/question-store

A CLI tool that generates a question paper based on specified requirements.

Primary LanguageTypeScriptMIT LicenseMIT

question-store

A CLI tool that generates a question paper based on specified requirements.

This is a solution to Backend Developer Assignment

Usage

Usage: index [options] [command]

Question Store - A CLI tool to store and retrive questions based on marks.

Options:
  -V, --version                                                               output the version number
  -h, --help                                                                  display help for command

Commands:
  length                                                                      Outputs number of questions stored
  add <question> <subject> <topic> <difficulty> <marks>                       Add a question to the store
  remove <id>                                                                 Remove a question from the store
  list                                                                        List all questions in the store
  generate <totalMarks> <easyPercentage> <mediumPercentage> <hardPercentage>  Generate a question paper

Approach Overview

The goal of this backend developer assignment is to create a robust system that caters to the needs of a user (teacher) interacting with a Command Line Interface (CLI) application. This CLI application communicates with a globally distributed database. The assignment includes the handling of a real-world problem, where the user interacts with the CLI to manage and query a database containing questions of varying difficulty levels.

Assumptions

  1. Real-World Problem Scenario: The selected approach addresses a real-world problem, simulating a teacher interacting with a CLI application to manage and query questions within a globally distributed database.

  2. Question Difficulty and Marking System:

    • Easy Questions: 1-2 marks
    • Medium Questions: 3-6 marks
    • Hard Questions: 6-10 marks

  3. Sample Data Generator:

    • Utilizes the openTDB API to generate questions.
    • Assigns marks to questions based on the specified difficulty level and marking system.

  4. Database Seeding:

    • The generated data from the Sample Data Generator can be seeded into the database.

  5. Database Interaction:

    • Prisma is employed for querying the database.

Implementation Steps

  1. CLI Application Design:

    • The CLI application is designed to offer an intuitive interface for the user (teacher) to interact with the system.
    • Commands are structured to facilitate actions such as adding, querying, and managing questions.

  2. Sample Data Generation:

    • The Sample Data Generator fetches questions from openTDB.
    • Marks are assigned based on the defined difficulty and marking system.
    • Generated data is formatted and ready for seeding into the database.

  3. Database Schema:

    • Prisma is utilized to define the schema for the globally distributed database.
    • Schemas include tables for questions, users, and any other relevant entities.

  4. Database Seeding:

    • The generated data is seamlessly seeded into the database using Prisma.
    • This ensures that the database is populated with questions of varying difficulty levels.

  5. Querying the Database:

    • Prisma provides a reliable mechanism for querying the database.
    • Queries are designed to retrieve questions based on user requirements, such as difficulty level and marks.

  6. Generate Command:

    • Marks Count Query:
      • The database is scanned to determine the count of questions for each mark, using an aggregation query.
    • Marks Distribution:
      • Marks distribution is calculated using a recursive backtracking approach.
      • This step defines how many questions are needed for each mark.
    • Parallel Fetching:
      • Questions are fetched in parallel using Promise.all in JavaScript and Prisma ORM.
      • This ensures efficient and speedy retrieval of questions based on the calculated distribution.

  7. Error Handling:

    • The system incorporates robust error handling mechanisms to gracefully manage any unexpected scenarios during CLI interactions, data generation, seeding, or database queries.

  8. npm Scripts:

    • The project utilizes npm scripts for various development and build tasks. Key scripts include:
      • start: Launches the application using the compiled index file.
      • build:watch: Monitors TypeScript source files for changes and triggers a rebuild.
      • build: Compiles TypeScript files using SWC, and then removes unnecessary build artifacts.
      • clean: Removes the dist directory.
      • lint: Runs ESLint to check and fix coding style issues.
      • init-dataset: Transpiles and runs the Sample Data Generator script to initialize the dataset.
      • db:generate: Generates Prisma client.
      • db:push: Pushes changes to the database using Prisma.
      • db:seed: Seeds the database with sample data using Prisma.
      • db:reset: Resets the database by rolling back migrations, forcing a reapply.

Area Of Improvements

  • API Integration:

    • This system can be easily integrated into an API server, providing a scalable and accessible solution for users.

  • OpenAI API Integration:

    • The openTDB API can be complemented or replaced with the OpenAI API for question generation, enhancing the variety and complexity of questions.

  • Assumption Adjustment:

    • Assumptions about the marking system and difficulty levels can be adjusted to optimize the calculation of marks distribution for faster execution.

Dependencies

  • Postgres : Database
  • Prisma : ORM
  • Typescript : Language
  • Commander : CLI management
  • OpenTDB : Question generation

Prerequisites

  • Node.js 10+
  • pnpm or yarn or npm

Getting Started

  • Clone this repository
git clone https://github.com/vishalx360/question-store.git
  • Install dependencies
pnpm install
  • Build application
pnpm build
  • Start Application
pnpm start

Follow usage instructions to use the CLI tool.