Data processing method and apparatus

Abstract

The application provides a data processing method and device, wherein the data processing method is applied to a cloud native platform, and includes receiving to-be-processed service data of a target service, wherein the to-be-processed service data is database data of the target service; reading service configuration information of the to-be-processed service data, and determining version information of the to-be-processed service data; determining to-be-updated service data in the to-be-processed service data based on the version information; generating a target data image based on the service configuration information and the to-be-updated service data, and uploading the target data image to a target image warehouse of the target service; in this way, part of the database data that needs to be updated under different versions is encapsulated and stored in the image warehouse, so that data images of all the database data do not need to be generated, a large amount of computing resources can be reduced, and bandwidth transmission pressure can be avoided.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to data processing methods. This application also relates to a data processing apparatus, a data processing system, a computing device, and a computer-readable storage medium. Background Technology

[0002] Container images are standardized encapsulations of application code and its runtime environment, which can run directly on any operating system with containers installed. Containers are a unified platform for building, distributing, and running applications, used to automate application installation, deployment, and upgrades. Therefore, before automating application deployment based on container technology, a container image of the application needs to be built first.

[0003] In cloud-native scenarios, since database data depends on application execution, if database data needs to be updated during application installation and upgrades, all data files of the application must be fully updated in order to install and upgrade the database data. This will not only consume a lot of computing resources, but also put a lot of pressure on bandwidth transmission. Summary of the Invention

[0004] In view of this, embodiments of this application provide a data processing method. This application also relates to a data processing apparatus, a data processing system, a computing device, and a computer-readable storage medium, to solve the aforementioned problems existing in the prior art.

[0005] According to a first aspect of the embodiments of this application, a data processing method is provided, applied to a cloud-native platform, including:

[0006] Receive pending service data of the target service, wherein the pending service data is the database data of the target service;

[0007] Read the service configuration information of the service data to be processed and determine the version information of the service data to be processed;

[0008] Based on the version information, determine the business data to be updated from the business data to be processed;

[0009] Based on the business configuration information and the business data to be updated, a target data image is generated, and the target data image is uploaded to the target image repository of the target business.

[0010] According to a second aspect of the embodiments of this application, another data processing method is provided, applied to a client, including:

[0011] Receive a target data image sent by the target image repository, wherein the target data image is determined by the target image repository based on the local data image version information of the target business;

[0012] Read the tool image configuration information from the configuration information of the target data image, and obtain the target tool image based on the tool image configuration information;

[0013] Based on the target tool image, the target data image is parsed to obtain the business data to be updated, and the business data to be updated is imported into the local database.

[0014] According to a third aspect of the embodiments of this application, a data processing system is provided, including a cloud-native platform, a target image repository, and a client;

[0015] The cloud-native platform is configured to receive pending business data of a target business, wherein the pending business data is database data of the target business; read the configuration information of the pending business data to determine the version information of the pending business data; based on the version information, determine the business data to be updated in the pending business data; generate a target data image based on the configuration information and the business data to be updated, and upload the target data image to the target image repository of the target business;

[0016] The target image repository is configured to receive a data image acquisition request for the target service sent by the client, and determine the target data image based on the local data image version information of the target service carried in the data image acquisition request;

[0017] The client is configured to receive the target data image sent by the target image repository, obtain the business data to be updated based on the target data image, and import the business data to be updated into the local database.

[0018] According to a fourth aspect of the embodiments of this application, a data processing apparatus is provided, applied to a cloud-native platform, comprising:

[0019] The first data receiving module is configured to receive pending service data of the target service, wherein the pending service data is the database data of the target service;

[0020] The version information determination module is configured to read the business configuration information of the business data to be processed and determine the version information of the business data to be processed.

[0021] The update data determination module is configured to determine the business data to be updated from the business data to be processed based on the version information.

[0022] The data image generation module is configured to generate a target data image based on the business configuration information and the business data to be updated, and upload the target data image to the target image repository of the target business.

[0023] According to a fifth aspect of the embodiments of this application, another data processing apparatus is provided, applied to a client, comprising:

[0024] The second data receiving module is configured to receive a target data image sent by the target image repository, wherein the target data image is determined by the target image repository based on the local data image version information of the target business;

[0025] The tool image acquisition module is configured to read the tool image configuration information in the configuration information of the target data image, and acquire the target tool image based on the tool image configuration information;

[0026] The data mirroring and parsing module is configured to parse the target data mirror based on the target tool mirroring, obtain the business data to be updated, and import the business data to be updated into the local database.

[0027] According to a sixth aspect of the present application, a computing device is provided, including a memory, a processor, and computer instructions stored in the memory and executable on the processor, wherein the processor executes the computer instructions to implement the steps of the data processing method.

[0028] According to a seventh aspect of the present application, a computer-readable storage medium is provided that stores computer instructions which, when executed by a processor, implement the steps of the data processing method.

[0029] The data processing method provided in this application, applied to a cloud-native platform, includes: receiving pending business data of a target business, wherein the pending business data is database data of the target business; reading business configuration information of the pending business data and determining version information of the pending business data; determining business data to be updated in the pending business data based on the version information; generating a target data image based on the business configuration information and the business data to be updated, and uploading the target data image to the target image repository of the target business.

[0030] In one embodiment of this application, within a cloud-native platform, version information of the business data to be processed is determined to obtain the database data to be updated corresponding to the target business. After completing the operation of encapsulating the business data to be updated into a data image, the data image is then uploaded to the target image repository of the target business. This approach avoids the cloud-native platform uploading all the business data to be processed for the target business in the application to the image repository, thus enabling subsequent installation and upgrades of the target business by the client. This method of encapsulating the database data to be updated under different versions and storing it in the image repository eliminates the need to generate data images for all database data, thereby reducing a significant amount of computing resources and avoiding bandwidth transmission pressure. Attached Figure Description

[0031] Figure 1 This is a system architecture diagram of a data processing system provided in one embodiment of this application;

[0032] Figure 2 This is a flowchart of a data processing method provided in an embodiment of this application;

[0033] Figure 3 This is a schematic diagram of the data mirror structure of a data processing method provided in an embodiment of this application;

[0034] Figure 4 This is a schematic diagram of a data mirror storage directory for a data processing method provided in an embodiment of this application;

[0035] Figure 5 This is a schematic diagram of the tool image structure of a data processing method provided in an embodiment of this application;

[0036] Figure 6 This is a schematic diagram of a patch data mirroring application of a data processing method provided in an embodiment of this application;

[0037] Figure 7 This is a flowchart of another data processing method provided in an embodiment of this application;

[0038] Figure 8 This is a flowchart illustrating the upload of a data image and the application of a data processing method according to an embodiment of this application;

[0039] Figure 9 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application;

[0040] Figure 10 This is a schematic diagram of the structure of another data processing device provided in an embodiment of this application;

[0041] Figure 11 This is a structural block diagram of a computing device provided in one embodiment of this application. Detailed Implementation

[0042] Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below.

