Performance test method and device based on cloud native architecture, equipment and medium
By using a cloud-native architecture-based performance testing method, a standardized virtual testing environment is automatically built and data is collected in real time. This solves the problems of cumbersome testing processes and scattered data in existing technologies, and achieves efficient and reliable performance testing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- PING AN TECH (SHENZHEN) CO LTD
- Filing Date
- 2026-01-06
- Publication Date
- 2026-04-17
AI Technical Summary
Existing business system performance testing processes are cumbersome and inefficient, test environment preparation is time-consuming and labor-intensive, test results are inconsistent, data is scattered and difficult to analyze, testers are highly skilled, and the process relies on the expertise of senior engineers.
The cloud-native architecture-based performance testing method automatically builds a standardized virtual test environment by receiving integrated test requests, collects performance indicator data in real time, and stores the data in a structured performance baseline database, thus achieving end-to-end automatic closed loop.
It simplifies test environment preparation, reduces error rates, improves test efficiency, ensures the reliability and reproducibility of test results, lowers the requirements for testers, and provides quantitative data for decision-making.
Smart Images
Figure CN121880145A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of artificial intelligence technology and intelligent decision-making technology, and in particular to a performance testing method, apparatus, computer equipment and storage medium based on cloud-native architecture. Background Technology
[0002] In industries such as healthcare, finance, and insurance, the performance of core business systems (such as transaction systems, clearing systems, and online claims platforms) impacts the normal operation of business, user experience, and even market stability. Therefore, rigorous and thorough performance testing of these systems is crucial.
[0003] However, existing business system performance testing processes have the following problems: 1) Testers need to manually prepare the test environment or prepare it through semi-automatic scripts. For example, preparing the test environment involves applying for virtual machines, configuring operating systems, configuring networks, deploying dependent software, and deploying test tools. This makes test environment preparation cumbersome, error-prone, time-consuming, and labor-intensive. Inconsistent test results due to configuration differences are also likely, making it difficult to accurately reproduce the results. 2) The environment preparation, script execution, performance monitoring, and result collection stages in the test execution process are separated. Testers rely on manually switching between different systems and tools, which is inefficient and prone to errors due to human operation. 3) Test results are usually scattered raw log files and non-persistent performance monitoring data. Testers need to spend a lot of time manually organizing and cleaning the raw data and importing it into other tools for analysis and graphing. Cross-platform performance data is difficult to compare effectively, data analysis is difficult, and a traceable and queryable performance baseline knowledge base cannot be formed, making it difficult to support long-term performance evolution analysis and capacity planning decisions. 4) Traditional performance testing relies on the professional knowledge and experience of senior engineers and requires familiarity with the complex parameter configurations of various testing tools, which places high demands on testers. Summary of the Invention
[0004] This invention provides a performance testing method, apparatus, device, and medium based on cloud-native architecture to solve the technical problems of cumbersome processes, low efficiency, scattered data making comparison and analysis difficult, and high requirements for testers in existing business system performance testing.
[0005] Firstly, a performance testing method based on cloud-native architecture is provided, including: Receive an integrated test request input by the user; wherein, the integrated test request includes environment construction parameters and test task parameters; The environment construction parameters and the test task parameters are parsed to generate a standardized virtual test environment that conforms to the definition of the environment construction parameters. The corresponding test tool package and dependencies are obtained according to the identifier of the test tool to be deployed in the test task parameters and deployed to the generated standardized virtual test environment. Based on the test task parameters, the performance test script is executed in a standardized virtual test environment, and performance indicator data is collected in real time through the monitoring agent of the cloud-native architecture. After the performance test script is executed, the raw result logs output by the test tool and the performance indicator data collected by the monitoring agent are collected. The raw result logs and performance indicator data are parsed and cleaned to extract key performance indicators. The extracted key performance indicators are associated with the metadata of the current test and stored in the performance baseline database in a structured format.
