Modern Reservation Management System

Document Information

1. Executive Summary

1.1 Project Overview

The Modern Reservation Management System is a comprehensive, cloud-native hospitality management platform designed to streamline all aspects of hotel operations. Built on a microservices architecture using Node.js LTS, Next.js with React and Tailwind CSS, PostgreSQL, Redis, and Apache Kafka, this system provides end-to-end management capabilities from reservations to housekeeping, integrated with modern payment systems and channel managers.

1.2 Vision Statement

To create a unified, scalable, and user-friendly reservation management system that empowers hospitality businesses to efficiently manage their operations, optimize revenue, and enhance guest experiences through real-time data processing and intelligent automation.

1.3 Technology Stack - Hybrid Architecture Approach

Strategic Decision: Hybrid Node.js + Java Architecture

For ultra-scale performance handling 10,000 reservations per minute with 100,000+ concurrent users, this system implements a strategic hybrid approach combining the strengths of both Node.js and Java ecosystems.

Frontend Technologies:

API Layer:

Backend Architecture - Service Distribution:

Data Layer:

Development Framework:

Infrastructure & DevOps:

Quality & Testing:

2. Business Objectives

2.1 Primary Goals

  1. Operational Efficiency: Reduce manual processes by 75% through automation
  2. Revenue Optimization: Increase revenue by 20% through dynamic pricing and channel management
  3. Guest Satisfaction: Improve guest experience with seamless booking and real-time updates
  4. Data-Driven Decisions: Provide comprehensive analytics and reporting for informed decision-making
  5. Scalability: Support properties from 10 to 1000+ rooms with multi-property chains

2.2 Success Metrics

3. Hybrid Architecture Strategy

3.1 Architectural Decision Framework

Performance Requirements Analysis:

graph TB
    subgraph "Ultra-Scale Load Requirements"
        L1[10,000 reservations/minute<br/>167 transactions/second]
        L2[100,000+ concurrent users<br/>WebSocket connections]
        L3[500,000+ Kafka messages/second<br/>Real-time event streaming]
        L4[1,000+ database writes/second<br/>Multi-master PostgreSQL]
        L5[10,000+ properties<br/>Global distribution]
    end

    subgraph "Node.js Optimal Use Cases"
        N1[High I/O Throughput<br/>API Gateway: 50,000+ req/sec]
        N2[WebSocket Connections<br/>100,000+ concurrent connections]
        N3[Event-Driven Architecture<br/>Real-time notifications]
        N4[Rapid Development<br/>TypeScript ecosystem]
        N5[Memory Efficiency<br/>60% less RAM for I/O operations]
    end

    subgraph "Java Optimal Use Cases"
        J1[CPU-Intensive Processing<br/>Complex availability calculations]
        J2[Multi-Threading Excellence<br/>Parallel reservation processing]
        J3[Enterprise Integration<br/>Payment gateway connections]
        J4[Long-Running Processes<br/>Superior garbage collection]
        J5[Mathematical Operations<br/>Dynamic pricing algorithms]
    end

    subgraph "Hybrid Architecture Benefits"
        H1[Best-of-Both Performance<br/>Optimal resource utilization]
        H2[Specialized Service Placement<br/>Right tool for right job]
        H3[Scalability Optimization<br/>Independent scaling strategies]
        H4[Risk Mitigation<br/>Technology diversification]
        H5[Team Expertise<br/>Leverage existing skills]
    end

    L1 --> N1
    L2 --> N2
    L1 --> J1
    L4 --> J2

    N1 --> H1
    J1 --> H1
    N2 --> H2
    J2 --> H2

3.2 Service Distribution Strategy

Technology Assignment by Service Characteristics:

graph LR
    subgraph "Node.js Services - I/O Intensive"
        direction TB
        N1[API Gateway<br/>High throughput routing]
        N2[WebSocket Service<br/>Real-time connections]
        N3[Notification Service<br/>Multiple channel delivery]
        N4[Channel Manager<br/>OTA API integrations]
        N5[Housekeeping Service<br/>Simple CRUD operations]
        N6[Audit Service<br/>Event log processing]

        N1 -.->|50,000+ req/sec| N2
        N3 -.->|Event-driven| N6
        N4 -.->|External APIs| N5
    end

    subgraph "Java Services - CPU Intensive"
        direction TB
        J1[Reservation Engine<br/>Complex business logic]
        J2[Availability Calculator<br/>Multi-dimensional algorithms]
        J3[Rate Management<br/>Dynamic pricing calculations]
        J4[Payment Processor<br/>Security-critical operations]
        J5[Analytics Engine<br/>Heavy data processing]
        J6[Batch Processor<br/>Large dataset operations]

        J1 -.->|Business rules| J2
        J2 -.->|Optimization| J3
        J4 -.->|Security| J5
    end

    subgraph "Shared Infrastructure"
        direction TB
        S1[PostgreSQL Cluster<br/>Multi-master setup]
        S2[Redis Cluster<br/>21-node architecture]
        S3[Kafka Cluster<br/>15-broker setup]
        S4[Service Mesh<br/>Istio communication]
    end

    N1 --> S4
    N2 --> S2
    N3 --> S3
    N4 --> S1
    N5 --> S2
    N6 --> S3

    J1 --> S4
    J2 --> S1
    J3 --> S2
    J4 --> S1
    J5 --> S1
    J6 --> S3

3.3 Performance Benchmarking Analysis

Comparative Performance Metrics:

Operation Type Pure Node.js Pure Java Hybrid + GraphQL Improvement
API Gateway Latency 5ms (optimal) 15ms (overhead) 5ms (Node.js) Best of class
Reservation Processing 50ms (single-threaded) 10ms (optimized) 10ms (Java) 5x faster
Availability Calculation 100ms (limited CPU) 20ms (multi-threaded) 20ms (Java) 5x improvement
WebSocket Connections 100K/instance (native) 20K/instance (limited) 100K (Node.js) 5x capacity
Dashboard Load Time 200ms (8 REST calls) 180ms (6 REST calls) 50ms (1 GraphQL query) 4x faster loading
Mobile Data Usage 2MB/session (over-fetch) 1.8MB/session (optimized) 500KB (precise queries) 75% reduction
Memory Usage (1K req/s) 2GB (efficient) 4GB (overhead) 3GB (balanced) 25% optimized
Cold Start Time 200ms (fast) 2s (JVM warmup) Mixed (service-specific) Context-aware
Development Velocity Fast (single language) Moderate (enterprise) Fast (unified schema) Enhanced with GraphQL

3.4 Monorepo Architecture Benefits

Unified Development Environment:

graph TB
    subgraph "Nx Monorepo Structure"
        direction TB

        subgraph "Applications Layer"
            A1[Frontend Apps<br/>Next.js 14+ PWA]
            A2[Backend Services<br/>Node.js + Java hybrid]
            A3[Worker Processes<br/>Scheduled jobs & batch]
        end

        subgraph "Shared Libraries"
            L1[Schemas & Types<br/>Zod validation across services]
            L2[UI Components<br/>Tailwind CSS system]
            L3[Backend Utils<br/>Database, cache, messaging]
            L4[Testing Tools<br/>Mocks, fixtures, utilities]
        end

        subgraph "Infrastructure as Code"
            I1[Docker Configs<br/>Multi-language containers]
            I2[Kubernetes Manifests<br/>Service orchestration]
            I3[Terraform Modules<br/>Cloud infrastructure]
            I4[CI/CD Pipelines<br/>Automated deployment]
        end

        subgraph "Development Tools"
            T1[Nx Generators<br/>Consistent project setup]
            T2[Code Sharing<br/>Cross-service libraries]
            T3[Dependency Graph<br/>Impact analysis]
            T4[Atomic Commits<br/>Multi-service changes]
        end
    end

    A1 --> L1
    A2 --> L1
    A3 --> L3

    L1 --> T2
    L2 --> T2
    L3 --> T2

    I1 --> T1
    I2 --> T1
    I3 --> T4

    T1 --> T3
    T2 --> T3
    T4 --> T3

3.5 Technology Ecosystem Integration

Event-Driven Inter-Service Communication via Kafka:

sequenceDiagram
    participant UI as Next.js Frontend
    participant GW as Node.js API Gateway
    participant K as Kafka Event Streaming
    participant AC as Java Availability Calculator
    participant RE as Java Reservation Engine
    participant PP as Java Payment Processor
    participant NS as Node.js Notification Service
    participant WS as Node.js WebSocket Service
    participant DB as PostgreSQL Cluster
    participant RC as Redis Cluster

    Note over UI,RC: Event-Driven Architecture - Fully Decoupled Services

    UI->>GW: Create Reservation Request
    Note over GW: Fast I/O handling<br/>Authentication & validation

    GW->>K: Publish ReservationRequested Event
    Note over K: Event-driven decoupling<br/>Guaranteed delivery + ordering

    par Availability Check (Async)
        K->>AC: Consume ReservationRequested
        Note over AC: Java multi-threading<br/>Parallel availability calculation
        AC->>RC: Cache Lookup (Multi-room)
        RC-->>AC: Availability Data
        alt Cache Miss
            AC->>DB: Complex Availability Query
            DB-->>AC: Fresh Availability Data
            AC->>RC: Update Cache
        end
        AC->>K: Publish AvailabilityConfirmed Event
    end

    K->>RE: Consume AvailabilityConfirmed
    Note over RE: Java CPU power<br/>Business rule validation

    RE->>K: Publish PaymentRequested Event
    Note over K: Decoupled payment processing<br/>Async financial operations

    K->>PP: Consume PaymentRequested
    Note over PP: Java security<br/>PCI-DSS compliant processing
    PP->>PP: Validate & Charge Payment
    PP->>K: Publish PaymentCompleted Event

    K->>RE: Consume PaymentCompleted
    RE->>DB: Persist Reservation (ACID)
    RE->>K: Publish ReservationConfirmed Event

    par Real-time Notifications
        K->>NS: Consume ReservationConfirmed
        Note over NS: Node.js I/O efficiency<br/>Multi-channel delivery
        NS->>NS: Send Email/SMS/Push
        NS->>K: Publish NotificationSent Event
    and WebSocket Updates
        K->>WS: Consume ReservationConfirmed
        Note over WS: Real-time user experience<br/>100K+ concurrent connections
        WS->>UI: WebSocket Real-time Update
    and Response Coordination
        K->>GW: Consume ReservationConfirmed
        GW-->>UI: HTTP Response with Confirmation
    end

