Outline Order in BSO Databases

So what should the outline order be for your BSO databases? Many of you Essbase experts have heard this before but just in case…

Outline ordering (and in general Essbase tuning) is not an exact science. But I recommend you start here and then tune / test …

1. Largest Dense Dimensions
2. Smallest Dense Dimensions
3. Smallest Aggregating Sparse Dimensions
4. Largest Aggregating Sparse Dimensions
5. Non-aggregating Sparse Dimensions

Dense dimensions - define the data block and must reside at the top of the outline

Aggregating Sparse dimensions - dimensions that will be calculated to create new parent values

Non-Aggregating Sparse dimensions - dimensions that organizes the data into logical slices

• Placing these dimensions as the first Sparse dimensions positions them to be the first dimensions included in the calculator cache
• Data is often times more dispersed within the database
• Example - Scenario, Year or Version

Here’s an dimension ordering example based on member count:

Dimension	Type-Size
Accounts	D – 94
Time Periods	D – 21
Metrics (Hrs, AHR, $)	D – 14
Scenarios	AS – 9
Job Code	AS – 1,524
Organization	AS – 2,304
Versions	NAS – 7
Years	NAS – 7

Here’s an example based on dimension density (Ordering the dense dimensions from most dense to least dense maximizes the clustering of thedata):

Dimension	Type-Size	Density After Calc	Density After Load	Data Points Created
Time Periods	D – 21	85%	85%	-
Metrics (Hrs, AHR, $)	D – 14	22%	22%	-
Accounts	D – 94	3 %	2%	-
Scenarios	AS – 9	22%	11%	199
Job Code	AS – 1,524	.56%	.23%	853
Organization	AS – 2,304	.34%	.09%	783
Versions	NAS – 7	19%	19%	-
Years	NAS – 7	14%	14%	-

How do I find the density of each dimension?

1. Make the dimension the lone Dense dimension
2. Load and calculate just that dimension
3. Check the block density value in Administration Services >Database<> Statistics

Another consideration for outline ordering is your compression type. One compression type RLE (Run Length Encoding) is a good compression type when your data has many zeros or often repeats (found in our budgeting applications. RLE will evaluate and use RLE, Bitmap or IVP for compression. So how does outline ordering impact compression? The first dense dimension determines your “columns” in PAG file. Compression will take place from left to right, top to bottom.

The standard method is to place Accounts / Measures first in every outline. If we were to do that, the .pag file would look like the following (simplified version):

BUDGET	Sales	COGS	Margin	Exp.	Profit
January	100	50	50	30	20
February	100	50	50	30	20
March	100	50	50	30	20
April	120	50	70	30	40
May	120	50	70	30	40
June	120	50	70	30	40

But if we move Time to first dense dimension and we get the following (notice repeating values):

BUDGET	Jan	Feb	Mar	Apr	May	Jun
Sales	100	100	100	120	120	120
COGS	50	50	50	50	50	50
Margin	50	50	50	70	70	70
Exp.	30	30	30	30	30	30
Profit	20	20	20	40	40	40

So… Time should be dense and listed first in the outlinewhen using RLE compression.