#!/usr/bin/env python3
"""
Enterprise System Monitoring and Observability Framework
"""

import subprocess
import json
import yaml
import logging
import time
import threading
import requests
from typing import Dict, List, Optional, Tuple, Any, Union
from dataclasses import dataclass, asdict, field
from pathlib import Path
from enum import Enum
import concurrent.futures
import statistics
import datetime

class MetricType(Enum):
    COUNTER = "counter"
    GAUGE = "gauge"
    HISTOGRAM = "histogram"
    SUMMARY = "summary"

class AlertSeverity(Enum):
    CRITICAL = "critical"
    WARNING = "warning"
    INFO = "info"
    DEBUG = "debug"

@dataclass
class MetricDefinition:
    name: str
    metric_type: MetricType
    description: str
    labels: Dict[str, str] = field(default_factory=dict)
    unit: str = ""
    collection_interval: int = 60
    retention_period: str = "7d"

@dataclass
class AlertRule:
    name: str
    expression: str
    severity: AlertSeverity
    description: str
    duration: str = "5m"
    labels: Dict[str, str] = field(default_factory=dict)
    annotations: Dict[str, str] = field(default_factory=dict)
    enabled: bool = True

@dataclass
class Dashboard:
    name: str
    description: str
    panels: List[Dict] = field(default_factory=list)
    variables: List[Dict] = field(default_factory=list)
    time_range: str = "1h"
    refresh_interval: str = "30s"

class EnterpriseMonitoringFramework:
    def __init__(self, config_file: str = "monitoring_config.yaml"):
        self.config = self._load_config(config_file)
        self.metrics_registry = {}
        self.alert_rules = {}
        self.dashboards = {}
        self.collectors = {}
        self.exporters = {}
        
        # Initialize logging
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(levelname)s - %(message)s'
        )
        self.logger = logging.getLogger(__name__)
        
    def _load_config(self, config_file: str) -> Dict:
        """Load monitoring configuration from YAML file"""
        try:
            with open(config_file, 'r') as f:
                return yaml.safe_load(f)
        except FileNotFoundError:
            return self._create_default_config()
    
    def _create_default_config(self) -> Dict:
        """Create default monitoring configuration"""
        return {
            'prometheus': {
                'url': 'http://localhost:9090',
                'retention': '15d',
                'scrape_interval': '15s'
            },
            'grafana': {
                'url': 'http://localhost:3000',
                'admin_user': 'admin',
                'admin_password': 'admin'
            },
            'alertmanager': {
                'url': 'http://localhost:9093',
                'smtp_smarthost': 'localhost:587',
                'smtp_from': 'alerts@company.com'
            },
            'collectors': {
                'node_exporter': {
                    'enabled': True,
                    'port': 9100,
                    'collectors': ['cpu', 'memory', 'disk', 'network']
                },
                'application_metrics': {
                    'enabled': True,
                    'port': 8080,
                    'path': '/metrics'
                }
            }
        }
    
    def setup_infrastructure_monitoring(self) -> Dict[str, Any]:
        """Configure comprehensive infrastructure monitoring"""
        infrastructure_metrics = {
            # System Resource Metrics
            'cpu_usage': MetricDefinition(
                name='node_cpu_usage_percent',
                metric_type=MetricType.GAUGE,
                description='CPU usage percentage by core',
                labels={'cpu': 'core_id', 'mode': 'usage_mode'},
                unit='percent'
            ),
            'memory_usage': MetricDefinition(
                name='node_memory_usage_bytes',
                metric_type=MetricType.GAUGE,
                description='Memory usage in bytes',
                labels={'type': 'memory_type'},
                unit='bytes'
            ),
            'disk_usage': MetricDefinition(
                name='node_disk_usage_percent',
                metric_type=MetricType.GAUGE,
                description='Disk usage percentage',
                labels={'device': 'disk_device', 'mountpoint': 'mount_path'},
                unit='percent'
            ),
            'network_traffic': MetricDefinition(
                name='node_network_bytes_total',
                metric_type=MetricType.COUNTER,
                description='Network traffic in bytes',
                labels={'device': 'interface', 'direction': 'rx_tx'},
                unit='bytes'
            ),
            'load_average': MetricDefinition(
                name='node_load_average',
                metric_type=MetricType.GAUGE,
                description='System load average',
                labels={'period': 'time_period'},
                unit='ratio'
            ),
            