[0043] The terminology used in one or more embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the scope of one or more embodiments of this application. The singular forms “a,” “the,” and “the” used in one or more embodiments of this application and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” used in one or more embodiments of this application refers to and includes any or all possible combinations of one or more associated listed items.

[0044] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this application, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0045] First, the terms and concepts involved in one or more embodiments of this application will be explained.

[0046] Cloud Native: Cloud Native is a cloud technology product system built on distributed cloud based on distributed deployment and unified operation and management, and based on technologies such as containers, microservices, and DevOps.

[0047] Docker containers are open-source application container engines that allow developers to package their applications and dependencies into a portable container in a unified way, and then deploy it to any server with the Docker engine installed (including popular Linux machines and Windows machines), and also achieve virtualization.

[0048] A container image is a collection that combines everything needed for a container to run, including the operating system, dependent libraries, application, and configuration parameters. It contains all the dependency data required by the application, such as code, runtime, system tools, system libraries, and settings.

[0049] Tool images: can be understood as a series of program encapsulations. Since different programming languages ​​have different construction methods, it is necessary to design corresponding tool images for specific programming languages.

[0050] Image repository: A collection of images used to store and manage container images.

[0051] In cloud-native scenarios, applications run in containers, and the foundation of containers is the container image. Because a container image contains a complete operating system and application runtime environment, and its layered nature makes application iteration and delivery simpler and more efficient.

[0052] However, container images, to some extent, only standardize the delivery of the application itself. They lack a unified solution for delivering the data the application depends on (mainly database data), such as initializing database tables during installation, importing basic data, and updating tables during program upgrades. The most basic solution is for developers to manually import the data; some solutions place the initialization data directly in the image, which is then read and imported by the container started by the image; others include the SQL in the application package, which is then read and imported by external tools.

[0053] Currently, in cloud-native scenarios, there is no ideal solution for more efficiently and automatically managing and delivering the database data that applications rely on. Existing technologies mainly employ two methods, but both have their drawbacks and shortcomings: The first approach involves storing data and applications in an image, which is then read and imported by programs in containers launched from that image. The biggest drawback of this approach is that it violates the principle of program and data isolation. Firstly, it is insecure; image upgrades may overwrite production business data, leading to data loss. Secondly, requiring each program to have its own logic to handle this functionality is redundant. Different programs often rely on different database types, versions, and data formats, and each program only cares about its own application, making it impossible to standardize and extract this as a separate capability. Finally, large files in the application may cause the program to crash due to insufficient memory.

[0054] The second approach is to put the database data into the application package. However, this approach is actually less advanced in some aspects of cloud-native scenarios compared to the first approach. First, cloud-native application packages are generally lightweight, typically only a few KB to a few hundred KB, making them unsuitable for storing large amounts of data. They usually only contain some configuration and descriptive orchestration files. As the application is continuously upgraded and iterated, the size of this application package will grow larger and larger, and it will be impossible to use the image layering capability to push only the upgraded and iterated data. In addition, there is no unified repository product to interface with such application packages, making it difficult to synchronize data and deliver data effectively.

[0055] Based on this, the data processing method provided in this application mainly combines database data into a data image. The data and format stored in the image have certain specifications. Since the data image serves as its storage carrier, it can solve the problems of excessive data dependency and strong program coupling when installing and upgrading applications by leveraging the universality and layering characteristics of images in cloud-native scenarios. This makes the delivery of database data that applications depend on more standardized and ultimately unifies it into a common capability of the platform.

[0056] This application provides a data processing method, and also relates to a data processing apparatus, a data processing system, a computing device, and a computer-readable storage medium, which will be described in detail in the following embodiments.

[0057] Figure 1 A system architecture diagram of a data processing system according to an embodiment of this application is shown.

[0058] Figure 1 This includes a cloud-native platform A, a target image repository B, a client C, and a database corresponding to client C. It should be noted that cloud-native platform A can be understood as an application development platform in a cloud-native scenario, utilizing container technology to develop applications. Target image repository B can be understood as a storage repository for application development packages, providing storage space for clients to download application packages. Client C in this embodiment can be understood as a terminal, through which users can download and update applications. Client C also has a local database to store various application data during application execution.

[0059] This application provides a data processing system, including a cloud-native platform, a target image repository, and a client;

[0060] The cloud-native platform is configured to receive pending business data of a target business, wherein the pending business data is database data of the target business; read the configuration information of the pending business data to determine the version information of the pending business data; based on the version information, determine the business data to be updated in the pending business data; generate a target data image based on the configuration information and the business data to be updated, and upload the target data image to the target image repository of the target business;

[0061] The target image repository is configured to receive a data image acquisition request for the target service sent by the client, and determine the target data image based on the local data image version information of the target service carried in the data image acquisition request;

[0062] The client is configured to receive the target data image sent by the target image repository, obtain the business data to be updated based on the target data image, and import the business data to be updated into the local database.

[0063] The target service can be understood as the service that needs to be installed or updated in the application, such as the emoticon service in the application or the skin service displayed on the application's interface.

[0064] The business data to be processed can be understood as the database data that needs to be installed or upgraded in the application. The configuration information of the business data to be processed can be understood as the configuration file of the source data layer of the database data, including the name, version, data format, related description information and other attribute information of the business data to be processed. The version information of the business data to be processed can be understood as the development version corresponding to the business data to be processed in the application. For example, if the emoji data is the first development version of the application, then the version information of the emoji data can be determined as the initial version.

[0065] A target data image can be understood as an image of the database data that needs to be installed or upgraded in the application. It should be noted that a data image is a simple image that stores data and does not require a related operating system or runtime environment, and it is relatively lightweight.

