Method for realizing SQL grammar conversion of different databases based on middleware and middleware

Abstract

The invention discloses a method for realizing SQL (Structured Query Language) grammar conversion of different databases based on middleware and the middleware, belongs to the technical field of databases, and aims to solve the technical problem of how to realize SQL grammar conversion of different databases through the middleware. The following operations are executed through the middleware to realize the grammar conversion of the SQL between the source database and the target data constructing an abstract syntax tree AST by taking various SQL elements as nodes; operating nodes in the AST on the basis of requirements of a target database; based on the data type in the target database, performing data type mapping by applying a data type conversion rule, and generating an SQL statement adapted to the target database; performing security check on the generated SQL statement; caching the query result and the generated SQL statement based on a caching mechanism; and recording the conversion process and query processing of the SQL statement.

Description

Technical Field

[0001] The present invention relates to the technical field of databases, and more specifically, to a method for implementing SQL syntax conversion between different databases based on middleware and the middleware. Background Art

[0002] Whether in the Internet field or the information system field, it may be necessary to face the situation of database migration or supporting multiple different databases. The backgrounds and reasons for this situation are diverse. Common factors include: the existing database may not meet the performance requirements brought by business growth, new database solutions may offer lower costs, as technology develops, enterprises may choose to adopt more advanced database technologies to improve efficiency or implement new functions, certain industries need to migrate to domestic databases, or even the situation of using multiple databases simultaneously, etc.

[0003] Whether it is database migration or supporting multiple different databases, it will involve the problem of incompatible SQL syntax between different databases. To handle this problem, the traditional approach is to modify the application program, which will inevitably incur a large amount of modification costs.

[0004] Intercepting SQL requests through middleware technology and performing necessary conversions is an effective method for creating an abstraction layer between the application program and the database, enabling the application program to be independent of the specific implementation of the underlying database. This approach is particularly suitable for projects that need to support multiple different databases or migrate databases.

[0005] How to implement SQL syntax conversion between different databases through middleware is a technical problem that needs to be solved. Summary of the Invention

[0006] The technical task of the present invention is to address the above deficiencies and provide a method for implementing SQL syntax conversion between different databases based on middleware and the middleware, so as to solve the technical problem of how to implement SQL syntax conversion between different databases through middleware.

[0007] In a first aspect, a method for implementing SQL syntax conversion between different databases based on message middleware of the present invention configures a pluggable middleware and adjusts the parameters of the middleware through a configuration file or environment variables, and the middleware performs the following operations to implement the syntax conversion of SQL between a source database and a target database:

[0008] SQL Parsing: Parse the SQL statement of the source database through a custom SQL parser, identify various SQL elements in the SQL statement, and construct an abstract syntax tree AST with various SQL elements as nodes. The SQL parser is used to parse the input SQL statement based on lexical analysis and syntax parsing in the compilation principle method;

[0009] AST Optimization: Traverse the Abstract Syntax Tree (AST), access the node information and specific patterns of all nodes in the AST, operate on the nodes in the AST based on the requirements of the target database, and perform query optimization on the AST based on a custom query optimization strategy;

[0010] Target SQL Generation: Perform data type mapping based on the data types in the target database and apply data type conversion rules to generate SQL statements adapted to the target database;

[0011] Security Check and Parameterization: Perform security checks on the generated SQL statements to prevent SQL injection attacks, perform parameterized query processing on the SQL statements, send the generated SQL statements to the target database, and obtain the returned query results from the target database;

[0012] Cache Management: Cache the query results and the generated SQL statements based on the cache mechanism;

[0013] Error Handling and Logging: Record the conversion process of the SQL statements and the query processing to form a log record including the original SQL statement, the converted SQL statement, the execution time, and the status of the query processing result, and analyze the error information based on the log record.

