CN109495291A

CN109495291A - Call abnormal localization method, device and server

Info

Publication number: CN109495291A
Application number: CN201811160467.7A
Authority: CN
Inventors: 王少华
Original assignee: Alibaba Group Holding 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: 2019-03-19
Anticipated expiration: 2038-09-30
Also published as: CN109495291B

Abstract

Abnormal localization method, device and server are called present description provides a kind of.This method comprises: obtaining multiple calling service data；Multiple calling service data are divided into multiple example groupings；Determine regular instance grouping and exception example grouping；Count the quantity of multiple characteristic dimensions calling service data in regular instance grouping and in exception example grouping；According to multiple characteristic dimensions in regular instance grouping and in exception example grouping calling service data quantity, determine and occur calling abnormal position.It in this specification embodiment, is grouped by the way that multiple calling data are first divided into multiple examples, the level being grouped from example is normal efficiently to judge whether；Fine-grained analysis is carried out from the level of characteristic dimension again, according to each characteristic dimension, the quantity of calling service data determines occur calling abnormal position in regular instance grouping and in exception example grouping, improves the accuracy and efficiency for determining and occurring calling abnormal position.

Description

Call abnormal localization method, device and server

Technical field

This specification belongs to Internet technical field more particularly to a kind of localization method, device and service for calling exception Device.

Background technique

In the control to business processing, it is often necessary to analyze, determine that exception occurs in data call in operation system Specific location, so that can corresponding repair process be carried out to above-mentioned abnormal position in time.

Currently, existing localization method is by log of the technical staff to each construction module in operation system mostly Record carries out investigation analysis one by one；In conjunction with the experience of technical staff, according to investigation analysis as a result, artificial judgment is called Abnormal position.The above method needs when it is implemented, since the log recording for needing to check analysis in operation system is often more Data volume to be processed is relatively large, causes implementation speed relatively slow；Again due to will receive the experience of technical staff individual with The influence of knowledge, the appearance determined call the accuracy of abnormal position relatively poor.Therefore, it is abnormal to need a kind of calling Localization method, accurately and efficiently to determine to occur in operation system to call abnormal position.

Summary of the invention

This specification is designed to provide a kind of localization method, device and server for calling exception, to improve determining industry Occur calling the accuracy and efficiency of abnormal position in business system.

Localization method, device and the server for a kind of calling exception that this specification provides are achieved in that

It is a kind of to call abnormal localization method, comprising: to obtain multiple calling service data in preset time period；According to tune The calling service data are polymerize with form, obtain multiple example groupings；It determines in the multiple example grouping Regular instance grouping and exception example grouping；Count the quantity of multiple characteristic dimensions calling service data in regular instance grouping With the quantity of the calling service data in exception example is grouped；According to multiple characteristic dimensions regular instance grouping in calling service The quantity of data and in exception example grouping calling service data quantity, determine and occur calling abnormal position.

It is a kind of to call abnormal positioning device, comprising: module to be obtained, for obtaining multiple business tune in preset time period Use data；Division module, for obtaining multiple example groupings according to calling form to polymerize the calling service data； First determining module, for determining regular instance grouping and exception example grouping in the multiple example grouping；Second really Cover half block, for count multiple characteristic dimensions regular instance grouping in calling service data quantity and exception example be grouped The quantity of middle calling service data；Third determining module, for according to multiple characteristic dimensions regular instance grouping in business tune The quantity of calling service data, determines and occurs calling abnormal position with the quantity of data and in exception example grouping.

A kind of server, including processor and for the memory of storage processor executable instruction, the processor The multiple calling service data obtained in preset time period are realized when executing described instruction；According to calling form to the business tune It is polymerize with data, obtains multiple example groupings；Determine the regular instance grouping and exception in the multiple example grouping Example grouping；Count multiple characteristic dimensions regular instance grouping in calling service data quantity and exception example grouping in The quantity of calling service data；According to multiple characteristic dimensions regular instance grouping in calling service data quantity and in exception The quantity of calling service data in example grouping, determines and occurs calling abnormal position.

A kind of computer readable storage medium, is stored thereon with computer instruction, and described instruction is performed realization and obtains Multiple calling service data in preset time period；According to calling form to polymerize the calling service data, obtain more A example grouping；Determine the regular instance grouping and exception example grouping in the multiple example grouping；Count multiple features Dimension regular instance grouping in calling service data quantity and exception example grouping in calling service data quantity；Root According to multiple characteristic dimensions regular instance grouping in calling service data quantity and exception example grouping in calling service number According to quantity, determine and occur calling abnormal position.

Localization method, device and the server for a kind of calling exception that this specification provides, by first by acquired number It measures huge multiple calling data and is divided into multiple example groupings, efficiently remove to judge whether it is tune from the level of example grouping It is abnormal with normal or calling；Again from the level of characteristic dimension, distinguished according to each characteristic dimension in multiple characteristic dimensions Regular instance grouping in calling service data quantity and exception example grouping in calling service data quantity to each The specific calling situation of characteristic dimension parameter carries out fine-grained analysis, different appearance calling in operation system is precisely located out Normal position occurs calling the accuracy and efficiency of abnormal position in determining operation system to improve.

Detailed description of the invention

In order to illustrate more clearly of this specification embodiment or technical solution in the prior art, below will to embodiment or Attached drawing needed to be used in the description of the prior art is briefly described, it should be apparent that, the accompanying drawings in the following description is only The some embodiments recorded in this specification, for those of ordinary skill in the art, in not making the creative labor property Under the premise of, it is also possible to obtain other drawings based on these drawings.

Fig. 1 is a kind of reality of the system structure composition of the localization method of the calling exception provided using this specification embodiment Apply the schematic diagram of example；

Fig. 2 is in a Sample Scenario, and the abnormal localization method of the calling provided using this specification embodiment obtains Example grouping a kind of composition schematic diagram；

Fig. 3 is in a Sample Scenario, using the one of the localization method for calling exception that this specification embodiment provides The schematic diagram of kind embodiment；

Fig. 4 is in a Sample Scenario, and the abnormal localization method of the calling provided using this specification embodiment obtains Example grouping statistical index a kind of composition schematic diagram；

Fig. 5 is in a Sample Scenario, and the abnormal localization method of the calling provided using this specification embodiment obtains Example grouping another composition schematic diagram；

Fig. 6 is a kind of signal of embodiment of the process of the localization method for the calling exception that this specification embodiment provides Figure；

Fig. 7 is a kind of schematic diagram of embodiment of the structure for the server that this specification embodiment provides；

Fig. 8 is a kind of signal of embodiment of the structure of the positioning device for the calling exception that this specification embodiment provides Figure.

Specific embodiment

In order to make those skilled in the art more fully understand the technical solution in this specification, below in conjunction with this explanation Attached drawing in book embodiment is clearly and completely described the technical solution in this specification embodiment, it is clear that described Embodiment be only this specification a part of the embodiment, instead of all the embodiments.The embodiment of base in this manual, Every other embodiment obtained by those of ordinary skill in the art without making creative efforts, all should belong to The range of this specification protection.

It calls abnormal localization method often to there is certain blindness in view of existing, is embodied in, existing side Method needs technical staff first to obtain the log recording that modules structure is all in some period operation system, then to above-mentioned All log recordings carry out investigation analysis one by one.And the modular structure that an operation system is usually included is complex, industry The calling data that business treatment process is related to are also relatively more, lead to the data of the log recording of the operation system of analysis to be checked It measures relatively large.Therefore, when it is implemented, efficiency of the practice is relatively poor.In addition, existing method is needed mostly by technical staff According to the investigation analysis of log recording as a result, in conjunction with itself knowledge and experience, artificially go to determine occur calling it is abnormal Position.Therefore, the result determined is affected by people, is caused when it is implemented, accuracy is relatively low, reliability It is relatively poor.

For above situation, this specification considers, in available certain time period in operation system business procession Multiple calling data, by above-mentioned multiple calling data it is for statistical analysis come orient in operation system occur call it is different Normal position.Specifically, in view of the number for the calling data being related in operation system business procession in certain time period Often more huge according to measuring, single call can have many big and small differences again between data.At this moment if one by one to upper It states and data is called to be analyzed and processed, certainly will may require that consumption a large amount of time and resource.It is therefore proposed that can be according to calling number According to calling form data a large amount of will be called first to be divided into multiple and different example groupings by calling form polymerization, then from The level of example grouping more efficiently distinguishes normal and exception, i.e., marks off regular instance from the grouping of multiple examples Grouping and exception example grouping.As unit of being grouped again by example, multiple characteristic dimensions business tune in regular instance grouping is counted The quantity of calling service data with the quantity of data and in exception example grouping.And then it can be according to multiple characteristic dimensions just In normal example grouping the quantity of calling service data and in exception example grouping calling service data quantity, from characteristic dimension Level carry out fine-grained analyzing and positioning, occur calling abnormal specific location in operation system to be accurately determined to provide, To improve the accuracy for occurring the abnormal position of calling in determining operation system and determine efficiency.

