WO2023029592A1

WO2023029592A1 - Data processing method and apparatus

Info

Publication number: WO2023029592A1
Application number: PCT/CN2022/093272
Authority: WO
Inventors: 屠志强
Original assignee: 北京沃东天骏信息技术有限公司; 北京京东世纪贸易有限公司
Priority date: 2021-08-30
Filing date: 2022-05-17
Publication date: 2023-03-09
Also published as: CN113641713A

Abstract

Provided are a data processing method and apparatus, which relate to the technical field of big data. The method comprises: receiving an acquisition request for target data, wherein the acquisition request comprises a query input parameter (S101); parsing the query input parameter, so as to determine query identifiers corresponding to the query input parameter (S102); determining whether the query identifiers are present in a cache (S103); if so, acquiring, from the cache, query results corresponding to the query identifiers (S104); if not, acquiring, from a database, query results corresponding to the query identifiers (S105); and combining the query results of the query identifiers, so as to obtain the target data (S106). By means of the embodiments, the cache hit rate can be improved, the efficiency of data querying is improved, and the response time of a data service interface is shortened, thereby improving the user experience.

Description

Method and device for data processing

Cross References to Related Applications

This application claims priority to the Chinese patent application for invention with application number 202111005262.3 filed on August 30, 2021, the entire contents of which are incorporated herein by reference.

technical field

The present disclosure relates to the technical field of big data, and in particular to a data processing method and device.

Background technique

In the Internet field, websites and different mobile applications query data through data service interfaces. As users have higher and higher requirements for user experience, the requirements for query data are also higher and higher, and the interface response speed is required to be faster and faster. , and the scope of data query is required to be wider and wider.

When the user selects any combination of dimensions to query data, the data service interface needs to aggregate data through OLAP (Online Analytical Processing, Online Analytical Processing). When the interface is called frequently and the query data range is wide, the data processing efficiency is low, the data query results cannot be returned quickly, and the user experience is poor.

Contents of the invention

In view of this, the embodiments of the present disclosure provide a data processing method and device. By analyzing the query input parameters in the acquisition request of the target data, each query identifier corresponding to the query input parameters is obtained. First, the query identifier is obtained from the cache For the corresponding query result, if there is no corresponding query result in the cache, the corresponding query result is obtained from the database, and then the target data is obtained, which improves the query efficiency, improves the interface response speed, and thus improves the user experience.

To achieve the above purpose, according to an aspect of the embodiments of the present disclosure, a data processing method is provided, including:

receiving an acquisition request for target data, where the acquisition request includes query input parameters;

Analyzing the query input parameters to determine each query identifier corresponding to the query input parameters;

Judging whether the query identifier exists in the cache, if so, obtaining the query result corresponding to the query identifier from the cache; if not, obtaining the query result corresponding to the query identifier from the database;

The query results of the respective query identifiers are spliced to obtain the target data.

Optionally, parsing the query input parameters to determine each query identifier corresponding to the query input parameters includes:

Parsing out the time attribute from the query input parameter;

According to the time attribute, determine the number of return values to be acquired corresponding to the query input parameters;

According to the number of return values to be obtained, each query identifier corresponding to the query input parameter is determined.

Analyzing time attributes and dimension types from the query input parameters; the dimension types include grouping dimensions;

According to the time attribute and the grouping dimension, determine the number of return values to be acquired corresponding to the query input parameter;

Optionally, the dimension type further includes a sorting dimension, and the sorting dimension indicates a sorting field and a sorting value range;

Splicing the query results of the various query identifiers to obtain the target data includes:

sorting the query results of the respective query identifiers according to the sorting field, and obtaining the query results corresponding to the sorting value range according to the sorting value range, so as to obtain the target data.

Optionally, according to the time attribute and the grouping dimension, determining the number of return values to be acquired corresponding to the query input parameters includes:

judging whether the grouping dimension matches the configured grouping dimension;

If yes, determine the number of return values to be acquired according to the time attribute and the enumeration value of the grouping dimension; if not, determine the number of return values to be acquired according to the time attribute.

Parse the time attribute and dimension type from the query input parameter; the dimension type includes a restricted dimension, and the restricted dimension indicates an enumerated value;

According to the time attribute and the enumerated value, determine the number of return values to be obtained corresponding to the query input parameter;

Optionally, according to the number of return values to be obtained, determining each query identifier corresponding to the query input parameter includes:

judging whether the number of return values to be obtained is greater than 1;

If not, determine a query identifier corresponding to the query input parameter;

If yes, determine whether the number of return values to be obtained in the cache is not less than a preset threshold, if yes, disassemble the query input parameters, and determine each query identifier corresponding to the query input parameters, if No, disassembly is not performed, and a query identifier corresponding to the query input parameter is determined.

Optionally, parsing the query input parameters to determine each query identifier corresponding to the query input parameters further includes:

Analyzing the time attribute from the query input parameter, the time attribute includes start time, end time and time granularity,

Asynchronously generate a new time query identifier corresponding to the query input parameter according to the start time, end time and time granularity;

Wherein, the end time of the new time query identifier is the sum of the end time of the query input parameters and the time granularity.

Optionally, after obtaining the query result corresponding to the query identifier from the database, further include:

Judging whether the number of returned values in the query result is greater than 1;

If so, correspondingly storing the query result and the query identifier corresponding to the query result in the cache;

If not, according to the number of return values in the query result, the query result and the query identifier corresponding to the query result are disassembled to obtain the subquery identifier and the return value corresponding to the subquery identifier, and The sub-query identifier and the return value corresponding to the sub-query identifier are correspondingly stored in the cache.

Another aspect of the embodiments of the present disclosure provides a data processing device, including:

The receiving module receives an acquisition request for target data, and the acquisition request includes query input parameters;

The determining module is configured to analyze the query input parameters, and determine each query identifier corresponding to the query input parameters;

A judging module, judging whether the query identifier exists in the cache, if so, obtaining the query result corresponding to the query identifier from the cache; if not, obtaining the query result corresponding to the query identifier from the database;

The splicing module splices the query results of the respective query identifiers to obtain the target data.

