CN110175085A - A kind of Hadoop system abnormal cause diagnostic method and device using map analysis - Google Patents
A kind of Hadoop system abnormal cause diagnostic method and device using map analysis Download PDFInfo
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Abstract
The present invention relates to a kind of Hadoop system abnormal cause diagnostic methods and device using map analysis, and wherein method includes: step S1: importing abnormal log file, and obtain the abnormal log information in file；Step S2: it is based on abnormal log information, obtain the function calling relationship inside call stack, and combine the call relation graph model of priori knowledge drafting error stack, wherein call relation graph model includes multiple first kind nodes for indicating basic reason and multiple for indicating the second class node of function involved in call stack, and for call relation between representative function the first kind while and for representative function and basic reason incidence relation the second class while；Step S3: obtaining exception record to be diagnosed, and extracts its function being related to, and lead to abnormal basic reason incremental update call relation graph model simultaneously based on the output of call relation graph model.Compared with prior art, the present invention has many advantages, such as that visual abnormal basic reason analysis may be implemented.
Description
Technical field
The present invention relates to a kind of abnormity diagnosis technologies, more particularly, to a kind of extremely former using the Hadoop system of map analysis
Because of diagnostic method and device.
Background technique
With the scale of IT system and its being continuously increased for complexity, human cost needed for system O&M and material resources cost
It is consequently increased, or even can be more than the construction cost of system.Therefore, automation, intelligentized operational system can not only improve and be
The reliability and service efficiency of system, while also will significantly save O&M cost.It is each in the stable operation dependence system of system
A component cooperating and operating normally, and syslog data effectively has recorded about each during system operation
The operating status of component, so, analysis daily record data assists in the operating status that operation maintenance personnel understands system, to realize
Intelligent O&M.The content for mainly three aspects that now intelligent O&M is paid close attention at present: abnormality detection, predicting abnormality and abnormal cause
Analysis.Wherein seldom priori knowledge can in addition by means of existing mature machine learning techniques for abnormality detection and predicting abnormality
Realize relatively good automation and intelligence, Analysis on Abnormal is further due to being related to after having found exception only
Reasoning work, cause now at present no matter industry or academia all without can with less domain knowledge can well into
The method of row automation and intelligentification analysis.What is also relied primarily in practical application is operation maintenance personnel domain knowledge abundant and project
Experience carries out artificial analysis, so efficiency is usually lower.And when it come to arrive the reason analysis difficulty of multicomponent exception
It will increase considerably, so being always the hot and difficult issue studied and applied.
Basic reason analyzes a kind of the problem of (Root Cause Analysis, RCA) is structuring processing method, to
It gradually finds out the basic reason of problem and assists to solve, rather than be solely focused on the characterization of problem.It is abnormal basic in IT system
The analysis of causes effectively help system operation maintenance personnel can find the crux of system problem, and find out the solution of essence,
System O&M quality can effectively be improved.Wherein the analysis of unimodule exception seems more relative to the anomaly analysis of multicomponent
Simple and efficient, the anomaly analysis of multicomponent also relates to because being not only related to associated analysis between component internal exception
Association analysis between different components will accomplish that effective basic reason analysis seems in real time so practical reasoning difficulty is bigger
It is very difficult.
Existing basic reason analysis method mainly includes four kinds, is to establish cause-and-effect diagram respectively, brainstorming, is based on fish
The causality analysis of bone figure, the causality analysis based on WHY-WHY figure.But these methods are for the multicomponent exception in IT system
Basic reason analysis, which all seems, to be not efficient enough, so proposing the new method of one kind in the invention to carry out multiple groups in IT system
The abnormal basic reason of part is analyzed.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of using map analysis
Hadoop system abnormal cause diagnostic method and device.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of Hadoop system abnormal cause diagnostic method using map analysis, comprising:
Step S1: abnormal log file is imported, and obtains the abnormal log information in file；
Step S2: being based on abnormal log information, obtains the function calling relationship inside call stack, and draw in conjunction with priori knowledge
The call relation graph model of error stack processed, wherein the call graph model includes multiple for indicating the of basic reason
A kind of node and multiple the second class nodes for being used to indicate function involved in call stack, and closed for being called between representative function
The first kind of system while and for representative function and basic reason incidence relation the second class while；
Step S3: obtaining exception record to be diagnosed, and extracts its function being related to, and export based on call relation graph model
Lead to abnormal basic reason.
