Analysis of China's Distributed Database Market and Future Development Trends

Database importance: as the foundation of most information systems, databases provide the hardware compute power below and enable upper‑layer applications, making speed, usability, stability, scalability, and cost critical for enterprise business and growth resilience.

Without databases, programmers would face massive data relationships and unreliable systems; databases simplify data storage and processing to basic CRUD operations.

Definition and classification: a database is software that organizes, stores, and manages data according to specific structures. Distributed databases connect physically separated database units via networks into a logically unified system.

Distributed database concept and technical evolution: the field has progressed through academic research, commercial deployment, industrial implementation, and enterprise demand, with data models evolving from hierarchical, network, relational, object, object‑relational, to semi‑structured models.

Industry support system: China's distributed database growth benefits from population dividends, academic research, industry‑research collaboration, and talent training aligned with market needs.

Technology routes: mainstream solutions address data capacity expansion via sharding middleware, native distributed architectures, shared‑storage, and decentralized (shared‑nothing) designs, each with distinct advantages and trade‑offs.

Sharding + middleware: single‑node databases provide storage and execution, while a middleware layer adds distributed capabilities, managing data partitioning, SQL parsing, request routing, and result merging.

Shared‑storage distributed databases: compute nodes are independent and share a dynamically scalable storage cluster, enabling separate scaling of compute and storage layers while ensuring high availability and performance.

Decentralized (shared‑nothing) distributed databases: each node has independent compute and storage, using consensus algorithms like multi‑paxos or multi‑raft to ensure multi‑replica availability.

Heterogeneous multimodal databases: multimodal capabilities build upon mature single‑model technologies, embedding vertical engines to handle diverse data types with tailored performance.

HTAP (Hybrid Transaction/Analytical Processing): merges OLTP and OLAP workloads in a single distributed system to enable real‑time analytics, facing challenges of workload interference, data visibility, and latency.

HTAP solutions: separated architecture (dominant) and unified architecture; cloud‑native environments will drive new HTAP products and features.

From cloud‑hosted to cloud‑native databases: early cloud adoption used IaaS to host traditional RDBMS (RDS), which suffered performance and cost issues; cloud‑native databases are designed from the ground up for cloud characteristics across application, middleware, and service layers.

Future trends: while distributed databases have matured commercially, the broader database landscape will continue evolving with open‑source ecosystems, multimodal capabilities, HTAP adoption, and tighter integration of business and technology.

Open‑source development: driven by movements like Linux and Hadoop, the database open‑source community now focuses on scenario‑driven collaboration.