CN111240876A - Fault positioning method and device for microservice, storage medium and terminal - Google Patents
Fault positioning method and device for microservice, storage medium and terminal Download PDFInfo
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- CN111240876A CN111240876A CN202010009042.7A CN202010009042A CN111240876A CN 111240876 A CN111240876 A CN 111240876A CN 202010009042 A CN202010009042 A CN 202010009042A CN 111240876 A CN111240876 A CN 111240876A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/079—Root cause analysis, i.e. error or fault diagnosis
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The embodiment of the application discloses a micro-service fault positioning method and device, a storage medium and electronic equipment, and belongs to the field of fault detection and analysis. When the micro-service calling chain is abnormal, the micro-service calling chain is provided with a plurality of micro-services, a first key value is generated according to the micro-service name and the request method name of each micro-service, abnormal analysis data matched with the first key value is inquired in a pre-analysis library, and abnormal analysis data are displayed, so that the problem of low efficiency caused by manually positioning the reason of the abnormity of the micro-service calling chain in the related technology is solved, and the embodiment of the application can realize automatic detection and positioning of the reason of the abnormity of the micro-service calling chain.
Description
Technical Field
The present disclosure relates to the field of fault detection and analysis, and in particular, to a method and an apparatus for locating a fault of a microservice, a storage medium, and an electronic device.
Background
Microservice is a software service architecture that has become more popular in recent years, and is a method that uses a set of services, each running in a separate process, to build an overall application. With the continuous development of a complete application, a large number of micro services are deployed, and meanwhile, a large number of calling relations exist among the micro services, if a certain micro service instance in the system is abnormal, the external service provided by the whole system is affected by involvement, and a final user may not normally receive a service response, so that the user experience is reduced.
Therefore, it is necessary to provide fault detection for micro-service invocation, and in the related art, query of a micro-service invocation link and detection of an exception of the micro-service invocation have been implemented, but intelligent analysis and rapid positioning cannot be implemented for the invocation exception, and the invocation exception needs to be processed one by one through personal experience.
Disclosure of Invention
The method, the device, the storage medium and the electronic equipment for locating the fault of the micro-service can solve the problem of low efficiency in manually locating the abnormal calling of the micro-service in the related technology. The technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a method for locating a fault of a microservice, where the method includes:
when detecting that a micro service call chain is abnormal, generating a first key value according to the micro service name and the request method name of each micro service in the micro service call chain;
inquiring matched abnormal analysis data in an analysis library according to the first key value;
and displaying the abnormal analysis data through the display unit.
In a second aspect, an embodiment of the present application provides a microservice fault location apparatus, where the microservice fault location apparatus includes:
the system comprises a generating unit, a sending unit and a receiving unit, wherein the generating unit is used for generating a first key value according to the micro service name and the request method name of each micro service in a micro service calling chain when detecting that the micro service calling chain is abnormal;
the query unit is used for querying matched abnormal analysis data in an analysis library according to the key value;
and the display unit is used for displaying the abnormal analysis data.
In a third aspect, embodiments of the present application provide a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The beneficial effects brought by the technical scheme provided by some embodiments of the application at least comprise:
when the micro-service calling chain is abnormal, the micro-service calling chain is provided with a plurality of micro-services, a first key value is generated according to the micro-service name and the request method name of each micro-service, abnormal analysis data matched with the first key value is inquired in a pre-analysis library, and abnormal analysis data are displayed, so that the problem of low efficiency caused by manually positioning the reason of the abnormity of the micro-service calling chain in the related technology is solved, and the embodiment of the application can realize automatic detection and positioning of the reason of the abnormity of the micro-service calling chain. If the abnormal analysis data is not inquired in the analysis base or the abnormal level or frequency inquired by the abnormal data in the analysis base is low, the abnormal error report cannot be immediately determined, so that the abnormal error report quantity is reduced, and the abnormal error report condition of the micro-service call chain can be reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a diagram of a network architecture provided by an embodiment of the present application;
FIG. 2 is a flowchart illustrating a method for locating a fault in a microservice provided by an embodiment of the present application;
FIG. 3 is a schematic diagram of an apparatus according to an embodiment of the present disclosure;
fig. 4 is another schematic structural diagram of an apparatus provided in the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, which is a schematic diagram of a microservice calling chain provided in the embodiment of the present application, the microservice calling chain includes a plurality of microservices, a service user calls a plurality of microservice trip microservice calling chains according to a certain calling sequence, and the plurality of microservices may be deployed on the same host or on different hosts. According to fig. 1, the calling order of each microservice in a microservice calling chain is: service 1 → service 3 → service 5 → service 6.
