Go Runtime Metrics: Advanced Collection and Analysis

🎯 Learning Objectives

By the end of this tutorial, you will be able to:

Master Go's built-in runtime metrics for comprehensive application monitoring
Implement custom metric collection systems with real-time capabilities
Build automated analysis engines for performance optimization
Create production-ready monitoring dashboards with alerting
Design scalable metrics storage and aggregation systems
Apply advanced optimization techniques based on runtime data

📚 What You'll Learn

This tutorial provides hands-on experience with Go's runtime metrics ecosystem, from basic collection to advanced analysis and automated optimization. You'll build complete monitoring solutions suitable for production environments.

🔧 Prerequisites

Go 1.19+ (for runtime/metrics package)
Understanding of Go concurrency patterns
Basic knowledge of performance monitoring concepts
Familiarity with time series data

🎪 Tutorial Overview

Module	Focus Area	Key Skills
Fundamentals	Runtime metrics basics	Collection patterns, metric types
Collection	Advanced gathering	Custom collectors, real-time systems
Analysis	Data processing	Statistical analysis, trend detection
Optimization	Automated tuning	Performance improvements, alerting
Production	Enterprise deployment	Scalability, monitoring, dashboards

🚀 Introduction to Runtime Metrics

Runtime metrics offer unprecedented visibility into your Go application's internal behavior. Unlike external monitoring, these metrics provide direct access to the Go runtime's performance characteristics, memory management, and concurrency patterns.

Why Runtime Metrics Matter

Production Benefits:

Early Warning Systems: Detect performance degradation before user impact
Capacity Planning: Understand resource utilization patterns
Optimization Targets: Identify specific areas for performance improvement
Incident Response: Root cause analysis with detailed runtime data

Development Benefits:

Performance Validation: Verify optimization effectiveness
Regression Detection: Catch performance regressions early
Resource Management: Understand memory and goroutine usage
Debugging Support: Deep insights into application behavior

🛠️ Building a Runtime Metrics System

Let's start by building a practical runtime metrics collection system that you can use in production applications. We'll begin with a simple collector and progressively add advanced features.

Step 1: Basic Runtime Metrics Collector

First, let's create a foundation for collecting Go's built-in runtime metrics:

package main

import (
    "context"
    "fmt"
    "runtime"
    "runtime/metrics"
    "sync"
    "time"
)

// RuntimeMetricsCollector provides professional-grade metrics collection
type RuntimeMetricsCollector struct {
    config      CollectorConfig
    samples     []metrics.Sample
    storage     MetricsStorage
    monitor     *RealTimeMonitor
    scheduler   *CollectionScheduler
    mu          sync.RWMutex
    running     bool
    lastCollect time.Time
}

// CollectorConfig defines collection parameters
type CollectorConfig struct {
    // Collection settings
    Interval     time.Duration
    BufferSize   int
    MaxSamples   int

    // Feature flags
    EnableGC       bool
    EnableMemory   bool
    EnableRoutines bool
    EnableScheduler bool

    // Advanced options
    RealTimeMode   bool
    AutoOptimize   bool
    EnableAlerts   bool
}

// MetricsData represents collected runtime information
type MetricsData struct {
    Timestamp   time.Time
    GCStats     GCMetrics
    MemStats    MemoryMetrics
    GoRoutines  GoroutineMetrics
    Scheduler   SchedulerMetrics
    Custom      map[string]interface{}
}

Step 2: Essential Metric Types

Let's define the core metric structures that capture Go runtime behavior:

// GCMetrics captures garbage collection performance
type GCMetrics struct {
    NumGC          uint32
    PauseTotal     time.Duration
    PauseAvg       time.Duration
    PauseMax       time.Duration
    LastPause      time.Duration
    GCCPUFraction  float64
    NextGC         uint64
    HeapInUse      uint64
    HeapReleased   uint64
}

// MemoryMetrics tracks memory utilization
type MemoryMetrics struct {
    Alloc         uint64  // Current allocation
    TotalAlloc    uint64  // Cumulative allocation
    Sys           uint64  // System memory
    HeapAlloc     uint64  // Heap allocation
    HeapSys       uint64  // Heap system memory
    HeapIdle      uint64  // Idle heap memory
    HeapInuse     uint64  // In-use heap memory
    StackInuse    uint64  // Stack memory
    StackSys      uint64  // Stack system memory
}

// GoroutineMetrics monitors concurrency patterns
type GoroutineMetrics struct {
    NumGoroutine   int     // Current goroutines
    NumCgoCall     int64   // CGO calls
    MaxGoroutines  int     // Peak goroutines
    AvgGoroutines  float64 // Average over time
    GrowthRate     float64 // Goroutine growth rate
}

// SchedulerMetrics tracks scheduler performance
type SchedulerMetrics struct {
    NumCPU        int     // Available CPUs
    GOMAXPROCS    int     // Go max processes
    SchedLatency  time.Duration // Scheduling latency
    RunqueueSize  int     // Run queue size
    IdleTime      float64 // CPU idle percentage
}

🎯 Practical Exercise: Basic Collection

Let's implement the core collection logic that you'll use as a foundation:

// NewRuntimeCollector creates a production-ready collector
func NewRuntimeCollector(config CollectorConfig) *RuntimeMetricsCollector {
    return &RuntimeMetricsCollector{
        config:    config,
        samples:   make([]metrics.Sample, 0, config.BufferSize),
        storage:   NewInMemoryStorage(config.MaxSamples),
        monitor:   NewRealTimeMonitor(),
        scheduler: NewCollectionScheduler(config.Interval),
    }
}

// Start begins metric collection
func (c *RuntimeMetricsCollector) Start(ctx context.Context) error {
    c.mu.Lock()
    defer c.mu.Unlock()

    if c.running {
        return fmt.Errorf("collector already running")
    }

    c.running = true

    // Initialize metric samples
    if err := c.initializeMetrics(); err != nil {
        return fmt.Errorf("failed to initialize metrics: %w", err)
    }

    // Start collection goroutine
    go c.collectLoop(ctx)

    fmt.Printf("✅ Runtime metrics collector started (interval: %v)\n", c.config.Interval)
    return nil
}

// collectLoop performs periodic metric collection
func (c *RuntimeMetricsCollector) collectLoop(ctx context.Context) {
    ticker := time.NewTicker(c.config.Interval)
    defer ticker.Stop()

    for {
        select {
        case <-ctx.Done():
            c.stop()
            return
        case <-ticker.C:
            if err := c.collectMetrics(); err != nil {
                fmt.Printf("⚠️ Collection error: %v\n", err)
            }
        }
    }
}

