Continuous Profiling - Automated Analysis

Comprehensive guide to automated analysis systems for continuous profiling in Go applications. This guide covers automated performance analysis, anomaly detection, trend analysis, and intelligent alerting systems for production environments.

Table of Contents

Introduction

Automated analysis transforms continuous profiling from reactive monitoring to proactive performance management. This guide provides comprehensive strategies for building intelligent analysis systems that can automatically detect issues, analyze trends, and provide actionable insights for Go applications.

Automated Analysis Framework

package main

import (
    "context"
    "fmt"
    "math"
    "runtime"
    "sort"
    "sync"
    "sync/atomic"
    "time"
)

// AutomatedAnalyzer manages automated performance analysis
type AutomatedAnalyzer struct {
    dataCollector    *ProfileDataCollector
    trendAnalyzer    *TrendAnalyzer
    anomalyDetector  *AnomalyDetector
    alertManager     *AlertManager
    rcaEngine        *RootCauseAnalyzer
    predictor        *PerformancePredictor
    reportGenerator  *ReportGenerator
    config           AnalyzerConfig
    metrics          *AnalysisMetrics
    mu               sync.RWMutex
}

// AnalyzerConfig contains analyzer configuration
type AnalyzerConfig struct {
    EnableTrendAnalysis    bool
    EnableAnomalyDetection bool
    EnablePredictiveAnalysis bool
    EnableRootCauseAnalysis bool
    AnalysisInterval       time.Duration
    DataRetentionPeriod    time.Duration
    AlertingEnabled        bool
    ReportingEnabled       bool
    MinDataPoints          int
    ConfidenceThreshold    float64
    SensitivityLevel       SensitivityLevel
}

// SensitivityLevel defines detection sensitivity
type SensitivityLevel int

const (
    LowSensitivity SensitivityLevel = iota
    MediumSensitivity
    HighSensitivity
    UltraHighSensitivity
)

// ProfileDataCollector collects profiling data from various sources
type ProfileDataCollector struct {
    sources     map[string]DataSource
    storage     DataStorage
    preprocessor *DataPreprocessor
    aggregator  *DataAggregator
    config      CollectorConfig
    stats       *CollectionStatistics
    mu          sync.RWMutex
}

// CollectorConfig contains collector configuration
type CollectorConfig struct {
    CollectionInterval   time.Duration
    BatchSize           int
    BufferSize          int
    CompressionEnabled  bool
    ValidationEnabled   bool
    DeduplicationEnabled bool
    MaxRetries          int
    RetryBackoff        time.Duration
}

// DataSource represents a profiling data source
type DataSource interface {
    CollectData(ctx context.Context) ([]*ProfileData, error)
    GetMetadata() DataSourceMetadata
    GetHealth() HealthStatus
}

// ProfileData represents collected profiling data
type ProfileData struct {
    Timestamp    time.Time
    Source       string
    Type         ProfileType
    Metrics      map[string]float64
    Samples      []*ProfileSample
    Metadata     map[string]interface{}
    Quality      DataQuality
}

// ProfileType defines profile data types
type ProfileType int

const (
    CPUProfile ProfileType = iota
    MemoryProfile
    GoroutineProfile
    BlockProfile
    MutexProfile
    HeapProfile
    AllocProfile
    CustomProfile
)

// ProfileSample represents a single profiling sample
type ProfileSample struct {
    Function    string
    File        string
    Line        int
    Value       int64
    Stack       []string
    Labels      map[string]string
    Weight      float64
}

// DataQuality represents data quality metrics
type DataQuality struct {
    Completeness float64
    Accuracy     float64
    Consistency  float64
    Timeliness   float64
    Validity     float64
    OverallScore float64
}

// DataSourceMetadata contains metadata about data sources
type DataSourceMetadata struct {
    Name         string
    Type         string
    Version      string
    Capabilities []string
    SampleRate   float64
    Latency      time.Duration
}

// HealthStatus represents health status
type HealthStatus struct {
    Healthy      bool
    LastCheck    time.Time
    ErrorCount   int
    SuccessRate  float64
    Issues       []HealthIssue
}

