CN114356965B - Method, system, server and storage medium for generating dynamic form - Google Patents
Method, system, server and storage medium for generating dynamic form Download PDFInfo
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Abstract
The embodiment of the application provides a method, a system, a server and a storage medium for generating a dynamic form, and relates to the technical field of computers. The method comprises the steps of receiving a dynamic form query request initiated by terminal equipment; screening the preselected dimensions according to the incidence relation of the preselected dimensions in the dynamic form query request to obtain target dimensions; determining a target table set according to the target dimension, the preselected index, the aggregation algorithm and the filtering condition; generating a target data query instruction according to the target table set; and generating a dynamic form according to the target data query instruction, and sending the dynamic form to the terminal equipment. The method and the device can automatically screen and optimize the dynamic form query request initiated by a user and generate a target data query instruction, thereby avoiding errors caused by manually writing codes and improving the efficiency of data query.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, a system, a server, and a storage medium for generating a dynamic form.
Background
A Data Warehouse (DW or DWH for short) is a structured Data environment for decision support systems and online analytics application Data sources. Compared with a transaction-oriented database, the data warehouse focuses more on data analysis, can support enterprise-level complex analysis operation and data decision, and can provide intuitive and understandable query results for users. Since various physical tables with huge data size and complex relationships are generally stored in a data warehouse, how to improve the efficiency of data query and storage in data analysis becomes a problem that developers of the data warehouse must pay attention to.
At present, when a user performs custom query on a data warehouse, in order to improve efficiency of performing custom analysis on data stored in the data warehouse, a developer generally designates a certain physical table in the data warehouse as a query master table, designs a plurality of large-width tables including a plurality of fields at the same time, and determines a query result by performing query association on fields in other large-width tables and the master table. Therefore, excessive associated operations in query normal mode operation can be reduced, parallelization of processing is achieved, and accordingly data query efficiency is improved.
However, as the amount of information stored in the data warehouse increases, the number of large-width tables accumulated in the data warehouse also increases, and the large-width tables have large data size and complicated business logic, which results in increased occupation of storage and computing resources. In this case, the query result may be obtained by a developer through a query instruction written by the developer on the wide table, and the query result may be incorrect due to a complex logic. In addition, because a large number of dependencies exist among fields of the large-width tables, instability among the large-width tables is transmitted progressively, and the stability of the data warehouse is further reduced.
Disclosure of Invention
The object of the present application includes, for example, providing a method, a system, a server and a storage medium for generating a dynamic form, which can automatically screen and optimize a dynamic form query request initiated by a user, and generate a target data query instruction, thereby avoiding an error caused by manually writing a code, and improving the efficiency of data query.
The embodiment of the application can be realized as follows:
in a first aspect, the present application provides a method for generating a dynamic form, where the method includes:
receiving a dynamic form query request initiated by a terminal device, wherein the dynamic form query request comprises: a plurality of preselected dimensions, a plurality of preselected metrics, an aggregation algorithm for the preselected metrics, and a filter condition;
Screening the preselected dimensions according to the incidence relation of the preselected dimensions in the dynamic form query request to obtain target dimensions;
determining a target table set according to the target dimension, the preselected indicator, the aggregation algorithm, and the filter condition, the target table set comprising: at least one physical table, said physical table including fields having direct associations with said target dimensions, said aggregation algorithm, said filter conditions, and said preselected metrics;
generating a target data query instruction according to the target table set;
and generating a dynamic form according to the target data query instruction, and sending the dynamic form to the terminal equipment.
In an alternative embodiment, said determining a target table set according to said target dimension, said preselected metric, said aggregation algorithm, and said filter condition comprises:
determining a plurality of physical tables in which the target dimension, the preselected index, the aggregation algorithm and the filtering condition are located according to the target dimension, the preselected index, the aggregation algorithm and the filtering condition;
determining a plurality of table sets to be selected according to a plurality of physical tables, wherein each table set to be selected comprises at least one physical table, and each physical table comprises fields which have association relations with the target dimension, the preselected index, the aggregation algorithm and the filtering condition;
And selecting one table set to be selected from the plurality of table sets to be selected as a target table set according to a preset evaluation strategy.
In an optional implementation manner, the determining, according to a plurality of physical tables, a plurality of sets of tables to be selected includes:
and carrying out permutation and combination on the plurality of physical tables to determine a plurality of table sets to be selected.
In an optional implementation manner, the selecting, according to a preset evaluation policy, one table set to be selected from the multiple table sets to be selected as a target table set includes:
according to the feature information of each table set to be selected, scoring the plurality of table sets to be selected respectively to obtain scores corresponding to the plurality of table sets to be selected, wherein the feature information comprises at least one of the following items: the number, the depth and the aggregation complexity of the physical tables in the table set to be selected;
and using a Monte Carlo algorithm to cooperate the table set to be selected corresponding to the highest score in all the scores as a target table set.
In an optional implementation, the association relationship includes: containment relationships and/or dependency relationships.
In an optional implementation manner, the generating a target data query instruction according to the target table set includes:
Generating an initial data query instruction according to the target table set, wherein the initial data query instruction comprises: query statement and calculation formula of the target dimension and the preselected index;
and optimizing the initial data query instruction to obtain the target data query instruction.
