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Essbase for Beginners : Dense & Sparse - How well do we understand it ?


So what is Dense and Sparse Dimensions??

Essbase outline has a lot of components. One of the most important components is Dimension.
Dimensions are the representations of business components and might be of standard or attribute types.

Standard Dimensions are basically of two types: Dense & Sparse.

So basically by oracle documentation,
“A dense dimension has a high probability that one or more cells is occupied in every combination of dimensions.”

“A sparse dimension is one with a low percentage of available data positions filled.”


However, the above definition is valid only with reference of data positions or data cells filled or not. What about the case when we do not have any data.

How does Essbase identifies whether the added new dimension is a Sparse or Dense..

To understand that I did a simple test.

We create an application from scratch with just some random dimensions “A”, “B”, “C”, and “D” without any children. Then we check how Essbase has treated each of the members.

Guess what, Essbase has alternately assigned each member Dense & Sparse, starting with Dense.

-A
-B
-C
-D


Now we add two child under “A” as “A1” and “A2” and under “B” as “B1” and “B2”. Also I have added one more dimension for further analysis. Let’s check on the behavior now.

-A
+A1
+A2
-B
+B1
+B2
-C
-D
-E

So now we observe that Essbase checks for the dimension which might have a probability of higher number of data positions (filled or not for now) which relates to the member which has more number of children.

Since dimension “B” has more children so it is also assigned as Dense apart from “A” and “E” in alternate order.


Alternately, if we assign “C” & “D” dimensions more members and delete all other members previously created, as shown below, we find that now “A” , “C” & “D” is assigned Dense and rest will be Sparse.



Now when I delete the members from A and create 5 stored members each in “B” and “E” Dimensions, it takes “B” & “E” as dense apart from the first dimension “A” though we still have 3 stored members each in other two dimensions “C” & “D”.

This clarifies that no matter what Essbase assigns first member always as Dense.



Conclusions so far: 

So we observe that dense and sparse is assigned by Essbase keeping in view with which dimension there has higher data positions available till we have not assigned any tags to the dimensions.

But that’s not all, there’s more to Essbase, there is basically the automatic configuration setting of Essbase.

Essbase can make recommendations for the sparse-dense configuration of dimensions based on the following factors:
§  The time and accounts tags on dimensions
§  The probable size of the data blocks
§  Characteristics that you attribute to the dimensions
In case we do not have any of the above, it follows a simple alternate assignment procedure of Dense & Sparse.

However the automatic configuration of dense and sparse dimensions provides only an estimate. You can apply a recommended configuration, or you can turn off automatic configuration to false and manually set the sparse or dense property for each dimension.

Hope this post enlightens all newbies of Essbase a bit more on Dense and Sparse.

Cheers!!
Anvi

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