[0006] Secondly, a performance testing device based on a cloud-native architecture is provided, including: The test request receiving module is used to receive integrated test requests input by the user; wherein, the integrated test request includes environment construction parameters and test task parameters; The environment automation and test deployment module is used to parse the environment construction parameters and the test task parameters, generate a standardized virtual test environment that conforms to the definition of the environment construction parameters, obtain the corresponding test tool package and dependencies according to the identifier of the test tool to be deployed in the test task parameters, and deploy them to the generated standardized virtual test environment. The test execution module is used to trigger the execution of performance test scripts in a standardized virtual test environment according to the test task parameters, and to collect performance indicator data in real time through the monitoring agent of cloud-native architecture. The data processing and storage module is used to collect the raw result logs output by the test tool and the performance indicator data collected by the monitoring agent after the performance test script is executed. It parses and cleans the raw result logs and performance indicator data, extracts key performance indicators, associates the extracted key performance indicators with the metadata of the current test, and stores them in the performance baseline database in a structured format.
[0007] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described performance testing method based on cloud-native architecture.
[0008] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the aforementioned performance testing method based on a cloud-native architecture.
[0009] The aforementioned performance testing method, apparatus, equipment, and media based on cloud-native architecture can receive integrated test requests from users via a client. These integrated test requests include environment construction parameters and test task parameters. The system parses the environment construction parameters and test task parameters to generate a standardized virtual test environment that conforms to the definition of the environment construction parameters. It then obtains the corresponding test tool package and dependencies based on the identifier of the test tool to be deployed in the test task parameters and deploys them to the generated standardized virtual test environment. Based on the test task parameters, it triggers the execution of a performance test script in the standardized virtual test environment, and collects performance indicator data in real time through a monitoring agent based on the cloud-native architecture. After the performance test script execution is complete, it collects the raw result logs output by the test tool and the performance indicator data collected by the monitoring agent. The raw result logs and performance indicator data are parsed and cleaned to extract key performance indicators. These extracted key performance indicators are associated with the metadata of the current test and stored in a structured format in a performance baseline database. The key performance indicators can also be fed back to the client. This cloud-native architecture-based performance testing solution can be used for intelligent customer service in the medical field or intelligent assistants in the financial field by receiving user input including environment construction parameters and test task parameters. This system integrates test requests for service parameters, unifying the testing process and enabling end-to-end automated closed-loop testing, thus improving efficiency. Based on a cloud-native architecture, it automatically generates standardized virtual test environments by calling the API interfaces of the underlying virtualization management platform. Based on the identifiers of the test tools to be deployed in the parsed test task parameters, it retrieves the corresponding test tool packages and dependencies from a centralized test tool resource repository and deploys them to the standardized virtual test environment. This simplifies test environment preparation, eliminates the need for manual intervention, automates test environment construction, reduces error rates, saves preparation time, and improves testing efficiency. The system ensures efficient environment preparation and standardized test environment configuration, avoiding test result deviations due to configuration differences and guaranteeing the reliability and reproducibility of test results. By collecting raw result logs from testing tools and performance indicator data collected by monitoring agents, key performance indicators are extracted, associated with the current test metadata, and stored in a structured format in the performance baseline database. This structured and centralized storage of scattered data facilitates querying and comparison, providing quantitative data for subsequent technology selection and architecture optimization decisions. Furthermore, performance testing can be initiated simply by inputting an integrated test request, requiring minimal testing personnel and lowering the testing threshold. Attached Figure Description
[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0011] Figure 1 This is a schematic diagram of an application environment for a performance testing method based on cloud-native architecture according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a performance testing method based on cloud-native architecture in one embodiment of the present invention; Figure 3 This is a schematic diagram of a performance testing device based on a cloud-native architecture according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention; Figure 5 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation
