Data processing method, computer equipment, storage medium and program product

Abstract

The invention discloses a data processing method, computer equipment, a storage medium and a program product. The first cluster and the second cluster with different cluster types are determined based on the cluster selection instruction. And determining target tasks of the first cluster and the second cluster based on the task selection instruction. And then determining to-be-processed data according to the target task and the corresponding target task parameter. And finally, performing data processing corresponding to the target task on the first cluster and the second cluster based on the to-be-processed data. Therefore, personalized selection of the cluster type and the to-be-processed data can be realized through the self-defined cluster selection instruction and the task selection instruction to perform data processing, so that the flexibility of cross-cluster data processing is improved.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and specifically to a data processing method, computer equipment, storage medium and program product. Background Art

[0002] With the development of the big data era, if a data cluster encounters a catastrophic problem, it will lead to business interruption, data loss, and recovery delay. Therefore, multi-cluster management is usually used. Multi-cluster refers to the simultaneous management and operation of multiple independent data clusters in a distributed system. Each data cluster has its own computing and storage resources and can run and process workloads independently. Through the configuration and replication of multiple clusters, a cluster active-active environment is provided, data can be distributed and redundant in multiple data clusters, improving data availability and elasticity. Summary of the invention

[0003] In response to the above technical problems, embodiments of the present application provide a data processing method, a computer device, a storage medium, and a program product, which improve the flexibility of cross-cluster data processing.

[0004] In a first aspect, the present application provides a data processing method, comprising:

[0005] Determine a first cluster and a second cluster based on the cluster selection instruction, the first cluster and the second cluster being of different cluster types;

[0006] determining target tasks for the first cluster and the second cluster based on the task selection instruction;

[0007] Determine the data to be processed according to the target task and the task parameters of the target task;

[0008] Based on the data to be processed, the first cluster and the second cluster are processed with data corresponding to the target task. In a second aspect, the present application provides a computer device, including a processor and a memory, wherein the memory stores a plurality of instructions; the processor loads instructions from the memory to execute the steps of any data processing method provided in the embodiments of the present application.

[0009] In a third aspect, the present application provides a computer-readable storage medium, which stores a plurality of instructions, and the instructions are suitable for a processor to load to execute the steps of any data processing method provided in the embodiments of the present application.

[0010] In a fourth aspect, the present application provides a computer program product, including a computer program, which is executed by a processor to perform the steps of any data processing method provided in the embodiments of the present application.

[0011] The present application first determines the first cluster and the second cluster of different cluster types based on the cluster selection instruction. Then, the target tasks of the first cluster and the second cluster are determined based on the task selection instruction. Then, the data to be processed is determined according to the target task and the corresponding target task parameters. Finally, the first cluster and the second cluster are subjected to data processing corresponding to the target task based on the data to be processed. In this way, personalized selection of cluster types and data to be processed can be achieved through customized cluster selection instructions and task selection instructions for data processing, thereby improving the flexibility of cross-cluster data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 It is a schematic diagram of an application environment of a data processing method provided in an embodiment of the present application;

[0014] Figure 2 is a schematic structural diagram of a display device provided in an embodiment of the present application;

[0015] Figure 3 It is a flowchart of a data processing method provided in an embodiment of the present application;

[0016] Figure 4 is a flowchart of a method for determining a first cluster and a second cluster provided in an embodiment of the present application;

[0017] Figure 5 It is a flowchart of a data processing method provided in a specific embodiment of the present application;

[0018] Figure 6 It is a flowchart of a data migration method provided in a specific embodiment of the present application;

[0019] Figure 7 It is a flow chart of a data comparison method provided in a specific embodiment of the present application;

[0020] Figure 8 It is a schematic diagram of interaction between a data cluster and a client provided in an embodiment of the present application;

[0021] Fig. 9 is a structural schematic diagram of a data processing device provided in an embodiment of the present application;

[0022] Fig.10It is a schematic diagram of the internal structure of the computer device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application. At the same time, in the description of the embodiments of the present application, the terms "first", "second", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0024] For data processing with a large amount of data, the use of a single data cluster may not meet its operational requirements. For example, a data table in a relational database consists of a row key and multiple column families, and more nodes can be added to make the capacity of the data table larger. Therefore, a data table usually needs to manage data through multiple data clusters. The embodiment of the present application takes HBase as an example, and HBase includes an HBase table. By distributing the HBase table in multiple data clusters for cross-cluster management, the capacity of the data can be increased and the read and write performance of the data can be improved. Cross-cluster data processing based on HBase may include but is not limited to cross-cluster data migration, cross-cluster data recovery, cross-cluster data synchronization, and cross-cluster data comparison. In cross-cluster data processing, the initial location for storing data is the source cluster, the HBase table in the source cluster is the source table, the target location for operations such as migration or synchronization of the data is the target cluster, and the HBase table in the target cluster is the target table.

[0025] In the related art, the management of multiple data clusters is usually full data processing, that is, data processing is performed based on the complete data of the source cluster and the target cluster. Taking cross-cluster data migration as an example, after the source cluster opens the snapshot, the structure of the data table is first synchronized in the target cluster, and then the data is copied to the target cluster. After the data migration, if data comparison is performed, it is necessary to sample and verify the data in the source table and the target table in the source cluster and the target cluster. Due to the large amount of HBase data, data is generally extracted from the row key of the source table in sequence, and then the target table is queried for matching records. After the records are found, the information of the column family and the unit is compared. When the information of the column family and the unit corresponding to the row key is completely consistent, it means that the data comparison is consistent. If cross-cluster data recovery is required, it is still necessary to perform cross-cluster data comparison first to obtain abnormal data that does not match the source table, and then synchronize the data of the source table in the source cluster again.

