Dynamic Schema Database Handling Binary Blob Data
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Solution Overview
Problem
Existing database systems are inefficient in handling data with dynamic schemas, as they are designed to work with static predefined data schemas, limiting flexibility and scalability in data processing and storage.
Innovation Solution
A computer system converts feed data with fixed and varying lengths into binary blob data, allocating it in fixed-size portions and using dynamic server statements to efficiently upload and process data, allowing for dynamic schema handling and efficient storage in a BLOB column, while maintaining data in a binary format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static predefined data schemas are used in database systems, then data storage structure is simple and stable, but flexibility and scalability in handling varying data formats are limited
Solution Approach 1:
The patent implements dynamic schemas that can adapt to varying data formats at runtime. The system uses dynamic SQL statements and metadata-driven approaches to handle different data structures without requiring predefined static schemas, enabling the database to flexibly accommodate changing data formats while maintaining operational simplicity through automated schema generation and adaptation.
2Productivity
If data is stored in normalized relational structures with static schemas, then data integrity is maintained, but processing efficiency for dynamic data formats deteriorates
Solution Approach 1:
The system dynamically changes data storage parameters based on the actual data format being processed. It uses dynamic schema generation that adjusts table structures, column types, and relationships according to the incoming data characteristics, allowing efficient processing of varied data formats while maintaining integrity through consistent application of the generated schema rules.
Solution Approach 2:
The database system automatically generates and applies appropriate schemas based on metadata analysis of the incoming data. This self-service approach eliminates the need for manual schema definition and adjustment, enabling the system to autonomously adapt to different data formats while maintaining data integrity through automated validation and constraint application.
3Adaptability or versatility
If multiple static schemas are maintained for different data types, then data organization is clear and manageable, but system complexity and maintenance burden increase
Solution Approach 1:
The patent implements a universal dynamic schema system that can handle multiple data types through a single flexible framework. Instead of maintaining separate static schemas for different data types, the system uses a unified approach with dynamic schema generation that adapts to various data formats, reducing the number of predefined schemas while maintaining the capability to organize and manage diverse data types effectively.
Data Source
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AI summary
A computer system accesses rows of feed data, and converts the received feed data into portions of binary blob data. The computer system also sends the binary blob data to a database server which is configured to access metadata associated with a feed including a dynamic server statement to determine how to convert the binary blob data to a server table with a blob column configured to store the rows of feed data. The database server accesses feed data belonging to a particular feed and executes a dynamic server statement to create a relational dataset in an in-memory table of the server. A second dynamic statement applies data processing conditions indicated in the metadata. When feed data rows match conditions, the computer system places feed data row information into an alert table that includes references to the blob table with blob data, thereby triggering an alert.