            # Application Performance Metrics
            'request_rate': MetricDefinition(
                name='http_requests_total',
                metric_type=MetricType.COUNTER,
                description='Total HTTP requests',
                labels={'method': 'http_method', 'status': 'status_code', 'endpoint': 'api_endpoint'},
                unit='requests'
            ),
            'response_time': MetricDefinition(
                name='http_request_duration_seconds',
                metric_type=MetricType.HISTOGRAM,
                description='HTTP request duration',
                labels={'method': 'http_method', 'endpoint': 'api_endpoint'},
                unit='seconds'
            ),
            'error_rate': MetricDefinition(
                name='application_errors_total',
                metric_type=MetricType.COUNTER,
                description='Application error count',
                labels={'service': 'service_name', 'error_type': 'error_category'},
                unit='errors'
            ),
            
            # Database Performance Metrics
            'db_connections': MetricDefinition(
                name='database_connections_active',
                metric_type=MetricType.GAUGE,
                description='Active database connections',
                labels={'database': 'db_name', 'pool': 'connection_pool'},
                unit='connections'
            ),
            'query_duration': MetricDefinition(
                name='database_query_duration_seconds',
                metric_type=MetricType.HISTOGRAM,
                description='Database query execution time',
                labels={'database': 'db_name', 'query_type': 'operation'},
                unit='seconds'
            )
        }
        
        # Register metrics
        for metric_name, metric_def in infrastructure_metrics.items():
            self.metrics_registry[metric_name] = metric_def
            
        return infrastructure_metrics
    
    def configure_alert_rules(self) -> Dict[str, AlertRule]:
        """Configure comprehensive alerting rules"""
        alert_rules = {
            # Critical System Alerts
            'high_cpu_usage': AlertRule(
                name='HighCPUUsage',
                expression='avg(rate(node_cpu_seconds_total{mode!="idle"}[5m])) * 100 > 80',
                severity=AlertSeverity.CRITICAL,
                description='CPU usage is above 80%',
                duration='5m',
                annotations={
                    'summary': 'High CPU usage detected on {{ $labels.instance }}',
                    'description': 'CPU usage has been above 80% for more than 5 minutes'
                }
            ),
            'high_memory_usage': AlertRule(
                name='HighMemoryUsage',
                expression='(1 - (node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes)) * 100 > 90',
                severity=AlertSeverity.CRITICAL,
                description='Memory usage is above 90%',
                duration='5m',
                annotations={
                    'summary': 'High memory usage detected on {{ $labels.instance }}',
                    'description': 'Memory usage has been above 90% for more than 5 minutes'
                }
            ),
            'disk_space_low': AlertRule(
                name='DiskSpaceLow',
                expression='(1 - (node_filesystem_free_bytes / node_filesystem_size_bytes)) * 100 > 85',
                severity=AlertSeverity.WARNING,
                description='Disk space usage is above 85%',
                duration='10m',
                annotations={
                    'summary': 'Low disk space on {{ $labels.instance }}',
                    'description': 'Disk usage on {{ $labels.mountpoint }} is above 85%'
                }
            ),
            'service_down': AlertRule(
                name='ServiceDown',
                expression='up == 0',
                severity=AlertSeverity.CRITICAL,
                description='Service is down',
                duration='1m',
                annotations={
                    'summary': 'Service {{ $labels.job }} is down',
                    'description': 'Service has been down for more than 1 minute'
                }
            ),
            
            # Application Performance Alerts
            'high_error_rate': AlertRule(
                name='HighErrorRate',
                expression='rate(http_requests_total{status=~"5.."}[5m]) / rate(http_requests_total[5m]) * 100 > 5',
                severity=AlertSeverity.WARNING,
                description='HTTP error rate is above 5%',
                duration='5m',
                annotations={
                    'summary': 'High error rate detected',
                    'description': '5xx error rate is above 5% for more than 5 minutes'
                }
            ),
            'slow_response_time': AlertRule(
                name='SlowResponseTime',
                expression='histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m])) > 2',
                severity=AlertSeverity.WARNING,
                description='95th percentile response time is above 2 seconds',
                duration='5m',
                annotations={
                    'summary': 'Slow response time detected',
                    'description': '95th percentile response time is above 2 seconds'
                }
            ),
            
            # Database Performance Alerts
            'database_connection_pool_exhausted': AlertRule(
                name='DatabaseConnectionPoolExhausted',
                expression='database_connections_active / database_connections_max * 100 > 90',
                severity=AlertSeverity.CRITICAL,
                description='Database connection pool usage is above 90%',
                duration='2m',
                annotations={
                    'summary': 'Database connection pool nearly exhausted',
                    'description': 'Connection pool usage is above 90%'
                }
            )
        }
        
