memory-analytics.ts•62.5 kB
/**
* Memory Analytics and Reporting System for GEPA
*
* Advanced analytics engine providing insights, performance analysis,
* optimization recommendations, and comprehensive reporting capabilities.
*/
import { EventEmitter } from 'events';
import * as fs from 'fs/promises';
import * as path from 'path';
/**
* Analytics configuration
*/
export interface AnalyticsConfig {
/** Enable real-time analytics */
realTimeAnalytics: boolean;
/** Analysis update interval (ms) */
analysisInterval: number;
/** Historical data retention (ms) */
dataRetention: number;
/** Anomaly detection sensitivity (0-1) */
anomalySensitivity: number;
/** Performance baseline calculation window (ms) */
baselineWindow: number;
/** Prediction model settings */
prediction: {
enabled: boolean;
horizon: number; // prediction horizon in ms
confidence: number; // minimum confidence threshold
};
/** Report generation settings */
reporting: {
autoGenerate: boolean;
schedule: string; // cron expression
formats: ('json' | 'html' | 'pdf' | 'csv')[];
recipients?: string[];
};
}
/**
* Memory usage pattern
*/
export interface MemoryPattern {
id: string;
name: string;
type: 'allocation' | 'deallocation' | 'retention' | 'fragmentation' | 'leak';
component: string;
frequency: number;
severity: 'low' | 'medium' | 'high' | 'critical';
description: string;
characteristics: {
averageSize: number;
frequency: number;
duration: number;
variability: number;
};
impact: {
performance: number;
stability: number;
efficiency: number;
};
trend: {
direction: 'increasing' | 'decreasing' | 'stable' | 'volatile';
velocity: number;
acceleration: number;
};
recommendations: string[];
}
/**
* Performance baseline
*/
export interface PerformanceBaseline {
component: string;
metric: string;
baseline: {
mean: number;
median: number;
p95: number;
p99: number;
standardDeviation: number;
};
thresholds: {
warning: number;
critical: number;
};
confidence: number;
lastUpdated: number;
sampleSize: number;
}
/**
* Anomaly detection result
*/
export interface AnomalyDetection {
id: string;
timestamp: number;
component: string;
metric: string;
value: number;
expectedValue: number;
deviation: number;
severity: 'low' | 'medium' | 'high' | 'critical';
type: 'statistical' | 'pattern' | 'trend' | 'threshold';
confidence: number;
context: {
timeWindow: number;
relatedMetrics: Record<string, number>;
environmentalFactors: string[];
};
impact: {
immediate: string[];
potential: string[];
};
recommendations: string[];
}
/**
* Optimization opportunity
*/
export interface OptimizationOpportunity {
id: string;
category: 'memory' | 'performance' | 'efficiency' | 'stability';
priority: 'low' | 'medium' | 'high' | 'critical';
component: string;
title: string;
description: string;
currentState: {
metric: string;
value: number;
trend: string;
};
targetState: {
metric: string;
value: number;
improvement: number;
};
implementation: {
effort: 'low' | 'medium' | 'high';
complexity: 'low' | 'medium' | 'high';
timeline: string;
prerequisites: string[];
steps: string[];
};
impact: {
performance: number;
stability: number;
resource: number;
cost: number;
};
riskAssessment: {
level: 'low' | 'medium' | 'high';
factors: string[];
mitigation: string[];
};
}
/**
* Memory prediction
*/
export interface MemoryPrediction {
component: string;
metric: string;
timestamp: number;
horizon: number;
predictions: {
timestamp: number;
value: number;
confidence: number;
variance: number;
}[];
scenarios: {
optimistic: { value: number; probability: number };
realistic: { value: number; probability: number };
pessimistic: { value: number; probability: number };
};
alerts: {
thresholdReach?: number;
criticalLevel?: number;
recommendations: string[];
};
}
/**
* Component health assessment
*/
export interface ComponentHealth {
component: string;
timestamp: number;
overallScore: number; // 0-100
dimensions: {
memoryEfficiency: { score: number; trend: string; issues: string[] };
stability: { score: number; trend: string; issues: string[] };
performance: { score: number; trend: string; issues: string[] };
reliability: { score: number; trend: string; issues: string[] };
};
riskFactors: {
factor: string;
severity: 'low' | 'medium' | 'high' | 'critical';
impact: string;
}[];
recommendations: {
immediate: string[];
shortTerm: string[];
longTerm: string[];
};
historicalTrend: {
timeWindow: number;
dataPoints: { timestamp: number; score: number }[];
trend: 'improving' | 'degrading' | 'stable';
};
}
/**
* System-wide analytics summary
*/
export interface SystemAnalytics {
timestamp: number;
overview: {
totalComponents: number;
healthyComponents: number;
warningComponents: number;
criticalComponents: number;
overallEfficiency: number;
memoryUtilization: number;
stabilityIndex: number;
};
patterns: MemoryPattern[];
anomalies: AnomalyDetection[];
optimizations: OptimizationOpportunity[];
predictions: MemoryPrediction[];
componentHealth: ComponentHealth[];
insights: {
key: string;
description: string;
impact: 'low' | 'medium' | 'high' | 'critical';
actionable: boolean;
}[];
}
/**
* Analytics report
*/
export interface AnalyticsReport {
id: string;
timestamp: number;
type: 'daily' | 'weekly' | 'monthly' | 'custom';
timeRange: { start: number; end: number };
summary: {
executiveSummary: string;
keyFindings: string[];
criticalIssues: string[];
recommendations: string[];
};
analytics: SystemAnalytics;
trends: {
memoryUsage: { component: string; trend: string; change: number }[];
performance: { metric: string; trend: string; change: number }[];
stability: { component: string; incidents: number; mtbf: number }[];
};
comparisons: {
previousPeriod: {
memoryEfficiency: { previous: number; current: number; change: number };
stabilityScore: { previous: number; current: number; change: number };
performanceIndex: { previous: number; current: number; change: number };
};
baseline: {
deviation: number;
significance: 'low' | 'medium' | 'high';
explanation: string;
};
};
attachments: {
charts: string[]; // file paths or base64 data
data: string[]; // CSV/JSON data files
logs: string[]; // relevant log excerpts
};
}
/**
* Main Memory Analytics Engine
*/
export class MemoryAnalyticsEngine extends EventEmitter {
private config: AnalyticsConfig;
private isAnalyzing = false;
private analysisInterval?: NodeJS.Timeout;
// Data storage
private memoryData: Array<{ timestamp: number; component: string; metrics: Record<string, number> }> = [];
private patterns = new Map<string, MemoryPattern>();
private baselines = new Map<string, PerformanceBaseline>();
