Interactive API Design Explorer

API Design Overview

This interactive document presents the RESTful API design for a modern ride-sharing application. It provides a comprehensive guide to the data structures, endpoints, and core operational flows required to build a service like Uber or Lyft. The goal is to offer a clear, explorable, and practical blueprint for developers. Use the navigation to explore the different components of the API, from the fundamental data models to the step-by-step user workflows. Each section is designed to build upon the last, creating a complete picture of the system's architecture.

Data Models

The foundation of the API is built on four core data models: User, Ride, Location, and Payment. These models represent the primary entities within the system. Below are the JSON structures for each, illustrating the key fields and their relationships. Understanding these models is the first step to interacting with the API.

API Endpoints Explorer

This section provides a complete, interactive reference for all API endpoints. The endpoints are grouped by their functional area. Click on a specific endpoint from the list on the left to view its detailed information, including the HTTP method, URI path, and description. This explorer is the central reference for all API interactions.

Select an endpoint to see details here.

Interactive Workflows

Workflows illustrate the sequence of API calls for common user actions. The diagrams below show the step-by-step processes for a rider requesting a trip and a driver completing one. Click on any step in a workflow to jump to its corresponding entry in the API Endpoints Explorer above, connecting the process flow directly to the technical implementation.

Rider Workflow

Driver Workflow

Ride Status Simulation

This visualization represents the current distribution of all rides in the system by their status. The chart is dynamic and can be updated to simulate the system under load. Use the button below to simulate new rides being requested and completed, and watch how the distribution changes in real-time. This provides insight into the lifecycle of the `Ride` data model.