This specification embodiment provides a kind of localization method that application calling is abnormal.It can be as shown in fig.1, described Abnormal localization method is called specifically to can be applied to include in the system architecture of multiple services.

Wherein, the part server in above-mentioned multiple servers can be used as operation system and carry out specific business processing； The server of another part then can be used as monitoring server, for obtaining in operation system business processing in the preset period Multiple calling service data in the process；And multiple calling service data are first divided into multiple examples and are grouped, and then from example The level of grouping determines regular instance grouping and exception example grouping in multiple example groupings；Multiple feature dimensions are counted again Spend regular instance grouping in calling service data quantity and exception example grouping in calling service data quantity；To Can from the level of characteristic dimension, according to regular instance be grouped in calling service data quantity and the industry in exception example grouping The quantity of data is called in business, subtly determines and occurs calling abnormal position.It is subsequent to be directed in identified operation system Occur that abnormal position is called further to be checked or repaired.

In the present embodiment, the server can have data operation, store function and network interaction for one The electronic equipment of function；Or run in the electronic equipment, support is provided for data processing, storage and network interaction Software.Do not limit the quantity of the server specifically in the present embodiment.The server can be a server, also It can be several servers, alternatively, the server cluster formed by several servers.

In a Sample Scenario, the abnormal localization method of the calling that can be provided using this specification embodiment is to certain net Occur that abnormal position is called to be determined in network platform service system.

When it is implemented, can be acquired in preset time period by monitoring server, such as in nearest 1 hour, the network The calling service data that the operation system of platform is related in business procession.

In this Sample Scenario, operation system can usually be related to a variety of when carrying out specific business data processing The calling of functional module, supplemental characteristic etc., there are a large amount of calling processes.

For example, certain network platform operation system is verified in the log-on message to certain user, whether to determine the user Have permission using the network platform provide for member user function services when, need that input module is first called to obtain the use The log-on message of family input；Recall the identity information that extraction module extracts the user from above-mentioned log-on message；Then it calls Retrieval module retrieves the membership information library of the network platform, determines membership information according to the identity information of the extracted user With the presence or absence of the matched membership information of identity information with the user in library；The search result finally fed back according to retrieval module, Determine whether the user has permission using corresponding function services provided by the platform.

Correspondingly, above-mentioned calling service data specifically can be understood as characterizing the calling process in business data processing The information data of specific call flow in involved supplemental characteristic, and description calling process.Pass through above-mentioned business tune More complete, clear the specific calling process being related in business data processing can be restored with data.

Wherein, above-mentioned calling service data can specifically include: the information datas such as key parameter and calling structure.Above-mentioned pass Bond parameter at least may include: that key enters the ginseng primary data of input (can be understood as calling process) and crucial go out to join (can be with Be interpreted as calling process output result data) etc. supplemental characteristics.Above-mentioned calling structure at least may include: to relate in call flow And calling of the calling arrived with called functional module and above-mentioned calling and called functional module in call flow Sequence etc..The calling form of calling service data can accurately be depicted by above-mentioned key parameter and calling structure.When So, it should be noted that the content that above-mentioned cited calling service data are included is intended merely to that this explanation is better described Book embodiment.When it is implemented, calling service data can also include other according to specific application scenarios and processing requirement The information data of type or content, such as can also be specified comprising some among call flow in involved by functional module Between parameter etc..In this regard, this specification is not construed as limiting.

Specifically, for example, certain network platform operation system collected is authenticated in the log-on message to certain user Calling service data involved by journey may include the identity information of the log-on message (i.e. key enter ginseng) of user, user The functional module being related in search result (i.e. crucial to go out to join) and call flow: input module, extraction module and retrieval Module and calling sequence: first having invoked input module, recalled extraction module, finally has invoked retrieval module and (calls knot Structure) etc..

In this scene embodiment, when it is implemented, monitoring server can be by being preset in the network platform business system The collector on each server in system, server where acquiring in the preset period are executing business data processing when institute The record data for the calling process being related to are as calling service data.It certainly, can also be with when it is implemented, as the case may be Be calling service data involved in calling process are voluntarily recorded as each server in operation system, then by wireless or The calling data recorded are sent to monitoring server by wired mode.In this regard, this specification is not construed as limiting.

Monitoring server is after having obtained above-mentioned calling service data, it is contemplated that in a period involved by operation system Calling process it is more, the data volume of the calling service data of processing to be analyzed is larger, in conjunction with called in operation system normally with Abnormal specific feature is called, in order to improve treatment effeciency, monitoring server can be first according to the calling shape of calling service data State polymerize calling service data, and multiple calling service data are divided into multiple examples and are grouped, then are grouped into example Unit normally still calls exception to judge the middle calling service data call that example is grouped, from the grouping of multiple examples really Regular instance grouping and exception example grouping are made, so as to avoid directly being analyzed one by one a large amount of calling service data Judgement can complete the discrimination and really for calling data and abnormal traffic to call data regular traffic with lesser calculating cost It is fixed.

Wherein, examples detailed above grouping specifically can be understood as the same type calling service data with same or similar feature Set.A kind of grouping of example often can wrap containing the independent calling service data of one or more.Specifically, same instance Different business in grouping calls the characterization of data to call the key parameter of form and calling structure same or similar (such as not It is less than or equal to difference degree threshold value with the difference degree between the key parameter and calling structure of calling service data), it is corresponding Same calling service data type.

Specifically, an example grouping is included at least, there are two attributive character: key parameter and calling structure etc..Accordingly , it can be as shown in fig.2, at least may include two main content parts in the inclusive segment of example grouping.Wherein, One content part can specifically include key and enter ginseng and crucial ginseng out for indicating the key parameter of example grouping；Another Content part is used to indicate the calling structure of example grouping.The example can be depicted by the above-mentioned attributive character that example is grouped Calling form common to the calling service data of grouping.Wherein, the crucial ginseng of the calling service data in same instance grouping Number and calling structure are contained in the key parameter of example grouping and call structure.For example, the calling service in same instance grouping The key parameter of data can be identical as the key parameter that example is grouped, and the key parameter for being also possible to example grouping is included A supplemental characteristic in range.Corresponding with calling service data, the key parameter of example grouping also may include crucial ginseng out Enter the supplemental characteristics such as ginseng with key, the calling structure of example grouping also may include the calling being related in call flow and adjusted The information datas such as the calling sequence of functional module and above-mentioned calling and called functional module in call flow. Calling service data in the same example, above-mentioned key parameter is identical with structure is called or difference degree is less than or equal to poor Off course degree threshold value.

For example, the key of calling service data Q enters the name that ginseng is user's first, key goes out the address that ginseng is user's first；It adjusts It include: three a module, b module, c_3 module functional modules, and the sequence called with structure are as follows: first call a module, then adjust With b module, c_3 module is finally called.The key of calling service data W enters the name that ginseng is user's second, crucial ginseng out is user The address of second, calling structure includes: three a module, b module, c_2 module functional modules, and the sequence called are as follows: is first called A module recalls b module, finally calls c_2 module.By being respectively compared calling service data Q's and calling service data W Key parameter and calling structure find the key parameter of two calling service data and structure are called to have differences, but difference journey It spends smaller, is less than difference degree threshold value, i.e., the key parameter of two calling service data and call structure all approximate.Therefore, may be used The same example is divided into the two different calling service data by above-mentioned calling service data Q and calling service data W In grouping, so as to it is subsequent can be grouped by example as unit of carry out unified analysis processing.

Specific implementation, monitoring server can obtain more according to calling form to polymerize the calling service data A example grouping.Specifically, monitoring server can first extract retouching for each calling service data in multiple calling service data It states the key parameter for calling form and calls structure；According to the key parameter of the calling service data and structure is called, by institute It states multiple calling service data and divides multiple example groupings, wherein the key parameter of the calling data in the same example grouping It is identical with structure is called.It should be noted that above-mentioned key parameter and calling structure can be used for characterizing corresponding calling process, It can be used to describe the calling form of calling service data；It therefore can be according to the key parameter and tune of calling service data With structure, form close (i.e. key parameter is identical with structure is called or difference degree is less than difference degree threshold value) will be called Multiple calling service data aggregates are one kind, obtain an example grouping.It in the manner described above, can be by the business of substantial amounts Data are called to be divided into relatively small several examples grouping of quantity.

For example, can by as shown in fig.3, by call in the form of polymerize, can be by 1000 different calling service data (such as calling service data 1 to calling service data 1000) is divided into 10 different types of examples groupings (such as example point Group A to example grouping J).And then it is subsequent when judging whether is normal calling service data, it does not need to 1000 business tune Carry out analysis determination one by one with data, if to above-mentioned 10 examples grouping carry out analysis determinations be regular instance be grouped or it is different Normal example grouping.To effectively increase treatment effeciency, the consumption to time and resource is reduced.