[0066] In practice, developers create applications in a cloud-native environment and package these applications into application images. However, the database data corresponding to the target business that depends on this application—the business data to be processed—also needs to be packaged into data images and uploaded to the image repository. Further, after receiving the business data to be processed for the target business developed by the developers, the cloud-native platform determines the version information of the business data by reading the business configuration information within it. This version information determines the development version of the business data, such as the initial development version 1.0 or the iterative version 2.0 of the target business. Further still, based on the version information of the business data, it filters out the business data to be updated, which is a portion of the business data to be processed. Finally, based on the business configuration information and the business data to be updated, a target data image is generated and uploaded to the target image repository for the target business.

[0067] It should be noted that filtering the business data to be updated in the pending business data based on the version information means that the business data of the new version developed by the developers is used as the business data to be updated. If the business data to be processed is the first version developed, then all the data in the pending business data can be used as the business data to be updated. If the business data to be processed is an iterative development version, then the iterative version in the pending business data can be used as the business data to be updated.

[0068] In summary, by determining the version information of the business data to be processed, the cloud-native platform identifies the business data to be updated within the business data to be processed, and encapsulates the business data to be updated into a data image, which is then uploaded to the image repository. This makes it convenient for clients to download only the data image corresponding to the business data to be updated when performing a full installation or upgrade of the target business. This not only reduces computing resources but also reduces bandwidth transmission pressure.

[0069] Figure 2 A flowchart of a data processing method according to an embodiment of this application is shown, which specifically includes the following steps:

[0070] It should be noted that the data processing method provided in this application embodiment is applied to a cloud-native platform to achieve more efficient and automated management of database data that applications depend on. By leveraging the universality and layered characteristics of images in cloud-native scenarios, it can solve the shortcomings of excessively large dependent data and strong coupling of applications when clients install and upgrade applications. It standardizes the delivery of database data that applications depend on, and thus this data processing method can be called a general capability of cloud-native platforms.

[0071] Step 202: Receive the pending service data of the target service, wherein the pending service data is the database data of the target service.

[0072] The target service can be understood as the service that needs to be installed or updated in the application, such as the emoticon service in the application or the skin service displayed on the application's interface.

[0073] The business data to be processed can be understood as the database data that needs to be installed or upgraded in the application. The configuration information of the business data to be processed can be understood as the configuration file of the source data layer of the database data, including the name, version, data format, related description information and other attribute information of the business data to be processed.

[0074] In practical applications, cloud-native platforms can receive pending business data corresponding to the target business in applications developed by developers. For example, if application A needs to develop an emoji service, since emoji data can be stored in a database, the emoji data can be used as database data to run application A. The data processing method provided in this application embodiment can process the emoji data developed by developers in the form of container images to meet the needs of various applications in cloud-native scenarios.

[0075] Step 204: Read the service configuration information of the service data to be processed and determine the version information of the service data to be processed.

[0076] Among them, business configuration information can be understood as the business attribute information of the business data to be processed, such as the emoji data name, version information, data type, data-related description, storage location, data development time, and other attribute information related to the business data.

[0077] The version information of pending business data can be understood as the development version corresponding to the pending business data of the application. For example, if the emoji data is the first development version of the application, then the version information of the emoji data can be determined as the initial version.

[0078] In practical applications, cloud-native platforms can read all the business configuration information corresponding to the business data to be processed and determine the version information corresponding to the business data to be processed. For example, after receiving emoji business data, the cloud-native platform can determine the version information corresponding to this emoji business data from the business configuration information of the emoji business data, such as version 1.0 (first development), version 2.0 (iterative upgrade), version 1.0.0 patch, etc.

[0079] Step 206: Based on the version information, determine the business data to be updated from the business data to be processed.

[0080] Furthermore, in cloud-native scenarios, in order to reduce the transmission pressure of application updates to database data, it is not necessary to upload all versions of database data, or historical versions, to the image repository every time the database data is updated or upgraded. This is because as the frequency of database data version iterations increases and the amount of updated data becomes larger, uploading all versions of database data to the image repository every time the database data is updated or upgraded will consume a lot of computing resources and also bring bandwidth transmission pressure.

[0081] Based on this, the data processing method provided in this application embodiment involves the cloud-native platform determining the current development version of the business data to be processed based on its version information. Then, among all the business data to be processed corresponding to the target business, the iterative business data corresponding to the current development version is determined; this is the business data that needs to be updated in this version iteration. Taking the aforementioned emoji data as an example, if it is determined that the developers have developed the emoji data to version 1.2, then the business data to be updated can be determined from all the data in version 1.2 of the emoji data. This business data to be updated is the emoji data that differs between version 1.2 and version 1.1. For example, if version 1.2 of the emoji data includes 1.sql and 2.sql, and version 1.1 of the emoji data includes 1.sql, then the business data to be updated is 2.sql.

[0082] Furthermore, when determining the business data to be updated, the cloud-native platform needs to determine the data loading type of the current version iteration based on the version information corresponding to the business data to be processed, and determine different data as the business data to be updated based on different data loading types; specifically, determining the business data to be updated from the business data to be processed based on the version information includes:

[0083] The data loading type corresponding to the business data to be processed is determined based on the historical version records in the version information, wherein the data loading type includes the initial business loading type and the iterative business loading type;

[0084] Based on the data loading type, determine the business data to be updated from the business data to be processed.

[0085] It should be noted that the version information corresponding to the business configuration information of the business data to be processed also records the historical version record information of the business data to be processed, clearly indicating the historical update version of the business data to be processed, such as recording the historical update version of the business data to be processed as version 1.0, version 1.1, version 1.2, etc.

[0086] The initial business loading type can be understood as the database data of the target business in the application, which is the type loaded during the first installation in this application version upgrade; the iterative business loading type can be understood as the database data of the target business in the application, which is the type that needs to be upgraded and loaded based on the first installation version.

[0087] In practical applications, after determining the data loading type corresponding to the current business data to be processed based on the historical version records in the version information of the business data to be processed, the cloud-native platform can determine the business data to be updated in the business data to be processed based on the data loading type.

[0088] Furthermore, determining the business data to be updated from the pending business data based on the data loading type includes:

[0089] If the data loading type is the initial business loading type, the business data to be processed will be treated as business data to be updated; or

[0090] When the data loading type is a business iteration loading type, the iterative business data is determined as the business data to be updated from the business data to be processed.

