CN106803799B - Performance test method and device - Google Patents
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Abstract
The application provides a performance test method and a performance test device, wherein the method comprises the following steps: acquiring service platform operation data, wherein the service platform operation data comprises: service operation data and service environment data; acquiring resource consumption simulation parameters from the service environment data, and acquiring interface response delay parameters and service test data of platform services from the service operation data; and taking the resource consumption simulation parameter, the interface response delay parameter and the service test data as the input of a platform test system for testing the performance of the service platform, and carrying out performance test on the service platform. The method and the device improve the accuracy of the performance test result of the service platform and improve the reliability of the service platform.
Description
Technical Field
The present application relates to computer technologies, and in particular, to a performance testing method and apparatus.
Background
With the development of network technology, people's life is increasingly convenient, for example, users can perform various business operations such as payment (for example, payment of living expenses such as water, electricity, gas and the like), transfer accounts and repayment on the network. Correspondingly, the service operation may be executed through a service platform, and the service platform may receive a service request (such as the payment request) of the user and communicate with a service mechanism (such as a gas charging enterprise) corresponding to the requested service to complete the service of the user. Currently, more and more services are processed by a service platform, the number of involved interaction mechanisms is gradually increased, and the service processing performance (such as the number of requests that can be borne, the response time to the service, etc.) of the service platform is of great importance.
The performance test of the service platform is usually carried out in the development stage, and the performance of the service platform needs to be synchronously evaluated every time a new requirement is added. The current platform performance test data is generally constructed independently under the line, so that the actual environment of the service platform running on the line is difficult to effectively simulate, and the accuracy of the test result is reduced. Meanwhile, the inconsistency of the performance test results among the versions of the service platform also causes difficulty in performing transverse comparison by using the performance test results among the versions of the service platform, and is not beneficial to the optimization of the performance and the improvement of the reliability of the service platform.
Disclosure of Invention
In view of this, the present application provides a performance testing method and apparatus, so as to improve accuracy of a performance testing result of a service platform and improve reliability of the service platform itself.
Specifically, the method is realized through the following technical scheme:
in a first aspect, a performance testing method is provided, including:
acquiring service platform operation data, wherein the service platform operation data comprises: service operation data and service environment data;
acquiring resource consumption simulation parameters from the service environment data, and acquiring interface response delay parameters and service test data of platform services from the service operation data;
and taking the resource consumption simulation parameter, the interface response delay parameter and the service test data as the input of a platform test system for testing the performance of the service platform, and carrying out performance test on the service platform.
In a second aspect, a performance testing apparatus is provided, comprising:
the parameter acquisition module is used for acquiring service platform operation data, and the service platform operation data comprises: service operation data and service environment data; acquiring resource consumption simulation parameters from the service environment data, and acquiring interface response delay parameters and service test data of platform services from the service operation data;
and the performance testing module is used for taking the resource consumption simulation parameter, the interface response delay parameter and the service testing data as the input of a platform testing system for testing the performance of the service platform and carrying out performance testing on the service platform.
According to the performance testing method and device, parameters for constructing the testing system are obtained according to the service platform operation data, so that the constructed testing system is closer to an actual service platform, the accuracy of a performance testing result of the service platform is improved when the testing system is used for performing performance testing, and the reliability of the service platform is improved.
Drawings
FIG. 1 is a schematic diagram of a service platform shown in an exemplary embodiment of the present application;
FIG. 2 is a schematic diagram of a performance testing principle shown in an exemplary embodiment of the present application;
FIG. 3 is a flow chart of a performance testing method shown in an exemplary embodiment of the present application;
FIG. 4 is a block diagram of a performance testing apparatus according to an exemplary embodiment of the present application;
fig. 5 is a block diagram of another performance testing apparatus according to an exemplary embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The present disclosure takes a platform for processing life payment service as an example to illustrate how to perform more accurate and reliable tests on the performance of the platform. As the example of fig. 1 illustrates a service platform 11, the service platform 11 may interact with a plurality of external institutions, such as institution 12 (e.g., bank), institution 13 (e.g., shanghai water fee enterprise), institution 14 (e.g., beijing gas fee enterprise), etc., for processing services corresponding to the institution, for example, interacting with institution 12 to process transfer services, interacting with institution 13 to process water fee payment services, and interacting with institution 14 to process gas fee payment services.
