CN115905239A - Method and device for realizing highly-multiplexed performance index data retrieval - Google Patents
Method and device for realizing highly-multiplexed performance index data retrieval Download PDFInfo
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Abstract
The invention discloses a method and a device for realizing highly-multiplexed performance index data retrieval, wherein the method comprises the following steps: abstract classifying each column of the performance index data table into a dimension column, a data column and description of data time granularity, and storing the dimensional column, the data column and the description as a model of the data table in a database model table; through the data table name association, a virtual index data column can be configured; when the query is executed, screening a data source, and screening a model meeting the condition through the dimension column name and the data column name of the parameter; when in query, SQL for querying the target table is dynamically assembled according to the entry conditions by calling a component method, and the virtual index is queried in the database by an algorithm configured by the data columns actually existing in the target data table according to product requirements and a result is calculated and returned by using an aggregation function or a calculator. The invention improves the interface development efficiency of inquiring the performance data, and optimizes the original mode of customizing the development interface according to the inquiring performance index into the mode of acquiring the data through a universal interface.
Description
Technical Field
The invention relates to the field of data retrieval, in particular to a method and a device for realizing highly-multiplexed performance index data retrieval.
Background
The traditional performance data query needs to explicitly specify a table name and a corresponding column, and the operation of customized processing indexes, and the like, and has low reusability.
In the performance data query strategy in the prior art, a character string SQL statement is spliced at the back end according to the index column name and the screening condition of the front end input parameters, and then a query result is returned after the execution in a database. The interface reusability is low, and the SQL injection safety problem is easily generated due to the joining and splicing of SQL, so that the system safety loophole is caused.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a highly-multiplexed performance index data retrieval implementation method and device, which improve the interface development efficiency of querying performance data and optimize the original mode that a development interface needs to be customized according to query performance indexes into a mode that data can be acquired through a universal interface.
In order to realize the purpose, the invention adopts the following technical scheme:
in an embodiment of the present invention, a method for implementing highly-multiplexed performance index data retrieval is provided, where the method includes:
s01, abstractively classifying each column of the performance index data table into a dimension column, a data column and description of data time granularity, and storing the data columns as a model of the data table in a database model table;
s02, respectively configuring the table names, the dimension column names and the data column names of the data tables in the database, associating the table names with the dimension column names through the data table names, and configuring virtual index data columns;
s03, screening a data source when query is executed, and screening a model meeting conditions through the entered dimension column names and the data column names;
and S04, dynamically assembling SQL for inquiring the target table according to the entry conditions by using the open source JSqlParser component in a component calling method during inquiry, and inquiring the virtual indexes in the database by using an aggregation function or a calculator to calculate and return results through an algorithm configured by data columns actually existing in the target data table according to product requirements.
Further, the dimension column in S01 refers to each group description column of the data index column, such as city information/device information/customer information, and the like, the data column refers to the column where the performance index is located, and the data time granularity refers to the time interval of data acquisition.
Further, the virtual index data column in S02 is index data that is not directly obtained from an existing data column in the target data table, but a returned data index is calculated according to a configured algorithm through one or more columns in the target data table.
Furthermore, a plurality of table names meeting the conditions exist, and the table with the least number of dimension columns and data columns is screened out to serve as a target data table, so that the data source screening is completed.
Further, the S03 includes:
s031, screen out the data set that can meet all dimensionalities and can match;
s032, screening out a data set containing the query indexes. Screening the data sets in the last step to obtain all data sets containing the query indexes;
s033, matching a data set which can meet the conditions;
s034, converting a data set which can be correlated, and generating a correlation query SQL by the data set based on the data structure;
s035, constructing a query SQL statement.
In an embodiment of the present invention, an apparatus for implementing highly-multiplexed performance index data retrieval is further provided, where the apparatus includes:
the preliminary screening matching module abstractly classifies all columns of the performance index data table into dimension columns, data columns and description of data time granularity, and stores the dimensional columns, the data columns and the description of the data time granularity as a model of the data table in a database model table;
the secondary screening matching module is used for respectively configuring the table names, the dimension column names and the data column names of the data tables in the database, associating the table names with the dimension column names and configuring virtual index data columns;
the matching success module screens a data source when executing query, and screens a model meeting conditions through the entered dimension column names and the data column names;
and when the query statement module is assembled, the SQL for querying the target table is dynamically assembled according to the entry conditions in a component calling method, and the virtual index is queried in the database through an algorithm configured by data columns actually existing in the target data table according to product requirements and is calculated by using an aggregation function or a calculator to return a result.
In an embodiment of the present invention, a computer device is further provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the highly multiplexed performance index data retrieval implementation method is implemented.
In an embodiment of the present invention, a computer-readable storage medium is further provided, where a computer program for implementing the method for retrieving highly multiplexed performance index data is stored in the computer-readable storage medium.
