Research And Implementation Of Text-to-SQL Application For Hybrid Storage System

Accompanied by the development and popularity of computers,a large amount of data resources have been accumulated in various industries.With the increasing volume and types of data,a single type of database cannot meet the increasingly diverse storage needs,and more and more enterprises are deploying hybrid storage systems to meet the diverse data storage needs.However,the SQL syntax of different kinds of databases is different,which creates a huge obstacle for data analysis,and the nested relationships between database tables are getting more and more complicated due to the complexity of data,and it is a great challenge to write SQL statements that run correctly.The emergence of Text-to-SQL technology has made it possible to use deep learning models to convert natural language into SQL query statements that can automatically query databases.However,the following problems exist in applying Text-to-SQL technology to hybrid storage systems:(1)When oriented to hybrid storage systems,the natural language problem involves data that may be stored in multiple databases,but the heterogeneity of data patterns and the differences in query languages among databases pose great difficulties for Text-to-SQL.The unified query technology for hybrid storage systems becomes an urgent problem to be solved.(2)Existing Text-to-SQL models often use graph neural networks to model data schema information,which are weak in expressing the rich semantic information in data schema when performing multi-table queries,and have the problem of over-smoothing when using multi-layer graph neural networks.In response to the above-mentioned problems,the following studies and implementations are carried out in this paper:(1)A unified query algorithm for hybrid storage systems is proposed to alleviate the SQL"dialect" problem between different databases.By constructing a unified data knowledge graph,and based on the unified data view,a new federated query process is designed to obtain the query results of different databases and finally obtain the target query data,thus realizing the unified query for hybrid storage systems and shielding the SQL "dialect" problem between different databases.(2)A Text-to-SQL generation model based on graph neural networks is proposed,which mainly addresses the problem of weakness in modeling multi-hop paths in data pattern graphs in those models that use graph neural networks in Text-to-SQL tasks.By introducing the multihop graph attention neural network to model the multi-hop node information in the data pattern graph,it not only expands the "field of perception" of the graph attention neural network,but also alleviates the over-smoothing problem caused by using multi-layer graph neural networks to a certain extent.At the same time,based on the above unified query algorithm,it is possible to use natural language to query the data in the hybrid storage easily and quickly.(3)A prototype question-and-answer system for hybrid storage architecture is implemented,which can receive questions from users in natural language,generate corresponding SQL answers based on the above study,and query data from the hybrid storage system back to the users by the unified query algorithm.This article explores the application process of Text-to-SQL in hybrid storage systems through the above research and application.This can effectively reduce the difficulty of data processing and significantly improve data processing capabilities.