[0014] Preferably, parse the SQL statements of the source database through a custom SQL parser, including the following steps:

[0015] Lexical Analysis: Split the strings in the SQL statement into tokens. Tokens include keywords, identifiers, operators, table names, field names, conditional expressions, and aggregate functions;

[0016] Syntax Analysis: Use the tokens as nodes and construct an Abstract Syntax Tree (AST) representing the SQL structure based on the grammar rules of the SQL statement by means of a rule-based natural language understanding method.

[0017] Preferably, AST optimization includes the following steps:

[0018] Perform a depth optimization search on the AST in a recursive manner. Start from the root node, access the child nodes until unable to go deeper, then backtrack to the previous level of nodes and continue to access until all nodes in the AST have been visited to collect node information or check specific patterns;

[0019] Operate on the nodes, including replacing nodes, modifying node content, inserting or deleting nodes, and updating the attribute values of nodes;

[0020] Optimize the query of the Abstract Syntax Tree (AST) based on a customized query optimization strategy, where the query optimization strategy includes merging consecutive selection conditions, eliminating redundant subqueries, and changing the join order.

[0021] Preferably, for the data types in different target databases, configure the application data type conversion rules corresponding to each target database, and manage the application order and conflict resolution strategy of multiple application data type conversion rules through a rule engine;

[0022] When performing data type mapping based on the data types in the target database and the application data type conversion rules, identify the patterns that need to be converted in the target database, and apply the corresponding application data type conversion rules.

[0023] Preferably, record the conversion process and query processing of SQL statements through Fluentd technology.

[0024] In a second aspect, the present invention relates to a middleware for implementing SQL syntax conversion between different databases. The middleware is a plug-in middleware that supports adjusting the parameters of the middleware through a configuration file or environment variables. The middleware includes an SQL parsing module, an AST optimization module, a target SQL generation module, a security check and parameterization module, a cache management module, and an error handling and logging module. The middleware can implement the syntax conversion of SQL between the source database and the target database through a method for implementing SQL syntax conversion between different databases based on middleware as described in any one of claims 1-5;

[0025] The SQL parsing module is used to perform the following operations: parse the SQL statements of the source database through a customized SQL parser, identify various SQL elements in the SQL statements, and construct an Abstract Syntax Tree (AST) with various SQL elements as nodes. The SQL parser is used to parse the input SQL statements based on lexical analysis and syntax parsing in the compilation principle method;

[0026] The AST optimization module is used to perform the following operations: traverse the Abstract Syntax Tree (AST), access the node information and specific patterns of all nodes in the Abstract Syntax Tree (AST), operate on the nodes in the Abstract Syntax Tree (AST) based on the requirements of the target database, and optimize the query of the Abstract Syntax Tree (AST) based on a customized query optimization strategy;

[0027] The target SQL generation module is used to perform the following operations: perform data type mapping based on the data types in the target database and the application data type conversion rules, and generate SQL statements adapted to the target database;

[0028] The security check and parameterization module is used to perform the following operations: perform a security check on the generated SQL statement to prevent SQL injection attacks, perform parameterized query processing on the SQL statement, send the generated SQL statement to the target database, and obtain the returned query results from the target database;

[0029] The cache management module is used to perform the following operations: cache the query results and the generated SQL statements based on the cache mechanism;

[0030] The error handling and logging module is used to perform the following operations: record the conversion process of the SQL statement and the query processing, form a log record including the original SQL statement, the converted SQL statement, the execution time, and the status of the query processing result, and analyze the error information based on the log record.

[0031] Preferably, when parsing the SQL statement of the source database through a custom SQL parser, the SQL parsing module is used to perform the following operations through the SQL parser:

[0032] Lexical analysis: Split the strings in the SQL statement into tokens, where tokens include keywords, identifiers, operators, table names, field names, conditional expressions, and aggregate functions;

[0033] Syntax analysis: Use the tokens as nodes and, based on the rule-based natural language understanding method and according to the grammar rules of the SQL statement, construct an abstract syntax tree (AST) representing the SQL structure from the token sequence.

