I inherited some mdx code which uses calculated members and the sum function. I need to make a new member using SUM but and OR (union?) set. I have tried various syntaxes but they all error.
I have code as below:
-- uses this date filter
set [as_of_month] as {strtomember("[Date].[Year - Month].[Month].&[" + cstr(format(cdate("Jul 2019"),"yyyy-MM")) + "-01T00:00:00]")}
-- member 1
member [SIONLY_MTH] as sum([as_of_month] * [Incident Details].[Is SI].[Is SI], [Measures].[Environment Impact Count])
-- member 2
member [MajorNC_Month] as sum([as_of_month] *
[Impact].[Impact].&[3] *
[Non Compliance].[Non Compliance Type].&[Major non-compliance],
[Measures].[Non Compliance Count]) + 0
-- I need a new member which is an OR of the previous 2, ie, count of
-- SI_ONLYMONTH or [MajorNC_Month] filtered by [as_of_month]
member [LegalSI_EnvSI_Month] as SUM([as_of_month] * {[Incident Details].[Is SI].[Is SI] , [Non Compliance].[Non Compliance Type].&[Major non-compliance]}
, [Measures].[Environment Impact Count]) + 0
The set inside the last sum function doesnt work, it returns #Error.
Does anyone know how to use a unioned set as the argument to the SUM function in mdx?
Thanks
Your problem basicly deals with the concepts of dimensionality and hierarchility
Try this
sum(
{
([as_of_month] * [Incident Details].[Is SI].[Is SI][Impact].[Impact].defaultmember[Non Compliance].[Non Compliance Type].defaultmember),
([as_of_month] * [Incident Details].[Is SI].defaultmember*[Impact].[Impact].&[3]*[Non Compliance].[Non Compliance Type].&[Major non-compliance])
}
,
[Measures].[Environment Impact Count])
Edit: Includes explanation on how and why the above query works.
In your problem you have two sets that have diffrent hierarchies in them. So you have to balance them. Lets take a look at your first set
sum([as_of_month] * [Incident Details].[Is SI].[Is SI],
[Measures].[Environment Impact Count])
This doesnt explicitly include "[Impact].[Impact]" however before executing SSAS takes the liberty to include [Impact].[Impact].defaultmember in the query. Based on this fact I balanced both of your sets by expicitly including the default members of attribute hierarchies that were orignally not part of your Set.
Next I encapsulaed them in "()" to indicate that they are a tuple of a larger Set.Then both these tuples are encapsulated in "{}" like this "{(tuple1),(tuple2)}"
Related
I have a bunch of calculated members that I need to create which are referenced off a single date.
Rather than repeating the MDX that gets the date member for which the measure will be based off, is there a way to create the date member at the start, and then reference it throughout so that I don't have to repeat the MDX multiple times? I was thinking something like the below however it returns a NULL:
WITH MEMBER [Date].[Retail].[Closing Date] AS
IIF (
[Date].[Retail].CurrentMember.Level.Name = 'Date',
[Date].[Retail].CurrentMember.PrevMember,
[Date].[Retail].CurrentMember
)
MEMBER [Measures].[Closing New] AS
(
[Date].[Retail].[Closing Date],
[Measures].[On Hand Quantity]
)
SELECT
[Date].[Retail].[Date].Members ON ROWS,
{
[Measures].[On Hand Quantity],
[Measures].[Closing New]
} ON COLUMNS
FROM
Retail
WHERE
[Date].[Retail Year].&[2017]
As above, I want to use the Closing Date member multiple times for various calculations.
To investigate why it is returning null try something like the following to see if it is as you expect:
WITH
MEMBER [Measures].[nm] AS
[Date].[Retail].CurrentMember.Level.Name
SELECT
{
[Measures].[On Hand Quantity],
[Measures].[nm]
} ON 0,
[Date].[Retail].[Date].Members ON 1
FROM Retail
WHERE [Date].[Retail Year].&[2017];
(I also switched the declaration order inside the SELECT clause as it is standard to declare columns, as it is axis 0, first followed by rows)
Why yes, there is!
the pattern I follow here is to create a set of one member then reference the first item of that set.
with set currentDate as
[Date].[Retail].[Date].&[20160725]
then reference it in the remainder of your query as
currentDate.item(0)
I have a question related to creating a (more efficient) custom Distinct Count Measure using MDX.