[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0013] The performance testing method based on cloud-native architecture provided in this invention can be applied to applications such as... Figure 1In application environments, intelligent customer service or intelligent assistants are used in scenarios such as healthcare, finance, and insurance. They are usually implemented through a server, where the client communicates with the server via a network. The server can receive integrated test requests from users via clients. These requests include environment setup parameters and test task parameters. The server parses these parameters to generate a standardized virtual test environment that conforms to the defined environment setup parameters. Based on the identifier of the test tool to be deployed in the test task parameters, the server retrieves the corresponding test tool package and dependencies and deploys them to the generated standardized virtual test environment. According to the test task parameters, the server triggers the execution of a performance test script in the standardized virtual test environment, and collects performance metrics data in real time through a cloud-native architecture monitoring agent. After the performance test script execution is complete, the server collects the raw result logs output by the test tool and the performance metrics data collected by the monitoring agent. The server parses and cleans the raw result logs and performance metrics data, extracts key performance indicators, associates the extracted key performance indicators with the current test metadata, and stores them in a structured format in a performance baseline database. The key performance indicators can also be fed back to the client. In this invention, for intelligent customer service in the medical field, or intelligent assistants in the financial field, a cloud-native architecture-based performance testing solution can be used by receiving integrated test requests from users, including environment setup parameters and test task parameters. This approach integrates all testing stages, enabling end-to-end automated closed-loop testing and improving efficiency. Based on a cloud-native architecture, it automatically generates standardized virtual test environments by calling the API interfaces of the underlying virtualization management platform. Using the identifiers of the test tools to be deployed in the parsed test task parameters, it retrieves the corresponding test tool packages and dependencies from a centralized test tool resource repository and deploys them to the standardized virtual test environment. This simplifies test environment preparation, eliminates the need for manual intervention, automates test environment construction, reduces error rates, saves preparation time, and improves the efficiency of test environment preparation. The test environment is standardized to avoid deviations in test results due to configuration differences, ensuring the reliability and reproducibility of test results. By collecting raw result logs from testing tools and performance metrics data collected by monitoring agents, key performance indicators are extracted. These key performance indicators are then associated with the current test metadata and stored in a structured format in a performance baseline database. This structured and centralized storage of scattered data facilitates querying and comparison, providing quantitative data for subsequent technology selection and architecture optimization decisions. Furthermore, performance testing can be initiated simply by inputting an integrated test request, requiring minimal testing personnel and lowering the testing threshold. Clients can be, but are not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a standalone server or a server cluster consisting of multiple servers.The present invention will now be described in detail through specific embodiments.
[0014] Please see Figure 2 As shown, Figure 2 A flowchart illustrating a performance testing method based on cloud-native architecture provided in this embodiment of the invention includes the following steps: S10: Receive an integrated test request input by the user; wherein, the integrated test request includes environment construction parameters and test task parameters.
[0015] Environment construction parameters define the configuration of the virtual environment under test, including the number of CPU cores, memory size, disk type, disk capacity, and network configuration parameters of the virtual machine or container. Test task parameters define the performance test task configuration, including the identifier of the test tool to be deployed, the test execution script or model, the number of concurrent test tasks, and the test duration. A user interaction layer serves as the user interface for inputting information, including a user interface or API (Application Programming Interface) gateway. The cloud-native architecture-based performance testing method provided by this invention can be applied to intelligent customer service or intelligent assistants in various application scenarios such as healthcare, finance, and insurance. It is typically implemented through a server-side architecture. This server can receive integrated test requests from users in real time, integrating the testing process and enabling end-to-end automatic closed-loop testing, thus improving testing efficiency. For example, in the healthcare application field, users can input an integrated test request including environment construction parameters and test task parameters to perform performance testing on a medical intelligent customer service system. The integrated test request can be parsed for subsequent performance testing execution.
[0016] Alternatively, for example, in the field of financial applications, users can perform performance tests on intelligent assistants for financial transactions by inputting an integrated test request that includes environment setup parameters and test task parameters. The integrated test request input by the user can be parsed to facilitate the execution of subsequent performance tests.
[0017] S20: Parse the environment construction parameters and the test task parameters to generate a standardized virtual test environment that conforms to the definition of the environment construction parameters. Obtain the corresponding test tool package and dependencies according to the identifier of the test tool to be deployed in the test task parameters and deploy them to the generated standardized virtual test environment.