// HealthIssue represents a health issue
type HealthIssue struct {
    Type        string
    Severity    Severity
    Description string
    FirstSeen   time.Time
    LastSeen    time.Time
    Count       int
}

// Severity defines issue severity levels
type Severity int

const (
    InfoSeverity Severity = iota
    WarningSeverity
    ErrorSeverity
    CriticalSeverity
)

// DataStorage manages persistent storage of profiling data
type DataStorage interface {
    Store(data []*ProfileData) error
    Retrieve(query StorageQuery) ([]*ProfileData, error)
    Delete(criteria DeletionCriteria) error
    GetStats() StorageStatistics
}

// StorageQuery represents a data retrieval query
type StorageQuery struct {
    TimeRange   TimeRange
    Sources     []string
    Types       []ProfileType
    Filters     map[string]interface{}
    Aggregation AggregationSpec
    Limit       int
}

// TimeRange represents a time range
type TimeRange struct {
    Start time.Time
    End   time.Time
}

// AggregationSpec specifies data aggregation
type AggregationSpec struct {
    GroupBy   []string
    Functions []AggregationFunction
    Interval  time.Duration
}

// AggregationFunction defines aggregation functions
type AggregationFunction int

const (
    SumFunction AggregationFunction = iota
    AvgFunction
    MinFunction
    MaxFunction
    CountFunction
    PercentileFunction
    StdDevFunction
)

// DeletionCriteria specifies deletion criteria
type DeletionCriteria struct {
    OlderThan time.Time
    Sources   []string
    Types     []ProfileType
    Conditions map[string]interface{}
}

// StorageStatistics tracks storage metrics
type StorageStatistics struct {
    TotalRecords     int64
    TotalSize        int64
    OldestRecord     time.Time
    NewestRecord     time.Time
    CompressionRatio float64
    QueryLatency     time.Duration
}

// DataPreprocessor preprocesses raw profiling data
type DataPreprocessor struct {
    normalizers []DataNormalizer
    validators  []DataValidator
    enrichers   []DataEnricher
    config      PreprocessorConfig
    stats       *PreprocessingStatistics
}

// PreprocessorConfig contains preprocessor configuration
type PreprocessorConfig struct {
    EnableNormalization bool
    EnableValidation    bool
    EnableEnrichment    bool
    ValidationRules     []ValidationRule
    NormalizationRules  []NormalizationRule
    EnrichmentRules     []EnrichmentRule
}

// DataNormalizer normalizes data values
type DataNormalizer interface {
    Normalize(data *ProfileData) error
    GetType() NormalizationType
}

// NormalizationType defines normalization types
type NormalizationType int

const (
    UnitNormalization NormalizationType = iota
    ScaleNormalization
    TimeNormalization
    ValueNormalization
)

// DataValidator validates data integrity
type DataValidator interface {
    Validate(data *ProfileData) ValidationResult
    GetRules() []ValidationRule
}

// ValidationResult contains validation results
type ValidationResult struct {
    Valid    bool
    Score    float64
    Issues   []ValidationIssue
    Warnings []string
}

// ValidationIssue represents a validation issue
type ValidationIssue struct {
    Type        string
    Severity    Severity
    Field       string
    Description string
    Suggestion  string
}

// ValidationRule defines validation rules
type ValidationRule struct {
    Name        string
    Type        ValidationType
    Parameters  map[string]interface{}
    Severity    Severity
    Enabled     bool
}

// ValidationType defines validation types
type ValidationType int

const (
    RangeValidation ValidationType = iota
    FormatValidation
    ConsistencyValidation
    CompletenessValidation
    TimelinessValidation
)

// DataEnricher enriches data with additional context
type DataEnricher interface {
    Enrich(data *ProfileData) error
    GetType() EnrichmentType
}

// EnrichmentType defines enrichment types
type EnrichmentType int

const (
    MetadataEnrichment EnrichmentType = iota
    ContextEnrichment
    ReferenceEnrichment
    CalculatedEnrichment
)

