Presentation by Chris Barbieri, Practice Director and Oracle Ace for Edgewater Ranzal, at Kaleidoscope 2010 (ODTUG June 2010)

1. Best Practices in
HFM Application Design
Chris Barbieri
Consolidation Practice Director
Oracle ACE
Ranzal & Associates

2. Personal Background
Chris Barbieri
• Established HFM performance tuning techniques
and statistics widely used today
• 4+ years as Sr. Product Issues Manager at Hyperion
– HFM, Smart View, Shared Services, MDM
• Member of HFM launch team in 2001, certified in
HFM and Enterprise
• MBA, Babson College
• B.S. Finance & Accounting, Boston College
• Co-founded the HFM Performance Tuning Lab at
Ranzal with infrastructure expert Kurt Schletter

3. Application Design: the Foundation of
Performance
• Hyperion Financial
Management
• Metadata design as it impacts
performance
– Volume of members
– Impact of structures
• Data
– Content
– Density

4. Metadata

5. Designing HFM’s 12 Dimensions
Application Profile User controlled
1. Year 5. Entity
2. Period 6. Account
3. View 7. ICP
System 8. Scenario
4. Value dimension, User defined
includes currencies 9. Custom 1
10. Custom 2
11. Custom 3
12. Custom 4

6. Application Profile
Year
– No inherent impact on performance
– Cannot be changed after the application is built
– Impacts the number of tables that can be created in the
database
Period
– The base periods comprise the column structure of
every table, whether you use them or not.
– For this reason, avoid weekly or yearly profiles unless it
is key to your entire application’s design
View
– No impact, but only YTD is stored and Periodic, QTD are
on-the-fly derivations

7. System Dimension
Value Dimension
– Can not directly modify this
– “<Entity Currency>” is a simple variable directing you to the current
entity’s default currency
– “<Parent Currency>” points back to the currency of the entity’s
parent
Currencies
– Don’t add currencies you aren’t using
• Sets of calc status records for (every entity * every currency)
• Impact of loading metadata with entity or currency changes
– Normally translate from the entity’s currency only into it’s parent’s
currency.
– Beware of non-default translations
• Impacted calc status
• Data explosion

8. User Controlled Dimensions
Entity
– Sum of the data of the children
– Avoid Consolidate All or All With Data on each hierarchy
– Assign Adj flags sparingly
ICP
– “Hidden” dimension
Scenario
– Number of tables

9. Impact of Account Depth
4- Net Income 6- Net Income
3- Optg Income 5- EBIT
2- Gross Margin 4- Optg Income
1- Sales 3- Gross Profit
2- Gross Margin
1- Sales
Effect is multiplied when you consider the
custom dimensions
Parent accounts don’t lock

10. User Defined Dimensions
Custom 1..4
– Think dozens or hundreds, but not thousands
– Avoid:
• Employees
• Products
• Anything that is very dynamic
• One to one relationship with the entities

11. Metadata Efficiency Ratio
What does the average entity have in common with the top
entity?
– Density measurement of re-use of the accounts and customs
across all entities
top entity
children
unique custom 1

12. Metadata Volumes (Americas)
Dimension Average Recorded Comments
Volume High
Accounts 2,132 14,409
Entities 1,165 22,882
Currencies 16 233 use only 1 currency 30%
Custom1 388 19,410 use Custom 1 96%
Custom2 153 15,188 use Custom 2 86%
Custom3 61 26,816 use Custom 3 86%
Custom4 39 11,389 use Custom 4 62%
Scenarios 11 78
Entity hierarchies 3 24 the equivalent of Organizations in Hyperion Enterprise
ICP Accounts with Plug 41 1,223 use automated intercompany matching 56%
Accounts with Line Item Detail 36 1,667 16% use this, but only 10% have more than 1 account flagged
Consolidation Rules - - use consolidation rules 28%
Consolidation methods 5 10 use methods 14%
OrgByPeriod use organization by period 9%
ICP Members 86 1,407 track intercompany activity 81%
Entities flagged for Parent Adjs 143 7,698 Allow [Parent Adj] or [Contribution Adj] journals30%
Scenarios using Process Mgmt 5 53 use process management46%

