CN120162224B - Abnormal monitoring method and device for gray level test - Google Patents
Abnormal monitoring method and device for gray level testInfo
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- CN120162224B CN120162224B CN202510645355.4A CN202510645355A CN120162224B CN 120162224 B CN120162224 B CN 120162224B CN 202510645355 A CN202510645355 A CN 202510645355A CN 120162224 B CN120162224 B CN 120162224B
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- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
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Abstract
The invention discloses an anomaly monitoring method and device for gray level test. The method comprises the steps of determining a test container corresponding to a gray level test, obtaining at least one operation parameter of the test container when the gray level test is operated, wherein the operation parameter is data obtained by collecting operation state data of the test container in real time, and determining whether the gray level test is in a preset abnormal state or not based on a monitoring model and the operation parameter. According to the technical scheme, the test container to be tested is monitored, not all containers related to gray level release are detected, whether the gray level test is abnormal or not can be accurately determined by monitoring the change state of the operation parameters of the test container, and the accuracy of monitoring abnormal conditions in the gray level test process is improved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for monitoring anomalies in gray level testing.
Background
Greyscale testing (GRAY RELEASE) is a software release strategy, which refers to a strategy that releases new functions or versions step by step and in stages. Greyscale testing is typically used for software update versions or functions, and is therefore important for anomaly detection during greyscale testing in order to prevent potential risks in the new version from affecting the use of most users.
At present, the anomaly detection in the gray test process generally obtains a target index of a current version of a product in a first period and a target index of a gray version of the product in the first period, and compares the target index of the current version of the product in the first period with the target index of the gray version of the product in the first period to determine a comparison result so as to determine whether the gray version of the product is anomalous or not according to the comparison result. However, in the case of fewer anomalies during the gray scale test, the variation of the version index data may not be obvious, which makes it difficult to find whether the gray scale test is abnormal, and in addition, in the prior art, whether the gray scale test parameter and the trend are abnormal or not has not been automatically analyzed, so that a method capable of accurately monitoring the anomalies in the gray scale test parameter is highly demanded.
Disclosure of Invention
The invention provides an anomaly monitoring method and device for gray level test, which are used for improving the accuracy of monitoring the anomaly condition in the gray level test process.
According to an aspect of the present invention, there is provided an anomaly monitoring method for gray scale test, the method comprising:
Determining a test container corresponding to a gray level test, and acquiring at least one operation parameter of the test container when the gray level test is operated, wherein the operation parameter is data obtained by acquiring operation state data of the test container in real time;
And determining whether the gray scale test is in a preset abnormal state or not based on the monitoring model and the operation parameters.
According to another aspect of the present invention, there is provided an anomaly monitoring device for gray scale test, the device comprising:
The system comprises a parameter acquisition module, a gray test module and a gray test module, wherein the parameter acquisition module is used for determining a test container corresponding to the gray test and acquiring at least one operation parameter of the test container when the gray test is operated;
and the monitoring module is used for determining whether the gray test is in a preset abnormal state or not based on the monitoring model and the operation parameters.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor, and
A memory communicatively coupled to the at least one processor, wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the anomaly monitoring method for gray scale testing of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to execute an anomaly monitoring method for gray scale testing according to any one of the embodiments of the present invention.
According to the technical scheme, the test container corresponding to the gray test is determined, at least one operation parameter of the test container during operation of the gray test is obtained, the operation parameter is data obtained by collecting operation state data of the test container in real time, whether the gray test is in a preset abnormal state or not is further determined based on the monitoring model and the operation parameter, namely, the real-time parameter and trend of the test container needing to be tested are monitored, and the container data monitoring is more accurately carried out based on the real-time parameter and the trend of the real-time parameter, so that whether the gray test is abnormal or not can be effectively determined accurately through monitoring the change state of the operation parameter of the test container, and the accuracy of monitoring abnormal conditions in the gray test process is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an anomaly monitoring method for gray scale testing according to an embodiment of the present invention;
FIG. 2 is a flow chart of another anomaly monitoring method for gray scale testing provided in accordance with an embodiment of the present invention;
FIG. 3 is a schematic representation of an operating parameter trend graph suitable for use in accordance with embodiments of the present invention;
FIG. 4 is a flow chart of another anomaly monitoring method for gray scale testing provided in accordance with an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an anomaly monitoring device for gray scale test according to an embodiment of the present invention;
Fig. 6 is a schematic structural diagram of an electronic device implementing an anomaly monitoring method for gray scale test according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of an anomaly monitoring method for gray level test according to an embodiment of the present invention, where the method may be performed by an anomaly monitoring device for gray level test, the anomaly monitoring device for gray level test may be implemented in hardware and/or software, and the anomaly monitoring device for gray level test may be configured in any electronic device with a network communication function. As shown in fig. 1, the anomaly monitoring method for gray scale test of the present invention includes the following steps:
S110, determining a test container corresponding to the gray level test, and acquiring at least one operation parameter of the test container when the gray level test is operated, wherein the operation parameter is data obtained by acquiring operation state data of the test container in real time.
