A data persistence orchestration method, device and equipment of a Redis database on a cloud, medium and product

Abstract

This disclosure relates to a data persistence orchestration method, apparatus, device, medium, and product for a cloud-based Redis database. The method includes: obtaining persistence association information corresponding to multiple Redis database instances; wherein the persistence association information characterizes the shared storage resource status and instance data volume when the Redis database instances perform data persistence operations; determining the persistence evaluation result for each Redis database instance based on the persistence association information; determining the persistence orchestration information for the multiple Redis database instances based on the persistence evaluation results; and issuing data persistence commands to the corresponding Redis database instances based on the persistence orchestration information, so that the multiple Redis database instances perform data persistence operations according to the persistence orchestration information. This disclosure can reduce the situation where multiple Redis database instances perform data persistence operations simultaneously, reduce the instantaneous write pressure on shared storage resources, and improve the stability of the data persistence process.

Description

Technical Field

[0001] This disclosure relates to the field of database technology, and in particular to a data persistence orchestration method, apparatus, device, medium and product for a cloud-based Redis database. Background Technology

[0002] With the popularization of cloud computing and cloud database services, Remote Dictionary Server (Redis) has been widely used in cloud data processing scenarios. In order to reduce the risk of data loss caused by database instance failure, the industry often adopts data persistence strategies. However, in the face of scenarios where multiple instances share storage resources and dynamically generate persistent write requests, how to reduce the instantaneous write pressure of shared storage resources while ensuring data reliability is the main problem we are currently facing.

[0003] In related technologies, data persistence is usually triggered automatically by the database instance based on configuration parameters or by external commands. There is a lack of unified assessment of the status of shared storage resources and the persistence requirements of database instances. Furthermore, existing solutions are difficult to effectively coordinate the persistence execution sequence of multiple database instances, which leads to persistence operations easily occurring in a concentrated manner within a similar time frame. This makes it impossible to improve the stability of cloud database services while ensuring the reliability of data persistence. Summary of the Invention

[0004] This disclosure provides a method, apparatus, device, medium, and product for data persistence orchestration of a Redis database in the cloud.

[0005] According to a first aspect of this disclosure, a method for data persistence orchestration of a Redis database in the cloud is provided, including: Retrieves persistent association information for multiple Redis database instances; the persistent association information is used to characterize the shared storage resource status and instance data volume when the Redis database instance performs data persistence operations; Based on the persistence association information, determine the persistence evaluation result corresponding to each Redis database instance; Based on the persistence evaluation results, the persistence orchestration information for multiple Redis database instances is determined; Based on persistence orchestration information, data persistence commands are issued to the corresponding Redis database instances so that multiple Redis database instances can perform data persistence operations according to the persistence orchestration information.

[0006] Furthermore, the persistent association information includes shared storage resource status information and the instance data volume of each Redis database instance; the shared storage resource status information includes performance indicator information and operation and maintenance indicator information; among which, the performance indicator information includes at least one of the number of requests per unit time, read and write bandwidth, latency and number of connections, and the operation and maintenance indicator information includes at least one of the read and write file status and cloud disk monitoring status.

[0007] Furthermore, obtain persistent association information corresponding to multiple Redis database instances, including: The monitoring component collects the status information of shared storage resources of cloud disk services mounted on multiple Redis database instances; Obtain the instance data volume of each Redis database instance through the management component; Based on the shared storage resource status information and the instance data volume of each Redis database instance, the persistent association information corresponding to each Redis database instance is determined.

[0008] Furthermore, based on the persistence association information, the persistence evaluation results corresponding to each Redis database instance are determined, including: The shared storage resource status information is preprocessed to obtain preprocessed shared storage resource status information; wherein, the preprocessing includes at least one of data denoising, missing value handling, outlier handling, and data smoothing; Based on the preprocessed shared storage resource status information and the instance data volume of each Redis database instance, construct the feature vector corresponding to each Redis database instance; Based on the feature vectors corresponding to each Redis database instance, the persistence evaluation result for each Redis database instance is determined; the persistence evaluation result is used to characterize the predicted persistence time when the corresponding Redis database instance performs data persistence operations.

[0009] Furthermore, based on the feature vectors corresponding to each Redis database instance, the persistence evaluation results for each Redis database instance are determined, including: The feature vectors corresponding to each Redis database instance are input into the persistence time prediction model to obtain the initial predicted persistence time for each Redis database instance; the persistence time prediction model includes a recurrent neural network model. Based on the initial predicted persistence time, the persistence evaluation results for each Redis database instance are determined.

[0010] Furthermore, based on the initial predicted persistence time, the persistence evaluation results for each Redis database instance are determined, including: The corrected weight parameters are determined based on the operation and maintenance indicator information; the corrected weight parameters are used to characterize the degree of influence of the operation and maintenance status corresponding to the operation and maintenance indicator information on the initial prediction persistence time. The initial prediction persistence time is corrected based on the corrected weight parameters to obtain the target prediction persistence time; Based on the target predicted persistence time, the persistence evaluation result corresponding to each Redis database instance is determined.

[0011] Furthermore, based on the persistence evaluation results, persistence orchestration information for multiple Redis database instances is determined, including: Based on the persistence evaluation results, the data persistence triggering order for each Redis database instance is determined, and the data persistence triggering time for each Redis database instance is also determined based on the persistence evaluation results. Based on the data persistence triggering order and data persistence triggering time, the persistence orchestration information of multiple Redis database instances is determined.

[0012] Furthermore, based on the persistence evaluation results, the data persistence trigger time for each Redis database instance is determined, including: Based on the predicted persistence time and preset limit time represented by the persistence evaluation results corresponding to the current Redis database instance, the target trigger interval is determined; wherein, the target trigger interval is used to represent the data persistence trigger time interval between the current Redis database instance and the next Redis database instance; Based on the current Redis database instance's data persistence trigger time, target trigger interval, and data persistence trigger order, determine the next Redis database instance's data persistence trigger time.

