CN115544062A - Apache call data blood relationship and influence analysis based method and system - Google Patents
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Abstract
The invention provides a method and a system for analyzing blood margin and influence based on Apache calcium data, comprising the following steps: acquiring metadata information according to the acquired metadata, wherein the metadata information comprises a table and a field; integrating Apache Call, performing lexical and syntactic analysis on the SQL character string of the metadata information, and converting the SQL character string into an abstract syntax tree AST; acquiring a relation map between tables and fields by using an abstract syntax tree AST; and performing table-level and field-level blood relationship analysis and influence analysis according to the relationship map. The method analyzes SQL of various databases of different types such as Greenplus, gaussDB and the like, has high expandability aiming at special grammars such as different keywords, functions and the like of different databases, and can support the analysis of the special grammars of various databases.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a method and a system for analyzing data blood relationship and influence based on an Apache calcium dynamic data management framework.
Background
The data blood relationship analysis and the influence analysis are core functions of a data management and data management tool, and by establishing the blood relationship among data, on one hand, the source and the processing logic of downstream data can be traced, on the other hand, the influence range of the upstream data when the upstream data changes can be quickly analyzed, so that the change influence early warning and the matched transformation can be timely carried out. The Apache Call is a basic framework which provides a standard SQL language, multiple query optimization and connection with various data sources, and can access multiple data and realize the query by using SQL. However, apache call only supports parsing regular SQL, and the SQL parsing for different types of databases may report an error or the parsing result is incomplete.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method and a system for analyzing data blood relationship and influence based on Apache calcium.
The method for analyzing the blood margin and influence based on the Apache calcium data provided by the invention comprises the following steps:
step S1: acquiring metadata information according to the acquired metadata, wherein the metadata information comprises a table and a field;
step S2: integrating Apache Call, performing lexical and syntactic analysis on the SQL character string of the metadata information, and converting the SQL character string into an abstract syntax tree AST;
and step S3: acquiring a relation map between tables and fields by using an abstract syntax tree AST;
and step S4: and performing table-level and field-level blood relationship analysis and influence analysis according to the relationship map.
Preferably, the step 2 comprises the steps of:
step S2.1: transforming and adapting special grammars in databases such as Greenplus and GaussDB, and analyzing SQL sentences into an abstract syntax tree AST;
step S2.2: analyzing a node object of the AST by using a custom analyzer to acquire a field blood relationship dependency relationship, and writing the field blood relationship dependency relationship into a designated data table, wherein the analyzing comprises the following steps:
analyzing query and associated nodes in the AST to obtain a blood relationship dependency relationship and dependency details between fields and tables;
recursively analyzing the physical table information of the fields in the sub-query to obtain the blood relationship dependence;
different parts of the SQL statement are analyzed and encapsulated into different node objects through the Call, and the corresponding blood relationship dependency relationship is obtained through analyzing the node object information.
Preferably, the custom parser comprises a call SQL parser; adjusting a config.fmpp file to support the required keywords in the process of generating a Call SQL parser; the fimp file is a calling template configuration file to complete the related configuration of Freemarker and JavaCC; adjusting the parsing rule in the parser.jj file in the templates folder to adapt to the required database parsing, and using a custom parsing function to meet the parsing of a special grammar rule; jj file is a core file needed by JavaCC resolver; and fmpp automatically generates an analysis file Parser.jj according to the configuration file, the template file and the additional template file, and generates the SQL analyzer after compiling.
Preferably, based on Apache calcium, the relationship between technical metadata and service metadata is established on the metadata level, so that the relationship penetration of field level and table level is realized, and blood margin analysis and influence analysis of different granularities are performed; calling Call to analyze SQL; generating an abstract syntax tree SqlNode after analysis; and calling different analyzers to analyze the dependency relationship according to the type of the SqlNode.