[0034] Preferably, the AST optimization module is used to perform the following operations:

[0035] Perform a depth optimization search on the abstract syntax tree (AST) in a recursive manner, starting from the root node, visiting the child nodes until unable to go deeper, then backtracking to the previous level node to continue visiting until all nodes of the abstract syntax tree (AST) have been visited to collect node information or check specific patterns;

[0036] Operate on the nodes, including replacing nodes, modifying node content, inserting or deleting nodes, and updating the attribute values of nodes;

[0037] Perform query optimization on the abstract syntax tree (AST) based on a custom query optimization strategy, where the query optimization strategy includes merging consecutive selection conditions, eliminating redundant subqueries, and changing the join order.

[0038] Preferably, for the data types in different target databases, the target SQL generation module is used to configure the application data type conversion rules corresponding to each target database, and manage the application order and conflict resolution strategies of multiple application data type conversion rules through a rule engine;

[0039] When performing data type mapping based on the data types in the target database and the application data type conversion rules, the target SQL generation module is used to identify the patterns that need to be converted in the target database and apply the corresponding application data type conversion rules.

[0040] Preferably, the error handling and logging module is used to record the conversion process and query processing of SQL statements through Fluentd technology.

[0041] The method for implementing SQL syntax conversion between different databases based on middleware and the middleware of the present invention have the following advantages:

[0042] (1) The method for implementing SQL syntax conversion between different databases based on middleware technology creates an abstraction layer between the application program and the database, enabling the application program to be independent of the specific implementation of the underlying database;

[0043] (2) By adopting a dedicated SQL translation middleware, it has a powerful SQL statement conversion ability, and can not only handle the differences in SQL syntax, but also handle complex logics such as stored procedures and triggers;

[0044] (3) Higher flexibility, security and performance can be obtained by custom-developing middleware;

[0045] (4) By constructing an effective SQL conversion middleware, business developers can focus on the business and do not need to worry about the differences in database SQL syntax, simplifying the development of cross-database application programs;

[0046] (5) By constructing an effective SQL conversion middleware, the application program can flexibly switch between different database systems while maintaining the consistency and efficiency of the business logic;

[0047] (6) By constructing an effective SQL conversion middleware, independent deployment and maintenance can be carried out, providing the portability and maintainability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0049] The present invention will be further described below in conjunction with the accompanying drawings.

[0050] Figure 1 It is a flowchart of a method for implementing SQL syntax conversion between different databases based on a message middleware in Embodiment 1;

[0051] Figure 2 It is a flowchart of the working process of a middleware for implementing SQL syntax conversion between different databases in Embodiment 2. Specific implementation manners

[0052] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and be able to implement it. However, the embodiments cited are not intended to limit the present invention. Without conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0053] The embodiments of the present invention provide a method and middleware for implementing SQL syntax conversion between different databases based on a middleware, which are used to solve the technical problem of how to implement SQL syntax conversion between different databases through a middleware.

[0054] Embodiment 1:

[0055] A method for implementing SQL syntax conversion between different databases based on a message middleware of the present invention configures a plug-in middleware, and adjusts the parameters of the middleware through a configuration file or environment variables, and performs the following operations through the middleware to implement the syntax conversion of SQL between a source database and a target database. The method includes six steps: SQL parsing, AST optimization, target SQL generation, security check and parameterization, cache management, and error handling and logging.

[0056] Step S100, SQL parsing: Parse the SQL statement of the source database through a custom SQL parser, identify various SQL elements in the SQL statement, and construct an abstract syntax tree AST with various SQL elements as nodes. The SQL parser is used to parse the input SQL statement based on lexical analysis and syntax parsing in the compilation principle method.

[0057] In this embodiment, parsing the SQL statement of the source database through a custom SQL parser includes two steps: lexical parsing and syntax parsing.

[0058] Lexical analysis: Split the string in the SQL statement into tokens. The tokens include keywords, identifiers, operators, table names, field names, conditional expressions, and aggregate functions.

