Language specification and conversion method and system fused with multi-database dialects

Abstract

The invention provides a multi-database dialect fused language specification, a multi-database dialect fused language conversion method and a multi-database dialect fused language conversion system, and relates to the technical field of databases. A user inputs a unified SQL statement and selects the type of a target database, lexical analysis and grammatical analysis are performed on the unified SQL statement, an abstract syntax tree is constructed, semantic analysis is performed, dialect characteristics in the unified SQL statement are identified, the support condition of the dialect characteristics in the target database is verified, and a dialect conversion strategy is provided. And selecting a corresponding conversion rule in the unified SQL specification library, converting the grammar of the unified SQL statement into the dialect grammar of the target database, and generating the SQL statement. According to the method, one set of SQL and multi-database support is realized through unified SQL specification formulation, intelligent dialect adaptation and configuration driving, a complete technical solution is provided for cross-database application development, and technical challenges caused by database dialect differences are solved.

Description

Technical Field

[0001] This invention relates to the field of database technology, and in particular to a language standard, conversion method and system that integrates multiple database dialects. Background Technology

[0002] While mainstream relational databases (MySQL, Oracle, PostgreSQL, SQL Server, etc.) all adhere to the SQL standard, significant differences exist in their implementations. These differences primarily lie in syntax features, data types, and function implementations. For example, in pagination queries, MySQL uses the syntax `LIMIT offset,count` or `LIMIT countOFFSET offset`, Oracle uses `ROWNUM` or `ROW_NUMBER()OVER()`, PostgreSQL uses `LIMIT count OFFSET offset`, and SQL Server uses `OFFSET count ROWS FETCH NEXT count ROWS ONLY`. Regarding auto-incrementing primary keys, MySQL uses `AUTO_INCREMENT`, Oracle uses `SEQUENCE` or `IDENTITY`, PostgreSQL uses `SERIAL` or `IDENTITY`, and SQL Server uses `IDENTITY`. Regarding function syntax, string concatenation uses the `CONCAT()` function in MySQL, the `||` operator in Oracle, and the `+` operator in SQL Server; date functions also differ in function names and parameters across databases; and JSON manipulation functions also exhibit significant differences in syntax and functionality across different databases.

[0003] In today's software development environment, enterprises often need to use multiple different types of databases simultaneously, such as MySQL, Oracle, PostgreSQL, and SQL Server. While these databases all adhere to the SQL standard, significant dialect differences exist in their implementations. These differences present developers with substantial technical challenges when writing cross-database applications. They need to master the specific syntax of multiple databases, write extensive adaptation code, and are prone to compatibility issues. When migrating applications between different databases, it often requires rewriting a large number of SQL statements, increasing development and maintenance complexity. Furthermore, these differences increase learning costs, requiring developers to invest significant time learning the dialect characteristics of each database, thus reducing development efficiency.

[0004] Based on the above issues, current technologies for solving cross-database adaptation problems mainly adopt two approaches. One is the ORM framework-based adaptation solution. ORM framework solutions achieve cross-database support by configuring data sources and dialect adapters. These solutions work through mechanisms such as data source configuration, dialect adapters, object mapping, and result mapping, and feature support for basic CRUD operations, partial dialect adaptation capabilities, transaction management, and caching mechanisms. However, these solutions suffer from incomplete dialect adaptation, insufficient case sensitivity handling, and high learning costs. Developers still need to manually write native SQL to handle complex queries or database-specific functions, increasing development complexity and reducing code portability. The other approach is the natural language to SQL (Natural Language to SQL) solution. This lowers the SQL learning threshold by converting natural language descriptions into the target database's SQL dialect. However, this technology suffers from inaccurate semantic understanding, limited dialect adaptation capabilities, and a lack of syntax validation. Natural language is ambiguous and context-dependent; when handling complex query requirements, it often fails to accurately understand the user's true intent, resulting in generated SQL statements that do not match user expectations, reducing system reliability and user experience.

