1. 📌 Example: How YouTube shards its database to store billions of videos.

YouTube, with its vast user base and extensive video library, faces significant challenges in managing and scaling its database infrastructure. To address these challenges, YouTube uses a multifaceted sharding strategy that ensures efficient data management, high availability, and optimal performance.

The Scale Challenge

YouTube's platform experiences immense data throughput, including:

• 500+ hours of video uploads every minute
• Billions of views daily
• Massive amounts of metadata, such as comments, likes, and user interactions

Handling this scale necessitates a robust and scalable database architecture capable of managing vast amounts of data efficiently.

Why Sharding is Necessary

Traditional monolithic database systems struggle under such heavy loads due to:

• Storage and processing limitations of single servers
• The need for horizontal scalability to distribute data across multiple servers
• Ensuring high availability and fault tolerance to maintain service reliability

Sharding, the practice of distributing data across multiple databases or servers, becomes essential to meet these demands.

YouTube's Sharding Approach

YouTube's sharding strategy encompasses several key methods:

1. Video ID-based Sharding

Each video on YouTube is assigned a unique 11-character identifier. These IDs are systematically assigned to specific database shards using consistent hashing algorithms, ensuring: