JSON Adapter System for SQL Database Table Storage
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Solution Overview
Problem
Current SQL databases require multiple tables to manage diverse data types and operations, leading to significant programming overhead and inefficiencies in querying and maintaining JSON documents, especially when document layouts change.
Innovation Solution
A JSON adapter system that converts JSON documents into a single SQL table using a defined schema, allowing entire JSON documents to be stored and retrieved with a single SQL query, reducing the need for multiple tables and simplifying query processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multiple SQL tables are used to manage diverse data types and operations, then data organization and querying capability are improved, but programming complexity and maintenance overhead increase significantly
Solution Approach 1:
The patent merges multiple SQL tables into a single table structure that can accommodate diverse data types and operations. Instead of creating separate tables for different data types (JSON documents, key-value pairs, etc.), the invention uses one unified table with flexible column definitions that can store various data formats, thereby reducing the number of tables from potentially dozens to just one while maintaining full querying capability.
Solution Approach 2:
The single SQL table is designed to be universal and multi-functional, capable of storing and managing different data types (JSON documents, key-value pairs, arrays, objects) through a standardized schema. The table structure includes columns that can represent different data types and relationships, allowing the same table to perform multiple functions that previously required separate specialized tables.
2Adaptability or versatility
If multiple SQL tables are used to manage diverse data types, then data organization is improved, but the number of lines of code required increases exponentially
Solution Approach 1:
The patent combines the functionality of multiple tables into a single table structure, reducing the code required for table definitions, relationships, and queries. Instead of maintaining dozens of separate table schemas and the complex code to manage relationships between them, the invention uses one table with a flexible schema that can represent various data types, exponentially reducing the lines of code needed for data management.
Solution Approach 2:
The invention changes the parameters of the table schema to accommodate different data types dynamically. Rather than creating fixed schemas for each data type, the table structure uses variable column definitions and data types that can adapt to store JSON documents, key-value pairs, arrays, and nested objects, allowing versatile data type management without proportional increases in code complexity.
3Adaptability or versatility
If NoSQL databases are used to store JSON documents, then scalability and ease of expansion are improved, but query processing capability and data management functionality deteriorate
Solution Approach 1:
The patent introduces an intermediary layer between the NoSQL database and the application layer. This intermediary uses a single SQL table structure that can process and manage JSON documents and other data types with full query capability. The intermediary table acts as a mediator that preserves the scalability of NoSQL while adding the structured query processing capabilities of SQL, eliminating the need for custom query functions and extensive code rewrites.
4Adaptability or versatility
If document layouts change in NoSQL databases, then adaptability is improved, but application code stability deteriorates as query functions break
Solution Approach 1:
The invention uses parameter changes in the SQL table schema to accommodate document layout changes without breaking application code. The flexible column definitions and data types in the single table structure allow it to adapt to different JSON document formats and layouts dynamically. When document layouts change, the table schema can be adjusted through standard SQL operations rather than requiring custom query function rewrites, maintaining application code stability while preserving adaptability.
Data Source
AI summary
Embodiments for storing a JSON document tree in a single SQL database table by defining a schema to store JSON nodes in the table and converting each JSON document into a corresponding set of rows in the table using the schema to encode a hierarchical relationship of nodes and sub-nodes into a path that specifies a node path to a root node for each node. Embodiments further receiving a SQL query to retrieve a specific sub-document, responding to the query by returning all rows of the table belonging to the sub-document, and inflating the returned rows back into a JSON expression using the path to guide reconstruction of the original JSON document tree.