private anomalies: AnomalyDetection[] = [];
private optimizations = new Map<string, OptimizationOpportunity>();
private healthAssessments = new Map<string, ComponentHealth>();
// Analysis state
private lastAnalysisTime = 0;
constructor(config: Partial<AnalyticsConfig> = {}) {
super();
this.config = {
realTimeAnalytics: config.realTimeAnalytics ?? true,
analysisInterval: config.analysisInterval ?? 30000, // 30 seconds
dataRetention: config.dataRetention ?? 7 * 24 * 60 * 60 * 1000, // 7 days
anomalySensitivity: config.anomalySensitivity ?? 0.7,
baselineWindow: config.baselineWindow ?? 24 * 60 * 60 * 1000, // 24 hours
prediction: {
enabled: true,
horizon: 60 * 60 * 1000, // 1 hour
confidence: 0.6,
...config.prediction
},
reporting: {
autoGenerate: true,
schedule: '0 8 * * *', // 8 AM daily
formats: ['json', 'html'],
...config.reporting
}
};
this.initializeBaselines();
}
/**
* Start analytics engine
*/
startAnalytics(): void {
if (this.isAnalyzing) {
// eslint-disable-next-line no-console
console.warn('Analytics engine is already running');
return;
}
this.isAnalyzing = true;
this.emit('analyticsStarted');
if (this.config.realTimeAnalytics) {
this.analysisInterval = setInterval(() => {
this.performAnalysis();
}, this.config.analysisInterval);
}
// eslint-disable-next-line no-console
console.log(`Memory analytics started - analysis interval: ${this.config.analysisInterval}ms`);
}
/**
* Stop analytics engine
*/
stopAnalytics(): void {
this.isAnalyzing = false;
if (this.analysisInterval) {
clearInterval(this.analysisInterval);
delete this.analysisInterval;
}
this.emit('analyticsStopped');
// eslint-disable-next-line no-console
console.log('Memory analytics stopped');
}
/**
* Add memory data for analysis
*/
addMemoryData(data: { timestamp: number; component: string; metrics: Record<string, number> }): void {
this.memoryData.push(data);
// Maintain data retention window
const cutoff = Date.now() - this.config.dataRetention;
this.memoryData = this.memoryData.filter(d => d.timestamp > cutoff);
// Trigger real-time analysis if enabled
if (this.config.realTimeAnalytics && this.isAnalyzing) {
// Throttle to avoid excessive analysis
const now = Date.now();
if (now - this.lastAnalysisTime > this.config.analysisInterval / 4) {
this.performIncrementalAnalysis(data);
}
}
}
/**
* Perform comprehensive analysis
*/
private async performAnalysis(): Promise<void> {
try {
this.lastAnalysisTime = Date.now();
// Update baselines
this.updateBaselines();
// Detect patterns
this.detectPatterns();
// Find anomalies
this.detectAnomalies();
// Identify optimization opportunities
this.identifyOptimizations();
// Assess component health
this.assessComponentHealth();
// Generate predictions if enabled
if (this.config.prediction.enabled) {
this.generatePredictions();
}
// Emit analytics update
const analytics = this.getSystemAnalytics();
this.emit('analyticsUpdate', analytics);
} catch (error) {
this.emit('analyticsError', error);
// eslint-disable-next-line no-console
console.error('Error performing memory analysis:', error);
}
}
/**
* Perform incremental analysis for new data
*/
private performIncrementalAnalysis(data: { timestamp: number; component: string; metrics: Record<string, number> }): void {
// Quick anomaly check
this.checkForImmediateAnomalies(data);
// Update component health if significant change
this.updateComponentHealthIncremental(data);
}
/**
* Initialize performance baselines
*/
private initializeBaselines(): void {
const components = ['evolution-engine', 'pareto-frontier', 'cache-manager', 'llm-adapter', 'system'];
const metrics = ['memoryUsage', 'objectCount', 'growthRate', 'heapUsagePercent'];
for (const component of components) {
for (const metric of metrics) {
const baselineKey = `${component}-${metric}`;
this.baselines.set(baselineKey, {
component,
metric,
baseline: {
mean: 0,
median: 0,
p95: 0,
p99: 0,
standardDeviation: 0
},
thresholds: {
warning: 0,
critical: 0
},
confidence: 0,
lastUpdated: Date.now(),
sampleSize: 0
});
}
}
}
/**
* Update performance baselines
*/
private updateBaselines(): void {
const now = Date.now();
const baselineWindow = now - this.config.baselineWindow;
// Group data by component and metric
const dataByKey = new Map<string, number[]>();
for (const dataPoint of this.memoryData) {
if (dataPoint.timestamp < baselineWindow) continue;
for (const [metric, value] of Object.entries(dataPoint.metrics)) {
const key = `${dataPoint.component}-${metric}`;
if (!dataByKey.has(key)) {
dataByKey.set(key, []);
}
dataByKey.get(key)!.push(value);
}
}
// Update baselines
for (const [key, values] of Array.from(dataByKey.entries())) {
if (values.length < 10) continue; // Need minimum samples
const sorted = [...values].sort((a, b) => a - b);
const mean = values.reduce((sum, v) => sum + v, 0) / values.length;
const variance = values.reduce((sum, v) => sum + Math.pow(v - mean, 2), 0) / values.length;
const stdDev = Math.sqrt(variance);
const baseline: PerformanceBaseline = {
component: key.split('-')[0] || '',
metric: key.split('-').slice(1).join('-'),
baseline: {
mean,
median: this.percentile(sorted, 50),
p95: this.percentile(sorted, 95),
p99: this.percentile(sorted, 99),
standardDeviation: stdDev
},
thresholds: {
warning: mean + (2 * stdDev),
critical: mean + (3 * stdDev)
},
confidence: Math.min(values.length / 100, 1), // Full confidence at 100+ samples
lastUpdated: now,
sampleSize: values.length
};
this.baselines.set(key, baseline);
}
}
/**
* Detect memory usage patterns
*/
private detectPatterns(): void {
const components = Array.from(new Set(this.memoryData.map(d => d.component)));
for (const component of components) {
const componentData = this.memoryData.filter(d => d.component === component);
if (componentData.length < 20) continue; // Need minimum data points
// Detect allocation patterns
this.detectAllocationPattern(component, componentData);
// Detect growth patterns
this.detectGrowthPattern(component, componentData);
// Detect cyclic patterns
this.detectCyclicPattern(component, componentData);
// Detect leak patterns
this.detectLeakPattern(component, componentData);
}
}
/**
* Detect allocation patterns
*/
private detectAllocationPattern(component: string, data: any[]): void {
const memoryUsage = data.map(d => d.metrics.memoryUsage || 0);
if (memoryUsage.length === 0) return;
// Calculate allocation frequency and size
let allocationEvents = 0;
let totalAllocated = 0;
for (let i = 1; i < memoryUsage.length; i++) {
const increase = memoryUsage[i] - memoryUsage[i - 1];
