I have a calculated measure that needs to cross join Customer and Product dimension then cross join a total sales measure to get a percentage for a specific customer sale.
[Measures].[Sale Value] / [Measures].[Total Sales]
each measure has a link to the time dimension, and are set to last non empty.
The problem is that as I look at more information over longer periods (days, months, years etc) it gets slower and slower and slower. I am assuming this is because the calculated measure does its processing on the fly and there is no caching.
Is this correct? I have about 2000 customers and 50 products.
Please please help! any information about how to speed this up would be great.
The answer to this was to set a many to many relationship between Customer/Prodcut and the [Measures].[Total Sales] measure group.
Related
In a model that contains the following dimensions:
- Time - granularity month - 5 years - 20 quarters - 60 months
- Suppliers- 6000 suppliers at lowest level
- departments - 500 departments on lowest level
I need to have the distinct count of the suppliers for each department.
I use the function:
with member [measures].[#suppliers] as
distinctcount(([Supplier].[Supplier].[supplier].members
,[Measures].[amount]))
)
select [Measures].[#suppliers] on 0
, order([Department].[Department].[department].members, [#suppliers], BDESC) on 1
from [cube]
where [Time].[Time].[2017 10]:[Time].[Time].[2018 01]
The time component may vary, as the dashboard user is free to choose a reporting period.
But the MDX is very slow. It takes about 38ms to calculate the measure for each row. I want to use this measure to rank the departments and to calculate a cumulative % and assign scores to these values. As you can imagine performance will not improve.
I have tried to use functions and cache the result, but results - for me - got worse (according to the log 2x as bad).
What can I do to improve the performance?
To go fast adding a measure that calculates de Distinct Count on the Supplier ID of the table associated to[Measures].[Amount] will help. In the Schema definition.
The other ones are not scalable as Supplier is growing.
Nonetheless, why did you use DistinctCount instead of Count(NonEmpty())) ?
DistinctCount is mainly for calculating the number of members/tuples that are different in a set. It only makes sense if it's possible to have two same members in a set. As our initial members have no duplicated, it's useless.
Count(NonEmpty()) filters the set whith the nonempty and counts the number of items in the set. This can be easily calculated in parallel
I want to create a member based on this problem
I have a Product A being sold
I want to find the largest range of consecutive days without sale
example:
days 1,2,3 the product not sale, after that,it sold for 15 consecutive days, at 19th day it didnt sell for 2 days and after that it sold every day until the end of the month
so my maximum days without sale was 3
The following query delivers in the Microsoft sample cube Adventure Works what you want:
WITH Member Measures.[days without sales] AS
IIf( [Measures].[Internet Sales Amount] > 0
, 0
,(Measures.[days without sales], [Date].[Calendar].CurrentMember.PrevMember) + 1
)
Member Measures.[Max days without sales] AS
Max( [Date].[Calendar].[Date].Members
,Measures.[days without sales]
)
SELECT { [Measures].[Max days without sales] }
ON COLUMNS
FROM [Adventure Works]
WHERE [Product].[Product].&[486]
The measure days without sales is defined recursively, and returns how many days up to and including the current member of the [Date].[Calendar] hierarchy there was no sales. You may need to adapt the criteria for "without sale", bearing in mind that in MDX, numerical comparisons treat NULL as 0 - which is different from SQL.
This measure only works correctly if there is a member in this hierarchy for each day, i. e. there are no gaps in this hierarchy. And actually, the definition is more general than just working for days: If you use months for the [Date].[Calendar].CurrentMember, it would give you the number of months without sales, etc. It works with each level of the hierarchy.
The measure Max days without sales does not contain the product in its definition, it delivers the maximum days for whatever is in context (in this case the product in the WHERE clause).
Please note that - as actually there is a loop over all days in the [Date].[Calendar] hierarchy when calculating Measures.[Max days without sales], and within that the recursion again iterates along the previous days, and all this for each cell in the result set - this may be slow for large reports.
