RANKX–Analysis Services 2012 Tabular

RANKX was a new function made available in DAX at the launch of SQL Server 2012; however Ranking is a widely used function in most BI projects, and was very much anticipated. Having worked extensively with Tabular on a recent project we had the need to rank over large sets of data, with models based on billions of rows. Whilst we were extremely impressed with the performance of Tabular, we found something not quite right with RANKX. Suppose we had the following very simple format of data, and wanted to perform a simple Rank over it.

CountryKey Date Units SalesValue
53 24/01/2012 62 81.290619
156 03/01/2012 3575 5186.565208
48 07/05/2012 321 362.836524
157 16/04/2012 7 8.005637
134 19/08/2012 20 44.12
232 29/07/2012 40 49.313156
178 05/03/2012 47 61.25
63 26/03/2012 51 73.369509
153 17/06/2012 3061 6026.4
31 08/07/2012 3622 5005.038257
140 13/02/2012 9870 7713.968564
14 27/05/2012 29220 46728.26302
98 20/08/2012 297 377.653459
131 30/07/2012 172 182.591931
48 27/03/2012 348 411.801792
157 06/03/2012 3 8.01422

In order to a Rank the above data by country over all time we would probably write a simple DAX query as follows:

    1. DEFINE
    2.   MEASURE FactSales[UnitCount]= SUM(FactSales[Units])
    3.   MEASURE FactSales[Rank]= RANKX(ALL(FactSales[CountryKey]), FactSales[UnitCount])
    4. EVALUATE
    5.   SUMMARIZE(
    6.     FactSales
    7.    ,FactSales[CountryKey]
    8.    ,”ProductSalesUnits”,FactSales[UnitCount]
    9.    ,”Rank”, FactSales[Rank]
    10.   )
    11. ORDER BY
    12.   FactSales[UnitCount] DESC

Now you can see from the query above, in order to avoid any doubt we only rank over a single table and we have no joins to other tables at all. This works fine resulting in the data below.

FactSales[CountryKey] [ProductSalesUnits] [Rank]
227 91818113 1
226 21487836 2
39 9033389 3
14 7597054 4
82 4656585 5
109 3678302 6
75 3371681 7
140 2511239 8
107 1850520 9
208 1443241 10
31 1092047 11
153 997860 12
201 914827 13
59 870366 14
163 840927 15

Now assume we rank over a lot more data and wish to apply a simple CALCULATETABLE in order to filter some data out. We may write a DAX Query as follows:

  1. DEFINE
  2.   MEASURE FactSales[UnitCount]= SUM(FactSales[Units])
  3.   MEASURE FactSales[Rank]= RANKX(ALL(FactSales[CountryKey]), FactSales[UnitCount])
  4. EVALUATE
  5.   CALCULATETABLE (
  6.     SUMMARIZE(
  7.       FactSales
  8.      ,FactSales[CountryKey]
  9.      ,”ProductSalesUnits”,FactSales[UnitCount]
  10.      ,”Rank”, FactSales[Rank]
  11.     )
  12.   ,DATESBETWEEN(‘Date'[CalDate], DATE(2012,1,1), DATE(2012,8,31))
  13. )
  14. ORDER BY
  15.   FactSales[UnitCount] DESC

In this instance we are joining to a Date dimension, but that is it.  The above query yields the below result.

FactSales[CountryKey] [ProductSalesUnits] [Rank]
227 83147875 1
226 19446567 2
39 8137335 3
14 6769390 4
82 4100168 5
109 3321496 6
75 3010890 7
140 2165554 8
107 1655472 9
208 1274740 10
31 987122 11
153 908938 12
201 853322 13
59 771677 14

That works, we get results as we would expect within seconds. Now, if instead of ranking over an integer field, let’s apply the RANKX function to a real number. In this example we get a worrying result set using the DAX as follows:

  1. DEFINE
  2.   MEASURE FactSales[SalesValue]= FactSales[Sales Value]
  3.   MEASURE FactSales[Rank]= RANKX(ALL(FactSales[CountryKey]), FactSales[UnitCount])
  4. EVALUATE
  5.   CALCULATETABLE (
  6.     SUMMARIZE(
  7.       FactSales
  8.      ,FactSales[CountryKey]
  9.      ,”ProductSalesValue”,FactSales[Sales Value]
  10.      ,”Rank”, FactSales[Rank]
  11.     )
  12.   ,DATESBETWEEN(‘Date'[CalDate], DATE(2012,1,1), DATE(2012,8,31))
  13. )
  14. ORDER BY
  15.   FactSales[UnitCount] DESC

Which outputs:

