CN112667572A - Log calibration method and device - Google Patents
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Abstract
The invention discloses a log calibration method and device. Wherein, the method comprises the following steps: acquiring target log data; analyzing the target log data to generate an analysis result; comparing the log data in the target database with the analysis result to generate a comparison result; and generating log display data according to the comparison result and carrying out visual display. The invention solves the technical problem that the log calibration in the prior art can not accurately and efficiently carry out diversified log result calibration.
Description
Technical Field
The invention relates to the field of log calibration, in particular to a log calibration method and device.
Background
With the continuous development of big data deployment, people can use the beneficial effects brought by big data in life, study and work, for example, the log calibration efficiency can be greatly improved and the log calibration accuracy can be increased by matching the big data with the log calibration function of an equipment software platform, and risk and trend prediction information in the log calibration result can be generated by utilizing the diversified characteristics of a big data database.
At present, when log calibration is performed, log calibration is usually performed on locally stored data by using an inherent log calibration program at a terminal, and log calibration result data such as fault information is obtained, so that the log calibration result in the process is comprehensive, the analysis speed and efficiency are not high, and the technical purpose of outputting a diversified analysis result through log calibration cannot be achieved.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a log calibration method and a log calibration device, which at least solve the technical problem that the log calibration in the prior art cannot accurately and efficiently calibrate diversified log results.
According to an aspect of an embodiment of the present invention, there is provided a log calibration method, including: acquiring target log data; analyzing the target log data to generate an analysis result; comparing the log data in the target database with the analysis result to generate a comparison result; and calibrating the log data according to the comparison result.
Optionally, the obtaining the target log data includes: and acquiring log data in real time according to target equipment software, and generating the target log data.
Optionally, the analyzing the target log data, and generating an analysis result includes: classifying the target log data to obtain a classification result; and analyzing according to the classification result through a preset rule to obtain the analysis result.
Optionally, the comparing the log data in the target database with the analysis result, and generating a comparison result includes: selecting the target database according to the target log data; and comparing the data in the target database with the analysis result to obtain the comparison result.
Optionally, the alignment result includes: fault judgment results, risk assessment results and trend prediction results.
Optionally, the calibrating the log data according to the comparison result includes: judging whether the comparison result and the log data have a calibratable space or not; if the calibratable space exists, performing a calibration operation on the log data.
According to another aspect of the embodiments of the present invention, there is also provided a log calibration apparatus, including: the acquisition module is used for acquiring target log data; the analysis module is used for analyzing the target log data to generate an analysis result; the comparison module is used for comparing the log data in the target database with the analysis result to generate a comparison result; and the calibration module is used for calibrating the log data according to the comparison result.
Optionally, the obtaining module includes: and the acquisition unit is used for acquiring the log data in real time according to the target equipment software and generating the target log data.
Optionally, the analysis module includes: the classification unit is used for classifying the target log data to obtain a classification result; and the analysis unit is used for analyzing according to the classification result through a preset rule to obtain the analysis result.
Optionally, the alignment module includes: a selecting unit, configured to select the target database according to the target log data; and the comparison unit is used for comparing the data in the target database with the analysis result to obtain the comparison result.
In the embodiment of the invention, target log data are obtained; analyzing the target log data to generate an analysis result; comparing the log data in the target database with the analysis result to generate a comparison result; the log data are calibrated according to the comparison result, and the log files are compared and calibrated through big data, so that the purposes of monitoring log fault information and predicting risks and trends are achieved, and the technical problem that diversified log result calibration cannot be accurately and efficiently performed in the prior art is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a method of log calibration according to an embodiment of the invention;
fig. 2 is a block diagram of a log calibration apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided a method embodiment of a log calibration method, it should be noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer executable instructions and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than that presented herein.
Example one
Fig. 1 is a flowchart of a log calibration method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, target log data is obtained.