if (increase > 0) {
allocationEvents++;
totalAllocated += increase;
}
}
if (allocationEvents > 0) {
const avgAllocationSize = totalAllocated / allocationEvents;
const frequency = allocationEvents / (data.length - 1);
const pattern: MemoryPattern = {
id: `${component}-allocation`,
name: `${component} Allocation Pattern`,
type: 'allocation',
component,
frequency,
severity: this.calculatePatternSeverity(frequency, avgAllocationSize),
description: `Average allocation: ${(avgAllocationSize / 1024 / 1024).toFixed(2)}MB, Frequency: ${(frequency * 100).toFixed(1)}%`,
characteristics: {
averageSize: avgAllocationSize,
frequency,
duration: 0,
variability: this.calculateVariability(memoryUsage)
},
impact: {
performance: frequency > 0.3 ? 0.7 : 0.3,
stability: avgAllocationSize > 10 * 1024 * 1024 ? 0.8 : 0.2,
efficiency: frequency > 0.5 ? 0.6 : 0.4
},
trend: this.calculateTrend(memoryUsage),
recommendations: this.generatePatternRecommendations('allocation', frequency, avgAllocationSize)
};
this.patterns.set(pattern.id, pattern);
}
}
/**
* Detect growth patterns
*/
private detectGrowthPattern(component: string, data: any[]): void {
const memoryUsage = data.map(d => d.metrics.memoryUsage || 0);
if (memoryUsage.length < 10) return;
// Calculate growth rate using linear regression
const n = memoryUsage.length;
const x = Array.from({ length: n }, (_, i) => i);
const sumX = x.reduce((sum, val) => sum + val, 0);
const sumY = memoryUsage.reduce((sum, val) => sum + val, 0);
const sumXY = x.reduce((sum, val, i) => sum + val * memoryUsage[i], 0);
const sumXX = x.reduce((sum, val) => sum + val * val, 0);
const slope = (n * sumXY - sumX * sumY) / (n * sumXX - sumX * sumX);
const growthRate = slope * 1000; // per second to per 1000 samples
if (Math.abs(growthRate) > 1024) { // 1KB growth threshold
const pattern: MemoryPattern = {
id: `${component}-growth`,
name: `${component} Growth Pattern`,
type: 'retention',
component,
frequency: 1,
severity: Math.abs(growthRate) > 10 * 1024 * 1024 ? 'critical' : 'medium',
description: `Growth rate: ${(growthRate / 1024 / 1024).toFixed(2)}MB per 1000 samples`,
characteristics: {
averageSize: 0,
frequency: 1,
duration: data[data.length - 1].timestamp - data[0].timestamp,
variability: this.calculateVariability(memoryUsage)
},
impact: {
performance: Math.min(Math.abs(growthRate) / (50 * 1024 * 1024), 1),
stability: Math.min(Math.abs(growthRate) / (100 * 1024 * 1024), 1),
efficiency: Math.min(Math.abs(growthRate) / (25 * 1024 * 1024), 1)
},
trend: {
direction: growthRate > 0 ? 'increasing' : 'decreasing',
velocity: Math.abs(growthRate),
acceleration: 0 // Would need second derivative
},
recommendations: this.generateGrowthRecommendations(growthRate, component)
};
this.patterns.set(pattern.id, pattern);
}
}
/**
* Detect cyclic patterns
*/
private detectCyclicPattern(component: string, data: any[]): void {
const memoryUsage = data.map(d => d.metrics.memoryUsage || 0);
if (memoryUsage.length < 50) return;
// Simple cycle detection using autocorrelation
const cycles = this.findCycles(memoryUsage);
if (cycles.length > 0) {
const avgCycleLength = cycles.reduce((sum, c) => sum + c.length, 0) / cycles.length;
const avgAmplitude = cycles.reduce((sum, c) => sum + c.amplitude, 0) / cycles.length;
const pattern: MemoryPattern = {
id: `${component}-cyclic`,
name: `${component} Cyclic Pattern`,
type: 'allocation',
component,
frequency: cycles.length / memoryUsage.length,
severity: avgAmplitude > 50 * 1024 * 1024 ? 'high' : 'medium',
description: `Detected ${cycles.length} cycles, avg length: ${avgCycleLength.toFixed(1)}, avg amplitude: ${(avgAmplitude / 1024 / 1024).toFixed(2)}MB`,
characteristics: {
averageSize: avgAmplitude,
frequency: cycles.length / memoryUsage.length,
duration: avgCycleLength,
variability: this.calculateCycleVariability(cycles)
},
impact: {
performance: avgAmplitude > 100 * 1024 * 1024 ? 0.7 : 0.3,
stability: 0.4,
efficiency: 0.5
},
trend: this.calculateTrend(memoryUsage),
recommendations: [
'Consider implementing memory pooling to reduce allocation cycles',
'Optimize allocation timing to reduce memory pressure spikes',
'Investigate cyclic allocation sources'
]
};
this.patterns.set(pattern.id, pattern);
}
}
/**
* Detect leak patterns
*/
private detectLeakPattern(component: string, data: any[]): void {
const memoryUsage = data.map(d => d.metrics.memoryUsage || 0);
if (memoryUsage.length < 30) return;
// Check for sustained growth without significant drops
const windows = [];
const windowSize = 10;
for (let i = 0; i <= memoryUsage.length - windowSize; i += windowSize) {
const window = memoryUsage.slice(i, i + windowSize);
const avg = window.reduce((sum, v) => sum + v, 0) / window.length;
windows.push(avg);
}
// Check if each window is higher than the previous
let consecutiveIncreases = 0;
let maxConsecutive = 0;
for (let i = 1; i < windows.length; i++) {
const prevWindow = windows[i - 1];
const currentWindow = windows[i];
if (prevWindow !== undefined && currentWindow !== undefined && currentWindow > prevWindow * 1.02) { // 2% increase threshold
consecutiveIncreases++;
maxConsecutive = Math.max(maxConsecutive, consecutiveIncreases);
} else {
consecutiveIncreases = 0;
}
}
if (maxConsecutive >= 3) { // 3 consecutive increasing windows
const lastWindow = windows[windows.length - 1]!;
const firstWindow = windows[0]!;
if (lastWindow !== undefined && firstWindow !== undefined) {
const totalGrowth = lastWindow - firstWindow;
const growthRate = totalGrowth / windows.length;
const pattern: MemoryPattern = {
id: `${component}-leak`,
name: `${component} Potential Leak`,
type: 'leak',
component,
frequency: maxConsecutive / windows.length,
severity: growthRate > 10 * 1024 * 1024 ? 'critical' : 'high',
description: `Sustained growth over ${maxConsecutive} windows, total growth: ${(totalGrowth / 1024 / 1024).toFixed(2)}MB`,
characteristics: {
averageSize: growthRate,
frequency: maxConsecutive / windows.length,
duration: data[data.length - 1].timestamp - data[0].timestamp,
variability: 0.1 // Leaks typically have low variability
},
impact: {
performance: 0.8,
stability: 0.9,
efficiency: 0.7
},
trend: {
direction: 'increasing',
velocity: growthRate,
acceleration: 0
},
recommendations: [
'Investigate for memory leaks in component',
'Check for unclosed resources or retained references',
'Consider implementing automatic cleanup mechanisms',
'Monitor object lifecycle management'
]
};
this.patterns.set(pattern.id, pattern);
}
}
}
/**
* Detect anomalies in memory data
*/
private detectAnomalies(): void {
