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# Advanced Monitoring & Analytics
## Enterprise-Scale Monitoring and Analytics Platform for Enhanced BMAD System
The Advanced Monitoring & Analytics module provides sophisticated enterprise-grade monitoring, observability, and analytics capabilities that enable comprehensive visibility, predictive insights, and intelligent automation across all systems, processes, and business operations with real-time analytics and AI-powered monitoring.
### Advanced Monitoring & Analytics Architecture
#### Comprehensive Monitoring and Analytics Platform
```yaml
advanced_monitoring_analytics:
monitoring_domains:
infrastructure_monitoring:
- system_performance_monitoring: "Real-time system performance monitoring and alerting"
- network_monitoring_and_analysis: "Network performance monitoring and traffic analysis"
- storage_monitoring_and_optimization: "Storage performance monitoring and capacity planning"
- cloud_infrastructure_monitoring: "Multi-cloud infrastructure monitoring and optimization"
- container_and_kubernetes_monitoring: "Container orchestration monitoring and observability"
application_monitoring:
- application_performance_monitoring: "APM with distributed tracing and profiling"
- user_experience_monitoring: "Real user monitoring and synthetic testing"
- api_monitoring_and_analytics: "API performance monitoring and usage analytics"
- microservices_observability: "Microservices monitoring and service mesh observability"
- database_performance_monitoring: "Database performance monitoring and optimization"
business_process_monitoring:
- business_transaction_monitoring: "End-to-end business transaction monitoring"
- workflow_performance_monitoring: "Workflow execution monitoring and optimization"
- sla_and_kpi_monitoring: "SLA compliance and KPI performance monitoring"
- customer_journey_analytics: "Customer journey monitoring and experience analytics"
- operational_efficiency_monitoring: "Operational process efficiency monitoring"
security_monitoring:
- security_event_monitoring: "Real-time security event monitoring and correlation"
- threat_detection_and_analysis: "Advanced threat detection and behavioral analysis"
- compliance_monitoring: "Continuous compliance monitoring and reporting"
- access_pattern_monitoring: "User access pattern monitoring and anomaly detection"
- data_security_monitoring: "Data access monitoring and protection analytics"
log_management_and_analysis:
- centralized_log_aggregation: "Centralized log collection and aggregation"
- log_parsing_and_enrichment: "Intelligent log parsing and data enrichment"
- log_analytics_and_insights: "Advanced log analytics and pattern recognition"
- audit_trail_management: "Comprehensive audit trail management and analysis"
- log_retention_and_archival: "Intelligent log retention and archival strategies"
analytics_capabilities:
real_time_analytics:
- streaming_data_processing: "Real-time streaming data processing and analysis"
- event_correlation_and_analysis: "Real-time event correlation and impact analysis"
- anomaly_detection_algorithms: "ML-powered anomaly detection and alerting"
- threshold_based_alerting: "Intelligent threshold-based monitoring and alerting"
- real_time_dashboard_updates: "Real-time dashboard updates and visualizations"
predictive_analytics:
- capacity_planning_predictions: "Predictive capacity planning and resource forecasting"
- performance_degradation_prediction: "Performance degradation prediction and prevention"
- failure_prediction_and_prevention: "System failure prediction and proactive prevention"
- demand_forecasting: "Demand forecasting and resource optimization"
- trend_analysis_and_projection: "Trend analysis and future projection modeling"
behavioral_analytics:
- user_behavior_analytics: "User behavior analysis and pattern recognition"
- system_behavior_profiling: "System behavior profiling and deviation detection"
- application_usage_analytics: "Application usage patterns and optimization insights"
