Temporary data storage scheduling method and device, storage medium and computer equipment
By obtaining the type and access pattern of temporary data, determining the optimal storage partition and executing scheduling rules, the problems of limited storage space and lack of lifecycle management in front-end storage solutions are solved, realizing accurate storage and dynamic management of temporary data, and improving storage efficiency and performance stability.
Patent Information
- Application Number
- CN202511837387.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-08
- Publication Date
- 2026-03-13
AI Technical Summary
In existing technologies, front-end storage solutions face challenges such as limited storage space and lack of lifecycle management mechanisms when dealing with complex scenarios like multi-tab applications, long session interactions, and offline priority. This leads to disordered accumulation of temporary data, resulting in storage leaks and performance degradation.
By obtaining the data type and access pattern of temporary data, the optimal storage partition is determined, and scheduling rules and cleanup strategies are executed in the storage partition to achieve precise storage and dynamic management of temporary data, preventing disordered data accumulation and performance degradation.
It achieves efficient storage of temporary data, reduces the risk of storage leakage, and improves the storage efficiency and performance stability of the front-end website system.
Smart Images
Figure CN121658742A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of software system technology, and in particular to a temporary data storage scheduling method, apparatus, storage medium and computer equipment. Background Technology
[0002] As modern web applications become increasingly complex and user experience demands rise, front-end storage technology has become a core component supporting various interactive scenarios. Currently, mainstream browsers commonly use traditional storage solutions such as localStorage, SessionStorage, and IndexedDB. While these technologies are widely used and can meet basic data persistence and session state management needs, they are gradually revealing common problems such as limited storage space and a lack of lifecycle management mechanisms when facing complex scenarios such as multi-tab applications, long-term session interactions, and offline-first scenarios.
[0003] While the emerging Storage Buckets API provides partitioning capabilities for front-end storage, improving data isolation and space management flexibility to some extent, a complete policy-based lifecycle management mechanism has yet to be established. In practical applications, developers still need to manually write cleanup logic or rely on external libraries to manage the storage and release of temporary data. This makes it difficult to achieve unified scheduling and adaptive optimization of storage behavior, which can easily lead to storage leaks and performance degradation due to the disordered accumulation of temporary data. Summary of the Invention
[0004] The purpose of this application is to at least solve one of the aforementioned technical defects, particularly the technical defects in the prior art that still require manual writing of cleanup logic or reliance on external tool libraries to manage the storage and release of temporary data, making it difficult to achieve unified scheduling and adaptive optimization of storage behavior, thus easily leading to storage leaks and performance degradation due to the disorderly accumulation of temporary data.
[0005] In a first aspect, this application provides a temporary data storage scheduling method, the method comprising:
[0006] When temporary data is detected to be generated on the current webpage, the data type and access mode of the temporary data are obtained;
[0007] Based on the data type and the access mode, determine the optimal storage partition for the temporary data among the preset storage partitions;
[0008] The temporary data is written to the optimal storage partition, and when the temporary data triggers any scheduling rule corresponding to the optimal storage partition, the instruction corresponding to the triggered scheduling rule is executed on the temporary data.
[0009] During the storage scheduling of the temporary data, the performance of each storage partition is monitored. When the performance of any storage partition fails to meet expectations, the cleanup strategy for that storage partition is obtained and executed.
[0010] In one embodiment, determining the optimal storage partition for the temporary data among preset storage partitions based on the data type and the access mode includes:
[0011] Retrieve the preset data partition mapping table;
[0012] In each storage partition of the data partition mapping table, query the storage partition corresponding to the data type and the access mode, and determine the queried storage partition as the optimal storage partition for the temporary data.
[0013] In one embodiment, the process of creating the storage partition includes:
[0014] When a partition creation command is triggered, a partition identifier is generated;
[0015] The business scenario is determined based on the partition creation instructions, and the storage type, initial quota, and default scheduling rules are determined based on the business scenario.
[0016] A storage partition is generated based on the partition identifier, the storage type, the initial quota, and the default scheduling rule.
[0017] In one embodiment, writing the temporary data to the optimal storage partition includes:
[0018] Determine the encryption method corresponding to the optimal storage partition;
[0019] The temporary data is encrypted using the encryption method described above, and the encrypted temporary data is stored in the optimal storage partition.
