Request processing method and device, electronic equipment, storage medium and program product

Abstract

The invention provides a request processing method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of computer technology, big data technology and data processing, in particular to data query for a database. The request processing method comprises the steps that in response to a received query request for a target scene, evaluation information is determined according to a historical request related to the query request and historical information of at least two evaluation dimensions, and the evaluation information represents the influence degree of the at least two evaluation dimensions on the processing result of the historical request; according to the evaluation information, a target configuration strategy is determined in at least one first candidate configuration strategy used for the target scene, and the target configuration strategy indicates a target query operation used for the query request; and based on the target query operation, executing the query request to obtain a request processing result.

Description

Technical Field

[0001] The present disclosure relates to the fields of computer technology, big data technology and data processing technology, and in particular to data query for a database, and more specifically to a request processing method and device, electronic equipment, storage medium and program product. Background Art

[0002] With the advent of the big data era, the needs for data management and analysis are becoming increasingly complex. The amount and types of data involved in various industries are increasing, and the coupling complexity of data is also increasing. Summary of the invention

[0003] The present disclosure provides a request processing method and device, an electronic device, a storage medium, and a program product.

[0004] According to one aspect of the present disclosure, a request processing method is provided, comprising: in response to receiving a query request for a target scenario, determining evaluation information based on historical requests related to the query request and historical information of at least two evaluation dimensions, wherein the evaluation information characterizes the degree of influence of each of the at least two evaluation dimensions on the processing result of the historical request; determining a target configuration strategy from at least one first candidate configuration strategy for the target scenario based on the evaluation information, wherein the target configuration strategy indicates a target query operation for the query request; and, based on the target query operation, executing the query request to obtain a request processing result.

[0005] According to another aspect of the present disclosure, a request processing device is provided, including: a first determination module, for determining evaluation information in response to receiving a query request for a target scenario, based on historical requests related to the query request and historical information of at least two evaluation dimensions, wherein the evaluation information characterizes the degree of influence of each of the at least two evaluation dimensions on the processing result of the historical request; a second determination module, for determining a target configuration strategy from at least one first candidate configuration strategy for the target scenario based on the evaluation information, wherein the target configuration strategy indicates a target query operation for the query request; and an execution module, for executing the query request based on the target query operation to obtain a request processing result.

[0006] According to another aspect of the present disclosure, an electronic device is provided, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0007] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the steps of the above method are implemented.

[0008] According to another aspect of the present disclosure, a computer program product is provided, including a computer program or instructions, and the steps of the above method are implemented when the computer program or instructions are executed by a processor.

[0009] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0011] Figure 1 The system architecture to which the request processing method according to the embodiment of the present disclosure can be applied is schematically shown;

[0012] Figure 2 A flowchart of a request processing method according to an embodiment of the present disclosure is schematically shown;

[0013] Figure 3A An exemplary schematic diagram schematically illustrates a process of determining evaluation information according to historical requests related to a query request and respective historical information of at least two evaluation dimensions according to an embodiment of the present disclosure;

[0014] Figure 3B An exemplary schematic diagram schematically illustrates a process of determining an intermediate evaluation value according to respective sub-information to be evaluated of at least two evaluation indicators according to an embodiment of the present disclosure;

[0015] Figure 3C An exemplary schematic diagram schematically illustrates a process of determining a target evaluation value according to respective intermediate evaluation values ​​of at least two evaluation dimensions according to an embodiment of the present disclosure;

[0016] Figure 4 An exemplary schematic diagram schematically illustrates a process of determining a target configuration strategy from at least one first candidate configuration strategy for a target scenario according to evaluation information according to an embodiment of the present disclosure;

[0017] Figure 5 An example schematic diagram schematically shows a process of executing a query request and obtaining a request processing result based on a target query operation according to an embodiment of the present disclosure;

[0018] Figure 6An example schematic diagram schematically shows a request processing process according to an embodiment of the present disclosure;

[0019] Figure 7 A block diagram schematically shows a request processing device according to an embodiment of the present disclosure; and

[0020] Figure 8 A block diagram of an electronic device suitable for implementing a request processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0021] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0022] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0023] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0024] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0025] With the needs of business development and the development and popularization of big data technology, the data volume and data coupling complexity of the database in the enterprise service system are getting higher and higher, resulting in more and more complex analysis requirements for enterprise database SQL (Structured Query Language) queries, more and more complex calculation logic, larger data volume, hierarchical association conditions, exponential growth of request concurrency, etc., and the requirements for the availability, performance, and accuracy of database storage component instances are getting higher and higher.

[0026] SQL queries on databases have many and high requirements for database instances. On the other hand, the capabilities of databases are fixed after deployment. For example, the number of concurrent connections, CPU resources, memory resources, network IO resources and other technical parameters related to physical hardware all have upper limits, resulting in frequent database failures. Moreover, the availability and overall performance of data cluster instances built independently by enterprise services are difficult to satisfy.

[0027] In one example, at least one of the following methods may be used to implement an SQL query on a database.

[0028] Method 1: Expand from a single-node data source to a master-slave data source, and distribute query requests to different slave node instances for query based on load balancing strategies. However, since load balancing strategies generally use polling or hashing algorithms based on a certain parameter of the request to determine a specific slave node, regardless of the overall status of the slave node, such as the CPU is full, resulting in a sharp decline in the performance and availability of subsequent query requests. The reduction in database availability is often an avalanche scenario. Due to the continuous squeezing of slow queries, the performance of the overall data instance is reduced, which in turn causes the performance of ordinary queries to deteriorate, and the query resource requests for slow queries that have been enabled cannot be satisfied by the physical resources of the database instance and cannot be completed quickly, resulting in a vicious cycle, and ultimately causing all queries to fail and be unable to be processed, such as the scenario of the overall Weibo database downtime mentioned above.

[0029] Method 2: Store the data queried from the data source in a cache, such as a redis cache, based on the query parameters. When querying again, query directly from the cache. However, since the biggest problem with using a cache is the accuracy of the data, the cache is generally configured with an expiration policy. Within the expiration period, whether using double writing, asynchronous subscription updates, etc., there is a certain risk of data delay, resulting in differences with the data in the database.

[0030] Method 3: Enable the cache mechanism in the database cluster. When querying the same parameters multiple times, directly hit the database cache to increase the query rate. However, the best practice for the cache mechanism of storage databases such as MySQL is generally to disable it by default, because in scenarios that usually involve updates, its performance cost is higher.

[0031] To this end, the embodiment of the present disclosure proposes a request processing scheme. For example, in response to receiving a query request for a target scenario, based on historical requests related to the query request and historical information of at least two evaluation dimensions, evaluation information is determined, wherein the evaluation information characterizes the degree of influence of each of the at least two evaluation dimensions on the processing result of the historical request; based on the evaluation information, a target configuration strategy is determined in at least one first candidate configuration strategy for the target scenario, wherein the target configuration strategy indicates a target query operation for the query request; and based on the target query operation, the query request is executed to obtain a request processing result.

