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Looking to learn more about performance testing? This comprehensive guide has got you covered! From the basics to advanced concepts, we cover everything you need to know to get started with performance testing.
Performance testing is a testing methodology used to evaluate the performance of software, systems, or devices. Unlike other testing types, the purpose of performance testing is not to check whether the functions are working correctly, but to assess their performance under high load or stress. Through this testing, we can understand the performance, stability, and reliability of the system, as well as its behavior under different load conditions.
Performance testing is like testing a car. A car needs to meet its basic functions, like appearance, driving, and fuel consumption, but we also test it under different road conditions and loads to understand its performance. Similarly, performance testing helps us understand a system's performance under different loads and scenarios to improve the user experience.
In summary, the purpose of performance testing is to evaluate the system's performance under high load or stress and to identify any performance bottlenecks or issues.
Based on the testing objective and approach, performance testing can be classified into the following types:
Baseline testing is the most basic form of performance testing, and its purpose is to test the performance and behavior of the system under normal working loads.
Stress testing is used to test the performance and behavior of the system under extreme loads. In stress testing, testers will test the system's performance with maximum load. This test can determine the system's capacity to withstand heavy loads and its behavior under overload conditions. Stress testing can reveal the performance limits of the system and identify bottlenecks and issues under high loads.
Capacity testing is a testing methodology used to evaluate the maximum user load that a system can support. Testers will test the system's performance under normal and extreme loads and determine the maximum user load the system can support. This testing can help system administrators evaluate the system's scalability and capacity to meet user demands.
Stability testing is a testing methodology used to test whether a system has stability, reliability, and availability. In stability testing, testers will test the system's performance over a long time and under different loads to determine whether the system has stability and reliability. This testing can help identify stability issues, which can be improved.
Concurrent testing is a testing methodology used to test a system's ability to handle multiple requests at the same time. In concurrent testing, testers will simulate multiple users accessing the system at the same time and test the system's performance when handling concurrent requests. This testing can help system administrators evaluate the system's concurrency processing ability to improve system performance and user experience.
In performance testing, core metrics are key indicators used to evaluate system performance and behavior. These metrics can help testers understand how a system performs under different loads and scenarios, in order to identify bottlenecks and issues. Here are several core metrics in performance testing:
Response time is the amount of time it takes for a system to receive a request, process it, and return a response. In performance testing, response time is a critical metric because it directly affects system responsiveness and user experience. Lower response times can improve user experience, while higher response times may result in user churn and a negative reputation.
In automated testing software, the response time for each request is typically provided directly. For example, in Apidog, you can see the response time for each request in the API's execution result or test report.
What is throughput in performance testing? Throughput refers to the number of requests that a system can handle in a unit of time. In performance testing, throughput is a critical metric as it reflects the system's processing capacity and performance. Testers can test the throughput of a system by increasing the number of concurrent users or requests.
The following formula can be used to calculate throughput: Throughput = Total number of requests / Test duration
Where the total number of requests refers to the number of requests sent during the test, and the test duration refers to the duration of the test.
For example, if 10,000 requests were sent during a test that lasted for 60 seconds, the throughput would be: Throughput = 10,000 / 60 = 166.67 (requests per second)
Testers can test the system's throughput under different conditions by changing the system load or increasing the number of concurrent users.
Concurrent users refer to the maximum number of users a system can handle simultaneously. In performance testing, the number of concurrent users is an important metric as it reflects the system's concurrent processing capacity and performance. Testers can test the system's performance by increasing the number of concurrent users.
For example, in Apidog, testers can increase the number of threads in the test case to perform multi-concurrent testing.
CPU utilization refers to the percentage of CPU occupied during task execution. It is a critical indicator in performance testing, reflecting the system's load and resource usage. High CPU utilization may cause performance degradation or even crashes.
Memory utilization refers to the percentage of memory occupied during task execution. It is an important indicator in performance testing, reflecting the system's load and resource usage. High memory utilization may cause performance degradation or even crashes. Common performance testing tools include Apidog, which is an integrated collaboration platform for API documentation, debugging, mocking, and testing. It supports in-app performance testing by simulating concurrent requests with thread numbers and exporting JMeter files for advanced testing. Apidog also provides other features like interface document generation, interface mock services, and interface automation testing. Its advantage is its easy and convenient operation, and it can be integrated with API design and development. However, complex stress testing requires combining with JMeter to achieve better results.
When conducting performance testing, testers can use various performance testing tools to help design test cases, execute tests, and analyze test results. Common performance testing tools include:
Apidog is a collaborative platform that integrates API documentation, debugging, mocking, and testing. It can help you quickly design, develop, and test APIs, improving development efficiency. Apidog supports in-app performance testing and exporting JMeter files for performance testing.
In-app performance testing can simulate concurrent requests by setting the number of threads, with each thread running all selected steps in order. Exporting JMeter files can enable more advanced performance testing in JMeter, such as setting pressure parameters, assertions, and reports. Apidog also has many other features, such as interface documentation generation, interface mock services, and interface automation testing.
Compared with other tools, the advantage of Apidog is that it is easy to operate and can be integrated with API design and development. However, more complex stress testing requires a combination with JMeter to achieve better results.
Apache JMeter is a Java-based framework used for executing various load, performance, and functional tests. It supports multiple protocols and technologies, including web applications, databases, FTP, SMTP, SOAP, REST, etc., and can help testers design, execute, and analyze test cases.
JMeter is an old performance testing tool with complex operations and a steep learning curve, but it is very powerful in terms of functionality.
LoadRunner is a leading performance testing tool developed by Hewlett-Packard Enterprise. It supports multiple protocols and technologies, including web applications, databases, ERP systems, and mobile applications, and provides a visual test script editor, test scenario design tool, and analysis report functions.
LoadRunner is commercial software and requires payment for purchase and use, with a relatively high price compared to other tools. Similar to JMeter, LoadRunner also requires learning its specific terms and operating procedures, which requires a certain learning cost.
In general, Apidog is suitable for agile teams to perform simple and quick performance testing; JMeter is suitable for more comprehensive teams and businesses to perform comprehensive performance testing, while LoadRunner is suitable for large enterprise-level application performance testing and advanced load testing. Choosing the appropriate tool depends on specific testing requirements and application scenarios.
