Optimization Techniques in Essbase

The best technique to make large data loads faster is to have the optimal order of dimensions in source file, and to sort this optimally, order the fields in your source file (or SQL statement) by having hourglass dimension order, you data file should have dimensions listed from the bottom dimension upwards. Your dense dimensions should always be first, and if you have multiple data columns these should be dense dimension members. This will cause blocks to be created and filled with data in sequence, making the data load faster and the cube less fragmented.

As a part of Optimization we need to re-order the dimensions as follows
  • Large members Dense dimension
  • Small members Dense dimension
  • Small members Sparse dimension
  • Large members Sparse dimension
  • Attribute dimensions.

Calculation order of the dimensions.

  • Dimension tagged accounts if it is dense.
  • Dense dimensions in outline or CALC DIM statement order.
  • Dimensions tagged as Accounts if it is sparse.
  • Sparse dimensions in outline order or CALC DIM statement order.
  • Two-pass calculations on members in the Accounts tagged dimension.
Here are some more optimization techniques used in Essbase
For data loading:
  • Grouping Sparse Member Combinations
  • Positioning Data in the Same Order As the Outline
  • Loading from the Essbase OLAP Server
  • Making the Data Source As Small As Possible
  • Making Source Fields As Small As Possible
  • Managing Parallel Data Load Processing
For Calculation:
  • Using Parallel Calculation
  • Using Formulas
  • Managing Caches to Improve Performance
  • Using Two-Pass Calculation
  • Aggregating #MISSING Values
  • Removing #MISSSING Blocks



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