Different Types of Performance Test Metrics
Performance test metrics fall into two distinct categories:
- Client-side metrics
- Server-side metrics
Client-side Metrics
Client-side metrics are gauged on the client's or end-user device's side. This encompasses data collected from the user's browser, device, or similar software components like third-party tools such as Google Analytics, New Relic, or Pingdom. These metrics provide insights into the application or website's performance from the user's perspective. They include measurements like page load times, rendering times, and resource loading times. By scrutinizing client-side metrics, developers and testers can pinpoint potential issues affecting user experience and take corrective actions to optimize application or website performance.
| KPI Metric | Description | When to Use |
|---|---|---|
| Time to First Byte (TTFB) | Evaluates the time taken for a browser to receive the first byte of the response from the server after sending the HTTP request. | Use when analyzing initial server response time. |
| Page Load Time | Refers to the time it takes for a web page to load completely in the user's browser, including metrics like load time, number of requests, caching rate, speed index, and compression ratio. | Use to assess overall webpage loading performance. |
| Rendering Time | Measures the time it takes for a browser to render a web page after receiving it from the server, focusing on user wait time until the page is loaded and ready to interact. | Use to evaluate the time taken for webpage rendering. |
| Time to Interact (TTI) | Measures the time taken for a user to navigate between different pages or sections of a website or application, directly impacting user engagement. | Use to understand user interaction delays. |
| Conversion Rate | Measures the effectiveness of converting visitors into customers or achieving specific goals, including metrics like bounce rate, time on site, exit rate, and conversion funnel analysis. | Use to assess the success of conversion efforts. |
| Click-through Rate | Measures the number of times users click on a specific link, ad, or call to action relative to the times the link or ad was shown. | Use to gauge user engagement with specific elements. |
Server-side Metrics
Server-side metrics analyze the behavior and characteristics of the server-side infrastructure or the backend of the software delivering the web application. This includes metrics such as CPU utilization, memory usage, disk capacity, and disk I/O. Server-side metrics offer insights into system performance, aiding in identifying bottlenecks and issues impacting the user experience.
| KPI Metric | Description | When to Use |
|---|---|---|
| CPU Utilization | Measures the amount of CPU time spent by the server in processing requests. High CPU utilization can indicate performance degradation or system failure under prolonged heavy loads. | Use to monitor server processing capacity. |
| Memory Utilization | Measures the amount of memory used by the server to store and process requests, directly impacting the server's ability to handle requests. High memory utilization can cause slow response times and application crashes. | Use to assess server memory usage and potential performance issues. |
| Disk Capacity | Measures the total amount of storage space available on a physical or virtual disc drive, typically measured in bytes, kilobytes, megabytes, and gigabytes. | Use to monitor available disk storage. |
| Disk I/O (Input/Output) | Refers to the rate at which data is read from or written to a disc storage system, indicating the data transfer capabilities of a disc storage device within a specific time frame. | Use to evaluate disk performance and data transfer efficiency. |
Use Cases
The following use cases demonstrate how client-side and server-side metrics can be instrumental in identifying, analyzing, and resolving performance-related issues for both frontend and backend aspects of an application.
Client-Side Metrics Use Cases:
-
Page Load Time Optimization:
Issue: Users are experiencing slow page load times on an e-commerce website.
Client-Side Metrics Used:
- Time to First Byte (TTFB)
- Page Load Time
- Rendering Time
Analysis and Solution: Analyzing TTFB, Page Load Time, and Rendering Time metrics can help identify bottlenecks. If TTFB is high, server-side optimizations may be needed. If Page Load Time is prolonged, consider optimizing resources, leveraging browser caching, or optimizing images. Rendering Time insights can guide improvements in the frontend code to enhance the overall user experience.
-
Enhancing Conversion Rates:
Issue: The conversion rate on a web application is lower than expected.
Client-Side Metrics Used:
- Conversion Rate
- Click-through Rate
- Time to Interact (TTI)
Analysis and Solution: By closely monitoring Conversion Rate, Click-through Rate, and TTI, you can identify areas impacting user engagement. For instance, if TTI is high, users might be experiencing delays in interaction. Optimizing frontend code and reducing TTI can improve the overall conversion rate. Analyzing Click-through Rate helps in understanding user engagement with specific elements, aiding in targeted improvements.
Server-Side Metrics Use Cases:
-
High CPU Utilization During Peak Loads:
Issue: The server experiences high CPU utilization during peak hours.
Server-Side Metrics Used:
- CPU Utilization
- Memory Utilization
Analysis and Solution: Monitoring CPU Utilization and Memory Utilization helps identify resource bottlenecks. If CPU Utilization is consistently high, it may indicate the need for optimizations in the backend code or additional server resources. Memory Utilization insights guide improvements in memory management, preventing slowdowns and potential crashes.
-
Disk Capacity Planning:
Issue: The application's storage is reaching capacity limits.
Server-Side Metrics Used:
- Disk Capacity
- Disk I/O (Input/Output)
Analysis and Solution: Regularly monitoring Disk Capacity ensures timely awareness of storage limitations. If Disk I/O is high, it may impact overall performance. Analyzing these metrics helps plan for additional storage capacity or optimize data storage strategies, preventing potential disruptions due to insufficient disk space.