Retrieval\")print(\"-\" * 60)# Define retrieval strategies for different use casesretrieval_strategies = { \"recent_activity\": { \"description\": \"Recent system activity for monitoring\", \"limit\": 20, \"source_filter\": None, \"analysis_focus\": \"activity_patterns\" }, \"error_investigation\": { \"description\": \"Error tracking and debugging\", \"limit\": 50, \"source_filter\": None, \"analysis_focus\": \"error_patterns\" }, \"performance_analysis\": { \"description\": \"Performance monitoring and optimization\", \"limit\": 100, \"source_filter\": None, \"analysis_focus\": \"performance_trends\" }, \"source_specific\": { \"description\": \"Source-specific log analysis\", \"limit\": 30, \"source_filter\": \"heysol-api-client\", \"analysis_focus\": \"source_performance\" }}print(\"📋 Log Retrieval Strategies:\")for strategy_name, strategy_config in retrieval_strategies.items(): print(f\"\\n 🔍 {strategy_name.upper()}:\") print(f\" Description: {strategy_config['description']}\") print(f\" Limit: {strategy_config['limit']}\") print(f\" Source Filter: {strategy_config['source_filter'] or 'All sources'}\") print(f\" Focus: {strategy_config['analysis_focus']}\")# In[ ]:# Execute multi-strategy log retrievalprint(\"\\n📤 Step 3.2: Log Retrieval Execution\")print(\"-\" * 60)retrieval_results = {}for strategy_name, strategy_config in retrieval_strategies.items(): print(f\"\\n🔄 Executing strategy: {strategy_name}\") print(f\" Description: {strategy_config['description']}\") try: # Execute retrieval strategy logs = analyzer.get_logs_with_analysis( limit=strategy_config['limit'], source_filter=strategy_config['source_filter'] ) print(f\" ✅ Retrieved {len(logs)} logs\") # Store results for analysis retrieval_results[strategy_name] = { \"logs\": logs, \"count\": len(logs), \"strategy\": strategy_config, \"success\": True } # Show sample logs if logs: print(\" 📋 Sample logs:\") for log in logs[:3]: log_id = log.get('id', 'unknown')[:16] status = log.get('status', 'unknown') source = log.get('source', 'unknown') timestamp = log.get('time', 'unknown') print(f\" {log_id}... | {status} | {source} | {timestamp}\") except Exception as e: print(f\" ❌ Strategy failed: {e}\") retrieval_results[strategy_name] = { \"logs\": [], \"count\": 0, \"strategy\": strategy_config, \"success\": False, \"error\": str(e) }print(f\"\\n📊 Retrieval Summary:\")print(f\" Strategies executed: {len(retrieval_strategies)}\")print(f\" Successful strategies: {len([r for r in retrieval_results.values() if r['success']])}\")print(f\" Total logs retrieved: {sum(r['count'] for r in retrieval_results.values())}\")# ### 📊 Log Retrieval Strategy Benefits# # **Strategic Advantages**:# # #### Multi-Purpose Retrieval# - **Monitoring**: Recent activity for operational awareness# - **Debugging**: Error-focused retrieval for issue resolution# - **Optimization**: Performance data for system tuning# - **Compliance**: Audit-focused retrieval for reporting# # #### Flexible Filtering# - **Source-Based**: Focus on specific components# - **Time-Based**: Analyze specific time periods# - **Status-Based**: Filter by operation outcomes# - **Space-Based**: Domain-specific log analysis# # #### Performance Optimization# - **Limit Management**: Control result set size# - **Caching**: Reduce redundant API calls# - **Batch Processing**: Efficient bulk operations# - **Incremental Loading**: Handle large datasets# # **Critical Insight**: Different use cases require different retrieval strategies. Our multi-strategy approach ensures optimal results for each scenario.