// TrendAnalyzer analyzes performance trends
type TrendAnalyzer struct {
    analyzers   map[string]TrendAnalysisEngine
    models      map[string]*TrendModel
    config      TrendAnalysisConfig
    cache       *TrendCache
    stats       *TrendAnalysisStatistics
    mu          sync.RWMutex
}

// TrendAnalysisConfig contains trend analysis configuration
type TrendAnalysisConfig struct {
    WindowSize          time.Duration
    UpdateInterval      time.Duration
    MinDataPoints       int
    TrendThreshold      float64
    SeasonalityEnabled  bool
    ForecastHorizon     time.Duration
    ConfidenceLevel     float64
}

// TrendAnalysisEngine performs trend analysis
type TrendAnalysisEngine interface {
    AnalyzeTrend(data []*ProfileData) (*TrendResult, error)
    GetType() TrendAnalysisType
    GetAccuracy() float64
}

// TrendAnalysisType defines analysis types
type TrendAnalysisType int

const (
    LinearTrend TrendAnalysisType = iota
    ExponentialTrend
    SeasonalTrend
    CyclicalTrend
    PolynomialTrend
    MovingAverageTrend
)

// TrendResult contains trend analysis results
type TrendResult struct {
    Type         TrendType
    Direction    TrendDirection
    Strength     float64
    Confidence   float64
    StartTime    time.Time
    EndTime      time.Time
    Parameters   map[string]float64
    Forecast     *TrendForecast
    Seasonality  *SeasonalityInfo
    ChangePoints []ChangePoint
}

// TrendType defines trend types
type TrendType int

const (
    NoTrend TrendType = iota
    UpwardTrend
    DownwardTrend
    StableTrend
    VolatileTrend
)

// TrendDirection defines trend directions
type TrendDirection int

const (
    Increasing TrendDirection = iota
    Decreasing
    Stable
    Volatile
)

// TrendForecast contains forecast information
type TrendForecast struct {
    Values      []float64
    Timestamps  []time.Time
    UpperBound  []float64
    LowerBound  []float64
    Confidence  float64
    Method      ForecastMethod
}

// ForecastMethod defines forecasting methods
type ForecastMethod int

const (
    LinearRegression ForecastMethod = iota
    ExponentialSmoothing
    ARIMA
    NeuralNetwork
    EnsembleMethod
)

// SeasonalityInfo contains seasonality information
type SeasonalityInfo struct {
    Present    bool
    Period     time.Duration
    Amplitude  float64
    Phase      float64
    Patterns   []SeasonalPattern
}

// SeasonalPattern represents a seasonal pattern
type SeasonalPattern struct {
    Name      string
    Period    time.Duration
    Strength  float64
    Phase     float64
    Frequency float64
}

// ChangePoint represents a significant change in trend
type ChangePoint struct {
    Timestamp   time.Time
    Type        ChangeType
    Magnitude   float64
    Confidence  float64
    Description string
    Cause       string
}

// ChangeType defines change point types
type ChangeType int

const (
    LevelChange ChangeType = iota
    TrendChange
    VarianceChange
    SeasonalChange
)

// TrendModel represents a statistical trend model
type TrendModel struct {
    Type        ModelType
    Parameters  map[string]float64
    Accuracy    ModelAccuracy
    Training    TrainingInfo
    Validation  ValidationInfo
    LastUpdated time.Time
}

// ModelType defines model types
type ModelType int

const (
    LinearModel ModelType = iota
    PolynomialModel
    ExponentialModel
    LogisticModel
    TimeSeriesModel
    EnsembleModel
)

// ModelAccuracy contains model accuracy metrics
type ModelAccuracy struct {
    MAE        float64 // Mean Absolute Error
    MAPE       float64 // Mean Absolute Percentage Error
    RMSE       float64 // Root Mean Square Error
    R2         float64 // R-squared
    AIC        float64 // Akaike Information Criterion
    Confidence float64
}

// TrainingInfo contains model training information
type TrainingInfo struct {
    DataPoints     int
    TrainingTime   time.Duration
    Iterations     int
    Convergence    bool
    CrossValidated bool
    Features       []string
}