[0020] In one embodiment, when the temporary data triggers any scheduling rule corresponding to the optimal storage partition, executing the instruction corresponding to the triggered scheduling rule on the temporary data includes:
[0021] Determine the triggering conditions for each scheduling rule corresponding to the optimal storage partition;
[0022] Obtain the attribute information of the temporary data, and based on the attribute information, determine whether the temporary data meets any triggering condition;
[0023] If the temporary data satisfies any of the triggering conditions, then the triggering condition satisfied by the temporary data is determined as the target triggering condition;
[0024] Obtain the rule instructions for each target trigger condition, and execute the rule instructions on the temporary data.
[0025] In one embodiment, when the performance of any storage partition fails to meet expectations, obtaining the cleanup strategy for that storage partition and executing the cleanup strategy includes:
[0026] When the performance of any storage partition is found to be below expectations, obtain the cleanup priority for each target storage partition whose performance is below expectations;
[0027] Based on the cleanup priority of each target storage partition, a cleanup strategy for each target storage partition is determined, and the corresponding cleanup strategy is executed on each target storage partition.
[0028] In one embodiment, the method further includes:
[0029] When the partitioning strategy corresponding to the optimal storage partition changes, the optimal storage partition is re-determined for the data in the storage partition, and the data in the storage partition is migrated to its corresponding optimal storage partition.
[0030] Secondly, this application provides a temporary data storage scheduling device, the device comprising:
[0031] The data acquisition module is used to acquire the data type and access mode of the temporary data when it is detected that the current webpage generates temporary data;
[0032] The partition determination module is used to determine the optimal storage partition for the temporary data among preset storage partitions based on the data type and the access mode.
[0033] The storage scheduling module is used to write the temporary data into the optimal storage partition, and when the temporary data triggers any scheduling rule corresponding to the optimal storage partition, execute the instruction corresponding to the triggered scheduling rule on the temporary data;
[0034] The storage cleanup module is used to monitor the performance of each storage partition during the storage scheduling process of the temporary data. When the performance of any storage partition fails to meet expectations, the module obtains the cleanup strategy for that storage partition and executes the cleanup strategy.
[0035] Thirdly, this application provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the temporary data storage scheduling method as described in any of the above embodiments.
[0036] Fourthly, this application provides a computer device, including: one or more processors, and a memory;
[0037] The memory stores computer-readable instructions, and when the one or more processors execute the computer-readable instructions, they perform the steps of the temporary data storage scheduling method as described in any of the above embodiments.
[0038] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:
[0039] The temporary data storage scheduling method, apparatus, storage medium, and computer equipment provided in this application, when detecting temporary data generated by the current webpage, can accurately allocate the temporary data storage location by obtaining the data type and access mode of the temporary data, writing the data to the optimal storage partition. This avoids the disorderly accumulation of temporary data in unsuitable storage areas, reducing the risk of storage leakage. Simultaneously, temporary data is processed and dynamically partitioned in a timely manner according to scheduling rules to prevent excessive data accumulation, further reducing the probability of storage leakage. Furthermore, the performance of each storage partition is monitored during the storage scheduling process. When performance is found to be below expectations, a cleanup strategy is immediately executed, quickly optimizing the performance of the storage partition and avoiding performance degradation caused by the disorderly accumulation of temporary data. This achieves unified scheduling and adaptive optimization of temporary data storage behavior, improving the storage efficiency and performance stability of the entire front-end website system. Attached Figure Description
[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0041] Figure 1 A flowchart illustrating a temporary data storage scheduling method provided in an embodiment of this application;
[0042] Figure 2 A schematic diagram of a temporary data storage and scheduling device provided in an embodiment of this application;
[0043] Figure 3 This is an internal structural diagram of a computer device provided in an embodiment of this application. Detailed Implementation
[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0045] In one embodiment, this application provides a temporary data storage scheduling method. The following embodiments illustrate the application of this method to a front-end application system, hereinafter referred to as the system.
[0046] like Figure 1 As shown, this application provides a temporary data storage scheduling method, the method comprising:
[0047] S101: When temporary data is detected to be generated by the current webpage, obtain the data type and access mode of the temporary data.
