I have two fact tables:
Display_Ad_Impressions
Web_Site_Clicks
One of the measure fields in Web_Site_Clicks is Conversion. This is TRUE when the page is the "Order_Confirmed" page.
I have a Customers dimension that is shared by the two fact tables.
What MDX query would return the average number of conversions per consumer, grouped by the number of ad impressions viewed by the consumer?
Or is this only possible by integrating these two fact tables into a single Events fact table that includes both display ad impressions and web site clicks? If so, what would the MDX look like?
Feeling around in the dark as we currently have very little idea of dimensions and hierarchies within your cube.
WITH
MEMBER [Measures].[ConversionCount] AS
Sum(
[Customers].[Customers].[Customers],
Iif([Measures].[Conversion] = "TRUE",1,0))
MEMBER [Measures].[AvgDailyConversionCount] AS
Sum(
[Date].[Date].[Date],
[Measures].[ConversionCount]
)
SELECT
[Measures].[AvgDailyConversionCount] ON 0,
[ad impressions].[ad impressions].[ad impressions] ON 1 //<<this groups by Ad impressions
FROM [YourCube];
Related
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.
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 been recently working with GOOD-DATA platform. I don't have that much experience in MAQL, but I am working on it. I did some metric and reports in GOOD-DATA platform. Recently I tried to create a metric for calculating Total Buyers,First Time Buyers and Repeating Buyers. I created these three reports and working perfect.But when i try to add a order date parent filter the first time buyers and repeating buyers value getting wrong. please have Look at Following queries.
I can find out the correct values using sql queries.
MAQL Queries:
TOTAL ORDERS-
SELECT COUNT(NexternalOrderNo) BY CustomerNo WITHOUT PF
TOTAL FIRSTTIMEBUYERS-
SELECT COUNT(CustomerNo) WHERE (TOTAL ORDER WO PF=1) WITHOUT PF
TOTAL REPEATINGBUYERS-
SELECT COUNT(CustomerNo) WHERE (TOTAL ORDER WO PF>1) WITHOUT PF
Can any one suggest a logic for finding these values using MAQL
It's not clear what you want to do. If you could provide more details about the report you need to get, it would be great.
It's not necessary to put "without pf" into the metrics. This clause bans filter application, so when you remove it, the parent filter will be used there. And you will probably get what you want. Specifically, modify this:
SELECT COUNT(CustomerNo) WHERE (TOTAL ORDER WO PF>1) WITHOUT PF
to:
SELECT COUNT(CustomerNo) WHERE (TOTAL ORDER WO PF>1)
The only thing you miss here is "ALL IN ALL OTHER DIMENSIONS" aka "ALL OTHER".
This keyword locks and overrides all attributes in all other dimensions—keeping them from having any affect on the metric. You can read about it more in MAQL Reference Guide.
FIRSTTIMEBUYERS:
SELECT COUNT(CustomerNo)
WHERE (SELECT IFNULL(COUNT(NexternalOrderNo), 0) BY Customer ID, ALL OTHER) = 1
REPEATINGBUYERS:
SELECT COUNT(CustomerNo)
WHERE (SELECT IFNULL(COUNT(NexternalOrderNo), 0) BY Customer ID, ALL OTHER) > 1
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])
I am new to MDX and I know that this must be a simple question but I haven't been able to find an answer.
I am modeling a a questionnaire that has questions and answers. What I am trying to achieve is to find out the number of people who gave specific answers to questions., e.g. the number of males aged between 20-25
When I run the query below for the questions individually the correct result is returned
SELECT
[Measures].[Fact Demographics Count] ON Columns
FROM
[Dsv All]
WHERE
[Answer].[Dim Answer].&[1]
[Measures].[Fact Demographics Count] is a count of the primary key column
[Answer].[Dim Answer].&[1] is the key for the Male answer
Result for number of people who are male = 150
Result for number of people who are between 20-25 = 12
But when I run the next query below rather than getting the number people who are males and aged between 20-25. I get the sum of the number of people who are males and the number of people who are between 20-25.