After multiple calling service data to be divided into multiple example groupings, monitoring server can be from the layer of example grouping Face is grouped into processing unit with example to determine that calling service is the calling of normal calling or exception.Specifically, monitoring clothes Business device can be by judging that example grouping is that regular instance grouping or exception example are grouped, to determine the industry in each example grouping It is normal call or exception call that data are called in business.

Wherein, above-mentioned regular instance grouping specifically can be understood as calling normal, and with same or similar feature The set of same type calling service data.Above-mentioned exception example grouping specifically can be understood as calling exception, and have identical Or the set of the same type calling service data of close feature.

When it is implemented, monitoring server, can determine the instant example point in multiple example groupings in the following way Group whether regular instance grouping or exception example grouping: retrieve preset example grouping library, determine the preset example point It is matched with the presence or absence of template instances grouping with instant example grouping in group library；Exist in determining the preset example grouping library Template instances grouping is grouped in matched situation with instant example, determines that instant example is grouped into regular instance grouping；In determination There is no template instances groupings in the preset example grouping library is grouped in matched situation with instant example, determines current real Example is grouped into exception example grouping.

Wherein, above-mentioned preset example grouping library is understood that the example point to be made of multiple template example The set of group.Wherein, the grouping of each template instances is regular instance grouping in above-mentioned default example grouping library, is all corresponding An involved normal calling process in operation system business procession.Specifically, above-mentioned preset example grouping Library can be and pre-establish acquisition in the following way: obtain multiple calling in designated time period (such as testing time section) Normal calling service data；According to calling form to call normal calling service data to polymerize to the multiple, obtain Multiple corresponding template instances groupings；It is grouped according to the template instances, establishes the preset example grouping library.

If joined specifically, monitoring server retrieves in preset example grouping library with the crucial of instant example grouping It, can be by the template reality when number is identical with calling structure or difference degree is less than the template instances grouping of difference degree threshold value Example grouping is determined as being grouped matched example grouping with instant example, that is, determines that there are templates in the preset example grouping library Example grouping is matched with instant example grouping, determines that instant example is grouped into regular instance grouping.Opposite, if preset Retrieval is identical less than the key parameter and calling structure being grouped with instant example in example grouping library or difference degree is less than poor When the template instances grouping of off course degree threshold value, can determine in preset example grouping library there is no template instances grouping with it is current Example grouping matching determines that instant example is grouped into exception example grouping.

Monitor has quickly determined out the regular instance in multiple example groupings by the layer face treatment being grouped from example Grouping and exception example grouping, and then can determine that the corresponding calling data call of regular instance grouping is normal, exception example The corresponding calling data call of grouping is abnormal, to achieve the purpose that efficiently to judge to call data call normal or abnormal.

Further, it is possible to count multiple characteristic dimensions business in regular instance grouping from the level of characteristic dimension Call the quantity of data and in exception example grouping calling service data quantity, and then can be existed according to multiple characteristic dimensions Regular instance grouping in calling service data quantity and exception example grouping in calling service data quantity, to each spy The normal call and exception call situation for levying dimensional parameter carry out fine-grained analysis, occur calling abnormal position to determine.

Specifically, can be obtained by the quantity of the multiple characteristic dimensions of statistics calling service data in regular instance grouping To the corresponding calling service data of each characteristic dimension parameter institute in multiple characteristic dimensions can be characterized in regular instance grouping Quantity regular instance grouping statistical index；It can be by counting multiple characteristic dimensions business tune in exception example grouping With the quantity of data, obtain to characterize in exception example grouping that each characteristic dimension parameter is respectively corresponded in multiple characteristic dimensions Calling service data quantity exception example grouping statistical index；And then the system that can be grouped according to the regular instance The statistical index of meter index and exception example grouping, is processing unit with characteristic dimension, and fine granularity subtly determines industry Occur calling abnormal position in business system.

Wherein, whether features described above dimension specifically can be understood as to occur calling normal to specific calling process or adjust With the dimension factor impacted extremely.Features described above dimension is may include respectively there are many characteristic dimension parameter, wherein each A characteristic dimension parameter can serve to indicate that the specific features body position in operation system.It should be noted that above-mentioned specific spy Sign position can be corresponding to an actual physical location, can also be with for example, some physics computer room or some server etc. A virtual functional location is corresponded to, for example, some construction module etc..

Specifically, features described above dimension at least may include: deployment unit, business scenario, up-stream system, down-stream system, Logic computer room etc..Wherein, above-mentioned logic computer room (zone) can specifically refer to computer room physically, may also mean that virtual patrol Collect grouping.Above-mentioned deployment unit can specifically refer to the structural unit or function mould for calling the functional module in structure to be deployed in Block.Above-mentioned up-stream system specifically can be understood as any called side once specifically called in calling process.Above-mentioned downstream system System specifically can be understood as any called side once specifically called in calling process.Above-mentioned business scenario specifically can be understood as Business processing corresponding to calling process.Certainly, it should be noted that above-mentioned cited characteristic dimension is intended merely to preferably Illustrate this specification embodiment.When it is implemented, may be incorporated into other kinds of factor as the case may be as above-mentioned Characteristic dimension.This specification is not construed as limiting this.

When it is implemented, monitoring server can be from specific characteristic dimension, by reverse indexing according to lower section Formula counts the quantity of multiple characteristic dimensions calling service data in regular instance grouping: can count each deployment unit respectively The quantity of parameter corresponding calling service data in regular instance grouping；Each business scenario parameter is counted in regular instance point The quantity of corresponding calling service data in group；Count each up-stream system parameter corresponding business tune in regular instance grouping With the quantity of data；Count the quantity of each down-stream system parameter corresponding calling service data in regular instance grouping；System Count the quantity of each logic computer room parameter corresponding calling service data in regular instance grouping.Further, for user Just, above-mentioned statistical result can be summarized again, the statistical index of available regular instance group.

Wherein, above-mentioned deployment unit parameter specifically can be understood as characterizing this spy of deployment unit in example grouping Levy the design parameter data of dimension.Above-mentioned business scenario parameter specifically can be understood as characterizing business field in example grouping The design parameter data of this characteristic dimension of scape.Above-mentioned up-stream system parameter specifically can be understood as characterizing example grouping The design parameter data of this characteristic dimension of middle and upper reaches system.Above-mentioned down-stream system parameter specifically can be understood as characterizing this The design parameter data of example grouping this characteristic dimension of middle and lower reaches system.Above-mentioned logic computer room parameter specifically can be understood as using In the design parameter data for characterizing this characteristic dimension of logic computer room in example grouping.You need to add is that for same In example grouping, the design parameter data for characterizing same characteristic dimension may include belonging to a variety of ginsengs of same function service type Number data.

For example, being grouped for some regular instance, up-stream system parameter may include: the system a_1 for being responsible for extracting, be responsible for Two kinds of the system a_2 of extraction.By the calling service data in statistics instant example grouping, it can determine that being responsible for data extracts System a_1 it is corresponding call data quantity be 30, be responsible for data extract system a_2 it is corresponding call data quantity be 70。

The statistical index of above-mentioned regular instance grouping specifically can be each deployment unit parameter in regular instance grouping Corresponding corresponding quantity, each up-stream system parameter pair for calling data of quantity, each business scenario parameter for calling data The corresponding quantity for calling data of the quantity for the calling data answered, each down-stream system parameter, each logic computer room parameter are corresponding The statistical results of quantity of calling data summarize.According to above-mentioned statistical index, can determine to appoint in instant example grouping The corresponding quantity situation for calling data of the specific characteristic dimension parameter of meaning one.

In the way of the statistical index of above-mentioned determining regular instance grouping, determine one by one each in multiple example groupings The statistical index of a regular instance grouping obtains the statistical index of each regular instance grouping.

Similar, the multiple characteristic dimension calling service number in exception example grouping can be counted in the following way According to quantity: can count respectively each deployment unit parameter exception example grouping in corresponding calling service data number Amount；Count the quantity of each business scenario parameter corresponding calling service data in exception example grouping；Count each upstream The quantity of system parameter corresponding calling service data in exception example grouping；Each down-stream system parameter is counted abnormal real The quantity of corresponding calling service data in example grouping；Count each logic computer room parameter corresponding industry in exception example grouping The quantity of data is called in business.Further, for ease of use, above-mentioned statistical result can be summarized again, obtains exception example group Statistical index.

Based on above-mentioned cited specific characteristic dimension parameter, for example, as shown in fig.4, for each example point The statistical index of group (including: regular instance grouping and exception example grouping), specifically can be for above-mentioned in example grouping Summarizing for the statistical result of data bulk is called corresponding to the characteristic dimension parameter of each characteristic dimension of multiple characteristic dimensions. In this way by parsing the statistical index of each example grouping, each spy in example grouping can be quickly and accurately determined Levy the quantity that data are called in preset time period corresponding to the characteristic dimension parameter of dimension.

