[AIR-3][AIS-3][BPC-3][RES-3]

Anya Core Architecture

Overview

Add a brief overview of this document here.

Table of Contents

This document describes the high-level architecture of Anya Core.

System Overview

graph TD
    subgraph Client Applications
        Web[Web App]
        Mobile[Mobile App]
        CLI[CLI Tool]
    end

    subgraph API Layer
        REST[Rest API]
        RPC[JSON-RPC]
        WS[WebSocket]
    end

    subgraph Application Layer
        Services[Domain Services]
        UseCases[Use Cases]
        Events[Event Bus]
    end

    subgraph Domain Layer
        Models[Domain Models]
        Repositories[Repositories]
        Events2[Domain Events]
    end

    subgraph Infrastructure Layer
        DB[(Database)]
        Cache[(Cache)]
        MQ[Message Queue]
        External[External Services]
    end

    Web --> REST
    Mobile --> REST
    CLI --> RPC

    REST --> Services
    RPC --> Services
    WS --> Events

    Services --> UseCases
    UseCases --> Repositories
    Repositories --> DB
    Repositories --> Cache

    Events --> MQ
    Events2 --> MQ

    UseCases --> External

Component Descriptions

Client Applications

  • Web App: Browser-based user interface
  • Mobile App: Native mobile applications
  • CLI Tool: Command-line interface for developers

API Layer

  • REST API: HTTP/HTTPS endpoints for web and mobile clients
  • JSON-RPC: Remote procedure calls for CLI and system integration
  • WebSocket: Real-time event streaming

Application Layer

  • Domain Services: Core business logic
  • Use Cases: Application-specific workflows
  • Event Bus: Handles domain events and integration events

Domain Layer

  • Domain Models: Core business entities and value objects
  • Repositories: Data access interfaces
  • Domain Events: Events that represent state changes in the domain

Infrastructure Layer

  • Database: Persistent data storage
  • Cache: High-speed data access layer
  • Message Queue: Asynchronous message processing
  • External Services: Third-party integrations

Data Flow

  1. Client applications send requests through the API layer
  2. The application layer processes requests using domain services
  3. Domain services coordinate between domain models and repositories
  4. Repositories interact with the infrastructure layer for data persistence
  5. Domain events are published and processed asynchronously
  6. Responses are returned to clients through the API layer

Deployment Architecture

graph TD
    subgraph Cloud Provider
        LB[Load Balancer]

        subgraph Auto Scaling Group
            API1[API Instance]
            API2[API Instance]
            API3[API Instance]
        end

        subgraph Services
            DB[(Database Cluster)]
            Cache[(Redis Cluster)]
            MQ[Kafka Cluster]
        end

        subgraph Monitoring
            Prometheus
            Grafana
            ELK[ELK Stack]
        end
    end

    Internet --> LB
    LB --> API1
    LB --> API2
    LB --> API3

    API1 --> DB
    API2 --> DB
    API3 --> DB

    API1 --> Cache
    API2 --> Cache
    API3 --> Cache

    API1 --> MQ
    API2 --> MQ
    API3 --> MQ

    API1 --> Prometheus
    API2 --> Prometheus
    API3 --> Prometheus

    Prometheus --> Grafana

    API1 --> ELK
    API2 --> ELK
    API3 --> ELK
    DB --> ELK
    Cache --> ELK
    MQ --> ELK

Security Architecture

Authentication

  • JWT-based authentication
  • OAuth 2.0 / OpenID Connect
  • API key authentication for services

Authorization

  • Role-based access control (RBAC)
  • Attribute-based access control (ABAC)
  • Policy-based access control (PBAC)

Data Protection

  • Encryption at rest (AES-256)
  • Encryption in transit (TLS 1.3)
  • Field-level encryption for sensitive data

Performance Considerations

Caching Strategy

  • Multi-level caching (in-memory, distributed, CDN)
  • Cache invalidation policies
  • Stale-while-revalidate pattern

Database Optimization

  • Read replicas for scaling reads
  • Sharding for horizontal scaling
  • Connection pooling

Asynchronous Processing

  • Event-driven architecture
  • Background job processing
  • Batch processing for heavy operations

Monitoring and Observability

Metrics

  • System metrics (CPU, memory, disk, network)
  • Application metrics (request rate, error rate, latency)
  • Business metrics (transactions, active users)

Logging

  • Structured logging with correlation IDs
  • Log aggregation and analysis
  • Log retention policies

Tracing

  • Distributed tracing across services
  • Performance analysis
  • Dependency mapping

See Also