ML-Based Transpiler for Automated Query Code Migration
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Solution Overview
Problem
The process of transpiling query source code from one data processing system to another is often manual, error-prone, and resource-intensive, requiring specialized knowledge and understanding of both source and target query languages, leading to slow and costly migrations with a high risk of data retrieval issues and system malfunctions.
Innovation Solution
An automated system utilizing a trained machine learning data model architecture that decomposes source code into a syntax tree, identifies equivalent code pairs, and generates optimized output source code, coupled with telemetry metadata for continuous improvement, and retraining based on performance data and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual transpilation process is used, then accuracy and understanding of source code can be maintained, but productivity is reduced and loss of time increases
Solution Approach 1:
The patent introduces an Abstract Syntax Tree (AST) as an intermediary representation between source code and target code. The transpiler converts source code to AST, then transforms AST to target code, enabling automated transpilation while maintaining accuracy through structured intermediate representation.
Solution Approach 2:
The patent replaces manual mechanical transpilation processes with automated machine learning-based transpilation. The system uses trained ML models to automatically perform code transformation, eliminating the need for manual intervention while maintaining high accuracy through learned patterns from training data.
2Adaptability or versatility
If in-house transpiler is developed, then customization to specific requirements is improved, but device complexity and loss of time increase
Solution Approach 1:
The patent implements a dynamic transpiler architecture where the system can adapt to different source and target languages through configurable language specifications and trained ML models. The transpiler can be dynamically adjusted to handle various language pairs and customization requirements without requiring complete redevelopment.
Solution Approach 2:
The patent segments the transpiler into modular components: source code parsing, AST generation, transformation rules, and target code generation. This modular architecture allows selective customization of specific components while reusing others, reducing overall development complexity.
3Productivity
If transpilation process is automated, then productivity is improved, but manufacturing precision may worsen due to errors
Solution Approach 1:
The patent implements feedback mechanisms where the transpiler analyzes the correctness of generated target code and uses this information to refine transformation rules and ML models. The system continuously learns from transpilation results to improve accuracy while maintaining automated productivity.
Solution Approach 2:
The patent performs preliminary training of ML models using extensive training data before actual transpilation. The system pre-learns patterns and transformations from diverse code examples, enabling accurate automated transpilation without sacrificing precision in the actual transformation process.
Data Source
AI summary
The disclosed system and method automate the transpilation of query source code to a distributed data processing system. The process involves instantiating transpiler libraries and source code, executing the source code, and transpiling the source code into a transpiled code using the transpiler libraries. A machine learning algorithm is utilized to control the operation of the transpilation process wherever different transpilation options are available as an automated mechanism for adapting transpilation based on tracked downstream operational characteristics. The system is configured for automatic source code generation in the target language as an output, which can then be deployed to a distributed data processing or query system. Variants are described using a combination of test and production environments for initial testing and tuning of the machine learning model architecture until a performance threshold is met. A same code segment may be transpiled periodically for continual automatic optimization.