[0005] Existing cross-database adaptation methods still have many problems, including: (1) Insufficient case sensitivity handling. Different databases have significantly different rules for handling the case of identifiers, which is the root cause of the insufficient case sensitivity handling. For example, PostgreSQL and SQL Server are case-sensitive databases, requiring the use of double quotes to enclose identifiers to maintain case sensitivity; while MySQL is case-insensitive in the Windows environment and will automatically convert identifiers to lowercase; Oracle converts identifiers to uppercase by default. Existing frameworks lack intelligent recognition capabilities and cannot process them according to the case sensitivity requirements of the target database, which may cause the generated SQL statements to report errors due to case sensitivity issues. This problem is particularly prominent in cross-database applications, where developers need to manually handle case differences, increasing debugging time and reducing development efficiency. When applications migrate between different databases, case sensitivity issues often become one of the main causes of errors. (2) High learning cost. Developers need to understand the syntax differences and characteristics of different databases, which is the root cause of the high learning cost. Due to the large dialect differences among databases, developers need to master the specific syntax of multiple databases such as MySQL, Oracle, PostgreSQL, and SQL Server, and learn the SQL dialects of each database. This learning cost is not only reflected in the time investment, but also in the continuity of knowledge updates, because each database is constantly developing and updating, and new syntax features are constantly emerging. When developers need to handle complex queries or database-specific functions, they often need to consult a large number of documents and examples, which further increases the learning cost. This high learning cost leads to an increased development threshold, restricts the development of cross-database applications, and also increases the training costs for enterprises. (3) Performance overhead problem. The ORM framework adds an abstraction layer between the application and the database, which is the root cause of the performance overhead problem. Although this additional abstraction layer provides convenience, it inevitably brings performance loss. In complex query scenarios, the SQL generated by ORM may not be as optimized as the SQL written by hand, resulting in reduced query efficiency. Especially when dealing with large amounts of data or complex relational queries, the automatic optimization strategy of ORM may not be able to achieve the effect of manual optimization, causing unnecessary performance overhead. This performance problem is particularly obvious in high-concurrency, large-data-volume application scenarios, becoming a bottleneck restricting system performance. (4) Steep learning curve problem. Using the ORM framework requires learning new concepts and APIs, which is the root cause of the steep learning curve problem. For beginners, the abstract concepts and configuration complexity of ORMs increase the learning curve. The configuration of some ORM frameworks can be quite complex, requiring developers to invest significant time to master their usage and best practices. This learning cost is not only present in the initial learning phase but also in the ongoing need for knowledge updates, as ORM frameworks themselves are constantly evolving and changing.When new members join the team, additional training time is required to familiarize them with the use of ORM, increasing the team's management costs. (5) "Missing abstraction" problem. ORM frameworks cannot completely hide the complexity of SQL, which is the root cause of the "missing abstraction" problem. In some complex query scenarios, developers still need to write native SQL or understand the underlying SQL knowledge. Some advanced functions specific to certain databases may not be directly usable through ORM, forcing developers to bypass the ORM's abstraction layer and operate the database directly. This "missing abstraction" problem greatly diminishes the advantages of ORM, and developers need to make a trade-off between the convenience of ORM and the flexibility of native SQL. When encountering complex scenarios that ORM cannot handle, developers often need to use a mixture of ORM and native SQL, increasing the complexity of the code and the difficulty of maintenance. (6) Overuse risk problem. The convenience of ORM frameworks can easily lead to overuse, which is the main cause of performance problems. The N+1 query problem is a typical pitfall in ORM use, which can easily lead to performance problems if not handled carefully. ORM may load unnecessary data from the database when used incorrectly, causing waste of network transmission and memory usage. This overuse problem is particularly serious in large applications, which may lead to a decline in system performance and a worse user experience. Developers need to have a deep understanding of how ORM works to avoid these problems, which further increases the barrier to entry. (7) Debugging difficulties. The dynamic SQL generation mechanism of the ORM framework makes debugging difficult, which is an important factor affecting development efficiency. Since the SQL is dynamically generated, it is difficult to debug complex queries, and developers cannot directly view and optimize the generated SQL statements. ORM error messages are sometimes not as clear as direct SQL error messages, which increases the difficulty of locating problems. When performance problems or logical errors occur, developers need to spend more time analyzing and debugging, which reduces development efficiency. This debugging difficulty is particularly prominent in complex business scenarios and has become an important factor affecting the development experience. (8) Inaccurate semantic understanding. Natural language has ambiguity and context dependence, which is the root cause of inaccurate semantic understanding. When dealing with complex query requirements, natural language to SQL technology often cannot accurately understand the user's true intention because natural language has diverse ways of expression. The same requirement may have multiple ways of expression, and different ways of expression may lead to different understanding results. In addition, natural language lacks strict grammatical rules and is prone to ambiguity, especially when dealing with complex business logic, the system may not be able to accurately understand the user's true needs. This inaccurate semantic understanding leads to generated SQL statements that do not meet user expectations, reducing system reliability and user experience. When problems arise in the generated SQL statements, users struggle to understand the root cause and are unable to effectively debug and modify them.

[0006] Therefore, how to provide a cross-database language adaptation method to solve the complexity of cross-database adaptation has become an urgent problem to be solved. Summary of the Invention

[0007] To address the aforementioned issues, this invention provides a language standard, conversion method, and system that integrates multiple database dialects. The aim is to establish a unified SQL language standard, integrating the advantages of various database dialects, to provide developers with a unified SQL language standard, and to automatically convert it into the dialect syntax of each target database, fundamentally solving the complexity problem of cross-database adaptation.

[0008] To achieve the above objectives, this invention provides a language standardization and conversion method that integrates dialects from multiple databases, including: integrating the dialect characteristics of each database, establishing a standardized SQL language standard and verification mechanism, and obtaining a unified SQL standard library;

[0009] Users input a unified SQL statement and select the type of target database; the unified SQL statement undergoes preliminary syntax checking and standardization verification.

[0010] Lexical and syntactic analysis are performed on the unified SQL statement to construct an abstract syntax tree containing dialect feature tags. Then, semantic verification and error detection are performed on the nodes of the abstract syntax tree to identify and tag case sensitivity differences.

[0011] Semantic analysis is performed on the abstract syntax tree to identify dialect features in the unified SQL statement, and the support of the dialect features in the target database is verified. The semantic correctness and logical consistency of the unified SQL statement are analyzed, and a dialect conversion strategy is provided. The compatibility of the SQL statement is verified according to the target database type, and features not supported by the target database are identified and corresponding error messages and alternative solutions are generated.

[0012] Based on the target database type, select the corresponding conversion rule from the unified SQL specification library, convert the syntax of the unified SQL statement into the dialect syntax of the target database, handle dialect feature differences according to the dialect conversion strategy, error prompts and alternative solutions, and generate an SQL statement that conforms to the syntax specification of the target database.

