Load Testing Metrics

Load Testing Metrics

Various metrics help determine how well a system can handle high user traffic and stress conditions when performing load testing. These metrics provide valuable insights into the system’s performance and help identify potential bottlenecks.

Some of the important load-testing metrics are as follows:

Response Time

Response time is the time it takes for a request from the client to be sent and for the server to respond.

• Average Response Time: The average time taken for all requests during the test.
• Peak Response Time: The maximum response time observed during the test.
• 90th/95th Percentile Response Time: The response time below which 90% or 95% of the requests fall.

Throughput

Throughput is the amount of data transferred between the client and server over a period, usually measured in requests per second (RPS) or transactions per second (TPS). It indicates the load the system can handle efficiently. High throughput with low response time is ideal.

Error Rate

The error rate is the percentage of failed requests out of the total requests.
Formula:

Error Rate =  Number of Failed Requests
——————————–  x 100 %
Total Requests

A high error rate may indicate issues like server overload, connectivity problems, or application errors.

 

Resource Utilization

Resource Utilization measures the usage of system resources like CPU, memory, disk, and network bandwidth during the load test.

CPU Utilization: High values may indicate CPU bottlenecks.
Memory Utilization: High usage may suggest memory leaks or inefficient resource handling.
Disk I/O Utilization: Indicates the read/write speed of the disk under load.
Network Utilization: High values can point to bandwidth limitations.

Time to First Byte (TTFB)

The time taken for the server to send the first byte of data back to the client.
A low TTFB indicates good server performance.

Bandwidth

Bandwidth is the amount of data transferred per second during the test, typically measured in Mbps (megabits per second). It helps assess if the network can handle the data load.

Monitoring these metrics helps in understanding the performance of your application under load conditions. Analyzing the results allows you to identify bottlenecks and areas that need optimization, ensuring the application can handle expected and peak loads effectively.