CN111008093A - Fault log query method, device, equipment and medium - Google Patents
Fault log query method, device, equipment and medium Download PDFInfo
- Publication number
- CN111008093A CN111008093A CN201911332563.XA CN201911332563A CN111008093A CN 111008093 A CN111008093 A CN 111008093A CN 201911332563 A CN201911332563 A CN 201911332563A CN 111008093 A CN111008093 A CN 111008093A
- Authority
- CN
- China
- Prior art keywords
- log
- openstack
- fault
- logs
- analysis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000004458 analytical method Methods 0.000 claims abstract description 51
- 238000004590 computer program Methods 0.000 claims description 10
- 230000009286 beneficial effect Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000000007 visual effect Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Images
Classifications
-
- 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
- G06F11/0766—Error or fault reporting or storing
-
- 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
- G06F11/0706—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 the processing taking place on a specific hardware platform or in a specific software environment
- G06F11/0709—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 the processing taking place on a specific hardware platform or in a specific software environment in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
-
- 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
- G06F11/0766—Error or fault reporting or storing
- G06F11/0787—Storage of error reports, e.g. persistent data storage, storage using memory protection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
- G06F2201/875—Monitoring of systems including the internet
Abstract
The application discloses a fault log query method, a fault log query device, equipment and a medium, wherein the fault log query method comprises the following steps: obtaining OpenStack logs generated by OpenStack, and analyzing the OpenStack logs to obtain analysis logs; classifying the analysis logs according to a preset rule to obtain processing logs, and sending the processing logs to an elastic search; when the OpenStack fails, an elastic search is utilized to retrieve a fault log corresponding to the failure. Obviously, by the method, a worker can directly utilize the elastic search to retrieve the fault log corresponding to the OpenStack when the fault occurs, so that the tedious work that the worker needs to manually query the fault log is avoided, and the searching efficiency of the worker when the worker locates the fault log from the OpenStack log is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a fault log query method, a fault log query device, equipment and a medium.
Background
Since OpenStack is an open-source cloud computing management platform project, OpenStack has become more and more widely applied in the market. The scale of the platform managed by the system also rises from the first few, more than ten to hundreds or even thousands of platforms. In this case, OpenStack generates a large number of access logs, application logs, and error logs, among others. In the prior art, if OpenStack fails, a worker needs to manually retrieve a fault log from a large number of OpenStack logs, so that the difficulty of finding the fault log from the OpenStack logs by the worker when the worker locates the fault log is significantly increased, and the efficiency of finding the fault log by the worker in the locating process is reduced. At present, no effective solution exists for the problem.
Therefore, how to further improve the search efficiency of the worker when locating the fault log from the OpenStack log is a technical problem to be solved by the technical personnel in the field.
Disclosure of Invention
In view of this, the present invention provides a fault log query method, device, apparatus, and medium, so as to further improve the efficiency of searching when a worker locates a fault log from an OpenStack log. The specific scheme is as follows:
a fault log query method comprises the following steps:
the method comprises the steps of obtaining OpenStack logs generated by OpenStack, and analyzing the OpenStack logs to obtain analysis logs;
classifying the analysis logs according to a preset rule to obtain processing logs, and sending the processing logs to an elastic search;
and when the OpenStack fails, retrieving a fault log corresponding to the failure by using the elastic search.
Preferably, the process of analyzing the OpenStack log to obtain an analyzed log includes:
and analyzing the OpenStack log by using a regular expression to obtain the analysis log.
Preferably, the process of classifying the analysis log according to a preset rule to obtain a processed log includes:
and classifying the analysis log according to the functional attribute and/or name and/or directory and/or path of the analysis log to obtain the processing log.
Preferably, the sending the processing log to an elasticsearch includes:
sending the processing log to the elasticsearch in json form.
Preferably, after the process of retrieving, by using the elastic search, the fault log corresponding to the fault when the OpenStack fails, the method further includes:
and visually displaying the fault log.
Preferably, the process of visually displaying the fault log includes:
and visually displaying the fault log by utilizing Kibana.
Correspondingly, the invention also discloses a fault log query device, which comprises:
the log analysis module is used for acquiring OpenStack logs generated by OpenStack and analyzing the OpenStack logs to obtain analysis logs;
the log processing module is used for carrying out classification processing on the analysis logs according to a preset rule to obtain processing logs and sending the processing logs to an elastic search;
and the fault finding module is used for retrieving a fault log corresponding to the fault by using the elastic search when the OpenStack has the fault.
