I'm trying to combine multiple members from a single hierarchy, though this leads to the following error:
Query (11, 3) The Jr-Kw-Mnd-Dag hierarchy is used more than once in the Crossjoin function.
This is a basic version of the Query I'm using:
SELECT
NON EMPTY {
[Measures].[Amount]
} ON COLUMNS
, NON EMPTY {
[Realisatiedatum].[Jr-Kw-Mnd-Dag].[Jaar]
* [Realisatiedatum].[Jr-Kw-Mnd-Dag].[Maand])
} ON ROWS
FROM
[Cube]
Jaar equals year in English, Maand equals month in English. This is what I'm trying to accomplish:
...
november 2013
december 2013
januari 2014
februari 2014
...
Last but not least, the hierarchy:
I would normally create several hierarchies within the Date dimension, such as Calendar, Financial and others that contain just financial year, calendar year, quarters etc.
If you have another hierarchy that contains the month, you could crossjoin with the year of the hierarchy you are using at the moment; then you won't be using the same hierarchy twice in the crossjoin function.
E.g.
, NON EMPTY {
( [Date Dimension].[Financial].[Financial Year]
* [Date Dimension].[Calendar].[Month] ) }
If you want to skip some levels in a user hierarchy, the best is to CrossJoin the corresponding attribute hierarchies of the remaining levels.
Here under I use the attribute hierarchy ([Geography].[City].[City], ..) instead of the user hierachy ([Geography].[Geography].[City],..) form the AW cube:
SELECT
[Measures].[Internet Sales Amount] ON 0,
[Geography].[State-Province].[State-Province] * [Geography].[City].[City] ON 1 FROM [Adventure Works]
Philip,
This issue is related to trying to pull multiple levels from the same hierarchy using a crossjoin and indeed you cannot. As mentioned in other replies, a good work around is to pull the columns you need from places other than the same hierarchy. But that works only if the cube design allows for it.
Your specific problem may be related to the tool you are using to develop the query and which levels in the hierarchy it returns.
For example, the following query when executed in SQL Server Management Studio (versions through V12.0.2000.8) returns only the City level of the hierarchy. But when executed from within design mode in the PowerPivot table import wizard it returns all levels in the hierarchy down to the city level including Country, State-Province and City.
select
[Measures].[Internet Order Count] on columns,
non empty [Customer].[Customer Geography].[City] on rows
from [Adventure Works]
Related
Here are two simple queries which shows data on month level filtered by dates.
In the first query I am using Month level of "Date.Calendar" user hierarchy.
SELECT
NON EMPTY { [Measures].[Internet Sales Amount] } ON 0,
NON EMPTY { [Date].[Calendar].[Month].&[2013]&[1] } ON 1
FROM [Adventure Works]
WHERE {[Date].[Date].&[20130105]:[Date].[Date].&[20130106]}
And recieved - January 2013 -> $857,689.91
Results
In the second query I am using "Date.Month of Year" attribute hierarchy.
SELECT
NON EMPTY { [Measures].[Internet Sales Amount]} ON 0,
NON EMPTY { [Date].[Month of Year].&[1] } ON 1
FROM [Adventure Works]
WHERE { [Date].[Date].&[20130105] : [Date].[Date].&[20130106] }
And received - January -> $54,468.46
Results
I can not figure out why these two queries show different results. If the same dimension is used and data are filtered/sliced on the lovest possible level.
Here are values for each of these dates.
SELECT
NON EMPTY { [Measures].[Internet Sales Amount]} ON 0,
NON EMPTY { [Date].[Calendar].[Date] } ON 1
FROM [Adventure Works]
WHERE { [Date].[Date].&[20130105] : [Date].[Date].&[20130106] }
January 5, 2013 $32,681.44
January 6, 2013 $21,787.02
Result
Total value for these two dates is equal with the second querie's result - $54,468.46
I understand that in the first query it is user hierarchy and the second query it is attribute hierarchy from the Date dimension but I can not figure out which rule(s) tells to calculate these values differently.
