CN109495291B

CN109495291B - Calling abnormity positioning method and device and server

Info

Publication number: CN109495291B
Application number: CN201811160467.7A
Authority: CN
Inventors: 王少华
Original assignee: Advanced New Technologies Co Ltd
Current assignee: Advanced New Technologies Co Ltd; Advantageous New Technologies Co Ltd
Priority date: 2018-09-30
Filing date: 2018-09-30
Publication date: 2021-11-16
Anticipated expiration: 2038-09-30
Also published as: CN109495291A

Abstract

The specification provides a calling exception positioning method, device and server. The method comprises the following steps: acquiring a plurality of service calling data; dividing a plurality of service calling data into a plurality of example groups; determining a normal instance group and an abnormal instance group; counting the number of service call data of a plurality of characteristic dimensions in normal instance grouping and abnormal instance grouping; and determining the position of the calling exception according to the quantity of the service calling data of the plurality of characteristic dimensions in the normal instance grouping and the abnormal instance grouping. In the embodiment of the specification, a plurality of calling data are divided into a plurality of example groups, and whether the calling data are normal or not is judged efficiently from the aspect of the example groups; and then, fine-grained analysis is carried out from the aspect of the characteristic dimension, and the position where the call exception occurs is determined according to the quantity of the service call data of each characteristic dimension in the normal instance group and the abnormal instance group, so that the accuracy and the efficiency of determining the position where the call exception occurs are improved.

Description

Calling abnormity positioning method and device and server

Technical Field

The specification belongs to the technical field of internet, and particularly relates to a calling abnormity positioning method, device and server.

Background

In the management and control of the service processing, a specific position where an exception occurs in data call in a service system often needs to be analyzed and determined, so that the exception position can be repaired in time.

At present, most of the existing positioning methods rely on technical personnel to perform one-by-one investigation and analysis on the log records of each structure module in a service system; and then, manually judging the position where the calling abnormity occurs according to the result of the investigation and analysis by combining the experience of technicians. When the method is implemented specifically, because the log records needing to be checked and analyzed in the service system are often more, the data volume needing to be processed is relatively large, and the implementation speed is relatively slow; and the determined location of the call anomaly is relatively inaccurate, as it can be influenced by the individual experience and knowledge of the technician. Therefore, a method for locating call exception is needed to accurately and efficiently determine the location of the call exception in the service system.

Disclosure of Invention

The present specification aims to provide a method, an apparatus and a server for locating a call exception, so as to improve the accuracy and efficiency of determining a location of a call exception occurring in a business system.

The calling exception positioning method, device and server provided by the specification are realized as follows:

a calling exception positioning method comprises the following steps: acquiring a plurality of service calling data in a preset time period; aggregating the service calling data according to the calling form to obtain a plurality of example groups; determining a normal instance group and an abnormal instance group in the plurality of instance groups; counting the number of service call data of a plurality of characteristic dimensions in normal instance grouping and the number of service call data in abnormal instance grouping; and determining the position of the calling abnormity according to the number of the service calling data in the normal instance grouping and the number of the service calling data in the abnormal instance grouping of the plurality of characteristic dimensions.

A calling exception location device comprising: the acquisition module is used for acquiring a plurality of service calling data in a preset time period; the dividing module is used for aggregating the service calling data according to the calling form to obtain a plurality of example groups; the first determining module is used for determining a normal example group and an abnormal example group in the plurality of example groups; the second determining module is used for counting the number of the service calling data of the plurality of characteristic dimensions in the normal instance grouping and the number of the service calling data in the abnormal instance grouping; and the third determining module is used for determining the position of the calling abnormity according to the number of the service calling data in the normal example grouping and the number of the service calling data in the abnormal example grouping of the plurality of characteristic dimensions.

A server comprises a processor and a memory for storing processor executable instructions, wherein the processor executes the instructions to acquire a plurality of service calling data in a preset time period; aggregating the service calling data according to the calling form to obtain a plurality of example groups; determining a normal instance group and an abnormal instance group in the plurality of instance groups; counting the number of service call data of a plurality of characteristic dimensions in normal instance grouping and the number of service call data in abnormal instance grouping; and determining the position of the calling abnormity according to the number of the service calling data in the normal instance grouping and the number of the service calling data in the abnormal instance grouping of the plurality of characteristic dimensions.

A computer readable storage medium having stored thereon computer instructions which, when executed, enable obtaining a plurality of service invocation data within a preset time period; aggregating the service calling data according to the calling form to obtain a plurality of example groups; determining a normal instance group and an abnormal instance group in the plurality of instance groups; counting the number of service call data of a plurality of characteristic dimensions in normal instance grouping and the number of service call data in abnormal instance grouping; and determining the position of the calling abnormity according to the number of the service calling data in the normal instance grouping and the number of the service calling data in the abnormal instance grouping of the plurality of characteristic dimensions.

According to the calling abnormity positioning method, device and server provided by the specification, the acquired large quantity of calling data is divided into a plurality of example groups, and whether the calling data is normal or abnormal is efficiently judged from the aspect of the example groups; and starting from the aspect of the characteristic dimension, analyzing the specific calling condition of each characteristic dimension parameter in a fine-grained manner according to the quantity of the service calling data in the normal instance group and the quantity of the service calling data in the abnormal instance group of each characteristic dimension in the plurality of characteristic dimensions, so as to accurately position the position of the calling abnormality in the service system, thereby improving the accuracy and efficiency of determining the position of the calling abnormality in the service system.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

FIG. 1 is a diagram illustrating an embodiment of a system architecture for implementing a method for locating call exceptions according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram illustrating an example packet obtained by applying the method for locating a call exception provided by the embodiments of the present specification, in one example scenario;

FIG. 3 is a diagram illustrating an embodiment of a method for locating call exceptions provided by embodiments of the present specification, in one example scenario;

FIG. 4 is a schematic diagram illustrating an example of a statistical index of an instance packet obtained by applying the method for locating a call exception provided by the embodiments of the present specification, in one example scenario;

FIG. 5 is a schematic diagram of another example packet obtained by applying the method for locating a call exception provided by the embodiments of the present specification, in a scenario example;

FIG. 6 is a diagram illustrating an embodiment of a process for calling an exception location method provided by an embodiment of the present specification;

FIG. 7 is a diagram illustrating an embodiment of a structure of a server provided by an embodiment of the present specification;

fig. 8 is a schematic diagram of an embodiment of a structure of a location device for calling an exception according to an embodiment of the present disclosure.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step should fall within the scope of protection of the present specification.

In consideration of the fact that the existing calling abnormity positioning method is always blind, the specific expression is that a technician needs to acquire all log records of each module structure in a service system in a certain time period and then check and analyze all the log records one by one. However, a service system usually includes a complex module structure, and the number of call data involved in the service processing process is relatively large, which results in a relatively large amount of data recorded in the log of the service system to be examined and analyzed. Therefore, the implementation efficiency is relatively poor when the method is implemented. In addition, most of the existing methods depend on technicians to manually determine the position of the call exception according to the result of the investigation and analysis of the log record and by combining knowledge and experience of the technicians. Therefore, the determined result is greatly influenced by people, so that the accuracy is relatively low and the reliability is relatively poor when the method is implemented.

In view of the above situation, the present specification considers that multiple pieces of call data in a service processing process of a service system in a certain time period can be obtained, and a position where a call abnormality occurs in the service system is located by performing statistical analysis on the multiple pieces of call data. Specifically, it is considered that the data volume of the call data involved in the service processing process of the service system in a certain time period is often huge, and a plurality of small and large differences exist between the single call data. In this case, if the above-mentioned call data are analyzed one by one, it will inevitably consume a lot of time and resources. Therefore, the method can divide a large amount of call data into a plurality of different example groups according to the call form of the call data through call form aggregation, and then distinguish the normal and abnormal cases more efficiently from the example group level, namely divide the normal example group and the abnormal example group from the plurality of example groups. And counting the number of the service call data of the plurality of characteristic dimensions in the normal instance grouping and the number of the service call data in the abnormal instance grouping by taking the instance grouping as a unit. And then, fine-grained analysis and positioning can be carried out from the aspect of the characteristic dimensionality according to the quantity of the service calling data of the plurality of characteristic dimensionalities in the normal instance grouping and the quantity of the service calling data in the abnormal instance grouping so as to accurately determine the specific position of the calling abnormity in the given service system, thereby improving the accuracy and the determination efficiency of determining the position of the calling abnormity in the service system.

