Mixedrelational

Mixedrelational is a term used to describe data architectures and stores that blend relational database capabilities with non-relational data models. The aim is to support traditional structured SQL workloads alongside flexible data representations such as documents, graphs, or key-value pairs within a single system or tightly integrated set of components. The concept overlaps with ideas like polyglot persistence and multi-model databases, which acknowledge that different data patterns benefit from different models.

In practice, mixedrelational designs may be realized as multi-model databases that natively support several data models, or as relational systems augmented with non-relational modules, adapters, or external stores. Common techniques include JSON or JSONB storage in relational engines, graph extensions, document stores linked to relational tables, and federated query or data virtualization layers that present a unified query interface.

Benefits of the approach include the ability to run diverse workloads without extensive data duplication, reduced ETL, and more flexible schemas for evolving applications. It can improve performance for certain queries, enable richer data relationships, and simplify data access for microservices that require multiple models.

Challenges involve maintaining data consistency across models, coordinating transactions, complex query optimization, and increased operational and developmental complexity. Proper governance, clear data ownership, and careful performance tuning are important when adopting mixed-relational architectures.

Related terms include multi-model databases, polyglot persistence, and hybrid stores. Notable examples of systems employed in this space range from relational engines with JSON/graph capabilities to dedicated multi-model platforms.