According to another aspect of the embodiments of the present disclosure, an electronic device is provided, including:

one or more processors;

storage means for storing one or more programs,

When the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method provided in the present disclosure.

According to still another aspect of the embodiments of the present disclosure, there is provided a computer-readable medium on which a computer program is stored, and when the program is executed by a processor, the data processing method provided in the present disclosure is implemented.

An embodiment in the above disclosure has the following advantages or beneficial effects: by analyzing the query input parameters in the acquisition request of the target data, each query identifier corresponding to the query input parameters is obtained, and firstly, the query result corresponding to the query identifier is obtained from the cache , if the corresponding query result does not exist in the cache, the corresponding query result is obtained from the database, and then the target data is obtained, which improves the query efficiency, improves the interface response speed, and thus improves the user experience.

The further effects of the above-mentioned non-conventional alternatives will be described below in conjunction with specific embodiments.

Description of drawings

The accompanying drawings are for better understanding of the present disclosure, and do not constitute an improper limitation of the present disclosure. in:

FIG. 1 is a schematic diagram of the main flow of a data processing method according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of the main flow of another data processing method according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of the main flow of another data processing method according to an embodiment of the present disclosure;

FIG. 4 is a schematic flowchart of a data processing method according to an embodiment of the present disclosure;

Fig. 5 is a schematic diagram of the main modules of a device for data processing according to an embodiment of the present disclosure;

FIG. 6 is an exemplary system architecture diagram to which embodiments of the present disclosure can be applied;

Fig. 7 is a schematic structural diagram of a computer system suitable for implementing a terminal device or a server according to an embodiment of the present disclosure.

Detailed ways

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The existing data service interface is generally equipped with a cache based on the Key-Value storage system. When the caller queries data through the data service interface, the data service interface generates a Key according to the interface query input parameters or generated sql, and combines the Key and the returned Value is stored in the cache so that the cache can be queried next time. For example, caller A requests the data service interface to obtain the daily order volume from 2021-05-01 to 2021-05-02, and the cache generates a Key="order_2021-05-01_2021-05-02" and Value={ "2021-05-01": 100, "2021-05-02": 200}, the caller B requests to obtain the daily order volume from 2021-05-01 to 2021-05-03, but this Key=" order_2021-05-01_2021-05-02", you need to check the database again. However, this method will result in a large granularity of data stored in the cache, and a low cache hit rate. The granularity of the data in the cache is determined by the interface caller. Different callers cannot share the cache if the data range and data granularity are different. , resulting in low cache hit rate, low data query efficiency, long interface response time, and reduced user experience.

In view of the above problems, the embodiments of the present disclosure provide a data processing method, which can improve cache hit rate, improve data query efficiency, shorten interface response time, and improve interface performance.

Fig. 1 is a schematic diagram of the main flow of a method for data processing according to an embodiment of the present disclosure. As shown in Fig. 1, the method for data processing is applied to the server and includes the following steps:

Step S101: receiving an acquisition request for target data, where the acquisition request includes query input parameters;

Step S102: Analyzing the query input parameters, and determining each query identifier corresponding to the query input parameters;

Step S103: Determine whether there is a query identifier in the cache, if yes, execute step S104, if not, execute step S105;

Step S104: Obtain the query result corresponding to the query identifier from the cache;

Step S105: Obtain the query result corresponding to the query identifier from the database;

Step S106: Concatenate the query results of each query identifier to obtain the target data.

In the embodiment of the present disclosure, the caller sends a target data acquisition request to the service provider through the data service interface, so as to acquire the target data. Wherein, the caller is the party that calls the data service interface, and the service provider is the called party that provides the target data. The service provider can be the server, and the caller can be the client. Obtain the query input parameters included in the request, and the service provider can determine the target data according to the query input parameters and return it to the caller.

In the embodiments of the present disclosure, the identifier of the requesting party, the identifier of the called interface, and the identifier or index of the target data can be parsed out from the query input parameters, and the channel identifier can also be parsed out. Among them, the identifier of the called interface indicates the called interface, and the caller’s identifier is used to verify whether the caller has the permission to obtain the target data. It can be verified by judging whether the caller’s identifier is in the white list. If the verification passes , you can respond to the fetch request, query and return the target data, and refuse to respond to the fetch request if the verification fails. The identifier or indicator of the target data is the identifier of the target data requested by the acquisition request, such as the identifier of the accumulated order quantity. The channel ID is the ID of the channel through which the caller invokes the data service interface. If it is a market channel, the market channel ID can be parsed out. Optionally, the business content may also be parsed out, that is, the business scope of the acquired target data.

In an implementation manner of the embodiments of the present disclosure, step S102 further includes: parsing the time attribute from the query input parameter; determining the number of return values to be obtained corresponding to the query input parameter according to the time attribute; The number of returned values obtained determines the respective query identifiers corresponding to the query input parameters.

In the embodiment of the present disclosure, the time attribute can be parsed from the query input parameter, and the time attribute includes start time, end time and time granularity. The start time and end time determine the time range for querying the target data, and the time granularity can be a time interval, such as 1 day, 1 hour, 10 minutes, 1 minute, and so on. Then, according to the time attribute, the number of return values to be acquired can be determined. For example, caller A requests to obtain the order quantity per hour on 2021-05-01, and parses it from the query input parameters. The start time is 2021-05-01 00:00:00 and the end time is 2021-05-01 23:59: 59. If the time granularity is 1 hour, then the number of return values to be acquired is 12.

According to business requirements, when the caller uses a business-related dimension to obtain target data, it can parse out the dimension type from the query input parameter, and the dimension type includes at least one of grouping dimension, restriction dimension and sorting dimension.