The test containers may be containers storing updated data of the current version or the current function compared with the previous version or the previous function, and each gray level test is to test at least one container in the test containers, i.e. run the test containers, until all the test containers can be stably run, i.e. determine that the current version can be stably brought on line.
Specifically, when performing a gray scale test, the number of containers actually associated is very large, but the test containers for performing the gray scale test have a relatively small ratio, or may not reach 1% of the total number of service containers corresponding to the gray scale test, and in order to accurately detect whether the gray scale test is abnormal, the test containers corresponding to the gray scale test need to be accurately monitored. Namely, when each gray level test is performed, the test container corresponding to the gray level test is required to be determined, so that at least one operation parameter of the test container when the gray level test is performed is obtained in real time, whether the gray level test is abnormal or not is determined by monitoring the change of the operation parameter, the test container in the gray level test can be better highlighted, the test efficiency is improved, and abnormal leakage is avoided.
For example, if the target application update needs to be applied to the first number of containers in total, but the target application update needs to be tested step by step before being online, i.e. the updated content is tested step by step, not all issued to the user at one time, if the current gray level test involves the second number of test containers and no abnormality is found, the next gray level test will be increased to the third number of test containers, and so on, until the updated target application is all issued to the user. The first number is substantially greater than the second number and the third number, and the second number is less than the third number.
Optionally, at least one operation parameter of the test container in the operation of the gray scale test is obtained, wherein the sampling interval corresponding to each operation parameter is different from the sampling frequency, and the sampling interval can be a time range for collecting data. The sampling frequency may be understood as the collection of operational state data of the test container once every preset time period. The time interval from the beginning to the end of each acquisition is a sampling interval.
Specifically, the sampling interval and the sampling frequency corresponding to each operation parameter can be determined according to the importance and the fluctuation sensitivity of each operation parameter, the sampling interval corresponding to the operation parameter with high importance is longer, the sampling frequency is higher so as to monitor the gray test state in real time, the sampling interval corresponding to the operation parameter with low fluctuation sensitivity is longer, the sampling frequency is reduced, and conversely, the sampling interval corresponding to the operation parameter with high fluctuation sensitivity is shorter, and the sampling frequency is increased. For example, for operation parameters with high importance and high fluctuation sensitivity, the corresponding sampling frequency may be 15-45 s, and for operation parameters with low importance and low fluctuation sensitivity, the corresponding sampling frequency may be 60-90 s.
Further, the preset sampling interval and the preset sampling frequency corresponding to each operation parameter can be given by the monitoring model, and when the current gray level test starts, the sampling interval and the sampling frequency corresponding to each operation parameter are dynamically adjusted based on the preset sampling interval and the preset sampling frequency corresponding to each operation parameter, so as to obtain the sampling interval and the sampling frequency corresponding to each operation parameter of the current gray level test.
According to the technical scheme, different sampling intervals and sampling frequencies are set for each operation parameter, so that the cost is reduced, and meanwhile, the effectiveness and pertinence of test data are improved.
In this embodiment, optionally, in the early stage of the gray test, serious anomalies are relatively easy to occur, so that some operation parameters need to be monitored, and early warning is sent out in time. The method comprises the steps of sending out first alarm information if the running time of the gray level test is in a preset monitoring interval and the running parameter meets a first alarm condition, wherein the preset monitoring interval is the time which lasts for a preset duration from the gray level test, the first alarm information at least comprises the running parameter meeting the first alarm condition and the first alarm condition corresponding to the running parameter meeting the first alarm condition, and the gray level test is stopped running in response to the fact that the running parameter meets the first alarm condition.
Wherein, for a severe anomaly occurring and the gray scale test is still running, a first alert condition is used to indicate that the gray scale test is to be interrupted from execution. The method comprises the steps that at least one operation parameter meets a first alarm condition, and the method comprises the steps that if a preset parameter exists in the at least one operation parameter and/or the fluctuation amplitude of the operation parameter is greater than or equal to a fluctuation abnormal threshold value, the at least one operation parameter is determined to meet the first alarm condition, wherein the operation parameter is indicated to enable a gray level test to be interrupted to be executed when the fluctuation amplitude of the operation parameter is greater than or equal to the fluctuation abnormal threshold value.
