Construction principles for the IT architect (12): Choose a Storage Structure Based on Requirements
In this blog series, I delve into timeless principles of information science that ensure better "information constructions." This 12th blog in the series continues with the third computer science principle (see my introductory blog for the distinction). Why and when should you choose a relational structure (RDB and SQL), and when is linked data (RDF and SPARQL) a better storage option? Most people think of data in terms of tables to organize it, but this also imposes certain constraints when you want to later modify the structure. What trade-offs exist between the possible solutions?
Key concepts
Before diving into the choice, it is essential to outline the two options.
- RDB (Relational Database): A type of database where data is stored in tables (also called "relations") that can be linked through common data (keys). The concept of relational databases is based on the mathematical theory of sets and relations, allowing for the structured and efficient management of data.
- SQL (Structured Query Language): A standardized programming language for manipulating, managing, and querying relational databases. SQL is particularly useful for performing CRUD (Create, Read, Update, Delete) operations on relational databases. It is also used to define database structures, such as creating tables and modifying data types. SQL is powerful due to its simplicity and efficiency in managing large amounts of structured data in relational databases.
- RDF (Resource Description Framework): A standardized way to describe data to express meaningful, semantic relationships so they can be shared and reasoned over, regardless of the domain or application. In RDF, data is stored as subject-predicate-object triples. For example: “Book" (subject) "has title" (predicate) "The Discovery of the World" (object). This is also referred to as Linked Data and forms the foundation of the semantic web.
- SPARQL: A programming language for querying structured data from datasets using RDF.
Differences Between RDB and RDF Storage
Below, I compare the two data storage methods based on various themes:
1. Structure and Schema
- Relational Databases: Highly structured with a defined schema, making it easier to organize and validate data. This is useful for applications with well-defined data models but can be inflexible when adding new data types or relationships.
- RDF: Uses a strict data model where data is always stored as subject-predicate-object triples. This allows for great flexibility in handling diverse data and relationships without requiring a predefined schema (as in relational databases). This makes RDF databases ideal for storing heterogeneous data from multiple sources with semantic links, enabling the easy addition of new data.
RDF also allows for rich metadata descriptions, such as context, meaning, and relationships, enabling more detailed and meaningful data modeling than traditional relational databases, which often only store data (with the meaning described separately in a metamodel).
RDF is often referred to as a graph database, but it is based on nodes and relationships that can contain attributes, not necessarily triples. Graph databases are often more flexible and less strict than the RDF data model. Both have unique strengths depending on the use case.
2. Transactions
- RDB: Supports ACID transactions (Atomicity, Consistency, Isolation, Durability) with robust mechanisms for data integrity (e.g., foreign keys, constraints), making them reliable for data processing, critical for many enterprise applications.
- RDF: Lacks inherent mechanisms for data integrity, which may result in inconsistent or incomplete data.
3. Complex Queries
- RDB: Uses SQL, a powerful query language capable of handling complex queries and joins across tables. This makes it straightforward to establish relationships and perform aggregations.
- RDF: Uses SPARQL, which is effective for querying interconnected data and executing intricate queries in triples to find specific relationships between entities. However, it may be less efficient for complex queries typical of relational databases.
4. Performance
- RDB: Optimized for large datasets with complex queries, thanks to indexing and other optimizations, allowing targeted searches across datasets.
- RDF: May be slower for large data volumes, especially with complex queries, due to the graph structure often requiring traversal of the entire dataset.
5. Maturity and Support
- RDB: Long established, with a wide range of tools, libraries, and community support, making it easier to troubleshoot and find assistance.
- RDF: Relatively newer and requires a different mindset than traditional tabular approaches, with a stronger emphasis on semantic standardization of ontologies.
6. Security
- RDB: Offers robust security mechanisms, such as role-based access control and encryption, which are crucial for sensitive data.
- RDF: Security may be less standardized depending on the implementation. However, popular RDF databases like Virtuoso, Stardog, and GraphDB provide built-in support for common security mechanisms, such as encryption, logging, and validation.
Other considerations include scalability, data distribution, logical reasoning, and whether the technology adheres to open standards (portability).
Conclusion
As a computer scientist, you must evaluate the relative importance of the above characteristics to your solution. Generally, the choice between SQL and RDF depends on specific solution requirements, such as data model, performance needs, and the demand for flexibility or data integrity.
- RDBs are well-suited for traditional enterprise applications, ERP systems, and scenarios where consistent and structured data is required, and cross-table searching needs to be fast.
- RDF is ideal for applications that benefit from semantic relationships, such as linked data, ontologies, and knowledge graphs, offering flexibility in structure, even when the structure is not fully known in advance.
RDF is not necessarily the default choice for AI applications. The decision depends on specific needs. RDF is preferable when semantic reasoning and knowledge play a role (e.g., chatbots, medical or legal advice). For machine learning and deep learning (such as language models), alternative solutions optimized for speed, scalability, and unstructured data are often better.
Finally, you can also consider a hybrid solution, combining the “best of both worlds.” For instance, you might store flexible data and relationships in RDF while using relational storage for fast searches on specific objects or data history (e.g., a data warehouse).
References
- This blog was created using AI (ChatGPT) as an experimental knowledge source.
- Key-value databases were excluded from this article. While ideal for applications requiring fast, simple, and scalable data storage, they are less suitable for complex data relationships or advanced queries.
Read the other information science principles here:
- Meaningless identity designation, read here.
- Decoupling points for complexity reduction and flexibility, maximizing independence of components, read here.
- Language consistency, read here.
- Clear distribution of responsibilities and functional separation for administration, read here.
- Delegating decision-making authority as low as possible, read here.
- Detaching authorization from identification/authentication, read here.
- Single registration of master data, read here.
- Separating data and metadata in storage and processing, read here.
- Applying standard patterns without deviations, read here.
- Separating application function from data storage, read here.
- Device-independent development, read here.
- Choose a Storage Structure, read here.