CN112256548B - Abnormal data monitoring method and device, server and storage medium - Google Patents
Abnormal data monitoring method and device, server and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a method, a device, a server and a storage medium for monitoring abnormal data, wherein the method comprises the following steps: acquiring original abnormal data of a target application acquired by at least one abnormal data acquisition platform, and determining target abnormal data from the original abnormal data according to a preset screening condition; determining a historical error amount, and determining a target error amount and a temporary error amount; determining a target monitoring ratio according to the historical error amount, the target error amount and the temporary error amount; and if the target monitoring ratio is greater than or equal to a preset monitoring ratio corresponding to the target error amount, acquiring target abnormal data within a third preset time length to generate an abnormal analysis report. According to the technical scheme, the technical effects that third-party data are filtered according to the preset screening conditions, part of abnormal data are obtained according to the data timing acquisition task, the data maintenance cost is reduced, and the workload of a user is reduced by generating the abnormal analysis report in a period of time are achieved.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a method and a device for monitoring abnormal data, a server and a storage medium.
Background
In order to monitor whether data running at the front end is abnormal or not, an error data acquisition platform such as Sentry is usually used for acquiring abnormal data, an engineer can browse the abnormal data and evaluate the severity of a problem based on the abnormal data, and when the abnormal data is analyzed by adopting the method, the engineer needs to manually determine the abnormal data, so that the problems of time and labor waste exist. In order to solve the problems, a mail notification control can be arranged in the abnormal data acquisition platform, namely, as long as the abnormal data is acquired by the platform, the abnormal data can be sent to related personnel to achieve the effect of reminding a user. In addition, the Sentry platform can also acquire abnormal data of a third-party code base (namely, some embedded external links), but a user does not pay attention to the abnormal data, but a filter carried by the Sentry platform cannot identify whether the abnormal data is the abnormal data of the third code base, so that the problem of high false alarm probability is caused.
In order to solve the above problem, the currently adopted abnormal data monitoring scheme may also be: the webhook notification using the abnormal data acquisition platform is similar to a mail notification, for example, the platform configures a corresponding address through the webhook, and sends the notification by calling back an interface corresponding to the address when abnormal data is detected, in this way, although the interface can be customized to optimize the filtering and reminding of the abnormal data, the implementation of the service is based on the persistence of the webhook received data, that is, the webhook service also needs to rely on an additional database, at this time, there is a problem of high maintenance cost of the monitoring system, and there are also technical problems of high labor cost and low abnormal maintenance efficiency caused by the fact that a user needs to determine and analyze a corresponding abnormal problem according to the content of the mail.
Disclosure of Invention
The invention provides a method and a device for monitoring abnormal data, a server and a storage medium, which are used for automatically, conveniently and efficiently determining an abnormal reason corresponding to the abnormal data, thereby reducing the labor cost and monitoring and maintaining cost and improving the technical effect of abnormal detection efficiency.
In a first aspect, an embodiment of the present invention provides a method for monitoring abnormal data, where the method is applied to a microservice, and includes:
acquiring original abnormal data of a target application acquired by at least one abnormal data acquisition platform, and determining target abnormal data from the original abnormal data according to a preset screening condition;
determining historical time separated from access time by first preset time, and determining a target error amount corresponding to the target abnormal data and a temporary error amount within second preset time by taking the access time as end time by using the historical error amount within second preset time; the access time is the time when the target application accesses the micro service;
determining a target monitoring ratio corresponding to the target error amount according to the historical error amount, the target error amount and the temporary error amount;
and if the target monitoring ratio is greater than or equal to a preset monitoring ratio corresponding to the target error amount, acquiring target abnormal data within a third preset time length to generate an abnormal analysis report.
In a second aspect, an embodiment of the present invention further provides an apparatus for monitoring abnormal data, where the apparatus is configured in a microservice, and includes:
the target abnormal data determining module is used for acquiring original abnormal data of a target application acquired by at least one abnormal data acquisition platform and determining target abnormal data from the original abnormal data according to a preset screening condition;
the error amount determining module is used for determining historical time which is separated from the access time by first preset time, determining target error amount corresponding to the target abnormal data and temporary error amount in second preset time which takes the access time as the end time by taking the historical time as the historical error amount in second preset time of the end time; the access time is the time when the target application accesses the micro service;
a target monitoring ratio determining module, configured to determine a target monitoring ratio corresponding to the target error amount according to the historical error amount, the target error amount, and the temporary error amount;
and the anomaly analysis report generation module is used for acquiring target anomaly data within a third preset time length to generate an anomaly analysis report if the target monitoring ratio is greater than or equal to a preset monitoring ratio corresponding to the target error amount.
In a third aspect, an embodiment of the present invention further provides a server, where the server includes:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for snooping exception data according to any one of the embodiments of the present invention.
In a fourth aspect, the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method for snooping abnormal data according to any one of the embodiments of the present invention.
The technical scheme of the embodiment of the invention obtains the original abnormal data through at least one abnormal data acquisition platform, determines the target abnormal data according to the preset screening condition, generates the abnormal analysis report when the target monitoring ratio corresponding to the target error quantity determined according to the historical error quantity, the target error quantity and the temporary error quantity is more than or equal to the preset monitoring ratio corresponding to the target error quantity, solves the problems of higher false alarm rate caused by the fact that the abnormal data monitoring platform cannot filter the third-party data, higher maintenance cost caused by the fact that part of the abnormal data monitoring platform relies on an external database to monitor the target application and labor waste caused by the fact that a user repeatedly checks mails when the abnormal data is monitored, realizes the filtering of the third-party data according to the preset screening condition, the technical effects of obtaining part of abnormal data according to the data timing acquisition task, reducing maintenance cost and reducing workload of a user by generating an abnormal analysis report in a period of time are achieved.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, a brief description is given below of the drawings used in describing the embodiments. It should be clear that the described figures are only views of some of the embodiments of the invention to be described, not all, and that for a person skilled in the art, other figures can be derived from these figures without inventive effort.
