Amit_ Application Design for Performance

8/10/2019 Amit_ Application Design for Performance

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Copyright 2013 by Chris Barbieri, Edgewater RanzalCopyright 2013 by Chris Barbieri, Edgewater Ranzal

Hyperion Financial Management

Application Design for Performance

Chris Barbieri


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Copyright 2013 by Chris Barbieri, Edgewater Ranzal

Personal Background

Established HFM performance tuning techniques and

statistics widely used today

4+ years as Sr. Product Issues Manager at Hyperion

2001 HFM launch team 2001

Certified HFM, Hyperion Enterprise B.S. Finance & Accounting, Boston College

MBA, Babson College

Established HFM Performance Tuning Lab at Ranzal Vice President of worlds largest HFM practice


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Foundation of Performance

Focus first on single user

Metadata design as it

impacts performance

Volume of members

Impact of structures

Data

Content

Density

Rules

Environment


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Metadata


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Designing HFMs 12* Dimensions

Application Profile1. Year2. Period3. View

System4. Value dimension,

includes currencies

User controlled5. Entity6. Account

7. ICP

8. ScenarioUser defined

9. Custom 1

10. Custom 2

11. Custom 3

12. Custom 4

(*and more customs 11.1.2.2)


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Application Profile

Year

No inherent impact on performance Can be increased afterthe application is built Impacts database table volume

Period Base periods comprise column structure of every table,

whether you use them or not

Avoid weekly profiles unless it is key to your entireapplications design

Daily is inadvisableView

No impact, but only YTD is stored Other views are on-the-fly derivations

Consider number of UI clicks


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System DimensionValue Dimension

Can not directly modify this points to entitys default currency

points to default currency of the entitys parent Anything above must be Parent.Child format

Currencies Dont add currencies you arent using

Sets of calc status records for (every entity * every currency) Impact of loading metadata with entity or currency changes

Normally translate from the entitys currency only into its parentscurrency

Beware of non-default translations Impacted calc status

Data explosion

Adds to cycle time


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User Controlled Dimensions

Entity

Single biggest factor in consolidation time

Avoid Consolidate All or All With Data on eachhierarchy

Assign Adj flags sparingly

Dont disable if you ever had journals on entity

ICP

Hidden dimension

Scenario

Number of tables


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Impact of Account Depth

Effect is multiplied when you considerthe custom dimensions

Parent accounts dont lock

4- Net Income

3- Optg Income

2- Gross Margin

1- Sales

4- Optg Income

3- Gross Profit

2- Gross Margin

1- Sales

5- EBIT

6- Net Income


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User Defined Dimensions

Custom 1..4

Think dozens or hundreds; resist thousands

If Thousands are necessary, 64 bit makes this possible

Rules remain a major factor in performance

UI click

Avoid:

Employees

Detailed Products Anything that is very dynamic, changing greatly from year to

year

One to one relationship with the entities

Configurable dimensions in 11.1.2.2


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Metadata Efficiency Ratio

What does the average entity have in common with the

top entity? Measure re-use of accounts and customs across entities

top entity

base


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Metadata Volume Study: 82 appsMedian +1 Std Deviation High

Accounts 1,444 2,915 7,491

ICP Accounts With Plug 17 288 2,273Accounts With Data Audit 32 1,358 7,490

Consolidation Rules 45%

OrgBy Period 16%

Currencies 25 57 150

Custom1 181 3,219 23,897Custom2 72 2,374 20,484

Custom3 46 909 5,681

Custom4 19 182 1,199

Entity Hierarchies 4 12 44

Entities (unique) 672 4,352 21,199

ICP Members 208 1,160 7,770

Scenarios 12 29 81

Process Management

Scenarios

- 7 37

Scenarios Using Data Audit - 11 78

Using Phased Submission? 17%


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Data


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Whats a Subcube?

HFM data structure

Database tables stored by

Each record contains all periods for a [Year]

All records for a subcube are loaded into memory together

Parent subcube,

stored in DCN tables

Currencysubcubes, stored inDCE tables


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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

Lots of custom members

Smart View Subcubes impact server performance

Cell volume impacts bandwidth


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Data Design

Density Content

Specifically: zeros Tiny numbers Invalid Records

Metadata volume is interesting, but its

how you it that matters most


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Data Volume Measurement

No perfect method

Method How-To Pros Cons

Data

Extract

Extract all data,

count per entity

Simple, easy to see

input from calculated

Can only extract

FreeLRU Parse HFM eventlogs

Good sense ofaverage cube, easy tomonitor monthlygrowth

Cant identifyindividual cubes,harder to understand

DatabaseAnalysis

Query DCE, DCNtables and count

Easy for a DBA, seeall subcubes

Doesnt countdynamic members,includes invalidrecords


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Data Density Using FreeLRU

Number of applications reviewed

46

Median +1 Std Dev Min Max

NumCubesInRAM 1,369 9,426 72 51,840

NumDataRecordsInRAM 1,179,049 4,679,031 247,900 23,019,754

NumRecordsInLargestCube 53,089 167,085 2,508 5939!"

