Schema-Aware Data Request Conversion for Database Migration
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Solution Overview
Problem
Migrating data between different database schemas, such as from relational to non-relational, is time-consuming and tedious, and existing large language models (LLMs) do not efficiently convert data operations due to differences in data storage and structure, often requiring manual adjustments.
Innovation Solution
LLM-assisted techniques that preprocess data requests to account for schema differences, converting queries like SQL to MongoDB queries by reflecting how data is stored in the new schema, and post-process the results for efficient execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing LLMs are used to convert data operations between schemas, then conversion can be automated, but the conversion accuracy is poor due to differences in data storage and structure between schemas
Solution Approach 1:
The system performs preliminary actions by analyzing the target schema structure before conversion, generating schema-specific instructions that guide the LLM to account for differences in data storage and structure between source and target schemas, thereby improving conversion accuracy while maintaining automation
Solution Approach 2:
The system introduces an intermediary layer of schema analysis and instruction generation between the source data operations and the LLM conversion process. This intermediary component translates schema differences into actionable instructions that the LLM can follow, resolving the contradiction between automation and accuracy
2Manufacturing precision
If data operations are manually adjusted for schema conversion, then conversion accuracy improves, but the migration process becomes more time-consuming and tedious
Solution Approach 1:
The system enables self-service by automatically analyzing schema differences and generating appropriate conversion instructions without requiring manual intervention. The LLM processes the schema-specific instructions to perform accurate conversions autonomously, achieving both high accuracy and time efficiency
Solution Approach 2:
The system changes the parameters of the conversion process by introducing schema structure parameters and storage format parameters as inputs to the LLM. These parameter changes enable the system to automatically adapt to different schema types while maintaining high conversion accuracy without manual adjustment
3Ease of operation
If LLM assumes one-to-one mapping between source and target schema structures, then conversion is simpler, but it fails to account for actual schema differences and migration optimizations
Solution Approach 1:
The system applies local quality by generating specific instructions for each schema difference rather than applying a uniform one-to-one mapping assumption. The schema analysis identifies local structural differences and creates targeted conversion instructions that address each difference appropriately, ensuring reliable conversions while maintaining operational simplicity
Data Source
AI summary
Described herein are LLM-assisted techniques for data operation migration between a first schema and a second schema, which may take into account differences between a first dataset under the first schema and a second dataset under the second schema to which the first dataset has been or will be converted. In some embodiments, responsive to receiving a first data request targeting a subset of first data stored in a first database under a first schema, wherein second data is stored in a second database under a second schema and includes a migrated version of the first data, the first data request may be converted into a second data request targeting a subset of the second data that comprises a migrated version of the subset of the first data. Taking into account differences between the schemas may provide migrated data operations that are efficient to run on the destination schema.