    Note over UI,RC: Benefits: Zero coupling, fault tolerance<br/>Scalable event processing, audit trail

4. Target Users & Personas

4.1 Guest User

3.2 Front Desk Staff

3.3 Reservation Manager

3.4 Housekeeping Staff

3.5 Hotel Administrator

3.6 Finance Team

4. Functional Requirements

4.1 Core Modules Overview

graph TB
    subgraph "Guest Facing"
        A[Online Booking Portal]
        B[Guest Profile]
        C[Feedback System]
    end

    subgraph "Operations"
        D[Front Desk]
        E[Housekeeping]
        F[Maintenance]
        G[Point of Sale]
    end

    subgraph "Management"
        H[Reservation Management]
        I[Rate Management]
        J[Room Setup]
        K[Channel Manager]
    end

    subgraph "Backend Systems"
        L[Payment & Billing]
        M[Audit & Reports]
        N[Admin & Config]
        O[Security]
    end

    subgraph "Infrastructure"
        P[Kafka Events]
        Q[Redis Cache]
        R[PostgreSQL]
        S[OpenTelemetry]
    end

    A --> H
    D --> H
    H --> P
    I --> P
    J --> R
    K --> P
    E --> P
    F --> P
    G --> L
    L --> P
    P --> Q
    P --> R
    P --> S
    M --> R
    N --> O

4.2 Module Specifications

4.2.1 Reservation & Booking Module

Purpose: Core reservation engine handling all booking operations

Key Features:

Soft Delete Capabilities:

User Stories:

4.2.2 Availability Management

Purpose: Real-time inventory and availability tracking

Key Features:

Soft Delete Capabilities:

Integration Points:

4.2.3 Rates Management

Purpose: Dynamic pricing and rate plan management

Key Features:

Soft Delete Capabilities:

Rate Calculation Flow:

graph LR
    A[Base Rate] --> B{Check Season}
    B -->|Peak| C[Apply Peak Multiplier]
    B -->|Regular| D[Standard Rate]
    B -->|Off-Peak| E[Apply Discount]
    C --> F{Check Occupancy}
    D --> F
    E --> F
    F -->|High| G[Dynamic Increase]
    F -->|Low| H[Dynamic Decrease]
    G --> I[Apply Offers]
    H --> I
    I --> J[Final Rate]

4.2.4 Room Setup & Amenities Configuration

Purpose: Define and manage room types, features, and amenities

Key Features:

4.2.5 Offers & Promotions

Purpose: Create and manage special offers and packages

Key Features:

4.2.6 Seasonal Rates

Purpose: Manage rate variations based on seasons and events

Key Features:

4.2.7 Add-ons Management

Purpose: Upsell additional services and amenities

Key Features:

4.2.8 Customer Feedback Module

Purpose: Collect and analyze guest feedback

Key Features:

4.2.9 Night Audit & Date Roll

Purpose: Daily closing procedures and system date management

Key Features:

Night Audit Process:

sequenceDiagram
    participant S as System
    participant NA as Night Audit
    participant DB as Database
    participant K as Kafka
    participant R as Reports

    S->>NA: Initiate Night Audit
    NA->>DB: Lock current date transactions
    NA->>NA: Validate day's transactions
    NA->>NA: Process no-shows
    NA->>NA: Post room charges
    NA->>NA: Calculate statistics
    NA->>DB: Create audit snapshot
    NA->>R: Generate daily reports
    NA->>K: Publish audit-complete event
    NA->>DB: Advance system date
    NA->>S: Night audit complete

4.2.10 Audit & Compliance

Purpose: Maintain comprehensive audit trails and ensure compliance

Key Features:

4.2.11 Reports Module

Purpose: Comprehensive reporting and analytics

Key Reports:

Report Categories:

4.2.12 Back Office & Admin

Purpose: System administration and configuration

Key Features:

4.2.13 Point of Sale (POS)

Purpose: Manage all property sales and services

Key Features:

4.2.14 Housekeeping & Maintenance

Purpose: Manage room cleaning and property maintenance

Key Features:

Housekeeping Workflow:

stateDiagram-v2
    [*] --> Dirty: Guest Checkout
    Dirty --> Cleaning: Assigned to Staff
    Cleaning --> Inspection: Cleaning Complete
    Inspection --> Clean: Passed
    Inspection --> Cleaning: Failed
    Clean --> Occupied: Guest Check-in
    Occupied --> Dirty: Guest Checkout
    Clean --> Maintenance: Issues Found
    Maintenance --> Clean: Resolved

4.2.15 Payment & Billing

Purpose: Handle all payment processing and billing operations

Key Features:

4.2.16 Channel Manager (OTA/GDS Integration)

Purpose: Synchronize with Online Travel Agencies and Global Distribution Systems

Key Features:

Supported Channels:

4.2.17 Front Desk / Reception Module

Purpose: Streamline front desk operations

Key Features:

4.2.18 Security & Compliance

Purpose: Ensure system security and regulatory compliance

Key Features:

4.3 Soft Delete API Specifications

4.3.1 Core Soft Delete API Requirements

Universal Soft Delete Capabilities:

The system shall provide standardized soft delete operations for all entities with the following requirements:

4.3.2 Entity-Specific Soft Delete Requirements

Reservation Management: The system shall provide specialized reservation cancellation capabilities:

Rate Management: The system shall support rate plan archival with the following features:

4.3.3 Cleanup & Maintenance Requirements

Administrative Cleanup Management: The system shall provide comprehensive cleanup management capabilities:

4.3.4 Audit & Compliance Requirements

Comprehensive Audit Trail Management: The system shall provide complete audit trail capabilities:

4.3.5 Performance & Security Specifications

Performance Requirements:

Security & Authorization:

5. Non-Functional Requirements

5.1 Hybrid Architecture Performance Requirements

Ultra-High Performance Targets (10,000 Reservations per Minute Scale):

Node.js Service Performance Targets:

Java Service Performance Targets:

Combined System Performance:

Hybrid Performance Benefits:

graph TB
    subgraph "Performance Comparison Analysis"
        subgraph "Node.js Strengths (I/O Operations)"
            N1[API Gateway: 5ms<br/>vs Java: 15ms]
            N2[WebSocket: 100K connections<br/>vs Java: 20K connections]
            N3[Memory Usage: 2GB<br/>vs Java: 4GB]
            N4[Cold Start: 200ms<br/>vs Java: 2000ms]
        end

        subgraph "Java Strengths (CPU Operations)"
            J1[Business Logic: 10ms<br/>vs Node.js: 50ms]
            J2[Calculations: 20ms<br/>vs Node.js: 100ms]
            J3[Parallel Processing: 10x<br/>vs Node.js: 1x]
            J4[Security: Enterprise-grade<br/>vs Node.js: Standard]
        end

        subgraph "Hybrid Benefits"
            H1[Optimal Resource Utilization<br/>Right tool for right job]
            H2[5x Performance Gain<br/>Service-specific optimization]
            H3[Cost Efficiency<br/>25% reduction in compute costs]
            H4[Scalability<br/>Independent scaling strategies]
        end
    end

    N1 --> H1
    N2 --> H2
    J1 --> H1
    J2 --> H2
    N3 --> H3
    J3 --> H4

GraphQL Performance Targets:

Event-Driven Architecture Performance Gains:

graph TB
    subgraph "Traditional Synchronous vs Event-Driven Performance"
        subgraph "Synchronous Chain (Traditional)"
            S1[Request: 0ms]
            S2[Auth: 5ms]
            S3[Availability: 25ms]
            S4[Payment: 50ms]
            S5[Persistence: 65ms]
            S6[Notification: 85ms]
            S7[Response: 90ms]

            S1 --> S2 --> S3 --> S4 --> S5 --> S6 --> S7
            S8[❌ Total Latency: 90ms<br/>❌ Cascading failures<br/>❌ Single threaded processing<br/>❌ Blocking operations]
        end

        subgraph "Event-Driven Pipeline (Optimized)"
            E1[Request: 0ms]
            E2[Auth + Event: 5ms]
            E3[Response: 5ms]

            E4[Parallel Processing:<br/>Availability: 5-25ms<br/>Payment: 5-50ms<br/>Persistence: 5-15ms<br/>Notifications: 5-10ms]

            E1 --> E2 --> E3
            E2 -.->|Async Events| E4

            E5[✅ Response Latency: 5ms<br/>✅ Parallel processing<br/>✅ Non-blocking operations<br/>✅ Independent failure domains]
        end

        subgraph "Performance Metrics Comparison"
            P1[Response Time: 18x faster<br/>Throughput: 10x higher<br/>Failure Isolation: 100%<br/>Resource Efficiency: 60% better]
        end
    end

    S8 --> P1
    E5 --> P1

Event-Driven Scalability Benefits:

5.2 Scalability Requirements

Ultra-Scale Architecture for 10,000+ Properties:

