I have a requirement to display the measures of a cube in hierarchy (browsing in excel or SSRS). Say I have four measures in fact table revenue A, revenue B, expense A. I need to show it as
'+ Profit (calculated measure)
'--+Revenue (calculated measure)
'----+Revenue A (Fact measure)
'----+Revenue B (Fact measure)
'--+expense (calculated measure)
'----+Expense A (Fact measure)
'----+Expense B (Fact measure)
I created the calculated measure however I am unable to solve the hierarchy issue for the measures. I am not able to use Display folders or degenerate dimensions to achieve the result. Can anyone help?
You don't need to create a hierarchy if the purpose is just to be able to browse it. Create a named set in your cube(if changing cube structure is an option), or a query scoped named set(if it should be temporarily created) and put all the requisite measures into it.
WITH SET MySetOfMeasures AS
{
Measures.Profit,
Measures.Revenue,
Measures.[Revenue A],
Measures.[Revenue B],
Measures.expense,
Measures.[Expense A],
Measures.[Expense B]
}
Then you can invoke this set in your MDX.
SELECT MySetOfMeasures ON 0 FROM [YourCube]
Related
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.
I have problem in my calculated member. Whenever this member involve in calculation or query it take large time to execute. I am trying to narrow down execution time.
I have to remove IIF condition from the members and start using scope instead.
CREATE Member CurrentCube.[Measures].[AvgAmount] as
IIF(ISLeaf([Customer].[ParentCustomer].currentmember),
[Measures].[Value],
(SUM([CCube^Customer].[ParentCustomer].CURRENTMEMBER.CHILDREN) /
COUNT([Customer].[ParentCustomer].CURRENTMEMBER.CHILDREN))
) ,
Format_String = "#.0000000;-#.0000000;0;0",
Non_Empty_Behavior = [Measures].[Amout];
I have created hierarchy of customer which is [ParentCustomer] here. I want to see avg amount of all the children under the parent customer but when I am looking child level which does not have any children in it should only show the [Measures].[Amout].
Thanks in advance
Regards,
Sam
From your question, I assume you really want to have the average of the children, and not the average of all leaf level descendants. The latter could be implemented as follows:
Create a new measure group on the customer dimension table which has a single measure 'customer count' which would just be implemented as count - or of your customer dimension as a granularity that is finer than a single customer - countdistinct of the customer key or something like this.
Then just define your measure as
CREATE Member CurrentCube.[Measures].[AvgAmount] as
[Measures].[Value] / [Measures].[customer count],
Format_String = "#.0000000;-#.0000000;0;0",
Non_Empty_Behavior = [Measures].[Amout];
This assumes that the aggregation of [Measures].[Value] is defined as sum or one of the semi additive aggregations, but not max or min or something similar.
However, I assume from your question that this is not what you want. Instead you want to see the average of the children at each level. And I assume that [Customer].[ParentCustomer] is a standard user hierarchy and not a parent child hierarchy. Then, the approach suggested in the title, using SCOPE, would work. Let's assume you have three levels in your [Customer].[ParentCustomer] hierarchy:
The (implicitly defined) All level, just containing the All member
level A, built from attribute A of the dimension
level B, which is the leaf level and built from attribute B of the dimension
Then, under similar assumptions about the [Measures].[Value] aggregation, you could define the AvgAmount measure as follows:
// create the measure as it is correct for level B:
CREATE Member CurrentCube.[Measures].[AvgAmount] as
[Measures].[Value],
Format_String = "#.0000000;-#.0000000;0;0",
Non_Empty_Behavior = [Measures].[Amout];
// overwrite the definition for level A:
SCOPE([Customer].[ParentCustomer].[A].Members);
[Measures].[AvgAmount] = [Measures].[Value] / (EXISTING [Customer].[B].[B].Members).Count
END SCOPE;
// overwrite the definition for the ALl level:
SCOPE([Customer].[ParentCustomer].&[All]);
[Measures].[AvgAmount] = [Measures].[Value] / (EXISTING [Customer].[A].[A].Members).Count
END SCOPE;
This approach, using SCOPE, would not work for a parent child hierarchy, buta syou do not write you have one, I just assume you don`t.
I'm not very experienced in OLAP Cube + MDX, and I'm having a hard time trying to use twice the same measure in a cube.
Let's say that we have 3 Dimensions: D_DATE, D_USER, D_TYPE_OF_SALE_TARGET and 3 tables of Fact: F_SALE, F_MEETING, F_SALE_TARGET
F_SALE is linked to D_USER (who make the sale) and D_DATE (when)
F_SALE_TARGET is linked to D_USER, D_DATE, D_TYPE_OF_SALE_TARGET (meaning: user has to reach various goals/targets for a given month).