const now = Date.now();
const recentData = this.memoryData.filter(d => now - d.timestamp < 3600000); // Last hour
for (const dataPoint of recentData) {
for (const [metric, value] of Object.entries(dataPoint.metrics)) {
const baselineKey = `${dataPoint.component}-${metric}`;
const baseline = this.baselines.get(baselineKey);
if (!baseline || baseline.sampleSize < 10) continue;
const deviation = Math.abs(value - baseline.baseline.mean) / baseline.baseline.standardDeviation;
const threshold = 2 + (1 - this.config.anomalySensitivity) * 2; // 2-4 sigma threshold
if (deviation > threshold) {
const anomaly: AnomalyDetection = {
id: `${dataPoint.component}-${metric}-${dataPoint.timestamp}`,
timestamp: dataPoint.timestamp,
component: dataPoint.component,
metric,
value,
expectedValue: baseline.baseline.mean,
deviation,
severity: this.calculateAnomalySeverity(deviation),
type: 'statistical',
confidence: Math.min(deviation / threshold, 1),
context: {
timeWindow: 3600000,
relatedMetrics: dataPoint.metrics,
environmentalFactors: []
},
impact: {
immediate: this.getImmediateImpact(dataPoint.component, metric, deviation),
potential: this.getPotentialImpact(dataPoint.component, metric, deviation)
},
recommendations: this.getAnomalyRecommendations(dataPoint.component, metric, deviation)
};
this.anomalies.push(anomaly);
this.emit('anomalyDetected', anomaly);
}
}
}
// Clean old anomalies
const cutoff = now - 24 * 60 * 60 * 1000; // 24 hours
this.anomalies = this.anomalies.filter(a => a.timestamp > cutoff);
}
/**
* Check for immediate anomalies in new data
*/
private checkForImmediateAnomalies(data: { timestamp: number; component: string; metrics: Record<string, number> }): void {
for (const [metric, value] of Object.entries(data.metrics)) {
const baselineKey = `${data.component}-${metric}`;
const baseline = this.baselines.get(baselineKey);
if (!baseline || baseline.confidence < 0.5) continue;
// Check for extreme deviations
const deviation = Math.abs(value - baseline.baseline.mean) / baseline.baseline.standardDeviation;
if (deviation > 3) { // 3 sigma threshold for immediate alerts
const anomaly: AnomalyDetection = {
id: `${data.component}-${metric}-${data.timestamp}`,
timestamp: data.timestamp,
component: data.component,
metric,
value,
expectedValue: baseline.baseline.mean,
deviation,
severity: 'critical',
type: 'statistical',
confidence: 0.95,
context: {
timeWindow: 0,
relatedMetrics: data.metrics,
environmentalFactors: ['real-time-detection']
},
impact: {
immediate: ['Potential system instability'],
potential: ['Memory exhaustion', 'Performance degradation']
},
recommendations: ['Immediate investigation required', 'Consider emergency cleanup']
};
this.emit('criticalAnomaly', anomaly);
}
}
}
/**
* Identify optimization opportunities
*/
private identifyOptimizations(): void {
// Memory usage optimization
this.identifyMemoryOptimizations();
// Performance optimization
this.identifyPerformanceOptimizations();
// Efficiency optimization
this.identifyEfficiencyOptimizations();
// Stability optimization
this.identifyStabilityOptimizations();
}
/**
* Identify memory optimization opportunities
*/
private identifyMemoryOptimizations(): void {
const components = Array.from(new Set(this.memoryData.map(d => d.component)));
for (const component of components) {
const componentData = this.memoryData.filter(d => d.component === component);
if (componentData.length === 0) continue;
const currentUsage = componentData[componentData.length - 1]?.metrics.memoryUsage || 0;
// High memory usage optimization
if (currentUsage > 100 * 1024 * 1024) { // 100MB threshold
const optimization: OptimizationOpportunity = {
id: `${component}-memory-usage`,
category: 'memory',
priority: currentUsage > 500 * 1024 * 1024 ? 'critical' : 'high',
component,
title: 'Reduce Memory Usage',
description: `Component ${component} is using ${(currentUsage / 1024 / 1024).toFixed(2)}MB of memory`,
currentState: {
metric: 'memoryUsage',
value: currentUsage,
trend: 'increasing'
},
targetState: {
metric: 'memoryUsage',
value: currentUsage * 0.7,
improvement: 0.3
},
implementation: {
effort: 'medium',
complexity: 'medium',
timeline: '1-2 weeks',
prerequisites: ['Memory profiling', 'Code analysis'],
steps: [
'Profile memory allocation patterns',
'Identify largest memory consumers',
'Implement object pooling',
'Optimize data structures',
'Add periodic cleanup'
]
},
impact: {
performance: 0.7,
stability: 0.8,
resource: 0.9,
cost: 0.6
},
riskAssessment: {
level: 'medium',
factors: ['Code complexity', 'Testing requirements'],
mitigation: ['Gradual rollout', 'Comprehensive testing']
}
};
this.optimizations.set(optimization.id, optimization);
}
}
}
/**
* Identify performance optimization opportunities
*/
private identifyPerformanceOptimizations(): void {
// Look for patterns that indicate performance issues
for (const pattern of Array.from(this.patterns.values())) {
if (pattern.impact.performance > 0.6) {
const optimization: OptimizationOpportunity = {
id: `${pattern.component}-performance`,
category: 'performance',
priority: pattern.severity === 'critical' ? 'critical' : 'high',
component: pattern.component,
title: `Optimize ${pattern.name}`,
description: `Pattern affecting performance: ${pattern.description}`,
currentState: {
metric: 'performance',
value: pattern.impact.performance,
trend: pattern.trend.direction
},
targetState: {
metric: 'performance',
value: pattern.impact.performance * 0.5,
improvement: 0.5
},
implementation: {
effort: pattern.type === 'leak' ? 'high' : 'medium',
complexity: 'medium',
timeline: '2-4 weeks',
prerequisites: ['Performance profiling'],
steps: pattern.recommendations
},
impact: {
performance: 0.8,
stability: 0.6,
resource: 0.7,
cost: 0.5
},
riskAssessment: {
level: 'low',
factors: ['Well-defined patterns'],
mitigation: ['Pattern-based solutions']
}
};
this.optimizations.set(optimization.id, optimization);
}
}
}
/**
* Identify efficiency optimization opportunities
*/
private identifyEfficiencyOptimizations(): void {
// Check for low pool utilization
// This would integrate with GC optimizer data
const components = ['evolution-engine', 'pareto-frontier', 'cache-manager'];
for (const component of components) {
// Simulated pool utilization check
const utilizationData = this.memoryData
.filter(d => d.component === component)
.map(d => d.metrics.poolUtilization || 0);
if (utilizationData.length > 0) {
const avgUtilization = utilizationData.reduce((sum, u) => sum + u, 0) / utilizationData.length;
if (avgUtilization < 0.3) { // Low utilization
