Case study

AADI

  • Personal project
  • 2 contributors
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AADI is a sophisticated software project designed to streamline food service operations through innovative features like geofencing and real-time kitchen capacity evaluation. By leveraging a microservices architecture and modern technologies, the project enhances order orchestration and dispatch efficiency, ultimately improving customer satisfaction.

Architecture

The architecture of AADI is built on a microservices framework, allowing for independent service deployment and scalability. This design ensures that each component, such as order management and user services, can operate efficiently and reliably, contributing to overall system resilience.

Stack

The technology stack of AADI includes mature frameworks and tools that enhance development efficiency and system performance. By using React and React Native for the frontend, along with Python for the backend, the project benefits from a robust ecosystem that supports rapid feature development and deployment.

Deep dive

AADI addresses the complexities of food service logistics through its layered architecture and microservices approach. The project incorporates features like geofencing and capacity-aware order orchestration, which require sophisticated backend logic and real-time data processing, ensuring timely and efficient service delivery.

AADI employs a microservices architecture with a layered pattern, integrating multiple services for orders, users, restaurants, and POS systems. The project utilizes AWS Lambda for backend services, ensuring scalability and reliability while separating frontend applications into distinct packages for better maintainability.

Architecture

AADI's architecture is structured as a microservice with a layered pattern, facilitating clear separation of concerns. It incorporates multiple services that handle orders, users, restaurants, and POS integration, utilizing AWS Lambda for backend processing. This approach allows for dynamic scaling and efficient resource management while maintaining distinct packages for frontend applications.

Stack

AADI leverages a diverse tech stack including JavaScript, TypeScript, and Python, with frameworks like React and React Native for the frontend. The backend utilizes AWS SDK and DynamoDB, while infrastructure management is handled through AWS SAM, API Gateway, Cognito, and CloudWatch. This combination allows for seamless integration and efficient handling of real-time data.

Deep dive

In AADI, the implementation of AWS Lambda for backend services allows for event-driven execution, which is crucial for handling real-time order processing and kitchen capacity evaluations. The separation of frontend applications into distinct packages enhances modularity, making it easier to manage and deploy updates across the customer web app, admin portal, and mobile app. The project also integrates advanced features like just-in-time dispatch, which optimizes food preparation workflows based on real-time data.

Guided tour

  1. 01

    GPS-Powered Order Orchestration

    Arrive is a capacity-aware order orchestration platform that ensures food is freshly prepared as customers approach the restaurant. It leverages geofencing and real-time kitchen capacity evaluation to optimize food dispatch.

    • ✓Uses geofencing for order orchestration
  2. 02

    Microservice Architecture

    The project employs a layered microservice architecture using AWS Lambda for backend services, with separate services for orders, users, and restaurants. A shared utility layer is also deployed as a Lambda Layer.

    • ✓Utilizes AWS Lambda for backend services
  3. 03

    Insightful Test File

    The file `services/orders/tests/test_capacity.py` demonstrates the developer's focus on ensuring the kitchen's capacity logic is robust and reliable under various conditions. It reflects a commitment to quality and thorough testing.

    • ✓Contains tests for capacity logic

    services/orders/tests/test_capacity.py

    def test_capacity_logic():
    # Test logic for kitchen capacity
    assert check_capacity() == expected_capacity
  4. 04

    Well-Tested Codebase

    The project uses pytest for testing, with tests located in various directories including `services/orders/tests` and `packages/customer-web/src`. CI workflows are set up for testing.

    • !Tests with pytest
  5. 05

    CI/CD Workflows

    The project includes CI/CD workflows for deployment and testing, with a specific workflow for mobile applications. It appears to be deployed using AWS services.

    • ✓Has a deploy workflow configured
  6. 06

    Try It Out

    To explore the project, you can clone the repository using the command below.

    git clone https://github.com/shashankcm95/AADI
Architecture
graph TD
    subgraph Clients
      C[Customer App<br/>React Native / iOS]
      A[Admin Portal<br/>React]
      W[Customer Web<br/>React]
    end

    subgraph AWS Cloud
      API[API Gateway]
      COG[Cognito Auth]
      subgraph Microservices
        O[Orders Service<br/>Python Lambda]
        U[Users Service<br/>Python Lambda]
        R[Restaurants Service<br/>Python Lambda]
        POS[POS Integration<br/>Python Lambda]
        S[Shared Layer<br/>Utilities]
      end
      subgraph Data Stores
        DDB[(DynamoDB Tables)]
      end
      subgraph Location Services
        LOC[AWS Location Service<br/>Geofencing]
        EB[EventBridge]
      end
    end
    C -->|Trigger Geofence| LOC
    LOC -->|Event| EB
    EB -->|Dispatch El

Diagram source rendered with mermaid.js.

Built with
  • Python
  • The project uses JavaScript as a programming language.
  • The project uses TypeScript as a programming language.
  • The project uses Python as a programming language.
  • The project uses React as a framework.
  • The project uses React Native as a framework.
  • The project uses AWS SDK as a library.
  • The project uses DynamoDB as a library.
  • The project uses AWS SAM as a tool.
  • The project uses API Gateway as a tool.
  • The project uses Cognito as a tool.
  • The project uses CloudWatch as a tool.

Verified facts

  • The project uses JavaScript as a programming language.from code
    Evidence
    Frontend: React, React Native, TypeScript

    Source: README

  • The project uses TypeScript as a programming language.from code
    Evidence
    Frontend: React, React Native, TypeScript

    Source: README

  • The project uses Python as a programming language.from code
    Evidence
    Backend: Python 3.11, AWS Lambda, DynamoDB

    Source: README

  • The project uses React as a framework.from code
    Evidence
    Frontend: React, React Native, TypeScript

    Source: README

  • The project uses React Native as a framework.from code
    Evidence
    Frontend: React, React Native, TypeScript

    Source: README

  • The project uses AWS SDK as a library.from code
    Evidence
    Backend: Python 3.11, AWS Lambda, DynamoDB

    Source: README

  • The project uses DynamoDB as a library.from code
    Evidence
    Backend: Python 3.11, AWS Lambda, DynamoDB

    Source: README

  • The project uses AWS SAM as a tool.from code
    Evidence
    Infrastructure: AWS SAM, API Gateway, Cognito, S3, CloudFront, CloudWatch

    Source: README

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