Observability Beyond Metrics: The Art of System Storytelling

// What our metrics showed us
interface TraditionalMetrics {
  cpu: "40% average";
  memory: "6GB/8GB";
  responseTime: "200ms p50";
  errorRate: "0.1%";
  // Looks perfect, right?
}

// What the distributed trace revealed
interface TheActualStory {
  userJourney: "checkout_attempt";
  totalDuration: "8.3 seconds"; // User gave up after 5 seconds
  spanCount: 247; // Should have been ~15
  criticalPath: {
    service: "recommendation-service",
    operation: "get_related_products",
    calls: 47, // The smoking gun
    totalTime: "6.8 seconds"
  };
  businessImpact: {
    abandonedCarts: 1247,
    lostRevenue: "$186,000/hour"
  };
}

import { trace, context, SpanStatusCode } from '@opentelemetry/api';
import { BusinessContext } from './business-metrics';

class CheckoutService {
  private tracer = trace.getTracer('checkout-service', '1.0.0');
  
  async processCheckout(userId: string, cart: CartData): Promise<CheckoutResult> {
    // Start with business context, not technical details
    const span = this.tracer.startSpan('user.checkout.attempt', {
      attributes: {
        'user.id': userId,
        'user.tier': await this.getUserTier(userId),
        'business.cart_value': cart.totalValue,
        'business.revenue_impact': cart.totalValue,
        'journey.step': 'checkout_initiated',
        'journey.entry_point': cart.referrer
      }
    });
    
    // Propagate context across service boundaries
    return context.with(trace.setSpan(context.active(), span), async () => {
      try {
        // Each step adds to the story
        span.addEvent('inventory.validation.started', {
          items_to_check: cart.items.length
        });
        
        const inventory = await this.validateInventory(cart);
        
        if (!inventory.allAvailable) {
          // This tells us WHY the checkout failed
          span.setAttributes({
            'failure.reason': 'inventory_unavailable',
            'failure.items': inventory.unavailableItems.join(','),
            'business.impact': 'checkout_abandoned'
          });
          span.setStatus({ 
            code: SpanStatusCode.ERROR, 
            message: 'Inventory check failed' 
          });
          return { success: false, reason: 'out_of_stock' };
        }
        
        // Continue building the narrative...
        span.addEvent('payment.processing.initiated');
        const payment = await this.processPayment(cart, userId);
        
        span.setAttributes({
          'journey.completed': true,
          'business.order_value': payment.amount,
          'journey.total_duration_ms': Date.now() - span.startTime
        });
        
        return { success: true, orderId: payment.orderId };
        
      } catch (error) {
        // Capture the failure narrative
        span.recordException(error);
        span.setAttributes({
          'failure.stage': this.getCurrentStage(),
          'failure.recovery_attempted': true,
          'business.impact': 'revenue_lost'
        });
        throw error;
      } finally {
        span.end();
      }
    });
  }
}

interface BusinessImpactAnalyzer {
  async analyzeTraceImpact(traceId: string): Promise<ImpactReport> {
    const trace = await this.getTrace(traceId);
    const businessContext = this.extractBusinessContext(trace);
    
    return {
      // Technical story
      technicalNarrative: {
        entryPoint: trace.rootSpan.service,
        failurePoint: this.findFailureSpan(trace),
        cascadeEffect: this.analyzeCascade(trace),
        performanceBottleneck: this.findSlowestPath(trace)
      },
      
      // Business story
      businessNarrative: {
        userIntent: businessContext.journeyType, // "purchase", "browse", etc.
        valueAtRisk: businessContext.cartValue || businessContext.subscriptionValue,
        userSegment: businessContext.userTier,
        conversionStage: this.getConversionStage(trace),
        alternativePaths: this.findAlternativeJourneys(businessContext)
      },
      
      // The combined story
      impact: {
        immediateRevenueLoss: this.calculateImmediateLoss(businessContext),
        projectedChurnRisk: this.predictChurnImpact(trace, businessContext),
        brandDamageScore: this.assessBrandImpact(trace),
        recoveryActions: this.generateRecoveryPlan(trace, businessContext)
      }
    };
  }
}

class AITraceAnalyzer {
  async findFailurePattern(traces: DistributedTrace[]): Promise<Analysis> {
    const prompt = `
      Analyze these distributed traces from our e-commerce platform:
      ${JSON.stringify(traces, null, 2)}
      
      Context:
      - System handles 10K orders/minute during peak
      - 12 microservices involved in order processing
      - Recent deployment: inventory service v2.3.1 (3 hours ago)
      
      Find:
      1. Common patterns across failed transactions
      2. Temporal correlations with external events
      3. Service dependency anomalies
      4. Root cause hypothesis with confidence score
      
      Consider both technical and business patterns.
    `;
    
    const analysis = await this.llm.analyze(prompt);
    