[0091] In practical applications, when a cloud-native platform determines that the data loading type is the initial loading type of the business, it can indicate that the business data to be processed is data that has not yet been loaded in the application. Therefore, in order to configure the business data to be processed in the application, all the business data to be processed needs to be treated as business data to be updated, and then all the business data to be processed is packaged into image data and uploaded to the image repository.

[0092] When the cloud-native platform determines that the data loading type is the business iteration loading type, it means that only a portion of the data in the current pending business data is data that has not yet been loaded in the application, while a portion of the data has already been loaded during the initial full installation process. Therefore, iterative business data can be identified from the pending business data as business data to be updated.

[0093] The data processing method provided in this application embodiment can determine the business data that needs to be updated for this version upgrade in the cloud-native platform by determining different data loading types, so as to complete the application version update.

[0094] In practical implementation, regarding the determination of business data to be updated, if the cloud-native platform determines that the business data to be processed is of the business iteration loading type, it needs to first determine the initial business data, i.e., the business data of the first loaded version, before it can determine the incremental business data in the business data to be processed. Specifically, determining the iterative business data as the business data to be updated from the business data to be processed includes:

[0095] Based on the version information, determine the initial business data from the business data to be processed;

[0096] Based on the initial business data, iterative business data is determined, and the iterative business data is used as the business data to be updated.

[0097] Initial business data can be understood as the business data to be processed in the first version development of the target business; iterative business data can be understood as the business data developed iteratively based on the first version development.

[0098] In practical applications, when determining iterative business data in the business data to be processed, the cloud-native platform can first determine the initial business data through version information, that is, find the business data of the initial version, then determine the full amount of business data to be processed, and determine the iterative business data based on the full amount of business data and the initial business data, and then use the iterative business data as the business data to be updated.

[0099] In summary, the data processing method provided in this application determines the business data to be updated in this version update iteration by using the version information of the business data to be processed developed by the developers. The purpose is to upload the business data to be updated to the mirror repository so that the client can download and install the business data to be updated in the future.

[0100] Step 208: Generate a target data image based on the business configuration information and the business data to be updated, and upload the target data image to the target image repository of the target business.

[0101] In cloud-native scenarios, images are a common storage medium, and a data image is a simple image that stores data. It does not require a related operating system or runtime environment and is relatively lightweight. Therefore, in order to encapsulate database data and application data into images for storage in the same way, the business data to be updated also needs to be encapsulated into data images and stored in an image repository.

[0102] In practical applications, cloud-native platforms can generate a script for uploading an image, i.e., a target data image, based on the business configuration information of the business data to be processed and the business data to be updated. Then, the target data image is uploaded to the target image repository corresponding to the target business. Continuing with the previous example, when the emoji data is determined to be 2.sql, the business configuration information of the business data to be processed, including the data name, data format, data-related description information, etc., as well as the emoji data 2.sql, can be packaged together into a target data image and uploaded to the image repository.

[0103] Further, generating the target data mirror based on the service configuration information and the service data to be updated includes:

[0104] Based on the service configuration information, determine the image configuration information corresponding to the service data to be updated, and generate a target data image based on the image configuration information and the service data to be updated.

[0105] The image configuration information can be understood as the configuration information describing the business data to be updated, including the name, version, data format, dependent tool images, data-related descriptive information, and other attribute information of the data image.

[0106] In practical applications, cloud-native platforms can determine the image configuration information corresponding to the business data to be updated based on the business configuration information in the business data to be processed, and generate the target data image based on the image configuration information and the business data to be updated.

[0107] See Figure 3 , Figure 3 A schematic diagram of the data mirror structure of the data processing method provided in the embodiments of this specification is shown.

[0108] Figure 3 The left side of the image shows the data mirror. A data mirror typically consists of two layers: the data layer (data) primarily stores data (except for the bottom source data layer; all others can be considered data layers), and the source data layer (meta) primarily stores configuration files, recording necessary information about the data mirror. See [link to relevant documentation]. Figure 3 Example of a configuration file on the right side of the page.

[0109] The specific meanings of the configuration items can be found in Table 1 below:

[0110] Table 1

[0111]

[0112] Furthermore, due to the layered nature of data mirroring, the business data to be processed can be stored in different storage directories according to different version information, and then the next layer contains the corresponding version information, and then stores the corresponding data files; specifically, uploading the target data mirror to the target mirror repository of the target business includes:

[0113] Based on the version information of the business data to be processed, the data storage directory of the target data image is determined in the business configuration information;

[0114] Based on the data storage directory, the target data image is uploaded to the target image repository of the target business.

[0115] The data storage directory can be understood as the storage directory where the data image needs to be stored in the target image repository. For example, the data image can be stored in the image / data directory.

[0116] In practical applications, cloud-native platforms can determine the data storage directory of the target data image based on the version information of the business data to be processed and the configuration information read. It should be noted that the data storage directory of the data image can be pre-configured in the image configuration information. Then, after determining the data storage directory of the target data image, the target data image can be uploaded to the target image repository of the target business according to the data storage directory.

[0117] It should be noted that the specific location where the target data image is stored in the target image repository is further determined by the image repository based on the data storage directory.

[0118] See Figure 4 , Figure 4 A schematic diagram of the data mirror storage directory of the data processing method provided in the embodiments of this application is shown.

[0119] Specifically, all data images are stored in the image / data directory within the data image repository. The next level contains the corresponding version, which in turn stores the corresponding data files. For example... Figure 4 In version (1), only 1.sql is stored in version v1.0; in version v1.1, 2.sql and 3.sql can be stored; and in version v1.2, 4.sql can be stored.

[0120] Based on this, Figure 4 (2) in the diagram shows the hierarchical storage structure of the three versions. Version v1.0 is the initial version, and v1.1 and v1.2 are the iterative versions in turn. The current version is built on the basis of the previous version. In other words, the current version contains all the changes of the previous historical versions. For example, the layer where version v1.2 is located has added a 4.sql file compared to version v1.1, but the program can read all four sql files.

[0121] In addition, the business configuration information of the business data to be processed also includes the tool image on which the data image depends. The tool image can parse the data image, enabling the data image to run normally in the application; specifically, the business configuration information includes tool image information.

[0122] Accordingly, after receiving the pending service data of the target service, the process further includes:

[0123] Read the tool image identifier from the tool image information, and obtain the tool parsing file based on the tool image identifier;

[0124] The target tool image is generated based on the tool parsing file and the tool image information.