In an optional implementation manner, the optimizing the initial data query instruction to obtain the target data query instruction includes:
simplifying the connection path of the query statement in the initial data query instruction to obtain the simplified query statement, and taking the simplified query statement as the query statement in the target data query instruction;
determining a plurality of pre-calculated indicators according to the pre-selected indicators;
according to the pre-calculated index, optimizing the target dimension and the calculation formula of the pre-selected index to obtain a processed calculation formula;
and selecting a target formula from the processed calculation formulas according to the characteristic information of the target dimension, and taking the target formula as the calculation formula in the target data query instruction.
In a second aspect, the present application provides an apparatus for generating a dynamic form, including:
A receiving module, configured to receive a dynamic form query request initiated by a terminal device, where the dynamic form query request includes: a plurality of preselected dimensions, a plurality of preselected metrics, an aggregation algorithm for the preselected metrics, and a filter condition.
And the processing module is used for screening the preselected dimensions according to the incidence relation of the preselected dimensions in the dynamic form query request to obtain target dimensions.
A determining module, configured to determine a target table set according to the target dimension, the preselected indicator, the aggregation algorithm, and the filtering condition, where the target table set includes: at least one physical table including fields having direct associations with the target dimension, the aggregation algorithm, the filter condition, and the preselected metric.
And the generating module is used for generating a target data query instruction according to the target table set.
The generating module is further used for generating a dynamic form according to the target data query instruction and sending the dynamic form to the terminal equipment.
The determining module is further specifically configured to determine, according to the target dimension, the preselected indicator, the aggregation algorithm, and the filtering condition, a plurality of physical tables in which the target dimension, the preselected indicator, the aggregation algorithm, and the filtering condition are located; determining a plurality of table sets to be selected according to a plurality of physical tables, wherein each table set to be selected comprises at least one physical table, and each physical table comprises fields which have association relations with the target dimension, the preselected index, the aggregation algorithm and the filtering condition; and selecting one table set to be selected from the plurality of table sets to be selected as a target table set according to a preset evaluation strategy.
The determining module is specifically further configured to perform permutation and combination on the plurality of physical tables, and determine a plurality of table sets to be selected.
The determining module is further specifically configured to score the multiple table sets to be selected respectively according to feature information of each table set to be selected, so as to obtain scores corresponding to the multiple table sets to be selected, where the feature information includes at least one of: the number, depth and aggregation complexity of the physical tables in the table set to be selected; and using a Monte Carlo algorithm to cooperate the table set to be selected corresponding to the highest score in all the scores as a target table set.
The processing module is specifically further configured to, the association relationship includes: containment relationships and/or dependency relationships.
The generating module is specifically further configured to generate an initial data query instruction according to the target table set, where the initial data query instruction includes: query statement and calculation formula of the target dimension and the preselected index; and optimizing the initial data query instruction to obtain the target data query instruction.
The generating module is further specifically configured to simplify a connection path of the query statement in the initial data query instruction to obtain the simplified query statement, and use the simplified query statement as the query statement in the target data query instruction; determining a plurality of pre-calculated indicators according to the pre-selected indicators; according to the pre-calculation index, optimizing the target dimension and the calculation formula of the pre-selection index to obtain a processed calculation formula; and selecting a target formula from the processed calculation formulas according to the characteristic information of the target dimension, and taking the target formula as the calculation formula in the target data query instruction.
In a third aspect, the present application provides a system for generating a dynamic form, where the system includes: the system comprises a plurality of terminal devices and a server, wherein the terminal devices are respectively in communication connection with the server;
the terminal equipment is used for initiating a dynamic form query request to the server and receiving a dynamic form sent by the server;
the server is configured to store data in a metadata manner and perform the steps of the method for generating a dynamic form according to any one of the foregoing embodiments.
In a fourth aspect, the present application provides a server, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the server is running, the processor executing the machine-readable instructions to perform the steps of the method for generating a dynamic form according to any one of the preceding embodiments.
In a fifth aspect, the present application provides a computer readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method for generating a dynamic form according to any one of the foregoing embodiments.
The beneficial effects of the embodiment of the application include:
by adopting the method, the system, the server and the storage medium for generating the dynamic form, firstly, according to the dynamic form request of a user, a plurality of pre-dimensions in the dynamic form request are optimized, then, according to the determined target table set, a target data query instruction is automatically generated, and a dynamic form result is obtained. Compared with a large-width table, the data volume in the target table set is less, the calculation logic is clear, and the server can quickly obtain the dynamic form. Moreover, the target data query instruction is automatically generated by the server, and errors caused by manually writing codes are avoided. Secondly, the physical tables are not in the form of large-width tables during design and storage, so that the negative influence of instability transmission among the physical tables on the system is avoided.
Drawings
To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic structural diagram of a system for generating a dynamic form according to an embodiment of the present application;
fig. 2 is a schematic flowchart illustrating steps of a method for generating a dynamic form according to an embodiment of the present application;
fig. 3 is a flowchart illustrating another step of a method for generating a dynamic form according to an embodiment of the present application;
fig. 4 is a flowchart illustrating another step of the method for generating a dynamic form according to the embodiment of the present application;
fig. 5 is a flowchart illustrating another step of a method for generating a dynamic form according to an embodiment of the present application;
fig. 6 is a flowchart illustrating another step of a method for generating a dynamic form according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a dynamic form generation apparatus according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a server according to an embodiment of the present application.