The embodiment of the specification provides a method for positioning an application call exception. As shown in fig. 1, the method for locating a call exception may be applied to a system architecture including multiple services.

Wherein, some servers in the plurality of servers can be used as service systems to perform specific service processing; the other part of the servers can be used as monitoring servers for acquiring a plurality of service call data in the service processing process within a preset time period in the service system; dividing a plurality of service call data into a plurality of example groups, and determining a normal example group and an abnormal example group in the plurality of example groups from the level of the example groups; then, counting the number of service call data of a plurality of characteristic dimensions in the normal instance grouping and the number of service call data in the abnormal instance grouping; therefore, the position of the calling abnormity can be determined finely according to the quantity of the service calling data in the normal instance grouping and the quantity of the service calling data in the abnormal instance grouping from the aspect of the characteristic dimension. Further inspection or repair can be subsequently performed for the determined location in the business system where the call exception occurred.

In this embodiment, the server may be an electronic device having data operation, storage function and network interaction function; software may also be provided that runs in the electronic device to support data processing, storage, and network interaction. The number of servers is not particularly limited in the present embodiment. The server may be one server, several servers, or a server cluster formed by several servers.

In a scenario example, the location where the call exception occurs in a certain network platform service system may be determined by using the positioning method for call exception provided in the embodiment of the present specification.

In specific implementation, the service call data related to the service system of the network platform in the service processing process within a preset time period, for example, within the latest 1 hour, may be collected by the monitoring server.

In this scenario example, when a business system performs specific business data processing, it often involves calling various functional modules, parameter data, and the like, and there are a large number of calling procedures.

For example, when a certain network platform service system verifies login information of a certain user to determine whether the user has permission to use a functional service for a member user provided by the network platform, an input module needs to be called first to obtain the login information input by the user; then, calling an extraction module to extract the identity information of the user from the login information; then, calling a retrieval module to retrieve a member information base of the network platform according to the extracted identity information of the user, and determining whether member information matched with the identity information of the user exists in the member information base; and finally, determining whether the user has the right to use the corresponding functional service provided by the platform according to the retrieval result fed back by the retrieval module.

Correspondingly, the service invocation data may be specifically understood as parameter data used for characterizing an invocation process in service data processing, and information data describing a specific invocation flow in the invocation process. The specific calling process involved in the service data processing can be completely and clearly restored through the service calling data.

The service invocation data may specifically include: key parameters and calling structure and other information data. The key parameters at least include: parameter data such as key-in parameter (which can be understood as initial data input by the calling process) and key-out parameter (which can be understood as result data output by the calling process). The call structure at least comprises: the calling and called function modules involved in the calling process, the calling sequence of the calling and called function modules in the calling process, and the like. The calling form of the service calling data can be accurately described through the key parameters and the calling structure. It should be understood that the contents included in the service invocation data listed above are only for better explanation of the embodiments of the present specification. In specific implementation, according to a specific application scenario and a processing requirement, the service invocation data may further include other types or content of information data, for example, may further include an intermediate parameter related to a certain specified function module in the middle of the invocation flow. The present specification is not limited to these.

Specifically, for example, the collected service invocation data related to the process of verifying the login information of a certain user by a certain network platform service system may include the login information of the user (i.e., key entry), the retrieval result of the identity information of the user (i.e., key exit), and the related function modules in the invocation flow: the input module, the extraction module and the retrieval module, and the calling sequence: the input module is called first, then the extraction module is called, and finally the retrieval module (i.e. the calling structure) is called.

In this scenario embodiment, in specific implementation, the monitoring server may collect, as service call data, recorded data of a call process related to the server in a preset time period when executing service data processing, through collectors preset in each server in the network platform service system. Of course, in specific implementation, according to specific situations, each server in the service system may record service invocation data related in the invocation process by itself, and then send the recorded invocation data to the monitoring server in a wireless or wired manner. The present specification is not limited to these.

After the monitoring server obtains the service calling data, considering that the calling process related to the service system is more in a time period, the data volume of the service calling data to be analyzed and processed is larger, and combining the specific characteristics of normal calling and abnormal calling in the service system, in order to improve the processing efficiency, the monitoring server can firstly aggregate the service call data according to the call form of the service call data, divide a plurality of service call data into a plurality of example groups, judge whether the service call data in the example groups are called normally or abnormally by taking the example groups as units, determine a normal example group and an abnormal example group from the plurality of example groups, therefore, a large amount of service calling data are prevented from being analyzed and judged one by one, and the discrimination and determination of normal service calling data and abnormal service calling data can be completed with low calculation cost.

The above example group may be specifically understood as a set of the same type of service invocation data having the same or similar characteristics. An example packet may often contain one or more separate service invocation data. Specifically, the key parameters representing the calling forms of different service calling data in the same instance group are the same as or similar to the calling structure (for example, the difference degree between the key parameters and the calling structure of different service calling data is less than or equal to the difference degree threshold), and correspond to the same service calling data type.

Specifically, an example packet contains at least two attribute features: key parameters and call structures, etc. Accordingly, as can be seen in FIG. 2, at least two main content portions may be included in the content segment of an example packet. Wherein, one content part is used for representing the key parameters of the instance grouping, and specifically comprises key-related parameters and key-related parameters; another content portion is used to represent the calling structure of the instance packet. The calling form shared by the service calling data of the example packet can be described through the attribute characteristics of the example packet. And the key parameters and the calling structure of the service calling data in the same instance group are contained in the key parameters and the calling structure of the instance group. For example, the key parameter of the service invocation data in the same instance group may be the same as the key parameter of the instance group, or may be one parameter data within a range included in the key parameter of the instance group. Corresponding to the service call data, the key parameters of the instance packet may also include parameter data such as key out-parameters and key in-parameters, and the call structure of the instance packet may also include information data such as the call and called function modules involved in the call flow, and the call sequence of the call and called function modules in the call flow. In the service invocation data in the same example, the key parameters and the invocation structure are the same, or the difference degree is less than or equal to the difference degree threshold.

For example, the key entry of the service invocation data Q is referred to as the name of the user a, and the key exit is referred to as the address of the user a; the calling structure comprises: the module a, the module b and the module c _3 are called in the following sequence: the module a is called first, then the module b is called, and finally the module c _3 is called. The key input of the service calling data W is the name of the user B, the key output is the address of the user B, and the calling structure comprises: the module a, the module b and the module c _2 are called in the following sequence: the module a is called first, then the module b is called, and finally the module c _2 is called. By respectively comparing the key parameters and the calling structures of the service calling data Q and the service calling data W, the difference between the key parameters and the calling structures of the two service calling data is found, but the difference degree is smaller than the difference degree threshold value, namely the key parameters and the calling structures of the two service calling data are similar. Therefore, the two different service invocation data, i.e. the service invocation data Q and the service invocation data W, can be divided into the same instance packet, so that the subsequent uniform analysis processing can be performed by taking the instance packet as a unit.

In a specific implementation, the monitoring server may aggregate the service invocation data according to the invocation form to obtain a plurality of instance groups. Specifically, the monitoring server may extract a key parameter and a calling structure describing a calling form of each service calling data among the plurality of service calling data; and dividing the plurality of service calling data into a plurality of example groups according to the key parameters and calling structures of the service calling data, wherein the key parameters and calling structures of the calling data in the same example group are the same. It should be noted that the key parameters and the calling structure may be used to characterize the corresponding calling process, that is, may be used to describe the calling form of the service calling data; therefore, a plurality of service call data with similar call forms (namely the key parameters and the call structures are the same or the difference degree is smaller than the difference degree threshold) can be aggregated into a class according to the key parameters and the call structures of the service call data to obtain an example group. In the above manner, a large amount of service invocation data can be divided into a relatively small number of instance packets.

For example, as can be seen in fig. 3, 1000 different service invocation data (e.g., service invocation data 1 through 1000) may be divided into 10 different types of instance groupings (e.g., instance grouping a through instance grouping J) by invoking morphological aggregation. And then, when judging whether the data is normal service call data or not, the 1000 service call data are not required to be analyzed and determined one by one, and only the 10 example groups are analyzed and determined to be normal example groups or abnormal example groups. Therefore, the processing efficiency is effectively improved, and the consumption of time and resources is reduced.

