Rustik - Hyperscale Architecture Explorer

Welcome to Rustik, an interactive web application designed to help you explore, understand, and conceptualize high-performance system architectures. Rustik leverages AI-powered analysis to provide insights into how different architectural components interact and scale.

Key Features

• Component Library: Browse a curated list of architectural components (e.g., Anycast IP, Load Balancers, API Design Styles, Microservices Architecture, Database Strategies) with detailed explanations of their types, use cases, real-world examples, and implementation guidance.
• System Visualizer: Interactively select architectural components and their specific types.
  • Receive AI-powered analysis on potential interactions, benefits, and trade-offs of your selected configuration.
  • Get AI-driven suggestions for potential microservices based on your chosen infrastructure, if "Microservices Architecture" is selected.
  • Obtain a conceptual security posture analysis, including strengths, potential vulnerabilities, and recommendations.
  • Navigate to a dedicated page for a detailed conceptual scaling potential analysis.
• Capacity Analyzer: Get AI-driven conceptual insights into the scaling potential of your chosen components, exploring strengths and potential bottlenecks for handling large user loads. This is launched from the System Visualizer.
• Master-Flow: A comprehensive architectural analysis hub. Select components and their specific types, then trigger multiple AI evaluations in parallel, including:
  • Interaction Analysis
  • Conceptual Scaling Potential
  • Suggested User Capacity Tier (with detailed reasoning)
  • Conceptual Security Posture Analysis
  • AI-Suggested Potential Microservices (if applicable)
  • Downloadable Conceptual Architecture Document (Markdown)
• Builder Insights: A dedicated section discussing the challenges and considerations of building real-world, large-scale distributed systems, including detailed discussions on scaling up to 1 billion and even 5 billion users, and advanced architectural concepts.
• AI-Powered Analysis: Utilizes Genkit and Google AI models (Gemini) to provide dynamic, context-aware architectural insights across various features.
• Modern Tech Stack: Built with Next.js (App Router), React, ShadCN UI components, Tailwind CSS, and TypeScript.

How to Use Rustik

1. Explore Components: Navigate to the Components tab (homepage) to learn about individual architectural building blocks and their detailed characteristics.

2. Visualize Systems & Analyze Interactions: Go to the Visualizer tab.

   • Select various architectural components and their specific types using the checkboxes.
   • Click "Analyze Interactions" to get an AI-generated analysis of how your chosen components might work together.
   • If "Microservices Architecture" and other relevant infrastructure are selected, click "Suggest Potential Microservices" for AI-based service suggestions.
   • Click "Analyze Conceptual Security Posture" for an AI-driven security overview of your selections.

   System Visualizer Screenshot:

3. Analyze Scaling Potential: From the Visualizer, after selecting components, click "Analyze Scaling Potential". This will take you to the Capacity Analyzer page, showing an AI's conceptual take on how your selections might scale and handle large user loads.

4. Comprehensive Architectural Profile (Master-Flow): Use the Master-Flow tab.

   • Select your desired architectural components and their specific types.
   • Click "Analyze Full Architectural Profile" to receive a multi-faceted report including interaction analysis, scaling potential, a suggested capacity tier with reasoning, a conceptual security posture analysis, and AI-suggested potential microservices (if applicable).
   • Once analyses are complete, you can download a conceptual architectural document in Markdown format.

5. Learn About Scaling Challenges: Visit the Builder Insights tab to understand the complexities of designing and operating systems at massive scale, including specific considerations for 1 billion and 5 billion user systems, and cutting-edge architectural concepts.

⚠️ Purpose & Disclaimer

Educational Tool – Rustik is for conceptual learning only. Not a Production Blueprint – AI insights are suggestive, not definitive. Real-world system design demands detailed planning, performance testing, and domain expertise.

