Rschoustra.awalkAroundBlock.presentation

8/11/2019 Rschoustra.awalkAroundBlock.presentation

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A walk around (& through )the block

Essbase BSO & Calculation Scr ipts for Everyone!

Rich Schoustra, Managed Services

Practice Lead, Principle

ADI Strategies, Inc.


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Rich Schoustra

Managed Services Practice Lead, Principal, ADI

Strategies, Inc. Hyperion Consulting (1996)

Training (2000-2002)

Project Management/Consulting

You!

VERY Important!

Introduction


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Why learn about Dense & Sparse

Assigning Dense & Sparse Storage type

Dense Defined

Sparse Defined Essbase is a databasesort of

Useful Database Statistics

A simple Calc Script

Why is it important to know:

What makes blocks fill up?

What makes blocks multiply?

What makes blocks grow (or shrink)?

Agenda


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Become a blockhead

Basis for writing efficient calculation scripts

Basis for writing calculation scripts that work (well)

Helps in understanding report development Helps dictate data load design

Helps in architecting overall Hyperion Solution

Including multiple Plan Types

Multiple Essbase reporting cubes

ASO / BSO hybrid architected solutions Designing Partition definitions

Helps in understanding EssbasedHyperion Products

Hyperion Planning

Hyperion Profitability and Cost Management (HPCM)

Why learn about Dense & Sparse


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How to define Dense/Sparse settings

Dense defines the Block

Sparse defines the Address

Calculating Block Size

Calculating Number of Blocks

Calculating Dense vs. Calculating Sparse

Identifying (unique) Blocks in retrievals & Extracts

Important Database Statistics for Dense & Sparse

Dynamic Calc on Dense but not on Sparse

Implied Shares

Hourglass

What you will learn


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Quick overview of the Sample.Basic (modified!) Essbase Database

Hyperions version of Hello World!

Sample.Basic


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Dont play with Matches

How to set Dense and Sparse


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Hyperion Planning: As easy as


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Oracle Essbase: As easy as


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Dense Dimension Storage Property Defined

Designating which dimensions have the Dense storage property

means you have just defined and pre-created a Multi-dimensional

Array.

Which brings us to.


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Spreadsheets & Databases . . . a digression(Essbase is a Database, sort of. Absolutely!)

Spreadsheets store (numerical) data in arrays with labels to

relate/define the data Labels to create CONTEXT


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Spreadsheets & Databases . . . a digression.

Relational Databases store data in related tables

with indices and keys to relate the data


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Oracle Essbase is a Database, but

In computing, an Array is a construct for storing data and is

declared in most programming languages as an X by Y by Z by

n multidimensional Matrix.

Which brings us back to.

Designating which dimensions have the Dense storage property

means you have just defined and pre-created a Multi-dimensional

Array (for storing your datanumbers).

1 X 2 Array

2 X 3 Array3 X 3 X 2

Array (3D) 3 X 3 X 2

Array (2D)


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Dense Dimension Storage Property Defines The Block(orthe multidimensional array )

(stored) Members in Dense Dimension-1 (axis-A)

X (stored) Members in Dense Dimension-2 (axis-B)

X (stored) Members in Dense Dimension-n (axis-n)

X 8(bytes) = Bock Size (Array Size)


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Sparse Dimension Storage Property DefinesPOTENTIAL addresses (index-pointers) to Blocks

= Potential Number of Blocks (stored) Members in Sparse Dimension-1

X (stored) Members in Sparse Dimension-2

X (stored) Members in Sparse Dimension-n

= Potential Number of Blocks


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Sparse Dimension Storage Property DefinesPOTENTIAL addresses (index-pointers) to Blocks

= Potential Number of Blocks


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The Sample Basic Model1 Dense Dimension and some useful Database Statistics

1 Dense Dimension

with 8 stored members

4 Sparse Dimensions

with 4, 3, 19, 25 storedMembers


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The Sample Basic Model1 Dense Dimension

Dimensions: Members Stored. Stored Member Count excludes members:

Tagged to calculate Dynamically

Tagged as Label Only

Tagged as (Explicit) Shared Member

Implied Shared Member

Attribute Dimensions Members


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*Implied Share:

Technically: when a Parent Member has only 1 child member (profit in this case),

it will share the same data value as the single child and Essbase will not allocate

storage or an array cell for this value.