The method for locating a fault of a microservice provided by the embodiment of the present application will be described in detail below with reference to fig. 2. The microservice fault location device in the embodiment of the present application may be a terminal.
Referring to fig. 2, a flow chart of a method for locating a fault of a microservice is provided in an embodiment of the present application. As shown in fig. 2, the method of the embodiment of the present application may include the steps of:
s201, when the micro service call chain is detected to be abnormal, a first key value is generated according to the micro service name and the request method name of each micro service in the micro service call chain.
When the electronic equipment calls one micro service in the micro service call chain, if the micro service does not return a call result within a preset time length, the electronic equipment determines that the micro service call chain is abnormal, the electronic equipment generates a call chain log when the micro service call is performed each time, and the call chain log records whether the micro service call chain is abnormal or not. The microservice call chain associates a plurality of microservices with a particular call order between the plurality of microservices. The name of the micro service represents the name of the micro service, the name of the request method comprises the name of a calling method used by the micro service, and the calling method comprises GET or POST and the like. The electronic equipment carries out Hash operation according to each micro service name and the request method name in the micro service calling chain to generate a first key value, and the first key value is an index value.
S202, inquiring matched abnormal analysis data in an analysis library according to the first key value.
The electronic device is pre-stored or pre-configured with an analysis library, a mapping relationship between the key value and the abnormal analysis data is stored in the analysis library, and the abnormal analysis data represents a type of the micro service call chain that is abnormal, for example: a service instance exception or a program exception.
And S203, displaying the abnormal analysis data through the display unit.
In a possible implementation manner, before generating a key value according to a service name and a request method name of each micro service in the micro service call chain when the abnormality of the service call chain is detected, the method further includes:
acquiring the micro service name and the request method name of each micro service in the micro service calling chain;
generating a first key value according to the micro service name and the request method name;
acquiring one or more of time consumption, error reporting status flag bit, error reporting information and resource consumption information of each micro service to generate an attribute value of the first key value;
and binding the first key value and the attribute value and then storing the first key value and the attribute value into a knowledge base.
Further, the resource consumption information includes: one or more of CPU usage, memory usage, IO request volume and network traffic.
Further, the types of the anomaly include: a plurality of exception large classes, each exception large class comprising a plurality of exception fine classes, wherein the exception large classes are: service instance exceptions and program exceptions.
Further, the detecting that the micro service call chain is abnormal includes:
and determining that the micro-service call chain is abnormal according to the call chain log.
Further, the method further comprises:
when abnormal analysis data matched with the first key value are not inquired in the analysis library, calculating an abnormal type according to a second key value of the abnormal analysis data;
writing the second key value, the abnormal large class, the abnormal fine class, the detailed abnormal log, the first occurrence time, the occurrence times and the last occurrence time into the analysis library.
Further, the method also comprises the following steps:
and updating the occurrence times and the last occurrence time of the abnormal analysis data in the analysis library.
The fault positioning method of the embodiment of the application specifically comprises the following steps:
step 1: the method comprises the steps of collecting latest micro-service calling chain information detected by a monitoring system, constructing a key value according to all micro-service names and request method names called in a micro-service calling chain, forming attribute values corresponding to first key values by time consumption, error reporting information and consumed resource information (CPU usage, memory usage, IO calling number and network flow) of each request method corresponding to a micro-service, and storing the first key values and the attribute values in a classified mode to form a knowledge base.
Step 2: and analyzing while acquiring data in the previous step, filtering the abnormal call chain in the call chain log, acquiring a second key value, and analyzing the abnormal large class and the abnormal fine class of the abnormal call chain. The types of anomalies are divided into two main categories: service instance exception and program exception, wherein the exception subdivision is subdivided according to the two major categories, and the service instance problem subdivision mode is as follows: subdividing the system resource load (such as CPU load and memory load) and service abnormity down type subdivision (such as OOM) when the system resource load is too high; and subdividing the program exception according to the error-reporting keywords of the specific program.
And step 3: and comparing the same key value and the same abnormal type in the analysis library according to the current key value and the calculated abnormal type, searching whether corresponding abnormal analysis data exist, if not, executing the step 4, otherwise, executing the step 5.