[0032] According to the embodiments of the present disclosure, by intelligently analyzing historical requests and historical information of each evaluation dimension, it is possible to obtain evaluation information that accurately reflects the degree of influence of each evaluation dimension on the processing results of historical requests. By using the evaluation information, it is possible to determine the target configuration strategy in at least one first candidate configuration strategy for the target scenario based on the specific needs of the current target scenario, thereby ensuring the accuracy and real-time performance of the target query operation. On this basis, by executing the query request based on the target query operation, the accuracy of the request processing result is improved.

[0033] In the technical solution of the present invention, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0034] In the technical solution of the present invention, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0035] Figure 1 The system architecture to which the request processing method according to the embodiment of the present disclosure can be applied is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0036] like Figure 1As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104 and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0037] The user may use at least one of the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0038] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0039] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0040] It should be noted that the request processing method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the request processing device provided in the embodiment of the present disclosure can generally be set in the server 105. The request processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the request processing device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0041] Alternatively, the request processing method provided in the embodiment of the present disclosure may also be executed by the first terminal device 101, the second terminal device 102, or the third terminal device 103, or may also be executed by other terminal devices different from the first terminal device 101, the second terminal device 102, or the third terminal device 103. Accordingly, the request processing apparatus provided in the embodiment of the present disclosure may also be arranged in the first terminal device 101, the second terminal device 102, or the third terminal device 103, or may be arranged in other terminal devices different from the first terminal device 101, the second terminal device 102, or the third terminal device 103.

[0042] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0043] It should be noted that the sequence numbers of the operations in the following method are only used as representations of the operations for the purpose of description, and should not be regarded as representing the execution order of the operations. Unless explicitly stated, the method does not need to be executed completely in the order shown.

[0044] Figure 2 The flowchart of the request processing method according to the embodiment of the present disclosure is schematically shown.

[0045] like Figure 2 As shown, the request processing method 200 includes operations S210 to S230.

[0046] In operation S210, in response to receiving a query request for a target scenario, evaluation information is determined based on historical requests related to the query request and historical information of at least two evaluation dimensions, wherein the evaluation information characterizes a degree of influence of each of the at least two evaluation dimensions on a processing result of the historical request.

[0047] In operation S220, a target configuration strategy is determined among at least one first candidate configuration strategy for a target scenario according to the evaluation information, wherein the target configuration strategy indicates a target query operation for the query request.

[0048] In operation S230, based on the target query operation, the query request is executed to obtain a request processing result.

[0049] A query request refers to a query operation initiated by a user for a specific target scenario, and the query request includes information that the user wishes to retrieve or obtain. After receiving the query request, historical requests and historical information may be obtained. In an embodiment of the present disclosure, before obtaining historical requests and historical information, the user's consent or authorization may be obtained. For example, before operation S210, a request to obtain historical requests and historical information may be issued to the user. When the user agrees or authorizes that historical requests and historical information may be obtained, operation S210 is performed.

[0050] Historical requests refer to requests received in the historical time period before receiving the query request, and historical information refers to historical and near real-time data of both the query initiator and the query receiver. Historical requests and historical information can be configured according to actual business needs and are not limited here. For example, historical requests and historical information for the same target scenario can be obtained. Alternatively, historical requests and historical information related to the query request can be obtained.

[0051] Evaluation dimensions refer to different aspects or standards used to measure and evaluate the processing results of historical requests, that is, evaluation dimensions represent specific evaluation criteria. Specific evaluation dimensions can be configured according to actual business needs and are not limited here. For example, evaluation dimensions may include at least one of the following: performance dimension, success rate dimension, and accuracy dimension. The performance dimension is used to measure the efficiency and speed of the system in performing its functions, the success rate dimension is used to measure the frequency with which the system successfully completes its predetermined tasks, and the accuracy dimension is used to measure whether the system correctly performs tasks or produces correct results.

[0052] After obtaining the historical requests related to the query request and the historical information of at least two evaluation dimensions, the evaluation information about the degree of influence of each evaluation dimension on the processing result of the historical request can be obtained by analyzing the historical requests and the historical information of different evaluation dimensions. The evaluation information can be used to guide the subsequent configuration strategy selection. The specific form of the evaluation information can be configured according to actual business needs and is not limited here. For example, the evaluation information can be presented in numerical form, in which case the evaluation information can include at least one of the following: percentage, ratio, average, standard deviation, and ranking, etc. Alternatively, the evaluation information can also be presented in text form, in which case the evaluation information can include at least one of the following: trend description and explanation, etc.

[0053] In one example, the division method of candidate scenarios can be configured according to actual business needs and is not limited here. For example, candidate scenarios can be divided according to application fields, in which case the candidate scenarios can include at least one of the following: bank card scenarios, marketing recommendation scenarios, and online video scenarios. Alternatively, candidate scenarios can also be divided according to application requirements, in which case the candidate scenarios can include at least one of the following: scenarios with higher accuracy requirements, scenarios with fewer update requirements, scenarios where cache is recommended, and scenarios where multi-level cache is recommended.

[0054] Before executing the request processing method provided by the present disclosure, a candidate policy set may be configured for a candidate scenario in advance. Specifically, since there are multiple candidate scenarios, in the process of configuring the candidate policy set, the candidate policy set may be configured for each candidate scenario one by one in a one-to-one configuration manner, or the candidate policy set may be configured for multiple candidate scenarios at the same time in a many-to-many manner according to the division manner of multiple candidate scenarios.

[0055] The candidate policy set may include at least one second candidate configuration policy. The second candidate configuration policy may include configuration information and a candidate query operation corresponding to the configuration information. The specific form of the above-mentioned evaluation information may be adapted to the specific form of the configuration information, that is, when the configuration information is in numerical form, the evaluation information is also in numerical form; when the configuration information is in text form, the evaluation information is also in text form.

[0056] After obtaining the evaluation information, at least one candidate scenario can be matched according to the target scenario to determine the target policy set. On this basis, at least one configuration information included in the target policy set can be matched according to the evaluation information to determine the target configuration policy in at least one first candidate configuration policy. After determining the target configuration policy, the candidate query operation included in the target configuration policy can be determined as the target query operation for the query request. By executing the query request based on the target query operation, a request processing result can be obtained. The request processing result is a response to the query request.

[0057] According to the embodiments of the present disclosure, by intelligently analyzing historical requests and historical information of each evaluation dimension, it is possible to obtain evaluation information that accurately reflects the degree of influence of each evaluation dimension on the processing results of historical requests. By using the evaluation information, it is possible to determine the target configuration strategy in at least one first candidate configuration strategy for the target scenario based on the specific needs of the current target scenario, thereby ensuring the accuracy and real-time performance of the target query operation for the query request. On this basis, by executing the query request based on the target query operation, the accuracy of the request processing result is improved.

[0058] Figure 3AAn example schematic diagram of a process of determining evaluation information according to historical requests related to a query request and respective historical information of at least two evaluation dimensions according to an embodiment of the present disclosure is schematically shown.

[0059] like Figure 3A As shown, in 300, taking the evaluation information including the target evaluation value 300 as an example, the determination process of the target evaluation value 300 is explained.

[0060] In one example, the evaluation dimension refers to different aspects or criteria considered in the evaluation process. The evaluation dimension may include evaluation dimension 310, evaluation dimension 320, ..., evaluation dimension 3240. The specific evaluation dimension may be configured according to actual business needs and is not limited here. For example, the evaluation dimension may include at least one of the following: performance, success rate, accuracy, etc.

