CN113656270A - Application performance testing method, device, medium and computer program product - Google Patents

Application performance testing method, device, medium and computer program product Download PDF

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CN113656270A
CN113656270A CN202110879239.0A CN202110879239A CN113656270A CN 113656270 A CN113656270 A CN 113656270A CN 202110879239 A CN202110879239 A CN 202110879239A CN 113656270 A CN113656270 A CN 113656270A
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performance
similarity
application
curve
fluctuation
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CN113656270B (en
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许治华
陈运权
金清华
罗伟涌
陈睿扬
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China Merchants Bank Co Ltd
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China Merchants Bank Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
    • G06F11/3414—Workload generation, e.g. scripts, playback
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3452—Performance evaluation by statistical analysis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The application discloses a method, equipment, a medium and a computer program product for testing application performance, which are used for obtaining a node monitoring index curve to be tested and applied, and carrying out similarity calculation on the node monitoring index curve to obtain first similarity information; determining whether the application to be tested has performance fluctuation or not based on the first similarity information; if the application to be tested has performance fluctuation, determining an abnormal node where the performance fluctuation is located according to the first similarity information; acquiring an index data curve of the abnormal node, and performing similarity calculation on the index data curve to obtain second similarity information; determining a performance bottleneck point in the abnormal node according to the second similarity information; and determining the performance fluctuation root cause of the application to be tested according to the performance bottleneck point. The method and the device can quickly and accurately position the performance fluctuation root cause of the application to be tested, reduce the skill requirements of application related personnel, have higher accuracy in positioning the performance fluctuation problem of the application, and improve the efficiency of application performance testing.

Description

Application performance testing method, device, medium and computer program product
Technical Field
The present application relates to the field of communications technologies, and in particular, to a method, an apparatus, a medium, and a computer program product for testing application performance.
Background
With the development of internet technology, especially mobile internet technology, the user quantity and the access quantity of computer software systems both have exponentially increased, and the non-functional requirements on the reliability, stability, compatibility and the like of software are increasingly urgent. In order to meet the non-functional requirements of the system, the software system architecture is gradually changed from the traditional simple single application to the complex distributed architecture, the microservice architecture and the like, and great challenges are brought to the performance fluctuation analysis of the software system. The existing solution for the problem of performance fluctuation of application software depends on manual experience judgment, clear rule guidance and system support do not exist, the root cause of the software performance fluctuation cannot be accurately and comprehensively covered, and the accuracy of performance test on the application at present is low.
Disclosure of Invention
The present application mainly aims to provide a method, an apparatus, a medium, and a computer program product for testing application performance, and aims to solve the technical problem of low accuracy of the current performance test on an application.
In order to achieve the above object, an embodiment of the present application provides an application performance testing method, where the application performance testing method includes:
acquiring a node monitoring index curve to be tested and applied, and performing similarity calculation on the node monitoring index curve to obtain first similarity information;
determining whether the application to be tested has performance fluctuation or not based on the first similarity information;
if the to-be-tested application has performance fluctuation, determining an abnormal node where the performance fluctuation is located according to the first similarity information;
acquiring an index data curve of the abnormal node, and performing similarity calculation on the index data curve to obtain second similarity information;
determining a performance bottleneck point in the abnormal node according to the second similarity information;
and determining the performance fluctuation root cause of the application to be tested according to the performance bottleneck point.
Preferably, the step of calculating the similarity of the node monitoring index curve to obtain first similarity information includes:
carrying out quantization processing on the node monitoring index curve to obtain a quantized node monitoring index curve;
determining a concurrent pressure curve and a performance golden index of a source in the node monitoring index curve as a reference curve pair;
and respectively carrying out similarity calculation on the monitoring index curves of other nodes in the node monitoring index curve and the reference curve pair to obtain a plurality of first similarities and form first similarity information.
Preferably, the step of determining whether the application to be tested has performance fluctuation based on the first similarity information includes:
comparing each first similarity in the first similarity information with a first preset similarity threshold respectively;
and if the intermediate similarity smaller than the first preset similarity threshold exists in the first similarities, determining that the to-be-tested application has performance fluctuation.
Preferably, the step of determining the abnormal node where the performance fluctuation is located according to the first similarity information includes:
acquiring each intermediate similarity smaller than the first preset similarity threshold in the first similarity information;
comparing the numerical values of the intermediate similarities to determine a first target similarity with the smallest numerical value among the intermediate similarities;
and determining the node corresponding to the first target similarity as an abnormal node where the performance fluctuation is located.
Preferably, the step of determining a performance bottleneck point in the abnormal node according to the second similarity information includes:
comparing the numerical values of the second similarity in the second similarity information to determine a second target similarity with the smallest numerical value in the second similarity;
and determining the index item corresponding to the second target similarity as a performance bottleneck point in the abnormal node.
Preferably, the step of determining the performance fluctuation root cause of the application to be tested according to the performance bottleneck point comprises:
acquiring a bottleneck index data curve of the performance bottleneck point, and performing similarity calculation on the bottleneck index data curve and a preset characteristic model to obtain a third similarity;
comparing the third similarity with a second preset similarity threshold;
and if the third similarity is greater than or equal to the second preset similarity threshold, determining a performance fluctuation root cause of the application to be tested based on the third similarity.
Preferably, the step of determining the root cause of the performance fluctuation of the application to be tested based on the third similarity comprises:
determining a preset feature model corresponding to the third similarity as a target feature model;
indexing a performance fluctuation root factor corresponding to the target characteristic model in a preset fluctuation model library;
and determining the performance fluctuation root cause corresponding to the target characteristic model as the performance fluctuation root cause of the application to be tested.
Further, to achieve the above object, the present application also provides an application performance testing apparatus, where the application performance testing apparatus includes a memory, a processor, and an application performance testing program stored in the memory and executable on the processor, and the application performance testing program, when executed by the processor, implements the steps of the application performance testing method described above.
