Abnormal data analysis method and abnormal data analysis platform

Abstract

According to the abnormal data analysis method and the abnormal data analysis platform provided by the invention, when the abnormal data analysis platform receives the alarm information published by the alarm system, the corresponding abnormal analysis task script can be matched for the alarm information based on the pre-configured matching rule; as the exception analysis task script is configured according to different application scenes in the service system, when the exception analysis task script obtained after matching runs, the exception reason of the alarm information can be quickly and accurately positioned, and an accurate exception data solution is provided for a user.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to an abnormal data analysis method and abnormal data analysis platform. BACKGROUND

[0002] The abnormal data analysis platform is mainly used for abnormal analysis of alarm information published by an alarm system, so that business personnel can improve corresponding nodes according to the abnormal analysis result.

[0003] At present, the abnormal data analysis platform mainly performs abnormal analysis on various alarm information according to a general abnormal analysis method realized by research and development personnel, and the abnormal analysis process cannot accurately locate the abnormal reasons in different alarm information, so that the alarm problem cannot be solved. SUMMARY

[0004] The present application aims to at least solve one of the above technical defects, in particular, the technical defect that the abnormal analysis process in the prior art cannot accurately locate the abnormal reasons in different alarm information, resulting in the alarm problem cannot be solved.

[0005] The present application provides an abnormal data analysis method, which comprises:

[0006] receiving alarm information published by an alarm system;

[0007] matching the alarm information with an abnormal analysis task script in a database based on a pre-configured matching rule, wherein the abnormal analysis task script is an abnormal analysis strategy configured according to different application scenarios in a business system;

[0008] performing abnormal analysis on the alarm information according to the matched abnormal analysis task script, and displaying the abnormal analysis result.

[0009] Optionally, the configuration process of the abnormal analysis task script comprises:

[0010] obtaining abnormal analysis logic code uploaded by a user;

[0011] uploading the abnormal analysis logic code to a gitlab source code repository, determining version information of the abnormal analysis logic code in the gitlab source code repository, and sending the version information saved in the gitlab source code repository to a display page;

[0012] Receiving version information submitted by a user and publishing the version information to a task running platform, so that the task running platform pulls abnormal analysis logic code corresponding to the version information in the gitlab source code warehouse into the database and configures the abnormal analysis logic code in the database to form at least one abnormal analysis task script.

[0013] Optionally, the step of obtaining the abnormal analysis logic code uploaded by the user comprises:

[0014] Receiving a project template download instruction sent by a user;

[0015] Based on the project template download instruction, the corresponding project template is displayed to the user.

[0016] Receiving abnormal analysis logic code uploaded by a user based on the project template.

[0017] Optionally, the step of matching the alarm information with the abnormal analysis task script in the database based on the pre-configured matching rule comprises:

[0018] According to the alarm content carried by the alarm information, the abnormal analysis task script in the database matched with the alarm content is determined.

[0019] The alarm content includes at least one of alarm id, alarm title, alarm source, and alarm level.

[0020] Optionally, the step of performing abnormal analysis on the alarm information according to the matched abnormal analysis task script comprises:

[0021] The matched abnormal analysis task script is submitted to a task running platform, so that the task running platform runs the abnormal analysis task script and performs abnormal analysis on the alarm information during the running process.

[0022] Optionally, the alarm information includes a stress test abnormal alarm.

[0023] The step of performing abnormal data analysis on the stress test abnormal alarm comprises:

[0024] Based on a pre-configured matching rule, the stress test abnormal alarm is matched with an abnormal analysis task script in a database to determine a corresponding stress test abnormal analysis script.

[0025] According to the stress test abnormal analysis script, the stress test abnormal alarm is analyzed and the stress test is terminated.

[0026] Optionally, the alarm information includes a container restart alarm.

[0027] The step of performing abnormal data analysis on the container restart alarm comprises:

[0028] The container restart alarm is matched with an abnormal analysis task script in a database based on a preconfigured matching rule, and a corresponding container restart analysis script is determined;

[0029] The container restart alarm is subjected to abnormal analysis according to the container restart analysis script.

[0030] The application further provides an abnormal data analysis platform, comprising:

[0031] An alarm receiving module is configured to receive alarm information published by an alarm system;

[0032] A task matching module is configured to match the alarm information with an abnormal analysis task script in a database based on a preconfigured matching rule, wherein the abnormal analysis task script is an abnormal analysis strategy configured according to different application scenarios in a business system;

[0033] An abnormal analysis module is configured to perform abnormal analysis on the alarm information according to the matched abnormal analysis task script, and display the abnormal analysis result.

