Object-Functional Language Mapping to Database Queries
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Solution Overview
Problem
Current database systems, particularly those using SQL, face performance limitations due to their row-oriented approach and lack of support for object-oriented and functional programming models, leading to inefficiencies in handling digital objects and increasing database capacity demands as user interactions and data sizes grow.
Innovation Solution
The method and apparatus for mapping object-oriented/functional languages to database languages enable seamless database access using proxy objects and cached data, allowing developers to remain in an object-oriented or functional programming model while accessing SQL or NoSQL databases, with the capability to accelerate data access significantly, often making it 100 to 10,000 times faster than traditional methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If SQL databases use traditional row-oriented approach and SQL programming model, then database compatibility and standard support are maintained, but programming model flexibility and data access performance deteriorate
Solution Approach 1:
The patent introduces a mapping layer that acts as an intermediary between object-oriented/functional programming models and SQL database operations. This mapping layer translates high-level programming constructs into database-specific commands, enabling developers to use modern programming paradigms while maintaining compatibility with traditional SQL databases. The mapping layer resolves the contradiction by providing adaptability through multiple programming model support while preserving performance through optimized translation to efficient database operations.
Solution Approach 2:
The system dynamically changes operational parameters by switching between different programming models (object-oriented, functional, and SQL) based on the specific operation being performed. This allows the system to optimize for both programming flexibility and execution performance by selecting the most appropriate model for each task, rather than being constrained to a single approach.
2Quantity of substance
If database capacity is increased to handle growing data sizes and user interactions, then data storage capability improves, but system complexity and cost increase
Solution Approach 1:
The patent segments the database system into distinct functional layers: the mapping layer that handles programming model translation, the query optimization layer that processes operations, and the storage layer that manages data. This segmentation allows each layer to be independently optimized and scaled, reducing overall system complexity while maintaining the capability to handle growing data volumes through distributed or clustered deployments.
3Productivity
If more powerful servers are deployed to increase database capacity, then data handling capability improves, but licensing cost and infrastructure expense increase linearly
Solution Approach 1:
The mapping layer provides multi-functionality by supporting multiple programming models (object-oriented, functional, and SQL) and translating them into efficient database operations. This universal approach allows a single database system to serve diverse application requirements without needing separate specialized systems for each programming paradigm, thereby improving processing capability while avoiding linear increases in hardware resources.
Data Source
AI summary
In a pipeline of operations having a terminating operation and a source operation, a builder is built corresponding to the terminating operation. The builder may also correspond to one or more intermediate operations. A database query is generated corresponding to the builder and is sent to a database or a data source for efficient access to the database.