Background
My cube has several long many to many relationship chains between Facts and Dimensions and it is important for me to be able to track which members in certain Dimensions do and do not relate to other Dimensions. As such, I have created a "Not Related" record in each of my dimension tables and set those records' ID values to -1. Then in my intermediate mapping fact tables I use the -1 ID to connect to these "Not Related" records.
The issue arises when I try to run a normal out-of-the-box distinct count on any field where the -1 members are present. In the case that a -1 member exists, the distinct count measure will return a result of 1 more than the true answer.
To solve this issue I have written the following MDX:
CREATE MEMBER CURRENTCUBE.[Measures].[Provider DCount]
AS
//Oddly enough MDX seems to require that the PID (Provider ID) field be different from both the linking field and the user sliceable field.
SUM( [Providers].[PID Used For MDX].Children ,
//Don't count the 'No Related Record' item.
IIF( NOT([Providers].[PID Used For MDX].CURRENTMEMBER IS [Providers].[PID Used For MDX].&[-1])
//For some reason this seems to be necessary to calculate the Unknown Member correctly.
//The "Regular Provider DCount Measure" below is the out-of-the-box, non-MDX measure built off the same field, and is not shown in the final output.
AND [Measures].[Regular Provider DCount Measure] > 0 , 1 , NULL )
),
VISIBLE = 1 , DISPLAY_FOLDER = 'Distinct Count Measures' ;
The Issue
This MDX works and always shows the correct answer (yeah!), but it is EXTREMELY slow when users start pulling Pivot Tables with more than a few hundred cells that use this measure. For less than 100 cells, the results are nearly instantaneously. For a few thousand cells (which is not uncommon at all), the results could take up to an hour to resolve (uggghhh!).
Can anyone help show me how to write a more efficient MDX formula to accomplish this task? Your help would be GREATLY appreciated!!
Jon Oakdale
jonoakdale#hotmail.com
Jon
You can use predefined scope to nullify all unnecessary (-1) members and than create your measure.
SCOPE ([Providers].[PID Used For MDX].&[-1]
,[Measures].[Regular Provider DCount Measure]);
THIS = NULL;
END SCOPE;
CREATE MEMBER CURRENTCUBE.[Measures].[Provider DCount]
AS
SUM([Providers].[PID Used For MDX].Children
,[Measures].[Regular Provider DCount Measure]),
VISIBLE = 1;
By the way, I used in my tests [Providers].[PID Used For MDX].[All].Children construction since don't know, what is dimension / hierarchy / ALL-level in your case. It seems like [PID Used For MDX] is ALL-level and [Providers] is name of dimension and hierarchy, and HierarchyUniqueName is set to Hide.
I'm attempting to create a new Calculated Measure that is based on 2 different attributes. I can query the data directly to see that the values are there, but when I create the Calculated Member, it always returns null.
Here is what I have so far:
CREATE MEMBER CURRENTCUBE.[Measures].[Absorption]
AS sum
(
Filter([Expense].MEMBERS, [Expense].[Amount Category] = "OS"
AND ([Expense].[Account Number] >= 51000
AND [Expense].[Account Number] < 52000))
,
[Measures].[Amount - Expense]
),
VISIBLE = 1 , ASSOCIATED_MEASURE_GROUP = 'Expense';
Ultimately, I need to repeat this same pattern many times. A particular accounting "type" (Absorption, Selling & Marketing, Adminstrative, R&D, etc.) is based on a combination of the Category and a range of Account Numbers.
I've tried several combinations of Sum, Aggregate, Filter, IIF, etc. with no luck, the value is always null.