// collectMetrics gathers current runtime metrics
func (c *RuntimeMetricsCollector) collectMetrics() error {
    start := time.Now()

    // Collect Go runtime metrics
    metrics.Read(c.samples)

    // Build metrics data
    data := &MetricsData{
        Timestamp: start,
    }

    // Extract specific metrics
    c.extractGCMetrics(data)
    c.extractMemoryMetrics(data)
    c.extractGoroutineMetrics(data)
    c.extractSchedulerMetrics(data)

    // Store collected data
    if err := c.storage.Store(data); err != nil {
        return fmt.Errorf("storage error: %w", err)
    }

    // Update collection stats
    c.mu.Lock()
    c.lastCollect = start
    c.mu.Unlock()

    // Real-time monitoring
    if c.config.RealTimeMode {
        c.monitor.ProcessMetrics(data)
    }

    return nil
}

💡 Key Insight: The collector uses Go's runtime/metrics package for low-overhead access to internal runtime data. This provides more detailed information than the older runtime.MemStats approach. MaxLatency time.Duration LastCollection time.Time DataPoints int64 BytesCollected int64 }

// GCMetricsCollector collects garbage collection metrics type GCMetricsCollector struct { config GCMetricsConfig stats debug.GCStats metrics CollectorMetrics }

// GCMetricsConfig contains GC metrics configuration type GCMetricsConfig struct { DetailedStats bool HistogramEnabled bool PauseTracking bool AllocationTracking bool TriggerTracking bool }

// MemoryMetricsCollector collects memory metrics type MemoryMetricsCollector struct { config MemoryMetricsConfig memStats runtime.MemStats metrics CollectorMetrics samples []MemorySample allocTracker *AllocationTracker }

// MemoryMetricsConfig contains memory metrics configuration type MemoryMetricsConfig struct { DetailedHeapStats bool StackTracking bool AllocationTracking bool LeakDetection bool FragmentationAnalysis bool PoolTracking bool }

// MemorySample represents memory sample data type MemorySample struct { Timestamp time.Time Alloc uint64 TotalAlloc uint64 Sys uint64 Lookups uint64 Mallocs uint64 Frees uint64 HeapAlloc uint64 HeapSys uint64 HeapIdle uint64 HeapInuse uint64 HeapReleased uint64 HeapObjects uint64 StackInuse uint64 StackSys uint64 MSpanInuse uint64 MSpanSys uint64 MCacheInuse uint64 MCacheSys uint64 BuckHashSys uint64 GCSys uint64 OtherSys uint64 NextGC uint64 LastGC uint64 PauseTotalNs uint64 PauseNs []uint64 PauseEnd []uint64 NumGC uint32 NumForcedGC uint32 GCCPUFraction float64 EnableGC bool DebugGC bool }

// AllocationTracker tracks memory allocations type AllocationTracker struct { allocations map[string]*AllocationInfo threshold int64 enabled bool mu sync.RWMutex }

// AllocationInfo contains allocation information type AllocationInfo struct { Count int64 Size int64 LastSeen time.Time Stack []uintptr Location string }

// GoroutineMetricsCollector collects goroutine metrics type GoroutineMetricsCollector struct { config GoroutineMetricsConfig metrics CollectorMetrics samples []GoroutineSample profiler GoroutineProfiler leakDetector GoroutineLeakDetector }

// GoroutineMetricsConfig contains goroutine metrics configuration type GoroutineMetricsConfig struct { DetailedProfiling bool LeakDetection bool StateTracking bool LifecycleTracking bool BlockingAnalysis bool StackSampling bool }

// GoroutineSample represents goroutine sample data type GoroutineSample struct { Timestamp time.Time Count int Running int Runnable int Blocked int BlockedIO int BlockedSync int BlockedSyscall int Sleeping int Dead int Idle int MaxStack int64 TotalStack int64 AverageStack float64 }

// GoroutineProfiler profiles goroutine behavior type GoroutineProfiler struct { profiles map[string]*GoroutineProfile sampling bool interval time.Duration maxProfiles int mu sync.RWMutex }

// GoroutineProfile contains goroutine profile data type GoroutineProfile struct { ID int64 State string CreatedAt time.Time LastSeen time.Time Duration time.Duration Stack []uintptr Function string File string Line int BlockingOn string WaitReason string }

// GoroutineLeakDetector detects goroutine leaks type GoroutineLeakDetector struct { baseline int threshold int checkInterval time.Duration enabled bool alerts []GoroutineLeak mu sync.RWMutex }

// GoroutineLeak represents detected goroutine leak type GoroutineLeak struct { DetectedAt time.Time Count int GrowthRate float64 Severity LeakSeverity Suspects []GoroutineProfile Mitigation []string }

// LeakSeverity defines leak severity levels type LeakSeverity int

const ( LowLeakSeverity LeakSeverity = iota MediumLeakSeverity HighLeakSeverity CriticalLeakSeverity )

// SchedulerMetricsCollector collects scheduler metrics type SchedulerMetricsCollector struct { config SchedulerMetricsConfig metrics CollectorMetrics samples []SchedulerSample }

// SchedulerMetricsConfig contains scheduler metrics configuration type SchedulerMetricsConfig struct { ProcessorTracking bool QueueTracking bool PreemptionTracking bool LoadBalancing bool AffinityTracking bool }

// SchedulerSample represents scheduler sample data type SchedulerSample struct { Timestamp time.Time Processors int ActiveProcessors int IdleProcessors int RunqueueSize int GlobalRunqueue int LocalRunqueues []int Preemptions int64 Schedules int64 Steals int64 Migrations int64 LoadBalance float64 CPUUtilization float64 }

// RuntimeOptimizer provides runtime optimization type RuntimeOptimizer struct { config OptimizerConfig strategies []OptimizationStrategy history []OptimizationAction monitor OptimizationMonitor evaluator OptimizationEvaluator enabled bool mu sync.RWMutex }

// OptimizerConfig contains optimizer configuration type OptimizerConfig struct { AutoOptimization bool GCOptimization bool MemoryOptimization bool SchedulerOptimization bool ConservativeMode bool LearningEnabled bool RollbackEnabled bool EvaluationPeriod time.Duration MinBenefit float64 MaxRisk float64 }

// OptimizationStrategy defines optimization strategies type OptimizationStrategy struct { Type OptimizationType Name string Description string Conditions []OptimizationCondition Actions []OptimizationAction Impact ExpectedImpact Risk RiskAssessment Enabled bool }