[0013] As a further improvement to this invention, a standardized SQL language specification is established, including:

[0014] Obtain the differences between various versions of the SQL international standard;

[0015] Analyze the dialect differences among various databases and abstract the grammatical features of each database into a universal pattern;

[0016] Specify unified syntax design principles, including the principles of openness, standardization, and extensibility;

[0017] Design configuration mapping templates, including database dialect configuration templates and conversion rule configuration templates;

[0018] Design a plug-in architecture, including abstract interfaces for database adapter interface syntax features;

[0019] Establish an extension development framework to provide standardized extension interfaces.

[0020] As a further improvement of the present invention, the conversion rule configuration template defines conversion rules from a unified syntax to various database dialects, including pagination syntax conversion rules, function syntax conversion rules, data type conversion rules, dialect feature conversion rules, case handling rules, and auto-incrementing primary key conversion rules.

[0021] As a further improvement of the present invention, the abstract definition of syntactic features defines an abstract representation of syntactic features, including feature names, unified syntactic expressions, conversion methods, and support for verification.

[0022] As a further improvement of the present invention, the database dialect configuration template defines the dialect characteristics and supported functions of each database, including database type, version, supported characteristics, data type mapping, and function mapping information.

[0023] As a further improvement of the present invention

[0024] The ANTLR4 parser is used to automatically generate a lexical analyzer and a parser based on the syntax rules of the unified SQL statement.

[0025] The lexical analyzer decomposes the unified SQL statement into lexical units and identifies syntactic elements, including keywords, identifiers, operators, and literals.

[0026] The parser constructs an abstract syntax tree based on the syntax rules of the unified SQL statement, organizes lexical units into a tree structure to represent the syntax structure of the unified SQL statement, and obtains an abstract syntax tree containing dialect tags. The abstract syntax tree also contains context information and case sensitivity tags.

[0027] As a further improvement of the present invention, semantic analysis is performed on the abstract syntax tree, including:

[0028] Identify the dialect characteristics of the abstract syntax tree, and analyze the pagination syntax characteristics, function syntax characteristics, data type characteristics, and special syntax characteristics used in the unified SQL statement;

[0029] Check the support status of the dialect features in the abstract syntax tree in the target database, verify syntax compatibility, and identify unsupported features;

[0030] The abstract syntax tree is subjected to semantic correctness analysis, logical consistency check, data type compatibility analysis, and case sensitivity analysis.

[0031] Generate conversion strategies, provide conversion suggestions, generate error messages, and recommend alternative solutions.

[0032] As a further improvement of the present invention, the corresponding conversion rules are selected from the unified SQL specification library according to the target database type, including pagination syntax conversion rules, function syntax conversion rules, data type conversion rules, dialect feature conversion rules, case handling rules, and auto-incrementing primary key conversion rules;

[0033] The unified SQL syntax is converted into the dialect syntax of the target database using the application conversion rules, dialect differences are handled, and target SQL statements that conform to the syntax rules of the target database and have consistent semantics are generated.

[0034] Output the target SQL statement after the change.

[0035] This invention also provides a language standardization and conversion system that integrates dialects from multiple databases, including: a user interaction module, a core engine, a configuration management module, and a plugin management module;

[0036] The user interaction module is used for:

[0037] Provides a unified SQL statement input interface for users to input unified SQL statements that conform to the unified SQL specification;

[0038] Provides a target database selection interface for users to choose the type of target database;

[0039] Provides a conversion result display interface, showing the converted target SQL statement and related conversion information.

[0040] The core engine is used for:

[0041] It provides the core processing flow of the system, including the formulation of a unified SQL standard, the reception, parsing, analysis, and transformation of SQL statements;

[0042] The configuration management module is used for:

[0043] Responsible for system configuration management, including configuration loading, verification, and hot update functions;

[0044] The plugin management module is used for:

[0045] Responsible for system plugin management, including plugin loading, adapter management, and extension interface functionality.

[0046] As a further improvement of the present invention, the configuration management module loads and configures the configuration mapping template library, and obtains dialect configuration rules and conversion rules;

[0047] The configuration mapping template library includes dialect configuration templates and conversion rule configuration templates.

[0048] The dialect configuration template defines the dialect characteristics and supported functions of each database, thus obtaining the dialect configuration rules;

[0049] The conversion rule configuration template defines the conversion rules from a unified syntax to various database dialects.

[0050] Store the dialect configuration rules and conversion rules for each database.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] This invention, based on international standards such as SQL 2016, integrates the characteristics of various database dialects to formulate a unified SQL specification. This design redefines a unified SQL specification that integrates the advantages of various databases, providing a standardized technical foundation for the entire system. The unified SQL specification, based on international standards such as SQL 2016, ensures compatibility with the existing SQL ecosystem while incorporating the dialect characteristics of various databases, providing users with a unified interface. It avoids the problem of incomplete dialect adaptation in existing technologies. Through the unified specification design, the system can handle all major database dialect differences, including pagination syntax, function syntax, data types, and case sensitivity. The process of formulating the unified SQL specification includes steps such as standard research and analysis, formulation of unified syntax design principles, configuration mapping template design, plug-in architecture design, extension development framework, and standard documentation, ensuring the completeness and scalability of the specification.

