JMeter Result Analysis

After executing a load test we need to interpret the result. For result interpretation we use Listeners that provide different matrices about the load test. In this post we will see the most commonly used matrices, their meanings and the how they are calculated internally from raw data.

Result matrices by Aggregate Report-

Label- Label is the name of the sample or the Transaction Controller

# Samples - The total number of samples corresponding to a given sample

Average - The average time taken (in milliseconds) to execute the requests under a given label. So, if there are 10 samples getting executed then average time taken will be-
Average = Total time taken by all samples /#samples

Median - The median is basically the middle value of response time in the sorted list of samples

90% Line - The Apache JMeter manual describes 90% line as- "90% of the samples took no more than this time". It is actually the 90 percentile of the response times of the samples -
90 percentile = (90/100)*N+1/2 where N is the number of samples
So, if there are 10 samples then 90%line will be 9.5 or 9. It means the 9th value in the sorted list of samples (sorted according to ascending order of their response times) will be the 90% line value.

Min - The minimum time (in milliseconds) taken by the sample

Max - The maximum time (in milliseconds) taken by the sample

Error % - Percentage of errors in the samples

Throughput - Throughput as we all know is output per unit time. In JMeter terms we define throughput as the amount of load applied on the server. So, numerically-
Throughput = Total number of requests to the server/ Total time
or Total number of requests to the server/(End time of last sample -Start time of first sample)
Here we just defined Total time in which load was applied on the server as time duration between Start of first sample and end of last sample.

KB/sec - The metric KB/sec is nothing but the throughput measured in terms of bytes. So,
KB/sec = (Throughput*Average bytes) /1024
Here Average bytes is the average value of the sample response in bytes and the term 1024 is used to convert the value (Throughput*Average bytes) into kilobytes.