If someone could explain this logic behind - it would be very helpful. Any link to some resource which explains this logic also could help.
BTW: I have created simple cube with simple Date dimension which consists just of attribute hierrarchies (date, month, year) and it still works like in the first query so it is not clear why it behaves like that.
I explored the reason for this behavior, and I think I have figured out the reason. The explanation below is based on the book SQL SERVER 2008 MDX Step by Step pages 51-58(especially Avoiding Reference Conflicts).
Your problem is a typical Reference Conflict problem.In MDX a hierarchy cannot be used more than once in a given tuple, but if you are using a USER Hierarchy and its under lying Attribute Hierarchy, you essentially by-pass this check. This is what happened in your query
In your first query you are using the User Hierarchy
[Date].[Calendar].[Month].&[2013]&1
In MDX a User Hierarchy is translated to Attribute Hierarchies. In your first query
SELECT
NON EMPTY { [Measures].[Internet Sales Amount] } ON 0,
NON EMPTY { [Date].[Calendar].[Month].&[2013]&1 } ON 1
FROM [Adventure Works] WHERE
{[Date].[Date].&[20130105]:[Date].[Date].&[20130106]}
you are using a User Hierarchy "[Date].[Calendar].[Month].&[2013]&1", which in its last level has "[Date].[Date]". Then in the where clause you use the same "[Date].[Date]" Attribute Hierarchy to filter. Since in the USER Hierarchy you have not used the leaf level, hence you have made a partial address, therefore the members and its ancestors are resolved. All the descendants are ignored in translation. Take a look at the below query(This is based on your first query,I have purposely removed your where clause).
with member [Measures].[CalendarYear] as [Date].[Calendar Year].currentmember.name
member [Measures].[CalendarSemester] as [Date].[Calendar Semester of Year].currentmember.name
member [Measures].[CalendarQuater] as [Date].[Calendar Quarter of Year].currentmember.name
member [Measures].[CalendarMonth] as [Date].[Month of Year].currentmember.name
member [Measures].[CalendarDate] as [Date].[Date].currentmember.name
SELECT
NON EMPTY { [Measures].[Internet Sales Amount] ,[Measures].[CalendarYear],[Measures].[CalendarSemester],[Measures].[CalendarQuater],[Measures].[CalendarMonth],[Measures].[CalendarDate]} ON 0,
NON EMPTY { [Date].[Calendar].[Month].&[2013]&[1] } ON 1
FROM [Adventure Works]
Result.
Notice that Calendar year, Semester and quarter all are showing non-default values. But we never used them. This shows that translation of User Hierarchy is done into underlying Attribute Hierarchies. Now take a look at Calendar, it is still showing "All Period". Since it was ignored.
Now if you add your where clause back, the Date still shows "All Period", there are two reasons
1) Because it was ignored in User Hierarchy translation ,
2)You used a range in where. If you replace your row axis tuple with your where tuple it will still show "All Period" as a range is based. However while resolving it will take just two dates.
Based on this while resolving your query, it had two translation for Date attribute hierarchy, one said to ignore it based on User Hierarchy, the other provided a range. This is where due to conflict the result is in-correct.
Now lets consider the query you gave me in your comment earlier
with member [Measures].[CalendarYear] as [Date].[Calendar Year].currentmember.name
member [Measures].[CalendarSemester] as [Date].[Calendar Semester of Year].currentmember.name
member [Measures].[CalendarQuater] as [Date].[Calendar Quarter of Year].currentmember.name
member [Measures].[CalendarMonth] as [Date].[Month of Year].currentmember.name
member [Measures].[CalendarDate] as [Date].[Date].currentmember.name
SELECT
NON EMPTY { [Measures].[Internet Sales Amount] ,[Measures].[CalendarYear],[Measures].[CalendarSemester],[Measures].[CalendarQuater],[Measures].[CalendarMonth],[Measures].[CalendarDate]} ON 0,
NON EMPTY { [Date].[Calendar].[Month].&[2013]&[1] } ON 1
FROM [Adventure Works]
WHERE {[Date].[Date].&[20130105]}
Result:
Notice that this time the resolution of a single member, was used instead of the User hierarchy resolution. Now this behavior might be due to the fact that one translation is giving "All Period" and the next is giving a member, hence the member won.