Further, to achieve the above object, the present application also provides a storage medium, where the storage medium stores a test program of application performance, and the test program of application performance, when executed by a processor, implements the steps of the method for testing application performance.
Further, to achieve the above object, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the above method for testing application performance.
The embodiment of the application provides a method, equipment, a medium and a computer program product for testing application performance, which are used for obtaining a node monitoring index curve to be tested and applied, and performing similarity calculation on the node monitoring index curve to obtain first similarity information; determining whether the application to be tested has performance fluctuation or not based on the first similarity information; if the to-be-tested application has performance fluctuation, determining an abnormal node where the performance fluctuation is located according to the first similarity information; acquiring an index data curve of the abnormal node, and performing similarity calculation on the index data curve to obtain second similarity information; determining a performance bottleneck point in the abnormal node according to the second similarity information; and determining the performance fluctuation root cause of the application to be tested according to the performance bottleneck point. According to the method, similarity calculation is carried out on the node monitoring index curve of the application to be tested to automatically and accurately determine whether performance fluctuation exists in the application to be tested, when the performance fluctuation exists in the application to be tested, the abnormal node where the performance fluctuation exists is determined, then the performance bottleneck point in the abnormal node is determined, finally, through similarity matching between the curves, the performance fluctuation root cause of the application to be tested is quickly and accurately positioned, the technical requirements of personnel related to the application are reduced, high accuracy is achieved in the aspect of positioning the performance fluctuation problem of the application, and the efficiency of application performance testing is effectively improved.
Drawings
FIG. 1 is a schematic structural diagram of a hardware operating environment related to an embodiment of a method for testing application performance of the present application;
FIG. 2 is a schematic flow chart of a first embodiment of a method for testing application performance of the present application;
FIG. 3 is a schematic flow chart of a second embodiment of the application performance testing method of the present application;
FIG. 4 is a schematic flow chart of a third embodiment of the testing method for application performance of the present application;
FIG. 5 is a schematic flow chart of a fourth embodiment of the testing method for application performance of the present application;
FIG. 6 is a schematic flow chart of a fifth embodiment of the application performance testing method of the present application;
fig. 7 is a flowchart illustrating a sixth embodiment of the method for testing application performance of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The embodiment of the application provides a method, equipment, a medium and a computer program product for testing application performance, which are used for obtaining a node monitoring index curve to be tested and applied, and performing similarity calculation on the node monitoring index curve to obtain first similarity information; determining whether the application to be tested has performance fluctuation or not based on the first similarity information; if the to-be-tested application has performance fluctuation, determining an abnormal node where the performance fluctuation is located according to the first similarity information; acquiring an index data curve of the abnormal node, and performing similarity calculation on the index data curve to obtain second similarity information; determining a performance bottleneck point in the abnormal node according to the second similarity information; and determining the performance fluctuation root cause of the application to be tested according to the performance bottleneck point. According to the method, similarity calculation is carried out on the node monitoring index curve of the application to be tested to automatically and accurately determine whether performance fluctuation exists in the application to be tested, when the performance fluctuation exists in the application to be tested, the abnormal node where the performance fluctuation exists is determined, then the performance bottleneck point in the abnormal node is determined, finally, through similarity matching between the curves, the performance fluctuation root cause of the application to be tested is quickly and accurately positioned, the technical requirements of personnel related to the application are reduced, high accuracy is achieved in the aspect of positioning the performance fluctuation problem of the application, and the efficiency of application performance testing is effectively improved.
As shown in fig. 1, fig. 1 is a schematic structural diagram of a device for testing application performance of a hardware operating environment according to an embodiment of the present application.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for the convenience of description of the present application, and have no specific meaning by themselves. Thus, "module", "component" or "unit" may be used mixedly.
The test equipment of the application performance of the embodiment of the application performance can be a PC, and can also be a mobile terminal device such as a tablet computer and a portable computer.
As shown in fig. 1, the device for testing application performance may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the application capability test device configuration shown in FIG. 1 does not constitute a limitation of application capability test devices, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a test program for application performance.
In the device shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call a test program of application performance stored in the memory 1005 and perform the following operations:
acquiring a node monitoring index curve to be tested and applied, and performing similarity calculation on the node monitoring index curve to obtain first similarity information;
determining whether the application to be tested has performance fluctuation or not based on the first similarity information;
if the to-be-tested application has performance fluctuation, determining an abnormal node where the performance fluctuation is located according to the first similarity information;
acquiring an index data curve of the abnormal node, and performing similarity calculation on the index data curve to obtain second similarity information;
determining a performance bottleneck point in the abnormal node according to the second similarity information;
and determining the performance fluctuation root cause of the application to be tested according to the performance bottleneck point.
Further, the step of calculating the similarity of the node monitoring index curve to obtain first similarity information includes:
carrying out quantization processing on the node monitoring index curve to obtain a quantized node monitoring index curve;
determining a concurrent pressure curve and a performance golden index of a source in the node monitoring index curve as a reference curve pair;
and respectively carrying out similarity calculation on the monitoring index curves of other nodes in the node monitoring index curve and the reference curve pair to obtain a plurality of first similarities and form first similarity information.
Further, the step of determining whether the application to be tested has performance fluctuation based on the first similarity information includes:
comparing each first similarity in the first similarity information with a first preset similarity threshold respectively;
and if the intermediate similarity smaller than the first preset similarity threshold exists in the first similarities, determining that the to-be-tested application has performance fluctuation.
Further, the step of determining the abnormal node where the performance fluctuation is located according to the first similarity information includes:
acquiring each intermediate similarity smaller than the first preset similarity threshold in the first similarity information;
comparing the numerical values of the intermediate similarities to determine a first target similarity with the smallest numerical value among the intermediate similarities;
and determining the node corresponding to the first target similarity as an abnormal node where the performance fluctuation is located.
Further, the step of determining a performance bottleneck point in the abnormal node according to the second similarity information includes:
comparing the numerical values of the second similarity in the second similarity information to determine a second target similarity with the smallest numerical value in the second similarity;
and determining the index item corresponding to the second target similarity as a performance bottleneck point in the abnormal node.