The grouping dimension indicates the grouping field, which is used to indicate the dimension of grouping. For example, the grouping dimension can be platform dimension, department dimension, category dimension and other dimensions that can realize grouping. The platform dimension indicates the target data obtained by platform grouping, and the department dimension indicates The target data is obtained by grouping by department, and the category dimension indicates that the target data is obtained by grouping by category. Further analysis can obtain the enumeration value corresponding to the grouping dimension. For example, the enumeration value of the platform dimension includes all platforms; the enumeration value of the second-level department dimension is all the second-level departments; the enumeration value of the category dimension is all classes head. The restricted dimension restricts the enumeration value. For example, the restricted dimension can be to obtain the target data of a certain platform, department, province, etc. The sorting dimension indicates the order in which the target data is sorted according to a certain indicator, and can indicate the range of the target data obtained after sorting. For example, the sorting dimension can indicate the top ten data obtained by order volume.

In the embodiments of the present disclosure, the grouping dimension, the restricting dimension and the sorting dimension can be combined arbitrarily, so that the scope of data query is wider, so as to improve interface performance and user experience.

In another implementation manner of the embodiment of the present disclosure, as shown in FIG. 2, step S102 further includes:

Step S201: Parse the time attribute and dimension type from the query input parameter; the dimension type includes grouping dimension;

Step S202: According to the time attribute and the grouping dimension, determine the number of return values to be acquired corresponding to the query input parameters;

Step S203: According to the number of return values to be obtained, determine each query identifier corresponding to the query input parameters.

Optionally, step S202 further includes: judging whether the grouping dimension matches the configured grouping dimension; if so, determining the number of return values to be obtained according to the time attribute and the enumeration value of the grouping dimension number; if not, determine the number of return values to be obtained according to the time attribute.

In the embodiments of the present disclosure, the query identifier may be a keyword, a Key in a Key-Value (K-V for short) storage system, or other query parameters that can obtain query results from a cache or a database. Further optionally, the query identifier is Key, and one or more Keys corresponding to the query input parameters can be determined according to the query input parameters, and the query result is Value.

Parse the time attribute and dimension type from the query input parameters. The time attribute includes start time, end time, and time granularity. When the dimension type is a grouping dimension, the grouping dimension indicates the grouping field. Analyze the grouping dimension according to the grouping field and judge Whether the grouping dimension matches the configured grouping dimension, if yes, that is, the grouping dimension in the query input parameter is the configured grouping dimension, then get the enumeration value of the grouping dimension, and according to the time attribute and the enumeration value of the grouping dimension , to determine the number of return values to be obtained for the query input parameter. If not, that is, the grouping dimension is not configured, then determine the number of return values to be obtained according to the time attribute. By judging whether the grouping dimension matches the configured grouping dimension, it can be determined whether it can be disassembled according to the grouping dimension. For example, the grouping dimension is parsed as a second-level department field from the query input parameters. If the grouping dimension of the second-level department has been configured, the department dimension can be matched, and then the enumerated value of the configured second-level department dimension can be obtained ( Including department b1, department b2 and department b3), the number of return values to be obtained can be determined according to the time attribute and enumeration value. If the grouping dimension of the secondary department is not configured, the number of return values to be obtained can be determined according to the time attribute number. The return value to be obtained is the number of query input parameters or expected return values. For example, the start time = 2021-05-01 00:00:00 and the end time = 2021-05-02 from the query input parameters 23:59:59, the time granularity is 1 day, and the grouping field is the second-level department, that is, query the daily order volume of all second-level departments on 2021-05-01 and 2021-05-02. For example, the second-level department has a and b two, query the order volume of secondary department a on 2021-05-01 and 2021-05-02 respectively, and the order volume of secondary department b on 2021-05-01 and 2021-05-02 respectively , the number of return values to be obtained is 4, regardless of whether the value of the order quantity is empty or not.

In yet another implementation manner of the embodiment of the present disclosure, step S102 further includes:

Parse the time attribute and dimension type from the query input parameter; the dimension type includes the restricted dimension, and the restricted dimension indicates the enumerated value;

Determine the number of return values to be obtained corresponding to the query input parameters according to the time attribute and the enumeration value;

When the time attribute and limit dimension are parsed from the query input parameters, the number of return values to be obtained can be determined according to the time attribute and enumeration value. For example, the start time = 2021-05-01 00:00:00, the end time = 2021-05-02 23:59:59, the time granularity is 1 day, and the restriction dimension indicates that the enumeration value is two Level-level department a, query the order volume of level-level department a on 2021-05-01 and 2021-05-02 respectively, and the number of return values to be obtained is 2.

In the embodiment of the present disclosure, as shown in FIG. 3 , according to the number of return values to be obtained, each query identifier corresponding to the query input parameter is determined, including:

Step S301: Determine whether the number of return values to be acquired is greater than 1, if not, execute step S302, if yes, execute step S303;

Step S302: Determine a query identifier corresponding to the query input parameter;

Step S303: Determine whether the number of return values to be acquired in the cache is not less than the preset threshold, if yes, execute step S304, if not, execute step S305;

Step S304: disassemble the query input parameters, and determine each query identifier corresponding to the query input parameters;

Step S305: Determine a query identifier corresponding to the query input parameter without disassembling the query input parameter.

After determining the number of return values to be obtained, judge whether the number of return values to be obtained is greater than 1. If the number of return values to be obtained is 1, it means that the data obtained by the query is a point. Return If the query result is a value, there is no need to disassemble the query input parameter, and a query identifier can be determined according to the query input parameter. For example, platform = platform a, first-level department = department b, start time = 2020-01-01 00:00:00, end time = 2020-01-01 00:59:59, indicator = cumulative indicator v_1. That is, query platform a, department b, the cumulative index v_1 between 2020-01-01 00:00:00 ~ 2020-01-01 00:59:59, and return a value, so there is no need to disassemble. Just determine a query ID.