To further confirm this, I made a change to my AdventureWorks sample. The Date attribute hierarchy is based on "Simple Date" column. I exposed "Simple Date" as a separate attribute and processed my cube.
Take a look at the "Simple Date" query and results.
with member [Measures].[CalendarYear] as [Date].[Calendar Year].currentmember.name
member [Measures].[CalendarSemester] as [Date].[Calendar Semester of Year].currentmember.name
member [Measures].[CalendarQuater] as [Date].[Calendar Quarter of Year].currentmember.name
member [Measures].[CalendarMonth] as [Date].[Month of Year].currentmember.name
member [Measures].[CalendarDate] as [Date].[Date].currentmember.name
SELECT
NON EMPTY { [Measures].[Internet Sales Amount] ,[Measures].[CalendarYear],[Measures].[CalendarSemester],[Measures].[CalendarQuater],[Measures].[CalendarMonth],[Measures].[CalendarDate]} ON 0,
NON EMPTY { [Date].[Calendar].[Month].&[2013]&[1] } ON 1
FROM [Adventure Works]
WHERE
--{[Date].[Date].&[20130105]}
{[Date].[Simple Date].&[20130105]:[Date].[Simple Date].&[20130106]}
Results:
I am trying to learn MDX. I am an experienced SQL Developer.
I am trying to find an example of an MDX query that has more than two dimensions. Every single webpage that talks about MDX provides simple two dimensional examples link this:
select
{[Measures].[Sales Amount]} on columns,
Customer.fullname.members on rows
from [Adventure Works DW2012]
I am looking for examples that use the following aliases: PAGES (third dimension?), section (forth dimension?) and Chapter (fifth dimension?). I have tried this but I do not think it is correct:
select
{[Measures].[Sales Amount]} on columns,
Customer.fullname.members on rows,
customer.Location.[Customer Geography] as pages
from [Adventure Works DW2012]
I am trying to get this output using an MDX query (this is from AdventureWorks DW2012):
That's not a 3-dimensional resultset in your screenshot, unless there's something cropped from it.
Something like
SELECT [Geography].[Country].Members ON 0,
[Customer].[CustomerName].Members ON 1
FROM [whatever the cube is called]
WHERE [Measures].[Sales Amount]
(dimension/hierarchy/level names may not be exactly right)
would give a resultset like the one in your message.
The beyond 2nd-dimension dimensions and dimension names are not used in any client tool that I know. (Others may know different). They seem to be there in MDX so that MDX can hand >2-dimensional resultsets to clients that can handle them (e.g. an MDX subquery handing its results to the main query).
An often-used trick in MDX is to get the members of two dimensions onto one axis by cross-joining:
SELECT
{[Date].[Calendar Date].[Calendar Year].Members * [Geography].[Country].Members} ON 0,
[something else] ON 1
FROM [Cube]
How about the following - it does not send more than two dimensions back to a flat screen but it uses quite a few dimensions explicitly:
SELECT
[Measures].[Sales Amount] ON O,
[Customer].[fullname].MEMBERS ON 1
FROM
(
SELECT
[Date].[Calendar Month].[Calendar Month].&[February-2012] ON 0,
[Geography].[Country].[Country].&[Canada] ON 1,
[Product].[Product].&[Red Bike] ON 2,
[Customer].[Customer].&[foo bar] ON 3
FROM [Adventure Works DW2012]
)
I've made up the dimension | hierarchy | member combinations as I do not have access to the cube.