An icon: 1011-a first terminal device; 1012-a second terminal device; 1013-nth terminal device; 10-a dynamic form generating device; 1001-receiving module; 1002-a processing module; 1003-determination module; 1004-a generation module; 102-a server; 2001-a processor; 2002-memory.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as presented in the figures, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined or explained in subsequent figures.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are only used to distinguish one description from another and are not to be construed as indicating or implying relative importance.
It should be noted that the features of the embodiments of the present application may be combined with each other without conflict.
The large-width table is a database table containing many fields, and is usually a database table formed by associating a plurality of fields related to a certain index. In order to improve the query performance, different fields are put into the same table for storage in the large-width table, and with the increase of the data volume, the problem of data redundancy is caused, so that a large burden is brought to query and storage of a data warehouse. In addition, because the business logic in the data warehouse stored in the form of the large wide table is complex, developers who need to write query instructions have clear and integral understanding of the dependency relationship among the large wide tables. Moreover, even with such knowledge, because too much information and elements need to be paid attention to each time the index is extracted, it is inevitable that an instruction is written by a person in a wrong manner, resulting in an error in the result of the finally generated dynamic form.
Based on the above problems, the applicant provides a method, a system, a server and a storage medium for generating a dynamic form through research, which can optimize multiple preselected dimensions in a dynamic form query request, and automatically generate a target data query instruction according to a determined target table set, so as to obtain a dynamic form result. Compared with a large-width table, the data volume in the target table set is less, the calculation logic is clear, and the server can quickly obtain the dynamic form. Moreover, the target data query instruction is automatically generated by the server, and errors caused by manually writing codes are avoided.
The following explains a method, a system, a server, and a storage medium for generating a dynamic form provided in the embodiments of the present application, in conjunction with a plurality of specific application examples.
Fig. 1 is a schematic structural diagram of a dynamic form generation system provided in an embodiment of the present application, and as shown in fig. 1, the dynamic form generation system includes: a plurality of terminal devices, and a server 102, wherein the plurality of terminal devices are respectively connected with the server 102 in a communication way.
Alternatively, the plurality of terminal devices may include a plurality of terminal devices such as a first terminal device 1011, a second terminal device 1012 …, an nth terminal device 1013, and the like, each terminal device may be an entity device interacting with a user, and the user may log in a dynamic form generation system through a certain terminal device, initiate a dynamic form query request to the server 102 through the terminal device, and receive a dynamic form sent by the server 102. The terminal device may be a mobile phone, a tablet computer, a desktop computer, a notebook computer, or other devices in various forms, which is not limited in this application.
The server 102 may be a remote device with computing processing capabilities, such as a cloud server. Specifically, the server 102 is configured to store data in a metadata manner and execute the steps of the method for generating a dynamic form in the following embodiments.
The metadata may be an electronic catalog, which is data for describing data, or structural data for providing certain resource-related information, and describes information such as all dimensions and indexes supported by the data warehouse, dimensions and indexes contained in each physical table, a blood relationship between the dimensions and the indexes, and a polymerization algorithm supportable by each index. The server 102 may read the physical addresses of the dimensions and the index by reading the configuration information, and further obtain the data.
It should be noted that a dimension is a point of view for describing something, and depends on an index for describing attributes or features of data, for example, a year is a time dimension, and a place is a geographic dimension. The index is a value or a ratio, and is obtained through some calculation to measure the service. Such as order volume, goodness, revenue growth, etc.
Next, the operation process of the dynamic form generation system will be described by taking the interaction between the first terminal device 1011 and the server 102 as an example.
The user inputs information such as a plurality of preselected dimensions and preselected indexes through the first terminal device 1011, and the first terminal device 1011 generates a corresponding dynamic form query request according to the information input by the user and sends the request to the server 102.
After receiving the dynamic form query request sent by the first terminal device 1011, the server 102 first performs preliminary screening on the preselected dimensions contained therein, and removes the columns containing the relationship or the dependency relationship among the dynamic form requests, so as to obtain the target dimensions.
Then, the server 102 may determine the plurality of physical tables where the target dimension is located through the target dimension and the preselected index in the dynamic form query request. Since the data of the indexes are obtained by performing data calculation through the same physical table, for example, the monthly total order quantity may be obtained from the accumulation of the order quantities of different users in the user information table, and may also be obtained from the accumulation of the sold order quantities of different sales in the sales performance table, and therefore, there may be a plurality of combinations of the physical tables corresponding to each index. By arranging and combining the obtained multiple physical tables, the server 102 determines multiple table sets to be selected, and data contained in each table set to be selected can be calculated according to the target dimension on the corresponding data in the physical tables to obtain the preselected index.
And then, scoring the table sets to be selected respectively according to the characteristic information by using a Monte Carlo algorithm for the plurality of table sets to be selected, and determining a corresponding score for each table set to be selected. Wherein, the characteristic information may include: the number, depth and aggregation complexity of the physical tables in the table set to be selected. And then, after the scores of all the table sets to be selected are obtained, the table set to be selected with the highest score is combined as a target table.
