Database Technology Evolution: From Hierarchical to Vector Databases

This comprehensive article traces the evolution of database technology from early hierarchical and network models to modern specialized databases. It begins by establishing the foundational role of databases in internet infrastructure, then systematically explores the development of various database types including hierarchical IMS (1968), network IDS (1970), relational MySQL (1996), column-store HBase (2008), document-oriented MongoDB (2009), key-value Redis (2010), graph Neo4j (2010), time-series InfluxDB (2013), HTAP TiDB (2016), and vector Milvus (2019).

Each database model is examined in detail, covering its core characteristics, advantages, limitations, and typical use cases. The article provides in-depth technical analysis of MySQL's architecture, transaction isolation levels, indexing strategies, and locking mechanisms. It explores HBase's column-family storage model and distributed architecture, MongoDB's document model and WiredTiger storage engine, Redis's in-memory data structures and persistence mechanisms, Neo4j's native graph processing and storage, InfluxDB's time-series model and TSM engine, TiDB's HTAP architecture combining OLTP and OLAP capabilities, and Milvus's vector database design for AI applications.

The article concludes by discussing future trends in database technology including vertical specialization, distributed architectures, cloud-native solutions, and security enhancements. It emphasizes how each database innovation responds to specific technological challenges and application requirements, ultimately supporting the advancement of internet services, data analysis, and artificial intelligence.