SQL (Structured Query Language) scheduling resource exception handling method and device based on intelligent analysis engine

Abstract

The invention discloses an SQL (structured query language) scheduling resource exception handling method and device based on an intelligent analysis engine, by implementing the method and the device, the intelligent analysis engine can pair and determine scheduling resources by combining an SQL architecture on the basis of multiple data analysis functions, so that the data analysis function can be ensured to obtain correct resource allocation and support, that is to say, the data analysis efficiency is improved. The resource scheduling accuracy of each data analysis function is improved, and meanwhile, the operation stability of the data analysis function is improved; furthermore, a monitoring and processing method for SQL scheduling resource abnormity is set, the SQL scheduling resource use condition of each data analysis function can be monitored and analyzed in real time, the problem of insufficient resource allocation or excessive resource allocation can be found in time, abnormal scheduling resources can be processed in time, and the service life of the SQL scheduling resources is prolonged. The discovery speed and processing efficiency of the abnormal scheduling resources can be improved, and meanwhile the operation stability and reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and device for handling SQL scheduling resource exceptions based on an intelligent analysis engine. Background Art

[0002] With the rapid development of information technology, data analysis has become a key means for many industries, especially the gaming industry, to make business decisions and optimize user experience. In order to gain a deeper understanding of player behavior, improve game performance and optimize marketing strategies, game developers and operators need to rely on powerful data analysis tools to process and analyze massive amounts of game data.

[0003] At the same time, in the traditional data analysis process, SQL (Structured Query Language) plays a vital role as a standard language for data query and operation. The game data analysis system can realize intelligent resource scheduling based on SQL. However, in practical applications, due to the complexity and diversity of game data and the dynamic changes in analysis needs, the allocation of SQL scheduling resources often faces challenges. On the one hand, different data analysis functions have different requirements for SQL scheduling resources. On the other hand, the scale of game data continues to expand with the user base and the update of game versions, which further aggravates the tension of SQL scheduling resources and may cause some key data analysis tasks to be delayed or failed due to insufficient resources. It can be seen that it is particularly important to provide a corresponding solution to the technical problems of low accuracy and low efficiency in the allocation and management of SQL scheduling resources in the prior art. Summary of the invention

[0004] The present invention provides a method and device for handling SQL scheduling resource exceptions based on an intelligent analysis engine, which can improve the resource allocation accuracy of different data analysis functions, improve the operating stability and reliability of data analysis functions, and simultaneously improve the discovery speed and handling efficiency of SQL resource allocation exceptions.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses a method for handling SQL scheduling resource exceptions based on an intelligent analysis engine. The method is applied in the intelligent analysis engine and comprises:

[0006] The intelligent analysis engine obtains a set of data analysis functions required for the target game; the set of data analysis functions includes one or more of an event analysis function, a retention analysis function, an SQL analysis function, a distribution analysis function, and a tag analysis function;

[0007] The intelligent analysis engine determines the SQL scheduling resources required for each data analysis function in the SQL architecture based on the preset SQL architecture and according to the data analysis function set;

[0008] The intelligent analysis engine determines whether the SQL scheduling resources of each of the data analysis functions meet the scheduling requirements of the data analysis function;

[0009] When it is determined that there is an SQL scheduling resource of the data analysis function that does not meet the scheduling requirements of the data analysis function, the intelligent analysis engine determines the abnormal scheduling resources of the data analysis function from the SQL scheduling resources of the data analysis function, and executes exception handling operations matching the abnormal scheduling resources, wherein the exception handling operations include query statement correction and resource scheduling adjustment.

[0010] As an optional implementation manner, in the first aspect of the present invention, before the intelligent analysis engine executes the optimization operation matching the abnormal scheduling resource, the method further includes:

[0011] The intelligent analysis engine determines the data analysis function corresponding to the abnormal scheduling resource as the target data analysis function to be processed currently;

[0012] The intelligent analysis engine obtains a plurality of historical processing solutions, and uses the target data analysis function as a screening benchmark to screen a plurality of first processing solutions that are adapted to the target data analysis function from all the historical processing solutions;

[0013] The intelligent analysis engine screens all the first processing solutions according to a plurality of preset resource optimization directions to obtain a second processing solution;

[0014] And, the intelligent analysis engine executes an exception handling operation matching the exception scheduling resource, including:

[0015] The intelligent analysis engine performs an exception handling operation on the abnormal scheduling resource according to the second processing scheme.

[0016] As an optional implementation, in the first aspect of the present invention, the intelligent analysis engine determines, based on a preset SQL architecture and according to the data analysis function set, the SQL scheduling resources required for each data analysis function in the SQL architecture, including:

[0017] For each data analysis function in the data analysis function set, the intelligent analysis engine determines a function usage requirement corresponding to the data analysis function, and performs requirement identification on the function usage requirement to obtain a requirement identification result corresponding to the function usage requirement, wherein the requirement identification result includes key conditions and / or keywords that respond to the function identification requirement;

[0018] The intelligent analysis engine determines a target execution scenario and a corresponding scenario type that responds to the functional usage requirement; and selects a target operation mode that is adapted to the functional usage requirement from a plurality of preset set operation modes according to the target execution scenario and the corresponding scenario type;

[0019] The intelligent analysis engine determines the SQL scheduling resources required for the data analysis function in the SOL architecture based on a preset SQL architecture, in combination with the demand identification result and the target operation mode.

[0020] As an optional implementation, in the first aspect of the present invention, the key condition includes a filtering condition; the keyword includes a sorting keyword and / or an aggregation keyword; the multiple set operation modes include MapReduce processing, parallel computing or stream processing;

[0021] In the process of determining, by the intelligent analysis engine based on the preset SQL architecture, the SQL scheduling resources required for the data analysis function in the SOL architecture in combination with the demand identification result and the target operation mode, the method further includes:

[0022] The intelligent analysis engine monitors the real-time resource information of different data analysis functions; the real-time resource information of each data analysis function includes the resource information currently allocated to the data analysis function and its corresponding resource usage information;

[0023] The intelligent analysis engine determines the real-time resource usage priority of each data analysis function based on the real-time resource information of all the data analysis functions; and then performs real-time dynamic adjustment on the SQL scheduling resources allocated to each data analysis function based on the real-time resource usage priority of each data analysis function.

