R Language Interface Layer for Database Query Translation
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Solution Overview
Problem
Legacy approaches for interfacing R language clients with database engines require in-memory data storage, are not scalable, and require R users to have SQL skills, leading to inefficiencies and data governance issues, especially when dealing with large datasets and sensitive data.
Innovation Solution
A transparency layer that interprets R language code, transforms constructs into database language constructs, and executes them within the database engine, allowing for parallel processing and secure data handling without requiring R users to know SQL or database technologies, and enables deferred evaluation to reduce memory demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If legacy R client environments use in-memory data storage, then R language-based analysis can be performed, but the system cannot handle large datasets due to limited memory and data space
Solution Approach 1:
The patent transitions from in-memory storage to database engine storage, moving data from the client's memory space to a separate database environment. This dimensional shift allows R users to work with datasets that exceed local memory constraints by leveraging the database engine's storage capacity.
Solution Approach 2:
The patent introduces a transparency layer as an intermediary between the R client and database engine. This layer translates R language constructs into database queries, enabling seamless interaction without requiring users to manually transfer data or learn SQL, thus resolving the memory limitation while maintaining ease of use.
2Ease of operation
If R users directly query databases using SQL, then data can be accessed from the database engine, but R users must have SQL skills which is not extensively possessed by the R user community
Solution Approach 1:
The transparency layer serves as an intermediary that translates R language constructs into database queries automatically. Users interact with the database using familiar R syntax, and the transparency layer handles the conversion to SQL or other database queries, eliminating the need for users to learn SQL while maintaining direct database access capability.
Solution Approach 2:
The transparency layer provides universal functionality by supporting multiple database operations through a single R-based interface. It handles various query types, data manipulations, and database interactions using R language constructs, making the system versatile without requiring users to master multiple query languages or protocols.
3Productivity
If data is pulled from the database into the R engine for computation, then R language processing can be performed, but the solution does not scale and is limited to sequential execution
Solution Approach 1:
Instead of pulling data into the R engine for computation, the patent inverts the approach by pushing R language constructs to the database engine for execution. The database engine performs the computation on data that remains in its storage, and results are returned to the R client. This inversion enables parallel execution capabilities inherent in database engines while maintaining R language syntax for users.
4Productivity
If legacy approaches are used for data extraction and processing, then data can be accessed, but the process is inefficient and introduces data governance issues when accessing sensitive data
Solution Approach 1:
The transparency layer acts as a secure intermediary that enables direct database access while maintaining data governance compliance. It provides controlled access to sensitive data through R language constructs, eliminating the need for manual data extractions by IT departments, thereby improving efficiency while maintaining security and governance standards.
Data Source
AI summary
A method, system, and computer program product for interfacing an R language client with a separate database engine environment. The method commences by interpreting an R language code fragment to identify and select R language constructs and transforming the R language constructs into queries or other database language constructs to execute within the database engine environment. The method further implements techniques for transmitting marshalled results (resulting from the execution of the database language constructs) back to the R client environment. In some situations, the marshalled results include an XML schema or DTD or another metadata description of the structure of the results. User conveniences in the form of transparent database query construction, and transparent exploitation of parallelism is achieved by determining the computation resource requirements of executable language constructs, and mapping the execution to one (or more) of, a local R client, a database engine, or a remote execution engine.