JSON Persistence Service Using Object-Relational Mapping
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Solution Overview
Problem
Existing data object persistence methods, such as storing JSON objects in relational databases as BLOBs or TEXT, face challenges in querying specific attributes efficiently due to the lack of standardized indexing, leading to increased processing and memory resource usage when reloading data objects for querying purposes.
Innovation Solution
Implementing a data object persistence service that uses an object-relational mapping (ORM) framework to map data object attributes to corresponding database attributes, allowing for efficient storage, retrieval, and querying of property values within a database, reducing the need for manual API customization and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If JSON objects are stored in persistent storage as plain text files or formatted object files, then data can be stored for future access, but querying elements of the data object requires reloading the entire object into memory, consuming considerable processing and memory resources
Solution Approach 1:
The patent segments the JSON object into individual attributes and stores them as separate columns in a relational database table. Instead of storing the entire JSON object as a single unit (BLOB/TEXT), each attribute is extracted and stored independently, allowing selective querying without reloading the complete object.
Solution Approach 2:
The patent introduces a relational database as an intermediary between the JSON data and the querying application. The database serves as a mediator that stores decomposed attributes and provides efficient query capabilities, eliminating the need to reload entire JSON objects into application memory for querying purposes.
2Reliability
If JSON objects are stored as BLOB or TEXT in relational databases, then data can be stored in persistent storage, but it becomes difficult to query against specific attributes due to lack of standardized indexing
Solution Approach 1:
The patent segments the JSON object structure into individual database columns, where each attribute of the JSON object becomes a separate table column. This segmentation enables standard database indexing and querying operations on individual attributes without treating the entire object as an unqueryable BLOB.
Solution Approach 2:
The patent changes the storage parameter from storing JSON objects as monolithic BLOB/TEXT fields to storing them as decomposed relational table columns with standardized data types. This parameter change enables the database to recognize and index individual attributes, making them queryable through standard SQL operations.
3Productivity
If data objects are stored in relational databases with standardized columns, then querying is efficient, but the system requires complex object-relational mapping infrastructure to handle different data formats
Solution Approach 1:
The patent creates a universal JSON data model that can represent various data formats and structures through a standardized set of attributes. This universal model serves multiple purposes: storing different types of data, enabling efficient querying, and providing a consistent interface for object-relational mapping across diverse data objects.
Solution Approach 2:
The patent uses a standardized JSON data model template that can be copied and instantiated for different data objects. Instead of creating custom mapping infrastructure for each data type, the same universal JSON model structure is reused, simplifying the mapping infrastructure while maintaining query performance.
Data Source
AI summary
Technologies for storing and accessing data from persisted data objects of an object-oriented system are provided. The disclosed techniques include a system that receives, a first request to perform an operation on data of data objects, where the data objects are capable of being persisted in a database. The system generates a second request by translating the first request from a first programming language to a second programming language. The system uses an object relational mapping data structure, which maps data object attributes to database properties, to map the second request parameters to corresponding database operation parameters. The system generates one or more database requests based on the corresponding database operation parameters and causes the one or more database requests to be executed on the database. The disclosed techniques describe creating and running a RESTful microservice to persist JSON objects in a relational database. The disclosed microservice may be deployed using a Docker container for cloud or on-premises implementations.