Records #er cube 1,352 15,537 24 91,418

Metadata e$$icienc% 3.4% 12.2% 0.3% 39.7%


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Loaded vs. Consolidated Zeros

What percent of the loaded data is a

zero value?


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Data Growth Up the Entity Hierarchy

Number of EntitiesTop of hierarchy 1

Total in hierarchy 5,571

Base of hierarchy 2,980

Base Entityinput 91records

Base Entitycalculated 153records

AverageEntity 178records

Top Entity

16,829 records


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Loaded, Calculated, and Consolidated Data

Rough stats: median from 12 applications

+1 std deviation

MonthlyZeros

%MonthlyGrowth

RulesGrowth

Loaded Records153,826

534,239 4.3% 3.3%

Loaded + CalculatedRecords 349,360717,570 22.5% 2.7% 2.0

Consolidated

Records

62,090

142,432 8.7% 3.2%


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Invalid Records

Type 1: Orphaned records from metadata that hasbeen deleted Member is removed from dimension_Item table, but not

from the data tables

These can be removed by Database > Delete InvalidRecords

Type 2: the member still exists, but is no longer in avalid intersection Most often from changing CustomX Top Member on an

account

These cannot be removed by HFM, but are filtered out inmemory


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So How Much Memory Do I Really Need?

Calculation Company

Number of entities 11,321

* 2 cubes: entity currency + contribution 11,321

Non-USD entities 2,939

add another cube for parent currency 2,939

Entity_value cubes 14,260

Actual 2013, 2012 2

Forecast1 2013, Plan 2013, TestForecast 2013 3

Tests, etc. 10

Total Year_scenarios 15Total cubes 213,900

Average records per cube 175

Optimal NumDataRecordsinRAM setting 37,432,500

bytes per record 120

Records * bytes converted to MB = MaxDataCacheSizeInMB 4,284


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Rules Timing


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Data Density Calc Time

correlation between density and calc times

Most applications are rules bound

-

0.500

1.000

1.500

2.000

2.500

-

100

200

300

400

500

600

700

800

900

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Seconds

Records

Average Rule Execution Time in Contrast with Data Volume


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But Some Applications are I/O Bound

Time vs. Volume

050

100150200250300

350400450

_D

BM

.US

.US

.DE

.CN

BM

704

519

899

GO

808

545

801

828

851

.BE

.BR

820

856

S

econds

010,000

20,000

30,000

40,000

50,000

60,000

elapsedtotalrecords

HFM app server CPU is waiting for data


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How Long Should Rules Take?

Total consolidation time in seconds,12 periods

Consolidate All With Data for consistency, reliability Fastest of three consecutive runs

Divide by 12 periods and total number of entities Descendants inclusive of POV parent

0 0.25 2.0 4.0 10.0

Seconds Per Entity Per Period


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Rules Impact Ratio

Total consolidation time withrules

Divided by time with Blank

Rules

Typically 2- 5 times

More than that is an

opportunity for improvement


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Ideal for simple applications that do not need

consolidation rules

Skips writing [Proportion] to the database

Smaller DCN tables

If no [Elimination], [Parent Adjs] or [Contribution Adjs]

the DCN tables wont even exist

Studies show about 30% faster consolidation times

Must be enabled at app creation

Consolidation Rules = R


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Reference

Application


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Small but Constant Application

Applied across multiple environments

0:00:00

0:00:43

0:01:26

0:02:10

0:02:53

0:03:36

0:04:19

0:05:02

physical physical virtual virtual physical physical virtual physical virtual virtual virtual virtual virtual virtual virtual virtual virtual

HFM lab A: Dev B: Dev C: Dev K: FIT D: QA Ranzal:dev

L: Stage K: dev T-61laptop

E: non-prod

F: prod T-410laptop

G: Dev H: Dev H: prod I: prod

Full Rules

Blank Rules

Target


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Chris Barbieri

[email protected], MA

USA

+1.617.480.6173

www.ranzal.com

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