Data processing method, electronic equipment and storage medium

Abstract

The invention discloses a data processing method, electronic equipment and a storage medium. The method comprises the steps that in response to a received cache updating instruction of the data processing system, data configuration information of the data processing system is obtained, and the cache updating instruction is generated based on data updating operation of the data processing system; analyzing the data configuration information to obtain a data cache type of the data processing system, the data cache type being used for representing a data type for caching in the data processing system; based on the data cache type, a data updating instruction corresponding to a data cache region in the data processing system is generated, and the data cache region is used for storing cache data of the data processing system; and based on the data updating instruction and the updating data of the data processing system, updating the cache data of the data cache region. According to the method and the device, the technical problem of relatively low cache data updating efficiency in related technologies is solved.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more specifically, to a data processing method, an electronic device, and a storage medium. Background Technology

[0002] In data-intensive applications such as big data analytics, massively parallel processing (MPP) database systems serve as the core, significantly improving data processing efficiency. However, traditional caching strategies suffer from low cache update efficiency and frequent manual intervention under dynamic changes in MPP clusters, such as node scaling, becoming a performance bottleneck. This results in low cache data update efficiency and consequently, low overall data processing efficiency.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a data processing method, an electronic device, and a storage medium to at least solve the technical problem of low efficiency in updating cached data in related technologies.

[0005] According to one aspect of the present invention, a data processing method is provided, comprising: in response to receiving a cache update instruction from a data processing system, obtaining data configuration information of the data processing system, wherein the cache update instruction is generated based on a data update operation of the data processing system; parsing the data configuration information to obtain a data cache type of the data processing system, wherein the data cache type is used to represent the data type cached in the data processing system; generating a data update instruction corresponding to a data cache area in the data processing system based on the data cache type, wherein the data cache area is used to store cached data of the data processing system; and updating the cached data in the data cache area based on the data update instruction and the update data of the data processing system.

[0006] Furthermore, the method also includes: monitoring the identification information of multiple nodes in the data processing system based on a preset period to obtain monitoring results, wherein the monitoring results are used to indicate whether a target node exists among the multiple nodes, the target node is a node whose data has been adjusted, and different nodes are used to implement the data processing operations of the data processing system; if the monitoring results indicate that a target node exists among the multiple nodes, a cache update instruction is generated.

[0007] Furthermore, based on a preset period, the identification information of multiple nodes in the data processing system is monitored to obtain monitoring results, including: for any one of the multiple nodes, the node's identification information and the node's historical identification information are compared based on the preset period to obtain a comparison result, wherein the comparison result is used to indicate whether the identification information is the same as the historical identification information; in response to the comparison result indicating that the identification information is different from the historical identification information, the node is determined to be the target node, and the monitoring result is determined to be that the target node exists among the multiple nodes; in response to multiple comparison results indicating that the corresponding identification information is the same as the corresponding historical identification information, the monitoring result is determined to be that the target node does not exist among the multiple nodes.

[0008] Furthermore, based on the data cache type, a data update instruction corresponding to the data cache area in the data processing system is generated, including: generating a data cleanup instruction based on the first cache type in the data cache type, wherein the first cache type is used to indicate the cache type of cached data in the data cache area that needs to be cleaned; generating a data loading instruction based on the second cache type in the data cache type, wherein the second cache type is used to indicate the cache type of cached data that needs to be loaded into the data cache area; and determining the data update instruction based on the data cleanup instruction and the data loading instruction.

[0009] Furthermore, based on the data update instruction and the update data from the data processing system, the cached data in the data cache area is updated, including: based on the data cleanup instruction in the data update instruction, the first cached data in the data cache area is cleaned to obtain the updated data cache area, wherein the first cached data is the cached data to be cleaned in the cached data; based on the data loading instruction in the data update instruction, the second cached data in the updated data is determined, and the second cached data is loaded into the updated data cache area to update the cached data in the data cache area, wherein the second cached data is the data to be cached in the updated data.

[0010] Furthermore, the method also includes: obtaining the data access pattern of the data processing system, wherein the data access pattern is used to represent the access pattern of different types of data in the data cache area; configuring the data processing system based on the data access pattern to generate data configuration information.

[0011] Furthermore, the data processing system is configured based on the data access mode to generate data configuration information, including: configuring the data processing system based on the data access mode to obtain initial data configuration information and outputting the initial data configuration information; adjusting the initial data configuration information based on the adjustment instruction received within a preset time period to obtain data configuration information; and determining the initial data configuration information as data configuration information if the adjustment instruction is not received within the preset time period.

[0012] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0013] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0014] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0015] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0016] According to another aspect of the present invention, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of the present invention.

[0017] In this embodiment of the invention, in response to receiving a cache update instruction from a data processing system, the system obtains data configuration information of the data processing system, wherein the cache update instruction is generated based on the data update operation of the data processing system; the data configuration information is parsed to obtain the data cache type of the data processing system, wherein the data cache type is used to represent the data type cached in the data processing system; based on the data cache type, a data update instruction corresponding to the data cache area in the data processing system is generated, wherein the data cache area is used to store the cached data of the data processing system; based on the data update instruction and the update data of the data processing system, the cached data in the data cache area is updated. In this application, upon receiving a cache update instruction, the system immediately obtains and parses the data configuration information, intelligently identifies the data cache type, and generates a precise update instruction accordingly, performing efficient data updates for the cache area. The system intelligently identifies data that needs to be cleared and loaded, and combines the latest data updates to achieve consistency between cache and system data, automatically and efficiently updating, maintaining high processing performance, solving the problem of low efficiency in traditional cache updates, and thus solving the technical problem of low efficiency in cache data updates in related technologies.

