package observability

import (
    "context"
    "sync"
    "time"
    "fmt"
    "encoding/json"
    "net"
    "log"
    "github.com/prometheus/client_golang/prometheus"
    "github.com/prometheus/client_golang/prometheus/promauto"
    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/trace"
    "go.opentelemetry.io/otel/metric"
)

type TelemetryType int

const (
    TelemetryMetrics TelemetryType = iota
    TelemetryLogs
    TelemetryTraces
    TelemetryEvents
    TelemetryFlows
)

type ObservabilityLevel int

const (
    LevelBasic ObservabilityLevel = iota
    LevelStandard
    LevelDetailed
    LevelDeep
    LevelFull
)

type TelemetrySource struct {
    SourceID     string            `json:"source_id"`
    SourceType   string            `json:"source_type"`
    IPAddress    net.IP            `json:"ip_address"`
    Port         int               `json:"port"`
    Protocol     string            `json:"protocol"`
    Credentials  map[string]string `json:"credentials"`
    CollectionConfig TelemetryConfig `json:"collection_config"`
    Status       SourceStatus      `json:"status"`
    LastContact  time.Time         `json:"last_contact"`
    Capabilities []string          `json:"capabilities"`
}

type TelemetryConfig struct {
    Enabled         bool                           `json:"enabled"`
    CollectionLevel ObservabilityLevel             `json:"collection_level"`
    Interval        time.Duration                  `json:"interval"`
    Types           []TelemetryType                `json:"types"`
    Filters         map[string]interface{}         `json:"filters"`
    Enrichment      map[string]string              `json:"enrichment"`
    Retention       map[TelemetryType]time.Duration `json:"retention"`
    Compression     bool                           `json:"compression"`
    Encryption      bool                           `json:"encryption"`
}

type SourceStatus int

const (
    StatusActive SourceStatus = iota
    StatusInactive
    StatusError
    StatusMaintenance
)

type TelemetryRecord struct {
    RecordID     string                 `json:"record_id"`
    Timestamp    time.Time              `json:"timestamp"`
    SourceID     string                 `json:"source_id"`
    Type         TelemetryType          `json:"type"`
    Category     string                 `json:"category"`
    Data         map[string]interface{} `json:"data"`
    Metadata     map[string]string      `json:"metadata"`
    Labels       map[string]string      `json:"labels"`
    Annotations  map[string]string      `json:"annotations"`
    Severity     string                 `json:"severity"`
    TraceID      string                 `json:"trace_id,omitempty"`
    SpanID       string                 `json:"span_id,omitempty"`
}

type NetworkMetrics struct {
    InterfaceMetrics    map[string]InterfaceStats     `json:"interface_metrics"`
    ProtocolMetrics     map[string]ProtocolStats      `json:"protocol_metrics"`
    FlowMetrics         map[string]FlowStats          `json:"flow_metrics"`
    PerformanceMetrics  PerformanceStats              `json:"performance_metrics"`
    SecurityMetrics     SecurityStats                 `json:"security_metrics"`
    CustomMetrics       map[string]interface{}        `json:"custom_metrics"`
}

type InterfaceStats struct {
    InterfaceName    string    `json:"interface_name"`
    AdminStatus      string    `json:"admin_status"`
    OperStatus       string    `json:"oper_status"`
    Speed            uint64    `json:"speed"`
    MTU              int       `json:"mtu"`
    InOctets         uint64    `json:"in_octets"`
    OutOctets        uint64    `json:"out_octets"`
    InPackets        uint64    `json:"in_packets"`
    OutPackets       uint64    `json:"out_packets"`
    InErrors         uint64    `json:"in_errors"`
    OutErrors        uint64    `json:"out_errors"`
    InDiscards       uint64    `json:"in_discards"`
    OutDiscards      uint64    `json:"out_discards"`
    InUcastPackets   uint64    `json:"in_ucast_packets"`
    OutUcastPackets  uint64    `json:"out_ucast_packets"`
    InMcastPackets   uint64    `json:"in_mcast_packets"`
    OutMcastPackets  uint64    `json:"out_mcast_packets"`
    InBcastPackets   uint64    `json:"in_bcast_packets"`
    OutBcastPackets  uint64    `json:"out_bcast_packets"`
    Utilization      float64   `json:"utilization"`
    LastUpdated      time.Time `json:"last_updated"`
}

type ProtocolStats struct {
    Protocol         string                 `json:"protocol"`
    PacketCount      uint64                 `json:"packet_count"`
    ByteCount        uint64                 `json:"byte_count"`
    ErrorCount       uint64                 `json:"error_count"`
    DropCount        uint64                 `json:"drop_count"`
    SessionCount     uint64                 `json:"session_count"`
    ActiveSessions   uint64                 `json:"active_sessions"`
    ResponseTimes    []float64              `json:"response_times"`
    ProtocolSpecific map[string]interface{} `json:"protocol_specific"`
    LastUpdated      time.Time              `json:"last_updated"`
}

type FlowStats struct {
    FlowKey          string    `json:"flow_key"`
    SourceIP         net.IP    `json:"source_ip"`
    DestinationIP    net.IP    `json:"destination_ip"`
    SourcePort       uint16    `json:"source_port"`
    DestinationPort  uint16    `json:"destination_port"`
    Protocol         string    `json:"protocol"`
    FirstSeen        time.Time `json:"first_seen"`
    LastSeen         time.Time `json:"last_seen"`
    PacketCount      uint64    `json:"packet_count"`
    ByteCount        uint64    `json:"byte_count"`
    Direction        string    `json:"direction"`
    Application      string    `json:"application"`
    QoSClass         string    `json:"qos_class"`
    Flags            []string  `json:"flags"`
}