- resource_utilization_patterns: "Resource utilization pattern analysis and optimization"
- performance_pattern_recognition: "Performance pattern recognition and correlation"
business_analytics:
- operational_intelligence: "Operational intelligence and business insights"
- customer_analytics: "Customer behavior analytics and segmentation"
- financial_performance_analytics: "Financial performance monitoring and analysis"
- market_intelligence_integration: "Market intelligence integration and analysis"
- competitive_analysis_monitoring: "Competitive landscape monitoring and analysis"
observability_platform:
distributed_tracing:
- end_to_end_request_tracing: "End-to-end request tracing across microservices"
- service_dependency_mapping: "Service dependency mapping and visualization"
- performance_bottleneck_identification: "Performance bottleneck identification and analysis"
- error_propagation_tracking: "Error propagation tracking and root cause analysis"
- trace_sampling_and_optimization: "Intelligent trace sampling and storage optimization"
metrics_collection_and_analysis:
- custom_metrics_definition: "Custom business and technical metrics definition"
- metrics_aggregation_and_rollup: "Metrics aggregation and time-series rollup"
- multi_dimensional_metrics: "Multi-dimensional metrics collection and analysis"
- metrics_correlation_analysis: "Cross-metrics correlation and relationship analysis"
- metrics_based_alerting: "Metrics-based intelligent alerting and escalation"
event_driven_monitoring:
- event_stream_processing: "Real-time event stream processing and analysis"
- complex_event_processing: "Complex event processing and pattern matching"
- event_correlation_engines: "Multi-source event correlation and analysis"
- event_driven_automation: "Event-driven automation and response systems"
- event_sourcing_and_replay: "Event sourcing and historical event replay"
visualization_and_dashboards:
- interactive_dashboard_creation: "Interactive dashboard creation and customization"
- real_time_data_visualization: "Real-time data visualization and updates"
- drill_down_and_exploration: "Multi-level drill-down and data exploration"
- mobile_responsive_dashboards: "Mobile-responsive dashboard interfaces"
- collaborative_dashboard_sharing: "Collaborative dashboard sharing and annotation"
automation_and_intelligence:
intelligent_alerting:
- smart_alert_correlation: "Smart alert correlation and noise reduction"
- contextual_alert_enrichment: "Contextual alert enrichment and prioritization"
- predictive_alerting: "Predictive alerting based on trend analysis"
- escalation_and_routing: "Intelligent alert escalation and routing"
- alert_feedback_learning: "Alert feedback learning and optimization"
automated_remediation:
- self_healing_systems: "Self-healing system automation and recovery"
- automated_scaling_responses: "Automated scaling responses to demand changes"
- performance_optimization_automation: "Automated performance optimization actions"
- security_response_automation: "Automated security incident response"
- workflow_automation_triggers: "Monitoring-driven workflow automation triggers"
machine_learning_integration:
- anomaly_detection_models: "ML-powered anomaly detection and classification"
- predictive_maintenance_models: "Predictive maintenance and lifecycle management"
- optimization_recommendation_engines: "ML-driven optimization recommendation engines"
- natural_language_processing: "NLP for log analysis and alert interpretation"
- reinforcement_learning_optimization: "RL-based system optimization and tuning"
aiops_capabilities:
- intelligent_incident_management: "AI-powered incident management and resolution"
- root_cause_analysis_automation: "Automated root cause analysis and diagnosis"
- performance_optimization_ai: "AI-driven performance optimization recommendations"
- capacity_planning_ai: "AI-powered capacity planning and resource optimization"
- predictive_analytics_ai: "AI-enhanced predictive analytics and forecasting"