However, if I don't use Filter and just create a Tuple with 2 values, it does give me the data I'd expect, like this:
CREATE MEMBER CURRENTCUBE.[Measures].[Absorption]
AS sum
(
{( [Expense].[Amount Category].&[OS], [Expense].[Account Number].&[51400] )}
,
[Measures].[Amount - Expense]
),
VISIBLE = 1 , ASSOCIATED_MEASURE_GROUP = 'Expense';
But, I need to specify multiple account numbers, not just one.
In general, you should only use the FILTER function when you need to filter your fact table based on the value of some measure (for instance, all Sales Orders where Sales Amount > 10.000). It is not intended to filter members based on dimension properties (although it could probably work, but the performance would likely suffer).
If you want to filter by members of one or more dimension attributes, use tuples and sets to express the filtering:
CREATE MEMBER CURRENTCUBE.[Measures].[Absorption]
AS
Sum(
{[Expense].[Account Number].&[51000]:[Expense].[Account Number].&[52000].lag(1)} *
[Expense].[Amount Category].&[OS],
[Measures].[Amount - Expense]
),
VISIBLE = 1 , ASSOCIATED_MEASURE_GROUP = 'Expense';
Here, I've used the range operator : to construct a set consisting of all [Account Number] members greater than or equal to 51000 and less than 52000. I then cross-join * this set with the relevant [Amount Category] attribute, to get the relevant set of members that I want to sum my measure over.
Note that this only works if you actually have a member with the account number 51000 and 52000 in your Expense dimension (see comments).
An entirely different approach, would be to perform this logic in your ETL process. For example you could have a table of account-number ranges that map to a particular accounting type (Absorption, Selling & Marketing, etc.). You could then add a new attribute to your Expense-dimension, holding the accounting type for each account, and populate it using dynamic SQL and the aforementioned mapping table.
I don't go near cube scripts but do you not need to create some context via the currentmember function and also return some values for correct evaluation against the inequality operators (e.g.>) via the use of say the membervalue function ?
CREATE MEMBER CURRENTCUBE.[Measures].[Absorption]
AS sum
(
[Expense].[Amount Category].&[OS]
*
Filter(
[Expense].[Account Number].MEMBERS,
[Expense].[Account Number].currentmember.membervalue >= 51000
AND
[Expense].[Account Number].currentmember.membervalue < 52000
)
,
[Measures].[Amount - Expense]
),
VISIBLE = 1 , ASSOCIATED_MEASURE_GROUP = 'Expense';
EDIT
Dan has used the range operator :. Please make sure your hierarchy is ordered correctly and that the members you use with this operator actually exist. If they do not exist then they will be evaluated as null:
Against the AdvWks cube:
SELECT
{} ON 0
,{
[Date].[Calendar].[Month].&[2008]&[4]
:
[Date].[Calendar].[Month].&[2009]&[2]
} ON 1
FROM [Adventure Works];
Returns the following:
If the left hand member does not exist in the cube then it is evaluated as null and therefore open ended on that side:
SELECT
{} ON 0
,{
[Date].[Calendar].[Month].&[2008]&[4]
:
[Date].[Calendar].[Month].&[1066]&[2] //<<year 1066 obviously not in our cube
} ON 1
FROM [Adventure Works];
Returns:
I am trying to calculate percentile (for example 90th percentile point of my measure) in a cube and I think I am almost there. The problem I am facing is, I am able to return the row number of the 90th percentile, but do not know how to get my measure.
With
Member [Measures].[cnt] as
Count(NonEmpty(
-- dimensions to find percentile on (the same should be repeated again
[Calendar].[Hierarchy].members *
[Region Dim].[Region].members *
[Product Dim].[Product].members
,
-- add the measure to group
[Measures].[Profit]))
-- define percentile
Member [Measures].[Percentile] as 90
Member [Measures].[PercentileInt] as Int((([Measures].[cnt]) * [Measures].[Percentile]) / 100)
**-- this part finds the tuple from the set based on the index of the percentile point and I am using the item(index) to get the necessary info from tuple and I am unable to get the measure part
Member [Measures].[PercentileLo] as
(
Order(
NonEmpty(
[Calendar].[Hierarchy].members *
[Region Dim].[Region].members *
[Product Dim].[Product].members,
[Measures].[Profit]),
[Measures].[Profit].Value, BDESC)).Item([Measures].[PercentileInt]).Item(3)**
select
{
[Measures].[cnt],
[Measures].[Percentile],[Measures].[PercentileInt],
[Measures].[PercentileLo],
[Measures].[Profit]
}
on 0
from
[TestData]
I think there must a way to get measure of a tuple found through index of a set. Please help, let me know if you need any more information. Thanks!