Further, it after the statistical index for obtaining the grouping of each example, is used for the ease of subsequent reading, at raising Efficiency is managed, a kind of new attributive character that the statistical index that example is grouped can also be grouped as example, together with extracting before The key parameter and calling structure of determining example grouping collectively constitute an example grouping.It specifically can be as shown in fig.5, one It may include three content parts in the inclusive segment of a example grouping, wherein a content part is used to indicate example grouping Key parameter, a content part are used to indicate the calling structure of example grouping, another content part is for indicating example point The statistical index of group.

The statistical index that monitoring server is grouped in the statistical index and exception example for having obtained above-mentioned regular instance grouping, It can be based on above-mentioned statistical index, different characteristic dimensional parameter is normally counted with abnormal conditions from the level of characteristic dimension Analysis is subtly determined in operation system according to for characteristic dimension parameter is normal and the statistic analysis result of abnormal conditions Abnormal position is called out.Specifically, can be first according to the statistical index and the exception example point of regular instance grouping The statistical index of group determines the number of each characteristic dimension calling service data in regular instance grouping in multiple characteristic dimensions Amount and exception example grouping in calling service data quantity；Further according to multiple characteristic dimensions regular instance grouping in business Call data quantity and exception example grouping in calling service data quantity, determine each spy in multiple characteristic dimensions Levy the normal call of the characteristic dimension parameter of dimension and the distributed data of exception call；According to each in the multiple characteristic dimension The normal call of the characteristic dimension parameter of a characteristic dimension and the distributed data of exception call, determine off-note dimensional parameter, And position indicated by the off-note dimensional parameter is determined as occurring calling abnormal position.

For example, can determine to adjust for this characteristic dimension of up-stream system according to the statistical index that regular instance is grouped With distributed data (i.e. multiple characteristic dimensions ginseng of this feature dimension of the corresponding calling data volume of normal up-stream system parameter The quantity of number calling service data in regular instance) are as follows: system a_1 is 30, and system a_2 is 70, and system b_1 is 90, system B_2 is 10, and system c is 110, and system d is 290.According to the statistical index that exception example is grouped, can determine to be directed to upstream system It unites this characteristic dimension, calls corresponding distributed data (the i.e. this feature dimension for calling data volume of abnormal up-stream system parameter Multiple characteristic dimension parameter calling service data in exception example quantity) are as follows: system a_1 be 5, system a_2 be 5, be The b_1 that unites is 10, and system b_2 is 190, and system c is 30, and system d is 50.In summary statistical data, may further determine that out The calling of each corresponding up-stream system parameter is normal and calls abnormal distribution proportion are as follows: system a_1 is 6:1, and system a_2 is 14:1, system b_1 are 9:1, and system b_2 is 1:19, and system c is 3:11, and system d is 5:29 to get arriving characteristic dimension just The distributed data of normal characteristic dimension parameter and off-note dimensional parameter.

And then it can use the normal characteristics dimensional parameter of features described above dimension and the distribution number of off-note dimensional parameter According to as reference frame, position indicated by characteristic dimension parameter each in this feature dimension is carried out more with the presence or absence of abnormal Fine judgement determines.For example, this specific characteristic dimension ginseng for the system b_2 in this characteristic dimension of up-stream system Number, calling distributed data normal and that calling is abnormal is 1:19, low and preset outlier threshold, thus may determine that this feature is tieed up Position indicated by parameter is spent, i.e. system b_2 calls abnormal with the presence of greater probability.And then it is subsequent can for system b_2 into More comprehensive, the careful inspection and analysis of row, so as to solve to cause to call exception due to system b_2 in time, so that The business procession of operation system is stable, reliable.

By above-mentioned Sample Scenario as it can be seen that the abnormal localization method of the calling that provides of this specification, by first will be acquired Multiple calling data of substantial amounts are divided into multiple example groupings, efficiently go to judge whether to adjust from the level of example grouping It is abnormal with normal or calling；Again from the level of characteristic dimension, according to multiple characteristic dimensions in regular instance grouping business Call the quantity of data and in exception example grouping calling service data quantity to the specific tune of each characteristic dimension parameter Fine-grained analysis is carried out with situation, occurs calling abnormal position in operation system to be precisely located out, to improve Determine in operation system occur calling the accuracy of abnormal position and determining efficiency.

As shown in fig.6, this specification embodiment provides a kind of localization method for calling exception, wherein this method tool Body should can be used for monitoring server side.When it is implemented, this method may include the following contents:

S61: multiple calling service data in preset time period are obtained.

In the present embodiment, above-mentioned preset time period specifically can be understood as some period to be analyzed.For example, A nearest period.Wherein, the corresponding specific duration of above-mentioned preset time period can be 30 minutes, be also possible to 1 hour Deng.For above-mentioned preset time period, according to specific application scenarios and processing requirement, some period and right is neatly selected The duration answered.In this regard, this specification is not construed as limiting.

In the present embodiment, above-mentioned calling service data specifically can be understood as characterizing operation system business processing In calling process involved by supplemental characteristic, and the Information Number for describing specific call flow in calling process According to.More complete, clear the specific calling being related in business data processing can be restored by above-mentioned calling service data Process.

Specifically, above-mentioned calling service data may include: the information datas such as key parameter and calling structure.Wherein, on Stating key parameter at least may include: that key enters ginseng (primary data that can be understood as calling process input) and crucial ginseng out Supplemental characteristics such as (result datas that can be understood as calling process output).By above-mentioned key parameter and call structure can be compared with The calling form of calling service data is depicted well.Above-mentioned calling structure at least may include: to be related in call flow Call the calling sequence with called functional module and above-mentioned calling and called functional module in call flow Deng.Certainly, it should be noted that the content that above-mentioned cited calling service data are included is intended merely to that this is better described Specification embodiment.For the particular content that upper calling service data are included, this specification is not construed as limiting.

In the present embodiment, multiple calling service data in above-mentioned acquisition preset time period, when it is implemented, can be with It include: that monitoring server can record by the collector being preset in operation system and acquire business system in preset time period The relevant calling service data united during executing business data processing, then prison is sent to by network timing by collector Control server.Certainly, the mode of multiple calling service data in above-mentioned cited acquisition preset time period is that one kind is shown Meaning property explanation.When it is implemented, as the case may be, can also be obtained using other suitable modes more in preset time period A calling service data.In this regard, this specification is not construed as limiting.

S62: according to calling form to polymerize the calling service data, multiple example groupings are obtained.

In the present embodiment, examples detailed above grouping specifically can be understood as the same type industry with same or similar feature The set of data is called in business.A kind of grouping of example often can wrap containing multiple individual calling service data.Specifically, same Example grouping in different business call data key parameter and call structure it is same or similar (such as different business calling Difference degree between the key parameter and calling structure of data is less than or equal to difference degree threshold value), corresponding same business Call data type.

In the present embodiment, examples detailed above grouping can specifically include at least two attributive character, be respectively as follows: crucial ginseng Number and calling structure etc..The calling service data institute of example grouping can be depicted by the above-mentioned attributive character that example is grouped Shared calling form.Wherein, the key parameter of the calling service data in same instance grouping and calling structure are contained in reality The key parameter and calling structure of example grouping.For example, the key parameter of the calling service data in same instance grouping can be with The key parameter of example grouping is identical, a parameter number being also possible in the range of the key parameter that example is grouped includes According to.Corresponding with calling service data, the key parameter of example grouping also may include that crucial ginseng out and key enter the parameter numbers such as ginseng According to, the calling structure of example grouping also may include the calling and called functional module being related in call flow, and The information datas such as the calling sequence of above-mentioned calling and called functional module in call flow.

In the present embodiment, it is contemplated that it is often more for the business processing in one period an of operation system, The data volume of related calling service data is larger, if directly against calling service data it is normal to its calling one by one or Exception carries out analysis determination, will certainly occupy a large amount of time and resource.Accordingly, it is considered to can be first by the tune with same type Together with multiple calling service data aggregates of form, an example grouping is obtained.And then it is subsequent can be from example packet layer Face is set out, and using example grouping as processing unit, the calling of the calling form corresponding to an example grouping is normal or abnormal Analysis determination is carried out, the calling being grouped according to the example is normal or calls exception, determines that the example is grouped the multiple tools for being included Whether the calling service data of body are called normal or are called abnormal.Distinguish so as to avoid to multiple specific calling service data It carries out analysis one by one to handle, significantly reduces data processing amount, improve treatment effeciency.

In the present embodiment, above-mentioned according to calling form to polymerize the calling service data, obtain multiple realities Example grouping, when it is implemented, may include the following contents: monitoring server can extract calling service data key parameter and Call structure；Key parameter and calling structure further according to the calling service data, the multiple calling service data are drawn Divide multiple examples groupings.Specifically, can by key parameter with call that structure is identical or difference degree is less than or equal to difference journey The one or more example packet aggregations for spending threshold value are one group and are grouped to get to an example.

S63: the regular instance grouping and exception example grouping in the multiple example grouping are determined.

In the present embodiment, the grouping of above-mentioned regular instance specifically can be understood as calling normal, and have it is identical or The set of the same type calling service data of close feature.Above-mentioned exception example grouping specifically can be understood as calling exception, And the set of the same type calling service data with same or similar feature.