After dividing the plurality of service invocation data into a plurality of instance packets, the monitoring server may determine whether the service invocation is a normal invocation or an abnormal invocation by taking the instance packet as a processing unit from the level of the instance packet. Specifically, the monitoring server may determine whether the service invocation data in each instance packet is a normal invocation or an abnormal invocation by determining whether the instance packet is a normal instance packet or an abnormal instance packet.

The normal instance grouping may be specifically understood as a set of the same type of service invocation data that is normally invoked and has the same or similar characteristics. The exception instance grouping may be specifically understood as a collection of the same type of service invocation data that is exception to the invocation and has the same or similar characteristics.

In specific implementation, the monitoring server may determine whether a current instance group in the multiple instance groups is a normal instance group or an abnormal instance group according to the following manner: searching a preset example grouping library, and determining whether a template example grouping is matched with a current example grouping in the preset example grouping library; determining that the current instance group is a normal instance group under the condition that the template instance group is matched with the current instance group in the preset instance group library; and under the condition that the template instance grouping does not exist in the preset instance grouping library and is matched with the current instance grouping, determining the current instance grouping as an abnormal instance grouping.

The preset instance grouping library can be understood as a collection of instance groupings composed of a plurality of template instance groupings. Each template instance group in the preset instance group library is a normal instance group and corresponds to a normal calling process involved in the service processing process of the service system. Specifically, the preset example grouping library may be pre-established and obtained in the following manner: acquiring a plurality of normally called service calling data in a specified time period (such as a test time period); aggregating the plurality of normally called service calling data according to the calling form to obtain a plurality of corresponding template example groups; and establishing the preset example grouping library according to the template example grouping.

Specifically, if the monitoring server retrieves a template instance group in a preset instance group library, where the key parameters and the calling structure of the template instance group are the same as those of the current instance group, or the difference degree is smaller than the difference degree threshold, the monitoring server may determine that the template instance group is an instance group matched with the current instance group, that is, determine that the template instance group is matched with the current instance group in the preset instance group library, and determine that the current instance group is a normal instance group. In contrast, if the template instance group which has the same key parameter and calling structure as the current instance group or has a difference degree smaller than the difference degree threshold value is not retrieved from the preset instance group library, it may be determined that the template instance group does not exist in the preset instance group library and is matched with the current instance group, and it is determined that the current instance group is an abnormal instance group.

The monitor rapidly determines normal example groups and abnormal example groups in the example groups through layer processing of the example groups, and further can determine that calling data corresponding to the normal example groups are normal in calling and calling data corresponding to the abnormal example groups are abnormal in calling, so that the aim of efficiently judging whether calling data are normal or abnormal in calling is fulfilled.

Furthermore, the number of the service call data of the plurality of feature dimensions in the normal instance group and the number of the service call data of the plurality of feature dimensions in the abnormal instance group can be counted from the aspect of the feature dimensions, and then fine-grained analysis can be performed on the normal call and abnormal call conditions of each feature dimension parameter according to the number of the service call data of the plurality of feature dimensions in the normal instance group and the number of the service call data of the plurality of feature dimensions in the abnormal instance group, so as to determine the position of the call abnormality.

Specifically, a statistical index of the normal instance packet, which can represent the number of service call data respectively corresponding to each feature dimension parameter in the feature dimensions in the normal instance packet, can be obtained by counting the number of the service call data in the normal instance packet of the feature dimensions; the statistical index of the abnormal instance grouping which can represent the number of the service call data respectively corresponding to each characteristic dimension parameter in the plurality of characteristic dimensions in the abnormal instance grouping can be obtained by counting the number of the service call data in the abnormal instance grouping by the plurality of characteristic dimensions; and further, according to the statistical index of the normal example group and the statistical index of the abnormal example group, the position of the call abnormity in the service system can be determined in a fine-grained and fine manner by taking the characteristic dimension as a processing unit.

The feature dimension may be specifically understood as a dimension factor that may affect whether a call is normal or abnormal in a specific call process. The feature dimensions may include a plurality of feature dimension parameters, respectively, where each feature dimension parameter may be used to indicate a specific feature location in the service system. It should be noted that the specific feature location may correspond to an actual physical location, such as a certain physical machine room or a certain server, or may correspond to a virtual function location, such as a certain structural module.

Specifically, the characteristic dimensions at least include: deployment units, service scenarios, upstream systems, downstream systems, logical rooms, and the like. The logical room (zone) may specifically refer to a physical room, or may refer to a virtual logical grouping. The deployment unit may specifically refer to a structural unit or a functional module in which a functional module in the call structure is deployed. The upstream system may be specifically understood as a callee of any specific call in the calling process. The downstream system may be specifically understood as a caller specifically calling any one time in the calling process. The service scenario may be specifically understood as a service process corresponding to a calling process. Of course, it should be noted that the above-listed feature dimensions are only for better illustration of the embodiments of the present specification. In specific implementation, other types of factors can be introduced as the characteristic dimension according to specific situations. This is not a limitation of the present specification.

In specific implementation, the monitoring server may count the number of service call data of a plurality of feature dimensions in a normal instance group by using a reverse index according to the following method from a specific feature dimension: the number of the corresponding service call data of each deployment unit parameter in the normal instance group can be respectively counted; counting the number of service calling data corresponding to each service scene parameter in the normal example group; counting the number of service call data corresponding to each upstream system parameter in the normal example packet; counting the number of service call data corresponding to each downstream system parameter in the normal example packet; and counting the number of the service call data corresponding to each logic machine room parameter in the normal example grouping. Further, for convenience of use, the statistical results may be summarized, and a statistical index of the normal instance group may be obtained.

The deployment unit parameter may be specifically understood as specific parameter data for characterizing a feature dimension of the deployment unit in the example group. The service scenario parameters may be specifically understood as specific parameter data for characterizing a feature dimension of the service scenario in the example packet. The upstream system parameter mentioned above is specifically understood to be specific parameter data for characterizing this characteristic dimension of the upstream system in this example group. The downstream system parameters are specifically understood to be specific parameter data for characterizing this characteristic dimension of the downstream system in the example group. The above-mentioned logical room parameters can be specifically understood as specific parameter data for characterizing a characteristic dimension of the logical room in the example group. It should be added that, for the same instance group, the specific parameter data characterizing the same feature dimension may include multiple parameter data belonging to the same functional service type.

For example, for some normal instance packet, the upstream system parameters may include: the system a _1 responsible for extraction and the system a _2 responsible for extraction. By counting the service call data in the current example packet, it can be determined that the number of call data corresponding to the system a _1 responsible for data extraction is 30, and the number of call data corresponding to the system a _2 responsible for data extraction is 70.

The statistical index of the normal example packet may be a summary of statistical results of the number of call data corresponding to each deployment unit parameter, the number of call data corresponding to each service scene parameter, the number of call data corresponding to each upstream system parameter, the number of call data corresponding to each downstream system parameter, and the number of call data corresponding to each logic room parameter in the normal example packet. According to the statistical index, the quantity condition of the calling data corresponding to any specific characteristic dimension parameter in the current example group can be determined.

And according to the mode of determining the statistical indexes of the normal example groups, determining the statistical indexes of each normal example group in the plurality of example groups one by one to obtain the statistical indexes of each normal example group.

Similarly, the number of service invocation data in the abnormal instance packet of the plurality of characteristic dimensions can be counted according to the following modes: the number of the service call data corresponding to each deployment unit parameter in the abnormal instance group can be respectively counted; counting the number of service call data corresponding to each service scene parameter in the abnormal instance group; counting the number of service call data corresponding to each upstream system parameter in the abnormal instance packet; counting the number of service call data corresponding to each downstream system parameter in the abnormal instance packet; and counting the number of the service call data corresponding to each logic machine room parameter in the abnormal instance grouping. Further, for convenience of use, the statistical results may be summarized to obtain a statistical index of the abnormal instance group.