[0053] This invention employs a five-stage processing flow: specification formulation → receiving → parsing → analysis → conversion. The unified SQL specification formulation stage provides a standardized technical foundation for the entire system, ensuring the integrity and reliability from specification formulation to final conversion. The five-stage processing flow includes unified SQL specification formulation, receiving unified SQL statements, parsing into an abstract syntax tree, performing semantic analysis, and converting to SQL statements adapted to the target database. Each stage has a clearly defined function and responsibility. Through the semantic analysis stage, the system can accurately identify dialect characteristics in the SQL statement, verify the support of dialect characteristics in the target database, and provide accurate guidance for dialect conversion, ensuring the accuracy and reliability of the conversion and avoiding the problem of inaccurate semantic understanding in existing technologies.

[0054] This invention utilizes intelligent semantic analysis and conversion mechanisms to automatically handle dialect differences between databases. It supports not only basic syntax conversion but also the unique grammatical features of each database. Through intelligent semantic analysis, the system accurately identifies dialect features used in SQL statements, verifies their support in the target database, and provides conversion strategies and optimization suggestions. The intelligent conversion mechanism can handle dialect differences across databases, including pagination syntax, function syntax, data types, and case sensitivity, generating SQL statements that conform to the target database's syntax specifications. When unsupported features are detected in the target database, the system generates detailed error messages and modification suggestions, providing automatic conversion to transform incompatible syntax into equivalent syntax supported by the target database. This solves the problem of incomplete dialect adaptation in existing technologies and provides comprehensive cross-database compatibility support.

[0055] This invention supports infinitely expandable database types through configuration mapping templates and a plug-in architecture, achieving true openness and scalability. It supports access to any database, defining database dialect characteristics and conversion rules through configuration mapping templates, and allowing for the definition of dialect characteristics and conversion rules for any database via configuration files. The plug-in architecture design enables the system to dynamically load adapters for any database, adding new database support without modifying the core code. Standardized extension interfaces provide third-party developers with standardized extension methods, supporting community contributions and a plugin marketplace, avoiding the poor scalability issues of existing technologies. Through configuration-driven and plug-in architecture, the system possesses unlimited scalability, supporting not only existing mainstream databases but also easily extending to any new database type.

[0056] This invention achieves the goal of "one SQL, multiple database support" through unified SQL specification, intelligent dialect adaptation, and configuration-driven scalability, providing a complete technical solution for cross-database application development and fundamentally solving the technical challenges caused by database dialect differences. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of a language standardization and conversion method and system that integrates dialects from multiple databases, as disclosed in one embodiment of the present invention.

[0058] Figure 2 This is a flowchart of the unified SQL specification development process disclosed in one embodiment of the present invention;

[0059] Figure 3 This is a configuration mapping template structure diagram disclosed in one embodiment of the present invention;

[0060] Figure 4 This is a flowchart of an embodiment of the SQL parsing process disclosed in this invention;

[0061] Figure 5 This is a semantic analysis flowchart disclosed in one embodiment of the present invention;

[0062] Figure 6 This is a flowchart of an embodiment of the SQL conversion process disclosed in this invention;

[0063] Figure 7 This is a schematic diagram of a language standardization and conversion system that integrates multiple database dialects using a plug-in architecture, as disclosed in one embodiment of the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] The present invention will now be described in further detail with reference to the accompanying drawings:

[0066] like Figure 1 As shown, this invention provides a language standardization and conversion method that integrates dialects from multiple databases. It employs a five-stage processing flow to achieve unified access across databases. This design ensures the integrity and reliability from standardization to final conversion, including:

[0067] S1. Integrate the dialect characteristics of various databases, establish standardized SQL language specifications and verification mechanisms, and obtain a unified SQL specification library;

[0068] in,

[0069] The unified SQL specification is based on international standards such as SQL2016, integrates the dialect characteristics of various databases, and establishes a standardized SQL syntax specification and verification mechanism to provide a technical foundation for the entire system.

[0070] Furthermore, establish standardized SQL language specifications, including:

[0071] Obtain the differences between various versions of the SQL international standard;

[0072] Analyze the dialect differences among various databases and abstract the grammatical features of each database into a universal pattern;

[0073] Specify unified syntax design principles, including the principles of openness, standardization, and extensibility;

[0074] Design configuration mapping templates, including database dialect configuration templates and conversion rule configuration templates;

[0075] Design a plug-in architecture, including abstract interfaces for database adapter interface syntax features;

[0076] Establish an extension development framework to provide standardized extension interfaces.

[0077] Furthermore,

[0078] The conversion rule configuration template defines conversion rules from a unified syntax to various database dialects, including pagination syntax conversion rules, function syntax conversion rules, data type conversion rules, dialect feature conversion rules, case handling rules, and auto-incrementing primary key conversion rules.

[0079] Syntactic feature abstraction defines the abstract representation of syntactic features, including feature name, unified syntactic expression, conversion method, and support for validation.

[0080] The database dialect configuration template defines the dialect characteristics and supported functions of each database, including database type, version, supported features, data type mapping, and function mapping information.

[0081] Specifically,

[0082] like Figure 2 As shown, in the process of developing a unified SQL specification, this invention first conducts standard research and analysis, deeply studies international standard specifications such as SQL 2016, analyzes the dialect differences among various databases, and designs a scalable architecture. Through in-depth analysis of international standard specifications such as SQL 2016, SQL 2011, and SQL 2008, the evolution of SQL standards and the differences between various standard versions are understood, providing a theoretical basis for the unified syntax design. Simultaneously, the dialect differences among various databases are analyzed, and the syntactic characteristics of each database are abstracted into a universal pattern, providing a unified interface for different expressions of the same function in different databases. Then, unified syntax design principles are formulated, including the principles of openness, standardization, and scalability. The openness principle ensures that the unified syntax can support access to any database; the standardization principle ensures compatibility with international standards such as SQL 2016; and the scalability principle ensures that the system can adapt to future database technology developments.