The first kind while and when the second class be directed line segment, and the arrow on the first kind side is directed toward called letter
Number.
The step S3 is specifically included:
Step S31: obtaining exception record to be diagnosed, and extracts its function being related to；
Step S32: judge the function extracted in call relation graph model with the presence or absence of the road for being directed toward any basic reason
Diameter, if it is, S34 is thened follow the steps, conversely, thening follow the steps S33；
Step S33: it is public to find out longest of the function of extraction in call relation graph model for the principle based on map analysis
Subsequence, the basic reason which is directed toward to external feedback exception information and are based on simultaneously as abnormality diagnostic candidate reason
It returns to diagnostic result and updates call relation graph model；
Alternatively, step S3 is specifically included:
Step S31: obtaining exception record to be diagnosed, and extracts its function being related to；
Step S32: judge the function extracted in call relation graph model with the presence or absence of the road for being directed toward any basic reason
Diameter, if it is, S34 is thened follow the steps, conversely, thening follow the steps S33；
Step S33: externally sending exception information, and receives artificial feedback result incremental update call relation graph model；
Step S34: the basic reason of direction is exported as diagnostic result.
Diagnostic result in the step S33 is artificial diagnostic result.
A kind of Hadoop system abnormal cause diagnostic device using map analysis, including memory, processor, and storage
The program executed in the memory and by the processor, the processor perform the steps of when executing described program
Step S1: abnormal log file is imported, and obtains the abnormal log information in file；
Step S2: being based on abnormal log information, obtains the function calling relationship inside call stack, and draw in conjunction with priori knowledge
The call relation graph model of error stack processed, wherein the call graph model includes multiple for indicating the of basic reason
A kind of node and multiple the second class nodes for being used to indicate function involved in call stack, and closed for being called between representative function
The first kind of system while and for representative function and basic reason incidence relation the second class while；
Step S3: obtaining exception record to be diagnosed, and extracts its function being related to, and export based on call relation graph model
Lead to abnormal basic reason.
Compared with prior art, the invention has the following advantages:
1) error stack call graph is established using the abnormal log information of component each in IT system, visualizes mistake hair
Raw detailed process can be very good to give expression to the incidence relation between multicomponent exception according to error stack call graph
Come, the basic reason that analysis system is abnormal can be convenient based on the error stack call graph established.
2) generating process of mistake can be modeled according to the abnormal log information of component each in IT system, and used
The mode of figure indicates.
3) can will to be difficult to the incidence relation between the multicomponent exception analyzed according to error stack call graph fine
Ground is expressed, convenient for analysis.
4) analysis can be made inferences to the abnormal log of appearance, and operation maintenance personnel can be helped visual by figure
The complicated basic reason occurred extremely of positioning.
Detailed description of the invention
Fig. 1 is the key step flow diagram of the method for the present invention；
Fig. 2 is to be called stack information extraction schematic diagram for abnormal log；
Fig. 3 is to establish call relation graph model for error stack；
Fig. 4 is the flow diagram for generating system exception log and corresponding to error stack call relation graph model；
Fig. 5 is the flow diagram that abnormal basic reason analysis is carried out when there is system exception.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
A kind of Hadoop system abnormal cause diagnostic method using map analysis, this method in the form of a computer program by
Computer system realizes that the computer system utilizes the Hadoop system abnormal cause diagnostic device of map analysis for one, including deposits
Reservoir, processor, and the program for being stored in memory and being executed by processor, as shown in Figure 1, when processor executes program
It performs the steps of
Step S1: abnormal log file is imported, and obtains the abnormal log information in file；
Step S2: being based on abnormal log information, obtains the function calling relationship inside call stack, and draw in conjunction with priori knowledge
The call relation graph model of error stack processed, wherein call relation graph model includes multiple for indicating the first kind of basic reason
Node and multiple the second class nodes for being used to indicate function involved in call stack, and for call relation between representative function
The first kind while and for representative function and basic reason incidence relation the second class while, the first kind while and when the second class be oriented
Line segment, and the arrow on first kind side is directed toward called function.
