CN114443663A - Data table processing method, device, equipment and medium - Google Patents

Data table processing method, device, equipment and medium Download PDF

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Publication number
CN114443663A
CN114443663A CN202210103926.8A CN202210103926A CN114443663A CN 114443663 A CN114443663 A CN 114443663A CN 202210103926 A CN202210103926 A CN 202210103926A CN 114443663 A CN114443663 A CN 114443663A
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China
Prior art keywords
weight
association
query statement
information
transaction
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Chinese (zh)
Inventor
胡畔
雷颖
杨红远
曹文伟
王黎君
吴方义
聂莉
朱晓康
郑佳
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China Construction Bank Corp
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China Construction Bank Corp
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Priority to CN202210103926.8A priority Critical patent/CN114443663A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2282—Tablespace storage structures; Management thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/242—Query formulation
    • G06F16/2433—Query languages

Abstract

The present disclosure provides a data table processing method, which can be applied to the fields of computers and big data. The data table processing method comprises the following steps: analyzing the query statement to obtain an analysis result; determining table relation information and table field information corresponding to the query statement according to the analysis result, wherein the table relation information records names of a plurality of data tables with association relations, and the table field information records the names of the data tables and the association fields of the data tables; determining transaction association weight of each query statement according to the online transaction quantity respectively corresponding to different query statements in a preset time period; determining a data table weight of the table relation information and an associated field weight of the table field information according to the corresponding relation between each transaction associated weight, the table relation information and the table field information and the query statement; and storing the plurality of data table fragments according to the data table weight and the associated field weight. The present disclosure also provides a data table processing apparatus, a device, a storage medium, and a program product.

Description

Data table processing method, device, equipment and medium
Technical Field
The present disclosure relates to the field of computer technologies and big data technologies, and in particular, to a method, an apparatus, a device, a medium, and a program product for processing a data table.
Background
With the development of computer technology, a plurality of data tables can be stored in different database fragments, and the plurality of data tables can be queried according to the incidence relation among the data tables. The same data table can also be divided into a plurality of database fragments for storage, and the information of the same data table can be queried aiming at the data table through the association relation of the association fields.
In the process of implementing the inventive concept of the present disclosure, the inventors found that the efficiency of data table query is low, and the query requirements of the relevant users cannot be met.
Disclosure of Invention
In view of the foregoing, the present disclosure provides a data table processing method, apparatus, device, medium, and program product.
According to a first aspect of the present disclosure, there is provided a data table processing method, including:
analyzing a query statement to obtain an analysis result, wherein the analysis result is included in the query statement, an association relation among a plurality of data tables and an association field for associating the data tables in the plurality of data tables;
determining table relationship information and table field information corresponding to the query statement, wherein the table relationship information records names of a plurality of data tables having an association relationship, and the table field information records the names of the data tables and the association fields of the data tables;
determining transaction association weight of each query statement according to online transaction quantity respectively corresponding to different query statements in a preset time period;
determining a data table weight of the table relationship information and an associated field weight of the table field information according to the transaction associated weight, the table relationship information and the corresponding relationship between the table field information and the query statement;
and storing a plurality of data table fragments according to the data table weight and the associated field weight.
According to an embodiment of the present disclosure, the data table processing method further includes:
determining the response association weight of each query statement according to a preset response rule, wherein the preset response rule is determined according to the preset response duration of batch transaction;
and updating the table relationship information and the table field information according to the corresponding relationship between each response association weight, the table relationship information and the table field information and the query statement to obtain an updated data table weight and an updated association field weight.
According to the embodiment of the present disclosure, determining the response association weight of each query statement according to a preset response rule includes:
determining a preset response duration of batch transactions according to the preset response rule, wherein the batch transactions correspond to at least one query statement;
determining the batch transaction weight of each batch transaction according to preset response time lengths of different batch transactions;
and determining the batch transaction weight of the batch transaction as the response association weight of the query statement according to the corresponding relation between the batch transaction and the query statement.
According to an embodiment of the present disclosure, determining the transaction association weight of each query statement according to the online transaction number corresponding to each of the query statements in a preset time period includes:
and normalizing the online transaction quantity corresponding to each query statement in the preset time period to obtain the transaction association weight of each query statement in the preset time period.
According to an embodiment of the present disclosure, the preset time period includes a plurality of time periods;
determining the transaction association weight of each query statement according to the online transaction number corresponding to each query statement in a preset time period, further comprises:
and summing the transaction association weights corresponding to each same query statement in a plurality of preset time periods to obtain the transaction association weights of each query statement in the plurality of preset time periods.