Specifically, in order to obtain a result of log data analysis, the embodiment of the present invention first needs to obtain target log data, where the target log data may be a log record data set to be analyzed, which is being used by a user, and the log calibration is performed based on the ELK.
It should be noted that the elastic search in the ELK is an open source distributed search engine, and its features are: distributed, zero configuration, automatic discovery, index automatic fragmentation, index copy mechanism, restful style interface, multiple data sources, automatic search load, etc. He is a fully open source tool that he can collect, filter, and store your logs for later use (e.g., searching). Kibana is also an open source and free tool, and Kibana can calibrate a friendly Web interface for logs provided by Logstash and ElasticSearch, and can collect, analyze and search important data logs. Log calibration is a main means for solving system faults and finding problems by operation and maintenance engineers. The log includes various types including a program log, a system log, a security log, and the like. Through the calibration of the log, the occurrence of the fault is pre-sent, and when the fault occurs, spider silk horse traces can be found to quickly locate the fault point. And timely solving. Specifically, in the embodiment of the present invention, the ELK assembly structure may be: elastic search: the method is an open-source distributed real-time analysis search engine, is established on the basis of a full-text search engine library Apache Lucene, and simultaneously hides the complexity of the Apache Lucene. The Elasticissearch packs all functions into an independent cartoon, an index copy mechanism, a RESTful style interface and multiple data sources. Automatic search, and the like. Logstash: the method is a completely open-source tool, is mainly used for collecting logs, can process data and output the data to an ElasticchichKibana: also a fully open source tool, kibana can provide graphical log calibration for logstack and Elasticsearch. And the Web interface can collect, analyze and search important data logs.
It should be further noted that the log calibration method based on ELK may be that logstack is deployed on all services that need to collect logs, and is used as a logstack agent (logstack loader) for monitoring and filtering the collected logs, and sends the filtered content to Redis, and then logstack indexer collects the logs together and gives them to a full-text search service, namely an ElasticSearch, and may use the ElasticSearch to perform a custom search and perform a page display by using Kibana in combination with the custom search.
Optionally, the obtaining the target log data includes: and acquiring log data in real time according to target equipment software, and generating the target log data.
And step S104, analyzing the target log data to generate an analysis result.
Specifically, after the target log data is obtained, the log data needs to be analyzed through a processor, and a corresponding data analysis result is obtained, and the result is used for displaying functions such as subsequent risk prediction, wherein the target log data can be collected in a real-time collection and storage mode, and the log data is transmitted and analyzed within a certain preset period, so that the efficiency and the uniformity of the whole target log data analysis are improved.
Optionally, the analyzing the target log data, and generating an analysis result includes: classifying the target log data to obtain a classification result; and analyzing according to the classification result through a preset rule to obtain the analysis result.
Specifically, since the target log data obtained from the target device software platform are complex and multi-type, the obtained target log data needs to be classified, and different types of data in the target log data are labeled with labels of corresponding types, for example, if cache release record data exists in the target log file, the cache release record data belongs to the cache label, so that when the target log data is classified, the cache release record data needs to be labeled with the "cache" label, and all log data with the "cache" label are classified and uniformly collected to form a "cache" set. And finally, analyzing the classification result through a preset rule, and analyzing the analysis results of different types of log data for subsequent processing.
It should be noted that the preset rule may be a rule matrix preset in the processor according to the requirement of the user on the log data, and is used to input the classification result of the target log data and obtain a corresponding operation result according to the data content of the classification result. For example, in the classification result cache set, after analysis is performed through a preset rule matrix, an analysis result "normal cache release" is obtained.
And step S106, comparing the log data in the target database with the analysis result to generate a comparison result.
Optionally, the comparing the log data in the target database with the analysis result, and generating a comparison result includes: selecting the target database according to the target log data; and comparing the data in the target database with the analysis result to obtain the comparison result.