[0026] It can be seen from the above content that in the related art, sampling the data in the source table row key may result in omissions. In addition, cross-cluster data comparison takes a long time. If the computer device is stopped to migrate the data, and then the computer device is started again after verification, the working efficiency of the computer device will be affected. If the computer device is not stopped, if new data is written during data comparison, the data of the target cluster cannot be kept consistent with that of the source cluster. In addition, for cross-cluster data recovery, data recovery can only be performed based on the latest version. Therefore, in the related art, cross-cluster data processing has the problems of large computational complexity, low efficiency and poor flexibility. Based on this, the embodiment of the present application provides a data processing method that can customize cluster types, target tasks and target task parameters. By managing the data of the data cluster on demand, the computational complexity of the computer device can be reduced, making the cross-cluster data processing more efficient and more flexible.

[0027] In one embodiment of the present application, the data processing method can be run on a local terminal device or a remote computer device. When the data processing method is run on a remote computer device, the method can be implemented and executed based on a cloud interaction system, wherein the cloud interaction system includes a computer device and a client device.

[0028] In order to better understand a data processing method, a data processing device, a computer device, a computer storage medium and a computer program product provided in an embodiment of the present application, the application environment applicable to the embodiment of the present application is described below.

[0029] See also Figure 1 , Figure 1A schematic diagram of an application environment of a data processing method provided in an embodiment of the present application is shown. The data processing method provided in this embodiment can be applied to any scenario where cross-cluster data processing is required in at least two data clusters, and the method can be applied to products in these scenarios. As an example, the application scenario includes a computer device 100, a display device 200, and at least two data clusters 300, and the computer device 100, the display device 200, and the at least two data clusters 300 communicate with each other. A data processing device is integrated in the computer device 100 to run a computer-readable storage medium corresponding to the data processing method, and execute the steps of the data processing method.

[0030] Understandably, Figure 1 The computer device in the application scenario of the data processing method shown, or the device included in the computer device, does not constitute a limitation on the embodiments of the present application. That is, the number of devices and types of devices included in the application scenario of the data processing method, or the number of devices and types of devices included in each device, do not affect the overall implementation of the technical solution in the embodiments of the present application, and can all be regarded as equivalent replacements or derivatives of the technical solution claimed to be protected in the embodiments of the present application.

[0031] The computer device 100 in the embodiment of the present application may be an independent device, or a device network or device cluster composed of different devices. For example, the computer device 100 described in the embodiment of the present application includes but is not limited to a computer, a network host, a single network device, a plurality of network device sets or a cloud device composed of a plurality of devices. The cloud device is composed of a large number of computers or network devices based on cloud computing.

[0032] Those skilled in the art will understand that Figure 1 The application scenario shown in the figure is only an application scenario corresponding to the technical solution of the present application, and does not constitute a limitation on the application scenario of the technical solution of the present application. Other application scenarios may also include Figure 1 More or fewer computer devices shown in, or computer device network connection relationships, such as Figure 1 Only one computer device is shown. It can be understood that the scenario of the data processing method can also include one or more other computer devices, which are not limited here. The computer device 100 can also include a memory and a processor, and the memory is used to store information related to the data processing method.

[0033] In addition, in the application scenario of the data processing method of the device in the embodiment of the present application, the computer device 100 can be provided with a display device 200, or the computer device 100 is not provided with a display device 200 and is in communication connection with an external display device 200, and the display device 200 is used to receive the configuration data input by the user and output the result of the execution of the data processing method in the computer device. The computer device 100 can access at least two data clusters 300, for example, the data cluster 300 may include the following: Figure 1 The data cluster 300 shown is a cluster 1, a cluster 2, ..., a cluster N. The data cluster 300 may be a local memory of the computer device 100 or a cloud database set in the cloud. The data cluster 300 stores information related to the data processing method.

[0034] In the embodiment of the present application, the display device 200 may include an operation interface, through which the user can customize the relevant information designed in the data processing process, such as cluster type, target task, and target task parameters. Figure 2 , Figure 2 2 is a schematic diagram of the structure of a display device 200 provided in an embodiment of the present application. In the display device 200, the operation interface or operation options provided by the functional modules such as the cluster selection module 201, the data migration module 202, the data comparison module 203, the data backup module 204, the data verification module 205, the data recovery module 206 and the progress query module 207 can be displayed to realize the corresponding functions. Among them, the modules such as the cluster selection module 201, the data migration module 202, the data comparison module 203, the data backup module 204, the data verification module 205, the data recovery module 206 and the progress query module 207 can be run in the computer device 100, and the corresponding operation interface can be displayed through the display device 200 during operation.

[0035] The cluster selection module 201 may include a first cluster selection unit 2011 and a second cluster selection unit 2012. The first cluster and the second cluster correspond to a cluster type respectively. For example, the first cluster may be a source cluster, and the second cluster may be a target cluster. The first cluster and the second cluster may be one or more.

[0036] The data migration module 202 may include a pre-processing unit 2021, a migration range setting unit 2022, and a data synchronization unit 2023. The pre-processing unit 2021 is used to provide a selection for pre-processing the target table of the target cluster, such as deletion and reconstruction, data clearing, or original data retention. The migration range setting unit 2022 is used to receive the data to be processed that needs to be migrated, such as the data change time range, the data field range, and a field with a specific value. The data synchronization unit 2023 can determine whether to synchronize and update the data in the second cluster with the data in the first cluster by turning it on or off.

[0037] The data comparison module 203 may include a comparison range setting unit 2031 and a comparison mode setting unit 2032. The comparison range setting unit 2031 is used to provide a selection to set the data range that needs to be compared, such as data that failed to be migrated or data filtered by time period, etc. The comparison mode setting unit 2032 is used to provide a selection to set the mode of data comparison, such as comparing data according to the quantity, content, or quantity first and then content, etc.