[0018] Specifically, step S20, which involves generating a standardized virtual test environment that conforms to the defined environment construction parameters, and obtaining the corresponding test tool package and dependencies based on the identifier of the test tool to be deployed in the test task parameters and deploying them to the generated standardized virtual test environment, includes the following steps: Call the API interface of the underlying virtualization management platform of the cloud-native architecture to automatically generate a standardized virtual test environment that conforms to the definition of environment build parameters; Based on the identifier of the test tool to be deployed in the test task parameters, the corresponding test tool package and dependencies are obtained from the test tool resource library of the cloud-native architecture, and the obtained test tool package and dependencies are deployed to the generated standardized virtual test environment.
[0019] Based on a cloud-native architecture, a standardized virtual test environment that conforms to the defined environment construction parameters is automatically generated by calling the API interface of the underlying virtualization management platform. According to the identifier of the test tool to be deployed in the parsed test task parameters, the corresponding test tool package and dependencies are obtained from a centralized test tool resource library and deployed to the standardized virtual test environment. The test environment preparation steps are simple and do not require manual operation. The automated construction of the test environment reduces the error rate, saves test environment preparation time, and improves the efficiency of test environment preparation. The standardized test environment configuration avoids test result deviations caused by configuration differences, ensuring the reliability and reproducibility of test results.
[0020] The test task parameters include a natural language description of the test intent. Preferably, before parsing the environment construction parameters and the test task parameters in step S20, the performance testing method based on cloud-native architecture further includes: A pre-trained large language model is used to parse the natural language description of the test intent and convert it into structured test task parameters.
[0021] The pre-trained large language model can be a locally deployed large language model or a large language model invoked through an API interface. The natural language description of the test intent refers to the test intent described in natural language. Specifically, the process of parsing and converting the natural language description of the test intent into structured test task parameters using the pre-trained large language model includes: A pre-trained large language model is used to perform intent recognition and entity extraction on the natural language description of the test intent, thereby obtaining the structured test task parameters corresponding to the test intent.
[0022] S30: Based on the test task parameters, trigger the execution of the performance test script in a standardized virtual test environment, and collect performance indicator data in real time through the monitoring agent of the cloud-native architecture.
[0023] Preferably, the step S30, which involves collecting performance metrics data in real time through a monitoring agent based on a cloud-native architecture, specifically includes: The monitoring agent, built on a cloud-native architecture, calls the operating system interface to collect performance metrics data in real time during the execution of performance test scripts.
[0024] The monitoring agent is a lightweight background program pre-deployed in the virtual machine image. Performance metrics data can be divided into three categories based on performance type: resource utilization performance metrics, system performance performance metrics, and application service performance metrics. Resource utilization performance metrics include CPU utilization, memory utilization, disk read / write speed, network bandwidth (throughput), network latency, and packet loss rate. System performance performance metrics include context switch count and interrupt count. Application service performance metrics data require specific testing tools and include transaction volume, request response time, and error rate. Request response time includes average response time. Performance metrics are quantifiable values, and the standards for performance metric data can typically be industry-standard, internal performance targets, or historical test data. For example, for database transactions, the standard for performance metrics data usually adopts industry-standard, generally requiring higher transaction volume and lower response time. When the standard for performance metric data is historical test data, the current test results are compared with the performance baseline data obtained from previous tests in the same or similar standardized virtual test environment to observe whether performance has improved or degraded.
[0025] S40: After the performance test script is executed, collect the raw result logs output by the test tool and the performance indicator data collected by the monitoring agent, parse and clean the raw result logs and performance indicator data, extract key performance indicators, associate the extracted key performance indicators with the metadata of the current test, and store them in the performance baseline database in a structured format.