Specifically, the target database may be a diversified database data source based on big data, and since the analysis result is generated according to the classification result, the analysis result of the target log data is also diversified, the required information may be extracted from the big database according to different result types in the analysis result, and the information may be compared with the locally obtained analysis result to verify and calibrate the locally obtained log data analysis result, so that the comparison result corresponding to the local analysis result has diversified data characteristics for subsequent output and display.
Optionally, the alignment result includes: fault judgment results, risk assessment results and trend prediction results.
Specifically, through the process of comparing the big data database with the analysis result, an accurate fault judgment result, a risk evaluation result and a trend prediction result can be obtained, wherein the fault judgment result can include information such as whether a fault exists, the time when the fault occurs, the fault degree and the like, the risk evaluation result can include the risk index of the fault, and the trend prediction result can be how the trend of various stable and unstable conditions may appear in the log running on the target device software platform.
And S108, calibrating the log data according to the comparison result.
Optionally, the calibrating the log data according to the comparison result includes: judging whether the comparison result and the log data have a calibratable space or not; if the calibratable space exists, performing a calibration operation on the log data.
Specifically, through the comparison result, the display data generated according to the comparison result is visually displayed through the display screen, and then whether the log data can be calibrated or not is judged through the difference value between the comparison result and the log data. The display data can be arranged according to the comparison result and have graphical fault information, risk information and trend information, and finally the various information are displayed through the display equipment, so that a user can visually see the log data analysis condition.
Through the steps, the technical effect of accurately and efficiently displaying the log calibration result through the big data can be achieved.
Example two
Fig. 2 is a block diagram of a log calibration apparatus according to an embodiment of the present invention, and as shown in fig. 2, the system includes:
and an obtaining module 20, configured to obtain the target log data.
Specifically, in order to obtain a result of log data analysis, the embodiment of the present invention first needs to obtain target log data, where the target log data may be a log record data set to be analyzed, which is being used by a user, and the log calibration is performed based on the ELK.
It should be noted that the elastic search in the ELK is an open source distributed search engine, and its features are: distributed, zero configuration, automatic discovery, index automatic fragmentation, index copy mechanism, restful style interface, multiple data sources, automatic search load, etc. He is a fully open source tool that he can collect, filter, and store your logs for later use (e.g., searching). Kibana is also an open source and free tool, and Kibana can calibrate a friendly Web interface for logs provided by Logstash and ElasticSearch, and can collect, analyze and search important data logs. Log calibration is a main means for solving system faults and finding problems by operation and maintenance engineers. The log includes various types including a program log, a system log, a security log, and the like. Through the calibration of the log, the occurrence of the fault is pre-sent, and when the fault occurs, spider silk horse traces can be found to quickly locate the fault point. And timely solving. Specifically, in the embodiment of the present invention, the ELK assembly structure may be: elastic search: the method is an open-source distributed real-time analysis search engine, is established on the basis of a full-text search engine library Apache Lucene, and simultaneously hides the complexity of the Apache Lucene. The Elasticissearch packs all functions into an independent cartoon, an index copy mechanism, a RESTful style interface and multiple data sources. Automatic search, and the like. Logstash: the method is a completely open-source tool, is mainly used for collecting logs, can process data and output the data to an ElasticchichKibana: also a fully open source tool, kibana can provide graphical log calibration for logstack and Elasticsearch. And the Web interface can collect, analyze and search important data logs.
It should be further noted that the log calibration method based on ELK may be that logstack is deployed on all services that need to collect logs, and is used as a logstack agent (logstack loader) for monitoring and filtering the collected logs, and sends the filtered content to Redis, and then logstack indexer collects the logs together and gives them to a full-text search service, namely an ElasticSearch, and may use the ElasticSearch to perform a custom search and perform a page display by using Kibana in combination with the custom search.
Optionally, the obtaining module includes: and the acquisition unit is used for acquiring the log data in real time according to the target equipment software and generating the target log data.
And the analysis module 22 is configured to analyze the target log data to generate an analysis result.