Fig. 1 is a schematic flowchart of a method for monitoring abnormal data according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a method for monitoring abnormal data according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an abnormal data monitoring apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a server according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a schematic flowchart of a method for monitoring abnormal data according to an embodiment of the present invention, where the method is applicable to monitoring abnormal data of a target application of a front-end platform, and the method may be executed by a device for monitoring abnormal data, and the device may be implemented in a form of software and/or hardware.
As shown in fig. 1, the method of this embodiment includes:
s110, acquiring original abnormal data of the target application acquired by at least one abnormal data acquisition platform, and determining the target abnormal data from the original abnormal data according to a preset screening condition.
The abnormal data acquisition platform is a platform for monitoring abnormal data and extracting related data information, for example: the Sentry anomaly capturing platform is an open-source real-time anomaly monitoring platform and can collect anomaly information generated in application running. And the related data information acquired by the abnormal data acquisition platform can be used for analyzing the abnormal data subsequently. The target application is a published application, e.g., the target application may be a certain page published. After the target application is released, the target application and the abnormal data acquisition platform can be connected in order to monitor the abnormal data fluctuation condition of the target application. The original abnormal data refers to abnormal data directly collected by an abnormal data collection platform, namely unprocessed abnormal data. The preset screening condition is a condition for filtering the acquired abnormal data, for example: and eliminating abnormal data generated due to the embedded external link in the target application. The original abnormal data is all abnormal data of the target application acquired by at least one abnormal data acquisition platform, and correspondingly, the target abnormal data is the abnormal data filtered by preset screening conditions. The target abnormal data refers to abnormal data required by screening from the original abnormal data according to a preset screening condition, namely the target abnormal data can be partial data in the original data.
Specifically, the target application is accessed to various abnormal data acquisition platforms, so that the abnormal data generated in the running process of the target application can be acquired by the various abnormal data acquisition platforms. The advantage of accessing the target application to at least one abnormal data acquisition platform is that when a fault occurs in one of the abnormal data acquisition platforms, it can be ensured that the abnormal data acquisition is normally performed. And taking the abnormal data collected at the moment as original abnormal data. Since the original abnormal data includes various types of abnormal data, for example: the abnormal data of the target application may be generated by the application itself, for example, a fault occurs in the web page itself, or may be generated by an external link mounted on the target application, or may include abnormal data generated by a buried point and a plug-in the target application. However, in the actual application process, abnormal data occurring in other applications loaded on the target application cannot be processed, so that the target abnormal data needs to be screened from the original abnormal data, that is, the target abnormal data is abnormal data that can maintain the control or the content to which the target abnormal data belongs.
The target anomaly data selected from the original anomaly data may be: and screening according to the domain name, the keywords and the like.
Specifically, the original abnormal data can be screened according to the domain name related to the target application, and the abnormal data generated by the embedded point in the target application and the plug-in can be removed according to the keywords of the embedded point and the plug-in, so that the target abnormal data for analyzing the abnormal fluctuation of the target application can be obtained.
S120, determining historical time separated from the access time by first preset time, and determining a target error amount corresponding to the target abnormal data and a temporary error amount within second preset time by taking the access time as the end time according to the historical error amount within the second preset time by taking the historical time as the end time.
In this embodiment, the micro service is a communication monitoring service established with the abnormal data acquisition platform, and the monitoring service can acquire and process abnormal data from the abnormal data acquisition platform. The first preset time length is the time length between the access time and the historical time length and is used for determining the historical time length before the access time length, for example, the access time length is 10 and 20 days 12:00 in 2020, and if the first preset time length is 24 hours, the historical time length is 10 and 19 days 12:00 in 2020; the second preset time is the time for acquiring the error amount, and if the second preset time is 1 hour, the abnormal data amount acquired by the abnormal data acquisition platform in 10 and 19 days 11:00-12:00 in 2020 is the historical error amount. Correspondingly, the abnormal data volume acquired by the abnormal data acquisition platform in 11:00-12:00 in 20 days 10 and 10 in 2020 is a temporary error volume. The error amount corresponding to the target abnormal data is the target error amount, namely the abnormal data amount is acquired by the abnormal data acquisition platform before the current moment after the target application is accessed to the abnormal data acquisition platform and is processed.
Specifically, the historical time can be determined according to the access time and the first preset time, historical abnormal data in a second preset time before the historical time is obtained from the abnormal data acquisition platform, and historical error amount is obtained through statistics. And obtaining the target error amount according to the target abnormal data statistics. Further, temporary error data within a second preset time before the access time are obtained from the abnormal data acquisition platform, and the temporary error amount is obtained through statistics.
And S130, determining a target monitoring ratio corresponding to the target error amount according to the historical error amount, the target error amount and the temporary error amount.
Wherein the target monitoring ratio is a numerical value for indicating an abnormal fluctuation situation of the target application.
