To analyse the client side statistics we have to merge the following graphs inorder to identify the bottleneck.
Running Vusers.
Connections.
Hits per seconds.
Throughput.
Error per second.
Response time.
Merging: We can merge the graph in 3 ways.
Overlay Graph
Tile graph
Correlate graph
Scenario1:
Relation between Hits per second and throughput.
Both should be directlyproportional, if not
Cause1: That could be a network bandwidth issue.
Cause2: Web server might have issue.
Cause3: Application itself having the issue.
Note: Hits are increasing and throughputs are not increasing due to the application issues. We are receiving exception page which impacts high hits low throughput.
Scenario2:
Relation between running users and hits per second?
Both should be directly propositional, if not application itself having a problem (or)
application is not responding well.
Scenario3:
Relation between throughput and response time.
Both should be inversely proportional.
Note: As per the market standard both should be inversely proportional based on boundaries (if you are testing pages).
Scenario4:
Relation between running users and connections
Both should be directly proportional, if not
Cause1: Connection limit issues in the web server.
Cause2: Number of treads limit reached threshold point in the web server.
Controller output message for above issue:
1. Users permanently or prematurely shutdown
2. Web server log max client error.
WEB PAGE DIAGNOSTICS:
Using Web page diagnostics graph we drill down the issues related to component, network, and server level issues (break down drill).
Component Break down graph:
Which will allow you to analyze component level issue any one of the component getting delay to download which will reported as an issue.
Time Taken For First Buffer Graph (TTFB):
If the TTFB is high then the problem is with server or application.
If the TTFB is low and the page response time is very high then that is a network issue.
Note: After this we have to move to server side analysis.
Running Vusers.
Connections.
Hits per seconds.
Throughput.
Error per second.
Response time.
Merging: We can merge the graph in 3 ways.
Overlay Graph
Tile graph
Correlate graph
Scenario1:
Relation between Hits per second and throughput.
Both should be directlyproportional, if not
Cause1: That could be a network bandwidth issue.
Cause2: Web server might have issue.
Cause3: Application itself having the issue.
Note: Hits are increasing and throughputs are not increasing due to the application issues. We are receiving exception page which impacts high hits low throughput.
Scenario2:
Relation between running users and hits per second?
Both should be directly propositional, if not application itself having a problem (or)
application is not responding well.
Scenario3:
Relation between throughput and response time.
Both should be inversely proportional.
Note: As per the market standard both should be inversely proportional based on boundaries (if you are testing pages).
Scenario4:
Relation between running users and connections
Both should be directly proportional, if not
Cause1: Connection limit issues in the web server.
Cause2: Number of treads limit reached threshold point in the web server.
Controller output message for above issue:
1. Users permanently or prematurely shutdown
2. Web server log max client error.
WEB PAGE DIAGNOSTICS:
Using Web page diagnostics graph we drill down the issues related to component, network, and server level issues (break down drill).
Component Break down graph:
Which will allow you to analyze component level issue any one of the component getting delay to download which will reported as an issue.
Time Taken For First Buffer Graph (TTFB):
If the TTFB is high then the problem is with server or application.
If the TTFB is low and the page response time is very high then that is a network issue.
Note: After this we have to move to server side analysis.
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