[0083] Next, configuration mapping templates were designed, including database dialect configuration templates and conversion rule configuration templates. The database dialect configuration templates define the dialect characteristics and supported functions of each database, while the conversion rule configuration templates define the conversion rules from a unified syntax to each database dialect. These templates use standardized formats (such as JSON, YAML, XML, etc.) and support dynamic loading and hot updates. Finally, a plug-in architecture was designed, including database adapter interfaces and syntax feature abstract interfaces, and an extension development framework was established. The database adapter interface defines the standard interface for interacting with the database, the syntax feature abstract interface defines the abstract representation of syntax features, and the extension development framework provides standardized extension interfaces for third-party developers.

[0084] This invention abstracts database dialects through configuration mapping templates, including database dialect configuration, conversion rule configuration, syntactic feature abstraction, and database adapter interface. For example... Figure 3 As shown, the database dialect configuration defines the dialect characteristics and supported functions of each database, including database type, version, supported features, data type mapping, function mapping, and other information. The conversion rule configuration defines conversion rules from a unified syntax to each database dialect, including pagination syntax conversion rules, function syntax conversion rules, data type conversion rules, dialect feature conversion rules, case handling rules, and auto-incrementing primary key conversion rules. The syntax feature abstraction defines the abstract representation of syntax features, including feature name, unified syntax expression, conversion method, and supported validation. The database adapter interface defines the standard interface for interacting with the database, including methods for obtaining database information, converting SQL statements, validating SQL statements, and obtaining supported features. This design allows for the definition of dialect characteristics and conversion rules for any database through configuration files, achieving true openness and scalability.

[0085] S2. User interaction and SQL reception: The user inputs a unified SQL statement and selects the type of the target database. The unified SQL statement is initially checked for syntax and validated for standardization to ensure that the input statement conforms to the unified SQL standard.

[0086] S3. Parse into an abstract syntax tree: Perform lexical and syntactic analysis on the unified SQL statement, construct an abstract syntax tree containing dialect feature tags, and then perform semantic verification and error detection on the nodes of the abstract syntax tree to identify and mark case sensitivity differences.

[0087] in,

[0088] The ANTLR4 parser is used to perform lexical and syntactic analysis on SQL statements that conform to the Unified SQL Specification, construct an abstract syntax tree with dialect feature tags, perform semantic verification and error detection on syntax tree nodes, identify and tag case sensitivity differences, and provide structured data for semantic analysis.

[0089] Furthermore,

[0090] ANTLR4 is a powerful parser generation tool. The ANTLR4 parser can automatically generate lexical analyzers and parsers based on the syntax rules of unified SQL statements, and supports complex syntax rules and error handling.

[0091] In this invention, ANTLR4 is used to parse the syntax of a unified SQL statement. The lexical analyzer decomposes the unified SQL statement into lexical units and identifies grammatical elements such as keywords, identifiers, operators, and literals. The syntax analyzer constructs an abstract syntax tree based on the syntax rules of the unified SQL statement, organizes the lexical units into a tree structure to represent the grammatical structure of the unified SQL statement, and obtains an abstract syntax tree containing dialect tags. The abstract syntax tree also contains context information and case sensitivity tags.

[0092] Specifically, such as Figure 4 As shown, the ANTLR4 parser is used to perform lexical and syntactic analysis on SQL statements conforming to the Uniform SQL Specification. The process includes:

[0093] First, lexical analysis is performed to break down the SQL statement into lexical units (tokens) and identify syntactic elements such as keywords, identifiers, operators, and literals.

[0094] Then, grammatical analysis is performed, and an abstract syntax tree is constructed based on the grammatical rules. Lexical units are organized into a tree structure to represent the grammatical structure of the SQL statement. The generated abstract syntax tree contains rich semantic information such as contextual information, dialect feature tags, and case sensitivity tags, providing a complete data foundation for subsequent semantic analysis.

[0095] S4. Semantic Analysis: Perform semantic analysis on the abstract syntax tree, identify dialect features in the unified SQL statement, verify the support of dialect features in the target database, analyze the semantic correctness and logical consistency of the unified SQL statement, provide dialect conversion strategies, verify the compatibility of SQL statements according to the target database type, identify features not supported by the target database and generate corresponding error messages and alternative solutions.

[0096] in,

[0097] It also includes analyzing the semantic correctness and logical consistency of SQL statements, providing conversion strategies and optimization suggestions for dialect conversion, and handling differences in case sensitivity and data type compatibility issues;

[0098] Further semantic analysis includes:

[0099] Identify the dialect characteristics of the abstract syntax tree and analyze the pagination syntax characteristics, function syntax characteristics, data type characteristics, and special syntax characteristics used in the unified SQL statement;

[0100] Check the support of dialect features in the abstract syntax tree in the target database, verify syntax compatibility, and identify unsupported features;

[0101] Perform semantic correctness analysis, logical consistency check, data type compatibility analysis, and case sensitivity analysis on the abstract syntax tree;

[0102] Generate conversion strategies, provide conversion suggestions, generate error messages, and recommend alternative solutions.

[0103] Specifically, such as Figure 5 As shown, the semantic analysis process includes:

[0104] First, identify dialect characteristics and analyze the pagination syntax, function syntax, data type characteristics, and special syntax characteristics used in the SQL statement to provide basic information for subsequent conversion.