Then, the server 102 may generate an initial data query instruction according to the obtained target table set, where the initial data query instruction includes a query statement, for example, a lookup and a connection for each physical table, and the initial data query instruction further includes: the target dimension and the calculation formula of the preselected index, for example, when the target dimension is year and the preselected index is number of users, the total number of users in year can be obtained by performing cumulative calculation on fields related to the number of users in year in the physical table through the calculation formula prestored in the server 102.
Further, the server 102 may optimize the query statement in the initial data query instruction and the calculation formulas of the target dimension and the preselected index, respectively. For the query statement, whether the connection path can be shortened or not can be determined by searching the redundant fields to be obtained in the connected physical tables between the connected physical tables and whether the main connected physical table is provided with the redundant fields to be obtained, so that the query statement is optimized. For the calculation formula, the calculation speed of the server 102 may be increased by reading the pre-calculated index and reading the data in the physical table according to the data granularity of the dimension. After the optimization process, the server 102 obtains the target data query instruction.
Finally, the server 102 may read data from the physical table and perform calculation through the target data query instruction to obtain a dynamic form, and send the dynamic form to the first terminal device 1011.
Fig. 2 is a flowchart of steps of a method for generating a dynamic form according to an embodiment of the present application, where an execution subject of the method may be a server in a system for generating a dynamic form according to the foregoing embodiment, and the server may be a cloud server with computing processing capability. As shown in fig. 2, the method includes the following steps.
S201, receiving a dynamic form query request initiated by a terminal device.
Wherein, the dynamic form inquiry request comprises: a plurality of preselected dimensions, a plurality of preselected metrics, an aggregation algorithm for the plurality of preselected metrics, and a filter condition.
For example, for an order system, the dimension may be a geographic dimension or a time dimension corresponding to a region where the user is located, and the index information may be a rating rate, an order growth rate, a total amount, and the like of the user.
Optionally, the user may also add more data analysis conditions to the dynamic form query request, for example, an aggregation algorithm of each index, a filtering condition, and the like. The index aggregation algorithm is a calculation method for a certain index, and may be a method such as an average value, a maximum value, or a minimum value. For example, when calculating the revenue amount of nearly 3 months, if the aggregation algorithm of the index is an average value, a single-month average value may be obtained for the total revenue amount of the 3 months as the calculation result of the index. The filtering condition may refer to an item that needs to be removed or retained when a certain index is calculated, for example, only the total amount of orders of a certain user is calculated, or the amount of performance of a certain department is removed when the total amount of performance is calculated.
Of course, the user may also add other data analysis conditions to the dynamic form query request, which is not limited herein.
After the user completes the selection of the data analysis conditions, the terminal device can package the data analysis conditions into a dynamic form query request and send the dynamic form query request to the server.
S202, screening the preselected dimensions according to the incidence relation of the preselected dimensions in the dynamic form query request to obtain the target dimensions.
After receiving the dynamic form query request, the server can analyze the dynamic form query request and extract preselected dimensions and preselected indexes. Since the association relationship between the dimensions may not be clear when the user selects the dimensions, the preselected dimensions may include the dimensions having the association relationship. For example, the preselected dimensions include two dimensions of a commodity name and a commodity brand, but the commodity brand is a sub-category of the commodity name, and the server can directly perform aggregation operation according to the commodity brand to obtain the commodity name. At this point, the name of the item may be removed from the preselected dimension.
After the screening process, the server can obtain the target dimension.
S203, determining a target table set according to the target dimension, the preselected index, the aggregation algorithm and the filtering condition.
Wherein the target table set includes: at least one physical table, the physical table including fields directly associated with the target dimension, the aggregation algorithm, the filter condition, and the preselected metric.
And according to the dynamic form query request, pre-screening a plurality of physical tables in the data warehouse, and determining a physical table set as a target table set. It should be noted that through calculation of the target dimension, the preselected index, the aggregation algorithm, and the filtering condition, fields directly associated with the target dimension, the preselected index, the aggregation algorithm, and the filtering condition may be determined, and then the physical tables directly associated with the fields are found to form the target table set. Specifically, the target table set may include one or more physical tables, which are directly associated with fields corresponding to the aggregation algorithm, the filtering condition, and the preselected metric, and the result of the preselected metric can be obtained directly from the physical tables of the target table set under the data analysis conditions of the filtering condition, the aggregation algorithm, and the target dimension. That is, the target table set includes the physical table in which the field corresponding to the most economical calculation of the preselected metric and the manner of storing the resource is located.
And S204, generating a target data query command according to the target table set.
And after determining the target table set, the server automatically generates a corresponding target data query instruction according to the target table set. It can be understood that, when databases where physical tables in the data warehouse are located are different, the generated target data query instructions are different, the server may generate corresponding target data query instructions according to the corresponding languages according to the databases where the physical tables are located, and the same target data query instruction may include query instructions in different languages to respectively execute the query instructions to obtain query results.
And S205, generating a dynamic form according to the target data query instruction, and sending the dynamic form to the terminal equipment.
Through the target data query instruction, the server can obtain a physical table required by the calculation of the corresponding preselected indexes under the condition of a plurality of target dimensions, a plurality of aggregation algorithms of the preselected indexes and filtering conditions required by the user.