[0013] According to a second aspect of this disclosure, a data persistence orchestration apparatus for a cloud-based Redis database is provided, the apparatus comprising: The acquisition module is used to acquire persistent association information corresponding to multiple Redis database instances; the persistent association information is used to characterize the shared storage resource status and instance data volume when the Redis database instance performs data persistence operations; The evaluation module is used to determine the persistence evaluation result for each Redis database instance based on the persistence association information. The orchestration module is used to determine the persistence orchestration information for multiple Redis database instances based on the persistence evaluation results. The distribution module is used to distribute data persistence commands to the corresponding Redis database instances based on persistence orchestration information, so that multiple Redis database instances can perform data persistence operations according to the persistence orchestration information.

[0014] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the program to implement the method described above.

[0015] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the methods described above.

[0016] According to a fifth aspect of this disclosure, a computer program product is provided. The computer program product includes a computer program that, when executed by a processor, implements the methods described above.

[0017] This disclosure provides a data persistence orchestration method, apparatus, device, medium, and product for a cloud-based Redis database. In this embodiment, firstly, persistence association information corresponding to multiple Redis database instances is obtained; wherein, the persistence association information is used to characterize the shared storage resource status and instance data volume when the Redis database instances perform data persistence operations; then, based on the persistence association information, the persistence evaluation result corresponding to each Redis database instance is determined; subsequently, based on the persistence evaluation result, the persistence orchestration information of the multiple Redis database instances is determined; finally, based on the persistence orchestration information, a data persistence command is issued to the corresponding Redis database instances, so that the multiple Redis database instances perform data persistence operations according to the persistence orchestration information.

[0018] As described above, this embodiment of the present disclosure evaluates the persistence status of each Redis database instance when performing data persistence operations by obtaining persistent association information that characterizes the state of shared storage resources and the amount of instance data. This allows the persistence evaluation process to simultaneously consider the current state of the shared storage resources and the differences in the amount of data among different Redis database instances. Based on this, this embodiment of the present disclosure orchestrates the data persistence operations of multiple Redis database instances in a unified manner based on the persistence evaluation results. This allows data persistence commands to be sent to the corresponding Redis database instances according to the persistence orchestration information. As a result, this embodiment of the present disclosure can reduce the situation where multiple Redis database instances perform data persistence operations out of order or in a concentrated manner, avoid the shared storage resources from being subjected to excessively high write pressure in a short period of time, thereby reducing the risk of data persistence failure or database instance abnormal alarms and improving the stability and reliability of the data persistence process of Redis databases in the cloud. Attached Figure Description

[0019] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 A flowchart illustrating a data persistence orchestration method for a Redis database in the cloud, provided as an exemplary embodiment of this disclosure; Figure 2 The overall architecture of a data persistence orchestration system for a Redis database in the cloud provided as an exemplary embodiment of this disclosure; Figure 3 One of the flowcharts for a data persistence orchestration method for a Redis database in the cloud, provided as another exemplary embodiment of this disclosure; Figure 4 A schematic diagram illustrating the index types of shared storage resource status information provided for an exemplary embodiment of this disclosure; Figure 5 A second flowchart illustrating a data persistence orchestration method for a Redis database in the cloud, provided as another exemplary embodiment of this disclosure; Figure 6 Flowchart 3 of a data persistence orchestration method for a Redis database in the cloud, provided as another exemplary embodiment of this disclosure; Figure 7 A schematic diagram of the persistence time prediction model structure provided for an exemplary embodiment of this disclosure; Figure 8 Flowchart four of a data persistence orchestration method for a Redis database in the cloud, provided as another exemplary embodiment of this disclosure; Figure 9 A schematic diagram of component collaboration provided for an exemplary embodiment of this disclosure; Figure 10 A schematic block diagram of functional modules of a data persistence orchestration apparatus for a cloud-based Redis database provided as an exemplary embodiment of this disclosure; Figure 11 A structural block diagram of an electronic device provided as an exemplary embodiment of this disclosure; Figure 12 A structural block diagram of a computer system provided as an exemplary embodiment of this disclosure; Figure 13 A structural block diagram of a computer program product provided for an exemplary embodiment of this disclosure. Detailed Implementation

[0021] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0022] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0023] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc., used in this disclosure are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0024] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more". The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0025] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0026] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0027] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understood that the above notification and user authorization process is merely illustrative and does not constitute a limitation on the implementation of this disclosure; other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0028] Data persistence orchestration for Redis databases in the cloud is a complex data management problem driven by shared storage resource status monitoring, database instance persistence requirement assessment, persistence time prediction, and multi-instance trigger timing control. It involves not only obtaining shared storage resource status information such as cloud disk service, read / write bandwidth, latency, and connection count that Redis database instances rely on during data persistence, but also the data volume, persistence trigger requirements, and write pressure on shared storage resources caused by data persistence operations for different Redis database instances. It also requires comprehensive orchestration of the data persistence execution timing of multiple Redis database instances by combining the shared storage resource status and instance data volume. In existing technologies, Redis database data persistence typically relies on automatic triggering via parameter settings or commands. These solutions often focus on data persistence execution for a single database instance, lacking a unified evaluation and orchestration mechanism for scenarios involving multiple Redis database instances sharing storage resources. This can easily lead to multiple Redis database instances performing data persistence operations concurrently within a short period. Furthermore, when there are many database instances in the cloud, the shared storage resource status changes dynamically, and different database instances have varying data volumes, existing solutions often struggle to balance the relationship between the current state of the shared storage resources, the differences in instance data volumes, and the persistence triggering sequence. This can easily cause the shared storage resources to experience excessive write pressure in a short period, thus affecting the stability of the data persistence process. In addition, while some data persistence management solutions can trigger database instances to perform persistence operations via commands, they still suffer from insufficient persistence time assessment, unreasonable instance triggering order, and inaccurate determination of the next instance persistence triggering time when dealing with scenarios involving multiple instances sharing cloud disk services. Therefore, it is necessary to provide a cloud-based Redis database data persistence orchestration solution that can balance the shared storage resource status, the Redis database instance data volume, and the ability to orchestrate the persistence triggering sequence for multiple instances.