type PerformanceStats struct {
    Latency          float64              `json:"latency"`
    Jitter           float64              `json:"jitter"`
    PacketLoss       float64              `json:"packet_loss"`
    Throughput       float64              `json:"throughput"`
    Bandwidth        float64              `json:"bandwidth"`
    CPUUtilization   float64              `json:"cpu_utilization"`
    MemoryUtilization float64             `json:"memory_utilization"`
    QueueDepth       map[string]int       `json:"queue_depth"`
    BufferUtilization map[string]float64  `json:"buffer_utilization"`
    ErrorRates       map[string]float64   `json:"error_rates"`
    LastUpdated      time.Time            `json:"last_updated"`
}

type SecurityStats struct {
    ThreatCount        uint64                 `json:"threat_count"`
    BlockedConnections uint64                 `json:"blocked_connections"`
    PolicyViolations   uint64                 `json:"policy_violations"`
    AnomalousFlows     uint64                 `json:"anomalous_flows"`
    SecurityEvents     []SecurityEvent        `json:"security_events"`
    ThreatIndicators   map[string]interface{} `json:"threat_indicators"`
    LastUpdated        time.Time              `json:"last_updated"`
}

type SecurityEvent struct {
    EventID      string                 `json:"event_id"`
    Timestamp    time.Time              `json:"timestamp"`
    EventType    string                 `json:"event_type"`
    Severity     string                 `json:"severity"`
    Source       string                 `json:"source"`
    Destination  string                 `json:"destination"`
    Description  string                 `json:"description"`
    Indicators   map[string]interface{} `json:"indicators"`
    Actions      []string               `json:"actions"`
    Remediation  string                 `json:"remediation"`
}

type NetworkObservabilityEngine struct {
    TelemetryCollectors  map[string]TelemetryCollector
    MetricsProcessors    []MetricsProcessor
    LogProcessors        []LogProcessor
    TraceProcessors      []TraceProcessor
    EventProcessors      []EventProcessor
    AnalyticsEngine      *AnalyticsEngine
    AlertManager         *AlertManager
    DashboardManager     *DashboardManager
    DataPipeline         *DataPipeline
    StorageManager       *StorageManager
    ConfigManager        *ConfigManager
    mutex                sync.RWMutex
    
    // Prometheus metrics
    metricsCounter       prometheus.Counter
    processingDuration   prometheus.Histogram
    activeCollectors     prometheus.Gauge
}

func NewNetworkObservabilityEngine() *NetworkObservabilityEngine {
    engine := &NetworkObservabilityEngine{
        TelemetryCollectors: make(map[string]TelemetryCollector),
        MetricsProcessors:   []MetricsProcessor{},
        LogProcessors:       []LogProcessor{},
        TraceProcessors:     []TraceProcessor{},
        EventProcessors:     []EventProcessor{},
        AnalyticsEngine:     NewAnalyticsEngine(),
        AlertManager:        NewAlertManager(),
        DashboardManager:    NewDashboardManager(),
        DataPipeline:        NewDataPipeline(),
        StorageManager:      NewStorageManager(),
        ConfigManager:       NewConfigManager(),
        
        // Initialize Prometheus metrics
        metricsCounter: promauto.NewCounter(prometheus.CounterOpts{
            Name: "network_telemetry_records_total",
            Help: "Total number of telemetry records processed",
        }),
        processingDuration: promauto.NewHistogram(prometheus.HistogramOpts{
            Name:    "network_telemetry_processing_duration_seconds",
            Help:    "Duration of telemetry processing",
            Buckets: prometheus.DefBuckets,
        }),
        activeCollectors: promauto.NewGauge(prometheus.GaugeOpts{
            Name: "network_telemetry_active_collectors",
            Help: "Number of active telemetry collectors",
        }),
    }
    
    return engine
}

func (noe *NetworkObservabilityEngine) RegisterTelemetrySource(source TelemetrySource) error {
    noe.mutex.Lock()
    defer noe.mutex.Unlock()
    
    // Create appropriate collector based on source type
    var collector TelemetryCollector
    var err error
    
    switch source.SourceType {
    case "snmp":
        collector, err = NewSNMPCollector(source)
    case "netflow":
        collector, err = NewNetFlowCollector(source)
    case "sflow":
        collector, err = NewSFlowCollector(source)
    case "streaming_telemetry":
        collector, err = NewStreamingTelemetryCollector(source)
    case "syslog":
        collector, err = NewSyslogCollector(source)
    case "api":
        collector, err = NewAPICollector(source)
    default:
        return fmt.Errorf("unsupported source type: %s", source.SourceType)
    }
    
    if err != nil {
        return fmt.Errorf("failed to create collector: %v", err)
    }
    
    noe.TelemetryCollectors[source.SourceID] = collector
    noe.activeCollectors.Inc()
    
    log.Printf("Registered telemetry source: %s (%s)", source.SourceID, source.SourceType)
    return nil
}

func (noe *NetworkObservabilityEngine) StartCollection(ctx context.Context) error {
    // Start all telemetry collectors
    for sourceID, collector := range noe.TelemetryCollectors {
        go func(id string, c TelemetryCollector) {
            if err := c.Start(ctx, noe.processTelemetryRecord); err != nil {
                log.Printf("Collector %s failed: %v", id, err)
            }
        }(sourceID, collector)
    }
    