Specifically, the historical error amount and the temporary error amount may be used to characterize data abnormal conditions before the target application accesses the micro service, and the target error amount is used to characterize data abnormal conditions after the target application accesses the micro service. For example, the historical error amount per unit time and the temporary error amount per unit time may be determined from the historical error amount and the temporary error amount, and the average error amount may be obtained by averaging the determined error amounts. The average error amount may be used to indicate a data exception before the target application accesses the microservice. Further, the target error amount in the unit time may be determined according to the target error amount and the acquisition time of the target error amount. The ratio of the target error amount to the average error amount in the unit time may represent an abnormal change condition after the target application accesses the micro service, and may be used as a target monitoring ratio. The specific algorithm for indicating the error amount of the data abnormal condition before the target application accesses the micro service may be a maximum value calculation or the like.
In order to better understand the target monitoring ratio, if the target monitoring ratio calculated by the method is less than or equal to 1, the abnormal data volume is reduced or unchanged after the target application accesses the micro-service; if the target monitoring ratio calculated by the method is larger than 1, the abnormal data volume is increased after the target application accesses the micro-service.
And S140, if the target monitoring ratio is greater than or equal to a preset monitoring ratio corresponding to the target error amount, acquiring target abnormal data within a third preset time length to generate an abnormal analysis report.
The predetermined monitoring ratio is a threshold value for determining whether excessive abnormal data is caused after the target application accesses the microservice. The third preset duration is the duration of generating an anomaly analysis report.
Specifically, the target monitoring ratio is compared with a preset monitoring ratio, and if the target monitoring ratio is smaller than the preset monitoring ratio, it is indicated that the abnormal fluctuation condition after the target application accesses the micro-service is within an error range compared with that before the target application accesses the micro-service, and the target application can be continuously operated; if the target monitoring ratio is larger than or equal to the preset monitoring ratio, the abnormal fluctuation condition after the target application is accessed to the micro-service is out of an error range compared with that before the target application is accessed to the micro-service, and the abnormal fluctuation condition indicates that a large amount of abnormal data is generated by the target application, which is probably caused by the target application, so that the target abnormal data within a third preset time after the target application is accessed to the micro-service needs to be counted and analyzed, and an abnormal analysis report is generated for further analysis by target application publishers.
For example, if the preset monitoring ratio is 5, when the target monitoring ratio is 3, the target monitoring ratio is smaller than the preset monitoring ratio, and at this time, the error amount generated by the target application is within the error range and can be ignored; when the target monitoring ratio is 7, the target monitoring ratio is greater than the preset monitoring ratio, and at this time, the error amount generated by the target application is out of the error range, and an anomaly analysis needs to be performed. Further, if the third preset time duration is 1 hour, statistical analysis is performed on the abnormal data within 1 hour between the access times, which may be a graph plotting the abnormal data amount and time, or analyzing the abnormal data characteristics when the abnormality occurs.
The technical scheme of the embodiment of the invention obtains the original abnormal data through at least one abnormal data acquisition platform, determines the target abnormal data according to the preset screening condition, generates the abnormal analysis report when the target monitoring ratio corresponding to the target error quantity determined according to the historical error quantity, the target error quantity and the temporary error quantity is more than or equal to the preset monitoring ratio corresponding to the target error quantity, solves the problems of higher false alarm rate caused by the fact that the abnormal data monitoring platform cannot filter the third-party data, higher maintenance cost caused by the fact that part of the abnormal data monitoring platform relies on an external database to monitor the target application and labor waste caused by the fact that a user repeatedly checks mails when the abnormal data is monitored, realizes the filtering of the third-party data according to the preset screening condition, the technical effects of acquiring partial abnormal data according to the data timing acquisition task, reducing the data maintenance cost and reducing the workload of a user by generating an abnormal analysis report in a period of time are achieved.
Example two
Fig. 2 is a flowchart of a method for monitoring abnormal data according to a second embodiment of the present invention, and in this embodiment, based on the above embodiment, further optimization is performed on the step "if the target monitoring ratio is greater than or equal to the preset monitoring ratio corresponding to the target error amount, the target abnormal data within a third preset time period is obtained to generate an abnormal analysis report". Wherein explanations of the same or corresponding terms as those of the above embodiments are omitted.
As shown in fig. 2, the method of this embodiment includes:
s210, original abnormal data of the target application acquired by at least one abnormal data acquisition platform are acquired, and the target abnormal data are determined from the original abnormal data according to preset screening conditions.
And establishing communication connection between the abnormal data acquisition platform and the target application data so that the abnormal data acquisition platform can acquire abnormal data when the target application is abnormal.
In order to detect abnormal data issued by a target application within a period of time, a first data timing acquisition task can be preset, wherein the task comprises a delay time length and is used for creating the task to acquire original abnormal data from an abnormal data acquisition platform after the delay time length when the communication connection between the abnormal data acquisition platform and the target application data is detected, and the acquired original abnormal data are abnormal data within the delay time length.
Specifically, the original abnormal data collected by the at least one abnormal data collection platform and related to the target application may be obtained when it is detected that the interval duration between the current time and the creation time of creating the data timing acquisition task reaches the delay duration in the first data timing acquisition task.
The original exception data includes all exception data of the target application, for example: exception data due to an external link embedded in the target application, exception data due to an exception of the target application itself, and the like.
Illustratively, the current time is 12:10 at 10 months and 20 days in 2020, the creation time for creating the data timing acquisition task is 12:00 at 10 months and 20 days in 2020, the delay time in the first data timing acquisition task is 10 minutes, at this time, the first data timing acquisition task is started to acquire original abnormal data about the target application in 12:00-12:10 at 10 months and 20 days in 2020, which is acquired by at least one abnormal data acquisition platform
Optionally, in order to determine the abnormal data condition after the target application is released, the abnormal condition of the target application may be monitored according to abnormal data, which is acquired from the original abnormal data and is generated due to the abnormality of the target application itself.