[0048] Temporary data refers to data generated temporarily during webpage operation to meet the needs of the current session or specific operations. Data type refers to the format and type of temporary data. Access pattern refers to the frequency and pattern of reading and writing temporary data, including but not limited to access frequency, read / write ratio, and timing characteristics.
[0049] In this step, when the system detects temporary data generated on the current network surface, the generation event can be captured through a programming interface to obtain the temporary data. Then, the obtained temporary data is analyzed to determine its data type. This can be achieved using built-in type detection functions (such as `typeof` or `instanceof`). For example, `typeof` can be used to detect whether a variable is a string or a number. Simultaneously, based on the business scenario to which the temporary data belongs, the frequency and pattern of its read and write operations are predicted to determine its access pattern.
[0050] Specifically, by extending the metadata option in the data storage API, developers are allowed to directly declare business attributes when writing data, assigning meta-attributes such as business type, priority, and expected lifespan to each storage record. This helps determine access patterns and the matching of subsequent scheduling rules.
[0051] S102: Determine the optimal storage partition for temporary data among the preset storage partitions based on data type and access mode.
[0052] Storage partitions refer to independent storage areas within a unified storage resource, which are pre-defined logically based on different performance characteristics, capacity limitations, data persistence capabilities, and operational costs, and have specific management strategies.
[0053] In this step, when determining the optimal storage partition for temporary data, the storage requirements of the data can be assessed based on the data type and access patterns. Then, the optimal storage partition that best matches the temporary data can be determined based on the storage requirements, thereby optimizing storage resource utilization and improving storage efficiency. For example, for frequently accessed and fast-read temporary data, such as user session information or real-time interactive data, storage partitions with higher storage performance can be prioritized, while data with low-frequency access can be allocated to low-quota partitions.
[0054] Understandably, during the storage partition creation process, the system-provided declarative configuration interface can be called, and storage partition templates can be predefined based on factors such as business scenarios. During partition creation, the corresponding storage partition template can be directly selected. In one example, the template parameters for the storage partition include: partition name, storage type, initial quota, and default scheduling rules.
[0055] S103: Write temporary data to the optimal storage partition, and when the temporary data triggers any scheduling rule corresponding to the optimal storage partition, execute the instruction corresponding to the triggered scheduling rule on the temporary data.
[0056] The scheduling rules refer to a set of data management instructions that are pre-set for each storage partition and automatically triggered based on specific conditions. These specific conditions may include data existence time, access frequency, storage space usage, etc.
[0057] In this step, when the optimal storage partition for temporary data is determined, the temporary data is written to that partition. Through continuous monitoring and response, once the status of the temporary data meets a scheduling rule for that optimal storage partition, the system automatically executes the instructions associated with that scheduling rule, thereby achieving dynamic management of the data lifecycle.
[0058] Specifically, each storage partition can be pre-configured with a set of scheduling rules that align with its characteristics. For example, rules for a high-speed memory partition might focus on rapid turnover; its scheduling rules could be set to automatically trigger an instruction when the overall partition utilization exceeds 80%, identifying the least used data and removing or destroying it. Rules for a large-capacity database partition, on the other hand, might prioritize long-term validity; its rules could be set to automatically trigger an instruction to archive or delete data if its last access time exceeds seven days.
[0059] By writing temporary data to the optimal storage partition, the efficiency of its initial state is ensured. Furthermore, by continuously monitoring the data status through preset scheduling rules and automatically executing instructions when conditions are met, the storage system achieves intelligent self-maintenance, thereby effectively preventing the disorderly accumulation of temporary data and resource leakage.
[0060] S104: During the storage scheduling of temporary data, the performance of each storage partition is monitored. When the performance of any storage partition fails to meet expectations, the cleanup policy for that storage partition is obtained and executed.
[0061] The cleanup strategy refers to a data cleanup operation with a clear priority that is triggered when the performance index of a storage partition falls below a preset threshold, with the aim of restoring the performance of that partition.
[0062] In this step, during the temporary data storage scheduling process, the performance of each storage unit will be continuously monitored. When the performance of a storage partition deviates from the expected range, a pre-set cleanup policy will be retrieved, and the corresponding instructions will be executed according to the priority in the cleanup policy. If the performance of the storage partition still does not meet expectations after executing the instruction, the instruction corresponding to the next priority can be executed until the performance of the storage partition meets expectations.
