WO2023078130A1

WO2023078130A1 - Index creation method and apparatus, and computer-readable storage medium

Info

Publication number: WO2023078130A1
Application number: PCT/CN2022/127445
Authority: WO
Inventors: 魏铮
Original assignee: 中兴通讯股份有限公司
Priority date: 2021-11-03
Filing date: 2022-10-25
Publication date: 2023-05-11
Also published as: CN116069777A

Abstract

Provided are an index creation method and apparatus, and a computer-readable storage medium. The method comprises: acquiring an application algorithm set (100); according to the application algorithm set, obtaining the frequency of use of a data table and the frequency of a field use mode (200); and creating an index according to the frequency of use of the data table and the frequency of the field use mode (300).

Description

Index creation method, device and computer-readable storage medium

Cross References to Related Applications

This application is based on a Chinese patent application with application number 202111294583.X and a filing date of November 03, 2021, and claims the priority of this Chinese patent application. The entire content of this Chinese patent application is hereby incorporated by reference into this application.

technical field

The present application relates to the field of information technology, and in particular to an index creation method, device and computer-readable storage medium.

Background technique

In a relational database, an index is a separate, physical storage structure that sorts the values of one or more columns in a database table. It is a collection of one or several column values in a table and the corresponding pointers A list of logical pointers to the data pages in the table that physically identify these values. The function of the index is equivalent to the table of contents of the book, and the required content can be quickly found according to the page number in the table of contents.

In the method of creating an index in the related art, business developers need to judge which fields are suitable for creating an index based on past experience. However, this way of creating an index may result in inappropriate selection of index fields, which in turn leads to a decrease in data access performance.

Contents of the invention

The following is an overview of the topics described in detail in this article. This summary is not intended to limit the scope of the claims.

The main purpose of the embodiments of the present application is to provide an index creation method, device and computer-readable storage medium.

In the first aspect, the embodiment of the present application provides an index creation method, the method includes: obtaining a set of application algorithms; obtaining the frequency of data table usage and the frequency of field usage methods according to the set of application algorithms; and according to the data Indexes are created on how often the table is used and how often the fields are used.

In the second aspect, an embodiment of the present application provides an index creation device, the index creation device includes a memory, a processor, a program stored in the memory and executable on the processor, and used to implement the A data bus connecting and communicating between the processor and the memory, when the program is executed by the processor, implements the index creation method as described in the first aspect above.

In a third aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to realize The index creation method of the first aspect above.

Additional features and advantages of the application will be set forth in the description which follows, and, in part, will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Description of drawings

FIG. 1 is a flowchart of an index creation method provided by an embodiment of the present application;

Fig. 2 is the flowchart of frequency statistics provided by an embodiment of the present application;

Fig. 3 is a flow chart of data acquisition provided by an embodiment of the present application;

FIG. 4 is a flow chart of data screening provided by an embodiment of the present application;

FIG. 5 is a flow chart of field acquisition provided by an embodiment of the present application;

FIG. 6 is a flow chart of obtaining an index field provided by an embodiment of the present application; and

FIG. 7 is another flowchart of an index creation method provided by an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, not to limit the present application.

It should be noted that although the functional modules are divided in the schematic diagram of the system architecture and the logical order is shown in the flow chart, in some cases, the division of modules in the device or the order in the flow chart may be different. Perform the steps shown or described.

The present application provides an index creation method, device and computer-readable storage medium. Firstly, an application algorithm set is obtained. The application algorithm set can access and acquire multiple data sets, and each data set includes multiple different Fields, and then the usage method corresponding to each field can be obtained by applying the algorithm, so the usage frequency of the data table and the usage frequency of the field can be further obtained, and an index can be created. Through these two types of frequencies, we can know the high-frequency words that appear in the current retrieval process, and the final index can be applied to the current data, improve the efficiency of retrieval, improve the user's experience in using data, and improve the performance of data access .

The embodiments of the present application will be further described below in conjunction with the accompanying drawings.

As shown in FIG. 1 , FIG. 1 is a flowchart of an index creation method provided by an embodiment of the present application. It can be understood that the present application proposes a method for creating an index, which includes but is not limited to step S100, step S200 and step S300.

