Understanding Dense and Sparse Dimensions

Understanding Dense and Sparse Dimensions

• Essbase is an MOLAP product, it is supposed to store detail and pre-aggregated value for both members and their parents and grand parents in many levels.
• Essbase introduce the concept of  "Dense dimension" and "Sparse dimension" to avoid this storage waste.

• Sparse Dimension: 

                 A dimension which has low probability that data exists for every combination of dimension members. It may contain some empty tuples. In sparse dimension data is not uniformly distributed. 

Examples: product, customer, and region dimensions. 

               Most multidimensional databases are inherently sparse; they lack data values for the majority of member combinations. A sparse dimension is one with a low percentage of available data positions filled.

• Dense Dimension: 

        A dimension which has the high probability that data exists for every combination of dimension members.

Examples:  time, gross sales, net sales, discount, and Accounts dimension.


        Most multidimensional databases also contain dense dimensions. A dense dimension has a high probability that one or more cells is occupied in every combination of dimensions. For example, in the basic database, accounts data exists for almost all products in all markets, so Measures is chosen as a dense dimension. Year and Scenario are also chosen as dense dimensions. Year represents time in months, and Scenario represents whether the accounts values are budget or actual values.

• For all the sparse dimensions, Essbase will not do  pre-allocation of  cell storage for them.
• If the data value exists for a combination of sparse dimension, Essbase will construct a block for it, cells in the block will be the multiplication of all the dense dimension member.  And this specific sparse dimension combination act as an index that point to this block.  which is call index of Essbase.