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Multi-Engine Render Farm Architecture

🎯 Overview

The Multi-Engine Render Farm operates using a three-zone architecture that separates concerns and enables scalable, distributed rendering. This architecture manages the communication between render workers, queue management, and result processing.

🏗️ Three-Zone Architecture

Based on the Media Production diagram, the system is organized into three distinct zones:

🌐 1. Common Zone

Purpose: Shared resources and services used across all zones

Components: - S3 Storage: Central storage for templates, assets, and results - APIs: Shared API services for cross-zone communication - Database: Central database for job tracking and metadata

🖥️ 2. Frontend Zone

Purpose: User interface and job submission

Components: - Frontend: Web-based interface for job submission and monitoring

⚙️ 3. Backend Zone

Purpose: Distributed processing and rendering

Components: - Middleware: Job orchestration and management - Queue: Task distribution and status management - Workers: Distributed render workers (Worker 01, Worker 02, etc.)

📡 Communication Flow

📤 Job Submission Flow

  1. Frontend → Middleware: User submits job through frontend
  2. Middleware → Queue: Middleware adds tasks to queue
  3. Queue → Workers: Queue distributes tasks to available workers
  4. Workers → Queue: Workers report task status back to queue
  5. Queue → Frontend: Frontend receives status updates from queue
  6. Frontend → Database: Frontend updates job status in database

📁 Asset Management Flow

  1. APIs → S3: APIs manage asset storage in S3
  2. S3 → Frontend: Frontend retrieves templates and compressed assets
  3. S3 → Workers: Workers download templates and assets from S3
  4. Workers → S3: Workers upload completed renders (results) to S3

🔄 Backend Zone Internal Communication

📊 Task Distribution

  • Queue → Workers: Queue distributes tasks to available workers
  • Task Assignment: Each worker receives specific tasks (Task 01, Task 02, etc.)
  • Load Balancing: Queue ensures optimal task distribution

📈 Status Reporting

  • Workers → Queue: Workers report task status and progress
  • Real-time Updates: Continuous status updates during processing
  • Error Reporting: Workers report errors and failures to queue

📦 Result Management Flow

  • Workers → S3: Workers upload processed results directly to S3 storage
  • Result Storage: Results are stored in S3 and managed through APIs
  • Quality Control: Results undergo quality validation before final delivery

📊 Architecture Diagram

graph TD
    %% Common Zone
    subgraph Common["Common Zone"]
        S3[("S3 Storage")]
        APIs[("APIs")]
        DB[("Database")]
    end

    %% Frontend Zone  
    subgraph Frontend["Frontend Zone"]
        FE[("Frontend")]
    end

    %% Backend Zone
    subgraph Backend["Backend Zone"]
        MW[("Middleware")]
        Q[("Queue")]
        W1[("Worker 01")]
        W2[("Worker 02")]

        %% Backend internal connections
        Q ---|"Task 01"| W1
        Q ---|"Task 02"| W2
        W1 ---|"Task status"| Q
        W2 ---|"Task status"| Q
        MW ---|"tasks"| Q
        Q ---|"Status updates"| MW
    end

    %% Cross-zone connections
    APIs --> S3
    S3 ---|"Templates & Assets"| FE
    FE ---|"Job"| MW
    MW ---|"Job status"| Q
    Q ---|"Status updates"| FE
    FE ---|"Update status"| DB
    DB ---|"Job data"| FE
    MW ---|"3rd Party Info"| APIs
    S3 ---|"Download templates"| W1
    S3 ---|"Download templates"| W2
    W1 ---|"Upload results"| S3
    W2 ---|"Upload results"| S3

    %% Styling
    classDef commonStyle fill:#f9f9b7
    classDef frontendStyle fill:#b3b3ff
    classDef backendStyle fill:#ffb3ff

    class Common commonStyle
    class Frontend frontendStyle
    class Backend backendStyle

🎯 Component Responsibilities

🔧 Middleware

  • Job Orchestration: Manages job lifecycle and coordination
  • Task Creation: Creates tasks from user jobs
  • Resource Management: Manages worker resources and availability
  • Error Handling: Handles errors and recovery procedures

📋 Queue System

  • Task Distribution: Distributes tasks to available workers
  • Status Management: Tracks task status and progress
  • Load Balancing: Ensures optimal resource utilization
  • Priority Management: Manages task priorities and scheduling

👷 Workers

  • Task Execution: Executes assigned rendering tasks
  • Status Reporting: Reports progress and status to queue
  • Result Generation: Generates processed results
  • Error Handling: Handles task-specific errors and failures

📦 Results Management Flows

  • Result Upload: Workers upload processed results directly to S3
  • Result Storage: Results are stored in S3 and accessed via APIs
  • Quality Validation: Results undergo quality validation through APIs
  • Delivery Management: Results are delivered to users through Frontend

📈 Scalability Features

↔️ Horizontal Scaling

  • Worker Scaling: Add/remove workers based on demand
  • Queue Scaling: Scale queue capacity for high throughput
  • Storage Scaling: Scale S3 storage for large assets and results

⚖️ Load Distribution

  • Intelligent Routing: Route tasks to optimal workers
  • Capacity Management: Manage worker capacity and availability
  • Resource Optimization: Optimize resource usage across workers

🛡️ Fault Tolerance

  • Worker Failure Handling: Handle worker failures gracefully
  • Task Retry: Retry failed tasks on available workers
  • Data Redundancy: Ensure data redundancy and backup

🔗 Integration Points

🌍 External Systems

  • 3rd Party Services: Integration with external rendering services
  • Cloud Services: Integration with cloud providers
  • Monitoring Systems: Integration with monitoring and alerting

🏠 Internal Systems

  • Render Orchestrator: Integration with render orchestration
  • Quality Control: Integration with quality control systems
  • Asset Management: Integration with asset management systems

⚡ Performance Considerations

🚀 Throughput Optimization

  • Parallel Processing: Concurrent task execution across workers
  • Batch Processing: Efficient batch processing of similar tasks
  • Resource Pooling: Shared resource pools for efficiency

⚡ Latency Reduction

  • Proximity Optimization: Optimize worker proximity to data
  • Caching Strategies: Cache frequently accessed assets
  • Network Optimization: Optimize network communication

💾 Resource Management

  • CPU/GPU Utilization: Optimal utilization of compute resources
  • Memory Management: Efficient memory usage and management
  • Storage Optimization: Optimize storage usage and access patterns

🚧 Development Status

Status: TODO - Implementation details not yet documented

📚 Documentation Status

The following sections require additional information and are marked for future completion:

📋 Queue Implementation Details

Status: TODO - Queue system implementation details not yet documented

👥 Worker Management

Status: TODO - Worker management and orchestration details not yet specified

📡 Communication Protocols

Status: TODO - Inter-component communication protocols not yet documented

📊 Performance Benchmarks

Status: TODO - Performance benchmarks and optimization targets not yet defined

👁️ Monitoring and Observability

Status: TODO - Monitoring setup and observability procedures not yet documented