[0105] Then verify the target database support, check the support of dialect features in the target database, verify syntax compatibility, and identify unsupported features;

[0106] Next, semantic correctness is analyzed, including logical consistency checks, data type compatibility analysis, and case sensitivity analysis, to ensure the semantic correctness of the SQL statement.

[0107] Finally, a conversion strategy is generated, conversion suggestions are provided, error messages are generated, and alternative solutions are recommended, providing guidance for dialect conversion.

[0108] S5. Convert to SQL statements adapted to the target database: Select the appropriate conversion rules from the unified SQL standard library according to the target database type, convert the syntax of the unified SQL statement to the dialect syntax of the target database, handle dialect feature differences according to dialect conversion strategies, error messages and alternative solutions, including pagination syntax, function syntax, data types, etc., and generate SQL statements that conform to the syntax specifications of the target database.

[0109] in,

[0110] When a feature not supported by the target database is detected, detailed error messages and modification suggestions are generated. An automatic conversion function is provided to convert incompatible syntax into equivalent syntax supported by the target database. Users can choose whether to accept automatic conversion or make manual modifications.

[0111] Furthermore,

[0112] Select the appropriate conversion rules from the Unified SQL Standard Library based on the target database type, including pagination syntax conversion rules, function syntax conversion rules, data type conversion rules, dialect feature conversion rules, case handling rules, and auto-incrementing primary key conversion rules;

[0113] The application conversion rules convert the unified SQL syntax into the dialect syntax of the target database, handle dialect differences, and generate target SQL statements that conform to the syntax specifications of the target database and have consistent semantics.

[0114] Output the target SQL statement after the change.

[0115] Specifically, such as Figure 6 As shown, the conversion process includes:

[0116] First, select the conversion rules and load the corresponding conversion rules according to the target database type, including pagination syntax conversion rules, function syntax conversion rules, data type conversion rules, dialect feature conversion rules, case handling rules, auto-incrementing primary key conversion rules, etc.

[0117] Then, conversion rules are applied to convert the unified SQL syntax into the dialect syntax of the target database, handling various dialect differences;

[0118] Next, the target SQL is generated, ensuring that the generated SQL statements conform to the syntax rules of the target database and maintain semantic consistency.

[0119] like Figure 7 As shown, the present invention also provides a language standardization and conversion system that integrates multiple database dialects, adopting a plug-in architecture design, including: a user interaction module, a core engine, a configuration management module (configuration loader, configuration verifier, hot update manager) and a plug-in management module (plug-in loader, adapter manager, extension interface);

[0120] The user interaction module is used for:

[0121] Provides a unified SQL statement input interface for users to input unified SQL statements that conform to the unified SQL specification;

[0122] Provides a target database selection interface for users to choose the type of target database;

[0123] Provides a conversion result display interface, showing the converted target SQL statement and related conversion information.

[0124] The core engine is used for:

[0125] It provides the core processing flow of the system, including the formulation of a unified SQL standard, the reception, parsing, analysis, and transformation of SQL statements;

[0126] The configuration management module is used for:

[0127] Responsible for system configuration management, including configuration loading, verification, and hot update functions;

[0128] The plugin management module is used for:

[0129] Responsible for system plugin management, including plugin loading, adapter management, and extension interface functionality.

[0130] in,

[0131] The configuration management module loads and configures the configuration mapping template library, and obtains dialect configuration rules and conversion rules.

[0132] Configure a mapping template library, including dialect configuration templates and conversion rule configuration templates.

[0133] The dialect configuration template defines the dialect characteristics and supported functions of each database, resulting in dialect configuration rules;

[0134] The conversion rule configuration template defines conversion rules from a unified syntax to various database dialects.

[0135] Store the dialect configuration rules and conversion rules for each database.

[0136] Furthermore,

[0137] This invention supports dynamically loading adapters for any database, achieving scalability. Through a plug-in architecture, the system can easily add support for new databases without modifying the core code. This design gives the system high scalability and flexibility, enabling it to adapt to evolving database technologies.

[0138] Explanation of relevant terms in this invention:

[0139] Unified SQL Syntax: Designed based on international standards such as SQL 2016, this unified query language syntax integrates the characteristics of various database dialects. This syntax is not a simple syntax conversion, but a redesigned unified syntax that integrates the advantages of various databases, allowing users to utilize the features of all databases. During conversion, compatibility verification and error messages are provided based on the target database type.

[0140] Dialect-integrated grammar: An extended grammar that integrates the unique grammatical features of different databases (such as MySQL's LIMIT, Oracle's ROWNUM, and PostgreSQL's array types) into a unified grammar. Through in-depth analysis of the dialectal characteristics of various databases, a universal grammatical pattern is abstracted, providing a unified interface for different expressions of the same functionality across different databases.

[0141] Abstract Syntax Tree (AST): A tree-like structure formed after parsing an SQL statement, used to represent the syntactic structure of the SQL statement. In this invention, the AST not only contains basic syntactic structure information, but also rich semantic information such as dialect feature tags, case sensitivity tags, and contextual information, providing a complete data foundation for subsequent semantic analysis and transformation.

[0142] Semantic analysis: This involves analyzing the semantic correctness and logical consistency of SQL statements, identifying dialect characteristics and conversion requirements. This analysis includes not only basic syntax verification but also in-depth analysis across multiple dimensions, such as dialect characteristic identification, target database support verification, semantic correctness analysis, logical consistency checks, case sensitivity analysis, and data type compatibility verification.