[0029] In one embodiment, such as Figure 1 As shown, a data persistence orchestration method for a Redis database in the cloud is provided, including the following steps: Step 101: Obtain persistent association information corresponding to multiple Redis database instances.

[0030] Here, the executing entity can obtain persistence association information corresponding to multiple Redis database instances. The persistence association information is used to characterize the shared storage resource status and instance data volume when the Redis database instances perform data persistence operations. Multiple Redis database instances can be multiple database instances on a single cluster or multiple clusters within the same resource pool. Shared storage resources can be storage resources that multiple Redis database instances rely on when performing data persistence operations, such as cloud disk services. The shared storage resource status can be used to characterize the running status and performance status of the cloud disk service when the Redis database instances perform data persistence operations. The instance data volume can be used to characterize the scale of data that needs to be persisted in the corresponding Redis database instance.

[0031] In one possible embodiment, the persistent association information includes shared storage resource status information and instance data volume of each Redis database instance. The shared storage resource status information includes performance indicator information and operation and maintenance indicator information. The performance indicator information includes at least one of the following: request volume per unit time, read and write bandwidth, latency, and number of connections. The operation and maintenance indicator information includes at least one of the following: read and write file status and cloud disk monitoring status.

[0032] Specifically, such as Figure 2 As shown, Figure 2 This embodiment exemplifies the overall architecture of a cloud-based Redis database data persistence orchestration system provided by this disclosure. The data persistence orchestration method provided by this disclosure can be applied to this data persistence orchestration system. The system may include a monitoring component, a management component, Redis database instances, and a cloud disk service. The management component, also known as a control component, allows multiple Redis database instances to mount a cloud disk service, using it as a storage resource for persistent data files. The monitoring component collects the shared storage resource status information of the cloud disk service mounted by the Redis database instances and sends this information to the management component. The management component obtains the instance data volume of each Redis database instance and determines the persistence association information corresponding to each Redis database instance based on the shared storage resource status information and the instance data volume. In other words, in... Figure 2In the system architecture shown, persistent association information can be jointly determined by the shared storage resource side information collected by the monitoring component and the instance side information obtained by the management component. In subsequent embodiments, the monitoring component is mainly used to collect shared storage resource status information, and the management component is mainly used to perform instance data volume acquisition, persistence evaluation, persistence orchestration, and data persistence command issuance.

[0033] In one possible embodiment, such as Figure 3 As shown, obtaining persistent association information for multiple Redis database instances involves the following steps: Step 301: Collect the status information of shared storage resources of cloud disk services mounted on multiple Redis database instances through the monitoring component.

[0034] Here, the monitoring component can collect the status information of shared storage resources of cloud disk services mounted on multiple Redis database instances. The shared storage resource status information can be the status information of the cloud disk service during operation, and can include performance index information and operation and maintenance index information.

[0035] In one possible embodiment, such as Figure 4 As shown, Figure 4 This illustration demonstrates the types of metrics for shared storage resource status information provided in this embodiment. The monitoring component can collect information from the cloud disk service mounted on the Redis database instance in real time or periodically. Information collection methods include, but are not limited to, obtaining information through Prometheus, top, sar, node-export, and internal cloud monitoring. Performance metrics may include at least one of the following: cloud disk service request volume per unit time, read / write bandwidth, latency, and number of connections. Request volume per unit time can be queries per second (QPS). Operational metrics may include at least one of read / write file status and cloud disk monitoring status. After collecting the shared storage resource status information, the monitoring component can send it to the management component for further processing. Figure 4 The performance and maintenance metrics shown can serve as the data foundation for subsequently determining persistent association information.

[0036] In one possible embodiment, the monitoring component can also determine whether the read / write status and monitoring status of the cloud disk service are abnormal based on the operation and maintenance indicator information. If the operation and maintenance indicator information is abnormal, an alarm message can be generated to prompt troubleshooting of the cloud disk service.

[0037] Step 302: Obtain the instance data volume of each Redis database instance through the management component.

[0038] Here, the management component can obtain the instance data volume of each Redis database instance. The instance data volume can be used to characterize the scale of data that the corresponding Redis database instance needs to write to the persistence file when performing data persistence operations. The instance data volume of different Redis database instances can be different. Therefore, under the same or similar cloud disk service status, the time required for different Redis database instances to complete data persistence operations may also be different.

[0039] In one possible embodiment, after receiving the shared storage resource status information sent by the monitoring component, the management component can obtain the instance data volume of each Redis database instance. The instance data volume can be used as instance-side information to participate in the subsequent persistence evaluation process. In other words, when determining the persistence association information corresponding to each Redis database instance, the management component not only considers the shared storage resource status information of the cloud disk service, but also considers the instance data volume of each Redis database instance itself.

[0040] Step 303: Based on the shared storage resource status information and the instance data volume of each Redis database instance, determine the persistent association information corresponding to each Redis database instance.

[0041] Here, after receiving the shared storage resource status information sent by the monitoring component and obtaining the instance data volume of each Redis database instance, the management component can determine the persistence association information corresponding to each Redis database instance based on the shared storage resource status information and the instance data volume of each Redis database instance. The persistence association information can be used as input information for subsequently determining the persistence evaluation result.

[0042] In one possible implementation, if multiple Redis database instances are mounted on the same cloud disk service, then the multiple Redis database instances can correspond to the same shared storage resource status information. Furthermore, since the instance data volume of each Redis database instance can differ, the management component can associate the shared storage resource status information with the instance data volume of each Redis database instance to obtain the persistent association information corresponding to each Redis database instance. For example, for any Redis database instance, the management component can use the performance metrics and operational metrics of the cloud disk service mounted on that Redis database instance, as well as the instance data volume of that Redis database instance, as the persistent association information corresponding to that Redis database instance. This allows the subsequent persistence evaluation process to consider both the shared storage resource status and the instance data volume simultaneously, avoiding data persistence orchestration based solely on the triggering requirements of a single Redis database instance.