Further, the step of determining the root cause of the performance fluctuation of the application to be tested according to the performance bottleneck point comprises:
acquiring a bottleneck index data curve of the performance bottleneck point, and performing similarity calculation on the bottleneck index data curve and a preset characteristic model to obtain a third similarity;
comparing the third similarity with a second preset similarity threshold;
and if the third similarity is greater than or equal to the second preset similarity threshold, determining a performance fluctuation root cause of the application to be tested based on the third similarity.
Further, the step of determining the root cause of the performance fluctuation of the application to be tested based on the third similarity comprises:
determining a preset feature model corresponding to the third similarity as a target feature model;
indexing a performance fluctuation root factor corresponding to the target characteristic model in a preset fluctuation model library;
and determining the performance fluctuation root cause corresponding to the target characteristic model as the performance fluctuation root cause of the application to be tested.
For a better understanding of the above technical solutions, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Referring to fig. 2, a first embodiment of the present application provides a flowchart illustrating a method for testing application performance. In this embodiment, the method for testing the application performance includes the following steps:
step S10, acquiring a node monitoring index curve to be tested and applied, and performing similarity calculation on the node monitoring index curve to obtain first similarity information;
the performance fluctuation analysis system based on the graph trend matching can be deployed in a server or an intelligent terminal to realize the function of testing the application performance of the server or the intelligent terminal. It is to be understood that, for convenience of description, the performance fluctuation analysis system based on the graph trend matching will be hereinafter simply referred to as a system.
It can be understood that, from the theory of conduction of energy, energy is conducted from the center of energy to the periphery along the transmissible route of energy in the process of diffusion, certain similarity exists between waveforms on the transmissible route, and the obstruction in the conduction process can interfere with the conduction waveform, so that the similarity between the waveform in the conduction process and the source waveform is reduced. Similarly, in the performance pressure measurement scenario of the application, the business transaction pressure is conducted backward from the source along the transaction critical path step by step. If a performance bottleneck exists in a certain node on the critical path, pressure conduction abnormity is caused, and the similarity between the index curve of the abnormal node and the concurrent pressure curve and golden index curve of the pressure source is reduced. Based on the theory, the method starts from a source of concurrent pressure generation, determines abnormal nodes from the incidence relation between monitoring index curves on a forward and backward analysis pressure conduction path along a business transaction key path, and then drills down the positioning performance fluctuation generation reason step by step based on the abnormal nodes, so that the method has certain prejudgment performance and can assist software testing, development and operation and maintenance personnel in quickly positioning the performance bottleneck of application.
Further, after software development is completed to obtain a software application, in order to ensure that the application has better performance when being online to improve the user experience, performance testing needs to be performed on the application to determine whether a performance bottleneck exists in the application, and if not, the application can be online; if the performance bottleneck of the application is found after the test, the performance bottleneck generated by the application needs to be positioned to determine the corresponding performance problem, then an application performance optimization scheme is formed aiming at the performance problem, and finally the application performance optimization scheme is pushed to the performance tester and the developer for reference, so that the performance tester and the developer can optimize the application.
Specifically, for an application (i.e., an application to be tested) that needs to be subjected to a performance test, the application to be tested is deployed in a server and operated, and then a scene simulation is performed on an operated target application, so that the performance of the target application is tested according to operation information of the target application in the scene. For example: a shopping application is deployed and operated on a server, and then a business pressure environment generated by operations of shopping, order submission and the like of multiple people is simulated to test whether a performance bottleneck occurs in the environment of the application to be tested.
Further, the system obtains a node monitoring index curve of the application to be tested, where the node monitoring index curve includes a concurrent pressure curve and a performance golden index curve of a source on the pressure transmission critical path, and a server resource consumption curve and a response time trend curve of each other node on the pressure transmission critical path, and the concurrent pressure curve is a curve formed by concurrent pressures in a certain time (concurrent pressures in this embodiment can be understood as pressures generated when a certain number of virtual users use the application); the performance golden index curve can be a curve formed by flow class, error (success) rate class, response time class and capacity class indexes; the server resource consumption curve is a curve formed by resources consumed by the server within a certain time; the response time curve is a curve formed by the time required to respond to a certain operation. Specifically, the system can obtain a concurrent pressure curve and a performance golden index curve of a source on a pressure transmission critical path, and a server resource consumption curve and a response time trend curve of other nodes on the pressure transmission critical path in a simulated environment of the application to be tested, so as to obtain a node monitoring index curve of the application to be tested.
Further, the system carries out quantization processing on the node monitoring index curve, and then determines a source concurrent pressure curve and a performance golden index in the node monitoring index curve after quantization processing as a reference curve pair, wherein the reference curve pair is a curve pair serving as a reference and used for comparing with other curves, and the quantization processing is used for converting the node monitoring index curve into a data form capable of being calculated. Further, the system performs similarity calculation on monitoring index curves of other nodes in the node monitoring index curve with a concurrent pressure curve and a performance golden index which are taken as a reference curve pair respectively to obtain a plurality of first similarities, and first similarity information is formed by the plurality of first similarities, wherein the similarity calculation between the monitoring index curve and the concurrent pressure curve and the performance golden index which are taken as the reference curve pair in the embodiment specifically includes calculating Hamming Distance (Hamming Distance) between the two curves, and specifically includes performing difference operation on fluctuation characteristics of the two curves to obtain the Hamming Distance between the two curves, so that the node monitoring index curve needs to be quantized to obtain the fluctuation characteristics of each curve. By calculating the similarity between curves of nodes (including sources) of the application to be tested and forming first similarity information, whether performance fluctuation exists in the application to be tested is determined according to the first similarity information, performance bottleneck points are rapidly positioned based on the first similarity information when the performance fluctuation of the application to be tested is determined, and the efficiency of application performance testing is improved.