Correspondingly, the invention also discloses a fault log query device, which comprises:
a memory for storing a computer program;
a processor for implementing the steps of a fault log query method as disclosed in the foregoing when executing said computer program.
Accordingly, the present invention also discloses a computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of a fault log query method as disclosed in the foregoing.
Therefore, in the invention, firstly, OpenStack logs generated by OpenStack are obtained, and the obtained OpenStack logs are analyzed to obtain analysis logs; then, classifying the analysis logs according to a preset rule to obtain processing logs, and sending the processing logs to an elastic search; finally, when the OpenStack fails, an elastic search is used to retrieve a fault log corresponding to the OpenStack when the OpenStack fails. Obviously, compared with the prior art, in the fault log query method provided by the invention, firstly, OpenStack logs generated by OpenStack are classified in advance, and the classified logs after the classification are sent to the elasticsearch with the indexing capability, so that a worker can directly use the elasticsearch to retrieve fault logs corresponding to OpenStack when the fault occurs, and therefore, the worker can be saved from manually querying the fault logs, and the search efficiency of the worker when the worker locates the fault logs from the OpenStack logs is further improved. Correspondingly, the fault log query device, the equipment and the medium have the beneficial effects.
Drawings
In order to more clearly illustrate the embodiments of the present invention 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 embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a fault log query method according to an embodiment of the present invention;
fig. 2 is a structural diagram of a fault log query apparatus according to an embodiment of the present invention;
fig. 3 is a structural diagram of a fault log query device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart of a fault log query method according to an embodiment of the present invention, where the fault log query method includes:
step S11: obtaining OpenStack logs generated by OpenStack, and analyzing the OpenStack logs to obtain analysis logs;
step S12: classifying the analysis logs according to a preset rule to obtain processing logs, and sending the processing logs to an elastic search;
step S13: when the OpenStack fails, an elastic search is utilized to retrieve a fault log corresponding to the failure.
In this embodiment, a new fault log query method is provided, by which the efficiency of searching when a worker locates a fault log from an OpenStack log can be further improved.
Specifically, in this embodiment, an OpenStack log generated by OpenStack is first acquired, and when the OpenStack log generated by OpenStack is acquired, the OpenStack log is analyzed to obtain an analysis log. Since the OpenStack log contains a lot of operating information about the OpenStack during operation, in this embodiment, the OpenStack log is analyzed to further mine the content information contained in the OpenStack log.
It can be understood that, since the OpenStack log contains various kinds of running information generated by OpenStack during running, for example: data generated at the target time, configuration operation of the host, attribute information of the relevant components, access information of the user, and the like. Therefore, in order to enable a worker to search and retrieve a target log generated by OpenStack, the analysis log is classified according to a preset rule to obtain a processing log, and the processing log is sent to an elastic search.
It should be noted that, because the elastic search is a distributed, highly-extended, and highly-real-time search and data analysis engine, when sending the processing log to the elastic search, it is equivalent to store the OpenStack log generated by OpenStack in the elastic search, and also store the OpenStack log generated by OpenStack in the elastic search in a classified manner. Thus, when the OpenStack fails, the elastic search can directly search a fault log corresponding to the fault according to the fault type of the OpenStack.
Obviously, compared with the prior art, the fault log query method provided by the embodiment of the invention can avoid the tedious process that the worker needs to manually search the fault log generated by OpenStack, thereby further improving the search efficiency of the worker when the worker locates the fault log from the OpenStack log.
As can be seen, in this embodiment, first, an OpenStack log generated by OpenStack is obtained, and the obtained OpenStack log is analyzed to obtain an analysis log; then, classifying the analysis logs according to a preset rule to obtain processing logs, and sending the processing logs to an elastic search; finally, when the OpenStack fails, an elastic search is used to retrieve a fault log corresponding to the OpenStack when the OpenStack fails. Obviously, compared with the prior art, in the fault log query method provided in this embodiment, firstly, the OpenStack logs generated by the OpenStack are classified in advance, and the classified logs after the classification are sent to the elasticsearch with the indexing capability, so that a worker can directly use the elasticsearch to retrieve the fault log corresponding to the OpenStack when the fault occurs, and thus, the worker can be saved from manually querying the fault log, and the efficiency of searching when the worker locates the fault log from the OpenStack log is further improved.
Based on the above embodiments, this embodiment further describes and optimizes the technical solution, and as a preferred implementation, the above steps: the process of analyzing the OpenStack log to obtain the analyzed log comprises the following steps:
and analyzing the OpenStack log by using the regular expression to obtain an analysis log.