[0024] As an optional implementation, in the first aspect of the present invention, the intelligent analysis engine determines whether the SQL scheduling resource of each of the data analysis functions meets the scheduling requirements of the data analysis function, including:

[0025] For each SQL scheduling resource of the data analysis function, the intelligent analysis engine monitors multiple usage information of the SQL scheduling resource, the usage information including at least one of scheduling resource utilization, resource event log, SQL query execution time, CPU and memory consumption;

[0026] For each of the data analysis functions, the intelligent analysis engine performs information comparison on all the real-time usage information corresponding to the data analysis function to obtain an information comparison result of all the real-time usage information for the data analysis result;

[0027] Among them, for each of the data analysis functions, when the scheduling resource utilization corresponding to the data analysis function indicates that there is a resource bottleneck, and / or when there are record events / error logs of resource scheduling exceptions in the resource event log corresponding to the data analysis function, and / or when the SQL query execution time corresponding to the data analysis function is higher than the preset execution time, and / or when the CPU and memory consumption corresponding to the data analysis function indicates that it is higher than the preset running consumption, it is determined that the SQL scheduling resources of the data analysis function do not meet the scheduling requirements of the data analysis function.

[0028] As an optional implementation, in the first aspect of the present invention, the preset multiple resource optimization directions include at least one of SQL statement optimization, configuration optimization, and abnormal event processing; the configuration optimization includes memory configuration optimization and / or CPU configuration optimization and / or disk configuration optimization; the abnormal event processing includes deadlock processing or blocking processing;

[0029] The intelligent analysis engine performs an exception handling operation on the abnormal scheduling resource according to the second processing scheme, including:

[0030] When all of the resource optimization directions include the SQL statement optimization, the intelligent analysis engine determines the abnormal query statement corresponding to the abnormal scheduling resource; and performs statement analysis, statement disassembly, and statement reorganization on the abnormal query statement in sequence to obtain a reorganized query statement corresponding to the abnormal query statement, and performs resource reallocation on the data analysis function corresponding to the reorganized query statement.

[0031] As an optional implementation, in the first aspect of the present invention, the intelligent analysis engine performs an exception handling operation on the abnormal scheduling resource according to the second processing scheme, further comprising:

[0032] When all the resource optimization directions include the memory configuration optimization, the intelligent analysis engine performs at least one operation of memory value adjustment, memory release, and circular reference removal on the data analysis function according to the memory resources of the server; the memory value adjustment is used to adjust the maximum memory and minimum memory set for the data analysis function;

[0033] When all of the resource optimization directions include the CPU configuration optimization, the intelligent analysis engine performs parallelism adjustment on the data analysis function according to the CPU resources of the server;

[0034] When all of the resource optimization directions include the disk configuration optimization, the intelligent analysis engine performs IO read and write monitoring and / or disk layout adjustment on the data analysis function.

[0035] The second aspect of the present invention discloses a SQL scheduling resource exception handling device based on an intelligent analysis engine, the device is applied in the intelligent analysis engine, and the device includes:

[0036] An acquisition module, used to acquire a set of data analysis functions required for a target game; the set of data analysis functions includes one or more of an event analysis function, a retention analysis function, an SQL analysis function, a distribution analysis function, and a tag analysis function;

[0037] A first determination module is used to determine, based on a preset SQL architecture and according to the data analysis function set, the SQL scheduling resources required for each data analysis function in the SQL architecture;

[0038] A judgment module, used to judge whether the SQL scheduling resource of each data analysis function meets the scheduling requirements of the data analysis function;

[0039] A second determination module is used to determine an abnormal scheduling resource of the data analysis function from the SQL scheduling resources of the data analysis function when the determination module determines that there is an SQL scheduling resource of the data analysis function that does not meet the scheduling requirements of the data analysis function;

[0040] The exception handling module is used to execute the exception handling operation matching the abnormal scheduling resource, wherein the exception handling operation includes query statement correction and resource scheduling adjustment.

[0041] As an optional implementation, in the second aspect of the present invention, the second determination module is further used to determine the data analysis function corresponding to the abnormal scheduling resource as the target data analysis function to be processed currently before the abnormal processing module performs the optimization operation matching the abnormal scheduling resource;

[0042] The acquisition module is also used to acquire multiple historical processing solutions;

[0043] The device also includes:

[0044] A screening module, configured to screen a plurality of first processing schemes adapted to the target data analysis function from all the historical processing schemes, taking the target data analysis function as a screening criterion;

[0045] The screening module is further used to screen all the first processing solutions according to a plurality of preset resource optimization directions to obtain a second processing solution;

[0046] And, the manner in which the exception handling module performs the exception handling operation matching the exception scheduling resource specifically includes:

[0047] According to the second processing scheme, an exception processing operation is performed on the abnormal scheduling resource.

[0048] As an optional implementation, in the second aspect of the present invention, the first determination module determines, based on a preset SQL architecture and according to the data analysis function set, the SQL scheduling resources required for each data analysis function in the SQL architecture, specifically including:

[0049] For each data analysis function in the data analysis function set, determining a function usage requirement corresponding to the data analysis function, and performing requirement identification on the function usage requirement to obtain a requirement identification result corresponding to the function usage requirement, wherein the requirement identification result includes key conditions and / or keywords that respond to the function identification requirement;

[0050] Determine a target execution scenario and its corresponding scenario type that responds to the functional usage requirement; and select a target operation mode that is adapted to the functional usage requirement from a plurality of preset set operation modes according to the target execution scenario and its corresponding scenario type;

[0051] Based on the preset SQL architecture, combined with the demand identification result and the target operation mode, the SQL scheduling resources required for the data analysis function in the SOL architecture are determined.

[0052] As an optional implementation, in the second aspect of the present invention, the key condition includes a filtering condition; the keyword includes a sorting keyword and / or an aggregation keyword; the multiple set operation modes include MapReduce processing, parallel computing or stream processing;

[0053] The device also includes:

[0054] A monitoring module, configured to monitor the real-time resource information of different data analysis functions in the process in which the first determination module determines the SQL scheduling resources required for the data analysis function in the SOL architecture based on a preset SQL architecture, in combination with the demand identification result and the target operation mode; the real-time resource information of each data analysis function includes the resource information currently allocated to the data analysis function and its corresponding resource usage information;

[0055] The first determination module is further used to determine the real-time resource usage priority of each of the data analysis functions according to the real-time resource information of all the data analysis functions;

[0056] The resource adjustment module is used to perform real-time dynamic adjustment on the SQL scheduling resources allocated to each data analysis function according to the real-time resource usage priority of each data analysis function.