[0061] An evaluation dimension can have at least two evaluation indicators. An evaluation indicator refers to a specific standard or parameter used to measure and evaluate a specific dimension. The evaluation indicators of an evaluation dimension can be configured according to actual business needs and are not limited here. For example, the evaluation indicators may include at least one of the following: average query time in the past 10 times, average query time in the past hour, average query time in the same hour yesterday, average query time in the same hour last week, data source CPU, number of data source connections, data source disk IO, data source disk IO increase in the past 3 minutes, data source memory, data source memory increase in the past 3 minutes, and data source network IO, etc.

[0062] The historical information may include the sub-information to be evaluated of the evaluation indicator. The sub-information to be evaluated refers to the specific data of the evaluation indicator. For example, the sub-information to be evaluated of the evaluation indicator "average query time in the last 10 times" may be 5ms.

[0063] For the evaluation dimension, the intermediate evaluation value can be determined according to the sub-information to be evaluated of each of at least two evaluation indicators. Specifically, since there are multiple evaluation dimensions, in the process of determining the intermediate evaluation value, the intermediate evaluation value of each evaluation dimension can be determined one by one, or the sub-information to be evaluated of each evaluation indicator under multiple evaluation dimensions can be processed simultaneously in a many-to-many manner. For each evaluation dimension, the intermediate evaluation value refers to the evaluation value calculated based on the sub-information to be evaluated of at least two evaluation indicators under it.

[0064] For example, for the evaluation dimension 310, the evaluation dimension 310 may have evaluation indicators 310_1, ..., evaluation indicators 310_P1, and the historical information may include sub-information to be evaluated 301_1, ..., and sub-information to be evaluated 301_P1 of the evaluation indicator 310_1. Thus, the intermediate evaluation value 310_0 of the evaluation dimension 310 may be determined according to the sub-information to be evaluated 301_1, ..., and sub-information to be evaluated 301_P1.

[0065] For another example, for the evaluation dimension 320, the evaluation dimension 320 may have evaluation indicators 320_1, ..., and evaluation indicators 320_P2, and the historical information may include sub-information to be evaluated 302_1, ..., and sub-information to be evaluated 302_P2 of the evaluation indicator 320_1. Thus, the intermediate evaluation value 320_0 of the evaluation dimension 320 may be determined according to the sub-information to be evaluated 302_1, ..., and sub-information to be evaluated 302_P2.

[0066] By analogy, for the evaluation dimension 3M0, the evaluation dimension 3M0 may have evaluation indicators 3M0_1, ..., evaluation indicators 3M0_PM, and the historical information may include sub-information to be evaluated 30M_1, ..., and sub-information to be evaluated 30M_PM of the evaluation indicator 3M0_1. Thus, the intermediate evaluation value 3M0_0 of the evaluation dimension 3M0 may be determined based on the sub-information to be evaluated 30M_1, ..., and sub-information to be evaluated 30M_PM.

[0067] After determining the intermediate evaluation values ​​of the evaluation dimensions, the target evaluation value 300 can be determined according to the intermediate evaluation values ​​of at least two evaluation dimensions. The target evaluation value 300 refers to the final evaluation value calculated by integrating the intermediate evaluation values ​​of at least two evaluation dimensions. For example, the target evaluation value 300 can be determined according to the intermediate evaluation value 310_0, the intermediate evaluation value 320_0, ..., the intermediate evaluation value 3240_0.

[0068] Specifically, in the process of determining the target evaluation value 300, multiple intermediate evaluation values ​​may be processed simultaneously to determine the target evaluation value 300. Alternatively, in the process of determining the target evaluation value 300, each intermediate evaluation value may be processed one by one, and after generating a candidate evaluation value corresponding to each evaluation dimension, multiple candidate evaluation values ​​may be uniformly merged to determine the target evaluation value 300.

[0069] According to an embodiment of the present disclosure, by subdividing the evaluation dimension into at least two evaluation indicators and collecting the sub-information to be evaluated of the evaluation indicator, the detailed characteristics of the historical data can be fully captured. For the evaluation dimension, the intermediate evaluation value is calculated by using the sub-information to be evaluated of the subordinate evaluation indicators, thereby ensuring the refinement and pertinence of the evaluation. On this basis, by combining the intermediate evaluation values ​​of each evaluation dimension, the target evaluation value is obtained, which not only improves the accuracy of the evaluation, but also enhances the adaptability to different target scenarios. In addition, the target evaluation value can effectively guide the determination of the target query operation, help improve the efficiency of query request processing and the accuracy of request processing results, and meet the needs of complex and changing application scenarios.

[0070] Figure 3B An example schematic diagram of a process of determining an intermediate evaluation value according to respective sub-information to be evaluated of at least two evaluation indicators according to an embodiment of the present disclosure is schematically shown.

[0071] like Figure 3B As shown, in 300B, taking the evaluation dimension 310 as an example, the process of determining the intermediate evaluation value 310_0 of the evaluation dimension 310 is explained.

[0072] The evaluation dimension 310 may include evaluation indicators 310_1, ..., evaluation indicators 310_P1. For the evaluation indicators, the sub-information to be evaluated of the evaluation indicators may be evaluated according to the influence function used for the evaluation indicators to obtain expected values. Specifically, during the evaluation process, the expected value of each evaluation indicator may be determined in a one-by-one evaluation manner, or the sub-information to be evaluated of multiple evaluation indicators may be evaluated simultaneously in a many-to-many manner.

[0073] For example, for the evaluation indicator 310_1, the sub-information 301_1 to be evaluated of the evaluation indicator 310_1 can be evaluated according to the influence function 303_1 used for the evaluation indicator 310_1 to obtain the expected value 304_1. Similarly, for the evaluation indicator 310_P1, the sub-information 301_P1 to be evaluated of the evaluation indicator 310_P1 can be evaluated according to the influence function 303_P1 used for the evaluation indicator 310_P1 to obtain the expected value 304_P1.

[0074] In one example, the influence function may include an influence function and an influence coefficient. The influence function may be determined according to the degree of influence of the evaluation indicator on the intermediate evaluation value. For example, the degree of influence may include very high, relatively high, general, and low. For example, for the evaluation indicator "average query time in the last 10 times", it is very close to the expected value of the intermediate evaluation value of the performance dimension and is in a linear relationship. Therefore, the influence function for "average query time in the last 10 times" may be a first-order function of f = X. Alternatively, for the evaluation indicator "the increase value of the CPU of the data source in the last 3 minutes", its influence on the expected value of the intermediate evaluation value of the performance dimension is very large. Therefore, the influence function for "the increase value of the CPU of the data source in the last 3 minutes" may be an exponential function of f = ex. Alternatively, the influence function may also be a quadratic function, a cubic function, a logarithmic function, a power function, or the like.

[0075] In another example, the influence coefficient can be determined based on the correlation between the sub-information to be evaluated and the intermediate evaluation value. The influence coefficient can be used to scale the result obtained by processing the sub-information to be evaluated according to the influence function used for the evaluation indicator so that it is mapped to the intermediate evaluation value. For example, the correlation may include positive correlation and negative correlation. For example, for the evaluation indicator "10 average query times", it is very close to the expected value of the intermediate evaluation value of the performance dimension, so the influence coefficient can be set to 0.85. Alternatively, for the evaluation indicator "data source memory rise value in the past 3 minutes", since it expands very quickly and the value may be large, the influence coefficient can be set to 0.00000000013. Alternatively, for the evaluation indicator of negative correlation, the influence coefficient can use (a–kx) or 1 / kx to achieve the calculation of the intermediate evaluation value of positive correlation.