[0125] The tool image information can be understood as the configuration information of the tool image data, such as the name, version, data format and other attribute information of the tool image.

[0126] In practical applications, cloud-native platforms can determine the type of a tool image by reading the tool image identifier in the tool image information. This identifier can be a pre-set image type identifier or the name of the tool image; no specific limitation is made here. For example, the image identifiers dbtool-mysql5 and dbtool-mysql8 indicate the database type and version that the tool image targets. Furthermore, the corresponding tool parsing file can be obtained based on the tool image identifier. The tool parsing file can be understood as the executable binary program corresponding to the tool image, which can perform parsing operations on the data image. Finally, a target tool image is generated based on the tool parsing file and the tool image information. The target tool image is a program that can be used to perform parsing operations on the target data image.

[0127] See Figure 5 , Figure 5 A schematic diagram of the tool mirror structure of the data processing method provided in the embodiments of this specification is shown.

[0128] Figure 5 The image illustrates four types of tool images: dbtool-mysql5:1.0.0, dbtool-mysql8:1.0.0, dbtool-oracel10:1.0.0, and dbtool-plsql9:1.0.0. Different tool images are matched to different database types and versions. Each tool image contains two layers: a source data layer (meta) and a tool layer (tool). The source data layer mainly stores information such as which database types and versions the image supports, and the disk paths where the data needs to be mounted. The tool layer mainly stores the executable binary program for the corresponding database, used for importing data.

[0129] Furthermore, after generating the target tool image on the cloud-native platform, the target tool image can be uploaded to the target image repository; specifically, after generating the target tool image based on the tool parsing file and the tool image information, the process further includes:

[0130] Based on the version information of the business data to be processed, the data storage path of the target tool image is determined in the business configuration information;

[0131] Based on the data storage path, the target tool image is uploaded to the target image repository of the target business.

[0132] The data storage path can be understood as the storage path where the tool image needs to be stored in the image repository, but the specific storage location in the image repository needs to be determined by the image repository.

[0133] In practical applications, cloud-native platforms can also determine the data storage path of the tool image in the image repository based on the version information of the business data to be processed, and upload the target tool image to the target image repository of the target business based on the data storage path.

[0134] Furthermore, the data processing method provided in this application also includes a process for generating patch data images. For patch versions where historical database data needs to be modified, corresponding patch data images can also be generated, facilitating the subsequent download of the corresponding patch data image from the image repository to complete the patch update operation. Specifically, the data processing method further includes:

[0135] Receive the patch version service data of the target service;

[0136] A patch data mirror is generated based on the patch version information carried in the patch version service data and the patch version service data.

[0137] The patch data is uploaded to the target image repository of the target service.

[0138] Among them, patch version business data can be understood as patch data corresponding to the target business, and patch data mirror can be understood as mirror data generated based on patch data.

[0139] In practical applications, after receiving the patch version business data from developers for the target business, the cloud-native platform can generate a patch data image based on the patch version information and the patch version business data, and then upload the patch data image to the target image repository of the target business. It should be noted that the patch version business data can be database data or program data, and this embodiment does not limit it in any way.

[0140] Figure 6 This is a schematic diagram of a patch data mirroring application of a data processing method provided in an embodiment of this application.

[0141] It should be noted that the paths and directories where patch version data and different versions of the data mirror are stored are not the same. Figure 6 As shown, the data mirrors include v1.0, v1.1, and v1.2. Clients can apply the data mirrors for each version. v1.1.1 is the patch version data package.

[0142] In practice, the data mirror version and the application version should ideally be consistent; that is, one application release version should correspond to one data mirror version. However, for some historical releases that are patch versions where only database data needs to be changed, inconsistencies may arise between the application version and the data mirror version. Figure 6 In this context, the application version of the data mirror v1.1 is associated with the patch data mirror version v1.1.1.

[0143] In summary, the data processing method provided in this application involves a cloud-native platform determining the version information of the business data to be processed to obtain the database data to be updated corresponding to the target business. After completing the operation of encapsulating the business data to be updated into a data image, the data image is then uploaded to the target image repository of the target business. This approach avoids the cloud-native platform uploading all the business data to be processed for the target business in the application to the image repository, thus enabling subsequent installation and upgrades of the target business by the client. This method of encapsulating the database data to be updated under different versions and storing it in the image repository eliminates the need to generate a data image for all database data, thereby reducing a significant amount of computing resources and avoiding bandwidth transmission pressure.

[0144] Figure 7 A flowchart of another data processing method according to an embodiment of this application is shown, which specifically includes the following steps:

[0145] It should be noted that the data processing method provided in this embodiment is applied to the client. When the user updates the application version through the client, the client can download the corresponding updated version of the database data from the mirror repository to complete the version update of the database data.

[0146] Step 702: Receive the target data image sent by the target image repository, wherein the target data image is determined by the target image repository based on the local data image version information of the target business.

[0147] Furthermore, before receiving the target data image sent by the target image repository, the process further includes:

[0148] Send a data image retrieval request for the target service, wherein the data image retrieval request carries the local data image version information of the target service.

[0149] In practical applications, the client can send a data image retrieval request to the target image repository, and the request carries the local data image version information of the target business. The local data image version information can be understood as the data image version installed in the client's current application, which makes it easier for the image repository to determine the version of the data image to be upgraded based on the client's local data image version. Furthermore, after the image repository determines the target image data to be upgraded and installed, the application version upgrade can be completed.

[0150] Step 704: Read the tool image configuration information in the configuration information of the target data image, and obtain the target tool image based on the tool image configuration information.

[0151] To enable the installation of the data image, a corresponding tool image can be obtained, and the data image can be parsed. Specifically, the client can read the tool image configuration information from the target data image's configuration information. This tool image configuration information can be understood as the tool image's attribute information, such as the tool image name, version, data format, etc. Furthermore, the target tool image corresponding to the target data image can be obtained based on the tool image configuration information.

[0152] Further, obtaining the target tool image based on the tool image configuration information includes:

[0153] Determine whether the target tool image corresponding to the tool image configuration information exists in the local database.

[0154] If so, then obtain the target tool image;

[0155] If not, the target tool image is obtained based on the storage path information of the tool image in the tool image configuration information.