    // Start data processing pipeline
    go noe.DataPipeline.Start(ctx)
    
    // Start analytics engine
    go noe.AnalyticsEngine.Start(ctx)
    
    // Start alert manager
    go noe.AlertManager.Start(ctx)
    
    log.Println("Network observability engine started")
    return nil
}

func (noe *NetworkObservabilityEngine) processTelemetryRecord(record TelemetryRecord) {
    startTime := time.Now()
    defer func() {
        noe.processingDuration.Observe(time.Since(startTime).Seconds())
        noe.metricsCounter.Inc()
    }()
    
    // Enrich record with additional metadata
    enrichedRecord := noe.enrichTelemetryRecord(record)
    
    // Route to appropriate processors
    switch record.Type {
    case TelemetryMetrics:
        for _, processor := range noe.MetricsProcessors {
            processor.Process(enrichedRecord)
        }
    case TelemetryLogs:
        for _, processor := range noe.LogProcessors {
            processor.Process(enrichedRecord)
        }
    case TelemetryTraces:
        for _, processor := range noe.TraceProcessors {
            processor.Process(enrichedRecord)
        }
    case TelemetryEvents:
        for _, processor := range noe.EventProcessors {
            processor.Process(enrichedRecord)
        }
    case TelemetryFlows:
        // Special handling for flow data
        noe.processFlowData(enrichedRecord)
    }
    
    // Send to analytics engine
    noe.AnalyticsEngine.ProcessRecord(enrichedRecord)
    
    // Store record
    noe.StorageManager.StoreRecord(enrichedRecord)
}

func (noe *NetworkObservabilityEngine) enrichTelemetryRecord(record TelemetryRecord) TelemetryRecord {
    // Add correlation IDs if not present
    if record.TraceID == "" {
        record.TraceID = generateTraceID()
    }
    
    // Add geolocation information
    if sourceIP, exists := record.Data["source_ip"]; exists {
        if ip, ok := sourceIP.(string); ok {
            geoInfo := noe.getGeolocationInfo(ip)
            record.Metadata["geo_country"] = geoInfo.Country
            record.Metadata["geo_city"] = geoInfo.City
            record.Metadata["geo_coordinates"] = fmt.Sprintf("%f,%f", geoInfo.Latitude, geoInfo.Longitude)
        }
    }
    
    // Add network topology context
    if interfaceName, exists := record.Data["interface_name"]; exists {
        topologyContext := noe.getTopologyContext(record.SourceID, interfaceName.(string))
        for key, value := range topologyContext {
            record.Metadata[key] = value
        }
    }
    
    // Add business context
    businessContext := noe.getBusinessContext(record)
    for key, value := range businessContext {
        record.Annotations[key] = value
    }
    
    return record
}

type TelemetryCollector interface {
    Start(ctx context.Context, processor func(TelemetryRecord)) error
    Stop() error
    GetStatus() CollectorStatus
    GetMetrics() CollectorMetrics
}

type CollectorStatus struct {
    Status          string    `json:"status"`
    LastCollection  time.Time `json:"last_collection"`
    RecordsCollected uint64   `json:"records_collected"`
    ErrorCount      uint64    `json:"error_count"`
    LastError       string    `json:"last_error"`
}

type CollectorMetrics struct {
    CollectionRate    float64   `json:"collection_rate"`
    ProcessingLatency float64   `json:"processing_latency"`
    ErrorRate         float64   `json:"error_rate"`
    DataVolume        uint64    `json:"data_volume"`
    LastUpdated       time.Time `json:"last_updated"`
}

type StreamingTelemetryCollector struct {
    source       TelemetrySource
    connection   net.Conn
    subscription map[string]interface{}
    decoder      TelemetryDecoder
    processor    func(TelemetryRecord)
    status       CollectorStatus
    metrics      CollectorMetrics
    mutex        sync.RWMutex
}

func NewStreamingTelemetryCollector(source TelemetrySource) (*StreamingTelemetryCollector, error) {
    collector := &StreamingTelemetryCollector{
        source:       source,
        subscription: make(map[string]interface{}),
        decoder:      NewGRPCTelemetryDecoder(),
        status: CollectorStatus{
            Status: "initialized",
        },
        metrics: CollectorMetrics{
            LastUpdated: time.Now(),
        },
    }
    
    return collector, nil
}

func (stc *StreamingTelemetryCollector) Start(ctx context.Context, processor func(TelemetryRecord)) error {
    stc.processor = processor
    
    // Establish connection to telemetry source
    conn, err := net.Dial(stc.source.Protocol, fmt.Sprintf("%s:%d", stc.source.IPAddress, stc.source.Port))
    if err != nil {
        return fmt.Errorf("failed to connect to telemetry source: %v", err)
    }
    stc.connection = conn
    
    // Configure subscriptions
    err = stc.configureSubscriptions()
    if err != nil {
        return fmt.Errorf("failed to configure subscriptions: %v", err)
    }
    
    // Start data collection loop
    go stc.collectionLoop(ctx)
    
    stc.updateStatus("active", "")
    return nil
}

func (stc *StreamingTelemetryCollector) collectionLoop(ctx context.Context) {
    ticker := time.NewTicker(1 * time.Second)
    defer ticker.Stop()
    
    for {
        select {
        case <-ctx.Done():
            return
        case <-ticker.C:
            stc.collectData()
        }
    }
}

func (stc *StreamingTelemetryCollector) collectData() {
    stc.mutex.Lock()
    defer stc.mutex.Unlock()
    
    startTime := time.Now()
    
