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unique client ip addresses metric...

r_muresu
Participant

hi, I would like an explanation for the metric in question because the values are not what I expected.

the value of metric unique client ip addresses, for a day is 102k, against the size of the client IP addresses dimension is 7914...

why this difference?

these settings of CAS:

thanks for your help

Roberto

3 REPLIES 3

Adam_Tryba
Dynatrace Advisor
Dynatrace Advisor

Hi

So the thing you are concerned about, is the fact that
- system claims to have ~102k unique client IP addresses

- but at the same time, it is able to show only ~8k, with specific address

Based on the configuration you showed,

1. you are currently collecting data for users with user names (if such are configured and reported)

In fact in your situation, you do not have user names, as the 'unique client IP addresses' are equal to 'unique users'.

2. for those without user names, you are performing aggregation of their IP addresses, based on 'AS'.

So just one client IP address will be reported per 'AS'

3. at the same time, you are telling the system to count unique users visible in specific 'AS'

So under that one 'client IP' address from point '2', you may expect the number for 'unique client IP addresses' will be greater than 1.

Here you can find some examples of how user aggregation works:

user aggregation

So in your situation, those IP addresses you see, are the most representative ones, each one from specific 'AS', therefore they should not be treated literally. If you treated them literally, you would get false conclusion, that there is just one client in each 'AS', accessing resources which you are monitoring with DCRUM.

Best Regards

Adam Tryba

ok. I have changed the configuration as below.

I removed the option also count user IP addresses that are aggregated and i have added the network range ip.

after restart, the value of metric unique client ip addresses is equal of number of rows of dimension client ip address 7914, and unique users are 897

You made the change today, but you are browsing through the data from the past. Please focus of data that arrived after the change (the change is simply not effective, or you could say not fully effective for the past data).