Data processing method and device based on materialized view, equipment and storage medium

Abstract

The invention discloses a data processing method and device based on a materialized view, equipment and a storage medium, target data is acquired, and the target data is static data within preset time; determining materialized view data corresponding to the target data based on the target data; when the data query request is obtained, the target feedback information is generated based on the materialized view data, and the display efficiency of data query is improved by performing data processing before the request is obtained to generate the materialized view data.

Description

Technical Field

[0001] The present disclosure generally relates to the field of data processing technology, and in particular to a data processing method, apparatus, device and storage medium based on materialized view. Background Art

[0002] In the credit business scenario, users or account managers need to frequently check the user's credit and repayment status. In related technologies, usually when an access request is received, data is obtained and calculated and displayed based on the access request. This process takes a certain amount of time, especially when the user repeatedly checks the behavior, repeated operations are still required, resulting in serious time waste and affecting the user experience. Summary of the invention

[0003] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a data processing method, apparatus, device and storage medium based on materialized view, which can improve the display efficiency of data query by generating materialized view data through data processing before obtaining the request.

[0004] In a first aspect, an embodiment of the present application provides a data processing method based on a materialized view, including:

[0005] Acquire target data, where the target data is static data within a preset time;

[0006] Based on the target data, determining materialized view data corresponding to the target data;

[0007] When a data query request is obtained, target feedback information is generated based on the materialized view data.

[0008] In some embodiments, acquiring target data includes:

[0009] In response to a trigger condition that the target data changes, the changed target data is acquired.

[0010] In some embodiments, the target data includes trial calculation data, and determining the materialized view data corresponding to the target data based on the target data includes:

[0011] The target data is pre-calculated to obtain trial calculation result data corresponding to the target data, and the trial calculation result data is used as the materialized view data.

[0012] In some embodiments, the target data includes trial calculation result data, and the trial calculation determines the materialized view data corresponding to the implementation target data based on the trial calculation target data, including:

[0013] Aggregate the trial calculation result data to obtain aggregate data corresponding to the trial calculation result data;

[0014] Materialized modeling is performed based on the aggregated data to obtain the materialized view data.

[0015] In some embodiments, when a data query request is obtained, generating target feedback information based on the materialized view data includes:

[0016] Identify whether the data query request hits the materialized view data;

[0017] When the data query request hits the materialized view data, the materialized view data is used as the target feedback information.

[0018] In some embodiments, it also includes:

[0019] When the data query request does not hit the materialized view data, obtaining query data corresponding to the data query request;

[0020] The target feedback information is generated based on the query data.

[0021] In a second aspect, an embodiment of the present application provides a data processing device based on a materialized view, including:

[0022] An acquisition module, used to acquire target data, wherein the target data is static data within a preset time;

[0023] A determination module, configured to determine materialized view data corresponding to the target data based on the target data;

[0024] The feedback module is used to generate target feedback information based on the materialized view data when a data query request is obtained.

[0025] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the embodiment of the present application when executing the program.

[0026] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the embodiment of the present application.

[0027] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, characterized in that when the computer program is executed by a processor, it implements the method described in the embodiment of the present application.

[0028] The materialized view-based data processing method, apparatus, device, and storage medium proposed in the embodiments of the present application obtain materialized view data by performing data processing on target data before obtaining a data query request, so that when a data query request is received, target feedback information can be generated according to the materialized view data obtained in advance, so as to reduce the response time of the data query request, that is, there is no need to perform corresponding calculation and processing on the target data after receiving the data query request, thereby greatly accelerating the response efficiency to the data query request and improving the user experience.

[0029] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0031] Figure 1 The following is a diagram showing an implementation environment architecture of a data processing method based on materialized views provided in an embodiment of the present application;

[0032] Figure 2 A schematic diagram of a process flow of a data processing method based on a materialized view provided in an embodiment of the present application is shown;

[0033] Figure 3 A schematic diagram of a process flow of a data processing method based on a materialized view provided in another embodiment of the present application is shown;

[0034] Figure 4 A schematic diagram of a process flow of a data processing method based on materialized views provided in another embodiment of the present application is shown;

[0035] Figure 5 A schematic diagram of a process flow of a data processing method based on materialized views provided in yet another embodiment of the present application is shown;

[0036] Figure 6 A schematic diagram of a process flow of a data processing method based on materialized views provided in yet another embodiment of the present application is shown;

[0037] Figure 7 An application architecture diagram of a data processing method based on materialized views provided by an embodiment of the present application is shown;

[0038] Figure 8 An exemplary structural block diagram of a data processing device based on materialized views provided in an embodiment of the present application is shown;

[0039] Fig. 9A schematic diagram of the structure of a computer system of an electronic device or server suitable for implementing an embodiment of the present application is shown. DETAILED DESCRIPTION

[0040] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0041] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0042] For the specific implementation environment of the data processing method based on materialized views proposed in this application, see Figure 1 . Figure 1 The following is a diagram showing an implementation environment architecture of a materialized view-based data processing method provided in an embodiment of the present application.