[0034] Optionally, the configuration process of the abnormal analysis task script comprises:

[0035] A code obtaining module is configured to obtain abnormal analysis logic code uploaded by a user;

[0036] A version confirming module is configured to upload the abnormal analysis logic code to a gitlab source code repository, determine version information of the abnormal analysis logic code in the gitlab source code repository, and send the version information saved in the gitlab source code repository to a display page;

[0037] A script configuration module is configured to receive version information submitted by a user, and publish the version information to a task running platform, so that the task running platform pulls abnormal analysis logic code corresponding to the version information in the gitlab source code repository to the database, and performs task configuration on the abnormal analysis logic code in the database to form at least one abnormal analysis task script.

[0038] Optionally, the code obtaining module comprises:

[0039] An instruction receiving module is configured to receive a project template download instruction sent by a user;

[0040] A template display module is configured to display a corresponding project template to the user based on the project template download instruction;

[0041] A code uploading module is configured to receive an abnormality analysis logic code uploaded by a user based on the project template.

[0042] From the above technical solutions, the embodiments of the present application have the following advantages:

[0043] The abnormal data analysis method and abnormal data analysis platform provided by the present application can match a corresponding abnormal analysis task script for the alarm information based on the pre-configured matching rule when the abnormal data analysis platform receives the alarm information published by the alarm system, and the abnormal analysis task script is configured according to different application scenarios in the business system, so that the abnormal analysis task script obtained after matching can quickly and accurately locate the abnormal reason of the alarm information when running, thereby providing accurate abnormal data solutions for users. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0045] Figure 1 A flowchart of an abnormal data analysis method provided by the embodiment of the present application is shown.

[0046] Figure 2 A configuration process diagram of an abnormal analysis task script provided by the embodiment of the present application is shown.

[0047] Figure 3 A structure diagram of an abnormal data analysis platform provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0049] The abnormal data analysis platform is mainly used for abnormal analysis of the alarm information published by the alarm system, so that the business personnel can improve the corresponding nodes according to the abnormal analysis result.

[0050] At present, the abnormal data analysis platform mainly analyzes various alarm information according to a general abnormal analysis method realized by research and development personnel investigation and analysis coding, the abnormal analysis process cannot accurately locate the abnormal reasons in different alarm information, and the alarm problem cannot be solved.

[0051] Therefore, the purpose of the present application is to solve the technical problem that the abnormal analysis process in the prior art cannot accurately locate the abnormal reasons in different alarm information, and the alarm problem cannot be solved, and the following technical solutions are proposed:

[0052] The technical solutions of the present application will be described below from the perspective of the abnormal data analysis platform:

[0053] In one embodiment, as shown in Figure 1 , Figure 1 a flowchart of an abnormal data analysis method provided by an embodiment of the present application; the present application provides an abnormal data analysis method, which specifically includes the following:

[0054] S110: receiving alarm information published by an alarm system.

[0055] In this step, the abnormal data analysis platform can receive the alarm information published by the alarm system, and execute the corresponding abnormal data analysis process according to the alarm information.

[0056] It should be noted that the abnormal data analysis platform in the present application can realize many functions, for example, it can support multiple language development (such as supporting two languages of python and java), which is convenient for developers familiar with different languages to use; it can support flexible rule matching and accurately match different types of alarm information; it can support multi-task concurrency and withstand a large amount of alarm information impact; the distributed architecture can realize system horizontal expansion; the multi-node running environment guarantees the high availability of the system; the cloud deployment can quickly migrate the application; it provides services for obtaining important monitoring indicators of various monitoring systems, such as logs, changes, releases, alarms, networks, system indicators, business indicators, etc., which provides data support for analysis in different scenarios; multi-end result display (supporting mobile terminal and PC terminal analysis result display); supporting package management, external dependency management, virtual environment and multi-module for python development; providing script running life cycle management and real-time tracking of script running conditions.