In the embodiments of the present disclosure, the disassembly is to disassemble the query input parameters into multiple query identifiers, and obtain the query results corresponding to the query identifiers from the cache and/or database according to the query identifiers, because the time to query data from the cache is It is much shorter than the time to query data from the database, so by dismantling it into multiple query identifiers, querying, first querying from the cache and then querying from the database, the query efficiency can be improved.

In an implementation manner of the embodiments of the present disclosure, the cache may be a K-V cache, such as a Redis cache, and the database may be a Mysql database, or other caches or databases.

If the number of return values to be obtained is greater than 1, it is judged whether the number of return values to be obtained in the cache is not less than a preset threshold, and if so, dismantling is performed, otherwise, disassembly is not performed. Among them, the preset threshold is determined by the number of return values to be obtained corresponding to the query input parameters and the system threshold, and the coefficient threshold is a value between 0 and 1 estimated based on experience and log analysis (such as 0.6). Assume that the number of return values to be obtained corresponding to the query input parameter is N, the number of return values to be obtained in the cache is M, and the coefficient threshold is r. If M≥r*N, disassembly can be performed; otherwise, No disassembly is performed to ensure a short data query time. Because the time to query and obtain data from the cache is much shorter than the time to query and obtain data from the database. If the time required to query multiple values from the database is ts, after disassembly, some values hit the cache, and the query cache time is ts1 (much less than ts), and the remaining values are queried from the database once, and the query time is ts2, which will be Multiple values obtained by the query are spliced, and the splicing time is ts3. If ts2+ts3<ts, then it is worth dismantling. Otherwise, it is not worth dismantling. That is, the more values are checked from the cache, the shorter the query time and the better the performance of the data service interface.

In an implementation manner of the embodiments of the present disclosure, disassembling the query input parameters includes: disassembling the query input parameters according to the time attribute. Disassemble the query input parameters according to the start time, end time and time granularity. For querying data of a series of time trends, for example, from the analysis of query input parameters, platform = platform a, first-level department = department b, start time = 2020-01-01 00:00:00, end time = 2020-01 -01 23:59:59, time interval = 1 hour, indicator = cumulative indicator v_1. That is to query platform a, department b, between 2020-01-01 00:00:00～2020-01-01 23:59:59, the cumulative value of 0-1 point, the cumulative value of 0-2 point, 0 The cumulative value of -3 points... the cumulative value of 0-24 points, 24 values are returned, and these values are sorted in chronological order and form a continuous time trend, which can be disassembled according to the time granularity, so that the The acquisition request is disassembled into 24 query requests to query separately, and 24 query identifiers can be determined to query the cumulative value of 0-1 point, the cumulative value of 0-2 point, the cumulative value of 0-3 point... 0 -24 point cumulative value. If x of the 24 query identifiers are in the cache, the query results of the x query identifiers are queried from the cache, and the query results of the remaining 24-x query identifiers are obtained by querying the database.

In another implementation manner of the embodiments of the present disclosure, disassembling the query input parameters includes: disassembling the query input parameters according to time attributes and dimension types. When the dimension type is a grouping dimension, it can be disassembled according to the time granularity and grouping dimension. For example, it can be parsed from the query input parameters, platform = platform a, first-level department = department b, start time = 2020-01-01 00:00:00, end time = 2020-01-01 00:59:59, Time granularity = 1 day, grouping dimension = second-level department field, index = cumulative index v_1. That is to query platform a and all the second-level departments under the first-level department b, the accumulated data between 2020-01-01 00:00:00～2020-01-01 00:59:59. If the second-level department includes b1, b2, and b3, the acquisition request is disassembled into three query requests to be queried separately, and three query identifiers can be determined, which are the accumulated data of the second-level department b1 and the accumulated data of the second-level department b2 and cumulative data for secondary sector b3.

In an embodiment of the present disclosure, determining each query identifier corresponding to the query input parameter includes: determining the form of the corresponding query identifier. After the query identifier is determined, the form of the query identifier is determined according to the query input parameters, so that the form of the query identifier conforms to the form of the cache and/or database, so that the query result can be quickly obtained. For example, if among the 24 keys, there are x keys in the K-V cache, then convert the form of the x keys into the form of the K-V cache, for example, Key=time abbreviation_time granularity_platform_first-level department_second-level department , if platform = platform a, first-level department = department b, start time = 2020-01-01 00:00:00, end time = 2020-01-01 00:59:59, index = cumulative index v_1. Key="2020010101_h_a_b__", "h" means the time granularity is hours, and 2020010101 means the first hour of 2020-01-01.

After determining each query ID corresponding to the query input parameter, first determine whether there is a query ID in the cache, if it exists, obtain the query result of the query ID from the cache, if not, obtain the query corresponding to the query ID from the database As a result, it can be determined which query IDs can be queried from the cache, which query IDs can be queried from the database, and the query IDs that can be queried from the cache can be determined first, and the remaining query IDs can be queried from the database again, which can greatly reduce Data query time improves query efficiency, shortens interface response time, improves interface throughput, improves interface anti-concurrency capability, and improves interface performance. For example, if it is determined that among the 24 keys, x keys are in the K-V cache, the values corresponding to the x keys are obtained from the K-V cache, and then the values corresponding to the remaining 24-x keys are obtained from the database.

For example, if the caller requests to obtain order data from 2021.5.1 to 2020.5.3, and the time granularity is days, the request will be split into three requests, which are to obtain orders from 2021.5.1, to obtain orders from 2021.5.2 and Get orders for 2020.5.3. The cache of the existing technology is to store a key=order_d_2021-05-01_2021-05-03. If the caller needs to obtain the daily order data from 2021.5.1 to 2020.5.4, the cache cannot be hit without disassembly , need to go to the database to get 4 days of order data. In the embodiment of the present disclosure, after dismantling the acquisition request, three keys are stored in the cache, namely order_d_2021-05-01_2021-05-01, order_d_2021-05-02_2021-05-02 and order_d_2021-05-03_2021-05 -03. When the caller requests to obtain the order data from 2021.5.1 to 2020.5.4, first check order_d_2021-05-01_2021-05-01, order_d_2021-05-02_2021-05-02, order_d_2021-05-03_2021-05- in the cache 03, order_d_2021-05-04_2021-05-04, where 3 keys are hit, you only need to check order_d_2021-05-04_2021-05-04 from the database.