        # Register alert rules
        for rule_name, rule_def in alert_rules.items():
            self.alert_rules[rule_name] = rule_def
            
        return alert_rules
    
    def create_monitoring_dashboards(self) -> Dict[str, Dashboard]:
        """Create comprehensive monitoring dashboards"""
        dashboards = {
            'infrastructure_overview': Dashboard(
                name='Infrastructure Overview',
                description='High-level infrastructure health and performance metrics',
                panels=[
                    {
                        'title': 'CPU Usage',
                        'type': 'stat',
                        'targets': [
                            {
                                'expr': 'avg(rate(node_cpu_seconds_total{mode!="idle"}[5m])) * 100',
                                'legendFormat': 'CPU Usage %'
                            }
                        ]
                    },
                    {
                        'title': 'Memory Usage',
                        'type': 'stat',
                        'targets': [
                            {
                                'expr': '(1 - (avg(node_memory_MemAvailable_bytes) / avg(node_memory_MemTotal_bytes))) * 100',
                                'legendFormat': 'Memory Usage %'
                            }
                        ]
                    },
                    {
                        'title': 'Disk Usage',
                        'type': 'stat',
                        'targets': [
                            {
                                'expr': 'avg((1 - (node_filesystem_free_bytes / node_filesystem_size_bytes)) * 100)',
                                'legendFormat': 'Disk Usage %'
                            }
                        ]
                    },
                    {
                        'title': 'Network Traffic',
                        'type': 'graph',
                        'targets': [
                            {
                                'expr': 'rate(node_network_receive_bytes_total[5m])',
                                'legendFormat': 'Inbound - {{ device }}'
                            },
                            {
                                'expr': 'rate(node_network_transmit_bytes_total[5m])',
                                'legendFormat': 'Outbound - {{ device }}'
                            }
                        ]
                    }
                ]
            ),
            'application_performance': Dashboard(
                name='Application Performance',
                description='Application-specific performance metrics and SLA tracking',
                panels=[
                    {
                        'title': 'Request Rate',
                        'type': 'graph',
                        'targets': [
                            {
                                'expr': 'rate(http_requests_total[5m])',
                                'legendFormat': '{{ method }} {{ endpoint }}'
                            }
                        ]
                    },
                    {
                        'title': 'Response Time Percentiles',
                        'type': 'graph',
                        'targets': [
                            {
                                'expr': 'histogram_quantile(0.50, rate(http_request_duration_seconds_bucket[5m]))',
                                'legendFormat': '50th percentile'
                            },
                            {
                                'expr': 'histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))',
                                'legendFormat': '95th percentile'
                            },
                            {
                                'expr': 'histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m]))',
                                'legendFormat': '99th percentile'
                            }
                        ]
                    },
                    {
                        'title': 'Error Rate',
                        'type': 'stat',
                        'targets': [
                            {
                                'expr': 'rate(http_requests_total{status=~"5.."}[5m]) / rate(http_requests_total[5m]) * 100',
                                'legendFormat': 'Error Rate %'
                            }
                        ]
                    }
                ]
            )
        }
        
        # Register dashboards
        for dashboard_name, dashboard_def in dashboards.items():
            self.dashboards[dashboard_name] = dashboard_def
            
        return dashboards
    
    def setup_log_aggregation(self) -> Dict[str, Any]:
        """Configure centralized log aggregation and analysis"""
        log_config = {
            'collectors': {
                'fluentd': {
                    'enabled': True,
                    'config': {
                        'sources': [
                            {
                                'type': 'tail',
                                'path': '/var/log/nginx/access.log',
                                'tag': 'nginx.access',
                                'format': 'nginx'
                            },
                            {
                                'type': 'tail',
                                'path': '/var/log/application/*.log',
                                'tag': 'application.*',
                                'format': 'json'
                            }
                        ],
                        'filters': [
                            {
                                'type': 'parser',
                                'key_name': 'message',
                                'format': 'json'
                            },
                            {
                                'type': 'record_transformer',
                                'record': {
                                    'hostname': '#{Socket.gethostname}',
                                    'environment': '${ENVIRONMENT}'
                                }
                            }
                        ],
                        'outputs': [
                            {
                                'type': 'elasticsearch',
                                'host': 'elasticsearch.monitoring.svc.cluster.local',
                                'port': 9200,
                                'index_name': 'logs-${tag}-%Y.%m.%d'
                            }
                        ]
                    }
                },
                'vector': {
                    'enabled': True,
                    'config': {
                        'sources': {
                            'internal_logs': {
                                'type': 'internal_logs'
                            },
                            'host_metrics': {
                                'type': 'host_metrics',
                                'scrape_interval_secs': 30
                            }
                        },
                        'transforms': {
                            'log_enrichment': {
                                'type': 'lua',
                                'inputs': ['internal_logs'],
                                'source': '''
                                    function process(event, emit)
                                        event.log.datacenter = os.getenv("DATACENTER")
                                        event.log.cluster = os.getenv("CLUSTER_NAME")
                                        emit(event)
                                    end
                                '''
                            }
                        },
                        'sinks': {
                            'prometheus_metrics': {
                                'type': 'prometheus_exporter',
                                'inputs': ['host_metrics'],
                                'address': '0.0.0.0:9598'
                            },
                            'log_storage': {
                                'type': 'elasticsearch',
                                'inputs': ['log_enrichment'],
                                'endpoints': ['http://elasticsearch:9200'],
                                'index': 'vector-logs-%Y-%m-%d'
                            }
                        }
                    }
                }
            },
            'processors': {
                'logstash': {
                    'enabled': True,
                    'pipelines': [
                        {
                            'name': 'application_logs',
                            'config': '''
                                input {
                                    beats {
                                        port => 5044
                                    }
                                }
                                