```
#### Advanced Monitoring & Analytics Implementation
```python
import asyncio
import pandas as pd
import numpy as np
from typing import Dict, List, Any, Optional, Tuple, Union
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime, timedelta
import json
import uuid
from collections import defaultdict, deque
import logging
from abc import ABC, abstractmethod
import time
import threading
import multiprocessing
from concurrent.futures import ThreadPoolExecutor
import psutil
import networkx as nx
from sklearn.ensemble import IsolationForest
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
import plotly.graph_objects as go
import plotly.express as px
from scipy import stats
import warnings
warnings.filterwarnings('ignore')
class MonitoringType(Enum):
INFRASTRUCTURE = "infrastructure"
APPLICATION = "application"
BUSINESS = "business"
SECURITY = "security"
NETWORK = "network"
USER_EXPERIENCE = "user_experience"
class AlertSeverity(Enum):
CRITICAL = "critical"
HIGH = "high"
MEDIUM = "medium"
LOW = "low"
INFO = "info"
class MetricType(Enum):
COUNTER = "counter"
GAUGE = "gauge"
HISTOGRAM = "histogram"
SUMMARY = "summary"
TIMER = "timer"
class AnalyticsType(Enum):
DESCRIPTIVE = "descriptive"
DIAGNOSTIC = "diagnostic"
PREDICTIVE = "predictive"
PRESCRIPTIVE = "prescriptive"
@dataclass
class MonitoringMetric:
"""
Represents a monitoring metric with metadata and configuration
"""
metric_id: str
name: str
type: MetricType
monitoring_type: MonitoringType
description: str
unit: str
collection_interval: int # seconds
retention_period: int # days
labels: Dict[str, str] = field(default_factory=dict)
thresholds: Dict[str, float] = field(default_factory=dict)
aggregation_rules: List[str] = field(default_factory=list)
alerting_enabled: bool = True
@dataclass
class MonitoringAlert:
"""
Represents a monitoring alert with context and severity
"""
alert_id: str
title: str
description: str
severity: AlertSeverity
monitoring_type: MonitoringType
triggered_time: datetime
source_metric: str
current_value: float
threshold_value: float
labels: Dict[str, str] = field(default_factory=dict)
context: Dict[str, Any] = field(default_factory=dict)
escalation_rules: List[Dict[str, Any]] = field(default_factory=list)
resolution_time: Optional[datetime] = None
acknowledged: bool = False
@dataclass
class AnalyticsInsight:
"""
Represents an analytics insight generated from monitoring data
"""
insight_id: str
title: str
description: str
analytics_type: AnalyticsType
confidence_score: float
impact_level: str # high, medium, low
time_horizon: str # immediate, short_term, medium_term, long_term
affected_systems: List[str] = field(default_factory=list)
recommendations: List[str] = field(default_factory=list)
supporting_data: Dict[str, Any] = field(default_factory=dict)
created_time: datetime = field(default_factory=datetime.utcnow)
class AdvancedMonitoringAnalytics:
"""
Enterprise-scale monitoring and analytics platform
"""
def __init__(self, claude_code_interface, config=None):
self.claude_code = claude_code_interface
self.config = config or {
'real_time_processing': True,
'predictive_analytics': True,
'anomaly_detection': True,
'automated_remediation': True,
'alert_correlation': True,
'data_retention_days': 365,
'metrics_collection_interval': 60,
'alert_evaluation_interval': 30,
'ml_model_training_interval_hours': 24
}
# Core monitoring components
self.metrics_collector = MetricsCollector(self.claude_code, self.config)
self.log_manager = LogManager(self.config)
self.event_processor = EventProcessor(self.config)
self.trace_manager = DistributedTraceManager(self.config)
# Analytics engines
self.real_time_analytics = RealTimeAnalyticsEngine(self.config)
self.predictive_analytics = PredictiveAnalyticsEngine(self.config)
self.behavioral_analytics = BehavioralAnalyticsEngine(self.config)
self.business_analytics = BusinessAnalyticsEngine(self.config)
# Alerting and automation
self.alert_manager = AlertManager(self.config)
self.automation_engine = MonitoringAutomationEngine(self.config)