For another example, the first-level department a is composed of the second-level department b1, the second-level department b2, and the second-level b3, then a can be disassembled into b1, b2, and b3.

Existing technology: the caller requests the order quantity of the secondary department b1 on May 12, 2021 for the first time, and generates key=order_d_20210512_b1, and stores K=order_d_20210512_b1, V={"order":100} in the cache. The next time the caller requests the order data of all the second-level departments under the first-level department a on May 12, 2021, without dismantling, it will first search for K=order_d_20210512_a in the cache, and will query the database if it is not found.

Embodiment of the present disclosure: the caller requests the order quantity of the secondary department b1 on May 12, 2021 for the first time, then generates the key, order_d_20210512_b1, and stores K=order_d_20210512_b1, V={"order": 100} in the cache. The next time the caller requests the order data of all the second-level departments under the first-level department a on 2021.5.12, it will be disassembled to query the order data of the second-level departments b1, b2, and b3 on 2021.5.12, that is, query order_d_20210512_b1, order_d_20210512_b2, order_d_20210512_b3, first query the cache, where order_d_20210512_b1 can hit the cache, order_d_20210512_b2, order_d_20210512_b3 cannot be found in the cache, and then query the database. Compared with the prior art, the embodiment of the present disclosure improves the cache hit ratio and reduces the data volume of the query data volume.

After the query results corresponding to each query identifier are obtained, the query results are spliced, that is, the query results obtained from the cache and the query results obtained from the database are spliced, converted into a unified form, the target data is obtained, and the target Data is returned to the caller.

In the method of the embodiment of the present disclosure, if the grouping dimension and sorting dimension are parsed from the query input parameters, the grouping dimension indicates the grouping field, and the sorting dimension indicates the sorting field and sorting value range, then splicing the query of each query identifier As a result, target data is obtained, including:

According to the sorting field, the query results of the respective query identifiers are sorted, and according to the sorting value range, the query results corresponding to the sorting value range are obtained, so as to obtain the target data. Wherein, the range of sorting values is the selection range after sorting is performed according to the sorting field.

For example, starting time = 2021-05-01 00:00:00, ending time = 2021-05-02 23:59:59 from the query input parameters, the time granularity is 1 day, the grouping field is the second-level department, sorting The field is the order quantity, and the sorting value range is the first two results with the largest order quantity. First, query the order quantity of the second-level department a on 2021-05-01 and 2021-05-02 respectively, and the order quantity of the second-level department b respectively For the daily order volume on 2021-05-01 and 2021-05-02, the number of values to be returned is 4. The acquisition request can be disassembled into 4 requests, and 4 Keys can be determined. If 3 Keys are in In the cache, if one key is in the database, the query results of three of the keys are obtained from the cache, and the query results of the remaining one key are obtained from the database, and then the query results obtained from the cache and the database are converted into a unified form , and sort according to the size of the order volume, and select the top 2 query results with the largest order volume, get the target data and return it.

If the current caller's acquisition request is to query the target data of a certain dimension under a certain time granularity, the data at the next moment under this time granularity may also be queried. For example, when some businesses compare data in the past time, the scenario For example: Comparing real-time data with last year’s data, if the real-time data is based on 1-minute time granularity, the order volume in a day can be 1440 points of data, and the same period last year is also based on 1-minute time granularity, last year’s order data.

In an embodiment of the present disclosure, step S102 further includes:

Parse the time attribute from the query input parameter, and the time attribute includes start time, end time and time granularity;

According to the start time, end time and time granularity, asynchronously generate a new time query identifier corresponding to the query input parameter;

Wherein, the end time of the new time query identifier is the sum of the end time of the query input parameter and the time granularity.

After parsing the start time, end time, and time granularity from the query input parameters, a new time query ID can be generated asynchronously. The new time query ID is the query ID at the next moment in each query ID determined according to the query input parameters, as a prediction query ID. The end time of the new time stamp can be calculated by summing the end time of the query input parameters and the time granularity, for example, query platform a, department b, between 2020-01-01 00:00:00 ~ 2020-01-01 00 For the cumulative index v_1 between :59:59 and the time granularity is 1 hour, the end time of the new time query ID is 2020-01-01 01:59:59, that is, the query for the new time query ID is 2020-01-01 Cumulative indicator v_1 between 00:00:00 and 2020-01-01 01:59:59.

In the embodiment of the present disclosure, after the new time query identifier is generated asynchronously, the query result corresponding to the new time query identifier is obtained from the database according to the new time query identifier, and then the new time query identifier and the query result corresponding to the new time query identifier are Correspondingly stored in the cache to update the cache, so that when the query result of the new time query identifier is obtained at the next moment or later, it can be obtained directly from the cache instead of from the database, thereby improving the cache hit rate. Improve the efficiency of data query and shorten the interface response time.

Optionally, in order to avoid the high pressure of querying the database in a short time, the new time query ID can be asynchronously sent to a message component, such as a message queue, to query the database by listening to messages and staggering the time, and store the new time query ID and corresponding query The result is in the cache, and the cache has been updated.