Step S100, acquiring a set of application algorithms.

It can be understood that the set of applied algorithms refers to the set of all algorithms that are currently used or may be used in the retrieval process. When a user needs to use a certain type of database or a certain type of big data component to clean and process massive data, the set of all algorithms in the database or big data component can be used as a set of application algorithms, and the application algorithm An index is created for a collection through the index creation method of this application.

It can be understood that, in the embodiments provided by this application, a set of multiple SQL (Structured Query Language, Structured Query Language) algorithms in the application program is used as the set of application algorithms to illustrate the specifics of the index creation method. process. In the process of using the index creation method proposed in this application, a set of SQL algorithms can be used as a set of application algorithms, or a set of other types of algorithms can be used as a set of application algorithms, or other types of databases and big data components can be used. The algorithm set is used as the application algorithm set, and this application does not specifically limit the objects targeted by the index creation method.

In step S200, the usage frequency of the data table and the frequency of the field usage mode are obtained according to the application algorithm set.

It is understandable that multiple initial data tables will be used in the application algorithm set, and each initial data table includes multiple fields, and the fields used in the initial data table and how the fields are used can be obtained through the application algorithm . By making statistics on the content in the application algorithm set, the usage frequency of the data table and the frequency of the field usage can be obtained. Frequency refers to the number of times the object corresponding to the frequency appears in the application algorithm set. By counting the content analyzed by the application algorithm set, after obtaining the frequency of data table usage and the frequency of field usage, it can be obtained according to the current frequency of each parameter, which content appears frequently in the process of actual engineering application higher degree.

Step S300, creating an index according to the usage frequency of the data table and the usage frequency of the fields.

It is understandable that after obtaining the data table usage frequency and field usage frequency, statistical analysis will be performed on these two types of frequencies, and an index will be created according to the analysis results. After obtaining these two types of frequencies, determine which current data tables, fields, and usage modes correspond to the frequencies that meet the preset conditions, and then create indexes based on these high-frequency contents. The index established by the index creation method fully combines the current retrieval requirements in the process of processing big data, which can improve the user's experience in using data and improve the performance of data access.

It should be noted that the frequency of use of data tables proposed in this application refers to the frequency of use of each data table among multiple data tables used in the application algorithm set. There are also different types of fields in each data table, and multiple fields are used in different ways in the application algorithm. Therefore, in the data table, the fields and the corresponding usage methods of the fields can be counted to obtain the field Frequency of use and How often the field is used. Then determine which information is suitable for index creation according to their frequency of use, thereby improving data access performance.

As shown in FIG. 2 , FIG. 2 is a flow chart of frequency statistics provided by an embodiment of the present application. It can be understood that step S200 in the embodiment shown in FIG. 1 includes but not limited to step S210, step S220, step S230 and step S240.

In step S210, a plurality of initial data tables and usage frequencies of the data tables corresponding to the initial data tables are obtained according to the application algorithm set.

It can be understood that after obtaining the application algorithm set, multiple initial data tables must be obtained according to the application algorithm set. After counting these initial data tables, the usage frequency of the data table corresponding to each initial data table can be obtained.

It should be noted that when the SQL algorithm set is used as the application algorithm set in this application, the initial data table obtained according to the SQL algorithm set is the data table. During the actual application of the SQL algorithm, the fields in the algorithm will be stored in different data tables according to the preset categories. At the same time, the calling relationship of each data table by the SQL algorithm is used to reflect the data tables belonging to different levels. The concept of table level is equivalent to the difference between the first-level catalog and the multi-level catalog in the book catalog. Specifically, the frequency of use of the data table mentioned above refers to the frequency of access to each data table during the running of the SQL algorithm. Multiple fields are stored in each data table, and the usage method corresponding to each field obtained through the SQL algorithm is recorded at the same time. In this application, the index creation method is briefly described through the SQL algorithm set and the storage form of the data table, but the SQL algorithm set and the recording method of the data table do not constitute a limitation to the implementation of the application.

