One of the major challenges is dealing with complex relationships between entities. In the real world, relationships between things can be very complex with many nested relationships. Relational databases work best with simple 1:1, 1:many and many:many relationships. It can be difficult to represent highly complex nested relationships within the relational data model. This often requires normalization of data across multiple tables and denormalization of some aspects to simplify certain queries. But this balancing act is not always straightforward.
Another challenge comes from enforcing referential integrity constraints between multiple tables. While RDBMS offer functionality like foreign keys to enforce RI, this adds complexity in the schema and can impact performance for mass data loads and updates. It also requires significant thought around how to model the primary-foreign key relationships between entities. Getting this part of the model wrong can impair data consistency down the line.
A third challenge is around handling changing or evolving requirements over time. In the real world, needs change but relational schemas are not as flexible to changes in requirements compared to some NoSQL data models. Adding or removing columns, tables and relationships in a relational DB after it has been populated can be tricky, require schema changes using ALTER commands, and the need for migrations and transforming existing data. This impacts the ability to respond quickly to new business needs.
Scalability of the solution for large volumes of data and high transaction loads can also be challenging with a relational model depending on the specific use case and query patterns. While relational databases are highly optimized, some data and access patterns just don’t fit well within the SQL paradigm to achieve best performance and utilization of resources at scale. Factors like normalization, indexes needed, types of queries used need careful consideration.
Another issue arises from the fact that object-oriented domains rarely map easily to the tabular structure of relational tables, rows and columns. Much real-world data incorporates complex object models which are not intuitively represented in relational form. The process of mapping objects and their relationships and attributes to a relational structure requires transformations that can result in redundancy, additional columns to handle polymorphism, or denormalization for performance.
Next, enforcing data types and constraints in a relational database that match the kinds of attributes and validation applied to objects and their properties in code can require significant mapping specifications and transformations. Data types have fixed sizes in a RDBMS and do not have the same kind of polymorphism and validation as programmatic data types and classes. Adapting behavior and constraints from code to the database adds design complexity.
Another concern relates to queries and access of data. Object-relational impedance mismatch occurs because objects are designed to be accessed from code, whereas relational data is designed to be queried via SQL. Mapping code-based access of objects to equivalent SQL queries and result handling requires mappings that often result in less optimal SQL with more joins than ideal. This impacts performance for object graph retrieval.
The relational model also lacks flexibility in handling semi-structured or unstructured data types that are common in real-world domains like content management systems or sensor telemetry. Trying to fit JSON, XML documents or sparse dimensional data into relational structures usually requires normalization that impacts scalability, increases storage overhead and complexifies query patterns to assemble the full objects/documnets from multiple tables.
There is also a challenge around mapping domain-specific business terminologies and concepts to logical relational constructs like tables, rows and attributes. Real-world domains often come with deeply embedded domain-specific language, concepts and taxonomies that must be translated for the database environment. Getting this translation and communication of mapped relational structures back to developers, analysts and business users correctly requires expertise.
Relationships in object models can naturally evolve in code as requirements change by adding properties, associations etc. But evolved relationships usually require changes to relational schemas which then need managed through revision control and tracked against application code. Keeping the database schema and object mapping configurations synchronized with the domain objects as they evolve adds ongoing maintenance overhead.
While relational databases provide benefits around structure, performance and scalability – mapping rich object models and evolving real-world requirements correctly into relational schemas in a way that is sustainable and meets evolving needs can present significant challenges even for experienced database experts and architects if not properly addressed. It requires careful consideration of patterns, optimization of queries vs consistency needs, and openness to refactoring of mappers and schemas over time.