Native JSON Data Type Validation in RDBMS
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Solution Overview
Problem
Current database systems face inefficiencies in validating JSON objects against schemas and executing queries on JSON data, particularly in relational database management systems (RDBMS) that lack effective mechanisms for schema-based validation and efficient query execution.
Innovation Solution
Implementing techniques that enhance JSON schema validation and query execution by using native JSON data types, coincident validation, schema-informed rewrites, and augmented data types, along with caching metadata in volatile memory for quicker access, to improve performance and efficiency in JSON schema validation and query execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If JSON objects are stored in varchar or CLOB columns with SQL/JSON operators, then JSON text storage is supported, but validation efficiency and query performance deteriorate
Solution Approach 1:
The patent creates JSON schema constraints that define validation rules in advance before data is inserted or updated. The database system pre-compiles these schemas into an optimized format, performing validation preparation work beforehand rather than during data operations, thus improving validation efficiency without sacrificing reliability
Solution Approach 2:
The patent segments JSON validation into distinct phases: schema compilation, constraint creation, and actual validation execution. By separating schema processing from data validation and caching compiled schemas, the system reduces the computational overhead during validation operations, resolving the contradiction between thorough validation and efficiency
2Productivity
If native JSON data type is implemented, then query execution efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements a native JSON data type that serves multiple functions: it provides efficient storage, enables optimized query execution with JSON operators, and integrates with the existing relational database architecture. This multi-functional approach allows the system to gain query performance benefits without proportionally increasing complexity, as the JSON type leverages existing database infrastructure
Solution Approach 2:
The patent introduces JSON operators as intermediary tools that bridge the gap between native JSON data types and SQL query language. These operators provide a standardized interface for querying JSON data, allowing the database system to handle JSON complexity through a layer of abstraction while maintaining efficient native JSON processing underneath
3Manufacturing precision
If schema validation is performed thoroughly, then data quality is improved, but computational resources increase
Solution Approach 1:
The patent pre-compiles JSON schemas into an optimized internal format during constraint creation, performing the computationally intensive schema processing work beforehand. This preliminary compilation allows the actual validation operations to execute more efficiently with reduced computational resources, maintaining high data quality standards while lowering runtime resource consumption
Solution Approach 2:
The patent caches compiled JSON schemas in memory, allowing the database system to reuse pre-processed validation rules across multiple validation operations. This continuous caching approach eliminates redundant compilation work and reduces computational resource usage for subsequent validations, maintaining thorough data quality checks with optimized resource consumption
Data Source
AI summary
JSON schemas are implemented efficiently within a DBMS. Through these techniques, the power and benefit of schema-based paradigm are realized in a more cost-effective manner in terms of computer system performance. JSON schema-based techniques described herein improve execution efficiency of database statements that access JSON objects and improve software development productivity.