Knowledge Hub System Architecture

This document describes the overall architecture of the Delta Lake & Apache Iceberg Knowledge Hub, including its automation systems, workflows, and data flows.

System Overview

The knowledge hub is a self-sustaining ecosystem built on GitHub, leveraging GitHub Actions for automation and community engagement.

graph TB
    subgraph "Content Layer"
        A[Documentation]
        B[Code Recipes]
        C[Tutorials]
        D[Comparisons]
    end
    
    subgraph "Automation Layer"
        E[CI/CD Workflows]
        F[Content Freshness Bot]
        G[Resource Aggregator]
        H[Gamification Engine]
    end
    
    subgraph "Community Layer"
        I[Contributors]
        J[Reviewers]
        K[Maintainers]
    end
    
    subgraph "Data Layer"
        L[Contributors DB]
        M[Processed URLs]
        N[Git History]
    end
    
    I --> B
    I --> A
    J --> E
    E --> A
    E --> B
    F --> A
    G --> D
    H --> L
    I --> L
    N --> F
    M --> G

Workflow Architecture

1. Code Recipe Validation Flow

sequenceDiagram
    participant Dev as Developer
    participant GH as GitHub
    participant CI as CI Workflow
    participant Linter as Linters
    participant Val as Validator
    
    Dev->>GH: Push code recipe PR
    GH->>CI: Trigger workflow
    CI->>CI: Detect changed recipes
    CI->>Linter: Run black & flake8
    Linter-->>CI: Linting results
    CI->>Val: Execute validate.sh
    Val-->>CI: Validation results
    CI->>GH: Report status
    GH->>Dev: Notify results

2. Documentation Validation Flow

sequenceDiagram
    participant Dev as Developer
    participant GH as GitHub
    participant CI as Doc CI
    participant MD as Markdownlint
    participant Link as Link Checker
    participant Mermaid as Mermaid Validator
    
    Dev->>GH: Push docs PR
    GH->>CI: Trigger workflow
    CI->>MD: Lint markdown
    MD-->>CI: Style results
    CI->>Link: Check links
    Link-->>CI: Link status
    CI->>Mermaid: Validate diagrams
    Mermaid-->>CI: Diagram status
    CI->>GH: Report status

3. Stale Content Detection Flow

sequenceDiagram
    participant Cron as Scheduled Trigger
    participant Script as Stale Bot
    participant Git as Git History
    participant GH as GitHub API
    participant Issue as Issue Tracker
    
    Cron->>Script: Weekly trigger
    Script->>Git: Query file history
    Git-->>Script: Last modified dates
    Script->>Script: Check threshold
    Script->>GH: Query existing issues
    GH-->>Script: Open issues
    Script->>Issue: Create new issues
    Issue-->>Script: Issue created
    Script->>Script: Log results

4. Gamification Flow

sequenceDiagram
    participant Event as GitHub Event
    participant Workflow as Gamification
    participant Parser as Event Parser
    participant Stats as Stats Updater
    participant DB as Contributors DB
    participant Board as Leaderboard
    
    Event->>Workflow: PR merged/Review
    Workflow->>Parser: Parse event
    Parser->>Stats: Calculate points
    Stats->>DB: Update contributor
    DB-->>Stats: Confirmation
    Workflow->>Board: Trigger update
    Board->>DB: Read stats
    Board->>Board: Generate markdown
    Board->>GH: Update README

5. Resource Aggregation Flow

sequenceDiagram
    participant Cron as Weekly Trigger
    participant Agg as Aggregator
    participant RSS as RSS Feeds
    participant Web as Websites
    participant AI as AI Summary
    participant PR as Pull Request
    
    Cron->>Agg: Start aggregation
    Agg->>RSS: Fetch feeds
    RSS-->>Agg: New articles
    Agg->>Web: Scrape websites
    Web-->>Agg: New links
    Agg->>Agg: Filter by keywords
    Agg->>AI: Generate summaries
    AI-->>Agg: Summaries
    Agg->>PR: Create PR
    PR-->>Agg: PR created

Component Architecture

Automation Scripts

graph LR
    subgraph "Python Scripts"
        A[find_stale_docs.py]
        B[update_contributor_stats.py]
        C[generate_leaderboard.py]
        D[find_new_articles.py]
    end
    
    subgraph "GitHub Actions"
        E[stale-content-bot.yml]
        F[gamification-engine.yml]
        G[update-leaderboard.yml]
        H[awesome-list-aggregator.yml]
    end
    
    subgraph "Data Storage"
        I[contributors.json]
        J[processed_urls.json]
        K[Git History]
    end
    
