Multi-Dimensional Scaling (MDS) is a distributed database scaling approach that separates and scales individual services such as query, index, and data to improve performance and resource utilization. This allows for distribute data without distributing query execution and indexes, optimizing hardware usage by running different services on different nodes with varying requirements. MDS eliminates resource contention by isolating services to prevent queries and indexes from slowing down reads and writes. It improves performance by storing data and indexes on separate nodes, executing queries on separate nodes, and reducing network overhead. Additionally, it optimizes resource utilization by configuring query service nodes with fast processors, index service nodes with fast SSDs, and data service nodes with more memory. MDS offers flexible deployment options, allowing administrators to decide whether or not to leverage the technology. The approach addresses limitations of traditional sharding methods, such as custom-column sharding, which can lead to cross-node traffic for basic queries. Instead, MDS enables automatic-range-partitioning, reducing network overhead and improving scalability.