Also if we consider implicit dimensions then take the following:
SELECT
[Customer].[Location].[Customer Geography] ON 0,
[Customer].[fullname].[fullname].&[Aaron Flores] ON 1
FROM [Adventure Works DW2012]
WHERE
(
[Measures].[Sales Amount]
);
On the slicer I've used braces (..) which indicate a tuple, but this is actually shorthand for the following:
SELECT
[Customer].[Location].[Customer Geography] ON 0,
[Customer].[fullname].[fullname].&[Aaron Flores] ON 1
FROM [Adventure Works DW2012]
WHERE
(
[Measures].[Sales Amount]
,[Date].[Calendar Month].[Calendar Month].[All],
,[Geography].[Country].[Country].[All],
,[Product].[Product].[All]
,...
,...
....
);
The All member from every dimension in the cube could be included in this slicer without affecting the result.
So the whole nature of mdx is multi-dimensional - yes you do not get more than a 2 dimensional table returned to your screen but the way you get to that cellset could well involve many dimensions.
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.
I am using Performance Point Dashboard Designer 2013 and SharePoint Server 2013 for building dashboards. I am using SSAS2012 for Cube.
I have a scenario similar to the one illustrated by figure below. I am required to find Previous Non-Empty value for purpose of finding Trends.
Measure: [Quota]
Dimension: [Date].[Calendar Date].[Date]
The script ([Measures].[Quota], [Date].[Calendar Date].PrevMember) gives you a previous date. Lets say for date 27-Jan-13 whose Quota value is 87, it returns 26-Jan-13 which has null value. I want it to return 21-Jan-13 that has some Quota value. And for date 21-Jan-13, I want to return 15-Jan-13.
I wonder if this is possible.
Thanks,
Merin
After long searches and hits & trials and so on, I think I invented a solution of my own for myself.
Following is the script for my Calculated Member.
(
[Quota],
Tail
(
Nonempty
( LastPeriods(15, [Date].[Calendar Date].PrevMember)
,[Quota]
)
).Item(0)
)
Explanation
The number 15 means it will look for non-empty measures up to 15 siblings.
Now we know up to how many siblings to traverse back, in this case 15.
Lets find 15 previous siblings (both empty and non-empty) excluding current member.
(LastPeriods(15, [Date].[Calendar Date].PrevMember)
Since it will yield both empty and non-empty members, lets filter out empty members in terms of measure [Quota]. If we don't specify measure here, it will use default measure whatever it is and we may not get desired result.
Nonempty(LastPeriods(15, [Date].[Calendar Date].PrevMember),[Quota])
We may have several members in the output. And we will choose the last one.
Tail
(
Nonempty
( LastPeriods(15, [Date].[Calendar Date].PrevMember)
,[Quota]
)
)
So far, the script above gives previous non-empty member. Now we want to implement this member for our measure [Quota].
Hence we get the script below ready to create a Calculated Member.
(
[Quota],
Tail
(
Nonempty
( LastPeriods(15, [Date].[Calendar Date].PrevMember)
,[Quota]
)
).Item(0)
)
You can use recursion to define this.
The following query delivers something similar for the Adventure Works cube:
WITH member [Measures].[Prev non empty] AS
IIf(IsEmpty(([Date].[Calendar].CurrentMember.PrevMember, [Measures].[Internet Sales Amount])),
([Date].[Calendar].CurrentMember.PrevMember, [Measures].[Prev non empty]),
([Date].[Calendar].CurrentMember.PrevMember, [Measures].[Internet Sales Amount])
), format_String = '$#,##0.00'
SELECT {[Measures].[Internet Sales Amount], [Measures].[Prev non empty]}
ON COLUMNS,
non empty
Descendants([Date].[Calendar].[Month].&[2007]&[12], [Date].[Calendar].[Date])
ON ROWS
FROM [Adventure Works]
WHERE [Customer].[Customer].&[12650]
You would have to replace the name of the date hierarchy, as well as the measure name from Internet Sales Amount to Quota in the recursive definition of the measure Prev non empty.
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.