This is probably quite simple but I have a piece of MDX that filters all my customers, that have a balance over 100. This returns a set of tuples containing the customer and the balance. How do i return just a set of the customers?
Filter(
[Customer].[Customer Name].Children,
[Measures].[Balance] > 100
)
I intend to use this as the expression for a named set in my cube.
Thanks in advance :)
Note sure to understand how you get ( customer, balance ) tuples; anyway, you might be looking at the extract function. It allows you to retrieve the tuples with members of the specified hierarchy: e.g.,
Extract( my-set , [Customer].[Customer Name] )
Related
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:
The following script gives exactly the result I want.
It feels like a hack as I've added the custom members VALUE and VALUE_MTD onto the hierarchy [Customer].[Country]. I've chosen this hierarchy arbitrarily - just not used [Measures] or [Date].[Calendar] as they are already in use.
Is there a more standard approach to returning exactly the same set of cells?
WITH
MEMBER [Customer].[Country].[VALUE] AS
Aggregate([Customer].[Country].[(All)].MEMBERS)
MEMBER [Customer].[Country].[VALUE_MTD] AS
Aggregate
(
PeriodsToDate
(
[Date].[Calendar].[Month]
,[Date].[Calendar].CurrentMember
)
,[Customer].[Country].[VALUE]
)
SELECT
{
[Customer].[Country].[VALUE]
,[Customer].[Country].[VALUE_MTD]
} ON 0
,NON EMPTY
{
[Measures].[Internet Sales Amount]
,[Measures].[Internet Order Quantity]
}
*
Descendants
(
{
[Date].[Calendar].[Month].&[2007]&[12]
:
[Date].[Calendar].[Month].&[2008]&[01]
}
,[Date].[Calendar].[Date]
) ON 1
FROM [Adventure Works];
The standard approach is called utility dimension. If you Google this term, you will find several descriptions of this approach. A "utility dimension" is one which does not reference any data, but is just added to the cube for the purpose of being able to cross join them with all other dimensions for calculations. You can have one or more of them.
Thus, in most cases, physically there is nothing in the dimension. It is just used for calculated members. (Depending on the implementation, you may have the attribute members defined physically, if you want to have some properties for them. But then, only the default member is referenced in the star schema from the fact tables. The attribute member values are then overwritten in the calculation script.)
Typical applications for this are time calculations like YTD, MTD, MAT (Moving Annual Total, i. e. a full year of data ending in the selected date), or comparisons like growth vs. a previous period.
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 am very new to MDX, so probably I'm missing something very simple.
In my cube, I have a dimension [Asset] and a measure [Visits], calculating (in this case) how many visits an asset has been consumed by. An important thing to note is that not every visit is associated with an asset.
What I need to find out is how many visits there are that consumed at least one asset. I wrote the following query:
SELECT
[Asset].[All] ON COLUMNS,
[Measures].[Visits] ON ROWS
FROM
[Analytics]
But this query just returns the total number of visits in the cube. I tried applying the NON EMPTY modifier to both axes, but that doesn't help.
This query should give you what you expect:
WITH MEMBER [Asset].[Asset Name].[All Assets] AS
AGGREGATE( EXCEPT( [Asset].[Asset Name].MEMBERS, { [Asset].[All] } ) )
SELECT
{ [Asset].[Asset Name].[All Assets] } ON COLUMNS,
[Measures].[Visits] ON ROWS
FROM
[Analytics]
You may need to put {[Asset].[Asset Name].[All]} as second argument of Except if the All member was not excluded.
In the query I create a calculated member [Asset].[Asset Name].[all assets] that should represent all your existing assets. I supposed that your existing assets are all the members of the level [Asset].[Asset Name] but the All member.
You can find more information about the Aggregate function here.
This works as well:
SELECT
[Measures].[Visits] ON 0
FROM
[Analytics]
WHERE
DRILLDOWNLEVEL([Asset].[All])
Update: as well as this:
SELECT
[Measures].[Visits] ON 0
FROM
[Analytics]
WHERE
[Asset].[All].CHILDREN
with member [Measures].[BoughtDispenser] as
Sum(Descendants([Customer].[Customer].CurrentMember, [Customer].[Customer]),
Iif(
(IsEmpty(([Item].[ItemNumber].&[011074], [Measures].[Sale Amount]))
And IsEmpty(([Item].[ItemNumber].&[011069], [Measures].[Sale Amount]))
)
Or IsEmpty([Measures].[Sale Amount]),
0 , 1
)
)
select
{[Measures].[Sale Amount]} on columns,
non empty filter([Customer].[Customer].children, [Measures].[BoughtDispenser])
* {[Item].[ItemNumber].members}
on rows
from [Sales]
where [EnteredDate].[Quarter].&[2010-01-01T00:00:00]
;
The object is to show all the items purchased by customers who also bought either of the two dispensers (011069 and 011074).
I based the calculated member on a query I found to do basket analysis. I feel like there should be a way to write it with the set {[Item].[ItemNumber].&[011074], [Item].[ItemNumber].&[011069]} instead of the two IsEmpty tests. Everything I've tried ended up having every Customer in the result.
My environment is SQL Server Analysis Services 2005.
Yes I can! It just required a slightly different approach to the calculated member:
with member [Measures].[BoughtDispenser] as
Sum(Descendants([Customer].[Customer].CurrentMember, [Customer].[Customer])
* {[Item].[ItemNumber].&[011069], [Item].[ItemNumber].&[011074]},
[Measures].[Quantity Shipped]
)
select
{[Measures].[Sale Amount]} on columns,
non empty filter([Customer].[Customer].children, [Measures].[BoughtDispenser])
* {[Item].[ItemNumber].members}
on rows
from [Sales]
where [EnteredDate].[Quarter].&[2010-01-01T00:00:00]
;