For each external organization, the service platform may communicate with the organization through at least one service interface, such as interface 15a, interface 15b, interface 15c, etc. illustrated in fig. 1; for example, the service platform 11 may call the interface 15b to start communication with the institution 13 when receiving a service request for water payment. The performance test of the service platform can be used for testing how large the service volume can be borne by the platform, or the response time of the platform to the service request can be used for completing the requested service; the performance may also be expressed by the performance index of the service interface, such as the concurrency number of the service interface, the response time of the interface, and the like.
Fig. 2 illustrates the principle of the performance testing method of the present disclosure, please refer to fig. 2, assuming that a new version of the service platform is developed, and the testing on the new version of the platform is performed on-line, while the service process of fig. 1 is performed on-line, which may be referred to as "off-line system" and "on-line system", respectively. The off-line system comprises a platform test system, wherein the platform test system is used for testing a new-version service platform, and then, parameters for testing need to be input into the platform test system for building a test environment, so that the new-version service platform can be tested in the built simulation test environment.
The off-line system in fig. 2 further includes a "big data modeling system" for establishing a service performance model for evaluating the service interface performance of the service platform, and using the service platform operation data in the modeling process, but in this embodiment, the resource consumption simulation parameter, the interface response delay parameter, and the service test data may be obtained from the service platform operation data, and these data are input to the platform test system to build a test environment for the platform test system. For example, the resource consumption simulation parameter may simulate the consumption of resources such as CPU, memory, etc. of the service platform, the interface response delay parameter may be used to simulate the response delay time of an external entity, and the service test data is used to simulate the service data received by the service platform, such as requesting the entity 12 to execute the transfer service.
The principle of the performance testing method of the present disclosure illustrated in fig. 2 is that a testing environment of an offline system is constructed according to service platform operation data obtained by the online system, and this way can better simulate the online environment and get closer to the online service, so that a more accurate testing result can be obtained. How to obtain the input parameters required by the platform test system according to the service platform operation data is described in detail as follows:
data acquisition and cleaning:
the data acquisition module 24 can be used for acquiring the service platform operation data of the online system, including the acquisition of online logs and service performance data. For example, some log data of calling the external mechanism interface by the service platform may be obtained, such as a certain type of service executed to a certain external mechanism at a certain time, a response time of the external mechanism to the service, and a CPU consumption of the platform at that time. Other types of parameters are also included in the log data and are not described in detail. The data of platform CPU consumption or platform memory consumption belonging to platform resource consumption can be collected by a snapshot tool. For example, platform resource consumption data corresponding to a plurality of sampling time points, respectively, may be collected by a snapshot tool. The data of platform CPU consumption or platform memory consumption may be referred to as resource consumption simulation parameters, and belongs to service environment data of a system level during the operation of a service platform. The log data that a certain type of service is executed to a certain external mechanism at a certain time may be referred to as service operation data of an application layer during operation of the service platform, and the service platform operation data includes the service environment data and the service operation data.
After the acquired online data is obtained, the data can be subjected to data cleaning according to predefined metadata to obtain a formatted data set used for subsequent modeling. That is, the metadata is data describing data attributes, and may be predefined, and then the acquired online data is cleaned according to the metadata, and finally a data set satisfying the metadata format is obtained. The data cleansing here is to select target data to be used from collected data, and not all data is used for modeling. Table 1 below illustrates the format of the partial metadata:
table 1 metadata format
| Name of field | Description of field | Data type |
| biz_time | Time of occurrence of service | String |
| biz_type | Type of service | String |
| sub_biz_type | Business subtype | String |
| charge_inst | Account-out mechanism | String |
| chargeoff_inst | Mechanism for selling account | String |
| biz_cpu | CPU occupancy of service occurrence time slice | Integer |
| biz_mem | Memory occupancy rate of service generation time slice | Integer |
| biz_load | Load of service occurrence time slice | Float |
| biz_tps | TPS for service occurrence time slice | Integer |
| biz_concurrency | Interface concurrency number of service occurrence time slice | Integer |
| biz_rspTime | Interface response time of service occurrence time slice | Integer |
| biz_errorTimes | Interface error number of service occurrence time slice | Integer |
Through the data cleaning in this step, the finally obtained formatted data is the data set corresponding to the metadata in table 1, and the service model is composed of part of necessary metadata in table 1, such as concurrency number, TPS, interface response time, and the like.