It should be noted that there are multiple fields and corresponding usage methods stored in the data table. Among them, the fields come from the original data that needs to be indexed. There are multiple fields in the original data, and then the original data is processed by the SQL algorithm. Only by processing the data can we know the corresponding usage method of each field, and finally store multiple fields and their corresponding usage methods in the data table, so that the subsequent statistics on the frequency of field usage methods can be used to determine which ones should be used Information is used as a retrieval field, thereby improving the performance of data access.

It should be noted that there are multiple fields and corresponding usage methods stored in the initial data table. Among them, the fields come from the original data that needs to be indexed. The original data includes multiple fields, and then the SQL algorithm is used to Only by processing the original data can we know the corresponding use method of each field, and finally store multiple fields and their corresponding use methods in the initial data table, so that the initial data table can be screened later to obtain candidate data tables.

Step S220, screening multiple initial data tables to obtain candidate data tables.

It can be understood that after obtaining multiple initial data tables, the initial data tables need to be screened to obtain candidate data tables. In the process of actually searching based on the index, the retrieval requirements for different data are also different, and the retrieval requirements are determined according to the algorithm content in the application algorithm set. Therefore, it is necessary to screen the initial data table according to the actual retrieval requirements to obtain the initial data table that meets the retrieval requirements, that is, to obtain the candidate data table, so as to make statistics on the fields in the candidate data table and the usage methods corresponding to the fields, and finally perform Create indexes to improve data access performance.

It should be noted that, in the process of screening the initial data tables and obtaining candidate data tables, screening refers to judging whether each initial data table satisfies the first preset condition. Specifically, the first preset condition is that the usage frequency of the data table corresponding to the initial data table is not less than the first preset value. When the usage frequency of the data table corresponding to the initial data table is greater than or equal to the first preset value, it can be considered that the initial data table satisfies the first preset condition, and the initial data table is marked as a candidate data table; if the initial If the usage frequency of the data table corresponding to the data table is less than the first preset value, the initial data table will not be marked as a candidate data table, and then other initial data tables will be judged until all the initial data tables are judged .

It should be noted that the first preset value may be a preset threshold according to actual needs, or may be a preset algorithm and a value obtained through algorithm calculation. In the process of judging whether the initial data table satisfies the first preset condition, it is only necessary to classify the initial data table according to the actual retrieval requirement, and the present application does not specifically limit the method of obtaining the first preset value.

In step S230, the candidate data table is scanned to obtain a plurality of fields, usage modes corresponding to the fields, and frequency of usage modes of the fields.

It can be understood that the candidate data table stores a plurality of fields and corresponding usage methods of the fields. After the candidate data table is obtained, the candidate data table needs to be scanned to obtain these fields and corresponding usage methods. It should be noted that the usage of fields mentioned above refers to the operations performed on fields in the application algorithm set, including but not limited to having output fields, using fields as filter conditions, using fields for association, and using fields as the aggregation condition. In the process of actual algorithm operation, there may be a variety of usage methods for fields, but not all usage methods can be used to judge whether a field is suitable as a search field, for example, a simple output function is not enough as a judgment condition of. For judging purposes, including but not limited to filter fields, correlation fields, and aggregation fields, each field in the statistical data table is used as the overall frequency of filtering, aggregation, or association in the algorithm, so as to select index fields to improve Data access performance.

It should be noted that there are multiple fields and corresponding usage methods stored in the candidate data table. Among them, the fields come from the original data that needs to be indexed. The original data includes multiple fields, and then the SQL algorithm is used to Only by processing the original data can we know the corresponding use method of each field, and finally store multiple fields and their corresponding use methods in the initial data table, and filter the initial data table to obtain candidate data tables for subsequent Make statistics on the usage frequency of fields, etc., so as to determine which information should be used as search fields.

It is understandable that, after obtaining the fields and their corresponding usage methods, it is necessary to make statistics on these information in order to obtain the frequency of the usage methods of the fields. After obtaining the frequency of the usage method corresponding to each field, determine which information to use to create the index according to the frequency and other methods.

