CN117176623B - Pressure testing method and system based on flow playback - Google Patents
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Abstract
The invention relates to a pressure test method and a system based on flow playback, which record the actual flow of user behavior and service requests; normalizing the dimension of the real flow pressure test by using an analytic hierarchy process, and further constructing an expert evaluation model according to the dimension normalization; moreover, the expert evaluation model is checked by utilizing big data, and the expert evaluation model is optimized; and inputting the data to be evaluated obtained by executing the pressure test based on the real flow into an expert evaluation system to generate an evaluation result. The invention can avoid the pressure measurement error caused by inconsistent ID generation in the actual flow and actual pressure test, and can update the evaluation standard based on the actual flow pressure test in real time, so that the evaluation standard has more objectivity and comprehensiveness.
Description
Technical Field
The invention relates to the technical field of computer system testing, in particular to a pressure testing method and system based on flow playback.
Background
Stress testing is a method of evaluating the performance and stability of a computer system under load. By simulating a high load situation, the performance of a computer system in handling a large number of tasks or requests may be tested.
The common stress test of the computer system is to simulate the behavior of an actual user and the load brought by a request, so as to test the performance of the computer system under different load conditions, evaluate performance indexes such as response time, throughput, resource utilization rate and the like of the computer system under different load conditions, and test the elasticity and fault tolerance of the computer system in the face of sudden load or large-scale request. When performing stress testing, it is necessary to define test targets, scenarios, and indicators, and monitor the performance and behavior of the system.
The pressure test can simulate and generate a large number of requests by using a mode of recording and playing back the flow of the real business load so as to be close to the real scene and the behavior of the user as much as possible. Real traffic recording may employ capturing network traffic of a computer system using data capture tools that may be run during testing, recording all network data packets into and out of the system, for example using probes as traffic recorders; the real flow record can also be used for guiding the flow of the computer system to the proxy server by configuring the proxy server, and the proxy server can record the flow and generate a corresponding log file for subsequent analysis.
However, after the actual flow is recorded, when the pressure test executes playback of the recorded flow, the situation that the actual flow is inconsistent when data is written due to problems such as an ID generation strategy, a pressure measurement environment, a pressure measurement time and the like often occurs, so that all subsequent modification and update requests cannot hit the actual data through the ID, and the pressure test fails. On the other hand, the prior art does not establish an effective real flow pressure test evaluation system, and has the problems of low quantization degree, low operability, no establishment of an evaluation model and system, incapability of accurately performing development evaluation on a pressure test execution process in real time and the like.
Disclosure of Invention
Object of the invention
In view of the above problems, the present invention aims to provide a pressure testing method and system based on flow playback, which can avoid a pressure testing error caused by inconsistent ID generation during actual flow and actual pressure testing, and can update an evaluation standard based on the actual flow pressure testing in real time, so that the evaluation standard is more objective and comprehensive.
(II) technical scheme
As a first aspect of the present invention, the present invention discloses a pressure testing method based on flow playback, including:
through each application end facing the computer system, mounting probes serving as flow recorders, and recording the actual flow of user behaviors and service requests;
when the pressure test is executed by the playback of the real flow, the actual data which is called in the recorded real flow can be accurately hit through the replacement of the data ID;
normalizing the dimension of the real flow pressure test by using an analytic hierarchy process, and further constructing an expert evaluation model according to the dimension normalization; wherein the expert evaluation model comprises a quantization curve;
utilizing the big data to check the expert evaluation model, and optimizing the expert evaluation model to obtain an expert evaluation system;
and inputting the data to be evaluated obtained by executing the pressure test based on the real flow into an expert evaluation system to generate an evaluation result.
Preferably, the server platform distributes flow recording and playback instructions to the probes of each application end, and the probes are used for completely recording data packets generated by the requests of all calling application ends; and in the process of executing the real flow recording by the server, synchronously executing the function of rewriting the generated data ID.
Preferably, when the real flow playback executes the stress test, a function calling the data ID is generated, and the data ID at the time of flow recording is found through the mapping relation of the flow request ID, so as to replace the calling data ID.