[0057] As an optional implementation, in the second aspect of the present invention, the judgment module judges whether the SQL scheduling resource of each data analysis function meets the scheduling requirements of the data analysis function in a manner specifically including:

[0058] For each SQL scheduling resource of the data analysis function, monitoring multiple usage information of the SQL scheduling resource, the usage information including at least one of scheduling resource utilization, resource event log, SQL query execution time, CPU and memory consumption;

[0059] For each of the data analysis functions, perform information comparison on all the real-time usage information corresponding to the data analysis function to obtain an information comparison result of all the real-time usage information for the data analysis result;

[0060] Among them, for each of the data analysis functions, when the scheduling resource utilization corresponding to the data analysis function indicates that there is a resource bottleneck, and / or when there are record events / error logs of resource scheduling exceptions in the resource event log corresponding to the data analysis function, and / or when the SQL query execution time corresponding to the data analysis function is higher than the preset execution time, and / or when the CPU and memory consumption corresponding to the data analysis function indicates that it is higher than the preset running consumption, it is determined that the SQL scheduling resources of the data analysis function do not meet the scheduling requirements of the data analysis function.

[0061] As an optional implementation, in the second aspect of the present invention, the preset multiple resource optimization directions include at least one of SQL statement optimization, configuration optimization, and abnormal event processing; the configuration optimization includes memory configuration optimization and / or CPU configuration optimization and / or disk configuration optimization; the abnormal event processing includes deadlock processing or blocking processing;

[0062] The manner in which the exception handling module performs the exception handling operation on the abnormal scheduling resource according to the second processing scheme specifically includes:

[0063] When all the resource optimization directions include the SQL statement optimization, determine the abnormal query statement corresponding to the abnormal scheduling resource; and perform statement analysis, statement disassembly, and statement reorganization on the abnormal query statement in sequence to obtain a reorganized query statement corresponding to the abnormal query statement, and perform resource reallocation on the data analysis function corresponding to the reorganized query statement.

[0064] As an optional implementation, in the second aspect of the present invention, the manner in which the exception handling module performs the exception handling operation on the abnormal scheduling resource according to the second processing scheme specifically further includes:

[0065] When all the resource optimization directions include the memory configuration optimization, at least one of memory value adjustment, memory release, and circular reference removal is performed on the data analysis function according to the memory resources of the server; the memory value adjustment is used to adjust the maximum memory and minimum memory set for the data analysis function;

[0066] When all of the resource optimization directions include the CPU configuration optimization, performing parallelism adjustment on the data analysis function according to the CPU resources of the server;

[0067] When all of the resource optimization directions include the disk configuration optimization, IO read and write monitoring and / or disk layout adjustment are performed on the data analysis function.

[0068] The third aspect of the present invention discloses another SQL scheduling resource exception handling device based on an intelligent analysis engine, the device comprising:

[0069] A memory storing executable program code;

[0070] a processor coupled to the memory;

[0071] The processor calls the executable program code stored in the memory to execute the SQL scheduling resource exception handling method based on the intelligent analysis engine disclosed in the first aspect of the present invention.

[0072] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the SQL scheduling resource exception handling method based on the intelligent analysis engine disclosed in the first aspect of the present invention.

[0073] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0074] In an embodiment of the present invention, a method for handling SQL scheduling resource exceptions based on an intelligent analysis engine is provided. The method is applied to an intelligent analysis engine, and the method includes: the intelligent analysis engine obtains a set of data analysis functions required for a target game; the set of data analysis functions includes one or more of an event analysis function, a retention analysis function, an SQL analysis function, a distribution analysis function, and a tag analysis function; the intelligent analysis engine determines the SQL scheduling resources required for each data analysis function in the SQL architecture based on the data analysis function set based on a preset SQL architecture; the intelligent analysis engine determines whether the SQL scheduling resources of each data analysis function meet the scheduling requirements of the data analysis function; when it is determined that the SQL scheduling resources of a certain data analysis function do not meet the scheduling requirements of the data analysis function, the intelligent analysis engine determines the abnormal scheduling resources of the data analysis function from the SQL scheduling resources of the data analysis function, and executes an exception handling operation that matches the abnormal scheduling resources, and the exception handling operation includes query statement correction and resource scheduling adjustment. It can be seen that by implementing the present invention, the intelligent analysis engine is based on multiple data analysis functions and combines the SQL architecture to pair and determine scheduling resources, thereby ensuring that data analysis tasks / functions can obtain correct resource allocation and support; improving the operational stability and reliability of the data analysis function; further, a processing method for SQL scheduling resource anomalies is set up, which can monitor and analyze the SQL scheduling resource usage of each data analysis function in real time, promptly discover problems of insufficient or excessive resource allocation, and improve the discovery speed and processing efficiency of abnormal scheduling resources; thereby improving the operational stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0076] Figure 1 It is a flow chart of a method for handling SQL scheduling resource exceptions based on an intelligent analysis engine disclosed in an embodiment of the present invention;

[0077] Figure 2 It is a flowchart of another method for handling SQL scheduling resource exceptions based on an intelligent analysis engine disclosed in an embodiment of the present invention;

[0078] Figure 3 It is a structural schematic diagram of a SQL scheduling resource exception processing device based on an intelligent analysis engine disclosed in an embodiment of the present invention;

[0079] Figure 4 It is a structural schematic diagram of another SQL scheduling resource exception handling device based on an intelligent analysis engine disclosed in an embodiment of the present invention;

[0080] Figure 5 It is a structural diagram of another SQL scheduling resource exception handling device based on an intelligent analysis engine disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0081] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0083] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0084] The present invention discloses a method and device for handling SQL scheduling resource exceptions based on an intelligent analysis engine. The intelligent analysis engine is based on multiple data analysis functions and combines the SQL architecture to pair and determine scheduling resources, thereby ensuring that data analysis tasks / functions can obtain correct resource allocation and support; improving the operational stability and reliability of data analysis functions; further, a method for handling SQL scheduling resource exceptions is set up, which can monitor and analyze the SQL scheduling resource usage of each data analysis function in real time, timely discover the problem of insufficient or excessive resource allocation, and improve the discovery speed and processing efficiency of abnormal scheduling resources; and then improve the operational stability and reliability of the system. The following are detailed descriptions.

[0085] Embodiment 1

[0086] See also Figure 1 , Figure 1 This is a flow chart of a method for handling SQL scheduling resource exceptions based on an intelligent analysis engine disclosed in an embodiment of the present invention. Figure 1 The described method for handling SQL scheduling resource exceptions based on an intelligent analysis engine can be applied to a device for handling SQL scheduling resource exceptions based on an intelligent analysis engine, and the embodiments of the present invention are not limited thereto. Figure 1 As shown, the SQL scheduling resource exception handling method based on the intelligent analysis engine may include the following operations:

[0087] 101. The intelligent analysis engine obtains the set of data analysis functions required for the target game.