[0043] like Figure 1 As shown, the implementation environment architecture includes: a terminal device 101 and a server 102 .

[0044] The terminal device 101 is a terminal device used by a user or customer experience, etc., for sending a data query request to the server, and receiving target feedback information returned by the server for display, etc. The terminal device can be a desktop computer, a laptop computer, a smart phone, a tablet computer, an e-book reader, smart glasses, a smart watch, etc., but is not limited thereto.

[0045] The server 102 is used to obtain the target data and determine the materialized view data corresponding to the target data based on the target data before obtaining the data query request, and generate target feedback information based on the materialized view data when obtaining the data query request. The server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0046] The terminal device 101 and the server 102 are directly or indirectly connected via wired or wireless communication. Optionally, the wireless network or wired network uses standard communication technology and / or protocols. The network is usually the Internet, and can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or any combination of a virtual private network.

[0047] The materialized view-based data processing method proposed in the present application can be implemented by a materialized view-based data processing device, and the materialized view-based data processing device can be installed on a terminal device or a server.

[0048] In order to further illustrate the technical solution provided by the embodiment of the present application, this is described in detail below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiment of the present application provides the method operation instruction steps shown in the following embodiments or drawings, more or less operation instruction steps may be included in the method based on routine or no creative labor. In the steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiment of the present application. The method may be executed in the order of the method shown in the embodiment or drawings or in parallel during the actual processing process or when the device is executed.

[0049] Please refer to Figure 2 , Figure 2 FIG. 1 is a flow chart of a data processing method based on a materialized view provided in an embodiment of the present application. Figure 2 As shown, the method includes:

[0050] Step 201, obtaining target data, where the target data is static data within a preset time.

[0051] Step 202: Based on the target data, determine the materialized view data corresponding to the target data.

[0052] Step 203: When a data query request is obtained, target feedback information is generated based on the materialized view data.

[0053] It should be noted that the target data is the basic data used to generate the materialized view data. The target data can be selected in advance according to the type and content of the materialized view data, and can be selected and set according to the application scenario, which is not specifically limited in this application. The materialized view is a database object including a query result, which is a local copy of the remote data. The materialized view data is the database storage content itself, which is the information of the remote data itself.

[0054] It should also be noted that the target data is static data within a preset time, that is, the target data will not change within a preset time period, so that the result of the data query request at any time within the preset time is the same.

[0055] Based on this, the materialized view-based data processing method proposed in the embodiment of the present application processes the target data to obtain materialized view data before obtaining the data query request, so that when the data query request is received, target feedback information can be generated according to the materialized view data obtained in advance, so as to reduce the response time of the data query request, that is, there is no need to perform corresponding calculation and processing on the target data after receiving the data query request, thereby greatly speeding up the response efficiency to the data query request and improving the user experience.

[0056] In a possible embodiment, the target data includes trial calculation data.

[0057] It should be noted that trial calculation usually refers to testing and verifying the expected results of a business or process in a small range or simulated environment. In the embodiment of the present application, trial calculation data refers to the data used for the test and verification. In other words, target data is computable and verifiable value data. In the credit business, target data can include various numerical data such as loan data and repayment data within an account period.

[0058] Specifically, based on the target data, determining the materialized view data corresponding to the target data includes: pre-calculating the target data to obtain trial calculation result data corresponding to the target data, and using the trial calculation result data as the materialized view data.

[0059] That is to say, when the target data is numerical data, various types of foreseeable data queries can be calculated in advance based on trial calculation behaviors to obtain trial calculation result data, and then the trial calculation result data can be used as materialized view data.

[0060] For example, taking housing loans as an example, the user's borrowing value and repayment value in a monthly account period are static data, so the data of related categories can be pre-calculated to obtain the corresponding materialized view data. Taking credit card monthly loans as an example, since even the user's daily consumption data requires at least one accounting date for accounting operations, the borrowing value, repayment value, and generated credit equity within a natural day are all static data, so the data of related categories can be pre-calculated to obtain the corresponding materialized view data. As a result, when receiving the corresponding data query request, the materialized view data can be directly rendered and displayed without further calculation, thereby greatly improving the response efficiency of the platform.