According to an embodiment of the present disclosure, determining the data table weight of the table relationship information and the associated field weight of the table field information according to each of the transaction associated weight, the table relationship information, and the corresponding relationship between the table field information and the query statement includes:
determining an initial data table weight of each table relationship information according to each transaction association weight and the corresponding relationship between the table relationship information and the query statement;
summing the initial data table weights recorded with the same data table name to obtain the data table weight of the table relation information;
determining an initial association field weight of each table field information according to each transaction association weight and the corresponding relation between the table field information and the query statement;
and summing the names recorded with the same data table and the initial associated field weights of the same associated fields to obtain the associated field weights of the table field information.
According to an embodiment of the present disclosure, storing a plurality of the data table segments according to the data table weight and the associated field weight includes:
processing the data table weight and the associated field weight by using a clustering algorithm to obtain a first clustering result and a second clustering result;
and storing a plurality of data table fragments according to the first clustering result and the second clustering result, wherein the fragment storage comprises vertical fragment storage and horizontal fragment storage.
According to an embodiment of the present disclosure, the data table processing method further includes:
displaying first association degree information among a plurality of data tables with association relations in a page according to the data table weight of the table relation information; and
and displaying second association degree information between the data table and the associated field of the data table in the page according to the associated field weight of the table field information.
A second aspect of the present disclosure provides a data table processing apparatus including:
the analysis module is used for analyzing a query statement to obtain an analysis result, wherein the analysis result comprises an association relation among a plurality of data tables and an association field for associating the data tables in the plurality of data tables in the query statement;
a first determining module, configured to determine, according to the analysis result, table relationship information and table field information corresponding to the query statement, where the table relationship information records names of multiple data tables having an association relationship, and the table field information records names of the data tables and association fields of the data tables;
the transaction association weight determining module is used for determining the transaction association weight of each query statement according to the online transaction quantity respectively corresponding to different query statements in a preset time period;
a second determining module, configured to determine a data table weight of the table relationship information and an associated field weight of the table field information according to each transaction associated weight, the table relationship information, and a corresponding relationship between the table field information and the query statement; and
and the fragment storage module is used for storing a plurality of data table fragments according to the data table weight and the associated field weight.
A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to perform the above-described data table processing method.
The fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-mentioned data table processing method.
The fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above-described data sheet processing method.
Drawings
The foregoing and other objects, features and advantages of the disclosure will be apparent from the following description of embodiments of the disclosure, which proceeds with reference to the accompanying drawings, in which:
FIG. 1 schematically shows an application scenario diagram of a data table processing method and apparatus according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow diagram of a data table processing method according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow chart for determining data table weights for table relationship information and associated field weights for table field information in accordance with an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow chart of a data table processing method according to another embodiment of the present disclosure;
FIG. 5 schematically illustrates an application scenario of a data table processing method according to another embodiment of the present disclosure;
FIG. 6 schematically illustrates an application scenario of a data table processing method according to another embodiment of the present disclosure;
FIG. 7 schematically shows a block diagram of a data table processing apparatus according to an embodiment of the present disclosure; and
FIG. 8 schematically illustrates a block diagram of an electronic device suitable for implementing a data table processing method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
With the development of computer technology, a plurality of data tables can be stored in different database fragments, and the plurality of data tables can be queried according to the incidence relation among the data tables. The same data table can also be divided into a plurality of database fragments for storage, and the information of the same data table can be queried aiming at the data table through the association relation of the association fields. The data table can be vertically sliced and horizontally sliced according to actual service requirements so as to meet the actual service requirements, but the slicing storage of the data table may cause longer query execution time for the data table, so that the query efficiency is lower, and the actual service requirements cannot be met.
The embodiment of the disclosure provides a data table processing method, which includes:
analyzing the query statement to obtain an analysis result, wherein the analysis result is included in the query statement, the association relation among the multiple data tables and an association field for associating the data tables in the multiple data tables; determining table relation information and table field information corresponding to the query statement according to the analysis result, wherein the table relation information records names of a plurality of data tables with association relations, and the table field information records the names of the data tables and the association fields of the data tables; determining transaction association weight of each query statement according to the online transaction quantity respectively corresponding to different query statements in a preset time period; determining a data table weight of the table relation information and an associated field weight of the table field information according to the corresponding relation between each transaction associated weight, the table relation information and the table field information and the query statement; and storing the plurality of data table fragments according to the data table weight and the associated field weight.
According to the embodiment of the disclosure, the transaction association weight of each query statement can be determined by counting the online transaction quantity ratio corresponding to the query statement. Because the query statement can correspond to the association relationship between the data tables and the association fields contained in the data tables, the data table weight of the table relationship information and the association field weight of the table field information can be determined according to the association weight of each transaction. The data table weight can represent the association degree between the data tables, and the association field weight can represent the association degree between the data tables and the association fields, so that the horizontal slicing storage and the vertical slicing storage are performed on the data tables according to the data table weight and the association field weight, that is, the query speed between the data tables can be increased, the query execution time for acquiring the data tables through query statements is reduced, and the execution efficiency for database operation is improved.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, necessary security measures are taken, and the customs of the public order is not violated.