self.remediation_engine = AutomatedRemediationEngine(self.config)
self.escalation_manager = EscalationManager(self.config)
# Observability platform
self.observability_platform = ObservabilityPlatform(self.config)
self.dashboard_service = MonitoringDashboardService(self.config)
self.visualization_engine = VisualizationEngine(self.config)
self.reporting_engine = MonitoringReportingEngine(self.config)
# AI and ML components
self.anomaly_detector = AnomalyDetector(self.config)
self.ml_engine = MonitoringMLEngine(self.config)
self.aiops_engine = AIOpsEngine(self.config)
self.nlp_processor = LogNLPProcessor(self.config)
# State management
self.metric_repository = MetricRepository()
self.alert_repository = AlertRepository()
self.insight_repository = InsightRepository()
self.monitoring_state = MonitoringState()
# Integration and data management
self.data_pipeline = MonitoringDataPipeline(self.config)
self.integration_manager = MonitoringIntegrationManager(self.config)
self.storage_manager = MonitoringStorageManager(self.config)
async def setup_comprehensive_monitoring(self, monitoring_scope, requirements):
"""
Setup comprehensive monitoring across all domains
"""
monitoring_setup = {
'setup_id': generate_uuid(),
'start_time': datetime.utcnow(),
'monitoring_scope': monitoring_scope,
'requirements': requirements,
'infrastructure_monitoring': {},
'application_monitoring': {},
'business_monitoring': {},
'security_monitoring': {},
'analytics_configuration': {}
}
try:
# Analyze monitoring requirements
monitoring_analysis = await self.analyze_monitoring_requirements(
monitoring_scope,
requirements
)
monitoring_setup['monitoring_analysis'] = monitoring_analysis
# Setup infrastructure monitoring
infrastructure_monitoring = await self.setup_infrastructure_monitoring(
monitoring_analysis
)
monitoring_setup['infrastructure_monitoring'] = infrastructure_monitoring
# Setup application monitoring
application_monitoring = await self.setup_application_monitoring(
monitoring_analysis
)
monitoring_setup['application_monitoring'] = application_monitoring
# Setup business process monitoring
business_monitoring = await self.setup_business_monitoring(
monitoring_analysis
)
monitoring_setup['business_monitoring'] = business_monitoring
# Setup security monitoring
security_monitoring = await self.setup_security_monitoring(
monitoring_analysis
)
monitoring_setup['security_monitoring'] = security_monitoring
# Configure analytics and AI
analytics_configuration = await self.configure_monitoring_analytics(
monitoring_analysis
)
monitoring_setup['analytics_configuration'] = analytics_configuration
# Setup alerting and automation
alerting_setup = await self.setup_alerting_and_automation(
monitoring_setup
)
monitoring_setup['alerting_setup'] = alerting_setup
# Configure dashboards and visualization
dashboard_setup = await self.setup_monitoring_dashboards(
monitoring_setup
)
monitoring_setup['dashboard_setup'] = dashboard_setup
# Initialize data pipeline
data_pipeline_setup = await self.initialize_monitoring_data_pipeline(
monitoring_setup
)
monitoring_setup['data_pipeline_setup'] = data_pipeline_setup
except Exception as e:
monitoring_setup['error'] = str(e)
finally:
monitoring_setup['end_time'] = datetime.utcnow()
monitoring_setup['setup_duration'] = (
monitoring_setup['end_time'] - monitoring_setup['start_time']
).total_seconds()
return monitoring_setup
async def analyze_monitoring_requirements(self, monitoring_scope, requirements):
"""
Analyze monitoring requirements and scope
"""
monitoring_analysis = {
'infrastructure_requirements': {},
'application_requirements': {},
'business_requirements': {},
'compliance_requirements': {},
'performance_requirements': {},
'scalability_requirements': {},
'integration_requirements': {}
}
# Analyze infrastructure requirements
infrastructure_requirements = await self.analyze_infrastructure_monitoring_requirements(
monitoring_scope,
requirements
)
monitoring_analysis['infrastructure_requirements'] = infrastructure_requirements