Preferably, the constructing an expert evaluation model specifically includes: assigning values to dimension standard items of the pressure test evaluation of the computer system based on the expert experience model; and obtaining the weight proportion of the dimension standard item of each evaluation by using a matrix algorithm.
Preferably, based on the assignment result of the expert experience model, an expert assignment matrix is established:
；
wherein A represents the weight proportion of a certain dimension standard item in the expert evaluation model,representing assignment of the j-th expert empirical model in the i-th group to the A-dimension criterion item,/-, and>the method comprises the steps of carrying out a first treatment on the surface of the Further, solving for the weight vector corresponding to matrix A>Element in vector->The specific calculation formula for representing the weight ratio of each group of expert experience models to the dimension standard terms is as follows:
。
preferably, the constructed expert evaluation model includes a quantization curve; the quantization curve is a final quantization curve, the abscissa of the final quantization curve is a final dimension standard term, and the ordinate is the weight proportion of a final dimension labeling term.
Preferably, the optimizing the expert evaluation model specifically includes: based on the preset number of sample data, calculating the sample weight proportion of each dimension standard item, and fusing the sample weight proportion with the weight proportion of the dimension standard item of the expert evaluation model by using a harmonic correction algorithm.
Preferably, the algorithm for utilizing harmonic correction fuses the weight proportion of the sample with the weight proportion of the dimension standard item of the expert evaluation model, and specifically includes: the evaluation setting curve expression based on the K-order harmonic function is as follows:
wherein S is an evaluation setting curve, k is an integer of 2 or more,is a coefficient of->For the first amplitude, +>For the first angular frequency +.>A second amplitude, t is time; a stage of determining K; adding local control points, fusing key points and the local control points by using a least square method and solving to obtain +.>、/>、/>、/>Obtaining a final quantization curve expression and a graph;
order the；
In the method, in the process of the invention,is the initial phase angle; />Is the amplitude; obtaining a sample curve expression:
combining the final-stage quantization curve expression and the sample curve expression to obtain a fused curve expression:
in the method, in the process of the invention,for the nth angular frequency, +.>For phase angle, adding local control points in the order of K, fitting key points and the local control points by using a least square method and solving +.>、/>、/>And further obtaining a fused curve expression and a map, and further optimizing the weight proportion of each dimension standard item.
Preferably, based on the quantization curve, a preliminary score of each piece of data to be evaluated is obtained, a final score of each piece of data to be evaluated is obtained according to the preliminary score of each piece of data to be evaluated and the weight proportion of each evaluation standard, and an evaluation result is generated by using the final score of each piece of data to be evaluated.
The invention further provides a pressure testing system based on flow playback, which is characterized by comprising: the system comprises a flow recording module, a flow playback module, an expert evaluation model construction module, an expert evaluation system optimization module and a pressure test evaluation module;
the flow recording module comprises probes and a server platform, and records the actual flow of user behaviors and service requests by mounting probes serving as flow recorders to each application end of the computer system; the server platform distributes flow recording and playback instructions to the probes of each application end, and the probes are used for completely recording data packets generated by the requests of all calling application ends, so that the platform realizes the scheduling and integration of recording real flow;
when the flow playback module performs pressure test on real flow playback, the flow playback module finds out the data ID during flow recording through the mapping relation of the flow request ID, replaces the call data ID and inserts the call data ID into a database;
the expert evaluation model construction module is used for normalizing the dimension of the real flow pressure test by using an analytic hierarchy process, and further constructing an expert evaluation model according to the dimension normalization; wherein the expert evaluation model comprises a quantization curve;
the expert evaluation system optimization module is used for verifying the expert evaluation model by using big data and optimizing the expert evaluation model to obtain an expert evaluation system;
and the pressure test evaluation module inputs data to be evaluated obtained by executing the pressure test based on the real flow into an expert evaluation system to generate an evaluation result.