    // Read data from connection
    buffer := make([]byte, 65536)
    n, err := stc.connection.Read(buffer)
    if err != nil {
        stc.updateStatus("error", err.Error())
        return
    }
    
    // Decode telemetry data
    records, err := stc.decoder.Decode(buffer[:n])
    if err != nil {
        stc.status.ErrorCount++
        stc.updateStatus("error", err.Error())
        return
    }
    
    // Process each record
    for _, record := range records {
        record.SourceID = stc.source.SourceID
        record.Timestamp = time.Now()
        stc.processor(record)
        stc.status.RecordsCollected++
    }
    
    // Update metrics
    processingTime := time.Since(startTime)
    stc.metrics.ProcessingLatency = processingTime.Seconds()
    stc.metrics.CollectionRate = float64(len(records)) / processingTime.Seconds()
    stc.metrics.DataVolume += uint64(n)
    stc.metrics.LastUpdated = time.Now()
    
    stc.status.LastCollection = time.Now()
}

func (stc *StreamingTelemetryCollector) updateStatus(status, errorMsg string) {
    stc.status.Status = status
    if errorMsg != "" {
        stc.status.LastError = errorMsg
        stc.status.ErrorCount++
    }
}

type AnalyticsEngine struct {
    PatternDetector     *PatternDetector
    AnomalyDetector     *AnomalyDetector
    TrendAnalyzer       *TrendAnalyzer
    CorrelationEngine   *CorrelationEngine
    PredictiveModels    map[string]PredictiveModel
    RealTimeProcessor   *RealTimeProcessor
    BatchProcessor      *BatchProcessor
    MLPipeline          *MLPipeline
    mutex               sync.RWMutex
}

func NewAnalyticsEngine() *AnalyticsEngine {
    return &AnalyticsEngine{
        PatternDetector:   NewPatternDetector(),
        AnomalyDetector:   NewAnomalyDetector(),
        TrendAnalyzer:     NewTrendAnalyzer(),
        CorrelationEngine: NewCorrelationEngine(),
        PredictiveModels:  make(map[string]PredictiveModel),
        RealTimeProcessor: NewRealTimeProcessor(),
        BatchProcessor:    NewBatchProcessor(),
        MLPipeline:        NewMLPipeline(),
    }
}

func (ae *AnalyticsEngine) Start(ctx context.Context) error {
    // Start real-time processing
    go ae.RealTimeProcessor.Start(ctx)
    
    // Start batch processing
    go ae.BatchProcessor.Start(ctx)
    
    // Start ML pipeline
    go ae.MLPipeline.Start(ctx)
    
    return nil
}

func (ae *AnalyticsEngine) ProcessRecord(record TelemetryRecord) {
    ae.mutex.Lock()
    defer ae.mutex.Unlock()
    
    // Real-time pattern detection
    patterns := ae.PatternDetector.DetectPatterns(record)
    if len(patterns) > 0 {
        ae.handleDetectedPatterns(record, patterns)
    }
    
    // Real-time anomaly detection
    anomalies := ae.AnomalyDetector.DetectAnomalies(record)
    if len(anomalies) > 0 {
        ae.handleDetectedAnomalies(record, anomalies)
    }
    
    // Update trend analysis
    ae.TrendAnalyzer.UpdateTrends(record)
    
    // Correlation analysis
    correlations := ae.CorrelationEngine.FindCorrelations(record)
    if len(correlations) > 0 {
        ae.handleCorrelations(record, correlations)
    }
    
    // Feed to real-time processor
    ae.RealTimeProcessor.ProcessRecord(record)
}

func (ae *AnalyticsEngine) handleDetectedPatterns(record TelemetryRecord, patterns []Pattern) {
    for _, pattern := range patterns {
        // Create pattern event
        event := TelemetryRecord{
            RecordID:  generateRecordID(),
            Timestamp: time.Now(),
            SourceID:  "analytics_engine",
            Type:      TelemetryEvents,
            Category:  "pattern_detection",
            Data: map[string]interface{}{
                "pattern_type":        pattern.Type,
                "pattern_description": pattern.Description,
                "confidence":          pattern.Confidence,
                "original_record":     record.RecordID,
            },
            Metadata: map[string]string{
                "severity": pattern.Severity,
            },
            TraceID: record.TraceID,
        }
        
        // Process pattern event
        ae.ProcessRecord(event)
    }
}

type Pattern struct {
    Type        string                 `json:"type"`
    Description string                 `json:"description"`
    Confidence  float64                `json:"confidence"`
    Severity    string                 `json:"severity"`
    Attributes  map[string]interface{} `json:"attributes"`
    StartTime   time.Time              `json:"start_time"`
    EndTime     time.Time              `json:"end_time"`
}

type Anomaly struct {
    Type        string                 `json:"type"`
    Description string                 `json:"description"`
    Score       float64                `json:"score"`
    Threshold   float64                `json:"threshold"`
    Severity    string                 `json:"severity"`
    Context     map[string]interface{} `json:"context"`
    DetectedAt  time.Time              `json:"detected_at"`
}

import asyncio
import numpy as np
import pandas as pd
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
import time
import logging
from datetime import datetime, timedelta
from sklearn.ensemble import IsolationForest, RandomForestRegressor
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
from scipy import stats
import tensorflow as tf

class AnalyticsType(Enum):
    REAL_TIME = "real_time"
    BATCH = "batch"
    STREAMING = "streaming"
    PREDICTIVE = "predictive"

class InsightType(Enum):
    ANOMALY = "anomaly"
    PATTERN = "pattern"
    TREND = "trend"
    CORRELATION = "correlation"
    PREDICTION = "prediction"
    RECOMMENDATION = "recommendation"