As shown in FIG. 3 , FIG. 3 is a flow chart of data acquisition provided by an embodiment of the present application. It can be understood that step S230 in the embodiment shown in FIG. 2 includes but not limited to step S231 and step S232.

Step S231, adding candidate data tables to the set of index data tables to be created.

Step S232, scan the candidate data tables in the set of index data tables to be created, and obtain the fields, the usage modes corresponding to the fields, and the usage frequency of the fields.

It can be understood that after judging whether each initial data table satisfies the first preset condition, and marking the initial data table satisfying the first preset condition as a candidate data table, step S231 is executed to add the candidate data table to the pending Create a collection of indexed data tables. After all the candidate data tables are added to the set of index data tables to be created, step S232 is performed to scan each initial data table (i.e. candidate data table) in the set of index data tables to be created to obtain the fields and the fields corresponding to each field way of using. Only after obtaining the field, the usage method corresponding to the field, and the frequency of the field usage method, can the statistics be made on this information, so as to create the index.

It should be noted that in the index creation method proposed in this application, the scanning of the candidate data tables is not limited to "first add the candidate data tables to the set of index data tables to be created, and then uniformly treat the scan the initial data table", and after each initial data table is determined to meet the first preset condition, after marking the initial data table as a candidate data table and adding it to the set of index data tables to be created, the candidate The data table is scanned, and after the candidate data table is scanned and the fields and usage methods corresponding to the fields are obtained, the remaining initial data tables are confirmed one by one, and successively judge whether the remaining initial data tables meet the first preset condition, and correspondingly process the initial data table satisfying the first preset condition. In the above method, it is only necessary to realize the judgment of the initial data table, and then add the initial data table that satisfies the conditions to the set of index data tables to be created and scan to obtain the corresponding fields and corresponding usage methods. Both technical solutions can realize the scanning of the candidate data table, and this application does not specifically limit the execution sequence of this step.

As shown in FIG. 4 , FIG. 4 is a flow chart of data screening provided by an embodiment of the present application. It can be understood that step S300 in the embodiment shown in FIG. 1 includes but not limited to step S310 and step S320.

Step S310, filter the data table use frequency and field use mode frequency to obtain index fields.

Step S320, creating an index according to the index field.

It can be understood that, in the index creation method in the prior art, business developers usually judge which fields should be used as index fields to create indexes based on past experience. The experience of business developers does not guarantee accuracy. Therefore, when the frequency of data access is low, after creating an index for this application data, the read, write and retrieval performance of the application data may be different from that when no index is created. There is not much difference in performance. If the selected index field is not the keyword required to access the application data, that is, no suitable index field is selected to create the index, which not only has no obvious benefit when searching and accessing the data, but also increases the cost of creating the index. resource overhead.

Therefore, in the index creation method proposed in this application, after obtaining the data table usage frequency and field usage frequency, step S310 needs to be performed to filter the data table usage frequency and field usage frequency to obtain the index field , and then execute step S320 to create an index according to the index field. Since the selected index fields are data tables, fields and corresponding usage methods that appear frequently in the application algorithm set, that is, this part of information with high frequency appears during the process of accessing and retrieving data Therefore, after knowing which fields and their corresponding usage methods appear frequently, using this information as index fields can create indexes according to actual application requirements, thereby improving data access performance, but also reduces the cost and threshold of user development.

As shown in FIG. 5 , FIG. 5 is a flow chart of field acquisition provided by an embodiment of the present application. It can be understood that step S310 in the embodiment shown in FIG. 4 includes but not limited to step S311 and step S312.

Step S311 , adding fields satisfying a second preset condition to the set of index fields to be created, where the second preset condition is that the field usage frequency corresponding to the field is not less than a second preset value.

Step S312, obtaining index fields according to the set of index data tables to be created and the set of index fields to be created.