const optimization: OptimizationOpportunity = {
id: `${component}-efficiency`,
category: 'efficiency',
priority: 'medium',
component,
title: 'Improve Pool Efficiency',
description: `Low pool utilization (${(avgUtilization * 100).toFixed(1)}%) indicates inefficient resource usage`,
currentState: {
metric: 'poolUtilization',
value: avgUtilization,
trend: 'stable'
},
targetState: {
metric: 'poolUtilization',
value: 0.7,
improvement: 0.7 - avgUtilization
},
implementation: {
effort: 'low',
complexity: 'low',
timeline: '1 week',
prerequisites: ['Pool analysis'],
steps: [
'Analyze pool usage patterns',
'Adjust pool sizes',
'Optimize allocation strategies',
'Implement dynamic sizing'
]
},
impact: {
performance: 0.4,
stability: 0.3,
resource: 0.8,
cost: 0.7
},
riskAssessment: {
level: 'low',
factors: ['Configuration changes only'],
mitigation: ['Easy rollback']
}
};
this.optimizations.set(optimization.id, optimization);
}
}
}
}
/**
* Identify stability optimization opportunities
*/
private identifyStabilityOptimizations(): void {
// Look for components with high anomaly counts
const anomaliesByComponent = new Map<string, number>();
for (const anomaly of this.anomalies) {
const count = anomaliesByComponent.get(anomaly.component) || 0;
anomaliesByComponent.set(anomaly.component, count + 1);
}
for (const [component, count] of Array.from(anomaliesByComponent.entries())) {
if (count > 5) { // High anomaly threshold
const optimization: OptimizationOpportunity = {
id: `${component}-stability`,
category: 'stability',
priority: count > 15 ? 'high' : 'medium',
component,
title: 'Improve Component Stability',
description: `Component has ${count} anomalies, indicating stability issues`,
currentState: {
metric: 'anomalyCount',
value: count,
trend: 'increasing'
},
targetState: {
metric: 'anomalyCount',
value: Math.max(2, count * 0.3),
improvement: 0.7
},
implementation: {
effort: 'high',
complexity: 'high',
timeline: '4-6 weeks',
prerequisites: ['Root cause analysis', 'Code review'],
steps: [
'Analyze anomaly patterns',
'Identify root causes',
'Implement stability improvements',
'Add monitoring and alerts',
'Test under stress conditions'
]
},
impact: {
performance: 0.6,
stability: 0.9,
resource: 0.5,
cost: 0.4
},
riskAssessment: {
level: 'medium',
factors: ['Complex changes required'],
mitigation: ['Phased implementation', 'Extensive testing']
}
};
this.optimizations.set(optimization.id, optimization);
}
}
}
/**
* Assess component health
*/
private assessComponentHealth(): void {
const components = Array.from(new Set(this.memoryData.map(d => d.component)));
for (const component of components) {
const assessment = this.calculateComponentHealth(component);
this.healthAssessments.set(component, assessment);
}
}
/**
* Calculate component health score
*/
private calculateComponentHealth(component: string): ComponentHealth {
const componentData = this.memoryData.filter(d => d.component === component);
const componentAnomalies = this.anomalies.filter(a => a.component === component);
const componentPatterns = Array.from(this.patterns.values()).filter(p => p.component === component);
// Memory efficiency score
const memoryData = componentData.map(d => d.metrics.memoryUsage || 0);
const memoryTrend = this.calculateTrend(memoryData);
const memoryScore = this.calculateMemoryEfficiencyScore(memoryData, memoryTrend);
// Stability score
const stabilityScore = Math.max(0, 100 - (componentAnomalies.length * 10));
// Performance score
const performanceImpact = componentPatterns.reduce((sum, p) => sum + p.impact.performance, 0) / Math.max(componentPatterns.length, 1);
const performanceScore = Math.max(0, 100 - (performanceImpact * 100));
// Reliability score
const criticalAnomalies = componentAnomalies.filter(a => a.severity === 'critical').length;
const reliabilityScore = Math.max(0, 100 - (criticalAnomalies * 20));
// Overall score
const overallScore = (memoryScore + stabilityScore + performanceScore + reliabilityScore) / 4;
return {
component,
timestamp: Date.now(),
overallScore,
dimensions: {
memoryEfficiency: {
score: memoryScore,
trend: memoryTrend.direction,
issues: memoryScore < 70 ? ['High memory usage', 'Memory growth detected'] : []
},
stability: {
score: stabilityScore,
trend: componentAnomalies.length > 5 ? 'degrading' : 'stable',
issues: componentAnomalies.length > 0 ? [`${componentAnomalies.length} anomalies detected`] : []
},
performance: {
score: performanceScore,
trend: performanceImpact > 0.5 ? 'degrading' : 'stable',
issues: componentPatterns.filter(p => p.impact.performance > 0.6).map(p => p.name)
},
reliability: {
score: reliabilityScore,
trend: criticalAnomalies > 0 ? 'degrading' : 'stable',
issues: criticalAnomalies > 0 ? ['Critical anomalies detected'] : []
}
},
riskFactors: this.identifyRiskFactors(component, componentAnomalies, componentPatterns),
recommendations: {
immediate: this.getImmediateRecommendations(component, overallScore),
shortTerm: this.getShortTermRecommendations(component, componentPatterns),
longTerm: this.getLongTermRecommendations(component, memoryTrend)
},
historicalTrend: {
timeWindow: 24 * 60 * 60 * 1000,
dataPoints: this.getHistoricalHealthData(component),
trend: overallScore > 80 ? 'improving' : overallScore < 60 ? 'degrading' : 'stable'
}
};
}
/**
* Update component health incrementally
*/
private updateComponentHealthIncremental(data: { timestamp: number; component: string; metrics: Record<string, number> }): void {
const currentHealth = this.healthAssessments.get(data.component);
if (!currentHealth) return;
// Quick health check for significant changes
const memoryUsage = data.metrics.memoryUsage || 0;
const baselineKey = `${data.component}-memoryUsage`;
const baseline = this.baselines.get(baselineKey);
if (baseline && baseline.confidence > 0.5) {
const deviation = Math.abs(memoryUsage - baseline.baseline.mean) / baseline.baseline.standardDeviation;
if (deviation > 2) {
// Significant change detected - trigger immediate health update
const updatedHealth = this.calculateComponentHealth(data.component);
this.healthAssessments.set(data.component, updatedHealth);
this.emit('healthUpdate', updatedHealth);
}
}
}
/**
* Generate memory predictions
*/
private generatePredictions(): void {
const components = Array.from(new Set(this.memoryData.map(d => d.component)));
for (const component of components) {
const prediction = this.generateComponentPrediction(component);
if (prediction) {
this.emit('predictionGenerated', prediction);
}
}
}
/**
* Generate prediction for a component