@dataclass
class NetworkInsight:
    insight_id: str
    timestamp: datetime
    insight_type: InsightType
    title: str
    description: str
    severity: str
    confidence: float
    affected_entities: List[str]
    metrics: Dict[str, Any]
    context: Dict[str, Any]
    recommendations: List[str] = field(default_factory=list)
    correlation_id: Optional[str] = None

@dataclass
class AnalyticsJob:
    job_id: str
    job_type: AnalyticsType
    data_sources: List[str]
    analysis_config: Dict[str, Any]
    schedule: Optional[str] = None
    dependencies: List[str] = field(default_factory=list)
    status: str = "pending"
    created_at: datetime = field(default_factory=datetime.now)

class NetworkAnalyticsEngine:
    def __init__(self):
        self.real_time_analyzers = {
            'anomaly': RealTimeAnomalyDetector(),
            'pattern': RealTimePatternDetector(),
            'correlation': RealTimeCorrelationAnalyzer()
        }
        self.batch_analyzers = {
            'trend': TrendAnalyzer(),
            'capacity': CapacityAnalyzer(),
            'performance': PerformanceAnalyzer(),
            'security': SecurityAnalyzer()
        }
        self.predictive_models = PredictiveModelManager()
        self.insight_generator = InsightGenerator()
        self.recommendation_engine = RecommendationEngine()
        self.data_warehouse = NetworkDataWarehouse()
        
    async def analyze_network_data(self, data: Dict[str, Any], 
                                 analysis_config: Dict[str, Any]) -> List[NetworkInsight]:
        """Comprehensive network data analysis"""
        insights = []
        
        # Real-time analysis
        if analysis_config.get('real_time_enabled', True):
            real_time_insights = await self._perform_real_time_analysis(data)
            insights.extend(real_time_insights)
        
        # Batch analysis
        if analysis_config.get('batch_enabled', True):
            batch_insights = await self._perform_batch_analysis(data)
            insights.extend(batch_insights)
        
        # Predictive analysis
        if analysis_config.get('predictive_enabled', True):
            predictive_insights = await self._perform_predictive_analysis(data)
            insights.extend(predictive_insights)
        
        # Generate meta-insights from correlation analysis
        meta_insights = await self._generate_meta_insights(insights)
        insights.extend(meta_insights)
        
        return insights
    
    async def _perform_real_time_analysis(self, data: Dict[str, Any]) -> List[NetworkInsight]:
        """Perform real-time network analysis"""
        insights = []
        
        # Anomaly detection
        anomaly_insights = await self.real_time_analyzers['anomaly'].detect_anomalies(data)
        insights.extend(anomaly_insights)
        
        # Pattern detection
        pattern_insights = await self.real_time_analyzers['pattern'].detect_patterns(data)
        insights.extend(pattern_insights)
        
        # Correlation analysis
        correlation_insights = await self.real_time_analyzers['correlation'].analyze_correlations(data)
        insights.extend(correlation_insights)
        
        return insights
    
    async def _perform_batch_analysis(self, data: Dict[str, Any]) -> List[NetworkInsight]:
        """Perform batch network analysis"""
        insights = []
        
        # Historical data retrieval
        historical_data = await self.data_warehouse.get_historical_data(
            time_range=timedelta(days=30),
            data_types=['metrics', 'flows', 'events']
        )
        
        # Trend analysis
        trend_insights = await self.batch_analyzers['trend'].analyze_trends(historical_data)
        insights.extend(trend_insights)
        
        # Capacity analysis
        capacity_insights = await self.batch_analyzers['capacity'].analyze_capacity(historical_data)
        insights.extend(capacity_insights)
        
        # Performance analysis
        performance_insights = await self.batch_analyzers['performance'].analyze_performance(historical_data)
        insights.extend(performance_insights)
        
        # Security analysis
        security_insights = await self.batch_analyzers['security'].analyze_security(historical_data)
        insights.extend(security_insights)
        
        return insights
    
    async def _perform_predictive_analysis(self, data: Dict[str, Any]) -> List[NetworkInsight]:
        """Perform predictive network analysis"""
        insights = []
        
        # Load predictive models
        models = await self.predictive_models.get_active_models()
        
        for model_name, model in models.items():
            predictions = await model.predict(data)
            
            for prediction in predictions:
                insight = NetworkInsight(
                    insight_id=f"pred_{model_name}_{int(time.time())}",
                    timestamp=datetime.now(),
                    insight_type=InsightType.PREDICTION,
                    title=f"Predictive Analysis: {prediction['title']}",
                    description=prediction['description'],
                    severity=prediction['severity'],
                    confidence=prediction['confidence'],
                    affected_entities=prediction['affected_entities'],
                    metrics=prediction['metrics'],
                    context={
                        'model_name': model_name,
                        'prediction_horizon': prediction['horizon'],
                        'model_accuracy': model.get_accuracy()
                    }
                )
                insights.append(insight)
        
        return insights

class RealTimeAnomalyDetector:
    """Real-time anomaly detection using multiple algorithms"""
    
    def __init__(self):
        self.isolation_forest = IsolationForest(contamination=0.1, random_state=42)
        self.statistical_detector = StatisticalAnomalyDetector()
        self.baseline_tracker = BaselineTracker()
        self.ensemble_detector = EnsembleAnomalyDetector()
        
    async def detect_anomalies(self, data: Dict[str, Any]) -> List[NetworkInsight]:
        """Detect network anomalies in real-time"""
        insights = []
        