[0038] The data backup module 204 may include a backup mode setting unit 2041 and a backup date setting unit 2042. The backup mode setting unit 2041 is used to provide a selection for setting the data backup mode, such as incremental, full, etc. The backup date setting unit 2042 is used to provide a selection for setting the date for incremental backup, such as a date selected on demand, fixed days, fixed weeks, or fixed months, etc.

[0039] The data verification module 205 may include a verification field setting unit 2051 and a verification rule setting unit 2052. The verification field setting unit 2051 is used to provide options for setting the fields that need to be verified. For example, the fields that need to be verified can be divided into maximum value, minimum value, enumeration value or average value according to the numerical type; according to the non-numerical type, they can be divided into enumeration value, number of repeated items greater than or less than a certain value or number of repeated items. The verification rule setting unit 2052 is used to provide options for setting the verification rules, such as setting the verification rules according to the table size, the number of table rows, the table size period volatility or the table row number period volatility.

[0040] The data recovery module 206 may include a recovery range setting unit 2061 and a recovery version number setting unit 2062. The recovery range setting unit 2061 is used to provide a selection to set the range of data recovery. The recovery version number setting unit 2062 is used to provide a selection to set the version number of data recovery. The progress query module 207 is used to receive the query instruction input by the user and display the progress information corresponding to the current task.

[0041] Through Figure 2 The display device 200 shown allows the user to perform operations such as setting custom parameters and entering queries for cross-cluster data processing on the operation interface of the display device 200, and can also obtain feedback such as the processing status and progress of tasks in cross-cluster data processing through the operation interface.

[0042] It should be noted that Figure 1 and Figure 2 The application scenario of the data processing method of the device shown is only an example. The application scenario of the data processing method described in the embodiment of the present application is to more clearly illustrate the technical solution of the embodiment of the present application, and does not constitute a limitation on the technical solution provided by the embodiment of the present application. Based on the application scenario of the data processing method of the above-mentioned device, an embodiment of the data processing method is proposed. It is described in detail below with reference to the accompanying drawings.

[0043] The following is a detailed description in conjunction with the accompanying drawings. In this embodiment, the execution subject is taken as an example of a data processing device in a computer device 100. It should be noted that the description order of the following embodiments is not intended to limit the preferred order of the embodiments. Although the logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in an order different from that shown in the accompanying drawings.

[0044] See also Figure 3 , Figure 3 301-303 are steps 302-303, which are described in detail below.

[0045] Step 301: Determine a first cluster and a second cluster based on a cluster selection instruction, where the first cluster and the second cluster are of different cluster types.

[0046] In an embodiment of the present application, a cluster selection instruction refers to an instruction for selecting a cluster type, and the first cluster is the initial location for data storage in cross-cluster data processing, i.e., the source cluster. The second cluster is the target location for operations based on the data of the source cluster in cross-cluster data processing, i.e., the target cluster. In the related technology of cross-cluster data processing, the source cluster and the target cluster in at least two data clusters are usually determined in advance based on the needs, so that the data of the source cluster flows to the target cluster to realize cross-cluster data processing. In this way, the type of each data cluster is fixed, the interaction direction between data clusters is fixed, and the flexibility is poor. However, by providing a selection of data cluster types, after receiving a cluster selection instruction for a data cluster type from a user, the data cluster selected by the user can be configured with a corresponding type according to the cluster selection instruction. In one example, different types of software packages configured in advance can be matched with the cluster identifier corresponding to the data cluster selected by the user, so as to assign a corresponding type to the data cluster selected by the user, so that data clusters can be arbitrarily selected as source clusters and target clusters in at least two data clusters according to the user's custom settings, respectively, for flexible data processing.

[0047] For example, a software package containing source cluster attribute information and a software package containing target cluster attribute information are configured in advance. Figure 1 In the example, cluster 1 is selected as the source cluster and cluster 2 is selected as the target cluster. Then, the software package containing the source cluster attribute information can be deployed to cluster 1, and the software package containing the target cluster attribute information can be deployed to cluster 2. In this way, the source cluster and the target cluster can be determined according to the user's selection, which improves the flexibility of cluster type determination.

[0048] Step 302: Determine target tasks for the first cluster and the second cluster based on the task selection instruction.

[0049] In the embodiment of the present application, the target task refers to the task to be performed in the cross-cluster data processing. In one example, the target task may include but is not limited to data migration, data comparison, data backup, data verification and data recovery. The user selects the target task to be performed in the operation interface of the display device 200, making the data processing more flexible.

[0050] Step 303: determine the data to be processed according to the set target task and the corresponding target task parameters.

[0051] In an embodiment of the present application, the target task parameter corresponding to the target task refers to a parameter that can be customized and selected by the user. The selection of the target task parameter can be displayed to the user through the operation interface of the display device 200, so that the user can select the target task parameter corresponding to the target task through the operation interface. The target task parameter corresponding to each target task can be set based on demand. After receiving the target task set by the user and the corresponding target task parameter, the corresponding data to be processed can be generated. The data to be processed refers to the parameters for performing data processing operations on the first cluster and the second cluster. Through user-defined selection, there is no need to perform full operations on the task, which can improve the efficiency of cross-cluster data processing.

[0052] Step 304: Perform data processing corresponding to the target task on the first cluster and the second cluster based on the data to be processed.