Step S3: obtaining exception record to be diagnosed, and extracts its function being related to, and export based on call relation graph model
Lead to abnormal basic reason incremental update call relation graph model simultaneously, specifically include:
Step S31: obtaining exception record to be diagnosed, and extracts its function being related to；
Step S32: judge the function extracted in call relation graph model with the presence or absence of the road for being directed toward any basic reason
Diameter, if it is, S34 is thened follow the steps, conversely, thening follow the steps S33；
Step S33: it is public to find out longest of the function of extraction in call relation graph model for the principle based on map analysis
Subsequence, the basic reason which is directed toward to external feedback exception information and are based on simultaneously as abnormality diagnostic candidate reason
Artificial Diagnosis result incremental update call relation graph model is returned to, certain process, which can be, receives artificial diagnostic result；
Step S34: the basic reason of direction is exported as diagnostic result.
Specifically, this method can use the abnormal log data that system itself generates, mistake wherein included is extracted
Stack information obtains the corresponding letter of the error stack every call stack in the inside with schema extraction by the quick scanning of a pass coding
Breath, is converted to a node in figure for every recalls information, to convert the abnormal log of the wrong storehouse of every band to
The subgraph of digraph can establish whole error stack call graph, according to the figure energy in conjunction with certain priori knowledge
The corresponding basic reason of each abnormal log is determined, to realize visual abnormal basic reason analysis.
The purpose of the application is at 3 points: one is utilizing the abnormal day of component each in IT system using the method for graph theory
Will information establishes error stack call graph, visualizes the detailed process that mistake occurs.The second is being called according to error stack
Relational graph can be very good to express the incidence relation between multicomponent exception.The third is based on the error stack established
Call graph can be convenient the basic reason that analysis system is abnormal.The core of this method is by the log of multicomponent exception
Data are converted into the form for being easy to the figure of reasoning, and by the property of figure, carry out to the abnormal basic reason of multicomponent in system
The basic reason that multicomponent occurs extremely is found out in induction.
Specifically, for the abnormal log information of all components occurred in system, we scan it one by one, statistics
The function information that all call stacks are included is with generating function set.A kind of function of each element representation (side in set
Method), corresponding to a node in figure.For example, the abnormal log information of system some component is scanned first and is mentioned
Required functional form is obtained, extraction process and the result form of expression are as shown in Figure 2.The corresponding corresponding tune of wherein each row
It is called with the linear function inside stack, also corresponds to a node in error stack call graph.
After being scanned extraction to all abnormal logs that global component generates, the present invention uses digraph G (V, E)
Call relation in the corresponding error stack of abnormal log to characterize system between each function, as shown in Figure 3.Wherein V is
The vertex set of digraph G, V include the vertex of two categories, are C respectivelyiMethod node and R in corresponding call stackiIt is corresponding
Basic reason node, E is the line set of digraph G, and the called method of bottom is directed toward from upper layer method in the direction on side.
Side between node indicates the incidence relation between the method for different call stacks.It include 9 in model as shown in Figure 3
The corresponding method information of the call stack of a classification, it is this kind of in overall error stack call graph corresponding to the vertex C in figure
Node is generated by the information of the corresponding method of call stack of the resulting all categories of pre-treatment scan, R vertex representation in figure
The basic reason node analyzed by certain priori knowledge, wherein (C3→C2→C1) mean that an error stack information
Call relation between corresponding functional based method.Generate the process that system exception log corresponds to error stack call relation graph model
As shown in Figure 4.