[0088] In an embodiment of the present invention, the data analysis function set includes one or more of an event analysis function, a retention analysis function, an SQL analysis function, a distribution analysis function, and a label analysis function.

[0089] 102. The intelligent analysis engine determines the SQL scheduling resources required for each data analysis function in the SQL architecture based on the preset SQL architecture and the data analysis function set.

[0090] 103. The intelligent analysis engine determines whether the SQL scheduling resources of each data analysis function meet the scheduling requirements of the data analysis function.

[0091] 104. When it is determined that the SQL scheduling resources of a certain data analysis function do not meet the scheduling requirements of the data analysis function, the intelligent analysis engine determines the abnormal scheduling resources of the data analysis function from the SQL scheduling resources of the data analysis function, and executes the exception handling operations that match the abnormal scheduling resources. The exception handling operations include query statement correction and resource scheduling adjustment.

[0092] It can be seen that the implementation Figure 1 The described method for handling SQL scheduling resource exceptions based on an intelligent analysis engine is based on multiple data analysis functions and combines the SQL architecture to pair and determine scheduling resources, thereby ensuring that data analysis tasks / functions can receive correct resource allocation and support; improving the operational stability and reliability of data analysis functions; further, a method for handling SQL scheduling resource exceptions is set up, which can monitor and analyze the SQL scheduling resource usage of each data analysis function in real time, promptly discover problems of insufficient or excessive resource allocation, and improve the discovery speed and processing efficiency of abnormal scheduling resources; thereby improving the operational stability and reliability of the system.

[0093] Embodiment 2

[0094] See also Figure 2 , Figure 2 It is a flow chart of another method for handling SQL scheduling resource exceptions based on an intelligent analysis engine disclosed in an embodiment of the present invention. Figure 2 The described method for handling SQL scheduling resource exceptions based on an intelligent analysis engine can be applied to a device for handling SQL scheduling resource exceptions based on an intelligent analysis engine, and the embodiments of the present invention are not limited thereto. Figure 2 As shown, the SQL scheduling resource exception handling method based on the intelligent analysis engine may include the following operations:

[0095] 201. The intelligent analysis engine obtains a set of data analysis functions required for the target game.

[0096] 202. The intelligent analysis engine determines the SQL scheduling resources required for each data analysis function in the SQL architecture based on the preset SQL architecture and the data analysis function set.

[0097] 203. The intelligent analysis engine determines whether the SQL scheduling resources of each data analysis function meet the scheduling requirements of the data analysis function.

[0098] 204. When it is determined that there are SQL scheduling resources of a certain data analysis function that do not meet the scheduling requirements of the data analysis function, the intelligent analysis engine determines abnormal scheduling resources of the data analysis function from the SQL scheduling resources of the data analysis function.

[0099] 205. The intelligent analysis engine determines the data analysis function corresponding to the abnormal scheduling resource as the target data analysis function to be processed currently.

[0100] 206. The intelligent analysis engine obtains multiple historical processing solutions, and uses the target data analysis function as a screening criterion to screen multiple first processing solutions that are adapted to the target data analysis function from all historical processing solutions.

[0101] 207. The intelligent analysis engine screens all first processing solutions according to the preset multiple resource optimization directions to obtain a second processing solution;

[0102] 208. The intelligent analysis engine performs an exception handling operation on the abnormal scheduling resource according to the second processing solution.

[0103] In the embodiment of the present invention, for other descriptions of step 201 to step 204, please refer to other specific descriptions of step 101 to step 104 in embodiment 1, and the embodiment of the present invention will not be repeated here.

[0104] It can be seen that implementation Figure 2The described SQL scheduling resource exception handling method based on the intelligent analysis engine can perform an adaptation analysis on the scheduling requirements of the SQL scheduling resources allocated for each data analysis function by the intelligent analysis engine, and in the case of abnormal scheduling resources, obtain multiple historical processing solutions, screen the solutions step by step according to the target data analysis function and multiple resource optimization directions, so as to obtain the final required second processing solution, and then perform exception handling on the abnormal scheduling resources based on this, thereby improving the efficiency and accuracy of determining the second processing solution, and also improving the exception handling speed and accuracy of abnormal scheduling resources.

[0105] In an optional embodiment, the intelligent analysis engine determines the SQL scheduling resources required for each data analysis function in the SQL architecture based on the preset SQL architecture and according to the data analysis function set, specifically including:

[0106] For each data analysis function in the data analysis function set, the intelligent analysis engine determines a function usage requirement corresponding to the data analysis function, and performs requirement identification on the function usage requirement to obtain a requirement identification result corresponding to the function usage requirement, wherein the requirement identification result includes key conditions and / or keywords that respond to the function identification requirement;

[0107] The intelligent analysis engine determines the target execution scenario and the corresponding scenario type of the response function usage requirement; and selects the target operation mode that adapts to the function usage requirement from a plurality of preset set operation modes according to the target execution scenario and the corresponding scenario type;

[0108] Based on the preset SQL architecture, the intelligent analysis engine combines the demand identification results and the target operation mode to determine the SQL scheduling resources required for the data analysis function in the SOL architecture.

[0109] It can be seen that in this optional embodiment, by identifying the function usage requirements, it helps to understand the specific requirements of each data analysis function; by determining the target execution scenario and its corresponding scenario type and selecting an adaptive target operation mode, it is ensured that the selected operation mode is highly matched with the actual requirements of the data analysis function, thereby improving the accuracy and efficiency of resource allocation.

[0110] In another optional embodiment, the key condition includes a filtering condition; the keyword includes a sorting keyword and / or an aggregation keyword; the multiple set operation modes include MapReduce processing, parallel computing or stream processing;

[0111] In the process of the intelligent analysis engine determining the SQL scheduling resources required for the data analysis function in the SOL architecture based on the preset SQL architecture, combined with the demand identification result and the target operation mode, the method further includes:

[0112] The intelligent analysis engine monitors the real-time resource information of different data analysis functions; the real-time resource information of each data analysis function includes the resource information currently allocated to the data analysis function and its corresponding resource usage information;

[0113] The intelligent analysis engine determines the real-time resource usage priority of each data analysis function based on the real-time resource information of all data analysis functions; then, based on the real-time resource usage priority of each data analysis function, it performs real-time dynamic adjustment on the SQL scheduling resources allocated to each data analysis function.

