Trying to create the Measure below Cumulative that is basically a cumulative YTD calculation applied to the Amount column (Amount = Count(ORDER_ID)).
That print screen was taken from the desktop app, that natively integrates a cumulative option within the graph components. That's not an option in my context where I need to use the Saas version.
Qlik Desktop - Cumulative YTD
I've seen many articles in the community that suggest to use a combination of rangesum and above but I'm trying to avoid that.
Coming from the Power BI world, I was used to calculate it with a Calculate(Count(ORDER_ID), ALL(Date), DatesYTD(Date), Date<= max(Date)). The All() removes all context filtering to the measure, and we then filter according to what we need, applying a set varibale max(Date), to ensure a cumulative calculation of the amount month to month.
I haven't found yet a good equivalent of the All() Power BI function that actually removes the context filtering in a pivot table/graph (here the breakdown by the month dimension), to allow us a to then apply a different filtering for instance in our case on a cumulative YTD basis.
I've tried something with total() in my set analysis, see below, but it's not working:
Count ( Distinct total< [Date.autoCalendar.Year], Date> ${<RECORD_TYPE = {'Declared Transactions'}, Date >=${"=YearStart(MAX(Date))"}, Date<=${"=MAX(Date)"} >} ORDER_ID )
which gives that result:
Qlik Saas - Wrong cumulative YTD measure attempt
Basically that's my problem:
Qliksense documentation
Related
All of the measure that I want to cumulative has the same formula. So, is there any way to use the thing like function or any thing in calculate measure to resolve this issue?
There are two ways to achieve your aim:
1- the first solution is based on using the business intelligence wizard to add time intelligence to your solution.
The time intelligence enhancement is a cube enhancement that adds time calculations (or time views) to a selected hierarchy. This enhancement supports the following categories of calculations:
List item
Period to date.
Period over period growth.
Moving averages.
Parallel period comparisons.
The wizard will let you chose the calculations and measures you want to apply.
Visit : https://learn.microsoft.com/en-us/analysis-services/multidimensional-models/define-time-intelligence-calculations-using-the-business-intelligence-wizard
Visit : http://www.ssas-info.com/analysis-services-articles/62-design/2465-ssas-time-intelligence-wizard
2- Use a dimension table to calculate your calculations, this solution is more complicated, but very powerful and one of the best practices.
The first step is to create a new physical dimension, with real
members for each of the calculations we're going to need. We don't
actually need to create a table in our data warehouse for this
purpose, we can do this with an SQL view like this
CREATE VIEW DateTool AS SELECT ID_Calc = 1, Calc = 'Real Value' UNION ALL SELECT ID_Calc = 2, Calc = 'Year To Date'
Next, we need to add this view to our DSV and create a dimension based
on it. The dimension must have one hierarchy and this hierarchy must
have its IsAggregatable property set to False. The DefaultMember
property of this hierarchy should then be set to the Real Value
member. Giving this dimension a name can be quite difficult, as it
should be something that helps the users understand what it does –
here we've called it Date Tool. It needs no relationship to any
measure group at all to work.
Our next task is to overwrite the value returned by each member so
that they return the calculations we want. We can do this using a
simple SCOPE statement in the MDX Script of the cube:
this code let you create the YEAR-TO-DATE aggregation for all your measures.
SCOPE ([Date Tool].[Calculation].[Year To Date]); THIS = AGGREGATE ( YTD ([Date Order].[Calendar].CurrentMember), [Date Tool].[Calculation].[Real Value]); END SCOPE;
Visit:https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781849689908/6/ch06lvl1sec35/calculation-dimensions
In my SSAS cube, I've several measures defined in MDX which work fine except in one type of aggregation across time periods. Some don't aggregate (and aren't meant to) but one does aggregate but gives the wrong answers. I can see why, but not what to do to prevent it.
The total highlighted in the Excel screenshot below (damn, not allowed to include an image, reverting to old-fashion table) is the simplest case of what goes wrong. In that example, 23,621 is not the grand total of 5,713 and 6,837.
Active Commitments Acquisitions Net Lost Commitments Growth in Commitments
2009 88,526 13,185 5,713 7,472
2010 92,125 10,436 6,837 3,599
Total 23,621 23,621
Active Commitments works fine. It is calculated for a point in time and should not be aggregated across time periods.
Acquisitions works fine.
[Measures].[Growth in Commitments] = ([Measures].[Active Commitments],[Date Dimension].[Fiscal Year Hierarchy].currentMember) - ([Measures].[Active Commitments],[Date Dimension].[Fiscal Year Hierarchy].prevMember)
[Measures].[Net Lost Commitments] = ([Measures].[Acquisitions] - [Measures].[Growth in Commitments])
What's happening in the screenshot is that the total of Net Lost Commitments is calculated from the total of Acquisitions (23,621) minus the total of Growth in Commitments (which is null).
Aggregation of Net Lost Commitments makes sense and works for non-time dimensions. But I want it to show null when multiple time periods are selected rather than an erroneous value. Note that this is not the same as simply disabling all aggregation on the time dimension. The aggregation of Net Lost Commitment works fine up the time hierarchy -- the screenshot shows correct values for 2009 and 2010, and if you expand to quarters or months you still get correct values. It is only when multiple time periods are selected that the aggregation fails.