Has the advantages that:
the invention discloses a method for realizing highly-multiplexed performance index data retrieval, which is characterized in that data is queried through a dimension column abstracted by a configuration data table and the relationship between the data column and the table, and the configured virtual index is calculated and returned according to a configured algorithm through the data column actually existing in the data table, so that the dynamic operational capability of the data is improved. And a mode of dynamically assembling query statements by using open source component functions is used, so that the data query is simplified into a data column name and a dimension column name which only need to be transmitted in the required data and optional screening condition parameters. The problems of reusability of performance index data query and system safety are solved.
Drawings
FIG. 1 is a schematic flow chart of a highly multiplexed performance index data retrieval implementation method of the present invention;
FIG. 2 is a schematic diagram of a highly-multiplexed apparatus for retrieving performance index data according to the present invention;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The principles and spirit of the present invention will be described below with reference to several exemplary embodiments, it being understood that these embodiments are presented only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As will be appreciated by one skilled in the art, embodiments of the present invention may be embodied as a system, apparatus, device, method, or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
The terms and their translations and explanations to which this invention relates are set forth in Table 1 below:
| english name or abbreviation | Chinese explanation |
| SQL | Structured query language (structured query language) |
| JSqlParser | Java structured query language parser (JavaStructuredQueryLanguageparser) |
TABLE 1
According to the embodiment of the invention, the method and the device for realizing the data retrieval of the highly-multiplexed performance indexes are provided, the interface development efficiency of inquiring the performance data is improved, and the original mode that a development interface is customized according to the inquiring performance indexes is optimized into a mode that the data can be obtained through a universal interface.
The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments of the invention.
As shown in fig. 1, the method includes:
s01, abstractively classifying each column of the performance index data table into a dimension column, a data column and description of data time granularity, and storing the dimensional column, the data column and the description of the data time granularity as a model of the data table in a database model table;
s02, respectively configuring the table names, the dimension column names and the data column names of the data tables in the database, associating the table names with the dimension column names through the data table names, and configuring virtual index data columns;
s03, screening a data source when executing query, and screening a model meeting the conditions through the dimension column names and the data column names of the parameters;
and S04, dynamically assembling SQL for inquiring the target table according to the entry conditions by using the open source JSqlParser component in a component calling method during inquiry, and inquiring the virtual indexes in the database by using an aggregation function or a calculator to calculate and return results through an algorithm configured by data columns actually existing in the target data table according to product requirements.
The dimension column in S01 refers to each group description column of the data index column, such as city information/device information/customer information, the data column refers to the column where the performance index is located, and the data time granularity refers to the time interval of data acquisition.
The virtual index data column in S02 is index data that is not directly obtained from an existing data column in the target data table, but a returned data index is calculated according to a configured algorithm by one or more columns in the target data table.
And screening the table with the least number of dimension columns and data columns as a target data table to finish screening the data source.
The S03 comprises:
s031, screen out the data set that can meet all dimensionalities and can match;
s032, screening out a data set containing the query indexes. Screening the data sets in the last step to obtain all data sets containing the query indexes;
s033, matching a data set which can meet the conditions;
s034, converting a data set which can be associated, and generating an associated query SQL by the data set based on the data structure;
s035, constructing a query SQL statement.
It should be noted that although the operations of the method of the present invention have been described in the above embodiments and the accompanying drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the operations shown must be performed, to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
In order to clearly explain the above-mentioned method for retrieving highly-multiplexed performance index data, a specific embodiment is described below, but it should be noted that this embodiment is only for better explaining the present invention, and should not be construed as an undue limitation to the present invention.
The following performance index retrieval process is a process that a general performance query interface developed by the government-enterprise operation and maintenance system according to the performance index retrieval implementation method is used for dynamically assembling an SQL statement query database after a data source and a target data table are screened out through analyzing front-end parameters, and returning a query result to the front end.
S01: the front-end parameters are transmitted into index names to be inquired, dimension names (group column names during index data acquisition), data time granularity, data time range, paging page numbers and single-page data quantity;
as in table 2 below:
table 2 requests parameter examples:
s02: after the interface analyzes the input parameters, inquiring whether a matched data table exists in the database model table by using SQL according to the index name, the dimension name and the data time granularity, and if a plurality of data table models are matched, taking the data table with the least column number as a target data table. If no data table model is matched, returning query failure information to the front end;
request parameter example:
s03, dynamically assembling the index name, the dimension name, the data time range, the paging page number and the single-page data quantity into an SQL (structured query language) statement through a JSqlParser component, executing the SQL statement in a database where a data table is located, assembling a return result into a return message body of a specified data structure, and returning the return message body to the front end;
and S04, after receiving the return message body, the front end analyzes the required performance index data and displays the data on a front end page.