[0043] In this embodiment, firstly, the execution entity collects the shared storage resource status information of the cloud disk service mounted by multiple Redis database instances through the monitoring component, and obtains the instance data volume of each Redis database instance through the management component; then, based on the shared storage resource status information and the instance data volume of each Redis database instance, the execution entity determines the persistent association information corresponding to each Redis database instance.

[0044] As described above, this embodiment collects the shared storage resource status information of the cloud disk service mounted on multiple Redis database instances through a monitoring component, and obtains the instance data volume of each Redis database instance through a management component. This allows the determination of persistence association information to consider both the shared storage resource status on which multiple Redis database instances depend when performing data persistence operations, and the differences in data volume between different Redis database instances. As a result, the persistence association information corresponding to each Redis database instance can more accurately reflect the actual situation of the corresponding Redis database instance when performing data persistence operations under the current shared storage resource status, providing a more reliable data foundation for determining the persistence evaluation results and persistence orchestration information.

[0045] Step 102: Based on the persistence association information, determine the persistence evaluation result corresponding to each Redis database instance.

[0046] Here, after obtaining the persistence association information corresponding to multiple Redis database instances, the executing entity can determine the persistence evaluation result corresponding to each Redis database instance based on the persistence association information. The persistence evaluation result can be used to characterize the predicted persistence time when the corresponding Redis database instance performs data persistence operations. In other words, the executing entity can combine the shared storage resource status information of the cloud disk service mounted on the Redis database instance and the instance data volume of the Redis database instance to evaluate the time required for the Redis database instance to complete the data persistence operation under the current shared storage resource status.

[0047] In one possible embodiment, such as Figure 5 As shown, based on persistence association information, the persistence evaluation result corresponding to each Redis database instance is determined, including the following steps: Step 501: Preprocess the shared storage resource status information to obtain preprocessed shared storage resource status information.

[0048] Here, after obtaining the persistent association information corresponding to multiple Redis database instances, the executing entity can preprocess the shared storage resource status information to obtain the preprocessed shared storage resource status information. The preprocessing includes at least one of data denoising, missing value handling, outlier handling, and data smoothing.

[0049] In one possible embodiment, the shared storage resource status information may include performance index information and operation and maintenance index information of cloud disk service. Since the shared storage resource status information collected by the monitoring component may contain noisy data, missing data or abnormal data, the execution entity can process the shared storage resource status information through at least one of the following preprocessing methods: data denoising, missing value processing, outlier processing and data smoothing, in order to remove dirty data in the shared storage resource status information, so as to facilitate subsequent aggregation analysis and performance analysis of the shared storage resource status.

[0050] Step 502: Based on the preprocessed shared storage resource status information and the instance data volume of each Redis database instance, construct the feature vector corresponding to each Redis database instance.

[0051] Here, after the execution entity preprocesses the shared storage resource status information and obtains the preprocessed shared storage resource status information, it can construct the feature vector corresponding to each Redis database instance based on the preprocessed shared storage resource status information and the instance data volume of each Redis database instance. The feature vector can be used to characterize the performance status of the shared storage resource and the instance data volume of the corresponding Redis database instance.

[0052] In one possible embodiment, the feature vector may include the number of requests per unit time, read / write bandwidth, latency, number of connections, and the instance data size of the corresponding Redis database instance. For example, if the number of requests per unit time is QPS, the read / write bandwidth is BandW, the latency is Time, the number of connections is Cli, and the instance data size is Mem, then after obtaining N samples through multiple load tests, the feature vector corresponding to the i-th sample can be represented as:

[0053] Where QPS_i represents the number of requests per unit time corresponding to the i-th sample, BandW_i represents the read / write bandwidth corresponding to the i-th sample, Time_i represents the latency corresponding to the i-th sample, Cli_i represents the number of connections corresponding to the i-th sample, and Mem_i represents the amount of instance data corresponding to the i-th sample. It should be noted that the above feature vector is only an illustrative example. In practical applications, feature vectors for characterizing the persistence-related states of the corresponding Redis database instance can be constructed based on the collection of shared storage resource status information and the amount of instance data of the Redis database instance. This disclosure does not impose specific limitations on this.

[0054] Step 503: Based on the feature vectors corresponding to each Redis database instance, determine the persistence evaluation result corresponding to each Redis database instance.

[0055] Here, after constructing the feature vectors corresponding to each Redis database instance, the executing entity can determine the persistence evaluation result corresponding to each Redis database instance based on the feature vectors corresponding to each Redis database instance. The evaluation result is used to characterize the predicted persistence time when the corresponding Redis database instance performs data persistence operations.

[0056] In one possible embodiment, such as Figure 6 As shown, based on the feature vectors corresponding to each Redis database instance, the persistence evaluation result for each Redis database instance is determined, including the following steps: Step 601: Input the feature vector corresponding to each Redis database instance into the persistence time prediction model to obtain the initial predicted persistence time for each Redis database instance.

[0057] Here, after constructing the feature vectors corresponding to each Redis database instance, the execution entity can input the feature vectors corresponding to each Redis database instance into the persistence time prediction model to obtain the initial predicted persistence time for each Redis database instance. The persistence time prediction model includes a recurrent neural network model.

[0058] In one possible embodiment, such as Figure 7 As shown, Figure 7 An exemplary schematic diagram of the persistence time prediction model structure provided in this disclosure embodiment is shown. This persistence time prediction model can be a Recurrent Neural Network (RNN) model. The RNN model can be used to process shared storage resource state information and instance data collected in time series. For the RNN model, in one time step, the hidden state h... t It can be determined by the current input X tThe hidden state h of the previous time step (t-1) Determined, exemplarily, h t It can be represented as:

[0059] Among them, X t h can represent the feature vector input at the current time step. (t-1) h can represent the hidden state of the previous time step. t The hidden state or prediction-related state at the current time step can be represented. The recurrent neural network layer receives the feature vector from the input layer and transforms the feature vector into the hidden state. The recurrent neural network layer processes the sequence data through time steps, updating the hidden state and outputting the prediction result at each time step. Then, the hidden state of the recurrent neural network layer can be mapped to the output layer. The output layer can be a fully connected layer used to transform the hidden state into the time prediction result. Based on this, the execution entity can obtain the initial predicted persistence time corresponding to each Redis database instance through the persistence time prediction model.

