Skip to main content

New to Qlik Sense

If you’re new to Qlik Sense, start with this Discussion Board and get up-to-speed quickly.

Announcements
QlikWorld 2023, a live, in-person thrill ride. Save $300 before February 6: REGISTER NOW!
cancel
Showing results for 
Search instead for 
Did you mean: 
jschrader
Contributor III
Contributor III

AAALR() greater than 1.000000

Can anyone point me to the appropriate place in Sense to fix the AAALR() greater than 1.000000 issue?

2 Replies
balabhaskarqlik

Response regarding this issue, from Qlik was, when raised by different people:

Check this link: https://community.qlik.com/thread/176761

Response 1:

AAALR refers to the average length of the aggregation array. For some customers who have large data sets, poor response times ,and/or complex calculations this warning will present in their logs. The general fix for this is to add this entry to the [Settings 7] portion of the Settings.ini for QlikView Server on all nodes:

DisableNewRowApplicator=0

This will switch to a new aggregation array which improves performance.

Response 2:

The warning to which you are referring - QVGeneral: when AAALR(1.293973) is greater than 1.000000, we suggest using new row applicator to improve time and mem effeciency. AAALR" is a very low level concept deep in the QlikView engine. Generally speaking it means the average length of aggregation array. The longer this array is, the more RAM usage and CPU power are to be consumed by the engine to get aggregation result for every hypercube node and can thus effect performance.

When AAALR is greater than 1.0, normally the customer has a large data set and suffers slow responses and high memory usage in their app.

To mitigate this, you can add the below setting to the Settings.ini file located in the QlikTech\QlikViewServer:

DisableNewRowApplicator=0

By setting this parameter to “0”, QlikView will use an new algorithm which is optimized for large data set to do the aggregation, and will consume much less RAM and CPU power.

balabhaskarqlik

Tweak your data model as per this:

The Importance Of Being Distinct