This document discusses sharding architectures for scaling databases and applications. Sharding involves splitting data across multiple databases to improve performance and allow parallelization. The key aspects covered include: 1. The problem sharding aims to address is scalability for applications with high read/write traffic and large amounts of data. 2. Sharding is described and distinguished from replication/backup/clustering approaches. 3. Implementation involves splitting tables or data by identifier and distributing them across database shards using a technique like modulo. 4. Optimization strategies are discussed like caching, connection pooling, and using aggregation daemons to coordinate shards. 5. Challenges with joins, relations, and migrating