And performing classification calculation, denoising and other processing on the processed data sets to obtain training data sets of all service interfaces, wherein the data sets corresponding to different service interfaces are stored in different data files respectively. For example, after classification and denoising, each service interface generates a corresponding data file list in a format of "BizType _ SubBizType _ charge _ off inst _ date. The metadata in a single data file is shown in table 2 below:
table 2 data file list
| The format is as follows: BizType _ SubBizType _ Charge Inst _ Charge OffInst _ date |
| HK_HK_CMB_CMB_20150728.csv |
| JF_WATER_BJWATER_BJCEB_20150728.csv |
| JF_ELECTRIC_WUHANELECTRIC_BJCEB_20150728.csv |
| HK_HK_ABC_ABC_20150728.csv |
| HK_HK_ICBC_ICBC_20150728.csv |
In table 2, each data file is data corresponding to each processed service model, and is used as a training data set of the decision tree algorithm, and metadata corresponding to each data set is shown in table 3:
TABLE 3 metadata for decision Tree Algorithm
| Name of field | Description of field | Data type |
| week | Service occurrence time: sunday table | Integer |
| hour | Service occurrence time: hour(s) | Integer |
| slice | Time slice of minute scale | Integer |
| calledTimes | Number of interface calls | Integer |
| rspPerCalled | Mean value of interface response time in time slice | Float |
| rspTime | Interface response time after noise removal in time slice | Float |
| volatility | Example of the ripple coefficient algorithm: rspTime/3/rspPerCalled | Float |
For example, as shown in table 4 below, each row of data in table 2 represents statistical information of services in a certain time slice, where the time slice may be 15 minutes, or may be 1 minute, or even a second, depending on the specific traffic volume.
TABLE 4 data files
The performance modeling of the service interface can be carried out according to the performance data training set to obtain a service performance model of the service interface. For example, the business performance model of this step may be established by a big data modeling system. A service performance model W of the week dimension can be established through a decision tree algorithm; or, according to the peri-dimensional model W (W1/W2/. the.) and outputting the monthly reference peri-dimensional model W' by using a cosine similarity algorithm, so that the performance evaluation is more accurate.
In the embodiment of the present disclosure, some parameters used for inputting the platform test system of the service platform to construct the test environment may be obtained from the performance data used in the modeling process of the performance model, which are respectively described as follows:
resource consumption simulation parameters: the method can be obtained according to monitoring data of a monitoring system (operation and maintenance platform for monitoring service operation), namely service environment data, and is mainly used for realizing playback of resource consumption of an online system and load simulation of an operating system level of a service platform.
For example, in the business model described above, platform resource consumption may include: the CPU consumption of the processor when the service occurs, the memory consumption when the service occurs, and the like, and these platform resource consumption data may include data sets respectively corresponding to a plurality of sampling time points, such as the CPU occupancy of the service occurrence time slice, the memory occupancy of the service occurrence time slice, and the like in table 1. The resource consumption of the test system can be simulated in the platform test system according to the data, and the simulation is used as the resource consumption simulation of the service platform at the system level.
Interface response delay parameter: the simulation method can be obtained according to the service operation data, and is mainly used for realizing the simulation of response delay to the external mechanism, for example, when the service platform receives the service request and carries out interactive processing with the external mechanism, the external mechanism usually has certain processing response time. For example, the interface response delay parameter may be the interface response time after removing noise in the rspTime-slice in Table 4. Interface delay of an external system can be simulated in the platform test system according to the data.