[0053] In an embodiment of the present application, after the corresponding data to be processed is determined according to the target task set by the user and the corresponding target task parameters, the operation corresponding to the target task can be performed on the first cluster and the second cluster based on the data to be processed. Taking the target task as data comparison as an example, assuming that the comparison time range set by the user is from January 1 to January 7, the target task parameter is the time range from January 1 to January 7. Based on this, the data to be processed for comparing the data during the period from January 1 to January 7 can be generated, thereby comparing the data of the first cluster and the second cluster within the time range from January 1 to January 7. In this way, the corresponding operation is performed on the task required by the user according to the user's customized selection, and customized cross-cluster data processing is realized, making cross-cluster data processing more flexible.

[0054] The embodiment of the present application can determine the first cluster and the second cluster in at least two data clusters by determining the cluster selection instruction input by the user, thereby realizing a custom cluster type, thereby flexibly selecting the source cluster and the target cluster that need to be processed in at least two data clusters. In addition, the task type of the task to be executed and its corresponding task parameters are determined according to the target task and target task parameters selected by the user, thereby determining the data to be processed of the target task, so as to process the data of at least two data clusters, thereby realizing personalized selection of cluster type and data to be processed, and improving the flexibility of cross-cluster data processing.

[0055] In the embodiment of the present application, the cluster selection instruction may include a first selection instruction and a second selection instruction. The first selection instruction is an instruction for the user to select the first cluster, and the second selection instruction is an instruction for the user to select the second cluster. Through the first selection instruction and the second selection instruction, a corresponding type of software package can be configured in the data cluster, thereby determining the type of the data cluster. Figure 4 , Figure 44 is a flow chart of a method for determining a first cluster and a second cluster provided in an embodiment of the present application. In step 301 of the embodiment of the present application, the method for determining the first cluster and the second cluster can be implemented through the following steps 401-404.

[0056] Step 401: Configure a first software package and a second software package.

[0057] Step 402: Receive a first selection instruction and a second selection instruction, wherein the first selection instruction includes a first cluster identifier, and the second selection instruction includes a second cluster identifier.

[0058] Step 403: deploy a first software package in a data cluster corresponding to the first cluster identifier, and deploy a second software package in a data cluster corresponding to the second cluster identifier.

[0059] Step 404: determine the data cluster where the first software package is deployed as the first cluster, and determine the data cluster where the second software package is deployed as the second cluster.

[0060] In an embodiment of the present application, the first software package includes the first attribute information of the first cluster, and the second software package includes the second attribute information of the second cluster. Take the first cluster as the source cluster, the second cluster as the target cluster, and the software package as a JAR (Java Archive) package as an example. Developers can compile source segment s and target segment t when developing Java programs. The source segment s corresponds to the attribute information of the source cluster, and the target segment t corresponds to the attribute information of the target cluster. If the source segment s is packaged into the first software package, the first software package can include the attribute information of the source cluster. If the target segment t is packaged into the second software package, the second software package can include the attribute information of the target cluster. In this way, when the user's customized selection of the first cluster and the second cluster is received, the data cluster selected by the user can be deployed based on the configured first software package and second software package.

[0061] Each data cluster has a corresponding identifier for identifying the data cluster. In an embodiment of the present application, the identifier of the data cluster selected by the user as the first cluster can be determined as the first cluster identifier, and the identifier of the data cluster selected by the user as the second cluster can be determined as the second cluster identifier. Among them, the first selection instruction includes the first cluster identifier, indicating that the user uses the data cluster corresponding to the first cluster identifier as the first cluster. When the first selection instruction is received, the first software package can be deployed in the data cluster corresponding to the first cluster identifier in at least two data clusters, and when the first selection instruction and the second selection instruction are received, the second software package is deployed in the data cluster corresponding to the second cluster identifier. Finally, the data cluster with the first software package deployed is determined as the first cluster, and the data cluster with the second software package deployed is determined as the second cluster.

[0062] In this way, based on the user's first selection instruction and the second selection instruction, the first and second software packages are deployed in at least two data clusters to the corresponding data clusters, thereby determining the first cluster and the second cluster, thereby improving the flexibility of cluster type determination. Figure 1 For example, the first software package JAR package s and the second software package JAR package t are configured in advance. Assume that the cluster corresponding to the first cluster identifier included in the first selection instruction input by the user is cluster 1, and the cluster corresponding to the second cluster identifier included in the second selection instruction input is cluster 2. In other words, the user selects Figure 1 In the example, cluster 1 is selected as the first cluster and cluster 2 is selected as the second cluster. Then, JAR package s can be deployed to cluster 1 and JAR package t can be deployed to cluster 2. In this way, cluster 1 can be determined as the first cluster and cluster 2 can be determined as the second cluster.

[0063] In an embodiment of the present application, for a task whose target task is data migration, its task parameters may include a migration range, and the data to be processed may be data within the migration range. Data migration refers to transferring data from a first cluster to a second cluster. In step 303, first data in the first cluster that is within the migration range may be migrated to the second cluster. The migration range may include, but is not limited to, a data change time range, a data field range, and a field being a specific value.

[0064] In order to ensure consistency and integrity during the data migration process, data synchronization can also be performed in the data migration task. Therefore, in step 303 of the embodiment of the present application, when the first data in the first cluster is updated, the second data associated with the first data in the second cluster can be synchronously updated. Among them, the first data is the data in the first cluster, and the second data is the data in the second cluster. In an example, the scope of data synchronization can be determined according to whether the real-time synchronization function is turned on for the second cluster. If the real-time synchronization function is not turned on for the second cluster, data synchronization is performed based on the time range of data migration, that is, data synchronization is only performed within the time range of data migration. If the real-time synchronization function is turned on for the second cluster, then after the data migration is completed, data written after the deadline can still be synchronized. When it is detected that the first data of the first cluster is updated, the second data of the second cluster is synchronously updated. In this way, the consistency of the data of the first cluster and the second cluster can be guaranteed.