[0061] In another feasible embodiment, the target data includes trial calculation result data.

[0062] It should be noted that the trial calculation result data is the result data obtained by pre-calculating the target data in the previous embodiment. That is to say, the target data in the embodiment of the present application is the calculation result of the previous embodiment. It should be understood that since the target data used for calculation in the previous embodiment is static data within a preset time (for example, an account period), its calculation result (trial calculation result data) is also static data within the preset time, and can also be used as target data for further data preprocessing.

[0063] Specifically, based on the target data, materialized view data corresponding to the target data is determined, including: aggregating the trial calculation result data to obtain aggregated data corresponding to the trial calculation result data; and performing materialized modeling based on the aggregated data to obtain materialized view data.

[0064] That is to say, in the embodiment of the present application, the target data has completed the required pre-calculation process based on the trial calculation behavior in the previous embodiment. Therefore, what is implemented in this stage is the operation of clustering and materialized modeling of the trial calculation result data to obtain materialized view data based on the trial calculation result data.

[0065] It should be understood that in the embodiment of the present application, the materialized view data based on the trial calculation data is numerical data, and the materialized view data based on the trial calculation result data is non-numerical data, which can be data with high visualization effect such as charts and model views.

[0066] For example, the materialized view data may be a graph, a model, or the like that expresses the proportional relationship between the credit value and the repayment value in the trial calculation result data.

[0067] In a specific embodiment, Figure 3 As shown in the figure, the data processing method based on materialized view includes:

[0068] Step 301, obtaining first target data.

[0069] Step 302 : Based on the first target data, calculate and obtain trial calculation result data corresponding to the first target data, and use the trial calculation result data as the first materialized view data and the second target data corresponding to the first target data.

[0070] Step 303: performing aggregation processing based on the second target data to obtain aggregated data corresponding to the second target data.

[0071] Step 304: perform materialized modeling based on the aggregated data to obtain second materialized view data.

[0072] Optionally, the first materialized view data can be used for users to display simple numerical values ​​on the lending platform, such as a homepage or a query page, and the second materialized view data can be used for users to display on the lending platform, such as a detailed data or change analysis page.

[0073] Preferably, the second materialized view data can form different aggregate information based on different aggregation operations, and then be displayed in the page information corresponding to different query requests. For example, the user's loan value can be reflected in multiple queries such as loan query, repayment query, equity query, personal information query, etc., that is, when clustering loan information, the clustering result can include loan data, when clustering repayment information, the clustering result can also include loan data, when clustering equity information, the clustering result can also include loan data, and when clustering personal information, it can still include loan data.

[0074] In a feasible embodiment, the credit platform can use different attribute pages to display the above information separately, and this application does not make any specific limitation.

[0075] Therefore, the embodiment of the present application can utilize target data of different stages at different analysis stages, perform classification processing to obtain corresponding materialized view data, thereby meeting the query requirements of different users, enriching the query content of users, and improving the query experience of users.

[0076] In a feasible embodiment, since the trial calculation data is not always constant, that is, it is static data only within a corresponding period range, the changed target data is acquired in response to a trigger condition in which the target data changes.

[0077] That is, when the preset time of the target data arrives, it is determined that a trigger condition for the target data to change is obtained. At this time, in response to the trigger condition for the target data to change, the changed target data is obtained.

[0078] Specifically, the preset time can be determined according to the user's account period, repayment cycle, accounting cycle, etc., and the corresponding target data can be updated according to the corresponding preset time, so as to achieve the purpose of updating the pre-calculated materialized view data, thereby ensuring that the target feedback information obtained by the user when querying the data is consistent with the query time, thereby achieving the purpose of improving data reliability and accuracy while improving the query response speed.

[0079] For example, Figure 4As shown, taking credit card lending as an example, in response to the daily date changes, the target data after the changes on the day is obtained, such as the lending data after the previous day's consumption data is recorded, and then the trial calculation result data is calculated based on the target data, such as borrowing data, repayment data, overpayment data and equity data, and then the calculated trial calculation result data is stored in the memory, waiting for the user's data query request.

[0080] Among them, the response trigger can be executed during the server's idle period to obtain the changed target data, such as 2-3 am every day. This period not only completes the time update, but also has less server occupancy, such as user access, thereby effectively improving the server's resource utilization.

[0081] Furthermore, when the target data is trial calculation result data, the corresponding data update can be performed in response to the data update operation in the previous stage, or the trial calculation result data corresponding to the current materialized view data can be compared based on the calculation result of the previous stage. If the two are different, the materialized view data is updated; if they are the same, no update is required, thereby effectively reducing the amount of data processing while ensuring data accuracy.