Step S20, determining whether the application to be tested has performance fluctuation based on the first similarity information;
after the first similarity information is obtained, the system determines whether the performance fluctuation exists in the application to be tested according to a plurality of first similarities contained in the first similarity information, specifically, the system detects whether an intermediate similarity smaller than a first preset similarity threshold exists in the plurality of first similarities, and if the intermediate similarity exists, the performance fluctuation exists in the application to be tested; conversely, if there is no intermediate similarity, it is determined that the application to be tested does not have performance fluctuations. The first preset similarity threshold is a numerical value that can be set based on the actual environment and the requirement. Whether performance fluctuation exists in the application to be tested can be quickly and accurately determined through the first similarity information, and the efficiency of application performance testing can be improved.
Step S30, if the application to be tested has performance fluctuation, determining an abnormal node where the performance fluctuation is located according to the first similarity information;
further, if it is determined that the to-be-tested application has performance fluctuation, the system acquires all intermediate similarities smaller than a first preset similarity threshold in the first similarity information, determines the intermediate similarity with the minimum value from the intermediate similarity thresholds as a first target similarity, and takes a node corresponding to the first target similarity as an abnormal node where the performance fluctuation is located. After the performance fluctuation of the application to be tested is determined, the abnormal node where the performance fluctuation is located can be quickly and accurately determined through the first similarity information, so that the root cause can be generated conveniently in the subsequent step-by-step drilling-down positioning performance fluctuation based on the abnormal node, and the efficiency of the application performance test can be improved.
Step S40, acquiring an index data curve of the abnormal node, and performing similarity calculation on the index data curve to obtain second similarity information;
after detecting that the application to be tested has performance fluctuation and determining the abnormal node where the performance fluctuation is located, the system acquires an index data curve of the abnormal node, and specifically, can acquire a pressure measurement data fluctuation curve of the abnormal node at a pressure machine end, a resource consumption data curve of a server end, a performance data curve of an application program layer and other curves as the index data curve of the abnormal node. Further, the system carries out quantization processing on the index data curve to obtain the index data curve after quantization processing, similarity calculation is carried out on the index data curve and a concurrent pressure curve and a performance golden index curve which are taken as a reference curve pair respectively to obtain a plurality of second similarities, and second similarity information is formed by the plurality of second similarities. Specifically, the system respectively calculates the similarity of a pressure measurement data fluctuation curve of the abnormal node at the end of the pressure machine after the quantitative processing, a concurrent pressure curve and a performance golden index curve, averages the two obtained similarities, and then calculates by combining the weight of the index item to obtain a second similarity of the index item; similarity calculation is carried out on the resource consumption data curve of the server side after the quantitative processing and the concurrent pressure curve and the performance golden index curve respectively, the two obtained similarities are averaged and then calculated by combining the weight of the index item, and a second similarity of the index item is obtained; and respectively carrying out similarity calculation on the curve of the performance data of the application program layer after the quantization processing and the concurrent pressure curve and the performance golden index curve, averaging the two obtained similarities, and then combining the two obtained similarities with the weight of the index item to carry out calculation so as to obtain a second similarity of the index item. By calculating the similarity between the index data curve and the reference curve pair and forming second similarity information, the performance bottleneck point can be conveniently and rapidly positioned according to the second similarity information, the performance fluctuation root cause of the application to be tested can be determined according to the performance bottleneck point, and the efficiency of application performance testing is improved.
Step S50, determining a performance bottleneck point in the abnormal node according to the second similarity information;
after the second similarity information is obtained through calculation, the system determines the second similarity with the minimum value from the second similarity threshold values as the second target similarity, and takes the index item corresponding to the second target similarity as a performance bottleneck point in the abnormal node. After the abnormal node where the performance fluctuation is located is determined, the performance bottleneck point in the abnormal node can be quickly and accurately located through the second similarity information, the root cause generated by the performance fluctuation can be conveniently determined based on the performance bottleneck point, and the efficiency of the application performance test can be improved.
And step S60, determining the performance fluctuation root cause of the application to be tested according to the performance bottleneck point.
After the performance bottleneck point is determined, the system firstly obtains a bottleneck index data curve of the performance bottleneck point, and similarity calculation is carried out on the bottleneck index data curve and a preset characteristic model to obtain third similarity information; comparing each third similarity in the third similarity information with a second preset similarity threshold respectively; and if a third target similarity with the numerical value larger than or equal to a second preset similarity threshold exists in the third similarities, determining a preset feature model corresponding to the third target similarity as a target feature model, and indexing a performance fluctuation root factor corresponding to the target feature model in a preset fluctuation model library as a performance fluctuation root factor of the application to be tested. By calculating the third similarity information between the bottleneck index data curve of the performance bottleneck point and the preset characteristic model, the performance fluctuation root cause of the application to be tested can be quickly and accurately determined according to the third similarity information, and the efficiency of the application performance test can be improved.
