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Here's how YouTube scaled MySQL to handle its massive user base: Why MySQL? YouTube chose MySQL in 2005 due to its open-source nature, reliability, and compatibility with programming languages like Python and PHP. Challenges of Scaling MySQL High write traffic, complex queries, the need for data sharding, and latency requirements posed significant challenges. Custom Sharding YouTube distributed data across multiple MySQL instances, with each shard storing data for a specific subset of users or videos. For example, videos were sharded based on video IDs, and user data was partitioned by user IDs. Replication at Scale YouTube implemented master-slave replication, where a primary database handled writes, and multiple replicas handled read requests. Custom scripts and eventual consistency principles ensured reliability. Caching with Memcached YouTube heavily relied on caching layers like Memcached for frequently accessed data, such as video metadata, which significantly reduced database load and led to faster responses. Query Optimization YouTube’s engineering team restructured complex queries and introduced indexes to minimize the execution time. Bigtable for Metadata YouTube transitioned from MySQL to Bigtable (a NoSQL solution by Google) for non-relational data like video descriptions and tags, while MySQL remained the primary database for transactional operations. #systemdesign #softwarearchitecture #mysql #scalability #microservices I make you a better software engineer than yesterday with my videos. #bettersdethanyesterday Let's begin with step 1: Click the link below. / @devmonk.preetam Connect with me: Stay updated with more tech content, tutorials, and insights by following me on social media: 🔗 LinkedIn: / preetamnahak 🔗 Twitter: https://x.com/preetamnahak 🔗 Instagram: / preetamnahak 🔗 GitHub: https://github.com/preetamnahak 🔗 Blog/Website: / preetamnahak Don't forget to subscribe to the channel for more videos on tech, cloud computing, distributed systems, and startup tips!