Key Performance Test Metrics to Track

Key Performance Test Metrics to Track

In our fast-moving digital world, nobody likes waiting around. If a website is slow to load or crashes halfway through, people quickly switch to another one — often a competitor. This can lead to significant losses, making performance testing crucial.

Performance testing is what QA teams or developers do to check how a system responds, its speed, scalability, stability, and overall behavior under different conditions. To ensure apps run smoothly and keep users satisfied, it’s crucial to track certain performance metrics. In this post, we’ll go over the key metrics you should keep an eye on.

So, let’s take a look at the metrics.

What are Performance Test Metrics?

Performance test metrics are measurements used to evaluate how well a system or application performs under various conditions. These metrics identify potential bottlenecks, areas for improvement, and ensure that the application meets the desired performance standards.

To illustrate the importance of performance test metrics, consider this everyday example:

Imagine you run a popular coffee shop in a busy neighborhood. During peak hours, your shop is flooded with customers, and you need to ensure everyone gets their coffee quickly and efficiently. To manage this, you track several key aspects: the time it takes to serve each customer, the number of customers you can handle simultaneously, and how smoothly operations run, avoiding issues like running out of supplies or equipment malfunctions.

In a similar way, performance test metrics for a website or application measure aspects such as page load time (akin to serving time), the capacity to handle multiple users (like customer volume), and system stability (comparable to equipment functionality). By monitoring these metrics, you can optimize your website or app’s performance, ensuring user satisfaction just as you would aim to please customers in your coffee shop.

Key Performance Test Metrics

Response Time: Measures the time it takes for a system to respond to a user request, which is crucial for meeting user expectations. This includes:

• Minimum Response Time: The quickest response.
• Maximum Response Time: The slowest response.
• Average Response Time: The typical response.
• 90th Percentile: Time taken for 90% of requests to be completed.

Throughput: Tracks the number of requests a system can handle within a specified timeframe, indicating the system’s capacity to process workloads, usually measured in requests per second or bytes per second.

Error Rate: Shows the percentage of requests that fail or do not receive a response, highlighting performance issues and bottlenecks. It’s calculated as (Number of failed requests / Total number of requests) x 100.

CPU Utilization: Measures the percentage of CPU capacity utilized during processing, helping assess CPU efficiency. Calculated as (1 — (Idle time / Total time)) x 100.

Memory Utilization: Indicates the percentage of used memory relative to total available, aiding in the identification of memory usage patterns and potential issues. Calculated as (Used memory / Total memory) x 100.

Average Latency Time: Measures the total time it takes for a system to respond to a user’s request, including processing and network transit time, typically expressed in milliseconds.

Network Latency: Refers to the delay in data transmission over a network, influenced by factors such as distance, bandwidth limitations, or network technology, which adds extra time to data transfers.

Wait Time: Represents the time from when a request is sent until the first byte is received, including both the users’ wait time and the system’s processing time.

Concurrent Users: Determines the maximum number of users that can simultaneously use the system without degrading performance or causing errors.

Transaction Success/Fail: Measures the success rate of transactions, indicating the percentage of transactions that are successfully completed versus those that fail, useful for identifying transaction-related issues.

Client-side Metrics and Server-side Metrics

Client-side Metrics

Client-side metrics in performance testing evaluate the user’s experience when interacting with an application or website. These metrics are essential as they reveal the application’s performance across different devices and networks, directly influencing user satisfaction and engagement. Key client-side metrics typically monitored include:
 
Load Time: Measures the time it takes for a page to fully display on the user’s screen, critical for assessing the initial user experience, particularly on content-heavy sites.

Rendering Time: Represents the time needed for the browser to render the page after receiving content from the server. This includes parsing HTML, CSS, and script execution, which are crucial for interactive and visually appealing websites.

Time to Interactive (TTI): Measures how long it takes for a page to become fully interactive and responsive to user inputs, with a shorter TTI being vital for a seamless user experience.

First Contentful Paint (FCP): Tracks the time from when a user starts loading the page to when any part of the page’s content is visually displayed. It’s a key metric for gauging how quickly users perceive the page as loading.

First Input Delay (FID): Measures the time from a user’s first interaction (e.g., clicking a link or tapping a button) to when the browser can start processing event handlers in response to that interaction.

Client-side Error Rate: Monitors errors that occur on the client side, such as JavaScript errors or failed resource loads, impacting the user experience.

Network Timing: Details the time spent during various stages of network requests and responses, including DNS lookup times, TCP connection times, and the time to receive the first byte of a response (TTFB).

Server-side Metrics

Server-side metrics in performance testing assess the performance and efficiency of the backend systems that drive applications or websites. These metrics provide crucial insights into how the server, database, and other backend services handle requests, process data, and respond under varying conditions. Monitoring these metrics is vital for identifying bottlenecks, optimizing resource usage, and ensuring servers can manage expected user loads. Key server-side metrics include:

CPU Utilization: Measures the percentage of CPU capacity utilized by the server. High CPU utilization may indicate extensive request processing or intensive computations, potentially leading to slowdowns if the CPU becomes a bottleneck.

Memory Utilization: Tracks the amount of RAM used by the server. High memory usage can cause performance issues, particularly if it forces the server to swap to slower disk storage.

Disk I/O: Monitors the disk’s input/output operations, including read and write actions crucial for performance in data-intensive applications.

Network I/O: Measures the volume of data sent and received over the network. Elevated network traffic can cause latency and packet loss, degrading the user experience.

Error Rate: Calculates the percentage of requests that result in errors, such as HTTP error codes (e.g., 500 Internal Server Error), exceptions, or other issues that hinder successful processing.

Throughput: Indicates the number of requests a server can handle per unit of time, reflecting the server’s capacity to manage workloads, typically measured in requests per second.

Response Time: Tracks the duration the server takes to respond to requests, including processing and response transmission times.

Database Performance Metrics: Encompasses various indicators like query execution time, transaction rates, and log write times, helping gauge the database’s impact on overall application performance.

Thread and Connection Pools: Monitors the usage and availability of threads and connections within pools. Overutilization can delay request processing.

Garbage Collection: In environments using garbage collection (e.g., Java), this metric tracks the frequency and impact of these events on performance.

Conclusion

Performance testing is essential for developing reliable and efficient applications. By monitoring both client-side and server-side metrics, teams can ensure a seamless user experience and optimize backend efficiency. Regular assessment of these metrics helps identify and solve potential issues, maintaining optimal application performance. Integrating these insights into testing strategies ensures that applications meet modern performance standards, making them both functional and competitive in today’s digital landscape.

Loadium provides you with all the metrics you need in every test run in the most detailed way. Visit Loadium to try it now!

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