In the present embodiment, the above-mentioned regular instance grouping and exception example point determined in the multiple example grouping Group, when it is implemented, may include the following contents: being grouped library according to preset example, determine preset example grouping library respectively In be grouped with the presence or absence of being grouped matched template instances with each example in the grouping of the multiple example, by the multiple example point Example grouping in group in preset example grouping library there are the grouping of matched template instances is determined as regular instance grouping, will It is grouped and determines there is no the example of matched template instances grouping in preset example grouping library in the multiple example grouping For exception example grouping.So as to efficiently determine regular instance grouping and exception example respectively from the grouping of multiple examples Grouping, wherein the included calling service data call of regular instance grouping is normal, and exception example is grouped included business tune With data call exception.It can sentence in this way to avoid the analysis directly carried out one by one to the calling situation of quantity biggish example grouping Level that is disconnected, but being grouped from example is grouped using example as processing unit, by each example of determination be grouped whether be Perhaps exception example grouping is completed whether to call a large amount of calling service data normal or be called abnormal for regular instance grouping It is determined, significantly reduces data processing amount, improve treatment effeciency.

S64: multiple characteristic dimensions quantity of calling service data and in exception example point in regular instance grouping is counted The quantity of calling service data in group.

In the present embodiment, features described above dimension specifically can be understood as specific calling process whether can be adjusted The dimension factor impacted extremely with normal or calling.Features described above dimension is can wrap respectively containing one or more specific Characteristic dimension parameter, wherein each characteristic dimension parameter, which can serve to indicate that, is directed to the one of this feature dimension in operation system A specific features body position.It should be noted that above-mentioned specific features position can be corresponding to an actual physical location, For example, some physics computer room or some server etc., are also possible to correspond to a virtual logical place, for example, some Construction module etc..

Specifically, features described above dimension may include: deployment unit, business scenario, up-stream system, down-stream system, logic Computer room etc..Wherein, above-mentioned logic computer room (zone) can specifically refer to computer room physically, may also mean that virtual logic point Group.Above-mentioned deployment unit can specifically refer to the structural unit for calling the functional module in structure to be deployed in or functional module. Above-mentioned up-stream system specifically can be understood as any called side once specifically called in calling process.Above-mentioned down-stream system tool Body can be understood as any called side once specifically called in calling process.Above-mentioned business scenario specifically can be understood as calling Business processing corresponding to process.Certainly, it should be noted that above-mentioned cited characteristic dimension is intended merely to be better described This specification embodiment.When it is implemented, may be incorporated into other kinds of factor as the case may be as features described above Dimension.This specification is not construed as limiting this.

In present embodiment, determine to occur in operation system to call abnormal specific position in order to accurately find It sets, can be that processing unit is subtly called abnormal positioning with characteristic dimension from the level of characteristic dimension.Cause This, can first count the data that the corresponding calling service data of normal characteristic dimension parameter are called in regular instance grouping respectively Amount obtains the statistical index of regular instance grouping, calls abnormal characteristic dimension parameter corresponding in statistics exception example grouping The data volume of calling service data obtains the statistical index of exception example grouping.It is subsequent to be directed to characteristic dimension based on above-mentioned Regular instance grouping statistical index and exception example grouping statistics be accurately determined from the level of characteristic dimension Occur calling abnormal position in operation system out.

In the present embodiment, specifically, multiple characteristic dimensions calling service number in regular instance grouping can be counted According to quantity, obtain regular instance grouping statistical index；Count multiple characteristic dimensions calling service in exception example grouping The quantity of data obtains the statistical index of exception example grouping.

In the present embodiment, the statistical index of above-mentioned regular instance grouping specifically can be understood as in regular instance grouping The corresponding calling of characteristic dimension parameter (i.e. normal characteristics dimensional parameter) institute of each characteristic dimension of each example grouping The statistical result of the quantity of data summarizes.The statistical index of above-mentioned exception example grouping specifically can be understood as exception example point The corresponding tune of characteristic dimension parameter (i.e. off-note dimensional parameter) of each characteristic dimension of each example grouping in group With summarizing for the statistical result of the quantity of data.According to the statistical index that above-mentioned regular instance is grouped, can accurately determine out The calling data of normal dimensional parameter different in some characteristic dimension in some example grouping in regular instance grouping Amount.For example, the statistical index being grouped by regular instance, can determine the calling data of different normal up-stream system parameters It is 30 that amount, which is respectively as follows: the corresponding quantity for calling data of system a_1, is responsible for the corresponding calling data of system a_2 that data are extracted Quantity be 70.

In the present embodiment, after the statistical index that statistics has obtained the grouping of above-mentioned regular instance, can by it is above-mentioned just The statistical index of normal example grouping as a kind of new attributive character increase in original example packet configuration, so as to it is subsequent can Use is read out with more convenient relevant information data to example grouping.It is each in the grouping of regular instance obtained by this way It may include three attributive character that example, which is grouped in structure, i.e. key parameter, the statistics for calling structure and regular instance to be grouped Index.Similar, it may include three attributive character that each example, which is grouped in business in structure, in exception example grouping, i.e., crucial Parameter, the statistical index for calling structure and exception example grouping.Certainly, above-mentioned cited regular instance grouping, exception example The structure of grouping is that one kind schematically illustrates, and should not constitute the improper restriction to this specification.

S65: according to multiple characteristic dimensions in regular instance grouping the quantity of calling service data and in exception example point The quantity of calling service data in group determines and occurs calling abnormal position.

It in the present embodiment, specifically, can also be according to the statistical index and the exception that the regular instance is grouped The statistical index of example grouping determines and occurs calling abnormal position.

In the present embodiment, monitoring server can be according to the statistics of the above-mentioned regular instance grouping for characteristic dimension The statistical index of index and exception example grouping is that specific processing is single with characteristic dimension from this level of characteristic dimension Position, can be analyzed by normal or abnormal situation of the aggregate statistics to characteristic dimension parameter each in operation system, thus Subtly determine occur calling abnormal specific location in operation system.

It in the present embodiment, when it is implemented, can be according to the statistical dimension and exception example point that regular instance is grouped The statistical dimension of group, determines the normal call and exception of specific characteristic dimension parameter in each characteristic dimension in operation system The distributed data of calling is (for example, the normal call data bulk and exception call data number of the corresponding same characteristic dimension parameter The ratio of amount)；And then it can be with the normal call of characteristic dimension parameter specific in each characteristic dimension in above-mentioned operation system With the distributed data of exception call as reference frame, guidance, which is found, to be occurred calling abnormal position, so as to it is subsequent can be to industry Occur that the position of exception is called to carry out timely, targetedly adjustment, reparation in business system.

Therefore the localization method for calling exception that this specification embodiment provides, by first by acquired quantity Huge multiple calling data are divided into multiple example groupings, are efficiently going to judge whether from the level of example grouping to call just Often or call abnormal；Again from the level of characteristic dimension, according to multiple characteristic dimensions in regular instance grouping calling service The quantity of data and in exception example grouping calling service data quantity to the specific calling feelings of each characteristic dimension parameter Condition carries out fine-grained analysis, occurs calling abnormal position in operation system to be precisely located out, to improve determination Occur calling the accuracy of abnormal position in operation system and determines efficiency.

In one embodiment, above-mentioned according to calling form to polymerize the calling service data, it obtains multiple Example grouping, when it is implemented, may include the following contents: extracting the key parameter of calling service data and call structure；Root According to the key parameter and calling structure of the calling service data, the multiple calling service data are divided into multiple examples point Group, wherein the key parameter of the calling data in the same example grouping is identical with structure is called.

It in the present embodiment, can be by each business tune in acquired multiple calling service data when specific implementation It is parsed respectively with data, obtains the key parameter and calling structure of each calling service data to extract.

In the present embodiment, the above-mentioned key parameter according to the calling service data and calling structure, will be described more A calling service data divide multiple example groupings, when it is implemented, may include: the key parameter according to calling service data With call structure, by key parameter and call that structure is identical or difference degree less than difference degree threshold value one or more industry It is that one kind is grouped to get corresponding example has been arrived that data aggregate is called in business.

In one embodiment, the above-mentioned regular instance grouping and exception example determined in the multiple example grouping Grouping, when it is implemented, may include the following contents: whether being normal reality with instant example grouping in the multiple example groupings of determination Example is grouped into for exception example grouping, specifically, preset example grouping library can first be retrieved, determines the preset example It is matched with the presence or absence of template instances grouping with instant example grouping in grouping library；It is deposited in determining the preset example grouping library In the case where template instances grouping is grouped matched situation with instant example, determine that instant example is grouped into regular instance grouping；True There is no template instances groupings in the fixed preset example grouping library is grouped in matched situation with instant example, determines current Example is grouped into exception example grouping.

In the present embodiment, above-mentioned preset example grouping library is specifically understood that as by multiple template example grouping group At an example grouping set.Wherein, the grouping of each template instances is normal reality in above-mentioned default example grouping library Example grouping all corresponds to an involved normal calling process in operation system business procession.