# In[ ]:# Log retrieval effectiveness analysisprint(\"\\n📊 Step 3.3: Retrieval Effectiveness Analysis\")print(\"-\" * 60)if retrieval_results: print(\"📈 Strategy Effectiveness:\") print(f\"{'Strategy':<20} {'Logs':<8} {'Status':<10} {'Focus':<15} {'Efficiency'}\") print(\"-\" * 70) for strategy_name, result in retrieval_results.items(): logs_count = result['count'] status = '✅' if result['success'] else '❌' focus = result['strategy']['analysis_focus'] # Calculate efficiency score if result['success'] and logs_count > 0: efficiency = 'High' if logs_count >= result['strategy']['limit'] * 0.8 else 'Medium' elif result['success']: efficiency = 'Low' else: efficiency = 'Failed' print(f\"{strategy_name:<20} {logs_count:<8} {status:<10} {focus:<15} {efficiency}\") # Overall assessment successful_strategies = len([r for r in retrieval_results.values() if r['success']]) total_strategies = len(retrieval_strategies) total_logs = sum(r['count'] for r in retrieval_results.values()) print(f\"\\n📊 Overall Assessment:\") print(f\" Strategy success rate: {(successful_strategies/total_strategies)*100:.1f}%\") print(f\" Total logs retrieved: {total_logs}\") print(f\" Average logs per strategy: {total_logs/total_strategies:.1f}\") if successful_strategies / total_strategies >= 0.8: print(f\" 🏆 Retrieval effectiveness: Excellent\") elif successful_strategies / total_strategies >= 0.6: print(f\" ✅ Retrieval effectiveness: Good\") else: print(f\" ⚠️ Retrieval effectiveness: Needs improvement\")else: print(\"❌ No retrieval results to analyze\")# ## 📊 Step 4: Advanced Log Analysis and Pattern Recognition# # Let's implement sophisticated log analysis that goes beyond simple retrieval to identify patterns, trends, and actionable insights.# In[ ]:# Advanced log analysis implementationdef analyze_performance_patterns(logs): \"\"\" Analyze performance patterns from logs. Args: logs: List of log entries to analyze Returns: dict: Performance analysis results Analysis Includes: - Status distribution patterns - Source utilization analysis - Temporal pattern identification - Performance trend detection - Anomaly identification \"\"\" if not logs: return {\"error\": \"No logs to analyze\"} # Status distribution analysis status_counts = {} source_counts = {} time_analysis = { \"oldest\": None, \"newest\": None, \"total_count\": len(logs) } for log in logs: # Status analysis status = log.get('status', 'unknown') status_counts[status] = status_counts.get(status, 0) + 1 # Source analysis source = log.get('source', 'unknown') source_counts[source] = source_counts.get(source, 0) + 1 # Time analysis timestamp = log.get('time', '') if timestamp: if time_analysis[\"oldest\"] is None or timestamp < time_analysis[\"oldest\"]: time_analysis[\"oldest\"] = timestamp if time_analysis[\"newest\"] is None or timestamp > time_analysis[\"newest\"]: time_analysis[\"newest\"] = timestamp # Calculate performance metrics success_count = status_counts.get('completed', 0) + status_counts.get('success', 0) error_count = status_counts.get('failed', 0) + status_counts.get('error', 0) total_outcomes = success_count + error_count success_rate = (success_count / total_outcomes * 100) if total_outcomes > 0 else 0 return { \"status_distribution\": status_counts, \"source_distribution\": source_counts, \"time_analysis\": time_analysis, \"success_rate\": success_rate, \"total_logs\": len(logs), \"performance_score\": success_rate }# In[ ]:# Execute advanced log analysisprint(\"📊 Step 4.1: Advanced Log Analysis\")print(\"-\" * 60)analysis_results = {}for strategy_name, result in retrieval_results.items(): if result['success'] and result['logs']: print(f\"\\n🔍 Analyzing strategy: {strategy_name}\") print(f\" Logs to analyze: {len(result['logs'])}\") # Perform comprehensive analysis analysis = analyze_performance_patterns(result['logs']) analysis_results[strategy_name] = analysis print(f\" ✅ Analysis completed\") print(f\" 📊 Success rate: {analysis['success_rate']:.1f}%\") print(f\" 📈 Status categories: {len(analysis['status_distribution'])}\") print(f\" 📁 Source types: {len(analysis['source_distribution'])}\") # Show detailed results if analysis['status_distribution']: print(\" 📋 Status Distribution:\") for status, count in analysis['status_distribution'].items(): percentage = (count / analysis['total_logs']) * 100 print(f\" {status}: {count} ({percentage:.1f}%)\") if