// ValidationInfo contains model validation information
type ValidationInfo struct {
    TestDataPoints int
    ValidationTime time.Duration
    Accuracy       float64
    Overfitting    bool
    Underfitting   bool
    Recommendations []string
}

// AnomalyDetector detects performance anomalies
type AnomalyDetector struct {
    detectors   map[string]AnomalyDetectionEngine
    config      AnomalyDetectionConfig
    baseline    *PerformanceBaseline
    alerter     *AnomalyAlerter
    stats       *AnomalyDetectionStatistics
    mu          sync.RWMutex
}

// AnomalyDetectionConfig contains detection configuration
type AnomalyDetectionConfig struct {
    EnableStatisticalDetection bool
    EnableMLDetection         bool
    EnableRuleBasedDetection  bool
    SensitivityLevel          SensitivityLevel
    FalsePositiveThreshold    float64
    MinAnomalyDuration        time.Duration
    BaselineUpdateInterval    time.Duration
    AdaptiveLearning          bool
}

// AnomalyDetectionEngine performs anomaly detection
type AnomalyDetectionEngine interface {
    DetectAnomalies(data []*ProfileData, baseline *PerformanceBaseline) ([]*Anomaly, error)
    GetType() DetectionType
    GetAccuracy() DetectionAccuracy
    UpdateModel(data []*ProfileData) error
}

// DetectionType defines detection algorithm types
type DetectionType int

const (
    StatisticalDetection DetectionType = iota
    MachineLearningDetection
    RuleBasedDetection
    ThresholdBasedDetection
    PatternBasedDetection
    EnsembleDetection
)

// DetectionAccuracy contains detection accuracy metrics
type DetectionAccuracy struct {
    Precision   float64
    Recall      float64
    F1Score     float64
    Specificity float64
    AUC         float64
    Confusion   ConfusionMatrix
}

// ConfusionMatrix represents a confusion matrix
type ConfusionMatrix struct {
    TruePositives  int
    TrueNegatives  int
    FalsePositives int
    FalseNegatives int
}

// Anomaly represents a detected anomaly
type Anomaly struct {
    ID          string
    Type        AnomalyType
    Severity    AnomalySeverity
    Timestamp   time.Time
    Duration    time.Duration
    Metric      string
    Value       float64
    Expected    float64
    Deviation   float64
    Confidence  float64
    Context     AnomalyContext
    RootCause   *RootCause
    Impact      AnomalyImpact
    Resolution  *Resolution
}

// AnomalyType defines anomaly types
type AnomalyType int

const (
    SpikeAnomaly AnomalyType = iota
    DipAnomaly
    TrendAnomaly
    SeasonalAnomaly
    PatternAnomaly
    CorrelationAnomaly
)

// AnomalySeverity defines anomaly severity levels
type AnomalySeverity int

const (
    LowAnomalySeverity AnomalySeverity = iota
    MediumAnomalySeverity
    HighAnomalySeverity
    CriticalAnomalySeverity
)

// AnomalyContext provides context for anomalies
type AnomalyContext struct {
    Application   string
    Environment   string
    Version       string
    Host          string
    Service       string
    Component     string
    Tags          map[string]string
    Correlations  []Correlation
}

// Correlation represents correlations with other metrics
type Correlation struct {
    Metric      string
    Coefficient float64
    Lag         time.Duration
    Strength    CorrelationStrength
}

// CorrelationStrength defines correlation strength levels
type CorrelationStrength int

const (
    WeakCorrelation CorrelationStrength = iota
    ModerateCorrelation
    StrongCorrelation
    VeryStrongCorrelation
)

// RootCause represents the root cause of an anomaly
type RootCause struct {
    Category    RootCauseCategory
    Description string
    Confidence  float64
    Evidence    []Evidence
    Hypothesis  string
    Verification *Verification
}

// RootCauseCategory defines root cause categories
type RootCauseCategory int

const (
    CodeChange RootCauseCategory = iota
    ConfigChange
    InfrastructureChange
    DataChange
    ExternalDependency
    ResourceExhaustion
    Unknown
)