[0063] Specifically, during the performance expectation determination process, the system sets key performance indicators for different types of storage partitions. For example, for memory partitions, the core indicators are current memory usage and remaining available memory; for local database partitions, it may monitor their storage space utilization or read / write operation response latency. Real-time data for these indicators is compared with preset expected thresholds. Once it is found that the performance of a partition does not meet expectations—for example, if the memory partition's utilization exceeds the 95% safety threshold, or the database partition's response latency exceeds the 300-millisecond tolerance limit—the system will determine that the partition's performance is substandard.
[0064] In the above embodiments, when temporary data is detected to be generated by the current webpage, the data type and access mode of the temporary data can be obtained to accurately allocate the temporary data storage location and write the data to the optimal storage partition. This avoids the disorderly accumulation of temporary data in unsuitable storage areas and reduces the risk of storage leakage. Simultaneously, temporary data is processed and dynamically partitioned in a timely manner according to scheduling rules to prevent excessive data accumulation and further reduce the probability of storage leakage. Furthermore, the performance of each storage partition is monitored during storage scheduling. When performance is found to be below expectations, a cleanup strategy is immediately executed to quickly optimize the performance of the storage partition, avoiding performance degradation caused by the disorderly accumulation of temporary data. This achieves unified scheduling and adaptive optimization of temporary data storage behavior, improving the storage efficiency and performance stability of the entire front-end website system.
[0065] In one embodiment, the optimal storage partition for temporary data is determined from preset storage partitions based on data type and access mode, including:
[0066] S1: Get the preset data partition mapping table.
[0067] S2: In each storage partition of the data partition mapping table, query the storage partition corresponding to the data type and access mode, and determine the queried storage partition as the optimal storage partition for temporary data.
[0068] The data partition mapping table is used to record the mapping relationship between storage partitions and data types and access modes.
[0069] In this embodiment, the system needs to load a data partition mapping table from a configuration file or database. This table lists in detail the storage partitions corresponding to different data types and access modes. After loading, the system queries the data partition mapping table based on the data type and access mode of the temporary data to determine the optimal storage partition for the temporary data.
[0070] In this way, the system can quickly find the optimal storage partition based on the specific characteristics of the data, ensuring the efficiency and adaptability of data storage. Furthermore, the data partition mapping table can be dynamically adjusted according to actual needs. For example, if the performance of a certain storage partition is found to be degrading, the mapping relationship can be readjusted, mapping some data to other storage partitions with better performance, further optimizing system performance.
[0071] In one embodiment, the process of creating a storage partition includes:
[0072] S1: When a partition creation command is triggered, a partition identifier is generated.
[0073] S2: Determine the business scenario based on the partition creation instruction, and determine the storage type, initial quota, and default scheduling rules based on the business scenario.
[0074] S3: Generate storage partitions based on partition identifiers, storage types, initial quotas, and default scheduling rules.
[0075] The partition identifier serves as a unique identifier for logical isolation. The storage type refers to the type of storage medium or technology used by the storage partition; for example, IndexedDB is used for structured data, and Cache Storage is used for network resources. The initial quota refers to the upper limit of the partition's storage space, used to prevent a single service from excessively consuming global resources.
[0076] In this embodiment, when a partition creation command is triggered, the system first generates a unique partition identifier to distinguish different storage partitions. Next, the system determines the specific business scenario based on the information in the partition creation command. For example, if the command is to create a storage partition for the shopping cart function of an online shopping system, the business scenario is "shopping cart data storage." Based on this business scenario, the system determines the appropriate storage type, initial quota, and default scheduling rules. For the shopping cart function, the storage type might be memory storage, as it needs to respond quickly to users adding or deleting items; the initial quota can be set based on the average shopping cart size, such as 1MB per user. The default scheduling rule could be to automatically clean up expired product data when memory usage reaches 80%. These configurations ensure that the storage partition accurately meets specific business needs and operates efficiently.