[0059] Syntax analysis: Using tokens as nodes, a rule-based natural language understanding method constructs an abstract syntax tree (AST) representing the SQL structure from the token sequence according to the grammar rules of the SQL statement.

[0060] In this embodiment, this step splits the SQL string into tokens, and through a rule-based natural language understanding method, i.e., context-free grammar (CFG), constructs a tree representing the SQL structure from the token sequence according to the grammar rules of SQL - namely, the abstract syntax tree (AST). Through the above technology, the SQL parser can recognize various SQL elements, such as table names, field names, conditional expressions, aggregate functions, etc.

[0061] Step S200, AST optimization: Traverse the abstract syntax tree (AST), access the node information and specific patterns of all nodes in the AST, operate on the nodes in the AST based on the requirements of the target database, and perform query optimization on the AST based on a custom query optimization strategy.

[0062] As a specific implementation of step S200, AST optimization includes the following steps:

[0063] (1) Perform a depth optimization search on the AST in a recursive manner. Starting from the root node, access the child nodes until no further depth can be reached, then backtrack to the previous level of nodes and continue accessing until all nodes in the AST have been visited to collect node information or check specific patterns;

[0064] (2) Operate on the nodes, including replacing nodes, modifying node content, inserting or deleting nodes, and updating the attribute values of nodes;

[0065] (3) Perform query optimization on the AST based on a custom query optimization strategy. The query optimization strategy includes merging consecutive selection conditions, eliminating redundant subqueries, and changing the join order.

[0066] After SQL parsing is completed, the SQL statement is converted into an AST. Each node represents a part of the SQL statement, such as a table name, column name, conditional expression, etc. The AST provides a clear representation of the SQL logical structure. Then operations can be performed on the AST. The main ways to operate on the AST include the following:

[0067] Traversal: Perform a depth optimization search (DFS) in a recursive manner. Starting from the root node, access the child nodes as deeply as possible until no further depth can be reached, then backtrack to the previous level of nodes and continue accessing until all nodes in the AST have been visited for collecting information or checking specific patterns.

[0068] Replace nodes: Replace old nodes with new ones, such as changing table names or field names.

[0069] Modify: Change the content of certain nodes, such as replacing function calls, adjusting data types, etc.

[0070] Insert / delete nodes: Add new child nodes or remove unnecessary parts to meet the requirements of the target database.

[0071] Attribute update: Adjust the attribute values of nodes, such as changing function parameters, the values of conditional expressions, etc.

[0072] Step S300, Target SQL generation: Based on the data types in the target database and applying data type conversion rules, perform data type mapping to generate SQL statements adapted to the target database.

[0073] In this embodiment, for the data types in different target databases, configure the application data type conversion rules corresponding to each target database, and manage the application order and conflict resolution strategies of multiple application data type conversion rules through a rule engine. When performing data type mapping based on the data types in the target database and the application data type conversion rules, identify the patterns that need to be converted in the target database and apply the corresponding application data type conversion rules.

[0074] To achieve the conversion from one database to another, this step defines a series of conversion rules that can specify how to modify the AST according to different SQL elements (such as SELECT statements, JOIN conditions, aggregate functions, etc.). This method implements the application of rules through the following two techniques:

[0075] (1) Pattern matching: Identify the specific patterns that need to be converted and apply the corresponding rules.

[0076] (2) Rule engine: Implement a rule engine to manage the application order and conflict resolution strategies of multiple conversion rules.

[0077] Step S400, Security check and parameterization: Perform a security check on the generated SQL statements to prevent SQL injection attacks, perform parameterized query processing on the SQL statements, send the generated SQL statements to the target database, and obtain the returned query results from the target database.

[0078] In this embodiment, this step uses AST to detect potential security issues, such as SQL injection attacks, etc. And a rule engine is used to verify whether the AST complies with security standards and automatically corrects insecure parts. After the above conversion, the next step is to regenerate the SQL statement from the modified AST. This process involves converting the tree structure back to the linear SQL file format. The following are the key points of this process:

[0079] (1) Ensure correctness and consistency: The newly generated SQL maintains the logic of the original query unchanged and complies with the syntax requirements of the target database.