// OptimizationType defines optimization types type OptimizationType int

const ( GCOptimization OptimizationType = iota MemoryOptimization SchedulerOptimization AllocationOptimization ConcurrencyOptimization IOOptimization )

// OptimizationCondition defines optimization conditions type OptimizationCondition struct { Metric string Operator ComparisonOperator Value float64 Duration time.Duration Enabled bool }

// ComparisonOperator defines comparison operators type ComparisonOperator int

const ( GreaterThan ComparisonOperator = iota LessThan Equal GreaterThanOrEqual LessThanOrEqual NotEqual )

// OptimizationAction defines optimization actions type OptimizationAction struct { Type ActionType Parameter string Value interface{} Timestamp time.Time Applied bool Success bool Result ActionResult Rollback *OptimizationAction }

// ActionType defines action types type ActionType int

const ( SetGCPercent ActionType = iota SetGCTarget SetMaxProcs SetMemoryLimit SetGCMode TriggerGC ReleaseMemory AdjustScheduler )

// ActionResult contains action result data type ActionResult struct { Success bool Error string Before map[string]interface{} After map[string]interface{} Improvement float64 SideEffects []string Duration time.Duration }

// ExpectedImpact represents expected optimization impact type ExpectedImpact struct { Performance float64 Memory float64 Latency float64 Throughput float64 Reliability float64 Confidence float64 }

// RiskAssessment represents optimization risk assessment type RiskAssessment struct { Performance float64 Stability float64 Compatibility float64 Reversibility float64 Overall float64 Mitigation []string }

// OptimizationMonitor monitors optimization effects type OptimizationMonitor struct { baseline map[string]float64 current map[string]float64 evaluations []OptimizationEvaluation thresholds map[string]float64 mu sync.RWMutex }

// OptimizationEvaluation represents optimization evaluation type OptimizationEvaluation struct { Timestamp time.Time Action OptimizationAction Metrics map[string]float64 Improvement float64 Success bool Recommendation EvaluationRecommendation }

// EvaluationRecommendation represents evaluation recommendations type EvaluationRecommendation struct { Action RecommendationAction Confidence float64 Rationale string NextSteps []string }

// RecommendationAction defines recommendation actions type RecommendationAction int

const ( KeepOptimization RecommendationAction = iota RollbackOptimization ModifyOptimization DisableOptimization EvaluateLonger )

// OptimizationEvaluator evaluates optimization effectiveness type OptimizationEvaluator struct { config EvaluatorConfig evaluations []OptimizationEvaluation models []EvaluationModel predictor PerformancePredictor classifier OptimizationClassifier }

// EvaluatorConfig contains evaluator configuration type EvaluatorConfig struct { EvaluationWindow time.Duration MinDataPoints int ConfidenceLevel float64 StatisticalTests []StatisticalTest MLEnabled bool PredictionEnabled bool }

// StatisticalTest defines statistical tests type StatisticalTest int

const ( TTestStatistical StatisticalTest = iota MannWhitneyTest WilcoxonTest KSTest AndersonDarlingTest )

// EvaluationModel represents evaluation models type EvaluationModel struct { Type ModelType Name string Parameters map[string]interface{} Accuracy float64 Precision float64 Recall float64 F1Score float64 Trained bool }

// ModelType defines model types type ModelType int

const ( LinearRegressionModel ModelType = iota RandomForestModel SVMModel NeuralNetworkModel EnsembleModel )

// PerformancePredictor predicts performance impact type PerformancePredictor struct { models []PredictionModel features []PerformanceFeature history []PerformancePrediction enabled bool }

// PredictionModel represents prediction models type PredictionModel struct { Type ModelType Features []string Target string Accuracy float64 Trained bool LastUpdate time.Time }

// PerformanceFeature represents performance features type PerformanceFeature struct { Name string Type FeatureType Importance float64 Correlation float64 Enabled bool }

// FeatureType defines feature types type FeatureType int

const ( NumericFeature FeatureType = iota CategoricalFeature BooleanFeature TextFeature TimeSeriesFeature )

// PerformancePrediction represents performance predictions type PerformancePrediction struct { Timestamp time.Time Action OptimizationAction Predicted map[string]float64 Actual map[string]float64 Error map[string]float64 Confidence float64 Accuracy float64 }

// OptimizationClassifier classifies optimization types type OptimizationClassifier struct { models []ClassificationModel features []string categories []OptimizationCategory accuracy float64 enabled bool }

// ClassificationModel represents classification models type ClassificationModel struct { Type ModelType Classes []string Features []string Accuracy float64 Precision map[string]float64 Recall map[string]float64 F1Score map[string]float64 Trained bool }

// OptimizationCategory represents optimization categories type OptimizationCategory struct { Name string Description string Strategies []OptimizationStrategy Priority int Enabled bool }

// RuntimeMonitor provides real-time runtime monitoring type RuntimeMonitor struct { config MonitorConfig watchers map[string]MetricWatcher alerts AlertManager dashboard MonitorDashboard analyzer RealTimeAnalyzer enabled bool mu sync.RWMutex }

// MonitorConfig contains monitor configuration type MonitorConfig struct { MonitoringInterval time.Duration AlertingEnabled bool DashboardEnabled bool AnalysisEnabled bool PredictionEnabled bool AutoResponseEnabled bool ThresholdSensitivity float64 NoiseReduction bool }

// MetricWatcher watches specific metrics type MetricWatcher struct { metric string thresholds []Threshold history []MetricValue status WatcherStatus alerts []Alert enabled bool }

// Threshold defines metric thresholds type Threshold struct { Type ThresholdType Value float64 Duration time.Duration Severity AlertSeverity Action []ThresholdAction Enabled bool }

// ThresholdType defines threshold types type ThresholdType int

const ( AbsoluteThreshold ThresholdType = iota RelativeThreshold TrendThreshold AnomalyThreshold PercentileThreshold )

// ThresholdAction defines threshold actions type ThresholdAction struct { Type ActionType Parameters map[string]interface{} Enabled bool }

// MetricValue represents metric values type MetricValue struct { Timestamp time.Time Value float64 Quality float64 Source string }

// WatcherStatus defines watcher status type WatcherStatus int

const ( WatcherIdle WatcherStatus = iota WatcherActive WatcherAlerting WatcherError WatcherStopped )