// Evidence represents evidence supporting a root cause
type Evidence struct {
    Type        EvidenceType
    Description string
    Weight      float64
    Source      string
    Timestamp   time.Time
    Data        interface{}
}

// EvidenceType defines evidence types
type EvidenceType int

const (
    MetricEvidence EvidenceType = iota
    LogEvidence
    TraceEvidence
    EventEvidence
    CorrelationEvidence
)

// Verification contains root cause verification information
type Verification struct {
    Method      VerificationMethod
    Confidence  float64
    Status      VerificationStatus
    Results     map[string]interface{}
    Timestamp   time.Time
}

// VerificationMethod defines verification methods
type VerificationMethod int

const (
    StatisticalVerification VerificationMethod = iota
    ExperimentalVerification
    CorrelationVerification
    ExpertVerification
)

// VerificationStatus defines verification status
type VerificationStatus int

const (
    Pending VerificationStatus = iota
    Verified
    Rejected
    Inconclusive
)

// AnomalyImpact represents the impact of an anomaly
type AnomalyImpact struct {
    Scope       ImpactScope
    Magnitude   float64
    Duration    time.Duration
    Metrics     map[string]float64
    Users       int
    Services    []string
    Business    BusinessImpact
}

// ImpactScope defines impact scope
type ImpactScope int

const (
    LocalImpact ImpactScope = iota
    ServiceImpact
    SystemImpact
    GlobalImpact
)

// BusinessImpact represents business impact
type BusinessImpact struct {
    Revenue     float64
    Users       int
    SLA         SLAImpact
    Reputation  ReputationImpact
    Operational OperationalImpact
}

// SLAImpact represents SLA impact
type SLAImpact struct {
    Violated    bool
    Metrics     map[string]float64
    Penalties   float64
    Credits     float64
}

// ReputationImpact represents reputation impact
type ReputationImpact struct {
    Score       float64
    Mentions    int
    Sentiment   float64
    Escalations int
}

// OperationalImpact represents operational impact
type OperationalImpact struct {
    Incidents   int
    Escalations int
    Resources   float64
    Downtime    time.Duration
}

// Resolution represents anomaly resolution information
type Resolution struct {
    Status      ResolutionStatus
    Actions     []ResolutionAction
    Results     ResolutionResults
    Timeline    ResolutionTimeline
    Automation  AutomationInfo
}

// ResolutionStatus defines resolution status
type ResolutionStatus int

const (
    UnresolvedStatus ResolutionStatus = iota
    InProgressStatus
    ResolvedStatus
    IgnoredStatus
)

// ResolutionAction represents a resolution action
type ResolutionAction struct {
    Type        ActionType
    Description string
    Automated   bool
    Results     ActionResults
    Timestamp   time.Time
    Actor       string
}

// ActionType defines action types
type ActionType int

const (
    Investigation ActionType = iota
    Mitigation
    Remediation
    Prevention
    Monitoring
)

// ActionResults contains action results
type ActionResults struct {
    Success     bool
    Impact      float64
    Duration    time.Duration
    SideEffects []string
    Metrics     map[string]float64
}

// ResolutionResults contains overall resolution results
type ResolutionResults struct {
    Effectiveness float64
    Duration      time.Duration
    Cost          float64
    Prevention    bool
    Lessons       []string
}

// ResolutionTimeline tracks resolution timeline
type ResolutionTimeline struct {
    Detection   time.Time
    Investigation time.Time
    Mitigation  time.Time
    Resolution  time.Time
    Verification time.Time
}

// AutomationInfo contains automation information
type AutomationInfo struct {
    Triggered   bool
    Actions     []string
    Success     bool
    Confidence  float64
    Override    bool
}

// PerformanceBaseline represents performance baselines
type PerformanceBaseline struct {
    metrics     map[string]*MetricBaseline
    timeRange   TimeRange
    confidence  float64
    quality     BaselineQuality
    lastUpdated time.Time
    mu          sync.RWMutex
}