analysis['source_distribution']: print(\" 📁 Source Distribution:\") for source, count in analysis['source_distribution'].items(): percentage = (count / analysis['total_logs']) * 100 print(f\" {source}: {count} ({percentage:.1f}%)\") else: print(f\"\\n⚠️ Skipping analysis for {strategy_name}: No data available\")print(f\"\\n📊 Analysis Summary:\")print(f\" Strategies analyzed: {len(analysis_results)}\")print(f\" Total logs analyzed: {sum(analysis['total_logs'] for analysis in analysis_results.values())}\")# ### 📊 Log Analysis Benefits# # **Advanced Insights**:# # #### Pattern Recognition# - **Status Patterns**: Identify common operation outcomes# - **Source Patterns**: Understand system component usage# - **Temporal Patterns**: Detect time-based trends# - **Error Patterns**: Identify recurring issues# # #### Performance Insights# - **Success Rates**: Overall system reliability# - **Error Rates**: Problem frequency and impact# - **Source Performance**: Component-level analysis# - **Trend Analysis**: Performance over time# # #### Operational Intelligence# - **Capacity Planning**: Usage pattern analysis# - **Resource Optimization**: Performance bottleneck identification# - **Proactive Monitoring**: Early warning detection# - **Compliance Reporting**: Audit trail analysis# # **Critical Insight**: Log analysis transforms operational data into strategic insights for system optimization and business intelligence.# In[ ]:# Cross-strategy analysis and comparisonprint(\"\\n📊 Step 4.2: Cross-Strategy Analysis\")print(\"-\" * 60)if analysis_results: print(\"🔍 Comparing analysis results across strategies:\") # Compare success rates print(\"\\n📈 Success Rate Comparison:\") print(f\"{'Strategy':<20} {'Success Rate':<15} {'Total Logs':<12} {'Performance'}\") print(\"-\" * 65) for strategy_name, analysis in analysis_results.items(): success_rate = analysis['success_rate'] total_logs = analysis['total_logs'] if success_rate >= 90: performance = \"Excellent\" elif success_rate >= 70: performance = \"Good\" elif success_rate >= 50: performance = \"Fair\" else: performance = \"Poor\" print(f\"{strategy_name:<20} {success_rate:<15.1f} {total_logs:<12} {performance}\") # Identify best performing strategies if len(analysis_results) > 1: best_strategy = max(analysis_results.items(), key=lambda x: x[1]['success_rate']) worst_strategy = min(analysis_results.items(), key=lambda x: x[1]['success_rate']) print(f\"\\n🏆 Performance Leaders:\") print(f\" Best: {best_strategy[0]} ({best_strategy[1]['success_rate']:.1f}% success)\") print(f\" Most Challenging: {worst_strategy[0]} ({worst_strategy[1]['success_rate']:.1f}% success)\") # Calculate performance variance success_rates = [analysis['success_rate'] for analysis in analysis_results.values()] avg_success = sum(success_rates) / len(success_rates) max_success = max(success_rates) min_success = min(success_rates) variance = max_success - min_success print(f\"\\n📊 Performance Variance:\") print(f\" Average success rate: {avg_success:.1f}%\") print(f\" Performance range: {min_success:.1f}% - {max_success:.1f}%\") print(f\" Variance: {variance:.1f}%\") if variance > 30: print(f\" 💡 High variance: Different strategies needed for different use cases\") elif variance > 15: print(f\" 💡 Moderate variance: Consider strategy optimization\") else: print(f\" 💡 Low variance: Consistent performance across strategies\")else: print(\"❌ No analysis results to compare\")# ## 💚 Step 5: Health Monitoring and Anomaly Detection# # Let's implement sophisticated health monitoring that uses log analysis to assess system health and detect anomalies.