// Alert represents monitoring alerts type Alert struct { ID string Metric string Type AlertType Severity AlertSeverity Message string Timestamp time.Time Value float64 Threshold float64 Context map[string]interface{} Actions []AlertAction Status AlertStatus Acknowledged bool Resolved bool }

// AlertType defines alert types type AlertType int

const ( ThresholdAlert AlertType = iota AnomalyAlert TrendAlert PredictionAlert SystemAlert )

// AlertSeverity defines alert severity levels type AlertSeverity int

const ( InfoAlert AlertSeverity = iota WarningAlert ErrorAlert CriticalAlert )

// AlertAction defines alert actions type AlertAction struct { Type AlertActionType Target string Message string Parameters map[string]interface{} Executed bool Success bool Timestamp time.Time }

// AlertActionType defines alert action types type AlertActionType int

const ( LogAction AlertActionType = iota EmailAction SlackAction WebhookAction PagerDutyAction AutoOptimizationAction EscalationAction )

// AlertStatus defines alert status type AlertStatus int

const ( ActiveAlert AlertStatus = iota AcknowledgedAlert ResolvedAlert SuppressedAlert ExpiredAlert )

// AlertManager manages alerts type AlertManager struct { config AlertManagerConfig rules []AlertRule channels map[string]AlertChannel history []Alert mu sync.RWMutex }

// AlertManagerConfig contains alert manager configuration type AlertManagerConfig struct { MaxAlerts int RetentionPeriod time.Duration DeduplicationWindow time.Duration EscalationEnabled bool AutoResolution bool NotificationLimits map[string]int }

// AlertRule defines alert rules type AlertRule struct { Name string Expression string Duration time.Duration Severity AlertSeverity Annotations map[string]string Labels map[string]string Enabled bool }

// AlertChannel defines alert channels type AlertChannel interface { Send(ctx context.Context, alert Alert) error Configure(config interface{}) error Test() error }

// MonitorDashboard provides monitoring dashboard type MonitorDashboard struct { config DashboardConfig widgets []DashboardWidget layouts []DashboardLayout filters []DashboardFilter enabled bool }

// DashboardConfig contains dashboard configuration type DashboardConfig struct { RefreshInterval time.Duration MaxDataPoints int AutoRefresh bool ExportEnabled bool SharingEnabled bool ThemeMode ThemeMode }

// ThemeMode defines theme modes type ThemeMode int

const ( LightTheme ThemeMode = iota DarkTheme AutoTheme )

// DashboardWidget represents dashboard widgets type DashboardWidget struct { Type WidgetType Title string Metrics []string Timerange TimeRange Options map[string]interface{} Position WidgetPosition Enabled bool }

// WidgetType defines widget types type WidgetType int

const ( LineChartWidget WidgetType = iota BarChartWidget GaugeWidget StatWidget TableWidget HeatmapWidget )

// TimeRange defines time ranges type TimeRange struct { Start time.Time End time.Time Step time.Duration }

// WidgetPosition defines widget positions type WidgetPosition struct { X int Y int Width int Height int }

// DashboardLayout defines dashboard layouts type DashboardLayout struct { Name string Widgets []string Grid GridConfig Enabled bool }

// GridConfig contains grid configuration type GridConfig struct { Columns int Rows int Spacing int }

// DashboardFilter defines dashboard filters type DashboardFilter struct { Name string Type FilterType Values []string Default string Enabled bool }

// FilterType defines filter types type FilterType int

const ( DropdownFilter FilterType = iota TextFilter DateFilter NumericFilter BooleanFilter )

// RealTimeAnalyzer provides real-time analysis type RealTimeAnalyzer struct { config AnalyzerConfig detectors []AnomalyDetector predictors []TrendPredictor correlator *MetricCorrelator enabled bool }

// AnalyzerConfig contains analyzer configuration type AnalyzerConfig struct { WindowSize time.Duration UpdateInterval time.Duration AnomalyDetection bool TrendAnalysis bool CorrelationAnalysis bool PredictiveAnalysis bool StatisticalAnalysis bool MLAnalysis bool }

// AnomalyDetector detects anomalies in metrics type AnomalyDetector struct { algorithm AnomalyAlgorithm sensitivity float64 baseline []float64 model interface{} enabled bool }

// AnomalyAlgorithm defines anomaly detection algorithms type AnomalyAlgorithm int

const ( StatisticalAnomaly AnomalyAlgorithm = iota IsolationForestAnomaly LocalOutlierAnomaly OneClassSVMAnomaly GaussianMixtureAnomaly AutoencoderAnomaly )

// TrendPredictor predicts metric trends type TrendPredictor struct { algorithm TrendAlgorithm horizon time.Duration model interface{} accuracy float64 enabled bool }

// TrendAlgorithm defines trend prediction algorithms type TrendAlgorithm int

const ( LinearTrend TrendAlgorithm = iota ExponentialSmoothing ARIMATrend ProphetTrend LSTMTrend )

// MetricCorrelator finds correlations between metrics type MetricCorrelator struct { correlations map[string]map[string]float64 threshold float64 window time.Duration enabled bool }

// Component interfaces and implementations type MetricProcessor interface{} type MetricAnalyzer interface{} type MetricsStorage interface{} type MetricsNotifier interface{} type MetricsScheduler struct{} type MetricsAggregator struct{} type MetricsExporter struct{} type MetricsDashboard struct{} type MetricsAlerting struct{}

// NewRuntimeMetricsCollector creates a new runtime metrics collector func NewRuntimeMetricsCollector(config RuntimeMetricsConfig) *RuntimeMetricsCollector { rmc := &RuntimeMetricsCollector{ config: config, collectors: make(map[string]MetricCollector), processors: make(map[string]MetricProcessor), analyzers: make(map[string]MetricAnalyzer), scheduler: &MetricsScheduler{}, aggregator: &MetricsAggregator{}, exporter: &MetricsExporter{}, dashboard: &MetricsDashboard{}, alerts: &MetricsAlerting{}, }