In the technical scheme of the disclosure, before the personal information of the user is acquired or collected, the authorization or the consent of the user is acquired.
Fig. 1 schematically shows an application scenario diagram of a data table processing method and apparatus according to an embodiment of the present disclosure.
As shown in fig. 1, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the data table processing method provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the data table processing apparatus provided by the embodiments of the present disclosure may be generally disposed in the server 105. The data table processing method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Correspondingly, the data table processing apparatus provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The data table processing method of the disclosed embodiment will be described in detail below with fig. 2 to 6 based on the scenario described in fig. 1.
FIG. 2 schematically shows a flow chart of a data table processing method according to an embodiment of the disclosure.
As shown in fig. 2, the data table processing method may include operations S201 to S205.
In operation S201, the query statement is parsed to obtain a parsing result, where the parsing result includes an association relationship between the plurality of data tables and an association field for associating the data tables in the plurality of data tables in the query statement.
According to the embodiment of the disclosure, a plurality of data tables can be associated through the same association field, and correspondingly, the same data table can contain a plurality of different association fields, so that the data table can be associated with different data tables through different association fields.
According to the embodiment of the disclosure, the same query statement can be queried to a plurality of different data tables through the associated fields contained in the data tables, so that the plurality of different data tables and the associated fields among the different data tables can have corresponding relations with the query statement. Therefore, the query statement is analyzed, and the obtained analysis result may include the association relationship between the plurality of data tables and the association field for associating the data tables in the plurality of data tables in the query statement.
In operation S202, table relationship information and table field information corresponding to the query statement are determined according to the parsing result, where the table relationship information records names of a plurality of data tables having an association relationship, and the table field information records the names of the data tables and associated fields of the data tables.
According to an embodiment of the present disclosure, the table relation information may include information in which names of different data tables having an association relation are recorded, such as a resolved query statement SQL1The obtained analysis result is included in the query statement SQL1The data table name with the association relationship may include (T)1，T2，......，Ti)。TiIndicating the name of the data table. The parsing result may further include an association field (C) for associating data tables of the plurality of data tables1，C2，......，Ci)，CiIndicating the association field. Table relation information RSQL obtained according to analysis result1mCan be expressed as (T)1，Tm) Characterization data Table T1And data table TmAnd (4) associating. Table field information CSQL1mCan be expressed as (T)1，Cm) Characterization data Table T1And associating field CmAnd data table TmAnd (4) associating. Table relation information RSQL1mSum table field information CSQL1mAnd query statement SQL1And correspondingly.
It should be understood that, according to different parsing results obtained after parsing different query statements, a plurality of different table relationship information and a plurality of different table field information may be obtained, where the plurality of different table relationship information may include a record with the same data table name. For example, with the query statement SQL1Corresponding table relation information RSQL1mCan be expressed as (T)1，Tm) And query statement SQL2Corresponding table relation information RSQL2mCan also be expressed as (T)1，Tm)。
In operation S203, transaction association weights of each query statement are determined according to online transaction amounts respectively corresponding to different query statements within a preset time period.
According to embodiments of the present disclosure, online transactions may include, for example, transfer transactions, rebate transactions, and the like. For the same online transaction, it can be completed through one or more query statements. One or more online transactions may be associated with the same query statement. Therefore, within the preset time period, the online transaction quantities corresponding to different query statements may be the same or different, the transaction association weight of each query statement is determined according to the online transaction quantity corresponding to each query statement, and the importance of the query statement related to the online transaction within the preset time period can be represented by the transaction association weight. The transaction association weight may be represented by a numerical value.
It should be noted that the online transaction amount in the preset time period may be a total amount of online transactions in the preset time period, or in a case that the preset time period includes a plurality of online transactions, the online transaction amount in the preset time period may also be an average amount of the online transaction amounts in each preset time period.
In operation S204, a data table weight of the table relationship information and an associated field weight of the table field information are determined according to each transaction associated weight, the table relationship information, and a correspondence between the table field information and the query statement.
In operation S205, a plurality of data table fragments are stored according to the data table weight and the associated field weight.
According to an embodiment of the present disclosure, for example, the query statement SQL1Is expressed as 0.3, can be associated with the query statement SQL1Corresponding table relation information RSQL1mData table weight and table field information CSQL of1mThe associated field weight of (a) is determined to be 0.3. The sum of the data table weights of the table relationship information in which the same data table name is recorded can be determined from the data table weight of each table relationship information. The sum of the names of the data tables recorded with the same data and the associated field weight of the associated field can be determined according to the associated field weight of each table field information. So that the data table weights between different data tables and the associated field weights between the data tables and the associated fields can be determined.