[0026] The raw result logs can be in formats such as text log, CSV, JSON, and XML. The performance indicator data collected by the monitoring agent is a performance indicator data stream in chronological order. By collecting the raw result logs output by the testing tool and the performance indicator data collected by the monitoring agent, the raw dataset scattered in the standardized virtual testing environment is centralized. Metadata includes the environment construction parameters, the test task parameters, the hardware information of the physical host machine where the standardized virtual testing environment is located, the operating system version information, the kernel version information, and the testing tool version information. The key performance indicators include throughput, latency, error rate, resource utilization, and system stability indicators. Throughput includes average TPS (transactions per second), latency includes average response time, P95 (95% of request response times are not greater than this value) response time, and P99 (99% of request response times are not greater than this value) response time. Error rate includes HTTP request error rate. Resource utilization includes average CPU utilization, maximum memory usage, and average disk IOPS (Input / Output Operations Per Second) during the test. System stability indicators include CPU utilization curves and memory usage curves throughout the entire run. By associating key performance indicators with the metadata of the current test and storing them in a structured format in the performance baseline database, scattered data is structured and centrally stored, making it easier to query and compare.
[0027] Specifically, the performance baseline database in step S40, which associates the extracted key performance indicators with the metadata of the current test and stores them in a structured format, is either a time-series database or a relational database.
[0028] Specifically, the raw result logs are typically unstructured data, while the performance metric data collected by the monitoring agent is typically structured data. Therefore, the step S40 of parsing and cleaning the raw result logs and performance metric data includes: Extract meaningful fields from the raw result logs according to predefined rules or templates. Meaningful fields refer to fields including timestamps, response times, request tags, or response codes. Transform performance metric data into a unified data model to obtain unified performance metric data for the data model. Noise and invalid data are removed from the performance metrics data of the meaningful fields and the unified data model. Missing time points in the performance metrics data are interpolated or labeled. The performance metrics data of the meaningful fields and the unified data model are then aligned and aggregated according to a unified timestamp. Invalid data includes data from the initial stage of testing before reaching a stable state (warm-up phase) and abnormal peak points caused by external interference, such as momentary network jitter. Aligning and aggregating the performance metrics data of the meaningful fields and the unified data model according to a unified timestamp ensures that their time granularity is aligned.
[0029] Specifically, the extraction of key performance indicators in step S40 includes: Based on the cleaned raw result logs and performance index data, calculate the key performance indicators that represent the system performance.
[0030] Specifically, step S40, which involves associating the extracted key performance metrics with the metadata of the current test and storing them in a structured format in the performance baseline database, includes: The extracted key performance indicators are converted into a structured format to obtain structured key performance indicators. These structured key performance indicators are then associated with the metadata of the current test and stored in the performance baseline database.
[0031] Specifically, after step S40, that is, after associating the extracted key performance indicators with the metadata of the current test and storing them in the performance baseline database in a structured format, the cloud-native architecture-based performance testing method further includes: A standardized test report is generated based on the key performance indicators and associated metadata of historical tests stored in the performance baseline database; wherein the standardized test report includes key performance indicators and performance trend charts.
[0032] Performance trend charts are generated based on key performance indicators (KPIs) and associated metadata from historical tests stored in the performance baseline database. By using any fixed dimension of metadata as the x-axis and any KPI as the y-axis, the corresponding information from historical tests in the performance baseline database is queried to generate data points, which are then plotted to create the performance trend chart. Performance trend charts are used to illustrate the changing trends of performance indicators over time. For example, version comparison trends show the changes in system throughput and response time when version information changes, facilitating the assessment of the impact of code changes on performance; capacity planning trends show the growth trends of business volume (data volume and user volume) and system resource (such as CPU and memory) utilization, predicting future hardware resource capacity requirements; or optimization effect trends clearly demonstrate the performance improvement brought about by optimization by comparing performance trend charts before and after optimization.
[0033] Specifically, after generating a standardized test report based on key performance indicators from historical tests stored in the performance baseline database, the process further includes: Generate a data analysis interface to allow users to input at least one dimension of metadata information. Based on the user-input metadata information, query key performance indicators of historical tests in the performance baseline database and perform visual comparative analysis.