Specifically, abnormal data screening may be performed according to a preset screening condition. And screening target abnormal data from the original abnormal data according to the internal domain name, the external chain domain name and the keywords in the preset screening condition. For example: if the link information in the abnormal data contains the internal domain name belonging to the company, the abnormal data of the third-party JavaScript code outer link can be filtered.
In order to accurately acquire the abnormal data of the target application, basic information of the target application needs to be determined, a communication connection condition between the target application and the abnormal data acquisition platform is detected, and a data timing acquisition task is created for the target application to acquire the abnormal data of the target application. Therefore, before acquiring the raw abnormal data of the target application acquired by the at least one abnormal data acquisition platform, the following steps can be further included:
and receiving the associated data of the target application sent by the application publishing platform.
The associated data comprises an application name of the target application, a publishing time for executing the publishing, a completing time for completing the publishing and a target user corresponding to the target application. The release time when the release is performed is the time when the release operation is triggered, and the release process takes a certain time, so the completion time when the release is completed is after the release time. The target user corresponding to the target application is a target user who publishes the target application, and may be a user name of the target user. Illustratively, before abnormal data collection is performed, target application associated data sent by an application publishing platform is received, where the target application associated data includes an application name a of a target application, a publishing time for executing publishing is 12:00 in 10 months and 20 days in 2020, a completion time for completing publishing is 12:05 in 10 months and 20 days in 10 months and 2020, and a target user corresponding to the target application is B.
And sending a query instruction for querying whether the target application is accessed to the at least one abnormal data acquisition platform, so that the at least one abnormal data acquisition platform queries the target application according to the target application identifier carried by the query instruction, and feeds back a query result.
The target application identifier is an identifier used for distinguishing from other applications, and application identifiers corresponding to applications of different versions are different.
Specifically, in order to determine whether the target application establishes a connection with the abnormal data collection platform, a query instruction needs to be sent for confirmation. Therefore, an inquiry instruction for inquiring whether the target application is accessed is sent to the abnormal data acquisition platform, when the abnormal data acquisition platform receives the inquiry instruction, the target application identifier in the inquiry instruction is searched in the application identifier in the accessed application, if the target application identifier is searched, the abnormal data acquisition platform is connected with the target application, and the abnormal data of the target application can be acquired; if the target application identifier is not found, it is indicated that the abnormal data acquisition platform and the target application are not connected, and abnormal data of the target application cannot be acquired.
The abnormal data can be collected by using a plurality of abnormal data collection platforms, so that the normal collection of the abnormal data can be ensured when the communication connection of any one of the abnormal data collection platforms is disconnected.
And receiving a query result, creating at least one data timing acquisition task corresponding to the target application according to the query result, and acquiring original abnormal data of the target application acquired by at least one abnormal data acquisition platform according to the at least one data timing acquisition task.
The data timing acquisition task is used for acquiring abnormal data with a time interval with the completion time of the target application as a delay time as a data acquisition starting time and an acquisition time as a set time. It should be noted that the delay duration in at least one data timing acquisition task is different. For example: the completion time of releasing is 10, month, 20, day 12:05 in 2020, the delay time of the first data timing acquisition task is 10 minutes, and the task may be a task for acquiring original abnormal data of the target application in 12:05 in 10, month, 20, month, 10, 20, day 12:15 in 2020, and the delay time of the second data timing acquisition task is 60 minutes, and the task may be a task for acquiring original abnormal data of the target application in 12:05 in 10, month, 20, day 13:05 in 2020.
S220, determining historical time separated from the access time by first preset time, and determining a target error amount corresponding to the target abnormal data and a temporary error amount within second preset time by taking the access time as the end time according to the historical error amount within the second preset time by taking the historical time as the end time.
The access time is the time when the target application accesses the micro service; the first preset time duration is used to determine a historical time before the access time, the second preset time duration is a time duration used to collect an error amount, and may be determined by multiplying the delay time duration by a preset time coefficient, and the time coefficient may be set according to a collection time duration requirement, and may be set to a value between 2 and 6 in a general case, which is not specifically limited in the embodiment. The advantage of setting the time coefficient lies in avoiding the contingency that only obtains the data in the delay duration and causes, can enlarge the time interval of taking a value to the peak value and valley are cut off, reduce the error.
Illustratively, the completion time is 10, month, 20 and day 12:00 in 2020, if the first preset time is 24 hours, the historical time is 10, month, 19 and day 12:00 in 2020, the delay time is 10 minutes, and the time coefficient is 6, the second preset time is 60 minutes, and the abnormal data volume acquired by the abnormal data acquisition platform in 10, month, 19 and day 11:00-12:00 in 2020 is the historical error volume. The amount of errors in the target anomaly data is the target error amount. At this time, the abnormal data volume collected by the abnormal data collection platform in 20 days 11:00-12:00 in 10 months in 2020 is a temporary error volume.
And S230, determining a target monitoring ratio corresponding to the target error amount according to the historical error amount, the target error amount and the temporary error amount.
Specifically, a target monitoring ratio corresponding to the target error amount is determined based on the history error amount, the target error amount, and the temporary error amount. The historical error amount and the temporary error amount can be used for representing the data abnormal condition before the target application is published and the target error amount is used for representing the data abnormal condition after the target application is published and completed.
Optionally, S230 may include the following steps:
step one, determining a historical error rate corresponding to the historical error amount according to the historical error amount and a time coefficient.
Specifically, the historical error amount acquired by the abnormal data acquisition platform is acquired, and the historical error amount in the unit delay time duration, that is, the historical error rate, can be acquired by dividing the historical error amount by the time coefficient.