And the server executes the target data query instruction, generates a dynamic form and sends the dynamic form to the terminal equipment to be displayed to the user. The dynamic form may be a result display page generated according to a dynamic form query request initiated by a user, and data in the dynamic form query request may be displayed on the dynamic form in a manner of numbers, curves, tables, statistical graphs, and the like, which is not limited to this.
In this embodiment, the dynamic form request of the user can be automatically optimized, and the target data query instruction is automatically generated according to the optimized dynamic form request, so as to obtain a dynamic form result. The optimized dynamic form requests have the advantages that the data quantity required to be acquired is reduced, the calculation logic is clearer, and the calculation efficiency of the server is improved. Moreover, the target data query instruction is automatically generated by the server, and errors caused by manually writing codes are avoided.
Alternatively, as shown in fig. 3, in the step S203, the target table set is determined according to the target dimension, the preselected index, the aggregation algorithm and the filtering condition, which may be implemented by the following steps S301 to S303.
S301, determining a plurality of physical tables of the target dimension, the preselected index, the aggregation algorithm and the filtering condition according to the target dimension, the preselected index, the aggregation algorithm and the filtering condition.
Firstly, the server analyzes the target dimension, the preselected index, the aggregation algorithm and the filtering condition to determine the fields required for obtaining the information, and further determines the physical table where the information is located. Further, each preselected metric may be calculated from a plurality of different fields, or a field may be located in a different physical table. Therefore, for a certain preselected index, the number of corresponding physical tables may be multiple.
S302, determining a plurality of table sets to be selected according to the plurality of physical tables.
Each table set to be selected comprises at least one physical table, and each physical table comprises fields which are in incidence relation with the target dimension, the preselected index, the aggregation algorithm and the filtering condition.
And dividing the plurality of physical tables to obtain a plurality of table sets to be selected, wherein each table set to be selected comprises one or more physical tables. The division standard may be that a corresponding preselected index result is obtained by calculation through data included in each physical table in each table set to be selected under the data analysis condition of the target dimension, the aggregation algorithm and the filtering condition. The association relationship may be a direct association relationship or an indirect association relationship.
For example, for a preselected index of total sales amount of commodities of a company, under the condition that the target dimension is year, the aggregation algorithm is null, and the filtering condition is to filter out the sales amount of the last half year, the determined table set to be selected can be a customer order table, and the accumulated calculation is carried out through the order amount field of each customer. The table set to be selected can also be a department performance table, and the sales performance fields of all departments are used for carrying out accumulation calculation. Or the combination of the commodity selling price table and the customer order table, and the total commodity selling amount in the next half year is calculated according to the commodity unit price in the commodity selling price table and the customer order amount in the customer order table.
And S303, selecting a table set to be selected from a plurality of table sets to be selected as a target table set according to a preset evaluation strategy.
Optionally, for the multiple candidate table sets, the server may evaluate the multiple candidate table sets respectively according to a preset evaluation policy, and select one of the multiple candidate table sets as the target table set. The preset evaluation strategy can be a method which is stored in the server in advance and used for evaluating a plurality of table sets to be selected and calculating the efficiency of the result according to data such as target dimensions, preselected indexes, aggregation algorithms, filtering conditions and the like.
In this embodiment, a plurality of candidate table sets are determined by analyzing data required by the target dimension, the preselected index, the aggregation algorithm, and the filtering condition. The multiple tables to be selected obtained by enumeration can contain possible modes of calculating final results as much as possible, and selectable items are provided for finding the optimal calculation path.
Optionally, in step S302, determining a plurality of table sets to be selected according to a plurality of physical tables may include: and carrying out permutation and combination on the plurality of physical tables to determine a plurality of table sets to be selected.
It is understood that, in the above embodiment, the plurality of physical tables may be divided in a manner of permutation and combination, for example, three different physical table combinations may be determined for the pre-selected index of the total sales amount of the commodity in the annual target dimension, and two different physical table combinations may be determined for the pre-selected index of the customer number growth rate in the annual target dimension. One of the three physical table combinations for calculating the total sales amount of the annual commodities and one of the two physical table combinations for calculating the number increase rate of the annual customers can be combined pairwise to obtain query results, so that the five physical tables are arranged and combined to obtain six table sets to be selected.
It can be understood that the physical tables in each candidate table set can obtain the query result through calculation, but the calculation efficiency and the occupation of the storage resources may be different.
In this embodiment, the table set to be selected is obtained by permutation and combination of the physical tables, so that all possible situations of the physical tables of the dynamic form can be enumerated as much as possible, and a possibility of seeking an optimal path is provided.
Alternatively, as shown in fig. 4, in the step S303, one table set to be selected is selected from a plurality of table sets to be selected as the target table set according to a preset evaluation policy, which may be implemented by the following steps S401 to S402.
S401, scoring the multiple table sets to be selected respectively according to the characteristic information of each table set to be selected to obtain scores corresponding to the multiple table sets to be selected.
Wherein the characteristic information includes at least one of: the number, depth and aggregation complexity of the physical tables in the table set to be selected.
It can be understood that, in the candidate table set, the smaller the number of the physical tables, the higher the parallelization degree is, the higher the efficiency of data query is, and the higher the score is obtained more easily.