                                filter {
                                    if [fields][log_type] == "application" {
                                        json {
                                            source => "message"
                                        }
                                        
                                        if [level] in ["ERROR", "FATAL"] {
                                            mutate {
                                                add_tag => ["alert_required"]
                                            }
                                        }
                                        
                                        date {
                                            match => ["timestamp", "ISO8601"]
                                        }
                                    }
                                }
                                
                                output {
                                    elasticsearch {
                                        hosts => ["elasticsearch:9200"]
                                        index => "application-logs-%{+YYYY.MM.dd}"
                                    }
                                    
                                    if "alert_required" in [tags] {
                                        http {
                                            url => "http://alertmanager:9093/api/v1/alerts"
                                            http_method => "post"
                                            format => "json"
                                        }
                                    }
                                }
                            '''
                        }
                    ]
                }
            }
        }
        
        return log_config
    
    def implement_distributed_tracing(self) -> Dict[str, Any]:
        """Implement comprehensive distributed tracing"""
        tracing_config = {
            'jaeger': {
                'enabled': True,
                'collector_endpoint': 'http://jaeger-collector:14268/api/traces',
                'sampling_config': {
                    'type': 'probabilistic',
                    'param': 0.1  # Sample 10% of traces
                },
                'instrumentation': {
                    'http_requests': True,
                    'database_queries': True,
                    'cache_operations': True,
                    'message_queues': True
                }
            },
            'opentelemetry': {
                'enabled': True,
                'exporters': [
                    {
                        'type': 'jaeger',
                        'endpoint': 'http://jaeger-collector:14250'
                    },
                    {
                        'type': 'prometheus',
                        'endpoint': 'http://prometheus:9090/api/v1/write'
                    }
                ],
                'processors': [
                    {
                        'type': 'batch',
                        'config': {
                            'timeout': '1s',
                            'send_batch_size': 1024
                        }
                    },
                    {
                        'type': 'resource',
                        'config': {
                            'attributes': [
                                {'key': 'service.name', 'value': '${SERVICE_NAME}'},
                                {'key': 'service.version', 'value': '${SERVICE_VERSION}'},
                                {'key': 'deployment.environment', 'value': '${ENVIRONMENT}'}
                            ]
                        }
                    }
                ]
            }
        }
        
        return tracing_config
    
    def setup_anomaly_detection(self) -> Dict[str, Any]:
        """Configure AI-powered anomaly detection"""
        anomaly_detection_config = {
            'prometheus_anomaly_detector': {
                'enabled': True,
                'models': [
                    {
                        'name': 'cpu_anomaly_detection',
                        'metric': 'node_cpu_usage_percent',
                        'algorithm': 'isolation_forest',
                        'training_window': '7d',
                        'detection_window': '1h',
                        'threshold': 0.95
                    },
                    {
                        'name': 'response_time_anomaly',
                        'metric': 'http_request_duration_seconds',
                        'algorithm': 'lstm',
                        'training_window': '14d',
                        'detection_window': '30m',
                        'threshold': 0.9
                    }
                ]
            },
            'custom_ml_pipeline': {
                'enabled': True,
                'framework': 'scikit-learn',
                'features': [
                    'cpu_usage', 'memory_usage', 'disk_io',
                    'network_traffic', 'response_time', 'error_rate'
                ],
                'algorithms': [
                    'isolation_forest',
                    'one_class_svm',
                    'local_outlier_factor'
                ],
                'ensemble_method': 'voting',
                'retraining_schedule': '0 2 * * 0'  # Weekly retraining
            }
        }
        
        return anomaly_detection_config
    
    def generate_monitoring_config_files(self) -> Dict[str, str]:
        """Generate complete monitoring configuration files"""
        configs = {}
        