[0076] It should be noted that in the process of determining the influence function and the influence coefficient, the influence function for each evaluation indicator can be determined one by one according to the degree of influence of the evaluation indicator on the intermediate evaluation value, and the influence system for the evaluation indicator can be determined according to the correlation between the sub-information to be evaluated and the intermediate evaluation value of the evaluation indicator. Alternatively, the influence functions and influence coefficients of multiple evaluation dimensions can be determined simultaneously in a many-to-many manner. The method for determining the influence function and influence coefficient of the evaluation indicator can be configured according to actual business needs and is not limited here. For example, the method for determining the influence function and the influence coefficient can include at least one of the following: a determination method based on historical experience and a determination method based on machine learning.

[0077] The determination method based on historical experience refers to adjusting the influence function and influence coefficient of each evaluation indicator based on the experience of the technicians and the deviation between the calculated value and the actual value. The determination method based on machine learning refers to using a machine learning model for training, obtaining the deviation between the calculated value and the actual value as feedback for model training, and optimizing and adjusting the mathematical model based on continuous feedback. Alternatively, a determination method based on machine learning can be used to train a set of results, and then fine-tune the parameters based on the experience of the technicians to achieve a more precise effect.

[0078] After obtaining the expected value of each evaluation indicator, for the evaluation dimension, an intermediate evaluation value can be determined according to at least two expected values. Specifically, in the process of determining the intermediate evaluation value, the intermediate evaluation value of each evaluation dimension can be determined one by one in a one-to-one manner. Alternatively, at least two expected values ​​of multiple evaluation dimensions can be processed in a many-to-many manner to determine the intermediate evaluation values ​​of multiple evaluation dimensions. The intermediate evaluation value can be used to optimize and process the query request processing result. For example, the intermediate evaluation value 310_0 can be determined according to the expected value 304_1 of the evaluation indicator 310_1, ..., the expected value 304_P1 of the evaluation indicator 310_P1.

[0079] In a specific example, taking the performance dimension as an example, the evaluation indicators may include at least one of the following: average query time in the past 10 times, average query time in the past hour, average query time in the same hour yesterday, average query time in the same hour last week, data source CPU, number of data source connections, data source disk IO, data source disk IO increase value in the past 3 minutes, data source memory, data source memory increase value in the past 3 minutes and data source network IO, etc.

[0080] Based on the evaluation indicators of the above performance dimensions, the intermediate evaluation value of the performance dimension can be determined using the following formula (1).

[0081]

[0082] in, Characterizes the intermediate evaluation value of the performance dimension, Characterizes the influence coefficient used to evaluate the indicator, Characterizes the influence function used to evaluate the indicator, The sub-information to be evaluated that represents the evaluation indicator, Characterize the first evaluation indicator, Characterizes the Ith evaluation indicator. It should be noted that is the specific value of each evaluation indicator. For example, for the evaluation indicator "average query time of the last 10 times", which is 210ms, the value is 210.

[0083] In another specific example, taking the success rate dimension as an example, the evaluation indicators may include at least one of the following: average availability in the past 10 times, average availability in the past hour, average availability in the same hour yesterday, average availability in the same hour last week, data source CPU, data source CPU increase value in the past 3 minutes, number of data source connections, data source disk IO, data source memory, data source memory increase value in the past 3 minutes and data source network IO, etc.

[0084] Based on the evaluation indicators of the above success rate dimensions, the intermediate evaluation value of the success rate dimension can be determined using the following formula (2).

[0085]

[0086] in, Characterizes the intermediate evaluation value of the success rate dimension, Characterizes the influence coefficient used to evaluate the indicator, Characterizes the influence function used to evaluate the indicator, The sub-information to be evaluated that represents the evaluation indicator, Characterize the first evaluation indicator, Represents the Jth evaluation indicator.

[0087] It should be noted that the specific selection of evaluation indicators for each evaluation dimension may vary depending on the data source. Each of the above evaluation indicators is data at a certain moment. For example, for the data at 12:00:00 on November 11, 2024, based on the data at these moments, a lot of management data can also be calculated and analyzed. For example, the average, maximum, 95th percentile, minimum and median values ​​of various time dimensions, including the number of statistical times, hours, minutes, days, weeks, months, years, year-on-year time and month-on-month time.

[0088] According to the embodiments of the present disclosure, the sub-information to be evaluated of the evaluation index is refined by introducing an influence function, and the setting of the influence function is based on the degree of influence of the evaluation index on the intermediate evaluation value and the correlation between the sub-information to be evaluated and the intermediate evaluation value, which enables the evaluation process to more objectively reflect the actual contribution and correlation of each index, and helps to obtain a more accurate expected value. On this basis, the intermediate evaluation value is determined by combining at least two expected values, thereby achieving a comprehensive evaluation of the evaluation dimension, which not only improves the accuracy of the evaluation, but also enhances the adaptability and flexibility of the system to different scenarios, and helps to ensure the efficiency and accuracy of subsequent query operations.

[0089] Figure 3C An example schematic diagram of a process of determining a target evaluation value according to respective intermediate evaluation values ​​of at least two evaluation dimensions according to an embodiment of the present disclosure is schematically shown.

[0090] like Figure 3C As shown, in 300C, taking the intermediate evaluation values ​​of the evaluation dimension including the intermediate evaluation values ​​310_0, ..., and the intermediate evaluation value 320_0 as an example, the process of determining the target evaluation value 300 according to each intermediate evaluation value is explained.

[0091] For the evaluation dimensions, the intermediate evaluation values ​​can be normalized respectively to convert the dimensional data of multiple expected values ​​into dimensionless data to obtain normalized evaluation values. Specifically, in the process of determining the normalized evaluation values, the normalized evaluation values ​​of each evaluation dimension can be determined one by one, or the intermediate evaluation values ​​of multiple evaluation dimensions can be processed simultaneously in a many-to-many manner. The normalized evaluation value can be a value in the range of 0 to 1. Normalization processing can convert data of different dimensions or ranges to the same standard, so that the evaluation values ​​of different evaluation dimensions can be calculated under the same standard.

[0092] For example, the intermediate evaluation value 310_0 is normalized to obtain the normalized evaluation value 305_1, ..., and the intermediate evaluation value 3M0_0 is normalized to obtain the normalized evaluation value 305_M. In a specific example, the intermediate evaluation values ​​are normalized to obtain the normalized evaluation values ​​as shown in the following equations (3) and (4).

[0093]

[0094] Among them, x represents the intermediate evaluation value, Characterizes the average value.

[0095] It should be noted that, for the performance dimension, the smaller the intermediate evaluation value of the performance dimension, the closer the normalized evaluation value is to 1; the larger the intermediate evaluation value of the performance dimension, the closer the normalized evaluation value is to 0. For the success rate dimension, its normalized evaluation value can be rounded off. If the rounded evaluation value is 1, it indicates that the query is expected to succeed; if the rounded evaluation value is 0, it indicates that the query is expected to fail.