[0065] In an embodiment of the present application, when synchronizing and updating the second data associated with the first data in the second cluster, it can be first verified whether the second cluster has the first data table corresponding to the first data of the first cluster. Among them, the first data table is a data table stored in the first cluster. If data synchronization is to be performed, the same data table needs to exist in the second cluster. If the first data table does not exist in the second cluster, it is necessary to create the first data table in the second cluster. If the first data table exists in the second cluster, it is necessary to further verify whether the structures of the data tables in the two data clusters match in order to ensure the normal data synchronization. If the second structure of the first data table in the second cluster does not match the first structure in the first cluster, it means that the structure of the first data table in the second cluster is faulty, and a data update fault prompt needs to be generated to prompt the user to correct the structure of the first data table in the second cluster for subsequent data synchronization. In one example, the data update fault prompt may include but is not limited to the following options: abandoning data migration, deleting the second data in the second cluster and performing full migration, and trying to continue migration, etc.

[0066] In addition, before data migration, the data of the second cluster can be processed in advance according to needs, that is, the processing of pre-data. Wherein, pre-data refers to the data already in the second cluster before data migration. In an embodiment of the present application, before the step of migrating the first data in the first cluster within the migration range to the second cluster, a pre-processing instruction can also be received first, and the pre-processing instruction may include a deletion and reconstruction instruction, a data clearing instruction, or an original data retention instruction. The deletion and reconstruction instruction refers to deleting the second data table of the second cluster. Wherein, the second data table refers to the data table existing in the second cluster before data migration. The data clearing instruction refers to retaining the second data table in the second cluster, but clearing the second data in the second data table. The original data retention instruction refers to retaining the second data table and the second data existing in the second cluster before executing the data migration task. In this way, the pre-data processing of the second cluster can be performed according to the pre-processing instruction selected by the user. Specifically, the pre-processing step may include: deleting the second data table corresponding to the second data of the second cluster based on a delete and rebuild instruction, and re-establishing the first data table corresponding to the first data in the second cluster; or clearing the second data in the second data table based on a data clearing instruction; or retaining the second data in the second data table based on an original data retaining instruction. By providing users with multiple pre-processing options before executing the data migration task, the needs of users are met, so that historical data required by users will not be lost during data migration, and the flexibility of data migration is improved.

[0067] After executing the data migration task, the result of migration success or failure can be returned to the operation interface of the display device 200 to prompt the user, and specific migration result details can also be returned, such as the number of successful migrations, the reason for failure, etc., and options such as failed data comparison and retry functions can be provided.

[0068] In an embodiment of the present application, for a task whose target task is data comparison, its task parameters may include a comparison range and a comparison method, and the data to be processed may be data within the comparison range. The embodiment of the present application may be compared based on the data that failed to migrate data, or may be compared with the data in the selection instruction according to the selection instruction input by the user, for example, the user may choose to compare the data filtered by a certain time period. At the same time, the user may also select a comparison method, such as comparison by quantity, comparison by content, or comparison by quantity and then by content. Therefore, in step 303, the first data in the first cluster located within the comparison range may be compared with the second data in the second cluster located within the comparison range based on the comparison method to obtain a comparison result. Further, the abnormal data in the comparison result may also be corrected by data comparison. The abnormal data is the data in the second cluster that does not match the first cluster. Therefore, when it is confirmed according to the comparison result that the second cluster has the second data that does not match the first cluster, the unmatched second data may be corrected so that the second data of the second cluster is consistent with the first data of the first cluster. In this way, the scope and method of data comparison can be set according to the user's customization, which greatly saves the time of cross-cluster data comparison and improves the efficiency of cross-cluster data comparison. In addition, there is no need to compare by sampling, which reduces the situation of data omission and improves the accuracy of data comparison.

[0069] In an embodiment of the present application, for a task whose target task is data backup, its task parameters may include a backup method and a backup date, and the data to be processed may be data within the backup date. Among them, the backup method may include but is not limited to incremental and full. The backup date is the time when the user needs to back up, such as a date selected on demand, a fixed number of days, a fixed number of weeks, or a fixed month. In step 303, the first data of the first cluster within the backup date can be backed up to the second cluster based on the backup method. For example, incremental backups can be performed regularly by day, week, or month, or a full backup can be performed before the deadline. After the data backup task is started, the first data is backed up to the second cluster so that the first data can exist in the second cluster so that the data is distributed in different clusters to improve the security of data storage.

[0070] In an embodiment of the present application, for a task whose target task is data verification, its task parameters may include a verification field and a verification rule, and the task parameters may include the data of the verification field. The verification field refers to a field that needs to be verified. For example, the field that needs to be verified can be divided into a maximum value, a minimum value, an enumeration value, or an average value according to the numerical type; it can be divided into an enumeration value, a number of repeated items greater than or less than a certain value, or a number of repeated items according to the non-numerical type. The verification rule refers to a rule for verifying data, such as by table size, number of table rows, periodic volatility of table size, or periodic volatility of number of table rows. In step 303, the data of the verification field in the second cluster can be verified based on the verification rule. For example, the abnormal data in the second cluster that does not match the first cluster can be verified, etc. When the data verification of the verification field fails, an early warning prompt can be generated, and the user can be prompted through the operation interface of the display device, so that the user can process the abnormal data in time, thereby improving the efficiency of fault handling across clusters. In one example, after the data verification task is run, through the verification rules, for example, the number of rows in the data table within a certain time period is greater than the expected value of 100 rows, then after the task is run, the number of data tables within the time range is counted, and then the number is compared with 100 rows. If the expected value is greater than 100 rows, the verification rule is met; if the expected value is less than or equal to 100 rows, the verification rule is not met and an early warning prompt is required.