    E --> A
    F --> B
    G --> C
    H --> D
    B --> I
    C --> I
    D --> J
    A --> K

Data Flow Architecture

Contributor Points System

graph TD
    A[GitHub Event] --> B{Event Type?}
    B -->|PR Merged| C[Calculate Lines Changed]
    B -->|Review| D[Check Review Type]
    B -->|Issue Closed| E[Award Issue Points]
    B -->|Discussion| F[Award Discussion Points]
    
    C --> G{Lines Changed?}
    G -->|>500| H[50 Points]
    G -->|100-500| I[25 Points]
    G -->|<100| J[10 Points]
    
    D --> K{Review State?}
    K -->|Approved| L[5 Points]
    K -->|Changes Req| M[3 Points]
    
    E --> N[3 Points]
    F --> O[1 Point]
    
    H --> P[Update DB]
    I --> P
    J --> P
    L --> P
    M --> P
    N --> P
    O --> P
    
    P --> Q[Generate Leaderboard]

Deployment Architecture

GitHub Actions Runtime

graph TB
    subgraph "GitHub Infrastructure"
        A[GitHub Events]
        B[GitHub Actions]
        C[Workflow Runner]
    end
    
    subgraph "Workflow Execution"
        D[Setup Environment]
        E[Install Dependencies]
        F[Run Scripts]
        G[Process Results]
    end
    
    subgraph "Output"
        H[Commit Changes]
        I[Create Issues]
        J[Create PRs]
        K[Update README]
    end
    
    A --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H
    G --> I
    G --> J
    G --> K

Security Architecture

Access Control

graph TD
    A[GitHub User] --> B{Authentication}
    B -->|Authenticated| C{Authorization}
    B -->|Not Auth| D[Public Read Only]
    
    C -->|Contributor| E[Create PRs]
    C -->|Reviewer| F[Review PRs]
    C -->|Maintainer| G[Merge PRs]
    
    E --> H[Submit Code]
    F --> I[Approve/Request Changes]
    G --> J[Merge to Main]
    
    J --> K[Trigger Workflows]
    K --> L{Has Secrets?}
    L -->|Yes| M[Use GitHub Secrets]
    L -->|No| N[Standard Execution]

Scalability Considerations

Handling Growth

1. Content Volume: Git is designed for large repositories
2. Workflow Executions: GitHub Actions auto-scales
3. Community Size: JSON-based storage for thousands of contributors
4. Automation Load: Rate-limited, scheduled jobs

Performance Optimization

graph LR
    A[Optimization Strategy] --> B[Caching]
    A --> C[Parallel Jobs]
    A --> D[Incremental Processing]
    A --> E[Efficient Queries]
    
    B --> F[Action Caching]
    B --> G[Dependency Caching]
    
    C --> H[Matrix Builds]
    
    D --> I[Changed Files Only]
    
    E --> J[Git Log Filtering]

Monitoring and Observability

Workflow Monitoring

graph TB
    A[Workflow Execution] --> B[GitHub Actions UI]
    A --> C[Workflow Logs]
    A --> D[Status Badges]
    
    B --> E[View Run History]
    C --> F[Debug Failures]
    D --> G[Public Status]
    
    E --> H[Metrics Dashboard]
    F --> I[Error Analysis]
    G --> J[README Display]

Future Enhancements

Planned Architecture Improvements

1. Advanced AI Integration: Full LLM API integration for summaries
2. Real-time Notifications: Discord/Slack integration
3. Advanced Analytics: Contributor insights dashboard
4. Multi-language Support: Internationalization
5. API Gateway: REST API for programmatic access

graph TB
    subgraph "Future Additions"
        A[API Gateway]
        B[Analytics Dashboard]
        C[Notification Service]
        D[LLM Integration]
    end
    
    subgraph "Existing System"
        E[Core Workflows]
        F[Content Repository]
    end
    
    A --> F
    B --> E
    C --> E
    D --> E
    
    F --> G[External Consumers]
    E --> H[Real-time Updates]

References

• GitHub Actions Documentation
• Mermaid.js Documentation
• Python Best Practices

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Last Updated: 2025-11-14
Maintainers: Community