In the present embodiment, matched template instances are grouped with instant example to be grouped, are specifically can be understood as preset Example is grouped key parameter in library and calls structure identical as instant example grouping, or the difference degree with instant example grouping Template instances less than or equal to difference degree threshold value are grouped.If in preset example grouping library, there are above-mentioned template instances point Group, it is determined that there are template instances groupings in the preset example grouping library matches with instant example grouping, determines current real Example is grouped into regular instance grouping.If in preset example grouping library, there is no above-mentioned template instances to be grouped, it is determined that described There is no template instances groupings in preset example grouping library matches with instant example grouping, determines that instant example is grouped into exception Example grouping.

In one embodiment, the preset example grouping library can specifically be established to obtain in the following way: obtain Take the normal calling service data of multiple calling in designated time period；Normal industry is called to the multiple according to calling form Business calls data to be polymerize, and obtains multiple corresponding template instances groupings；It is grouped, is established described pre- according to the template instances If example be grouped library.

In the present embodiment, above-mentioned designated time period specifically can be normally carried out business processing with operation system in history Some period, the calling service data in the period are the calling service data of normal call.For example, specifically can be One period of test phase.Wherein, the specific duration of above-mentioned designated time period can be 30 minutes, be also possible to 1 hour Deng.The selection of above-mentioned designated time period is determined, can flexibly be determined as the case may be.In this regard, this specification does not limit It is fixed.

In one embodiment, the multiple characteristic dimension can specifically include: deployment unit, business scenario, upstream The factors such as system, down-stream system, logic computer room.Wherein, above-mentioned logic computer room (zone) can specifically refer to computer room physically, It may also mean that virtual logic groups.Above-mentioned deployment unit, which can specifically refer to, calls the functional module in structure to be deployed in Structural unit or functional module.Above-mentioned up-stream system specifically can be understood as any quilt once specifically called in calling process Called side.Above-mentioned down-stream system specifically can be understood as any called side once specifically called in calling process.Above-mentioned business Scene specifically can be understood as business processing corresponding to calling process.Certainly, it should be noted that above-mentioned cited feature Dimension is intended merely to that this specification embodiment is better described.When it is implemented, may be incorporated into other as the case may be The factor of type is as features described above dimension.This specification is not construed as limiting this.

In the present embodiment, each of multiple characteristic dimensions characteristic dimension can wrap containing one or more Specific characteristic dimension parameter.Wherein, each characteristic dimension parameter can serve to indicate that in operation system and tie up for this feature One specific features body position of degree.For example, it is directed to this characteristic dimension of up-stream system, and it is corresponding, it may include following more A specific characteristic dimension parameter: system a_1, system a_2, system b, system c and system d.For business scenario, this is special Dimension is levied, corresponding includes business scenario parameter: payment transaction, this characteristic dimension parameter.Certainly, above-mentioned cited Characteristic dimension parameter be that one kind schematically illustrates, the improper restriction to this specification should not be constituted.

In one embodiment, the quantity of multiple characteristic dimensions calling service data in regular instance grouping is counted, When it is implemented, may include: each deployment unit parameter of statistics corresponding calling service data in regular instance grouping Quantity；Count the quantity of each business scenario parameter corresponding calling service data in regular instance grouping；On counting each Swim the quantity of system parameter corresponding calling service data in regular instance grouping；Each down-stream system parameter is counted normal The quantity of corresponding calling service data in example grouping；It is corresponding in regular instance grouping to count each logic computer room parameter The quantity of calling service data.For ease of use, progress can summarize above-mentioned statistical result, obtain the statistics of regular instance Index.

In the present embodiment, it is referred to aforesaid way, multiple characteristic dimensions can be counted in exception example grouping The quantity of calling service data, and then obtain the statistical index of exception example grouping.

In the present embodiment, after the statistical index for obtaining above-mentioned regular instance grouping (or exception example grouping), it is Convenient for the attributive character that subsequent reading use the statistical index that can be grouped regular instance new as one kind, together with example The key parameter and calling structure of grouping are recorded in together in the content structure of corresponding instance grouping, are used after being convenient for.

In the present embodiment, it should be noted that it is above-mentioned only to be grouped by counting multiple characteristic dimensions in regular instance With the quantity of calling service data in exception example, for obtaining corresponding statistical result, the positioning to exception is called is carried out.Tool It, as the case may be, can also be by counting multiple characteristic dimensions industry in regular instance grouping and exception example when body is implemented Business calls the calling of data time-consuming, and so as to describe, determine from another angle, characteristic dimension is called just in operation system Often or abnormal situation is called, to be called abnormal positioning.In this regard, this specification is not construed as limiting.

In one embodiment, the above-mentioned number according to multiple characteristic dimensions calling service data in regular instance grouping Amount and in exception example grouping calling service data quantity, determine and occur calling abnormal position, comprising: according to multiple spies Levy dimension regular instance grouping in calling service data quantity and exception example grouping in calling service data quantity, Determine the normal call of the characteristic dimension parameter of each characteristic dimension in multiple characteristic dimensions and the distributed data of exception call； According to the distribution of the normal call of the characteristic dimension parameter of each characteristic dimension in the multiple characteristic dimension and exception call Data determine off-note dimensional parameter, and position indicated by the off-note dimensional parameter are determined as calling Abnormal position.

In the present embodiment, the statistical index and exception example being grouped in the regular instance obtained for characteristic dimension After the statistical index of grouping, the statistical index of above-mentioned regular instance grouping and the statistics rope of exception example grouping also can use Draw, determines each feature dimensions in operation system respectively from the level of characteristic dimension using characteristic dimension as processing unit The corresponding quantity number for calling data corresponding with off-note dimensional parameter for calling data of the normal characteristics dimensional parameter of degree Distributed data of the ratio of amount as the normal reconciliation exception call of the characteristic dimension parameter of features described above dimension.For example, determining Out for the corresponding quantity of data and different called of the normal characteristics dimensional parameter of this specific characteristic dimension parameter of system b_2 Often the corresponding quantity for calling data of characteristic dimension parameter is than being 1:19 to get the characteristic dimension parameter for having arrived this feature dimension Normal call and the distributed data of exception call are 1:19.

In the present embodiment, the characteristic dimension parameter of each characteristic dimension in above-mentioned multiple characteristic dimensions is being obtained Normal call and exception call distributed data after, can according to above-mentioned data in operation system each characteristic dimension join The indicated specific location of number carries out fine-grained explication de texte and determines each feature dimensions in operation system based on the analysis results It is abnormal whether position indicated by degree parameter occurs calling, and occurs calling abnormal specific location and coverage etc..

For example, can be by the normal tune of the characteristic dimension parameter of each characteristic dimension in identified multiple characteristic dimensions It is compared respectively with preset outlier threshold with the distributed data with exception call, by the distribution of normal call and exception call Data are less than position indicated by the characteristic dimension parameter of preset outlier threshold and are determined as occurring calling abnormal position.In turn It is subsequent, it can be for occurring determined by above-mentioned that abnormal position is called to carry out more accurate, careful test detection, with into one Step demonstrate,proves whether the position really occurs calling abnormal position；Exception directly can also be called to above-mentioned identified appearance Position is adjusted reparation, to eliminate exception, so that business processing is reliable and stable.Certainly, it should be noted that above-mentioned cited The characteristic dimension parameter according to each characteristic dimension in the multiple characteristic dimension normal call and exception call point Cloth data determine off-note dimensional parameter, and position indicated by the off-note dimensional parameter are determined as adjusting With abnormal position, and determining that occurring calling the processing mode behind abnormal position is that one kind schematically illustrates, no The improper restriction to this specification should be constituted.

Therefore the localization method for calling exception that this specification embodiment provides, due to first by acquired quantity Huge multiple calling data are divided into multiple example groupings, are efficiently going to judge whether from the level of example grouping to call just Often or call abnormal；Again from the level of characteristic dimension, according to multiple characteristic dimensions in regular instance grouping calling service The quantity of data and in exception example grouping calling service data quantity to the specific calling feelings of each characteristic dimension parameter Condition carries out fine-grained analysis, occurs calling abnormal position in operation system to be precisely located out, to improve determination Occur calling the accuracy of abnormal position in operation system and determines efficiency；Mould also by being grouped with preset example in library Plate example is grouped into judgment criteria, retrieves preset example grouping library, determine in preset example grouping library with the presence or absence of with it is more The key parameter of a example grouping is identical with structure is called or template instances grouping of the difference degree less than difference degree threshold value comes It determines that the regular instance in multiple example groupings is grouped and exception example is grouped, improves determining regular instance grouping and exception is real The efficiency and accuracy of example grouping.

This specification embodiment additionally provides a kind of server, including processor and refers to for storage processor to be executable The memory of order, the processor can be according to instruction execution following steps when being embodied: obtaining more in preset time period A calling service data；According to calling form to polymerize the calling service data, multiple example groupings are obtained；It determines Regular instance grouping and exception example grouping in the multiple example grouping；Multiple characteristic dimensions are counted to be grouped in regular instance The quantity of middle calling service data and exception example grouping in calling service data quantity；According to multiple characteristic dimensions just In normal example grouping the quantity of calling service data and in exception example grouping calling service data quantity, determination adjusts With abnormal position.