The preset parameter is operation data for indicating that the gray test is interrupted. The preset parameters can comprise a first parameter, a second parameter and a third parameter, wherein the first parameter can be that the service related to the gray test is not started, the second parameter is that a core dump file (core dump) related to the gray test is detected, namely, when a tested program crashes, an operating system generates the core dump file, the memory state, stack information and the like when the program crashes are recorded, and the third parameter is data generated by the kernel crash (KERNEL PANIC) related to the gray test, namely, when the kernel of the operating system encounters serious errors which cannot be processed, KERNEL PANIC is triggered, so that the system crashes and is forced to restart or enter a debugging mode;
and if the fluctuation amplitude of the operation parameter is larger than or equal to the fluctuation abnormal threshold value, the operation parameter also meets the first alarm condition. The method includes the steps of setting a first alarm condition if a gateway success rate associated with a gray test is reduced by at least a preset threshold value when an operation parameter is the gateway success rate associated with the gray test, and setting the first alarm condition if the operation parameter is the error log amount associated with the gray test, the error log amount is increased by a preset percentage amount and is larger than the preset amount when the error log amount associated with the gray test is increased by a steep percentage amount.
In addition, it should be noted that the monitoring model may monitor the operation parameters of the gray scale test, where the operation time is within the preset monitoring interval, to determine whether at least one operation parameter meets the first alarm condition. The monitoring model is an artificial intelligent model.
According to the technical scheme, whether the operation parameters meet the first alarm condition or not is timely found by monitoring the operation parameters in the preset monitoring interval, so that the phenomenon that serious abnormality is not timely found and the efficiency of gray level test is influenced is avoided.
S120, determining whether the gray test is in a preset abnormal state or not based on the monitoring model and the operation parameters.
The monitoring model is an artificial intelligent model, is configured with a function of identifying the change state of the operation parameter, wherein the change state can be a normal change state and an abnormal change state of the operation parameter, and when the data of the operation parameter are preset operation state data, the abnormal change state of the operation parameter is determined. The monitoring model can be advanced
Specifically, the operation parameters are input into the monitoring model, the monitoring model can analyze the operation parameters to determine whether preset operation state data appear in the operation parameters so as to output whether abnormal change states appear in the change states of the operation parameters, and accordingly whether the gray level test is in the preset abnormal states or not can be accurately obtained.
As an alternative embodiment, determining whether the gray test is in a preset abnormal state based on the monitoring model and the operation parameters comprises determining the data change trend of each operation parameter according to the operation parameters, and identifying the data change trend of each operation parameter based on the monitoring model to determine whether the gray test is in the preset abnormal state. The monitoring model is provided with a function of identifying the data change trend of the operation parameters.
The data trend may be a trend of the operating parameter over time.
The method for determining the data change trend of each operation parameter according to the operation parameters comprises the steps of fitting the operation parameters by adopting a preset data processing method, and determining the data change trend of the operation parameters. The preset data processing method may be a method for determining a data change trend of an operation parameter by using a mathematical method, which includes, but is not limited to, a linear regression method, a moving average method, and an exponential smoothing method. Or drawing the acquired operation parameter values at different moments into a data trend graph, and acquiring the change trend of each operation parameter in the graph.
Further, the monitoring model can analyze the data change trend of each operation parameter to determine whether the data change trend of the operation parameter meets the preset data change trend, and if the data change trend of the operation parameter meets the preset data change trend, the monitoring model outputs the gray level test in a preset abnormal state. The preset data change trend can be understood as a data change trend shown by the operation parameters when the gray level test is in a preset abnormal state.
According to the technical scheme, the data change trend of each operation parameter is determined firstly, the data change trend of each operation parameter is further identified based on the monitoring model, whether the gray level test is in a preset abnormal state is determined, the situation that the output result is inaccurate due to the fact that a large number of operation parameters of the monitoring model are not sensitive and are directly processed is avoided, the data change trend of each operation parameter is determined firstly, the data change trend is identified by the monitoring model, accuracy of the output result can be greatly improved, and efficiency of the model on data analysis is improved.