I can browse my cube:
Rows = Date * User
Cols = Number of sale, Total amount of sale + the value of 1 target (in the WHERE clause, I filter on [Dim TYPE SALE TARGET].[Code].&[code.numberOfSales])
How can I add other columns for other targets? As all the targets are in the same table, I don't see how to add a second measure from [Measures].[Value - F_SALE_TARGET] linked to a different code, ie. [Dim TYPE SALE TARGET].[Code].&[code.amountOfSale].
your question is not clear to me but it seems like one way to accomplish that is by creating Calculated Members. Basically, select you cube in BIDS, go to the Calculations tab and create Calculated Members. You would be able to insert your MDX query there. For each target type you can create a different calculation such as: ([Measures].[Value - F_SALE_TARGET], [Dim TYPE SALE TARGET].[Code].&[code.amountOfSale])
SELECT
NON EMPTY {[Measures].[Fact Order Count]}
ON COLUMNS,
{ ([Front Manager].[Front Manager Id].[Front Manager Id].ALLMEMBERS * [Order Type].[Order Type].[Order Type].ALLMEMBERS ) }
ON ROWS
FROM
[TEST_DW] CELL PROPERTIES VALUE
So, I have three columns in the output:
Front Manager, Order Type, Order Count
The above query shows me the counts for each manager and order type combination. I need a fourth column which would be a percentage of the types of orders for each front manager.
So, if there are four types of orders (A, B, C, D), and a manager had 25 of each order totaling 100. The fourth column would read 25%.....
I have scoured the web on how to do this, but have really come up short on this one. Any direction on this would be greatly appreciated, I am definitely new to MDX. Thanks.
What you're looking for are MDX Calculated members.
Let's assume the member for order A is called : [Order Type].[Order Type].[Order A] and we want to calculate the percentage from the total.
WITH
MEMBER [Order A] AS ([Order Type].[Order Type].[Order A],[Measures].[Fact Order Count]) / ([Measures].[Fact Order Count]) , FORMAT_STRING = 'Percent'
SELECT
{[Measures].[Fact Order Count],[Measures].[Order A]} on 0
...
What is important in the calculated members is that you can evaluate any MDX tuple (e.g ([Order Type].[Order Type].[Order A],[Measures].[Fact Order Count]) ). This changing if needed the values coming from the pivot axis (defined in on 0 and on 1..). Note you can add calculated members for the measures as well as the other dimensions.
Say I have a measure, foo, in a cube, and I have a reporting requirement that users want to see the following measures in a report:
total foo
total foo excluding instances where foo > 10
total foo excluding instances where foo > 30
What is the best way to handle this?
In the past, I have added Named Calculations which return NULL if foo > 10 or just foo otherwise.
I feel like there has to be a way to accomplish this in MDX (something like Filter([Measures].[foo], [Measures].[foo] > 10)), but I can't for the life of me figure anything out.
Any ideas?
The trick is that you need to apply the filter on your set, not on your measure.
For example, using the usual Microsoft 'warehouse and sales' demo cube, the following MDX will display the sales for all the stores where sales were greater than $2000.
SELECT Filter([Store].[Stores].[Store].members, [Unit Sales] > 2000) ON COLUMNS,
[Unit Sales] ON ROWS
FROM [Warehouse and Sales]
I met similar problem when use saiku (backend with Mondrain), as I haven't found any clear solution of "add filter on measure", I added it here, and that may be useful for other guy.
In Saiku3.8, you could add filter on UI: "column"->"filter"->"custom", then you may see a Filter MDX Expression.
Let's suppose we want clicks in Ad greater than 1000, then add the following line there:
[Measures].[clicks] > 1000
Save and close, then that filter will be valid for find elem with clicks greater than 1000.
The MDX likes below (suppose dt as dimension and clicks as measure, we want to find dt with clicks more than 1000)
WITH
SET [~ROWS] AS
Filter({[Dt].[dt].[dt].Members}, ([Measures].[clicks] > 1000))
SELECT
NON EMPTY {[Measures].[clicks]} ON COLUMNS,
NON EMPTY [~ROWS] ON ROWS
FROM [OfflineData]
i think you have two choices:
1- Add column to your fact(or view on data source view that is based on fact table)like:
case when unit_Price>2000 then 1
else 0
end as Unit_Price_Uper_Or_Under_10
and add a fictitious Dimension based on this columns value.
and add named query for New Dimension(say Range_Dimension in datasourceview :
select 1 as range
union all
select 0 as range
and after taht you cant used this filter like other dimension and attribute.
SELECT [Store].[Stores].[Store].members ON COLUMNS,
[Unit Sales] ON ROWS
FROM [Warehouse and Sales]
WHERE [Test_Dimension].[Range].&[1]
the problem is for every range you must add When condition and only if the range is static this solution is a good solution.
and for dynamic range it's better to formulate the range (based on disceretizing method )
2- add dimension with granularity near fact table based on fact table
for example if we have fact table with primary key Sale_id.we can add
dimension based on fact table with only one column sale_Id and in dimension Usage tab
we can relate this new dimension and measure group with relation type Fact and
after that in mdx we can use something like :
filter([dim Sale].[Sale Id].[Sale Id].members,[Measures].[Unit Price]>2000)