Based on the specific characteristic dimension parameters listed above, for example, as shown in fig. 4, the statistical index for each instance group (including a normal instance group and an abnormal instance group) may specifically be a summary of statistical results of the number of call data corresponding to the characteristic dimension parameter of each characteristic dimension of the multiple characteristic dimensions in the instance group. Therefore, by analyzing the statistical index of each example group, the quantity of the calling data in the preset time period corresponding to the characteristic dimension parameter of each characteristic dimension in the example group can be quickly and accurately determined.

Further, after the statistical index of each instance group is obtained, in order to facilitate subsequent reading and use and improve processing efficiency, the statistical index of the instance group can be used as a new attribute feature of the instance group, and the instance group is formed together with the key parameters and the calling structure of the instance group which are extracted and determined in the prior art. Specifically, as shown in fig. 5, the content segment of an example packet may include three content parts, where one content part is used to represent the key parameters of the example packet, one content part is used to represent the invocation structure of the example packet, and another content part is used to represent the statistical index of the example packet.

The monitoring server obtains the statistical index of the normal example grouping and the statistical index of the abnormal example grouping, can perform statistical analysis on normal and abnormal conditions of different feature dimension parameters from the aspect of feature dimension based on the statistical indexes, and finely determines the position of call abnormity in the service system according to the statistical analysis result aiming at the normal and abnormal conditions of the feature dimension parameters. Specifically, the number of service call data in the normal instance group and the number of service call data in the abnormal instance group of each feature dimension of the plurality of feature dimensions may be determined according to the statistical index of the normal instance group and the statistical index of the abnormal instance group; determining the distribution data of normal calling and abnormal calling of the characteristic dimension parameters of each characteristic dimension in the plurality of characteristic dimensions according to the quantity of the service calling data of the plurality of characteristic dimensions in the normal example group and the quantity of the service calling data of the plurality of characteristic dimensions in the abnormal example group; and determining abnormal characteristic dimension parameters according to the distribution data of normal calling and abnormal calling of the characteristic dimension parameters of each characteristic dimension in the plurality of characteristic dimensions, and determining the position indicated by the abnormal characteristic dimension parameters as the position where calling abnormality occurs.

For example, according to the statistical index of the normal instance group, it may be determined that, for the feature dimension of the upstream system, the distribution data of the call data amount corresponding to the call data amount for calling the normal upstream system parameter (i.e. the number of service call data in the normal instance of the multiple feature dimension parameters of the feature dimension) is: system a _1 is 30, system a _2 is 70, system b _1 is 90, system b _2 is 10, system c is 110, and system d is 290. According to the statistical index of the abnormal instance grouping, it can be determined that, for the feature dimension of the upstream system, the distribution data of the calling data volume corresponding to the parameter of the upstream system calling the abnormality (that is, the number of the service calling data in the abnormal instance of the plurality of feature dimension parameters of the feature dimension) is: system a _1 is 5, system a _2 is 5, system b _1 is 10, system b _2 is 190, system c is 30, and system d is 50. By integrating the statistical data, the distribution ratio of call normality and call abnormality corresponding to each upstream system parameter can be further determined as follows: the distribution data of the normal characteristic dimension parameters and the abnormal characteristic dimension parameters of the characteristic dimension are obtained, wherein the ratio of the system a _1 is 6:1, the ratio of the system a _2 is 14:1, the ratio of the system b _1 is 9:1, the ratio of the system b _2 is 1:19, the ratio of the system c is 3:11 and the ratio of the system d is 5: 29.

And then, by using the distribution data of the normal characteristic dimension parameters and the abnormal characteristic dimension parameters of the characteristic dimensions as reference bases, more precise judgment and determination can be performed on whether the positions indicated by the characteristic dimension parameters in the characteristic dimensions are abnormal or not. For example, for a specific feature dimension parameter, system b _2, in the feature dimension of the upstream system, the distribution data of call normal and call exception is 1:19, which is lower than the preset exception threshold, so that the position indicated by the feature dimension parameter, that is, system b _2 has a greater probability of having a call exception. And then, more comprehensive and detailed examination and analysis can be carried out on the system b _2 subsequently, so that the problem of calling abnormity caused by the system b _2 can be solved in time, and the service processing process of the service system is stable and reliable.

As can be seen from the above scene example, the method for locating a call exception provided in this specification efficiently determines whether to call normally or call abnormally from the aspect of the instance grouping by dividing the acquired large amount of call data into the instance grouping; and starting from the aspect of the characteristic dimension, analyzing the specific calling condition of each characteristic dimension parameter in a fine-grained manner according to the number of the service calling data of the plurality of characteristic dimensions in the normal instance group and the number of the service calling data in the abnormal instance group so as to accurately position the position of the calling abnormality in the service system, thereby improving the accuracy and the efficiency of determining the position of the calling abnormality in the service system.

Referring to fig. 6, an embodiment of the present disclosure provides a method for locating a call exception, where the method may be specifically used on a side of a monitoring server. In specific implementation, the method may include the following:

s61: and acquiring a plurality of service call data in a preset time period.

In this embodiment, the preset time period may be specifically understood as a certain time period to be analyzed. For example, the most recent time period. The specific duration corresponding to the preset time period may be 30 minutes, or 1 hour, and the like. And for the preset time period, flexibly selecting a certain time period and corresponding duration according to a specific application scene and processing requirements. The present specification is not limited to these.

In this embodiment, the service invocation data may be specifically understood as parameter data used for characterizing an invocation process in service processing of the service system, and information data used for describing a specific invocation flow in the invocation process. The specific calling process involved in the service data processing can be completely and clearly restored through the service calling data.

Specifically, the service invocation data may include: key parameters and calling structure and other information data. Wherein, the key parameters at least include: parameter data such as key-in parameter (which can be understood as initial data input by the calling process) and key-out parameter (which can be understood as result data output by the calling process). The calling form of the service calling data can be better described through the key parameters and the calling structure. The call structure at least comprises: the calling and called function modules involved in the calling process, the calling sequence of the calling and called function modules in the calling process, and the like. It should be understood that the contents included in the service invocation data listed above are only for better explanation of the embodiments of the present specification. The present specification is not limited to the specific content included in the service invocation data.

In this embodiment, the obtaining of the plurality of service invocation data within the preset time period may include, in specific implementation: the monitoring server can record and collect related service calling data of the service system in the process of executing service data processing in a preset time period through a collector preset in the service system, and then the collector sends the related service calling data to the monitoring server at regular time through a network. Of course, the above listed manner of acquiring the plurality of service invocation data within the preset time period is only an illustrative illustration. In specific implementation, according to specific situations, a plurality of service invocation data in a preset time period may also be obtained in other suitable manners. The present specification is not limited to these.

S62: and aggregating the service call data according to the call form to obtain a plurality of example groups.

In the present embodiment, the above example group may be specifically understood as a set of service invocation data of the same type having the same or similar characteristics. An example packet may often contain multiple individual service invocation data. Specifically, the key parameters and the call structures of different service call data in the same instance group are the same or similar (for example, the difference degree between the key parameters and the call structures of different service call data is less than or equal to the difference degree threshold), and correspond to the same service call data type.

In this embodiment, the example group may specifically include at least two attribute features, which are respectively: key parameters and call structures, etc. The calling form shared by the service calling data of the example packet can be described through the attribute characteristics of the example packet. And the key parameters and the calling structure of the service calling data in the same instance group are contained in the key parameters and the calling structure of the instance group. For example, the key parameter of the service invocation data in the same instance group may be the same as the key parameter of the instance group, or may be one parameter data within a range included in the key parameter of the instance group. Corresponding to the service call data, the key parameters of the instance packet may also include parameter data such as key out-parameters and key in-parameters, and the call structure of the instance packet may also include information data such as the call and called function modules involved in the call flow, and the call sequence of the call and called function modules in the call flow.

In the embodiment, considering that service processing in a time period of one service system is often more and the data volume of related service call data is larger, if the call normality or abnormality of the service call data is directly analyzed and determined one by one, a large amount of time and resources are inevitably occupied. Thus, it is contemplated that multiple service invocation data having the same type of invocation modality may be first aggregated together, resulting in one instance grouping. And then, starting from an instance grouping layer, taking the instance grouping as a processing unit, analyzing and determining the call normality or exception of the call form corresponding to the instance grouping, and determining whether the specific service call data contained in the instance grouping is call normality or call exception according to the call normality or call exception of the instance grouping. Therefore, the method avoids analyzing and processing a plurality of specific service calling data one by one, greatly reduces the data processing amount and improves the processing efficiency.