For example, type 1, query platform a, department b, the cumulative index v_1 between 2020-01-01 00:00:00 ~ 2020-01-01 00:59:59, it is likely to need to query at the next moment Platform a, department b, cumulative index v_1 between 2020-01-01 00:00:00 and 2020-01-01 01:59:59, so that a new time Key can be determined. Type 2, query platform a, department b, between 2020-01-01 00:00:00～2020-01-01 11:59:59, cumulative value of 0-1 point, cumulative value of 0-2 point , the accumulative value of 0-3 o'clock...the accumulative value of 0-12 o'clock, it is very likely that the accumulative value of 0-13 o'clock will need to be checked at the next moment, so that a new time Key can be determined. Type 3, query the cumulative data between 2020-01-01 00:00:00～2020-01-01 00:59:59 of platform a and all the second-level departments under the first-level department b, it is likely to download It is necessary to check the cumulative data between 2020-01-01 00:00:00 and 2020-01-01 01:59:59 of platform a and all the second-level departments under the first-level department b, such as the second-level There are 3 departments, the number of return values to be obtained is 3, and 3 Keys can be determined, and each Key can generate a new time Key, so a total of 3 new time Keys will be generated, and the new time Key and the data from the database The query result of the new time Key obtained in the corresponding storage in the cache, so that when the caller calls next time, the cache can be hit without querying from the database, which improves the cache hit rate.

For another example, when the current time is 2021.5.12, when the caller queries the cumulative order data between the 0 time point and the 100th point data, the comparison history will also query the cumulative order data between the 0 time point and the 100th point data on 2020.5.12 Order data, at this time, in addition to querying the cumulative order data between the 0 moment of 2020.5.12 - the 100th point, the cumulative order data from the 0 moment of 2020.5.12 - the 101st point will be generated asynchronously and stored in the cache. In this way, at the next moment, if the caller calls, the cache can be hit, which improves the cache hit rate.

In an embodiment of the present disclosure, after obtaining the query result corresponding to the query identifier from the database, it further includes:

Determine whether the number of returned values in the query result is greater than 1;

If not, store the query result and the query identifier corresponding to the query result in the cache;

If so, according to the number of returned values in the query result, disassemble the query result and the query identifier corresponding to the query result, obtain the subquery identifier and the return value corresponding to the subquery identifier, and obtain the subquery identifier and the return value corresponding to the subquery identifier The correspondence is stored in the cache.

When among the determined query identifiers, there is a query identifier that needs to obtain query results from the database, after obtaining the query results from the database, determine whether the number of returned values in the query results is greater than 1, and if so, indicate the query If the value is a value, the query identifier and the corresponding query result are directly stored in the cache without disassembly; if the number of returned values in the query result is greater than 1, the query The result and the corresponding query identifier are disassembled to obtain the subquery identifier and the return value corresponding to the subquery identifier. The number of subquery identifiers is equal to the number of return values in the query result, and then store the subquery identifier and the return value corresponding to the subquery identifier To the cache to update the cache, so that the granularity of the data stored in the cache is finer, that is, the coarse-grained query identifier is disassembled into multiple fine-grained sub-query identifiers, thereby improving the cache hit rate, and the higher the cache hit rate , the better the interface performance. Fine-grained data is stored in the cache, and when different callers or query different data ranges or combinations of dimensions, the cache can also be shared.

As shown in FIG. 4 , it is a schematic flow diagram of a data processing method according to an embodiment of the present disclosure. The method includes: receiving an acquisition request for target data, where the acquisition request includes query input parameters; analyzing the query input parameters to obtain time attributes and grouping dimension, generate a new time Key asynchronously according to the time attribute, and store it in the message queue, obtain the new time Key from the message queue, query the Value corresponding to the new time Key from the database, and map the new time Key to the corresponding Value Store in the cache; determine whether the grouping dimension matches the configured grouping dimension, if so, determine the number of return values to be obtained according to the time attribute and the enumeration value of the grouping dimension, if not, determine the number of return values to be obtained according to the time attribute The number of return values; determine whether the number of return values to be obtained is greater than 1, if not, splice a Key; if so, determine whether the number of return values to be obtained in the cache is not less than the preset threshold; if yes, According to the number of return values to be obtained, the query input parameters are disassembled, and multiple Keys are spliced. If not, a Key is spliced without disassembly; to determine whether the Key is in the cache, if it is in the cache, then Obtain the corresponding Value from the cache. If it is not in the cache, obtain the corresponding Value from the database. Combine the Value obtained from the cache with the Value obtained from the database to obtain the target data and return it; at the same time, it is judged to obtain it from the database. Whether the number of return values in the Value is greater than 1, if not, store the Value and the corresponding Key in the cache, if yes, according to the number of return values in the Value, disassemble the Value and Key and store them correspondingly into the cache.

In the data processing method provided by the embodiments of the present disclosure, each query identifier corresponding to the query input parameter is determined by analyzing the query input parameters in the acquisition request, and by determining the storage location of each query identifier, successively through the query cache and the database Obtain the query results corresponding to each query identifier in the same way. When the query input parameters can be disassembled, multiple query IDs are determined according to the query input parameters. When the query IDs are not in the cache, the query results obtained from the database and the corresponding query IDs are disassembled and stored in the cache, so that The granularity of the data stored in the cache is small, which is convenient for subsequent data query, and the cache hit rate is high, and the query ID of the new time is generated according to the time attribute, and the query ID of the new time is compared with the corresponding query ID obtained from the database. The query results are stored in the cache to improve the cache hit rate of subsequent data queries. The data processing method provided by the embodiments of the present disclosure can improve cache hit rate, improve data query efficiency, shorten interface response time, improve interface throughput, improve interface anti-concurrency capability, improve interface service performance, and further improve user experience.

As shown in FIG. 5, an embodiment of the present disclosure also provides a data processing device 500, including:

The receiving module 501 receives an acquisition request for target data, where the acquisition request includes query parameters;

Determining module 502, analyzing the query input parameters, and determining each query identifier corresponding to the query input parameters;

Judging module 503, judging whether there is a query identifier in the cache, if so, obtaining the query result corresponding to the query identifier from the cache; if not, obtaining the query result corresponding to the query identifier from the database;

The splicing module 504 splices the query results of each query identifier to obtain the target data.

In an implementation manner of the embodiments of the present disclosure, the determination module 502 is further configured to: parse out the time attribute from the query input parameter; The number of return values; according to the number of return values to be obtained, each query identifier corresponding to the query input parameters is determined.