Application scheme may be implemented to be directed to by the error stack call graph constructed more in same system
Incidence relation between component exception is analyzed.
Specifically, error stack call graph is primarily to the detailed process that visual expression mistake occurs,
And incidence relation and its basic reason between multicomponent exception are effectively determined according to the process.For same component and not
With the incidence relation between component abnormal log, in this invention mainly by carrying out path to error stack call graph
It searches for obtain, as shown in figure 3, for two directed walks in figure, it, can by judging the similitude between two paths
To effectively determine different abnormal correlation degrees between component.Judgement for similitude between path, can be according in figure
The coincidence degree of point and side that the origin-to-destination on road passes through defines.Such as two paths (C in Fig. 36→C5→C1)→R1
With path (C6→C5→C4)→R1It is all from C6It sets out and eventually arrives at R1, centre is also while by C5, then this two different
Original anomaly log corresponding to path may similitude with higher, the correlation degree between two logs is also larger.Knot
Specific log information is closed, we can be further discovered that the basic reason of two abnormal logs is all due to bottom calling pair
The component answered is abnormal caused.So error stack call graph in this way we can be very clear
Induction go out the incidence relation between component exception, to preferably position abnormal basic reason.
In addition, by error stack call graph, it is visual to analyze abnormal basic reason and carry out online updating.Tool
For body, the call stack information for including in abnormal log error stack is extracted conversion first by the present invention, specific abnormal
Generating process corresponds in digraph, when each component is abnormal in systems, by looking into error stack call graph
Search is ask to determine the corresponding basic reason of each exception, and the structure of energy online updating error stack call graph.
Usual operation maintenance personnel is to be in accordance with existing background knowledge carrying out abnormal basic reason analysis, is subject to certain engineering experience
Check log one by one and then position abnormal cause, there is no the structure by means of a complete clearly figure come it is visual
Carry out the analysis of abnormal basic reason.There is the error stack call graph established, can be very good that operation maintenance personnel is helped to exist
System is abnormal, and when generating abnormal log, is tracked the process that mistake occurs, is efficiently positioned abnormal basic reason.Specifically exist
Line exception basic reason analytic process is as shown in Figure 5.
Claims (8)
1. a kind of Hadoop system abnormal cause diagnostic method using map analysis characterized by comprising
Step S1: abnormal log file is imported, and obtains the abnormal log information in file；
Step S2: being based on abnormal log information, obtains the function calling relationship inside call stack, and it is wrong to combine priori knowledge to draw
The accidentally call relation graph model of storehouse, wherein the call graph model includes multiple for indicating the first kind of basic reason
Node and multiple the second class nodes for being used to indicate function involved in call stack, and for call relation between representative function
The first kind while and for representative function and basic reason incidence relation the second class while；
Step S3: obtaining exception record to be diagnosed, and extracts its function being related to, and cause based on the output of call relation graph model
Abnormal basic reason.
2. a kind of Hadoop system abnormal cause diagnostic method using map analysis according to claim 1, feature exist
In, the first kind while and when the second class be directed line segment, and the arrow on the first kind side is directed toward called function.
3. a kind of Hadoop system abnormal cause diagnostic method using map analysis according to claim 1, feature exist
In the step S3 is specifically included:
Step S31: obtaining exception record to be diagnosed, and extracts its function being related to；
Step S32: judging that the function extracted whether there is the path for being directed toward any basic reason in call relation graph model, if
Be it is yes, S34 is thened follow the steps, conversely, thening follow the steps S33；
Step S33: the principle based on map analysis finds out the public sub- sequence of longest of the function of extraction in call relation graph model
Column, the basic reason which is directed toward to external feedback exception information and are based on returning simultaneously as abnormality diagnostic candidate reason
Diagnostic result incremental update call relation graph model；
Step S34: the basic reason of direction is exported as diagnostic result.