[0034] The data analysis interface can be a natural language query interface. Through a large language model, it can perform intent recognition and entity extraction on the natural language query input by the user, and convert the natural language query into a structured query language or equivalent query instruction for the performance baseline database, so as to perform subsequent queries and return query results.
[0035] As can be seen, in the above solution, for intelligent customer service in the medical field or intelligent assistant in the financial field, an integrated test request is received from the user, including environment construction parameters and test task parameters. This integrates the testing process, enabling end-to-end automated closed-loop testing and improving efficiency. Based on a cloud-native architecture, a standardized virtual test environment is automatically generated by calling the API interface of the underlying virtualization management platform. The corresponding test tool packages and dependencies are retrieved from a centralized test tool resource library based on the identifier of the test tool to be deployed in the parsed test task parameters. These packages and dependencies are then deployed to the standardized virtual test environment, simplifying the test environment preparation process and eliminating the need for manual intervention, thus automating the process. Building a test environment reduces error rates, saves time in test environment preparation, and improves the efficiency of test environment preparation. Standardized test environment configuration avoids test result deviations due to configuration differences, ensuring the reliability and reproducibility of test results. By collecting raw result logs from testing tools and performance indicator data collected by monitoring agents, key performance indicators are extracted. These key performance indicators are then associated with the metadata of the current test and stored in a structured format in a performance baseline database. This structured and centralized storage of scattered data facilitates querying and comparison, providing quantitative data for subsequent technology selection and architecture optimization decisions. Furthermore, performance testing can be initiated simply by inputting an integrated test request, requiring minimal testing personnel and lowering the testing threshold.
[0036] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
[0037] In one embodiment, a performance testing device based on a cloud-native architecture is provided, which corresponds one-to-one with the performance testing method based on a cloud-native architecture described in the above embodiments. For example... Figure 3 As shown, this cloud-native architecture-based performance testing device includes a test request receiving module 101, an environment automation and test deployment module 102, a test execution module 103, and a data processing and storage module 104. Detailed descriptions of each functional module are as follows: The test request receiving module 101 is used to receive an integrated test request input by the user; wherein, the integrated test request includes environment construction parameters and test task parameters; The environment automation and test deployment module 102 is used to parse the environment construction parameters and the test task parameters, generate a standardized virtual test environment that conforms to the definition of the environment construction parameters, obtain the corresponding test tool package and dependencies according to the identifier of the test tool to be deployed in the test task parameters, and deploy them to the generated standardized virtual test environment. The test execution module 103 is used to trigger the execution of the performance test script in a standardized virtual test environment according to the test task parameters, and to collect performance indicator data in real time through the monitoring agent of the cloud-native architecture. The data processing and storage module 104 is used to collect the raw result logs output by the test tool and the performance index data collected by the monitoring agent after the performance test script is executed, parse and clean the raw result logs and performance index data, extract key performance indicators, associate the extracted key performance indicators with the metadata of the current test, and store them in the performance baseline database in a structured format.
[0038] In one embodiment, the environment automation and test deployment module 102 is specifically used for: Call the API interface of the underlying virtualization management platform of the cloud-native architecture to automatically generate a standardized virtual test environment that conforms to the definition of environment build parameters; Based on the identifier of the test tool to be deployed in the test task parameters, the corresponding test tool package and dependencies are obtained from the test tool resource library of the cloud-native architecture, and the obtained test tool package and dependencies are deployed to the generated standardized virtual test environment.
[0039] In one embodiment, the test execution module 103 is specifically used for: The monitoring agent, built on a cloud-native architecture, calls the operating system interface to collect performance metrics data in real time during the execution of performance test scripts.
[0040] In one embodiment, the data processing and storage module 104 is specifically used for: The extracted key performance indicators are converted into a structured format to obtain structured key performance indicators. These structured key performance indicators are then associated with the metadata of the current test and stored in the performance baseline database.
[0041] In one embodiment, the data processing and storage module 104 is further configured to: Standardized test reports are generated based on key performance indicators and associated metadata from historical tests stored in the performance baseline database.
