How to interpret and analyze the results of performance testing?
The results of the performance test need to be carefully interpreted and analyzed in order to find out the bottlenecks and problems of the system and make suggestions for improvement. Here are some common performance test result metrics and how to interpret them:
1. Response time : Response time refers to the time required for the system to process a request, usually measured by indicators such as average response time, maximum response time, and 95% response time. Long response times may indicate a bottleneck or performance issue in the system and require further analysis.
2. Throughput : Throughput refers to the number of requests processed by the system per unit time, usually measured in requests per second (QPS). Lower throughput may indicate a bottleneck or performance issue in the system, requiring further analysis.
3. Error rate : The error rate refers to the proportion of errors that occur when the system processes requests, usually measured as a percentage. A high error rate may indicate a bug or anomaly in the system that requires further analysis.
4. Resource utilization : Resource utilization refers to the utilization of resources (such as CPU, memory, network bandwidth, etc.) used by the system when processing requests, usually measured in percentages. High resource utilization may indicate a bottleneck or performance issue in the system that requires further analysis.
5. Bottleneck analysis : Through comprehensive analysis of the above indicators, find out the bottlenecks and problems of the system, and put forward suggestions for improvement. For example, if the response time is long, it may be due to low efficiency of database query or insufficient network bandwidth, etc., which needs targeted optimization.
How to analyze the response time index of the stress test?
1. Average Response Time : The average response time is the average time it takes for the system to process a request. A lower average response time usually indicates better system performance and users are responding quickly. A high average response time may indicate a bottleneck or performance issue in the system and requires further analysis.
2. Maximum Response Time : The maximum response time is the maximum time required for the system to process a request. A long maximum response time may mean that the system performs poorly in some situations and users may experience long wait times. Need to pay attention to whether the maximum response time exceeds the user acceptable threshold.
3. 95% response time : 95% response time refers to the average time after the slowest 5% of requests are excluded from the time required for the system to process requests. This indicator can help exclude the impact of extreme conditions on the average response time, and more accurately reflect the performance of the system. A high 95% response time may indicate that the system has performance issues on a subset of requests.
4. Response time distribution : In addition to the above indicators, you can also observe the distribution of response time. By plotting a response time distribution graph or histogram, you can see the number of requests for different response time intervals. If there is an obvious concentration of response times in a long interval, further analysis of the characteristics and causes of these requests may be required.
How to judge whether the average response time meets the system performance requirements?
1. Determine the performance requirements : First, the performance requirements of the system need to be clarified. This can be determined through communication and consultation with relevant stakeholders (eg, business units, users). Performance requirements may include metrics such as maximum response time, average response time, etc.
2. Set the threshold : According to the performance requirements, set a reasonable threshold as the judgment standard. The threshold should be determined according to the actual situation of the system and user needs, and information such as historical data and user feedback can be referred to. For example, if the system requires an average response time of less than 1 second, then 1 second can be used as the threshold.
3. Conduct stress testing : Use appropriate tools and methods to conduct stress testing to simulate the performance of the system under different loads. During the test, the response time of each request is recorded and the average response time is calculated.
4. Comparative analysis : compare and analyze the average response time obtained from the test with the set threshold. If the average response time is less than or equal to the threshold, it indicates that the performance of the system meets the requirements; if the average response time exceeds the threshold, it indicates that there may be a problem with the system performance.
5. Consider the actual situation : In addition to the average response time, other performance indicators and actual conditions of the system need to be considered. For example, factors such as the number of concurrent users of the system and network delay may affect the response time. Therefore, when judging whether the average response time meets the system performance requirements, these factors need to be considered comprehensively.
So, how to judge the standard when the performance requirements of the system are not clear?
1. Refer to industry standards : Understand the standards and best practices of related industries, which can be used as a reference. For example, for web applications, you can refer to some general guidelines for web performance optimization, such as Google's PageSpeed Insights, Yahoo's YSlow, etc.
2. Refer to competitors : observe the system performance of competitors, and understand their average response time, number of concurrent users and other indicators. This can be used as a reference to help you determine performance standards for your own system.
3. User feedback and requirements : Communicate with the end users of the system to understand their expectations and requirements for system performance. By collecting user feedback and requirements, you can better understand user expectations for system performance, and set performance standards based on user needs.
4. Conduct user research : through questionnaires, user interviews, etc., actively collect users' evaluation and expectations of system performance. This allows for more direct, specific user feedback to help you determine performance metrics.
5. Conduct experiments and evaluations : In the early stages of system development, some experiments and evaluations can be performed to understand how the system will perform under different loads. Through these tests and evaluations, the performance bottlenecks and requirements of the system can be preliminarily judged, thereby setting performance standards.
In general, page load times should be as short as possible to provide a better user experience. According to Google's recommendation, the page load time should be controlled within 3 seconds, because a page load time of more than 3 seconds will lead to an increase in user churn.
In addition to page load times, Google provides several other performance metrics and recommendations for optimizing web page performance. Here are some common suggestions:
1. Compress and optimize resources : Use compression algorithms such as Gzip to reduce file size, optimize image and video resources to reduce page load time.
2. Use browser cache : By setting an appropriate cache policy, let the browser cache static resources, reduce repeated network requests, and improve page loading speed.
3. Reduce redirection : Avoid excessive page redirection, because each redirection will add additional network requests and delays.
4. Load resources asynchronously : Use asynchronous loading for resources that do not affect page rendering (such as scripts and style sheets) to avoid blocking page loading.
5. Delay loading content : For long pages or pages with a lot of content, you can delay loading part of the content and only load it when the user scrolls to the visible area.
6. Use CDN to accelerate : use content distribution network (CDN) to distribute static resources, and cache them on servers around the world to improve resource loading speed.
7. Responsive design : Responsive design is adopted to enable web pages to adapt to different devices and screen sizes, providing a better user experience.
In addition to page load times, Google provides some performance metrics and recommendations for:
1. First Contentful Paint (FCP) : refers to the time when the first content element (such as text, image) appears on the page, and it is recommended to complete it within 1 second.
2. Time to Interactive (TTI) : refers to the time when the page can be interacted with, that is, the time when the user can click, input and other operations. It is recommended to complete within 5 seconds.
3. Total Blocking Time (TBT) : Refers to the total time of blocking user input during page loading, and it is recommended to be within 300 milliseconds.
4. Largest Contentful Paint (LCP) : refers to the time when the largest content element (such as pictures, videos) on the page appears, and it is recommended to complete it within 2.5 seconds.
5. Cumulative Layout Shift (CLS) : Refers to the sum of element layout changes in the page, and it is recommended to be less than 0.1. These performance indicators can help developers more comprehensively evaluate the performance of web pages and optimize them accordingly.
Practical case
Optical theory is useless, you have to learn to follow along, and you have to do it yourself, so that you can apply what you have learned to practice. At this time, you can learn from some practical cases.
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