[0060] Step 602: Based on the initial predicted persistence time, determine the persistence evaluation result for each Redis database instance.

[0061] Here, after obtaining the initial predicted persistence time for each Redis database instance, the executing entity can determine the persistence evaluation result for each Redis database instance based on the initial predicted persistence time.

[0062] In one possible implementation, the persistence evaluation result for each Redis database instance is determined based on the initial predicted persistence time, including the following steps: Determine the adjusted weight parameters based on operation and maintenance indicator information; The initial prediction persistence time is corrected based on the corrected weight parameters to obtain the target prediction persistence time; Based on the target predicted persistence time, the persistence evaluation result corresponding to each Redis database instance is determined.

[0063] Specifically, after obtaining the initial predicted persistence time for each Redis database instance, the executing entity first determines the correction weight parameter based on the operation and maintenance indicator information. The correction weight parameter is used to characterize the degree of influence of the operation and maintenance status corresponding to the operation and maintenance indicator information on the initial predicted persistence time. Then, the initial predicted persistence time is corrected based on the correction weight parameter to obtain the target predicted persistence time. Finally, the executing entity determines the persistence evaluation result for each Redis database instance based on the target predicted persistence time.

[0064] In one possible embodiment, the initial prediction of persistence time can be the time prediction result h output by the persistence time prediction model. t Operational metrics information can include file read / write status and cloud disk monitoring status. The executing entity can determine the adjustment weight parameter λ based on the file read / write status and cloud disk monitoring status, and adjust the time prediction result h using the adjustment weight parameter λ. t Dynamic adjustments are made to obtain the target prediction persistence time. For example, the target prediction persistence time can be expressed as λ·h. t , where h t The initial predicted persistence time output by the persistence time prediction model is represented by λ, and the correction weight parameter is determined based on the operation and maintenance indicator information. In this way, the initial predicted persistence time output by the persistence time prediction model can be corrected when the operation and maintenance status of the shared storage resources changes, so that the target predicted persistence time can further reflect the current read and write status and monitoring status of the cloud disk service.

[0065] In this embodiment, firstly, the execution entity inputs the feature vectors corresponding to each Redis database instance into the persistence time prediction model to obtain the initial predicted persistence time for each Redis database instance; then, based on the initial predicted persistence time, the execution entity determines the persistence evaluation result for each Redis database instance.

[0066] As described above, this embodiment obtains the initial predicted persistence time for each Redis database instance by inputting the feature vector corresponding to each Redis database instance into the persistence time prediction model, and determines the persistence evaluation result based on the initial predicted persistence time. Thus, the time required for each Redis database instance to perform data persistence operations can be evaluated, providing a basis for subsequently determining persistence orchestration information.

[0067] Step 103: Based on the persistence evaluation results, determine the persistence orchestration information for multiple Redis database instances.

[0068] Here, after determining the persistence evaluation results for each Redis database instance, the executing entity can determine the persistence orchestration information for multiple Redis database instances based on the persistence evaluation results. The persistence orchestration information can be used to characterize the triggering arrangement when multiple Redis database instances perform data persistence operations. For example, the persistence orchestration information may include the data persistence triggering order and data persistence triggering time for each Redis database instance.

[0069] In one possible embodiment, such as Figure 8As shown, based on the persistence evaluation results, the persistence orchestration information for multiple Redis database instances is determined, including the following steps: Step 801: Based on the persistence evaluation results, determine the data persistence triggering order for each Redis database instance, and based on the persistence evaluation results, determine the data persistence triggering time for each Redis database instance.

[0070] Here, after determining the persistence evaluation results for each Redis database instance, the executing entity can determine the data persistence triggering order for each Redis database instance based on the persistence evaluation results, and determine the data persistence triggering time for each Redis database instance based on the persistence evaluation results. The data persistence triggering order can be used to characterize the order in which multiple Redis database instances perform data persistence operations, and the data persistence triggering time can be used to characterize the time when the corresponding Redis database instance starts performing data persistence operations.

[0071] In one possible embodiment, such as Figure 9 As shown, Figure 9 An exemplary schematic diagram of component collaboration provided in this embodiment of the disclosure is shown. In this diagram, the management component can start a scheduled persistence task according to custom-configured parameters and filter Redis database instances in normal status within the cluster. The management component performs polling-style persistence orchestration on the normal Redis database instances. Specifically, the management component can determine the Redis database instances that can perform data persistence operations in the current orchestration process based on the persistence evaluation results, and determine the data persistence triggering order of each Redis database instance in combination with the polling traversal order of the scheduled task. Thus, multiple Redis database instances can perform data persistence operations sequentially according to the determined triggering order, reducing the situation where multiple Redis database instances perform data persistence operations simultaneously within a similar time period.

[0072] In one possible implementation, the data persistence trigger time for each Redis database instance is determined based on the persistence evaluation results, including the following steps: Based on the predicted persistence time and preset limit time represented by the persistence evaluation results corresponding to the current Redis database instance, the target trigger interval is determined; wherein, the target trigger interval is used to represent the data persistence trigger time interval between the current Redis database instance and the next Redis database instance; Based on the current Redis database instance's data persistence trigger time, target trigger interval, and data persistence trigger order, determine the next Redis database instance's data persistence trigger time.

[0073] Specifically, the executing entity can determine the predicted persistence time for the current Redis database instance based on the persistence evaluation result, and compare the predicted persistence time with a preset limit time. If the predicted persistence time is less than or equal to the preset limit time, the executing entity can determine the predicted persistence time as the target trigger interval; if the predicted persistence time is greater than the preset limit time, the executing entity can determine the preset limit time as the target trigger interval. Then, the executing entity can determine the data persistence trigger time for the next Redis database instance based on the data persistence trigger time of the current Redis database instance and the target trigger interval, and determine the data persistence trigger times for multiple Redis database instances sequentially based on the data persistence trigger order.