[0082] For example, Figure 5 As shown, when the first materialized view data changes, in response to the trigger condition of the target data change (the change of the first materialized view data), the changed target data is obtained, and then aggregation processing and materialized modeling are performed based on the target data to obtain materialized view data of various categories such as loan information modeling, repayment information modeling, equity information modeling, and personal information modeling, and then the materialized view data obtained by modeling is stored in the memory, waiting for the user's data query request.

[0083] In a feasible embodiment, when a data query request is obtained, target feedback information is generated based on materialized view data, including: identifying whether the data query request hits the materialized view data, and when the data query request hits the materialized view data, using the materialized view data as target feedback information.

[0084] When the data query request does not hit the materialized view data, query data corresponding to the data query request is obtained, and target feedback information is generated based on the query data.

[0085] It should be noted that the data query request hitting the materialized view data may be that the query data corresponding to the data query request is the materialized view data, for example, the data query request obtains the loan value, and the loan value has been pre-calculated and stored as the first materialized view data, in this case, it is determined that the data query request hits the materialized view data. Alternatively, the data query request hitting the materialized view data may be that the data query request contains information corresponding to the materialized view data, for example, the data query request is the loan information, and the loan information has been aggregated and modeled and stored as the second materialized view data, in this case, it is determined that the data query request hits the materialized view data.

[0086] Specifically, whether the data query request hits the materialized view data may be determined by judging whether the query data corresponding to the data query request is consistent with the data identifier corresponding to the materialized view data.

[0087] For example, Figure 6 As shown, a data query request is received, and then it is determined whether the materialized view data is hit. If the materialized view data is hit, the materialized view data is directly fed back as the target feedback information. If the materialized view data is not hit, the query data corresponding to the data query request is obtained, and the target feedback information is generated based on the query data. Finally, the feedback result is displayed based on the target feedback information.

[0088] In another specific embodiment, in order to implement the data processing method based on materialized view proposed in the embodiment of the present application, it is proposed as follows Figure 7 The application architecture shown in FIG. Specifically, it includes a data source layer, a pre-calculation layer, a dimensional modeling layer, and a data storage layer. The data source layer includes the Jingtian data storage device MySQL database, the redis cache database, etc., a calculation API interface, and a data query API for receiving data query requests. The pre-calculation layer includes various operators for calculating numerical data, such as loan calculation operators, repayment calculation operators, overpayment calculation operators, and equity calculation operators. The dimensional modeling layer is used for data aggregation and page modeling, such as including IOU information aggregation, repayment information aggregation, loan page modeling, repayment page modeling, equity page modeling, historical IOU page modeling, historical repayment page modeling, overpayment page modeling, etc. The data storage layer is used to store the materialized view data obtained by the pre-calculation layer and the data storage layer. The data access that can be provided based on this architecture includes but is not limited to app card page access, mini program card page access, open platform card page access, etc.

[0089] In summary, the materialized view-based data processing method proposed in the embodiment of the present application processes the target data to obtain materialized view data before obtaining the data query request, so that when the data query request is received, target feedback information can be generated according to the materialized view data obtained in advance, so as to reduce the response time of the data query request, that is, there is no need to perform corresponding calculation and processing on the target data after receiving the data query request, thereby greatly speeding up the response efficiency to the data query request and improving the user experience.

[0090] It should be noted that although the operations of the method of the present invention are described in a particular order in the drawings, this does not require or imply that the operations must be performed in this particular order or that all illustrated operations must be performed to achieve the desired results.

[0091] Figure 8 An exemplary structural block diagram of a data processing device based on materialized views provided in an embodiment of the present application is shown.

[0092] like Figure 8 As shown, the materialized view-based data processing device 10 includes:

[0093] An acquisition module 11 is used to acquire target data, where the target data is static data within a preset time;

[0094] A determination module 12, configured to determine materialized view data corresponding to the target data based on the target data;

[0095] The feedback module 13 is used to generate target feedback information based on the materialized view data when a data query request is obtained.

[0096] In some embodiments, the acquisition module 11 is further used to:

[0097] In response to a trigger condition that the target data changes, the changed target data is acquired.

[0098] In some embodiments, the target data includes trial calculation data, and the determination module 12 is further configured to:

[0099] The target data is pre-calculated to obtain trial calculation result data corresponding to the target data, and the trial calculation result data is used as the materialized view data.

[0100] In some embodiments, the target data includes trial calculation result data, and the determination module 12 is further configured to:

[0101] Aggregate the trial calculation result data to obtain aggregate data corresponding to the trial calculation result data;

[0102] Materialized modeling is performed based on the aggregated data to obtain the materialized view data.