(III) beneficial effects
The pressure testing method based on flow playback disclosed by the invention has the following beneficial effects: the recorded real flow is ensured to be consistent with the actual flow in the process of performing playback recording in the pressure test, and various requests can hit actual data through the ID; the expert evaluation model is constructed, the expert evaluation system is obtained by optimizing the model, the evaluation standard of the computer system pressure test is more objective and comprehensive, a large number of samples are checked by regression analysis through the expert evaluation system by using big data to obtain an actual evaluation curve, the quantized curve is fused with the actual evaluation curve, and the weight proportion coefficient of each evaluation standard item in the expert evaluation model is optimized, so that the weight proportion coefficient of each evaluation standard item is more objective.
Drawings
The embodiments described below with reference to the drawings are exemplary and intended to illustrate and describe the invention and should not be construed as limiting the scope of the invention.
FIG. 1 is a flow diagram of a flow playback-based pressure testing method of the present disclosure;
fig. 2 is a flowchart of step S100 provided in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention become more apparent, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention.
It should be noted that: in the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, embodiments of the invention, and the embodiments and features of the embodiments in this application may be combined with each other without conflict. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A first embodiment of a pressure testing method based on flow playback according to the present disclosure is described in detail below with reference to fig. 1, and mainly includes the following steps as shown in fig. 1.
First, step S1: and (3) by mounting probes serving as flow recorders on each application end facing the computer system, recording the user behaviors and the actual flow of the service request.
Specifically, for the real flow brought by user behavior and service request facing the computer system, the flow recording of the invention is realized by mounting probes serving as flow recorders to each application end of the computer system and automatically registering the probes to a service end platform of the flow recording to form a loop for recording the real flow. And the server platform distributes flow recording and playback instructions to the probes of all application ends, and the probes are used for completely recording data packets generated by the requests of all calling application ends, so that the platform realizes the scheduling and integration of recording real flow.
And synchronously executing the function of rewriting the generated data ID in the process of recording the real flow by the server, so that the mapping relation between the flow request ID and the data ID is recorded and compiled into a jar packet while the data ID is generated when the real flow is recorded.
Further, step S2: when the pressure test is executed through real flow playback, a function generating calling data ID loads the jar packet, and the original calling data ID is replaced in batches under the condition of not modifying source codes through a java agent byte code replacement reloading technology; in this way, the data ID during flow recording is found through the mapping relation of the flow request ID, and the call data ID is replaced and inserted into the database. Therefore, in the actual flow pressure test process, the actual data which is called in the recorded actual flow can be accurately hit through the replacement of the data ID, so that all requests in the pressure test process can be correctly responded.
Step S3: normalizing the dimension of the real flow pressure test by using an analytic hierarchy process, and further constructing an expert evaluation model according to the dimension normalization; wherein the expert evaluation model comprises a quantization curve.
Specifically, the analytic hierarchy process follows the principles of comprehensiveness, feasibility, comparability, guidance and emphasis, and sets a dimension standard for evaluating the pressure test index of the computer system based on the real flow. According to the characteristics of the problems and the total target to be achieved, the analytic hierarchy process decomposes the problems into different component factors, and aggregates and combines the factors according to different hierarchies according to the mutual correlation influence among the factors and the membership to form a multi-hierarchy analytic structure model; the basic steps of the analytic hierarchy process are as follows: establishing a hierarchical structure model, wherein the model comprises a target layer, a criterion layer and a scheme layer; constructing a comparison matrix to calculate single-order weight vectors and carrying out consistency check; and calculating the total sorting weight vector and performing consistency check.
The expert evaluation model is constructed, and specifically comprises the following steps: assigning values to dimension standard items of the pressure test evaluation of the computer system based on the expert experience model; and obtaining the weight proportion of the dimension standard item of each evaluation by using a matrix algorithm. Specifically, expert experience model assignment is carried out on standard items of the same dimension of pressure test evaluation; further, the weight ratio of the standard term of the dimension is calculated by using a matrix algorithm. The specific process is as follows: firstly, based on the assignment result of the expert experience model, an expert assignment matrix is established:
；
wherein A represents the weight proportion of a certain dimension standard item in the expert evaluation model,representing assignment of the j-th expert empirical model in the i-th group to the A-dimension criterion item,/-, and>the method comprises the steps of carrying out a first treatment on the surface of the Further, solving for the weight vector corresponding to matrix A>Element in vector->The specific calculation formula for representing the weight ratio of each group of expert experience models to the dimension standard terms is as follows:
。
and, the expert evaluation model constructed includes a quantization curve; the quantization curve is a final quantization curve, the abscissa of the final quantization curve is a final dimension standard term, and the ordinate is the weight proportion of a final dimension labeling term. The weight proportion of each dimension criterion term can be calculated by using a recursive algorithm.