In this embodiment, the aggregating the service invocation data according to the invocation form to obtain a plurality of example groups may include the following contents: the monitoring server can extract key parameters and calling structures of service calling data; and dividing the plurality of service call data into a plurality of example groups according to the key parameters and the call structure of the service call data. Specifically, one or more example groups with the same key parameter and calling structure or with the difference degree smaller than or equal to the difference degree threshold value may be aggregated into a group, so as to obtain an example group.

S63: normal and abnormal instance groupings of the plurality of instance groupings are determined.

In this embodiment, the normal instance group may be specifically understood as a set of the same type of service invocation data that is normally invoked and has the same or similar characteristics. The exception instance grouping may be specifically understood as a collection of the same type of service invocation data that is exception to the invocation and has the same or similar characteristics.

In this embodiment, the determining of the normal example packet and the abnormal example packet in the plurality of example packets may include the following steps: according to a preset instance grouping library, respectively determining whether a template instance grouping which is matched with each instance grouping in the plurality of instance groupings exists in the preset instance grouping library, determining the instance grouping which has the matched template instance grouping in the preset instance grouping library in the plurality of instance groupings as a normal instance grouping, and determining the instance grouping which does not have the matched template instance grouping in the preset instance grouping library in the plurality of instance groupings as an abnormal instance grouping. Therefore, the normal example packet and the abnormal example packet can be efficiently and respectively determined from the plurality of example packets, wherein the service call data contained in the normal example packet is called normally, and the service call data contained in the abnormal example packet is called abnormally. Therefore, the calling condition of the large number of example packets can be prevented from being directly analyzed and judged one by one, the example packets are taken as processing units from the aspect of the example packets, and whether the calling of a large amount of service calling data is normal or abnormal is determined by determining whether each example packet is a normal example packet or an abnormal example packet, so that the data processing amount is greatly reduced, and the processing efficiency is improved.

S64: and counting the number of the service call data of the plurality of characteristic dimensions in the normal instance packet and the number of the service call data in the abnormal instance packet.

In this embodiment, the feature dimension may be specifically understood as a dimension factor that may affect whether a call is normal or abnormal in a specific call process. The characteristic dimension may include one or more specific characteristic dimension parameters, where each characteristic dimension parameter may be used to indicate a specific feature location in the business system for the characteristic dimension. It should be noted that the specific characteristic location may correspond to an actual physical location, such as a certain physical machine room or a certain server, or may correspond to a virtual logical location, such as a certain structural module.

Specifically, the characteristic dimensions may include: deployment units, service scenarios, upstream systems, downstream systems, logical rooms, and the like. The logical room (zone) may specifically refer to a physical room, or may refer to a virtual logical grouping. The deployment unit may specifically refer to a structural unit or a functional module in which a functional module in the call structure is deployed. The upstream system may be specifically understood as a callee of any specific call in the calling process. The downstream system may be specifically understood as a caller specifically calling any one time in the calling process. The service scenario may be specifically understood as a service process corresponding to a calling process. Of course, it should be noted that the above-listed feature dimensions are only for better illustration of the embodiments of the present specification. In specific implementation, other types of factors can be introduced as the characteristic dimension according to specific situations. This is not a limitation of the present specification.

In this embodiment, in order to accurately find and determine a specific location where a call exception occurs in a business system, the call exception may be finely located by using a feature dimension as a processing unit from the aspect of the feature dimension. Therefore, the data volume of the service call data corresponding to the characteristic dimension parameter calling normally in the normal example packet can be respectively counted to obtain the statistical index of the normal example packet, and the data volume of the service call data corresponding to the characteristic dimension parameter calling abnormally in the abnormal example packet can be counted to obtain the statistical index of the abnormal example packet. Subsequently, based on the above statistical index of the normal instance packet and the statistics of the abnormal instance packet for the feature dimension, the position of the call abnormality in the service system is accurately determined from the aspect of the feature dimension.

In this embodiment, specifically, the number of service call data in the normal instance packet of a plurality of feature dimensions may be counted to obtain a statistical index of the normal instance packet; and counting the quantity of service call data of the plurality of characteristic dimensions in the abnormal instance packet to obtain a statistical index of the abnormal instance packet.

In this embodiment, the statistical index of the normal instance group may be specifically understood as a summary of statistical results of the number of call data respectively corresponding to the feature dimension parameters (i.e., normal feature dimension parameters) of each feature dimension of each instance group in the normal instance group. The statistical index of the abnormal case group may be specifically understood as a summary of statistical results of the number of call data corresponding to the feature dimension parameters (i.e., abnormal feature dimension parameters) of each feature dimension of each case group in the abnormal case group. According to the statistical indexes of the normal example groups, the calling data volume of different normal dimension parameters in a characteristic dimension in one example group in the normal example groups can be accurately determined. For example, through the statistical indexes of the normal instance groups, it can be determined that the call data volumes of different normal upstream system parameters are respectively: the number of call data corresponding to the system a _1 is 30, and the number of call data corresponding to the system a _2 responsible for data extraction is 70.

In this embodiment, after the statistical index of the normal instance packet is obtained through statistics, the statistical index of the normal instance packet may be added to an original instance packet structure as a new attribute feature, so that the information data related to the instance packet may be read and used more conveniently in the following. Each instance grouping of the resulting normal instance groupings may structurally include three attribute features, namely, key parameters, call structures, and statistical indices of the normal instance groupings. Similarly, each of the exception instance groupings may structurally include three attribute features, namely a key parameter, a call structure, and a statistical index of the exception instance grouping. Of course, the structures of the normal example packet and the abnormal example packet listed above are only schematic illustrations, and should not be construed as unduly limiting the present specification.

S65: and determining the position of the calling abnormity according to the number of the service calling data in the normal instance grouping and the number of the service calling data in the abnormal instance grouping of the plurality of characteristic dimensions.

In this embodiment, specifically, the position where the call exception occurs may be further determined according to the statistical index of the normal instance group and the statistical index of the abnormal instance group.

In this embodiment, the monitoring server may analyze the normal or abnormal situation of each feature dimension parameter in the service system by using the feature dimension as a specific processing unit according to the statistical index of the normal instance group and the statistical index of the abnormal instance group for the feature dimension, and thus may finely determine the specific location where the call abnormality occurs in the service system.

In this embodiment, in specific implementation, distribution data (for example, a ratio of the number of normal call data to the number of abnormal call data corresponding to the same feature dimension parameter) of normal call and abnormal call of a specific feature dimension parameter in each feature dimension in a service system may be determined according to the statistical dimension of the normal instance group and the statistical dimension of the abnormal instance group; and then the distribution data of the specific feature dimension parameters in each feature dimension in the service system can be used as reference data to guide and search the position with the calling abnormality, so that the position with the calling abnormality in the service system can be timely and pertinently adjusted and repaired.

As can be seen from the above, in the method for positioning call exception provided in the embodiments of the present specification, the obtained large amount of call data is first divided into a plurality of example groups, and whether the call is normal or abnormal is efficiently determined from the example group level; and starting from the aspect of the characteristic dimension, analyzing the specific calling condition of each characteristic dimension parameter in a fine-grained manner according to the number of the service calling data of the plurality of characteristic dimensions in the normal instance group and the number of the service calling data in the abnormal instance group so as to accurately position the position of the calling abnormality in the service system, thereby improving the accuracy and the efficiency of determining the position of the calling abnormality in the service system.

In an embodiment, the aggregating the service invocation data according to the invocation form to obtain a plurality of example groups may include the following contents: extracting key parameters and a calling structure of service calling data; and dividing the plurality of service calling data into a plurality of example groups according to the key parameters and calling structures of the service calling data, wherein the key parameters and calling structures of the calling data in the same example group are the same.

In this embodiment, in a specific implementation, each of the obtained plurality of service invocation data may be analyzed, so as to extract and obtain a key parameter and an invocation structure of each of the service invocation data.