Service test data: the data can be obtained according to the service operation data, which mainly refers to what kind of service is received by the service platform, and for example, the data may include service occurrence time, service type, sub-service type, charge-off mechanism, and the like.
For example, in the process of establishing the service performance model, many service models are obtained according to the metadata, and the service models are classified, so that the service models passing through the same service interface can be classified and collected according to the parameters of the organization, the service and the like. The service test data structure can select a service model with lower performance according to the performance data so as to optimize the performance of the service platform. For example, each service model has performance indexes of service interfaces such as concurrency number and interface response time, and the service models can be sorted and screened according to the performance indexes, and a service model with a sorting digit in a preset range is selected, for example, a service model of Top N is selected, such as a service of Top3 consumed by a CPU or a service of Top3 comprehensively sorted by CPU + interface response time; and constructing service test data according to the service models to form a service environment for platform test, wherein the service test data can comprise: service type, sub-service type, charge-off mechanism and other parameters. The construction of the traffic test data may be done using orthogonal tables, for example.
The obtained resource consumption simulation parameters, interface response delay parameters and service test data can be input into a platform test system for testing the service platform so as to perform performance test on the service platform. As can be seen from the above process, the performance testing method provided by the present disclosure is as the process of fig. 3:
in step 301, service platform operation data is obtained, where the service platform operation data includes: service operation data and service environment data; and acquiring resource consumption simulation parameters from the service environment data, and acquiring interface response delay parameters and service test data of the platform service from the service operation data.
In step 302, the resource consumption simulation parameter, the interface response delay parameter, and the service test data are used as inputs of a platform test system for testing the performance of the service platform, so as to perform a performance test on the service platform.
When the performance test is performed on the service platform, a performance baseline curve can be output, and the performance baseline data is used for measuring the change of the performance index of the service interface during each version. For example, a service platform is usually continuously developed and evolved according to requirements, and the test mode disclosed by the present disclosure may use online service data as input data of the test platform to perform daily construction of each version and iteration, and continuously monitor performance change trends of each version and each stage.
According to the performance testing method, modeling is carried out according to the big data of the online system, and the input parameters for testing the system are obtained, so that the online operating environment can be well simulated, and the testing result can be more accurately obtained; moreover, the service test data is also obtained according to the on-line data, so that the service model coverage rate in the current interface test is improved, and the service model coverage rate is closer to the on-line service; the method can construct and update the test environment continuously according to the online data to obtain a dynamic performance change trend.
Fig. 4 provides a performance testing apparatus for implementing the performance testing method shown in fig. 3, and as shown in fig. 4, the apparatus may include: a parameter acquisition module 41 and a performance testing module 42.
A parameter obtaining module 41, configured to obtain service platform operation data, where the service platform operation data includes: service operation data and service environment data; acquiring resource consumption simulation parameters from the service environment data, and acquiring interface response delay parameters and service test data of platform services from the service operation data;
and the performance testing module 42 is configured to use the resource consumption simulation parameter, the interface response delay parameter, and the service testing data as input of a platform testing system for testing performance of the service platform, so as to perform performance testing on the service platform.
Further, referring to fig. 5, the parameter obtaining module 41 may include: a classification sub-module 411, an ordering sub-module 412, and a construction sub-module 413.
The classification submodule 411 is configured to obtain multiple service models corresponding to the same service interface from service platform operation data;
a sorting submodule 412, configured to sort the plurality of service models according to performance data in the service platform operation data;
and the constructing submodule 413 is used for selecting the service model with the sequencing digit in the preset range and constructing the service test data.
Further, the performance testing module 42 is further configured to output performance baseline data of a service interface of the service platform, where the performance baseline data is used to measure changes of performance indexes of the service interface during each version.
For example, the resource consumption simulation parameters include: and a processor of the service platform consumes resources and memory consumes resources. The resource consumption simulation parameters include, but are not limited to, the parameters described above.