In a specific embodiment of the application, when a pressure end initiates pressure, taking the pressure source as an example, a pressure source is described, a concurrent pressure curve of the pressure source and a performance golden index curve model are depicted, a numerical matrix of curve indexes such as wave crests, amplitude, duration and the like is taken as a model characteristic, a hamming distance between characteristic matrixes is calculated as a similarity, if the model characteristic charging similarity between curve pairs is lower than 0.3, the curve model pairs are considered to be mismatched, it can be determined that the pressure is not normally transmitted, then a bottleneck point of the performance is at the pressure end, and the problem of pressure initiation can be further positioned by checking scripts, test data, parameter configuration and the like; and in the case that the similarity of the pressure end concurrent pressure curve and the reference curve is higher than 0.8, the pressure end is considered to be a bottleneck point on the non-pressure conduction path. Further, for each node on the pressure conduction path, the operation is similar to that of the pressure source. And (3) extracting the model characteristic values of the resource consumption curves of all the nodes one by one along the pressure conduction path, carrying out similarity matching with the concurrent pressure curve and the golden index curve of the pressure source, and identifying the node with the characteristic similarity of the fluctuation curve model lower than 0.3 as a performance bottleneck point. And further calculating the similarity between the bottleneck point fluctuation curve and various fluctuation models in the historical characteristic library, and if the model characteristic similarity is higher than 0.8, confirming the fluctuation classification of the node index curve so as to identify the fluctuation rule classification of the curve. Based on the curve's fluctuation rule classification, the root cause can be further confirmed in combination with the following means. Firstly, whether a golden index curve of a pressure end source belongs to regular fluctuation classification is confirmed, whether system resource consumption exists in each node on a key path or not and whether fluctuation curves of network delay end and database end resource consumption are similar to the golden index curve of the pressure end source with regular fluctuation can be further judged (namely, the similarity of model characteristics is greater than 0.8), if similar fluctuation curves exist, the fact that performance bottlenecks exist in responding nodes and index points can be judged, and the resource consumption or application state of a single system can be further analyzed to determine bottleneck root causes. If there is no similar fluctuation curve in regular fluctuation, the fluctuation range of the golden index curve at a certain time node is reduced or tends to be converged, which can assist the server to monitor to determine that the resource consumption reaches the system limit or the system parameter setting is unreasonable. If the golden index curve fluctuates irregularly in the curve trend analysis and the system monitoring indexes are normal, further analyzing the resource condition of the application end, and if the utilization rate of the heap memory of the application end shows a trend of fluctuation upwards, preliminarily judging that the application has the problem of memory leakage; if the trend of the thread number of the application end is normal, and the cpu consumption shows a trend of downward fluctuation, whether the application has data cache or not can be preliminarily judged, the data volume of the application is reduced, and the like.
The embodiment provides a method, equipment, a medium and a computer program product for testing application performance, which are used for obtaining a node monitoring index curve to be tested and applied, and performing similarity calculation on the node monitoring index curve to obtain first similarity information; determining whether the application to be tested has performance fluctuation or not based on the first similarity information; if the to-be-tested application has performance fluctuation, determining an abnormal node where the performance fluctuation is located according to the first similarity information; acquiring an index data curve of the abnormal node, and performing similarity calculation on the index data curve to obtain second similarity information; determining a performance bottleneck point in the abnormal node according to the second similarity information; and determining the performance fluctuation root cause of the application to be tested according to the performance bottleneck point. According to the method, similarity calculation is carried out on the node monitoring index curve of the application to be tested to automatically and accurately determine whether performance fluctuation exists in the application to be tested, when the performance fluctuation exists in the application to be tested, the abnormal node where the performance fluctuation exists is determined, then the performance bottleneck point in the abnormal node is determined, finally, through similarity matching between the curves, the performance fluctuation root cause of the application to be tested is quickly and accurately positioned, the technical requirements of personnel related to the application are reduced, high accuracy is achieved in the aspect of positioning the performance fluctuation problem of the application, and the efficiency of application performance testing is effectively improved.
Further, referring to fig. 3, a second embodiment of the testing method for application performance of the present application is proposed based on the first embodiment of the testing method for application performance of the present application, and in the second embodiment, the step of performing similarity calculation on the node monitoring index curve to obtain first similarity information includes:
step S11, carrying out quantization processing on the node monitoring index curve to obtain a quantized node monitoring index curve;
step S12, determining a concurrent pressure curve of a source in the node monitoring index curve and a performance golden index as a reference curve pair;
and step S13, respectively carrying out similarity calculation on the monitoring index curves of other nodes in the node monitoring index curve and the reference curve pair to obtain a plurality of first similarities and form first similarity information.
It will be appreciated that the present embodiment is based on a concurrent pressure curve CpHarmony performance golden index curve CtaSeparately extracting [ t ] in a unit voltage time periodb，te]The fluctuation characteristic factors of all relevant fluctuation curves mainly comprise the fluctuation frequency WfAmplitude of fluctuation WaAnd duration TpFunctional relation between them, extracting the fluctuation characteristics F of the fluctuation curvecEstablishing a reference curve pair model M based on the functional relation and the fluctuation characteristicsc＝F(Wf,Wa,Tp) And quantizing the acquired curve according to the reference curve so that the quantized curve can be subjected to similarity calculation. The node monitoring index curves of the model are quantized through the reference curves, so that the node monitoring index curves on the key path under the current pressure measurement scene can be analyzed in unit time period [ ct ]b,cte]The internal fluctuation characteristics are calculated and matched with the model characteristics of a reference curve pair formed by a concurrent pressure curve and a performance golden index curve of the source one by one from the source of the pressure conduction critical pathfTo identify pairs of index curves on the critical path that are outside the similarity threshold range from the concurrent pressure curve, the fluctuating characteristic of the performance golden index curve, and the model.
Because concurrent pressure is conducted backwards from a pressure source along a business transaction critical path, under normal conditions, monitoring indexes of all nodes on the critical path must have certain similarity with performance gold indexes. Therefore, the system inputs the acquired node monitoring index curve into the reference curve pair model, extracts the fluctuation characteristics of each node monitoring index curve through the reference curve pair model according to the function relation among the fluctuation frequency, the fluctuation amplitude and the duration of the node monitoring index curve, achieves the quantization processing of the node monitoring index curve, and outputs the node monitoring index curve after the quantization processing. Further, the system defines a source concurrent pressure curve and a performance golden index curve as a reference curve pair in advance, and takes fluctuation characteristics of the source concurrent pressure curve and the performance golden index curve as calculation contents of the reference curve pair.
Further, the system respectively carries out similarity calculation on the monitoring index curves of other nodes except the source node in the quantized node monitoring index curves and a reference curve, specifically, because the monitoring index curves of all the nodes comprise a server resource consumption curve and a response time trend curve, the similarity between the server resource consumption curve and a concurrent pressure curve in a reference curve pair, the similarity between the server resource consumption curve and a performance golden index curve in the reference curve pair, the similarity between the response time trend curve and the concurrent pressure curve in the reference curve pair, and the similarity between the response time trend curve and the performance golden index curve in the reference curve pair are respectively calculated, when the similarity calculation is carried out, the fluctuation characteristics of the two curves are respectively obtained, and then the Hamming distance between the two curves is calculated according to the fluctuation characteristics of the two curves, the calculated hamming distance is taken as the similarity between the two curves. After obtaining the plurality of similarities, the system may take an average value of the plurality of similarities as a first similarity between the node monitoring index curve and the reference curve pair. Or the system can also acquire a similarity weight preset for the node according to the position of the node in the link, and calculate according to the similarity weight and an average value of a plurality of similarities to obtain a first similarity between the node monitoring index curve and the reference curve pair.