[0018] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0019] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0021] Figure 1 This is a flowchart of a data processing method according to an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of a data processing apparatus according to an embodiment of this application. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] According to an embodiment of the present invention, a method embodiment of a data processing method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] Figure 1 This is a flowchart of a data processing method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0027] Step S102: In response to receiving the cache update instruction from the data processing system, obtain the data configuration information of the data processing system.

[0028] Among them, the cache update instruction is generated based on the data update operation of the data processing system.

[0029] The aforementioned cache update instruction is triggered by the data processing system after detecting a data update operation. It is used to instruct the system to update the cached data in order to maintain the consistency between the cached data and the main data storage.

[0030] The aforementioned data configuration information refers to the data management configuration information stored in the data processing system, including data storage location, caching strategy, etc., which serves as the basis for the system to manage data and cache.

[0031] When the data processing system detects a data update operation, it will automatically issue a cache update command. In response to the cache update command, the system then obtains the current data configuration information. This is the starting point of the entire cache update process, ensuring real-time linkage between cache updates and data updates, and providing the necessary configuration information for subsequent steps.

[0032] The aforementioned data processing systems are core components of modern information technology architectures, responsible for receiving, parsing, storing, and processing raw data from various data sources to support decision-making, project operation analysis, and other data-driven tasks. These systems typically include modules for data storage, data retrieval, data transformation, and data analysis, capable of handling both structured data (such as data in relational databases) and unstructured data (such as text, images, or web page data). In the context of big data and cloud computing, data processing systems often adopt a distributed architecture, utilizing multiple computers working collaboratively to improve processing power and flexibility.

[0033] When the data processing system detects a data update operation, such as data insertion, deletion, or modification, it generates a cache update instruction. This is part of the system's self-regulation to ensure consistency between cached data and main storage data. In response to this instruction, the next step is to retrieve the data processing system's data configuration information. This data configuration information is a set of pre-defined rules and parameters within the system that define key attributes such as data storage methods, caching strategies, and data access permissions, and is crucial for efficient system operation.

[0034] In this embodiment, the unique design of this mechanism lies in its ability to intelligently determine the necessity of cache updates based on the nature of the data updates and immediately obtain data configuration information. This rapid response and acquisition of data configuration information enables the system to quickly identify which data should be cached and how to cache it after a data update, thereby avoiding redundant data occupying cache space and reducing the time required for subsequent cache cleanup and reloading. In this way, this embodiment significantly improves data processing efficiency, especially when dealing with large-scale, high-speed updated datasets, ensuring that the data in the system cache is always up-to-date, reducing latency, improving data access speed, and thus improving the overall system performance.

[0035] The cache update mechanism initiation and data configuration information acquisition steps in this embodiment are key steps in improving the performance of the data processing system. This innovative design, through automated response to cache update commands and real-time acquisition of data configuration information, enables dynamic adjustment of the caching strategy, ensuring the real-time nature and relevance of cached data. Compared with traditional static caching solutions, this embodiment can respond more flexibly to data changes, significantly reducing query latency caused by inconsistencies between cached and master data, thereby improving the overall data processing efficiency and user experience of the system. Especially in audit off-site data processing scenarios, the frequent updates of massive amounts of data require the system to have the ability to respond quickly and cache efficiently. This embodiment, through the above steps, effectively meets this requirement, demonstrating its significant benefits in improving the real-time performance of data processing and reducing processing costs.

[0036] Step S104: Parse the data configuration information to obtain the data cache type of the data processing system.

[0037] Among them, the data cache type is used to represent the data type that is cached in the data processing system.

[0038] The data caching types mentioned above are classifications used by data processing systems to distinguish the appropriate caching levels for different data types. They guide the formulation of caching strategies based on characteristics such as data access frequency, size, and type.

[0039] Parsing the acquired data configuration information is a crucial step in identifying the data cache type. By parsing the data cache type, the system can understand the current caching strategy and data layout, thereby identifying which data types (such as hot data, historical data, etc.) are suitable for caching, providing a basis for formulating specific cache update strategies.

[0040] The aforementioned Data Configuration Information (DCI) is a core component of a data processing system, containing detailed specifications at multiple levels regarding data storage, management strategies, access permissions, and caching strategies. This information determines how data is effectively organized and utilized within the system, and also defines which data types are suitable for storage in the cache to improve access speed and system performance.

[0041] Data processing systems require in-depth analysis of Data Caching Interface (DCI) to extract definitions of Data Caching Types (DCTs). DCTs are intelligent classifications of data attributes, categorized based on factors such as access frequency, importance, size, and purpose, guiding which data should be prioritized for caching and how to cache it. Analyzing DCIs and identifying DCTs are crucial steps in improving caching efficiency. Through this process, the system can intelligently determine which data types will yield the greatest performance improvement when cached, thus generating targeted cache update instructions. This data-characteristic-based caching strategy improvement avoids the waste of cache space and inefficiency caused by blindly caching all data.

[0042] By implementing this step, the data processing system ensures the effective utilization of cache space, improves data access speed, and thus enhances the overall system efficiency and responsiveness. Especially in big data processing and real-time analysis, this improved intelligent caching strategy significantly reduces data latency and increases system throughput, serving as a crucial guarantee for achieving high-performance operation of the data processing system.

[0043] Step S106: Based on the data cache type, generate a data update instruction corresponding to the data cache area in the data processing system.

[0044] The data cache area is used to store cached data from the data processing system.

[0045] The aforementioned data cache area is a physical or logical area in the data processing system specifically used to store cached data in order to improve data access speed and system efficiency.

[0046] Based on the identified data cache types, the data processing system generates data update instructions for the Data Cache Area (DCA). These instructions include commands for cleaning and loading data in the data cache area, ensuring that cached data remains consistent with the master data state, while improving the efficiency of cache space utilization.

[0047] The data processing system generates specific data update instructions (DUIs) based on the previously identified Data Caching Type (DCT). These instructions guide the system on how to update and manage data within the Data Caching Area (DCA). The DCA refers to the physical or virtual space in the data processing system used to store cached data. Its main function is to accelerate data access and reduce the time spent loading data from main storage, thereby improving overall system performance.