[0156] In practical applications, the version iteration frequency of tool images is different from that of data images. In other words, a single development of a tool image can support parsing multiple versions of data images. Therefore, if the client is not installing the data image for the first time, a search operation can be performed on the local database to determine whether the local database has a tool image that can parse the target image data. If the local database exists, the corresponding target tool image can be obtained. If the local database does not exist, the target tool image can be retrieved from the image repository according to the storage path information of the tool image in the tool image configuration information.

[0157] By determining whether the target tool image exists in the local database, the target data image can be parsed, which can avoid repeated downloads of the tool image and avoid wasting computing resources and bandwidth.

[0158] Step 706: Based on the target tool image, parse the target data image to obtain the business data to be updated, and import the business data to be updated into the local database.

[0159] In practical applications, after obtaining the target tool image, the client can parse the target data through the target tool image. The specific parsing process is not specifically limited in this embodiment. After obtaining the business data to be updated, that is, the database data corresponding to the current version iteration of the target business, the business data to be updated can be imported into the local database to complete the update operation of the version iteration of the database data in the current application, or a full installation operation.

[0160] In addition, after the target tool image is re-downloaded by the client, it can be stored in the local database, which facilitates the reuse of the target tool image in the future and avoids repeated downloads from the image repository, thus saving computing resources and reducing bandwidth pressure.

[0161] In addition, the client can also download patch data images to resolve runtime errors that occur during version upgrades; specifically, the data processing method provided in this embodiment also includes:

[0162] Send a patch data image retrieval request for the target service, wherein the patch data image retrieval request carries the target service identifier;

[0163] Receive patch data image sent by target image repository, wherein the patch data image is determined by target image repository based on target service identifier;

[0164] The target service is updated by performing a patch update based on the patch data mirror.

[0165] In practical applications, the client can send a request to the image repository to obtain the patch data image for the target business. The request also carries the target business identifier. The image repository can determine the corresponding patch data image based on the received target business identifier. After obtaining the patch data image, the image repository sends the patch data image to the client. After receiving the patch data image, the client can complete the patch update operation for the target business of the application.

[0166] It is important to emphasize that for any application installation or upgrade, the first step on the client side is to perform data import. If it is the first installation, all data layer files that the program can see are directly imported. If it is a patch upgrade, all data layer files between the previous and new versions are imported. Using the database data upgrade example above, if you directly install the v1.1 version of the application, three SQL files are executed. If you are upgrading from v1.1 to v1.2, only the 4.sql file needs to be executed.

[0167] In summary, the data processing method provided in this application allows the client to complete the version update of the application for the target business by downloading the updated business data from the mirror repository without having to perform a full update of all database data. This not only reduces the bandwidth of data transmission but also saves a significant amount of computing and storage resources.

[0168] See Figure 8 , Figure 8 The flowchart illustrates the upload data image and application data image of a data processing method provided in an embodiment of this application.

[0169] It should be noted that by logically combining database data and related toolkits into a special package image, which can be parsed and executed by the platform, it can be better orchestrated. Since the data is carried by the image, and the image is a common storage medium in cloud-native scenarios, combined with the platform's orchestration capabilities, data delivery can be more automated and standardized. Ultimately, in cloud-native scenarios, it can be abstracted into a platform capability.

[0170] Step 802: The cloud-native platform determines the business configuration information corresponding to the business data to be processed, and determines the version information of the business data to be processed.

[0171] Step 804: The cloud-native platform determines the business data to be updated based on the version information corresponding to the business data to be processed.

[0172] Step 806: The cloud-native platform generates a target data image based on the business data to be updated and the business configuration information.

[0173] Step 808: The cloud-native platform uploads the target data image to the target image repository.

[0174] Step 810: The target image repository stores the target data image in the target storage location.

[0175] Step 812: The client sends a data image retrieval request to the target image repository. This data image retrieval request carries the version information of the client's local data image.

[0176] Step 814: The target image repository determines the target data image based on the version information of the client's local data image and sends the target data image to the client.

[0177] Step 816: After receiving the target data image, the client can determine in its local database whether there is a matching target tool image.

[0178] Step 818: After the client determines that there is a corresponding target tool image in the local database, it can directly use the target tool image to parse and process the target data image to obtain the business data to be updated and complete the version update of the database data in the application.

[0179] Step 820: After determining that there is no corresponding target tool image in the local database, the client can obtain the corresponding target tool image from the target image repository according to the tool storage path in the business configuration information. Then, the target data image is parsed and processed according to the target tool image to obtain the business data to be updated, thus completing the version update of the database data in the application.

[0180] It should be noted that steps 818 and 820 are parallel steps.

[0181] In summary, the data processing method provided in this application has the following beneficial effects: 1. Tool images are used to adapt to different databases and versions, turning the ability to import database data into a general capability of cloud-native platforms. 2. Database data that programs depend on is transmitted and delivered using images as carriers, just as many application protocols are built on the HTTP protocol, solving the problem of universality of transmission and storage media. 3. Data relies on the layered characteristics of images, and many basic layers can be shared. If only some data is modified, only this layer of data needs to be pushed, which can save bandwidth transmission and storage. 4. Because the directory structure of data files is defined, there is no need to write a special script to upload image layers; it can be generated by tools, reducing the workload of developers. 5. By organically combining the version iteration of delivered data with image layering, and separating data and tools, and separating dependent data and applications, applications can be made lighter in cloud-native scenarios, suitable for platforms that need to maintain a large number of product applications.

[0182] Corresponding to the above method embodiments, this application also provides data processing apparatus embodiments. Figure 9 A schematic diagram of the structure of a data processing apparatus according to an embodiment of this application is shown. Figure 9 As shown, this device is applied to a cloud-native platform and includes:

[0183] The first data receiving module 902 is configured to receive pending service data of the target service, wherein the pending service data is the database data of the target service;

[0184] Version information determination module 904 is configured to read the service configuration information of the service data to be processed and determine the version information of the service data to be processed.

[0185] The update data determination module 906 is configured to determine the business data to be updated from the business data to be processed based on the version information.

[0186] The data image generation module 908 is configured to generate a target data image based on the business configuration information and the business data to be updated, and upload the target data image to the target image repository of the target business.

[0187] Optionally, the data mirroring generation module 908 is further configured to:

[0188] Based on the version information of the business data to be processed, the data storage directory of the target data image is determined in the business configuration information;

[0189] Based on the data storage directory, the target data image is uploaded to the target image repository of the target business.