Illustratively, the completion time is 10, month, 20, day and 12:00 in 2020, if the first preset time duration is 24 hours, the historical time is 10, month, 19, day and 12:00 in 2020, the delay time duration is 10 minutes, the time coefficient is 6, the second preset time duration is 60 minutes, the abnormal data volume acquired by the abnormal data acquisition platform in 10, month, 19, day and 11:00-12:00 in 2020 is the historical error volume, and is recorded as C, and the historical error rate is C/6, which represents the average historical error volume of 10 minutes in the previous hour of the day before the target application is released.
And step two, determining a temporary error rate corresponding to the temporary error amount according to the temporary error amount and the time coefficient.
Specifically, the temporary error amount acquired by the abnormal data acquisition platform is acquired, and the temporary error amount in the unit delay time duration, that is, the temporary error rate, can be acquired by dividing the temporary error amount by the time coefficient.
On the basis of the above example, the abnormal data volume acquired by the abnormal data acquisition platform within 10/20/11: 00-12:00 of 2020 is a temporary error volume, which is denoted as B, and the temporary error volume is B/6, which represents an average temporary error volume of 10 minutes in the hour before the target application is released.
And step three, determining a target monitoring ratio corresponding to the target error amount according to the target error amount, the historical error rate and the temporary error rate.
The target error amount, the historical error rate, and the temporary error rate are error amounts or average error amounts in a unit time length, and the target monitoring ratio corresponding to the target error amount may be obtained by further performing calculation processing on the error amounts. The historical error rate and the temporary error rate can be used for measuring the abnormal data generation condition before the target application completes the release, and the target error amount can be used for measuring the abnormal data generation condition after the target application completes the release. And comparing the abnormal data quantity before and after the target application is released to obtain a target monitoring ratio, and determining the abnormal data generation condition after the target application is released according to the target monitoring ratio.
Alternatively, the target monitoring ratio may be determined in the following manner.
And obtaining a target error rate with a higher error rate value from the historical error rate and the temporary error rate, and determining a target monitoring coefficient according to the target error rate and a preset coefficient.
In order to determine the error rate of the target application before release, the error rate value of the historical error rate and the temporary error rate may be used as a reference, that is, the target error rate. For example, if the history error rate is C and the temporary error rate is B, the target error rate is D max (B, C).
And determining a target monitoring ratio according to the target error amount and the target monitoring coefficient.
On the basis of the above example, if the target error amount is a, the target monitoring ratio is a/D, which can be used to measure the abnormal data change after the target application completes publishing.
In order to better understand the target monitoring ratio, if the target monitoring ratio calculated by the method is smaller than or equal to 1, the abnormal data volume is reduced or unchanged after the target application is released; if the target monitoring ratio calculated by the method is larger than 1, the abnormal data volume is increased after the target application is released. Optionally, for convenience of understanding, the target monitoring ratio may be reduced by 1, in which case, if the value is positive, it indicates that the abnormal data amount is reduced or unchanged after the target application completes publishing; and if the value is negative, the abnormal data volume is increased after the target application finishes releasing.
Alternatively, the target monitoring ratio may be calculated as follows:
where A is the target error amount, B is the temporary error rate, C is the historical error rate, and P is the target monitoring rate.
S240, judging whether the target monitoring ratio is larger than or equal to a preset monitoring ratio corresponding to the target error amount, if so, executing S250; if not, go to S260.
The preset monitoring ratio is used for judging whether the abnormal data after the target application completes the release needs to be further analyzed, corresponds to the target error amount, and can be set in multiple stages according to the target error amount.
If the target monitoring ratio is greater than or equal to a preset monitoring ratio corresponding to the target error amount, an abnormal data analysis report needs to be further generated. If the target monitoring ratio is smaller than the preset monitoring ratio corresponding to the target error amount, the monitoring of the next stage is required to more accurately monitor the abnormal fluctuation of the target application.
Exemplarily, setting the target error amount as A, presetting the monitoring ratio as p, and neglecting when A is less than or equal to 100; when A is more than 100 and less than or equal to 200, p is 10; when A is more than 200 and less than or equal to 300, p is 6; when A is more than 300 and less than or equal to 400, p is 4; when A is more than 400 and less than or equal to 600, p is 3; when A is more than 600 and less than or equal to 1000, p is 2; when A > 1000, p is 1.5. If the collected target error amount is 80, the target error rate is smaller than the lowest target error rate critical value and can be ignored; if the collected target error amount is 150, when the target error rate is 10, the target monitoring ratio is 15, which is greater than the preset monitoring ratio of 10, and an abnormal analysis report needs to be further generated; if the acquired target error amount is 500, when the target error rate is 250, the target monitoring ratio is 2 and is smaller than the preset monitoring ratio 3, which indicates that the abnormal data amount is within the acceptable range after the target application is released, and further monitoring can be performed for a longer time.
And S250, acquiring target abnormal data within a third preset time length to generate an abnormal analysis report.
The third preset time duration is a time period for generating the abnormal analysis report, may be a time period before the abnormal analysis report is triggered to be generated, and may be determined according to a delay time duration of the data timing acquisition task in a normal case. For example: the delay time of the data timing acquisition task is 10 minutes, and then the third preset time may be 10 minutes; the delay time period of the data timing acquisition task is 3 hours, and then the third preset time period may be 1 hour. The abnormal analysis report is provided for a target application publisher, is used for analyzing abnormal data fluctuation, and may include an application name, a publishing time and the like of the target application, and may also include an abnormal data oscillogram and the like within a third preset time period.