        # Prometheus configuration
        prometheus_config = {
            'global': {
                'scrape_interval': '15s',
                'evaluation_interval': '15s'
            },
            'rule_files': [
                '/etc/prometheus/rules/*.yml'
            ],
            'alerting': {
                'alertmanagers': [
                    {
                        'static_configs': [
                            {
                                'targets': ['alertmanager:9093']
                            }
                        ]
                    }
                ]
            },
            'scrape_configs': [
                {
                    'job_name': 'prometheus',
                    'static_configs': [
                        {
                            'targets': ['localhost:9090']
                        }
                    ]
                },
                {
                    'job_name': 'node_exporter',
                    'static_configs': [
                        {
                            'targets': ['node-exporter:9100']
                        }
                    ]
                },
                {
                    'job_name': 'application',
                    'kubernetes_sd_configs': [
                        {
                            'role': 'pod'
                        }
                    ],
                    'relabel_configs': [
                        {
                            'source_labels': ['__meta_kubernetes_pod_annotation_prometheus_io_scrape'],
                            'action': 'keep',
                            'regex': 'true'
                        }
                    ]
                }
            ]
        }
        
        configs['prometheus.yml'] = yaml.dump(prometheus_config, default_flow_style=False)
        
        # Alertmanager configuration
        alertmanager_config = {
            'global': {
                'smtp_smarthost': 'smtp.company.com:587',
                'smtp_from': 'alerts@company.com'
            },
            'route': {
                'group_by': ['alertname'],
                'group_wait': '10s',
                'group_interval': '10s',
                'repeat_interval': '1h',
                'receiver': 'web.hook'
            },
            'receivers': [
                {
                    'name': 'web.hook',
                    'email_configs': [
                        {
                            'to': 'admin@company.com',
                            'subject': 'Alert: {{ .GroupLabels.alertname }}',
                            'body': '''
                                {{ range .Alerts }}
                                Alert: {{ .Annotations.summary }}
                                Description: {{ .Annotations.description }}
                                {{ end }}
                            '''
                        }
                    ],
                    'slack_configs': [
                        {
                            'api_url': 'https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK',
                            'channel': '#alerts',
                            'title': 'Alert: {{ .GroupLabels.alertname }}',
                            'text': '{{ range .Alerts }}{{ .Annotations.description }}{{ end }}'
                        }
                    ]
                }
            ]
        }
        
        configs['alertmanager.yml'] = yaml.dump(alertmanager_config, default_flow_style=False)
        
        # Grafana datasource configuration
        grafana_datasources = {
            'apiVersion': 1,
            'datasources': [
                {
                    'name': 'Prometheus',
                    'type': 'prometheus',
                    'url': 'http://prometheus:9090',
                    'access': 'proxy',
                    'isDefault': True
                },
                {
                    'name': 'Elasticsearch',
                    'type': 'elasticsearch',
                    'url': 'http://elasticsearch:9200',
                    'access': 'proxy',
                    'database': 'logs-*',
                    'timeField': '@timestamp'
                },
                {
                    'name': 'Jaeger',
                    'type': 'jaeger',
                    'url': 'http://jaeger-query:16686',
                    'access': 'proxy'
                }
            ]
        }
        
        configs['grafana-datasources.yml'] = yaml.dump(grafana_datasources, default_flow_style=False)
        