In this embodiment, the dividing the plurality of service invocation data into a plurality of example groups according to the key parameter and the invocation structure of the service invocation data may include, in specific implementation: and according to the key parameters and the calling structure of the service calling data, aggregating one or more service calling data of which the key parameters and the calling structure are the same or the difference degree is smaller than the difference degree threshold value into one class, namely obtaining the corresponding example group.

In an embodiment, the determining of the normal instance group and the abnormal instance group in the plurality of instance groups may include the following steps: taking the example of determining whether the current instance grouping in the multiple instance groupings is a normal instance grouping or an abnormal instance grouping, specifically, a preset instance grouping library may be retrieved first, and it is determined whether a template instance grouping is matched with the current instance grouping in the preset instance grouping library; determining that the current instance group is a normal instance group under the condition that the template instance group is matched with the current instance group in the preset instance group library; and under the condition that the template instance grouping does not exist in the preset instance grouping library and is matched with the current instance grouping, determining the current instance grouping as an abnormal instance grouping.

In this embodiment, the preset instance grouping library may be specifically understood as a set of instance groupings composed of a plurality of template instance groupings. Each template instance group in the preset instance group library is a normal instance group and corresponds to a normal calling process involved in the service processing process of the service system.

In this embodiment, the template instance group matched with the current instance group may be specifically understood as a template instance group in which key parameters and a calling structure in a preset instance group library are the same as the current instance group, or a difference degree from the current instance group is less than or equal to a difference degree threshold. And if the template instance group exists in a preset instance group library, determining that the template instance group exists in the preset instance group library and is matched with the current instance group, and determining that the current instance group is a normal instance group. And if the template instance group does not exist in the preset instance group library, determining that the template instance group does not exist in the preset instance group library and the current instance group are matched, and determining that the current instance group is an abnormal instance group.

In an embodiment, the preset instance group library may be specifically established in the following manner: acquiring a plurality of normally called service calling data in a specified time period; aggregating the plurality of normally called service calling data according to the calling form to obtain a plurality of corresponding template example groups; and establishing the preset example grouping library according to the template example grouping.

In this embodiment, the specified time period may be a certain time period in which the service system normally performs service processing in history, and the service invocation data in the time period is normally invoked service invocation data. For example, it may be a time period of the test phase. The specific time of the above-mentioned specified time period may be 30 minutes, or 1 hour, etc. The selection determination of the designated time period can be flexibly determined according to specific situations. The present specification is not limited to these.

In one embodiment, the plurality of feature dimensions may specifically include: deployment units, service scenarios, upstream systems, downstream systems, logic rooms, and the like. The logical room (zone) may specifically refer to a physical room, or may refer to a virtual logical grouping. The deployment unit may specifically refer to a structural unit or a functional module in which a functional module in the call structure is deployed. The upstream system may be specifically understood as a callee of any specific call in the calling process. The downstream system may be specifically understood as a caller specifically calling any one time in the calling process. The service scenario may be specifically understood as a service process corresponding to a calling process. Of course, it should be noted that the above-listed feature dimensions are only for better illustration of the embodiments of the present specification. In specific implementation, other types of factors can be introduced as the characteristic dimension according to specific situations. This is not a limitation of the present specification.

In this embodiment, each of the plurality of feature dimensions may include one or more specific feature dimension parameters. Each feature dimension parameter may be used to indicate a specific feature location for the feature dimension in the business system. For example, for the feature dimension of the upstream system, the following specific feature dimension parameters may be included: system a _1, system a _2, system b, system c, and system d. Aiming at one characteristic dimension of the service scene, the corresponding service scene only comprises the service scene parameters: payment transaction, this one characteristic dimension parameter. Of course, the characteristic dimensional parameters listed above are merely illustrative and should not be construed as unduly limiting this specification.

In an embodiment, counting the number of service invocation data in a normal instance group by a plurality of feature dimensions, which may include: counting the number of service call data corresponding to each deployment unit parameter in the normal instance group; counting the number of service calling data corresponding to each service scene parameter in the normal example group; counting the number of service call data corresponding to each upstream system parameter in the normal example packet; counting the number of service call data corresponding to each downstream system parameter in the normal example packet; and counting the number of the service call data corresponding to each logic machine room parameter in the normal example grouping. For convenience of use, the statistical results can be summarized to obtain a statistical index of normal examples.

In this embodiment, with reference to the above manner, the number of service invocation data in an abnormal instance group by a plurality of feature dimensions may be counted, and then a statistical index of the abnormal instance group is obtained.

In this embodiment, after obtaining the statistical index of the normal instance packet (or the abnormal instance packet), the statistical index of the normal instance packet may be used as a new attribute feature for facilitating subsequent reading and use, and is recorded in the content structure of the corresponding instance packet together with the key parameter and the call structure of the instance packet, so as to facilitate subsequent use.

In this embodiment, it should be noted that the above-mentioned locating of the call exception is performed only by taking the example that the corresponding statistical result is obtained by counting the number of the service call data in the normal instance group and the exception instance of the plurality of feature dimensions. During specific implementation, according to specific situations, time consumed for calling the service calling data in the normal instance group and the abnormal instance by the multiple feature dimensions can be counted, so that the condition that the feature dimensions in the service system are normally called or abnormally called can be described and determined from another angle, and calling abnormity can be positioned. The present specification is not limited to these.

In an embodiment, the determining, according to the number of service invocation data in the normal instance packet and the number of service invocation data in the abnormal instance packet, the position where the invocation exception occurs includes: determining the distribution data of normal calling and abnormal calling of the characteristic dimension parameters of each characteristic dimension in the plurality of characteristic dimensions according to the quantity of the service calling data of the plurality of characteristic dimensions in the normal instance group and the quantity of the service calling data of the plurality of characteristic dimensions in the abnormal instance group; and determining abnormal characteristic dimension parameters according to the distribution data of normal calling and abnormal calling of the characteristic dimension parameters of each characteristic dimension in the plurality of characteristic dimensions, and determining the position indicated by the abnormal characteristic dimension parameters as the position where calling abnormality occurs.

In this embodiment, after obtaining the statistical index of the normal instance group and the statistical index of the abnormal instance group for the feature dimension, the statistical index of the normal instance group and the statistical index of the abnormal instance group may be used to determine, from the aspect of the feature dimension, a ratio of the number of call data corresponding to the normal feature dimension parameter of each feature dimension in the service system to the number of call data corresponding to the abnormal feature dimension parameter as the distribution data of the normal harmonic abnormal call of the feature dimension parameter, respectively, with the feature dimension as a processing unit. For example, it is determined that the ratio of the number of call data corresponding to the normal feature dimension parameter to the number of call data corresponding to the abnormal feature dimension parameter of the specific feature dimension parameter of the system b _2 is 1:19, that is, the distribution data of the normal call and the abnormal call of the feature dimension parameter of the feature dimension is 1: 19.

In this embodiment, after the distribution data of the normal call and the abnormal call of the feature dimension parameter of each feature dimension in the plurality of feature dimensions is obtained, fine-grained analysis may be performed on the specific position indicated by each feature dimension parameter in the service system according to the data, and according to the analysis result, whether a call abnormality occurs at the position indicated by each feature dimension parameter in the service system, and the specific position and the influence range where the call abnormality occurs, and the like are determined.

For example, the distribution data of the normal call and the abnormal call of the feature dimension parameter of each of the determined feature dimensions may be respectively compared with a preset abnormal threshold, and the position indicated by the feature dimension parameter of which the distribution data of the normal call and the abnormal call is smaller than the preset abnormal threshold may be determined as the position where the call abnormality occurs. Then, more accurate and detailed test detection can be performed on the determined position with the call exception so as to further verify whether the position really has the call exception position; the determined position where the call exception occurs can be directly adjusted and repaired to eliminate the exception, so that the service processing is stable and reliable. Of course, it should be noted that, the above-listed processing manner of determining the abnormal feature dimension parameter according to the distribution data of the normal call and the abnormal call of the feature dimension parameter of each feature dimension in the plurality of feature dimensions, and determining the position indicated by the abnormal feature dimension parameter as the position where the call abnormality occurs, and after determining the position where the call abnormality occurs, is only an illustrative description, and should not be an improper limitation to this specification.

