HyperionSolutions

An Essbase outline has a number of dimensions.  The number of dimensions can range in quantity and size, but each dimension is identified as a dense or sparse dimension.  As we understood the concept of Dense and Sparse in our last discussion, let’s begin with the cocept of data blocks now. To understand this, let’s carry forward our hierarchy of 5 dimensions in a test application in my previous post.. We have 3 dense and 2 sparse dimensions each with 3 stored members. So let’s begin: Number of Existing Blocks = (potential number of blocks) * estimated density= 9 Block Size = Dense Stored * Dense Stored * Dense Stored * 8 bytes = 3*3*3 *8 = 216 Potential number of blocks = Sparse Stored * Sparse Stored = 9 If all dimensions are assigned as Dense, Number of Existing Blocks = (potential number of blocks) * estimated density= 1 Block Size = Dense Stored * Dense Stored * Dense Stored * 8 bytes = 3*3*3*3*3 *8 = 1944 Potential number of blocks = Spar

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 Essba