[0077] Specifically, there are several ways to trigger partition creation commands. One is based on page state: when a single-page application navigates to a specific view, a dedicated temporary partition is automatically created to store computational cache, and it is immediately destroyed and its resources reclaimed when the user leaves the view. Another is based on user identity: the user ID is embedded in the partition name (e.g., user_123_session) to achieve secure isolation of data across multiple accounts. A dedicated partition is created when a user logs in and automatically destroyed upon logout, thoroughly clearing private data. A third is based on business stages: in a multi-step process, an independent draft partition is created for each step, and intermediate data is automatically cleaned up after the process is completed to avoid unnecessary storage space occupation. Besides the above methods, trigger settings can also be configured according to actual circumstances; this application does not impose specific restrictions on this.
[0078] Furthermore, when creating a storage partition, the business scenario can be determined based on the creation command, and a storage partition template for that scenario can be obtained. Based on this template, relevant data such as storage type, initial quota, and default scheduling rules can be retrieved. Then, based on this data and the storage partition template, the storage partition is generated. This enables rapid storage partition creation.
[0079] In one embodiment, writing temporary data to the optimal storage partition includes:
[0080] S1: Determine the encryption method corresponding to the optimal storage partition.
[0081] S2: Encrypt temporary data using encryption methods and store the encrypted temporary data in the optimal storage partition.
[0082] In this embodiment, before storing temporary data, it is first necessary to determine the encryption method corresponding to the optimal storage partition. This is typically done by querying the storage partition's configuration information, which explicitly specifies the encryption algorithm (such as AES, RSA, etc.) used by the optimal storage partition. After determining the encryption method, the system calls the corresponding encryption module and uses the key to encrypt the temporary data. The encryption process converts the original data into ciphertext, ensuring data security during storage and transmission. After encryption, the system stores the encrypted temporary data in the optimal storage partition. This effectively prevents unauthorized access and theft of data. Furthermore, when temporary data is encrypted and stored in the optimal storage partition, integrity checks can be performed during access to prevent data corruption or malicious modification.
[0083] In one embodiment, the temporary data storage scheduling method further includes setting independent read and write permissions for each storage partition to prevent unauthorized access and cross-partition data contamination. Furthermore, each created storage partition is an independent storage domain accessed through a unique StorageBucket object handle. Any JavaScript code that does not explicitly obtain a handle to a specific partition cannot perform any read, write, or enumeration operations on the data within that storage partition, ensuring that even within the same page, the storage behavior of different business modules does not interfere with each other.
[0084] In one embodiment, when temporary data triggers any scheduling rule corresponding to the optimal storage partition, the instruction corresponding to the triggered scheduling rule is executed on the temporary data, including:
[0085] S1: Determine the triggering conditions for each scheduling rule corresponding to the optimal storage partition.
[0086] S2: Obtain the attribute information of the temporary data, and based on the attribute information, determine whether the temporary data meets any triggering condition.
[0087] S3: If the temporary data satisfies any trigger condition, then the trigger condition satisfied by the temporary data is determined as the target trigger condition.
[0088] S4: Obtain the rule instructions for each target trigger condition and execute the rule instructions on the temporary data.
[0089] Among these, attribute information refers to information related to the popularity or obscurity of data. Examples include data creation time, last access time, access frequency, and data importance.
[0090] In this embodiment, the system first determines the triggering conditions for each scheduling rule corresponding to the optimal storage partition. These triggering conditions may include storage space utilization, data access interval, data size limit, etc. Next, the system obtains the attribute information of the temporary data, which may include data type, size, creation time, last access time, etc. Based on this attribute information, the system determines whether the temporary data meets any triggering condition. For example, if the triggering condition of the scheduling rule is that the data access interval exceeds two weeks, the system will check whether the last access time of the temporary data meets this condition. If the temporary data meets any triggering condition, then that triggering condition is determined as the target triggering condition. Subsequently, the system obtains the rule instructions corresponding to the target triggering condition and executes these instructions on the temporary data. In this way, the system can automatically adjust the storage location and processing method of data according to the actual data usage and the state of the storage partition.
[0091] In addition, the scheduling rules for each storage partition can be modified, added, and deleted. By dynamically managing these scheduling rules, storage efficiency can be optimized, ensuring that the system can operate efficiently and stably.