Step S4: and (5) checking the expert evaluation model by using the big data, and optimizing the expert evaluation model to obtain an expert evaluation system.
In step S4, the expert evaluation model is verified with big data, the actual data of the preset number of pressure test samples (i.e. a large number of sample data) are calculated by algorithms such as regression analysis, and the weight ratio of each dimension standard item of each evaluation is optimized, wherein the expert evaluation model is verified with big data to include a sample curve. Further, based on the sample data of the preset quantity, calculating the sample weight proportion of the dimension standard items of each evaluation by using a regression analysis method, specifically, firstly, setting a regression model according to the existing data and the relation between the independent variable and the dependent variable; secondly, a reasonable regression coefficient is obtained; and finally, performing correlation test to determine a correlation coefficient.
Specifically, a large number of samples are preprocessed, the large number of samples are subjected to grouping processing, and the number of samples in each group is the same; secondly, establishing a regression model by utilizing the score of a certain dimension standard item in a plurality of groups of samples:
；
wherein B represents the weight proportion of a certain dimension standard item of the sample,sample assignment indicating that the jth sample in the ith group is in the B dimension standard term, ++>. Further, solving the weight vector corresponding to the matrix BElement in vector->The concrete calculation formula for representing the sample weight of each group of sample to dimension standard items and the sample weight proportion of each group of sample to dimension standard items is as follows:
；
further, calculating the sample weight proportion of the dimension standard terms of different sample groups according to the weight vector W, and grouping samples with similar sample weight proportions, wherein the number of the sample groups in each group is the same, and the number of samples in each sample group is the same, for example, 50 samples in sample group 1, 50 samples in sample group 2, the samples in sample group 2 are different from the samples in sample group 1, and calculating the similarity degree of similar sample weights to obtain a similarity matrix R:
；
wherein,a sample weight ratio representing an nth sample in the mth group of samples; further, the similarity degree corresponding to R is solved, and the calculation formula is as follows:
；
as can be seen from the formula of the present invention,the closer to 1, the more similar the weight ratio of the sample group, so R is a central symmetric matrix and the center is 1; further, by comparing the sample groups, a similarity coefficient between the sample groups can be obtained, and in order to compare the deviation degree of the overall situation, the calculation can be performed by the following formula:
；
wherein,representing the sum of elements of each row in the similarity matrix R, namely the degree of deviation between the sample weight proportion of the dimension standard item of the i-th group sample group and the weight proportion of the dimension standard item of the expert evaluation system, ">The smaller the value is, the larger the deviation degree of the weight proportion of the dimension standard item of the expert evaluation system is, namely, the greater divergence or ingress exists between the sample weight proportion of the i-th sample and the results of other sample groups;
wherein,i=1, 2, …, n, X respectively list the degree of deviation between the weight ratios of the sample groups of each group, +.>The larger the gap value is, the larger the sample weight proportion of the sample group deviates from the overall level, when ∈>When the value of (2) is greater than a certain limit, it should be discarded. Through the steps, the weight proportion of each dimension standard item in the expert evaluation model is checked by utilizing big data, so that the expert evaluation model is optimized, and an optimized expert evaluation system is obtained.