# Analyze application requirements
application_requirements = await self.analyze_application_monitoring_requirements(
monitoring_scope,
requirements
)
monitoring_analysis['application_requirements'] = application_requirements
# Analyze business requirements
business_requirements = await self.analyze_business_monitoring_requirements(
monitoring_scope,
requirements
)
monitoring_analysis['business_requirements'] = business_requirements
# Analyze compliance requirements
compliance_requirements = await self.analyze_compliance_monitoring_requirements(
requirements
)
monitoring_analysis['compliance_requirements'] = compliance_requirements
return monitoring_analysis
async def perform_real_time_analytics(self, data_stream):
"""
Perform real-time analytics on streaming monitoring data
"""
analytics_session = {
'session_id': generate_uuid(),
'start_time': datetime.utcnow(),
'data_points_processed': 0,
'anomalies_detected': [],
'patterns_identified': [],
'alerts_generated': [],
'insights_generated': []
}
try:
# Process data stream in real-time
async for data_batch in data_stream:
# Update data points counter
analytics_session['data_points_processed'] += len(data_batch)
# Perform anomaly detection
anomalies = await self.anomaly_detector.detect_anomalies_batch(data_batch)
if anomalies:
analytics_session['anomalies_detected'].extend(anomalies)
# Generate alerts for anomalies
anomaly_alerts = await self.generate_anomaly_alerts(anomalies)
analytics_session['alerts_generated'].extend(anomaly_alerts)
# Identify patterns
patterns = await self.real_time_analytics.identify_patterns(data_batch)
analytics_session['patterns_identified'].extend(patterns)
# Generate real-time insights
insights = await self.generate_real_time_insights(
data_batch,
anomalies,
patterns
)
analytics_session['insights_generated'].extend(insights)
# Update monitoring state
await self.monitoring_state.update_from_batch(data_batch)
# Process alerts and automation
for alert in anomaly_alerts:
await self.alert_manager.process_alert(alert)
except Exception as e:
analytics_session['error'] = str(e)
finally:
analytics_session['end_time'] = datetime.utcnow()
analytics_session['processing_duration'] = (
analytics_session['end_time'] - analytics_session['start_time']
).total_seconds()
return analytics_session
async def generate_predictive_insights(self, historical_data, prediction_horizon="7d"):
"""
Generate predictive insights from historical monitoring data
"""
prediction_session = {
'session_id': generate_uuid(),
'start_time': datetime.utcnow(),
'prediction_horizon': prediction_horizon,
'data_analyzed': len(historical_data),
'predictions_generated': [],
'risk_assessments': [],
'recommendations': []
}
try:
# Prepare data for prediction
prepared_data = await self.predictive_analytics.prepare_prediction_data(
historical_data
)
# Generate capacity predictions
capacity_predictions = await self.predictive_analytics.predict_capacity_requirements(
prepared_data,
prediction_horizon
)
prediction_session['predictions_generated'].extend(capacity_predictions)
# Generate performance predictions
performance_predictions = await self.predictive_analytics.predict_performance_trends(
prepared_data,
prediction_horizon
)
prediction_session['predictions_generated'].extend(performance_predictions)
# Generate failure risk predictions
failure_predictions = await self.predictive_analytics.predict_failure_risks(
prepared_data,
prediction_horizon
)
prediction_session['predictions_generated'].extend(failure_predictions)
# Assess risks based on predictions
risk_assessments = await self.assess_prediction_risks(
prediction_session['predictions_generated']
)
prediction_session['risk_assessments'] = risk_assessments
# Generate recommendations
recommendations = await self.generate_predictive_recommendations(
prediction_session['predictions_generated'],
risk_assessments
)
prediction_session['recommendations'] = recommendations
# Create predictive alerts
predictive_alerts = await self.create_predictive_alerts(