// Initialize collectors
if config.EnableGCMetrics {
    rmc.collectors["gc"] = &GCMetricsCollector{
        config: GCMetricsConfig{
            DetailedStats:    true,
            HistogramEnabled: true,
            PauseTracking:    true,
        },
    }
}

if config.EnableMemoryMetrics {
    rmc.collectors["memory"] = &MemoryMetricsCollector{
        config: MemoryMetricsConfig{
            DetailedHeapStats:     true,
            AllocationTracking:    true,
            LeakDetection:        true,
            FragmentationAnalysis: true,
        },
        allocTracker: &AllocationTracker{
            allocations: make(map[string]*AllocationInfo),
            threshold:   1024 * 1024, // 1MB
            enabled:     true,
        },
    }
}

if config.EnableGoroutineMetrics {
    rmc.collectors["goroutine"] = &GoroutineMetricsCollector{
        config: GoroutineMetricsConfig{
            DetailedProfiling: true,
            LeakDetection:     true,
            StateTracking:     true,
            BlockingAnalysis:  true,
        },
        profiler: &GoroutineProfiler{
            profiles:    make(map[string]*GoroutineProfile),
            sampling:    true,
            interval:    time.Second,
            maxProfiles: 1000,
        },
        leakDetector: &GoroutineLeakDetector{
            baseline:      10,
            threshold:     100,
            checkInterval: time.Minute,
            enabled:       true,
        },
    }
}

if config.EnableSchedulerMetrics {
    rmc.collectors["scheduler"] = &SchedulerMetricsCollector{
        config: SchedulerMetricsConfig{
            ProcessorTracking:  true,
            QueueTracking:      true,
            PreemptionTracking: true,
            LoadBalancing:      true,
        },
    }
}

// Initialize optimizer
if config.EnableOptimization {
    rmc.optimizer = RuntimeOptimizer{
        config: OptimizerConfig{
            AutoOptimization:      config.AutoOptimization,
            GCOptimization:        true,
            MemoryOptimization:    true,
            SchedulerOptimization: true,
            ConservativeMode:      true,
            LearningEnabled:       true,
            RollbackEnabled:       true,
            EvaluationPeriod:      time.Minute * 5,
            MinBenefit:            0.05, // 5% improvement threshold
            MaxRisk:               0.1,  // 10% risk threshold
        },
        strategies: []OptimizationStrategy{
            {
                Type:        GCOptimization,
                Name:        "Adaptive GC Tuning",
                Description: "Automatically adjust GC target based on allocation patterns",
                Conditions: []OptimizationCondition{
                    {
                        Metric:   "gc_pause_avg",
                        Operator: GreaterThan,
                        Value:    10.0, // 10ms
                        Duration: time.Minute,
                    },
                },
            },
        },
        monitor: &OptimizationMonitor{
            baseline:   make(map[string]float64),
            current:    make(map[string]float64),
            thresholds: make(map[string]float64),
        },
        evaluator: &OptimizationEvaluator{
            config: EvaluatorConfig{
                EvaluationWindow: time.Minute * 10,
                MinDataPoints:    20,
                ConfidenceLevel:  0.95,
                MLEnabled:        true,
                PredictionEnabled: true,
            },
        },
        enabled: true,
    }
}

// Initialize monitor
if config.EnableRealTimeMonitoring {
    rmc.monitor = RuntimeMonitor{
        config: MonitorConfig{
            MonitoringInterval:   time.Second * 10,
            AlertingEnabled:      config.EnableAlerting,
            DashboardEnabled:     true,
            AnalysisEnabled:      config.AnalysisEnabled,
            PredictionEnabled:    true,
            AutoResponseEnabled:  config.AutoOptimization,
            ThresholdSensitivity: 0.8,
            NoiseReduction:       true,
        },
        watchers: make(map[string]*MetricWatcher),
        alerts: &AlertManager{
            config: AlertManagerConfig{
                MaxAlerts:           1000,
                RetentionPeriod:     time.Hour * 24,
                DeduplicationWindow: time.Minute * 5,
                EscalationEnabled:   true,
                AutoResolution:      true,
            },
            rules:    []AlertRule{},
            channels: make(map[string]AlertChannel),
            history:  []Alert{},
        },
        enabled: true,
    }
}

return rmc

}

// Start starts the runtime metrics collection func (rmc *RuntimeMetricsCollector) Start(ctx context.Context) error { rmc.mu.Lock() defer rmc.mu.Unlock()

if rmc.running {
    return fmt.Errorf("runtime metrics collector is already running")
}

// Start collectors
for name, collector := range rmc.collectors {
    if err := collector.Start(ctx); err != nil {
        return fmt.Errorf("failed to start collector %s: %w", name, err)
    }
}

// Start collection loop
go rmc.collectionLoop(ctx)

// Start optimization if enabled
if rmc.config.EnableOptimization {
    go rmc.optimizationLoop(ctx)
}

// Start monitoring if enabled
if rmc.config.EnableRealTimeMonitoring {
    go rmc.monitoringLoop(ctx)
}

rmc.running = true
return nil

}

// Stop stops the runtime metrics collection func (rmc *RuntimeMetricsCollector) Stop() error { rmc.mu.Lock() defer rmc.mu.Unlock()

if !rmc.running {
    return fmt.Errorf("runtime metrics collector is not running")
}

// Stop collectors
for _, collector := range rmc.collectors {
    collector.Stop()
}

rmc.running = false
return nil

}

// Collect collects runtime metrics func (rmc *RuntimeMetricsCollector) Collect(ctx context.Context) ([]MetricData, error) { var allMetrics []MetricData

// Collect from all collectors
for name, collector := range rmc.collectors {
    metrics, err := collector.Collect(ctx)
    if err != nil {
        return nil, fmt.Errorf("collection failed for %s: %w", name, err)
    }
    allMetrics = append(allMetrics, metrics...)
}

rmc.lastCollect = time.Now()
return allMetrics, nil

}

func (rmc *RuntimeMetricsCollector) collectionLoop(ctx context.Context) { ticker := time.NewTicker(rmc.config.CollectionInterval) defer ticker.Stop()

for {
    select {
    case <-ctx.Done():
        return
    case <-ticker.C:
        metrics, err := rmc.Collect(ctx)
        if err != nil {
            // Log error and continue
            continue
        }

        // Process metrics
        if rmc.config.EnableAggregation {
            // Aggregate metrics
        }

        // Store metrics
        if rmc.config.PersistenceEnabled {
            // Store to storage
        }

        // Export metrics
        if rmc.config.EnableExport {
            // Export metrics
        }

        // Analyze metrics
        if rmc.config.AnalysisEnabled {
            // Perform analysis
        }

        // Update monitoring
        if rmc.config.EnableRealTimeMonitoring {
            rmc.updateMonitoring(metrics)
        }
    }
}

}

func (rmc *RuntimeMetricsCollector) optimizationLoop(ctx context.Context) { ticker := time.NewTicker(rmc.optimizer.config.EvaluationPeriod) defer ticker.Stop()

for {
    select {
    case <-ctx.Done():
        return
    case <-ticker.C:
        if err := rmc.evaluateOptimizations(ctx); err != nil {
            // Log error and continue
        }
    }
}

