Federated Data Source Connector Automation
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Solution Overview
Problem
The development of federated data source connectors is hindered by user errors and the need to fully support diverse database systems that may not conform to formal SQL specifications, requiring significant time and resources to verify every possible query, especially when dealing with pseudo-SQL languages.
Innovation Solution
The implementation utilizes a directed graph-based automaton to create components for each connector, analyzing database query logs to assess and improve feature coverage, translating federated queries into native queries, and identifying unsupported queries to correct errors, thereby reducing development time and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual connector development is performed for each database system, then the connector can be customized for specific database types, but the development time and resources increase significantly
Solution Approach 1:
The system automatically generates connectors by having the database system itself provide metadata about its capabilities. The connector generator extracts information directly from the database system without requiring manual verification of every possible query, enabling self-service connector creation that reduces development time while maintaining adaptability
Solution Approach 2:
A universal connector generation framework is implemented that can work with multiple different database systems through a common interface. The system uses standardized metadata extraction and automaton generation that adapts to various database types, allowing one tool to serve multiple purposes across different database systems
2Reliability
If every possible ANSI-SQL query is verified manually, then complete functionality support is achieved, but the complexity and resources required for connector development increase
Solution Approach 1:
Manual verification processes are replaced with automated computational methods. The system uses automata theory and formal language processing to automatically verify query translation correctness, substituting manual mechanical verification with algorithmic automation that reduces complexity while maintaining reliability
Solution Approach 2:
The system implements automated feedback loops where query translations are systematically tested and verified. The connector generator receives feedback from automated query verification processes and iteratively improves connector accuracy, ensuring complete functionality support without manual intervention for every query
3Adaptability or versatility
If connectors are manually developed for database systems using pseudo-SQL language, then support for non-standard databases is achieved, but user errors may limit connector functionality
Solution Approach 1:
The database system provides self-service by automatically supplying metadata about its pseudo-SQL capabilities. The connector generator extracts capability information directly from the database system itself, reducing reliance on manual user input and minimizing errors while maintaining support for non-standard databases
Data Source
AI summary
A processing device translates a set of federated queries submitted to a federated data source server into a set of native data source queries for the particular type of data source using a federated data source connector for a particular type of data source, inputs the set of native data source queries for the particular type of data source into a component corresponding to the particular type of data source, marks the component in view of the set of native data source queries, and generates output indicating whether one or more of the set of native data source queries is not acceptable as input for the component. The marked component and the output indicate whether the federated data source connector for the particular type of data source includes an error.