4. a kind of Hadoop system abnormal cause diagnostic method using map analysis according to claim 3, feature exist
In the step S3 is specifically included:
Step S31: obtaining exception record to be diagnosed, and extracts its function being related to；
Step S32: judging that the function extracted whether there is the path for being directed toward any basic reason in call relation graph model, if
Be it is yes, S34 is thened follow the steps, conversely, thening follow the steps S33；
Step S33: externally sending exception information, and receives artificial feedback result incremental update call relation graph model；
Step S34: the basic reason of direction is exported as diagnostic result.
5. a kind of Hadoop system abnormal cause diagnostic device using map analysis, which is characterized in that including memory, processing
Device, and the program for being stored in the memory and being executed by the processor, the processor execute real when described program
Existing following steps:
Step S1: abnormal log file is imported, and obtains the abnormal log information in file；
Step S2: being based on abnormal log information, obtains the function calling relationship inside call stack, and it is wrong to combine priori knowledge to draw
The accidentally call relation graph model of storehouse, wherein the call graph model includes multiple for indicating the first kind of basic reason
Node and multiple the second class nodes for being used to indicate function involved in call stack, and for call relation between representative function
The first kind while and for representative function and basic reason incidence relation the second class while；
Step S3: obtaining exception record to be diagnosed, and extracts its function being related to, and cause based on the output of call relation graph model
Abnormal basic reason.
6. a kind of Hadoop system abnormal cause diagnostic device using map analysis according to claim 5, feature exist
In, the first kind while and when the second class be directed line segment, and the arrow on the first kind side is directed toward called function.
7. a kind of Hadoop system abnormal cause diagnostic device using map analysis according to claim 5, feature exist
In the step S3 is specifically included:
Step S31: obtaining exception record to be diagnosed, and extracts its function being related to；
Step S32: judging that the function extracted whether there is the path for being directed toward any basic reason in call relation graph model, if
Be it is yes, S34 is thened follow the steps, conversely, thening follow the steps S33；
Step S33: the principle based on map analysis finds out the public sub- sequence of longest of the function of extraction in call relation graph model
Column, the basic reason which is directed toward feed back exception information as abnormality diagnostic candidate reason simultaneously and are based on Artificial Diagnosis
As a result incremental update call relation graph model；
Step S34: the basic reason of direction is exported as diagnostic result.
8. a kind of Hadoop system abnormal cause diagnostic device using map analysis according to claim 7, feature exist
In the step S3 is specifically included:
Step S31: obtaining exception record to be diagnosed, and extracts its function being related to；
Step S32: judging that the function extracted whether there is the path for being directed toward any basic reason in call relation graph model, if
Be it is yes, S34 is thened follow the steps, conversely, thening follow the steps S33；
Step S33: externally sending exception information, and receives artificial feedback result incremental update call relation graph model；
Step S34: the basic reason of direction is exported as diagnostic result.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110609761A (en) * | 2019-09-06 | 2019-12-24 | 北京三快在线科技有限公司 | Method and device for determining fault source, storage medium and electronic equipment |
| CN112395159A (en) * | 2020-11-17 | 2021-02-23 | 华为技术有限公司 | Log detection method, system, device and medium |
| CN112948154A (en) * | 2019-12-11 | 2021-06-11 | 中移(苏州)软件技术有限公司 | System abnormity diagnosis method, device and storage medium |
| CN113098723A (en) * | 2021-06-07 | 2021-07-09 | 新华三人工智能科技有限公司 | Fault root cause positioning method and device, storage medium and equipment |