FactSales[CountryKey] [ProductSalesValue] [Rank]
227 84074007.25 1
226 29928143.25 3
14 10859628.74 4
39 8451588.111 4
109 7964922.769 6
82 6254219.85 6
75 4730390.37 7
107 2466064.97 9
208 1904009.18 10
140 1862708.961 11
153 1311217.35 11
22 1207366.72 13
59 1182179.95 15

Now let’s be clear, all we have done is simply change the measure from an Integer to a Float, the rest of the data is the same. You will notice that there are tie’s in the data that there should not be. Having scratched our heads for hours, rebuilt the model, re wrote the DAX, and had a number of colleagues check it over we found that when no calculate table is applied, then we get the correct answer again as follows:

  1. DEFINE
  2.   MEASURE FactSales[SalesValue]= FactSales[Sales Value]
  3.   MEASURE FactSales[Rank]= RANKX(ALL(FactSales[CountryKey]), FactSales[UnitCount])
  4. EVALUATE
  5.   SUMMARIZE(
  6.     FactSales
  7.    ,FactSales[CountryKey]
  8.    ,”ProductSalesValue”,FactSales[Sales Value]
  9.    ,”Rank”, FactSales[Rank]
  10. )
  11. ORDER BY
  12.   FactSales[UnitCount] DESC
FactSales[CountryKey] [ProductSalesValue] [Rank]
227 92885561.31 1
226 33237033.3 2
14 12253005.68 3
39 9414266.358 4
109 8928147.606 5
82 7120811.54 6
75 5296490.13 7
107 2756500.54 8
208 2146627.18 9
140 2115750.609 10
153 1479858.53 11
22 1339255.82 12
59 1324799.98 13
105 1320651.83 14
31 1277065.779 15

So what we have learnt here is that RANKX seems to give the wrong answer, but only when ranking over real numbers, and only when we filter the data set in some way using CALCULATETABLE. Not being able to find a clear reason for this behaviour we eventually gave in and raised it with Microsoft. Having spent a week or so working it through with the great support team at Microsoft, it seems that this is a current “feature” of RANKX. It is believed to be a floating point arithmetic issue that is driven from how floating point numbers are stored. It is documented here:

http://support.microsoft.com/kb/78113/EN-US

There is also some further information documented here:

http://technet.microsoft.com/en-us/library/gg492146.aspx

http://www.microsoft.com/en-us/download/details.aspx?id=4106

I have been told by Microsoft that this is the same as it is in Excel, and Analysis Services, however still does not explain why the CALCULATETABLE makes a difference.  I am still working with Microsoft to see if we can get to the bottom of it.   However, if precision beyond 15 significant figures within the rank is not important to you (up to 99 Billion with 2DP) this issue is very easily worked around. Take the following DAX query:

  1. DEFINE
  2.   MEASURE FactSales[UnitCount]= FactSales[Royalty Value]
  3.   MEASURE FactSales[Rank]= RANKX(ALL(FactSales[CountryKey]), ROUND(FactSales[UnitCount],2))
  4. EVALUATE
  5.   CALCULATETABLE (
  6.     SUMMARIZE(
  7.       FactSales
  8.      ,FactSales[CountryKey]
  9.      ,”ProductSalesValue”,FactSales[UnitCount]
  10.      ,”Rank”, FactSales[Rank]
  11.     )
  12. ,DATESBETWEEN(‘Date'[CalDate], DATE(2012,1,1), DATE(2012,8,31))
  13. )
  14. ORDER BY
  15.   FactSales[UnitCount] DESC

Which now correctly outputs:

FactSales[CountryKey] [ProductSalesValue] [Rank]
227 84074007.25 1
226 29928143.25 2
14 10859628.74 3
39 8451588.111 4
109 7964922.769 5
82 6254219.85 6
75 4730390.37 7
107 2466064.97 8
208 1904009.18 9
140 1862708.961 10
153 1311217.35 11
22 1207366.72 12
59 1182179.95 14
105 1196551.61 13
31 1132926.109 15

You will notice that all I have done is ROUND the measure in the RANKX function to 2 decimal places, this will stop the calculation engine from ranking over an approximated value, and force it RANK on a decimal with less than 15 significant figures, which will in turn fix the ranking. Further to this, rounding the values prior to it being loaded into the model will also work, just be sure to keep below the 15 Significant figures if possible.

So in summary, it seems that the calculation engine, when using the RANKX function does not handle the float data type as we may expect. This can be easily rectified by losing some precision on the RANK measure you define. We have been informed that using the Currency function will also fix the problem. I hope this stops others going through the same pain. As and When i get any further information on this issue I will post it up.

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