[0057] Also, the abnormal data analysis platform in the present application can open the abnormal data analysis and recovery logic to the outside, and personnel outside the system can flexibly code to realize the abnormal data analysis and recovery logic provided by individuals, and the platform provides a script packaging running environment. The abnormal data analysis platform based on the present application can flexibly construct abnormal data analysis logic of different abnormal scenarios, and system development and operation personnel can focus on the logic implementation of abnormal data analysis and recovery, help business systems to analyze fault root causes and quickly recover services. In this process, the abnormal data analysis platform can also provide public services for common monitoring system important indicators, which facilitates developers to call and obtain system indicator data without repeated development of underlying third-party monitoring system interface logic code.

[0058] In addition, the abnormal data analysis platform in the present application can be connected with Janitors, Noah and other external platforms to realize self-recovery services. Among them, the abnormal data analysis platform and the Janitors platform can realize various operation operations, such as ping operation, network port viewing, domain name resolution, etc. (remote control of physical machines); the abnormal data analysis platform and the Noah platform can realize container restart, capacity expansion and contraction, migration, etc. (container cloud management), which helps fault self-healing.

[0059] S120: matching the alarm information with the abnormal analysis task scripts in the database based on the pre-configured matching rules.

[0060] In this step, after the abnormal data analysis platform receives the alarm information published by the alarm system through S110, the abnormal data analysis platform can match the alarm information with the abnormal analysis task scripts in the database based on the pre-configured matching rules, and determine the abnormal analysis task scripts matched with the alarm information.

[0061] Specifically, a rule matcher is configured in the abnormal data analysis platform, which will configure alarm matching rules for each abnormal analysis task script after the abnormal analysis task script is published, to determine the corresponding relationship between the alarm triggering task class or python script, so that the abnormal data analysis platform can match the abnormal analysis task scripts in the database according to the rule matcher after receiving the alarm information published by the alarm system, determine the corresponding abnormal analysis task script, and trigger the abnormal analysis task script.

[0062] When the rule matcher performs rule matching, it can match according to the id in the alarm information, or match according to the title of the alarm information, or match according to the source of the alarm information, or match according to the domain of the alarm information, the level of the alarm information (warning, serious, general serious, disaster), which is not limited here.

[0063] Further, the abnormality analysis task script in the present application is an abnormality analysis strategy configured according to different application scenarios in a business system. It can be understood that there are many executable code classes or modules in a project, and the functions performed by each module are also different. The rule matcher in the present application can classify the modules according to functions so as to be associated with rule matching subsequently.

[0064] For example, there are many function classes in a java project, such as a class for checking a disk, a class for checking a network, a class for checking a cpu, etc. When an alarm occurs, if the alarm information is a disk alarm, the function class for checking the disk can be configured through the rule matcher, and other function classes can be associated with other alarm information. In addition, there are also many function modules in a python project, so the python project can also be split according to functions and associated with alarms.

[0065] It should be noted that the abnormality analysis task script in the present application can be a script for performing abnormality analysis on various alarm information according to a general abnormality analysis method implemented by a developer through research and analysis, or can be abnormality data analysis and recovery logic provided by a person outside the system and implemented through a script packaging running environment provided by the abnormality data analysis platform to achieve the publishing of the task and the script obtained after rule matching.

[0066] S130: performing abnormality analysis on the alarm information according to the matched abnormality analysis task script, and displaying the abnormality analysis result.

[0067] In this step, after matching the alarm information with the abnormality analysis task script in the database through S120, the abnormality data analysis platform can perform abnormality analysis on the alarm information according to the matched abnormality analysis task script, and display the abnormality analysis result.

[0068] Specifically, when the rule matcher performs rule matching, one rule matching can be associated with one abnormality analysis task script. When an alarm occurs, the rule matcher will find the abnormality analysis task script according to the configured rule, and then trigger the abnormality analysis task script to run.

[0069] After the rule matcher triggers the abnormality analysis task script to run, the abnormality analysis task script can obtain important monitoring index services in various monitoring systems, such as logs, changes, releases, alarms, networks, system indexes, business indexes, etc., so as to analyze the cause of the alarm through the obtained data, and display the alarm result.

[0070] For example, when the abnormal data analysis platform receives an alarm information with a title of "high disk usage", the rule matcher can match the disk checking task class script for it and trigger the disk checking task class script to run to check the disk usage, whether it needs to be cleaned up, and whether it needs to notify the manual online processing. When the disk checking task class script analysis is completed, the abnormal analysis result can be displayed for the user to handle in time.