[0143] Dialectal characteristics: These are unique syntactic features specific to each database, such as pagination syntax, function syntax, data types, and case sensitivity. The implementation of these features varies across different databases. This invention uses a unified abstraction and conversion mechanism to make these differences transparent to the user.

[0144] Case sensitivity: Databases have different rules for handling the case of identifiers (table names, field names, etc.). This invention adopts a case-sensitive design principle and uses an intelligent conversion mechanism to adapt to the case sensitivity requirements of different databases, ensuring full compatibility with case-sensitive databases while also adapting to case-insensitive databases.

[0145] Configuration mapping template: A configuration file used to define database dialect features and conversion rules, supporting scalable database types. This template uses a standardized format (such as JSON, YAML, XML, etc.) and includes information such as database dialect configuration, conversion rule configuration, syntax feature abstraction, and database adapter interface, supporting dynamic loading and hot updates.

[0146] Advantages of this invention:

[0147] This invention reduces the learning cost for users, as learning a unified SQL syntax is a prerequisite for this reduction. By designing a unified SQL syntax that incorporates the dialectal characteristics of various databases, this invention simplifies the learning process significantly, eliminating the need for users to master the specific grammatical differences between databases. The unified syntax is based on international standards such as SQL 2016, exhibiting a high degree of standardization. Users only need to master one set of syntax rules to develop in various database environments. This design avoids the complex process of learning multiple database dialects such as MySQL, Oracle, PostgreSQL, and SQL Server, significantly reducing the learning cost. Through an intelligent conversion mechanism, the system automatically handles the dialectal differences between databases, further reducing the learning difficulty as users do not need to concern themselves with the specific grammatical requirements of the target database. Therefore, this invention greatly reduces the learning cost and development complexity, while improving development efficiency.

[0148] This invention improves cross-database development efficiency. Ensuring the correctness of converted SQL statements through intelligent semantic analysis and conversion mechanisms is a prerequisite for improved development efficiency. This invention employs a five-stage processing flow, particularly the semantic analysis stage, which accurately identifies dialect characteristics in SQL statements and verifies their support in the target database, providing accurate guidance for dialect conversion. The intelligent conversion mechanism automatically handles dialect differences between databases, including pagination syntax, function syntax, data types, and case sensitivity, reducing the workload of manual adaptation for developers. The system provides detailed error messages and modification suggestions. When unsupported features are detected in the target database, detailed error messages and modification suggestions are generated to help developers quickly locate and resolve problems. The automatic conversion function converts incompatible syntax into equivalent syntax supported by the target database, further improving development efficiency. Therefore, this invention improves development efficiency and code quality while reducing development and maintenance workload.

[0149] This invention enhances the database compatibility of the development program, handling all major database dialect differences. Enhanced compatibility is a prerequisite for improved compatibility, including differences in pagination syntax, function syntax, data types, and case sensitivity. Through a unified SQL syntax design, this invention integrates the dialect characteristics of various databases, providing a unified interface for different expressions of the same functionality across different databases. Through compatibility verification and error message mechanisms, the system can promptly detect and resolve database compatibility issues, ensuring that the converted SQL statements can be correctly executed in the target database. The intelligent conversion mechanism can handle dialect differences across databases, including differences in pagination syntax, function syntax, data types, and case sensitivity, providing comprehensive cross-database compatibility support. The system supports an unlimited number of database types; through configuration mapping templates and a plug-in architecture, new database types can be easily supported, further enhancing compatibility. Therefore, this invention provides comprehensive cross-database compatibility support, solving the problem of incomplete dialect adaptation in existing technologies.

[0150] This invention improves system reliability by employing a case-sensitive design principle and utilizing an intelligent conversion mechanism to adapt to different databases, which is a prerequisite for enhanced system reliability. The case-sensitive design principle aligns with international standards such as SQL Server 2016, ensuring a high degree of system standardization. Through the intelligent conversion mechanism, the system can process data according to the case sensitivity requirements of the target database, ensuring full compatibility with case-sensitive databases while also adapting to case-insensitive databases. Multiple verification mechanisms, including syntax verification, semantic verification, and compatibility verification, ensure the correctness of the conversion results and reduce the possibility of runtime errors. Intelligent error detection and repair mechanisms can promptly identify and fix potential problems, improving system stability. Comprehensive compatibility testing ensures that the converted SQL statements can be correctly executed in the target database, further enhancing system reliability. Therefore, this invention provides comprehensive cross-database compatibility support, improving system reliability.

[0151] This invention's architecture achieves system scalability. Support for any database is a prerequisite for unlimited expansion, achieved through configuration mapping templates and a plug-in architecture. This invention defines database dialect characteristics and conversion rules through configuration mapping templates, supporting the definition of dialect characteristics and conversion rules for any database via configuration files, thus achieving true openness and scalability. The plug-in architecture design allows the system to dynamically load adapters for any database, adding new database support without modifying the core code. Standardized extension interfaces enable developers to easily develop new database adapters, further enhancing the system's scalability. Therefore, this invention achieves true openness and scalability, supports multiple databases, and provides a complete technical solution for cross-database application development.