According to the technical scheme, the test container corresponding to the gray test is determined, at least one operation parameter of the test container during operation of the gray test is obtained, the operation parameter is data obtained by collecting operation state data of the test container in real time, whether the gray test is in a preset abnormal state or not is further determined based on the monitoring model and the operation parameter, namely, the real-time parameter and trend of the test container needing to be tested are monitored, and the container data monitoring is more accurately carried out based on the real-time parameter and the trend of the real-time parameter, so that whether the gray test is abnormal or not can be effectively determined accurately through monitoring the change state of the operation parameter of the test container, and the accuracy of monitoring abnormal conditions in the gray test process is improved.
Example two
Fig. 2 is a flowchart of another anomaly monitoring method for gray level test according to an embodiment of the present invention, where the technical solution of the present embodiment is further described in detail on the basis of the foregoing embodiment by "determining a trend of data change of each operating parameter according to the operating parameters", and determining whether the gray level test is in a preset anomaly state based on the identification of the trend of data change of each operating parameter by the monitoring model ", where the present embodiment may be combined with each of the alternatives in one or more embodiments. As shown in fig. 2, the anomaly monitoring method for gray scale test includes:
s210, determining a test container corresponding to the gray level test, and acquiring at least one operation parameter of the test container when the gray level test is operated, wherein the operation parameter is data obtained by acquiring operation state data of the test container in real time.
And S220, drawing each operation parameter into an operation parameter trend chart corresponding to each operation parameter, wherein the operation parameter trend chart is used for representing the data change trend of the operation parameter along with the time change.
The operation parameters may include at least one of concurrency number, memory occupancy, CPU occupancy, thread number, success rate, time delay, and Error log amount (Error log amount). The concurrency may be the number of requests per second QPS, the success rates may include, but are not limited to, program execution success rate, gateway success rate, and call success rate, the call success rate may include interface call success rate, service call success rate, etc., and the time delay may be a delay parameter associated with gray scale testing, including, but not limited to, P50, P90, P99, and average delay. The P99 and P90 results can reveal sporadic problems such as resource contention, slow query, network jitter, etc.
The operating parameter trend graph can be understood as a visual graph, which can reflect the trend of data changes. The operation parameter trend graph can be displayed in the form of a line graph, a scatter graph and an area graph. For example, a line graph as shown in fig. 3.
And S230, determining whether an operation parameter trend graph corresponding to the operation parameter meets a second alarm condition or not based on the monitoring model, wherein the second alarm condition is used for indicating that the gray test is in a preset abnormal state.
The preset abnormal state can be understood as a state when the running state data of the gray scale container is abnormal to different degrees.
Specifically, the operation parameter trend graph is identified based on the monitoring model, first trend data corresponding to the operation parameters are obtained, and when the first trend data meets the preset data trend change range, whether the operation parameter trend graph corresponding to the operation parameters meets the second alarm condition is determined. The preset data trend change range can be understood as trend change information of the data when the operation parameters are in a preset abnormal state.
Alternatively, the monitoring model may have the ability to analyze the operating parameter trend graph. The generation of the monitoring model can comprise the steps of obtaining at least one historical operation parameter of different gray scale test stages of the history, drawing the historical operation parameter into a historical operation parameter trend chart, enabling different historical operation parameter trend charts to correspond to different trend chart analysis results, taking the historical operation parameter trend chart and the trend chart analysis results corresponding to the historical operation parameter trend chart as data to be trained, and training the monitoring model to be trained based on the data to be trained to obtain the monitoring model. The trend graph analysis result may be an analysis result of a historical operation parameter trend graph corresponding to whether the gray level test is in a preset abnormal state.
Further, based on the data to be trained, training the monitoring model to be trained to obtain the monitoring model, wherein the data to be trained is divided into training data, test data and verification data, the training of chart analysis capability is carried out on the monitoring model to be trained based on the training data to obtain a trained monitoring model, whether the chart analysis capability of the trained monitoring model meets the standard is verified based on the test data and the verification data, and if the chart analysis capability of the trained monitoring model meets the standard, the trained monitoring model is used as a monitoring model of gray level test. The invention ensures the accuracy of the capability of analyzing the running parameter trend graph of the monitoring model for the training of the monitoring model.
S240, if the operation parameter trend graph corresponding to the operation parameter meets the second alarm condition, determining that the gray test is in a preset abnormal state, and sending out second alarm information.
The second alarm information may include, but is not limited to, an operation parameter corresponding to the second alarm condition, corresponding change trend data, the number of test containers tested corresponding to the gray level test, environment configuration information of the test corresponding to the gray level test, and gray level test items, where the change trend data may be trend data obtained by analyzing an operation parameter trend graph corresponding to the operation parameter by the monitoring model.
