Factors that affect system setup and sizing

This article explains the main factors that can affect Board On-premises setup and sizing.

This article covers:

• Number of users

• Data Model complexity

  • Hierarchy size

  • Cube size

  • Sparse size

  • Data Model complexity score

• Application complexity

Number of users

The number of users who access the Board server at the same time affects server sizing. These users are called concurrent users.

Concurrent users affect the server setup, but not the client setup.

Processor speed, processor power, number of cores, and clock rate are important when many concurrent users access Board at the same time.

RAM use is only partly affected by the number of concurrent users. Disk space is not affected by the number of concurrent users, but by the size of the Data Model.

Data Model complexity

Data Model complexity is based on these factors:

• Hierarchy size

• Cube size

• Sparse size

Each factor has a score. Add the scores together to calculate the final Data Model complexity score.

Hierarchy size

Hierarchy size depends on these values:

• Number of Entities in the tree

• Number of levels of the tree

• Number of items in Entities, i.e., Entity members

Complex hierarchies can increase processor and RAM needs. Processor clock rate is important for complex hierarchies.

RAM can vary from one Data Model to another because direct Relationships, indirect Relationships, and data patterns are loaded into RAM when the Data Model is opened.

Cube size

Cube size can vary based on the number of dimensions, the number of versions, sparse structure settings, and the Time range.

Cube size is measured in MB or GB.

Sparsity size

Sparsity size is the number of actual combinations of Entity members that are part of a sparse structure.

You can view sparsity size in the Cube sparsity analysis area.

Sparse structures help Board allocate disk space only for combinations that exist in the loaded data. This can reduce disk use and improve interaction with affected Cubes.

Data Model complexity score

Use this score matrix to calculate the overall Data Model complexity.

Data Model complexity score = Hierarchy size score + Cube size score + Sparsity size score

Application complexity

Application complexity affects system sizing. It depends on how users interact with the application and how much processing the application requires.

Processor clock speed and number of cores are important when you assess application complexity.