According to the embodiment of the disclosure, the transaction association weight of each query statement can be determined by counting the online transaction quantity ratio corresponding to the query statement. Because the query statement can correspond to the association relationship between the data tables and the association fields contained in the data tables, the data table weight of the table relationship information and the association field weight of the table field information can be determined according to the association weight of each transaction. The data table weight can represent the association degree between the data tables, and the association field weight can represent the association degree between the data tables and the association fields, so that the horizontal slicing storage and the vertical slicing storage are performed on the data tables according to the data table weight and the association field weight, that is, the query speed between the data tables can be increased, the query execution time for acquiring the data tables through query statements is reduced, and the execution efficiency for database operation is improved.
According to an embodiment of the present disclosure, the determining the transaction association weight of each query statement according to the online transaction amounts respectively corresponding to different query statements within the preset time period in operation S203 may include the following operations.
And normalizing the online transaction quantity corresponding to each query statement in a preset time period to obtain the transaction association weight of each query statement in the preset time period.
For example, the query statement included in the online transaction includes the query statement SQL within a preset time period1And query statement SQL2. Query statement SQL1The corresponding online transaction quantity is 100, and the query statement SQL2The corresponding number of online transactions is 50. Normalizing the online transaction quantity corresponding to each query statement in the preset time period to obtain the query statement SQL in the preset time period1The transaction associated weight of (1) is 0.66, query statement SQL2Is 0.34. By normalizing the online transaction quantity corresponding to each query statement in the preset time period, the numerical range of the transaction association weight can be narrowed, and the determination of the subsequent data table weight and the association field weight is facilitated.
According to an embodiment of the present disclosure, the preset time period includes a plurality of times.
Operation S203, determining the transaction association weight of each query statement according to the online transaction amounts respectively corresponding to different query statements in the preset time period may further include the following operations.
And summing the transaction association weights corresponding to each same query statement in a plurality of preset time periods to obtain the transaction association weight of each query statement in the plurality of preset time periods.
For example, in a first preset time period, the query statement contained in the online transaction comprises a query statement SQL1And query statement SQL2. The query statement SQL in the first preset time period1The transaction associated weight of (1) is 0.66, query statement SQL2Is 0.34. In a second preset time period, the query statement contained in the online transaction comprises a query statement SQL2And query statement SQL3. The query statement SQL in the second preset time period2The transaction associated weight of (1) is 0.34, query statement SQL3Is 0.66. Therefore, the query statement SQL is used for the first preset time period and the second preset time period which are the plurality of preset time periods1The transaction associated weight of (1) is 0.66, query statement SQL2The transaction associated weight of (1), query statement SQL3Is 0.66.
According to the embodiment of the disclosure, the transaction association weights corresponding to the same query statements in a plurality of preset time periods are summed, the association degree of each query statement and the online transaction can be comprehensively reflected through the online transaction number in the plurality of preset time periods, and the association degree is represented through the transaction association weight, so that a foundation is laid for the subsequent generation of the data table weight and the association field weight.
FIG. 3 schematically illustrates a flow chart for determining data table weights for table relationship information and associated field weights for table field information according to an embodiment of the disclosure.
As shown in fig. 3, determining the data table weight of the table relationship information and the associated field weight of the table field information according to the correspondence of each transaction associated weight, the table relationship information, and the table field information to the query statement in operation S204 may include operations S301 to S304.
In operation S301, an initial data table weight of each table relationship information is determined according to each transaction association weight and a corresponding relationship between the table relationship information and the query statement.
In operation S302, the initial data table weights recorded with the same name of the data table are summed to obtain a data table weight of the table relation information.
According to an embodiment of the present disclosure, for example, the query statement SQL1The transaction-associated weight of 1.1, with query statement SQL1Corresponding table relation information RSQL1mCan be expressed as (T)1，Tm) Table relation information RSQL1mThe initial data table weight of (a) may be a query statement SQL1Is associated with a weight of 1.1. And query statement SQL2Corresponding table relation information RSQL2mCan be expressed as (T)1，Tm) Query statement SQL2Has a transaction association weight of 1.9 and table relationship information RSQL2mThe initial data table weight of (a) may be a query statement SQL2Is associated with a weight of 1.9. Due to table relation information RSQL1mAnd table relation information RSQL2mRecord the same name (T) of the data table1，Tm) Therefore, the table relation information RSQL1mAnd table relation information RSQL2mIs summed to obtain the initial data table weight represented as (T)1，Tm) The data table weight of the table relation information of (1) is 3. Further, for convenience of representation, the names of the data tables in the data table weight and table relation information may be passed through a vector (T)1，Tm，LJ1m) Is represented by wherein LJ1mRepresenting table relationship information (T)1，Tm) The data table weight of (c).
In operation S303, an initial association field weight of each table field information is determined according to each transaction association weight and a corresponding relationship between the table field information and the query statement.
In operation S304, the names recorded with the same data table and the initial associated field weights of the same associated field are summed to obtain the associated field weights of the table field information.