The system for analyzing the blood margin and influence based on the Apache calcium data provided by the invention comprises the following components:
a module M1: acquiring metadata information according to the acquired metadata, wherein the metadata information comprises a table and a field;
a module M2: integrating Apache Call, performing lexical and syntactic analysis on the SQL character string of the metadata information, and converting the SQL character string into an abstract syntax tree AST;
a module M3: acquiring a relation map between tables and fields by using the AST;
a module M4: and performing table-level and field-level blood relationship analysis and influence analysis according to the relation map.
Preferably, the step 2 comprises the steps of:
module M2.1: transforming and adapting special grammars in databases such as Greenplus and GaussDB, and analyzing SQL sentences into an abstract syntax tree AST;
module M2.2: analyzing a node object of the AST by using a custom analyzer to acquire a field blood relationship dependency relationship, and writing the field blood relationship dependency relationship into a designated data table, wherein the analyzing comprises the following steps:
analyzing query and associated nodes in the AST to obtain a blood relationship dependency relationship and dependency details between fields and tables;
recursively analyzing the physical table information of the fields in the sub-query to obtain the blood relationship dependence;
different parts of the SQL statement are analyzed and encapsulated into different node objects through the Call, and the corresponding blood relationship dependency relationship is obtained through analyzing the node object information.
Preferably, the custom parser comprises a call SQL parser; in the process of generating the Call SQL parser, adjusting a config. Fmpp file to support the required keywords; the fimp file is a calling template configuration file to complete the related configuration of Freemarker and JavaCC; adjusting the parsing rule in the parser.jj file in the templates folder to adapt to the required database parsing, and using a custom parsing function to meet the parsing of a special grammar rule; jj file is a core file needed by JavaCC resolver; and fmpp automatically generates an analysis file Parser.jj according to the configuration file, the template file and the additional template file, and generates the SQL analyzer after compiling.
Preferably, based on Apache calcium, the relationship between technical metadata and service metadata is established on the metadata level, so that the relationship penetration of field level and table level is realized, and blood margin analysis and influence analysis of different granularities are performed; calling Calcite to analyze SQL; generating an abstract syntax tree SqlNode after analysis; and calling different analyzers to analyze the dependency relationship according to the type of the SqlNode.
According to the present invention, a computer readable storage medium is provided, having a computer program stored thereon, which, when being executed by a processor, carries out the steps of the method for blood margin and impact analysis based on Apache call data.
According to the present invention, an electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method based on Apache call data blood margin and impact analysis.
Compared with the prior art, the invention has the following beneficial effects:
1. the method is based on Apache call, the relationship between technical metadata and service metadata is established on the metadata level, the relationship penetration of field level, operation level and table level is realized, and the data can be better understood and used through different granularity blood margin analysis and influence analysis.
2. According to the method, the bloody border dependency relationship and the dependency details between the fields and the table are obtained by inquiring and analyzing the associated nodes in the abstract tree; in addition, for the fields in the sub-query, the physical table information to which the fields belong is recursively analyzed, so that the dependency relationship is obtained. Different parts of the SQL statement are analyzed and encapsulated into different node objects through the Call, and the corresponding blood relationship dependency relationship is obtained through analyzing the node object information.
3. The method analyzes the SQL of various databases of different types such as Greenplus, gaussDB and the like, and the different databases have different special grammars such as keywords, functions and the like, so that the method can support the analysis of the special grammars of various databases.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is a schematic diagram illustrating a process of implementing parsing.
FIG. 2 is a schematic diagram of a resolver.
FIG. 3 is a schematic diagram of the flow steps for parsing an SQL statement.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the concept of the invention. All falling within the scope of the present invention.
The method is based on Apache call, the relationship between technical metadata and service metadata is established on the metadata level, the relationship penetration of field level, operation level and table level is realized, and the data can be better understood and used through different granularity blood margin analysis and influence analysis.
According to the method, the bloody border dependency relationship and the dependency details between the fields and the table are obtained by inquiring and analyzing the associated nodes in the abstract tree; in addition, for the fields in the sub-query, the physical table information to which the fields belong is recursively analyzed, so that the dependency relationship is obtained. Different parts of the SQL statements are analyzed and encapsulated into different node objects through calling, and corresponding blood relationship dependency relationships are obtained through analyzing node object information.