It can be understood that, because the Regular Expression (Regular Expression) is a powerful tool that can be used for pattern matching and replacement, and is generally applied in many lexical analyses, and the Regular Expression is also an open-source log parsing tool, when an OpenStack log generated by OpenStack is parsed by using the Regular Expression, not only can the universality in the parsing process of the OpenStack log be relatively improved, but also the parsing cost in the parsing process of the OpenStack log can be reduced.
Here, the specific description is given by taking the nova log in the OpenStack log as an example, and the nova log is assumed to be:
format1/^(？<Timestamp>\S+\S+)(？<Pid>\d+)(？<log_level>\S+)(？<python_module>\S+)(\[(req-(？<request_id>\S+)(？<user_id>\S+)(？<tenant_id>\S+)(？<domain_id>\S+)(？<user_domain>\S+)(？<project_domain>\S+)|-)\])？(？<Payload>.*)？$/
if the regular expression is used for analyzing the nova log, the analysis content shown in the table 1 can be obtained.
TABLE 1
| Canonical field | Description of the invention |
| (？<Timestamp>\S+\S+) | Time of day |
| (？<Pid>\d+) | Program ID |
| (？<log_level>\S+ | Log level |
| (？<python_module>\S+ | python module |
| (\[(req-(？<request_id>\S+) | Request ID |
| (？<user_id>\S+) | User ID |
| (？<tenant_id>\S+) | Tenant ID |
| (？<domain_id>\S+) | Domain ID |
| (？<user_domain>\S+) | User Domain |
| (？<project_domain>\S+)|-)\]) | OpenStack module |
| (？<Payload>.*) | Others |
Based on the foregoing embodiment, this embodiment further describes and optimizes the technical solution, and as a preferred implementation, the process of obtaining the processed log by classifying the analysis log according to the preset rule includes:
and classifying the analysis logs according to the functional attributes and/or names and/or directories and/or paths of the analysis logs to obtain processing logs.
In this embodiment, a specific implementation manner of classifying the parsing log by using a preset rule is provided. It can be understood that, because the functional attribute of the analysis log is fixed and unchanged, and the functional attribute of the analysis log can also be used to represent the attribute information of the analysis log, in the actual operation process, if the analysis log is classified according to the functional attribute of the analysis log, the classification result of the analysis log can be more accurate and reliable.
Specifically, in practical applications, when the analysis logs are classified according to the functional attributes of the analysis logs, the analysis logs can be classified according to a time dimension, a host dimension, a user dimension, a tenant dimension, a level dimension, a component dimension, a Python module dimension, and an OpenStack module dimension corresponding to the analysis logs.
In addition to classifying the resolution logs according to the functional attributes of the resolution logs, the resolution logs can be classified by using names, directories and/or paths of the resolution logs, and because the inherent logical relationship among various OpenStack logs can be further deduced according to the names, directories and/or paths of the resolution logs, when the resolution logs are classified according to the names, directories and/or paths of the resolution logs, the classification results of the resolution logs can be more credible and comprehensive.
Obviously, by such a processing manner, the accuracy and reliability in classifying the analysis logs can be further improved.
Based on the foregoing embodiment, this embodiment further describes and optimizes the technical solution, and as a preferred implementation, the process of sending the processing log to the elastic search includes:
the processing log is sent to the elasticsearch in json.
Specifically, in this embodiment, the processing log is sent to the elastic search in a json (json Object Notation) form, because json is a lightweight data exchange format and stores and represents data in a text format completely independent of a programming language, so that the processing log can be more convenient for the analysis of the elastic search.
Here, description is made by way of an example, for example:
<store>
@type elasticsearch
Host XXX
Port 9200
Logstash_format true
Logstash_prefix flog
Flush_interval 15s。
obviously, by the technical scheme provided by the embodiment, the receiving efficiency of the elastic search to the processing log can be relatively improved.
Based on the foregoing embodiment, this embodiment further describes and optimizes the technical solution, and as a preferred implementation, after the process of retrieving the fault log corresponding to the fault by using the elastic search, the method further includes:
and visually displaying the fault log.
In practical application, when a fault log corresponding to the OpenStack when a fault occurs is obtained by utilizing an elastic search, the fault log can be visually displayed. It can be thought that after the fault log is visualized, the worker can more clearly and intuitively view the fault type and the fault reason corresponding to the OpenStack when the fault occurs. Under the condition, the user experience of the working personnel in fault handling can be relatively improved, and the repairing efficiency of the working personnel in the fault repairing process can be relatively improved.