5.2.1 Ultra-Scale Architecture for 10,000 Reservations/Minute

graph TB
    subgraph "Global Infrastructure"
        GLB[Global Load Balancer<br/>CloudFlare / Route 53]
        CDN[Global CDN<br/>CloudFlare / CloudFront]
        DNS[GeoDNS Routing]
    end

    subgraph "Region 1 - US East"
        subgraph "Load Balancing Tier"
            ALB1[Application LB<br/>Multi-AZ]
            NLB1[Network LB<br/>10Gbps]
        end

        subgraph "API Gateway Cluster (100-200 Pods)"
            AG1[API Gateway 1]
            AG2[API Gateway 2]
            AG3[API Gateway N]
        end

        subgraph "Ultra-Scale Microservices"
            RS1[Reservation Service<br/>Pods: 200-400]
            AS1[Availability Service<br/>Pods: 100-300]
            PS1[Payment Service<br/>Pods: 50-100]
            NS1[Notification Service<br/>Pods: 20-50]
            CS1[Cache Service<br/>Pods: 30-60]
            ES1[Event Stream Service<br/>Pods: 40-80]
        end

        subgraph "Multi-Master Database Layer"
            PG1[(Master DB 1<br/>Properties 1-2500)]
            PG2[(Master DB 2<br/>Properties 2501-5000)]
            PG3[(Master DB 3<br/>Properties 5001-7500)]
            PG4[(Master DB 4<br/>Properties 7501-10000)]
            RR1[(Read Replicas<br/>20 Instances)]
        end

        subgraph "Enhanced Redis Architecture"
            RA1[Availability Cache<br/>12 Master-Slave Pairs]
            RS1_Cache[Session Cache<br/>6 Nodes]
            RL1[Lock Manager<br/>3 Dedicated Nodes]
            RLM1[Rate Limiter<br/>3 Nodes]
        end

        subgraph "Ultra-Scale Kafka"
            K1[Kafka Cluster<br/>15 Brokers]
            KP1[100+ Partitions]
            KS1[Kafka Streams<br/>Real-time Processing]
        end
    end

    subgraph "Region 2 - EU West (Mirror)"
        subgraph "Load Balancing Tier EU"
            ALB2[Application LB<br/>Multi-AZ]
            NLB2[Network LB<br/>10Gbps]
        end

        subgraph "EU Microservices"
            RS2[Reservation Service<br/>Pods: 200-400]
            AS2[Availability Service<br/>Pods: 100-300]
            PS2[Payment Service<br/>Pods: 50-100]
            CS2[Cache Service<br/>Pods: 30-60]
        end

        subgraph "EU Database Layer"
            PG5[(Master DB 5<br/>EU Properties)]
            PG6[(Master DB 6<br/>EU Properties)]
            RR2[(Read Replicas<br/>20 Instances)]
        end

        subgraph "EU Redis Architecture"
            RA2[Availability Cache<br/>12 Master-Slave Pairs]
            RS2_Cache[Session Cache<br/>6 Nodes]
            RL2[Lock Manager<br/>3 Nodes]
        end

        subgraph "EU Kafka"
            K2[Kafka Cluster<br/>15 Brokers]
            KP2[100+ Partitions]
        end
    end

    subgraph "Cross-Region Services"
        CR[Cross-Region Replication]
        GS[Global State Sync]
        DR[Disaster Recovery]
    end

    GLB --> ALB1
    GLB --> ALB2
    DNS --> GLB
    CDN --> AG1
    CDN --> AG2

    ALB1 --> AG1
    ALB1 --> AG2
    AG1 --> RS1
    AG1 --> AS1
    AG1 --> PS1
    AG1 --> CS1

    RS1 --> PG1
    RS1 --> PG2
    AS1 --> RA1
    PS1 --> K1
    CS1 --> RS1_Cache

    PG1 <--> CR
    PG2 <--> CR
    CR <--> PG5
    CR <--> PG6

    K1 <--> GS
    GS <--> K2

5.2.2 Multi-Master Database Architecture for Ultra-Scale

Advanced Sharding Strategy:

Ultra-Scale Database Architecture:

graph TB
    subgraph "Application Services Layer"
        AS[200-400 Service Pods]
        WR[Write Router<br/>Smart Load Balancer]
        RR[Read Router<br/>Query Distribution]
        CP[Connection Pool<br/>5000+ Connections]
    end

    subgraph "Master Database Cluster"
        M1[(Master 1<br/>Properties 1-2500<br/>64 vCPU, 256GB RAM)]
        M2[(Master 2<br/>Properties 2501-5000<br/>64 vCPU, 256GB RAM)]
        M3[(Master 3<br/>Properties 5001-7500<br/>64 vCPU, 256GB RAM)]
        M4[(Master 4<br/>Properties 7501-10000<br/>64 vCPU, 256GB RAM)]
    end

    subgraph "Read Replica Farm"
        R1[(Read Replicas M1<br/>5 Instances)]
        R2[(Read Replicas M2<br/>5 Instances)]
        R3[(Read Replicas M3<br/>5 Instances)]
        R4[(Read Replicas M4<br/>5 Instances)]
    end

    subgraph "Enhanced Cache Infrastructure"
        AC[Availability Cache<br/>21-Node Redis Cluster<br/>500GB Total Memory]
        SC[Session Cache<br/>6-Node Cluster]
        LC[Lock Manager<br/>3 Dedicated Nodes]
        QC[Query Cache<br/>6-Node Cluster]
    end

    subgraph "Write Optimization"
        WQ[Write Queue<br/>Batch Processing]
        WAL[Write-Ahead Log<br/>Event Sourcing]
        BC[Bulk Coordinator<br/>Batch Operations]
    end

    AS --> CP
    CP --> WR
    CP --> RR

    WR --> M1
    WR --> M2
    WR --> M3
    WR --> M4

    RR --> R1
    RR --> R2
    RR --> R3
    RR --> R4

    M1 --> R1
    M2 --> R2
    M3 --> R3
    M4 --> R4

    AS --> AC
    AS --> SC
    AS --> LC
    AS --> QC

    WR --> WQ
    WQ --> WAL
    WAL --> BC

Database Specifications for 10,000 Reservations/Minute:

5.2.3 Ultra-Performance Caching Architecture

Five-Tier Caching Strategy:

Enhanced Redis Architecture:

graph TB
    subgraph "Redis Infrastructure (21 Nodes Total)"
        subgraph "Availability Cache Cluster"
            AC1[Master 1<br/>32GB RAM]
            AC2[Slave 1<br/>32GB RAM]
            AC3[Master 2<br/>32GB RAM]
            AC4[Slave 2<br/>32GB RAM]
            AC5[Master 3<br/>32GB RAM]
            AC6[Slave 3<br/>32GB RAM]
            AC7[Master 4<br/>32GB RAM]
            AC8[Slave 4<br/>32GB RAM]
            AC9[Master 5<br/>32GB RAM]
            AC10[Slave 5<br/>32GB RAM]
            AC11[Master 6<br/>32GB RAM]
            AC12[Slave 6<br/>32GB RAM]
        end

        subgraph "Session Management Cluster"
            SC1[Session Master 1<br/>16GB RAM]
            SC2[Session Slave 1<br/>16GB RAM]
            SC3[Session Master 2<br/>16GB RAM]
            SC4[Session Slave 2<br/>16GB RAM]
            SC5[Session Master 3<br/>16GB RAM]
            SC6[Session Slave 3<br/>16GB RAM]
        end

        subgraph "Lock Manager Cluster"
            LC1[Lock Manager 1<br/>8GB RAM]
            LC2[Lock Manager 2<br/>8GB RAM]
            LC3[Lock Manager 3<br/>8GB RAM]
        end
    end

    subgraph "Cache Request Flow"
        CR[Cache Request<br/>10,000/minute]
        AR[Availability Router]
        SR[Session Router]
        LR[Lock Router]
    end

    CR --> AR
    CR --> SR
    CR --> LR

    AR --> AC1
    AR --> AC3
    AR --> AC5
    SR --> SC1
    SR --> SC3
    SR --> SC5
    LR --> LC1
    LR --> LC2
    LR --> LC3

Ultra-Fast Cache Performance Specifications:

5.2.4 Ultra-High Volume Transaction Processing (10,000/Minute)

Critical Performance Target: 167 Reservations per Second Sustained Load

1. CQRS + Event Sourcing Pipeline

sequenceDiagram
    participant U as User/OTA
    participant LB as Load Balancer
    participant API as API Gateway (200 Pods)
    participant WTC as Write-Through Cache
    participant CMD as Command Handler
    participant ES as Event Store
    participant DB as Multi-Master DB
    participant EVT as Event Bus (Kafka)
    participant VIEW as Read Model
    participant WS as WebSocket

    U->>LB: Create Reservation
    LB->>API: Route Request (< 5ms)
    API->>WTC: Immediate Cache Write
    WTC-->>API: Cache Confirmed (< 1ms)
    API-->>U: Immediate Response (< 25ms)

    API->>CMD: Async Command
    CMD->>ES: Store Event
    ES->>EVT: Publish Event (< 5ms)
    EVT->>DB: Async DB Write
    EVT->>VIEW: Update Read Model
    EVT->>WS: Real-time Notification
    WS-->>U: Confirmation (WebSocket/Polling)

2. Database Optimization Techniques

3. Availability Cache Strategy

4. Queue Management Architecture

graph TB
    subgraph "Request Processing Queues"
        PQ[Priority Queue<br/>VIP/Direct Bookings]
        SQ[Standard Queue<br/>Regular Bookings]
        BQ[Bulk Queue<br/>OTA/Channel Manager]
    end

    subgraph "Processing Workers"
        PW1[Priority Workers<br/>Pool: 10]
        SW1[Standard Workers<br/>Pool: 50]
        BW1[Bulk Workers<br/>Pool: 20]
    end

    subgraph "Database Connections"
        PC[Priority DB Pool<br/>50 connections]
        SC[Standard DB Pool<br/>200 connections]
        BC[Bulk DB Pool<br/>100 connections]
    end

    PQ --> PW1
    SQ --> SW1
    BQ --> BW1

    PW1 --> PC
    SW1 --> SC
    BW1 --> BC

2. Ultra-Scale Resource Allocation for 10,000 Reservations/Minute

Kubernetes Ultra-Scale Resource Planning:

Ultra-Performance Database Infrastructure:

Network and Load Balancing:

3. Critical Monitoring and Alerting for Ultra-Scale

5.2.5 Circuit Breaker and Fault Tolerance Patterns

Circuit Breaker Implementation:

stateDiagram-v2
    [*] --> Closed: System Healthy
    Closed --> Open: Failure Threshold Reached<br/>(5 failures in 10 seconds)
    Open --> Half_Open: Timeout Period<br/>(30 seconds)
    Half_Open --> Closed: Success
    Half_Open --> Open: Failure

    Closed: Allow All Requests<br/>Monitor Failures
    Open: Fail Fast<br/>Return Cached Response
    Half_Open: Limited Requests<br/>Test System Health

Fault Tolerance Strategy:

5.2.6 CQRS and Event Sourcing Architecture

Command Query Responsibility Segregation:

graph TB
    subgraph "Command Side (Write)"
        CMD[Commands<br/>Create/Update/Delete]
        CH[Command Handlers]
        ES[Event Store]
        WDB[(Write Database<br/>Event Stream)]
    end

    subgraph "Query Side (Read)"
        QH[Query Handlers]
        RM[Read Models]
        RDB[(Read Database<br/>Materialized Views)]
        CACHE[Ultra-Fast Cache]
    end

    subgraph "Event Processing"
        EB[Event Bus<br/>Kafka Streams]
        EP[Event Processors]
        PROJ[Projections]
    end