And 4, step 4: and writing the second key value, the abnormal large class, the abnormal fine class, the detailed abnormal log, the first mode time, the occurrence times and the final occurrence time as a group of data into an analysis library according to the second key value of the abnormal analysis data and the calculated abnormal type.
And 5: and updating the analysis library to find the occurrence times and the final occurrence time of the same abnormal analysis data.
Step 6: and when the occurrence frequency is greater than the preset frequency, performing autonomous learning, generating an abnormal error report, and sending the abnormal error report to a terminal of a corresponding operation and maintenance or developer for processing according to the abnormal type.
And 7: and if an abnormal error report is generated, storing the abnormal error report into a knowledge base for subsequent abnormal problem analysis.
According to the embodiment, the server resources under the micro-service architecture and the call link data between the services are comprehensively collected, the analysis library is formed through built-in analysis and learning, and when the micro-service call is abnormal, the analysis library is used for qualitatively and positioning the current abnormal problem. Self-learning is carried out in the acquisition and analysis process to form a gradually improved analysis model, and the learning contents comprise: 1. detailed calling information of all micro-service calling chains; 2. all microservice related server resource data: CPU usage, memory usage, IO read-write data volume, network transmission and reception volume; 3. counting micro-service calling data: the total number of calls, the total number of call failures and the average response time; 4. extracting abnormal keywords from the abnormal log of the micro service call chain; 5. the above information is integrated to form an analysis library of each micro service call chain through a built-in analyzer.
When the micro service call chain is found to have an abnormal problem, performing similarity contrast analysis by combining the data collected in the previous step at different time points, wherein the main contents comprise: 1. whether the micro service call chain has a match in an analysis library; 2. if matching exists, finding out corresponding analysis data; 3. and combining the analysis data obtained in the last step to generate the analysis and positioning of the problem. Therefore, the method helps developers or operation and maintenance personnel of the system to quickly and efficiently process the abnormal service calling problem under the micro-service architecture.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Referring to fig. 3, a schematic structural diagram of a fault location device for microservices provided by an exemplary embodiment of the present application is shown. Hereinafter, the generating apparatus 3 is simply referred to as the generating apparatus 3, and the generating apparatus 3 may be implemented by software, hardware, or a combination of both as a whole or a part of a terminal. The apparatus 3 comprises: a generating unit 301, an inquiring unit 302 and a display unit 303.
The generation unit 301 is configured to generate a first key value according to a micro service name and a request method name of each micro service in a micro service call chain when detecting that the micro service call chain is abnormal;
the query unit 302 is configured to query the matched abnormal analysis data in the analysis library according to the first key value;
a display unit 303, configured to display the abnormality analysis data through the display unit.
In one possible implementation, obtaining a micro-service name and a request method name of each micro-service in the micro-service call chain;
generating a first key value according to the micro service name and the request method name;
acquiring one or more of time consumption, error reporting status flag bit, error reporting information and resource consumption information of each micro service to generate an attribute value of the first key value;
and binding the first key value and the attribute value and then storing the first key value and the attribute value into a knowledge base.
In one possible embodiment, the resource consumption information includes: one or more of CPU usage, memory usage, IO request volume and network traffic.
In one possible embodiment, the types of anomalies include: a plurality of exception large classes, each exception large class comprising a plurality of exception fine classes, wherein the exception large classes are: service instance exceptions and program exceptions.
In a possible embodiment, the detecting that the micro service call chain is abnormal includes:
and determining that the micro-service call chain is abnormal according to the call chain log.
In a possible embodiment, the device 3 further comprises:
the writing unit is used for calculating an abnormal type according to a second key value of the abnormal analysis data when the abnormal analysis data matched with the first key value is not inquired in the analysis library;
writing the second key value, the abnormal large class, the abnormal fine class, the detailed abnormal log, the first occurrence time, the occurrence times and the last occurrence time into the analysis library.
In a possible embodiment, the device 3 further comprises:
and the updating unit is used for updating the occurrence frequency and the last occurrence time of the abnormal analysis data in the analysis library.
It should be noted that, when the apparatus 3 provided in the foregoing embodiment executes the method for locating a fault of a microservice, the above-mentioned division of each functional module is merely used as an example, and in practical applications, the above-mentioned function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the above-mentioned functions. In addition, the embodiments of the method for locating a fault of a microservice provided by the above embodiments belong to the same concept, and details of the implementation process are referred to as the embodiments of the method, which are not described herein again.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
When the micro-service calling chain is abnormal, the device 3 generates a first key value according to the micro-service name and the request method name of each micro-service because the micro-service calling chain has a plurality of micro-services, inquires abnormal analysis data matched with the first key value in a pre-analysis library, and displays the abnormal analysis data, so that the problem of low efficiency caused by manually positioning the reason of the abnormality of the micro-service calling chain in the related technology is solved. If the abnormal analysis data is not inquired in the analysis base or the abnormal level or frequency inquired by the abnormal data in the analysis base is low, the abnormal error report cannot be immediately determined, so that the abnormal error report quantity is reduced, and the abnormal error report condition of the micro-service call chain can be reduced.