prediction_session['predictions_generated'],
risk_assessments
)
prediction_session['predictive_alerts'] = predictive_alerts
except Exception as e:
prediction_session['error'] = str(e)
finally:
prediction_session['end_time'] = datetime.utcnow()
prediction_session['prediction_duration'] = (
prediction_session['end_time'] - prediction_session['start_time']
).total_seconds()
return prediction_session
async def setup_infrastructure_monitoring(self, monitoring_analysis):
"""
Setup comprehensive infrastructure monitoring
"""
infrastructure_monitoring = {
'system_monitoring': {},
'network_monitoring': {},
'storage_monitoring': {},
'cloud_monitoring': {},
'container_monitoring': {}
}
# Setup system performance monitoring
system_monitoring = await self.setup_system_monitoring()
infrastructure_monitoring['system_monitoring'] = system_monitoring
# Setup network monitoring
network_monitoring = await self.setup_network_monitoring()
infrastructure_monitoring['network_monitoring'] = network_monitoring
# Setup storage monitoring
storage_monitoring = await self.setup_storage_monitoring()
infrastructure_monitoring['storage_monitoring'] = storage_monitoring
# Setup cloud monitoring
cloud_monitoring = await self.setup_cloud_monitoring()
infrastructure_monitoring['cloud_monitoring'] = cloud_monitoring
return infrastructure_monitoring
async def setup_system_monitoring(self):
"""
Setup system performance monitoring
"""
system_monitoring = {
'cpu_monitoring': True,
'memory_monitoring': True,
'disk_monitoring': True,
'process_monitoring': True,
'service_monitoring': True
}
# Configure CPU monitoring
cpu_metrics = [
MonitoringMetric(
metric_id="system_cpu_usage",
name="CPU Usage Percentage",
type=MetricType.GAUGE,
monitoring_type=MonitoringType.INFRASTRUCTURE,
description="System CPU utilization percentage",
unit="percent",
collection_interval=60,
retention_period=90,
thresholds={
'warning': 70.0,
'critical': 90.0
}
),
MonitoringMetric(
metric_id="system_load_average",
name="System Load Average",
type=MetricType.GAUGE,
monitoring_type=MonitoringType.INFRASTRUCTURE,
description="System load average (1, 5, 15 minutes)",
unit="load",
collection_interval=60,
retention_period=90,
thresholds={
'warning': 2.0,
'critical': 4.0
}
)
]
# Configure memory monitoring
memory_metrics = [
MonitoringMetric(
metric_id="system_memory_usage",
name="Memory Usage Percentage",
type=MetricType.GAUGE,
monitoring_type=MonitoringType.INFRASTRUCTURE,
description="System memory utilization percentage",
unit="percent",
collection_interval=60,
retention_period=90,
thresholds={
'warning': 80.0,
'critical': 95.0
}
)
]
# Register metrics
for metric in cpu_metrics + memory_metrics:
await self.metric_repository.register_metric(metric)
system_monitoring['metrics_configured'] = len(cpu_metrics + memory_metrics)
return system_monitoring
async def setup_application_monitoring(self, monitoring_analysis):
"""
Setup comprehensive application monitoring
"""
application_monitoring = {
'apm_configuration': {},
'user_experience_monitoring': {},
'api_monitoring': {},
'database_monitoring': {},
'microservices_monitoring': {}
}
# Configure APM
apm_configuration = await self.configure_application_performance_monitoring()
application_monitoring['apm_configuration'] = apm_configuration
# Configure user experience monitoring
ux_monitoring = await self.configure_user_experience_monitoring()
application_monitoring['user_experience_monitoring'] = ux_monitoring
# Configure API monitoring
api_monitoring = await self.configure_api_monitoring()
application_monitoring['api_monitoring'] = api_monitoring
return application_monitoring
async def configure_application_performance_monitoring(self):
"""
Configure application performance monitoring
"""
apm_config = {
'distributed_tracing': True,
'transaction_profiling': True,
'error_tracking': True,
'performance_profiling': True,
'dependency_mapping': True
}
# Configure application metrics
app_metrics = [
MonitoringMetric(
metric_id="app_response_time",