The depth of the physical tables in the table set to be selected can be understood as that a dependency relationship exists between the physical tables in the data warehouse, and a certain preselected index can be obtained according to a required physical table after connecting the physical tables in a hierarchical drilling mode or a non-hierarchical drilling mode through connection of a plurality of physical tables. In the process, the number of layers of the connected physical tables is the depth corresponding to the preselected index, and the sum of the depths required by the computation of all the preselected indexes is the depth of the table set to be selected. It is understood that the greater the depth, the greater the number of connections, the less computationally efficient, and the lower the score obtained.
The aggregation complexity of the candidate table set can be understood as that, when calculating an average value for a target dimension of annual order total amount, aggregation operation is performed from a physical table in which monthly order total amount is located, and the aggregation operation is performed from a physical table in which daily order total amount is located, the complexity of dimension aggregation is different, and it can be understood that the larger the data granularity is, the higher the aggregation complexity score is.
Optionally, the server may pre-store the above feature information with the corresponding relationship between different values and scores of the feature information. Therefore, each table set to be selected can be scored respectively according to the characteristic information, and scores corresponding to each table set to be selected are obtained.
S402, using a Monte Carlo algorithm to cooperate the table set to be selected corresponding to the highest score in all the scores as a target table set.
The Monte Carlo algorithm is a generalization of the characteristics of a random algorithm, and has the core idea that all possible solutions are enumerated as far as possible, and then a local optimal solution is selected from the possible solutions to serve as a global optimal solution for the problem. It can be understood that, because the monte carlo algorithm adopts a limited sampling mode, the obtained solution can only be an optimal solution within a sampling range, but as the sampling range is expanded, the probability that the obtained local optimal solution is a global optimal solution is gradually increased.
In the application, one of enumerated table sets to be selected is selected to serve as a target table set in the scoring process, and the table set to be selected with the highest score is obtained. The calculation efficiency and the occupied storage space of the target table set are optimal in all the table sets to be selected.
In this embodiment, each table set to be selected is scored according to the feature information of each table set to be selected, so that the table sets to be selected can be comprehensively and completely evaluated at multiple angles. Then, the idea of the Monte Carlo algorithm is used, and a target table set is determined, so that the target table set is the optimal solution in the table set to be selected.
Optionally, the association relationship in the above embodiment includes: containment relationships and/or dependency relationships.
The inclusion relation refers to a relation that one preselected dimension and another preselected dimension are parent-child dimensions which are all included in each preselected dimension, for example, two time dimensions of month and year are dependency relations.
The dependency relationship refers to that one preselected dimension among the preselected dimensions can be derived from other preselected dimensions, and thus it can be said that the preselected dimension exists in dependence on other preselected dimensions. Generally, when a dimension is established, a data warehouse is established through normalization processing by a snowflake mode, but the situation that domains corresponding to the dimension overlap is inevitable. For example, for a company that contains two regions, the geographic dimension of the first region is summed with the physical dimension of the second region to obtain the overall geographic dimension of the company.
For the preselected dimension with the association relationship, the preselected dimension can be eliminated, and the target dimension is obtained.
In this embodiment, the specific situation of the association relationship is defined, and a basis is provided for the screening of the target dimension.
Alternatively, as shown in fig. 5, in the step S204, the target data query command is generated according to the target table set, and may be obtained through the following steps S501 to S502.
S501, generating an initial data query instruction according to the target table set.
Wherein, the initial data query instruction comprises: and query statements, and calculation formulas of the target dimension and the preselected index.
It can be understood that the query statement in the initial data query instruction is a database query statement that is automatically generated by the server according to the database in which each physical table in the target table set is located, and is used for acquiring data that can be used for generating a dynamic form by calculation from each physical table in the database.
In the initial data query statement, the calculation formula of the target dimension and the preselected index is a calculation formula of the process of acquiring data in the physical table, summarizing, calculating and generating the dynamic form.
S502, optimizing the initial data query instruction to obtain a target data query instruction.
In the initial data query instruction, the data query and calculation mode with the highest efficiency may not be performed, and the server may optimize the data acquisition path, the calculation path of the calculation mode, and the like in the query instruction, so as to speed up the efficiency of data acquisition and the calculation efficiency.
In the embodiment, the initial data query instruction is automatically generated through the target table set, so that tedious manual development is avoided, and the condition that errors may occur in manual instruction writing is reduced.
Alternatively, as shown in fig. 6, in the step S502, the initial data query command is optimized to obtain the target data query command, which can be implemented by the following steps S601 to S604.
And S601, simplifying a connection path of the query statement in the initial data query instruction to obtain a simplified query statement, and taking the simplified query statement as the query statement in the target data query instruction.
The connection path of the query statement may be understood as a query statement in which an upper physical table and a lower physical table are connected to each other through a common field in order to acquire data in a plurality of physical tables. Sometimes, in order to obtain data in a lowest physical table, multiple physical tables may need to be connected to obtain data.
In order to reduce excessive association operations, when a physical table of the data warehouse is designed, a field which is frequently used in a lower physical table can be stored in an upper physical table at the same time, and the field is used as a shortcut redundancy field in the upper physical table. Therefore, after the server searches the upper physical table in sequence, the shortcut redundancy field is read, the longer connection path connected to the lower physical table is changed into the shorter connection path connected to the upper physical table, and the length of the query statement in the initial data query instruction is shortened.