The method has multiple advantages as a blood margin analysis tool, and can rapidly and accurately identify the direct mapping relation (including the dependency relation and conversion operation between fields), the correlation between table fields, the filtering condition and other dependency relations between fields by analyzing DDL (distributed data language) and DML (digital document language) statements in the SQL script, so that the data processing is more convenient and efficient. The invention, as a blood margin analysis tool, comprises an integrated Apache Call SQL analysis framework for analyzing SQL sentences into abstract syntax trees and a custom analyzer for analyzing blood margin relations.
The method for analyzing the blood margin and influence based on the Apache calcium data provided by the invention comprises the following steps:
step 1: acquiring metadata, and acquiring metadata information such as tables and fields according to the metadata;
step 2: integrating Apache Call, performing lexical and syntactic analysis on SQL character strings of metadata information such as tables and fields, and converting the SQL character strings into AST (Abstract Syntax Tree);
and step 3: acquiring a relation map between tables and fields by using AST;
and 4, step 4: completing table-level and field-level blood relationship analysis and influence analysis through the generated relation map;
and 5: and (4) completing blood relationship analysis and influence analysis of table level, operation level and field level through the generated relation map.
The step 2 comprises the following steps:
step 2.1: transforming and adapting part of specific grammars in databases such as Greenplus and GaussDB, and analyzing SQL sentences in the databases into abstract syntax trees;
step 2.2: and analyzing the abstract syntax tree node object by using the custom analyzer, acquiring field dependency relationship data, generating an analysis result and writing the analysis result into a specified data source. The implementation flow is shown in fig. 1.
The custom parser comprises a Call SQL parser. In the process of generating the Call SQL parser, required related files are all placed in a codegen folder and comprise custom parsed files such as gusParserImpls. Ftl, gpParserImpls. Ftl, parsserImpls. Ftl, tcl. Ftl and the like in an include folder; fmpp file and parser jj file in templates folder. Files such as GuassParserImpls. Ftl, gpParserImpls. Ftl, parsserImpls. Ftl, tcl. Ftl and the like are additional grammar template files, a Parser. Jj file is a parsing core file (such as a main modification file when a function is added) required by a JavaCC parser, a config. Fmpp file is a CALCITE template configuration file, and the related configuration of Freemarker and JavaCC is completed; and f mpp automatically generates a final analysis file Parser. Jj of the f mpp/java folder according to the configuration file, the template file and the additional template file, and generates an SQL analyzer java cc/. J after compiling. The specific process of generating the parser is shown in fig. 2. The method analyzes the SQL of various databases such as GP, gauss and the like, and the different databases have different special grammars such as keywords, functions and the like, so that the method can support the analysis of the special grammars of the various databases. The method correspondingly adjusts the Apache Call framework source code and configuration, for example, adjusts config.
The core process of parsing the SQL statement using the blood relationship analysis tool is shown in fig. 3, which includes: calling Call to analyze SQL; generating an abstract syntax tree SqlNode after analysis; and calling different resolvers to resolve the dependency relationship according to the type (create update and the like) of the SqlNode.
The invention also provides a system based on the Apache calcium data blood margin and influence analysis, which can be realized by executing the flow steps of the method based on the Apache calcium data blood margin and influence analysis, namely, the method based on the Apache calcium data blood margin and influence analysis can be understood as the preferred implementation mode of the system based on the Apache calcium data blood margin and influence analysis by the technical personnel in the field. Specifically, the system based on the blood relationship and influence analysis of the Apache calcium data provided by the invention comprises the following steps:
a module M1: acquiring metadata information according to the acquired metadata, wherein the metadata information comprises a table and a field;
a module M2: integrating Apache Call, performing lexical and syntactic analysis on the SQL character string of the metadata information, and converting the SQL character string into an abstract syntax tree AST;
a module M3: acquiring a relation map between tables and fields by using an abstract syntax tree AST;
a module M4: and performing table-level and field-level blood relationship analysis and influence analysis according to the relation map.