[0048] By generating DUI based on DCT, the system can ensure that cache updates follow an efficient strategy, rather than indiscriminately updating all data. The benefit of this is that the system can selectively update data types that are determined by DCT to be frequently accessed or critical to the current computational task, thereby maintaining the timeliness and accuracy of cached data.

[0049] For example, in off-site data processing for auditing, the system might identify transaction logs, account balances, and other data as high-frequency access types (belonging to hot or critical data types), while classifying target files, historical reports, and other data as low-frequency access types (relatively less common data types). When a cache update instruction is received, the system does not blindly update all cached data. Instead, it generates a precise Data Access Context (DUI) based on Data Access Patterns (DCTs), prioritizing the removal of inactive low-frequency access data while loading the latest high-frequency access data into the cache area. This intelligent cache update based on data access patterns avoids invalid cache space usage, reduces unnecessary data reads and writes, and significantly improves data processing efficiency and response speed. Furthermore, the precise location and implementation of the DUI also reduces the system load during the cache update process, helping to maintain stable system operation.

[0050] Step S108: Update the cached data in the data cache area based on the data update instruction and the update data from the data processing system.

[0051] Based on the generated data update instructions and the latest data update information, the data processing system updates the cached data in the data cache area. This process, through intelligent identification and precise operation, achieves efficient synchronization of cached data, ensuring that the data processing system maintains high performance and stability in environments with frequent data updates.

[0052] The above steps constitute the execution phase of cache management in the data processing system. The system updates data in real-time based on previously generated data update instructions and the cached data within the specific data update content. DCA is a dedicated storage area in the system designed to accelerate data access. By storing frequently used or recently accessed data blocks, it reduces the latency of reading data from the main storage area, thereby improving overall data processing efficiency.

[0053] The data processing system not only responds swiftly to data update commands but also precisely executes cached data update operations, ensuring that the data in the cache is synchronized with the main storage data. By updating the cache immediately upon data changes, the system ensures that the data read from the cache is always up-to-date and accurate, avoiding inconsistencies in query results caused by outdated cached data. Since the cache area stores updated frequently accessed data, queries can retrieve it directly from the cache, eliminating the need for frequent access to the main storage area. This significantly reduces disk I / O operations, speeds up data access, and improves query response time and system processing capacity. Based on a customized DUI for data cache types, the system intelligently determines which data needs to be retained and which can be replaced, avoiding wasted cache space and achieving effective resource management. Real-time cache updates reduce system load fluctuations caused by cache data mismatches and prevent sudden query delays caused by cache invalidation, resulting in smoother system operation.

[0054] For example, in a data processing system for off-site auditing, when new transaction data is detected being inserted or old transaction data being modified, the system automatically issues a cache update instruction. Based on the instruction and the updated transaction data, the system updates the data in the cache area, ensuring that the latest transaction records, rather than outdated information, can be quickly accessed during the audit process. This process effectively improves the real-time performance and accuracy of audit analysis and reduces potential risks caused by data delays.

[0055] By implementing this step, the data processing system not only maintains the timeliness and accuracy of cached data, but also significantly improves data query performance, enhances resource utilization efficiency, and strengthens the overall stability and reliability of the system. It has important application value and practical significance for scenarios involving large-scale, rapidly changing data, such as auditing off-site data and real-time data analysis.

[0056] This application's embodiments achieve efficient and automated data cache updates by intelligently identifying data cache types and dynamically generating data update instructions. This technical solution minimizes the impact of data updates on cache consistency, significantly improves the response speed and processing efficiency of data processing systems in scenarios with frequent data changes, and effectively solves the technical problem of low cache update efficiency.

[0057] Through the above steps, in response to receiving a cache update instruction from the data processing system, the system obtains the data configuration information of the data processing system, wherein the cache update instruction is generated based on the data update operation of the data processing system; the data configuration information is parsed to obtain the data cache type of the data processing system, wherein the data cache type is used to represent the data type cached in the data processing system; based on the data cache type, a data update instruction corresponding to the data cache area in the data processing system is generated, wherein the data cache area is used to store the cached data of the data processing system; based on the data update instruction and the update data of the data processing system, the cached data in the data cache area is updated. In this application, upon receiving a cache update instruction, the system immediately obtains and parses the data configuration information, intelligently identifies the data cache type, and generates a precise update instruction based on this, performing efficient data updates for the cache area. The system intelligently identifies the data that needs to be cleared and loaded, and combines the latest data updates to achieve consistency between cache and system data, automatically and efficiently updating, maintaining high processing performance, solving the problem of low efficiency in traditional cache updates, and thus solving the technical problem of low efficiency in cache data updates in related technologies.

[0058] Optionally, the method further includes: monitoring the identification information of multiple nodes in the data processing system based on a preset period to obtain monitoring results, wherein the monitoring results are used to indicate whether a target node exists among the multiple nodes, the target node is a node whose data has been adjusted, and different nodes are used to implement the data processing operation of the data processing system; if the monitoring results indicate that a target node exists among the multiple nodes, a cache update instruction is generated.

[0059] The aforementioned preset period is a fixed time interval set in the data processing system to monitor system status and data changes. This periodic monitoring mechanism ensures the real-time performance and stability of the system.

[0060] The aforementioned identification information is unique information used to distinguish and mark each node in the data processing system, such as node name and node ID. Identification information plays an important role in system management and maintenance, especially in distributed environments, where it helps the system identify and track node status.

[0061] The monitoring results described above are feedback information on the status of data processing system nodes obtained through periodic monitoring, used to determine whether nodes have made data adjustments. These monitoring results provide the system with a basis for decision-making, determining whether a cache update instruction needs to be issued.