In step S4, the expert evaluation model may be optimized using a harmonic correction algorithm. Specifically, based on the preset number of sample data, calculating the sample weight proportion of each dimension standard item, and fusing the sample weight proportion with the weight proportion of the dimension standard item of the expert evaluation model by using a harmonic correction algorithm, namely fusing a sample curve and a final-stage quantization curve. For example, the evaluation setting curve expression based on the K-order harmonic function is:
wherein S is an evaluation setting curve, k is an integer of 2 or more,is a coefficient of->For the first amplitude, +>For the first angular frequency +.>A second amplitude, t is time; a stage of determining K; adding local control points, fusing key points and the local control points by using a least square method and solving to obtain +.>、/>、/>、/>Obtaining a final quantization curve expression and a graph;
order the；
In the method, in the process of the invention,is the initial phase angle; />Is the amplitude; obtaining a sample curve expression:
combining the final-stage quantization curve expression and the sample curve expression to obtain a fused curve expression:
in the method, in the process of the invention,for the nth angular frequency, +.>For phase angle, adding local control points in the order of K, fitting key points and the local control points by using a least square method and solving +.>、/>、/>And further obtaining a fused curve expression and a map, and further optimizing the weight proportion of each dimension standard item.
S5, inputting data to be evaluated obtained by executing the pressure test based on the real flow into an expert evaluation system, and generating an evaluation result.
In step S5, data to be evaluated obtained by performing a pressure test on the real flow is input to an expert evaluation system, and is calculated by an evaluation algorithm to generate an evaluation result. The evaluation algorithm calculation is based on a quantization curve, the preliminary score of each piece of data to be evaluated is obtained, the final score of each piece of data to be evaluated is obtained according to the preliminary score of each piece of data to be evaluated and the weight proportion of each evaluation standard, and the final score of each piece of data to be evaluated is utilized to generate an evaluation result. Wherein, the calculation sequence of the evaluation algorithm is calculated from the last stage evaluation standard to the first stage evaluation standard.
As shown in fig. 2, a second embodiment of the present invention provides a pressure testing system based on flow playback, mainly including: the system comprises a flow recording module 100, a flow playback module 200, an expert evaluation model construction module 300, an expert evaluation system optimization module 400 and a pressure test evaluation module 500.
The flow recording module 100 comprises a probe and a server platform, and records the actual flow of user behaviors and service requests by mounting the probe as a flow recorder to each application end of the computer system; the server platform distributes flow recording and playback instructions to the probes of all application ends, and the probes are used for completely recording data packets generated by the requests of all calling application ends, so that the platform realizes the scheduling and integration of recording real flow. And in the process of recording the real traffic, the server synchronously executes the function of rewriting the generated data ID, so that the mapping relation between the traffic request ID and the data ID is recorded and compiled into the jar packet while the data ID is generated when the real traffic is recorded.
When the flow playback module 200 performs pressure test in real flow playback, a function for generating call data ID loads the jar packet, and the original call data ID is replaced in batches under the condition of not modifying source codes by using a java agent byte code replacement reloading technology; in this way, the data ID during flow recording is found through the mapping relation of the flow request ID, and the call data ID is replaced and inserted into the database.
The expert evaluation model construction module 300 normalizes the dimension of executing the real flow pressure test by using an analytic hierarchy process, and further constructs an expert evaluation model according to the dimension normalization; wherein the expert evaluation model comprises a quantization curve. The expert evaluation model construction module 300 specifically includes: assigning values to dimension standard items of the pressure test evaluation of the computer system based on the expert experience model; and obtaining the weight proportion of the dimension standard item of each evaluation by using a matrix algorithm.
The expert evaluation system optimization module 400 uses the big data to check the expert evaluation model and optimizes the expert evaluation model to obtain the expert evaluation system. The module utilizes a big data test expert evaluation model, calculates the sample weight proportion of the dimension standard items of each evaluation by using the actual data of a preset number of pressure test samples through algorithms such as a regression analysis method and the like, and optimizes the weight proportion of each dimension standard item in the expert evaluation model.