I am currently trying to create a calculated measure for an SSAS 2008 R2 cube. In a financial cube dealing with accounts receivable data, I have a "Gross Balance" measure, and a "Days Since DOS" measure. The "Days Since DOS" measure is invisible to the user because it is only used in combination with a couple others to gain an average.
I would like the new calculated measure to show the percent of the total gross balance that has a Days Since DOS value > 90.
For example, if the total gross balance were $1000, the total gross balance for records with days since DOS > 90 being $500, the Percent Over 90 Days calculated measure would show 50%.
Would it be possible to do this with my current setup, and if so, how would I go about writing the expression?
I found out that it is in fact possible.
First, create a new named calculation in the DSV using a case statement (for example, call it [Gross Bal Over 90]):
CASE
WHEN [Days Since DOS] > 90 THEN [Gross Balance]
ELSE 0
END
Then, the calculated measure would simply be:
Sum([Gross Bal Over 90])/Sum([Gross Balance])
You can then make [Gross Bal Over 90] invisible to the user, keeping a cleaner look.
I looked at this topic, Calculating the number of days in a time dimension node - with Grand Total, but can't seem to get it.
I have a Time Dimension; [Invoice Date].
I want to count the number of Work Days in that dimension for a specified time period. I'm new to MDX.
Here's what I have.
Count(
Descendants(
[Invoice Date].CurrentMember,
[Invoice Date].[Work Date].[Work Date]
)
)
I'm getting a cube error now.
An easy way to implement this reliably would be to create a physical measure "Day Count". To do this, create a new measure group on the Date dimension table, and define "Day Count" as the Count. On the dimension usage tab, make sure you set a relationship from this measure group to the Invoice Date cube dimension and not the other dimensions.
I have cost center allocation data that is currently fully populated, one record per day, each with one cost center dimension key. The cube has a head count measure (the data is set to "1"), and the aggregate function is set to "LastChild". This means that a head count report will count a person only once in a cost center in any given time period.
Introducing partial allocations - a new measure will have a percentage value for the allocation, allowing for multiple concurrent cost centers where the allocation should add up to 100% (with "day" being the granular level). I am trying to figure out how to configure the aggregation over other time periods. I thought that "Average" should work just fine, i.e. a person who is allocated to a cost center at 50% for half the time period will be reported at 25% for that period.
The problem that I see is that my facts are not populated for days where the allocation to a cost center was 0%. To illustrate:
Employee1 CostCenterA 1/1/2013 50%
Employee1 CostCenterB 1/1/2013 50%
Employee1 CostCenterA 1/2/2013 100%
Employee1 CostCenterA 1/3/2013 100%
... etc with 100% in CostCenterA for all days
The above data on a report by month shows 50% for the allocation to CostCenterB, even though the person was only allocated for one day, and the average percentage on a monthly basis should be 1.6%.
I suppose I could generate the 0%-allocations in the data, but my fact table would explode as a result, so I'd much rather change how the "average" aggregation treats percentage values in facts that are sparsely populated, i.e. the average should be calculated based on the number of granular units in the reporting period (days in the month, in this case 0.5/31), not the number of rows in the fact table (0.5/1). Can this be done in SSAS?
If the measure with the average allocation is off by a factor that is proportional to the "sparseness" of my facts, i.e. the ratio of days in a period and actual fact rows, then it can be corrected as follows:
adjusted average allocation =
(calculated average allocation) * (fact count)/(number of days in period)
I created two new hidden measures, one named [Fact Count] for the fact count (a measure using the AggregateFunction "Count") and a calculated measure named [Days In Period Count] for the number of days, using the expression
COUNT(Descendants([Date].[Calendar].CurrentMember,5),INCLUDEEMPTY)
with [Calendar] being the name of the hierarchy in my Date dimension.
Finally, I added a calculated measure that implements the corrective formula:
[Measures].[Allocative Head Count]/
(
[Measures].[Days In Period Count]/[Measures].[Fact Count]
)
and named it [Adjusted Average Allocation]. This I can now use in reports and it appears to somewhat approximate the average cost center allocation over longer periods.
The formular for [Days In Period Count] btw does not work for the row totals when filters are involved. I opened another question for this.