[0062] The target nodes mentioned above are those nodes in the data processing system that have undergone data adjustments, i.e., nodes whose cache state needs to be updated due to data insertion, deletion, or modification operations. Identifying the target nodes is a crucial part of the cache update mechanism.

[0063] The aforementioned cache update instruction is an instruction automatically generated by the data processing system when the monitoring result indicates the existence of a target node. It is used to guide the system to update cached data and ensure the consistency between the cache and the master data.

[0064] This application's embodiments design a periodic monitoring mechanism to monitor the identification information of multiple nodes in the data processing system at preset intervals. This action aims to promptly detect changes in system architecture or data distribution, ensuring that the caching strategy can adapt to these changes and maintain the timeliness and accuracy of cached data. The results generated during the monitoring process are used to determine whether target nodes exist, i.e., those nodes whose data has been adjusted. By effectively identifying target nodes, unnecessary cache updates for all nodes are avoided, thereby reducing system resource consumption and improving the targeting of cache updates. Once the monitoring results confirm the existence of target nodes, the system will automatically generate cache update instructions to guide subsequent data cache update operations. The generation of this instruction is the trigger point for the entire cache update process, ensuring that cached data can reflect the data adjustment status in a timely manner.

[0065] In off-site auditing data processing scenarios, the periodic node monitoring and cache update mechanism of this application embodiment can significantly improve the efficiency and accuracy of data processing. Specifically, the system monitors node identification information according to a preset period, enabling timely detection of node status changes, such as the addition or removal of nodes in an MPP cluster or changes in data distribution. The use of monitoring results allows the system to intelligently identify target nodes that truly require cache updates, rather than blindly clearing and loading caches from all nodes, thus avoiding resource waste. Furthermore, when a target node is identified, the system automatically generates a cache update instruction, guiding precise updates to the relevant data cache area, ensuring consistency between cached data and master data, reducing query latency caused by data inconsistency, and improving the real-time performance of audit analysis and data access speed.

[0066] For example, taking a non-site audit data processing system as an example, facing a monthly data update volume of 1.2PB, the traditional method may require clearing all caches and reloading, which is time-consuming and inefficient. However, using the mechanism of this application embodiment, the system automatically generates tasks every half hour at a preset cycle to monitor the identification information of all nodes, such as node names. By comparing the node names monitored before and after, if a node name is found to be added or disappeared, it is determined that a target node exists, indicating that the architecture or data distribution of the MPP cluster has changed. At this time, the system will automatically generate cache update instructions, precisely guiding the update of the data cache area of ​​the relevant target node, avoiding unnecessary updates to the cache of unchanged nodes, saving time, and improving the speed and efficiency of audit data processing. Under this mechanism, the system can better control cache updates, reduce resource consumption, and is an effective technical solution for improving the performance of large-scale data processing systems.

[0067] Optionally, the identification information of multiple nodes in the data processing system is monitored based on a preset period to obtain monitoring results, including: for any one of the multiple nodes, the identification information of the node and the historical identification information of the node are compared based on the preset period to obtain a comparison result, wherein the comparison result is used to indicate whether the identification information is the same as the historical identification information; in response to the comparison result indicating that the identification information is different from the historical identification information, the node is determined to be the target node, and the monitoring result is determined to be that the target node exists among the multiple nodes; in response to multiple comparison results indicating that the corresponding identification information is the same as the corresponding historical identification information, the monitoring result is determined to be that the target node does not exist among the multiple nodes.

[0068] The aforementioned identification information refers to the unique identifier that each node in a data processing system possesses, used for internal system identification and management. This typically includes the node name, node ID, or other attributes that uniquely identify the node.

[0069] The historical identification information mentioned above is a recorded version of the identification information, reflecting the state of the node in the previous preset period. By comparing it with the current identification information, the system can determine whether the node has changed within this period.

[0070] In this embodiment, for any node in the data processing system, the system performs an identification information comparison based on a preset period. This action ensures that the system can periodically check changes in node status, including node additions, deletions, and changes in data distribution. The comparison of the identification information with historical identification information determines whether they are consistent. If the identification information differs from the historical identification information, it indicates a change in node status, such as node addition, removal, or data distribution adjustment. In this case, the system identifies the node as the target node. The determination of the target node is the basis for generating cache update instructions, ensuring that the system can take action on nodes that truly need cache updates, avoiding unnecessary update operations on stable nodes. Finally, based on the comparison results of all nodes, the system generates monitoring results indicating the existence status of the target node in the data processing system. If a target node exists, the monitoring results will instruct the system to take cache update measures; otherwise, it will instruct the system to maintain the cache state unchanged.

[0071] In off-site audit data processing scenarios, the system faces a large amount of constantly updated data, requiring efficient processing using an MPP cluster. The node monitoring mechanism designed in this application's embodiment can periodically (e.g., every half hour) compare the identification information of each node in the MPP cluster to check for changes in node status. This automated monitoring process provides the system with real-time node status feedback, enabling the system to respond quickly to node changes and avoid cached data mismatch issues caused by node adjustments.

[0072] For example, a remote auditing system uses an MPP cluster for data processing, where each node is responsible for storing and processing a specific data partition. At each preset period (e.g., every half hour), the system reads the current identification information of all nodes and compares it with the historical identification information recorded in the previous period. If a discrepancy is found in the node identification information, the system immediately identifies that node as the target node for data adjustment and generates a cache update instruction to address potential changes in data distribution. If all node identification information remains unchanged, the system determines that no cache update is needed and maintains the existing cache state.

[0073] By implementing the node monitoring and comparison mechanism of this application embodiment, the data processing system can achieve real-time node status monitoring, quickly identify and respond to node changes, and avoid cached data invalidation caused by node adjustments. This mechanism not only ensures the continuity and efficiency of data processing, but also reduces the system maintenance costs caused by cache inconsistency, and is a key technical solution for improving the performance and stability of large-scale data processing systems.