[0114] It can be seen that in this optional embodiment, by real-time monitoring of the resource usage of different data analysis functions, the intelligent analysis engine can quickly capture the dynamic changes in resource requirements, thereby realizing real-time dynamic adjustment of SQL scheduling resources. This mechanism of dynamic resource monitoring and adjustment ensures the timeliness and flexibility of resource allocation, allowing the system to better respond to sudden resource demands or resource surpluses, which is conducive to improving the stability and reliability of the system.

[0115] In another optional embodiment, the intelligent analysis engine determines whether the SQL scheduling resource of each data analysis function meets the scheduling requirements of the data analysis function in the following manner:

[0116] For each SQL scheduling resource of a data analysis function, the intelligent analysis engine monitors multiple usage information of the SQL scheduling resource, the usage information including at least one of scheduling resource utilization, resource event log, SQL query execution time, CPU and memory consumption;

[0117] For each data analysis function, the intelligent analysis engine performs information comparison on all real-time usage information corresponding to the data analysis function, and obtains information comparison results for all real-time usage information of the data analysis result;

[0118] Among them, for each data analysis function, when the scheduling resource utilization corresponding to the data analysis function indicates that there is a resource bottleneck, and / or when there are record events / error logs of resource scheduling exceptions in the resource event log corresponding to the data analysis function, and / or when the SQL query execution time corresponding to the data analysis function is higher than the preset execution time, and / or when the CPU and memory consumption corresponding to the data analysis function is higher than the preset operating consumption, it is determined that the SQL scheduling resources of the data analysis function do not meet the scheduling requirements of the data analysis function.

[0119] It can be seen that in this optional embodiment, by monitoring multiple usage information of SQL scheduling resources, including scheduling resource utilization, resource event logs, SQL query execution time, CPU and memory consumption, etc., the detail and accuracy of the evaluation of the usage of SQL scheduling resources are improved; thereafter, through information comparison, it can be identified whether there are resource bottlenecks, resource scheduling anomalies, long execution time or excessive resource consumption, so as to accurately judge whether the SQL scheduling resources meet the scheduling requirements of the data analysis function, thereby improving the judgment accuracy of the adaptability of the SQL scheduling resources and scheduling requirements of each data analysis function.

[0120] In another optional embodiment, the preset multiple resource optimization directions include at least one of SQL statement optimization, configuration optimization, and abnormal event processing; configuration optimization includes memory configuration optimization and / or CPU configuration optimization and / or disk configuration optimization; abnormal event processing includes deadlock processing or blocking processing;

[0121] The above intelligent analysis engine performs the exception handling operation on the abnormal scheduling resource according to the second processing scheme, specifically including:

[0122] When all resource optimization directions include SQL statement optimization, the intelligent analysis engine determines the abnormal query statement corresponding to the abnormal scheduling resource; and performs statement analysis, statement disassembly, and statement reorganization on the abnormal query statement in sequence to obtain the reorganized query statement corresponding to the abnormal query statement, and performs resource reallocation on the data analysis function corresponding to the reorganized query statement.

[0123] In this optional embodiment, in the case where resource optimization includes SQL statement optimization, optimized query statements are obtained through statement analysis, disassembly and reorganization. This optimization not only reduces the execution time of SQL queries, reduces CPU and memory consumption, but also improves the accuracy and completeness of query results.

[0124] In this optional embodiment, optionally, the intelligent analysis engine performs the exception handling operation on the abnormal scheduling resource according to the second processing scheme, and specifically includes:

[0125] When all resource optimization directions include memory configuration optimization, the intelligent analysis engine performs at least one operation of memory value adjustment, memory release, and circular reference removal on the data analysis function according to the memory resources of the server; the memory value adjustment is used to adjust the maximum memory and minimum memory set for the data analysis function;

[0126] When all resource optimization directions include CPU configuration optimization, the intelligent analysis engine performs parallelism adjustment on the data analysis function based on the server's CPU resources;

[0127] When all resource optimization directions include disk configuration optimization, the intelligent analysis engine performs IO read and write monitoring and / or disk layout adjustment on the data analysis function.

[0128] In this optional embodiment, for resource optimization directions including configuration optimization, operations such as memory value adjustment, memory release, and circular reference removal help release occupied or wasted memory resources and improve memory utilization; the parallelism adjustment can make full use of the server's CPU resources and improve data processing speed; IO read and write monitoring and disk layout adjustment can optimize disk access efficiency and help reduce disk I / O bottlenecks.

[0129] It can be seen that in this optional embodiment, it covers optimization from the SQL statement level to the adjustment at the system configuration level, as well as processing strategies for specific abnormal matters, thereby improving the comprehensiveness of monitoring of abnormal scheduling resources; wherein, the processing mechanism of the abnormal scheduling resources can improve the execution efficiency of SQL statements, optimize system resource configuration, efficiently handle abnormal matters and other aspects, thereby improving the processing flexibility and accuracy of abnormal scheduling resources; and then helping to improve the stability and reliability of the system.

[0130] In another optional embodiment, when all resource optimization directions include abnormal event processing, the intelligent analysis engine obtains a record log for abnormal scheduling resources, and the record log at least includes abnormal record details corresponding to the abnormal scheduling resources; the abnormal record details include deadlock processing details or blocking processing details;

[0131] When the exception record details include deadlock processing details, the intelligent analysis engine locates the abnormal transaction and abnormal SQL statement associated with the exception processing resource according to the deadlock processing details;

[0132] The intelligent analysis engine analyzes the execution order and resource occupancy of the abnormal transaction and the abnormal SQL statement to determine the generation information of the deadlock event; and according to the generation information of the deadlock event, adjusts and updates the execution statement of the abnormal SQL statement, and / or adjusts the execution order of the abnormal transaction and isolates the transaction;

[0133] When the exception record details include blocking processing details, the intelligent analysis engine determines the target task currently waiting to be executed and its task type and target resource according to the exception scheduling resources and blocking processing details;

[0134] The intelligent analysis engine performs blocking condition matching according to the target task, its task type and the target resource to obtain a corresponding blocking condition matching result; the blocking condition corresponding to the blocking condition matching includes at least one of slow query, missing index and lock contention;

[0135] When the blocking condition matching result indicates that the conditions corresponding to the slow query and / or the conditions corresponding to the missing index are met, the intelligent analysis engine performs index addition and query statement optimization operations on the target task and target resource;

[0136] When the blocking condition matching result indicates that the condition corresponding to the lock contention is satisfied, the intelligent analysis engine performs lock retrieval and lock release, and / or transaction concurrency adjustment.