// MetricBaseline contains baseline for a specific metric
type MetricBaseline struct {
    Name        string
    Mean        float64
    StdDev      float64
    Percentiles map[int]float64
    Range       ValueRange
    Seasonality *SeasonalBaseline
    Trend       *TrendBaseline
}

// ValueRange represents a value range
type ValueRange struct {
    Min float64
    Max float64
}

// SeasonalBaseline contains seasonal baseline information
type SeasonalBaseline struct {
    Patterns map[string]*SeasonalPattern
    Enabled  bool
    Accuracy float64
}

// TrendBaseline contains trend baseline information
type TrendBaseline struct {
    Direction TrendDirection
    Rate      float64
    Stability float64
    Enabled   bool
}

// BaselineQuality represents baseline quality metrics
type BaselineQuality struct {
    DataPoints   int
    Completeness float64
    Stability    float64
    Accuracy     float64
    Freshness    float64
    OverallScore float64
}

// NewAutomatedAnalyzer creates a new automated analyzer
func NewAutomatedAnalyzer(config AnalyzerConfig) *AutomatedAnalyzer {
    return &AutomatedAnalyzer{
        dataCollector:   NewProfileDataCollector(),
        trendAnalyzer:   NewTrendAnalyzer(),
        anomalyDetector: NewAnomalyDetector(),
        alertManager:    NewAlertManager(),
        rcaEngine:       NewRootCauseAnalyzer(),
        predictor:       NewPerformancePredictor(),
        reportGenerator: NewReportGenerator(),
        config:          config,
        metrics:         &AnalysisMetrics{},
    }
}

// Start starts the automated analysis process
func (aa *AutomatedAnalyzer) Start(ctx context.Context) error {
    aa.mu.Lock()
    defer aa.mu.Unlock()

    // Start data collection
    if err := aa.dataCollector.Start(ctx); err != nil {
        return fmt.Errorf("failed to start data collector: %w", err)
    }

    // Start analysis loops
    go aa.analysisLoop(ctx)
    go aa.alertingLoop(ctx)
    go aa.reportingLoop(ctx)

    return nil
}

// analysisLoop performs periodic analysis
func (aa *AutomatedAnalyzer) analysisLoop(ctx context.Context) {
    ticker := time.NewTicker(aa.config.AnalysisInterval)
    defer ticker.Stop()

    for {
        select {
        case <-ctx.Done():
            return
        case <-ticker.C:
            aa.performAnalysis(ctx)
        }
    }
}

// performAnalysis performs comprehensive analysis
func (aa *AutomatedAnalyzer) performAnalysis(ctx context.Context) {
    // Collect recent data
    query := StorageQuery{
        TimeRange: TimeRange{
            Start: time.Now().Add(-aa.config.AnalysisInterval),
            End:   time.Now(),
        },
    }

    data, err := aa.dataCollector.QueryData(query)
    if err != nil {
        return
    }

    if len(data) < aa.config.MinDataPoints {
        return
    }

    // Perform trend analysis
    if aa.config.EnableTrendAnalysis {
        trendResults := aa.trendAnalyzer.AnalyzeAll(data)
        aa.processTrendResults(trendResults)
    }

    // Perform anomaly detection
    if aa.config.EnableAnomalyDetection {
        baseline := aa.getOrCreateBaseline(data)
        anomalies := aa.anomalyDetector.DetectAll(data, baseline)
        aa.processAnomalies(ctx, anomalies)
    }

    // Perform predictive analysis
    if aa.config.EnablePredictiveAnalysis {
        predictions := aa.predictor.GeneratePredictions(data)
        aa.processPredictions(predictions)
    }
}