You should extract the tuple at position [Measures].[PercentileInt] from your set and add the measure to it to build a tuple of four elements. Then you want to return its value as the measure PercentileLo, i. e. define
Member [Measures].[PercentileLo] as
(
[Measures].[Profit],
Order(
NonEmpty(
[Calendar].[Hierarchy].members *
[Region Dim].[Region].members *
[Product Dim].[Product].members,
[Measures].[Profit]),
[Measures].[Profit], BDESC)).Item([Measures].[PercentileInt])
)
The way you implemented it, you tried to extract the fourth (as Item() starts counting from zero) item from a tuple containing only three elements. Your ordered set only has three hierarchies.
Just another unrelated remark: I think you should avoid using complete hierarchies for [Calendar].[Hierarchy].members, [Region Dim].[Region].members, and [Product Dim].[Product].members. Your code looks like you are including all levels (including the all member) in the calculation. But I do not know the structure and names of your cube, hence I may be wrong with this.
An alternate method could be to find the median of the last 20% of the records in the table. I've used this combination of functions to find the 75th percentile. By dividing the record count by 5, you can use the TopCount function to return a set of tuples that make up 20% of the whole table sorted in descending order by your target measure. The median function should then land you at the correct 90th percentile value without having to find the record's coordinates. In my own use, I use the same measure for the last parameter in both the Median and TopCount functions.
Here's my code:
WITH MEMBER Measures.[90th Percentile] AS MEDIAN(
TOPCOUNT(
[set definition]
,Measures.[Fact Table Record Count] / 5
,Measures.[Value by which to sort the set so the first 20% of records are chosen]
)
,Measures.[Value from which the median should be determined]
)
Based on what you've supplied in your problem definition, I would expect your code to look something like this:
WITH MEMBER Measures.[90th Percentile] AS MEDIAN(
TOPCOUNT(
{
[Calendar].[Hierarchy].members *
[Region Dim].[Region].members *
[Product Dim].[Product].members
}
,Measures.[Fact Table Record Count] / 5
,[Measures].[Profit]
)
,[Measures].[Profit]
)
BACKGROUND: I've been using MDX for a bit but I am by no means an expert at it - looking for some performance help. I'm working on a set of "Number of Stores Authorized / In-Stock / Selling / Etc" calculated measures (MDX) in a SQL Server Analysis Services 2012 Cube. I had these calculations performing well originally, but discovered that they weren't aggregating across my product hierarchy the way I needed them to. The two hierarchies predominantly used in this report are Business -> Item and Division -> Store.
For example, in the original MDX calcs the Stores In-Stock measure would perform correctly at the "Item" level but wouldn't roll up a proper sum to the "Business" level above it. At the business level, we want to see the total number of store/product combinations in-stock, not a distinct or MAX value as it appeared to do originally.
ORIGINAL QUERY RESULTS: Here's an example of it NOT working correctly (imagine this is an Excel Pivot Table):
[FILTER: CURRENT WEEK DAYS]
[BUSINESS] [AUTH. STORES] [STORES IN-STOCK] [% OF STORES IN STOCK]
[+] Business One 2,416 2,392 99.01%
[-] Business Two 2,377 2,108 93.39%
-Item 1 2,242 2,094 99.43%
-Item 2 2,234 1,878 84.06%
-Item 3 2,377 2,108 88.68%
-Item N ... ... ...
FIXED QUERY RESULTS: After much trial and error, I switched to using a filtered count of a CROSSJOIN() of the two hierarchies using the DESCENDANTS() function, which yielded the correct numbers (below):
[FILTER: CURRENT WEEK DAYS]
[BUSINESS] [AUTH. STORES] [STORES IN-STOCK] [% OF STORES IN STOCK]
[+] Business One 215,644 149,301 93.90%
[-] Business Two 86,898 55,532 83.02%
-Item 1 2,242 2,094 99.43%
-Item 2 2,234 1,878 99.31%
-Item 3 2,377 2,108 99.11%
-Item N ... ... ...