According to an embodiment of the present disclosure, for example, the query statement SQL1The transaction-associated weight of 1.1, with query statement SQL1Corresponding table field information CSQL1mCan be expressed as (T)1，Cm) Table field information CSQL1mThe initial associated field weight of (a) may be a query statement SQL1Is associated with a weight of 1.1. And query statement SQL2Corresponding table field information CSQL2mCan be expressed as (T)1，Cm) Query statement SQL2The transaction associated weight of (1.9), table field information CSQL2mThe initial associated field weight of (a) may be a query statement SQL2Is associated with a weight of 1.9. Due to table field information CSQL1mSum table field information CSQL2mRecords the same name and the same associated field (T) of the data table1，Tm) Thus table field information CSQLlmSum table field information CSQL2mIs summed to obtain the weight of the associated field represented by (T)1，Tm) The associated field weight of the table field information of (1) is 3.
FIG. 4 schematically shows a flow diagram of a data table processing method according to another embodiment of the present disclosure.
As shown in fig. 4, the data table processing method may further include operations S301 to S302.
In operation S401, a response association weight of each query statement is determined according to a preset response rule, where the preset response rule is determined according to a preset response duration of a batch transaction.
In operation S402, the table relationship information and the table field information are updated according to the corresponding relationship between each response association weight, the table relationship information, and the table field information and the query statement, so as to obtain an updated data table weight and an updated association field weight.
According to an embodiment of the present disclosure, the operation S401 of determining the response association weight of each query statement according to the preset response rule includes the following operations.
Determining a preset response duration of batch transaction according to a preset response rule, wherein the batch transaction corresponds to at least one query statement; determining the batch transaction weight of each batch transaction according to the preset response duration of different batch transactions; and determining the batch transaction weight of the batch transaction as the response association weight of the query statement according to the corresponding relation between the batch transaction and the query statement.
According to an embodiment of the present disclosure, the batch transaction may include a transaction processed according to a preset batch processing rule, for example, a reconciliation transaction or the like may be included. The same batch transaction can correspond to a plurality of query statements, and one or more batch transactions can be corresponding to the same query statement. The preset response time duration may include a response time duration for performing a batch transaction. Different preset response durations can be set according to actual requirements and importance degrees of different batch transactions, a preset response rule is determined according to the preset response durations, and response association weights corresponding to the batch transactions can be determined. According to the corresponding relation between the batch transaction and the query statement, the corresponding associated weight of the batch transaction can be determined as the response associated weight of the query statement, and the data table weight and the associated field weight are updated according to the corresponding relation between the query statement and the table information and the corresponding relation between the query statement and the table field information. Therefore, the data table weight and the associated field weight can reflect the preset response time corresponding to batch transaction, and a basis is provided for performing fragment storage on a plurality of data tables subsequently. For example, the data tables corresponding to the batch transactions with shorter preset response time may be stored in the same data segment, so as to satisfy the preset response time of the batch transactions.
According to an embodiment of the present disclosure, for example, the query statement SQL1The weight of the response association of (1.1) with the query statement SQL1Corresponding table relation information RSQL1mCan be expressed as (T)1，Tm) Query statement SQL2The weight of the response association of (1.9) with the query statement SQL2Corresponding table relation information RSQL2mCan be expressed as (T)1，Tm). Due to table relation information RSQL1mAnd table relation information RSQL2mRecord the same name (T) of the data table1，Tm) Therefore, the table relation information RSQL1mAnd table relation information RSQL2mAfter summing the response association weights (T), the table relationship information (T) is updated1，Tm，LJ1m) The updated table relationship information may be represented as (T)1，Tm，LJ1m，PL1m)，(LJ1m，PL1m) May represent updated data table weights, or (LJ)1m+PL1m) Or may represent updated data table weights. It should be appreciated that the updated associated field weights may be obtained using the same or similar methods.
According to an embodiment of the present disclosure, storing the plurality of data table segments according to the data table weight and the associated field weight includes:
processing the weight of the data table and the weight of the associated field by using a clustering algorithm to obtain a first clustering result and a second clustering result;
and storing the plurality of data tables in a fragmentation mode according to the first clustering result and the second clustering result, wherein the fragmentation storage comprises vertical fragmentation storage and horizontal fragmentation storage.
According to embodiments of the present disclosure, the clustering algorithm may include, for example, a K-Means algorithm, a mean-shift clustering algorithm, and the like. The clustering algorithm is utilized to process the weight of the data tables, the weight of the table relation information can be used as the input of the clustering algorithm, and because the table relation information records the names of the data tables with the association relation, the first clustering result output by the clustering algorithm can identify the data tables with close association degree into the same group, namely, the data tables in the same group can be stored in the same fragment. According to the first clustering result, the number of fragments stored in the vertical fragments can be determined, and a plurality of data tables with close association degrees can be stored in the same database fragment.