}

func (rmc *RuntimeMetricsCollector) monitoringLoop(ctx context.Context) { ticker := time.NewTicker(rmc.monitor.config.MonitoringInterval) defer ticker.Stop()

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

}

func (rmc *RuntimeMetricsCollector) updateMonitoring(metrics []MetricData) { for _, metric := range metrics { if watcher, exists := rmc.monitor.watchers[metric.Name]; exists { watcher.history = append(watcher.history, MetricValue{ Timestamp: metric.Timestamp, Value: rmc.convertToFloat64(metric.Value), Quality: metric.Quality.Score, Source: metric.Source, })

        // Check thresholds
        rmc.checkThresholds(watcher, metric)
    }
}

}

func (rmc *RuntimeMetricsCollector) convertToFloat64(value interface{}) float64 { switch v := value.(type) { case float64: return v case float32: return float64(v) case int: return float64(v) case int64: return float64(v) case uint64: return float64(v) default: return 0 } }

func (rmc RuntimeMetricsCollector) checkThresholds(watcher MetricWatcher, metric MetricData) { value := rmc.convertToFloat64(metric.Value)

for _, threshold := range watcher.thresholds {
    if !threshold.Enabled {
        continue
    }

    var triggered bool
    switch threshold.Type {
    case AbsoluteThreshold:
        triggered = value > threshold.Value
    case RelativeThreshold:
        if len(watcher.history) > 1 {
            previous := watcher.history[len(watcher.history)-2].Value
            change := (value - previous) / previous
            triggered = change > threshold.Value
        }
    case TrendThreshold:
        // Implement trend analysis
    case AnomalyThreshold:
        // Implement anomaly detection
    }

    if triggered {
        alert := Alert{
            ID:        fmt.Sprintf("%s-%d", metric.Name, time.Now().Unix()),
            Metric:    metric.Name,
            Type:      ThresholdAlert,
            Severity:  threshold.Severity,
            Message:   fmt.Sprintf("Threshold exceeded for %s: %.2f > %.2f", metric.Name, value, threshold.Value),
            Timestamp: time.Now(),
            Value:     value,
            Threshold: threshold.Value,
            Status:    ActiveAlert,
        }

        watcher.alerts = append(watcher.alerts, alert)
        rmc.monitor.alerts.history = append(rmc.monitor.alerts.history, alert)

        // Execute threshold actions
        for _, action := range threshold.Action {
            rmc.executeThresholdAction(action, metric)
        }
    }
}

}

func (rmc *RuntimeMetricsCollector) executeThresholdAction(action ThresholdAction, metric MetricData) { switch action.Type { case TriggerGC: runtime.GC() case SetGCPercent: if percent, ok := action.Parameters["percent"].(int); ok { debug.SetGCPercent(percent) } case ReleaseMemory: debug.FreeOSMemory() } }

func (rmc *RuntimeMetricsCollector) evaluateOptimizations(ctx context.Context) error { // Collect current metrics metrics, err := rmc.Collect(ctx) if err != nil { return err }

// Evaluate each optimization strategy
for _, strategy := range rmc.optimizer.strategies {
    if !strategy.Enabled {
        continue
    }

    // Check conditions
    shouldOptimize := true
    for _, condition := range strategy.Conditions {
        if !rmc.evaluateCondition(condition, metrics) {
            shouldOptimize = false
            break
        }
    }

    if shouldOptimize {
        // Apply optimization
        if err := rmc.applyOptimization(strategy); err != nil {
            return err
        }
    }
}

return nil

}

func (rmc *RuntimeMetricsCollector) evaluateCondition(condition OptimizationCondition, metrics []MetricData) bool { for _, metric := range metrics { if metric.Name == condition.Metric { value := rmc.convertToFloat64(metric.Value) switch condition.Operator { case GreaterThan: return value > condition.Value case LessThan: return value < condition.Value case Equal: return value == condition.Value case GreaterThanOrEqual: return value >= condition.Value case LessThanOrEqual: return value <= condition.Value case NotEqual: return value != condition.Value } } } return false }

func (rmc *RuntimeMetricsCollector) applyOptimization(strategy OptimizationStrategy) error { for _, action := range strategy.Actions { switch action.Type { case SetGCPercent: if percent, ok := action.Value.(int); ok { debug.SetGCPercent(percent) action.Applied = true action.Success = true action.Timestamp = time.Now() } case TriggerGC: runtime.GC() action.Applied = true action.Success = true action.Timestamp = time.Now() case ReleaseMemory: debug.FreeOSMemory() action.Applied = true action.Success = true action.Timestamp = time.Now() }

    rmc.optimizer.history = append(rmc.optimizer.history, action)
}

return nil

}

func (rmc *RuntimeMetricsCollector) performMonitoring(ctx context.Context) { // Collect current metrics metrics, err := rmc.Collect(ctx) if err != nil { return }

// Update watchers
rmc.updateMonitoring(metrics)

// Perform real-time analysis
if rmc.monitor.analyzer != nil && rmc.monitor.config.AnalysisEnabled {
    // Anomaly detection
    for _, detector := range rmc.monitor.analyzer.detectors {
        // Implement anomaly detection
    }

    // Trend prediction
    for _, predictor := range rmc.monitor.analyzer.predictors {
        // Implement trend prediction
    }

    // Correlation analysis
    if rmc.monitor.analyzer.correlator != nil {
        // Implement correlation analysis
    }
}

}

// Implement collector methods func (gmc *GCMetricsCollector) Collect(ctx context.Context) ([]MetricData, error) { var metrics []MetricData

// Read GC stats
debug.ReadGCStats(&gmc.stats)