QUERY THAT NEEDS HELP: Here is the "new" query that yields the results above:
CREATE MEMBER CURRENTCUBE.[Measures].[Num Stores In-Stock]
AS COUNT(
FILTER(
CROSSJOIN(
DESCENDANTS(
[Product].[Item].CURRENTMEMBER,
[Product].[Item].[UPC]
),
DESCENDANTS(
[Division].[Store].CURRENTMEMBER,
[Division].[Store].[Store ID]
)
),
[Measures].[Inventory Qty] > 0
)
),
FORMAT_STRING = "#,#",
NON_EMPTY_BEHAVIOR = { [Inventory Qty] },
This query syntax is used in a bunch of other "Number of Stores Selling / Out of Stock / Etc."-type calculated measures in the cube, with only a variation to the [Inventory Qty] condition at the bottom or by chaining additional conditions.
In its current condition, this query can take 2-3 minutes to run which is way too long for the audience of this reporting. Can anyone think of a way to reduce the query load or help me rewrite this to be more efficient?
Thank you!
UPDATE 2/24/2014: We solved this issue by bypassing a lot of the MDX involved and adding flag values to our named query in the DSV.
For example, instead of doing a filter command in the MDX code for "number of stores selling" - we simply added this to the fact table named query...
CASE WHEN [Sales Qty] > 0
THEN 1
ELSE NULL
END AS [Flag_Selling]
...then we simply aggregated these measures as LastNonEmpty in the cube. They roll up much faster than the full-on MDX queries.
It should be much faster to model your conditions into the cube, avoiding the slow Filter function:
If there are just a handful of conditions, add an attribute for each of them with two values, one for condition fulfilled, say "cond: yes", and one for condition not fulfilled, say "cond: no". You can define this in a view on the physical fact table, or in the DSV, or you can model it physically. These attributes can be added to the fact table directly, defining a dimension on the same table, or more cleanly as a separate dimension table referenced from the fact table. Then define your measure as
CREATE MEMBER CURRENTCUBE.[Measures].[Num Stores In-Stock]
AS COUNT(
CROSSJOIN(
DESCENDANTS(
[Product].[Item].CURRENTMEMBER,
[Product].[Item].[UPC]
),
DESCENDANTS(
[Division].[Store].CURRENTMEMBER,
[Division].[Store].[Store ID]
),
{ [Flag dim].[cond].[cond: yes] }
)
)
Possibly, you even could define the measure as a standard count measure of the fact table.
In case there are many conditions, it might make sense to add just a single attribute with one value for each condition as a many-to-many relationship. This will be slightly slower, but still faster than the Filter call.
I believe you can avoid the cross join as well as filter completely. Try using this:
CREATE MEMBER CURRENTCUBE.[Measures].[Num Stores In-Stock]
AS
CASE WHEN [Product].[Item Name].CURRENTMEMBER IS [Product].[Item Name].[All]
THEN
SUM(EXISTS([Product].[Item Name].[Item Name].MEMBERS,[Business].[Business Name].CURRENTMEMBER),
COUNT(
EXISTS(
[Division].[Store].[Store].MEMBERS,
(
[Business].[Business Name].CURRENTMEMBER,
[Product].[Item Name].CURRENTMEMBER
),
"Measure Group Name"
)
))
ELSE
COUNT(
EXISTS(
[Division].[Store].[Store].MEMBERS,
(
[Business].[Business Name].CURRENTMEMBER,
[Product].[Item Name].CURRENTMEMBER
),
"Measure Group Name"
)
)
END
I tried it using a dimension in my cube and using Area-Subsidiary hierarchy.
The case statement handles the situation of viewing data at Business level. Basically, the SUM() across all members of Item Names used in CASE statement calculates values for individual Item Names and then sums up all the values. I believe this is what you needed.