[0071] In an embodiment of the present application, for a task whose target task is data recovery, the task parameters may include a recovery range and a recovery version number, and the data to be processed may be the data within the recovery range in the recovery version number. When establishing a data table, the version number of the data table that needs to be retained can be set according to demand. In this way, when performing data recovery, data recovery can be performed on the data table corresponding to the recovery version number based on the recovery version number selected by the user. Therefore, in step 103, the data to be recovered within the recovery range in the first data corresponding to the recovery version number can be first searched from the first cluster based on the recovery version number. Then, the second data of the second cluster within the recovery range is recovered based on the data to be recovered. In this way, data can be recovered based on the recovery version number required by the user, which improves data integrity and is more flexible than the related art in which data can only be recovered with the latest version number.

[0072] The following example takes the first cluster as the source cluster, the second cluster as the target cluster, the first software package as the JAR package s, and the second software package as the JAR package t. Figure 5 , Figure 5 This is a flow chart of a data processing method provided in a specific embodiment of the present application. In a specific embodiment of the present application, the specific flow of the data processing method can be implemented by the following steps 501-507, which are described in detail below.

[0073] Step 501: Receive a source cluster selection instruction and a target cluster selection instruction sent by a display device. The source cluster selection instruction and the target cluster selection instruction sent by the display device may refer to operation instructions generated by a user by operating an operation interface displayed by the display device.

[0074] Step 502: Determine a source cluster according to a source cluster identifier included in a source cluster selection instruction, and determine a target cluster according to a target cluster identifier included in a target cluster selection instruction.

[0075] Step 503: Install JAR package s in the source cluster, and install JAR package t in the target cluster.

[0076] Step 504: determine the data to be processed according to the target task selected by the user and the corresponding target task parameters sent by the display device.

[0077] Step 505: Perform data processing corresponding to the target task on the source cluster and the target cluster based on the data to be processed.

[0078] Step 506: upon receiving the query progress instruction, return the current task execution progress to the display device.

[0079] Step 507: When the target task is completed, the task execution result is returned to the display device.

[0080] In an embodiment of the present application, the user selects the source cluster and the target cluster through the display device. For example, click the options of the source cluster and the target cluster on the operation interface, and then select the corresponding data clusters respectively. Data clusters can be distinguished based on different identifiers such as names. For example, each data cluster has a corresponding name, such as cluster 1, cluster 2, etc. The name of each cluster corresponds to a cluster identifier. In this way, when the user selects the corresponding data cluster, the location of the data cluster can be determined according to the cluster identifier corresponding to the data cluster. Then, deploy JAR package s for the data cluster as the source cluster, and deploy JAR package t for the data cluster as the target cluster to determine the source cluster and the target cluster in the data cluster. In addition, the user can also select the target task to be executed through the operation interface, and customize the target task to determine the target task parameters. When the target task and the corresponding target task parameters sent by the display device are received, the corresponding data to be processed can be generated based on the target task and the corresponding target task parameters. When executing the target task, the user can also click the option of querying progress through the operation interface. When the query progress instruction is received, the execution status of the current task is queried, and the current task execution progress is returned to the display device, which is displayed through the operation interface of the display device, so that the user can timely understand the execution status of the target task. Finally, when the target task is completed, the execution result can also be returned to the display device, and the execution result of the target task can be displayed through the operation interface. In this way, the execution status of the target task can be presented more intuitively, and when there is a fault, the user can be notified in time to handle the fault, which improves the security of data processing.

[0081] The following uses the data migration task as an example. Figure 6 , Figure 6 601-611 are steps 602-613.

[0082] Step 601: Receive a pre-processing instruction sent by a display device; wherein the pre-processing instruction may include a deletion and reconstruction instruction, a data clearing instruction, or an original data retention instruction.

[0083] Step 602, perform pre-data processing on the target cluster according to the pre-processing instruction; wherein the pre-data processing includes: deleting the second data table corresponding to the second data of the target cluster based on the delete and rebuild instruction, and re-establishing the first data table corresponding to the first data in the target cluster; or clearing the second data in the second data table based on the data clear instruction; or retaining the second data in the second data table based on the original data retention instruction.

[0084] Step 603: Receive a migration range selected by the user based on an operation interface of the display device.

[0085] Step 604: Migrate the first data in the source cluster within the migration range to the target cluster.

[0086] Step 605 , determine whether the target cluster has enabled the real-time synchronization function; if so, proceed to step 606 , otherwise, proceed to step 611 .

[0087] Step 606: When the first data of the source cluster is updated, check whether the target cluster has a first data table corresponding to the first data of the source cluster; if not, proceed to step 607 to create the first data table. If yes, proceed to step 608.

[0088] Step 608 , verify whether the second structure of the first data table in the target cluster matches the first structure of the source cluster; if so, proceed to step 610 ; otherwise, proceed to step 609 .

[0089] Step 609: Generate a data update failure prompt, wherein the data update failure prompt may include abandoning data migration, deleting the second data in the target cluster and performing full migration, and attempting to continue migration.

[0090] Step 610: Synchronously update the second data associated with the first data in the target cluster.

[0091] Step 611: After the data migration is completed, the migration result is returned to the display device. The migration result may include migration success, migration failure, the number of successful migrations, and the abnormal error reason of migration failure. At the same time, the comparison and retry functions of migration failure data can also be provided.

[0092] The embodiment of the present application performs pre-data processing before data migration, which can better retain the data of the target cluster and reduce the situation of accidental deletion. And according to the task parameters of the data migration task set by the user, the data can be migrated more flexibly. At the same time, the data can also be synchronized in real time according to whether the synchronization function is enabled in the target cluster, so that when the source cluster data is updated, the target cluster can also be updated synchronously, which improves the consistency of the source cluster and target cluster data.