Specifically, the process of visually displaying the fault log includes:
and visually displaying the fault log by utilizing Kibana.
In this embodiment, Kibana is used to visually display a fault log corresponding to the OpenStack when a fault occurs, because Kibana is a free open source visualization tool, the log can be analyzed and analyzed by Kibana, and Kibana further provides powerful and easy-to-use functions, such as: histograms, line graphs, pie graphs, heat maps, and the like. Therefore, when the Kibana is used for visually displaying the fault log, the development cost of the visual software in the development process can be relatively reduced, and the convenience degree of the visual display process of the fault log can be further improved.
Referring to fig. 2, fig. 2 is a structural diagram of a fault log query device according to an embodiment of the present invention, where the fault log query device includes:
the log analyzing module 21 is configured to obtain an OpenStack log generated by OpenStack, and analyze the OpenStack log to obtain an analysis log;
the log processing module 22 is configured to perform classification processing on the analysis logs according to a preset rule to obtain processing logs, and send the processing logs to an elastic search;
and the fault finding module 23 is configured to, when the OpenStack fails, retrieve, by using an elastic search, a fault log corresponding to the failure.
Preferably, the log parsing module 21 includes:
and the log analyzing unit is used for analyzing the OpenStack log by using the regular expression to obtain an analysis log.
Preferably, the log processing module 22 includes:
and the log classifying unit is used for classifying the analysis logs according to the functional attributes and/or names and/or directories and/or paths of the analysis logs to obtain the processing logs.
Preferably, the log processing module 22 includes:
and the log sending unit is used for sending the processing log to the elastic search in a json form.
Preferably, the method further comprises the following steps:
and the log display module is used for visually displaying the fault log after the process of retrieving the fault log corresponding to the fault by using the elastic search when the OpenStack has the fault.
Preferably, the log presentation module includes:
and the log display unit is used for visually displaying the fault log by utilizing Kibana.
The fault log query device provided by the embodiment of the invention has the beneficial effects of the fault log query method disclosed by the embodiment of the invention.
Referring to fig. 3, fig. 3 is a structural diagram of a fault log query device according to an embodiment of the present invention, where the fault log query device includes:
a memory 31 for storing a computer program;
a processor 32 for implementing the steps of a fault log query method as disclosed in the foregoing when executing the computer program.
The fault log query device provided by the embodiment of the invention has the beneficial effects of the fault log query method disclosed by the embodiment of the invention.
Correspondingly, the invention also discloses a computer readable storage medium, on which a computer program is stored, and when being executed by a processor, the computer program realizes the steps of the fault log query method as disclosed in the foregoing.
The computer-readable storage medium provided by the embodiment of the invention has the beneficial effects of the fault log query method disclosed in the foregoing.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The fault log query method, the fault log query device, the fault log query equipment and the fault log query medium provided by the invention are described in detail, specific examples are applied in the method to explain the principle and the implementation mode of the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (9)
1. A fault log query method is characterized by comprising the following steps:
the method comprises the steps of obtaining OpenStack logs generated by OpenStack, and analyzing the OpenStack logs to obtain analysis logs;
classifying the analysis logs according to a preset rule to obtain processing logs, and sending the processing logs to an elastic search;
and when the OpenStack fails, retrieving a fault log corresponding to the failure by using the elastic search.
2. The fault log query method according to claim 1, wherein the process of parsing the OpenStack log to obtain a parsed log comprises:
and analyzing the OpenStack log by using a regular expression to obtain the analysis log.
3. The method according to claim 1, wherein the step of classifying the analysis log according to a preset rule to obtain a processed log comprises:
and classifying the analysis log according to the functional attribute and/or name and/or directory and/or path of the analysis log to obtain the processing log.
4. The fault log query method according to claim 1, wherein the sending the processing log to an elastic search includes:
sending the processing log to the elasticsearch in json form.
5. The fault log query method according to any one of claims 1 to 4, wherein after the process of retrieving, by using the elastic search, the fault log corresponding to the fault when the OpenStack fails, the method further comprises:
and visually displaying the fault log.
6. The fault log query method according to claim 5, wherein the process of visually displaying the fault log comprises:
and visually displaying the fault log by utilizing Kibana.