        return configs
    
    def deploy_monitoring_stack(self) -> Dict[str, Any]:
        """Deploy complete monitoring stack using Docker Compose"""
        docker_compose = {
            'version': '3.8',
            'services': {
                'prometheus': {
                    'image': 'prom/prometheus:latest',
                    'container_name': 'prometheus',
                    'ports': ['9090:9090'],
                    'volumes': [
                        './prometheus.yml:/etc/prometheus/prometheus.yml',
                        './rules:/etc/prometheus/rules',
                        'prometheus_data:/prometheus'
                    ],
                    'command': [
                        '--config.file=/etc/prometheus/prometheus.yml',
                        '--storage.tsdb.path=/prometheus',
                        '--web.console.libraries=/etc/prometheus/console_libraries',
                        '--web.console.templates=/etc/prometheus/consoles',
                        '--storage.tsdb.retention.time=15d',
                        '--web.enable-lifecycle'
                    ]
                },
                'grafana': {
                    'image': 'grafana/grafana:latest',
                    'container_name': 'grafana',
                    'ports': ['3000:3000'],
                    'volumes': [
                        'grafana_data:/var/lib/grafana',
                        './grafana-datasources.yml:/etc/grafana/provisioning/datasources/datasources.yml'
                    ],
                    'environment': {
                        'GF_SECURITY_ADMIN_PASSWORD': 'admin123',
                        'GF_INSTALL_PLUGINS': 'grafana-piechart-panel,grafana-worldmap-panel'
                    }
                },
                'alertmanager': {
                    'image': 'prom/alertmanager:latest',
                    'container_name': 'alertmanager',
                    'ports': ['9093:9093'],
                    'volumes': [
                        './alertmanager.yml:/etc/alertmanager/alertmanager.yml'
                    ]
                },
                'node_exporter': {
                    'image': 'prom/node-exporter:latest',
                    'container_name': 'node_exporter',
                    'ports': ['9100:9100'],
                    'volumes': [
                        '/proc:/host/proc:ro',
                        '/sys:/host/sys:ro',
                        '/:/rootfs:ro'
                    ],
                    'command': [
                        '--path.procfs=/host/proc',
                        '--path.sysfs=/host/sys',
                        '--collector.filesystem.ignored-mount-points',
                        '^/(sys|proc|dev|host|etc|rootfs/var/lib/docker/containers|rootfs/var/lib/docker/overlay2|rootfs/run/docker/netns|rootfs/var/lib/docker/aufs)($$|/)'
                    ]
                },
                'elasticsearch': {
                    'image': 'elasticsearch:7.17.0',
                    'container_name': 'elasticsearch',
                    'ports': ['9200:9200'],
                    'environment': {
                        'discovery.type': 'single-node',
                        'ES_JAVA_OPTS': '-Xms512m -Xmx512m'
                    },
                    'volumes': ['elasticsearch_data:/usr/share/elasticsearch/data']
                },
                'kibana': {
                    'image': 'kibana:7.17.0',
                    'container_name': 'kibana',
                    'ports': ['5601:5601'],
                    'environment': {
                        'ELASTICSEARCH_HOSTS': 'http://elasticsearch:9200'
                    },
                    'depends_on': ['elasticsearch']
                },
                'jaeger': {
                    'image': 'jaegertracing/all-in-one:latest',
                    'container_name': 'jaeger',
                    'ports': [
                        '16686:16686',
                        '14268:14268'
                    ],
                    'environment': {
                        'COLLECTOR_ZIPKIN_HTTP_PORT': '9411'
                    }
                }
            },
            'volumes': {
                'prometheus_data': {},
                'grafana_data': {},
                'elasticsearch_data': {}
            }
        }
        
        return docker_compose

def main():
    """Main execution function"""
    # Initialize monitoring framework
    monitoring = EnterpriseMonitoringFramework()
    
    # Setup comprehensive monitoring
    print("Setting up infrastructure monitoring...")
    infrastructure_metrics = monitoring.setup_infrastructure_monitoring()
    print(f"Configured {len(infrastructure_metrics)} infrastructure metrics")
    
    print("Configuring alert rules...")
    alert_rules = monitoring.configure_alert_rules()
    print(f"Configured {len(alert_rules)} alert rules")
    
    print("Creating monitoring dashboards...")
    dashboards = monitoring.create_monitoring_dashboards()
    print(f"Created {len(dashboards)} monitoring dashboards")
    
    print("Setting up log aggregation...")
    log_config = monitoring.setup_log_aggregation()
    print("Log aggregation configured")
    
    print("Implementing distributed tracing...")
    tracing_config = monitoring.implement_distributed_tracing()
    print("Distributed tracing implemented")
    
    print("Setting up anomaly detection...")
    anomaly_config = monitoring.setup_anomaly_detection()
    print("Anomaly detection configured")
    
    print("Generating configuration files...")
    config_files = monitoring.generate_monitoring_config_files()
    for filename, content in config_files.items():
        with open(filename, 'w') as f:
            f.write(content)
        print(f"Generated {filename}")
    
    print("Generating Docker Compose deployment...")
    docker_compose = monitoring.deploy_monitoring_stack()
    with open('docker-compose.monitoring.yml', 'w') as f:
        yaml.dump(docker_compose, f, default_flow_style=False)
    print("Generated docker-compose.monitoring.yml")
    
    print("\nMonitoring framework setup complete!")
    print("Next steps:")
    print("1. Review and customize configuration files")
    print("2. Deploy monitoring stack: docker-compose -f docker-compose.monitoring.yml up -d")
    print("3. Access Grafana at http://localhost:3000 (admin/admin123)")
    print("4. Configure additional dashboards and alerts as needed")

if __name__ == "__main__":
    main()

#!/bin/bash
# Enterprise SLO Monitoring and SLA Management Script

set -euo pipefail

# SLO Configuration
declare -A SLO_DEFINITIONS=(
    ["availability"]="99.9"
    ["response_time_p95"]="500"  # milliseconds
    ["error_rate"]="0.1"         # percentage
    ["throughput"]="1000"        # requests per second
)