    // The AI found something we missed entirely
    return {
      pattern: "Failures occur exactly 47 seconds after cache invalidation",
      rootCause: "Race condition between cache refresh and inventory updates",
      confidence: 0.94,
      evidence: [
        "All failures have cache_invalidated event 47±2 seconds prior",
        "Inventory service v2.3.1 introduced async cache refresh",
        "Load balancer retry timeout is 45 seconds"
      ],
      businessImpact: "Affects high-value customers during cart updates",
      suggestedFix: "Implement distributed lock on cache refresh operations"
    };
  }
}

class StoryDrivenAlerting {
  async evaluateAlert(anomaly: TraceAnomaly): Promise<AlertDecision> {
    // Don't alert on technical metrics alone
    if (!anomaly.hasBusinessContext()) {
      return { shouldAlert: false, reason: "No business impact detected" };
    }
    
    // Build the complete story
    const story = await this.buildNarrative(anomaly);
    
    // Only alert if the story matters
    const impactScore = this.calculateImpactScore({
      affectedUsers: story.userCount,
      revenueAtRisk: story.potentialLoss,
      customerTier: story.primaryUserSegment,
      timeOfDay: story.isBusinessHours,
      similarIncidents: await this.findSimilarStories(story)
    });
    
    if (impactScore < this.alertThreshold) {
      // Log it, but don't wake anyone up
      await this.logForLaterAnalysis(story);
      return { shouldAlert: false, reason: "Below impact threshold" };
    }
    
    // Create an alert that tells the whole story
    return {
      shouldAlert: true,
      channel: this.getChannelForImpact(impactScore),
      message: this.createNarrativeAlert(story),
      suggestedActions: await this.generatePlaybook(story),
      autoRemediation: this.canAutoRemediate(story)
    };
  }
  
  private createNarrativeAlert(story: IncidentStory): string {
    return `
      📖 Incident Story:
      
      What's happening: ${story.summary}
      Who's affected: ${story.affectedUsers} users (${story.userSegments})
      Business impact: $${story.revenueImpact}/hour potential loss
      
      The journey that's broken:
      ${story.brokenJourney.map(step => `→ ${step}`).join('\n')}
      
      Root cause (${story.confidence}% confident): ${story.rootCause}
      
      Similar incident: ${story.previousIncident?.summary || 'No similar incidents found'}
      
      Suggested actions:
      ${story.suggestedActions.map((action, i) => `${i+1}. ${action}`).join('\n')}
    `;
  }
}

interface ObservabilityCosts {
  infrastructure: {
    openTelemetryCollectors: 800,  // 3 instances, high-memory
    distributedTracing: 1200,       // Jaeger with 30-day retention
    aiAnalysis: 400,                // GPT-4 API calls for pattern analysis
    correlationPlatform: 2500,      // Custom built on top of Grafana
    incidentAutomation: 600         // Workflow automation tools
  },
  
  hiddenCosts: {
    engineerTime: "20% of DevOps capacity",
    storageGrowth: "50GB/day of trace data",
    networkOverhead: "5-10% increased traffic",
    cpuOverhead: "2-5% per instrumented service"
  },
  
  benefits: {
    mttrReduction: "2.5 hours → 18 minutes",
    incidentPrevention: "60% fewer production issues",
    onCallQualityOfLife: "70% fewer false alerts",
    revenueProtection: "$200K+ annual savings"
  }
}

# Start here, not everywhere
priority_instrumentation:
  phase_1:
    - user_registration_flow
    - checkout_process
    - search_to_purchase
  
  phase_2:
    - admin_operations
    - background_jobs
    - third_party_integrations
  
  phase_3:
    - internal_tools
    - reporting_systems
    - everything_else

class SmartSampling {
  getSampleRate(span: Span): number {
    // Always sample errors and high-value transactions
    if (span.status === 'ERROR') return 1.0;
    if (span.attributes['user.tier'] === 'premium') return 1.0;
    if (span.attributes['business.value'] > 1000) return 1.0;
    
    // Sample based on business hours
    const hour = new Date().getHours();
    if (hour >= 9 && hour <= 17) return 0.1;  // 10% during business hours
    
    // Minimal sampling during quiet periods
    return 0.01; // 1% overnight
  }
}

interface TeamTrainingPlan {
  week1: "Distributed tracing fundamentals",
  week2: "OpenTelemetry instrumentation workshop",
  week3: "Reading and interpreting trace narratives",
  week4: "Correlating traces with business metrics",
  week5: "AI-assisted incident analysis",
  week6: "Building custom dashboards that tell stories",
  
  ongoing: {
    monthlyReviews: "Analyze interesting incidents together",
    documentationDays: "Everyone writes one observability guide",
    rotationProgram: "Everyone does one week of incident command",
    knowledgeSharing: "Weekly 'trace detective' sessions"
  }
}

interface PredictiveObservability {
  pattern: "Cache invalidation spike detected",
  prediction: "Order processing will fail in ~47 seconds",
  confidence: 0.89,
  preventiveAction: "Preemptively scale inventory service",
  businessImpact: "Prevent $45K in abandoned carts"
}