| CN115964673A (en) * | 2023-03-14 | 2023-04-14 | 江苏省生态环境监测监控有限公司 | Enterprise power utilization condition abnormity early warning analysis method |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050053009A1 (en) * | 2003-08-19 | 2005-03-10 | Lorraine Denby | Method and apparatus for automatic determination of performance problem locations in a network |
| CN103947156A (en) * | 2011-09-30 | 2014-07-23 | 瑞典爱立信有限公司 | A method, apparatus and communication network for root cause analysis |
| CN104298225A (en) * | 2014-09-25 | 2015-01-21 | 中国石油化工股份有限公司 | Modeling and graphical displaying method for causal relationship reasoning model of unusual working conditions of chemical process |
| US20170068581A1 (en) * | 2015-09-04 | 2017-03-09 | International Business Machines Corporation | System and method for relationship based root cause recommendation |
| CN107391353A (en) * | 2017-07-07 | 2017-11-24 | 西安电子科技大学 | Complicated software system anomaly detection method based on daily record |
| CN109032918A (en) * | 2018-05-31 | 2018-12-18 | 长安大学 | A kind of sensing node program exception diagnostic method based on abnormal task function trace |
| CN109213616A (en) * | 2018-09-25 | 2019-01-15 | 江苏润和软件股份有限公司 | A kind of micro services software systems method for detecting abnormality based on calling map analysis |
| CN109345121A (en) * | 2018-10-09 | 2019-02-15 | 湖南工学院 | A kind of method and apparatus for Operation Event of Nuclear Power Plants analysis |
| CN109522209A (en) * | 2018-09-29 | 2019-03-26 | 中国平安人寿保险股份有限公司 | Log stack information analysis method and device, computer installation and storage medium |
-
2019
- 2019-04-19 CN CN201910318337.XA patent/CN110175085B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050053009A1 (en) * | 2003-08-19 | 2005-03-10 | Lorraine Denby | Method and apparatus for automatic determination of performance problem locations in a network |
| CN103947156A (en) * | 2011-09-30 | 2014-07-23 | 瑞典爱立信有限公司 | A method, apparatus and communication network for root cause analysis |
| CN104298225A (en) * | 2014-09-25 | 2015-01-21 | 中国石油化工股份有限公司 | Modeling and graphical displaying method for causal relationship reasoning model of unusual working conditions of chemical process |
| US20170068581A1 (en) * | 2015-09-04 | 2017-03-09 | International Business Machines Corporation | System and method for relationship based root cause recommendation |
| CN107391353A (en) * | 2017-07-07 | 2017-11-24 | 西安电子科技大学 | Complicated software system anomaly detection method based on daily record |
| CN109032918A (en) * | 2018-05-31 | 2018-12-18 | 长安大学 | A kind of sensing node program exception diagnostic method based on abnormal task function trace |
| CN109213616A (en) * | 2018-09-25 | 2019-01-15 | 江苏润和软件股份有限公司 | A kind of micro services software systems method for detecting abnormality based on calling map analysis |
| CN109522209A (en) * | 2018-09-29 | 2019-03-26 | 中国平安人寿保险股份有限公司 | Log stack information analysis method and device, computer installation and storage medium |
| CN109345121A (en) * | 2018-10-09 | 2019-02-15 | 湖南工学院 | A kind of method and apparatus for Operation Event of Nuclear Power Plants analysis |
Non-Patent Citations (1)
| Title |
|---|
| YANG ZHIMING;ZHANG CHENGCHENG;LIU MINZHUANG;PENG XIYUAN: "Graph-based fault diagnosis method for complex electronic systems", 《IEEE》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110609761A (en) * | 2019-09-06 | 2019-12-24 | 北京三快在线科技有限公司 | Method and device for determining fault source, storage medium and electronic equipment |
| CN110609761B (en) * | 2019-09-06 | 2020-10-16 | 北京三快在线科技有限公司 | Method and device for determining fault source, storage medium and electronic equipment |
| CN112948154A (en) * | 2019-12-11 | 2021-06-11 | 中移(苏州)软件技术有限公司 | System abnormity diagnosis method, device and storage medium |
| CN112395159A (en) * | 2020-11-17 | 2021-02-23 | 华为技术有限公司 | Log detection method, system, device and medium |
| CN113098723A (en) * | 2021-06-07 | 2021-07-09 | 新华三人工智能科技有限公司 | Fault root cause positioning method and device, storage medium and equipment |
| CN115964673A (en) * | 2023-03-14 | 2023-04-14 | 江苏省生态环境监测监控有限公司 | Enterprise power utilization condition abnormity early warning analysis method |
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| CN110175085B (en) | 2020-11-27 |
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