[0096] According to the embodiments of the present disclosure, for the evaluation dimensions, by normalizing the intermediate evaluation values, each intermediate evaluation value can be adjusted to the same standard scale, eliminating the impact of the differences in the dimensions and magnitudes of different evaluation dimensions, thereby ensuring the comparability and accuracy of the evaluation results. On this basis, by comprehensively determining the target evaluation value based on the normalized evaluation values ​​of at least two evaluation dimensions, not only the rationality of the evaluation is improved, but also the adaptability to different scenarios is enhanced, thereby ensuring the efficiency and accuracy of the query operation.

[0097] It should be noted that for enterprise service instances, the business scenarios of query requests are diverse, and the trade-off strategies for the comprehensive analysis of the request processing results of a query request are different, that is, query requests in some scenarios focus more on success rate, while query requests in some scenarios focus more on performance.

[0098] In one example, query requests may be hierarchically divided and managed based on a preset method. The preset method may be a North Star coordinate, for example, the horizontal coordinate is the weight of the success rate dimension of the query request, the vertical coordinate is the weight of the performance dimension of the query request, and the weight may be quantified using a percentage in the interval [0, 1].

[0099] In one example, a target configuration parameter 307 can be determined from at least one candidate configuration parameter for the target scene 306 according to the target scene 306. The target scene 306 can be a scene to which the query request is applicable. The candidate configuration parameters can be configured for the candidate scenes in advance according to the importance of each candidate scene for the evaluation dimension of the query request. Specifically, since there are multiple candidate scenes, during the configuration of the candidate configuration parameters, the candidate configuration parameters of each candidate scene can be configured one by one in a one-to-one manner, or the importance of the evaluation dimensions of the query request for multiple candidate scenes can be processed simultaneously in a many-to-many manner. The candidate configuration parameters all include weights for each evaluation dimension for the candidate scene.

[0100] For example, in the scenario of user's bank card account, the user can accept the occasional query failure caused by network anomaly or database anomaly, such as the prompt (system anomaly, please try again later!), but it is difficult to find that the user balance is not added in time after the deposit, that is, the tolerance for data accuracy anomaly is low, and the tolerance for success rate has a certain space. The candidate configuration parameters for the scenario of user's bank card account can be shown in the following formula (5).

[0101]

[0102] in, candidate configuration parameters characterizing a scenario for a user's bank card account, The ratio of success rate to performance is 2.5, which means that in the scenario of user's bank card account, more emphasis is placed on success rate; Characterize the weights of performance dimensions, Characterize the weight of the success rate dimension.

[0103] For another example, in the promotional marketing scenario of a merchant, related products will be recommended to users to increase sales. When the number of user visits increases sharply and the database pressure is high, it is obvious that the default recommendation scheme of the cache can be used as a backup, instead of the most accurate and customized detailed recommendation scheme for the user, to improve the access success rate and performance of the user page and thus improve the conversion rate of orders. That is, the tolerance for data accuracy is high, and the tolerance for data availability and performance is low. The candidate configuration parameters for the promotional marketing scenario of a merchant can be shown in the following formula (6).

[0104]

[0105] in, Characterize candidate configuration parameters for promotional marketing scenarios for merchants, The ratio of success rate to performance is 1.33, which means that in the promotion and marketing activities of merchants, more attention is paid to the success rate; Characterize the weights of performance dimensions, Characterize the weight of the success rate dimension.

[0106] For another example, in the video playback scenario, a surge in user visits can easily lead to a surge in video query database requests. At this time, there are generally two strategies. One is to keep circling and waiting for the cache query results to be returned, which is essentially an unavailable state; the other is to reduce the image quality and reduce the number of video frames. At this time, the picture is actually very different from the original picture and is a distorted picture. There is also a combination of the two solutions. Obviously, no matter which solution is used, it reflects a high tolerance for data accuracy and data availability. The candidate configuration parameters for the video playback scenario can be shown in the following formula (7).

[0107]

[0108] in, Characterize candidate configuration parameters for video playback scenarios, The ratio of the characterization success rate to the performance is 0.5, which means that in the video playback scenario, more emphasis is placed on performance; Characterize the weights of performance dimensions, Characterize the weight of the success rate dimension.

[0109] After obtaining the normalized evaluation values ​​of the evaluation dimensions, the target evaluation value can be determined according to the normalized evaluation values ​​of at least two evaluation dimensions. Specifically, in the process of determining the target evaluation value, the target evaluation value of each evaluation dimension can be determined one by one in a one-to-one manner. In one example, for each evaluation dimension, the normalized evaluation values ​​can be accumulated according to the weight to obtain the target evaluation value. Alternatively, the normalized evaluation values ​​of multiple evaluation dimensions can also be processed simultaneously in a many-to-many manner.

[0110] For example, the target configuration parameter 307 for the target scenario 306 may include a weight 307_1 for the first evaluation dimension, ..., a weight 307_M for the Mth evaluation dimension. For the first evaluation dimension, the normalized evaluation value 305_1 of the evaluation dimension may be processed according to the weight 307_1 for the first evaluation dimension to obtain a cumulative value 308_1 of the first evaluation dimension. Similarly, for the Mth evaluation dimension, the normalized evaluation value 305_M of the evaluation dimension may be processed according to the weight 307_M for the Mth evaluation dimension to obtain a cumulative value 308_M of the Mth evaluation dimension. On this basis, the cumulative values ​​of the evaluation dimensions may be accumulated to obtain a target evaluation value 300.

[0111] In a specific example, the target evaluation value 300 may be determined using the following formula (8).

[0112]

[0113] in, Characterize the target evaluation value, represents the normalized evaluation value of the qth evaluation dimension, Characterizes the weight of the qth evaluation dimension, Characterizes the first evaluation dimension, Represents the Qth evaluation dimension, Belongs to [0,Q]. For example, when the evaluation dimension includes the performance dimension and the success rate dimension, Q=2.

[0114] According to an embodiment of the present disclosure, a target configuration parameter is determined from at least one candidate configuration parameter for the target scenario according to the target scenario, and the normalized evaluation value is accumulated based on the weight of the evaluation dimension in the target configuration parameter to obtain the target evaluation value, thereby achieving accurate calculation of the target evaluation value. Since the weight represents the importance of the evaluation dimension to the target evaluation value, the pertinence of each evaluation dimension is improved by accumulating the normalized evaluation value according to the weight.

[0115] Figure 4 An example schematic diagram of a process of determining a target configuration strategy from at least one first candidate configuration strategy for a target scenario based on evaluation information according to an embodiment of the present disclosure is schematically shown.

[0116] like Figure 4As shown, in 400, a candidate configuration policy set 402 may be configured for a candidate scenario 401. Specifically, since there may be multiple candidate scenarios 401, during the configuration process of the candidate configuration policy set 402, the candidate configuration policy set 402 of each candidate scenario 401 may be configured one by one in a one-to-one configuration manner, or the candidate policy set 402 may be configured for multiple candidate scenarios 401 at the same time in a many-to-many manner according to the division manner of the multiple candidate scenarios. The candidate configuration policy set 402 may include a second candidate configuration policy 401_1, ..., a second candidate configuration policy 401_S.