It can be understood that after obtaining the data table usage frequency, field usage frequency, and field usage mode frequency, step S311 needs to be executed first to determine whether each field in each candidate data table satisfies the second preset condition, After adding the fields satisfying the second preset condition to the set of index fields to be created, step S312 is performed to obtain index fields according to the set of index data tables to be created and the set of index fields to be created, so as to create the index. Specifically, the second preset condition is that the field usage frequency corresponding to the field is not less than the second preset value. When the field use frequency is greater than or equal to the second preset value, it can be considered that this field meets the second preset condition, indicating that this field is a field that is often used in the process of large data retrieval access, so This field needs to be added to the set of index fields to be created; if the frequency of use of the field corresponding to this field is less than the second preset value, it means that this field appears less often in the process of large data retrieval access, so It is not necessary to add this field to the set of index fields to be created, and it is necessary to judge other fields until all fields satisfying the second preset condition are added to the set of index fields to be created.

It should be noted that the second preset value may be a preset threshold according to actual needs, or may be a preset algorithm and a value obtained through algorithm calculation. In the process of judging whether a field satisfies the second preset condition, it is only necessary to classify the fields according to the actual retrieval requirement, and the present application does not specifically limit the manner of obtaining the second preset value.

As shown in FIG. 6 , FIG. 6 is a flow chart of obtaining an index field provided by an embodiment of the present application. It can be understood that step S312 in the embodiment shown in FIG. 5 includes but not limited to step S3121 , step S3122 and step S3123 .

Step S3121, according to the set of index data tables to be created and the set of index fields to be created, a first mapping relationship is obtained, and the first mapping relationship is the mapping relationship between each candidate data table and a field.

In step S3122, a second mapping relationship is obtained according to the set of index fields to be created and the usage frequency of the fields. The second mapping relationship is the mapping relationship between each field and the corresponding usage mode of the field.

Step S3123, according to the first mapping relationship and the second mapping relationship, the index field is obtained.

It can be understood that there are multiple data tables in the set of index data tables to be created, and each data table corresponds to a field set, and each data table that satisfies the first preset condition satisfies the second preset condition The fields are added to the field set corresponding to the data table, and finally all the fields together constitute the set of index fields to be created.

It can be understood that, in the process of obtaining the index fields according to the set of index data tables to be created, the set of index fields to be created, and the frequency of field usage, step S3121 needs to be executed first to obtain the set of index data tables to be created and the index fields to be created. The first mapping relationship between the field sets, that is, to obtain the mapping relationship between each data table and the corresponding field; and then perform step S3122 to obtain the second mapping relationship between the index field set to be created and the field usage frequency , that is, to obtain the mapping relationship between the field and the corresponding usage mode of the field. Finally, step S3123 is executed to obtain index fields according to the first mapping relationship and the second mapping relationship, and create an index.

It should be noted that, after adding the fields satisfying the second preset condition in each candidate data table to the set of index fields to be created, that is, after finding all suitable fields in the set of applied algorithms, according to the index data to be created Index fields are obtained from the table set, the set of index fields to be created, and the frequency of field usage. For data access, whether it is table-level data table usage frequency, field usage frequency or field usage frequency, it will affect the efficiency of data access to varying degrees according to different actual situations. Therefore, in the process of creating an index based on the index field, you can assign different weights to the data such as the data table and the usage method corresponding to the field according to actual needs, and choose the most suitable scheme to create the index, so that the indexed data Access performance has been greatly improved.

It should be noted that, in addition to only using the two types of data, the data table and the field usage method, as the criteria for selecting index fields, you can also perform data analysis on the data table, fields, and the usage methods corresponding to the fields according to actual needs. Different combinations or different weights are assigned to determine the search fields.

It should be noted that when the business application algorithm changes, such as when the algorithm is added, deleted, or updated, such changes will change the data tables, fields, and usage methods corresponding to the fields, and the corresponding frequencies will also change. will change. Therefore, after the business application algorithm is changed, the index creation method proposed in this application also actively obtains the updated application algorithm set, and updates the data through the above index creation method, so as to create the index to the greatest extent. Guarantees the performance improvement of data retrieval access brought by indexing.

It should be noted that after the business application algorithm is changed, the business personnel can actively input the updated application algorithm set into the index creation method proposed in this application, or the processor can periodically update the business application algorithm. Detection, after detecting a change in the business application algorithm, actively acquire new data and update the application algorithm set to create an index so that the index can adapt to the change of the business algorithm to improve the credit-to-price ratio of the index and the data access performance. This application does not specifically limit the update method of the application algorithm set.

