Thursday, September 22, 2011

Essbase ASO “A Quick Reference Guide”


Aggregate storage technique is used when application needs more dimensions and members in order to support higher degree of analysis without compromising the cube performance. Aggregate storage is mainly used for applications where reporting on business data is considered as primary requirements. Data load in aggregate storage is faster than block storage and the data consolidation at the higher level is done automatically. Aggregate storage required less space in disk and data retrieval is also faster because data is always available in aggregated form. Aggregate storage application is approximate is similar as block storage application but it has so many new features. Aggregate storage database used where application require large dimensionality.

Customer analysis - Data is analyzed from any dimension, and there are potentially millions of customers.

Procurement analysis - Many products are tracked across many vendors.

Logistics analysis - Near real-time updates of product shipments are provided.

Below are some benefits of ASO.

1. Faster load and calc times provide
2. Lower hardware costs
3. Lower maintenance costs
4. Higher availability

Key Difference between Aggregate storage and block storage

1. Data load can be possible at level 0 only and write back functionality

In aggregate storage you can’t load data at any level. In this example “Total Expenses” is level 1 member and if you load data in to it, Essbase will give you’re an error.

Data load at any level is possible in Block Storage Application. Edit data field and click on update button for verification refresh data grid.

This example also shows that you can’t write back in aggregate storage but it allow in block storage.

2. No need to run consolidation operation

When you load data in to aggregate storage, data will immediately available at all parent level of hierarchy. Load data in below combination of dimension, sales is level 0 member. We will load data in sales and verify that data will be immediately available for “Margin” level 1 member.
Data is not available for below combination.

Data load text file

Right Click on data base :- select load data

Select data file and data load value method then click ok.

Data is loaded successfully.

Without running any calculation script or consolidate operation data is available at level 0.

Data at level 1
Data is consolidating automatically for parent level. Data is available for “Margin”.

No Execute calculation option is available for aggregate storage application.

3. Set system resource utilization

While loading data aggregate storage allows you to set resource utilization. Resource utilization option supports to execute other tasks simultaneously. Some other options those are available for aggregate storage.

Dataload in aggregate storage

Dataload in block storage

4. Calculation done through MDX

Calculation script is not supported in aggregate storage applications. You have write calculation script for any calculation.

5. Data access is faster

Data extraction in aggregate storage is relatively faster than block storage database.



6. Aggregate storage dimension supports

Aggregate storage application supports more dimensions in comparison with block storage. The performance of block storage will be decrease as you increase number of dimensions in database. Aggregate storage database performance does not effects by number of dimension.

7. No sparse and dense dimension

In aggregate storage application does not have dense and sparse dimension concepts.

8. Restriction on data export

Aggregate storage database restrict to export data only for level 0 data block. Block storage allows you to use all data export options.

9. Creating currency database

You can create currency data base in block storage database.

You can’t create currency data base in aggregate storage database. Because database type of currency or normal is not applicable to aggregate storage databases therefore it is not selectable.

Aggregate Storage Overview

Aggregate storage is relatively newer the block storage application. It has additional features as compare to block storage. Aggregate storage database is aggregation-intensive cubes. It supports large numbers of dimensions and members. There is no concept of dense dimension in aggregate storage. It only supports extremely sparse data sets. Aggregate storage reduced calculation times and disk footprint and also reduced complexity in database development.

Key Aggregate Storage Characteristics

1. Data is loaded only at level 0
2. Member formulas are MDX queries
3. All formulas and aggregations are executed at runtime
4. Aggregation algorithm selects and stores most expensive queries
5. Outlines are paged
6. Block storage outlines can be converted to aggregate storage outlines
7. Hierarchy types follow formalized rules
8. Data is stored in table spaces
9. Creating Aggregate storage manually

Design Considerations


Ragged hierarchies supported- Ragged hierarchy means it is not necessary that all members of hierarchy contain equal number of child.

No limit to dimensions- There is no limit on creating dimensions in aggregate storage database outline.

Maximum level combinations

The maximum level of combinations between outline dimensions are 2^52, which is very large. Large amount of data can be store in single database.

Limitation on Database-

1. One database per application – Restriction for ASO application
2. MaxL commands Eecuted on application level – Because there is only one database in each application.
3. No currency conversion - Restriction for ASO application

Member Formulas

When working with aggregate storage databases, you must write all member formulas in MDX. The Hyperion implementation of MDX is a customized version; it contains a series of commands that are specific to Essbase and is embedded in the MaxL shell.

Aggregate storage supports MDX, so write all member formulas in MDX. When converting an outline from block storage to aggregate storage, you may have difficulty converting block storage member formulas to MDX. You have to convert all member formulas in to MDX manually.

Aggregate Storage Production Cycle

The production cycle for aggregate storage databases is similar as block storage database.