The mode of sending the second alarm information by the invention can include, but is not limited to, a popup window, a new message prompt, an audio prompt and other common alarm modes. The invention can display the second alarm information, and the platform for displaying the second alarm information can be a webpage end, an application end, a mobile end app which is in synchronous communication with the webpage end, a short message, a server end and the like, and the invention is not particularly limited to the above.
In this embodiment, optionally, the second alarm condition may include a stability alarm condition, a first abnormal alarm condition and a second abnormal alarm condition, where the stability alarm condition is used to indicate that the gray scale test has a stability problem, the first abnormal alarm condition is used to indicate that the gray scale test is in a first abnormal state, the second abnormal alarm condition is used to indicate that the gray scale test is in a second abnormal state, and the severity of the first abnormal state is greater than the severity of the second abnormal state;
correspondingly, if the operating parameter trend graph corresponding to the operating parameter meets the second alarm condition, determining that the gray test is in a preset abnormal state, including the steps of A1-A3:
and A1, if the running parameter trend graph corresponding to the running parameter meets the stability alarm condition, controlling the monitoring model to output a gray level test with the stability problem and output second alarm information corresponding to the stability alarm condition, wherein the second alarm information corresponding to the stability alarm condition can comprise, but is not limited to, the running parameter meeting the stability alarm condition and the change trend data corresponding to the running parameter meeting the stability alarm condition.
And A2, if the operation parameter trend graph corresponding to the operation parameter meets the first abnormal alarm condition, controlling the monitoring model to output the gray level test in a first abnormal state and output second alarm information corresponding to the first abnormal alarm condition, wherein the second alarm information corresponding to the first abnormal alarm condition can comprise, but is not limited to, the operation parameter meeting the first abnormal alarm condition and the change trend data corresponding to the operation parameter meeting the first abnormal alarm condition.
And A3, if the operating parameter trend graph corresponding to the operating parameter meets a second abnormal alarm condition, controlling the monitoring model to output a gray level test in a second abnormal state and output second alarm information corresponding to the second abnormal alarm condition, wherein the second alarm information corresponding to the second abnormal alarm condition can comprise, but is not limited to, the operating parameter meeting the second abnormal alarm condition and the change trend data corresponding to the operating parameter meeting the second abnormal alarm condition.
According to the technical scheme, the second alarm conditions are divided into alarm conditions of different degrees, so that when the gray level test is abnormal, the abnormal state of the type corresponding to the gray level test at present can be accurately judged, the abnormal inspection can be carried out in a targeted manner, the problem corresponding to the gray level test can be quickly adjusted, and the efficiency of the gray level test is improved.
In this embodiment, optionally, when certain specific variation trends of the operation parameters occur, because the specific variation trends are different from the normal variation trends of the operation parameters, the specific variation trends may be mistaken for abnormal data and alarm is performed, but in actual situations, the specific variation trends need to be monitored in order to avoid false alarm principles, specifically, based on a monitoring model, the data variation trends of each operation parameter are identified, whether the gray test is in a preset abnormal state is determined, and further, based on the monitoring model, whether an operation parameter trend graph corresponding to the operation parameters meets a false alarm condition is determined, wherein the false alarm condition is a condition indicating that the operation parameters are matched with the preset operation data, and if the operation parameter trend graph corresponding to the operation parameters meets the false alarm condition, the gray test is not in the preset abnormal state and alarm information is not output.
The preset operation data may be understood as data which is not matched with the normal data change trend, but is still data change trend information in a normal state. The preset operation data is determined according to the characteristics of the actual service.
Specifically, the operation parameter trend graph is identified based on the monitoring model, second trend data corresponding to the operation parameters are obtained, and if the second trend data is matched with preset operation data, it is determined that the operation parameter trend graph corresponding to the operation parameters meets the false alarm condition.
For example, the preset operation data corresponding to the operation parameter may be trend information that the operation parameter is continuously increased or decreased for a first period of time according to the first preset change rate, and then returns to normal.
If the operation parameter is a QPS value, the preset operation data may be that a preset number of QPS values are preset values, and the preset values may be zero.
If the operating parameter is a delay, in particular P90 and P99, the predetermined operating data may be that the increase in the delay parameter is less than a predetermined magnitude, e.g. the increase in P90 and/or P99 is below 50%.
If the operation parameter is one of the memory occupancy rate, the CPU occupancy rate and the time delay, the preset operation data corresponding to the operation parameter may be trend information of restarting the test program corresponding to the gray scale test and of the condition that the operation parameter is reduced in the second time period after restarting.
