Technical patterns for solving DDPs

Once you've completed breaking down a Deep Domain Problem, implementation requires careful technical consideration. This guide draws from successful implementations at Betalectic, including our supply chain optimization case study and lending decision-making analysis.

1. Domain-Driven Architecture Patterns

Core Domain Implementation

Based on Finezzy's experience with loan against mutual funds:

// Core Domain Types
interface LoanApplication {
  id: string;
  applicant: Applicant;
  creditScore: number;
  financials: FinancialData;
  status: ApplicationStatus;
  mutualFundDetails: MutualFundPortfolio; // Finezzy-specific
}

// Value Objects
class Money {
  constructor(private amount: number, private currency: string) {
    if (amount < 0) throw new Error("Amount cannot be negative");
  }

  add(other: Money): Money {
    if (this.currency !== other.currency) {
      throw new Error("Currency mismatch");
    }
    return new Money(this.amount + other.amount, this.currency);
  }
}

Regional Operations Patterns

Drawing from Evoilve's oil and gas industry expertise:

// Domain model for regional oil and gas operations
interface RegionalOperation {
  regionId: string;
  operationalMetrics: {
    production: ProductionMetrics;
    safety: SafetyCompliance;
    environmental: EnvironmentalMetrics;
  };
  localRegulations: RegionalCompliance;
}

class RegionalOperationsHandler {
  async handleOperation(
    operation: RegionalOperation
  ): Promise<OperationResult> {
    // Abstract regional complexities into domain-specific operations
    const localizedParams = await this.localizeOperationParams(operation);
    const complianceChecks = await this.validateRegionalCompliance(operation);

    // Handle region-specific operational nuances
    return this.executeWithRegionalContext(localizedParams, complianceChecks);
  }

  private async localizeOperationParams(
    operation: RegionalOperation
  ): Promise<LocalizedParams> {
    return {
      standardizedMetrics: this.convertToRegionalStandards(
        operation.operationalMetrics
      ),
      safetyProtocols: await this.getRegionalSafetyRequirements(
        operation.regionId
      ),
      environmentalFactors: this.assessLocalEnvironmentalImpact(operation),
    };
  }

  private async validateRegionalCompliance(
    operation: RegionalOperation
  ): Promise<ComplianceResult> {
    // Implement regional regulatory requirements
    const regulations = await this.fetchRegionalRegulations(operation.regionId);
    return this.validateCompliance(operation, regulations);
  }
}

Domain-Specific Adaptations

Drawing from WiredUp's forex risk management expertise:

class ForexRiskAnalyzer {
  async analyzeRisk(transaction: ForexTransaction): Promise<RiskAssessment> {
    return {
      volatilityMetrics: await this.calculateVolatility(transaction),
      exposureLevel: this.determineExposure(transaction),
      regulatoryCompliance: await this.validateCompliance(transaction),
    };
  }
}

Event Sourcing Pattern

From our supply chain optimization case study:

interface DomainEvent {
  readonly type: string;
  readonly timestamp: Date;
  readonly aggregateId: string;
  readonly data: unknown;
}

class RouteAggregate {
  private state: Route;
  private events: DomainEvent[] = [];

  apply(event: DomainEvent): void {
    switch (event.type) {
      case "ROUTE_PLANNED":
        this.handleRoutePlanning(event.data);
        break;
      case "WEATHER_ALERT":
        this.handleWeatherAlert(event.data);
        break;
      case "TRAFFIC_UPDATE":
        this.handleTrafficUpdate(event.data);
        break;
    }
    this.events.push(event);
  }
}

Saga Pattern for Long-Running Processes

Based on the loan approval workflow from our lending case study:

class LoanApprovalSaga {
  private steps: SagaStep[] = [
    new ValidateApplicationStep(),
    new CheckCreditStep(),
    new AssessRiskStep(),
    new GenerateOfferStep(),
  ];

  async execute(application: LoanApplication): Promise<void> {
    const compensation: CompensationAction[] = [];

    try {
      for (const step of this.steps) {
        await step.execute(application);
        compensation.unshift(step.compensate.bind(step));
      }
    } catch (error) {
      // Rollback on failure
      for (const compensate of compensation) {
        await compensate(application);
      }
      throw error;
    }
  }
}

3. Decision Engine Implementation

Rule Engine Pattern

Implementing the decision tree from our lending case study:

interface Rule {
  evaluate(context: EvaluationContext): boolean;
  priority: number;
}

class CreditScoreRule implements Rule {
  priority = 10;
  evaluate(context: EvaluationContext): boolean {
    return context.creditScore >= 600;
  }
}

class DebtToIncomeRule implements Rule {
  priority = 8;
  evaluate(context: EvaluationContext): boolean {
    return context.debtToIncomeRatio <= 0.4;
  }
}

4. AI Integration Patterns

As discussed in Coding with AI and Sunk Cost Fallacy and The Evolving Role of Programmers:

class AIAssistantIntegration {
  async getRecommendation(context: DomainContext): Promise<Recommendation> {
    try {
      const aiSuggestion = await this.aiService.analyze(context);
      return this.validateAndAdjust(aiSuggestion);
    } catch (error) {
      // Fallback to traditional rules-based approach
      return this.fallbackStrategy.process(context);
    }
  }
}

5. Performance Optimization

Drawing from our supply chain optimization case study:

class PerformanceOptimizer {
  async optimizeRoute(route: Route): Promise<OptimizedRoute> {
    const metrics = await this.calculateMetrics(route);

    // Target 7% delay reduction (from case study)
    if (metrics.delayProbability > 0.07) {
      return this.findAlternativeRoute(route);
    }

    // Target 10% fuel cost savings (from case study)
    if (metrics.fuelCost > this.threshold) {
      return this.optimizeFuelEfficiency(route);
    }

    return route;
  }
}

Conclusion

The key to successful DDP implementation lies in combining robust technical patterns with deep domain understanding. As shown through our examples from Betalectic, WiredUp, and Finezzy, effective solutions require both technical excellence and domain expertise. For more insights on developing shared understanding between technical and domain teams, see our guide on Ubiquitous Language.