[0190] Optionally, the service configuration information includes tool image information;

[0191] Optionally, the device further includes:

[0192] The tool image generation module is configured to read the tool image identifier from the tool image information and obtain the tool parsing file based on the tool image identifier;

[0193] The target tool image is generated based on the tool parsing file and the tool image information.

[0194] Optionally, the tool image generation module is further configured to:

[0195] Based on the version information of the business data to be processed, the data storage path of the target tool image is determined in the business configuration information;

[0196] Based on the data storage path, the target tool image is uploaded to the target image repository of the target business.

[0197] Optionally, the update data determination module 906 is further configured to:

[0198] The data loading type corresponding to the business data to be processed is determined based on the historical version records in the version information, wherein the data loading type includes the initial business loading type and the iterative business loading type;

[0199] Based on the data loading type, determine the business data to be updated from the business data to be processed.

[0200] Optionally, the update data determination module 906 is further configured to:

[0201] If the data loading type is the initial business loading type, the business data to be processed will be treated as business data to be updated; or

[0202] When the data loading type is a business iteration loading type, the iterative business data is determined as the business data to be updated from the business data to be processed.

[0203] Optionally, the update data determination module 906 is further configured to:

[0204] Based on the version information, determine the initial business data from the business data to be processed;

[0205] Based on the initial business data, iterative business data is determined, and the iterative business data is used as the business data to be updated.

[0206] Optionally, the device further includes:

[0207] The patch data mirroring generation module is configured to receive patch version service data of the target service;

[0208] A patch data mirror is generated based on the patch version information carried in the patch version service data and the patch version service data.

[0209] The patch data is uploaded to the target image repository of the target service.

[0210] Optionally, the data mirroring generation module 908 is further configured to:

[0211] Based on the service configuration information, determine the image configuration information corresponding to the service data to be updated, and generate a target data image based on the image configuration information and the service data to be updated.

[0212] This application provides a data processing apparatus that, within a cloud-native platform, determines the version information of the business data to be processed to obtain the database data to be updated corresponding to the target business. After completing the operation of encapsulating the business data to be updated into a data image, the data image is then uploaded to the target image repository of the target business. This approach avoids the cloud-native platform uploading all the business data to be processed for the target business in the application to the image repository, thus enabling subsequent installation and upgrades of the target business by the client. This method of encapsulating the database data to be updated under different versions and storing it in the image repository eliminates the need to generate a data image for all database data, thereby reducing a significant amount of computing resources and avoiding bandwidth transmission pressure.

[0213] The above is an illustrative scheme of a data processing apparatus according to this embodiment. It should be noted that the technical solution of this data processing apparatus and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the data processing apparatus, please refer to the description of the technical solution of the data processing method described above.

[0214] Corresponding to the above method embodiments, this application also provides another data processing apparatus embodiment. Figure 10 A schematic diagram of another data processing apparatus provided in one embodiment of this application is shown. Figure 10 As shown, the device is used on a client side and includes:

[0215] The second data receiving module 1002 is configured to receive a target data image sent by the target image repository, wherein the target data image is determined by the target image repository based on the local data image version information of the target business;

[0216] The tool image acquisition module 1004 is configured to read the tool image configuration information in the configuration information of the target data image, and acquire the target tool image based on the tool image configuration information;

[0217] The data mirroring parsing module 1006 is configured to parse the target data mirror based on the target tool mirroring, obtain the business data to be updated, and import the business data to be updated into the local database.

[0218] Optionally, the tool image acquisition module 1004 is further configured to:

[0219] Determine whether the target tool image corresponding to the tool image configuration information exists in the local database.

[0220] If so, then obtain the target tool image;

[0221] If not, the target tool image is obtained based on the storage path information of the tool image in the tool image configuration information.

[0222] Optionally, the device further includes:

[0223] The request sending module is configured to send a data image acquisition request for the target service, wherein the data image acquisition request carries the local data image version information of the target service.

[0224] Optionally, the device further includes:

[0225] The patch update module is configured to send a patch data image retrieval request for the target service, wherein the patch data image retrieval request carries the target service identifier;

[0226] Receive patch data image sent by target image repository, wherein the patch data image is determined by target image repository based on target service identifier;

[0227] The target service is updated by performing a patch update based on the patch data mirror.

[0228] The data processing apparatus provided in this application embodiment allows the client to complete the version update of the application for the target business by downloading the updated business data from a mirror repository without having to perform a full update of all database data. This not only reduces the bandwidth of data transmission but also saves a lot of computing and storage resources.

[0229] The above is an illustrative scheme of a data processing apparatus according to this embodiment. It should be noted that the technical solution of this data processing apparatus and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the data processing apparatus, please refer to the description of the technical solution of the data processing method described above.

[0230] Figure 11 A structural block diagram of a computing device 1100 according to an embodiment of this application is shown. The components of the computing device 1100 include, but are not limited to, a memory 1110 and a processor 1120. The processor 1120 is connected to the memory 1110 via a bus 1130, and a database 1150 is used to store data.

[0231] The computing device 1100 also includes an access device 1140, which enables the computing device 1100 to communicate via one or more networks 1160. Examples of these networks include a Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 1140 may include one or more of any type of wired or wireless network interface (e.g., a Network Interface Card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) interface, a Wi-MAX interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, and so on.

[0232] In one embodiment of this application, the aforementioned components of the computing device 1100 and Figure 11 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 11 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this application. Those skilled in the art can add or replace other components as needed.

[0233] The computing device 1100 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or PCs. The computing device 1100 can also be a mobile or stationary server.

[0234] The processor 1120 implements the data processing method when executing the computer instructions.

[0235] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the data processing method described above.

[0236] An embodiment of this application also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the steps of the data processing method described above.

[0237] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the data processing method described above.

[0238] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0239] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0240] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0241] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0242] The preferred embodiments disclosed above are merely illustrative of this application. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this application. These embodiments are selected and specifically described in this application to better explain the principles and practical applications of this application, thereby enabling those skilled in the art to better understand and utilize this application. This application is limited only by the claims and their full scope and equivalents.