The pressure test evaluation module 500 inputs data to be evaluated obtained by performing a pressure test based on the real flow rate to an expert evaluation system, and generates an evaluation result.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. A pressure testing method based on flow playback, comprising:
by mounting probes serving as flow recorders on each application end facing a computer system, recording the actual flow of user behaviors and service requests, synchronously executing a function of rewriting and generating a data ID (identity) in the process of executing the actual flow recording on a server, recording the mapping relation between the flow request ID and the data ID while generating the data ID when recording the actual flow, and compiling the data ID into a jar packet;
when the pressure test is executed by the playback of the real flow, generating a function for calling the data ID, finding the data ID when the flow is recorded according to the mapping relation of the flow request ID, and accurately hitting the actual data called in the recorded real flow by replacing the data ID;
normalizing the dimension of the real flow pressure test by using an analytic hierarchy process, and further constructing an expert evaluation model according to the dimension normalization; wherein the expert evaluation model comprises a quantization curve;
utilizing the big data to check the expert evaluation model, and optimizing the expert evaluation model to obtain an expert evaluation system;
inputting data to be evaluated obtained by executing a pressure test based on the real flow into an expert evaluation system to generate an evaluation result;
the construction of the expert evaluation model specifically comprises the following steps: performing expert experience model assignment on standard items of the same dimension of the pressure test evaluation; furthermore, a matrix algorithm is utilized to calculate the weight proportion of the standard item of the dimension, and based on the assignment result of the expert experience model, the following expert assignment matrix is established:
；
wherein, the matrix A represents the weight proportion of a certain dimension standard item in the expert evaluation model,assignment of the j-th expert empirical model in the i-th group to the weight ratio of the dimension criterion in matrix A,/the matrix A>The method comprises the steps of carrying out a first treatment on the surface of the Solving the weight vector corresponding to matrix A>Element in vector->The specific calculation formula for representing the weight ratio of each group of expert experience models to the dimension standard terms is as follows:
；
the constructed expert evaluation model comprises a quantization curve, wherein the quantization curve is a final-stage quantization curve, the abscissa of the final-stage quantization curve is a final-stage dimension standard item, and the ordinate of the final-stage quantization curve is the weight proportion of the final-stage dimension labeling item.
2. The flow playback-based pressure testing method of claim 1, wherein: and the server platform distributes flow recording and playback instructions to the probes of all application ends, and the probes are used for completely recording the data packets generated by the requests of all calling application ends.
3. The flow playback-based pressure testing method of claim 1, wherein: the optimizing the expert evaluation model specifically comprises the following steps: based on the preset number of sample data, calculating the sample weight proportion of each dimension standard item, and fusing the sample weight proportion with the weight proportion of the dimension standard item of the expert evaluation model by using a harmonic correction algorithm.
4. A method of pressure testing based on flow playback as claimed in claim 3, wherein: the algorithm for utilizing harmonic correction fuses the weight proportion of the sample with the weight proportion of the dimension standard item of the expert evaluation model, and specifically comprises the following steps: the evaluation setting curve expression based on the K-order harmonic function is as follows:
wherein S is an evaluation setting curve, k is an integer of 2 or more, c 0 Is a coefficient, a n At the first amplitude of the light is of a first amplitude,at a first angular frequency b n A second amplitude, t is time; a stage of determining K; adding local control points, fusing key points and the local control points by using a least square method and solving to obtain c 0 、a n 、/>、b n Obtaining a final quantization curve expression and a graph;
order the；
In the method, in the process of the invention,is the initial phase angle; />Is the amplitude; obtaining a sample curve expression:
combining the final-stage quantization curve expression and the sample curve expression to obtain a fused curve expression:
in the method, in the process of the invention,for the nth angular frequency, +.>Adding local control points for determining the order of K for the phase angle, fitting key points and the local control points by using a least square method and solving c n 、/>、/>And further obtaining a fused curve expression and a map, and further optimizing the weight proportion of each dimension standard item.
5. The flow playback based pressure testing method of claim 4, wherein: based on the quantization curve, obtaining a preliminary score of each piece of data to be evaluated, obtaining a final score of each piece of data to be evaluated according to the preliminary score of each piece of data to be evaluated and the weight proportion of each evaluation standard, and generating an evaluation result by utilizing the final score of each piece of data to be evaluated.
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