[0074] This application embodiment, by periodically monitoring changes in node identification information, can promptly detect the addition, removal, or status changes of computing nodes. Furthermore, by analyzing the comparison results, it identifies the target node, ensuring that cache update instructions are generated only for those nodes that truly require cache updates. This avoids unnecessary operations on the entire system or stable nodes, significantly improving the efficiency and accuracy of cache management. In large-scale data processing scenarios, the introduction of this mechanism greatly reduces system response latency and improves data processing speed and overall system operating efficiency.

[0075] Optionally, based on the data cache type, a data update instruction corresponding to the data cache area in the data processing system is generated, including: generating a data cleanup instruction based on a first cache type in the data cache type, wherein the first cache type is used to indicate the cache type of cached data in the data cache area that needs to be cleaned; generating a data loading instruction based on a second cache type in the data cache type, wherein the second cache type is used to indicate the cache type of cached data that needs to be loaded into the data cache area; and determining a data update instruction based on the data cleanup instruction and the data loading instruction.

[0076] The first cache type mentioned above refers to those cache types that require cache cleanup in the data processing system. The first cache type typically includes data blocks that are no longer active or are about to be replaced, to ensure efficient use of cache space.

[0077] The second cache type mentioned above refers to the cache type that needs to be loaded into the data processing system's cache area. This typically includes hot data, critical data, or newly inserted data to accelerate data access and processing speed.

[0078] The aforementioned data cleanup instruction is generated based on the first cache type, guiding the system to perform cleanup operations on the data cache area, removing data that no longer needs to be cached, and making room for new data.

[0079] The aforementioned data loading instruction is generated based on the second cache type and is used to guide the system to load new data or frequently accessed data into the data cache area to improve data access efficiency.

[0080] According to the first cache type in this embodiment, the system generates a data cleanup instruction to clean up cached data that is no longer active or accessed infrequently. This action frees up cache space, preparing for subsequent data loading. Similarly, according to the second cache type, the system generates a data loading instruction to load frequently accessed data or new data into the data cache area, ensuring that the cached data matches the current processing needs. Finally, the system combines the data cleanup instruction and the data loading instruction to form a data update instruction, which guides the data cache area to perform precise update operations, i.e., clean up outdated data and load new or hot data.

[0081] In off-site audit data processing scenarios, data processing systems (such as MPP clusters) face the challenges of massive data volumes and high-frequency updates. Through the steps described above, this embodiment of the application enables dynamic adjustment of the data cache, ensuring that cached data always meets current computational needs and avoiding reduced computational efficiency due to outdated or mismatched cached data. Specifically, the generation and execution of data cleanup and data loading instructions can precisely control cache updates, reduce unnecessary data read / write operations, lower disk I / O and network transmission overhead, and improve query performance.

[0082] By implementing the data cleaning and data loading instruction generation mechanism in the embodiments of this application, the data processing system can achieve dynamic management of the data cache area. This mechanism ensures that the cache area always stores the latest hot and critical data, avoids query delays caused by invalid cached data, significantly improves the efficiency of data query and processing, and reduces system resource consumption. It is a key technical solution for improving the performance and stability of large-scale data processing systems.

[0083] Under this mechanism, the data processing system no longer needs to periodically clear all cached data. Instead, it can intelligently adjust the cache content, that is, remove inactive data and load the latest high-frequency data. This maximizes the use of cache space while ensuring data timeliness and accuracy, reduces data reading latency, and improves data processing speed. It has significant advantages, especially in scenarios that require rapid response, such as off-site auditing.

[0084] Optionally, based on the data update instruction and the update data from the data processing system, the cached data in the data cache area is updated, including: cleaning the first cached data in the data cache area based on the data cleanup instruction in the data update instruction to obtain the updated data cache area, wherein the first cached data is the cached data to be cleaned in the cached data; determining the second cached data in the updated data based on the data loading instruction in the data update instruction, and loading the second cached data into the updated data cache area to update the cached data in the data cache area, wherein the second cached data is the data to be cached in the updated data.

[0085] The aforementioned First Caching Data (FCD) is data marked as outdated or accessed infrequently in the data processing system and needs to be cleared from the data cache area to make room for more critical data.

[0086] The aforementioned Second Caching Data (SCD) consists of recently generated or frequently accessed data. This type of data will be loaded into the data cache area of ​​the data processing system to accelerate data access and processing speed.

[0087] The aforementioned Data Caching Area (DCA) is a physical or logical area that stores cached data. Its purpose is to provide fast data access and reduce the latency of reading data from the main storage area.

[0088] First, based on the data cleanup command, the system identifies the first cached data in the DCA that needs to be cleared. Through data cleanup, this data is removed from the cache, resulting in an updated DCA, ensuring efficient use of cache space. Then, based on the data loading command, the system determines the second cached data to be cached in the updated data and loads it into the updated DCA. This process ensures that the data in the DCA is always up-to-date and maintains its hotspot characteristics, improving data access efficiency.

[0089] In this embodiment, the system dynamically manages and updates the data cache area by executing data cleanup and data loading instructions within the data update command. First, based on the data cleanup instruction, the system accurately identifies the first cached data in the DCA (Data Cache Area), i.e., data that is no longer active or is about to be replaced, and performs a data cleanup operation to release cache space. Next, according to the data loading instruction, the system determines the second cached data in the update data; this data is typically hot or critical data. Then, the SCD (Data Cache Area) is loaded into the updated DCA, ensuring that the data processing system can quickly access the latest and most relevant information, improving query performance and data processing speed.