As can be seen from the above, in the method for positioning call exception provided in the embodiments of the present specification, since the obtained large amount of call data is first divided into a plurality of instance groups, whether to call normally or call abnormally is efficiently determined from the instance group level; starting from the aspect of the characteristic dimension, analyzing the specific calling condition of each characteristic dimension parameter in a fine-grained manner according to the number of the service calling data of the plurality of characteristic dimensions in the normal instance group and the number of the service calling data in the abnormal instance group so as to accurately position the position of the calling abnormity in the service system, thereby improving the accuracy and the efficiency of determining the position of the calling abnormity in the service system; the template instance grouping in the preset instance grouping library is used as a judgment standard, the preset instance grouping library is retrieved, whether the template instance grouping which is the same as the key parameters and calling structures of the multiple instance groupings or has the difference degree smaller than the difference degree threshold exists in the preset instance grouping library or not is determined, and the normal instance grouping and the abnormal instance grouping in the multiple instance groupings are determined, so that the efficiency and the accuracy of determining the normal instance grouping and the abnormal instance grouping are improved.

Embodiments of the present specification further provide a server, including a processor and a memory for storing processor-executable instructions, where the processor, when implemented, may perform the following steps according to the instructions: acquiring a plurality of service calling data in a preset time period; aggregating the service calling data according to the calling form to obtain a plurality of example groups; determining a normal instance group and an abnormal instance group in the plurality of instance groups; counting the number of service call data of a plurality of characteristic dimensions in normal instance grouping and the number of service call data in abnormal instance grouping; and determining the position of the calling abnormity according to the number of the service calling data in the normal instance grouping and the number of the service calling data in the abnormal instance grouping of the plurality of characteristic dimensions.

In order to complete the above instructions more accurately, referring to fig. 7, the present specification further provides another specific server, wherein the server includes a network communication port 701, a processor 702 and a memory 703, and the above structures are connected by an internal cable, so that the structures can perform specific data interaction.

The network communication port 701 may be specifically configured to acquire a plurality of service invocation data within a preset time period.

The processor 702 may be specifically configured to aggregate the service invocation data according to an invocation form to obtain a plurality of instance groups; determining a normal instance group and an abnormal instance group in the plurality of instance groups; counting the number of service call data of a plurality of characteristic dimensions in normal instance grouping and the number of service call data in abnormal instance grouping; and determining the position of the calling abnormity according to the number of the service calling data in the normal instance grouping and the number of the service calling data in the abnormal instance grouping of the plurality of characteristic dimensions.

The memory 703 may be specifically configured to store a plurality of service call data in a preset time period, which are obtained through the network communication port 701, and a corresponding instruction program used by the processor 702.

In this embodiment, the network communication port 701 may be a virtual port that is bound to different communication protocols so as to transmit or receive different data. For example, the network communication port may be port No. 80 responsible for web data communication, port No. 21 responsible for FTP data communication, or port No. 25 responsible for mail data communication. In addition, the network communication port can also be a communication interface or a communication chip of an entity. For example, it may be a wireless mobile network communication chip, such as GSM, CDMA, etc.; it can also be a Wifi chip; it may also be a bluetooth chip.

In this embodiment, the processor 702 may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth. The description is not intended to be limiting.

In this embodiment, the memory 703 may include multiple layers, and in a digital system, the memory may be any memory as long as it can store binary data; in an integrated circuit, a circuit without a physical form and with a storage function is also called a memory, such as a RAM, a FIFO and the like; in the system, the storage device in physical form is also called a memory, such as a memory bank, a TF card and the like.

An embodiment of the present specification further provides a computer storage medium for a positioning method based on the above-mentioned call exception, where the computer storage medium stores computer program instructions, and when the computer program instructions are executed, the computer program instructions implement: aggregating the service calling data according to the calling form to obtain a plurality of example groups; determining a normal instance group and an abnormal instance group in the plurality of instance groups; counting the number of service call data of a plurality of characteristic dimensions in normal instance grouping and the number of service call data in abnormal instance grouping; and determining the position of the calling abnormity according to the number of the service calling data in the normal instance grouping and the number of the service calling data in the abnormal instance grouping of the plurality of characteristic dimensions.

In the present embodiment, the storage medium includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard disk (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.

In this embodiment, the functions and effects specifically realized by the program instructions stored in the computer storage medium can be explained by comparing with other embodiments, and are not described herein again.

Referring to fig. 8, in a software level, an embodiment of the present disclosure further provides a positioning apparatus for calling an exception, where the apparatus may specifically include the following structural modules:

the obtaining module 801 may be specifically configured to obtain multiple service invocation data in a preset time period;

the dividing module 802 may be specifically configured to aggregate the service invocation data according to an invocation form to obtain a plurality of instance groups;

the first determining module 803 may be specifically configured to determine a normal instance group and an abnormal instance group in the multiple instance groups;

the second determining module 804 may be specifically configured to count the number of the service invocation data in the normal instance group and the number of the service invocation data in the abnormal instance group for the plurality of feature dimensions;

the third determining module 805 may be specifically configured to determine a position where the call exception occurs according to the number of the service call data in the normal instance group and the number of the service call data in the exception instance group of the plurality of feature dimensions.

In an embodiment, the dividing module 802 may specifically include the following structural units:

the extraction unit is used for extracting key parameters and a calling structure of the service calling data;

and the dividing unit is used for dividing the plurality of service calling data into a plurality of example groups according to the key parameters and the calling structures of the service calling data, wherein the key parameters and the calling structures of the calling data in the same example group are the same.

In one embodiment, the first determining module 803 may specifically include the following structural units:

the searching unit may be specifically configured to search a preset instance grouping library, and determine whether a template instance grouping exists in the preset instance grouping library and matches the current instance grouping;

the first determining unit may be specifically configured to determine that the current instance group is a normal instance group when it is determined that the template instance group matches the current instance group in the preset instance group library;

the second determining unit may be specifically configured to determine that the current instance group is an abnormal instance group when it is determined that the template instance group does not exist in the preset instance group library and the current instance group matches.

In an embodiment, the apparatus may further include an establishing module, where the establishing module may be specifically configured to obtain multiple pieces of service invocation data that are invoked normally in a specified time period; aggregating the plurality of normally called service calling data according to the calling form to obtain a plurality of corresponding template example groups; and establishing the preset example grouping library according to the template example grouping.

In one embodiment, the plurality of feature dimensions may specifically include at least one of: deployment units, service scenarios, upstream systems, downstream systems, logical rooms, and the like. Of course, the above listed feature dimensions are only illustrative. In specific implementation, other relevant factors can be introduced as the characteristic dimension according to specific situations. The present specification is not limited to these.

In an embodiment, the second determining module 804 may be specifically configured to count the number of service invocation data corresponding to each deployment unit parameter in a normal instance group; counting the number of service calling data corresponding to each service scene parameter in the normal example group; counting the number of service call data corresponding to each upstream system parameter in the normal example packet; counting the number of service call data corresponding to each downstream system parameter in the normal example packet; and counting the number of the service call data corresponding to each logic machine room parameter in the normal example grouping. Correspondingly, the second determining module 804 may be further specifically configured to count the number of service call data corresponding to each deployment unit parameter in the normal instance group; counting the number of service calling data corresponding to each service scene parameter in the normal example group; counting the number of service call data corresponding to each upstream system parameter in the normal example packet; counting the number of service call data corresponding to each downstream system parameter in the normal example packet; and counting the number of the service call data corresponding to each logic machine room parameter in the normal example grouping.

In one embodiment, the third determining module 805 may specifically include the following structural units:

the third determining unit may be specifically configured to determine, according to the number of the service call data in the normal instance group and the number of the service call data in the abnormal instance group of the plurality of feature dimensions, distribution data of normal call and abnormal call of the feature dimension parameter of each feature dimension of the plurality of feature dimensions;

the fourth determining unit may be specifically configured to determine an abnormal feature dimension parameter according to distribution data of normal calls and abnormal calls of the feature dimension parameter of each feature dimension of the plurality of feature dimensions, and determine a position indicated by the abnormal feature dimension parameter as a position where a call abnormality occurs.