As shown in FIG. 7 , FIG. 7 is another flowchart of an index creation method provided by an embodiment of the present application. It can be understood that the index creation method proposed in other embodiments of the present application also includes but is not limited to step S400, step S410, step S420, step S430, step S440, step S450, step S460, step S470, step S48 and step S490.

Step S400, acquiring a set of application algorithms.

Step S410, scanning table-level usage frequency.

Step S420, judging whether the data in all data tables has been scanned.

Step S430, judging whether the usage frequency of the data table is not less than a first preset threshold value.

Step S440, adding the data table into the set of index tables to be created.

In step S450, the fields in the data table are scanned one by one to obtain the frequency of use of the fields and the frequency of use modes corresponding to the fields.

Step S460, judging whether the fields in the data table have been scanned.

Step S470, judging one by one whether the usage frequency of fields in the data table is not less than a second preset threshold value.

Step S480, adding the field to the set of index fields to be created corresponding to the data table.

In step S490, an index is created according to the index field corresponding to the data table and the usage method.

It can be understood that, in the index creation method, step S400 is firstly executed to acquire the application algorithm set, the algorithm statement in the application algorithm set is parsed, and the information of the data tables and fields used in the algorithm is identified; then step S410 is executed to Statistical analysis is performed on the data tables and fields identified in step S400 to obtain data tables at different levels and usage frequencies of data tables corresponding to the data tables. Then execute step S420 to judge whether all field information in all data tables has been scanned at present, if so, then enter step S490 to create an index; if the data in all data tables has not been scanned yet, Then enter step S430 to judge each data table, specifically to judge that the usage frequency of the data table is not less than the first preset threshold value, if it is determined that it is not less than the first preset threshold value, then execute step S440 , adding the data table to the set of index tables to be created; if the usage frequency of the data table is less than the first preset threshold value, skip this data table and continue to judge other unjudged data tables. After the suitable data table is added to the set of index tables to be created, step S450 is executed to scan the fields in each data table in the set of index tables to be created one by one to obtain the usage frequency corresponding to the field. Then execute step S460 to judge whether the fields in the current data table have been scanned, if not, then execute step S470 to judge the fields in the data table, specifically to judge whether the usage frequency of each field is different is less than the second preset threshold value, until all fields whose usage frequency is not less than the second preset threshold value are screened out, and step S480 is executed to add these fields to the set of index fields to be created corresponding to the data table; if After the fields in the current data table have been scanned, and all fields satisfying the conditions have been screened out, return to step S420 to determine whether the field information in each data table has been scanned. Corresponding index fields and usage information to create an index. By obtaining the data tables, fields and usage methods corresponding to the fields, according to the data table usage frequency and field usage frequency and other information, the current high-frequency words can be known, and the final index can be set according to the current input application algorithm Adaptively create high-efficiency indexes, improve retrieval efficiency, improve user experience in data use, improve data access performance, and avoid data access performance degradation caused by improper index configuration.

In addition, another embodiment of the present application also provides an index creation device, which includes: a memory, a processor, and a computer program stored in the memory and operable on the processor.

The processor and memory can be connected by a data bus or otherwise.

As a non-transitory computer-readable storage medium, memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The non-transitory software programs and instructions required to realize the index creation method of the above-mentioned embodiment are stored in the memory, and when executed by the processor, the index creation method in the above-mentioned embodiment is executed, for example, the execution of the above-described FIG. Method steps S100 to S300, method steps S210 to S230 among Fig. 2, method steps S231 to S232 among Fig. 3, method steps S310 to S320 among Fig. 4, method steps S311 to S312 among Fig. 5, Fig. 6 Method steps S3121 to S3123 and method steps S400 to S490 in FIG. 7 .