In an implementation manner of the embodiments of the present disclosure, the determination module 502 is further configured to: parse out the time attribute and dimension type from the query input parameter; the dimension type includes a grouping dimension;

According to the time attribute and grouping dimension, determine the number of return values to be obtained corresponding to the query input parameters;

In another embodiment of the present disclosure, the determining module 502 is further configured to: parse out the time attribute and dimension type from the query input parameter; the dimension type includes a restricted dimension, and the restricted dimension indicates an enumerated value;

In an embodiment of the present disclosure, the determining module 502 is further configured to: judge whether the grouping dimension matches the configured grouping dimension; if so, determine the pending The number of return values obtained; if not, determine the number of return values to be obtained according to the time attribute.

In the embodiment of the present disclosure, the determination module 502 is further used to: determine whether the number of return values to be obtained is greater than 1; if not, determine a query identifier corresponding to the query input parameter; if so, determine the Whether the number of return values to be obtained is not less than the preset threshold, if yes, disassemble the query input parameters, and determine the query identifiers corresponding to the query input parameters, if not, do not disassemble, and determine the corresponding query input parameters A query ID for .

In the embodiment of the present disclosure, the determination module 503 is further configured to: parse out the time attribute from the query input parameter, the time attribute includes start time, end time and time granularity, according to the start time, end time and time granularity , asynchronously generate a new time query identifier corresponding to the query input parameter; wherein, the end time of the new time query identifier is the sum of the end time of the query input parameter and the time granularity.

In the embodiment of the present disclosure, the dimension type also includes a sorting dimension, and the sorting dimension indicates a sorting field and a sorting value range; the splicing module 504 is also configured to: sort the query results of each query identifier according to the sorting field, and According to the sorting value range, the query result corresponding to the sorting value range is obtained to obtain the target data.

In an embodiment of the present disclosure, the device further includes a storage module, configured to, after obtaining the query result corresponding to the query identifier from the database, further include: judging whether the number of returned values in the query result is greater than 1; if No, correspondingly store the query result and the query identifier corresponding to the query result in the cache; if yes, store the query result and the query identifier corresponding to the query result according to the number of returned values in the query result Perform disassembly to obtain the sub-query identifier and the return value corresponding to the sub-query identifier, and correspondingly store the sub-query identifier and the return value corresponding to the sub-query identifier in the cache.

Embodiments of the present disclosure also provide an electronic device, including: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, so that One or more processors implement the data processing method provided by the embodiments of the present disclosure.

Embodiments of the present disclosure also provide a computer-readable medium on which a computer program is stored, and when the program is executed by a processor, the data processing method provided by the embodiments of the present disclosure is implemented.

FIG. 6 shows an exemplary system architecture 600 to which the data processing method or data processing device of the embodiments of the present disclosure can be applied.

As shown in FIG. 6 , a system architecture 600 may include

terminal devices

601 , 602 , and 603 , a network 604 and a server 605 . The network 604 is used as a medium for providing communication links between the

terminal devices

601 , 602 , 603 and the server 605 . Network 604 may include various connection types, such as wires, wireless communication links, or fiber optic cables, among others.

Users can use

terminal devices

601 , 602 , 603 to interact with server 605 via network 604 to receive or send messages and the like. Various communication client applications can be installed on the

terminal devices

601, 602, 603, such as search applications, shopping applications, web browser applications, instant messaging tools, email clients, social platform software, etc. (just for example).

The

terminal devices

601, 602, 603 may be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers and the like.

The server 605 may be a server that provides various services, such as a background management server that provides support for search applications used by users using the

terminal devices

601 , 602 , 603 (just an example). The background management server can analyze and process the received data such as the target data acquisition request, and feed back the processing result (target data—just an example) to the terminal device.

It should be noted that the data processing method provided by the embodiments of the present disclosure is generally executed by the server 605 , and correspondingly, the data processing device is generally disposed in the server 605 .

It should be understood that the numbers of terminal devices, networks and servers in FIG. 6 are only illustrative. According to the implementation needs, there can be any number of terminal devices, networks and servers.

Referring now to FIG. 7 , it shows a schematic structural diagram of a computer system 700 suitable for implementing a terminal device according to an embodiment of the present disclosure. The terminal device shown in FIG. 7 is only an example, and should not limit the functions and application scope of the embodiments of the present disclosure.

As shown in FIG. 7 , a computer system 700 includes a central processing unit (CPU) 701 that can operate according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage section 708 into a random-access memory (RAM) 703 Instead, various appropriate actions and processes are performed. In the RAM 703, various programs and data required for the operation of the system 700 are also stored. The CPU 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to the bus 704 .

The following components are connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 708 including a hard disk, etc. and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc. is mounted on the drive 710 as necessary so that a computer program read therefrom is installed into the storage section 708 as necessary.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, where the computer program includes program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via communication portion 709 and/or installed from removable media 711 . When this computer program is executed by a central processing unit (CPU) 701, the above-described functions defined in the system of the present disclosure are performed.

It should be noted that the computer-readable medium shown in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. A computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. . Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

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 a flowchart or block diagram may represent a module, program segment, or portion of code that includes one or more logical functions for implementing specified executable instructions. 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 they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block in the block diagrams or flowchart illustrations, and combinations of blocks in the block diagrams or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a A combination of dedicated hardware and computer instructions.

The modules involved in the embodiments described in the present disclosure may be implemented by software or by hardware. The described modules may also be set in a processor, for example, it may be described as: a processor includes a receiving module, a determining module, a judging module and a splicing module. Wherein, the names of these modules do not constitute a limitation on the module itself under certain circumstances, for example, the receiving module can also be described as "a module that receives an acquisition request for target data".