[0074] For example, if the data persistence trigger time of the current Redis database instance is T, the predicted persistence time corresponding to the current Redis database instance is t, and the preset limit time is Tmax, then when t is less than or equal to Tmax, the data persistence trigger time of the next Redis database instance can be determined based on T and t; when t is greater than Tmax, the data persistence trigger time of the next Redis database instance can be determined based on T and Tmax. Thus, when the predicted persistence time does not exceed the preset limit time, the data persistence trigger time of the next Redis database instance can be arranged according to the predicted persistence time, and when the predicted persistence time exceeds the preset limit time, it can prevent the subsequent Redis database instance from being unable to be triggered to perform data persistence operations for a long time.

[0075] Step 802: Determine the persistence orchestration information for multiple Redis database instances based on the data persistence triggering order and data persistence triggering time.

[0076] Here, the executing entity can determine the persistence orchestration information of multiple Redis database instances based on the data persistence triggering order and data persistence triggering time. The persistence orchestration information can include the correspondence between each Redis database instance and its corresponding data persistence triggering order and data persistence triggering time.

[0077] In one possible embodiment, the management component can combine the data persistence trigger order and data persistence trigger time of multiple Redis database instances to obtain persistence orchestration information. This persistence orchestration information can be used to guide subsequent issuance of data persistence commands to the corresponding Redis database instances, enabling multiple Redis database instances to perform data persistence operations according to the orchestrated trigger order and trigger time, thereby achieving unified orchestration of data persistence operations for multiple Redis database instances.

[0078] In this embodiment, firstly, the executing entity determines the data persistence triggering order of each Redis database instance based on the persistence evaluation results, and determines the data persistence triggering time of each Redis database instance based on the persistence evaluation results; then, the executing entity determines the persistence orchestration information of multiple Redis database instances based on the data persistence triggering order and the data persistence triggering time.

[0079] As described above, this embodiment determines the data persistence triggering order and time of each Redis database instance through persistence evaluation results, and further forms persistence orchestration information for multiple Redis database instances. Thus, multiple Redis database instances can perform data persistence operations in the orchestrated order and time, reducing the situation where multiple Redis database instances trigger data persistence operations at the same time, and reducing the instantaneous write pressure on shared storage resources.

[0080] Step 104: Based on the persistence orchestration information, issue a data persistence command to the corresponding Redis database instance so that multiple Redis database instances can perform data persistence operations according to the persistence orchestration information.

[0081] Here, after determining the persistence orchestration information of multiple Redis database instances, the executing entity can issue data persistence commands to the corresponding Redis database instances based on the persistence orchestration information, so that the multiple Redis database instances can perform data persistence operations according to the persistence orchestration information.

[0082] In one possible embodiment, the Redis database instance can be a database service instance container that mounts a cloud disk service. For example, the database service instance container may include... Figure 9 The StatefulSet pod shown can utilize cloud disk services as persistent file storage resources for Redis database instances. It should be noted that before executing the data persistence orchestration method provided in this embodiment, the persistent directory of the Redis database instance can be initialized to the cloud disk directory, and the save parameter of the Redis database instance can be set to empty. This prevents the data persistence operation of the Redis database instance from being automatically triggered by data writing within a certain period, but rather by a data persistence command issued by the management component. Based on the above configuration, the monitoring component can collect the shared storage resource status information of the cloud disk service, and the management component can orchestrate the data persistence operations of multiple Redis database instances based on the shared storage resource status information and the instance data volume, and dynamically adjust the data persistence trigger time of the Redis database instances.

[0083] Furthermore, combined Figure 9 The component collaboration relationships shown allow the executing entity to determine the Redis database instance that needs to trigger data persistence operations based on persistence orchestration information, and establish a communication connection with that Redis database instance. For example, the executing entity can connect to the Redis database instances sequentially via Transmission Control Protocol (TCP), and issue data persistence commands to the corresponding Redis database instances according to the data persistence trigger order and data persistence trigger time in the persistence orchestration information. After receiving the data persistence command, the Redis database instance can execute the data persistence operation according to the persistence orchestration information and write the corresponding data to its mounted cloud disk service. In this way, multiple Redis database instances can be triggered sequentially to execute data persistence operations according to the persistence orchestration information, reducing the situation where multiple Redis database instances execute data persistence operations simultaneously.

[0084] As described above, this disclosed technical solution obtains the persistence association information corresponding to multiple Redis database instances and determines the persistence evaluation result for each Redis database instance based on the shared storage resource status information and instance data volume. This allows the persistence evaluation process to simultaneously consider the current state of the cloud disk service and the differences in data volume among different Redis database instances. Furthermore, this disclosed technical solution determines the persistence orchestration information of multiple Redis database instances based on the persistence evaluation results and issues data persistence commands to the corresponding Redis database instances according to the persistence orchestration information. This enables multiple Redis database instances to execute data persistence operations according to the orchestrated trigger order and trigger time. Therefore, this disclosed technical solution can reduce the situation where multiple Redis database instances execute data persistence operations out of order or in a concentrated manner, reduce the write pressure on the cloud disk service in a short period of time, and reduce the risk of data persistence failure or database instance abnormal alarms due to cloud disk service performance exceeding limits, thereby improving the stability and reliability of the cloud-based Redis database data persistence process.

[0085] By dividing each function into corresponding functional modules, this disclosure provides a data persistence orchestration device for a cloud-based Redis database. This data persistence orchestration device for a cloud-based Redis database can be a server or a chip applied to a server. Figure 10 This is a schematic block diagram of the functional modules of a data persistence orchestration apparatus for a cloud-based Redis database provided as an exemplary embodiment of this disclosure. Figure 10 As shown, the data persistence orchestration device for this cloud-based Redis database includes: The acquisition module 1001 is used to acquire persistent association information corresponding to multiple Redis database instances; wherein, the persistent association information is used to characterize the shared storage resource status and instance data volume when the Redis database instance performs data persistence operation; Evaluation module 1002 is used to determine the persistence evaluation result corresponding to each Redis database instance based on persistence association information; The orchestration module 1003 is used to determine the persistence orchestration information of multiple Redis database instances based on the persistence evaluation results. The distribution module 1004 is used to distribute data persistence commands to the corresponding Redis database instances based on persistence orchestration information, so that multiple Redis database instances can perform data persistence operations according to the persistence orchestration information.