S602, determining a plurality of pre-calculation indexes according to the pre-selection indexes.
In order to simplify the calculation process, for the acquired data of some fields, the server may pre-calculate the data according to the pre-selected indexes which often correspond to the fields in the past, and then, generate a temporary view together with some fields by using the result obtained by the pre-calculation as a temporary field. Therefore, each part of the field can be calculated by the calculation formula respectively, and the calculation efficiency is improved.
For example, for both the number of pieces and the unit price, the corresponding indicator may be the total amount in most cases. The server can pre-calculate the total amount and store it in the temporary view.
And S603, optimizing the target dimension and the calculation formula of the preselected index according to the pre-calculation index to obtain the processed calculation formula.
The server searches the temporary view according to the calculation formula, and if a certain field in the temporary view is a result which needs to be calculated and obtained by the calculation formula, the field in which the result is located can be directly replaced by the calculation of the part in the calculation formula, so that the complexity of the calculation formula is reduced, and the calculation efficiency is improved.
S604, selecting a target formula from the processed calculation formulas according to the characteristic information of the target dimension, and taking the target formula as the calculation formula in the target data query instruction.
Next, the calculation formula may be further optimized by using an angle of a certain dimension in the target dimension, so as to obtain the target formula. For example, annual revenue may be obtained by accumulation of monthly revenue or daily revenue, and obviously, the efficiency of annual revenue obtained from monthly revenue accumulation is higher than that obtained from daily revenue accumulation. Therefore, when both daily revenue and monthly revenue data are present, monthly revenue data can be preferentially used.
The method improves the generation efficiency of the dynamic form from the algorithm level, and optionally, the server can also improve the query efficiency of the query statement from the physical equipment level in a table partitioning mode when designing a data warehouse. Specifically, a physical table with a large data size can be divided and stored in a plurality of partitions, so that the plurality of partitions of the physical table can be searched at the same time, and the searching speed is improved.
The server can also store the data with different heat degrees in databases with different efficiencies and storage spaces according to the time dimension of the data. It will be appreciated that the closer the time dimension of the data, the higher the heat. Therefore, hot data can be stored in a MySQL database with a faster retrieval speed, warm data in a Holo database, and cold data in a MaxCompute database. When retrieving data with different heat degrees, the server can generate different query statements according to the corresponding databases, and respectively acquire and summarize the analysis data.
In the embodiment, the initial data query instruction is optimized from the two aspects of the query instruction and the calculation formula to obtain the target data query instruction, and the automatically generated initial data query instruction is further optimized, so that the generation efficiency of the dynamic form is improved.
An embodiment of the present application further provides an apparatus 10 for generating a dynamic form, as shown in fig. 7, the apparatus includes:
a receiving module 1001, configured to receive a dynamic form query request initiated by a terminal device, where the dynamic form query request includes: a plurality of preselected dimensions, a plurality of preselected indicators aggregation algorithm and a filtering condition.
The processing module 1002 is configured to screen the preselected dimensions according to the association relationship of each preselected dimension in the dynamic form query request, so as to obtain target dimensions.
A determining module 1003, configured to determine a target table set according to the target dimension, the preselected index, the aggregation algorithm, and the filtering condition, where the target table set includes: at least one physical table, the physical table including fields having direct associations with the target dimensions, the aggregation algorithm, the filter condition, and the preselected metric.
The generating module 1004 is configured to generate a target data query instruction according to the target table set.
The generating module 1004 is further configured to generate a dynamic form according to the target data query instruction, and send the dynamic form to the terminal device.
The determining module 1003 is further specifically configured to determine, according to the target dimension, the preselected index, the aggregation algorithm, and the filtering condition, a plurality of physical tables in which the target dimension, the preselected index, the aggregation algorithm, and the filtering condition are located. And determining a plurality of table sets to be selected according to the plurality of physical tables, wherein each table set to be selected comprises at least one physical table, and each physical table comprises fields which have association relations with the target dimension, the preselected index, the aggregation algorithm and the filtering condition. And selecting one table set to be selected from a plurality of table sets to be selected as a target table set according to a preset evaluation strategy.
The determining module 1003 is further specifically configured to perform permutation and combination on the multiple physical tables to determine multiple table sets to be selected.
The determining module 1003 is further specifically configured to score the multiple to-be-selected table sets according to the feature information of each to-be-selected table set, to obtain scores corresponding to the multiple to-be-selected table sets, where the feature information includes at least one of the following items: the number, depth and aggregation complexity of the physical tables in the table set to be selected. And using a Monte Carlo algorithm to cooperate the table set to be selected corresponding to the highest score in all the scores as a target table set.
The processing module 1002 is further specifically configured to associate the relationship: containment relationships and/or dependency relationships.
The generating module 1004 is specifically further configured to generate an initial data query instruction according to the target table set, where the initial data query instruction includes: and query statements, and calculation formulas of the target dimension and the preselected index. And optimizing the initial data query instruction to obtain a target data query instruction.
The generating module 1004 is further specifically configured to simplify a connection path of the query statement in the initial data query instruction, obtain a simplified query statement, and use the simplified query statement as the query statement in the target data query instruction. A plurality of pre-calculated indicators are determined based on the pre-selected indicators. And according to the pre-calculation index, optimizing the calculation formula of the target dimension and the pre-selection index to obtain a processed calculation formula. And selecting a target formula from the processed calculation formulas according to the characteristic information of the target dimension, and taking the target formula as the calculation formula in the target data query instruction.