Optionally, S250 may include the following steps:
step one, generating a report theme of an exception analysis report according to the application name of the target application, the release time and the target exception data of the target application.
Specifically, the application name and the release time of the target application may be determined according to pre-received associated data of the target application sent by the application release platform. In order to enable the target user to clearly acquire the required information according to the topic information, the acquired application name and release time of the target application and the target abnormal data of the target application may be connected by a connector, for example: report subject: health portal-10/2020/20/12: 00-anomalous data for the target application.
And step two, acquiring target abnormal data within a third preset time length, generating an abnormal curve based on the target abnormal data, and taking the abnormal curve as an accessory of a report subject.
Specifically, the abnormal data of the abnormal data acquisition platform within the third preset time can be acquired, and a curve graph between the abnormal data amount and the time can be drawn according to the abnormal data, so that a target user can visually determine the time when the abnormal data is generated. To facilitate viewing and storage of the exception curve by the target user, the exception curve may be used as an attachment to the subject of the report.
And thirdly, generating an abnormal analysis report based on the report subject and the accessory, and sending the abnormal analysis report to a target user.
Specifically, according to the report topic and the accessory, corresponding statistical analysis may be performed on the abnormal data within the third preset time period, which may be data statistics on an abnormal curve, for example: the third preset time is evenly divided into a plurality of segments according to time, and the average value, the maximum value, the variance and the like of the abnormal data in each segment of time are respectively calculated, which may include the name of an abnormal data acquisition platform, for example: sentry, may also be a different cause of statistical anomalies, such as: the amount of abnormal data when the a operation is performed is 200, the amount of abnormal data when the B operation is performed is 500, and the like. And the abnormal analysis content can be used as the text content of the abnormal analysis report for reference of the target user. Further, according to the relevant information of the target user in the relevant data of the target application, an abnormity analysis report is sent to the target user so as to remind the target user that the target application is abnormal.
S260, obtaining delay time in the task at regular time based on the second data, obtaining original abnormal data which are collected by at least one abnormal data collection platform and related to the target application, determining target abnormal data from the original abnormal data, and repeatedly executing to determine a target monitoring ratio corresponding to the target abnormal data.
When the delay time in the second data timing acquisition task is reached, the original abnormal data of the target application can be acquired again, the target abnormal data can be determined according to the original abnormal data, and the specific determination method of the original abnormal data and the target abnormal data is described in detail in methods shown in S110 and S210.
Further, it is necessary to determine a target monitoring ratio corresponding to the target abnormality data, and a specific determination method of the target monitoring ratio is described in detail in methods S220 to S230.
S270, judging whether the target monitoring ratio corresponding to the second data timing acquisition task is larger than or equal to a preset monitoring ratio corresponding to the target error amount or not, and if so, executing S250; if not, go to step S280.
The preset monitoring ratio is used to determine whether the abnormal data after the target application completes the release needs to be further analyzed, the preset monitoring ratio corresponds to the target error amount, and may be set in multiple stages according to the target error amount, and the specific determination method is described in detail in S240.
It should be noted that the preset monitoring ratio in this step is determined according to the target error amount corresponding to the second data timing acquisition task, and may be different from the preset monitoring ratio corresponding to the first data timing acquisition task.
And S280, generating no abnormal analysis report.
According to the technical scheme of the embodiment of the invention, at least one data timing acquisition task corresponding to the target application is created, the target application data is compared in stages, when the target monitoring rate corresponding to the target error amount is smaller than the preset monitoring rate corresponding to the target error amount, the abnormal data of the next stage is monitored, so that the problem of a large amount of resource waste caused by the fact that abnormal mails are fed back when an abnormal data acquisition platform monitors the abnormal data is solved, the problem of high labor cost caused by lack of stage abnormal analysis is also avoided, the purpose of acquiring and analyzing the abnormal data in stages according to the data timing and avoiding the user from receiving a large amount of invalid abnormal mails is realized, and the technical effect of the user on the abnormal fluctuation analysis speed of the target application is improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an abnormal data monitoring apparatus according to a third embodiment of the present invention, where the apparatus configuration and the micro service include: a target anomaly data determination module 310, an error amount determination module 320, a target monitoring ratio determination module 330, and an anomaly analysis report generation module 340. Wherein the content of the first and second substances,
the target abnormal data determining module 310 is configured to obtain original abnormal data of a target application, which is acquired by at least one abnormal data acquisition platform, and determine target abnormal data from the original abnormal data according to a preset screening condition; an error amount determining module 320, configured to determine a historical time that is separated from an access time by a first preset time, and determine a target error amount corresponding to the target abnormal data and a temporary error amount within a second preset time that is the access time and takes the historical time as an end time, where the historical error amount is within a second preset time; the access time is the time when the target application accesses the micro service; a target monitoring ratio determining module 330, configured to determine a target monitoring ratio corresponding to the target error amount according to the historical error amount, the target error amount, and the temporary error amount; and an anomaly analysis report generating module 340, configured to obtain target anomaly data within a third preset time duration to generate an anomaly analysis report if the target monitoring ratio is greater than or equal to a preset monitoring ratio corresponding to the target error amount.
On the basis of the foregoing technical solution, the target abnormal data determining module 310, before being configured to obtain the original abnormal data of the target application acquired by the at least one abnormal data acquisition platform, is further configured to:
receiving the associated data of the target application sent by an application publishing platform; the associated data comprises an application name of the target application, a release time for executing the release, a release completion time for completing the release and a target user corresponding to the target application; sending an inquiry instruction for inquiring whether the target application is accessed to the at least one abnormal data acquisition platform, so that the at least one abnormal data acquisition platform inquires the target application according to a target application identifier carried by the inquiry instruction and feeds back an inquiry result; receiving the query result, and creating at least one data timing acquisition task corresponding to the target application according to the query result so as to acquire original abnormal data of the target application acquired by at least one abnormal data acquisition platform according to the at least one data timing acquisition task; the delay duration in the at least one data timing acquisition task is different.