    CMD --> CH
    CH --> ES
    ES --> WDB
    ES --> EB

    EB --> EP
    EP --> PROJ
    PROJ --> RM
    RM --> RDB

    QH --> CACHE
    CACHE --> RDB

Event Sourcing Benefits for Ultra-Scale:

5.3 Ultra-High Reliability & Availability

5.4 Security Requirements

5.4.1 Soft Delete Security Framework

5.5 Usability Requirements

5.6 Observability Requirements

6. Hybrid System Architecture

6.1 Ultra-Scale Hybrid Architecture Overview

graph TB
    subgraph "Client Layer - Multi-Channel Access"
        A1[Next.js PWA<br/>Guest Portal]
        A2[Next.js PWA<br/>Staff Portal]
        A3[Next.js PWA<br/>Admin Portal]
        A4[Mobile Apps<br/>iOS/Android]
        A5[POS Terminals<br/>Kiosk Integration]
    end

    subgraph "API Gateway Layer - Node.js"
        B1[Primary Gateway<br/>Kong/Express]
        B2[Authentication Service<br/>JWT/OAuth2]
        B3[Rate Limiter<br/>Redis-based]
        B4[Load Balancer<br/>50,000+ req/sec]
        B5[Circuit Breaker<br/>Fault tolerance]
    end

    subgraph "Node.js Services - I/O Intensive"
        N1[WebSocket Service<br/>100K+ connections]
        N2[Notification Service<br/>Multi-channel delivery]
        N3[Channel Manager<br/>OTA integrations]
        N4[Housekeeping Service<br/>Simple CRUD ops]
        N5[Audit Service<br/>Event processing]
        N6[File Upload Service<br/>Media handling]
    end

    subgraph "Java Microservices Infrastructure"
        subgraph "Core Infrastructure Services"
            JI1[Config Server<br/>Spring Cloud Config]
            JI2[Service Discovery<br/>Eureka Server]
            JI3[API Gateway<br/>Spring Cloud Gateway]
            JI4[Circuit Breaker<br/>Resilience4j]
            JI5[Tracing Service<br/>Zipkin Server]
        end

        subgraph "Business Logic Services"
            J1[Reservation Engine<br/>Core business logic]
            J2[Availability Calculator<br/>Complex algorithms]
            J3[Rate Management<br/>Dynamic pricing]
            J4[Payment Processor<br/>Security critical]
            J5[Analytics Engine<br/>Heavy computation]
            J6[Batch Processor<br/>Large datasets]
        end
    end

    subgraph "Data Layer - Multi-Master"
        D1[(PostgreSQL Master 1<br/>Properties 1-2500)]
        D2[(PostgreSQL Master 2<br/>Properties 2501-5000)]
        D3[(PostgreSQL Master 3<br/>Properties 5001-7500)]
        D4[(PostgreSQL Master 4<br/>Properties 7501-10000)]
        D5[(Read Replicas<br/>20 instances)]
    end

    subgraph "Cache Layer - Redis Cluster"
        R1[Availability Cache<br/>12 master-slave pairs]
        R2[Session Cache<br/>6-node cluster]
        R3[Lock Manager<br/>3 dedicated nodes]
        R4[Rate Limiter Cache<br/>High-speed access]
    end

    subgraph "Event Streaming - Kafka Cluster"
        K1[Reservation Events<br/>100 partitions]
        K2[Payment Events<br/>50 partitions]
        K3[Availability Updates<br/>200 partitions]
        K4[Notification Queue<br/>20 partitions]
        K5[Audit Logs<br/>30 partitions]
    end

    subgraph "Service Mesh - Istio"
        I1[Traffic Management<br/>Load balancing]
        I2[Security Policies<br/>mTLS encryption]
        I3[Observability<br/>Distributed tracing]
        I4[Circuit Breaking<br/>Fault injection]
    end

    subgraph "Observability Stack"
        O1[OpenTelemetry<br/>Distributed tracing]
        O2[Prometheus<br/>Metrics collection]
        O3[Grafana<br/>Dashboards]
        O4[Jaeger<br/>Trace analysis]
        O5[ELK Stack<br/>Log aggregation]
    end

    subgraph "External Integrations"
        E1[Payment Gateways<br/>Stripe, PayPal, etc.]
        E2[OTA Platforms<br/>Booking.com, Expedia]
        E3[Communication<br/>Email, SMS, Push]
        E4[Third-party APIs<br/>Maps, Weather, etc.]
    end

    A1 --> B1
    A2 --> B1
    A3 --> B1
    A4 --> B4
    A5 --> B4

    B1 --> B2
    B2 --> B3
    B3 --> B4
    B4 --> B5

    B5 --> JI3
    JI3 --> N1
    JI3 --> N2
    JI3 --> J1
    JI3 --> J2

    N1 --> R2
    N2 --> K4
    N3 --> E2
    N4 --> D5
    N5 --> K5

    J1 --> D1
    J1 --> D2
    J2 --> R1
    J3 --> R1
    J4 --> E1
    J5 --> D3
    J6 --> D4

    JI1 --> JI2
    JI2 --> J1
    JI2 --> J2
    JI2 --> J3
    JI2 --> J4
    JI2 --> J5
    JI2 --> J6
    JI4 --> J1
    JI4 --> J2
    JI4 --> J3
    JI4 --> J4
    JI5 --> J1
    JI5 --> J2
    JI5 --> J3
    JI5 --> J4

    J1 --> K1
    J2 --> K3
    J3 --> K3
    J4 --> K2

    K1 --> N2
    K2 --> N5
    K3 --> N1
    K4 --> N2

    I1 --> N1
    I1 --> J1
    I2 --> N2
    I2 --> J4
    I3 --> O1
    I4 --> B5

    O1 --> O2
    O2 --> O3
    O1 --> O4
    N5 --> O5

6.2 Event-Driven Microservices Communication

sequenceDiagram
    participant UI as Next.js PWA
    participant GW as Node.js API Gateway
    participant K as Kafka Event Bus
    participant AC as Java Availability Calculator
    participant RE as Java Reservation Engine
    participant PP as Java Payment Processor
    participant NS as Node.js Notification Service
    participant WS as Node.js WebSocket Service
    participant AS as Node.js Audit Service
    participant DB as PostgreSQL Multi-Master
    participant RC as Redis Cluster

    Note over UI,RC: Event-Driven Ultra-Scale Architecture (10,000/min)

    UI->>GW: Create Reservation Request
    Note over GW: Node.js I/O efficiency<br/>Authentication & validation

    GW->>GW: Validate & Rate Limit
    GW->>K: Publish ReservationRequested Event
    Note over K: Event-driven decoupling<br/>Zero service dependencies

    par Availability Processing Pipeline
        K->>AC: Consume ReservationRequested
        Note over AC: Java multi-threading<br/>Complex availability algorithms

        AC->>RC: Multi-Room Cache Lookup
        RC-->>AC: Availability Status

        alt Cache Miss
            AC->>DB: Complex Availability Query
            Note over DB: Multi-master read distribution
            DB-->>AC: Fresh Availability Data
            AC->>RC: Update Availability Cache
        end

        AC->>K: Publish AvailabilityChecked Event
        Note over K: Async availability confirmation<br/>Non-blocking processing
    end

    K->>RE: Consume AvailabilityChecked
    Note over RE: Java business logic<br/>Reservation validation & creation

    alt Availability Confirmed
        RE->>K: Publish PaymentRequested Event
        Note over K: Decoupled payment flow<br/>Financial service isolation

        par Payment Processing
            K->>PP: Consume PaymentRequested
            Note over PP: Java security & compliance<br/>PCI-DSS payment processing
            PP->>PP: Secure Payment Validation
            PP->>K: Publish PaymentProcessed Event
        end

        K->>RE: Consume PaymentProcessed

        alt Payment Success
            RE->>DB: Persist Reservation (ACID)
            Note over DB: Multi-master write<br/>Guaranteed consistency
            RE->>K: Publish ReservationConfirmed Event

            par Multi-Channel Response Handling
                K->>NS: Consume ReservationConfirmed
                Note over NS: Node.js I/O optimization<br/>Multi-channel notifications
                NS->>NS: Send Email/SMS/Push
                NS->>K: Publish NotificationsSent Event
            and Real-Time Updates
                K->>WS: Consume ReservationConfirmed
                Note over WS: Real-time user experience<br/>100K+ WebSocket connections
                WS->>UI: Instant WebSocket Update
            and Audit Trail Processing
                K->>AS: Consume ReservationConfirmed
                Note over AS: Node.js event processing<br/>Compliance & audit logging
                AS->>DB: Store Audit Trail
                AS->>K: Publish AuditCompleted Event
            and Response Coordination
                K->>GW: Consume ReservationConfirmed
                GW->>GW: Prepare Success Response
                GW-->>UI: HTTP 201 Created Response
            end
        else Payment Failed
            RE->>K: Publish ReservationFailed Event
            K->>GW: Consume ReservationFailed
            GW-->>UI: HTTP 402 Payment Required
        end
    else Availability Denied
        RE->>K: Publish ReservationRejected Event
        K->>GW: Consume ReservationRejected
        GW-->>UI: HTTP 409 Conflict (No Availability)
    end

    Note over UI,RC: Benefits: Zero coupling, fault tolerance<br/>Independent scaling, eventual consistency<br/>Complete audit trail, error resilience

6.3 Kafka Event Architecture & Decoupling Strategy

Event-Driven Service Decoupling Benefits:

graph TB
    subgraph "Traditional Tightly Coupled Approach"
        direction TB
        T1[API Gateway] -->|Direct HTTP| T2[Reservation Service]
        T2 -->|Direct HTTP| T3[Availability Service]
        T2 -->|Direct HTTP| T4[Payment Service]
        T2 -->|Direct HTTP| T5[Notification Service]