[0080] (2) Optimize the output: In addition to simple syntax conversion, query optimization is also performed at this stage by adjusting the JOIN order, constant folding, dead code elimination, and effective index strategies.

[0081] Step S500, cache management: Cache the query results and the generated SQL statements based on the cache mechanism.

[0082] In this embodiment, the Fluentd technology is used to record the conversion process of SQL statements and query processing.

[0083] To improve query performance, the method implements query caching in the middleware. For frequently used SQL statements, the previously converted results are directly returned to avoid repeated parsing and conversion.

[0084] Step S600, error handling and logging: Record the conversion process of SQL statements and query processing to form a log record including the original SQL statement, the converted SQL statement, the execution time, and the status of the query processing result, and analyze the error information based on the log record.

[0085] The method implements a powerful error handling mechanism to ensure that detailed error information can be given when encountering SQL statements that cannot be converted. The method uses the Fluentd technology to record all important conversion activities, including the original SQL, the converted SQL, the execution time, and the result status, which facilitates debugging and auditing.

[0086] In this embodiment, the following two designs and technologies are used to implement the extensible architecture in SQL conversion:

[0087] (1) Plug-in design: Allows developers to add support for new databases or introduce new features by writing plug-ins. The plug-ins can be dynamically loaded without affecting the stability of the existing system.

[0088] (2) Configuration-driven: Parameterize as many behaviors as possible and allow adjustment through configuration files or environment variables.

[0089] Embodiment 2:

[0090] An intermediate ware for implementing SQL syntax conversion between different databases according to the present invention is a plug-in intermediate ware, which supports adjusting the parameters of the intermediate ware through a configuration file or an environment variable. The intermediate ware includes an SQL parsing module, an AST optimization module, a target SQL generation module, a security check and parameterization module, a cache management module, and an error handling and logging module. The intermediate ware can implement the syntax conversion of SQL between a source database and a target database through a method for implementing SQL syntax conversion between different databases based on an intermediate ware as described in any item of the first aspect.

[0091] The SQL parsing module is used to perform the following operations: parse the SQL statement of the source database through a custom SQL parser, identify various SQL elements in the SQL statement, and construct an abstract syntax tree AST with various SQL elements as nodes. The SQL parser is used to parse the input SQL statement based on lexical analysis and syntax parsing in the compilation principle method.

[0092] In this embodiment, parsing the SQL statement of the source database through a custom SQL parser includes two steps: lexical parsing and syntax parsing.

[0093] Lexical analysis: Split the string in the SQL statement into tokens. The tokens include keywords, identifiers, operators, table names, field names, conditional expressions, and aggregate functions.

[0094] Syntax analysis: Using the tokens as nodes, based on the rule-based natural language understanding method, construct an abstract syntax tree AST representing the SQL structure according to the grammar rules of the SQL statement.

[0095] In this embodiment, this module splits the SQL string into tokens, and through the rule-based natural language understanding method, that is, context-free grammar (CFG), according to the grammar rules of SQL, constructs a tree representing the SQL structure - that is, an abstract syntax tree (AST). Through the above technology, the SQL parser can identify various SQL elements, such as table names, field names, conditional expressions, aggregate functions, etc.

[0096] The AST optimization module is used to perform the following operations: traverse the abstract syntax tree AST, access the node information and specific patterns of all nodes in the abstract syntax tree AST, operate on the nodes in the abstract syntax tree AST based on the requirements of the target database, and perform query optimization on the abstract syntax tree AST based on a custom query optimization strategy.