// Convert to metric data
metrics = append(metrics, MetricData{
    Name:      "gc_pause_total",
    Value:     gmc.stats.PauseTotal.Nanoseconds(),
    Type:      CounterMetric,
    Unit:      "nanoseconds",
    Timestamp: time.Now(),
    Category:  GCCategory,
    Source:    "gc_collector",
})

if len(gmc.stats.Pause) > 0 {
    lastPause := gmc.stats.Pause[0]
    metrics = append(metrics, MetricData{
        Name:      "gc_pause_last",
        Value:     lastPause.Nanoseconds(),
        Type:      GaugeMetric,
        Unit:      "nanoseconds",
        Timestamp: time.Now(),
        Category:  GCCategory,
        Source:    "gc_collector",
    })
}

metrics = append(metrics, MetricData{
    Name:      "gc_num_collections",
    Value:     gmc.stats.NumGC,
    Type:      CounterMetric,
    Unit:      "count",
    Timestamp: time.Now(),
    Category:  GCCategory,
    Source:    "gc_collector",
})

gmc.metrics.Collections++
gmc.metrics.LastCollection = time.Now()
gmc.metrics.DataPoints += int64(len(metrics))

return metrics, nil

}

func (gmc *GCMetricsCollector) Start(ctx context.Context) error { return nil }

func (gmc *GCMetricsCollector) Stop() error { return nil }

func (gmc *GCMetricsCollector) GetMetrics() CollectorMetrics { return gmc.metrics }

func (gmc *GCMetricsCollector) Configure(config interface{}) error { if c, ok := config.(GCMetricsConfig); ok { gmc.config = c return nil } return fmt.Errorf("invalid config type") }

func (mmc *MemoryMetricsCollector) Collect(ctx context.Context) ([]MetricData, error) { var metrics []MetricData

// Read memory stats
runtime.ReadMemStats(&mmc.memStats)

// Create memory sample
sample := MemorySample{
    Timestamp:     time.Now(),
    Alloc:         mmc.memStats.Alloc,
    TotalAlloc:    mmc.memStats.TotalAlloc,
    Sys:           mmc.memStats.Sys,
    Lookups:       mmc.memStats.Lookups,
    Mallocs:       mmc.memStats.Mallocs,
    Frees:         mmc.memStats.Frees,
    HeapAlloc:     mmc.memStats.HeapAlloc,
    HeapSys:       mmc.memStats.HeapSys,
    HeapIdle:      mmc.memStats.HeapIdle,
    HeapInuse:     mmc.memStats.HeapInuse,
    HeapReleased:  mmc.memStats.HeapReleased,
    HeapObjects:   mmc.memStats.HeapObjects,
    StackInuse:    mmc.memStats.StackInuse,
    StackSys:      mmc.memStats.StackSys,
    MSpanInuse:    mmc.memStats.MSpanInuse,
    MSpanSys:      mmc.memStats.MSpanSys,
    MCacheInuse:   mmc.memStats.MCacheInuse,
    MCacheSys:     mmc.memStats.MCacheSys,
    BuckHashSys:   mmc.memStats.BuckHashSys,
    GCSys:         mmc.memStats.GCSys,
    OtherSys:      mmc.memStats.OtherSys,
    NextGC:        mmc.memStats.NextGC,
    LastGC:        mmc.memStats.LastGC,
    PauseTotalNs:  mmc.memStats.PauseTotalNs,
    NumGC:         mmc.memStats.NumGC,
    NumForcedGC:   mmc.memStats.NumForcedGC,
    GCCPUFraction: mmc.memStats.GCCPUFraction,
    EnableGC:      mmc.memStats.EnableGC,
    DebugGC:       mmc.memStats.DebugGC,
}

mmc.samples = append(mmc.samples, sample)

// Convert to metrics
metrics = append(metrics, MetricData{
    Name:      "memory_alloc",
    Value:     mmc.memStats.Alloc,
    Type:      GaugeMetric,
    Unit:      "bytes",
    Timestamp: time.Now(),
    Category:  MemoryCategory,
    Source:    "memory_collector",
})

metrics = append(metrics, MetricData{
    Name:      "memory_total_alloc",
    Value:     mmc.memStats.TotalAlloc,
    Type:      CounterMetric,
    Unit:      "bytes",
    Timestamp: time.Now(),
    Category:  MemoryCategory,
    Source:    "memory_collector",
})

metrics = append(metrics, MetricData{
    Name:      "memory_sys",
    Value:     mmc.memStats.Sys,
    Type:      GaugeMetric,
    Unit:      "bytes",
    Timestamp: time.Now(),
    Category:  MemoryCategory,
    Source:    "memory_collector",
})

metrics = append(metrics, MetricData{
    Name:      "memory_heap_alloc",
    Value:     mmc.memStats.HeapAlloc,
    Type:      GaugeMetric,
    Unit:      "bytes",
    Timestamp: time.Now(),
    Category:  MemoryCategory,
    Source:    "memory_collector",
})

metrics = append(metrics, MetricData{
    Name:      "memory_heap_objects",
    Value:     mmc.memStats.HeapObjects,
    Type:      GaugeMetric,
    Unit:      "count",
    Timestamp: time.Now(),
    Category:  MemoryCategory,
    Source:    "memory_collector",
})

// Track allocations if enabled
if mmc.config.AllocationTracking && mmc.allocTracker.enabled {
    // Implement allocation tracking
}

mmc.metrics.Collections++
mmc.metrics.LastCollection = time.Now()
mmc.metrics.DataPoints += int64(len(metrics))
mmc.metrics.BytesCollected += int64(len(metrics) * 64) // Estimate

return metrics, nil

}

func (mmc *MemoryMetricsCollector) Start(ctx context.Context) error { return nil }

func (mmc *MemoryMetricsCollector) Stop() error { return nil }

func (mmc *MemoryMetricsCollector) GetMetrics() CollectorMetrics { return mmc.metrics }

func (mmc *MemoryMetricsCollector) Configure(config interface{}) error { if c, ok := config.(MemoryMetricsConfig); ok { mmc.config = c return nil } return fmt.Errorf("invalid config type") }

func (gmc *GoroutineMetricsCollector) Collect(ctx context.Context) ([]MetricData, error) { var metrics []MetricData

// Get goroutine count
numGoroutines := runtime.NumGoroutine()

// Create goroutine sample
sample := GoroutineSample{
    Timestamp: time.Now(),
    Count:     numGoroutines,
    // Additional fields would be populated from goroutine profiling
}

gmc.samples = append(gmc.samples, sample)

metrics = append(metrics, MetricData{
    Name:      "goroutines_count",
    Value:     numGoroutines,
    Type:      GaugeMetric,
    Unit:      "count",
    Timestamp: time.Now(),
    Category:  GoroutineCategory,
    Source:    "goroutine_collector",
})

