Formula and Business Rule Efficiency in OneStream

July 29, 2016

Aaron Bolshoun

Finit Services

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Upcoming OneStream Webinar

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Friday August 19th

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Presented by Jay Hampton & Christine Ong-Estrada

About the Presenter

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Aaron Bolshoun(abolshoun@finit.com)

Certification/Education

• MBA - Business InformationTechnology, University of Denver

• BA - Northwestern University

• HFM Certified

Experience

• 3 years with OneStream

• 10 years with HFM

• 10 years with FDM

• 10 years of EPM / CPM Implementation experience

Agenda

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• Formula Efficiency

• Data Buffers

• New Functionality available in 3.7 Release

OneStream XF Formulas

Elements of OneStream that can be Customized

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• Member Formulas• Formula Pass

• Dynamic Calc

• Business Rules• Finance

• Parser

• Connector

• Conditional

• Derivative

• Dashboards: Method Queries, SQL Scripts

• Transformation Rules: Complex Expressions

• Confirmation Rules

• Data Cells

• Dashboard Data Set

• Dashboard Extender

• Dashboard XFBR String

• Extensibility

• Event Handler

• Drill-Down

Efficiency

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Types of Formula Efficiency

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Different Types of Formula “Costs”:

1. Efficient Processing

2. Efficient Maintenance

3. Efficient Storage

4. Efficient Script Creation

Efficient Processing

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Preferred Methodology1. Can I do this without any formulas? Is it possible to produce result

via metadata attributes and hierarchy? (Account type and/or Aggregation Weight)

• No impact to processing

2. Can I build this as a Dynamic Calculation?• No impact to consolidation time, but takes time to calculate

in memory when a report is run.

3. Can I build this as a Member Formula (Formula Pass)?• Stored in database, so increases consolidation time,

but no impact to report generation.

4. Can I build this as a Business Rule?• Stored in database, so increases consolidation time,

but no impact to report generation.

Dynamic vs. Stored Calculations

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Dynamic Calculation

Formula Pass

Dynamic Calculation vs. Member Formula

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Dynamic Calculation

MemberFormula

No Impact Consolidation Time Much Slower

Much Slower Report Generation No Impact

Unnecessary Consolidation Required

Can use Formula Pass or Dynamic Calc in Source

Use with other formulas

Can use Formula Pass in Source

Data must already be consolidated if source contains a Formula Pass

DependenciesMust assign Formula

Pass 1-16 accordingly

Do not aggregate Aggregation Aggregate naturally

Efficient Processing

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• From the XF Design and Reference Guide:

“Consolidation performance is directly impacted by the volume and complexity of Stored Calculations. Careful consideration should be given to each Stored Calculation since …. a large amount of data being written to the Cube would negatively impact Consolidation performance …. The quantity of stored numbers is the most important factor when optimizing Consolidation performance.”

Member Formula vs. Business Rule

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• Member Formulas will always perform better than Business Rules: Member formulas are multi-threaded (parallel) while Business Rules run sequentially (serial)

• Some logic, like custom Consolidation or Member Lists, must occur within a Business Rule—API vs. BRAPI

Efficient Maintenance

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• We need to build all logic with the understanding that we will eventually hand it over to someone else to maintain

• We need to make troubleshooting easier

• Formulas should be as dynamic as possible

• Use Comments!!!!

• HFM vs. OneStream: With great flexibility comes great complexity! Where exactly does that logic reside?• HFM: It’s in the rules file.

• OneStream: • Member Formulas (on Account, Entity, Flow, UD’s, etc.)

• Business Rules

Lists of Formulas by Formula Pass

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Grid View

Formula List Dashboard

Formula for Calculation Drill-Down

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• Entering a formula in the Formula for Calculation Drill Down property allows drilling on calculated Scenario, Account, Flow and UD members

Efficient Storage

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• Storage isn’t free—it has costs associated

• Every Stored Member creates a record which takes up storage space in the database when data is consolidated

• Be Specific: much more efficient to write to one specific member instead of all of them

Efficient Storage: Data Explosion

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• OneStream warns upon consolidation about a formula that will cause data explosion:

API.Data.Calculate

• Tips to Avoid Data Explosion• Use same level of detail on target (left side of =) as on the source

(right side of =)

• Do not use #All in Stored Member formulas

Formula Result

“A#Profit = A#Sales” No Data Explosion

“A#Profit:U2#Cust1 = A#Sales * 1.05” No Data Explosion

“A#Profit = A#Sales:U2#Cust1” Some Data Explosion

“A#Profit = 2.0” Large scale data explosion

Efficient Script Creation – Script Editor

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• Color Coding:

• VB is blue

• API is black

• Strings are red

• Comments are green

• IntelliSense

• Integrated Script Library with filter:

• No need to “re-invent the wheel” – use existing code as starting point (GolfStream as a reference)

• OneStream Script Editor provides many tools to simplify the process:

Efficient Script Creation – Rule Snippets

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• Sample code for common calculations that integrates with Script Editor is available in the XF MarketPlace• Common Account formulas (e.g. Current Ratio)

• Formula for Calc Drill Down

• Writing to Error Log

• Confirmation Rules

• Member Lists

• Eval

• Many more & list

continues to grow!