The performance testing device of the embodiment can better simulate the on-line operating environment and more accurately obtain the testing result by modeling according to the big data of the on-line system and acquiring the input parameters for testing the system.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (6)
1. A method of performance testing, comprising:
acquiring service platform operation data, wherein the service platform operation data comprises: service operation data and service environment data;
acquiring resource consumption simulation parameters from the service environment data, and acquiring interface response delay parameters and service test data of platform services from the service operation data;
taking the resource consumption simulation parameter, the interface response delay parameter and the service test data as the input of a platform test system for testing the performance of the service platform, and carrying out performance test on the service platform; the platform test system is used for testing a tested service platform, and the input is used for building a test environment of the platform test system so that the tested service platform can be tested in a built simulation test environment;
the performance test of the service platform comprises the following steps: outputting performance baseline data of a service interface of a service platform, wherein the performance baseline data is used for measuring the change of performance indexes of the service interface during each version;
the acquiring of the service test data includes:
acquiring a plurality of service models corresponding to the same service interface from the service platform operation data;
sequencing the plurality of service models according to performance data in the service platform operation data;
and selecting a service model with the sequencing digit in a preset range, and constructing the service test data.
2. The method of claim 1, wherein obtaining resource consumption simulation parameters from the business environment data comprises:
and the platform resource consumption data which are acquired by the snapshot tool and respectively correspond to the plurality of sampling time points are used as the resource consumption simulation parameters.
3. The method of claim 1, wherein the resource consumption simulation parameters comprise: and a processor of the service platform consumes resources and memory consumes resources.
4. A performance testing device, comprising:
the parameter acquisition module is used for acquiring service platform operation data, and the service platform operation data comprises: service operation data and service environment data; acquiring resource consumption simulation parameters from the service environment data, and acquiring interface response delay parameters and service test data of platform services from the service operation data;
the performance testing module is used for taking the resource consumption simulation parameter, the interface response delay parameter and the service testing data as the input of a platform testing system for testing the performance of the service platform and carrying out performance testing on the service platform; the platform test system is used for testing a tested service platform, and the input is used for building a test environment of the platform test system so that the tested service platform can be tested in a built simulation test environment;
the performance test module is further configured to output performance baseline data of a service interface of a service platform, where the performance baseline data is used to measure changes of performance indexes of the service interface during each version;
the parameter acquisition module comprises:
the classification submodule is used for acquiring a plurality of service models corresponding to the same service interface from the service platform operation data;
the sequencing submodule is used for sequencing the plurality of service models according to performance data in the service platform operation data;
and the construction submodule is used for selecting the service model with the sequencing digit in the preset range and constructing the service test data.
5. The apparatus of claim 4,
the parameter acquisition module is used for acquiring platform resource consumption data which respectively correspond to a plurality of sampling time points and are acquired by a snapshot tool when acquiring the resource consumption simulation parameters from the service environment data, and taking the platform resource consumption data as the resource consumption simulation parameters.
6. The apparatus of claim 4, wherein the resource consumption simulation parameters comprise: and a processor of the service platform consumes resources and memory consumes resources.
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| CN109299006A (en) * | 2018-09-11 | 2019-02-01 | 平安科技（深圳）有限公司 | The method and apparatus for determining system stability |
| CN109383567B (en) * | 2018-10-11 | 2020-12-11 | 北京市地铁运营有限公司 | Detection method and device for subway comprehensive monitoring system |
| CN110808877A (en) * | 2019-10-30 | 2020-02-18 | 深圳前海环融联易信息科技服务有限公司 | Statistical analysis method and device based on interface response duration and computer equipment |
| CN111884882A (en) * | 2020-07-29 | 2020-11-03 | 北京千丁互联科技有限公司 | Monitoring coverage rate detection method and device |
| CN112422315B (en) * | 2020-10-14 | 2022-09-02 | 深圳壹账通智能科技有限公司 | Cluster performance test method, device, equipment and storage medium |
| CN112286798A (en) * | 2020-10-19 | 2021-01-29 | 长春创世麒麟科技有限公司 | Full link pressure measurement system and method capable of simulating real user scene |
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