If the operation parameter is the memory occupancy rate and/or the CPU occupancy rate, the preset operation data may be that the memory occupancy rate and/or the CPU occupancy rate is smaller than the preset occupancy rate, for example, the memory occupancy rate and/or the CPU occupancy rate is lower than 0.4.
According to the technical scheme, whether the operating parameter trend graph corresponding to the operating parameter meets the false alarm condition is determined based on the monitoring model, so that whether the change trend of the operating parameter has certain specific change trend is accurately judged, the situation that the normal condition is mistakenly reported as abnormal is avoided, and the accurate monitoring of the operating state of the gray level test is realized.
According to the technical scheme, the test container corresponding to the gray level test is determined, at least one operation parameter of the test container during operation of the gray level test is obtained, and the operation parameter is data obtained by collecting operation state data of the test container in real time. The operation parameter trend graph is used for representing the data change trend of the operation parameters changing along with time, the operation parameter trend graph can effectively reflect the change trend of the data so as to realize the follow-up based on a monitoring model, whether the operation parameter trend graph corresponding to the operation parameters meets the second alarm condition can be rapidly and accurately determined, the condition that the output result is inaccurate due to the fact that the monitoring model is insensitive to a large number of operation parameters and directly processes a large number of operation parameters is effectively avoided, the accuracy of the output result is greatly improved, the efficiency of the model for data analysis is also improved, further if the operation parameter trend graph corresponding to the operation parameters meets the second alarm condition, the gray level test is determined to be in a preset abnormal state, the second alarm information is sent, the purpose of accurately determining whether the gray level test is abnormal or not is realized, and the accuracy of the abnormal condition in the gray level test monitoring process is improved.
Example III
Fig. 4 is a flowchart of another anomaly monitoring method for gray level test according to an embodiment of the present invention, where the technical solution of this embodiment is to describe in further detail "determining whether an operation parameter trend chart corresponding to an operation parameter satisfies a second alarm condition based on a monitoring model" based on the foregoing embodiment, and this embodiment may be combined with each of the alternatives in one or more embodiments. As shown in fig. 4, the anomaly monitoring method of the gradation test includes:
S310, determining a test container corresponding to the gray level test, and acquiring at least one operation parameter of the test container when the gray level test is operated, wherein the operation parameter is data obtained by acquiring operation state data of the test container in real time.
And S320, drawing each operation parameter into an operation parameter trend chart corresponding to each operation parameter, wherein the operation parameter trend chart is used for representing the data change trend of the operation parameter along with the time change.
S330, responding to a data input operation, controlling the monitoring model to call preset information corresponding to the operation parameter trend graphs from a preset information base, wherein the data input operation is an operation of inputting the operation parameter trend graphs of each operation parameter to the monitoring model, and the preset information base stores preset information for judging whether the operation parameter trend graphs meet the second alarm condition.
The monitoring model is associated with a preset information base, that is, the preset information base can be arranged inside or outside the monitoring model and is used for assisting the monitoring model in judging whether the gray test meets the second alarm condition. The preset information may be understood as trend information satisfying the second alarm condition corresponding to different trend graphs of the operation parameters.
S340, identifying the operation parameter trend graph and preset information based on the monitoring model, determining whether the gray test meets the second alarm condition, if the operation parameter trend graph corresponding to the operation parameter meets the second alarm condition, determining that the gray test is in a preset abnormal state, and sending out the second alarm information.
Specifically, the operation parameter trend graph is identified based on the monitoring model, third trend data corresponding to the operation parameters are obtained, when the third trend data is matched with preset information corresponding to the operation parameter trend graph, the gray test is determined to meet the second alarm condition, the monitoring model is controlled to output that the gray test is in a preset abnormal state, and second alarm information is sent out.
Further, the preset information can comprise first preset information, second preset information and third preset information, wherein the first preset information can be parameter change trend information corresponding to the gray test in the first abnormal state, the second preset information can be parameter change trend information corresponding to the gray test in the second abnormal state, and the third preset information can be parameter change trend information corresponding to the running parameter change trend with stability problem;
The second alarm condition can comprise a stability alarm condition, a first abnormal alarm condition and a second abnormal alarm condition, wherein the first abnormal alarm condition is used for indicating that the gray test is in a first abnormal state, the second abnormal alarm condition is used for indicating that the gray test is in a second abnormal state, and the severity of the first abnormal state is greater than that of the second abnormal state;
correspondingly, when the third trend data is matched with the preset information corresponding to the operation parameter trend graph, determining that the gray test meets the second alarm condition, and controlling the monitoring model to output the gray test in the preset abnormal state can comprise the steps of B1-B3:
and B1, if the third trend data meets the first preset information, determining whether the gray test meets the first abnormal alarm condition, and controlling the monitoring model to output the gray test in the first abnormal state.