[0092] Specifically, scheduling rules can be mainly divided into the following types:
[0093] (1) Time-based: Perform expiration cleanup or data archiving based on the data creation time or last access time.
[0094] (2) Capacity type: When the storage usage of a partition reaches the threshold, the cleanup process is automatically started.
[0095] (3) Access frequency type: Automatically identify "cold data" that has not been accessed for a long time and delete it.
[0096] (4) Event-based: Respond to specific system events (such as page unloading) and execute related optimization tasks.
[0097] In one embodiment, when the performance of any storage partition fails to meet expectations, the cleanup policy for that storage partition is obtained and executed, including:
[0098] S1: When the performance of any storage partition is found to be below expectations, obtain the cleanup priority of each target storage partition whose performance is below expectations.
[0099] S2: Based on the cleanup priority of each target storage partition, determine the cleanup strategy for each target storage partition, and execute the corresponding cleanup strategy for each target storage partition.
[0100] Among them, cleanup priority refers to the order in which different data in a storage partition are cleaned up when the performance of the storage partition does not meet expectations.
[0101] In this embodiment, when the monitoring system detects that the performance of any storage partition is not up to expectations, it first obtains the cleanup priority of each substandard storage partition. The cleanup priority is set based on the importance of the data in the storage partition and its impact on system performance, and is used to determine the order of cleanup operations. For example, the system may set the cleanup priority based on factors such as data type, access frequency, and creation time. For high-priority data, the cleanup strategy may be compression or migration, while for low-priority data, the cleanup strategy may be direct deletion.
[0102] Furthermore, when monitoring whether the performance of a storage partition meets expectations, performance data can be collected based on predefined performance metrics for that partition, such as storage usage, remaining quota, and operational performance. Then, based on the collected data, the partition's score on each performance metric is determined. These scores are then weighted and summed to obtain a performance score. Finally, whether the performance score falls within a preset range or is below a preset threshold determines whether the storage partition's performance meets expectations.
[0103] Specifically, when executing the corresponding cleanup strategy on each target storage partition, a gradual cleanup approach is adopted to avoid blocking the main thread, and soft deletion and recycle bin mechanisms are supported to prevent accidental data deletion.
[0104] Understandably, when monitoring reveals that storage partition performance is not meeting expectations, the system acquires cleanup priorities and determines cleanup strategies based on these priorities. This optimizes storage performance while ensuring the security and availability of critical data. In this way, the system can prioritize processing low-priority data that has a significant impact on performance, quickly freeing up storage space and improving performance, while avoiding unnecessary cleanup operations on high-priority critical data.
[0105] In one embodiment, the temporary data storage scheduling method further includes:
[0106] When the partitioning strategy corresponding to the optimal storage partition changes, the optimal storage partition is re-determined for the data in the storage partition, and the data in the storage partition is migrated to its corresponding optimal storage partition.
[0107] In this embodiment, when the partitioning policy corresponding to the optimal storage partition changes, the system first triggers a re-evaluation process. In this process, the system re-analyzes each piece of data in the storage partition, determining the optimal storage partition for each piece of data based on its current attributes (such as data type, access frequency, size, etc.) and the new partitioning policy. This dynamically adapts to changes in the partitioning policy, ensuring efficient and cost-effective data storage. Furthermore, data migration can be triggered based on the current load on the optimal storage partition, thereby dynamically adjusting the load balancing among the various storage partitions.
[0108] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.
[0109] The temporary data storage scheduling device provided in the embodiments of this application is described below. The temporary data storage scheduling device described below can be referred to in correspondence with the temporary data storage scheduling method described above.
[0110] like Figure 2 As shown, this application provides a temporary data storage scheduling device 200, the device comprising:
[0111] The data acquisition module 201 is used to acquire the data type and access mode of the temporary data when it is detected that the current webpage generates temporary data;
[0112] The partition determination module 202 is used to determine the optimal storage partition for temporary data among the preset storage partitions based on the data type and access mode.
[0113] The storage scheduling module 203 is used to write temporary data to the optimal storage partition, and when the temporary data triggers any scheduling rule corresponding to the optimal storage partition, execute the instruction corresponding to the triggered scheduling rule on the temporary data;
[0114] The storage cleanup module 204 is used to monitor the performance of each storage partition during the storage scheduling process of temporary data. When the performance of any storage partition fails to meet expectations, the cleanup strategy for that storage partition is obtained and the cleanup strategy is executed.