        T6[❌ Single Point of Failure<br/>❌ Cascading Failures<br/>❌ Tight Coupling<br/>❌ Synchronous Blocking<br/>❌ Hard to Scale]
    end

    subgraph "Event-Driven Decoupled Architecture"
        direction TB

        subgraph "Producer Services"
            P1[Node.js API Gateway<br/>Event Producer]
            P2[Java Services<br/>Event Producers]
        end

        subgraph "Kafka Event Bus - Ultra Scale"
            K1[reservation.events<br/>100 partitions]
            K2[availability.events<br/>200 partitions]
            K3[payment.events<br/>50 partitions]
            K4[notification.events<br/>20 partitions]
            K5[audit.events<br/>30 partitions]
        end

        subgraph "Consumer Services"
            C1[Java Availability Calculator<br/>High-throughput consumer]
            C2[Java Reservation Engine<br/>Business logic processor]
            C3[Java Payment Processor<br/>Security-critical consumer]
            C4[Node.js Notification Service<br/>Multi-channel consumer]
            C5[Node.js WebSocket Service<br/>Real-time consumer]
            C6[Node.js Audit Service<br/>Compliance consumer]
        end

        P1 --> K1
        P2 --> K2
        P2 --> K3

        K1 --> C1
        K1 --> C2
        K2 --> C2
        K3 --> C3
        K1 --> C4
        K1 --> C5
        K1 --> C6

        D1[✅ Zero Service Dependencies<br/>✅ Independent Scaling<br/>✅ Fault Tolerance<br/>✅ Async Processing<br/>✅ Event Sourcing]
    end

Kafka Topic Strategy for Ultra-Scale:

Topic Partitions Replication Retention Key Strategy Consumer Groups
reservation.requested 100 3 7 days property_id + date availability-calculators, reservation-engines
availability.checked 200 3 24 hours property_id + room_type reservation-engines, cache-invalidators
payment.requested 50 3 30 days payment_id payment-processors, fraud-detectors
payment.completed 50 3 90 days payment_id reservation-engines, billing-services
reservation.confirmed 100 3 30 days reservation_id notification-services, websocket-services, audit-services
notification.sent 20 3 7 days user_id analytics-services, delivery-trackers
audit.logged 30 3 365 days tenant_id compliance-services, reporting-engines

Event-Driven Architecture Benefits:

graph LR
    subgraph "Decoupling Benefits"
        D1[Service Independence<br/>Zero runtime dependencies]
        D2[Fault Isolation<br/>Service failures don't cascade]
        D3[Technology Diversity<br/>Best tool for each job]
        D4[Independent Deployment<br/>Deploy services separately]
    end

    subgraph "Scalability Benefits"
        S1[Horizontal Scaling<br/>Scale consumers independently]
        S2[Load Distribution<br/>Partition-based load balancing]
        S3[Throughput Optimization<br/>Parallel event processing]
        S4[Elastic Scaling<br/>Auto-scale based on lag]
    end

    subgraph "Reliability Benefits"
        R1[Event Durability<br/>Persistent event storage]
        R2[Replay Capability<br/>Reprocess events from any point]
        R3[At-Least-Once Delivery<br/>Guaranteed event processing]
        R4[Dead Letter Queues<br/>Handle failed events]
    end

    subgraph "Business Benefits"
        B1[Audit Trail<br/>Complete event history]
        B2[Event Sourcing<br/>Rebuild state from events]
        B3[Real-time Analytics<br/>Stream processing capabilities]
        B4[Compliance<br/>Immutable event logs]
    end

    D1 --> S1
    D2 --> S2
    D3 --> S3
    D4 --> S4

    S1 --> R1
    S2 --> R2
    S3 --> R3
    S4 --> R4

    R1 --> B1
    R2 --> B2
    R3 --> B3
    R4 --> B4

Event Schema Standardization:

The system shall implement standardized event schemas for consistent inter-service communication:

6.4 Data Flow Architecture

graph LR
    subgraph "Data Sources"
        A[User Actions]
        B[System Events]
        C[External APIs]
        D[Scheduled Jobs]
    end

    subgraph "Processing Layer"
        E[Event Stream<br/>Apache Kafka]
        F[Real-time Processing<br/>Node.js Services]
        G[Batch Processing<br/>Cron Jobs]
    end

    subgraph "Storage Layer"
        H[(Transactional Data<br/>PostgreSQL)]
        I[Cache Layer<br/>Redis]
        J[Analytics Data<br/>Data Warehouse]
    end

    subgraph "Consumption Layer"
        K[Real-time Dashboard]
        L[Reports]
        M[Notifications]
        N[External Systems]
    end

    A --> E
    B --> E
    C --> F
    D --> G

    E --> F
    F --> H
    F --> I
    G --> H
    G --> J

    H --> K
    I --> K
    J --> L
    E --> M
    F --> N

6.5 GraphQL Federation Architecture

Unified Data Graph for Ultra-Scale Performance

The GraphQL Federation layer provides a unified API that dramatically improves frontend performance while maintaining microservices architecture benefits.

graph TB
    subgraph "Client Applications"
        A1[Guest Portal<br/>PWA]
        A2[Staff Dashboard<br/>Real-time updates]
        A3[Mobile Apps<br/>Bandwidth optimized]
        A4[Admin Console<br/>Complex queries]
    end

    subgraph "GraphQL Federation Gateway"
        GW[Apollo Gateway<br/>Query planning & composition]
        GS[GraphQL Subscriptions<br/>WebSocket management]
        GC[Query Complexity Analyzer<br/>Rate limiting & security]
        GR[Response Cache<br/>Redis-backed caching]
    end

    subgraph "GraphQL Subgraphs"
        SG1[Reservation Subgraph<br/>Node.js service]
        SG2[Availability Subgraph<br/>Java service]
        SG3[Guest Profile Subgraph<br/>Node.js service]
        SG4[Payment Subgraph<br/>Java service]
        SG5[Analytics Subgraph<br/>Java service]
        SG6[Property Subgraph<br/>Node.js service]
    end

    subgraph "Data Sources & Events"
        DB[(PostgreSQL<br/>Multi-master)]
        RC[(Redis Cluster<br/>21 nodes)]
        KF[Kafka Events<br/>Real-time streams]
    end

    A1 --> GW
    A2 --> GW
    A3 --> GW
    A4 --> GW

    A1 -.-> GS
    A2 -.-> GS

    GW --> GC
    GC --> SG1
    GC --> SG2
    GC --> SG3
    GC --> SG4
    GC --> SG5
    GC --> SG6

    GW <--> GR

    SG1 --> DB
    SG1 --> RC
    SG2 --> DB
    SG2 --> RC
    SG3 --> DB
    SG4 --> DB
    SG5 --> DB
    SG6 --> RC

    KF -.-> GS

GraphQL Query Optimization Pipeline

sequenceDiagram
    participant C as Client
    participant GW as GraphQL Gateway
    participant QA as Query Analyzer
    participant DL as DataLoader
    participant RC as Redis Cache
    participant MS as Microservices
    participant DB as Database

    Note over C,DB: Complex Dashboard Query Example
    C->>GW: Query { guest, reservations, payments }
    GW->>QA: Analyze complexity & validate
    QA->>RC: Check query cache
    RC-->>QA: Cache miss
    QA->>DL: Plan data loading strategy
    DL->>MS: Batch requests per service
    MS->>DB: Optimized database queries
    DB-->>MS: Structured result sets
    MS-->>DL: Service responses
    DL->>GW: Composed data graph
    GW->>RC: Cache result (TTL-based)
    GW-->>C: Single response < 50ms

    Note over C,DB: Subsequent Similar Query
    C->>GW: Similar query pattern
    GW->>RC: Cache lookup
    RC-->>GW: Cache hit
    GW-->>C: Cached response < 5ms

Performance Benefits Comparison

Operation REST API Approach GraphQL Approach Performance Gain
Guest Dashboard Load 8 API calls, 200ms total 1 query, 50ms total 4x faster loading
Availability Search 500KB response data 150KB response data 70% bandwidth reduction
Booking Flow 12 sequential API calls 3 optimized mutations 75% fewer requests
Real-time Updates Polling every 5 seconds Push-based subscriptions Instant updates
Mobile Data Usage 2MB per session 500KB per session 75% data reduction
Complex Reports 20+ REST endpoints 1 federated query 95% complexity reduction

GraphQL Implementation Features

1. Query Optimization:

2. Real-time Capabilities:

3. Federation Benefits:

4. Performance Monitoring:

6.6 Nx Monorepo Structure & Organization

Enterprise-Scale Monorepo Architecture:

graph TB
    subgraph "Nx Monorepo - modern-reservation/"
        direction TB

        subgraph "Applications Layer"
            subgraph "Frontend Applications"
                F1[guest-portal/<br/>Next.js 14+ PWA<br/>Guest booking interface]
                F2[staff-portal/<br/>Next.js 14+ PWA<br/>Staff operations]
                F3[admin-portal/<br/>Next.js 14+ PWA<br/>Administrative interface]
                F4[mobile-pwa/<br/>Progressive Web App<br/>Mobile-first experience]
            end

            subgraph "Backend Services - Node.js"
                N1[api-gateway/<br/>Kong/Express Gateway<br/>50K+ req/sec capacity]
                N2[notification-service/<br/>Multi-channel delivery<br/>Event-driven architecture]
                N3[websocket-service/<br/>Real-time connections<br/>100K+ concurrent users]
                N4[channel-manager/<br/>OTA integrations<br/>External API orchestration]
                N5[housekeeping-service/<br/>Simple CRUD operations<br/>Mobile-optimized APIs]
                N6[audit-service/<br/>Event log processing<br/>Compliance tracking]
            end

            subgraph "Java Microservices Infrastructure"
                subgraph "Spring Cloud Infrastructure"
                    JI1[config-server/<br/>Spring Cloud Config<br/>Centralized configuration]
                    JI2[service-discovery/<br/>Eureka Server<br/>Service registration]
                    JI3[api-gateway/<br/>Spring Cloud Gateway<br/>Traffic routing & filtering]
                    JI4[circuit-breaker/<br/>Resilience4j<br/>Fault tolerance]
                    JI5[tracing-service/<br/>Zipkin Server<br/>Distributed tracing]
                end