Future Enhancements (Potential Ideas)

Here are some advanced directions Rustik could explore in the future to become an even more powerful tool for architectural design and operations:

• Collaboration & Versioning: Add real-time multi-user editing with change history so teams can co-design, comment on, and roll back architectural changes—just like a Google Doc for infra diagrams.
  • A Robust Backend System: We'd need a backend to handle user authentication, manage real-time data synchronization (e.g., using WebSockets), and store the architectural designs, their versions, and comments in a database.
  • Real-time Collaboration Logic: Technologies like Conflict-free Replicated Data Types (CRDTs) or operational transforms would likely be needed to manage simultaneous edits from multiple users smoothly.
  • Significant UI/UX Overhaul: The current interface would need to be substantially redesigned to incorporate editing tools, comment threads, version history viewers, etc.
  • Database Integration: A database (SQL or NoSQL) would be essential for persisting all this collaborative data.
• Diagram Export & Codegen: Enable one-click exports to PlantUML/C4 notation, SVG/PNG, or even Terraform/CloudFormation stubs so your conceptual designs can flow straight into IaC pipelines.
• Cost & Performance Simulation: Integrate cloud-provider APIs (AWS, GCP, Azure) to attach real cost estimates and latency profiles to each component, then run “what-if” scenarios to forecast budget vs. performance trade-offs.
• Security Posture Assessment (Advanced): Build in a rule-based or AI-powered security auditor that flags missing mTLS, weak isolation zones, or single points of failure—turning your visual model into a living threat-model. (Beyond the current conceptual analysis).
• Plugin & API Ecosystem: Expose a plugin framework or REST API so the community (or your own teams) can add new component libraries, custom AI flows (e.g. compliance checks), or embed Rustik analyses in other tooling.
• Saved Profiles & Templates: Offer templates for common patterns (e.g. “Global CDN + Multi-Region DB”) and let users snapshot and share “best practice” configurations across projects or orgs. This would likely require user accounts.
• Alerting & Drift Detection: If you connect your visual models to live infrastructure (via metrics or Terraform state), detect and notify when real-world topology drifts away from the designed architecture or exhibits unusual behavior.
• CI/CD Integration (Advanced): Plug Rustik into your pipeline: break the build if your IaC deviates from approved architectural patterns, or auto-generate compliance reports on every PR.
• AI-Driven Architectural Pattern Recommendation Engine: Users describe a high-level problem or goal, and the AI suggests suitable architectural components and patterns.
• Conceptual Scalability Simulation / "What-if" Scenarios (Advanced): AI analyzes how a selected architecture might conceptually respond to different load levels or failure scenarios in more detail.
• Automated Conceptual Architectural Document Generation: AI generates a more comprehensive high-level architectural design document (Markdown) based on user selections and analysis results.
• Digital Twin & “What-If” Simulator: Spin up a sandboxed, code-generated replica of your design (e.g. in Docker or Kubernetes) and run synthetic load tests to validate performance and failure modes before you build.
  • Advanced Code Generation: Would require sophisticated capabilities to generate functional code for various components and their interactions based on user selections.
  • Infrastructure Orchestration Integration: Needs to interface with tools like Docker or Kubernetes to dynamically create and manage sandboxed environments.
  • Load Testing Framework Integration: Would need to integrate with synthetic load testing tools to apply workloads to the simulated environment.
  • Performance Monitoring in Sandbox: Requires mechanisms to collect, aggregate, and analyze performance metrics from the temporary "digital twin."
• Policy-as-Code Enforcement: Define organizational rules (e.g. “all traffic between regions must use mTLS” or “no single-AZ database”) in a declarative language, then block or flag any design that violates them.
• Adaptive Blueprint Optimization: Use reinforcement-learning or genetic-algorithms to evolve your design automatically based on cost, latency, and resilience objectives—letting the AI propose “next-generation” topologies.
• Extensible Plugin Marketplace: Let users publish and share new component definitions, AI flow templates, compliance policies, or even custom cost-model providers.
• ChatOps & Collaboration Bots: Integrate with Slack/Teams so you can “@rustik analyze this config” or get architecture change notifications in your dev channels.
• Mobile Companion App: Carry your architecture explorer on your phone or tablet, complete with tap-to-zoom diagrams and on-the-go commentary.
• User Accounts & Saved Configurations: Allow users to create accounts, save their architectural explorations, and revisit them later. (Note: This would require significant backend changes).
• Interactive Diagram Rendering in Visualizer: Dynamically generate a visual (graphical) diagram of selected components and their conceptual connections. (Note: Requires a client-side diagramming library).

Cloning and Running Locally

To clone and run Rustik on your local machine, follow these steps:

Prerequisites

• Node.js (v18 or later recommended)
• npm or yarn

Setup Instructions

1. Clone the Repository: Open your terminal and run the following command to clone the project.

   git clone <YOUR_REPOSITORY_URL_HERE> rustik-app
   cd rustik-app

   Replace <YOUR_REPOSITORY_URL_HERE> with the actual URL of your Git repository.