The device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, an embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by a processor or a controller, for example, by the above-mentioned Execution by a processor in the embodiment of the index creation device can cause the above-mentioned processor to execute the index creation method in the above-mentioned embodiment, for example, perform the above-described method steps S100 to S300 in FIG. 1 and method step S210 in FIG. 2 Method steps S231 to S232 in Fig. 3, method steps S310 to S320 in Fig. 4, method steps S311 to S312 in Fig. 5, method steps S3121 to S3123 in Fig. 6 and method step S400 in Fig. 7 to S230, Fig. 3 to S490.

Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, the functional modules/units in the system, and the device can be implemented as software, firmware, hardware, and an appropriate combination thereof.

In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be composed of several physical components. Components cooperate to execute. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit . Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). As known to those of ordinary skill in the art, the term computer storage media includes both volatile and nonvolatile media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. permanent, removable and non-removable media. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, tape, magnetic disk storage or other magnetic storage devices, or can Any other medium used to store desired information and which can be accessed by a computer. In addition, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media .

The preferred embodiments of the present application have been described above with reference to the accompanying drawings, and the scope of rights of the present application is not limited thereby. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the present application shall fall within the scope of rights of the present application.

Claims

An index creation method comprising:

Obtain a set of application algorithms;

Obtain the data table usage frequency and the field usage mode frequency according to the set of application algorithms; and

An index is created according to the usage frequency of the data table and the usage frequency of the field.
The index creation method according to claim 1, wherein said obtaining the data table usage frequency and field usage frequency according to the set of application algorithms includes:

Obtaining a plurality of initial data tables and the frequency of use of the data tables corresponding to the initial data tables according to the set of application algorithms;

Screening a plurality of the initial data tables to obtain candidate data tables; and

The candidate data table is scanned to obtain a plurality of fields, usage modes corresponding to the fields, and frequency of usage modes of the fields.
The index creation method according to claim 2, wherein said screening a plurality of said initial data tables to obtain candidate data tables comprises:

marking the initial data table that satisfies a first preset condition as the candidate data table, the first preset condition being that the usage frequency of the data table corresponding to the initial data table is not less than a first preset value .
The index creation method according to claim 2, wherein the scanning of the candidate data table to obtain a plurality of fields, usage modes corresponding to the fields, and frequency of usage modes of the fields includes:

adding the candidate data table to the set of index data tables to be created; and

The candidate data tables in the set of index data tables to be created are scanned to obtain the fields, the usage modes corresponding to the fields, and the frequency of usage modes of the fields.
The method for creating an index according to claim 2, wherein said creating an index according to the frequency of use of the data table and the frequency of use of the field includes:

Filtering the frequency of use of the data table and the frequency of use of the field to obtain an index field; and

An index is created according to the index field.
The method for creating an index according to claim 5, wherein said filtering said data table usage frequency and said field usage frequency to obtain an index field includes:

Adding the fields satisfying the second preset condition to the set of index fields to be created, the second preset condition being that the field usage frequency corresponding to the field is not less than a second preset value; and

The index fields are obtained according to the set of index data tables to be created and the set of index fields to be created.
The index creation method according to claim 6, wherein said obtaining said index fields according to said set of index data tables to be created and said set of index fields to be created comprises:

According to the set of index data tables to be created and the set of index fields to be created, a first mapping relationship is obtained, and the first mapping relationship is a mapping relationship between each of the candidate data tables and the fields;

Obtaining a second mapping relationship according to the set of index fields to be created and the usage frequency of the fields, the second mapping relationship is a mapping relationship between each of the fields and the usage mode corresponding to the field; and

The index field is obtained according to the first mapping relationship and the second mapping relationship.
The method for creating an index according to claim 1, wherein said acquiring a set of application algorithms comprises:

When it is determined that the content of the application algorithm set changes, the updated application algorithm set is acquired.
An index creation device, comprising a memory, a processor, a program stored on the memory and operable on the processor, and a data bus for realizing connection and communication between the processor and the memory, Wherein, when the program is executed by the processor, the steps of the index creation method according to any one of claims 1 to 8 are realized.
A computer-readable storage medium storing a computer-executable program, wherein the computer-executable program is used to make a computer execute the index creation method according to any one of claims 1-8.