        # Extract numerical features for ML-based detection
        numerical_features = self._extract_numerical_features(data)
        
        if numerical_features:
            # Machine learning-based detection
            ml_anomalies = await self._detect_ml_anomalies(numerical_features, data)
            insights.extend(ml_anomalies)
        
        # Statistical anomaly detection
        statistical_anomalies = await self._detect_statistical_anomalies(data)
        insights.extend(statistical_anomalies)
        
        # Baseline deviation detection
        baseline_anomalies = await self._detect_baseline_deviations(data)
        insights.extend(baseline_anomalies)
        
        # Ensemble detection for high-confidence anomalies
        ensemble_anomalies = await self.ensemble_detector.detect(data, insights)
        insights.extend(ensemble_anomalies)
        
        return insights
    
    async def _detect_ml_anomalies(self, features: np.ndarray, 
                                 context: Dict[str, Any]) -> List[NetworkInsight]:
        """Detect anomalies using machine learning"""
        insights = []
        
        # Normalize features
        scaler = StandardScaler()
        normalized_features = scaler.fit_transform(features.reshape(1, -1))
        
        # Isolation Forest detection
        anomaly_score = self.isolation_forest.decision_function(normalized_features)[0]
        is_anomaly = self.isolation_forest.predict(normalized_features)[0] == -1
        
        if is_anomaly:
            insight = NetworkInsight(
                insight_id=f"ml_anomaly_{int(time.time())}",
                timestamp=datetime.now(),
                insight_type=InsightType.ANOMALY,
                title="Machine Learning Anomaly Detected",
                description=f"Isolation Forest detected anomaly with score {anomaly_score:.3f}",
                severity=self._calculate_severity(abs(anomaly_score)),
                confidence=min(abs(anomaly_score), 1.0),
                affected_entities=self._extract_affected_entities(context),
                metrics={
                    'anomaly_score': anomaly_score,
                    'feature_vector': features.tolist(),
                    'detection_method': 'isolation_forest'
                },
                context=context
            )
            insights.append(insight)
        
        return insights
    
    async def _detect_statistical_anomalies(self, data: Dict[str, Any]) -> List[NetworkInsight]:
        """Detect statistical anomalies using time series analysis"""
        insights = []
        
        # Check each metric for statistical anomalies
        for metric_name, metric_value in data.get('metrics', {}).items():
            if isinstance(metric_value, (int, float)):
                # Get historical values for this metric
                historical_values = await self._get_historical_metric_values(metric_name)
                
                if len(historical_values) > 10:  # Need enough history
                    # Calculate z-score
                    mean_val = np.mean(historical_values)
                    std_val = np.std(historical_values)
                    
                    if std_val > 0:
                        z_score = abs((metric_value - mean_val) / std_val)
                        
                        if z_score > 3:  # 3-sigma rule
                            insight = NetworkInsight(
                                insight_id=f"stat_anomaly_{metric_name}_{int(time.time())}",
                                timestamp=datetime.now(),
                                insight_type=InsightType.ANOMALY,
                                title=f"Statistical Anomaly in {metric_name}",
                                description=f"Metric {metric_name} deviates {z_score:.2f} standard deviations from normal",
                                severity=self._calculate_severity(z_score / 3),  # Normalize to 0-1
                                confidence=min(z_score / 3, 1.0),
                                affected_entities=[data.get('source_id', 'unknown')],
                                metrics={
                                    'current_value': metric_value,
                                    'historical_mean': mean_val,
                                    'historical_std': std_val,
                                    'z_score': z_score,
                                    'detection_method': 'statistical'
                                },
                                context=data
                            )
                            insights.append(insight)
        
        return insights
    
    def _calculate_severity(self, score: float) -> str:
        """Calculate severity based on anomaly score"""
        if score >= 0.9:
            return "critical"
        elif score >= 0.7:
            return "high"
        elif score >= 0.5:
            return "medium"
        else:
            return "low"

class TrendAnalyzer:
    """Analyze long-term trends in network data"""
    
    def __init__(self):
        self.seasonal_decomposer = SeasonalDecomposer()
        self.change_point_detector = ChangePointDetector()
        self.forecast_engine = ForecastEngine()
        
    async def analyze_trends(self, historical_data: Dict[str, Any]) -> List[NetworkInsight]:
        """Analyze trends in historical network data"""
        insights = []
        
        # Analyze trends for each metric type
        for metric_type, time_series_data in historical_data.items():
            if len(time_series_data) > 100:  # Need sufficient data
                # Seasonal decomposition
                seasonal_insights = await self._analyze_seasonal_patterns(metric_type, time_series_data)
                insights.extend(seasonal_insights)
                
                # Change point detection
                change_point_insights = await self._detect_change_points(metric_type, time_series_data)
                insights.extend(change_point_insights)
                
                # Trend forecasting
                forecast_insights = await self._generate_forecasts(metric_type, time_series_data)
                insights.extend(forecast_insights)
        
        return insights
    
    async def _analyze_seasonal_patterns(self, metric_type: str, 
                                       time_series: List[Dict[str, Any]]) -> List[NetworkInsight]:
        """Analyze seasonal patterns in time series data"""
        insights = []
        
        # Convert to pandas DataFrame for analysis
        df = pd.DataFrame(time_series)
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        df.set_index('timestamp', inplace=True)
        
        # Perform seasonal decomposition for each numeric column
        for column in df.select_dtypes(include=[np.number]).columns:
            decomposition = self.seasonal_decomposer.decompose(df[column])
            