On the basis of the foregoing technical solutions, the target abnormal data determining module 310 is further configured to obtain, when it is detected that an interval duration between the current time and a creation time at which the data timing obtaining task is created reaches a delay duration in a first data timing obtaining task, original abnormal data about the target application, which is collected by the at least one abnormal data collection platform; and screening target abnormal data from the original abnormal data according to the internal domain name, the external chain domain name and the keywords in the preset screening condition.
On the basis of the above technical solutions, the second preset time duration is determined according to the delay time duration and a preset time coefficient.
On the basis of the above technical solutions, the target monitoring ratio determining module 330 is further configured to:
determining a historical error rate corresponding to the historical error amount according to the historical error amount and the time coefficient; determining a temporary error rate corresponding to the temporary error amount according to the temporary error amount and the time coefficient; acquiring a target error rate with a higher error rate value from the historical error rate and the temporary error rate, and determining a target monitoring coefficient according to the target error rate and a preset coefficient; and determining the target monitoring ratio according to the target error amount and the target monitoring coefficient.
On the basis of each technical scheme, the device further comprises: a cycle module to:
if the target monitoring ratio is smaller than a preset monitoring ratio corresponding to the target error amount, acquiring delay time in a task at regular time based on second data, acquiring original abnormal data which are acquired by the at least one abnormal data acquisition platform and are related to the target application, determining target abnormal data from the original abnormal data, and repeatedly executing to determine the target monitoring ratio corresponding to the target abnormal data; and if the target monitoring ratio is smaller than a preset monitoring ratio corresponding to the target abnormal data, not generating the abnormal analysis report.
On the basis of the above technical solutions, the anomaly analysis report generating module 340 is further configured to:
generating a report subject of the anomaly analysis report according to the application name of the target application, the release time and the target anomaly data of the target application; acquiring target abnormal data within third preset time, generating an abnormal curve based on the target abnormal data, and taking the abnormal curve as an accessory of the report subject; and generating the abnormal analysis report based on the report subject and the accessory, and sending the abnormal analysis report to the target user.
The technical scheme of the embodiment of the invention obtains the original abnormal data through at least one abnormal data acquisition platform, determines the target abnormal data according to the preset screening condition, generates the abnormal analysis report when the target monitoring ratio corresponding to the target error quantity determined according to the historical error quantity, the target error quantity and the temporary error quantity is more than or equal to the preset monitoring ratio corresponding to the target error quantity, solves the problems of higher false alarm rate caused by the fact that the abnormal data monitoring platform cannot filter the third-party data, higher maintenance cost caused by the fact that part of the abnormal data monitoring platform relies on an external database to monitor the target application and labor waste caused by the fact that a user repeatedly checks mails when the abnormal data is monitored, realizes the filtering of the third-party data according to the preset screening condition, the technical effects of acquiring partial abnormal data according to the data timing acquisition task, reducing the data maintenance cost and reducing the workload of a user by generating an abnormal analysis report in a period of time are achieved.
The abnormal data monitoring device provided by the embodiment of the invention can execute the abnormal data monitoring method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
It should be noted that, the units and modules included in the apparatus are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiment of the invention.
Example four
Fig. 4 is a schematic structural diagram of a server according to a fourth embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary server 40 suitable for use in implementing embodiments of the present invention. The server 40 shown in fig. 4 is only an example, and should not bring any limitation to the function and the scope of use of the embodiment of the present invention.
As shown in fig. 4, the server 40 is in the form of a general purpose computing device. The components of server 40 may include, but are not limited to: one or more processors or processing units 401, a system memory 402, and a bus 403 that couples the various system components (including the system memory 402 and the processing unit 401).
The server 40 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by server 40 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 402 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)404 and/or cache memory 405. The server 40 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 406 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 403 by one or more data media interfaces. Memory 402 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 408 having a set (at least one) of program modules 407 may be stored, for example, in memory 402, such program modules 407 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 407 generally perform the functions and/or methods of the described embodiments of the invention.
The server 40 may also communicate with one or more external devices 409 (e.g., keyboard, pointing device, display 410, etc.), with one or more devices that enable a user to interact with the server 40, and/or with any devices (e.g., network card, modem, etc.) that enable the server 40 to communicate with one or more other computing devices. Such communication may be through input/output (I/O) interface 411. Also, server 40 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via network adapter 412. As shown, the network adapter 412 communicates with the other modules of the server 40 over the bus 403. It should be appreciated that although not shown in FIG. 4, other hardware and/or software modules may be used in conjunction with the server 40, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 401 executes various functional applications and data processing by running a program stored in the system memory 402, for example, implementing a method for snooping exception data provided by an embodiment of the present invention.
EXAMPLE five
The fifth embodiment of the present invention further provides a storage medium containing computer-executable instructions, which are used to perform a snooping method of abnormal data when executed by a computer processor.