[0086] In one embodiment, the persistent association information includes shared storage resource status information and instance data volume of each Redis database instance; the shared storage resource status information includes performance indicator information and operation and maintenance indicator information; wherein, the performance indicator information includes at least one of the number of requests per unit time, read and write bandwidth, latency and number of connections, and the operation and maintenance indicator information includes at least one of the read and write file status and cloud disk monitoring status.

[0087] In one embodiment, the acquisition module 1001 includes: The collection unit is used to collect the status information of shared storage resources of cloud disk services mounted on multiple Redis database instances through the monitoring component; The acquisition unit is used to obtain the instance data volume of each Redis database instance through the management component; The first determining unit is used to determine the persistent association information corresponding to each Redis database instance based on the shared storage resource status information and the instance data volume of each Redis database instance.

[0088] In one embodiment, the evaluation module 1002 includes: The preprocessing unit is used to preprocess the shared storage resource status information to obtain preprocessed shared storage resource status information; wherein, the preprocessing includes at least one of data denoising, missing value handling, outlier handling, and data smoothing. The building unit is used to construct the feature vector corresponding to each Redis database instance based on the preprocessed shared storage resource status information and the instance data volume of each Redis database instance; The second determining unit is used to determine the persistence evaluation result corresponding to each Redis database instance based on the feature vector corresponding to each Redis database instance; wherein, the evaluation result is used to characterize the predicted persistence time when the corresponding Redis database instance performs data persistence operation.

[0089] In one embodiment, the evaluation module 1002 includes: The input unit is used to input the feature vectors corresponding to each Redis database instance into the persistence time prediction model to obtain the initial predicted persistence time for each Redis database instance; wherein, the persistence time prediction model includes a recurrent neural network model; The third determining unit is used to determine the persistence evaluation result corresponding to each Redis database instance based on the initial predicted persistence time.

[0090] In one embodiment, the evaluation module 1002 includes: The fourth determining unit is used to determine the corrected weight parameters based on the operation and maintenance indicator information; wherein, the corrected weight parameters are used to characterize the degree of influence of the operation and maintenance status corresponding to the operation and maintenance indicator information on the initial prediction persistence time. The correction unit is used to correct the initial prediction persistence time based on the correction weight parameters to obtain the target prediction persistence time. The fifth determining unit is used to determine the persistence evaluation result corresponding to each Redis database instance based on the target predicted persistence time.

[0091] In one embodiment, the orchestration module 1003 includes: The sixth determining unit is used to determine the data persistence triggering order of each Redis database instance based on the persistence evaluation results, and to determine the data persistence triggering time of each Redis database instance based on the persistence evaluation results; The seventh determining unit is used to determine the persistence orchestration information of multiple Redis database instances based on the data persistence triggering order and data persistence triggering time.

[0092] In one embodiment, the orchestration module 1003 includes: The eighth determining unit is used to determine the target trigger interval based on the predicted persistence time and preset limit time represented by the persistence evaluation result corresponding to the current Redis database instance; wherein, the target trigger interval is used to represent the data persistence trigger time interval between the current Redis database instance and the next Redis database instance; The ninth determining unit is used to determine the data persistence trigger time of the next Redis database instance based on the data persistence trigger time, target trigger interval, and data persistence trigger order of the current Redis database instance.

[0093] Figure 11 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of this disclosure. For example... Figure 11 As shown, the electronic device 1100 includes at least one processor 1101 and a memory 1102 coupled to the processor 1101. The processor 1101 can perform the corresponding steps in the methods disclosed in the embodiments of this disclosure.

[0094] The processor 1101 described above can also be called a central processing unit (CPU), which can be an integrated circuit chip with signal processing capabilities. Each step in the method disclosed in this embodiment can be implemented by the integrated logic circuitry in the processor 1101 or by software instructions. The processor 1101 can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this embodiment can be directly implemented by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can be located in the memory 1102, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor 1101 reads information from the memory 1102 and, in conjunction with its hardware, completes the steps of the method described above.

[0095] Furthermore, various operations / processes according to this disclosure, implemented via software and / or firmware, can be transmitted from a storage medium or network to a computer system with a dedicated hardware architecture, such as... Figure 12 The computer system 1200 shown is equipped with the programs that constitute the software. When various programs are installed, the computer system is able to perform various functions, including functions such as those described above. Figure 12 A block diagram of a computer system provided for an exemplary embodiment of this disclosure.

[0096] Computer system 1200 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0097] like Figure 12 As shown, the computer system 1200 includes a computing unit 1201, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 1202 or a computer program loaded from a storage unit 1208 into a random access memory (RAM) 1203. The RAM 1203 may also store various programs and data required for the operation of the computer system 1200. The computing unit 1201, ROM 1202, and RAM 1203 are interconnected via a bus 1204. An input / output (I / O) interface 1205 is also connected to the bus 1204.

[0098] Multiple components in the computer system 1200 are connected to the I / O interface 1205, including: an input unit 1206, an output unit 1207, a storage unit 1208, and a communication unit 1209. The input unit 1206 can be any type of device capable of inputting information into the computer system 1200. The input unit 1206 can receive input numerical or character information and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 1207 can be any type of device capable of presenting information and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. The storage unit 1208 may include, but is not limited to, a hard disk and an optical disk. The communication unit 1209 allows the computer system 1200 to exchange information / data with other devices via a network such as the Internet, and may include, but is not limited to, a modem, network card, infrared communication device, wireless communication transceiver, and / or chipset, such as Bluetooth™ device, WiFi device, WiMax device, cellular communication device, and / or the like.