                subgraph "Business Logic Services"
                    J1[reservation-engine/<br/>Spring Boot Core<br/>Complex business logic]
                    J2[availability-calculator/<br/>Multi-threaded processor<br/>Optimization algorithms]
                    J3[rate-management/<br/>Dynamic pricing engine<br/>Revenue optimization]
                    J4[payment-processor/<br/>Security-critical service<br/>PCI-DSS compliance]
                    J5[analytics-engine/<br/>Heavy data processing<br/>Business intelligence]
                    J6[batch-processor/<br/>Large dataset operations<br/>ETL processes]
                end
            end

            subgraph "Worker Processes"
                W1[cleanup-worker/<br/>Scheduled maintenance<br/>Soft delete processing]
                W2[kafka-consumer/<br/>High-throughput processing<br/>Event stream handling]
                W3[batch-worker/<br/>Background jobs<br/>Data synchronization]
            end
        end

        subgraph "Shared Libraries"
            subgraph "Common Schemas & Types"
                S1[schemas/<br/>Zod validation schemas<br/>Cross-service consistency]
                S2[types/<br/>TypeScript definitions<br/>Shared type safety]
                S3[constants/<br/>System-wide constants<br/>Configuration management]
                S4[proto/<br/>Protocol Buffer definitions<br/>Service communication]
                S5[graphql/<br/>GraphQL schema definitions<br/>Federation type safety]
            end

            subgraph "Frontend Libraries"
                L1[ui-components/<br/>Tailwind CSS system<br/>Design consistency]
                L2[state-management/<br/>Zustand stores<br/>Application state]
                L3[guards/<br/>Authentication guards<br/>Route protection]
                L4[interceptors/<br/>HTTP interceptors<br/>Cross-cutting concerns]
                L5[themes/<br/>Dark/Light themes<br/>Accessibility support]
                L6[graphql-client/<br/>Apollo Client setup<br/>GraphQL utilities]
            end

            subgraph "Backend Libraries"
                B1[database/<br/>Connection pooling<br/>Query optimization]
                B2[cache/<br/>Multi-tier caching<br/>Redis cluster management]
                B3[kafka/<br/>Producer/Consumer patterns<br/>Event streaming]
                B4[auth/<br/>JWT/OAuth2 handling<br/>RBAC implementation]
                B5[monitoring/<br/>OpenTelemetry setup<br/>Observability patterns]
                B6[soft-delete/<br/>Audit trail system<br/>Recovery mechanisms]
                B7[graphql-federation/<br/>Schema federation utilities<br/>DataLoader patterns]
                B7[circuit-breaker/<br/>Resilience patterns<br/>Fault tolerance]
            end
        end

        subgraph "Development Tools & Infrastructure"
            subgraph "Build & Deploy Tools"
                T1[generators/<br/>Custom Nx generators<br/>Project scaffolding]
                T2[executors/<br/>Custom build executors<br/>Deployment automation]
                T3[scripts/<br/>Development scripts<br/>Environment setup]
            end

            subgraph "Testing Infrastructure"
                TE1[fixtures/<br/>Test data management<br/>Consistent test scenarios]
                TE2[mocks/<br/>Service mocks<br/>Isolated testing]
                TE3[e2e-utils/<br/>End-to-end utilities<br/>Integration testing]
                TE4[performance/<br/>Load testing tools<br/>Performance validation]
            end

            subgraph "Infrastructure as Code"
                I1[docker/<br/>Multi-language containers<br/>Standardized deployment]
                I2[kubernetes/<br/>Orchestration manifests<br/>Environment management]
                I3[terraform/<br/>Cloud infrastructure<br/>Resource provisioning]
                I4[helm/<br/>Package management<br/>Application deployment]
            end
        end
    end

    F1 -.->|Shared Components| L1
    F2 -.->|State Management| L2
    F3 -.->|Authentication| L3
    F4 -.->|Themes| L5

    N1 -.->|Validation| S1
    N2 -.->|Event Schemas| S4
    J1 -.->|Business Types| S2
    J2 -.->|Constants| S3

    N1 -.->|Database Access| B1
    N3 -.->|Caching| B2
    J1 -.->|Event Streaming| B3
    J4 -.->|Authentication| B4

    T1 -.->|Scaffolding| F1
    T1 -.->|Scaffolding| N1
    T1 -.->|Scaffolding| J1

    TE1 -.->|Test Data| N1
    TE2 -.->|Mocking| J1
    TE3 -.->|E2E Testing| F1

Monorepo Benefits for Ultra-Scale Development:

Aspect Traditional Multi-Repo Nx Monorepo Performance Impact
Code Sharing Duplicate implementations Shared libraries across services 40% reduction in code duplication
Dependency Management Version conflicts across repos Unified dependency resolution 60% faster dependency updates
Refactoring Manual coordination required Atomic cross-service changes 5x faster large-scale refactoring
Testing Independent CI/CD per repo Smart affected testing 70% reduction in test execution time
Type Safety Interface drift between services Compile-time validation 90% reduction in integration errors
Development Setup Multiple clone/setup steps Single repository setup 80% faster onboarding
Build Optimization Rebuild everything always Smart caching & affected builds 75% faster build times
Release Coordination Manual versioning sync Coordinated releases 50% reduction in deployment issues

Nx Configuration Highlights:

6.7 Development Workflow & Dependency Management

graph LR
    subgraph "Developer Experience"
        D1[Developer<br/>Local Setup]
        D2[Feature Branch<br/>Creation]
        D3[Code Changes<br/>Multiple Services]
        D4[Smart Testing<br/>Affected Only]
        D5[Build Validation<br/>Incremental]
        D6[Pull Request<br/>Atomic Changes]
    end

    subgraph "Nx Intelligence"
        N1[Project Graph<br/>Dependency Analysis]
        N2[Affected Detection<br/>Smart Filtering]
        N3[Computation Cache<br/>Distributed Storage]
        N4[Parallel Execution<br/>Optimal Scheduling]
    end

    subgraph "Quality Gates"
        Q1[Type Checking<br/>Cross-Service Validation]
        Q2[Unit Tests<br/>Affected Projects Only]
        Q3[Integration Tests<br/>Contract Validation]
        Q4[E2E Tests<br/>Critical Path Only]
        Q5[Performance Tests<br/>Benchmark Validation]
    end

    D1 --> N1
    D2 --> D3
    D3 --> N2
    N2 --> D4
    D4 --> N3
    N3 --> D5
    D5 --> N4
    N4 --> D6

    D4 --> Q1
    Q1 --> Q2
    Q2 --> Q3
    Q3 --> Q4
    Q4 --> Q5

6.8 Deployment Architecture

graph TB
    subgraph "Kubernetes Cluster"
        subgraph "Namespace: Production"
            subgraph "Frontend Pods"
                A1[Next.js App<br/>Replica 1]
                A2[Next.js App<br/>Replica 2]
                A3[Next.js App<br/>Replica N]
            end

            subgraph "Service Pods"
                B1[Reservation<br/>Service]
                B2[Payment<br/>Service]
                B3[Availability<br/>Service]
                B4[Other Services]
            end

            subgraph "Infrastructure Pods"
                C1[Redis Master]
                C2[Redis Slave]
                D1[Kafka Broker 1]
                D2[Kafka Broker 2]
                E1[OpenTelemetry<br/>Collector]
            end
        end

        subgraph "Namespace: Monitoring"
            F1[Prometheus]
            F2[Grafana]
            F3[Alert Manager]
        end
    end

    subgraph "External Services"
        G1[(PostgreSQL<br/>Managed DB)]
        G2[Object Storage<br/>S3/GCS]
        G3[CDN]
    end

    subgraph "CI/CD"
        H1[GitHub]
        H2[Jenkins/GitLab CI]
        H3[Container Registry]
    end

    H1 --> H2
    H2 --> H3
    H3 --> A1

    B1 --> G1
    B2 --> G1
    B3 --> G1

    A1 --> G3
    E1 --> F1

7. Database-Agnostic Schema Implementation with Zod

7.1 Schema-First Architecture

The application implements a database-agnostic approach using Zod for TypeScript-first schema validation, ensuring the system can run with any database or data source that matches our schema definitions.

7.2 Zod Schema Benefits

7.3 Core Data Models

The system will define comprehensive data models for all entities using Zod schema validation:

7.3.1 User Entity (Enhanced with Soft Delete)

7.3.2 Reservation Entity (Enhanced with Soft Delete)

7.3.3 Room Entity (Enhanced with Soft Delete)

7.3.4 Rate Entity (Enhanced with Soft Delete)

7.4 Database-Agnostic Architecture

7.4.1 Repository Pattern

The system implements a repository pattern to abstract data access:

7.5 Supported Data Sources

The schema-based approach allows easy integration with multiple data sources:

7.6 Migration Strategy

graph TB
    subgraph "Schema Layer"
        A[Zod Schemas]
        B[Type Definitions]
        C[Validation Rules]
    end

    subgraph "Abstraction Layer"
        D[Repository Interface]
        E[Data Source Interface]
        F[Query Builder]
    end

    subgraph "Implementation Layer"
        G[PostgreSQL Adapter]
        H[MongoDB Adapter]
        I[Redis Adapter]
        J[REST API Adapter]
    end

    subgraph "Migration Tools"
        K[Schema Migrator]
        L[Data Transformer]
        M[Validation Engine]
    end

    A --> D
    B --> D
    C --> E
    D --> G
    D --> H
    D --> I
    D --> J
    E --> K
    F --> L
    G --> M
    H --> M
    I --> M
    J --> M

7.7 Implementation Benefits

7.8 Soft Delete System Implementation

7.8.1 Soft Delete Architecture

The system implements a fail-safe soft delete mechanism to ensure data integrity, audit compliance, and recovery capabilities while maintaining ultra-high performance.

Core Principles:

7.8.2 Enhanced Zod Schema with Soft Delete Fields

All entities shall include standardized soft delete and audit fields with the following requirements:

Base Soft Delete Schema Requirements:

Entity-Specific Schema Extensions: Entities like reservations shall extend the base schema with domain-specific fields while maintaining all soft delete capabilities and audit requirements.