The method comprises the following steps:
acquiring original abnormal data of a target application acquired by at least one abnormal data acquisition platform, and determining target abnormal data from the original abnormal data according to a preset screening condition;
determining historical time separated from the release time by first preset time, and determining a target error amount corresponding to the target abnormal data and a temporary error amount within second preset time by taking the release time as the end time by taking the historical time as the historical error amount within second preset time of the end time; the release moment is the moment when the target application accesses the micro service;
determining a target monitoring ratio corresponding to the target error amount according to the historical error amount, the target error amount and the temporary error amount;
and if the target monitoring ratio is larger than a preset monitoring ratio corresponding to the target error amount, acquiring target abnormal data within a third preset time length to generate an abnormal analysis report.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for monitoring abnormal data is applied to micro service, and comprises the following steps:
acquiring original abnormal data of a target application acquired by at least one abnormal data acquisition platform, and determining target abnormal data from the original abnormal data according to a preset screening condition;
determining historical time separated from access time by first preset time, and determining a target error amount corresponding to the target abnormal data and a temporary error amount within second preset time by taking the access time as end time by using the historical error amount within second preset time; the access time is the time when the target application accesses the micro service;
determining a target monitoring ratio corresponding to the target error amount according to the historical error amount, the target error amount and the temporary error amount;
if the target monitoring ratio is larger than or equal to a preset monitoring ratio corresponding to the target error amount, acquiring target abnormal data within a third preset time length to generate an abnormal analysis report;
and if the target monitoring ratio is smaller than the preset monitoring ratio, continuing to run the target application.
2. The method of claim 1, prior to said obtaining raw anomaly data for a target application collected by at least one anomaly data collection platform, further comprising:
receiving the associated data of the target application sent by an application publishing platform; the associated data comprises an application name of the target application, a release time for executing the release, a release completion time for completing the release and a target user corresponding to the target application; and the number of the first and second groups,
sending a query instruction for querying whether a target application is accessed to the at least one abnormal data acquisition platform, so that the at least one abnormal data acquisition platform queries the target application according to a target application identifier carried by the query instruction, and feeds back a query result;
receiving the query result, creating at least one data timing acquisition task corresponding to the target application according to the query result, and acquiring original abnormal data of the target application acquired by at least one abnormal data acquisition platform according to the at least one data timing acquisition task;
the delay duration in the at least one data timing acquisition task is different.
3. The method according to claim 2, wherein the obtaining of the original abnormal data of the target application acquired by the at least one abnormal data acquisition platform and the determining of the target abnormal data from the original abnormal data according to a preset screening condition comprises:
when detecting that the interval duration between the current time and the creation time for creating the data timing acquisition task reaches the delay duration in the first data timing acquisition task, acquiring original abnormal data which is acquired by the at least one abnormal data acquisition platform and is related to the target application;
and screening target abnormal data from the original abnormal data according to the internal domain name, the external chain domain name and the keywords in the preset screening condition.
4. The method of claim 3, wherein the second predetermined time period is determined according to the delay time period and a predetermined time coefficient.
5. The method of claim 4, wherein determining a target monitoring ratio corresponding to the target error amount based on the historical error amount, the target error amount, and the temporary error amount comprises:
determining a historical error rate corresponding to the historical error amount according to the historical error amount and the time coefficient;
determining a temporary error rate corresponding to the temporary error amount according to the temporary error amount and the time coefficient;
acquiring a target error rate with a higher error rate value from the historical error rate and the temporary error rate, and determining a target monitoring coefficient according to the target error rate and a preset coefficient;
and determining the target monitoring ratio according to the target error amount and the target monitoring coefficient.
6. The method of claim 4, further comprising:
if the target monitoring ratio is smaller than a preset monitoring ratio corresponding to the target error amount, acquiring delay time in a task at regular time based on second data, acquiring original abnormal data which are acquired by the at least one abnormal data acquisition platform and are related to the target application, determining target abnormal data from the original abnormal data, and repeatedly executing to determine the target monitoring ratio corresponding to the target abnormal data;
and if the target monitoring ratio is smaller than a preset monitoring ratio corresponding to the target abnormal data, not generating the abnormal analysis report.
7. The method according to claim 1, wherein the obtaining of the target abnormal data within the third preset time period generates an abnormal analysis report, which includes:
generating a report theme of the exception analysis report according to the application name of the target application, the release time and the target exception data of the target application;
acquiring target abnormal data within a third preset time length, generating an abnormal curve based on the target abnormal data, and taking the abnormal curve as an accessory of the report subject;
and generating the abnormal analysis report based on the report subject and the accessory, and sending the abnormal analysis report to the target user.
8. An apparatus for monitoring abnormal data, configured in a microservice, comprising:
the target abnormal data determining module is used for acquiring original abnormal data of a target application acquired by at least one abnormal data acquisition platform and determining target abnormal data from the original abnormal data according to a preset screening condition;
the error amount determining module is used for determining historical time which is separated from the access time by first preset time, determining target error amount corresponding to the target abnormal data and temporary error amount in second preset time which takes the access time as the end time by taking the historical time as the historical error amount in second preset time of the end time; the access time is the time when the target application accesses the micro service;
a target monitoring ratio determining module, configured to determine a target monitoring ratio corresponding to the target error amount according to the historical error amount, the target error amount, and the temporary error amount;
the anomaly analysis report generation module is used for acquiring target anomaly data within a third preset time length to generate an anomaly analysis report if the target monitoring ratio is greater than or equal to a preset monitoring ratio corresponding to the target error amount;
the apparatus is further configured to:
and if the target monitoring ratio is smaller than the preset monitoring ratio, continuing to run the target application.
9. A server, characterized in that the server comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method of snooping exception data as claimed in any one of claims 1 to 7.
10. A storage medium containing computer-executable instructions for performing a method of snooping of exception data as claimed in any one of claims 1 to 7 when executed by a computer processor.
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