// Check for goroutine leaks if enabled
if gmc.config.LeakDetection && gmc.leakDetector.enabled {
    if numGoroutines > gmc.leakDetector.baseline+gmc.leakDetector.threshold {
        leak := GoroutineLeak{
            DetectedAt: time.Now(),
            Count:      numGoroutines,
            GrowthRate: float64(numGoroutines-gmc.leakDetector.baseline) / float64(gmc.leakDetector.baseline),
            Severity:   HighLeakSeverity,
        }
        gmc.leakDetector.alerts = append(gmc.leakDetector.alerts, leak)
    }
}

gmc.metrics.Collections++
gmc.metrics.LastCollection = time.Now()
gmc.metrics.DataPoints += int64(len(metrics))

return metrics, nil

}

func (gmc *GoroutineMetricsCollector) Start(ctx context.Context) error { if gmc.config.DetailedProfiling && gmc.profiler.sampling { go gmc.profilingLoop(ctx) } return nil }

func (gmc *GoroutineMetricsCollector) Stop() error { return nil }

func (gmc *GoroutineMetricsCollector) GetMetrics() CollectorMetrics { return gmc.metrics }

func (gmc *GoroutineMetricsCollector) Configure(config interface{}) error { if c, ok := config.(GoroutineMetricsConfig); ok { gmc.config = c return nil } return fmt.Errorf("invalid config type") }

func (gmc *GoroutineMetricsCollector) profilingLoop(ctx context.Context) { ticker := time.NewTicker(gmc.profiler.interval) defer ticker.Stop()

for {
    select {
    case <-ctx.Done():
        return
    case <-ticker.C:
        // Implement goroutine profiling
        // This would involve stack sampling and analysis
    }
}

}

func (smc *SchedulerMetricsCollector) Collect(ctx context.Context) ([]MetricData, error) { var metrics []MetricData

// Get number of processors
numProcs := runtime.NumCPU()
maxProcs := runtime.GOMAXPROCS(0)

sample := SchedulerSample{
    Timestamp:        time.Now(),
    Processors:       numProcs,
    ActiveProcessors: maxProcs,
}

smc.samples = append(smc.samples, sample)

metrics = append(metrics, MetricData{
    Name:      "scheduler_processors",
    Value:     numProcs,
    Type:      GaugeMetric,
    Unit:      "count",
    Timestamp: time.Now(),
    Category:  SchedulerCategory,
    Source:    "scheduler_collector",
})

metrics = append(metrics, MetricData{
    Name:      "scheduler_max_procs",
    Value:     maxProcs,
    Type:      GaugeMetric,
    Unit:      "count",
    Timestamp: time.Now(),
    Category:  SchedulerCategory,
    Source:    "scheduler_collector",
})

smc.metrics.Collections++
smc.metrics.LastCollection = time.Now()
smc.metrics.DataPoints += int64(len(metrics))

return metrics, nil

}

func (smc *SchedulerMetricsCollector) Start(ctx context.Context) error { return nil }

func (smc *SchedulerMetricsCollector) Stop() error { return nil }

func (smc *SchedulerMetricsCollector) GetMetrics() CollectorMetrics { return smc.metrics }

func (smc *SchedulerMetricsCollector) Configure(config interface{}) error { if c, ok := config.(SchedulerMetricsConfig); ok { smc.config = c return nil } return fmt.Errorf("invalid config type") }

// Example usage func ExampleRuntimeMetrics() { config := RuntimeMetricsConfig{ CollectionInterval: time.Second 10, BufferSize: 1000, MaxSamples: 10000, EnableGCMetrics: true, EnableMemoryMetrics: true, EnableGoroutineMetrics: true, EnableSchedulerMetrics: true, EnableRealTimeMonitoring: true, EnableOptimization: true, EnableAlerting: true, RetentionPeriod: time.Hour 24, AutoOptimization: true, ThresholdMonitoring: true, }

collector := NewRuntimeMetricsCollector(config)

ctx := context.Background()
if err := collector.Start(ctx); err != nil {
    fmt.Printf("Failed to start runtime metrics collector: %v\n", err)
    return
}
defer collector.Stop()

fmt.Println("Runtime Metrics Collection Started")

// Simulate some work
for i := 0; i < 5; i++ {
    // Allocate some memory
    data := make([]byte, 1024*1024) // 1MB
    _ = data

    // Create some goroutines
    for j := 0; j < 10; j++ {
        go func() {
            time.Sleep(time.Millisecond * 100)
        }()
    }

    time.Sleep(time.Second)
}

// Collect metrics manually
metrics, err := collector.Collect(ctx)
if err != nil {
    fmt.Printf("Failed to collect metrics: %v\n", err)
    return
}

fmt.Printf("Collected %d metrics:\n", len(metrics))
for _, metric := range metrics {
    fmt.Printf("  %s: %v %s (category: %v)\n", 
        metric.Name, metric.Value, metric.Unit, metric.Category)
}

// Display collector metrics
fmt.Println("\nCollector Metrics:")
for name, collector := range collector.collectors {
    metrics := collector.GetMetrics()
    fmt.Printf("  %s: %d collections, %d data points, %.2fms avg latency\n",
        name, metrics.Collections, metrics.DataPoints, 
        float64(metrics.AvgLatency.Nanoseconds())/1e6)
}

} ```

Go Runtime Metrics

Comprehensive collection of Go runtime metrics for performance monitoring.

Memory Metrics

Detailed memory allocation and usage tracking.

Garbage Collection Metrics

GC performance and behavior analysis.

Goroutine Metrics

Goroutine lifecycle and leak detection.

Scheduler Metrics

Go scheduler performance monitoring.

Real-time Monitoring

Advanced real-time monitoring capabilities for immediate insights.

Threshold Monitoring

Configurable threshold monitoring with automated responses.

Anomaly Detection

Machine learning-based anomaly detection for runtime metrics.

Trend Analysis

Predictive trend analysis for capacity planning.

Best Practices

Minimal Overhead: Design collectors for minimal performance impact
Selective Collection: Enable only necessary metrics to reduce overhead
Aggregation: Use proper aggregation to reduce data volume
Alerting: Configure meaningful alerts for actionable insights
Optimization: Use automated optimization conservatively
Monitoring: Monitor the monitoring system itself
Documentation: Document metric meanings and thresholds
Baseline: Establish performance baselines for comparison

Summary

Runtime metrics provide essential insights into Go application behavior:

Comprehensive Collection: Complete coverage of Go runtime metrics
Real-time Analysis: Advanced real-time monitoring and analysis
Automated Optimization: Intelligent runtime optimization based on metrics
Alerting System: Sophisticated alerting with correlation and deduplication
Performance Impact: Minimal overhead design for production use
Machine Learning: Advanced ML-based analysis and prediction

These capabilities enable organizations to maintain optimal application performance through continuous monitoring and automated optimization of runtime characteristics.

Runtime Metrics