🎵 Mini-Spotify Backend API

A clean and efficient Java backend application built using Spring Boot and MySQL to manage music playlists and provide smart, mood-based analytical insights.

I architected this project entirely from scratch to break away from AI dependency, master core backend engineering patterns, and implement memory-efficient data structures.

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🚀 Key Features

• Layered Enterprise Architecture: Implements a strict decoupled separation of concerns across Controller, Service, and Repository (Data JPA) layers.
• Relational Persistence: Seamlessly integrated with MySQL using Hibernate Object-Relational Mapping (ORM) to ensure permanent data durability.
• Optimized Data Aggregation: Leverages advanced Java collections (HashMaps) combined with Java Lambda Expressions for single-pass data transformations.
• Batch Processing Verified: Rigorously tested endpoints capable of handling dense JSON payload streams (Successfully validated using a custom 100+ production song dataset).

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🛠️ Tech Stack & Ecosystem

• Core Language: Java (JDK 17+)
• Framework: Spring Boot (Spring Web, Spring Data JPA)
• Database Backend: MySQL
• API Simulation & Debugging: Postman

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🔌 API Documentation & Endpoints

1. Register a Song

• Endpoint: POST /addsong
• Request Body (JSON):

  {
    "title": "Chaleya",
    "artist": "Arijit Singh",
    "mood": "Romantic"
  }

2. Batch Bulk Insertion

• Endpoint: POST /add-multiple-songs
• Description: Receives large-volume song arrays for efficient, synchronous database storage.

3. Retrieve Entire Database Playlist

• Endpoint: GET /playlist
• Response: Pulls a complete live matrix of all songs actively stored inside the relational schema.

4. Real-time Mood Analytics

• Endpoint: GET /mood-count
• Description: Evaluates the current catalog dynamically using modern functional streaming blocks to count aggregate distributions.
• Response Sample:

  {
    "Romantic": 35,
    "Sad": 24,
    "Gym": 18,
    "Party": 23
  }

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🧠 Core Competencies & Technical Growth

• Autonomous Problem Solving: Mastered dry-running core programmatic execution trees on paper to track and safely eliminate critical logical failures (such as state-tracking flag issues and variable reset overflows).
• Automated Database Lifecycle Management: Deepened comprehension of operational property layers like relational mapping states (ddl-auto=update), entity keys (@Id, @GeneratedValue), and active connection string drivers.
• Functional Programming Concepts: Migrated standard, verbose multi-line collection loops into concise and immutable functional blocks using methods like .merge() and specialized lambda notation.