According to the same or similar method, the association degree of the data table and the associated field can be represented according to the second clustering result, and the data table and the associated field are stored in a horizontal slicing mode according to the second clustering result, so that the query requirement for the database is met.
It should be noted that, a person skilled in the art may also use the first clustering result and the second clustering result as references to facilitate performing a vertical slicing policy and a horizontal slicing policy on multiple data tables manually.
According to an embodiment of the present disclosure, the data table processing method may further include the following operations.
Displaying first association degree information among a plurality of data tables with association relations in a page according to the data table weight of the table relation information; and displaying second association degree information between the data table and the associated field of the data table in the page according to the associated field weight of the table field information.
According to an embodiment of the present disclosure, the first association degree information may be updated data table weights, a plurality of data tables having association relationships shown in a page may be represented by nodes, and the first association degree information may be represented as edge relationships between the plurality of data tables. Specifically, the first relevance information may be characterized by a line segment width used for characterizing the edge relationship, for example.
Accordingly, the second relevance information may characterize the edge relationship between the data table and the relevance field, and may characterize the second relevance information by the line segment width for characterizing the edge relationship.
Fig. 5 schematically shows an application scenario of a data table processing method according to another embodiment of the present disclosure.
As shown in fig. 5, the page 500 may include nodes Node1, Node2, Node3, and Node 4. The nodes Node1, Node2, Node3 and Node4 respectively represent the data table T with the incidence relation1、T2、T3、T4. Data table T1And data table T2Can be identified by associating field C2And (6) associating. Data table T1And data table T2The first degree of association information between can be characterized by the line segment width of the edge relation 512. Data table T1And data table T3Can be identified by associating field C3And (6) associating. Data table T1And data table T3The first degree of association information between can be through the side relation513. Data table T1And data table T4Can be identified by associating field C4And (6) associating. Data table T1And data table T4The first degree of association information between can be characterized by the line segment width of the edge relation 514.
In the page 500, the first association degree information between the data tables with association relations is represented by the line segment width showing the edge relations, so that the association degree between the data tables can be visually shown for related personnel, and an intuitive basis can be provided for vertically slicing and storing a plurality of data tables.
Fig. 6 schematically shows an application scenario of a data table processing method according to another embodiment of the present disclosure.
As shown in fig. 6, the page 600 may include nodes Node1, Node2, Node3, and Node 4. The Node1 may characterize the data table T1The nodes Node2, Node3 and Node4 respectively represent the data table T1Associated field C of2、C3、C4. Data table T1And data table T2Can be identified by associating field C2Association, data table T1And data table T3Can be identified by associating field C3Association, data table T1And data table T4Can be identified by associating field C4And (6) associating.
Data table T1And associated field C2Second degree of association information therebetween may be characterized by a line segment width of the edge relation 612. Data table T1And associated field C3Second degree of association information therebetween may be characterized by the line segment width of the edge relation 613. Data table T1And associated field C4Second degree of association information therebetween may be characterized by a line segment width of the edge relation 614.
In the page 600, the second association degree information of the data table and the associated field is represented by the line segment width of the edge relationship, so that the association degree between the data table and the associated field can be visually displayed for related personnel, and an intuitive basis can be provided for performing horizontal fragmentation storage on a plurality of data tables.
Based on the data table processing method, the disclosure also provides a data table processing device. The apparatus will be described in detail below with reference to fig. 7.
Fig. 7 schematically shows a block diagram of a data table processing apparatus according to an embodiment of the present disclosure.
As shown in fig. 7, the data table processing apparatus 700 of this embodiment includes a parsing module 710, a first determining module 720, a transaction association weight determining module 730, a second determining module 740, and a fragmentation storage module 750.
The parsing module 710 is configured to parse the query statement to obtain a parsing result, where the parsing result includes an association relationship between the plurality of data tables and an association field for associating data tables in the plurality of data tables in the query statement.
The first determining module 720 is configured to determine, according to the parsing result, table relationship information and table field information corresponding to the query statement, where the table relationship information records names of multiple data tables having an association relationship, and the table field information records names of the data tables and association fields of the data tables.
The transaction association weight determining module 730 is configured to determine a transaction association weight of each query statement according to the online transaction number corresponding to each query statement in a preset time period.
The second determining module 740 is configured to determine a data table weight of the table relationship information and an associated field weight of the table field information according to the corresponding relationship between each transaction associated weight, the table relationship information, and the table field information and the query statement.
The shard storage module 750 is configured to store the plurality of data table shards according to the data table weight and the associated field weight.
According to an embodiment of the present disclosure, the data table processing method may further include: a response association weight determination module and a data table weight update module.
The response association weight determining module is used for determining the response association weight of each query statement according to a preset response rule, wherein the preset response rule is determined according to the preset response time of the batch transaction.
And the data table weight updating module is used for updating the table relation information and the table field information according to the corresponding relation between each response association weight, the table relation information and the table field information and the query statement to obtain the updated data table weight and the updated association field weight.