[0097] As a specific implementation of the AST optimization module, this module is used to perform the following operations:

[0098] (1) Perform a depth - optimized search on the Abstract Syntax Tree (AST) recursively. Starting from the root node, visit the child nodes until no further depth can be reached, then backtrack to the upper - level node and continue visiting until all nodes of the AST have been visited, in order to collect node information or check for specific patterns;

[0099] (2) Operate on the nodes, including replacing nodes, modifying node content, inserting or deleting nodes, and updating the attribute values of nodes;

[0100] (3) Perform query optimization on the said Abstract Syntax Tree (AST) based on a custom query optimization strategy. The query optimization strategy includes merging consecutive selection conditions, eliminating redundant sub - queries, and changing the join order.

[0101] After SQL parsing is completed, the SQL statement is converted into an AST. Each node represents a part of the SQL statement, such as table names, column names, conditional expressions, etc. The AST provides a clear representation of the SQL logical structure. Then operations can be performed on the AST. The operations on the AST mainly include the following ways:

[0102] Traversal: Perform a depth - optimized search (DFS) recursively. Starting from the root node, visit the child nodes as deep as possible until no further depth can be reached, then backtrack to the upper - level node and continue visiting until all nodes of the AST have been visited, for collecting information or checking for specific patterns.

[0103] Replace nodes: Replace the old nodes with new nodes, for example, changing the table name or field name.

[0104] Modification: Change the content of certain nodes, such as replacing function calls, adjusting data types, etc.

[0105] Insert / Delete nodes: Add new child nodes or remove unnecessary parts to meet the requirements of the target database.

[0106] Attribute update: Adjust the attribute values of nodes, such as changing function parameters, values of conditional expressions, etc.

[0107] The target SQL generation module is used to perform the following operations: Based on the data types in the target database and applying data type conversion rules, perform data type mapping to generate SQL statements adapted to the target database.

[0108] In this embodiment, for the data types in different target databases, application data type conversion rules corresponding to each target database are configured, and the application order and conflict resolution strategies of multiple application data type conversion rules are managed through a rule engine. When performing data type mapping based on the data types in the target database and the application data type conversion rules, the patterns that need to be converted in the target database are identified, and the corresponding application data type conversion rules are applied.

[0109] To achieve the conversion from one database to another, this step defines a series of conversion rules, which can specify how to modify the AST according to different SQL elements (such as SELECT statements, JOIN conditions, aggregation functions, etc.). This method implements the application of rules through the following two techniques:

[0110] (1) Pattern matching: Identify the specific patterns that need to be converted and apply the corresponding rules.

[0111] (2) Rule engine: Implement a rule engine to manage the application order and conflict resolution strategies of multiple conversion rules.

[0112] The security check and parameterization module is used to perform the following operations: perform security checks on the generated SQL statements to prevent SQL injection attacks, perform parameterized query processing on the SQL statements, send the generated SQL statements to the target database, and obtain the returned query results from the target database.

[0113] In this embodiment, the security check and parameterization module uses the AST to detect potential security problems, such as SQL injection attacks, etc. And through the rule engine, it verifies whether the AST meets the security standards and automatically corrects the insecure parts. After the above conversion, the next step is to regenerate the SQL statement from the modified AST. This process involves converting the tree structure back to the linear SQL file format. The following are the key points of this process:

[0114] (1) Ensure correctness and consistency: The newly generated SQL maintains the same logic as the original query and conforms to the syntax requirements of the target database.

[0115] (2) Optimize the output: In addition to simple syntax conversion, query optimization is also performed at this stage by adjusting the JOIN order, constant folding, dead code elimination, and effective index strategies.

[0116] The cache management module is used to perform the following operations: cache the query results and the generated SQL statements based on the cache mechanism.

[0117] In this embodiment, the conversion process of the SQL statement and the query processing are recorded through Fluentd technology.

[0118] To improve query performance, the method implements query caching in the middleware. For frequently used SQL statements, the previously converted results are directly returned to avoid repeated parsing and conversion.

[0119] The error handling and logging module is used to perform the following operations: record the SQL statement conversion process and query processing to form a log record including the original SQL statement, the converted SQL statement, the execution time, and the query processing result status, and analyze the error information based on the log record.