SELECT
[Measures].[Fact Demographics Count] ON Columns
FROM
[Dsv All]
WHERE
{[Answer].[Dim Answer].&[1],[Answer].[Dim Answer].&[9]}
result = 162
The structure of the fact table is
FactDemographicsKey,
RespodentKey,
QuestionKey,
AnswerKey
Any help would be greatly appreciated
Thanks
Take a look at the MDX function FILTER - this may give you what you need. A combination of FILTER and Member Properties to filter against the ID's might do it. You're having a problem because what you're trying to do is a little against the grain of how an OLAP cube is structured (from my experience) because Age and Gender are both members of the same dimension (Answers), which means that they each get their own cells for aggregation, but unlike if Age and Gender were each on their own dimension, they don't get aggregated with respect to one another except to get added together. In an OLAP cube, each combination of each member of each dimension with each member of every other dimension gets a "cell" with the value of each measure that is unique to that combination - that is what you want, but members of the same dimension (such as Answers) aren't cross-calculated in that way.
If possible, I would recommend breaking out the individual answers into individual dimensions, i.e. Age and Gender each have their own dimensions with their own members, then what you want to do will naturally flow out of your cube. Otherwise, I'm afraid you will have lots of MDX fiddelry to do. (I am not an MDX expert, though, so I could be completely off base on this one, but that is my understanding)
Also, definitely read the book previously mentioned, MDX Solutions, unless this is the only MDX query you think you'll need to write. MDX and Multidimensional analysis are nothing like SQL, and a solid understanding of the structure of an OLAP database and MDX in general is absolutely essential, and that book does a very, very nice job of getting you where you need to be in that department.
When trying to figure out problems with where-criteria or slices I find it helpful to breakdown the items that you're slicing on into dimensions, rather than measures.
select
[Measures].[Fact Demographics Count] on Columns
from [Dsv All]
where
{
[Answer].[Dim Answer].&[1],
[Dim Age Band].[20-25]
}
Although then you're not really using the power of MDX - you're getting just one value.
select
[Dim Answer].Members on Columns,
[Dim Age Band].Members on Rows
from [Dsv All]
where ( [Measures].[Fact Demographics Count] )
Will give you a pivot table (or crosstable) breaking down gender (on columns) by age-bands (on rows).
BTW - ff you're learning MDX this book: MDX Solutions is far and away the best starting point that I've found.
Firstly thanks to everyone for their replies. This was an interesting one to solve and for anyone new to MDX and coming from SQL its an easy trap to fall into.
So for those interested here is a brief overview of the solution.
I have 3 tables
factDemographics: holds respondents and their answers (who answered what)
dimAnswer: the answers
dimRespondent: the respondents
In the datasource view for the cube I duplicated factDemographics 5 times using Named Queries and I named these fact1, fact2, ..., fact5. (which will create 5 measure groups)
Using VS Studio's create cube wizard I set the following fact tables
fact1, fact2, ... as fact tables
dimRespondent a fact table. I use this table to get the number of respondents.
Removed the original factDemographics table.
Once the cube was created I duplicated the dimAnswer dimension 5 times, naming them filter1, filter2, ...
Finally in the Cube Structure's Dimension Usage tab I linked these together as follows
filter1 many to many dimRespondent
filter2 many to many dimRespondent
filter3 many to many dimRespondent
filter4 many to many dimRespondent
filter5 many to many dimRespondent
filter1 regular relationship fact1
filter2 regular relationship fact2
filter3 regular relationship fact3
filter4 regular relationship fact4
filter5 regular relationship fact5
This now enables me to rewrite the query I used in my original post as
SELECT
[Measures].[Dim Respondent Count] On 0
FROM
[DemographicsCube]
WHERE
(
[Filter1].[Answer].&[Male],
[Filter2].[Answer].&[20-25]
)
My query can now be filtered by up to 5 questions.
Although this works I'm sure that there is a more elegant solution. If anyone knows what that is I'd love to hear it.
Thanks
If you are using MSSQL, you can use the "WITH ROLLUP" to get some extra rows which would have the information you want. Also, you are not using a "GROUP BY" which you will need.
Use the GROUP BY to break up the set into groups and then use aggregate functions to get your counts and other stats.
Example:
select AGE, GENDER, count(1)
from MY_TABLE
group by AGE, GENDER
with rollup
This would give you the number of each gender of person in your table in each age group, and the "rollup" would give you the total number of people in your table, the numbers in each age group regardless of gender, and the numbers of each gender regardless of age. Something like
AGE GENDER COUNT
--- ------ -----
20 M 1245
21 M 1012
20 F 942
21 F 838
M 2257
F 1780
20 2187
21 1850
4037