In the embodiment, the node monitoring index curve is firstly subjected to quantization processing, then the similarity between each curve in the node monitoring index curve (including the source) to be tested and a reference curve pair is calculated, and first similarity information is formed, so that whether performance fluctuation exists in the application to be tested is automatically and accurately determined according to the first similarity information, a performance bottleneck point is quickly positioned based on the first similarity information when the performance fluctuation exists in the application to be tested, and the efficiency of application performance testing is improved.
Further, referring to fig. 4, a third embodiment of the testing method for application performance of the present application is proposed based on the first embodiment of the testing method for application performance of the present application, and in the third embodiment, the step of determining whether the application to be tested has performance fluctuation based on the first similarity information includes:
step S21, comparing each first similarity in the first similarity information with a first preset similarity threshold;
step S22, if there is an intermediate similarity smaller than the first preset similarity threshold in the first similarities, determining that the application to be tested has performance fluctuation.
After first similarity information formed by a plurality of first similarities is obtained, in order to determine whether performance fluctuation exists in the application to be tested, the system respectively obtains each first similarity in the first similarity information, and compares each first similarity with a first preset similarity threshold value respectively to determine a magnitude relation between each first similarity and the first preset similarity threshold value. Specifically, the system may use each of the first similarities as a subtree, use a first preset similarity threshold as a subtree, perform difference operation on each of the first similarities and the first preset similarity threshold through a calculation device, such as a calculator, to obtain a plurality of differences, and determine whether an intermediate similarity smaller than the first preset similarity threshold exists in each of the first similarities according to the plurality of differences. Further, if it is determined that there is an intermediate similarity smaller than the first similarity threshold in each first similarity according to the plurality of difference values, for example, there is one or more difference values in the difference values that are negative numbers, that is, it is described that part of the first similarities are smaller than the first preset similarity threshold, it is determined that there is a performance fluctuation in the application to be tested. On the contrary, if all the difference values are numerical values not less than 0, which indicates that all the first similarity values are greater than or equal to the first preset similarity threshold value, it is determined that the performance test does not exist in the application to be tested, and the test process of the performance of the application can be ended.
According to the embodiment, the limitation of manual testing can be avoided according to the similarity between the curves in the node monitoring index curve of the application to be tested, whether the performance fluctuation exists in the application to be tested or not can be automatically and accurately determined, and the efficiency of application performance testing can be improved.
Further, referring to fig. 5, a fourth embodiment of the testing method for application performance of the present application is proposed based on the first embodiment of the testing method for application performance of the present application, and in the fourth embodiment, the step of determining the abnormal node where the performance fluctuation is located according to the first similarity information includes:
step S31, obtaining each intermediate similarity in the first similarity information that is smaller than the first preset similarity threshold;
step S32, comparing the intermediate similarity to determine a first target similarity with the smallest value among the intermediate similarities;
step S33, determining the node corresponding to the first target similarity as an abnormal node where the performance fluctuation is located.
When the abnormal node where the performance fluctuation is located is determined according to the first similarity information, the system firstly identifies all first similarities which are smaller than a first preset similarity threshold value in the first similarity information, and takes all the first similarities which are smaller than the first preset similarity threshold value as the intermediate similarity. Further, the system compares the values of the intermediate similarities, specifically, may compare the values of the intermediate similarities one by one, and determine the intermediate similarity with the smallest value from the intermediate similarities as the first target similarity.
Further, the system identifies the monitoring index curve corresponding to the first target similarity, locates the node where the monitoring index curve corresponding to the first target similarity is located, and determines the node as an abnormal node where the performance fluctuation is located. It can be understood that, if there are a plurality of intermediate similarities with the smallest value among the plurality of intermediate similarities, that is, there are a plurality of intermediate similarities with the same value and the same value as the smallest value, the node where the monitoring index data corresponding to each intermediate similarity is located is determined, and then the node closest to the source among the nodes is determined as the abnormal node.
In this embodiment, by comparing the intermediate similarity values smaller than the first preset similarity threshold value in the first similarity information, the node with the minimum similarity distance (i.e., the similarity value) in the pressure conduction process is quickly and accurately identified as the abnormal node where the performance fluctuation is located, so that the efficiency of the application performance test can be improved.
Further, referring to fig. 6, a fifth embodiment of the testing method for application performance of the present application is proposed based on the first embodiment of the testing method for application performance of the present application, and in the fifth embodiment, the step of determining the performance bottleneck point in the abnormal node according to the second similarity information includes:
step S51, comparing the second similarity in the second similarity information to determine a second target similarity with a minimum value among the second similarities;
step S52, determining the index item corresponding to the second target similarity as a performance bottleneck point in the abnormal node.
After the second similarity information is obtained, the system compares the second similarities contained in the second similarity information, specifically, may compare the second similarity values one by one, and determine the second similarity with the smallest value from the second similarities as the second target similarity. Further, the system identifies an index data curve corresponding to the second target similarity, locates an index item of the index data curve corresponding to the second target similarity, and determines the index item as a performance bottleneck point in the abnormal node.
After the abnormal node where the performance fluctuation is located is determined, the performance bottleneck point in the abnormal node is quickly and accurately located by analyzing each second similarity in the second similarity information, and the efficiency of the application performance test can be improved.