So my question is how to change the definition of Net Lost Commitments so that it does not aggregate when multiple time periods are selected, but continues to aggregate across all other dimensions? For instance, is there a way of writing in MDX:
CREATE MEMBER CURRENTCUBE.[Measures].[Net Lost Commitments]
AS (iif([Date Dimension].[Fiscal Year Hierarchy].**MultipleMembersSelected**
, null
, [Measures].[Acquisitions] - [Measures].[Growth in Commitments]))
ADVthanksANCE,
Matt.
A suggestion from another source has solved this for me. I can use --
iif(iserror([Date Dimension].[Fiscal Year Hierarchy].CurrentMember),
, null
, [Measures].[Acquisitions] - [Measures].[Growth in Commitments]))
CurrentMember will return an error when multiple members have been selected.
I didn't understand much of the first part of the question, sorry...but at the end I think you ask how to detect if multiple members from a particular dimension are in use in the MDX.
You can examine either of the two axes as a string, and use that to form a true/false test. Remember you can use VBA functions in Microsoft implementations of MDX.
I suggest InStr(1, SetToStr(StrToSet("Axis(1)")), "whatever") = 0 as a way to craft the first argument of your IIF.
This gets the set of members on axis number one, converts it to a string, and looks to see if a certain string is present (it returns the position of that string within the other). Zero means not found (so it returns true). You may need to use axis zero instead, or maybe check both.
To see if multiple members from the same dimension were used, the test string above would have to be more complicated. You want to know if whatever occurs once or twice. You could test if the first occurance of the string was at the same position as the last occurance (by searching backwards); though that could also mean the string wasn't found at all:
IIF(
InStr(1, bigstring, littlestring) = InStrRev(bigstring, littlestring),
'used once',
'used twice or not at all'
)
I came across this post while researching a solution for my own issue with grand totals of calculated measures over time when filters are involved. I think you could have fixed the calculations instead of suppressing them by using dynamic sets. This worked for me.
In my cube there are certain measures which are non-additive, however I can compute a value for every drill down level. Unfortunately the computation is too complex for it to be done in in Analysis Services.
If I precompute the drill down levels I'm interested in, I have to put those values into a separate fact-table / measure group for each drill down level, or don't I? Is it possible to do this in a way that is transparent to the end user? So it should look like there is only one fact table and SSAS automatically selects the value from the correct fact table based on the drill-down level?
I found the answer in a Microsoft forum: http://social.msdn.microsoft.com/Forums/en-US/sqlanalysisservices/thread/d5998b23-936b-4e7b-b593-bd164b20c022
On the Calculate tab you can define a scope statement:
In this really trivial example, internet sales amount will be shown when reseller sales amount is chosen (at calendar quarters).
scope([Measures].[Reseller Sales Amount], [Date].[Calendar].[Calendar Quarter]);
this=[Measures].[Internet Sales Amount];
end scope;
I'm using the follow mdx to keep a running total of the Period Balance measure in my cube:
SUM({[Due Date].[Date].CurrentMember.Level.Item(0):[Due Date].[Date].CurrentMember}, [Measures].[Period Balance])
It works great, however it's really slow as the amount of data displayed increases. I can't use a MTD or YTD because the users may be analyzing data that overlaps years. Any way I can speed this up?
Thanks in advance.
I take it you've seen this? http://sqlblog.com/blogs/mosha/archive/2006/11/17/performance-of-running-sum-calculations-in-sp2.aspx
Failing that, there is another sample which uses the technique of taking the parent's prior totals and the parent's current child from first sibling to current - So you'd sum the prior months and then this month's days - That'll only work if you have a date hierarchy though:
http://www.ssas-info.com/analysis-services-articles/62-design/367-inventory-management-calculations-in-sql-server-analysis-services-2005-by-richard-tkachuk
I think the pictures there explain it better, its the "Summing Increments" section.
Are you query-logging and doing usage-based aggregations?
First some background: I have the typical Date dimension (similar to the one in the Adventure Works cube) and an Account dimension. In my fact table I have daily transaction amounts for the accounts.
I need to calculate cumulative transaction amounts for different accounts for different periods of time. The catch is that whatever is the first period shown on the resulting report should get its transaction amount as-is from the fact table and all the following periods in the report should have cumulative amounts.
For example, I might have a single account on rows and on columns I could have [Date].[Calendar].[Calendar Year].[&2005]:[Date].[Calendar].[Calendar Year].[&2010]. The transaction amount for 2005 should have the sum of transaction amounts that took place in 2005 for that specific account. For the following year, 2006, the transaction amount should be TransactionAmountsIn2005 + TransactionAmountsIn2006. Same goes for the remaining of the years.
My problem is that I don't really know how to specify this kind of calculated member in the cube because the end-user who is responsible for writing the actual MDX queries that produce the reports could use any range of periods on any hierarchy level of the Date dimension.
Hope this made some sense.
Teeri,
I would avoid letting the end-user actually write MDX queries and just force them to use ranges you defined. To clarify, just give them a start and end date, or a range if you will, to select and then go from there. I've worked with accounting and finance developing cubes (General Ledger, etc) for years and this is usually what they were ultimately looking for.
Good luck!