            # Analyze seasonal strength
            seasonal_strength = self._calculate_seasonal_strength(decomposition)
            
            if seasonal_strength > 0.3:  # Significant seasonality
                insight = NetworkInsight(
                    insight_id=f"seasonal_{metric_type}_{column}_{int(time.time())}",
                    timestamp=datetime.now(),
                    insight_type=InsightType.PATTERN,
                    title=f"Seasonal Pattern Detected in {metric_type}.{column}",
                    description=f"Strong seasonal pattern with strength {seasonal_strength:.2f}",
                    severity="medium",
                    confidence=seasonal_strength,
                    affected_entities=[metric_type],
                    metrics={
                        'seasonal_strength': seasonal_strength,
                        'trend_component': decomposition.trend.tolist(),
                        'seasonal_component': decomposition.seasonal.tolist(),
                        'residual_component': decomposition.resid.tolist()
                    },
                    context={
                        'metric_type': metric_type,
                        'column': column,
                        'analysis_period': f"{df.index.min()} to {df.index.max()}"
                    }
                )
                insights.append(insight)
        
        return insights
    
    async def _detect_change_points(self, metric_type: str,
                                  time_series: List[Dict[str, Any]]) -> List[NetworkInsight]:
        """Detect significant change points in time series"""
        insights = []
        
        df = pd.DataFrame(time_series)
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        
        for column in df.select_dtypes(include=[np.number]).columns:
            change_points = self.change_point_detector.detect(df[column].values)
            
            for change_point in change_points:
                change_time = df.iloc[change_point['index']]['timestamp']
                
                insight = NetworkInsight(
                    insight_id=f"change_point_{metric_type}_{column}_{change_point['index']}",
                    timestamp=datetime.now(),
                    insight_type=InsightType.TREND,
                    title=f"Change Point Detected in {metric_type}.{column}",
                    description=f"Significant change detected at {change_time}",
                    severity=self._assess_change_severity(change_point),
                    confidence=change_point['confidence'],
                    affected_entities=[metric_type],
                    metrics={
                        'change_point_index': change_point['index'],
                        'change_time': change_time.isoformat(),
                        'before_mean': change_point['before_mean'],
                        'after_mean': change_point['after_mean'],
                        'change_magnitude': change_point['magnitude']
                    },
                    context={
                        'metric_type': metric_type,
                        'column': column
                    }
                )
                insights.append(insight)
        
        return insights

class PredictiveModelManager:
    """Manage predictive models for network forecasting"""
    
    def __init__(self):
        self.models = {}
        self.model_registry = ModelRegistry()
        self.training_pipeline = TrainingPipeline()
        self.model_evaluator = ModelEvaluator()
        
    async def get_active_models(self) -> Dict[str, Any]:
        """Get all active predictive models"""
        active_models = {}
        
        # Load pre-trained models
        model_configs = await self.model_registry.get_active_models()
        
        for model_config in model_configs:
            model = await self._load_model(model_config)
            if model and model.is_ready():
                active_models[model_config['name']] = model
        
        return active_models
    
    async def _load_model(self, model_config: Dict[str, Any]) -> Optional['PredictiveModel']:
        """Load a predictive model"""
        model_type = model_config['type']
        
        if model_type == 'capacity_forecasting':
            return CapacityForecastingModel(model_config)
        elif model_type == 'anomaly_prediction':
            return AnomalyPredictionModel(model_config)
        elif model_type == 'performance_prediction':
            return PerformancePredictionModel(model_config)
        elif model_type == 'failure_prediction':
            return FailurePredictionModel(model_config)
        
        return None

class CapacityForecastingModel:
    """Predictive model for network capacity forecasting"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.model = self._build_model()
        self.scaler = StandardScaler()
        self.is_trained = False
        
    def _build_model(self) -> tf.keras.Model:
        """Build LSTM model for time series forecasting"""
        model = tf.keras.Sequential([
            tf.keras.layers.LSTM(50, return_sequences=True, input_shape=(30, 1)),
            tf.keras.layers.Dropout(0.2),
            tf.keras.layers.LSTM(50, return_sequences=True),
            tf.keras.layers.Dropout(0.2),
            tf.keras.layers.LSTM(50),
            tf.keras.layers.Dropout(0.2),
            tf.keras.layers.Dense(1)
        ])
        
        model.compile(optimizer='adam', loss='mse', metrics=['mae'])
        return model
    
    async def predict(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Generate capacity predictions"""
        predictions = []
        
        if not self.is_trained:
            await self._train_model(data)
        
        # Extract capacity-related metrics
        capacity_metrics = self._extract_capacity_metrics(data)
        
        for metric_name, values in capacity_metrics.items():
            if len(values) >= 30:  # Need enough history
                # Prepare data for prediction
                scaled_values = self.scaler.fit_transform(np.array(values).reshape(-1, 1))
                input_data = scaled_values[-30:].reshape(1, 30, 1)
                
                # Generate prediction
                prediction = self.model.predict(input_data)[0][0]
                prediction = self.scaler.inverse_transform([[prediction]])[0][0]
                
