Optimized Aggregations or sums

[#1]

Hello.

I'm currently building an application and according to the selection of the user, i calculated 20 values and set them as ranges of sales for the chart.  I want to calculate how many customers are in each range using the customer's sales volume data.

In the chart i'm setting the dimensions like this:

=ValueList(

    '$(vRange_001)',

    '$(vRange_002)',

    '$(vRange_003)',

    '$(vRange_004)',

    '$(vRange_005)',

    '$(vRange_006)',

    '$(vRange_007)',

    '$(vRange_008)',

    '$(vRange_009)',

    '$(vRange_010)')

And the expression, i'm using the next:

Pick(RowNo(),
Sum({$<Customer_ID = {"=$($(=sum(Volume)))>=$(vBins_1a) and $($(=sum(Volume)))<$(vBins_1b)"}>} Counter),
Sum({$<Customer_ID = {"=$($(=sum(Volume)))>=$(vBins_2a) and $($(=sum(Volume)))<$(vBins_2b)"}>} Counter),
Sum({$<Customer_ID = {"=$($(=sum(Volume)))>=$(vBins_3a) and $($(=sum(Volume)))<$(vBins_3b)"}>} Counter),
Sum({$<Customer_ID = {"=$($(=sum(Volume)))>=$(vBins_4a) and $($(=sum(Volume)))<$(vBins_4b)"}>} Counter),
Sum({$<Customer_ID = {"=$($(=sum(Volume)))>=$(vBins_5a) and $($(=sum(Volume)))<$(vBins_5b)"}>} Counter),
Sum({$<Customer_ID = {"=$($(=sum(Volume)))>=$(vBins_6a) and $($(=sum(Volume)))<$(vBins_6b)"}>} Counter),
Sum({$<Customer_ID = {"=$($(=sum(Volume)))>=$(vBins_7a) and $($(=sum(Volume)))<$(vBins_7b)"}>} Counter),
Sum({$<Customer_ID = {"=$($(=sum(Volume)))>=$(vBins_8a) and $($(=sum(Volume)))<$(vBins_8b)"}>} Counter),
Sum({$<Customer_ID = {"=$($(=sum(Volume)))>=$(vBins_9a) and $($(=sum(Volume)))<$(vBins_9b)"}>} Counter),
Sum({$<Customer_ID = {"=$($(=sum(Volume)))>=$(vBins_10a) and $($(=sum(Volume)))<$(vBins_10b)"}>} Counter))

So, the performance is slow for the user.

any way to improve this?

Thanks

[Nicole-Smith · Accepted solution]

You should be able to improve on this by using the class() function:

class(expression, interval [ , label [ , offset ]])

Creates a classification of expressions. The bin width is determined by the number set as interval. The result is shown as a<=x<b, where a and b are the upper and lower limits of the bin. The x can be replaced by an arbitrary string stated in label. 0 is normally the default starting point of the classification. This can be changed by adding an offset.

Examples:

class( var,10 ) with var = 23 returns '20<=x<30'

class( var,5,'value' ) with var = 23 returns '20<= value <25'

class( var,10,'x',5 ) with var = 23 returns '15<=x<25'

[#3]

Great, the performance is a lot better! Thanks!