2. Install Dependencies: Install the project dependencies using either npm or yarn:

   npm install

   or

   yarn install

3. Set Up Environment Variables: The application uses Genkit, which connects to Google AI models (like Gemini) for its AI-powered features. You'll need a Google AI API key.

   • Create a new file named .env in the root directory of the project (i.e., inside the rustik-app folder).
   • Add your Google AI API key to this .env file. The content should look like this:

     GOOGLE_API_KEY=your_google_ai_api_key_here

     Replace your_google_ai_api_key_here with your actual API key.
   • You can obtain a Google AI API key by visiting Google AI Studio (formerly MakerSuite). Create a new API key if you don't have one.

4. Run the Development Servers: Rustik requires two development servers to run concurrently:

   • One for the Next.js frontend application.
   • One for the Genkit AI flows (which act as the backend for AI features).

   You will need to open two separate terminal windows or tabs for these commands.

   • Terminal 1: Start the Genkit Development Server: In your first terminal window, navigate to the project directory (rustik-app) and run:

     npm run genkit:dev

     This command starts the Genkit development server. It will typically run on port 3400 (e.g., http://localhost:3400). This server handles the AI flow executions. It will also automatically recompile your flows if you make changes to them in the src/ai/flows/ directory.

   • Terminal 2: Start the Next.js Development Server: In your second terminal window, navigate to the project directory (rustik-app) and run:

     npm run dev

     This command starts the Next.js frontend application. It will typically run on port 9002 (e.g., http://localhost:9002).

5. Access the Application: Once both servers are running:

   • Open your web browser.
   • Navigate to http://localhost:9002 (or the port specified in your terminal output for the Next.js server).

   You should now be able to use the Rustik application, including its AI-powered analysis features.

Automated Workflows with GitHub Actions

This project uses GitHub Actions to automate several quality checks and can be extended for more advanced CI/CD capabilities:

• Linting and Type Checking: Automatically checks your code for style issues (ESLint) and type errors (TypeScript) on every push or pull request. This helps catch common mistakes early. (See: .github/workflows/lint-typecheck.yml)
• Build Check: Ensures your Next.js application successfully builds whenever changes are made. This prevents merging code that breaks the production build. (See: .github/workflows/build-check.yml)
  • Note on Build Check: The build process might require a GOOGLE_API_KEY. For CI, you can set a dummy key as a GitHub repository secret named DUMMY_GOOGLE_API_KEY_FOR_BUILD. If the build doesn't require it, this can be omitted from the workflow file.
• Automated Testing (Conceptual): A conceptual workflow is set up to run unit tests (npm test). This would need actual tests to be written and the package.json test script to be configured. (See: .github/workflows/unit-tests.yml)
• Security Scanning (SAST - Conceptual): A workflow using GitHub CodeQL is set up to perform Static Analysis Security Testing. Results appear in the "Security" tab of your repository. (See: .github/workflows/codeql-analysis.yml)
• Automated Dependency Updates (Dependabot): Configured via .github/dependabot.yml to automatically check for and create PRs for npm dependency updates.

Further GitHub Actions Capabilities (Potential Future Setup)

• Comprehensive Automated Testing:
  • Unit Tests: Expand on the conceptual setup by writing tests (e.g., with Jest or Vitest) and ensuring the npm test script executes them.
  • Integration Tests: For testing how different parts of your application interact.
  • End-to-End (E2E) Tests: Using tools like Cypress or Playwright, you can automate browser interactions to test user flows. This is very powerful for catching regressions in the UI and overall application behavior.
• Deployment Automation (CI/CD):
  • Deploy to Staging/Preview: Automatically deploy your application to a staging or preview environment (like Vercel, Netlify, Firebase App Hosting, or your own cloud infrastructure) whenever you push to a specific branch (e.g., develop or feature branches).
  • Deploy to Production: Set up a workflow to deploy to your production environment when you merge to main or create a release tag. This often includes steps like building the optimized production version of your app.
• Code Coverage Reporting:
  • After running tests, you can have a workflow that calculates code coverage (what percentage of your code is covered by tests) and uploads the report (e.g., to Codecov or Coveralls).
• Release Management:
  • Automate the process of creating release notes, tagging releases, and publishing packages (if your project becomes a library).

We hope you find Rustik insightful for exploring the fascinating world of hyperscale system architecture!