// processTrendResults processes trend analysis results
func (aa *AutomatedAnalyzer) processTrendResults(results map[string]*TrendResult) {
    for metric, result := range results {
        // Check for significant trends
        if result.Strength > 0.7 && result.Confidence > aa.config.ConfidenceThreshold {
            event := &AnalysisEvent{
                Type:        TrendEvent,
                Metric:      metric,
                Severity:    aa.calculateTrendSeverity(result),
                Result:      result,
                Timestamp:   time.Now(),
            }

            aa.alertManager.ProcessEvent(event)
        }
    }
}

// processAnomalies processes detected anomalies
func (aa *AutomatedAnalyzer) processAnomalies(ctx context.Context, anomalies []*Anomaly) {
    for _, anomaly := range anomalies {
        // Perform root cause analysis if enabled
        if aa.config.EnableRootCauseAnalysis {
            rootCause, err := aa.rcaEngine.AnalyzeAnomaly(ctx, anomaly)
            if err == nil {
                anomaly.RootCause = rootCause
            }
        }

        // Send alert
        alert := &Alert{
            Type:      AnomalyAlert,
            Severity:  anomaly.Severity,
            Anomaly:   anomaly,
            Timestamp: time.Now(),
        }

        aa.alertManager.SendAlert(alert)
    }
}

// calculateTrendSeverity calculates trend severity
func (aa *AutomatedAnalyzer) calculateTrendSeverity(result *TrendResult) AnomalySeverity {
    // Calculate severity based on trend strength and direction
    severity := LowAnomalySeverity

    if result.Strength > 0.9 {
        severity = CriticalAnomalySeverity
    } else if result.Strength > 0.8 {
        severity = HighAnomalySeverity
    } else if result.Strength > 0.7 {
        severity = MediumAnomalySeverity
    }

    return severity
}

// Component implementations and placeholder types
type AnalysisMetrics struct{}
type ProfileDataCollector struct{}
type TrendAnalyzer struct{}
type AnomalyDetector struct{}
type AlertManager struct{}
type RootCauseAnalyzer struct{}
type PerformancePredictor struct{}
type ReportGenerator struct{}
type AnomalyAlerter struct{}
type TrendCache struct{}
type CollectionStatistics struct{}
type TrendAnalysisStatistics struct{}
type AnomalyDetectionStatistics struct{}
type DataAggregator struct{}
type PreprocessingStatistics struct{}
type NormalizationRule struct{}
type EnrichmentRule struct{}

// Analysis event types
type AnalysisEvent struct {
    Type      EventType
    Metric    string
    Severity  AnomalySeverity
    Result    interface{}
    Timestamp time.Time
}

type EventType int
const (
    TrendEvent EventType = iota
    AnomalyEvent
    PredictionEvent
)

type Alert struct {
    Type      AlertType
    Severity  AnomalySeverity
    Anomaly   *Anomaly
    Timestamp time.Time
}

type AlertType int
const (
    AnomalyAlert AlertType = iota
    TrendAlert
    PredictionAlert
)

// Constructor functions
func NewProfileDataCollector() *ProfileDataCollector { return &ProfileDataCollector{} }
func NewTrendAnalyzer() *TrendAnalyzer { return &TrendAnalyzer{} }
func NewAnomalyDetector() *AnomalyDetector { return &AnomalyDetector{} }
func NewAlertManager() *AlertManager { return &AlertManager{} }
func NewRootCauseAnalyzer() *RootCauseAnalyzer { return &RootCauseAnalyzer{} }
func NewPerformancePredictor() *PerformancePredictor { return &PerformancePredictor{} }
func NewReportGenerator() *ReportGenerator { return &ReportGenerator{} }

// Method implementations
func (pdc *ProfileDataCollector) Start(ctx context.Context) error { return nil }
func (pdc *ProfileDataCollector) QueryData(query StorageQuery) ([]*ProfileData, error) { return nil, nil }
func (ta *TrendAnalyzer) AnalyzeAll(data []*ProfileData) map[string]*TrendResult { return nil }
func (ad *AnomalyDetector) DetectAll(data []*ProfileData, baseline *PerformanceBaseline) []*Anomaly { return nil }
func (pp *PerformancePredictor) GeneratePredictions(data []*ProfileData) interface{} { return nil }
func (aa *AutomatedAnalyzer) getOrCreateBaseline(data []*ProfileData) *PerformanceBaseline { return nil }
func (aa *AutomatedAnalyzer) processPredictions(predictions interface{}) {}
func (rca *RootCauseAnalyzer) AnalyzeAnomaly(ctx context.Context, anomaly *Anomaly) (*RootCause, error) { return nil, nil }
func (am *AlertManager) ProcessEvent(event *AnalysisEvent) {}
func (am *AlertManager) SendAlert(alert *Alert) {}