[0137] It can be seen that in this optional embodiment, corresponding and accurate intelligent processing flows are set for the two situations of deadlock and blocking. By optimizing transaction design, query statements, lock sequence and transaction isolation level, etc., the occurrence of deadlock and blocking can be effectively reduced, and the processing efficiency of deadlock and blocking can be improved, which in turn helps to improve the concurrency performance and stability of the database.

[0138] Embodiment 3

[0139] See also Figure 3 , Figure 3 It is a structural diagram of a SQL scheduling resource exception handling device based on an intelligent analysis engine disclosed in an embodiment of the present invention. Among them, the SQL scheduling resource exception handling device based on an intelligent analysis engine can be applied to an intelligent analysis engine; in addition, the SQL scheduling resource exception handling device based on an intelligent analysis engine can be a SQL scheduling resource exception handling terminal, device, system or server based on an intelligent analysis engine. The server can be a local server, a remote server, or a cloud server (also called a cloud server). When the server is a non-cloud server, the non-cloud server can communicate with the cloud server, which is not limited in the embodiment of the present invention. Figure 3 As shown, the SQL scheduling resource exception handling device based on the intelligent analysis engine may include an acquisition module 301, a first determination module 302, a judgment module 303, a second determination module 304 and an exception handling module 305, wherein:

[0140] The acquisition module 301 is used to acquire the data analysis function set required for the target game; the data analysis function set includes one or more of event analysis function, retention analysis function, SQL analysis function, distribution analysis function and tag analysis function.

[0141] The first determination module 302 is used to determine the SQL scheduling resources required for each data analysis function in the SQL architecture based on a preset SQL architecture and according to a set of data analysis functions.

[0142] The judgment module 303 is used to judge whether the SQL scheduling resources of each data analysis function meet the scheduling requirements of the data analysis function.

[0143] The second determination module 304 is used to determine abnormal scheduling resources of a data analysis function from the SQL scheduling resources of the data analysis function when the determination module 303 determines that there is an SQL scheduling resource of a certain data analysis function that does not meet the scheduling requirements of the data analysis function.

[0144] The exception handling module 305 is used to execute exception handling operations that match the abnormal scheduling resources. The exception handling operations include query statement correction and resource scheduling adjustment.

[0145] It can be seen that implementation Figure 3 The described SQL scheduling resource exception handling device based on the intelligent analysis engine, the intelligent analysis engine is based on multiple data analysis functions, combined with the SQL architecture to match and determine scheduling resources, thereby ensuring that data analysis tasks / functions can obtain correct resource allocation and support; improving the operating stability and reliability of the data analysis function; further, a processing method for SQL scheduling resource exceptions is set up, which can monitor and analyze the SQL scheduling resource usage of each data analysis function in real time, and promptly discover problems of insufficient or excessive resource allocation, thereby improving the discovery speed and processing efficiency of abnormal scheduling resources; thereby improving the operating stability and reliability of the system.

[0146] In an optional embodiment, the second determination module 304 is further used to determine the data analysis function corresponding to the abnormal scheduling resource as the target data analysis function to be processed currently before the abnormal processing module 305 performs the optimization operation matching the abnormal scheduling resource;

[0147] The acquisition module 301 is also used to acquire multiple historical processing solutions;

[0148] See also Figure 4 , Figure 4 is a structural diagram of another SQL scheduling resource exception handling device based on an intelligent analysis engine disclosed in an embodiment of the present invention, such as Figure 4 As shown, the device also includes a screening module 306, wherein:

[0149] A screening module 306 is used to screen multiple first processing schemes that are adapted to the target data analysis function from all historical processing schemes based on the target data analysis function as a screening criterion;

[0150] The screening module 306 is further used to screen all first processing solutions according to the preset multiple resource optimization directions to obtain a second processing solution;

[0151] And, the manner in which the exception handling module 305 performs the exception handling operation matching the exception scheduling resource specifically includes:

[0152] According to the second processing scheme, an exception processing operation is performed on the abnormal scheduling resource.

[0153] It can be seen that in this optional embodiment, for the SQL scheduling resources allocated to each data analysis function, the intelligent analysis engine can perform adaptation analysis on the scheduling requirements, and for the situation where there are abnormal scheduling resources, by obtaining multiple historical processing solutions, the solutions are screened step by step according to the target data analysis function and multiple resource optimization directions, so as to obtain the final required second processing solution, and then perform exception processing on the abnormal scheduling resources based on this, which improves the efficiency and accuracy of determining the second processing solution, and also improves the exception processing speed and accuracy of abnormal scheduling resources.

[0154] In another optional embodiment, the first determining module 302 determines the SQL scheduling resources required for each data analysis function in the SQL architecture based on the preset SQL architecture and according to the data analysis function set, specifically including:

[0155] For each data analysis function in the data analysis function set, determining a function usage requirement corresponding to the data analysis function, and performing requirement identification on the function usage requirement to obtain a requirement identification result corresponding to the function usage requirement, the requirement identification result including key conditions and / or keywords that respond to the function identification requirement;

[0156] Determine the target execution scenario and its corresponding scenario type that responds to the functional usage requirement; and select the target operation mode that adapts to the functional usage requirement from a plurality of preset set operation modes according to the target execution scenario and its corresponding scenario type;

[0157] Based on the preset SQL architecture, combined with the demand identification results and target operation mode, determine the SQL scheduling resources required for the data analysis function in the SOL architecture.

[0158] It can be seen that in this optional embodiment, by identifying the function usage requirements, it helps to understand the specific requirements of each data analysis function; by determining the target execution scenario and its corresponding scenario type and selecting an adaptive target operation mode, it is ensured that the selected operation mode is highly matched with the actual requirements of the data analysis function, thereby improving the accuracy and efficiency of resource allocation.

[0159] In yet another optional embodiment, the key condition includes a filtering condition; the keyword includes a sorting keyword and / or an aggregation keyword; the multiple set operation modes include MapReduce processing, parallel computing or stream processing;

[0160] like Figure 4 As shown, the device also includes a monitoring module 307, wherein:

[0161] The monitoring module 307 is used to monitor the real-time resource information of different data analysis functions in the process of determining the SQL scheduling resources required for the data analysis function in the SOL architecture by the first determination module 302 based on the preset SQL architecture, combined with the demand identification result and the target operation mode; the real-time resource information of each data analysis function includes the resource information currently allocated to the data analysis function and its corresponding resource usage information;

[0162] The first determination module 302 is further used to determine the real-time resource usage priority of each data analysis function according to the real-time resource information of all data analysis functions;

[0163] The resource adjustment module 308 is used to perform real-time dynamic adjustment on the SQL scheduling resources allocated to each data analysis function according to the real-time resource usage priority of each data analysis function.