[0117] The specific form of the second candidate configuration strategy can be configured according to actual business needs and is not limited here. For example, the second candidate configuration strategy can be in the form of Pair "A, B", where A represents the evaluation value interval and B represents the query operation for the evaluation value interval. The candidate configuration strategy set 402 can be a sequential set of the above-mentioned second candidate configuration strategies.

[0118] For example, for business scenarios with high accuracy requirements, no matter what the calculated target evaluation value is, only the main database that is 100% accurate but cannot ensure high availability and performance can be queried. Therefore, the candidate configuration strategy set 402 for business scenarios with high accuracy requirements can be {Pair《[0,2], query data source main database》}.

[0119] Alternatively, for a business scenario with few update scenarios, the candidate configuration strategy set 402 for the business scenario with few update scenarios may be {Pair《[0.5,2], query the database instance with database cache enabled》, Pair《[0,0.5], data source from library》}.

[0120] Alternatively, for general business scenarios, the candidate configuration strategy set 402 for the general business scenario can be {Pair "(0.8, 2], query the data source main library", Pair "[0, 0.8], query the data source slave library"}. Taking the above general business scenario as an example, when the target evaluation value is 1.6, the target configuration strategy can be determined to be Pair "(0.8, 2], query the data source main library", which indicates that the target query operation for the query request is to query the data source main library. Alternatively, when the target evaluation value is 0.6, the target configuration strategy can be determined to be Pair "[0, 0.8], query the data source slave library", which indicates that the target query operation for the query request is to query the data source slave library.

[0121] Alternatively, for business scenarios where the use of cache is recommended, the candidate configuration strategy set 402 for the business scenarios where the use of cache is recommended can be {Pair "(0.8, 2], query the data source slave library", Pair "[0.5, 0.8], query the data source slave library and synchronously maintain the cached value, that is, the redis cache preheating buffer", Pair "[0, 0.5], query the cache first, and query the database slave library if no result is found"}.

[0122] Alternatively, for business scenarios that recommend the use of multi-level cache, such as redis cache and memory cache, the candidate configuration strategy set 402 for the business scenario that recommends the use of multi-level cache can be {Pair "(0.8,2], query the data source slave library", Pair "[0.5,0.8], query the data source slave library and synchronously maintain the value of the redis cache, that is, the redis cache preheating buffer", Pair "[0.3,0.5], prioritize querying the redis cache, and if no result is found, query the database slave library, and synchronously maintain the value of the memory cache, that is, the memory cache preheating buffer", Pair "[0,0.3], prioritize querying the memory cache, and if no result is found, query the redis cache, and if no result is found, query the database slave library"}.

[0123] Alternatively, for business scenarios with higher availability tolerance, the candidate configuration strategy set 402 for the business scenarios with higher availability tolerance can be {Pair《(1,2], query the data source slave library", Pair《[0.8,1], query the data source slave library and synchronously maintain the value of the redis cache, that is, the redis cache preheating buffer zone", Pair《[0.5,0.8], prioritize querying the redis cache, and if the query is not found, query the database slave library, and synchronously maintain the value of the memory cache, that is, the memory cache preheating buffer zone", Pair《[0.3,0.5], prioritize querying the memory cache, and if the query is not found, query the redis cache, and if the query is still not found, query the database slave library", Pair《[0,0.3], prioritize querying the memory cache, and if the query is not found, query the redis cache, and if the query is still not found, directly throw an error to prevent continuous backlog of pressure on the database》}.

[0124] It should be noted that the above-mentioned candidate query operations are only examples. In actual applications, different candidate query operations can be set in combination with the design characteristics of the specific business system and the actual technical architecture. Candidate query operations may include at least one of the following: real-time expansion and reduction of the slave database cluster; real-time closing or reopening of the first or some levels of cache in the multi-level cache; fusing the return value of the interface, not querying, and directly throwing an exception; sending fault emails to inform technical personnel to follow up and handle the corresponding timely; sending early warning notifications to customers to let users have psychological expectations that the current system has a large number of visits and may have abnormal scenarios; in order to save costs, cloud platform database instances that do not usually distribute traffic can turn on query request traffic distribution or turn off traffic distribution, etc.

[0125] After receiving the query request, at least one first candidate configuration strategy 404 may be determined from at least two candidate configuration strategy sets for different candidate scenarios according to the target scenario 403 of the query request. For example, at least one first candidate configuration strategy 404 may include first candidate configuration strategy 404_1, ..., first candidate configuration strategy 404_T.

[0126] According to the embodiments of the present disclosure, by determining the first candidate configuration strategy according to the target scene in a plurality of candidate configuration strategy sets for different candidate scenes, refined management of query operations is achieved. Since the second candidate configuration strategy provides corresponding candidate query operations for different candidate evaluation value intervals, this hierarchical and targeted configuration strategy selection mechanism enables the most suitable target configuration strategy to be accurately selected from a large number of candidate registration strategies according to the specific needs of the target scene, which helps to execute query requests more efficiently and improves the accuracy and reliability of request processing results.

[0127] The above-mentioned first candidate configuration strategy may include a candidate evaluation value interval and a candidate query operation for the candidate evaluation value interval. After obtaining at least one first candidate configuration strategy 404, the candidate evaluation value intervals of each first candidate configuration strategy may be matched according to the evaluation information 405 to obtain a target evaluation value interval 407. Each candidate evaluation value interval may be used to define a range of evaluation values, and the candidate evaluation value interval is associated with a specific candidate configuration strategy to guide which query operation is specifically selected.

[0128] For example, the evaluation information 405 can be matched with the candidate evaluation value intervals 405_1, ..., and the candidate evaluation value intervals 405_T in the first candidate configuration strategy 404_1, respectively, to determine the target evaluation value interval 407 that matches the evaluation information 405. On this basis, the first candidate configuration strategy for the target evaluation value interval 407 can be determined as the target configuration strategy 408.

[0129] According to the embodiments of the present disclosure, by comparing and matching the evaluation information with the candidate evaluation value intervals, the target evaluation value interval that best matches the current evaluation result can be identified, and the strategy for the query request that best suits the target scenario can be quickly and accurately screened out from a large number of candidate configuration strategies, ensuring that the selection of the configuration strategy has a high degree of relevance and applicability, which helps to ensure the efficiency and accuracy of subsequent query operations.

[0130] Figure 5 An example schematic diagram of a process of executing a query request and obtaining a request processing result based on a target query operation according to an embodiment of the present disclosure is schematically shown.

[0131] like Figure 5 As shown, in 500, the hit evaluation value interval is determined according to the evaluation information, and the candidate query operation corresponding to the evaluation value interval is determined as the target query operation 501. In the case where the target query operation 501 includes N query sub-operations and N is greater than 1, each query sub-operation has an execution order. For example, the target query operation 501 includes query sub-operations 501_1, ..., query sub-operations 501_N.

[0132] For each of the above query sub-operations, the nth query sub-operation 502 can be determined in the target query operation 501 according to the execution order, and based on the nth query sub-operation 502, the query request 503 is executed, and operation S510 is executed. In operation S510, is the query request 503 executed successfully? If so, the request processing result 504 can be determined.