7.8.3 Soft Delete Workflow Architecture

graph TB
    subgraph "User Action Layer"
        UA[User Delete Request]
        UI[Admin Interface]
        API[API Endpoint]
    end

    subgraph "Soft Delete Processing"
        VL[Validation Layer]
        BL[Business Logic]
        AL[Authorization Check]
        SDS[Soft Delete Service]
    end

    subgraph "Database Operations"
        UQ[Update Query]
        AT[Audit Trail]
        KE[Kafka Event]
        CI[Cache Invalidation]
    end

    subgraph "Automated Cleanup System"
        CS[Cleanup Scheduler]
        RP[Retention Policy]
        HD[Hard Delete Job]
        BU[Backup Service]
    end

    subgraph "Monitoring & Alerts"
        DM[Delete Metrics]
        AL2[Audit Logging]
        EM[Error Monitoring]
        RA[Recovery Alerts]
    end

    UA --> VL
    UI --> API
    API --> VL
    VL --> AL
    AL --> BL
    BL --> SDS
    SDS --> UQ
    SDS --> AT
    SDS --> KE
    SDS --> CI

    CS --> RP
    RP --> HD
    HD --> BU

    UQ --> DM
    AT --> AL2
    SDS --> EM
    HD --> RA

7.8.4 Soft Delete Service Specifications

High-Performance Soft Delete Operations:

Query Performance Requirements:

7.8.5 Automated Cleanup & Retention Policy

Retention Policy Framework:

The system shall implement configurable retention policies with the following requirements:

7.8.6 Automated Cleanup Jobs

Daily Cleanup Scheduler:

Cleanup Process Flow:

graph TB
    subgraph "Daily Cleanup Job - 2:00 AM UTC"
        ST[Cleanup Start]
        BC[Backup Check]
        RP[Retention Policy Evaluation]
        RR[Records Ready for Hard Delete]
    end

    subgraph "Safety Validation"
        LH[Legal Hold Check]
        CC[Compliance Validation]
        BR[Business Rule Validation]
        AC[Admin Confirmation]
    end

    subgraph "Batch Processing"
        BP[Batch Processing - 10K records]
        BU2[Pre-Delete Backup]
        HD2[Hard Delete Execution]
        KN[Kafka Notification]
    end

    subgraph "Post-Processing"
        AL3[Audit Logging]
        MU[Metrics Update]
        ER[Error Recovery]
        CR[Completion Report]
    end

    ST --> BC
    BC --> RP
    RP --> RR
    RR --> LH
    LH --> CC
    CC --> BR
    BR --> AC
    AC --> BP
    BP --> BU2
    BU2 --> HD2
    HD2 --> KN
    KN --> AL3
    AL3 --> MU
    MU --> CR

    HD2 --> ER
    ER --> CR

7.8.7 Recovery & Restore Capabilities

Data Recovery Service:

Recovery Workflow Requirements:

The system shall support comprehensive data recovery with the following capabilities:

7.8.8 Performance Monitoring & Metrics

Key Performance Indicators:

Monitoring Dashboard Requirements:

The system shall provide comprehensive monitoring and metrics with the following capabilities:

8. Theme Support Implementation

7.1 Theme Architecture

7.2 Theme Components

graph LR
    A[User Profile] --> B[Theme Preference]
    B --> C{Theme Selector}
    C -->|Light| D[Light Theme]
    C -->|Dark| E[Dark Theme]
    D --> G[Apply CSS Variables]
    E --> G
    G --> H[Update UI]
    H --> I[Save Preference]
    I --> A

9. Ultra-Scale Kafka Implementation for Real-time Processing

9.1 Enhanced Kafka Cluster Architecture

Ultra-Performance Kafka Configuration:

graph TB
    subgraph "Kafka Ultra-Scale Cluster (15 Brokers)"
        subgraph "AZ-1 (5 Brokers)"
            K1[Broker 1<br/>32GB RAM]
            K2[Broker 2<br/>32GB RAM]
            K3[Broker 3<br/>32GB RAM]
            K4[Broker 4<br/>32GB RAM]
            K5[Broker 5<br/>32GB RAM]
        end

        subgraph "AZ-2 (5 Brokers)"
            K6[Broker 6<br/>32GB RAM]
            K7[Broker 7<br/>32GB RAM]
            K8[Broker 8<br/>32GB RAM]
            K9[Broker 9<br/>32GB RAM]
            K10[Broker 10<br/>32GB RAM]
        end

        subgraph "AZ-3 (5 Brokers)"
            K11[Broker 11<br/>32GB RAM]
            K12[Broker 12<br/>32GB RAM]
            K13[Broker 13<br/>32GB RAM]
            K14[Broker 14<br/>32GB RAM]
            K15[Broker 15<br/>32GB RAM]
        end
    end

    subgraph "Optimized Topic Configuration"
        T1[reservation.events<br/>100 partitions<br/>Key: property_id]
        T2[payment.events<br/>50 partitions<br/>Key: payment_id]
        T3[availability.updates<br/>200 partitions<br/>Key: property_id + room_type]
        T4[notification.queue<br/>20 partitions<br/>Key: user_id]
        T5[audit.logs<br/>30 partitions<br/>Compacted]
        T6[system.metrics<br/>10 partitions<br/>Time-based retention]
    end

    subgraph "Ultra-Scale Producers (10,000 msg/minute)"
        P1[Reservation Service<br/>200-400 Pods]
        P2[Availability Service<br/>100-300 Pods]
        P3[Payment Service<br/>50-100 Pods]
        P4[Cache Service<br/>30-60 Pods]
    end

    subgraph "High-Performance Consumers"
        C1[Notification Service<br/>100+ consumers]
        C2[Analytics Service<br/>50+ consumers]
        C3[Audit Service<br/>20+ consumers]
        C4[Real-time Dashboard<br/>30+ consumers]
        C5[Cache Invalidation<br/>50+ consumers]
    end

    P1 --> T1
    P2 --> T3
    P3 --> T2
    P4 --> T3

    T1 --> C1
    T1 --> C2
    T1 --> C3
    T2 --> C1
    T2 --> C3
    T3 --> C5
    T4 --> C1
    T5 --> C3
    T6 --> C4

9.2 Ultra-Scale Topic Configuration

Topic Partitions Replication Factor Retention Period Key Strategy Compaction
reservation.events 100 3 30 days property_id + date No
payment.events 50 3 90 days payment_id No
availability.updates 200 3 7 days property_id + room_type Yes
notification.queue 20 3 24 hours user_id No
audit.logs 30 3 365 days tenant_id Yes
system.metrics 10 3 7 days metric_type No
cache.invalidation 50 3 6 hours cache_key No
analytics.events 40 3 180 days event_type + date No

9.3 Ultra-Performance Specifications

9.3.1 Throughput Requirements

9.3.2 Latency & Performance Targets

9.3.3 Availability & Reliability

9.4 Notification Flow

  1. Event Generation: Service publishes event to Kafka
  2. Event Processing: Notification service consumes event
  3. Template Selection: Choose notification template based on event type
  4. Channel Selection: Determine delivery channel (email/SMS/push/in-app)
  5. Delivery: Send notification through selected channel
  6. Tracking: Log delivery status and user engagement

10. OpenTelemetry Integration

10.1 Observability Stack

graph TB
    subgraph "Application Layer"
        A1[Node.js Services]
        A2[Next.js App]
    end

    subgraph "OpenTelemetry"
        B1[OTel SDK]
        B2[Auto-instrumentation]
        B3[Manual Instrumentation]
        B4[OTel Collector]
    end

    subgraph "Storage & Visualization"
        C1[Prometheus<br/>Metrics]
        C2[Jaeger<br/>Traces]
        C3[Elasticsearch<br/>Logs]
        C4[Grafana<br/>Dashboards]
    end

    A1 --> B1
    A2 --> B1
    B1 --> B2
    B1 --> B3
    B2 --> B4
    B3 --> B4
    B4 --> C1
    B4 --> C2
    B4 --> C3
    C1 --> C4
    C2 --> C4
    C3 --> C4

10.2 Logging Strategy

11. Docker & Kubernetes Deployment

11.1 Container Strategy

11.2 Kubernetes Resources

graph TB
    subgraph "Kubernetes Objects"
        A[Deployments]
        B[Services]
        C[ConfigMaps]
        D[Secrets]
        E[Ingress]
        F[HPA]
        G[PVC]
        H[NetworkPolicy]
    end

    subgraph "Resource Configuration"
        A --> A1[Replica Sets]
        A --> A2[Rolling Updates]
        B --> B1[ClusterIP]
        B --> B2[LoadBalancer]
        C --> C1[App Config]
        D --> D1[Credentials]
        E --> E1[TLS Termination]
        F --> F1[Auto-scaling]
        G --> G1[Persistent Storage]
        H --> H1[Security Rules]
    end

11.3 Deployment Pipeline

  1. Code Commit: Push to Git repository
  2. Build Trigger: CI/CD pipeline activation
  3. Test Execution: Unit, integration, and security tests
  4. Image Build: Docker image creation
  5. Image Push: Upload to container registry
  6. Deployment: Kubernetes rolling update
  7. Health Check: Readiness and liveness probes
  8. Smoke Test: Automated validation
  9. Monitoring: Metrics and log verification

12. Hybrid Monorepo Development & Deployment Strategy

12.1 Development Workflow for Hybrid Architecture

Nx-Powered Development Process:

graph TB
    subgraph "Developer Workflow"
        D1[Feature Request<br/>JIRA/GitHub Issue]
        D2[Branch Creation<br/>feature/RES-123-payment-integration]
        D3[Multi-Service Development<br/>Node.js + Java changes]
        D4[Nx Affected Detection<br/>Smart dependency analysis]
        D5[Local Testing<br/>Affected projects only]
        D6[Pre-commit Validation<br/>Type safety + linting]
        D7[Pull Request<br/>Atomic cross-service changes]
        D8[Code Review<br/>Architecture compliance]
        D9[CI/CD Pipeline<br/>Automated deployment]
    end

    subgraph "Nx Intelligence Layer"
        N1[Project Graph Analysis<br/>Dependency visualization]
        N2[Affected Command<br/>nx affected:test/build/lint]
        N3[Computation Caching<br/>Distributed cache hits]
        N4[Parallel Execution<br/>Optimal task scheduling]
        N5[Code Generation<br/>Consistent scaffolding]
    end

    subgraph "Quality Assurance"
        Q1[TypeScript Compilation<br/>Cross-service type checking]
        Q2[Unit Tests<br/>Service-specific validation]
        Q3[Integration Tests<br/>Contract testing]
        Q4[E2E Tests<br/>Critical user journeys]
        Q5[Performance Tests<br/>Load & stress testing]
        Q6[Security Scans<br/>Vulnerability assessment]
    end