13. Data

14. What’s a Subcube?
• HFM data structure
• Database tables stored by
– Each record contains all periods for the [Year]
– All records for a subcube are loaded into memory together
Parent subcube, stored
in DCN tables
Currency subcubes,
stored in DCE tables

15. Take it to the Limit
Reports, Grids, or Forms that:
– Pull lots of entities
– Lots of years
– Lots of scenarios
Not so problematic:
– Lots of accounts
– Or Custom dimension members
Smart View
– Cell volume impacts bandwidth
– Subcubes impact server performance

16. HFM Urban Legends
• 100,000 records per subcube
• Increase MaxNumDataRecordsInRAM = better
performance
• 500 children to a parent
• System 9 allows an unlimited sub cube size
• Customs should be ordered largest to smallest
• Limit to the Account dimension depth
• 64 bit is faster (this requires some explanation)

17. Data Design
“Metadata volume is interesting, but it’s
how you it that matters most”
• Density
• Content
– Specifically: zeros
– Tiny numbers
– Invalid Records

18. Data Volume Measurement
• No perfect method
Method How-To Pros Cons
Data Extract Extract all data, Simple, easy to see input Can only extract
count per entity from calculated <Entity Currency>
FreeLRU Parse HFM event Good sense of average Can’t identify
logs cube, easy to monitor individual cubes,
monthly growth harder to understand
Database Query DCE, DCN Easy for a DBA, see all Doesn’t count dynamic
Analysis tables and count subcubes members, includes
invalid records

19. Data Density Using FreeLRU
• Survey of data density using FreeLRU method
Number of applications reviewed: 32 Average Min Max Median ABC
Customer
NumCubesInRAM 2,672 72 10,206 1,345 577
NumDataRecordsInRAM 1,502,788 247,900 5,627,748 1,170,908 1,107,614
NumRecordsInLargestCube 86,415 2,508 593,924 53,089 31,446
Average records per cube 6,309 24 91,418 1,352 2,288
Average metadata efficiency: 7.3% 0.3% 39.7% 3.4% 7.3%
average cube/densest cube

20. Loaded Data
• What percent of the loaded data is a zero value?
– No hard rule, but <5% may be reasonable
– No zeros are best, watch ZeroView settings on the scenarios
• Watch out for tiny values, resulting from allocations
• How much does the data expand from Sub Calculate?
– Am I generating zeros, or tiny numbers?
Input Base Records Input Plus Calculated Base Records % Increase
From Rules
Total 2,031,976 Total 4,387,520 116 %
Input zeros 18,024 Calculated zeros 413,837 2,196 %
% zero loaded 0.9% % zeros calculated at base 9.4%
Values > -1 and < 1 373,226 Values > -1 and < 1 calculated 593,981 59 %
% values > -1 and < 1 18.4% % values > -1 and < 1 calculated 13.5%

21. Effect of Sparsity on Record Volume
• Most dense data is at the top entity
– Greatest number of populated intersections
(account _ custom 1..4 combinations)

22. Consolidated Data
• Total volume of data in any
subcube Consolidated Base Records
Total 991,587
• How many zeros are generated Consolidated zeros 194,204
by the consolidation process? % zeros 19.6%
– Intercompany eliminations Values > -1 and < 1 84,251
% values > -1 and < 1 8.5%
– Allocations
– Empty variables
Consolidated
19.6%
Calculated
9.4%
Loaded 0.9%

23. Data Density <> Calc Time
Average Rule Execution Time in Contrast with Data Volume
900 2.500
800
700 2.000
600
1.500
Seconds
Records
500
400
1.000
300
200 0.500
100
- -
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
correlation between density and calc times
• Most applications are rules bound

24. Invalid Records
• Type 1: Orphaned records from metadata that has
been deleted
– Member is removed from dimension_Item table, but not
from the data tables
– These can be removed by Database > Delete Invalid Records
• Type 2: the member still exists, but is no longer in a
valid intersection
– Most often from changing CustomX Top Member on an
account
– These cannot be removed by HFM, but are filtered out in
memory

26. Chris Barbieri
cbarbieri@ranzal.com
Needham, MA
USA
+1.617.480.6173
www.ranzal.com