Further, referring to fig. 7, a sixth embodiment of the testing method for application performance of the present application is proposed based on the first embodiment of the testing method for application performance of the present application, and in the sixth embodiment, the step of determining the performance fluctuation root cause of the application to be tested according to the performance bottleneck point includes:
step S61, acquiring a bottleneck index data curve of the performance bottleneck point, and performing similarity calculation on the bottleneck index data curve and a preset characteristic model to obtain a third similarity;
step S62, comparing the third similarity with a second preset similarity threshold;
step S63, if the third similarity is greater than or equal to the second preset similarity threshold, determining a performance fluctuation root cause of the application to be tested based on the third similarity.
After the performance bottleneck point in the abnormal node is determined, the system acquires a bottleneck index data curve of the performance bottleneck point, and as can be understood, the bottleneck index data curve of the performance bottleneck point in this embodiment is an index data curve of one index item in the index data curve of the abnormal node, that is, the bottleneck index data curve of the performance bottleneck point includes one of a pressure measurement data fluctuation curve of the abnormal node at a pressure machine end, a resource consumption data curve of a server end, a performance data curve of an application program layer and the like. Further, the system firstly quantizes the bottleneck index data curve of the performance bottleneck point through the reference curve, then carries out similarity calculation on the quantized bottleneck index data curve (or directly obtains the quantized index data curve of the abnormal node) and a preset characteristic model, specifically, obtains a preset characteristic model from a preset fluctuation model base, carries out Hamming distance calculation on the bottleneck index data curve and the preset characteristic model, and the calculated Hamming distance is used as a third similarity between the bottleneck index data curve and the preset characteristic model, wherein the preset characteristic model comprises index data curves corresponding to performance bottleneck points of different index items when performance fluctuation exists, so that similarity calculation is carried out on the bottleneck index data curves and the preset characteristic model, the similarity between the bottleneck index data curve of the current performance bottleneck point and the index data curve when performance fluctuation exists can be determined. Further, the system compares the third similarity with a second preset similarity threshold, specifically, the third similarity may be used as a subtree, the second preset similarity threshold is used as a subtree to perform difference operation, and it is determined whether the difference obtained by the operation is less than 0, if so, it indicates that the bottleneck index data curve of the index item is not matched with the index data curve during performance fluctuation; if the difference obtained by the operation is not less than 0, the third similarity is larger than or equal to a second preset similarity threshold, the bottleneck index data curve of the index item is matched with the index data curve during performance fluctuation, and the root cause causing the application performance fluctuation can be found through the preset characteristic model. The system therefore determines a performance fluctuation root cause for the application to be tested based on the third similarity. The second preset similarity threshold is a numerical value that can be set based on the actual environment and the requirement. In addition, the first preset similarity threshold and the second preset similarity threshold can be dynamically adjusted, and the first preset similarity threshold and the second preset similarity threshold may be the same or different before or after adjustment. It can be understood that, the present application analyzes and models various concurrent pressure curves and golden index curves in the existing performance test scene, characterizes the fluctuation of each type of performance fluctuation curve pair, marks the corresponding performance root, stores the performance root into a fluctuation model pair-root resource library, and establishes a preset fluctuation model library containing the index relationship between each type of fluctuation model pair and the performance bottleneck root causing the fluctuation, for example, the index formula can be in the form as shown below:
Index([Modelta,Modelapp],Apppb,Para[hd,conf…])
further, the step of determining the root cause of the performance fluctuation of the application to be tested based on the third similarity comprises:
step S631, determining a preset feature model corresponding to the third similarity as a target feature model;
step S632, indexing a performance fluctuation root factor corresponding to the target characteristic model in a preset fluctuation model library;
step S633, determining the performance fluctuation root cause corresponding to the target feature model as the performance fluctuation root cause of the application to be tested.
It can be understood that the application lists several common performance golden index curve models, the first type is a concurrency number curve, and the concurrency number curve presents a mode increasing along with the pressure and the time; the normal performance response time or TPS curve is increased or decreased in a step mode along with the increase of the concurrency number, regular small-amplitude fluctuation occurs after the concurrency number is stable, the fluctuation amplitude and the frequency of the fluctuation are within a threshold range, and the fluctuation of the curve keeps a stable extension trend along with the continuation of the concurrency time. Similarly, based on the process of pressure conduction, the performance monitoring index curve in the subsequent node is similar to the performance golden index curve, and is changed in a stepwise manner along with the increase of concurrency, and after the concurrency number is stable, the monitoring index also has stable fluctuation and extension trend within the threshold range. The abnormal fluctuation conditions are relatively more, and may be increased or decreased in a stepwise manner along with the increase of the concurrency number, but after the concurrency number is stable, large-amplitude regular or irregular fluctuation occurs, the fluctuation amplitude or frequency of the fluctuation exceeds the threshold range, and there is also fluctuation with an obvious trend upwards or downwards or irregular fluctuation without an obvious trend.
When the performance fluctuation root cause of the application to be tested is determined based on the third similarity, the system firstly determines the preset feature model corresponding to the third similarity as the target feature model, and because the preset fluctuation model library comprises the index relationship between each type of fluctuation model and the performance bottleneck root cause causing the fluctuation, the system can index the performance fluctuation root cause from the target feature model through the index relationship by inputting the target feature model into the preset fluctuation model library, and determines the performance fluctuation root cause corresponding to the target feature model as the performance fluctuation root cause of the application to be tested, thereby completing the analysis of the performance fluctuation root cause. Furthermore, the system can also output a diagnosis report according to the performance fluctuation root cause to assist software testing, development and operation and maintenance personnel to quickly locate the software performance bottleneck. The root cause classification for performance fluctuation in this embodiment includes: the method comprises the following steps that a pressure end [ script, parameter data ], a link side [ network, peripheral system, parameter configuration ], a server end [ resource consumption, application performance and parameter configuration ], and each type of fluctuation root can be subdivided and explained, wherein in script type fluctuation, in a common Jmeter script example, the use of a bearer shell or the use of various press machine resource consumption plug-ins can be compared with a reference curve or trend of a template script to prompt a user to have risks; the judgment logic of network fluctuation starts from the fluctuation frequency, and whether the fluctuation of network delay/packet loss/jitter is consistent with the fluctuation of a golden curve or not is judged; for the fluctuation of the parameters of the operating system, the related parameters of the network protocol are common, and whether regular fluctuation exists can be judged by combining a thread number change curve; response time fluctuations caused by peripheral system performance problems, the fluctuation curve of which is generally consistent with the fluctuation of the golden curve, wherein the performance problems of the database are similar to those of the peripheral system; the fluctuation caused in the aspect of server resources mostly appears at a resource consumption bottleneck point, and the golden curve fluctuates irrelatively with the step-increasing of the concurrency number under the condition of resource shortage. In general, the resource consumption of an application server, the number of application threads and the number of concurrency numbers have no obvious similarity relation, a golden index curve of the performance fluctuation rating caused by application code design or parameter configuration tends to be an upper limit of the performance, and the fluctuation amplitude does not change along with the increase of the number of concurrency numbers.