*/
private generateComponentPrediction(component: string): MemoryPrediction | null {
const componentData = this.memoryData
.filter(d => d.component === component)
.sort((a, b) => a.timestamp - b.timestamp);
if (componentData.length < 30) return null; // Need minimum data points
const memoryUsage = componentData.map(d => d.metrics.memoryUsage || 0);
const timestamps = componentData.map(d => d.timestamp);
// Simple linear prediction model
const trend = this.calculateTrend(memoryUsage);
const currentValue = memoryUsage[memoryUsage.length - 1];
const currentTime = timestamps[timestamps.length - 1];
if (currentValue === undefined || currentTime === undefined) {
return null;
}
// Generate predictions for the next hour
const predictions = [];
const stepSize = 5 * 60 * 1000; // 5 minutes
const horizon = this.config.prediction.horizon;
for (let t = stepSize; t <= horizon; t += stepSize) {
const futureTimestamp = currentTime + t;
const steps = t / stepSize;
// Linear prediction with some variance
const predictedValue = currentValue + (trend.velocity * steps);
const confidence = Math.max(0.2, 1 - (steps * 0.1)); // Confidence decreases over time
const variance = Math.abs(predictedValue * 0.1 * steps); // Variance increases over time
predictions.push({
timestamp: futureTimestamp,
value: Math.max(0, predictedValue),
confidence,
variance
});
}
// Generate scenarios
const finalPrediction = predictions[predictions.length - 1];
if (!finalPrediction) {
return null;
}
const scenarios = {
optimistic: {
value: finalPrediction.value * 0.8,
probability: 0.2
},
realistic: {
value: finalPrediction.value,
probability: 0.6
},
pessimistic: {
value: finalPrediction.value * 1.5,
probability: 0.2
}
};
// Check for alerts
const alerts: any = { recommendations: [] };
const baseline = this.baselines.get(`${component}-memoryUsage`);
if (baseline && finalPrediction.value > baseline.thresholds.critical) {
alerts.criticalLevel = currentTime + horizon;
alerts.recommendations.push('Critical memory level predicted - immediate action required');
} else if (baseline && finalPrediction.value > baseline.thresholds.warning) {
alerts.thresholdReach = currentTime + horizon * 0.7;
alerts.recommendations.push('Warning threshold predicted to be reached');
}
return {
component,
metric: 'memoryUsage',
timestamp: Date.now(),
horizon,
predictions,
scenarios,
alerts
};
}
/**
* Get system-wide analytics
*/
getSystemAnalytics(): SystemAnalytics {
const components = Array.from(new Set(this.memoryData.map(d => d.component)));
const healthAssessments = Array.from(this.healthAssessments.values());
const healthyComponents = healthAssessments.filter(h => h.overallScore >= 80).length;
const warningComponents = healthAssessments.filter(h => h.overallScore >= 60 && h.overallScore < 80).length;
const criticalComponents = healthAssessments.filter(h => h.overallScore < 60).length;
const overallEfficiency = healthAssessments.length > 0
? healthAssessments.reduce((sum, h) => sum + h.overallScore, 0) / healthAssessments.length / 100
: 1;
// Calculate memory utilization
const latestData = new Map<string, number>();
for (const component of components) {
const componentData = this.memoryData.filter(d => d.component === component);
if (componentData.length > 0) {
latestData.set(component, componentData[componentData.length - 1]?.metrics?.memoryUsage || 0);
}
}
const totalMemory = Array.from(latestData.values()).reduce((sum, usage) => sum + usage, 0);
const memoryUtilization = totalMemory / (1024 * 1024 * 1024); // GB
// Calculate stability index
const recentAnomalies = this.anomalies.filter(a => Date.now() - a.timestamp < 3600000); // Last hour
const stabilityIndex = Math.max(0, 1 - (recentAnomalies.length / 100));
return {
timestamp: Date.now(),
overview: {
totalComponents: components.length,
healthyComponents,
warningComponents,
criticalComponents,
overallEfficiency,
memoryUtilization,
stabilityIndex
},
patterns: Array.from(this.patterns.values()),
anomalies: this.anomalies.slice(-50), // Last 50 anomalies
optimizations: Array.from(this.optimizations.values()),
predictions: [], // Would be populated by generatePredictions
componentHealth: healthAssessments,
insights: this.generateInsights()
};
}
/**
* Generate actionable insights
*/
private generateInsights(): Array<{ key: string; description: string; impact: 'low' | 'medium' | 'high' | 'critical'; actionable: boolean }> {
const insights = [];
// Memory usage insights
const highMemoryComponents = Array.from(this.healthAssessments.values())
.filter(h => h.dimensions.memoryEfficiency.score < 60);
if (highMemoryComponents.length > 0) {
insights.push({
key: 'high-memory-usage',
description: `${highMemoryComponents.length} components showing high memory usage`,
impact: highMemoryComponents.length > 2 ? 'high' : 'medium' as any,
actionable: true
});
}
// Pattern insights
const criticalPatterns = Array.from(this.patterns.values())
.filter(p => p.severity === 'critical');
if (criticalPatterns.length > 0) {
insights.push({
key: 'critical-patterns',
description: `${criticalPatterns.length} critical memory patterns detected`,
impact: 'critical',
actionable: true
});
}
// Anomaly insights
const recentAnomalies = this.anomalies.filter(a => Date.now() - a.timestamp < 3600000);
if (recentAnomalies.length > 10) {
insights.push({
key: 'high-anomaly-rate',
description: `High anomaly rate: ${recentAnomalies.length} anomalies in the last hour`,
impact: 'high',
actionable: true
});
}
// Optimization insights
const criticalOptimizations = Array.from(this.optimizations.values())
.filter(o => o.priority === 'critical');
if (criticalOptimizations.length > 0) {
insights.push({
key: 'critical-optimizations',
description: `${criticalOptimizations.length} critical optimization opportunities available`,
impact: 'high',
actionable: true
});
}
return insights;
}
/**
* Generate analytics report
*/
async generateReport(type: 'daily' | 'weekly' | 'monthly' | 'custom' = 'daily', timeRange?: { start: number; end: number }): Promise<AnalyticsReport> {
const now = Date.now();
const reportId = `report-${type}-${now}`;
// Determine time range
let reportTimeRange = timeRange;
if (!reportTimeRange) {
switch (type) {
case 'daily':
reportTimeRange = { start: now - 24 * 60 * 60 * 1000, end: now };
break;
case 'weekly':
reportTimeRange = { start: now - 7 * 24 * 60 * 60 * 1000, end: now };
break;
case 'monthly':
reportTimeRange = { start: now - 30 * 24 * 60 * 60 * 1000, end: now };
break;
default:
reportTimeRange = { start: now - 24 * 60 * 60 * 1000, end: now };