An embodiment of the present application further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executing the method steps in the embodiment shown in fig. 2, and a specific execution process may refer to a specific description of the embodiment shown in fig. 2, which is not described herein again.
The present application further provides a computer program product storing at least one instruction, which is loaded and executed by the processor to implement the method for locating a fault of a microservice according to the above embodiments.
Fig. 4 is a schematic structural diagram of a microservice fault location apparatus according to an embodiment of the present application, which is hereinafter referred to as an apparatus 4, where the apparatus 4 may be integrated in the electronic device, as shown in fig. 4, the apparatus includes: memory 402, processor 401, input device 403, output device 404, and communication interface.
The memory 402 may be a separate physical unit, and may be connected to the processor 401, the input device 403, and the output device 8404 by a bus. The memory 402, processor 401, transceiver 403 may also be integrated, implemented in hardware, etc.
The memory 402 is used for storing a program implementing the above method embodiment, or various modules of the apparatus embodiment, which is called by the processor 401 to perform the operations of the above method embodiment.
Input devices 402 include, but are not limited to, a keyboard, a mouse, a touch panel, a camera, and a microphone; the output device includes, but is not limited to, a display screen.
Communication interfaces are used to send and receive various types of messages and include, but are not limited to, wireless interfaces or wired interfaces.
Alternatively, when part or all of the distributed task scheduling method of the above embodiments is implemented by software, the apparatus may also include only a processor. The memory for storing the program is located outside the device and the processor is connected to the memory by means of circuits/wires for reading and executing the program stored in the memory.
The processor may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP.
The processor may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.
The memory may include volatile memory (volatile memory), such as random-access memory (RAM); the memory may also include a non-volatile memory (non-volatile memory), such as a flash memory (flash memory), a Hard Disk Drive (HDD) or a solid-state drive (SSD); the memory may also comprise a combination of memories of the kind described above.
Wherein the processor 401 calls the program code in the memory 402 for performing the following steps:
when detecting that a micro service call chain is abnormal, generating a first key value according to the micro service name and the request method name of each micro service in the micro service call chain;
inquiring matched abnormal analysis data in an analysis library according to the first key value;
and displaying the abnormal analysis data through the display unit.
In one possible implementation, the processor 401 is further configured to perform: acquiring the micro service name and the request method name of each micro service in the micro service calling chain;
generating a first key value according to the micro service name and the request method name;
acquiring one or more of time consumption, error reporting status flag bit, error reporting information and resource consumption information of each micro service to generate an attribute value of the first key value;
and binding the first key value and the attribute value and then storing the first key value and the attribute value into a knowledge base.
In one possible embodiment, the resource consumption information includes: one or more of CPU usage, memory usage, IO request volume and network traffic.
In one possible embodiment, the types of anomalies include: a plurality of exception large classes, each exception large class comprising a plurality of exception fine classes, wherein the exception large classes are: service instance exceptions and program exceptions.
In a possible embodiment, the detecting that the micro service call chain is abnormal includes:
and determining that the micro-service call chain is abnormal according to the call chain log.
In one possible implementation, the processor 401 is further configured to perform: when abnormal analysis data matched with the first key value are not inquired in the analysis library, calculating an abnormal type according to a second key value of the abnormal analysis data;
writing the second key value, the abnormal large class, the abnormal fine class, the detailed abnormal log, the first occurrence time, the occurrence times and the last occurrence time into the analysis library.
In one possible implementation, the processor 401 is further configured to perform: and updating the occurrence times and the last occurrence time of the abnormal analysis data in the analysis library.
The embodiment of the application also provides a computer storage medium, which stores a computer program, and the computer program is used for executing the fault location method of the microservice provided by the embodiment.
Embodiments of the present application further provide a computer program product containing instructions, which when run on a computer, cause the computer to execute the method for fault location of microservice provided by the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (10)
1. A method for fault location of microservice, the method comprising:
when detecting that a micro service call chain is abnormal, generating a first key value according to the micro service name and the request method name of each micro service in the micro service call chain;
inquiring matched abnormal analysis data in an analysis library according to the first key value;
and displaying the abnormal analysis data through the display unit.