According to the embodiment, similarity calculation is carried out according to the bottleneck index data curve of the performance bottleneck point and the preset characteristic model, the performance fluctuation root cause of the application to be tested is determined according to the obtained third similarity, the root cause causing the performance fluctuation can be quickly and accurately positioned according to the similarity between the bottleneck index curve of the performance bottleneck point and the preset characteristic model, and the efficiency of application performance testing is improved.
In addition, the present application also provides a medium, preferably a computer readable storage medium, on which a test program of application performance is stored, and when the test program of application performance is executed by a processor, the steps of the embodiments of the test method of application performance are implemented.
In addition, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the embodiments of the testing method for application performance described above are implemented.
In the embodiments of the application performance testing apparatus, the computer-readable medium, and the computer program product of the present application, all technical features of the embodiments of the application performance testing method are included, and the descriptions and explanations of the embodiments of the application performance testing method are substantially the same as those of the embodiments of the application performance testing method, and are not repeated herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present application or a part contributing to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g., a ROM/RAM, a magnetic disk, and an optical disk), and includes a plurality of instructions for enabling a terminal device (which may be a fixed terminal, such as an internet of things smart device including smart homes, such as a smart air conditioner, a smart lamp, a smart power supply, and a smart router, or a mobile terminal, including a smart phone, a wearable networked AR/VR device, a smart sound box, and a network device such as an auto-driven automobile) to execute the method according to the embodiments of the present application.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.

Claims (10)

1. A method for testing application performance is characterized in that the method for testing application performance comprises the following steps:
acquiring a node monitoring index curve to be tested and applied, and performing similarity calculation on the node monitoring index curve to obtain first similarity information;
determining whether the application to be tested has performance fluctuation or not based on the first similarity information;
if the to-be-tested application has performance fluctuation, determining an abnormal node where the performance fluctuation is located according to the first similarity information;
acquiring an index data curve of the abnormal node, and performing similarity calculation on the index data curve to obtain second similarity information;
determining a performance bottleneck point in the abnormal node according to the second similarity information;
and determining the performance fluctuation root cause of the application to be tested according to the performance bottleneck point.
2. The method for testing application performance of claim 1, wherein the step of performing similarity calculation on the node monitoring index curve to obtain first similarity information comprises:
carrying out quantization processing on the node monitoring index curve to obtain a quantized node monitoring index curve;
determining a concurrent pressure curve and a performance golden index of a source in the node monitoring index curve as a reference curve pair;
and respectively carrying out similarity calculation on the monitoring index curves of other nodes in the node monitoring index curve and the reference curve pair to obtain a plurality of first similarities and form first similarity information.
3. The method for testing application performance of claim 1, wherein the step of determining whether the application to be tested has performance fluctuation based on the first similarity information comprises:
comparing each first similarity in the first similarity information with a first preset similarity threshold respectively;
and if the intermediate similarity smaller than the first preset similarity threshold exists in the first similarities, determining that the to-be-tested application has performance fluctuation.
4. The method for testing application performance of claim 1, wherein the step of determining the abnormal node where the performance fluctuation is located according to the first similarity information comprises:
acquiring each intermediate similarity smaller than the first preset similarity threshold in the first similarity information;
comparing the numerical values of the intermediate similarities to determine a first target similarity with the smallest numerical value among the intermediate similarities;
and determining the node corresponding to the first target similarity as an abnormal node where the performance fluctuation is located.
5. The method for testing application performance of claim 1, wherein the step of determining the performance bottleneck point in the abnormal node according to the second similarity information comprises:
comparing the numerical values of the second similarity in the second similarity information to determine a second target similarity with the smallest numerical value in the second similarity;
and determining the index item corresponding to the second target similarity as a performance bottleneck point in the abnormal node.
6. The method for testing application performance of claim 1, wherein the step of determining the root cause of performance fluctuation of the application to be tested according to the performance bottleneck point comprises:
acquiring a bottleneck index data curve of the performance bottleneck point, and performing similarity calculation on the bottleneck index data curve and a preset characteristic model to obtain a third similarity;
comparing the third similarity with a second preset similarity threshold;
and if the third similarity is greater than or equal to the second preset similarity threshold, determining a performance fluctuation root cause of the application to be tested based on the third similarity.
7. The method for testing application performance of claim 6, wherein the step of determining the root cause of performance fluctuation of the application to be tested based on the third similarity comprises:
determining a preset feature model corresponding to the third similarity as a target feature model;
indexing a performance fluctuation root factor corresponding to the target characteristic model in a preset fluctuation model library;
and determining the performance fluctuation root cause corresponding to the target characteristic model as the performance fluctuation root cause of the application to be tested.
8. An application performance testing device, characterized in that the application performance testing device comprises a memory, a processor and an application performance testing program stored on the memory and executable on the processor, the application performance testing program, when executed by the processor, implementing the steps of the application performance testing method according to any one of claims 1-7.
9. A medium, which is a computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a test program of application performance, which when executed by a processor implements the steps of the method for testing application performance according to any one of claims 1-7.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the method for testing the performance of an application according to any one of claims 1 to 7.
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