                # Calculate prediction confidence
                confidence = self._calculate_prediction_confidence(values, prediction)
                
                # Assess prediction severity
                severity = self._assess_capacity_severity(values[-1], prediction)
                
                predictions.append({
                    'title': f"Capacity Forecast for {metric_name}",
                    'description': f"Predicted value: {prediction:.2f}",
                    'severity': severity,
                    'confidence': confidence,
                    'affected_entities': [data.get('source_id', 'unknown')],
                    'metrics': {
                        'current_value': values[-1],
                        'predicted_value': prediction,
                        'prediction_horizon': self.config.get('horizon', '1_hour'),
                        'metric_name': metric_name
                    },
                    'horizon': self.config.get('horizon', '1_hour')
                })
        
        return predictions
    
    def _extract_capacity_metrics(self, data: Dict[str, Any]) -> Dict[str, List[float]]:
        """Extract capacity-related metrics from data"""
        capacity_metrics = {}
        
        # Look for utilization metrics
        metrics = data.get('metrics', {})
        for key, value in metrics.items():
            if 'utilization' in key.lower() or 'usage' in key.lower():
                if isinstance(value, (int, float)):
                    # Get historical values for this metric
                    historical_values = self._get_historical_values(key)
                    if historical_values:
                        capacity_metrics[key] = historical_values
        
        return capacity_metrics
    
    def _calculate_prediction_confidence(self, historical_values: List[float], 
                                       prediction: float) -> float:
        """Calculate confidence in prediction based on historical variance"""
        if len(historical_values) < 10:
            return 0.5
        
        # Calculate historical variance
        variance = np.var(historical_values)
        mean_value = np.mean(historical_values)
        
        # Calculate coefficient of variation
        cv = np.sqrt(variance) / mean_value if mean_value > 0 else 1.0
        
        # Confidence inversely related to coefficient of variation
        confidence = max(0.1, min(0.9, 1.0 - cv))
        
        return confidence
    
    def _assess_capacity_severity(self, current_value: float, predicted_value: float) -> str:
        """Assess severity based on capacity prediction"""
        # Calculate percentage change
        if current_value > 0:
            change_percent = abs(predicted_value - current_value) / current_value
        else:
            change_percent = 0
        
        # Check if approaching capacity limits
        if predicted_value > 90:  # Approaching 90% utilization
            return "critical"
        elif predicted_value > 80:  # Approaching 80% utilization
            return "high"
        elif change_percent > 0.2:  # 20% change
            return "medium"
        else:
            return "low"
    
    def is_ready(self) -> bool:
        """Check if model is ready for predictions"""
        return True  # Simplified for example
    
    def get_accuracy(self) -> float:
        """Get model accuracy"""
        return 0.85  # Simplified for example

class NetworkObservabilityDashboard:
    """Comprehensive network observability dashboard"""
    
    def __init__(self):
        self.dashboard_builder = DashboardBuilder()
        self.widget_factory = WidgetFactory()
        self.data_aggregator = DataAggregator()
        self.alert_integrator = AlertIntegrator()
        
    def create_comprehensive_dashboard(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Create comprehensive network observability dashboard"""
        dashboard = {
            'dashboard_id': config.get('id', 'network_observability'),
            'title': 'Enterprise Network Observability',
            'layout': 'grid',
            'refresh_interval': config.get('refresh_interval', 30),
            'widgets': []
        }
        
        # Network overview widget
        overview_widget = self._create_network_overview_widget()
        dashboard['widgets'].append(overview_widget)
        
        # Real-time metrics widget
        metrics_widget = self._create_real_time_metrics_widget()
        dashboard['widgets'].append(metrics_widget)
        
        # Topology widget
        topology_widget = self._create_topology_widget()
        dashboard['widgets'].append(topology_widget)
        
        # Performance analytics widget
        performance_widget = self._create_performance_analytics_widget()
        dashboard['widgets'].append(performance_widget)
        
        # Security monitoring widget
        security_widget = self._create_security_monitoring_widget()
        dashboard['widgets'].append(security_widget)
        
        # Capacity planning widget
        capacity_widget = self._create_capacity_planning_widget()
        dashboard['widgets'].append(capacity_widget)
        
        # Alerts and incidents widget
        alerts_widget = self._create_alerts_widget()
        dashboard['widgets'].append(alerts_widget)
        
        return dashboard
    
    def _create_network_overview_widget(self) -> Dict[str, Any]:
        """Create network overview widget"""
        return {
            'widget_id': 'network_overview',
            'title': 'Network Overview',
            'type': 'overview',
            'size': {'width': 12, 'height': 6},
            'data_sources': ['network_summary'],
            'visualization': {
                'type': 'summary_cards',
                'metrics': [
                    'total_devices',
                    'active_interfaces',
                    'total_flows',
                    'network_health_score'
                ]
            },
            'refresh_interval': 30
        }
    
    def _create_real_time_metrics_widget(self) -> Dict[str, Any]:
        """Create real-time metrics widget"""
        return {
            'widget_id': 'real_time_metrics',
            'title': 'Real-Time Network Metrics',
            'type': 'time_series',
            'size': {'width': 12, 'height': 8},
            'data_sources': ['interface_metrics', 'flow_metrics'],
            'visualization': {
                'type': 'line_chart',
                'metrics': [
                    'bandwidth_utilization',
                    'packet_rate',
                    'latency',
                    'packet_loss'
                ],
                'time_range': '1h'
            },
            'refresh_interval': 10
        }
    
    def _create_topology_widget(self) -> Dict[str, Any]:
        """Create network topology widget"""
        return {
            'widget_id': 'network_topology',
            'title': 'Network Topology',
            'type': 'topology',
            'size': {'width': 8, 'height': 10},
            'data_sources': ['topology_data', 'link_status'],
            'visualization': {
                'type': 'network_graph',
                'layout': 'hierarchical',
                'show_labels': True,
                'show_metrics': True
            },
            'refresh_interval': 60
        }