In order to more accurately complete above-metioned instruction, as shown in fig.7, this specification additionally provides another kind specifically Server, wherein the server includes network communications port 701, processor 702 and memory 703, and above structure is logical It crosses Internal cable to be connected, so that each structure can carry out specific data interaction.

Wherein, the network communications port 701 specifically can be used for obtaining multiple calling service numbers in preset time period According to.

The processor 702 specifically can be used for obtaining according to calling form to polymerize the calling service data Multiple example groupings；Determine the regular instance grouping and exception example grouping in the multiple example grouping；Count multiple spies Levy dimension regular instance grouping in calling service data quantity and exception example grouping in calling service data quantity； According to multiple characteristic dimensions regular instance grouping in calling service data quantity and exception example grouping in calling service The quantity of data determines and occurs calling abnormal position.

The memory 703 specifically can be used for storing in the preset time period obtained by network communications port 701 The corresponding instruction repertorie that multiple calling service data and processor 702 use.

In the present embodiment, the network communications port 701 can be is bound from different communication protocol, thus The virtual port of different data can be sent or received.Lead to for example, the network communications port can be responsible for progress web data No. 80 ports of letter are also possible to No. 21 ports for being responsible for carrying out FTP data communication, can also be that responsible progress mail data is logical No. 25 ports of letter.In addition, the network communications port can also be the communication interface or communication chip of entity.For example, its It can be mobile radio network communication chip, such as GSM, CDMA；It can also be Wifi chip；It can also be bluetooth core Piece.

In the present embodiment, the processor 702 can be implemented in any suitable manner.For example, processor can be with Take such as microprocessor or processor and storage can by (micro-) processor execute computer readable program code (such as Software or firmware) computer-readable medium, logic gate, switch, specific integrated circuit (Application Specific Integrated Circuit, ASIC), programmable logic controller (PLC) and the form etc. for being embedded in microcontroller.This specification is simultaneously It is not construed as limiting.

In the present embodiment, the memory 703 may include many levels, in digital display circuit, as long as can save Binary data can be memory；In integrated circuits, the circuit with store function of a not no physical form Also memory, such as RAM, FIFO are；In systems, the storage equipment with physical form is also memory, such as memory bar, TF Card etc..

This specification embodiment additionally provides a kind of computer storage medium of localization method based on above-mentioned calling exception, The computer storage medium is stored with computer program instructions, is performed realization in the computer program instructions: according to It calls form to polymerize the calling service data, obtains multiple example groupings；It determines in the multiple example grouping Regular instance grouping and exception example grouping；Count the number of multiple characteristic dimensions calling service data in regular instance grouping Amount and exception example grouping in calling service data quantity；According to multiple characteristic dimensions regular instance grouping in business tune The quantity of calling service data, determines and occurs calling abnormal position with the quantity of data and in exception example grouping.

In the present embodiment, above-mentioned storage medium includes but is not limited to random access memory (Random Access Memory, RAM), read-only memory (Read-Only Memory, ROM), caching (Cache), hard disk (Hard DiskDrive, ) or storage card (Memory Card) HDD.The memory can be used for storing computer program instructions.Network communication unit It can be according to standard setting as defined in communication protocol, for carrying out the interface of network connection communication.

In the present embodiment, the function and effect of the program instruction specific implementation of computer storage medium storage, can To compare explanation with other embodiment, details are not described herein.

As shown in fig.8, this specification embodiment additionally provides a kind of positioning dress for calling exception on software view It sets, which can specifically include construction module below:

Module 801 is obtained, specifically can be used for obtaining multiple calling service data in preset time period；

Division module 802 specifically can be used for obtaining more according to calling form to polymerize the calling service data A example grouping；

First determining module 803, the regular instance grouping being specifically determined for out in the grouping of the multiple example and Exception example grouping；

Second determining module 804 specifically can be used for counting multiple characteristic dimensions calling service in regular instance grouping The quantity of data and exception example grouping in calling service data quantity；

Third determining module 805, specifically can be used for according to multiple characteristic dimensions regular instance grouping in calling service The quantity of data and in exception example grouping calling service data quantity, determine and occur calling abnormal position.

In one embodiment, the division module 802 can specifically include following structural unit:

Extraction unit, for extracting the key parameter of calling service data and calling structure；

Division unit, for the key parameter and calling structure according to the calling service data, by the multiple business Calling data divide multiple examples groupings, wherein the key parameter and calling structure of the calling data in the same example grouping It is identical.

In one embodiment, first determining module 803 can specifically include following structural unit:

Retrieval unit specifically can be used for retrieving preset example grouping library, determine in the preset example grouping library It is matched with the presence or absence of template instances grouping with instant example grouping；

First determination unit, specifically can be used in determining preset example grouping library that there are template instances groupings It is grouped in matched situation with instant example, determines that instant example is grouped into regular instance grouping；

Second determination unit, specifically can be used in determining preset example grouping library that there is no template instances point Group is grouped in matched situation with instant example, determines that instant example is grouped into exception example grouping.

In one embodiment, described device specifically can also include establishing module, wherein above-mentioned to establish module specific It can be used for obtaining the normal calling service data of multiple calling in designated time period；According to calling form to the multiple tune It is polymerize with normal calling service data, obtains multiple corresponding template instances groupings；It is grouped according to the template instances, Establish the preset example grouping library.

In one embodiment, the multiple characteristic dimension can specifically include at least one of: deployment unit, industry Business scene, up-stream system, down-stream system, logic computer room etc..Certainly, above-mentioned cited characteristic dimension is that one kind is schematically said It is bright.When it is implemented, may be incorporated into other relevant factors as the case may be as features described above dimension.In this regard, this theory Bright book is not construed as limiting.

In one embodiment, second determining module 804 specifically can be used for counting each deployment unit parameter The quantity of corresponding calling service data in regular instance grouping；Each business scenario parameter is counted in regular instance grouping The quantity of corresponding calling service data；Count each up-stream system parameter corresponding calling service number in regular instance grouping According to quantity；Count the quantity of each down-stream system parameter corresponding calling service data in regular instance grouping；Statistics is each The quantity of a logic computer room parameter corresponding calling service data in regular instance grouping.Corresponding, described second determines mould Block 804 specifically can be also used for counting the number of each deployment unit parameter corresponding calling service data in regular instance grouping Amount；Count the quantity of each business scenario parameter corresponding calling service data in regular instance grouping；Count each upstream The quantity of system parameter corresponding calling service data in regular instance grouping；Each down-stream system parameter is counted normal real The quantity of corresponding calling service data in example grouping；Count each logic computer room parameter corresponding industry in regular instance grouping The quantity of data is called in business.

In one embodiment, the third determining module 805 can specifically include following structural unit:

Third determination unit, specifically can be used for according to multiple characteristic dimensions regular instance grouping in calling service data Quantity and exception example grouping in calling service data quantity, determine each characteristic dimension in multiple characteristic dimensions The normal call of characteristic dimension parameter and the distributed data of exception call；

4th determination unit specifically can be used for the feature dimensions according to each characteristic dimension in the multiple characteristic dimension The normal call of parameter and the distributed data of exception call are spent, determines off-note dimensional parameter, and the off-note is tieed up Position indicated by degree parameter is determined as occurring calling abnormal position.

It should be noted that unit, device or module etc. that above-described embodiment illustrates, specifically can by computer chip or Entity is realized, or is realized by the product with certain function.For convenience of description, it describes to divide when apparatus above with function It is described respectively for various modules.It certainly, can be the function of each module in same or multiple softwares when implementing this specification And/or realized in hardware, the module for realizing same function can also be realized by the combination of multiple submodule or subelement etc..With Upper described Installation practice is only schematical, for example, the division of the unit, only a kind of logic function is drawn Point, there may be another division manner in actual implementation, such as multiple units or components may be combined or can be integrated into separately One system, or some features can be ignored or not executed.Another point, shown or discussed mutual coupling or straight Connecing coupling or communication connection can be through some interfaces, and the indirect coupling or communication connection of device or unit can be electrical property, Mechanical or other forms.

Therefore the positioning device for calling exception that this specification embodiment provides, due to first will by division module Acquired multiple calling data are divided into the grouping of multiple examples, by level that the first determining module be grouped from example come efficient Ground goes to judge whether to call normal；Again by third determining module from the level of characteristic dimension, according to multiple characteristic dimensions Regular instance grouping in calling service data quantity and exception example grouping in calling service data quantity to each The specific calling situation of characteristic dimension parameter carries out fine-grained analysis, different appearance calling in operation system is precisely located out There is calling the accuracy of abnormal position and determining efficiency to improve in determining operation system in normal position.