Specifically, after the target log data is obtained, the log data needs to be analyzed through a processor, and a corresponding data analysis result is obtained, and the result is used for displaying functions such as subsequent risk prediction, wherein the target log data can be collected in a real-time collection and storage mode, and the log data is transmitted and analyzed within a certain preset period, so that the efficiency and the uniformity of the whole target log data analysis are improved.
Optionally, the analysis module includes: the classification unit is used for classifying the target log data to obtain a classification result; and the analysis unit is used for analyzing according to the classification result through a preset rule to obtain the analysis result.
Specifically, since the target log data obtained from the target device software platform are complex and multi-type, the obtained target log data needs to be classified, and different types of data in the target log data are labeled with labels of corresponding types, for example, if cache release record data exists in the target log file, the cache release record data belongs to the cache label, so that when the target log data is classified, the cache release record data needs to be labeled with the "cache" label, and all log data with the "cache" label are classified and uniformly collected to form a "cache" set. And finally, analyzing the classification result through a preset rule, and analyzing the analysis results of different types of log data for subsequent processing.
It should be noted that the preset rule may be a rule matrix preset in the processor according to the requirement of the user on the log data, and is used to input the classification result of the target log data and obtain a corresponding operation result according to the data content of the classification result. For example, in the classification result cache set, after analysis is performed through a preset rule matrix, an analysis result "normal cache release" is obtained.
And the comparison module 24 is configured to compare the log data in the target database with the analysis result to generate a comparison result.
Optionally, the alignment module includes: a selecting unit, configured to select the target database according to the target log data; and the comparison unit is used for comparing the data in the target database with the analysis result to obtain the comparison result.
Specifically, the target database may be a diversified database data source based on big data, and since the analysis result is generated according to the classification result, the analysis result of the target log data is also diversified, the required information may be extracted from the big database according to different result types in the analysis result, and the information may be compared with the locally obtained analysis result to verify and calibrate the locally obtained log data analysis result, so that the comparison result corresponding to the local analysis result has diversified data characteristics for subsequent output and display.
Optionally, the alignment result includes: fault judgment results, risk assessment results and trend prediction results.
Specifically, through the process of comparing the big data database with the analysis result, an accurate fault judgment result, a risk evaluation result and a trend prediction result can be obtained, wherein the fault judgment result can include information such as whether a fault exists, the time when the fault occurs, the fault degree and the like, the risk evaluation result can include the risk index of the fault, and the trend prediction result can be how the trend of various stable and unstable conditions may appear in the log running on the target device software platform.
A calibration module 26, configured to calibrate the log data according to the comparison result.
Optionally, the display data includes: fault information, risk information, trend information.
Specifically, through the comparison result, the display data generated according to the comparison result is visually displayed through the display screen, wherein the display data can be arranged according to the comparison result and have graphical fault information, risk information and trend information, and finally, the various information is displayed through the display device, so that a user can visually see the log data analysis condition.
According to another aspect of embodiments of the present invention, there is also provided a computer program product comprising instructions which, when run on a computer, cause the computer to perform a log calibration method.
Specifically, the log calibration method includes: acquiring target log data; analyzing the target log data to generate an analysis result; comparing the log data in the target database with the analysis result to generate a comparison result; and generating log display data according to the comparison result and carrying out visual display.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium, which includes a stored program, wherein the program controls a device in which the non-volatile storage medium is located to execute a log calibration method when running.
Specifically, the log calibration method includes: acquiring target log data; analyzing the target log data to generate an analysis result; comparing the log data in the target database with the analysis result to generate a comparison result; and generating log display data according to the comparison result and carrying out visual display.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions when executed perform a log calibration method.
Specifically, the log calibration method includes: acquiring target log data; analyzing the target log data to generate an analysis result; comparing the log data in the target database with the analysis result to generate a comparison result; and generating log display data according to the comparison result and carrying out visual display.