[0133] If not, then the n+1th query sub-operation 502 may be determined in the target query operation 501 according to the execution order. Based on the n+1th query sub-operation 502, the query request 503 is executed, and the above operations are repeatedly performed until the query request 503 is successfully executed, thereby the result obtained when the query request 503 is successfully executed can be used as the request processing result 504.

[0134] According to an embodiment of the present disclosure, when a target query operation includes the opening permissions of multiple query sub-operations, these query sub-operations are executed sequentially in a predetermined order until the query request is successfully executed. Then, the query sub-operation (n+1) can be automatically transferred to the execution order to continue execution until a successful query path is found, thereby ensuring the continuity and robustness of the query process and improving the flexibility and success rate of the query operation.

[0135] Figure 6 An example schematic diagram of a request processing process according to an embodiment of the present disclosure is schematically shown.

[0136] like Figure 6As shown, in 600 , in response to receiving a query request 601 for a target scenario, a target evaluation value 603 is determined according to historical requests related to the query request 601 and historical information 602 of at least two evaluation dimensions.

[0137] According to the target scenario of the query request 601, at least one first candidate configuration strategy 604 is determined in the candidate configuration strategy set for different candidate scenarios, for example, first candidate configuration strategy 604_1, ..., first candidate configuration strategy 604_U. The first candidate configuration strategy may include a candidate evaluation value interval and a candidate query operation for the candidate evaluation value interval. For example, the first candidate configuration strategy 604_1 corresponds to the candidate evaluation value interval 605_1, ..., and the first candidate configuration strategy 604_U corresponds to the candidate evaluation value interval 605_U.

[0138] After obtaining the target evaluation value 603, the target evaluation value 603 can be matched with the candidate evaluation value intervals 605_1, ..., and candidate evaluation value intervals 605_U respectively to determine the target evaluation value interval 606 hit by the target evaluation value 603. On this basis, the first candidate configuration strategy corresponding to the target evaluation value interval 606 can be determined as the target configuration strategy 607.

[0139] The target configuration strategy 607 indicates a target query operation 608 for the target evaluation value interval 606. The target query operation 608 may include query sub-operations 608_1, ..., query sub-operations 608_V, and each query sub-operation has an execution order.

[0140] For each of the above query sub-operations, the nth query sub-operation 609 can be determined in the target query operation 608 according to the execution order, and the query request 601 is executed based on the nth query sub-operation 609, and operation S610 is performed. In operation S610, is the query request 601 executed successfully? If so, the request processing result 610 can be determined.

[0141] If not, then the n+1th query sub-operation 609 can be determined in the target query operation 608 according to the execution order. Based on the n+1th query sub-operation 609, the query request 601 is executed, and the above operations are repeatedly performed until the query request 601 is successfully executed, thereby, the result obtained when the query request 601 is successfully executed can be used as the request processing result 610.

[0142] The above are only exemplary embodiments, but are not limited thereto, and may also include other request processing methods known in the art, as long as the accuracy of the request processing result can be improved.

[0143] Figure 7The block diagram of a request processing device according to an embodiment of the present disclosure is schematically shown.

[0144] like Figure 7 As shown, the request processing device 700 may include a first determination module 710 , a second determination module 720 and an execution module 730 .

[0145] The first determination module 710 is used to determine evaluation information in response to receiving a query request for a target scenario, based on historical requests related to the query request and historical information of at least two evaluation dimensions, wherein the evaluation information characterizes the degree of influence of each of the at least two evaluation dimensions on the processing results of the historical requests.

[0146] The second determination module 720 is configured to determine a target configuration strategy from at least one first candidate configuration strategy for the target scenario according to the evaluation information, wherein the target configuration strategy indicates a target query operation for the query request.

[0147] The execution module 730 is used to execute the query request based on the target query operation and obtain the request processing result.

[0148] According to an embodiment of the present disclosure, the evaluation information includes a target evaluation value, the evaluation dimension has at least two evaluation indicators, and the historical information includes sub-information to be evaluated of the evaluation indicator.

[0149] According to an embodiment of the present disclosure, the first determination module 710 may include a first determination submodule and a second determination submodule.

[0150] The first determination submodule is used to determine an intermediate evaluation value according to the sub-information to be evaluated of each of at least two evaluation indicators with respect to the evaluation dimension.

[0151] The second determination submodule is used to determine the target evaluation value according to the respective intermediate evaluation values ​​of at least two evaluation dimensions.

[0152] According to an embodiment of the present disclosure, the first determining submodule may include an evaluation unit and a first determining unit.

[0153] The evaluation unit is used to evaluate the sub-information to be evaluated of the evaluation indicator according to the influence function used for the evaluation indicator to obtain an expected value, wherein the influence function is determined according to the influence degree of the evaluation indicator on the intermediate evaluation value and the correlation between the sub-information to be evaluated and the intermediate evaluation value.

[0154] The first determining unit is used to determine an intermediate evaluation value according to at least two expected values ​​for the evaluation dimension.

[0155] According to an embodiment of the present disclosure, the second determining submodule may include a normalization processing unit and a second determining unit.

[0156] The normalization processing unit is used to normalize the intermediate evaluation values ​​according to the evaluation dimension to obtain a normalized evaluation value.

[0157] The second determining unit is used to determine the target evaluation value according to the normalized evaluation values ​​of at least two evaluation dimensions.

[0158] According to an embodiment of the present disclosure, the second determining unit may include a determining subunit and an accumulating subunit.

[0159] A determination subunit is used to determine a target configuration parameter from at least one candidate configuration parameter for the target scenario according to the target scenario, wherein the target configuration parameter includes a weight of each evaluation dimension, and the weight represents the importance of the evaluation dimension to the target evaluation value.

[0160] The accumulation subunit is used to accumulate the normalized evaluation values ​​according to the weights of the evaluation dimensions to obtain the target evaluation value.

[0161] According to an embodiment of the present disclosure, the first candidate configuration strategy is determined from at least two candidate configuration strategy sets for different candidate scenarios according to the target scenario. The candidate configuration strategy set includes at least one second candidate configuration strategy, which indicates a candidate query operation for a candidate evaluation value interval.

[0162] According to an embodiment of the present disclosure, the second determination module 720 may include a matching submodule and a third determination submodule.

[0163] The matching submodule is used to match at least one candidate evaluation value interval according to the evaluation information to obtain a target evaluation value interval.

[0164] The third determination submodule is used to determine the first candidate configuration strategy for the target evaluation value interval as the target configuration strategy.

[0165] According to an embodiment of the present disclosure, when a target query operation includes N query sub-operations and N is greater than 1, each query sub-operation has an execution order.

[0166] According to an embodiment of the present disclosure, the execution module 730 may include an execution submodule and an acquisition submodule.

[0167] The execution submodule is used to execute the query request based on the nth query sub-operation in response to the failure of executing the query request based on the nth query sub-operation according to the execution order, wherein n≤N-1.

[0168] The submodule is used to obtain the result obtained when the query request is successfully executed as the request processing result.

[0169] Figure 8A block diagram of an electronic device suitable for implementing a request processing method according to an embodiment of the present disclosure is schematically shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0170] like Figure 8 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 to a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0171] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0172] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as the request processing method. For example, in some embodiments, the request processing method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the request processing method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the request processing method in any other appropriate manner (e.g., by means of firmware).