[0152] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for language standardization and conversion integrating dialects from multiple databases, characterized in that, include: By integrating the dialect characteristics of various databases, a standardized SQL language specification and verification mechanism are established to obtain a unified SQL specification library; Users input a unified SQL statement and select the type of target database; the unified SQL statement undergoes preliminary syntax checking and standardization verification. Lexical and syntactic analysis are performed on the unified SQL statement to construct an abstract syntax tree containing dialect feature tags. Then, semantic verification and error detection are performed on the nodes of the abstract syntax tree to identify and tag case sensitivity differences. Semantic analysis is performed on the abstract syntax tree to identify dialect features in the unified SQL statement, and the support of the dialect features in the target database is verified. The semantic correctness and logical consistency of the unified SQL statement are analyzed, and a dialect conversion strategy is provided. The compatibility of the SQL statement is verified according to the target database type, and features not supported by the target database are identified and corresponding error messages and alternative solutions are generated. Based on the target database type, select the corresponding conversion rule from the unified SQL specification library, convert the syntax of the unified SQL statement into the dialect syntax of the target database, handle dialect feature differences according to the dialect conversion strategy, error prompts and alternative solutions, and generate an SQL statement that conforms to the syntax specification of the target database.

2. The language standardization and conversion method for integrating dialects from multiple databases according to claim 1, characterized in that, Establish standardized SQL language specifications, including: Obtain the differences between various versions of the SQL international standard; Analyze the dialect differences among various databases and abstract the grammatical features of each database into a universal pattern; Specify unified syntax design principles, including the principles of openness, standardization, and extensibility; Design configuration mapping templates, including database dialect configuration templates and conversion rule configuration templates; Design a plug-in architecture, including abstract interfaces for database adapter interface syntax features; Establish an extension development framework to provide standardized extension interfaces.

3. The language standardization and conversion method for integrating dialects from multiple databases according to claim 2, characterized in that: The conversion rule configuration template defines conversion rules from a unified syntax to various database dialects, including pagination syntax conversion rules, function syntax conversion rules, data type conversion rules, dialect feature conversion rules, case handling rules, and auto-incrementing primary key conversion rules.

4. The language standardization and conversion method for integrating dialects from multiple databases according to claim 2, characterized in that: Syntactic feature abstraction defines the abstract representation of syntactic features, including feature name, unified syntactic expression, conversion method, and support for validation.

5. The language standardization and conversion method for integrating dialects from multiple databases according to claim 2, characterized in that: The database dialect configuration template defines the dialect characteristics and supported functions of each database, including database type, version, supported features, data type mapping, and function mapping information.

6. The language standardization and conversion method for integrating dialects from multiple databases according to claim 1, characterized in that: The ANTLR4 parser is used to automatically generate a lexical analyzer and a parser based on the syntax rules of the unified SQL statement. The lexical analyzer decomposes the unified SQL statement into lexical units and identifies syntactic elements, including keywords, identifiers, operators, and literals. The parser constructs an abstract syntax tree based on the syntax rules of the unified SQL statement, organizes lexical units into a tree structure to represent the syntax structure of the unified SQL statement, and obtains an abstract syntax tree containing dialect tags. The abstract syntax tree also contains context information and case sensitivity tags.

7. The language standardization and conversion method for integrating dialects from multiple databases according to claim 1, characterized in that: Semantic analysis of the abstract syntax tree includes: Identify the dialect characteristics of the abstract syntax tree, and analyze the pagination syntax characteristics, function syntax characteristics, data type characteristics, and special syntax characteristics used in the unified SQL statement; Check the support status of the dialect features in the abstract syntax tree in the target database, verify syntax compatibility, and identify unsupported features; The abstract syntax tree is subjected to semantic correctness analysis, logical consistency check, data type compatibility analysis, and case sensitivity analysis. Generate conversion strategies, provide conversion suggestions, generate error messages, and recommend alternative solutions.

8. The language standardization and conversion method for integrating dialects from multiple databases according to claim 1, characterized in that: Select the appropriate conversion rules from the Unified SQL Standard Library based on the target database type, including pagination syntax conversion rules, function syntax conversion rules, data type conversion rules, dialect feature conversion rules, case handling rules, and auto-incrementing primary key conversion rules; The unified SQL syntax is converted into the dialect syntax of the target database using the application conversion rules, dialect differences are handled, and target SQL statements that conform to the syntax rules of the target database and have consistent semantics are generated. Output the target SQL statement after the change.

9. A language standardization and conversion system integrating dialects from multiple databases, used to implement the language standardization and conversion method integrating dialects from multiple databases as described in any one of claims 1 to 8, characterized in that, include: User interaction module, core engine, configuration management module, and plugin management module; The user interaction module is used for: Provides a unified SQL statement input interface for users to input unified SQL statements that conform to the unified SQL specification; Provides a target database selection interface for users to choose the type of target database; Provides a conversion result display interface, showing the converted target SQL statement and related conversion information. The core engine is used for: It provides the core processing flow of the system, including the formulation of a unified SQL standard, the reception, parsing, analysis, and transformation of SQL statements; The configuration management module is used for: Responsible for system configuration management, including configuration loading, verification, and hot update functions; The plugin management module is used for: Responsible for system plugin management, including plugin loading, adapter management, and extension interface functionality.

10. The language standardization and conversion system integrating multiple database dialects according to claim 9, characterized in that: The configuration management module loads and configures the configuration mapping template library, and obtains dialect configuration rules and conversion rules; The configuration mapping template library includes dialect configuration templates and conversion rule configuration templates. The dialect configuration template defines the dialect characteristics and supported functions of each database, thus obtaining the dialect configuration rules; The conversion rule configuration template defines the conversion rules from a unified syntax to various database dialects. Store the dialect configuration rules and conversion rules for each database.

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