[0164] It can be seen that in this optional embodiment, by real-time monitoring of the resource usage of different data analysis functions, the intelligent analysis engine can quickly capture the dynamic changes in resource requirements, thereby realizing real-time dynamic adjustment of SQL scheduling resources. This mechanism of dynamic resource monitoring and adjustment ensures the timeliness and flexibility of resource allocation, allowing the system to better respond to sudden resource demands or resource surpluses, which is conducive to improving the stability and reliability of the system.

[0165] In another optional embodiment, the determination module 303 determines whether the SQL scheduling resource of each data analysis function meets the scheduling requirements of the data analysis function in the following manner:

[0166] For each SQL scheduling resource of the data analysis function, multiple usage information of the SQL scheduling resource is monitored, where the usage information includes at least one of scheduling resource utilization, resource event log, SQL query execution time, and CPU and memory consumption;

[0167] For each data analysis function, perform information comparison on all real-time usage information corresponding to the data analysis function to obtain an information comparison result of all real-time usage information for the data analysis result;

[0168] Among them, for each data analysis function, when the scheduling resource utilization corresponding to the data analysis function indicates that there is a resource bottleneck, and / or when there are record events / error logs of resource scheduling exceptions in the resource event log corresponding to the data analysis function, and / or when the SQL query execution time corresponding to the data analysis function is higher than the preset execution time, and / or when the CPU and memory consumption corresponding to the data analysis function is higher than the preset operating consumption, it is determined that the SQL scheduling resources of the data analysis function do not meet the scheduling requirements of the data analysis function.

[0169] It can be seen that in this optional embodiment, by monitoring multiple usage information of SQL scheduling resources, including scheduling resource utilization, resource event logs, SQL query execution time, CPU and memory consumption, etc., the detail and accuracy of the evaluation of the usage of SQL scheduling resources are improved; thereafter, through information comparison, it can be identified whether there are resource bottlenecks, resource scheduling anomalies, long execution time or excessive resource consumption, so as to accurately judge whether the SQL scheduling resources meet the scheduling requirements of the data analysis function, thereby improving the judgment accuracy of the adaptability of the SQL scheduling resources and scheduling requirements of each data analysis function.

[0170] In another optional embodiment, the preset multiple resource optimization directions include at least one of SQL statement optimization, configuration optimization, and abnormal event processing; configuration optimization includes memory configuration optimization and / or CPU configuration optimization and / or disk configuration optimization; abnormal event processing includes deadlock processing or blocking processing;

[0171] According to the second processing scheme, the exception handling module 305 performs the exception handling operation on the abnormal scheduling resource in the following manner:

[0172] When all resource optimization directions include SQL statement optimization, determine the abnormal query statement corresponding to the abnormal scheduling resource; and perform statement analysis, statement disassembly, and statement reorganization on the abnormal query statement in sequence to obtain the reorganized query statement corresponding to the abnormal query statement, and perform resource reallocation on the data analysis function corresponding to the reorganized query statement.

[0173] In this optional embodiment, the exception handling module 305 performs the exception handling operation on the abnormal scheduling resource according to the second processing scheme, and specifically includes:

[0174] When all resource optimization directions include memory configuration optimization, at least one of memory value adjustment, memory release, and circular reference removal is performed on the data analysis function according to the memory resources of the server; the memory value adjustment is used to adjust the maximum memory and minimum memory set for the data analysis function;

[0175] When all resource optimization directions include CPU configuration optimization, the parallelism of the data analysis function is adjusted according to the CPU resources of the server;

[0176] When all resource optimization directions include disk configuration optimization, the data analysis function performs IO read and write monitoring and / or disk layout adjustment.

[0177] It can be seen that in this optional embodiment, it covers optimization from the SQL statement level to the adjustment at the system configuration level, as well as processing strategies for specific abnormal matters, thereby improving the comprehensiveness of monitoring of abnormal scheduling resources; wherein, the processing mechanism of the abnormal scheduling resources can improve the execution efficiency of SQL statements, optimize system resource configuration, efficiently handle abnormal matters and other aspects, thereby improving the processing flexibility and accuracy of abnormal scheduling resources; and then helping to improve the stability and reliability of the system.

[0178] Embodiment 4

[0179] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of another structure of an SQL scheduling resource exception handling device based on an intelligent analysis engine disclosed in an embodiment of the present invention. Figure 5 As shown, the SQL scheduling resource exception handling device based on the intelligent analysis engine may include:

[0180] A memory 401 storing executable program codes;

[0181] a processor 402 coupled to the memory 401;

[0182] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the SQL scheduling resource exception handling method based on the intelligent analysis engine described in the first embodiment of the present invention or the second embodiment of the present invention.

[0183] Embodiment 5

[0184] An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the SQL scheduling resource exception handling method based on the intelligent analysis engine described in Embodiment 1 or Embodiment 2 of the present invention.

[0185] Embodiment 6

[0186] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the SQL scheduling resource exception handling method based on an intelligent analysis engine described in Embodiment 1 or Embodiment 2.

[0187] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0188] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0189] Finally, it should be noted that the SQL scheduling resource exception handling method and device based on the intelligent analysis engine disclosed in the embodiment of the present invention discloses only the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in the field should understand that it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for handling SQL scheduling resource exceptions based on an intelligent analysis engine, characterized in that: The method is applied in an intelligent analysis engine, and the method comprises: The intelligent analysis engine obtains a set of data analysis functions required for the target game; the set of data analysis functions includes one or more of an event analysis function, a retention analysis function, an SQL analysis function, a distribution analysis function, and a tag analysis function; The intelligent analysis engine determines the SQL scheduling resources required for each data analysis function in the SQL architecture based on the preset SQL architecture and according to the data analysis function set; The intelligent analysis engine determines whether the SQL scheduling resources of each of the data analysis functions meet the scheduling requirements of the data analysis function; When it is determined that there is an SQL scheduling resource of the data analysis function that does not meet the scheduling requirements of the data analysis function, the intelligent analysis engine determines the abnormal scheduling resources of the data analysis function from the SQL scheduling resources of the data analysis function, and executes exception handling operations matching the abnormal scheduling resources, wherein the exception handling operations include query statement correction and resource scheduling adjustment.