2. The method of claim 1, wherein before generating key values according to service names and request method names of respective micro services in the micro service call chain when detecting that the call chain for the service is abnormal, the method further comprises:
acquiring the micro service name and the request method name of each micro service in the micro service calling chain;
generating a first key value according to the micro service name and the request method name;
acquiring one or more of time consumption, error reporting status flag bit, error reporting information and resource consumption information of each micro service to generate an attribute value of the first key value;
and binding the first key value and the attribute value and then storing the first key value and the attribute value into a knowledge base.
3. The method of claim 2, wherein the resource consumption information comprises: one or more of CPU usage, memory usage, IO request volume and network traffic.
4. The method of claim 1, wherein the type of anomaly comprises: a plurality of exception large classes, each exception large class comprising a plurality of exception fine classes, wherein the exception large classes are: service instance exceptions and program exceptions.
5. The method of claim 1, wherein the detecting that the micro-service call chain is abnormal comprises:
and determining that the micro-service call chain is abnormal according to the call chain log.
6. The method of claim 1, further comprising:
when abnormal analysis data matched with the first key value are not inquired in the analysis library, calculating an abnormal type according to a second key value of the abnormal analysis data;
writing the second key value, the abnormal large class, the abnormal fine class, the detailed abnormal log, the first occurrence time, the occurrence times and the last occurrence time into the analysis library.
7. The method of claim 1, further comprising:
and updating the occurrence times and the last occurrence time of the abnormal analysis data in the analysis library.
8. A microservice fault location apparatus, the apparatus comprising:
the system comprises a generating unit, a sending unit and a receiving unit, wherein the generating unit is used for generating a first key value according to the micro service name and the request method name of each micro service in a micro service calling chain when detecting that the micro service calling chain is abnormal;
the query unit is used for querying matched abnormal analysis data in an analysis library according to the first key value;
and the display unit is used for displaying the abnormal analysis data.
9. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to carry out the method steps according to any one of claims 1 to 7.
10. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1 to 7.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112732476A (en) * | 2021-01-26 | 2021-04-30 | 长威信息科技发展股份有限公司 | Fault positioning analysis method and system based on micro-service |
| CN113014421A (en) * | 2021-02-08 | 2021-06-22 | 武汉大学 | Micro-service root cause positioning method for cloud native system |
| CN113271224A (en) * | 2021-05-17 | 2021-08-17 | 中国邮政储蓄银行股份有限公司 | Node positioning method and device, storage medium and electronic device |
| CN115314559A (en) * | 2022-08-03 | 2022-11-08 | 苏州创意云网络科技有限公司 | Network service system and abnormal response method thereof |
| CN117370052A (en) * | 2023-09-14 | 2024-01-09 | 广州宇中网络科技有限公司 | Microservice fault analysis method, device, equipment and storage medium |
| CN117370052B (en) * | 2023-09-14 | 2024-04-26 | 广州宇中网络科技有限公司 | Microservice fault analysis method, device, equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180267859A1 (en) * | 2017-03-17 | 2018-09-20 | International Business Machines Corporation | Event failure management |
| CN109559121A (en) * | 2018-12-03 | 2019-04-02 | 深圳前海微众银行股份有限公司 | Transaction path calls exception analysis method, device, equipment and readable storage medium storing program for executing |
| CN109672741A (en) * | 2018-12-25 | 2019-04-23 | 鼎信信息科技有限责任公司 | Micro services monitoring method, device, computer equipment and storage medium |
| CN110647447A (en) * | 2019-08-01 | 2020-01-03 | 百度时代网络技术（北京）有限公司 | Abnormal instance detection method, apparatus, device and medium for distributed system |
-
2020
- 2020-01-06 CN CN202010009042.7A patent/CN111240876B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180267859A1 (en) * | 2017-03-17 | 2018-09-20 | International Business Machines Corporation | Event failure management |
| CN109559121A (en) * | 2018-12-03 | 2019-04-02 | 深圳前海微众银行股份有限公司 | Transaction path calls exception analysis method, device, equipment and readable storage medium storing program for executing |
| CN109672741A (en) * | 2018-12-25 | 2019-04-23 | 鼎信信息科技有限责任公司 | Micro services monitoring method, device, computer equipment and storage medium |
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