[0093] The following uses the target tasks of data comparison and data verification as examples for explanation. Figure 7 , Figure 7 701-709 are steps 702-703.

[0094] Step 701: Receive a comparison range and a comparison method sent by a display device; assuming that the comparison range and the comparison method are the number of data items of a source cluster and a target cluster within a set time period.

[0095] Step 702: compare the number of first data in the source cluster with the number of second data in the target cluster within a set time period to obtain a comparison result.

[0096] Step 703 , confirm whether the number of the second data is the same as the number of the first data according to the comparison result; if not, proceed to step 704 ; if so, proceed to step 709 .

[0097] Step 704: Divide the abnormal data into batches according to the row key, and compare each batch; the abnormal data of the latest batch may be compared first.

[0098] Step 705: Determine whether the number of abnormal data in the latest batch is the same as the total number of abnormal data. If yes, proceed to step 706; otherwise, proceed to step 708;

[0099] Step 706: perform value verification on the latest batch of abnormal data or synchronize the first data first and then verify the number of abnormal data in the latest batch, and determine whether the verification is successful; if it still fails, proceed to step 707 and give a fault prompt; if the verification is successful, proceed to step 709;

[0100] Step 708: Verify the abnormal data in batches until the number of verified abnormal data is the same as the total number of data.

[0101] Step 709: End the data comparison task and return the comparison result to the display device.

[0102] When abnormal data appears in data comparison, the embodiment of the present application corrects the abnormal data through batch verification, which not only reduces the amount of calculation, but also can process the abnormal data in time, improves the efficiency of data comparison, and further improves the efficiency of data migration.

[0103] Based on the above embodiment, data processing between two data clusters, the first cluster and the second cluster, is taken as an example. Figure 8 A schematic diagram of the interaction between a data cluster and a client in an embodiment of the present application is shown. Figure 8, the user's client 800 first accesses the first cluster 910, and the first cluster 910 includes the first data. The data processing device 900 performs real-time synchronous data update on the data of the second cluster 920 through the above-mentioned cross-cluster data processing method. When a data disaster occurs in the first cluster 910, it can be switched to the second cluster 920. In this way, the client 800 originally connected to the first cluster 910 can automatically connect to the switched second cluster 920, and the imperceptible switching in the process of interaction between the client 800 and the data cluster can be realized.

[0104] It should be noted that in the embodiment of the present application, any of the at least two data clusters can be the first cluster or the second cluster. In this way, after the client interacts with any of the data clusters, the other data clusters can be used as backup clusters to perform cross-cluster data synchronization, migration and other operations, thereby achieving client-unperceived switching. When a disaster occurs in a cluster, it will not affect the client's data interaction process, thereby improving the stability and flexibility of the interaction between the data cluster and the client.

[0105] It should be understood that, although each step in the flowcharts involved in the above-mentioned embodiments is displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0106] Based on the same inventive concept, the embodiment of the present application also provides a data processing device for implementing the data processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more data processing devices provided below can refer to the limitations on the data processing method above, and the specific limitations will not be repeated here.

[0107] This embodiment also provides a data processing device, which can be integrated into Figure 1 In the computer device 100 shown. Fig. 9 is a structural diagram of a data processing device 900 provided in an embodiment of the present application. Fig. 9, the data processing device 900 may include a first selection module 901, a second selection module 902, a determination module 903 and a processing module 904. The first selection module 901 is used to determine the first cluster and the second cluster based on the cluster selection instruction, and the cluster types of the first cluster and the second cluster are different. The second selection module 902 is used to determine the target task for the first cluster and the second cluster based on the task selection instruction. The determination module 903 is used to determine the data to be processed according to the target task and the task parameters of the target task. The processing module 904 is used to perform data processing corresponding to the target task on the first cluster and the second cluster based on the data to be processed.

[0108] The above-mentioned first selection module 901, second selection module 902, determination module 903 and processing module 904 can respectively execute steps 301-304 in the above-mentioned method embodiment. For a detailed introduction of these modules, please refer to the introduction of the corresponding steps of the above-mentioned method, which will not be repeated here one by one.

[0109] Based on the same inventive concept, the embodiment of the present application also provides a computer device, which can be a server or a terminal device, and the computer device includes a memory and a processor, and a computer program is stored in the memory, and the processor implements the steps of the above data processing method when executing the computer program. Thereby realizing various functions, for example: determining the first cluster and the second cluster based on the cluster selection instruction, and the cluster types of the first cluster and the second cluster are different; determining the target tasks for the first cluster and the second cluster based on the task selection instruction; determining the data to be processed according to the target task and the task parameters of the target task; and performing data processing corresponding to the target task on the first cluster and the second cluster based on the data to be processed.

[0110] The embodiment of the present application is based on determining the first cluster and the second cluster of different cluster types in the cluster selection instruction. Then, the target tasks of the first cluster and the second cluster are determined based on the task selection instruction. Then, the data to be processed is determined according to the target task and the corresponding target task parameters. Finally, the first cluster and the second cluster are subjected to data processing corresponding to the target task based on the data to be processed. In this way, personalized selection of cluster types and data to be processed can be achieved through customized cluster selection instructions and task selection instructions to perform data processing, thereby improving the flexibility of cross-cluster data processing.

[0111] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0112] In one embodiment, the computer device is a terminal device, for example, its internal structure diagram can be as follows: Fig.10As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a data processing method is implemented. The display unit of the computer device is used to form a visually visible image, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse, etc.