[0071] In the above embodiment, when the abnormal data analysis platform receives the alarm information published by the alarm system, the corresponding abnormal analysis task script can be matched for the alarm information based on the pre-configured matching rule. Since the abnormal analysis task script is configured according to different application scenarios in the business system, the abnormal analysis task script obtained after matching can quickly and accurately locate the abnormal reason of the alarm information when running, thereby providing accurate abnormal data solution for the user.

[0072] The above embodiment describes the abnormal data analysis method in the application, and the configuration process of the abnormal analysis task script in the application will be described below.

[0073] In one embodiment, as shown in Figure 2 , the configuration process of the abnormal analysis task script provided by the embodiment of the application comprises: Figure 2 The configuration process of the abnormal analysis task script provided by the embodiment of the application comprises:

[0074] S141: Obtain the abnormal analysis logic code uploaded by the user.

[0075] S142: Upload the abnormal analysis logic code to the gitlab source code repository, determine the version information of the abnormal analysis logic code in the gitlab source code repository, and send the version information saved in the gitlab source code repository to the display page.

[0076] S143: Receive the version information submitted by the user, and publish the version information to the task running platform, so that the task running platform pulls the abnormal analysis logic code corresponding to the version information in the gitlab source code repository to the database, and performs task configuration on the abnormal analysis logic code in the database to form at least one abnormal analysis task script.

[0077] In the embodiment, as shown in Figure 2 , the open platform in Figure 2 is the abnormal data analysis platform. When the abnormal data analysis and recovery logic is opened to the outside, the abnormal data analysis platform can obtain the abnormal analysis logic code uploaded by the user, and then upload the abnormal analysis logic code to the gitlab source code repository.

[0078] It can be understood that gitlab is an open source project for repository management system, using Git as a code management tool, and building a web service on this basis. It has similar functions to Github, can browse source code, manage defects and comments, can manage team access to the repository, and gitlab can also provide a code snippet collection function to realize code reuse and facilitate future search.

[0079] In this application, the abnormal data analysis platform uploads the abnormal analysis logic code to the gitlab source code repository, and the gitlab source code repository assigns corresponding version information to the abnormal analysis logic code. The abnormal data analysis platform pulls multiple version information in the gitlab source code repository for user selection. When the version information submitted by the user is obtained, the version information can be published to the task running platform. The abnormal analysis logic code corresponding to the version information in the gitlab source code repository is pulled into the database through the task running platform, and the pulled abnormal analysis logic code is configured in the database to form at least one abnormal analysis task script.

[0080] Further, after the user selects a version information to initiate a version release application, the project administrator in the abnormal data analysis platform can audit the to-be-released version information according to the version release application, and determine whether the version information can be released. When the audit is passed, the project administrator can initiate a release operation. If the audit is not passed, the reason for rejection is fed back, and the release is terminated.

[0081] After the release operation is initiated, the abnormal data analysis platform can pass the release request to the powerjob-server end (powerjob-server is a platform management node for task class or module running) through the powerjob-client API (powerjob-client is an interface for calling powerjob-server). The powerjob-server will execute the release operation according to the following steps:

[0082] a) Pull the abnormal analysis logic code of the specified version in the gitlab source code repository and compile and package it. The python project is directly packaged into a zip file, and the java project is compiled and then packaged into a jar file. In the process of executing this step, the project source package will be parsed, and the configurable task class or module will be extracted. For example, the java project can extract the subclasses that implement the processor interface, and the python project can extract the user-defined modules except the common modules. The entire execution process can be pushed to the front end in real time in the form of websocket.

[0083] b) save the configurable task class extracted in the previous step to the database for task configuration;

[0084] c) initiate a deployment command to each powerjob-work node, and the powerjob-work node receives the release command and requests the powerjob-server to pull the packaged java jar file or python zip file. For the python zip file, it can be directly decompressed, and then the running preparation work is done, such as creating a virtual environment and installing dependent libraries. The java jar file can be loaded into the jvm through the classloader to prepare for running.

[0085] d) return the release result (the logs in the release process will be pushed to the release interface in real time through websocket, and the user can track the entire release process in real time).

[0086] It should be noted that powerjob here refers to a task running platform, which is modified from the open source project powerjob source code to manage the life cycle of the task running. Its modules include powerjob-client (encapsulating powerjob platform API), powerjob-server (powerjob management node), and powerjob-work (task running node).