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The Sample Basic Model4 Sparse Dimensions

Sparse Dimension Storage Type makes up the Address or Pointer to the Block

(Array). The total Potential number of addresses or Unique Blocks is:

(4 stored Year Dimension Members) *Attribute Dimensions

X (3 stored Scenario Dimension Members) are not included

X (19 stored Product Dimension Members)

X (25 stored Market Dimension Members) = 5,700 Potential uniq ue bloc ks


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Statistics: Block Size: Number of Stored Dense Dimension Members X 8 (Bytes)

defines the Block (or Array) Size

DENSE

SPARSE


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Index Cache is amount of memory available to load the index values into memory

from the ess0000n.IND file(s). The *.ind files contain all of the unique block

address pointersthe unique (complete) Sparse member intersections.

DENSE

SPARSE

Data Cach e is amount of memory available to load the (uncompressed) blocks into

memory from the ess0000n.PAG file(s). The *.PAG files contain all of the blocks.


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ess00001.IND This file contains all of the unique block address pointersthe

unique (complete) Sparse member intersections that exist (have been loaded toEssbase. This file can usually fit in memory (index cache) for rapid access to

pull (point to) blocks in memory or on disk to be pulled into memory

DENSE

(the Blocks)

SPARSE

(the Index)

ess00001.PAG This file (or files) contains the compressed blocks on disk. Blocks

are called into memory (data cache) based on complete sparse intersection data

requests (e.g. Smart View refreshes, Planning Web Forms, Running Calculation

Scripts and Business Rules.


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WOW! The Amazing & Fantastic:

Block-Size-Potential-Number-Of-Blocks-Hyperion-Essbase-

Database-Sizing-Conf igurator!


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2-Dense Dimensions Model . Statistics


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2 Dense Dimensions. Load 1 Block


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2 Dense Dimensions. Calc All;


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2 Dense Dimensions. Calc All;


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Calculating [Measures] in the block (Dense #1)

Calc All d oes jus t that. It Calculates All B loc ks in the data base (Dense), and

Ag gregates (Sparse) al l level 0 block s to upp er level block s i f th ey exist

Block[Actual], [100-10], [New York]

Context for 645:

Dense Context 1 = Sales

Dense Context 2 = Feb


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Calculating [Year] in the block (Dense #2)



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Calculating (Aggregating) [Scenario] (Sparse #1)



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Calculating (Aggregating) [Product] (Sparse #2)



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Calculating (Aggregating) [Product] (Sparse #2)

ess00001.PAG

BOOM! 2 new (upper level) Blocks:

[Actual], [100], [New York]

[Actual], [Product], [New York]


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Calculating (Aggregating) [Market] (Sparse #3)



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Calculating (Aggregating) [Region] (Sparse #3)

Set Msg Detail;


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Calculating (Aggregating) [Region] (Sparse #3)

ess00001.PAG

BOOM! 6 new (upper level) Blocks:

1. [Actual], [100-10], [East]

2. [Actual], [100], [East]

3. [Actual], [Product], [East]

4. [Actual], [100-10], [Market]

5. [Actual], [100], [Market]

6. [Actual], [Product], [Market]


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Dense(Calculation)

Fill up the blocks


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Sparse(Calculation)

Creates new(Aggregated) Blocks

[Actual]->[Cola]->[New York]

[Actual]->[Colas]->

[New York]

[Actual]->[Product]->

[New York]

[Actual]->[Cola]->

[East]

[Actual]->[Colas]->

[East]

[Actual]->[Cola]->

[Market]

[Actual]->[Colas]->

[Market]


[Market]


[East]

1 Block becomes 8 aggregated

[upperlevel] Blocks


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Simple Calculation

REQUIREMENT:

Load the Database with Actual data (25 level 0 Blocks)

Calculate the Database : 81 Upper Level Blocks =106 Total Blocks

Pre-Populate the Budget with Actual data [DATACOPY Actual to Budget; ]

212 Total Blocks

Assumption. Sales will be 10% higher in the Budget [If it is an Eastern RegionMember](Child of East)

Using IF /

ENDIF

& FIX /ENDFIX


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Simple Calculation


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Simple Calculation

This Calculation will iterate through all 106[Budget] Blocks,asking the question each of the 106 times:

Are you [Block-in-Memory] a child of the ParentMember [East] in the [Market] Dimension?