[0042] In one embodiment, the data processing and storage module 104 is specifically used for: Generate a data analysis interface for users to input at least one dimension of metadata information, and query key performance indicators of historical tests in the performance baseline database based on the user-input metadata information of at least one dimension.
[0043] This invention provides a performance testing device based on a cloud-native architecture. By receiving an integrated test request from the user, including environment setup parameters and test task parameters, it integrates the testing process, enabling end-to-end automated closed-loop testing and improving efficiency. Based on the cloud-native architecture, it automatically generates a standardized virtual test environment by calling the API interface of the underlying virtualization management platform. According to the identifier of the test tool to be deployed in the parsed test task parameters, it retrieves the corresponding test tool package and dependencies from a centralized test tool resource library and deploys them to the standardized virtual test environment. This simplifies the test environment preparation process and automates the test environment construction without manual intervention. It reduces error rates, saves time in test environment preparation, improves the efficiency of test environment preparation, and standardizes test environment configuration to avoid test result deviations due to configuration differences, ensuring the reliability and reproducibility of test results. By collecting raw result logs output by testing tools and performance indicator data collected by monitoring agents, key performance indicators are extracted, and key performance indicators are associated with the metadata of the current test and stored in a structured format in the performance baseline database. This structured and centralized storage of scattered data facilitates querying and comparison, and provides quantitative data for decision-making on subsequent technology selection and architecture optimization. Moreover, performance testing can be started by inputting an integrated test request, with low requirements for testers and a low testing threshold.
[0044] Specific limitations regarding cloud-native architecture-based performance testing devices can be found in the limitations of cloud-native architecture-based performance testing methods described above, and will not be repeated here. Each module in the aforementioned cloud-native architecture-based performance testing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.
[0045] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements server-side functions or steps of a performance testing method based on a cloud-native architecture.
[0046] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements client-side functions or steps of a performance testing method based on a cloud-native architecture.
[0047] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: Receive an integrated test request input by the user; wherein, the integrated test request includes environment construction parameters and test task parameters; The environment construction parameters and the test task parameters are parsed to generate a standardized virtual test environment that conforms to the definition of the environment construction parameters. The corresponding test tool package and dependencies are obtained according to the identifier of the test tool to be deployed in the test task parameters and deployed to the generated standardized virtual test environment. Based on the test task parameters, the performance test script is executed in a standardized virtual test environment, and performance indicator data is collected in real time through the monitoring agent of the cloud-native architecture. After the performance test script is executed, the raw result logs output by the test tool and the performance indicator data collected by the monitoring agent are collected. The raw result logs and performance indicator data are parsed and cleaned to extract key performance indicators. The extracted key performance indicators are associated with the metadata of the current test and stored in the performance baseline database in a structured format.
[0048] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Receive an integrated test request input by the user; wherein, the integrated test request includes environment construction parameters and test task parameters; The environment construction parameters and the test task parameters are parsed to generate a standardized virtual test environment that conforms to the definition of the environment construction parameters. The corresponding test tool package and dependencies are obtained according to the identifier of the test tool to be deployed in the test task parameters and deployed to the generated standardized virtual test environment. Based on the test task parameters, the performance test script is executed in a standardized virtual test environment, and performance indicator data is collected in real time through the monitoring agent of the cloud-native architecture. After the performance test script is executed, the raw result logs output by the test tool and the performance indicator data collected by the monitoring agent are collected. The raw result logs and performance indicator data are parsed and cleaned to extract key performance indicators. The extracted key performance indicators are associated with the metadata of the current test and stored in the performance baseline database in a structured format.
[0049] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.
[0050] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0051] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.
[0052] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.
