Introduction: Efforts to Resolve the 'Mismatch' Between Data and Code
Developers write code in object-oriented languages (Java, Python), but data is still stored in table-based RDBMS. This 'Object-Relational Impedance Mismatch' has been a challenge plaguing developers for the past 30 years. To solve this, pure Object-Oriented Databases (OODBMS) emerged, which then evolved into Object-Relational Databases (ORDBMS) that absorbed their benefits. This post analyzes the technical evolution of these three models and delves into how ORDBMS like PostgreSQL have come to dominate the market in 2025 from a practical perspective.
Deepening Core Principles: Limitations of RDBMS and the Rise of ORDBMS
The three systems differ fundamentally in their view of data.
RDBMS: Stable but Rigid Structure
RDBMS (Oracle, MySQL) normalizes data into 2-dimensional tables. While excellent for data integrity and transaction (ACID) management, storing complex objects (e.g., inheritance relationships, polymorphism) requires numerous JOIN operations, leading to performance degradation.
OODBMS: Leader in Niche Markets
OODBMS stores objects directly on disk. It retrieves data via object pointers without JOINs, showing overwhelming performance in processing complex graph data. However, due to the lack of a standard query language (SQL) and a closed ecosystem, it is currently limited to specialized fields like embedded systems or CAD/CAM.
ORDBMS: The Modern Standard (e.g., PostgreSQL)
ORDBMS layers object-oriented elements (inheritance, user-defined types, methods) on top of the RDBMS table structure. Recently, it perfectly supports unstructured data types like JSONB, effectively blurring the lines between RDBMS and NoSQL. It is the most realistic alternative that captures both development productivity and data integrity.
Latest Trends: Evolving into 'Multi-Model' Databases
The database trend for 2025 is the 'End of Single Models'. Modern ORDBMS are evolving into Multi-Model Databases that go beyond simply storing objects to support Vector search for AI model integration or processing time-series data.
Especially in cloud-native environments, the need for OODBMS has further diminished due to the advancement of ORM (Object-Relational Mapping) frameworks. Instead, hybrid patterns querying JSON documents directly within RDBMS have become mainstream.
Practical Application: When to Use What?
A practical architect needs clear criteria based on business requirements.
- Finance, Payments, ERP: If strong consistency is required, traditional RDBMS is still the answer.
- Complex Manufacturing Processes, Real-time Simulation: If data connections are extremely complex and speed is the top priority, OODBMS (or Graph DB) can be considered.
- General Web/App Services, AI Backends: If you need to handle structured and unstructured data (JSON, Vector) simultaneously, ORDBMS (PostgreSQL) is the best choice.
Expert Insight
💡 Database Architect's Note
Caution when Adopting Technology: Introducing a pure OODBMS requires caution. The developer talent pool is small, and integration costs with other systems are very high. Unless there is a specific reason, utilizing the JSON features of an ORDBMS to gain the benefits of object storage is advantageous for maintenance.
Future Outlook: Within the next 5 years, the distinction between 'relational' and 'object-oriented' will become meaningless. 'Autonomous DBs', where the engine learns data patterns and decides whether to store as tables or objects, will become commonplace.
Conclusion: Tools Change, But the Essence Remains
The shift from RDBMS to OODBMS, and then to ORDBMS, is ultimately a process of finding the answer to "How can we store data more efficiently and retrieve it more easily?" The current market winner is ORDBMS, which adds object flexibility to the stability of the relational model. However, with the advent of the AI era, data forms are changing rapidly again. Engineers must possess flexible thinking to choose the most appropriate tool for solving business problems, rather than being tied to a specific model.