Claims

1. A data processing method, characterized in that, Applied to cloud-native platforms, including: Receive pending service data of the target service, wherein the pending service data is the database data of the target service; Read the service configuration information of the service data to be processed and determine the version information of the service data to be processed; Based on the version information, business data to be updated is determined from the business data to be processed, wherein the business data to be updated is determined based on the data loading type, and the data loading type includes business initial loading type and business iteration loading type; A target data image is generated based on the business configuration information and the business data to be updated, and the target data image is uploaded to the target image repository of the target business. The target data image is a data image independent of the application image.

2. The method according to claim 1, characterized in that, Uploading the target data image to the target image repository of the target service includes: Based on the version information of the business data to be processed, the data storage directory of the target data image is determined in the business configuration information; Based on the data storage directory, the target data image is uploaded to the target image repository of the target business.

3. The method according to claim 1, characterized in that, The service configuration information includes tool image information; Accordingly, after receiving the pending service data of the target service, the process further includes: Read the tool image identifier from the tool image information, and obtain the tool parsing file based on the tool image identifier; The target tool image is generated based on the tool parsing file and the tool image information.

4. The method according to claim 3, characterized in that, After generating the target tool image based on the tool parsing file and the tool image information, the process further includes: Based on the version information of the business data to be processed, the data storage path of the target tool image is determined in the business configuration information; Based on the data storage path, the target tool image is uploaded to the target image repository of the target business.

5. The method according to claim 1, characterized in that, The step of determining the business data to be updated from the pending business data based on the version information includes: The data loading type corresponding to the business data to be processed is determined based on the historical version records in the version information; Based on the data loading type, determine the business data to be updated from the business data to be processed.

6. The method according to claim 5, characterized in that, The step of determining the business data to be updated from the business data to be processed based on the data loading type includes: If the data loading type is the initial business loading type, the business data to be processed will be treated as business data to be updated; or When the data loading type is a business iteration loading type, the iterative business data is determined as the business data to be updated from the business data to be processed.

7. The method according to claim 6, characterized in that, The step of determining iterative business data as business data to be updated from the business data to be processed includes: Based on the version information, determine the initial business data from the business data to be processed; Based on the initial business data, iterative business data is determined, and the iterative business data is used as the business data to be updated.

8. The method according to claim 1, characterized in that, Also includes: Receive the patch version service data of the target service; A patch data mirror is generated based on the patch version information carried in the patch version service data and the patch version service data. The patch data is uploaded to the target image repository of the target service.

9. The method according to claim 1, characterized in that, The process of generating a target data image based on the service configuration information and the service data to be updated includes: Based on the service configuration information, determine the image configuration information corresponding to the service data to be updated, and generate a target data image based on the image configuration information and the service data to be updated.

10. A data processing method, characterized in that, Applied to the client side, including: Receive a target data image sent by the target image repository, wherein the target data image is determined by the target image repository based on the local data image version information of the target business; Read the tool image configuration information from the configuration information of the target data image, and obtain the target tool image based on the tool image configuration information; Based on the target tool image, the target data image is parsed to obtain the business data to be updated, and the business data to be updated is imported into the local database. The business data to be updated is determined based on the data loading type, which includes the business initial loading type and the business iteration loading type. The target data image is a data image independent of the application image.

11. The method according to claim 10, characterized in that, The step of obtaining the target tool image based on the tool image configuration information includes: Determine whether the target tool image corresponding to the tool image configuration information exists in the local database. If so, then obtain the target tool image; If not, the target tool image is obtained based on the storage path information of the tool image in the tool image configuration information.

12. The method according to claim 10, characterized in that, Before receiving the target data image sent by the target image repository, the process also includes: Send a data image retrieval request for the target service, wherein the data image retrieval request carries the local data image version information of the target service.

13. The method according to claim 10, characterized in that, Also includes: Send a patch data image retrieval request for the target service, wherein the patch data image retrieval request carries the target service identifier; Receive patch data image sent by target image repository, wherein the patch data image is determined by target image repository based on target service identifier; The target service is updated by performing a patch update based on the patch data mirror.

14. A data processing system, characterized in that, This includes cloud-native platforms, target image repositories, and clients; The cloud-native platform is configured to receive pending business data of a target business, wherein the pending business data is database data of the target business; read configuration information of the pending business data to determine version information of the pending business data; based on the version information, determine business data to be updated in the pending business data; generate a target data image based on the configuration information and the business data to be updated, and upload the target data image to the target image repository of the target business, wherein the business data to be updated is determined based on data loading type, the data loading type includes business initial loading type and business iteration loading type, and the target data image is a data image independent of the application image; The target image repository is configured to receive a data image acquisition request for the target service sent by the client, and determine the target data image based on the local data image version information of the target service carried in the data image acquisition request; The client is configured to receive the target data image sent by the target image repository, obtain the business data to be updated based on the target data image, and import the business data to be updated into the local database.

15. A data processing apparatus, characterized in that, Applied to cloud-native platforms, including: The first data receiving module is configured to receive pending service data of the target service, wherein the pending service data is the database data of the target service; The version information determination module is configured to read the business configuration information of the business data to be processed and determine the version information of the business data to be processed. The update data determination module is configured to determine the business data to be updated from the pending business data based on the version information, wherein the business data to be updated is determined based on the data loading type, and the data loading type includes the business initial loading type and the business iteration loading type. The data image generation module is configured to generate a target data image based on the business configuration information and the business data to be updated, and upload the target data image to the target image repository of the target business, wherein the target data image is a data image independent of the application image.

16. A data processing apparatus, characterized in that, Applied to the client side, including: The second data receiving module is configured to receive a target data image sent by the target image repository, wherein the target data image is determined by the target image repository based on the local data image version information of the target business; The tool image acquisition module is configured to read the tool image configuration information in the configuration information of the target data image, and acquire the target tool image based on the tool image configuration information; The data mirror parsing module is configured to parse the target data mirror based on the target tool mirror, obtain the business data to be updated, and import the business data to be updated into the local database. The business data to be updated is determined based on the data loading type, which includes the business initial loading type and the business iteration loading type. The target data mirror is a data mirror independent of the application mirror.

17. A computing device, comprising a memory, a processor, and computer instructions stored in the memory and executable on the processor, characterized in that, When the processor executes the computer instructions, it implements the steps of the method according to any one of claims 1-9 or 10-13.

18. A computer-readable storage medium storing computer instructions, characterized in that, When executed by a processor, the computer instructions implement the steps of the method according to any one of claims 1-9 or 10-13.

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