Understanding how Essbase stores and retrieves data is essential for teams managing financial systems. At HollandParker, we help organizations leverage platforms like Essbase more effectively by explaining the core concepts behind multidimensional storage. In this article, we break down how BSO data blocks, sparse and dense dimensions, and index systems work together to deliver fast, reliable access to your organization’s data.
Not only is Essbase a leading calculation engine, but as a multidimensional database, it is also efficient at storing data. Essbase creates data blocks and an index system in order to access and store data into a cube. For more background on how Essbase manages data, you can also review our overview of Essbase data storage.
The cube above contains three dimensions:
- Scenario
- Accounts
- Period
Data values are intersections of members from different dimensions that are stored in one cell of the database. For instance, an example of a data value in the cube above is where Sales intersects with Actual and January.
As mentioned above, Essbase accesses and stores data by using data blocks and an index system. A data block is created for every sparse dimension intersection. When that block is created, the index is used to locate the block containing a data value, in this case, Sales -> Actual -> January. In other words, the index or the locator provides a pointer to the correct data block. Once that data block is located, Essbase can retrieve the data value and it can be viewed via Smartview. You can compare SmartView to other Essbase tools in our guide on SmartView vs. the Essbase Excel Add-In.
Note: Be sure to label dense and sparse correctly in order to maximized the efficiency of the block storage system. This can be done by turning autoconfigure to false in order to be able to change your storage type. Changing a dense dimension to sparse will reduce your block size. The recommended block size is 8KB-100KB for 32 bit servers and 100KB-1MB for 64 bit servers. For additional optimization tips, you may find our article on quick BSO tuning helpful.
Essentially,
- Data block is created
- Index created
- Once data value is located using the index, Essbase retrieves data.
The importance of Essbase and its multidimensionality play a vital role when discussing the use of data blocks and an index system. Using the multidimensional technique, users can gain insight and analyze data from any business perspective as shown in the example above (sales -> actual-> January).
If your organization is looking to optimize Essbase performance or better understand multidimensional storage, HollandParker’s experts can help you evaluate and enhance your data environment—reach out to our team anytime to get started.
Essbase BSO Storage FAQs
1. What is a data block in Essbase BSO storage?
A data block is created for every sparse dimension intersection and stores the actual data values for combinations of dense dimension members.
2. How does the Essbase index system work?
The index provides pointers to the correct data block, allowing Essbase to quickly locate and retrieve specific data intersections.
3. Why is it important to correctly label dense and sparse dimensions?
A proper dense/sparse configuration ensures optimal block size, improving storage efficiency and system performance.
4. What is the recommended data block size for Essbase environments?
Recommended block sizes are 8KB–100KB for 32-bit servers and 100KB–1MB for 64-bit servers.
5. How does Essbase retrieve data from a cube?
Essbase uses the index to locate the correct data block, retrieves the stored value, and displays it through tools like SmartView.