[0120] The error handling and logging module implements a powerful error handling mechanism to ensure that detailed error information can be given when encountering SQL statements that cannot be converted. This method uses Fluentd technology to record all important conversion activities, including the original SQL, the converted SQL, the execution time, and the result status, facilitating debugging and auditing.

[0121] In this embodiment, the following two designs and technologies are adopted to implement the extensible architecture in SQL conversion:

[0122] (1) Plug-in design: Allows developers to add support for new databases or introduce new features by writing plug-ins. The plug-ins can be dynamically loaded without affecting the stability of the existing system.

[0123] (2) Configuration-driven: Parameterize as many behaviors as possible and allow adjustment through configuration files or environment variables.

[0124] The above has introduced in detail the method and middleware for implementing SQL syntax conversion of different databases based on the middleware. Specific examples are used in this article to elaborate on the principle and implementation method of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for implementing SQL syntax conversion of different databases based on a message middleware, characterized in that Configure a pluggable middleware, and adjust the parameters of the middleware through a configuration file or environment variables. Perform the following operations through the middleware to achieve the syntax conversion of SQL between the source database and the target database: SQL Parsing: Parse the SQL statements of the source database through a custom SQL parser, identify various SQL elements in the SQL statements, and construct an Abstract Syntax Tree (AST) with various SQL elements as nodes. The SQL parser is used to parse the input SQL statements based on lexical analysis and syntax parsing in the compilation principle method; AST Optimization: Traverse the Abstract Syntax Tree (AST), access the node information and specific patterns of all nodes in the AST, operate on the nodes in the AST based on the requirements of the target database, and perform query optimization on the AST based on a custom query optimization strategy; Target SQL Generation: Perform data type mapping based on the data types in the target database and apply data type conversion rules to generate SQL statements adapted to the target database; Security Check and Parameterization: Perform a security check on the generated SQL statements to prevent SQL injection attacks, and perform parameterized query processing on the SQL statements. Send the generated SQL statements to the target database and obtain the returned query results from the target database; Cache Management: Cache the query results and the generated SQL statements based on the cache mechanism; Error Handling and Logging: Record the conversion process of the SQL statements and the query processing to form a log record including the original SQL statements, the converted SQL statements, the execution time, and the status of the query processing results. Analyze the log record to obtain error information; 2. The method for implementing SQL syntax conversion of different databases based on middleware according to claim 1, characterized in that Parse the SQL statements of the source database through a custom SQL parser, including the following steps: Lexical Analysis: Split the strings in the SQL statements into tokens. Tokens include keywords, identifiers, operators, table names, field names, conditional expressions, and aggregate functions; Syntax Analysis: Use the tokens as nodes and construct an Abstract Syntax Tree (AST) representing the SQL structure according to the grammar rules of the SQL statements based on the rule-based natural language understanding method; 3. The method for implementing SQL syntax conversion of different databases based on a message middleware according to claim 1, wherein AST Optimization includes the following steps: Perform a depth optimization search on the Abstract Syntax Tree (AST) in a recursive manner. Start from the root node, visit the child nodes until unable to go deeper, then backtrack to the previous level of nodes and continue to visit until all nodes in the AST have been visited to collect node information or check specific patterns; Operate on the nodes, including replacing nodes, modifying node content, inserting or deleting nodes, and updating the attribute values of nodes; Perform query optimization on the Abstract Syntax Tree (AST) based on a custom query optimization strategy. The query optimization strategy includes merging consecutive selection conditions, eliminating redundant subqueries, and changing the join order.

4. The method for implementing SQL syntax conversion of different databases based on a message middleware according to claim 3, characterized in that, For data types in different target databases, configure the corresponding application data type conversion rules for each target database, and manage the application order and conflict resolution strategies of multiple application data type conversion rules through a rule engine; When performing data type mapping based on the data types in the target database and the application data type conversion rules, identify the patterns that need to be converted in the target database and apply the corresponding application data type conversion rules.