name="Application Response Time",
type=MetricType.HISTOGRAM,
monitoring_type=MonitoringType.APPLICATION,
description="Application response time distribution",
unit="milliseconds",
collection_interval=30,
retention_period=30,
thresholds={
'warning': 1000.0,
'critical': 5000.0
}
),
MonitoringMetric(
metric_id="app_throughput",
name="Application Throughput",
type=MetricType.COUNTER,
monitoring_type=MonitoringType.APPLICATION,
description="Application requests per second",
unit="requests/second",
collection_interval=30,
retention_period=30,
thresholds={
'warning': 100.0,
'critical': 50.0
}
),
MonitoringMetric(
metric_id="app_error_rate",
name="Application Error Rate",
type=MetricType.GAUGE,
monitoring_type=MonitoringType.APPLICATION,
description="Application error rate percentage",
unit="percent",
collection_interval=30,
retention_period=90,
thresholds={
'warning': 1.0,
'critical': 5.0
}
)
]
# Register application metrics
for metric in app_metrics:
await self.metric_repository.register_metric(metric)
apm_config['metrics_configured'] = len(app_metrics)
return apm_config
class MetricsCollector:
"""
Collects metrics from various sources
"""
def __init__(self, claude_code, config):
self.claude_code = claude_code
self.config = config
self.collection_tasks = {}
async def start_collection(self, metrics_configuration):
"""
Start metrics collection based on configuration
"""
collection_session = {
'session_id': generate_uuid(),
'start_time': datetime.utcnow(),
'metrics_configured': len(metrics_configuration),
'collection_tasks_started': 0,
'data_points_collected': 0
}
# Start collection tasks for each metric type
for metric_config in metrics_configuration:
if metric_config.monitoring_type == MonitoringType.INFRASTRUCTURE:
task = asyncio.create_task(
self.collect_infrastructure_metrics(metric_config)
)
elif metric_config.monitoring_type == MonitoringType.APPLICATION:
task = asyncio.create_task(
self.collect_application_metrics(metric_config)
)
else:
task = asyncio.create_task(
self.collect_generic_metrics(metric_config)
)
self.collection_tasks[metric_config.metric_id] = task
collection_session['collection_tasks_started'] += 1
return collection_session
async def collect_infrastructure_metrics(self, metric_config):
"""
Collect infrastructure metrics
"""
while True:
try:
# Collect system metrics based on metric type
if 'cpu' in metric_config.metric_id:
value = psutil.cpu_percent(interval=1)
elif 'memory' in metric_config.metric_id:
value = psutil.virtual_memory().percent
elif 'disk' in metric_config.metric_id:
value = psutil.disk_usage('/').percent
elif 'load' in metric_config.metric_id:
value = psutil.getloadavg()[0] if hasattr(psutil, 'getloadavg') else 0.0
else:
value = 0.0 # Default value
# Create metric data point
data_point = {
'metric_id': metric_config.metric_id,
'timestamp': datetime.utcnow(),
'value': value,
'labels': metric_config.labels
}
# Store metric data point
await self.store_metric_data_point(data_point)
# Wait for next collection interval
await asyncio.sleep(metric_config.collection_interval)
except Exception as e:
logging.error(f"Error collecting metric {metric_config.metric_id}: {e}")
await asyncio.sleep(metric_config.collection_interval)
async def store_metric_data_point(self, data_point):
"""
Store metric data point
"""
# In practice, this would store to a time-series database
# For now, we'll just log it
logging.info(f"Metric collected: {data_point}")
class AnomalyDetector:
"""
AI-powered anomaly detection for monitoring data
"""
def __init__(self, config):
self.config = config
self.models = {}
self.scaler = StandardScaler()
async def detect_anomalies_batch(self, data_batch):
"""
Detect anomalies in a batch of monitoring data
"""
anomalies = []
try:
# Prepare data for anomaly detection
df = pd.DataFrame(data_batch)
if len(df) < 10: # Need minimum data points
return anomalies
# Extract numerical features
numerical_features = df.select_dtypes(include=[np.number]).columns
if len(numerical_features) == 0:
return anomalies