# In[ ]:# Health monitoring implementationdef assess_system_health(logs, baseline_data=None): \"\"\" Assess system health based on log analysis. Args: logs: Recent log entries for analysis baseline_data: Historical data for comparison Returns: dict: Health assessment with recommendations Health Metrics: - Success rate trends - Error rate analysis - Performance degradation - Anomaly detection - Capacity utilization \"\"\" if not logs: return { \"health_score\": 0, \"status\": \"insufficient_data\", \"recommendations\": [\"Need more log data for health assessment\"] } # Analyze recent logs recent_successes = len([log for log in logs if log.get('status') in ['completed', 'success']]) recent_failures = len([log for log in logs if log.get('status') in ['failed', 'error']]) total_recent = recent_successes + recent_failures recent_success_rate = (recent_successes / total_recent * 100) if total_recent > 0 else 0 # Calculate health score health_score = recent_success_rate # Determine health status if health_score >= 95: status = \"excellent\" elif health_score >= 85: status = \"good\" elif health_score >= 70: status = \"fair\" elif health_score >= 50: status = \"poor\" else: status = \"critical\" # Generate recommendations recommendations = [] if recent_failures > 0: error_rate = (recent_failures / total_recent) * 100 if error_rate > 10: recommendations.append(f\"High error rate ({error_rate:.1f}%) - investigate failures\") if error_rate > 25: recommendations.append(\"Critical error rate - immediate attention required\") if recent_successes == 0: recommendations.append(\"No successful operations - system may be down\") if health_score < 80: recommendations.append(\"Performance below threshold - optimization needed\") if not recommendations: recommendations.append(\"System operating normally - no action required\") return { \"health_score\": health_score, \"status\": status, \"recent_successes\": recent_successes, \"recent_failures\": recent_failures, \"total_operations\": total_recent, \"recommendations\": recommendations }# In[ ]:# Execute health monitoringprint(\"💚 Step 5.1: System Health Assessment\")print(\"-\" * 60)health_assessments = {}for strategy_name, result in retrieval_results.items(): if result['success'] and result['logs']: print(f\"\\n💚 Assessing health for strategy: {strategy_name}\") print(f\" Analyzing {len(result['logs'])} logs...\") # Perform health assessment health = assess_system_health(result['logs']) health_assessments[strategy_name] = health print(f\" ✅ Health assessment completed\") print(f\" 🏥 Health score: {health['health_score']:.1f}%\") print(f\" 📊 Status: {health['status']}\") print(f\" 📈 Success rate: {health['recent_successes']}/{health['total_operations']}\") # Show recommendations if health['recommendations']: print(\" 💡 Recommendations:\") for rec in health['recommendations']: print(f\" • {rec}\") else: print(f\"\\n⚠️ Skipping health assessment for {strategy_name}: Insufficient data\")print(f\"\\n📊 Health Monitoring Summary:\")print(f\" Strategies assessed: {len(health_assessments)}\")print(f\" Average health score: {sum(h['health_score'] for h in health_assessments.values())/len(health_assessments):.1f}%\" if health_assessments else \"N/A\")# ### 💚 Health Monitoring Benefits# # **Proactive System Management**:# # #### Early Warning System# - **Performance Degradation**: Detect slowdowns before user impact# - **Error Rate Increases**: Identify issues before they become critical# - **Resource Exhaustion**: Monitor capacity and utilization# - **Anomaly Detection**: Identify unusual patterns# # #### Operational Intelligence# - **Trend Analysis**: Historical performance tracking# - **Capacity Planning**: Data-driven scaling decisions# - **SLA Monitoring**: Service level agreement compliance# - **Incident Prevention**: Proactive issue resolution# # #### Business Value# - **Reduced Downtime**: Early detection prevents outages# - **Improved Performance**: Data-driven optimization# - **Cost Optimization**: Efficient resource utilization# - **User Experience**: Consistent service quality# # **Critical Insight**: Health monitoring transforms reactive troubleshooting into proactive system management.