If you are, then Your Sales are now = to 1.1 timesyour current Sales!

If you are not a child of the Parent Member [East]in the [Market] Dimension then get outta here! Backto disk with you!

Blocks Before Calculation Blocks After Calculation


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Simple Calculation

Just needto calculatethese 12Blocks

Not these 94Blocks94 =106-12

[non-children-of-East]Blocks


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Simple Calculation

Just need tocalculate these12 Blocks

Change IF logicto FIX

and ignoreall the other

blocks


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Generalization regarding IF & FIX

Somet imes* It depends. Usually after FIXing on othergroups or groupings of SPARSE member intersections.

Rarely** It depends. Also after Fixing down onmembers. There are other challenges with associatedwith Ifingon Sparse dimension members.


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Dynamic Calculation Storage Option

Why Dynam ic Calc on Dense?

Smaller Block Size

Calculation occurs IN THE block


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Dynamic Calculation Storage Option

Why Not Dynamic Calc on Sparse?

Need Multiple Blocks in Memory toderive Virtual Blocks in Memory

[Actual]->[Cola]->

[New York]

[Actual]->[Colas]->

[New York]


[New York]


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Data Load. Extract Layout Tip

SORT ON SPARSE

Dimensions First andDENSE DimensionsLast :

Avoids excessive I/O(Reads / Writes fromdiskspecifically theess00001.PAG file)

Prevents unnecessaryFragmentation of the

ess00001.PAG file, orfiles)

GOOD! BAD!


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The Hourglass.

The Hourglass

For DENSE Dimension Members this (usually) occurs naturally since [Accounts]usually has the most (Dense) members, followed by Time, Scenario, and sometimesanother DENSE dimension. The Top part of the hourglass doesnt really matter forcalculation performanceUser Presentation (how the members naturally appear inSmartView is more important.

For SPARSE dimensions, the aggregations do occur more efficiently if the dimensionswith fewest aggregations (not members!) are aggregated 1st, followed by next with

most aggregations, etc.


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Wheres Waldo?...

If Waldo is [SPARSE] or [DENSE] can you spot Waldo

In SmartView Retrievals?

Calculation Scripts?

Data Extracts?

Substitution Variable Assignment Grids?

In a plane? On a Train? .


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Whats Next?

Where to go from here

Check out some of the more technical sessions here at KScope

Use your own environment and look at and watch the Statistics we talked about

Open up those Calculation Scripts that may have seemed daunting

On DEV, play around with Calculation Scripts and make small adjustments

HAVE FUN!


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Contact: Rich Schoustra: [email protected]

Join Rich Schoustra: Managing Hyperion: A Success Guide for CIOs, CFOs, Finance,

Accounting, and IT Managers When: Jun 24, 2014, Session 7, 11:15 am12:15 pm

Join Sumit Deo: EPMA Tips and Tricks: Lessons in Metadata and Data Management

When: Jun 25, 2014, Session 13, 11:15 am12:15 pm

Join Eric Helmer: Saas? Cloud? Exalytics? On-premise?

Exploring Oracle EPM Deployment Options

When: Jun 25, 2014, Session 15, 3:15 pm - 4:15 pm

For more information
mailto:[email protected]:[email protected]


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T H A N K Y O U

Rich SchoustraHyperion Managed ServicesPractice Lead, [email protected]
mailto:[email protected]:[email protected]:[email protected]

Documents

Rschoustra.awalkAroundBlock.presentation