[0115] In the above embodiments, when temporary data is detected to be generated by the current webpage, the data type and access mode of the temporary data can be obtained to accurately allocate the temporary data storage location and write the data to the optimal storage partition. This avoids the disorderly accumulation of temporary data in unsuitable storage areas and reduces the risk of storage leakage. Simultaneously, temporary data is processed and dynamically partitioned in a timely manner according to scheduling rules to prevent excessive data accumulation and further reduce the probability of storage leakage. Furthermore, the performance of each storage partition is monitored during storage scheduling. When performance is found to be below expectations, a cleanup strategy is immediately executed to quickly optimize the performance of the storage partition, avoiding performance degradation caused by the disorderly accumulation of temporary data. This achieves unified scheduling and adaptive optimization of temporary data storage behavior, improving the storage efficiency and performance stability of the entire front-end website system.
[0116] In one embodiment, the partition determination module includes:
[0117] The mapping table retrieval submodule is used to retrieve the preset data partition mapping table;
[0118] The partition query submodule is used to query the storage partitions corresponding to the data type and access mode in each storage partition of the data partition mapping table, and determine the queried storage partitions as the optimal storage partitions for temporary data.
[0119] In one embodiment, the partition determination module includes:
[0120] The identifier generation submodule is used to generate partition identifiers when a partition creation command is triggered.
[0121] The information determination submodule is used to determine the business scenario based on the partition creation instructions, and to determine the storage type, initial quota and default scheduling rules based on the business scenario;
[0122] The partition generation submodule is used to generate storage partitions based on partition identifiers, storage types, initial quotas, and default scheduling rules.
[0123] In one embodiment, the storage scheduling module includes:
[0124] The encryption determination submodule is used to determine the encryption method corresponding to the optimal storage partition;
[0125] The data encryption submodule is used to encrypt temporary data based on encryption methods and store the encrypted temporary data in the optimal storage partition.
[0126] In one embodiment, the storage scheduling module includes:
[0127] The condition acquisition submodule is used to determine the triggering conditions for each scheduling rule corresponding to the optimal storage partition;
[0128] The condition judgment submodule is used to obtain the attribute information of temporary data and, based on the attribute information, determine whether the temporary data meets any triggering condition.
[0129] The condition determination submodule is used to determine the trigger condition that the temporary data meets as the target trigger condition if the temporary data meets any trigger condition.
[0130] The instruction execution submodule is used to obtain the rule instructions for each target trigger condition and execute the rule instructions on the temporary data.
[0131] In one embodiment, the storage cleanup module includes:
[0132] The performance monitoring submodule is used to obtain the cleanup priority of each target storage partition whose performance does not meet expectations when the performance of any storage partition is detected to be below expectations.
[0133] The policy execution submodule is used to determine the cleanup policy for each target storage partition based on the cleanup priority of each target storage partition, and to execute the corresponding cleanup policy for each target storage partition.
[0134] In one embodiment, the temporary data storage scheduling device further includes:
[0135] The partition migration module is used to re-determine the optimal storage partition for the data in the storage partition when the partitioning policy corresponding to the optimal storage partition changes, and then migrate the data in the storage partition to its corresponding optimal storage partition.
[0136] The division of modules in the aforementioned temporary data storage scheduling device is merely illustrative. In other embodiments, the temporary data storage scheduling device can be divided into different modules as needed to complete all or part of its functions. Each module in the aforementioned temporary data storage scheduling device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.
[0137] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the temporary data storage scheduling method as described in any of the above embodiments.
[0138] In one embodiment, this application also provides a computer device storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the temporary data storage scheduling method as described in any of the above embodiments.
[0139] Indicatively, such as Figure 3 As shown, Figure 3 This is a schematic diagram of the internal structure of a computer device 300 provided in an embodiment of this application. The computer device 300 can be provided as a server. (Refer to...) Figure 3 The computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions, such as application programs, that can be executed by the processing component 302. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 302 is configured to execute instructions to perform the temporary data storage scheduling method of any of the above embodiments.