// Additional methods for complete interface
func (aa *AutomatedAnalyzer) alertingLoop(ctx context.Context) {
    // Implementation for alerting loop
}

func (aa *AutomatedAnalyzer) reportingLoop(ctx context.Context) {
    // Implementation for reporting loop
}

// Example usage
func ExampleAutomatedAnalysis() {
    // Create analyzer configuration
    config := AnalyzerConfig{
        EnableTrendAnalysis:      true,
        EnableAnomalyDetection:   true,
        EnablePredictiveAnalysis: true,
        EnableRootCauseAnalysis:  true,
        AnalysisInterval:         time.Minute,
        DataRetentionPeriod:      24 * time.Hour,
        AlertingEnabled:          true,
        ReportingEnabled:         true,
        MinDataPoints:            10,
        ConfidenceThreshold:      0.8,
        SensitivityLevel:         MediumSensitivity,
    }

    // Create analyzer
    analyzer := NewAutomatedAnalyzer(config)

    // Start analysis
    ctx := context.Background()
    if err := analyzer.Start(ctx); err != nil {
        fmt.Printf("Failed to start analyzer: %v\n", err)
        return
    }

    fmt.Println("Automated analysis started successfully")
    fmt.Printf("Analysis interval: %v\n", config.AnalysisInterval)
    fmt.Printf("Confidence threshold: %.2f\n", config.ConfidenceThreshold)
    fmt.Printf("Features enabled: trend=%t, anomaly=%t, prediction=%t, rca=%t\n",
        config.EnableTrendAnalysis,
        config.EnableAnomalyDetection,
        config.EnablePredictiveAnalysis,
        config.EnableRootCauseAnalysis)

    // Simulate running for some time
    time.Sleep(5 * time.Second)

    fmt.Println("Analysis completed")
}

Performance Trend Analysis

Advanced techniques for analyzing performance trends and identifying patterns in profiling data.

Statistical Trend Analysis

Using statistical methods to identify significant trends in performance metrics.

Seasonal Pattern Detection

Detecting and modeling seasonal patterns in application performance.

Change Point Detection

Identifying significant changes in performance characteristics.

Anomaly Detection

Sophisticated anomaly detection algorithms for identifying performance issues.

Statistical Anomaly Detection

Using statistical methods for robust anomaly detection.

Machine Learning Detection

Implementing ML-based anomaly detection for complex patterns.

Ensemble Detection

Combining multiple detection methods for improved accuracy.

Root Cause Analysis

Automated root cause analysis for performance anomalies.

Correlation Analysis

Analyzing correlations between different metrics and events.

Causal Inference

Using causal inference techniques to identify root causes.

Evidence Collection

Systematic collection and analysis of evidence for root cause determination.

Best Practices

  1. Data Quality: Ensure high-quality profiling data for accurate analysis
  2. Baseline Management: Maintain accurate and up-to-date performance baselines
  3. Alert Tuning: Carefully tune alert thresholds to minimize false positives
  4. Model Validation: Regularly validate and update analysis models
  5. Automation: Automate routine analysis tasks while maintaining human oversight
  6. Documentation: Document analysis results and decisions for future reference
  7. Continuous Improvement: Continuously improve analysis algorithms based on feedback
  8. Integration: Integrate with existing monitoring and alerting systems

Summary

Automated analysis transforms continuous profiling into an intelligent system:

  1. Trend Analysis: Automatically identify and analyze performance trends
  2. Anomaly Detection: Detect performance anomalies with high accuracy
  3. Root Cause Analysis: Automatically identify likely root causes
  4. Predictive Analytics: Predict future performance issues
  5. Intelligent Alerting: Generate actionable alerts with context

These techniques enable organizations to proactively manage application performance and quickly resolve issues before they impact users.

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