[0090] In off-site audit data processing scenarios, facing massive amounts of data and high-frequency changes, data processing systems (such as MPP clusters) need to maintain the timeliness and accuracy of DCA data. By periodically monitoring node identification information and historical identification information, this embodiment can promptly identify the target node for data adjustment and generate data update instructions, including data cleanup instructions and data loading instructions. The data cleanup instruction clears the first cached data on the target node to make room for new data; the data loading instruction guides the system to load the second cached data from the updated data into the updated DCA. This mechanism ensures that the data stored in the DCA is consistent with the data in the main storage area, avoiding query delays or errors caused by outdated cached data, and improving the efficiency and accuracy of audit analysis.

[0091] For example, suppose that during off-site data processing auditing, the system detects that a node has added new transaction log data (SCD). At this point, the system will generate a data cleanup instruction to clear the old target document data (FCD) in the node's DCA, freeing up storage space. Subsequently, according to the data loading instruction, the system loads the new transaction log data (SCD) into the updated DCA, ensuring that audit calculations can access the latest transaction information in real time, rather than outdated target document data, thereby improving the real-time performance of the audit.

[0092] By implementing the data cleaning and loading mechanism in this embodiment, the data processing system can achieve precise management of the DCA (Distributed Data Area), namely, timely clearing of non-hotspot first-cache data and timely loading of the latest hotspot second-cache data, ensuring that the stored content of the DCA closely matches the current computing needs. This mechanism effectively improves data access speed, reduces disk I / O operations, and lowers data read latency. For processing large-scale, rapidly changing data environments, such as off-site audit data processing, it has significant practical benefits and can significantly improve data processing efficiency and system responsiveness.

[0093] Optionally, the method further includes: obtaining the data access pattern of the data processing system, wherein the data access pattern is used to represent the access pattern of different types of data in the data cache area; configuring the data processing system based on the data access pattern to generate data configuration information.

[0094] The data access patterns described above represent the frequency, timing, method, and context of access to different types of data in the data cache area within a data processing system. Data Access Pattern (DAP) analysis helps identify hot data, regularly accessed data, and occasionally accessed data, thereby guiding improvements in data configuration.

[0095] The aforementioned Data Configuration Information (DCI) is configuration information generated by the data processing system based on data access pattern analysis results. It guides data caching, storage, and access strategies. DCI may include settings such as caching strategies, data partitioning rules, and data lifecycle management, aiming to improve data processing efficiency and resource utilization.

[0096] First, embodiments of this application collect and analyze access records of different types of data in the data processing system to determine data access patterns. This may involve statistical analysis, machine learning prediction, or mining of historical access logs to identify which data is accessed frequently and which is accessed occasionally. After obtaining the Data Access Buffer (DAP), the system automatically generates data configuration information based on the data access frequency and patterns. The Data Access Buffer (DCI) guides the system on how to more effectively configure the data caching area, ensuring that frequently accessed data can be loaded quickly, while infrequently accessed data can be placed in more economical storage locations or have longer data retention periods set.

[0097] In off-site auditing data processing systems, such as MPP clusters, data access patterns differ significantly. Transaction log data may be frequently accessed for real-time audit analysis, while older target document data may only be used occasionally for historical comparison. By implementing the DAP acquisition and DCI generation steps in this application embodiment, the system can intelligently identify these differences in data access, thereby generating customized data configuration information to guide data caching and storage strategies.

[0098] By implementing DAP acquisition and DCI generation steps, the data processing system can dynamically adjust data configuration, intelligently improving data caching strategies and storage layout based on actual access patterns. This not only ensures rapid access to frequently accessed data but also rationally manages the storage of infrequently accessed data, reducing resource waste and unnecessary data transmission. For large-scale data processing systems, such as MPP clusters processing off-site audit data, this can significantly improve data processing efficiency and system response speed while reducing operating costs.

[0099] For example, taking an MPP cluster that processes off-site audit data as an example, the system analyzes its data access patterns and finds that the access frequency of transaction log data is much higher than that of target document data. Based on this pattern, this application embodiment generates data configuration information, including but not limited to improving transaction log data to the first cache type to ensure its priority caching and fast access; while setting the target document data as the second cache type to appropriately reduce its caching frequency or priority. Under this configuration, audit calculations can quickly obtain the required transaction log data without frequently reading target document data from storage devices, greatly improving the real-time performance and efficiency of audit analysis.

[0100] In the above embodiments, by accurately analyzing data access patterns and generating corresponding data configuration information, the data processing system can intelligently adjust data caching and storage strategies. For frequently accessed transaction log data, the system ensures its priority in the cache, improving data query speed; while for infrequently accessed target document data, its cache usage is reduced, lowering storage and maintenance costs. This flexible data configuration mechanism enables the data processing system to achieve more efficient and economical data management in complex environments with diverse data types and varying access requirements, significantly improving data processing efficiency and system performance in application scenarios such as off-site auditing.

[0101] Optionally, the data processing system is configured based on the data access mode to generate data configuration information, including: configuring the data processing system based on the data access mode to obtain initial data configuration information and outputting the initial data configuration information; adjusting the initial data configuration information based on the adjustment instruction received within a preset time period to obtain data configuration information; and determining the initial data configuration information as data configuration information if the adjustment instruction is not received within the preset time period.

[0102] The aforementioned adjustment instructions allow users or administrators to manually modify data configuration information based on specific needs or system changes.

[0103] Based on the analysis results of data access patterns, the system generates an initial set of data configuration information and outputs it for inspection or use. This process may be automated or may require manual confirmation. Within a preset time period, if the system receives adjustment instructions from users or administrators, it will adjust the initial DCI accordingly to adapt to new needs or environmental changes. Adjustments may include adding new caching strategies, modifying data sharding rules, etc. If no adjustment instructions are received within the preset time period, the system will default to the initial DCI as the final data configuration information to guide the actual operation of the data processing system. This ensures that the system can maintain a reasonable data configuration strategy based on the initial DAP analysis even without human intervention.

