Metadata-Driven RDBMS Migration with Dynamic Resource Allocation
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Solution Overview
Problem
Existing data migration tools face inefficiencies, lack data security, and require complex infrastructure for large-scale data transfer between different sources and targets, often necessitating custom connectors and lacking quality controls.
Innovation Solution
A framework for dynamically allocating system resources and utilizing metadata tables to facilitate parallel data transfer between RDBMS systems, including a Metadata Detail Table, Audit Table, and History Table, with preprocessing and post-processing modules for efficient data migration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data migration tools are used, then data can be transferred between RDBMS systems, but resource allocation is inefficient and transfer speed is slow
Solution Approach 1:
The patent segments the data migration process into multiple parallel threads that can simultaneously read from source tables, process data, and write to target tables. This segmentation enables concurrent execution of migration tasks, significantly improving throughput and reducing overall migration time compared to sequential processing.
Solution Approach 2:
The patent implements dynamic resource allocation where the system automatically adjusts the number of parallel threads and their resource allocation based on system load, available memory, and migration progress. This dynamic adaptation optimizes resource utilization during the migration process, preventing both resource exhaustion and idle capacity.
2Adaptability or versatility
If custom connectors are written for each platform, then data migration between specific platforms is enabled, but device complexity and development effort increase
Solution Approach 1:
The patent creates a universal data migration framework that can operate across multiple RDBMS platforms without requiring custom connectors for each platform. The system uses standardized metadata schemas and generic database interaction patterns that work across different RDBMS types, eliminating the need for platform-specific connector development while maintaining broad compatibility.
Solution Approach 2:
The patent introduces a metadata-driven intermediary layer that sits between the migration engine and the target database. This metadata layer abstracts platform-specific details, allowing the core migration logic to remain platform-agnostic while still supporting migrations to and from various RDBMS systems through standardized metadata schemas.
3Productivity
If data migration is performed without validation, then transfer speed is faster, but data quality deteriorates and corrupt data is transferred
Solution Approach 1:
The patent performs preliminary validation of source data before initiating the migration process. The system checks for data quality issues, constraints violations, and potential corruption in the source data upfront, allowing the migration to proceed only with valid data. This preliminary action prevents the transfer of corrupt data while maintaining high transfer speeds for valid records.
Solution Approach 2:
The patent implements feedback mechanisms during the migration process where the system continuously monitors data quality metrics and validates migrated records. When quality thresholds are met, the system maintains high transfer speeds; when issues are detected, the system automatically adjusts processing to revalidate or skip problematic records, ensuring data quality without permanently reducing throughput.
4Productivity
If parallel data reading is implemented, then resource utilization improves, but resource management complexity increases
Solution Approach 1:
The patent implements self-service resource management where the migration system automatically monitors system resources and dynamically adjusts parallel thread allocation without external intervention. The system self-regulates resource usage based on available memory, CPU load, and I/O capacity, eliminating the need for complex manual resource configuration while maintaining high parallelism and resource utilization.
Data Source
AI summary
A framework for dynamically allocating and utilizing resources of a computer system for transferring data between a source RDBMS and a target RDBMS is described, as is a method of dynamically optimize allocation and utilization of system resources to efficiently transfer data between a source RDBMS and a target RDBMS.