According to an embodiment of the present disclosure, the response association weight determination module may include: the system comprises a response time length determining unit, a batch transaction weight determining unit and a response association weight determining unit.
The response duration determining unit is used for determining the preset response duration of batch transactions according to a preset response rule, wherein the batch transactions correspond to at least one query statement.
The batch transaction weight determining unit is used for determining the batch transaction weight of each batch transaction according to the preset response time length of different batch transactions.
And the response association weight determining unit determines the batch transaction weight of the batch transaction as the response association weight of the query statement according to the corresponding relation between the batch transaction and the query statement.
According to an embodiment of the present disclosure, the transaction association weight determination module may include: a first transaction association weight determination unit.
The first transaction association weight determining unit is used for carrying out normalization processing on the online transaction quantity corresponding to each query statement in a preset time period to obtain the transaction association weight of each query statement in the preset time period.
According to an embodiment of the present disclosure, the preset time period includes a plurality of times.
The transaction association weight determination module may further include: a second transaction association weight determination unit.
The second transaction association weight determining unit is used for summing the transaction association weights corresponding to the same query statements in a plurality of preset time periods to obtain the transaction association weight of each query statement in the plurality of preset time periods.
According to an embodiment of the present disclosure, the second determining module may include: the device comprises a first initial determination unit, a data table weight determination unit, a second initial determination unit and an associated field weight determination unit.
The first initial determination unit is used for determining the initial data table weight of each table relation information according to each transaction association weight and the corresponding relation between the table relation information and the query statement.
The data table weight determining unit is used for summing the initial data table weights recorded with the same data table name to obtain the data table weight of the table relation information.
The second initial determination unit is used for determining the initial association field weight of each table field information according to each transaction association weight and the corresponding relation between the table field information and the query statement.
And the associated field weight determining unit is used for summing the names recorded with the same data table and the initial associated field weights of the same associated fields to obtain the associated field weights of the table field information.
According to an embodiment of the present disclosure, the fragmentation storage module may include: the device comprises a clustering processing unit and a slicing storage unit.
The clustering processing unit is used for processing the weight of the data table and the weight of the associated field by using a clustering algorithm to obtain a first clustering result and a second clustering result.
The fragmentation storage unit is used for performing fragmentation storage on a plurality of data tables according to the first clustering result and the second clustering result, wherein the fragmentation storage comprises vertical fragmentation storage and horizontal fragmentation storage.
According to an embodiment of the present disclosure, the data table processing apparatus may further include: a first display module and a second display module.
The first display module is used for displaying first association degree information among a plurality of data tables with association relations in a page according to the data table weight of the table relation information.
The second display module is used for displaying second association degree information between the data table and the associated fields of the data table in the page according to the associated field weight of the table field information.
According to an embodiment of the present disclosure, any of the parsing module 710, the first determining module 720, the transaction association weight determining module 730, the second determining module 740, and the fragmentation storage module 750 may be combined into one module to be implemented, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the parsing module 710, the first determining module 720, the transaction association weight determining module 730, the second determining module 740, and the fragmentation storage module 750 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or by any other reasonable manner of integrating or packaging a circuit, such as hardware or firmware, or by any one of three implementations of software, hardware, and firmware, or by any suitable combination of any of them. Alternatively, at least one of the parsing module 710, the first determining module 720, the transaction association weight determining module 730, the second determining module 740, and the shard storage module 750 may be at least partially implemented as a computer program module that, when executed, may perform corresponding functions.
FIG. 8 schematically illustrates a block diagram of an electronic device suitable for implementing a data table processing method according to an embodiment of the present disclosure.
As shown in fig. 8, an electronic device 800 according to an embodiment of the present disclosure includes a processor 801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. The processor 801 may include, for example, a general purpose microprocessor (e.g., CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., Application Specific Integrated Circuit (ASIC)), among others. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flows according to embodiments of the present disclosure.
In the RAM 803, various programs and data necessary for the operation of the electronic apparatus 800 are stored. The processor 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. The processor 801 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 802 and/or RAM 803. Note that the programs may also be stored in one or more memories other than the ROM 802 and RAM 803. The processor 801 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
Electronic device 800 may also include input/output (I/O) interface 805, input/output (I/O) interface 805 also connected to bus 804, according to an embodiment of the present disclosure. Electronic device 800 may also include one or more of the following components connected to I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a signal such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 802 and/or RAM 803 described above and/or one or more memories other than the ROM 802 and RAM 803.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the data sheet processing method provided by the embodiment of the disclosure.