[0103] In some embodiments, the feedback module 13 is further configured to:

[0104] Identify whether the data query request hits the materialized view data;

[0105] When the data query request hits the materialized view data, the materialized view data is used as the target feedback information.

[0106] In some embodiments, the feedback module 13 is further configured to:

[0107] When the data query request does not hit the materialized view data, obtaining query data corresponding to the data query request;

[0108] The target feedback information is generated based on the query data.

[0109] It should be understood that the modules or modules described in the materialized view-based data processing device 10 are similar to the modules described in the reference Figure 2 The various steps in the described method correspond to each other. Therefore, the operations and features described above for the method are also applicable to the data processing device 10 based on materialized views and the modules contained therein, and will not be repeated here. The data processing device 10 based on materialized views can be pre-implemented in a browser or other security application of an electronic device, or can be loaded into the browser or its security application of an electronic device by downloading or the like. The corresponding modules in the data processing device 10 based on materialized views can cooperate with the modules in the electronic device to implement the solution of the embodiment of the present application.

[0110] For the several modules or units mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0111] Reference below Fig. 9 , Fig. 9 A schematic diagram of the structure of a computer system of an electronic device or server suitable for implementing an embodiment of the present application is shown.

[0112] like Fig. 9As shown, the computer system includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 902 or the program loaded from the storage part 908 into the random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation instructions of the system are also stored. The CPU 901, the ROM 902 and the RAM 903 are connected to each other through a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0113] The following components are connected to the I / O interface 905: an input section 906 including a keyboard, a mouse, etc.; an output section 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, a modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed so that a computer program read therefrom is installed into the storage section 908 as needed.

[0114] In particular, according to an embodiment of the present application, the above reference flow chart Figure 2 The described process can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the central processing unit (CPU) 901, the above-mentioned functions defined in the system of the present application are executed.

[0115] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium such as a computer-readable storage medium that can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0116] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operating instructions of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the aforementioned module, program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, the boxes represented by two connections can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operating instruction, or can be implemented with a combination of dedicated hardware and computer instructions.

[0117] The units or modules involved in the embodiments described in the present application may be implemented by software or by hardware. The units or modules described may also be arranged in a processor, for example, they may be described as: a processor includes an acquisition module, a determination module, and a feedback module. Among them, the names of these units or modules do not constitute a limitation on the units or modules themselves under certain circumstances, for example, the acquisition module may also be described as "acquiring target data, wherein the target data is static data within a preset time".

[0118] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device. The above computer-readable storage medium stores one or more programs, and when the above programs are used by one or more processors to execute the materialized view-based data processing method described in the present application.

[0119] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A data processing method based on materialized view, characterized in that: include: Acquire target data, where the target data is static data within a preset time; Based on the target data, determining materialized view data corresponding to the target data; When a data query request is obtained, target feedback information is generated based on the materialized view data.

2. The data processing method based on materialized view according to claim 1, characterized in that: The obtaining of target data comprises: In response to a trigger condition that the target data changes, the changed target data is acquired.

3. The data processing method based on materialized view according to claim 1, characterized in that: The target data includes trial calculation data, and determining the materialized view data corresponding to the target data based on the target data includes: The target data is pre-calculated to obtain trial calculation result data corresponding to the target data, and the trial calculation result data is used as the materialized view data.

4. The data processing method based on materialized view according to claim 1, characterized in that: The target data includes trial calculation result data. The trial calculation is based on the trial calculation target data to determine the materialized view data corresponding to the implementation target data, including: Aggregate the trial calculation result data to obtain aggregate data corresponding to the trial calculation result data; Materialized modeling is performed based on the aggregated data to obtain the materialized view data.

5. The data processing method based on materialized view according to claim 1, characterized in that: When a data query request is obtained, generating target feedback information based on the materialized view data includes: Identify whether the data query request hits the materialized view data; When the data query request hits the materialized view data, the materialized view data is used as the target feedback information.

6. The data processing method based on materialized view according to claim 5, characterized in that: Also includes: When the data query request does not hit the materialized view data, obtaining query data corresponding to the data query request; The target feedback information is generated based on the query data.

7. A data processing device based on materialized view, characterized in that: include: An acquisition module, used to acquire target data, wherein the target data is static data within a preset time; A determination module, configured to determine materialized view data corresponding to the target data based on the target data; The feedback module is used to generate target feedback information based on the materialized view data when a data query request is obtained.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the materialized view-based data processing method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the materialized view-based data processing method as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the materialized view-based data processing method described in any one of claims 1 to 6 is implemented.

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