}
}
const analytics = this.getSystemAnalytics();
// Generate executive summary
const criticalIssues = [];
const keyFindings = [];
const recommendations = [];
if (analytics.overview.criticalComponents > 0) {
criticalIssues.push(`${analytics.overview.criticalComponents} components in critical state`);
}
if (analytics.anomalies.filter(a => a.severity === 'critical').length > 0) {
criticalIssues.push('Critical memory anomalies detected');
}
keyFindings.push(`Overall system efficiency: ${(analytics.overview.overallEfficiency * 100).toFixed(1)}%`);
keyFindings.push(`Memory utilization: ${analytics.overview.memoryUtilization.toFixed(2)}GB`);
keyFindings.push(`Stability index: ${(analytics.overview.stabilityIndex * 100).toFixed(1)}%`);
// Get top recommendations from optimizations
const topOptimizations = Array.from(this.optimizations.values())
.sort((a, b) => (b.priority === 'critical' ? 1 : 0) - (a.priority === 'critical' ? 1 : 0))
.slice(0, 5);
recommendations.push(...topOptimizations.map(o => o.title));
const executiveSummary = this.generateExecutiveSummary(analytics, criticalIssues, keyFindings);
const report: AnalyticsReport = {
id: reportId,
timestamp: now,
type,
timeRange: reportTimeRange,
summary: {
executiveSummary,
keyFindings,
criticalIssues,
recommendations
},
analytics,
trends: this.calculateTrends(reportTimeRange),
comparisons: {
previousPeriod: {
memoryEfficiency: { previous: 0.85, current: analytics.overview.overallEfficiency, change: 0 },
stabilityScore: { previous: 0.9, current: analytics.overview.stabilityIndex, change: 0 },
performanceIndex: { previous: 0.8, current: 0.8, change: 0 }
},
baseline: {
deviation: 0.1,
significance: 'medium',
explanation: 'System performance within normal parameters'
}
},
attachments: {
charts: [],
data: [],
logs: []
}
};
// Save report if enabled
if (this.config.reporting.autoGenerate) {
await this.saveReport(report);
}
this.emit('reportGenerated', report);
return report;
}
// Utility methods
private percentile(sortedValues: number[], percentile: number): number {
if (sortedValues.length === 0) return 0;
if (sortedValues.length === 1) return sortedValues[0] ?? 0;
const index = (percentile / 100) * (sortedValues.length - 1);
const lower = Math.floor(index);
const upper = Math.ceil(index);
if (lower === upper) {
return sortedValues[lower] ?? 0;
}
const lowerVal = sortedValues[lower] ?? 0;
const upperVal = sortedValues[upper] ?? 0;
return lowerVal + (upperVal - lowerVal) * (index - lower);
}
private calculateVariability(values: number[]): number {
if (values.length < 2) return 0;
const mean = values.reduce((sum, v) => sum + v, 0) / values.length;
const variance = values.reduce((sum, v) => sum + Math.pow(v - mean, 2), 0) / values.length;
return Math.sqrt(variance) / mean; // Coefficient of variation
}
private calculateTrend(values: number[]): { direction: 'increasing' | 'decreasing' | 'stable' | 'volatile'; velocity: number; acceleration: number } {
if (values.length < 3) return { direction: 'stable', velocity: 0, acceleration: 0 };
const n = values.length;
const x = Array.from({ length: n }, (_, i) => i);
const sumX = x.reduce((sum, val) => sum + val, 0);
const sumY = values.reduce((sum, val) => sum + val, 0);
const sumXY = x.reduce((sum, val, i) => sum + val * (values[i] ?? 0), 0);
const sumXX = x.reduce((sum, val) => sum + val * val, 0);
const slope = (n * sumXY - sumX * sumY) / (n * sumXX - sumX * sumX) || 0;
const mean = sumY / n;
let direction: 'increasing' | 'decreasing' | 'stable' | 'volatile';
if (Math.abs(slope) < mean * 0.01) {
direction = 'stable';
} else if (slope > 0) {
direction = 'increasing';
} else {
direction = 'decreasing';
}
// Check for volatility
const variance = values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / n;
if (Math.sqrt(variance) > mean * 0.3) {
direction = 'volatile';
}
return {
direction,
velocity: Math.abs(slope),
acceleration: 0 // Would need second derivative
};
}
private findCycles(values: number[]): Array<{ length: number; amplitude: number }> {
// Simple cycle detection using local maxima/minima
const cycles = [];
const peaks = [];
const valleys = [];
for (let i = 1; i < values.length - 1; i++) {
const current = values[i];
const prev = values[i - 1];
const next = values[i + 1];
if (current !== undefined && prev !== undefined && next !== undefined) {
if (current > prev && current > next) {
peaks.push({ index: i, value: current });
} else if (current < prev && current < next) {
valleys.push({ index: i, value: current });
}
}
}
// Match peaks with valleys to find cycles
for (let i = 0; i < peaks.length - 1; i++) {
const peak1 = peaks[i];
const peak2 = peaks[i + 1];
if (!peak1 || !peak2) continue;
const valleysBetween = valleys.filter(v => v.index > peak1.index && v.index < peak2.index);
if (valleysBetween.length > 0) {
const valley = valleysBetween[0];
if (valley && peak1.value !== undefined && valley.value !== undefined) {
cycles.push({
length: peak2.index - peak1.index,
amplitude: peak1.value - valley.value
});
}
}
}
return cycles;
}
private calculateCycleVariability(cycles: Array<{ length: number; amplitude: number }>): number {
if (cycles.length < 2) return 0;
const lengths = cycles.map(c => c.length);
const avgLength = lengths.reduce((sum, l) => sum + l, 0) / lengths.length;
const lengthVariance = lengths.reduce((sum, l) => sum + Math.pow(l - avgLength, 2), 0) / lengths.length;
return Math.sqrt(lengthVariance) / avgLength;
}
private calculatePatternSeverity(frequency: number, averageSize: number): 'low' | 'medium' | 'high' | 'critical' {
const sizeScore = Math.min(averageSize / (100 * 1024 * 1024), 1); // 100MB max
const freqScore = Math.min(frequency * 2, 1); // 50% frequency max
const combined = (sizeScore + freqScore) / 2;
if (combined > 0.8) return 'critical';
if (combined > 0.6) return 'high';
if (combined > 0.3) return 'medium';
return 'low';
}
private generatePatternRecommendations(type: string, frequency: number, size: number): string[] {
const recommendations = [];
if (type === 'allocation') {
if (frequency > 0.5) {
recommendations.push('Implement object pooling to reduce allocation frequency');
}
if (size > 50 * 1024 * 1024) {
recommendations.push('Consider breaking large allocations into smaller chunks');
}
recommendations.push('Monitor allocation patterns for optimization opportunities');
}
return recommendations;
}
private generateGrowthRecommendations(growthRate: number, component: string): string[] {
const recommendations = [];
if (growthRate > 0) {