TroubleShooting & Optimization: Logging

• api.LogMessage(message, detail) and api.LogError(errorLevel, description)

• Consolidate with Logging provides visibility into all calculations that run and how long each one takes

• Entries are logged in Task Activity Log

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Consolidate with Logging

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Balancing priorities:

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1. Efficient Processing

2. Efficient Maintenance

3. Efficient Storage

4. Efficient Script Creation

Data Buffers and Eval Buffers

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Data Cell vs. Data Unit vs. Data Buffer

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• Data Cell• One number uniquely represented by

a Cube and 18 dimension members

• Data Unit• All data for a single:

Cube, Entity, Parent, Cons, Scenario, Time

• E.g., Calculate one Entity/Period 1 Data Unit

• Data Buffer• A subset of data in a Data Unit

• E.g., A#Salary contains numbers for all Cost Centers

Data UnitDimensions

Account-type Dimensions

Data Unit Concepts

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Stored Formulas run for an entire Data Unit

Cube and 5 Data Unit dims are known Valid: If api.Pov.Entity.Name.XFEqualsIgnoreCase(“CO”) Then

Account-type dims can be anything Not Valid: If api.Pov.Account.Name … or If api.Pov.UD1.Name …

Conditional logic requires Eval

Avoid using api.Data.Calculate in a loop (performance)

APIs api.Data.Calculate (easiest to use)

Eval (most flexibility)

api.Data.GetDataBuffer (legacy cell-level processing)

Eval Buffer Example

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• api.Data.Calculate("A#Bonus = A#Salary * Eval(A#BonusLevel)", AddressOf OnEvalDataBuffer) • Bonus by Cost Center using BonusLevel and Eval

• Order of Operations• A#Salary – Data Buffer is retrieved from storage

• A#BonusLevel – Data Buffer is retrieved from storage

• Eval evaluates and modifies the BonusLevel Data Buffer

• Data Buffer Math creates a new Data Buffer

• A#Bonus - The new Data Buffer is stored

Eval Buffer Example - Formula

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Eval Filtering – Method 1

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• Filter using api.Data.Calculate:

• Syntax: api.Data.Calculate(formula, accountFilter, flowFilter, originFilter, icFilter, ud1Filter, ud2Filter, ud3Filter, ud4Filter, ud5Filter, ud6Filter, ud7Filter, ud8Filter, onEvalDataBuffer, userState)

• For example:

api.Data.Calculate("A#Bonus = A#Salary * 0.05", "", "", "", "", "U1#Selling.Base, U1#Eng")

Eval Filtering – Method 2

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• Eval Filters the new Data Buffer:

• For example:

api.Data.Calculate("A#Bonus = Eval(A#Salary * 0.05)”, AddressOf OnEvalDataBufferFilter)

• Generates the same result as Method 1

Eval Filtering – Method 2 Formula

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New Functionalityin Version 3.7

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Convert Unbalanced

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• AddUnbalanced, SubtractUnbalanced, MultiplyUnbalanced, and DivideUnbalanced

• Used to avoid data explosion when varying level of Account-Type dimensions are required in a formula

• Valid:

api.Data.Calculate("A#6050 = A#5000 * A#3000")

• Bad Idea:api.Data.Calculate("A#6050 = A#5000 * A#3000:O#Top")

api.Data.Calculate("A#6050:O#All = A#5000:O#All * A#3000:O#Top")

Convert Unbalanced

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• Instead, use new Convert Unbalanced functions:

api.Data.Calculate("A#6050 = MultiplyUnbalanced(A#5000, A#3000:O#Top,

O#Top)")

• First two parameters are Data Buffers to be multiplied (or added or subtracted or divided)

• Third parameter is the Account Type dimension member that will always be used with first parameter, without causing data explosion.

Get Data Buffer Using Formula

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• Allows use of an entire math function to calculate a final data buffer:

• Syntax: Dim objDataBuffer As DataBuffer = api.Data.GetDataBufferUsingFormula(formula, scriptMethodType, changeIdsToCommonIfNotUsingAll, expressionDestinationInfo, onEvalDataBuffer, userState)

• For example:

Dim myDataBuffer As DataBuffer = api.Data.GetDataBufferUsingFormula("A#Sales – A#Costs")

Get Data Buffer Using Formula Variables

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• After creating a data buffer variable, it can be named as a Formula Variable and then reference inside api.Data.Calculateor inside other calls to api.Data.GetDataBufferUsingFormula.

• This provides a lot of flexibility and it can even improve performance because you can calculate a data buffer once and re-use the variable multiple times instead of recreating it.

• For example:

Dim myDataBuffer As DataBuffer = api.Data.GetDataBufferUsingFormula("A#Sales – A#Costs")

api.Data.FormulaVariables.SetDataBufferVariable("myDataBuffer", myDataBuffer, False)

api.Data.Calculate("A#Profit = A#5000 + $myDataBuffer")

Get Member Names from ID’s

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• Allows identification of Member Names when looping:

• For example:

For Each sourceCell As DataBufferCell In myDataBuffer.DataBufferCells.Values

Dim accountName As String = sourceCell.DataBufferCellPk.GetAccountName(api)

Dim ud1Name As String = sourceCell.DataBufferCellPk.GetUD1Name(api)

Next

Log Contents of Data Buffer

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• Allows logging of Data Buffer contents for Debugging

• Third parameter indicates number of records to be logged.

• For example:myDataBuffer.LogDataBuffer(api, "MyDataBuffer Output", 1000)

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Questions?

Thank You for Attending!

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Today’s Presenter:

Aaron Bolshoun

abolshoun@finit.com

General Questions:

Greg Barrett

gbarrett@finit.com

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