    D1 --> D2
    D2 --> D3
    D3 --> N1
    N1 --> D4
    D4 --> N2
    N2 --> D5
    D5 --> N3
    N3 --> D6
    D6 --> N4
    D7 --> D8
    D8 --> D9

    D4 --> Q1
    D5 --> Q2
    Q1 --> Q3
    Q2 --> Q4
    Q3 --> Q5
    Q4 --> Q6

    N5 --> D3

12.2 CI/CD Pipeline Architecture

Multi-Language Pipeline for Nx Monorepo:

graph TB
    subgraph "Source Control"
        SC1[GitHub Repository<br/>Nx Monorepo]
        SC2[Feature Branches<br/>Atomic changes]
        SC3[Main Branch<br/>Production ready]
        SC4[Release Tags<br/>Semantic versioning]
    end

    subgraph "CI Pipeline - GitHub Actions"
        CI1[Trigger<br/>Push/PR events]
        CI2[Checkout & Cache<br/>Node modules + Maven deps]
        CI3[Nx Affected Analysis<br/>Determine changed projects]
        CI4[Parallel Matrix Build<br/>Node.js + Java services]
        CI5[Test Execution<br/>Unit + Integration tests]
        CI6[Quality Gates<br/>Coverage + Security scans]
        CI7[Container Build<br/>Multi-arch Docker images]
        CI8[Registry Push<br/>Versioned artifacts]
    end

    subgraph "CD Pipeline - ArgoCD"
        CD1[GitOps Repository<br/>Kubernetes manifests]
        CD2[Environment Promotion<br/>Dev → Staging → Prod]
        CD3[Canary Deployment<br/>Traffic splitting]
        CD4[Health Checks<br/>Readiness probes]
        CD5[Rollback Capability<br/>Automated failure recovery]
        CD6[Monitoring<br/>Deployment metrics]
    end

    subgraph "Testing Strategy"
        T1[Unit Tests<br/>Jest + JUnit coverage]
        T2[Contract Tests<br/>Pact consumer/provider]
        T3[Integration Tests<br/>Testcontainers]
        T4[E2E Tests<br/>Cypress automation]
        T5[Performance Tests<br/>K6 load testing]
        T6[Security Tests<br/>OWASP scanning]
    end

    subgraph "Deployment Environments"
        E1[Development<br/>Feature branch deploys]
        E2[Staging<br/>Integration testing]
        E3[Production<br/>Blue-Green deployment]
        E4[DR Environment<br/>Disaster recovery]
    end

    SC1 --> CI1
    SC2 --> CI2
    CI1 --> CI3
    CI2 --> CI4
    CI3 --> CI5
    CI4 --> CI6
    CI5 --> CI7
    CI6 --> CI8
    CI7 --> CD1
    CI8 --> CD2

    CD1 --> CD3
    CD2 --> CD4
    CD3 --> CD5
    CD4 --> CD6
    CD5 --> E1
    CD6 --> E2

    CI5 --> T1
    T1 --> T2
    T2 --> T3
    T3 --> T4
    T4 --> T5
    T5 --> T6

    E1 --> E2
    E2 --> E3
    E3 --> E4

12.3 Testing Strategy for Hybrid Services

Comprehensive Testing Framework:

Test Type Node.js Services Java Services Tools Coverage Target
Unit Tests Jest + Supertest JUnit 5 + Mockito Nx test runners > 80%
Integration Tests Testcontainers + Redis Spring Boot Test + PostgreSQL Docker Compose > 70%
Contract Tests Pact Consumer Pact Provider Pact Broker 100% API contracts
E2E Tests Cypress Selenium Grid Nx e2e runner Critical paths
Performance Tests K6 scenarios JMeter plans Grafana dashboards Load benchmarks
Security Tests npm audit OWASP dependency check Snyk scanning Zero high/critical

12.4 Container Strategy for Multi-Language Services

Optimized Docker Images:

graph TB
    subgraph "Node.js Container Strategy"
        N1[Base Image<br/>node:20-alpine]
        N2[Multi-stage Build<br/>Dependencies + Application]
        N3[Security Scanning<br/>Trivy vulnerability scan]
        N4[Size Optimization<br/>~100MB final image]
        N5[Runtime Optimization<br/>Non-root user + readonly fs]
    end

    subgraph "Java Container Strategy"
        J1[Base Image<br/>eclipse-temurin:21-jre-alpine]
        J2[Multi-stage Build<br/>Maven build + Runtime]
        J3[Security Scanning<br/>Trivy + OWASP checks]
        J4[Size Optimization<br/>~200MB final image]
        J5[Runtime Optimization<br/>JVM tuning + health checks]
    end

    subgraph "Container Registry"
        R1[Multi-arch Support<br/>AMD64 + ARM64]
        R2[Image Scanning<br/>Automated vulnerability detection]
        R3[Retention Policy<br/>30 latest versions]
        R4[Distribution<br/>Regional replication]
    end

    subgraph "Kubernetes Deployment"
        K1[Resource Limits<br/>Service-specific tuning]
        K2[Health Checks<br/>Liveness + readiness probes]
        K3[Auto-scaling<br/>HPA + VPA configuration]
        K4[Security Context<br/>Pod security standards]
    end

    N1 --> N2
    N2 --> N3
    N3 --> N4
    N4 --> N5

    J1 --> J2
    J2 --> J3
    J3 --> J4
    J4 --> J5

    N5 --> R1
    J5 --> R1
    R1 --> R2
    R2 --> R3
    R3 --> R4

    R4 --> K1
    K1 --> K2
    K2 --> K3
    K3 --> K4

12.5 Monitoring & Observability Strategy

Comprehensive Observability for Hybrid Architecture:

graph TB
    subgraph "Application Metrics"
        A1[Node.js Services<br/>Express metrics + Custom business metrics]
        A2[Java Services<br/>Micrometer + Spring Actuator]
        A3[Frontend Apps<br/>Next.js performance + User analytics]
    end

    subgraph "Infrastructure Metrics"
        I1[Kubernetes Metrics<br/>Pod performance + Resource usage]
        I2[Database Metrics<br/>PostgreSQL + Redis performance]
        I3[Message Queue<br/>Kafka throughput + Consumer lag]
        I4[Network Metrics<br/>Service mesh + Load balancer stats]
    end

    subgraph "Observability Stack"
        O1[OpenTelemetry<br/>Distributed tracing across services]
        O2[Prometheus<br/>Metrics collection + Alerting]
        O3[Grafana<br/>Dashboards + Visualization]
        O4[Jaeger<br/>Trace analysis + Performance]
        O5[ELK Stack<br/>Log aggregation + Search]
        O6[Alert Manager<br/>Intelligent alerting]
    end

    subgraph "Business Intelligence"
        B1[Performance KPIs<br/>Response time + Throughput]
        B2[Business KPIs<br/>Reservation rate + Revenue]
        B3[System Health<br/>Uptime + Error rates]
        B4[Capacity Planning<br/>Resource utilization trends]
    end

    A1 --> O1
    A2 --> O1
    A3 --> O2
    I1 --> O2
    I2 --> O3
    I3 --> O4
    I4 --> O5

    O1 --> B1
    O2 --> B2
    O3 --> B3
    O4 --> B4
    O5 --> O6
    O6 --> B1

12.6 Development Environment Setup

Standardized Development Stack:

Component Node.js Services Java Services Shared Tools
IDE Setup VSCode + Extensions IntelliJ IDEA Ultimate Nx Console plugin
Runtime Node.js 20 LTS OpenJDK 21 Docker Desktop
Package Managers pnpm (workspace support) Maven 3.9+ Nx CLI
Testing Jest + Supertest JUnit 5 + TestNG Testcontainers
Debugging Node.js Inspector Remote JVM debugging Docker Compose
Database PostgreSQL 15 local Same shared instance pgAdmin 4
Cache Redis 7 local Same shared instance RedisInsight
Message Queue Kafka local cluster Same shared instance Kafka UI
Monitoring Prometheus + Grafana Same stack Local observability

13. Development Timeline

Phase 1: Foundation (Weeks 1-6)

Phase 2: Core Modules (Weeks 7-14)

Phase 3: Advanced Features (Weeks 15-22)

Phase 4: Operations & Analytics (Weeks 23-28)

Phase 5: Testing & Deployment (Weeks 29-32)

13. Risk Management

13.1 Technical Risks

Risk Impact Probability Mitigation Strategy
Kafka cluster failure High Low Multi-broker setup, replication factor 3
Database performance issues High Medium Read replicas, query optimization, caching
OTA integration complexity Medium High Phased integration, fallback mechanisms
Real-time sync delays Medium Medium Redis caching, event-driven updates
Security vulnerabilities High Medium Regular audits, OWASP compliance
Scalability bottlenecks High Low Microservices architecture, auto-scaling

13.2 Business Risks

Risk Impact Probability Mitigation Strategy
User adoption resistance High Medium Comprehensive training, intuitive UI
Data migration issues High Low Thorough testing, phased migration
Regulatory compliance High Low Regular compliance audits
Integration partner changes Medium Medium Abstraction layers, multiple vendors
Scope creep Medium High Clear requirements, change control

14. Success Criteria

14.1 Technical Success Metrics

14.2 Business Success Metrics

15. Dependencies & Assumptions

15.1 Dependencies

15.2 Assumptions

16. Appendix

16.1 Glossary

16.2 Reference Documents

16.3 Technology Stack Versions

Document Approval:

Role Name Signature Date
Product Owner      
Technical Lead      
Business Stakeholder      
Project Manager      

Revision History:

Version Date Author Changes
1.0 Sept 24, 2025 Initial Initial draft
2.0 Sept 24, 2025 Team Comprehensive update with all modules

Next Steps:

  1. Review and approval from all stakeholders
  2. Technical architecture deep dive
  3. API specification documentation
  4. Database schema finalization
  5. Development environment setup
  6. Sprint planning for Phase 1