As another aspect, the present disclosure also provides a computer-readable medium, which may be included in the device described in the above embodiments, or may exist independently without being assembled into the device. The above-mentioned computer-readable medium carries one or more programs, and when the one or more programs are executed by the device, the device includes: receiving an acquisition request for target data, where the acquisition request includes query input parameters; Analyze the parameters to determine each query identifier corresponding to the query input parameter; determine whether there is a query identifier in the cache, if so, obtain the query result corresponding to the query identifier from the cache; if not, obtain the query result corresponding to the query identifier from the database; The query results of each query identifier are spliced to obtain the target data.

According to the technical solutions of the embodiments of the present disclosure, by analyzing the query input parameters in the acquisition request, each query identifier corresponding to the query input parameter is determined, and by judging the storage location of each query identifier, successively through the query cache and the database Obtain the query result corresponding to each query identifier. When the query input parameters can be disassembled, multiple query IDs are determined according to the query input parameters. When the query IDs are not in the cache, the query results obtained from the database and the corresponding query IDs are disassembled and stored in the cache, so that The granularity of the data stored in the cache is small, which is convenient for subsequent data query, and the cache hit rate is high, and the query ID of the new time is generated according to the time attribute, and the query ID of the new time is compared with the corresponding query ID obtained from the database. The query results are stored in the cache to improve the cache hit rate of subsequent data queries. The data processing method provided by the embodiments of the present disclosure can improve cache hit rate, improve data query efficiency, shorten interface response time, improve interface throughput, improve interface anti-concurrency capability, improve interface service performance, and further improve user experience.

The specific implementation manners described above do not limit the protection scope of the present disclosure. It should be apparent to those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

A data processing method, characterized in that, comprising:

receiving an acquisition request for target data, where the acquisition request includes query input parameters;

Analyzing the query input parameters to determine each query identifier corresponding to the query input parameters;

Judging whether the query identifier exists in the cache, if so, obtaining the query result corresponding to the query identifier from the cache; if not, obtaining the query result corresponding to the query identifier from the database;

The query results of the respective query identifiers are spliced to obtain the target data.
The method according to claim 1, wherein parsing the query input parameters to determine each query identifier corresponding to the query input parameters includes:

Parsing out the time attribute from the query input parameter;

According to the time attribute, determine the number of return values to be acquired corresponding to the query input parameters;

According to the number of return values to be obtained, each query identifier corresponding to the query input parameter is determined.
The method according to claim 1, wherein parsing the query input parameters to determine each query identifier corresponding to the query input parameters includes:

Analyzing time attributes and dimension types from the query input parameters; the dimension types include grouping dimensions;

According to the time attribute and the grouping dimension, determine the number of return values to be acquired corresponding to the query input parameter;

According to the number of return values to be obtained, each query identifier corresponding to the query input parameter is determined.
The method according to claim 3, wherein the dimension type further includes a sorting dimension, and the sorting dimension indicates a sorting field and a sorting value range;

Splicing the query results of the various query identifiers to obtain the target data includes:

sorting the query results of the respective query identifiers according to the sorting field, and obtaining the query results corresponding to the sorting value range according to the sorting value range, so as to obtain the target data.
The method according to claim 3, wherein, according to the time attribute and the grouping dimension, determining the number of return values to be obtained corresponding to the query input parameters includes:

judging whether the grouping dimension matches the configured grouping dimension;

If yes, determine the number of return values to be acquired according to the time attribute and the enumeration value of the grouping dimension; if not, determine the number of return values to be acquired according to the time attribute.
The method according to claim 1, wherein parsing the query input parameters to determine each query identifier corresponding to the query input parameters includes:

Parse the time attribute and dimension type from the query input parameter; the dimension type includes a restricted dimension, and the restricted dimension indicates an enumerated value;

According to the time attribute and the enumerated value, determine the number of return values to be obtained corresponding to the query input parameter;

According to the number of return values to be obtained, each query identifier corresponding to the query input parameter is determined.
The method according to any one of claims 2-6, characterized in that, according to the number of return values to be obtained, determining each query identifier corresponding to the query input parameters, including:

judging whether the number of return values to be obtained is greater than 1;

If not, determine a query identifier corresponding to the query input parameter;

If yes, determine whether the number of return values to be obtained in the cache is not less than a preset threshold, if yes, disassemble the query input parameters, and determine each query identifier corresponding to the query input parameters, if No, disassembly is not performed, and a query identifier corresponding to the query input parameter is determined.
The method according to claim 1, wherein parsing the query input parameters to determine each query identifier corresponding to the query input parameters further includes:

Analyzing time attributes from the query input parameters, the time attributes include start time, end time and time granularity;

Asynchronously generate a new time query identifier corresponding to the query input parameter according to the start time, end time and time granularity;

Wherein, the end time of the new time query identifier is the sum of the end time of the query input parameter and the time granularity.
The method according to claim 1, wherein after obtaining the query result corresponding to the query identifier from the database, further comprising:

Judging whether the number of returned values in the query result is greater than 1;

If not, correspondingly storing the query result and the query identifier corresponding to the query result in the cache;

If so, according to the number of returned values in the query result, the query result and the query identifier corresponding to the query result are disassembled, the subquery identifier and the return value corresponding to the subquery identifier are obtained, and the The sub-query identifier and the return value corresponding to the sub-query identifier are correspondingly stored in the cache.
A data processing device, characterized in that it comprises:

The receiving module receives an acquisition request for target data, and the acquisition request includes query input parameters;

The determining module is configured to analyze the query input parameters, and determine each query identifier corresponding to the query input parameters;

A judging module, judging whether the query identifier exists in the cache, if so, obtaining the query result corresponding to the query identifier from the cache; if not, obtaining the query result corresponding to the query identifier from the database;

A splicing module splices the query results of the respective query identifiers to obtain the target data.
An electronic device, characterized in that it comprises:

one or more processors;

storage means for storing one or more programs,

When the one or more programs are executed by the one or more processors, the one or more processors are made to implement the method according to any one of claims 1-9.
A computer-readable medium, on which a computer program is stored, wherein, when the program is executed by a processor, the method according to any one of claims 1-9 is realized.