recommendations.push(`Investigate memory growth in ${component}`);
if (growthRate > 10 * 1024 * 1024) {
recommendations.push('Consider implementing automatic cleanup mechanisms');
}
} else {
recommendations.push('Monitor memory deallocation patterns');
}
return recommendations;
}
private calculateAnomalySeverity(deviation: number): 'low' | 'medium' | 'high' | 'critical' {
if (deviation > 4) return 'critical';
if (deviation > 3) return 'high';
if (deviation > 2.5) return 'medium';
return 'low';
}
private getImmediateImpact(_component: string, metric: string, deviation: number): string[] {
const impact = [];
if (metric === 'memoryUsage' && deviation > 3) {
impact.push('Potential memory exhaustion');
impact.push('Performance degradation');
}
if (metric === 'objectCount' && deviation > 3) {
impact.push('Resource exhaustion');
impact.push('Allocation failures');
}
return impact;
}
private getPotentialImpact(_component: string, _metric: string, deviation: number): string[] {
const impact = [];
if (deviation > 3) {
impact.push('System instability');
impact.push('Cascading failures');
impact.push('Service degradation');
}
return impact;
}
private getAnomalyRecommendations(component: string, metric: string, deviation: number): string[] {
const recommendations = [];
recommendations.push(`Investigate ${component} ${metric} anomaly`);
if (deviation > 3) {
recommendations.push('Consider immediate intervention');
}
recommendations.push('Monitor for pattern development');
return recommendations;
}
private calculateMemoryEfficiencyScore(memoryData: number[], trend: any): number {
if (memoryData.length === 0) return 100;
const avgUsage = memoryData.reduce((sum, usage) => sum + usage, 0) / memoryData.length;
// Efficiency decreases with higher usage and growth
let score = 100;
// Penalize high absolute usage (assuming 1GB as high)
if (avgUsage > 1024 * 1024 * 1024) {
score -= Math.min(50, (avgUsage / (1024 * 1024 * 1024)) * 10);
}
// Penalize growth trends
if (trend.direction === 'increasing') {
score -= Math.min(20, trend.velocity / (10 * 1024 * 1024));
}
// Penalize volatility
if (trend.direction === 'volatile') {
score -= 15;
}
return Math.max(0, score);
}
private identifyRiskFactors(_component: string, anomalies: AnomalyDetection[], patterns: MemoryPattern[]): any[] {
const riskFactors = [];
const criticalAnomalies = anomalies.filter(a => a.severity === 'critical').length;
if (criticalAnomalies > 0) {
riskFactors.push({
factor: 'Critical Anomalies',
severity: 'critical' as const,
impact: 'System stability at risk'
});
}
const leakPatterns = patterns.filter(p => p.type === 'leak').length;
if (leakPatterns > 0) {
riskFactors.push({
factor: 'Memory Leak Pattern',
severity: 'high' as const,
impact: 'Progressive memory exhaustion'
});
}
return riskFactors;
}
private getImmediateRecommendations(component: string, score: number): string[] {
const recommendations = [];
if (score < 30) {
recommendations.push(`Emergency intervention required for ${component}`);
recommendations.push('Consider component restart or failover');
} else if (score < 60) {
recommendations.push(`Immediate attention required for ${component}`);
recommendations.push('Investigate and resolve critical issues');
}
return recommendations;
}
private getShortTermRecommendations(_component: string, patterns: MemoryPattern[]): string[] {
const recommendations = [];
for (const pattern of patterns) {
if (pattern.severity === 'high' || pattern.severity === 'critical') {
recommendations.push(...pattern.recommendations);
}
}
return Array.from(new Set(recommendations)); // Remove duplicates
}
private getLongTermRecommendations(component: string, trend: any): string[] {
const recommendations = [];
if (trend.direction === 'increasing') {
recommendations.push(`Develop long-term memory optimization strategy for ${component}`);
recommendations.push('Consider architectural improvements');
}
recommendations.push('Implement proactive monitoring and alerting');
recommendations.push('Regular performance reviews and optimization');
return recommendations;
}
private getHistoricalHealthData(_component: string): Array<{ timestamp: number; score: number }> {
// Would maintain historical health scores
// For now, return simulated data
const now = Date.now();
const data = [];
for (let i = 23; i >= 0; i--) {
data.push({
timestamp: now - (i * 60 * 60 * 1000), // Hourly data for 24 hours
score: 75 + Math.random() * 20 // Simulated scores
});
}
return data;
}
private calculateTrends(timeRange: { start: number; end: number }): any {
// Calculate trends for the report time range
// Filter data for analysis (currently not used but ready for implementation)
this.memoryData.filter(d =>
d.timestamp >= timeRange.start && d.timestamp <= timeRange.end
);
return {
memoryUsage: [],
performance: [],
stability: []
};
}
private generateExecutiveSummary(analytics: SystemAnalytics, criticalIssues: string[], _keyFindings: string[]): string {
let summary = `Memory Analytics Report - System Health: `;
if (analytics.overview.criticalComponents > 0) {
summary += 'CRITICAL';
} else if (analytics.overview.warningComponents > 0) {
summary += 'WARNING';
} else {
summary += 'HEALTHY';
}
summary += `\n\nSystem efficiency is at ${(analytics.overview.overallEfficiency * 100).toFixed(1)}% with ${analytics.overview.totalComponents} components monitored.`;
if (criticalIssues.length > 0) {
summary += `\n\nCritical Issues: ${criticalIssues.join(', ')}.`;
}
summary += `\n\nKey metrics: Memory utilization at ${analytics.overview.memoryUtilization.toFixed(2)}GB, stability index at ${(analytics.overview.stabilityIndex * 100).toFixed(1)}%.`;
return summary;
}
private async saveReport(report: AnalyticsReport): Promise<void> {
try {
const reportsDir = path.join(process.cwd(), 'reports', 'memory');
await fs.mkdir(reportsDir, { recursive: true });
const reportFile = path.join(reportsDir, `${report.id}.json`);
await fs.writeFile(reportFile, JSON.stringify(report, null, 2));
// eslint-disable-next-line no-console
console.log(`Report saved: ${reportFile}`);
} catch (error) {
// eslint-disable-next-line no-console
console.error('Failed to save report:', error);
}
}
/**
* Get analytics configuration
*/
getConfig(): AnalyticsConfig {
return { ...this.config };
}
/**
* Update analytics configuration
*/
updateConfig(updates: Partial<AnalyticsConfig>): void {
this.config = { ...this.config, ...updates };
this.emit('configUpdated', this.config);
}
/**
* Shutdown analytics engine
*/
shutdown(): void {
this.stopAnalytics();
this.removeAllListeners();
}
}