Although being based on routine or nothing present description provides the method operating procedure as described in embodiment or flow chart Creative means may include more or less operating procedure.The step of enumerating in embodiment sequence is only numerous steps One of rapid execution sequence mode does not represent and unique executes sequence.When device or client production in practice executes, Can be executed according to embodiment or the execution of method shown in the drawings sequence or parallel (such as parallel processor or multithreading The environment of processing, even distributed data processing environment).The terms "include", "comprise" or its any other variant are intended to Cover non-exclusive inclusion, so that the process, method, product or the equipment that include a series of elements not only include those Element, but also including other elements that are not explicitly listed, or further include for this process, method, product or setting Standby intrinsic element.In the absence of more restrictions, being not precluded is including process, method, the product of the element Or there is also other identical or equivalent elements in equipment.The first, the second equal words are used to indicate names, and are not offered as appointing What specific sequence.

It is also known in the art that other than realizing controller in a manner of pure computer readable program code, it is complete Entirely can by by method and step carry out programming in logic come so that controller with logic gate, switch, specific integrated circuit, programmable Logic controller realizes identical function with the form for being embedded in microcontroller etc..Therefore this controller is considered one kind Hardware component, and the structure that the device for realizing various functions that its inside includes can also be considered as in hardware component.Or Person even, can will be considered as realizing the device of various functions either the software module of implementation method can be hardware again Structure in component.

This specification can describe in the general context of computer-executable instructions executed by a computer, such as journey Sequence module.Generally, program module include routines performing specific tasks or implementing specific abstract data types, programs, objects, Component, data structure, class etc..This specification can also be practiced in a distributed computing environment, in these distributed computing rings In border, by executing task by the connected remote processing devices of communication network.In a distributed computing environment, program mould Block can be located in the local and remote computer storage media including storage equipment.

As seen through the above description of the embodiments, those skilled in the art can be understood that this specification It can realize by means of software and necessary general hardware platform.Based on this understanding, the technical solution of this specification Substantially the part that contributes to existing technology can be embodied in the form of software products in other words, the computer software Product can store in storage medium, such as ROM/RAM, magnetic disk, CD, including some instructions are used so that a computer Equipment (can be personal computer, mobile terminal, server or the network equipment etc.) execute each embodiment of this specification or Method described in certain parts of person's embodiment.

Each embodiment in this specification is described in a progressive manner, the same or similar portion between each embodiment Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.This specification can be used for In numerous general or special purpose computing system environments or configuration.Such as: personal computer, server computer, handheld device Or portable device, laptop device, multicomputer system, microprocessor-based system, set top box, programmable electronics set Standby, network PC, minicomputer, mainframe computer, distributed computing environment including any of the above system or equipment etc..

Although depicting this specification by embodiment, it will be appreciated by the skilled addressee that there are many become for this specification Shape and the spirit changed without departing from this specification, it is desirable to which the attached claims include these deformations and change without departing from this The spirit of specification.

Claims

1. a kind of call abnormal localization method, comprising:

Obtain multiple calling service data in preset time period；

According to calling form to polymerize the calling service data, multiple example groupings are obtained；

Determine the regular instance grouping and exception example grouping in the multiple example grouping；

Count multiple characteristic dimensions quantity of calling service data and business in exception example grouping in regular instance grouping Call the quantity of data；

According to multiple characteristic dimensions regular instance grouping in calling service data quantity and exception example grouping in business The quantity of data is called, determines and occurs calling abnormal position.

2. according to the method described in claim 1, being obtained multiple according to calling form to polymerize the calling service data Example grouping, comprising:

It extracts the key parameter of calling service data and calls structure；

According to the key parameter of the calling service data and structure is called, the multiple calling service data are divided into multiple Example grouping, wherein the key parameter of the calling data in the same example grouping is identical with structure is called.

3. according to the method described in claim 1, determining regular instance grouping in the grouping of the multiple example and abnormal real Example grouping, comprising:

Determine whether instant example grouping is regular instance grouping in the following way:

Retrieve preset example grouping library, determine in the preset example grouping library with the presence or absence of template instances grouping with it is current Example grouping matching；

Under being grouped matched situation with instant example there are template instances grouping in determining the preset example grouping library, really Determine instant example and is grouped into regular instance grouping；

Under being grouped matched situation with instant example there is no template instances grouping in determining the preset example grouping library, Determine that instant example is grouped into exception example grouping.

4. according to the method described in claim 3, the preset example grouping library is established in the following way:

Obtain the normal calling service data of multiple calling in designated time period；

According to calling form to call normal calling service data to polymerize to the multiple, it is real to obtain multiple corresponding templates Example grouping；

It is grouped according to the template instances, establishes the preset example grouping library.

5. according to the method described in claim 1, the multiple characteristic dimension includes at least one of: deployment unit, business Scene, up-stream system, down-stream system, logic computer room.

6. according to the method described in claim 5, counting multiple characteristic dimensions calling service data in regular instance grouping Quantity, comprising:

Count the quantity of each deployment unit parameter corresponding calling service data in regular instance grouping；

Count the quantity of each business scenario parameter corresponding calling service data in regular instance grouping；

Count the quantity of each up-stream system parameter corresponding calling service data in regular instance grouping；

Count the quantity of each down-stream system parameter corresponding calling service data in regular instance grouping；

Count the quantity of each logic computer room parameter corresponding calling service data in regular instance grouping.

7. according to the method described in claim 1, according to multiple characteristic dimensions in regular instance grouping calling service data Quantity and in exception example grouping calling service data quantity, determine and occur calling abnormal position, comprising:

According to multiple characteristic dimensions regular instance grouping in calling service data quantity and exception example grouping in business The quantity for calling data, determines the normal call and exception of the characteristic dimension parameter of each characteristic dimension in multiple characteristic dimensions The distributed data of calling；

According to the normal call of the characteristic dimension parameter of each characteristic dimension in the multiple characteristic dimension and exception call Distributed data determines off-note dimensional parameter, and position indicated by the off-note dimensional parameter is determined as occurring Call abnormal position.

8. a kind of call abnormal positioning device, comprising:

Module is obtained, for obtaining multiple calling service data in preset time period；

Division module, for obtaining multiple example groupings according to calling form to polymerize the calling service data；

First determining module, for determining regular instance grouping and exception example grouping in the multiple example grouping；

Second determining module, for count multiple characteristic dimensions regular instance grouping in calling service data quantity and different The quantity of calling service data in normal example grouping；

Third determining module, for according to multiple characteristic dimensions regular instance grouping in calling service data quantity and different The quantity of calling service data, determines and occurs calling abnormal position in normal example grouping.

9. device according to claim 8, the division module include:

Division unit, for the key parameter and calling structure according to the calling service data, by the multiple calling service Data are divided into multiple example groupings, wherein the key parameter and calling structure phase of the calling data in the same example grouping Together.

10. device according to claim 9, first determining module include:

Retrieval unit determines in the preset example grouping library for retrieving preset example grouping library with the presence or absence of template Example grouping is matched with instant example grouping；

First determination unit, for there are template instances groupings and instant example point in determining the preset example grouping library In the matched situation of group, determine that instant example is grouped into regular instance grouping；

Second determination unit, for there is no template instances grouping and instant examples in determining the preset example grouping library It is grouped in matched situation, determines that instant example is grouped into exception example grouping.

11. device according to claim 9, described device further includes establishing module, for obtaining in designated time period It is multiple to call normal calling service data；Normal calling service data are called to gather to the multiple according to calling form It closes, obtains multiple corresponding template instances groupings；It is grouped according to the template instances, establishes the preset example grouping library.

12. device according to claim 9, the multiple characteristic dimension includes at least one of: deployment unit, business Scene, up-stream system, down-stream system, logic computer room.

13. device according to claim 12, second determining module is specifically used for counting each deployment unit parameter The quantity of corresponding calling service data in regular instance grouping；Each business scenario parameter is counted in regular instance grouping The quantity of corresponding calling service data；Count each up-stream system parameter corresponding calling service number in regular instance grouping According to quantity；Count the quantity of each down-stream system parameter corresponding calling service data in regular instance grouping；Statistics is each The quantity of a logic computer room parameter corresponding calling service data in regular instance grouping.

14. device according to claim 9, the third determining module include:

Third determination unit, for according to multiple characteristic dimensions regular instance grouping in calling service data quantity and different The quantity of calling service data, determines the characteristic dimension parameter of each characteristic dimension in multiple characteristic dimensions in normal example grouping Normal call and exception call distributed data；

4th determination unit, for according to the normal of the characteristic dimension parameter of each characteristic dimension in the multiple characteristic dimension The distributed data with exception call is called, determines off-note dimensional parameter, and will be indicated by the off-note dimensional parameter Position be determined as occurring calling abnormal position.

15. a kind of server, including processor and for the memory of storage processor executable instruction, the processor is held The step of any one of claims 1 to 7 the method is realized when row described instruction.

16. a kind of computer readable storage medium is stored thereon with computer instruction, described instruction, which is performed, realizes that right is wanted The step of seeking any one of 1 to 7 the method.