# SLA Monitoring Functions
calculate_availability_sli() {
    local service_name="$1"
    local time_window="${2:-1h}"
    
    # Calculate availability SLI using Prometheus
    local uptime_query="avg_over_time(up{job=\"$service_name\"}[$time_window])"
    local availability=$(prometheus_query "$uptime_query")
    
    echo "$(echo "$availability * 100" | bc -l)"
}

calculate_latency_sli() {
    local service_name="$1"
    local percentile="${2:-0.95}"
    local time_window="${3:-1h}"
    
    # Calculate latency SLI
    local latency_query="histogram_quantile($percentile, rate(http_request_duration_seconds_bucket{job=\"$service_name\"}[$time_window]))"
    local latency=$(prometheus_query "$latency_query")
    
    # Convert to milliseconds
    echo "$(echo "$latency * 1000" | bc -l)"
}

calculate_error_rate_sli() {
    local service_name="$1"
    local time_window="${2:-1h}"
    
    # Calculate error rate SLI
    local error_query="rate(http_requests_total{job=\"$service_name\",status=~\"5..\"}[$time_window]) / rate(http_requests_total{job=\"$service_name\"}[$time_window]) * 100"
    local error_rate=$(prometheus_query "$error_query")
    
    echo "$error_rate"
}

prometheus_query() {
    local query="$1"
    local prometheus_url="${PROMETHEUS_URL:-http://localhost:9090}"
    
    curl -s -G "$prometheus_url/api/v1/query" \
        --data-urlencode "query=$query" | \
        jq -r '.data.result[0].value[1] // "0"'
}

# SLO Monitoring and Alerting
monitor_slos() {
    local service_name="$1"
    local time_window="${2:-1h}"
    
    echo "Monitoring SLOs for service: $service_name"
    echo "Time window: $time_window"
    echo "================================================"
    
    # Check availability SLO
    local availability=$(calculate_availability_sli "$service_name" "$time_window")
    local availability_threshold="${SLO_DEFINITIONS[availability]}"
    
    echo "Availability SLI: ${availability}%"
    echo "Availability SLO: ${availability_threshold}%"
    
    if (( $(echo "$availability < $availability_threshold" | bc -l) )); then
        echo "❌ Availability SLO BREACH detected!"
        send_slo_alert "availability" "$service_name" "$availability" "$availability_threshold"
    else
        echo "✅ Availability SLO met"
    fi
    
    # Check latency SLO
    local p95_latency=$(calculate_latency_sli "$service_name" "0.95" "$time_window")
    local latency_threshold="${SLO_DEFINITIONS[response_time_p95]}"
    
    echo "P95 Latency SLI: ${p95_latency}ms"
    echo "P95 Latency SLO: ${latency_threshold}ms"
    
    if (( $(echo "$p95_latency > $latency_threshold" | bc -l) )); then
        echo "❌ Latency SLO BREACH detected!"
        send_slo_alert "latency" "$service_name" "$p95_latency" "$latency_threshold"
    else
        echo "✅ Latency SLO met"
    fi
    
    # Check error rate SLO
    local error_rate=$(calculate_error_rate_sli "$service_name" "$time_window")
    local error_threshold="${SLO_DEFINITIONS[error_rate]}"
    
    echo "Error Rate SLI: ${error_rate}%"
    echo "Error Rate SLO: ${error_threshold}%"
    
    if (( $(echo "$error_rate > $error_threshold" | bc -l) )); then
        echo "❌ Error Rate SLO BREACH detected!"
        send_slo_alert "error_rate" "$service_name" "$error_rate" "$error_threshold"
    else
        echo "✅ Error Rate SLO met"
    fi
    
    echo "================================================"
}

send_slo_alert() {
    local slo_type="$1"
    local service_name="$2"
    local current_value="$3"
    local threshold="$4"
    
    local alert_payload=$(cat <<EOF
{
    "receiver": "slo-alerts",
    "status": "firing",
    "alerts": [
        {
            "status": "firing",
            "labels": {
                "alertname": "SLOBreach",
                "service": "$service_name",
                "slo_type": "$slo_type",
                "severity": "critical"
            },
            "annotations": {
                "summary": "$slo_type SLO breach for service $service_name",
                "description": "$slo_type SLI ($current_value) exceeds SLO threshold ($threshold)",
                "runbook_url": "https://runbooks.company.com/slo-breach"
            },
            "startsAt": "$(date -u +%Y-%m-%dT%H:%M:%S.%3NZ)"
        }
    ]
}
EOF
    )
    
    # Send alert to Alertmanager
    curl -X POST "http://localhost:9093/api/v1/alerts" \
        -H "Content-Type: application/json" \
        -d "$alert_payload"
}

# Error Budget Calculation
calculate_error_budget() {
    local service_name="$1"
    local time_period="${2:-30d}"  # 30 days by default
    
    echo "Calculating error budget for $service_name over $time_period"
    
