Database Transition Pattern Matching for Cloud Storage
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Solution Overview
Problem
The transition from legacy relational databases to cloud-based databases is inefficient due to the copying of database structures and content, which does not fully utilize cloud-based storage and results in prolonged processes with exponential growth of joining operations, leading to meaningless partitions and increased complexity.
Innovation Solution
The proposed solution involves parsing database instructions to identify patterns, suggesting replacements, and translating SQL-based instructions into JSON-based equivalents, incorporating domain-specific information to optimize database creation and maintenance, while preventing overlapping patterns that could render instructions inoperable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy database structures and content are copied to cloud-based databases, then database transition is achieved, but processing time increases exponentially due to massive joining operations
Solution Approach 1:
The patent segments the database transition process by identifying and separating meaningful data relationships from meaningless ones. It divides tables into meaningful and meaningless partitions, applying different processing strategies to each segment. This segmentation prevents exponential joining operations by avoiding unnecessary combinations of meaningless partitions while preserving essential data relationships.
Solution Approach 2:
The patent performs preliminary analysis of database schemas to identify meaningful relationships between tables before executing the transition. By pre-processing the schema to determine which partitions are meaningful and which are not, the system prepares optimization strategies in advance, avoiding unnecessary joining operations during the actual data migration process.
2Stability of the object's composition
If conventional database partitioning is copied to cloud-based environment, then structure preservation is achieved, but storage efficiency deteriorates due to meaningless partitions
Solution Approach 1:
The patent applies local quality by treating different partitions of the database differently based on their meaningfulness. Meaningful partitions retain their structure and data, while meaningless partitions are identified and eliminated or consolidated. This localized differentiation optimizes storage efficiency by removing redundant data while preserving necessary structural elements.
Solution Approach 2:
The patent changes the parameter of partition meaningfulness by analyzing and redefining which partitions should exist in the cloud-based environment. It transforms the database structure by modifying partition parameters, removing meaningless partitions and adjusting the organizational structure to match cloud-based storage optimization requirements.
3Productivity
If database transition process is accelerated, then productivity improves, but instruction complexity increases leading to inoperable instructions
Solution Approach 1:
The patent implements feedback mechanisms that analyze the results of database transition operations and adjust subsequent instructions accordingly. By monitoring the effectiveness of joining operations and partition handling, the system learns from outcomes and refines its approach, preventing the accumulation of complex or inoperable instructions while maintaining high transition speed.
Data Source
AI summary
A system, method, and computer-readable medium for proving feedback on database instructions, identifying, for example, existing patterns and providing suggested replacement instructions. This may have the effect of improving the efficiency of instructions used to create and/or manipulate databases. According to some aspects, these and other benefits may be achieved by parsing received instructions into an organizational structure, traversing the organizational structure for known patterns, and suggesting replacement patterns. In implementation, this may be effected by receiving one or more sets of known patterns and corresponding replacement patterns, parsing received instructions, comparing the known patterns with the parsed instructions, and providing suggested replacement patterns based on one or more known patterns matching the parsed instructions. A benefit of may include reducing Cartesian products during the merging of tables.