For example, if the operation parameter is a CPU occupancy rate, the first preset information may be information that the CPU occupancy rate is greater than the preset occupancy rate, and the case that the CPU occupancy rate is greater than the preset occupancy rate continues for at least the third period of time. For example, the CPU occupancy rate is greater than 80% for a long time.
If the operation parameter is the memory occupancy rate, the first preset information can be information of continuously increasing the memory occupancy rate, and the continuously increasing time of the memory occupancy rate is longer than the fourth time period and the absolute value of the memory occupancy rate is doubled. For example, the memory occupancy rate continues to rise, and the rise time exceeds 10min, doubling the absolute value.
If the operation parameter is a QPS value, the first preset information is that the QPS values are all preset values, and the preset values can be zero.
And B2, if the third trend data meets the second preset information, determining whether the gray test meets the first abnormal alarm condition, and controlling the monitoring model to output the gray test in the first abnormal state.
The second preset information is information that the CPU occupancy rate is continuously increased, and the time that the CPU occupancy rate is continuously increased is smaller than the fifth time period.
If the operation parameter is the memory occupancy rate, the second preset information is information of continuously increasing the memory occupancy rate, the continuously increasing time of the memory occupancy rate is smaller than a sixth time period, and the sixth time period is smaller than a fourth time period. For example, the memory occupancy continues to rise, but the rise time is shorter.
If the operation parameters are time delays, particularly P90 and P99, the second preset information is information of which the time delay is at least preset times of the time delay before the restarting of the test program corresponding to the gray scale test in at least a seventh time period, and the second preset information is information after the restarting of the test program corresponding to the gray scale test. For example, after the restart of the test program corresponding to the gradation test, the P90, P99 delays are 2 times and more than before the restart of the program, and the values are continued for a long time.
And B3, if the third trend data meets the third preset information, determining whether the gray test meets the stability alarm condition, and controlling the monitoring model to output the gray test to have the stability problem.
The third preset information may be information that the operation parameter continuously changes for at least an eighth time period according to the second preset change rate in the monitoring window corresponding to the gray scale test. For example, if there is a continuous significant change in the operating parameters within the monitoring window, then the test program has stability problems.
According to the technical scheme, the test container corresponding to the gray level test is determined, at least one operation parameter of the test container during operation of the gray level test is obtained, and the operation parameter is data obtained by collecting operation state data of the test container in real time. Drawing each operation parameter into an operation parameter trend graph corresponding to each operation parameter, wherein the operation parameter trend graph is used for representing the data change trend of the operation parameter along with the time change. And storing the preset information in the preset information base, so as to accurately acquire data compared with the running parameter trend graph, and accurately determine whether the gray level test meets the second alarm condition. And identifying the operation parameter trend graph and preset information based on the monitoring model, determining whether the gray test meets a second alarm condition, if the operation parameter trend graph corresponding to the operation parameter meets the second alarm condition, determining that the gray test is in a preset abnormal state, and sending out the second alarm information, thereby realizing the accurate determination of whether the gray test is abnormal, and improving the accuracy of monitoring the abnormal condition in the gray test process.
Example IV
Fig. 5 is a schematic structural diagram of an anomaly monitoring device for gray level test according to an embodiment of the present invention, where the anomaly monitoring device for gray level test may be implemented in hardware and/or software, and the anomaly monitoring device for gray level test may be configured in any electronic device having a network communication function. As shown in fig. 5, the abnormality monitoring device for gradation test of the present invention includes:
The parameter acquisition module 410 is configured to determine a test container corresponding to a gray level test, and acquire at least one operation parameter of the test container when the gray level test is performed, where the operation parameter is data obtained by acquiring operation state data of the test container in real time;
The monitoring module 420 determines whether the gray test is in a preset abnormal state based on the monitoring model and the operation parameters.
On the basis of the embodiment, the abnormal monitoring device for the gray level test optionally comprises a data monitoring module, wherein the data monitoring module is used for sending out first alarm information if the running time of the gray level test is in a preset monitoring interval and at least one running parameter meets a first alarm condition, the preset monitoring interval is a time which lasts for a preset duration from the gray level test, the first alarm information at least comprises the running parameter meeting the first alarm condition and the first alarm condition corresponding to the running parameter meeting the first alarm condition, and the gray level test is interrupted to run in response to the fact that the at least one running parameter meets the first alarm condition.