# Normalize data
normalized_data = self.scaler.fit_transform(df[numerical_features])
# Use Isolation Forest for anomaly detection
iso_forest = IsolationForest(contamination=0.1, random_state=42)
anomaly_labels = iso_forest.fit_predict(normalized_data)
# Identify anomalies
for i, label in enumerate(anomaly_labels):
if label == -1: # Anomaly detected
anomaly = {
'anomaly_id': generate_uuid(),
'data_point': data_batch[i],
'anomaly_score': iso_forest.score_samples([normalized_data[i]])[0],
'detection_time': datetime.utcnow(),
'features_affected': numerical_features.tolist()
}
anomalies.append(anomaly)
except Exception as e:
logging.error(f"Error in anomaly detection: {e}")
return anomalies
def generate_uuid():
"""Generate a UUID string"""
return str(uuid.uuid4())
# Additional classes would be implemented here:
# - LogManager
# - EventProcessor
# - DistributedTraceManager
# - RealTimeAnalyticsEngine
# - PredictiveAnalyticsEngine
# - BehavioralAnalyticsEngine
# - BusinessAnalyticsEngine
# - AlertManager
# - MonitoringAutomationEngine
# - AutomatedRemediationEngine
# - EscalationManager
# - ObservabilityPlatform
# - MonitoringDashboardService
# - VisualizationEngine
# - MonitoringReportingEngine
# - MonitoringMLEngine
# - AIOpsEngine
# - LogNLPProcessor
# - MetricRepository
# - AlertRepository
# - InsightRepository
# - MonitoringState
# - MonitoringDataPipeline
# - MonitoringIntegrationManager
# - MonitoringStorageManager
```
### Advanced Monitoring & Analytics Commands
```bash
# Infrastructure monitoring setup
bmad monitor infrastructure --setup --comprehensive --predictive
bmad monitor system --cpu --memory --disk --network --real-time
bmad monitor cloud --multi-cloud --auto-scaling --cost-optimization
# Application performance monitoring
bmad monitor application --apm --distributed-tracing --profiling
bmad monitor api --performance --usage-analytics --error-tracking
bmad monitor user-experience --real-user --synthetic --journey-analytics
# Business process monitoring
bmad monitor business --transactions --workflows --kpis
bmad monitor operations --efficiency --sla-compliance --process-analytics
bmad monitor customer --journey --satisfaction --behavior-analytics
# Security and compliance monitoring
bmad monitor security --events --threats --behavioral-analytics
bmad monitor compliance --continuous --regulatory --audit-trail
bmad monitor access --patterns --anomalies --privilege-escalation
# Real-time analytics and insights
bmad analytics real-time --streaming --event-correlation --anomaly-detection
bmad analytics predictive --capacity-planning --failure-prediction
bmad analytics behavioral --user-patterns --system-behavior --optimization
# AI-powered monitoring and AIOps
bmad monitor ai --anomaly-detection --root-cause-analysis --auto-remediation
bmad monitor ml --pattern-recognition --predictive-maintenance
bmad monitor nlp --log-analysis --alert-interpretation --insights
# Alerting and automation
bmad alert setup --intelligent --correlation --escalation
bmad alert automate --response --remediation --workflows
bmad alert optimize --noise-reduction --context-enrichment
# Dashboards and visualization
bmad monitor dashboard --create --real-time --executive --operational
bmad monitor visualize --interactive --drill-down --mobile-responsive
bmad monitor report --automated --stakeholder-specific --scheduled
# Data management and integration
bmad monitor data --pipeline --integration --retention --archival
bmad monitor integrate --tools --platforms --apis --webhooks
bmad monitor storage --time-series --optimization --compression
```
This Advanced Monitoring & Analytics module provides sophisticated enterprise-grade monitoring, observability, and analytics capabilities that enable comprehensive visibility, predictive insights, and intelligent automation across all systems, processes, and business operations with real-time analytics and AI-powered monitoring throughout the entire enterprise ecosystem.
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