Claims
1. A performance testing method based on cloud-native architecture, characterized in that, include: Receive an integrated test request input by the user; wherein, the integrated test request includes environment construction parameters and test task parameters; The environment construction parameters and the test task parameters are parsed to generate a standardized virtual test environment that conforms to the definition of the environment construction parameters. The corresponding test tool package and dependencies are obtained according to the identifier of the test tool to be deployed in the test task parameters and deployed to the generated standardized virtual test environment. Based on the test task parameters, the performance test script is executed in a standardized virtual test environment, and performance indicator data is collected in real time through the monitoring agent of the cloud-native architecture. After the performance test script is executed, the raw result logs output by the test tool and the performance indicator data collected by the monitoring agent are collected. The raw result logs and performance indicator data are parsed and cleaned to extract key performance indicators. The extracted key performance indicators are associated with the metadata of the current test and stored in the performance baseline database in a structured format.
2. The performance testing method based on cloud-native architecture as described in claim 1, characterized in that, The process of generating a standardized virtual test environment that conforms to the defined environment construction parameters, and obtaining the corresponding test tool package and dependencies based on the identifier of the test tool to be deployed in the test task parameters and deploying them to the generated standardized virtual test environment, includes: Call the API interface of the underlying virtualization management platform of the cloud-native architecture to automatically generate a standardized virtual test environment that conforms to the definition of environment build parameters; Based on the identifier of the test tool to be deployed in the test task parameters, the corresponding test tool package and dependencies are obtained from the test tool resource library of the cloud-native architecture, and the obtained test tool package and dependencies are deployed to the generated standardized virtual test environment.
3. The performance testing method based on cloud-native architecture as described in claim 1, characterized in that, The real-time collection of performance metric data through a cloud-native architecture monitoring agent specifically includes: The monitoring agent, built on a cloud-native architecture, calls the operating system interface to collect performance metrics data in real time during the execution of performance test scripts.
4. The performance testing method based on cloud-native architecture as described in claim 1, characterized in that, The performance baseline database, which associates the extracted key performance indicators with the metadata of the current test and stores them in a structured format, is either a time-series database or a relational database.
5. The performance testing method based on cloud-native architecture as described in claim 1, characterized in that, The step of associating the extracted key performance indicators with the metadata of the current test and storing them in a structured format in the performance baseline database includes: The extracted key performance indicators are converted into a structured format to obtain structured key performance indicators. These structured key performance indicators are then associated with the metadata of the current test and stored in the performance baseline database.
6. The performance testing method based on cloud-native architecture as described in claim 1, characterized in that, After associating the extracted key performance metrics with the metadata of the current test and storing them in a structured format in the performance baseline database, the cloud-native architecture-based performance testing method further includes: Standardized test reports are generated based on key performance indicators and associated metadata from historical tests stored in the performance baseline database.
7. The performance testing method based on cloud-native architecture as described in claim 6, characterized in that, After generating a standardized test report based on key performance indicators from historical tests stored in the performance baseline database, the process also includes: Generate a data analysis interface for users to input at least one dimension of metadata information, and query key performance indicators of historical tests in the performance baseline database based on the user-input metadata information of at least one dimension.
8. A performance testing device based on a cloud-native architecture, characterized in that, include: The test request receiving module is used to receive integrated test requests input by the user; wherein, the integrated test request includes environment construction parameters and test task parameters; The environment automation and test deployment module is used to parse the environment construction parameters and the test task parameters, generate a standardized virtual test environment that conforms to the definition of the environment construction parameters, obtain the corresponding test tool package and dependencies according to the identifier of the test tool to be deployed in the test task parameters, and deploy them to the generated standardized virtual test environment. The test execution module is used to trigger the execution of performance test scripts in a standardized virtual test environment according to the test task parameters, and to collect performance indicator data in real time through the monitoring agent of cloud-native architecture. The data processing and storage module is used to collect the raw result logs output by the test tool and the performance indicator data collected by the monitoring agent after the performance test script is executed. It parses and cleans the raw result logs and performance indicator data, extracts key performance indicators, associates the extracted key performance indicators with the metadata of the current test, and stores them in the performance baseline database in a structured format.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the performance testing method based on cloud-native architecture as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the performance testing method based on cloud-native architecture as described in any one of claims 1 to 7.