2. The SQL scheduling resource exception handling method based on the intelligent analysis engine according to claim 1 is characterized in that: Before the intelligent analysis engine executes the optimization operation matching the abnormal scheduling resource, the method further includes: The intelligent analysis engine determines the data analysis function corresponding to the abnormal scheduling resource as the target data analysis function to be processed currently; The intelligent analysis engine obtains a plurality of historical processing solutions, and uses the target data analysis function as a screening benchmark to screen a plurality of first processing solutions that are adapted to the target data analysis function from all the historical processing solutions; The intelligent analysis engine screens all the first processing solutions according to a plurality of preset resource optimization directions to obtain a second processing solution; And, the intelligent analysis engine executes an exception handling operation matching the exception scheduling resource, including: The intelligent analysis engine performs an exception handling operation on the abnormal scheduling resource according to the second processing scheme.

3. The SQL scheduling resource exception handling method based on the intelligent analysis engine according to claim 1 or 2 is characterized in that: The intelligent analysis engine determines the SQL scheduling resources required for each data analysis function in the SQL architecture based on the preset SQL architecture and according to the data analysis function set, including: For each data analysis function in the data analysis function set, the intelligent analysis engine determines a function usage requirement corresponding to the data analysis function, and performs requirement identification on the function usage requirement to obtain a requirement identification result corresponding to the function usage requirement, wherein the requirement identification result includes key conditions and / or keywords that respond to the function identification requirement; The intelligent analysis engine determines a target execution scenario and a corresponding scenario type that responds to the functional usage requirement; and selects a target operation mode that is adapted to the functional usage requirement from a plurality of preset set operation modes according to the target execution scenario and the corresponding scenario type; The intelligent analysis engine determines the SQL scheduling resources required for the data analysis function in the SOL architecture based on a preset SQL architecture, in combination with the demand identification result and the target operation mode.

4. The SQL scheduling resource exception handling method based on the intelligent analysis engine according to claim 3 is characterized in that: The key conditions include filtering conditions; the keywords include sorting keywords and / or aggregation keywords; The multiple collective operation modes include MapReduce processing, parallel computing or stream processing; In the process of determining, by the intelligent analysis engine based on the preset SQL architecture, the SQL scheduling resources required for the data analysis function in the SOL architecture in combination with the demand identification result and the target operation mode, the method further includes: The intelligent analysis engine monitors the real-time resource information of different data analysis functions; the real-time resource information of each data analysis function includes the resource information currently allocated to the data analysis function and its corresponding resource usage information; The intelligent analysis engine determines the real-time resource usage priority of each data analysis function based on the real-time resource information of all the data analysis functions; and then performs real-time dynamic adjustment on the SQL scheduling resources allocated to each data analysis function based on the real-time resource usage priority of each data analysis function.

5. The SQL scheduling resource exception handling method based on the intelligent analysis engine according to claim 2 is characterized in that: The intelligent analysis engine determines whether the SQL scheduling resource of each data analysis function meets the scheduling requirements of the data analysis function, including: For each SQL scheduling resource of the data analysis function, the intelligent analysis engine monitors multiple usage information of the SQL scheduling resource, the usage information including at least one of scheduling resource utilization, resource event log, SQL query execution time, CPU and memory consumption; For each of the data analysis functions, the intelligent analysis engine performs information comparison on all the real-time usage information corresponding to the data analysis function to obtain an information comparison result of all the real-time usage information for the data analysis result; Among them, for each of the data analysis functions, when the scheduling resource utilization corresponding to the data analysis function indicates that there is a resource bottleneck, and / or when there are record events / error logs of resource scheduling exceptions in the resource event log corresponding to the data analysis function, and / or when the SQL query execution time corresponding to the data analysis function is higher than the preset execution time, and / or when the CPU and memory consumption corresponding to the data analysis function indicates that it is higher than the preset running consumption, it is determined that the SQL scheduling resources of the data analysis function do not meet the scheduling requirements of the data analysis function.

6. The method for handling SQL scheduling resource exceptions based on an intelligent analysis engine according to claim 5, characterized in that: The preset multiple resource optimization directions include at least one of SQL statement optimization, configuration optimization, and abnormal event processing; the configuration optimization includes memory configuration optimization and / or CPU configuration optimization and / or disk configuration optimization; the abnormal event processing includes deadlock processing or blocking processing; The intelligent analysis engine performs an exception handling operation on the abnormal scheduling resource according to the second processing scheme, including: When all of the resource optimization directions include the SQL statement optimization, the intelligent analysis engine determines the abnormal query statement corresponding to the abnormal scheduling resource; The abnormal query statement is sequentially analyzed, disassembled, and reorganized to obtain a reorganized query statement corresponding to the abnormal query statement, and resources are reallocated for the data analysis function corresponding to the reorganized query statement.

7. The method for handling SQL scheduling resource exceptions based on an intelligent analysis engine according to claim 6, characterized in that: The intelligent analysis engine performs an exception handling operation on the abnormal scheduling resource according to the second processing scheme, further comprising: When all the resource optimization directions include the memory configuration optimization, the intelligent analysis engine performs at least one operation of memory value adjustment, memory release, and circular reference removal on the data analysis function according to the memory resources of the server; the memory value adjustment is used to adjust the maximum memory and minimum memory set for the data analysis function; When all of the resource optimization directions include the CPU configuration optimization, the intelligent analysis engine performs parallelism adjustment on the data analysis function according to the CPU resources of the server; When all of the resource optimization directions include the disk configuration optimization, the intelligent analysis engine performs IO read and write monitoring and / or disk layout adjustment on the data analysis function.

8. A SQL scheduling resource exception handling device based on an intelligent analysis engine, characterized in that: The device is applied to an intelligent analysis engine, and the device comprises: An acquisition module, used to acquire a set of data analysis functions required for a target game; the set of data analysis functions includes one or more of an event analysis function, a retention analysis function, an SQL analysis function, a distribution analysis function, and a tag analysis function; A first determination module is used to determine, based on a preset SQL architecture and according to the data analysis function set, the SQL scheduling resources required for each data analysis function in the SQL architecture; A judgment module, used to judge whether the SQL scheduling resource of each data analysis function meets the scheduling requirements of the data analysis function; A second determination module is used to determine an abnormal scheduling resource of the data analysis function from the SQL scheduling resources of the data analysis function when the determination module determines that there is an SQL scheduling resource of the data analysis function that does not meet the scheduling requirements of the data analysis function; The exception handling module is used to execute the exception handling operation matching the abnormal scheduling resource, wherein the exception handling operation includes query statement correction and resource scheduling adjustment.

9. A SQL scheduling resource exception handling device based on an intelligent analysis engine, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the SQL scheduling resource exception handling method based on the intelligent analysis engine as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the SQL scheduling resource exception handling method based on the intelligent analysis engine as described in any one of claims 1-7.

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