The step 2 comprises the following steps:
module M2.1: transforming and adapting special grammars in databases such as Greenplus and GaussDB, and analyzing SQL sentences into an abstract syntax tree AST;
module M2.2: analyzing the node object of the AST by using a custom analyzer to obtain the dependency relationship of the blood relationship of the field, and writing the dependency relationship into a specified data table, wherein the analyzing comprises the following steps: analyzing query and associated nodes in the AST to obtain a blood relationship dependency relationship and dependency details between fields and tables; recursively analyzing the information of the physical table to which the fields belong to the sub-query fields to obtain the blood relationship dependency relationship; different parts of the SQL statement are analyzed and encapsulated into different node objects through the Call, and the corresponding blood relationship dependency relationship is obtained through analyzing the node object information.
The custom parser comprises a Call SQL parser; in the process of generating the Call SQL parser, adjusting a config. Fmpp file to support the required keywords; the fimpp file is a calling template configuration file, and related configuration of Freemarker and JavaCC is completed; adjusting the parsing rules in the Parser.jj files in the templates folder to adapt to the needed database parsing, and using a custom parsing function to meet the parsing of special grammar rules; jj file is a core file needed by JavaCC resolver; and fmpp automatically generates an analysis file Parser.jj according to the configuration file, the template file and the additional template file, and generates the SQL analyzer after compiling.
Based on Apache calcium, the relationship between technical metadata and service metadata is established on a metadata level, so that the relationship penetration of field level and table level is realized, and blood margin analysis and influence analysis of different granularities are performed; calling Call to analyze SQL; generating an abstract syntax tree SqlNode after analysis; and calling different resolvers to resolve the dependency relationship according to the type of the SqlNode.
According to the present invention, a computer readable storage medium is provided, having a computer program stored thereon, which, when being executed by a processor, carries out the steps of the method for blood margin and impact analysis based on Apache call data.
According to the present invention, an electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program when executed by the processor implements the steps of the method based on Apache call data blood margin and influence analysis.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the apparatus, and the modules thereof provided by the present invention may be considered as a hardware component, and the modules included in the system, the apparatus, and the modules for implementing various programs may also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description has described specific embodiments of the present invention. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A method for analyzing blood margin and influence based on Apache calcium data is characterized by comprising the following steps:
step S1: acquiring metadata information according to the acquired metadata, wherein the metadata information comprises a table and a field;
step S2: integrating Apache Call, performing lexical and syntactic analysis on the SQL character string of the metadata information, and converting the SQL character string into an abstract syntax tree AST;
and step S3: acquiring a relation map between tables and fields by using an abstract syntax tree AST;
and step S4: and performing table-level and field-level blood relationship analysis and influence analysis according to the relation map.
2. The Apache calcium data based blood margin and influence analysis method according to claim 1, wherein said step 2 comprises the steps of:
step S2.1: transforming and adapting special grammar in a Greenplus and GaussDB database, and analyzing SQL sentences into an abstract grammar tree AST;
step S2.2: analyzing a node object of the AST by using a custom analyzer to acquire a field blood relationship dependency relationship, and writing the field blood relationship dependency relationship into a designated data table, wherein the analyzing comprises the following steps:
analyzing query and associated nodes in the AST to obtain a blood relationship dependency relationship and dependency details between fields and tables;
recursively analyzing the information of the physical table to which the fields belong to the sub-query fields to obtain the blood relationship dependency relationship;
different parts of the SQL sentences are analyzed and encapsulated into different node objects through Apache call, and the corresponding blood relationship dependency relationship is obtained through analyzing node object information.
3. The Apache call data consanguinity and impact analysis-based method of claim 2, wherein said custom parser comprises a call SQL parser; adjusting a config.fmpp file to support the required keywords in the process of generating a Call SQL parser; the fimp file is a calling template configuration file to complete the related configuration of Freemarker and JavaCC; adjusting the parsing rule in the parser.jj file in the templates folder to adapt to the required database parsing, and using a custom parsing function to meet the parsing of a special grammar rule; jj file is a core file needed by JavaCC resolver; and fmpp automatically generates an analysis file Parser.jj according to the configuration file, the template file and the additional template file, and generates the SQL analyzer after compiling.