5. The method for implementing SQL syntax conversion of different databases based on a message middleware according to claim 1, characterized in that Record the conversion process and query processing of SQL statements through Fluentd technology.

6. A middleware for implementing SQL syntax conversion between different databases, characterized in that, The middleware is a plug-in middleware that supports adjusting the parameters of the middleware through configuration files or environment variables. The middleware includes an SQL parsing module, an AST optimization module, a target SQL generation module, a security check and parameterization module, a cache management module, and an error handling and logging module. The middleware can implement the syntax conversion of SQL between the source database and the target database through a method for implementing SQL syntax conversion between different databases based on middleware as described in any one of claims 1-5; The SQL parsing module is used to perform the following operations: parse the SQL statements of the source database through a custom SQL parser, identify various SQL elements in the SQL statements, and construct an abstract syntax tree AST with various SQL elements as nodes. The SQL parser is used to parse the input SQL statements based on lexical analysis and syntax parsing in the compilation principle method; The AST optimization module is used to perform the following operations: traverse the abstract syntax tree AST, access the node information and specific patterns of all nodes in the abstract syntax tree AST, operate on the nodes in the abstract syntax tree AST based on the requirements of the target database, and perform query optimization on the abstract syntax tree AST based on a custom query optimization strategy; The target SQL generation module is used to perform the following operations: perform data type mapping based on the data types in the target database and the application data type conversion rules, and generate SQL statements adapted to the target database; The security check and parameterization module is used to perform the following operations: perform security checks on the generated SQL statements to prevent SQL injection attacks, perform parameterized query processing on the SQL statements, send the generated SQL statements to the target database, and obtain the returned query results from the target database; The cache management module is used to perform the following operations: cache the query results and the generated SQL statements based on the cache mechanism; The error handling and logging module is used to perform the following operations: record the conversion process and query processing of SQL statements, form a log record including the original SQL statement, the converted SQL statement, the execution time, and the status of the query processing result, and analyze the error information based on the log record; 7. The middleware for implementing SQL syntax conversion between different databases according to claim 6, characterized in that, When parsing the SQL statements of the source database through a custom SQL parser, the SQL parsing module is used to perform the following operations through the SQL parser: Lexical analysis: Split the strings in the SQL statement into tokens. Tokens include keywords, identifiers, operators, table names, field names, conditional expressions, and aggregate functions. Syntax analysis: Using tokens as nodes, construct an abstract syntax tree (AST) representing the SQL structure based on the grammar rules of the SQL statement through a rule-based natural language understanding method.

8. The middleware for implementing SQL syntax conversion between different databases according to claim 6, wherein The AST optimization module is used to perform the following operations: Perform a depth-optimized search on the abstract syntax tree (AST) recursively. Starting from the root node, visit the child nodes until no further depth is possible, then backtrack to the previous level and continue visiting until all nodes of the abstract syntax tree (AST) have been visited to collect node information or check for specific patterns. Operate on the nodes, including replacing nodes, modifying node content, inserting or deleting nodes, and updating the attribute values of nodes. Perform query optimization on the abstract syntax tree (AST) based on a custom query optimization strategy. The query optimization strategy includes merging consecutive selection conditions, eliminating redundant subqueries, and changing the join order.

9. The middleware for implementing SQL syntax conversion between different databases according to claim 8, characterized in that, For data types in different target databases, the target SQL generation module is used to configure the application data type conversion rules corresponding to each target database, and manage the application order and conflict resolution strategy of multiple application data type conversion rules through a rule engine. When performing data type mapping based on the data types in the target database and the application data type conversion rules, the target SQL generation module is used to identify the patterns that need to be converted in the target database and apply the corresponding application data type conversion rules.

10. The middleware for implementing SQL syntax conversion between different databases according to claim 8, characterized in that, The error handling and logging module is used to record the conversion process of the SQL statement and the query processing through Fluentd technology.

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