The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 801. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted in the form of a signal on a network medium, distributed, downloaded and installed via communication section 809, and/or installed from removable media 811. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program, when executed by the processor 801, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be appreciated by a person skilled in the art that various combinations or/and combinations of features recited in the various embodiments of the disclosure and/or in the claims may be made, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims (12)

1. A method of data table processing, comprising:
analyzing a query statement to obtain an analysis result, wherein the analysis result comprises an association relation among a plurality of data tables and an association field for associating the data tables in the plurality of data tables in the query statement;
determining table relationship information and table field information corresponding to the query statement according to the analysis result, wherein the table relationship information records names of a plurality of data tables with association relations, and the table field information records the names of the data tables and association fields of the data tables;
determining transaction association weight of each query statement according to online transaction quantity respectively corresponding to different query statements in a preset time period;
determining a data table weight of the table relationship information and an associated field weight of the table field information according to the corresponding relationship between each transaction associated weight, the table relationship information and the table field information and the query statement;
and storing a plurality of data table fragments according to the data table weight and the associated field weight.
2. The method of claim 1, further comprising:
determining a response association weight of each query statement according to a preset response rule, wherein the preset response rule is determined according to a preset response duration of batch transaction;
and updating the table relation information and the table field information according to the corresponding relation between each response association weight, the table relation information and the table field information and the query statement to obtain an updated data table weight and an updated association field weight.
3. The method of claim 2, wherein determining the response association weight of each query statement according to a preset response rule comprises:
determining a preset response duration of batch transactions according to the preset response rule, wherein the batch transactions correspond to at least one query statement;
determining the batch transaction weight of each batch transaction according to the preset response time length of different batch transactions;
and determining the batch transaction weight of the batch transaction as the response association weight of the query statement according to the corresponding relation between the batch transaction and the query statement.
4. The method of claim 1, wherein determining the transaction association weight of each query statement according to the online transaction amounts respectively corresponding to different query statements within a preset time period comprises:
and normalizing the online transaction quantity corresponding to each query statement in the preset time period to obtain the transaction association weight of each query statement in the preset time period.
5. The method of claim 4, wherein the preset time period comprises a plurality;
determining the transaction association weight of each query statement according to the online transaction quantity respectively corresponding to different query statements in a preset time period further comprises:
and summing the transaction association weights corresponding to each same query statement in the preset time periods to obtain the transaction association weight of each query statement in the preset time periods.
6. The method of claim 1, wherein determining a data table weight for the table relationship information and an associated field weight for the table field information from the correspondence of each of the transaction association weights, the table relationship information, and the table field information to the query statement comprises:
determining an initial data table weight of each table relationship information according to each transaction association weight and the corresponding relationship between the table relationship information and the query statement;
summing the initial data table weights recorded with the same data table name to obtain the data table weight of the table relation information;
determining the initial association field weight of each table field information according to each transaction association weight and the corresponding relation between the table field information and the query statement;
and summing the names recorded with the same data table and the initial associated field weights of the same associated fields to obtain the associated field weights of the table field information.
7. The method of claim 1, wherein storing a plurality of the data table shards according to the data table weight and the associated field weight comprises:
processing the data table weight and the associated field weight by using a clustering algorithm to obtain a first clustering result and a second clustering result;
and storing a plurality of data table fragments according to the first clustering result and the second clustering result, wherein the fragment storage comprises vertical fragment storage and horizontal fragment storage.
8. The method of claim 1, further comprising:
displaying first association degree information among a plurality of data tables with association relations in a page according to the data table weight of the table relation information; and
and displaying second association degree information between the data table and the associated field of the data table in the page according to the associated field weight of the table field information.
9. A data table processing apparatus comprising:
the analysis module is used for analyzing the query statement to obtain an analysis result, wherein the analysis result comprises an association relation among a plurality of data tables and an association field for associating the data tables in the plurality of data tables in the query statement;
a first determining module, configured to determine, according to the parsing result, table relationship information and table field information corresponding to the query statement, where the table relationship information records names of multiple data tables having an association relationship, and the table field information records names of the data tables and association fields of the data tables;
the transaction association weight determining module is used for determining the transaction association weight of each query statement according to the online transaction quantity respectively corresponding to different query statements in a preset time period;
a second determining module, configured to determine, according to each transaction association weight, the table relationship information, and a correspondence between the table field information and the query statement, a data table weight of the table relationship information and an association field weight of the table field information; and
and the fragment storage module is used for storing a plurality of data table fragments according to the data table weight and the associated field weight.
10. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-8.
11. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 8.
12. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 8.
CN202210103926.8A 2022-01-27 2022-01-27 Data table processing method, device, equipment and medium Pending CN114443663A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117349321A (en) * 2023-12-04 2024-01-05 凯美瑞德（苏州）信息科技股份有限公司 Multi-table connection query method and device for document database

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117349321A (en) * 2023-12-04 2024-01-05 凯美瑞德（苏州）信息科技股份有限公司 Multi-table connection query method and device for document database
CN117349321B (en) * 2023-12-04 2024-03-05 凯美瑞德（苏州）信息科技股份有限公司 Multi-table connection query method and device for document database

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