4. The method for analyzing blood margin and influence based on Apache calcium data according to claim 1, characterized in that based on Apache calcium, the relationship between technical metadata and service metadata is established in the metadata layer, so as to realize the relationship penetration of field level and table level, and perform blood margin analysis and influence analysis of different granularities; calling Call to analyze SQL; generating an analyzed abstract syntax tree SqlNode; and calling different resolvers to resolve the dependency relationship according to the type of the SqlNode.
5. A system for blood margin and impact analysis based on Apache call data, comprising:
a module M1: acquiring metadata information according to the acquired metadata, wherein the metadata information comprises a table and a field;
a module M2: integrating Apache Call, performing lexical and syntactic analysis on the SQL character string of the metadata information, and converting the SQL character string into an abstract syntax tree AST;
a module M3: acquiring a relation map between tables and fields by using the AST;
a module M4: and performing table-level and field-level blood relationship analysis and influence analysis according to the relationship map.
6. The Apache calcium data blood margin and impact analysis based system according to claim 5 wherein said step 2 comprises the steps of:
module M2.1: transforming and adapting special grammar in a Greenplus and GaussDB database, and analyzing SQL sentences into an abstract grammar tree AST;
module M2.2: analyzing a node object of the AST by using a custom analyzer to acquire a field blood relationship dependency relationship, and writing the field blood relationship dependency relationship into a designated data table, wherein the analyzing comprises the following steps:
analyzing query and associated nodes in the AST to obtain a blood relationship dependency relationship and dependency details between fields and tables;
recursively analyzing the information of the physical table to which the fields belong to the sub-query fields to obtain the blood relationship dependency relationship;
different parts of the SQL statements are analyzed and encapsulated into different node objects through calling, and corresponding blood relationship dependency relationships are obtained through analyzing node object information.
7. The Apache Call data blood margin and impact analysis based system according to claim 6, wherein said custom parser comprises a Call SQL parser; adjusting a config.fmpp file to support the required keywords in the process of generating a Call SQL parser; the fimp file is a calling template configuration file to complete the related configuration of Freemarker and JavaCC; adjusting the parsing rules in the Parser.jj files in the templates folder to adapt to the needed database parsing, and using a custom parsing function to meet the parsing of special grammar rules; jj file is a core file needed by JavaCC resolver; and f mpp automatically generates an analysis file Parser. Jj according to the configuration file, the template file and the additional template file, and generates the SQL analyzer after compiling.
8. The Apache calcium data blood margin and impact analysis based system according to claim 5, wherein based on Apache calcium, the relationship between technical metadata and service metadata is established in the metadata layer, so that the relationship penetration of field level and table level is realized, and blood margin analysis and impact analysis of different granularities are performed; calling Call to analyze SQL; generating an abstract syntax tree SqlNode after analysis; and calling different analyzers to analyze the dependency relationship according to the type of the SqlNode.
9. A computer-readable storage medium storing a computer program which, when executed by a processor, performs the steps of the method for blood margin and impact analysis based on Apache call data of any one of claims 1 to 4.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the Apache call data blood margin and impact analysis based method of any one of claims 1 to 4.
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| CN117743452A (en) * | 2023-12-25 | 2024-03-22 | 湖北省珍岛数字智能科技有限公司 | Multi-source data management system based on Datart technology |
| CN118170785A (en) * | 2024-01-12 | 2024-06-11 | 广州中康数字科技有限公司 | Data blood source relation analysis system and method |
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| CN117743452A (en) * | 2023-12-25 | 2024-03-22 | 湖北省珍岛数字智能科技有限公司 | Multi-source data management system based on Datart technology |
| CN118170785A (en) * | 2024-01-12 | 2024-06-11 | 广州中康数字科技有限公司 | Data blood source relation analysis system and method |
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