[0099] The computing unit 1201 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1201 performs the various methods and processes described above. For example, in some embodiments, the methods disclosed in this disclosure can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1208. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 1100 via ROM 1202 and / or communication unit 1209. In some embodiments, the computing unit 1201 can be configured to perform the methods disclosed in this disclosure by any other suitable means (e.g., by means of firmware).

[0100] This disclosure also provides a computer-readable storage medium, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is able to perform the methods disclosed in this disclosure.

[0101] The computer-readable storage medium in this disclosure can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The aforementioned computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specifically, the aforementioned computer-readable storage medium may include electrical connections based on one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0102] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0103] Figure 13 A computer program product 1300 is provided as an exemplary embodiment of the present disclosure. The computer program product 1300 includes a computer program 1301, wherein the computer program 1301, when executed by a processor, implements the methods disclosed in the embodiments of the present disclosure.

[0104] In embodiments of this disclosure, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or it can be connected to an external computer.

[0105] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0106] The modules, components, or units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the modules, components, or units do not necessarily constitute a limitation on the module, component, or unit itself.

[0107] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary hardware logic components that can be used include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0108] The above description is merely an embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0109] While specific embodiments of this disclosure have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. A data persistence orchestration method for a Redis database on cloud, characterized in that, include: Obtain persistent association information corresponding to multiple Redis database instances; wherein, the persistent association information is used to characterize the shared storage resource status and instance data volume when the Redis database instance performs data persistence operation; Based on the persistence association information, the persistence evaluation result corresponding to each Redis database instance is determined; Based on the persistence evaluation results, the persistence orchestration information of the multiple Redis database instances is determined; Based on the persistence orchestration information, a data persistence command is issued to the corresponding Redis database instance, so that the multiple Redis database instances perform data persistence operations according to the persistence orchestration information.

2. The method of claim 1, wherein, The persistent association information includes shared storage resource status information and instance data volume of each Redis database instance; the shared storage resource status information includes performance indicator information and operation and maintenance indicator information; wherein, the performance indicator information includes at least one of request volume per unit time, read and write bandwidth, latency and connection number, and the operation and maintenance indicator information includes at least one of read and write file status and cloud disk monitoring status.

3. The method of claim 2, wherein, The step of obtaining persistent association information corresponding to multiple Redis database instances includes: The monitoring component collects the status information of the shared storage resources of the cloud disk service mounted on the multiple Redis database instances; The instance data volume of each Redis database instance is obtained through the management component; Based on the shared storage resource status information and the instance data volume of each Redis database instance, the persistent association information corresponding to each Redis database instance is determined.

4. The method of claim 2, wherein, The step of determining the persistence evaluation result corresponding to each Redis database instance based on the persistence association information includes: The shared storage resource status information is preprocessed to obtain preprocessed shared storage resource status information; wherein, the preprocessing includes at least one of data denoising, missing value handling, outlier handling, and data smoothing; Based on the preprocessed shared storage resource status information and the instance data volume of each Redis database instance, a feature vector corresponding to each Redis database instance is constructed; Based on the feature vectors corresponding to each Redis database instance, the persistence evaluation result corresponding to each Redis database instance is determined; wherein, the persistence evaluation result is used to characterize the predicted persistence time when the corresponding Redis database instance performs data persistence operations.

5. The method of claim 4, wherein, The step of determining the persistence evaluation result for each Redis database instance based on its feature vector includes: The feature vectors corresponding to each Redis database instance are input into the persistence time prediction model to obtain the initial predicted persistence time for each Redis database instance; wherein, the persistence time prediction model includes a recurrent neural network model; Based on the initial predicted persistence time, the persistence evaluation result corresponding to each Redis database instance is determined.

6. The method of claim 5, wherein, The step of determining the persistence evaluation result for each Redis database instance based on the initial predicted persistence time includes: The correction weight parameters are determined based on the operation and maintenance indicator information; wherein, the correction weight parameters are used to characterize the degree of influence of the operation and maintenance status corresponding to the operation and maintenance indicator information on the initial prediction persistence time. The initial prediction persistence time is corrected based on the corrected weight parameters to obtain the target prediction persistence time; Based on the predicted persistence time of the target, the persistence evaluation result corresponding to each Redis database instance is determined.

7. The method according to claim 1, characterized in that, The determination of persistence orchestration information for the multiple Redis database instances based on the persistence evaluation results includes: Based on the persistence evaluation results, the data persistence triggering order of each Redis database instance is determined, and based on the persistence evaluation results, the data persistence triggering time of each Redis database instance is determined; Based on the data persistence triggering order and the data persistence triggering time, the persistence orchestration information of the multiple Redis database instances is determined.

8. The method according to claim 7, characterized in that, The process of determining the data persistence trigger time for each Redis database instance based on the persistence evaluation results includes: Based on the predicted persistence time and preset limit time represented by the persistence evaluation result corresponding to the current Redis database instance, the target trigger interval is determined; wherein, the target trigger interval is used to represent the data persistence trigger time interval between the current Redis database instance and the next Redis database instance; Based on the data persistence trigger time of the current Redis database instance, the target trigger interval, and the data persistence trigger order, the data persistence trigger time of the next Redis database instance is determined.

9. A data persistence orchestration device for a cloud-based Redis database, characterized in that, include: The acquisition module is used to acquire persistent association information corresponding to multiple Redis database instances; wherein, the persistent association information is used to characterize the shared storage resource status and instance data volume when the Redis database instance performs data persistence operation; The evaluation module is used to determine the persistence evaluation result corresponding to each of the Redis database instances based on the persistence association information. An orchestration module is used to determine the persistence orchestration information of the multiple Redis database instances based on the persistence evaluation results; The distribution module is used to distribute data persistence commands to the corresponding Redis database instances based on the persistence orchestration information, so that the multiple Redis database instances perform data persistence operations according to the persistence orchestration information.

10. An electronic device, characterized in that, include: At least one processor; Memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method as described in any one of claims 1-8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-8.

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