Through the steps, the technical effect of accurately and efficiently displaying the log calibration result through the big data can be achieved.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A log calibration method, comprising:
acquiring target log data;
analyzing the target log data to generate an analysis result;
comparing the log data in the target database with the analysis result to generate a comparison result;
and calibrating the log data according to the comparison result.
2. The method of claim 1, wherein obtaining target log data comprises: and acquiring log data in real time according to target equipment software, and generating the target log data.
3. The method of claim 1, wherein analyzing the target log data and generating an analysis result comprises:
classifying the target log data to obtain a classification result;
and analyzing according to the classification result through a preset rule to obtain the analysis result.
4. The method of claim 1, wherein comparing the log data in the target database with the analysis result and generating a comparison result comprises:
selecting the target database according to the target log data;
and comparing the data in the target database with the analysis result to obtain the comparison result.
5. The method of claim 4, wherein the aligning comprises: fault judgment results, risk assessment results and trend prediction results.
6. The method of claim 1, wherein calibrating the log data according to the comparison comprises:
judging whether the comparison result and the log data have a calibratable space or not;
if the calibratable space exists, performing a calibration operation on the log data.
7. A log calibration device, comprising:
the acquisition module is used for acquiring target log data;
the analysis module is used for analyzing the target log data to generate an analysis result;
the comparison module is used for comparing the log data in the target database with the analysis result to generate a comparison result;
and the calibration module is used for calibrating the log data according to the comparison result.
8. The system of claim 7, wherein the acquisition module comprises: and the acquisition unit is used for acquiring the log data in real time according to the target equipment software and generating the target log data.
9. The system of claim 7, wherein the analysis module comprises:
the classification unit is used for classifying the target log data to obtain a classification result;
and the analysis unit is used for analyzing according to the classification result through a preset rule to obtain the analysis result.
10. The system of claim 7, wherein the alignment module comprises:
a selecting unit, configured to select the target database according to the target log data;
and the comparison unit is used for comparing the data in the target database with the analysis result to obtain the comparison result.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109361573A (en) * | 2018-12-13 | 2019-02-19 | 武汉市硅丰科技发展有限责任公司 | Traffic log analysis method, system and computer readable storage medium |
| CN109525863A (en) * | 2017-09-20 | 2019-03-26 | 北京国双科技有限公司 | Viewing-data methods of exhibiting and device |
| CN110262949A (en) * | 2019-04-29 | 2019-09-20 | 北京邮电大学 | Smart machine log processing system and method |
| CN110309030A (en) * | 2019-07-05 | 2019-10-08 | 亿玛创新网络(天津)有限公司 | Log analysis monitoring system and method based on ELK and Zabbix |
| CN110716910A (en) * | 2019-10-14 | 2020-01-21 | 中国建设银行股份有限公司 | Log management method, device, equipment and storage medium |
| CN111552619A (en) * | 2020-04-29 | 2020-08-18 | 深圳市道旅旅游科技股份有限公司 | Log data display method and device, computer equipment and storage medium |
-
2020
- 2020-12-23 CN CN202011535092.5A patent/CN112667572A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109525863A (en) * | 2017-09-20 | 2019-03-26 | 北京国双科技有限公司 | Viewing-data methods of exhibiting and device |
| CN109361573A (en) * | 2018-12-13 | 2019-02-19 | 武汉市硅丰科技发展有限责任公司 | Traffic log analysis method, system and computer readable storage medium |
| CN110262949A (en) * | 2019-04-29 | 2019-09-20 | 北京邮电大学 | Smart machine log processing system and method |
| CN110309030A (en) * | 2019-07-05 | 2019-10-08 | 亿玛创新网络(天津)有限公司 | Log analysis monitoring system and method based on ELK and Zabbix |
| CN110716910A (en) * | 2019-10-14 | 2020-01-21 | 中国建设银行股份有限公司 | Log management method, device, equipment and storage medium |
| CN111552619A (en) * | 2020-04-29 | 2020-08-18 | 深圳市道旅旅游科技股份有限公司 | Log data display method and device, computer equipment and storage medium |
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