[0087] In addition, the purpose of loading the java jar into the jvm here is to make the java code execute faster, avoid the need for initialization every time the code runs, and reduce the working efficiency of the platform. Moreover, at runtime, java uses the classloader to work together with the project running powerjob-work as a bean component injected into the running framework, which not only reduces the size of the jar package, but also facilitates the combination and association with the running framework. For example, the log module class in the java jar file can directly use the powerjob-work framework.

[0088] In one embodiment, the step of obtaining the user uploaded exception analysis logic code in S141 can include:

[0089] S1411: receiving a project template download instruction sent by a user.

[0090] S1412: based on the project template download instruction, showing the corresponding project template to the user.

[0091] S1413: receiving the exception analysis logic code uploaded by the user based on the project template.

[0092] In this embodiment, asFigure 2 As shown, when acquiring the user uploaded abnormal analysis logic code, the project template download instruction sent by the user can be first received, the project template download instruction is used by the abnormal data analysis platform to provide the user with project templates in multiple languages, such as a python project template and a java project template, and the abnormal analysis logic code coded by the user on the selected project template is received, so as to construct a corresponding abnormal analysis task script according to the abnormal analysis logic code.

[0093] It can be understood that the project of a java developer can use a java project template, and the project of a python developer can use a python project template. The project template contains some sample code, which is irrelevant to logic, and the logic can be freely used. The purpose of the project template is mainly to constrain the developer to code according to the structure of the project template.

[0094] In one embodiment, the step of matching the alarm information with the abnormal analysis task script in the database based on the preconfigured matching rule in S120 can include:

[0095] S121: determining the abnormal analysis task script in the database that matches the alarm content according to the alarm content carried by the alarm information.

[0096] In this embodiment, when matching the alarm information with the abnormal analysis task script in the database, the abnormal analysis task script in the database that matches the alarm content can be determined according to the alarm content carried by the alarm information, wherein the alarm content includes but is not limited to alarm id, alarm title, alarm source, alarm level, etc.

[0097] In one embodiment, the step of performing abnormal analysis on the alarm information according to the matched abnormal analysis task script in S130 can include:

[0098] The matched abnormal analysis task script is submitted to the task running platform, so that the task running platform runs the abnormal analysis task script, and performs abnormal analysis on the alarm information during the running process.

[0099] In this embodiment, after the alarm system publishes the alarm information to the abnormal data analysis platform, the abnormal data analysis platform can perform rule matching on the alarm information through a rule matcher. If an abnormal analysis task script is matched, the abnormal data analysis platform can submit the abnormal analysis task script to the task running platform through a powerjob-client API. Task running parameter information such as timeout time, alarm information, debugging mode, and retry number can also be attached when submitting.

[0100] When the task running platform receives the exception analysis task script, the exception analysis task script can be dispatched to the powerjob-work running, and after the powerjob-work running is completed, an API in the exception data analysis platform can be called to save the exception analysis result. In addition, log information in the running process can be saved to the powerjob-server local (a database can be configured to save), and the powerjob-client API is used to save the log information for the exception data analysis platform to call and display.

[0101] In one embodiment, the alarm information can include a stress test exception alarm.

[0102] The step of performing exception data analysis on the stress test exception alarm can include:

[0103] S151: Based on the pre-configured matching rule, the stress test exception alarm is matched with the exception analysis task script in the database to determine the corresponding stress test exception analysis script.

[0104] S152: According to the stress test exception analysis script, the stress test exception alarm is analyzed, and the stress test is terminated.

[0105] In this embodiment, since the online system often evaluates the capacity of the system before the big promotion, and performs stress test analysis to detect the bottleneck of the application business code and the traffic ratio of the upstream and downstream, and further performs capacity planning. Therefore, when the stress test appears an exception, the alarm system publishes the stress test exception alarm to the exception data analysis platform, so that the exception data analysis platform matches the stress test exception alarm with the exception analysis task script in the database based on the pre-configured matching rule, determines the corresponding stress test exception analysis script, and uses the stress test exception analysis script to pull the stress test log, parses the stress test ID, and calls back the stress test system to terminate the stress test.

[0106] In this embodiment, the developer can establish an exception alarm according to the stress test log, and create a matching rule and a stress test exception analysis script association in the exception data analysis platform. When the stress test appears an alarm, the stress test exception analysis script is executed to terminate the stress test. The time from the stress test appearing an alarm to the stress test stopping is short, which can greatly improve the work efficiency of the business system.