[0104] In off-site auditing scenarios, MPP clusters need to handle large amounts of constantly changing data. By periodically analyzing Data Flow Analysis (DAP), this embodiment can generate an initial Data Cognition (DCI) to guide data caching and access strategies. For example, if DAP shows that transaction flow data is frequently accessed while policy document data is accessed less frequently, the system will generate a DCI suggesting a more aggressive caching strategy for transaction flow data and a more conservative caching strategy for policy document data. Furthermore, this embodiment also supports Acceptance of Audit Accounts (ACs), allowing administrators to manually adjust the DCI within a preset time period based on changes in audit requirements, further improving the adaptability and flexibility of data processing.

[0105] This application's embodiments achieve intelligent data management based on Data Access Optimization (DAP) by dynamically generating and adjusting Data Access Cache (DCI). This not only ensures that the data processing system can automatically adjust its caching strategy according to current data access needs, but also provides the flexibility for manual adjustment, allowing administrators to fine-tune the DCI based on specific scenarios (such as sudden events or changes in data quality), thereby improving data processing efficiency and system responsiveness. Overall, this mechanism reduces data read latency, improves cache utilization, and lowers operating costs. Especially for large-scale data processing environments, it effectively addresses rapid changes in data access patterns, maintaining the advanced nature and effectiveness of data management strategies.

[0106] Consider an audit off-site data processing system. Analysis of past months' Data Processing Actions (DAPs) reveals that transaction log data is accessed far more frequently than target document data. The system generates an initial Data Cache (DCI), prioritizing transaction log data and lowering the cache priority for target document data. After reviewing the initial DCI, administrators anticipate that target document data may also become a hotspot during the month-end audit period. Therefore, they send an adjustment instruction within a preset timeframe, requesting an increase in the cache priority of target document data. The system responds to the Audit Activity (AC) by adjusting the DCI to ensure that target document data is adequately cached during the month-end audit period, meeting peak access demands. If no AC is received, the system will default to using the initial DCI for data management after the preset timeframe, maintaining efficiency and cost-effectiveness in data processing under normal circumstances. This mechanism allows the audit data processing system to flexibly respond to different audit scenarios, improve data access speed, reduce data processing latency, and enhance the system's practicality and adaptability.

[0107] In MPP clusters, automatic data caching mechanisms can be used to improve query performance. The basic principle of this mechanism is to cache frequently accessed data (hot data) in memory to reduce disk input / output (I / O) operations and speed up data reading. Below are some characteristics of common automatic data caching mechanisms: Level 1 Cache (L1 Cache) is the closest cache to the CPU and is used to store recently used data. Due to its limited capacity, it typically only stores very hot data. Level 2 Cache: Larger than L1 Cache but still fast, it is used to store slightly less frequently used data. When an L1 Cache miss occurs, the query will be redirected to the L2 Cache. The buffer pool is the most commonly used caching mechanism in database management systems, used to store data pages already loaded into memory. When a new data page needs to be loaded, if there is free space in the buffer pool, it is loaded directly; otherwise, the old data page is evicted according to the Least Recently Used (LRU) replacement algorithm to make room for the new data page. Query result cache caches the result set of a query, especially for queries that are executed repeatedly with the same input parameters. This allows the cached results to be returned directly without re-executing the query plan.

[0108] This application addresses the issue of low data loading efficiency during off-site auditing. By designing an automatic data caching mechanism, frequently used data is always stored in the cache, reducing the time required for clearing and loading data, thus improving computational efficiency. A pre-defined scheduled task in the data processing system periodically monitors the cache status of massively parallel processing (MPP) nodes and detects node failures. This monitoring mechanism ensures the system can respond promptly to dynamic changes in the MPP cluster.

[0109] Upon initial monitoring, the system queries the configuration information of all MPP nodes, paying particular attention to the unique identifier of each node—the node name (node_name)—and records this information for subsequent comparison and analysis. Every half hour, the system re-queries the configuration information of the MPP nodes, records the node name again (the current node_name), and compares this recorded information with the previously recorded node name to detect changes in node status. When a change in node name is detected, it indicates that the MPP cluster may have undergone scaling down or scaling up, potentially causing cached data loss or staleness. In this case, the system automatically triggers a caching script. The execution of the caching script involves accessing the configuration table of commonly used tables in the data processing system, concatenating SQL statements for data cleanup and data caching, performing cleanup operations on the data cache area, and loading data from commonly used tables into the cache to ensure that the cached data remains consistent with current computing needs. If no change is found in the node name comparison, the system does not need to execute the caching script but continues monitoring, cyclically executing the script to ensure real-time monitoring of MPP cluster status changes and timely adjustments to the data caching strategy. The embodiments of this application can realize automatic caching management of MPP cluster data, ensuring that the data in the cache is both the most frequently used and the latest, thereby improving data processing efficiency and system performance.

[0110] According to an embodiment of the present invention, an apparatus embodiment of a data processing method is provided. It should be noted that the apparatus can be used to execute the above-described data processing method. Figure 2 This is a schematic diagram of a data processing apparatus according to an embodiment of this application, such as... Figure 2 As shown, the device includes: an acquisition module 202, a parsing module 204, a generation module 206, and an update module 208.

[0111] The system comprises the following modules: an acquisition module, which receives a cache update instruction from the data processing system and acquires the data configuration information of the data processing system (the cache update instruction is generated based on the data update operation of the data processing system); a parsing module, which parses the data configuration information to obtain the data cache type of the data processing system (the data cache type represents the data type cached in the data processing system); a generation module, which generates a data update instruction corresponding to the data cache area in the data processing system based on the data cache type (the data cache area is used to store the cached data of the data processing system); and an update module, which updates the cached data in the data cache area based on the data update instruction and the update data of the data processing system.