[0140] The computer device 300 may also include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate on an operating system stored in memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.
[0141] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0142] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, the singular forms "a," "an," and "the" may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having” specify the presence of the stated features, wholes, steps, operations, components, parts or combinations thereof, but do not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.
[0143] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.
[0144] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A temporary data storage scheduling method, characterized in that, The method includes: When temporary data is detected to be generated on the current webpage, the data type and access mode of the temporary data are obtained; Based on the data type and the access mode, determine the optimal storage partition for the temporary data among the preset storage partitions; The temporary data is written to the optimal storage partition, and when the temporary data triggers any scheduling rule corresponding to the optimal storage partition, the instruction corresponding to the triggered scheduling rule is executed on the temporary data. During the storage scheduling of the temporary data, the performance of each storage partition is monitored. When the performance of any storage partition fails to meet expectations, the cleanup strategy for that storage partition is obtained and executed.
2. The temporary data storage scheduling method according to claim 1, characterized in that, The step of determining the optimal storage partition for the temporary data among preset storage partitions based on the data type and the access mode includes: Retrieve the preset data partition mapping table; In each storage partition of the data partition mapping table, query the storage partition corresponding to the data type and the access mode, and determine the queried storage partition as the optimal storage partition for the temporary data.
3. The temporary data storage scheduling method according to claim 1, characterized in that, The process of creating the storage partition includes: When a partition creation command is triggered, a partition identifier is generated; The business scenario is determined based on the partition creation instructions, and the storage type, initial quota, and default scheduling rules are determined based on the business scenario. A storage partition is generated based on the partition identifier, the storage type, the initial quota, and the default scheduling rule.
4. The temporary data storage scheduling method according to claim 1, characterized in that, The step of writing the temporary data to the optimal storage partition includes: Determine the encryption method corresponding to the optimal storage partition; The temporary data is encrypted using the encryption method described above, and the encrypted temporary data is stored in the optimal storage partition.
5. The temporary data storage scheduling method according to claim 1, characterized in that, When the temporary data triggers any scheduling rule corresponding to the optimal storage partition, executing the instruction corresponding to the triggered scheduling rule on the temporary data includes: Determine the triggering conditions for each scheduling rule corresponding to the optimal storage partition; Obtain the attribute information of the temporary data, and based on the attribute information, determine whether the temporary data meets any triggering condition; If the temporary data satisfies any of the triggering conditions, then the triggering condition satisfied by the temporary data is determined as the target triggering condition; Obtain the rule instructions for each target trigger condition, and execute the rule instructions on the temporary data.
6. The temporary data storage scheduling method according to claim 1, characterized in that, When the performance of any storage partition fails to meet expectations, the process of obtaining and executing the cleanup strategy for that storage partition includes: When the performance of any storage partition is found to be below expectations, obtain the cleanup priority for each target storage partition whose performance is below expectations; Based on the cleanup priority of each target storage partition, a cleanup strategy for each target storage partition is determined, and the corresponding cleanup strategy is executed on each target storage partition.
7. The temporary data storage scheduling method according to any one of claims 1 to 6, characterized in that, The method further includes: When the partitioning strategy corresponding to the optimal storage partition changes, the optimal storage partition is re-determined for the data in the storage partition, and the data in the storage partition is migrated to its corresponding optimal storage partition.
8. A temporary data storage scheduling device, characterized in that, The device includes: The data acquisition module is used to acquire the data type and access mode of the temporary data when it is detected that the current webpage generates temporary data; The partition determination module is used to determine the optimal storage partition for the temporary data among preset storage partitions based on the data type and the access mode. The storage scheduling module is used to write the temporary data into the optimal storage partition, and when the temporary data triggers any scheduling rule corresponding to the optimal storage partition, execute the instruction corresponding to the triggered scheduling rule on the temporary data; The storage cleanup module is used to monitor the performance of each storage partition during the storage scheduling process of the temporary data. When the performance of any storage partition fails to meet expectations, the module obtains the cleanup strategy for that storage partition and executes the cleanup strategy.
9. A storage medium, characterized in that: The storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the temporary data storage scheduling method as described in any one of claims 1 to 7.
10. A computer device, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the temporary data storage scheduling method as described in any one of claims 1 to 7.