[0173] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0174] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0175] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0176] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0177] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0178] A computer system may include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises through computer programs running on respective computers and having a client-server relationship to each other. The server may be a cloud server, a server in a distributed system, or a server combined with a blockchain.

[0179] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0180] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A request processing method, comprising: In response to receiving a query request for a target scenario, determining evaluation information according to historical requests related to the query request and historical information of at least two evaluation dimensions, wherein the evaluation information represents the degree of influence of each of the at least two evaluation dimensions on a processing result of the historical request; determining, according to the evaluation information, a target configuration strategy from among at least one first candidate configuration strategy for the target scenario, wherein the target configuration strategy indicates a target query operation for the query request; and Based on the target query operation, the query request is executed to obtain a request processing result.

2. The method according to claim 1, wherein: The evaluation information includes a target evaluation value, the evaluation dimension has at least two evaluation indicators, and the historical information includes sub-information to be evaluated of the evaluation indicator; The determining of the evaluation information according to the historical requests related to the query request and the historical information of each of the at least two evaluation dimensions includes: For the evaluation dimension, determining an intermediate evaluation value according to the sub-information to be evaluated of each of the at least two evaluation indicators; as well as The target evaluation value is determined according to the respective intermediate evaluation values ​​of the at least two evaluation dimensions.

3. The method according to claim 2, wherein: The determining of the intermediate evaluation value according to the respective sub-information to be evaluated of the at least two evaluation indicators comprises: For the evaluation indicator, the sub-information to be evaluated of the evaluation indicator is evaluated according to an influence function for the evaluation indicator to obtain an expected value, wherein the influence function is determined according to the influence degree of the evaluation indicator on the intermediate evaluation value and the correlation between the sub-information to be evaluated and the intermediate evaluation value; and For the evaluation dimension, the intermediate evaluation value is determined according to at least two of the expected values.

4. The method according to claim 2, wherein: Determining the target evaluation value according to the respective intermediate evaluation values ​​of the at least two evaluation dimensions comprises: For the evaluation dimension, normalizing the intermediate evaluation value to obtain a normalized evaluation value; and The target evaluation value is determined according to the normalized evaluation values ​​of each of the at least two evaluation dimensions.

5. The method according to claim 4, wherein: Determining the target evaluation value according to the normalized evaluation values ​​of the at least two evaluation dimensions comprises: According to the target scenario, determining a target configuration parameter from at least one candidate configuration parameter for the target scenario, wherein the target configuration parameter includes a weight of the evaluation dimension, and the weight represents the importance of the evaluation dimension to the target evaluation value; and For the evaluation dimension, the normalized evaluation values ​​are accumulated according to the weights to obtain the target evaluation value.

6. The method according to any one of claims 1 to 5, wherein: The first candidate configuration strategy is determined according to the target scenario from at least two candidate configuration strategy sets for different candidate scenarios; The candidate configuration strategy set includes at least one second candidate configuration strategy, where the second candidate configuration strategy indicates a candidate query operation for a candidate evaluation value interval.

7. The method according to claim 6, wherein: Determining a target configuration strategy from at least one first candidate configuration strategy for the target scenario according to the evaluation information includes: According to the evaluation information, matching the at least one candidate evaluation value interval to obtain a target evaluation value interval; and A first candidate configuration strategy for the target evaluation value interval is determined as the target configuration strategy.

8. The method according to any one of claims 1 to 5, wherein: In the case where the target query operation includes N query sub-operations and N is greater than 1, each of the query sub-operations has an execution order; The executing the query request based on the target query operation to obtain the request processing result includes repeatedly performing the following operations until the query request is successfully executed: In response to a failure in executing the query request based on the nth query sub-operation, executing the query request based on the n+1th query sub-operation according to the execution order, where n≤N-1; and The result obtained when the query request is successfully executed is used as the request processing result.

9. A request processing device, comprising: A first determination module is configured to, in response to receiving a query request for a target scenario, determine evaluation information according to historical requests related to the query request and historical information of at least two evaluation dimensions, wherein the evaluation information represents the degree of influence of each of the at least two evaluation dimensions on a processing result of the historical request; a second determining module, configured to determine, according to the evaluation information, a target configuration strategy from at least one first candidate configuration strategy for the target scenario, wherein the target configuration strategy indicates a target query operation for the query request; and An execution module is used to execute the query request based on the target query operation to obtain a request processing result.

10. The device according to claim 9, wherein: The evaluation information includes a target evaluation value, the evaluation dimension has at least two evaluation indicators, and the historical information includes sub-information to be evaluated of the evaluation indicator; The first determining module comprises: A first determination submodule is used to determine an intermediate evaluation value for the evaluation dimension according to the sub-information to be evaluated of each of the at least two evaluation indicators; as well as The second determination submodule is used to determine the target evaluation value according to the respective intermediate evaluation values ​​of the at least two evaluation dimensions.

11. The device according to claim 10, wherein: The first determining submodule includes: an evaluation unit, configured to evaluate the sub-information to be evaluated of the evaluation indicator according to an influence function for the evaluation indicator, and obtain an expected value, wherein the influence function is determined according to the influence degree of the evaluation indicator on the intermediate evaluation value and the correlation between the sub-information to be evaluated and the intermediate evaluation value; and The first determining unit is used to determine the intermediate evaluation value according to at least two expected values ​​for the evaluation dimension.

12. The device according to claim 10, wherein: The second determining submodule includes: a normalization processing unit, configured to perform normalization processing on the intermediate evaluation value with respect to the evaluation dimension to obtain a normalized evaluation value; and The second determining unit is used to determine the target evaluation value according to the normalized evaluation values ​​of each of the at least two evaluation dimensions.

13. The device according to claim 12, wherein: The second determining unit includes: a determining subunit, configured to determine, according to the target scenario, a target configuration parameter from among at least one candidate configuration parameter for the target scenario, wherein the target configuration parameter includes a weight of the evaluation dimension, the weight representing an importance of the evaluation dimension to the target evaluation value; and The accumulation subunit is used to accumulate the normalized evaluation values ​​according to the weights for the evaluation dimensions to obtain the target evaluation values.

14. The device according to any one of claims 9 to 13, wherein: The first candidate configuration strategy is determined according to the target scenario from at least two candidate configuration strategy sets for different candidate scenarios; The candidate configuration strategy set includes at least one second candidate configuration strategy, where the second candidate configuration strategy indicates a candidate query operation for a candidate evaluation value interval.

15. The device according to claim 14, wherein: The second determining module comprises: a matching submodule, configured to match the at least one candidate evaluation value interval according to the evaluation information to obtain a target evaluation value interval; and The third determination submodule is used to determine the first candidate configuration strategy for the target evaluation value interval as the target configuration strategy.

16. The device according to any one of claims 9 to 13, wherein: In the case where the target query operation includes N query sub-operations and N is greater than 1, each of the query sub-operations has an execution order; The execution module includes repeatedly performing the following operations until the query request is successfully executed: an execution submodule, configured to, in response to a failure in executing the query request based on the nth query sub-operation, execute the query request based on the n+1th query sub-operation according to the execution order, wherein n≤N-1; and The obtaining submodule is used to use the result obtained when the query request is successfully executed as the request processing result.

17. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

18. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

19. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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