SQL Query Translation for Non-Relational Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Devices such as routers and servers often store data in various formats, making it challenging to retrieve data in a relational database format using structured query languages like SQL, especially when the data is initially stored in formats like binary, YANG, or JSON.
Innovation Solution
The system receives a query in a structured database query language, converts data from the initial format to a relational database format, and provides a query response in this format, allowing for flexible data retrieval by generating a temporary relational database based on the query and destroying it after use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in non-relational formats (binary, YANG, JSON), then data storage flexibility and device-specific optimization are improved, but data retrieval using structured query languages becomes difficult or impossible
Solution Approach 1:
The patent introduces an intermediary translation layer that converts queries from structured database query languages (like SQL) into operations compatible with non-relational data formats (binary, YANG, JSON). This mediator component enables seamless interaction between relational query interfaces and non-relational data storage, resolving the contradiction by allowing both flexible storage and easy retrieval without requiring data format conversion.
2Adaptability or versatility
If a temporary relational database is generated for each query, then data retrieval flexibility is improved, but system resource consumption and processing time increase
Solution Approach 1:
The patent implements preliminary action by pre-compiling query translation rules and data format mappings during system initialization or idle periods. This preparation work establishes ready-to-use translation templates that accelerate runtime query processing, reducing the overhead of generating temporary relational databases while maintaining retrieval flexibility.
Solution Approach 2:
The system dynamically adjusts its behavior based on query characteristics and system state. For simple queries, it uses pre-computed translation rules for fast processing. For complex queries or when resources are abundant, it generates temporary relational databases. This dynamic adaptation optimizes the balance between retrieval flexibility and processing efficiency.
3Speed
If data is converted directly from the first format to the second format, then conversion speed is improved, but conversion accuracy and data integrity may be compromised
Solution Approach 1:
The patent employs an intermediary translation layer that facilitates accurate data format conversion. This mediator component understands both the source format (binary, YANG, JSON) and target format (relational database), enabling precise mapping of data structures, types, and relationships while maintaining data integrity throughout the conversion process.
Solution Approach 2:
The system replaces direct mechanical format conversion with an intelligent translation approach using command-line interface commands and structured query language processing. This substitution enables sophisticated data mapping, validation, and transformation logic that preserves data accuracy while maintaining efficient conversion through automated processing.
Data Source
AI summary
Various example embodiments for supporting data retrieval flexibility are presented. Various example embodiments for supporting data retrieval flexibility may include supporting data retrieval flexibility for retrieval of data from a device that does not maintain that data using a relational database data format by supporting operation of the device as a device that supports retrieval of that data in a relational database data format using a structured query language. Various example embodiments for supporting data retrieval flexibility may include supporting data retrieval flexibility for retrieval of data from a device by supporting retrieval of data maintained at the device based on a first data format based on generation of a database storing that data in a second data format different than the first data format and using a structured query language configured to support retrieval of the data in the second data format from the database.