Modern distributed database architectures are designed to handle massive data volumes, global accessibility, and fault tolerance. These systems spread data across multiple servers or nodes, using mechanisms like sharding and replication. Sharding splits data into smaller pieces and distributes them across nodes for horizontal scalability, while replication ensures redundancy and fault tolerance. However, this benefits come with tradeoffs, such as potentially increased latency due to the need for consensus algorithms to maintain consistency. The primary-replica architecture is a foundational concept in distributed systems, offering simplicity and read scalability but limiting its ability to scale effectively for modern applications. In contrast, the shared-nothing architecture excels in horizontal scalability and fault tolerance but introduces challenges like metadata management overhead and uneven data distribution. These architectures represent the evolution of distributed systems, each addressing specific tradeoffs between scalability, consistency, and operational complexity.