    # Calculate actual availability over the period
    local actual_availability=$(calculate_availability_sli "$service_name" "$time_period")
    local target_availability="${SLO_DEFINITIONS[availability]}"
    
    # Calculate error budget
    local allowed_downtime=$(echo "100 - $target_availability" | bc -l)
    local actual_downtime=$(echo "100 - $actual_availability" | bc -l)
    local error_budget_consumed=$(echo "scale=2; $actual_downtime / $allowed_downtime * 100" | bc -l)
    
    echo "Target Availability: ${target_availability}%"
    echo "Actual Availability: ${actual_availability}%"
    echo "Allowed Downtime: ${allowed_downtime}%"
    echo "Actual Downtime: ${actual_downtime}%"
    echo "Error Budget Consumed: ${error_budget_consumed}%"
    
    # Check if error budget is exhausted
    if (( $(echo "$error_budget_consumed > 100" | bc -l) )); then
        echo "❌ ERROR BUDGET EXHAUSTED!"
        return 1
    elif (( $(echo "$error_budget_consumed > 80" | bc -l) )); then
        echo "⚠️  Error budget critically low"
        return 2
    else
        echo "✅ Error budget healthy"
        return 0
    fi
}

# Main execution
main() {
    if [[ $# -lt 1 ]]; then
        echo "Usage: $0 <service_name> [time_window]"
        echo "Example: $0 web-api 1h"
        exit 1
    fi
    
    local service_name="$1"
    local time_window="${2:-1h}"
    
    echo "Enterprise SLO Monitoring Report"
    echo "Generated at: $(date)"
    echo "Service: $service_name"
    echo ""
    
    # Monitor current SLOs
    monitor_slos "$service_name" "$time_window"
    
    echo ""
    echo "Error Budget Analysis"
    echo "===================="
    calculate_error_budget "$service_name"
}

# Execute if run directly
if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then
    main "$@"
fi

# Complete Kubernetes monitoring stack
apiVersion: v1
kind: Namespace
metadata:
  name: monitoring
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: prometheus
  namespace: monitoring
spec:
  replicas: 2
  selector:
    matchLabels:
      app: prometheus
  template:
    metadata:
      labels:
        app: prometheus
    spec:
      containers:
      - name: prometheus
        image: prom/prometheus:v2.40.0
        ports:
        - containerPort: 9090
        volumeMounts:
        - name: config
          mountPath: /etc/prometheus
        - name: storage
          mountPath: /prometheus
        args:
        - '--config.file=/etc/prometheus/prometheus.yml'
        - '--storage.tsdb.path=/prometheus'
        - '--storage.tsdb.retention.time=15d'
        - '--web.enable-lifecycle'
        - '--web.enable-admin-api'
        resources:
          requests:
            memory: "2Gi"
            cpu: "1000m"
          limits:
            memory: "4Gi"
            cpu: "2000m"
      volumes:
      - name: config
        configMap:
          name: prometheus-config
      - name: storage
        persistentVolumeClaim:
          claimName: prometheus-storage
---
apiVersion: v1
kind: Service
metadata:
  name: prometheus
  namespace: monitoring
spec:
  selector:
    app: prometheus
  ports:
  - port: 9090
    targetPort: 9090
  type: ClusterIP
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: grafana
  namespace: monitoring
spec:
  replicas: 1
  selector:
    matchLabels:
      app: grafana
  template:
    metadata:
      labels:
        app: grafana
    spec:
      containers:
      - name: grafana
        image: grafana/grafana:9.3.0
        ports:
        - containerPort: 3000
        env:
        - name: GF_SECURITY_ADMIN_PASSWORD
          valueFrom:
            secretKeyRef:
              name: grafana-admin
              key: password
        - name: GF_INSTALL_PLUGINS
          value: "grafana-piechart-panel,grafana-worldmap-panel,grafana-kubernetes-app"
        volumeMounts:
        - name: storage
          mountPath: /var/lib/grafana
        - name: datasources
          mountPath: /etc/grafana/provisioning/datasources
        - name: dashboards-config
          mountPath: /etc/grafana/provisioning/dashboards
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "1Gi"
            cpu: "1000m"
      volumes:
      - name: storage
        persistentVolumeClaim:
          claimName: grafana-storage
      - name: datasources
        configMap:
          name: grafana-datasources
      - name: dashboards-config
        configMap:
          name: grafana-dashboards-config
---
apiVersion: v1
kind: Service
metadata:
  name: grafana
  namespace: monitoring
spec:
  selector:
    app: grafana
  ports:
  - port: 3000
    targetPort: 3000
  type: LoadBalancer