[0113] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0114] Based on the same inventive concept, an embodiment of the present application also provides a computer-readable storage medium, which may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0115] Since the computer program stored in the computer-readable storage medium can execute any data processing method provided in the embodiments of the present application, the beneficial effects that can be achieved by any data processing method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0116] Based on the same inventive concept, the embodiment of the present application further provides a computer program product or a computer program, which includes a computer instruction, and the computer instruction is stored in a computer-readable storage medium. The processor of the computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the method provided in various optional implementations in the above embodiments.

[0117] It should be noted that the object data (including but not limited to user device information, user personal information, etc.) and conversation data involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions. A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0118] Any reference to the memory, database or other media used in the embodiments provided in the present application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (Re RAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0119] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0120] In the above data processing device, computer-readable storage medium, computer equipment, and computer program product embodiments, the description of each embodiment has its own emphasis. For parts not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and beneficial effects of the above-described data processing device, computer-readable storage medium, computer program product, computer equipment, and its corresponding units can refer to the description of the data processing method in the above embodiment, and will not be repeated here.

[0121] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] The above is a detailed introduction to a data processing method, a data processing device, a computer device, a computer-readable storage medium and a computer program product provided in an embodiment of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A data processing method, characterized in that: include: determining a first cluster and a second cluster based on the cluster selection instruction, the first cluster and the second cluster being of different cluster types; Determine target tasks for the first cluster and the second cluster based on a task selection instruction; Determining data to be processed according to the target task and task parameters of the target task; Based on the data to be processed, data processing corresponding to the target task is performed on the first cluster and the second cluster.

2. The data processing method according to claim 1, characterized in that: The cluster selection instruction includes a first selection instruction and a second selection instruction; and determining the first cluster and the second cluster based on the cluster selection instruction includes: configuring a first software package and a second software package, wherein the first software package includes first attribute information and the second software package includes second attribute information; receiving a first selection instruction and a second selection instruction, wherein the first selection instruction includes a first cluster identifier and the second selection instruction includes a second cluster identifier; deploying the first software package in a data cluster corresponding to the first cluster identifier, and deploying the second software package in a data cluster corresponding to the second cluster identifier; The data cluster in which the first software package is deployed is determined as the first cluster, and the data cluster in which the second software package is deployed is determined as the second cluster.

3. The data processing method according to claim 1, characterized in that: The target task includes data migration, the task parameters include a migration range, and the data to be processed is data within the migration range; The performing data processing corresponding to the target task on the first cluster and the second cluster based on the data to be processed includes: Migrating the first data in the first cluster and within the migration range to the second cluster; When the first data in the first cluster is updated, the second data associated with the first data in the second cluster is synchronously updated, where the second data is data migrated from the first cluster to the second cluster within the migration range.

4. The data processing method according to claim 3, characterized in that: The synchronously updating the second data associated with the first data in the second cluster includes: Checking whether the second cluster has a first data table corresponding to the first data of the first cluster; If the first data table does not exist in the second cluster, create the first data table in the second cluster, and synchronously update the second data associated with the first data in the second cluster; If the first data table exists in the second cluster, verify whether the second structure of the first data table in the second cluster matches the first structure of the first cluster; If the second structure matches the first structure, synchronously updating the second data associated with the first data in the second cluster; If the second structure does not match the first structure, a data update failure prompt is generated.

5. The data processing method according to claim 3, characterized in that: Before the step of migrating the first data in the first cluster and within the migration range to the second cluster, the method further includes: Receiving a pre-processing instruction, wherein the pre-processing instruction includes a deletion and reconstruction instruction, a data clearing instruction, or an original data retention instruction; Performing pre-processing on the second cluster according to the pre-processing instruction, wherein the pre-processing includes: Deleting the second data table corresponding to the second data of the second cluster based on the delete-rebuild instruction, and creating a first data table corresponding to the first data in the second cluster; or Clear the second data in the second data table based on the data clear instruction; or The second data in the second data table is retained based on the original data retaining instruction.

6. The data processing method according to claim 1, characterized in that: The target task includes a data comparison task, the task parameters include a comparison range and a comparison method, and the data to be processed is data within the comparison range; performing data processing corresponding to the target task on the first cluster and the second cluster based on the data to be processed includes: Based on the comparison method, first data in the first cluster and located within the comparison range are compared with second data in the second cluster and located within the comparison range to obtain a comparison result; When it is confirmed according to the comparison result that the second cluster has second data that does not match the first cluster, the second data that does not match is corrected.

7. The data processing method according to claim 1, characterized in that: The target task includes a data verification task, the task parameters include a verification field and a verification rule, and the data to be processed is the data of the verification field; The performing data processing corresponding to the target task on the first cluster and the second cluster based on the data to be processed includes: Verifying the data of the verification field in the second cluster based on the verification rule; When the data verification of the verification field fails, an early warning prompt is generated.

8. The data processing method according to claim 1, characterized in that: The target task includes a data recovery task, the task parameters include a recovery range and a recovery version number, and the data to be processed is the data in the recovery range in the recovery version number; The performing data processing corresponding to the target task on the first cluster and the second cluster based on the data to be processed includes: According to the recovery version number, searching the first cluster for the data to be recovered that is within the recovery range and in the first data corresponding to the recovery version number; The second data of the second cluster within the recovery range is recovered according to the data to be recovered.

9. A computer device, characterized in that: It comprises a processor and a memory, wherein the memory stores a plurality of instructions; the processor loads instructions from the memory to execute the steps of the data processing method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the steps of the data processing method according to any one of claims 1 to 8.

11. A computer program product, characterized in that The computer program product comprises a computer program, and the computer program is used by a processor to execute the steps of the data processing method according to any one of claims 1 to 8.

Images