[0112] Optionally, the device is also used to monitor the identification information of multiple nodes in the data processing system based on a preset period to obtain monitoring results. The monitoring results are used to indicate whether a target node exists among the multiple nodes. The target node is a node whose data has been adjusted. Different nodes are used to implement the data processing operation of the data processing system. If the monitoring results indicate that a target node exists among the multiple nodes, a cache update instruction is generated.

[0113] Optionally, the device is further configured to compare the identification information of any one of the multiple nodes with the node's historical identification information based on a preset period to obtain a comparison result, wherein the comparison result is used to indicate whether the identification information is the same as the historical identification information; in response to the comparison result indicating that the identification information is different from the historical identification information, the node is determined to be the target node, and the monitoring result is determined to be that the target node exists among the multiple nodes; in response to multiple comparison results indicating that the corresponding identification information is the same as the corresponding historical identification information, the monitoring result is determined to be that the target node does not exist among the multiple nodes.

[0114] Optionally, the generation module is further configured to generate a data cleanup instruction based on a first cache type in the data cache type, wherein the first cache type is used to indicate the cache type for which cached data in the data cache area needs to be cleaned up; generate a data loading instruction based on a second cache type in the data cache type, wherein the second cache type is used to indicate the cache type for which cached data needs to be loaded into the data cache area; and determine a data update instruction based on the data cleanup instruction and the data loading instruction.

[0115] Optionally, the update module is further configured to clean up the first cached data in the data cache area based on the data cleanup instruction in the data update instruction, to obtain the updated data cache area, wherein the first cached data is the cached data to be cleaned in the cached data; and to determine the second cached data in the updated data based on the data loading instruction in the data update instruction, and load the second cached data into the updated data cache area to update the cached data in the data cache area, wherein the second cached data is the data to be cached in the updated data.

[0116] Optionally, the device is also used to acquire the data access pattern of the data processing system, wherein the data access pattern is used to represent the access pattern of different types of data in the data cache area; and to configure the data processing system based on the data access pattern to generate data configuration information.

[0117] Optionally, the generation module is also used to configure the data processing system based on the data access mode, obtain initial data configuration information, and output the initial data configuration information; in response to receiving an adjustment instruction for the initial data configuration information within a preset time period, adjust the initial data configuration information based on the adjustment instruction to obtain data configuration information; and in response to not receiving an adjustment instruction for the initial data configuration information within a preset time period, determine the initial data configuration information as data configuration information.

[0118] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention during runtime.

[0119] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0120] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0121] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of the present invention.

[0122] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of the present invention.

[0123] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0124] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0125] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0126] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0127] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0128] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A data processing method, characterized in that, include: In response to receiving a cache update instruction from a data processing system, the system obtains data configuration information of the data processing system, wherein the cache update instruction is generated based on a data update operation of the data processing system. The data configuration information is parsed to obtain the data cache type of the data processing system, wherein the data cache type is used to represent the data type cached in the data processing system; Based on the data cache type, a data update instruction corresponding to the data cache area in the data processing system is generated, wherein the data cache area is used to store cached data of the data processing system; Based on the data update instruction and the update data from the data processing system, the cached data in the data cache area is updated.

2. The method according to claim 1, characterized in that, The method further includes: Based on a preset period, the identification information of multiple nodes in the data processing system is monitored to obtain monitoring results. The monitoring results are used to indicate whether there is a target node among the multiple nodes. The target node is a node whose data has been adjusted. Different nodes are used to implement the data processing operations of the data processing system. If the monitoring result indicates that the target node exists among the multiple nodes, the cache update instruction is generated.

3. The method according to claim 2, characterized in that, Based on a preset period, the identification information of multiple nodes in the data processing system is monitored to obtain monitoring results, including: For any one of the plurality of nodes, the identification information of the node and the historical identification information of the node are compared according to a preset period to obtain a comparison result, wherein the comparison result is used to indicate whether the identification information and the historical identification information are the same; In response to the comparison result indicating that the identification information is different from the historical identification information, the node is determined to be the target node, and the monitoring result is determined to indicate that the target node exists among the plurality of nodes; If multiple comparison results indicate that the corresponding identification information is the same as the corresponding historical identification information, the monitoring result is determined to be that the target node does not exist among the multiple nodes.

4. The method according to claim 1, characterized in that, Based on the data cache type, a data update instruction corresponding to the data cache area in the data processing system is generated, including: Based on the first cache type in the data cache type, a data cleanup instruction is generated, wherein the first cache type is used to indicate the cache type of cached data in the data cache area that needs to be cleaned up; Based on the second cache type in the data cache type, a data loading instruction is generated, wherein the second cache type is used to indicate the cache type that needs to load cached data into the data cache area; The data update instruction is determined based on the data cleanup instruction and the data loading instruction.

5. The method according to claim 4, characterized in that, Based on the data update instruction and the update data from the data processing system, the cached data in the data cache area is updated, including: Based on the data cleanup instruction in the data update instruction, the first cached data in the data cache area is cleaned to obtain the updated data cache area, wherein the first cached data is the cached data to be cleaned in the cached data; Based on the data loading instruction in the data update instruction, the second cached data in the updated data is determined, and the second cached data is loaded into the updated data cache area to update the cached data in the data cache area, wherein the second cached data is the data to be cached in the updated data.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Obtain the data access mode of the data processing system, wherein the data access mode is used to represent the access mode of different types of data in the data cache area; The data processing system is configured based on the data access mode to generate the data configuration information.

7. The method according to claim 6, characterized in that, Based on the data access mode, the data processing system is configured to generate the data configuration information, including: Based on the data access mode, the data processing system is configured to obtain initial data configuration information, and the initial data configuration information is output. In response to receiving an adjustment instruction for the initial data configuration information within a preset time period, the initial data configuration information is adjusted based on the adjustment instruction to obtain the data configuration information; In response to receiving an adjustment instruction for the initial data configuration information within the preset time period, the initial data configuration information is determined to be the data configuration information.

8. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

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