On the basis of the embodiment, optionally, at least one operation parameter meets a first alarm condition, wherein the method comprises the step of determining that at least one operation parameter meets the first alarm condition if a preset parameter exists in the at least one operation parameter and/or the fluctuation amplitude of the operation parameter is greater than or equal to a fluctuation abnormal threshold, and the step of indicating to interrupt the execution of the gray scale test when the fluctuation amplitude of the operation parameter is greater than or equal to the fluctuation abnormal threshold.
On the basis of the embodiment, the monitoring module comprises a data change trend determining unit and a first monitoring unit, wherein the data change trend determining unit is used for determining the data change trend of each operation parameter according to the operation parameters, and the monitoring unit is used for identifying the data change trend of each operation parameter based on the monitoring model and determining whether the gray level test is in a preset abnormal state or not.
On the basis of the embodiment, the optional data change trend determining unit is used for drawing each operation parameter into an operation parameter trend graph corresponding to each operation parameter, wherein the operation parameter trend graph is used for representing the data change trend of the operation parameter changing along with time.
On the basis of the embodiment, the first monitoring unit comprises a monitoring subunit and a first judging subunit, wherein the monitoring subunit is used for determining whether an operation parameter trend chart corresponding to the operation parameter meets a second alarm condition or not based on the monitoring model, the second alarm condition is used for indicating that the gray test is in a preset abnormal state, and the first judging subunit is used for determining that the gray test is in the preset abnormal state and sending out second alarm information if the operation parameter trend chart corresponding to the operation parameter meets the second alarm condition.
On the basis of the embodiment, the second alarm condition comprises a stability alarm condition, a first abnormal alarm condition and a second abnormal alarm condition, wherein the stability alarm condition is used for indicating that the gray test has a stability problem, the first abnormal alarm condition is used for indicating that the gray test is in a first abnormal state, the second abnormal alarm condition is used for indicating that the gray test is in a second abnormal state, and the severity of the first abnormal state is greater than that of the second abnormal state;
The first judging subunit is used for controlling the monitoring model to output the gray test to have a stability problem if the running parameter trend graph corresponding to the running parameter meets the stability alarm condition, controlling the monitoring model to output the gray test to be in a first abnormal state if the running parameter trend graph corresponding to the running parameter meets the first abnormal alarm condition, and controlling the monitoring model to output the gray test to be in a second abnormal state if the running parameter trend graph corresponding to the running parameter meets the second abnormal alarm condition.
On the basis of the embodiment, the monitoring subunit is optional and is used for responding to a data input operation, controlling the monitoring model to call preset information corresponding to the operation parameter trend graphs from the preset information base, wherein the data input operation is an operation of inputting the operation parameter trend graphs of each operation parameter to the monitoring model, preset information for judging whether the operation parameter trend graphs meet the second alarm condition is stored in the preset information base, identifying the operation parameter trend graphs and the preset information based on the monitoring model, and determining whether the gray test meets the second alarm condition.
On the basis of the embodiment, the monitoring module optionally comprises a second monitoring unit, wherein the second monitoring unit is used for determining whether an operation parameter trend chart corresponding to the operation parameter meets a false alarm condition or not based on the monitoring model, the false alarm condition is a condition indicating that the operation parameter is matched with preset operation data, and if the operation parameter trend chart corresponding to the operation parameter meets the false alarm condition, the gray level test is not in a preset abnormal state and alarm information is not output.
On the basis of the embodiment, optionally, at least one operation parameter of the test container is obtained, wherein a sampling interval corresponding to each operation parameter is different from a sampling frequency, and the sampling interval is a time range for collecting data.
On the basis of the embodiment, optionally, the operation parameters include at least one of concurrency number, memory occupancy rate, CPU occupancy rate, thread number, success rate and time delay.
The abnormality monitoring device for the gray level test provided by the embodiment of the invention can execute the abnormality monitoring method for the gray level test provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example five
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 6 is a schematic diagram showing the structure of an electronic device that can be used to implement the anomaly monitoring method of the gray scale test of the embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including an input unit 16, such as a keyboard, mouse, etc., an output unit 17, such as various types of displays, speakers, etc., a storage unit 18, such as a magnetic disk, optical disk, etc., and a communication unit 19, such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as an anomaly monitoring method of gradation test.
In some embodiments, the anomaly monitoring method of gray scale testing may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the anomaly monitoring method for gray scale testing described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the anomaly monitoring method of gray scale testing in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems-on-a-chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be a special or general purpose programmable processor, operable to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user, for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a Local Area Network (LAN), a Wide Area Network (WAN), a blockchain network, and the Internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
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