Throughput Graph

This has been noticed that the throughput graph is always less attentive. The reason could be either a performance tester does not understand its importance or does not care about it because it is not included in the NFRs. Of course, it is true that Throughput does not fall under the core performance metric category but it does not mean that it can be ignored. Let’s try to understand the importance of throughput graph in performance testing.

As per standard definition, the unit of information a system can process or transfer in a given amount of time represents Throughput. If you use both LoadRunner and JMeter then you may get two definitions of Throughput. In LoadRunner, throughput is the amount of data sent by the server to the client. On the other hand, JMeter represents the number of requests sent by a client to the server. Although JMeter has separate ‘sent bytes’ and ‘received bytes’ graphs.

Here, we will discuss LoadRunner’s ‘Throughput’ graph or JMeter’s ‘Received Bytes’ graph.

Throughput Graph
Figure 01: Throughput Graph


  1. To measure the amount of data received from the server in bytes, KB or MB
  2. To identify the network bandwidth issue

Throughput Graph axes represent:

  1. X-axis: This graph shows elapsed time on X-axis. The elapsed time may be relative time or actual time as per the setting of graph. The X-axis of the graph also shows the complete duration of the test (without applying any filter).
  2. Y-axis: It represents the amount of data received from server to client (in bytes/KB/MB)

How to read:

The graph line shows how much amount of data sent by the server in per second interval. As I explained in the graph’s purpose section using a throughput graph you can get an idea on network bandwidth issue (if any). A flat throughput graph with an increase in network latency and user load shows an issue in network bandwidth. Apart from this if throughput scales downward as time progresses and the number of users (load) increase, this indicates that the possible bottleneck is at the server end. In that case, you need to merge the throughput graph with error graph, user graph and response time graph to identify the exact bottleneck.

Merging of Throughput graph with others:

  1. With User graph: Merge User graph with Throughput graph and understand the pattern. Ideally, throughput should increase during the user ramp-up period. As the number of users progress, more data comes from the server. The throughput graph should remain in a range during the steady-state of the test. If you observe a sudden fall in the throughput graph during steady-state of the test then it indicates the server-side issue. What is the issue? That you need to investigate using server logs.
    Another scenario is when throughput becomes flat while increasing number of users then it may lead to the bandwidth issue. To confirm bandwidth issue you need to merge the throughput graph with Latency.
  2. With Network Latency graph: To confirm network bandwidth issue you have to look into Network latency graph by merging with throughput graph. If latency increases without an increase in throughput then it’s a Network Bandwidth issue.
  3. With Response Time graph: Response Time graph can be correlated with Throughput graph. Increase in response time with constant throughput may be due to network bandwidth issue. To confirm please refer Latency graph. If you see throughput degradation with an increase in response time then start the investigation at the server end.
  4. With Error per second graph: Throughput graph can be merged with error per second graph to identify the point when error starts to occur and what type of error?

Remember: Before making any conclusion, you should properly investigate the root cause of the performance bug by referring to all the related analysis graphs.

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