Referring to fig. 8, the present embodiment further provides a server, including: a processor 2001, a memory 2002 and a bus, wherein the memory 2002 stores machine-readable instructions executable by the processor 2001, and when the server runs, the machine-readable instructions are executed, the processor 2001 and the memory 2002 are communicated through the bus, and the processor 2001 is used for executing the steps of the dynamic form generation method in the above embodiments.
The memory 2002, processor 2001, and bus elements are electrically connected to one another, directly or indirectly, to enable data transfer or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The data processing device of the dynamic form generation system includes at least one software functional module which can be stored in the memory 2002 in the form of software or firmware (firmware) or solidified in an Operating System (OS) of the computer device. The processor 2001 is used to execute executable modules stored in the memory 2002, such as software functional modules and computer programs included in a data processing apparatus of a system for generating a dynamic form of an object.
The Memory 2002 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
Optionally, the present application further provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method embodiments are performed. The specific implementation and technical effects are similar, and are not described herein again.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process of the system and the apparatus described above may refer to the corresponding process in the method embodiment, and is not described in detail in this application. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some communication interfaces, indirect coupling or communication connection between devices or modules, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk or an optical disk, and various media capable of storing program codes.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A method for generating a dynamic form, the method comprising:
receiving a dynamic form query request initiated by a terminal device, wherein the dynamic form query request comprises: a plurality of preselected dimensions, a plurality of preselected indicators, an aggregation algorithm for the plurality of preselected indicators, and a filtering condition;
screening the preselected dimensions according to the incidence relation of the preselected dimensions in the dynamic form query request to obtain target dimensions;
determining a target table set according to the target dimension, the preselected indicator, the aggregation algorithm, and the filtering condition, the target table set comprising: at least one physical table including fields having direct associations with the target dimension, the aggregation algorithm, the filter condition, and the preselected metric;
generating a target data query instruction according to the target table set;
generating a dynamic form according to the target data query instruction, and sending the dynamic form to the terminal equipment;
generating a target data query instruction according to the target table set, including:
generating an initial data query instruction according to the target table set, wherein the initial data query instruction comprises: query statement and calculation formula of the target dimension and the preselected index;
Optimizing the initial data query instruction to obtain the target data query instruction;
the optimizing the initial data query instruction to obtain the target data query instruction includes:
simplifying the connection path of the query statement in the initial data query instruction to obtain a simplified query statement, and taking the simplified query statement as the query statement in the target data query instruction;
determining a plurality of pre-calculated indexes according to the pre-selected indexes;
according to the pre-calculation index, optimizing the target dimension and the calculation formula of the pre-selection index to obtain a processed calculation formula;
and selecting a target formula from the processed calculation formulas according to the characteristic information of the target dimension, and taking the target formula as the calculation formula in the target data query instruction.
2. The method of generating a dynamic form of claim 1, wherein said determining a set of target tables based on said target dimensions, said preselected metric, said aggregation algorithm, and said filtering criteria comprises:
determining a plurality of physical tables in which the target dimension, the preselected index, the aggregation algorithm and the filtering condition are located according to the target dimension, the preselected index, the aggregation algorithm and the filtering condition;
Determining a plurality of table sets to be selected according to the plurality of physical tables, wherein each table set to be selected comprises at least one physical table, and each physical table comprises fields which have association relations with the target dimension, the preselected index, the aggregation algorithm and the filtering condition;
and selecting one table set to be selected from the plurality of table sets to be selected as a target table set according to a preset evaluation strategy.
3. The method for generating a dynamic form according to claim 2, wherein the determining a plurality of candidate table sets according to the plurality of physical tables comprises:
and carrying out permutation and combination on the plurality of physical tables to determine a plurality of table sets to be selected.
4. The method for generating a dynamic form according to claim 2, wherein the selecting one table set to be selected from the plurality of table sets to be selected as a target table set according to a preset evaluation policy includes:
according to the feature information of each table set to be selected, scoring the plurality of table sets to be selected respectively to obtain scores corresponding to the plurality of table sets to be selected, wherein the feature information comprises at least one of the following items: the number, the depth and the aggregation complexity of the physical tables in the table set to be selected;
And using a Monte Carlo algorithm to cooperate the table set to be selected corresponding to the highest score in all the scores as a target table set.
5. The method of generating a dynamic form according to claim 1, wherein the association relationship comprises: containment relationships and/or dependency relationships.
6. A system for generating a dynamic form, the system comprising: the system comprises a plurality of terminal devices and a server, wherein the terminal devices are respectively in communication connection with the server;
the terminal equipment is used for initiating a dynamic form query request to the server and receiving a dynamic form sent by the server;
the server is configured to store data in the form of metadata and to perform the steps of the method for generating a dynamic form according to any one of claims 1 to 5.
7. A server, characterized in that the server comprises: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the server is running, the processor executing the machine-readable instructions to perform the steps of the method of generating a dynamic form according to any one of claims 1-5.
8. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the method for generating a dynamic form according to any one of claims 1-5.
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