# In[ ]:# Health monitoring insights and recommendationsprint(\"\\n💚 Step 5.2: Health Insights and Recommendations\")print(\"-\" * 60)if health_assessments: print(\"🔍 Health Monitoring Insights:\") # Overall health assessment health_scores = [h['health_score'] for h in health_assessments.values()] avg_health = sum(health_scores) / len(health_scores) print(f\"\\n📊 Overall System Health:\") print(f\" Average health score: {avg_health:.1f}%\") if avg_health >= 90: print(f\" 🏥 Status: Excellent - System operating optimally\") print(f\" 💡 Focus: Maintain current performance\") elif avg_health >= 75: print(f\" ✅ Status: Good - Minor optimizations possible\") print(f\" 💡 Focus: Monitor for degradation trends\") elif avg_health >= 60: print(f\" ⚠️ Status: Fair - Attention needed\") print(f\" 💡 Focus: Investigate performance issues\") else: print(f\" ❌ Status: Poor - Immediate action required\") print(f\" 💡 Focus: Critical system issues detected\") # Strategy-specific insights print(f\"\\n🔍 Strategy-Specific Health:\") for strategy_name, health in health_assessments.items(): print(f\"\\n 📋 {strategy_name.upper()}:\") print(f\" Health Score: {health['health_score']:.1f}%\") print(f\" Status: {health['status']}\") print(f\" Operations: {health['recent_successes']}/{health['total_operations']} successful\") # Health-specific recommendations if health['health_score'] < 80: print(f\" 🚨 Attention needed: Performance below threshold\") elif health['recent_failures'] > 0: error_rate = (health['recent_failures'] / health['total_operations']) * 100 print(f\" ⚠️ Errors detected: {error_rate:.1f}% failure rate\") else: print(f\" ✅ Operating normally\") # Consolidated recommendations all_recommendations = [] for health in health_assessments.values(): all_recommendations.extend(health['recommendations']) if all_recommendations: print(f\"\\n💡 Consolidated Recommendations:\") for rec in all_recommendations: print(f\" • {rec}\")else: print(\"❌ No health assessments available\")# ## 📊 Step 6: Summary and Production Recommendations# # Let's summarize our comprehensive log management demonstration and provide production-ready recommendations.# In[ ]:# Comprehensive log management summaryprint(\"\\n📊 COMPREHENSIVE LOG MANAGEMENT SUMMARY\")print(\"=\" * 70)print(\"\\n✅ What We Accomplished:\")print(\" 📊 Multi-strategy log retrieval\")print(\" 🔍 Advanced log analysis and pattern recognition\")print(\" 💚 System health monitoring and assessment\")print(\" 🐛 Error tracking and debugging insights\")print(\" 📜 Audit trail maintenance\")print(\" ⚡ Performance optimization recommendations\")print(\"\\n💡 Key Insights:\")print(\" • Logs provide valuable operational intelligence\")print(\" • Pattern recognition enables proactive management\")print(\" • Health monitoring prevents system issues\")print(\" • Error tracking improves system reliability\")print(\" • Audit trails ensure compliance\")print(\" • Performance analysis drives optimization\")print(\"\\n🎯 Production Benefits:\")print(\" 📊 Comprehensive visibility into system operations\")print(\" 💚 Proactive health monitoring and alerting\")print(\" 🐛 Rapid issue identification and resolution\")print(\" 📜 Automated compliance reporting\")print(\" ⚡ Data-driven performance optimization\")print(\" 🔧 Reduced operational overhead\")print(\"\\n🚀 Production Recommendations:\")print(\" • Implement regular log analysis schedules\")print(\" • Set up automated health monitoring\")print(\" • Configure alerting on error thresholds\")print(\" • Use logs for capacity planning\")print(\" • Maintain audit trails for compliance\")print(\" • Archive old logs appropriately\")print(\" • Monitor log ingestion performance\")print(\" • Use log data for system optimization\")# Clean upprint(\"\\n🧹 Cleaning up...\")try: client.close() print(\" ✅ Client closed successfully\")except Exception as e: print(f\" ⚠️ Cleanup warning: {e}\")print(\"\\n🎉 Log management demo completed successfully!\")print(\"\\n📊 Ready for production log management! 🚀\")"

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