1. Create a database outline with database dimensions and hierarchies
2. Load data, using load rules to map to the database dimensions
3. Optional: Aggregate data by using stored or ad hoc aggregations
4. Analyze data in Excel through Smart View or Spreadsheet Add-in

Database aggregations decrease query times because many data values at upper-level intersections are calculated and stored, rather than being calculated dynamically on retrieval.

Instruction for creating aggregate storage database

1. Application and database name should be in eight characters
2. You can create only one aggregate storage database for each application

Application and Database Trees

Block Storage application database tree has more than one database and calculation scripts.
Aggregate storage application database tree has only one database and no calculation script exists.

Directory Structures

Directory contains same components in both aggregate and block storage database like outlines (OTL), load rules (RUL), and report scripts (REP). Aggregate storage databases may also contain aggregation script files (CSC).
This is sample directory structure for block storage database.

This is sample directory structure for aggregate storage database.

Aggregate database objects

a. Outlines (OTL)
b. Load rules (RUL)
c. Report scripts (REP)
d. Aggregation scripts (CSC)

Rules Files for Building Outlines

Creating rule file and building outline is same in aggregate storage as block storage.

Go to file and create new rule file.

Go to file and open relative source file either text file or SQL file.

Set “Dimension Build Properties” for source file then click ok.

Set Dimension build settings


Save rule file and load data.

Select data load mode as “Build only” then data source and rule file click ok.

New outline dimension is loaded successfully.

Verify in existing outline.

Designing Aggregate Storage Outline Hierarchies

You can design outline manually by using toolbar. You can create new dimensions add siblings, add child and set properties through toolbar.

Adding Child in dimension member

There are three types of hierarchies in aggregate storage.

1. Multiple hierarchy
2. Stored hierarchy
3. Dynamic hierarchy

Aggregation hierarchies are structures usually comprising two or more levels of detail that must aggregate from the bottom up to provide a top-level total.

Multiple Hierarchy

When you tag a dimension as “Multiple hierarchies enabled” the dimension member is automatically tagged as Label Only. To use multiple hierarchies in a dimension, you must enable multiple hierarchies for that dimension.

Stored Hierarchy

Stored hierarchy has only addition as consolidation operator. You can use the stored hierarchy type where aggregation is the only mathematical requirement. If you have some shared member in hierarchy then use multiple hierarchy.


1. Potential to store aggregated data
2. Enhanced query performance


1. Limited use of unary operators
2. Limited use of Label Only
3. Support for only one instance
4. Dynamic Hierarchy

Dynamic hierarchy

The Dynamic hierarchy allows you to do complex calculations and member formulas. Dynamic hierarchies are calculated, the data retrieval time may be longer than for data retrieved from stored hierarchies.


1. Any consolidation operator
2. Member formulas
3. No Label Only restrictions
4. Unlimited shared members


1. Members calculated during retrieval (never preaggregated)
2. Potentially reduced query performance

Designing Alternate Hierarchies

Attribute dimension hierarchy

Attribute dimension hierarchy is an alternate hierarchy used for classify additional information of dimension.


1. Attribute dimension can be assign for any base dimension
2. Are treated like stored alternate hierarchies


1. Can perform only addition calculations
2. Are calculated dynamically during retrieval

Shared members hierarchy

Shared member hierarchy is also an alternate hierarchy all shared member refers to stored members of outline. In aggregate storage application only multiple hierarchies can have shared members.

“Jan” is a shared member …

But “Feb” is not a shared member, So Essbase will through the below error massages.

Make “Feb” as shared member and then save it.

Converting Block Storage to Aggregate Storage

There is simple way to converting block storage application to aggregate storage application through conversion wizard. There are many difference between block storage and aggregate storage, so when you convert block storage application to aggregate storage application, wizard will reject not applicable options.

Conversion steps for Block Storage to Aggregate Storage

1. Select a source outline
2. Verify and correct block storage-only features (either manually or automatically)
3. Select a destination for the converted outline.

Step #1 :-
Select Source Block storage Outline

Step #2 :-
Verify and correct block storage-only features

This wizard will give you the list of features which are only supported by block storage application.
Warning comes in conversion of block storage to aggregate storage, because some properties does not support in aggregate storage. This warning information says that shown features are not supported in aggregate storage like dynamic time series, shared member and member formula.

Modification information from BSO to ASO

Conversion wizard will automatically modify some member properties and delete invalid members.

Step #3 :-
Select Target Aggregate Storage Application

You can select target application and database outline then replace the existing outline from the new one. You also can create new aggregate storage application and convert block storage to aggregate storage.

Select Outline

Select and replace the existing outline

Click on finish…..

Converted Block Storage Application

Block storage application successfully converted into aggregate storage application.

The unsupported features replaced by supported features.

1) Year dimension is converted from dynamic to storage

2) Measures dimension hierarchy converted as dynamic

3) Product dimension storage hierarchy converted as Multiple Hierarchy

4) All member formulas are rejected

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