Based on the same inventive concept, the invention also provides a highly-multiplexed performance index data retrieval implementation device. The implementation of the device can refer to the implementation of the method, and repeated details are not repeated. The term "module," as used below, may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 2 is a schematic structural diagram of a highly-multiplexed performance index data retrieval implementation apparatus according to the present invention. As shown in fig. 2, the apparatus includes:
the preliminary screening matching module 110 abstractly classifies each column of the performance index data table into a dimension column, a data column and description of data time granularity, and stores the data columns as a model of the data table in a database model table;
the secondary screening matching module 120 is used for respectively configuring the table names, the dimension column names and the data column names of the data tables in the three tables in the database, associating the table names through the data tables, and configuring virtual index data columns;
the matching success module 130 screens data sources when executing query, and screens out a model meeting the conditions through the entered dimension column names and the data column names;
and the query statement assembling module 140 dynamically assembles SQL for querying the target table according to the entry conditions in a component calling method during query, and queries the database for the virtual index through an algorithm configured by data columns actually existing in the target data table according to product requirements by using an aggregation function or a calculator to calculate a return result.
It should be noted that although in the above detailed description reference is made to several modules of a highly multiplexed performance indicator data retrieval implementing apparatus, such division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the modules described above may be embodied in one module according to embodiments of the invention. Conversely, the features and functions of one module described above may be further divided into embodiments by a plurality of modules.
Based on the aforementioned inventive concept, as shown in fig. 3, the present invention further provides a computer device 200, which includes a memory 210, a processor 220, and a computer program 230 stored in the memory 210 and capable of running on the processor 220, wherein the processor 220 implements the aforementioned highly-multiplexed performance index data retrieval implementation method when executing the computer program 230.
Based on the above inventive concept, the present invention further provides a computer readable storage medium storing a computer program for executing the foregoing highly multiplexed performance index data retrieval implementation method.
The method for realizing the highly-multiplexed performance index data retrieval comprises the steps of inquiring data by configuring a dimension column abstracted by a data table and the relation between the data column and the table, and calculating a return result by a configured virtual index through the data column actually existing in the data table according to a configured algorithm, so that the dynamic operational capability of the data is improved. And a mode of dynamically assembling query statements by using open source component functions is used for simplifying data query into a data column name and a dimension column name which only need to be transmitted into required data and optional screening condition parameters. The problems of reusability of performance index data query and system safety are solved.
While the spirit and principles of the invention have been described with reference to several particular embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, nor is the division of aspects, which is for convenience only as the features in such aspects may not be combined to benefit. The invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
The limitation of the protection scope of the present invention is understood by those skilled in the art, and various modifications or changes which can be made by those skilled in the art without inventive efforts based on the technical solution of the present invention are still within the protection scope of the present invention.
Claims (8)
1. A method for realizing highly-multiplexed performance index data retrieval is characterized by comprising the following steps:
s01, abstractively classifying each column of the performance index data table into a dimension column, a data column and description of data time granularity, and storing the data columns as a model of the data table in a database model table;
s02, respectively configuring the table names, the dimension column names and the data column names of the data tables in the database, associating the table names with the dimension column names through the data table names, and configuring virtual index data columns;
s03, screening a data source when executing query, and screening a model meeting the conditions through the dimension column names and the data column names of the parameters;
and S04, dynamically assembling SQL for querying the target table according to the entry conditions by calling a component method during query, and querying the data columns actually existing in the target data table through the virtual indexes according to an algorithm configured by product requirements to use an aggregation function or a calculator to calculate a return result.
2. The method for realizing data retrieval of highly multiplexed performance indicators according to claim 1, wherein the dimension column in S01 is a packet description column of a data indicator column, the data column is a column in which the performance indicators are located, and the data time granularity is a time interval of data acquisition.
3. The method as claimed in claim 2, wherein the virtual index data column in S02 calculates the index of the returned data through one or more columns in the target data table according to a configured algorithm.
4. The method for realizing data retrieval of highly multiplexed performance indicators according to claim 3, wherein a plurality of table names meeting the conditions are selected, and the table with the least number of dimension columns and data columns is selected as the target data table to complete data source selection.
5. The method according to claim 1, wherein the S03 comprises:
s031, screening out a data set which can be matched with all dimensions;
s032, screening out a data set containing the query indexes. Screening the data sets in the last step to obtain all data sets containing the query indexes;
s033, matching a data set which can meet the conditions;
s034, converting a data set which can be correlated, and generating a correlation query SQL by the data set based on the data structure;
and S035, constructing a query SQL statement.
6. A highly multiplexed performance index data retrieval implementation apparatus, the apparatus comprising:
the preliminary screening matching module abstractly classifies all columns of the performance index data table into dimension columns, data columns and description of data time granularity, and stores the dimensional columns, the data columns and the description of the data time granularity as a model of the data table in a database model table;
the secondary screening matching module is used for respectively configuring the table names, the dimension column names and the data column names of the data tables in the database, associating the table names with the dimension column names and configuring virtual index data columns;
the matching success module screens a data source when executing query, and screens a model meeting the conditions through the entered dimension column names and the data column names;
and when the query statement module is assembled, the SQL for querying the target table is dynamically assembled according to the entry condition in a mode of calling a component method, and the virtual index is queried in the database by an algorithm configured by the data column actually existing in the target data table according to the product requirement and the result is calculated and returned by using an aggregation function or a calculator.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1-5 when executing the computer program.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1-5.
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