[0107] In one embodiment, the alarm information can include a container restart alarm.

[0108] The step of performing exception data analysis on the container restart alarm can include:

[0109] S161: Based on the pre-configured matching rule, the stress test exception alarm is matched with the exception analysis task script in the database to determine the corresponding stress test exception analysis script.

[0110] S162: Perform anomaly analysis on the container restart alarm based on the container restart analysis script.

[0111] In this embodiment, in actual production, containers sometimes restart for various reasons, a process that is time-consuming and labor-intensive to troubleshoot manually. Therefore, in this scenario, the anomaly data analysis platform of this application can be used to assist in analyzing the reasons for container restarts. A container restart analysis script can be developed and run to perform anomaly analysis on the container restarts. The analysis process includes:

[0112] Step 1: Obtain the host machine information and CMDB information of the container, and check the host machine status to determine if migration caused the container to restart;

[0113] Step 2: Obtain the container JVM pause duration metric to determine if the restart was caused by a failed container health check due to an excessively long pause.

[0114] Step 3: Obtain the release information to determine if the container restart was caused by the release.

[0115] Step 4: Obtain change information to determine if the container restart was caused by a change;

[0116] Step 5: Based on the above analysis of the reasons for container restart, give the root cause conclusion of container restart.

[0117] The anomaly analysis results obtained from the above analysis can quickly provide root cause conclusions, eliminating the tedious steps of manual investigation and saving location time.

[0118] The abnormal data analysis platform provided in the embodiments of this application is described below. The abnormal data analysis platform described below can be referred to in correspondence with the abnormal data analysis method described above.

[0119] In one embodiment, such as Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an abnormal data analysis platform provided in an embodiment of the present invention; the present invention also provides an abnormal data analysis platform, including an alarm receiving module 210, a task matching module 220, and an abnormal analysis module 230, specifically including the following:

[0120] The alarm receiving module 210 is used to receive alarm information issued by the alarm system.

[0121] The task matching module 220 is used to match the alarm information with the anomaly analysis task script in the database based on pre-configured matching rules, wherein the anomaly analysis task script is an anomaly analysis strategy configured according to different application scenarios in the business system.

[0122] The abnormality analysis module 230 is configured to perform abnormality analysis on the alarm information according to the matched abnormality analysis task script, and display the abnormality analysis result.

[0123] In the above embodiment, when the abnormality data analysis platform receives the alarm information published by the alarm system, the corresponding abnormality analysis task script is matched for the alarm information based on the preconfigured matching rule. Since the abnormality analysis task script is configured according to different application scenarios in the business system, the abnormality analysis task script obtained after matching can quickly and accurately locate the abnormal reason of the alarm information when running, thereby providing an accurate abnormal data solution for the user.

[0124] In one embodiment, the configuration process of the abnormality analysis task script can include:

[0125] The code obtaining module is configured to obtain the abnormality analysis logic code uploaded by the user.

[0126] The version confirmation module is configured to upload the abnormality analysis logic code to the gitlab source code repository, determine the version information of the abnormality analysis logic code in the gitlab source code repository, and send the version information saved in the gitlab source code repository to the display page.

[0127] The script configuration module is configured to receive the version information submitted by the user, and publish the version information to the task running platform, so that the task running platform pulls the abnormality analysis logic code corresponding to the version information in the gitlab source code repository to the database, and performs task configuration on the abnormality analysis logic code in the database to form at least one abnormality analysis task script.

[0128] In one embodiment, the code obtaining module can include:

[0129] The instruction receiving module is configured to receive the project template download instruction sent by the user.

[0130] The template display module is configured to display the corresponding project template to the user based on the project template download instruction.

[0131] The code uploading module is configured to receive the abnormality analysis logic code uploaded by the user based on the project template.

[0132] In one embodiment, the task matching module 220 can include:

[0133] The content matching module is configured to determine the abnormality analysis task script in the database that matches the alarm content carried by the alarm information.

[0134] The alarm content includes at least one of an alarm ID, an alarm title, an alarm source, and an alarm level.

[0135] In one embodiment, the anomaly analysis module 230 can include:

[0136] The analysis submodule is configured to submit the matched anomaly analysis task script to a task running platform, so that the task running platform runs the anomaly analysis task script, and performs anomaly analysis on the alarm information during the running process.