It should be noted that, the units, devices, modules, etc. illustrated in the above embodiments may be implemented by a computer chip or an entity, or implemented by a product with certain functions. For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. It is to be understood that, in implementing the present specification, functions of each module may be implemented in one or more pieces of software and/or hardware, or a module that implements the same function may be implemented by a combination of a plurality of sub-modules or sub-units, or the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

As can be seen from the above, in the positioning apparatus for call exception provided in the embodiment of the present specification, since the obtained multiple pieces of call data are first divided into multiple example groups by the dividing module, the first determining module efficiently determines whether the call is normal from the example group level; and then, starting from the aspect of the characteristic dimension, the third determining module analyzes the specific calling condition of each characteristic dimension parameter in a fine-grained manner according to the number of the service calling data of the plurality of characteristic dimensions in the normal instance group and the number of the service calling data in the abnormal instance group, so as to accurately position the position of the calling abnormity in the service system, and thus, the accuracy and the efficiency for determining the position of the calling abnormity in the service system are improved.

Although the present specification provides method steps as described in the examples or flowcharts, additional or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an apparatus or client product in practice executes, it may execute sequentially or in parallel (e.g., in a parallel processor or multithreaded processing environment, or even in a distributed data processing environment) according to the embodiments or methods shown in the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded. The terms first, second, etc. are used to denote names, but not any particular order.

Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

From the above description of the embodiments, it is clear to those skilled in the art that the present specification can be implemented by software plus a necessary general hardware platform. Based on such understanding, the technical solutions of the present specification may be embodied in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, or the like, and includes instructions for causing a computer device (which may be a personal computer, a mobile terminal, a server, or a network device) to execute the method according to the embodiments or some parts of the embodiments of the present specification.

The embodiments in the present specification are described in a progressive manner, and the same or similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. The description is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

While the specification has been described with examples, those skilled in the art will appreciate that there are numerous variations and permutations of the specification that do not depart from the spirit of the specification, and it is intended that the appended claims include such variations and modifications that do not depart from the spirit of the specification.

Claims

1. A calling exception positioning method comprises the following steps:

acquiring a plurality of service calling data in a preset time period;

aggregating the service calling data according to the calling form to obtain a plurality of example groups; the calling form is described by key parameters and calling structure of service calling data;

determining a normal instance group and an abnormal instance group in the plurality of instance groups;

counting the number of service call data of a plurality of characteristic dimensions in normal instance grouping and the number of service call data in abnormal instance grouping; wherein the normal instance packet comprises an instance packet with a matching template instance packet; the exception instance packet comprises an instance packet for which there is no template instance packet match;

and determining the position of the calling abnormity according to the number of the service calling data in the normal instance grouping and the number of the service calling data in the abnormal instance grouping of the plurality of characteristic dimensions.

2. The method of claim 1, aggregating the service invocation data according to invocation morphology to obtain a plurality of instance groupings, comprising:

extracting key parameters and a calling structure of service calling data;

and dividing the plurality of service calling data into a plurality of example groups according to the key parameters and calling structures of the service calling data, wherein the key parameters and calling structures of the calling data in the same example group are the same.

3. The method of claim 1, determining normal and abnormal instance groupings in the plurality of instance groupings, comprising:

determining whether the current instance packet is a normal instance packet as follows:

searching a preset example grouping library, and determining whether a template example grouping is matched with a current example grouping in the preset example grouping library;

determining that the current instance group is a normal instance group under the condition that the template instance group is matched with the current instance group in the preset instance group library;

and under the condition that the template instance grouping does not exist in the preset instance grouping library and is matched with the current instance grouping, determining the current instance grouping as an abnormal instance grouping.

4. The method of claim 3, wherein the preset instance group library is built as follows:

acquiring a plurality of normally called service calling data in a specified time period;

aggregating the plurality of normally called service calling data according to the calling form to obtain a plurality of corresponding template example groups;

and establishing the preset example grouping library according to the template example grouping.

5. The method of claim 1, the plurality of feature dimensions comprising at least one of: the system comprises a deployment unit, a service scene, an upstream system, a downstream system and a logic machine room.

6. The method of claim 5, counting the number of service invocation data in normal instance groups for a plurality of feature dimensions, comprising:

counting the number of service call data corresponding to each deployment unit parameter in the normal instance group;

counting the number of service calling data corresponding to each service scene parameter in the normal example group;

counting the number of service call data corresponding to each upstream system parameter in the normal example packet;

counting the number of service call data corresponding to each downstream system parameter in the normal example packet;

and counting the number of the service call data corresponding to each logic machine room parameter in the normal example grouping.

7. The method of claim 1, wherein determining the location of the call exception according to the number of the service call data in the normal instance packet and the number of the service call data in the exception instance packet comprises:

determining the distribution data of normal calling and abnormal calling of the characteristic dimension parameters of each characteristic dimension in the plurality of characteristic dimensions according to the quantity of the service calling data of the plurality of characteristic dimensions in the normal instance group and the quantity of the service calling data of the plurality of characteristic dimensions in the abnormal instance group;

and determining abnormal characteristic dimension parameters according to the distribution data of normal calling and abnormal calling of the characteristic dimension parameters of each characteristic dimension in the plurality of characteristic dimensions, and determining the position indicated by the abnormal characteristic dimension parameters as the position where calling abnormality occurs.

8. A calling exception location device comprising:

the acquisition module is used for acquiring a plurality of service calling data in a preset time period;

the dividing module is used for aggregating the service calling data according to the calling form to obtain a plurality of example groups; the calling form is described by key parameters and calling structure of service calling data;

the first determining module is used for determining a normal example group and an abnormal example group in the plurality of example groups;

the second determining module is used for counting the number of the service calling data of the plurality of characteristic dimensions in the normal instance grouping and the number of the service calling data in the abnormal instance grouping; wherein the normal instance packet comprises an instance packet with a matching template instance packet; the exception instance packet comprises an instance packet for which there is no template instance packet match;

and the third determining module is used for determining the position of the calling abnormity according to the number of the service calling data in the normal example grouping and the number of the service calling data in the abnormal example grouping of the plurality of characteristic dimensions.

9. The apparatus of claim 8, the partitioning module comprising:

10. The apparatus of claim 9, the first determining module comprising:

the retrieval unit is used for retrieving a preset example grouping library and determining whether a template example grouping is matched with the current example grouping in the preset example grouping library;

a first determining unit, configured to determine that the current instance packet is a normal instance packet when it is determined that there is a match between the template instance packet and the current instance packet in the preset instance packet library;

and the second determining unit is used for determining that the current instance group is an abnormal instance group under the condition that the template instance group is not matched with the current instance group in the preset instance group library.

11. The apparatus according to claim 9, further comprising a setup module, configured to obtain service invocation data that a plurality of invocations are normal in a specified time period; aggregating the plurality of normally called service calling data according to the calling form to obtain a plurality of corresponding template example groups; and establishing the preset example grouping library according to the template example grouping.

12. The apparatus of claim 9, the plurality of feature dimensions comprising at least one of: the system comprises a deployment unit, a service scene, an upstream system, a downstream system and a logic machine room.

13. The apparatus according to claim 12, wherein the second determining module is specifically configured to count the number of service invocation data corresponding to each deployment unit parameter in a normal instance group; counting the number of service calling data corresponding to each service scene parameter in the normal example group; counting the number of service call data corresponding to each upstream system parameter in the normal example packet; counting the number of service call data corresponding to each downstream system parameter in the normal example packet; and counting the number of the service call data corresponding to each logic machine room parameter in the normal example grouping.

14. The apparatus of claim 9, the third determination module comprising:

a third determining unit, configured to determine, according to the number of the plurality of feature dimensions in the normal instance group and the number of the service call data in the abnormal instance group, distribution data of normal call and abnormal call of feature dimension parameters of each feature dimension in the plurality of feature dimensions;

and the fourth determining unit is used for determining an abnormal characteristic dimension parameter according to the distribution data of the normal call and the abnormal call of the characteristic dimension parameter of each characteristic dimension in the plurality of characteristic dimensions, and determining the position indicated by the abnormal characteristic dimension parameter as the position where the call abnormality occurs.

15. A server comprising a processor and a memory for storing processor-executable instructions which, when executed by the processor, implement the steps of the method of any one of claims 1 to 7.

16. A computer readable storage medium having stored thereon computer instructions which, when executed, implement the steps of the method of any one of claims 1 to 7.