7. A fault log querying device, comprising:
the log analysis module is used for acquiring OpenStack logs generated by OpenStack and analyzing the OpenStack logs to obtain analysis logs;
the log processing module is used for carrying out classification processing on the analysis logs according to a preset rule to obtain processing logs and sending the processing logs to an elastic search;
and the fault finding module is used for retrieving a fault log corresponding to the fault by using the elastic search when the OpenStack has the fault.
8. A fault log querying device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of a fault log querying method as claimed in any one of claims 1 to 6 when executing said computer program.
9. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of a fault log query method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911332563.XA CN111008093A (en) | 2019-12-22 | 2019-12-22 | Fault log query method, device, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911332563.XA CN111008093A (en) | 2019-12-22 | 2019-12-22 | Fault log query method, device, equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111008093A true CN111008093A (en) | 2020-04-14 |
Family
ID=70116679
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911332563.XA Pending CN111008093A (en) | 2019-12-22 | 2019-12-22 | Fault log query method, device, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111008093A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116755992A (en) * | 2023-08-17 | 2023-09-15 | 青岛民航凯亚系统集成有限公司 | Log analysis method and system based on OpenStack cloud computing |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106980627A (en) * | 2016-01-18 | 2017-07-25 | 中兴通讯股份有限公司 | The display methods and device of log content |
| CN106992876A (en) * | 2017-03-04 | 2017-07-28 | 郑州云海信息技术有限公司 | Cloud platform blog management method and system |
| CN110362544A (en) * | 2019-05-27 | 2019-10-22 | 中国平安人寿保险股份有限公司 | Log processing system, log processing method, terminal and storage medium |
-
2019
- 2019-12-22 CN CN201911332563.XA patent/CN111008093A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106980627A (en) * | 2016-01-18 | 2017-07-25 | 中兴通讯股份有限公司 | The display methods and device of log content |
| CN106992876A (en) * | 2017-03-04 | 2017-07-28 | 郑州云海信息技术有限公司 | Cloud platform blog management method and system |
| CN110362544A (en) * | 2019-05-27 | 2019-10-22 | 中国平安人寿保险股份有限公司 | Log processing system, log processing method, terminal and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| DACID N.BLANK-EDELMAN著，盛春等 译, 东南大学出版社 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116755992A (en) * | 2023-08-17 | 2023-09-15 | 青岛民航凯亚系统集成有限公司 | Log analysis method and system based on OpenStack cloud computing |
| CN116755992B (en) * | 2023-08-17 | 2023-12-01 | 青岛民航凯亚系统集成有限公司 | Log analysis method and system based on OpenStack cloud computing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108427705B (en) | Electronic device, distributed system log query method and storage medium | |
| US8516309B1 (en) | Method of debugging a software system | |
| CN109284323B (en) | Management method and device for detection data | |
| CN111897625B (en) | Resource event backtracking method, system and electronic equipment based on Kubernetes cluster | |
| US20080098365A1 (en) | Performance analyzer | |
| CN112667697A (en) | Method and device for acquiring real estate information by combining RPA and AI | |
| CN110737689A (en) | Data standard conformance detection method, device, system and storage medium | |
| CN112925757A (en) | Method, equipment and storage medium for tracking operation log of intelligent equipment | |
| CN111966390A (en) | Project construction method, system, terminal device and storage medium | |
| CN111190807A (en) | Buried point testing method and device | |
| CN111324510B (en) | Log processing method and device and electronic equipment | |
| US9727666B2 (en) | Data store query | |
| CN111881183A (en) | Enterprise name matching method and device, storage medium and electronic equipment | |
| US20130275943A1 (en) | Determining interface differences between different versions of an operating system | |
| CN108763524B (en) | Electronic device, chatting data processing method, and computer-readable storage medium | |
| CN111008093A (en) | Fault log query method, device, equipment and medium | |
| CN108459949B (en) | Test method and terminal | |
| US8479163B2 (en) | Simplifying maintenance of large software systems | |
| CN116719843A (en) | Query method, storage medium and device for database system | |
| CN108197253B (en) | Equipment query method, device and equipment of cloud monitoring platform | |
| CN113010208B (en) | Version information generation method, device, equipment and storage medium | |
| CN114020813A (en) | Data comparison method, device and equipment based on Hash algorithm and storage medium | |
| CN114416489A (en) | System running state monitoring method and device, computer equipment and storage medium | |
| US10866993B2 (en) | Managing online help information in a data center | |
| CN109766480B (en) | Data query method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200414 |
|
| RJ01 | Rejection of invention patent application after publication |