[0137] Finally, it should be noted that in this document, the relationship terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0138] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The various embodiments can be combined as needed, and the same and similar parts refer to each other.

[0139] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An anomaly data analysis method, characterized in that, The method includes: Receive alarm information from the alarm system; Based on pre-configured matching rules, the alarm information is matched with the anomaly analysis task script in the database, wherein the anomaly analysis task script is an anomaly analysis strategy configured according to different application scenarios in the business system. The alarm information is analyzed based on the matched anomaly analysis task script, and the anomaly analysis results are displayed.

2. The abnormal data analysis method according to claim 1, characterized in that, The configuration process for the anomaly analysis task script includes: Obtain the anomaly analysis logic code for user-uploaded data; Upload the anomaly analysis logic code to the GitLab source code repository, determine the version information of the anomaly analysis logic code in the GitLab source code repository, and send the version information saved in the GitLab source code repository to the display page; The system receives version information submitted by the user and publishes the version information to the task execution platform. The task execution platform then pulls the exception analysis logic code corresponding to the version information from the GitLab source code repository into the database and configures the exception analysis logic code in the database to form at least one exception analysis task script.

3. The abnormal data analysis method according to claim 2, characterized in that, The step of obtaining the user-uploaded anomaly analysis logic code includes: Receive project template download instructions from users; Based on the project template download instruction, the corresponding project template is displayed to the user; Receive the exception analysis logic code uploaded by the user based on the project template.

4. The abnormal data analysis method according to claim 1, characterized in that, The step of matching the alarm information with the anomaly analysis task scripts in the database based on pre-configured matching rules includes: Based on the alarm content carried by the alarm information, determine the anomaly analysis task script in the database that matches the alarm content; The alarm content includes at least one of the following: alarm ID, alarm title, alarm source, and alarm level.

5. The abnormal data analysis method according to claim 1, characterized in that, The step of performing anomaly analysis on the alarm information based on the matched anomaly analysis task script includes: The matched anomaly analysis task script is submitted to the task execution platform, which then runs the script and performs anomaly analysis on the alarm information during the process.

6. The abnormal data analysis method according to claim 1, characterized in that, The alarm information includes stress test anomaly alarms; The steps for performing abnormal data analysis on the aforementioned stress test anomaly alarms include: Based on pre-configured matching rules, the load test anomaly alarms are matched with anomaly analysis task scripts in the database to determine the corresponding load test anomaly analysis scripts. The load test anomaly analysis script is used to analyze the load test anomaly alarms and then the load test is terminated.

7. The abnormal data analysis method according to claim 1, characterized in that, The alarm information includes container restart alarms; The steps for performing abnormal data analysis on the container restart alarm include: Based on pre-configured matching rules, the container restart alarm is matched with the anomaly analysis task script in the database to determine the corresponding container restart analysis script; Anomaly analysis is performed on the container restart alarm based on the container restart analysis script.

8. An anomaly data analysis platform, characterized in that, include: The alarm receiving module is used to receive alarm information issued by the alarm system; The task matching module is used to match the alarm information with the anomaly analysis task script in the database based on pre-configured matching rules. The anomaly analysis task script is an anomaly analysis strategy configured according to different application scenarios in the business system. The anomaly analysis module is used to perform anomaly analysis on the alarm information based on the matched anomaly analysis task script, and to display the anomaly analysis results.

9. The abnormal data analysis platform according to claim 8, characterized in that, The configuration process for the anomaly analysis task script includes: The code acquisition module is used to acquire the exception analysis logic code uploaded by the user; The version confirmation module is used to upload the anomaly analysis logic code to the GitLab source code repository, determine the version information of the anomaly analysis logic code in the GitLab source code repository, and send the version information saved in the GitLab source code repository to the display page. The script configuration module is used to receive version information submitted by the user and publish the version information to the task execution platform, so that the task execution platform can pull the exception analysis logic code corresponding to the version information from the GitLab source code repository into the database, and configure the exception analysis logic code in the database to form at least one exception analysis task script.

10. The abnormal data analysis platform according to claim 8, characterized in that, The code acquisition module includes: The instruction receiving module is used to receive project template download instructions sent by users; The template display module is used to display the corresponding project template to the user based on the project template download instruction; The code upload module is used to receive exception analysis logic code uploaded by users based on the project template.

Images