Groovy collections are used all throughout the ePBCS API.  If you are not familiar with collections, you may want to take a look at Adventures in Groovy – Part 27: Understanding Collections before you continue.  Maps, which are a type of collection, are very useful when moving data between different applications that have different member names representing the same data.  In a the example below, data is moving from a product revenue cube to a financial cube.  In the detailed cube, the member names are more descriptive, like Net Sales.  In the financial application, the same data is a true account number from the GL, and names 42001.  Mapping data between these two can easily be done with Groovy maps.

Introduction

There are two components to understanding the use of these maps.  First, the map must be defined for use.  The construction of the map is a delimited list of items.  Each of the items is made up of an key and a value.  These are separated by a colon.

//set account map
def acctMap = ['Units':'Units',
               '42001-Product Sales':'Net Sales',
               '50001-Cost of Sales':'COGS',
               '50015-Write-offs':'Write-offs',
               '56010-Chargebacks':'Customer Satisfaction Discount',
               '50010-Sales and Discounts':'Sales and Discounts',
               '56055-Overstock Discount':'Overstock Discount',
               '56300-Customer Satisfaction Discount':'Customer Satisfaction Discount',
               '56092-Multi-Purchase Discount':'Multi-Purchase Discount',
               '56230-Open Box Discount':'Open Box Discount',
               '56200-Damage Container Discount':'Damage Container Discount',
               '56205-Damaged Box Discount':'Damaged Box Discount',
               '56090-Group Purchase Discount':'Group Purchase Discount']

The second piece is retrieving the mapped value.  The value on the left of the colon is referenced and the value on the right will be returned.  The following would return 56230.

[acctMap.get("56230-Open Box Discount")]

A fully vetted example follows of moving data from one database to several others.  The function’s use is embedded in a loop, so rather than a hard coded value, the member of the account dimension is used as the accounts (rows in the form) are being iterated.  It looks like this.

[acctMap.get(it.getMemberName('Account'))]

Working Use Case

The map above is used in several places for several reasons.  First, the map is created.  Second, the map is iterated and the key is used to create a data grid for all the values that will be copied, or synchronized, to the destination cube.  Third, the map is used to lookup the converted value to create the grid connected to the destination.  this is a complete working example.  The items in red are specific to the map and its use.

//Dimension employeeDim = operation.application.getDimension("Account")

//****************************************************************************
// Data Movement between Apps
//****************************************************************************

// Get POV
String sCompany = operation.grid.getCellWithMembers().getMemberName("Company")
def sMaterialGroup = operation.grid.getCellWithMembers().getMemberName("Material_Group")
String sChannel = operation.grid.getCellWithMembers().getMemberName("Channel")

def lstProducts = []
operation.grid.dataCellIterator({DataCell cell -> cell.edited}).each{ 
 lstProducts.add(it.getMemberName("Product"))
}

String strProducts = """\"${lstProducts.unique().join('","')}\""""
println "data push running for " + strProducts

if(operation.grid.hasSmartPush("Prod_SmartPush") && lstProducts)
 operation.grid.getSmartPush("Prod_SmartPush").execute(["Product":strProducts,"Currency":'"USD","Local"'])

//set account map
def acctMap = ['Units':'Units',
               '42001-Product Sales':'Net Sales',
               '50001-Cost of Sales':'COGS',
               '50015-Write-offs':'Write-offs',
               '56010-Chargebacks':'Customer Satisfaction Discount',
               '50010-Sales and Discounts':'Sales and Discounts',
               '56055-Overstock Discount':'Overstock Discount',
               '56300-Customer Satisfaction Discount':'Customer Satisfaction Discount',
               '56092-Multi-Purchase Discount':'Multi-Purchase Discount',
               '56230-Open Box Discount':'Open Box Discount',
               '56200-Damage Container Discount':'Damage Container Discount',
               '56205-Damaged Box Discount':'Damaged Box Discount',
               '56090-Group Purchase Discount':'Group Purchase Discount']


Cube lookupCube = operation.application.getCube("rProd")
DataGridDefinitionBuilder builder = lookupCube.dataGridDefinitionBuilder()
builder.addPov(['Years', 'Scenario', 'Currency', 'Version', 'Company','Store_Type','Department','Source','Product','View'], [['&v_PlanYear'], ['OEP_Plan'], ['Local'], ['OEP_Working'], [sCompany],['Store_Type'],['Total_Department'],['Tot_Source'],['Tot_Product'],['MTD']])
builder.addColumn(['Period'], [ ['ILvl0Descendants("YearTotal")'] ])
for ( e in acctMap ) {
 builder.addRow(['Account'], [ [e.key] ]) 
}
DataGridDefinition gridDefinition = builder.build()

// Load the data grid from the lookup cube 
DataGrid dataGrid = lookupCube.loadGrid(gridDefinition, false) 
def povmbrs = dataGrid.pov
def rowmbrs = dataGrid.rows
def colmbrs = dataGrid.columns
def tmpColMbrs = []

//Fin Grid Setup
Cube finCube = operation.application.getCube("Fin")
Cube rfinCube = operation.application.getCube("rFin")
DataGridBuilder finGrid = finCube.dataGridBuilder("MM/DD/YYYY")
DataGridBuilder rfinGrid = rfinCube.dataGridBuilder("MM/DD/YYYY")
finGrid.addPov('&v_PlanYear','OEP_Plan','Local','OEP_Working',sCompany,'Prod_Model')
rfinGrid.addPov('&v_PlanYear','OEP_Plan','Local','OEP_Working',sCompany,'Prod_Model','MTD')
def colnames = colmbrs[0]*.essbaseMbrName

String scolmbrs = "'" + colnames.join("', '") + "'"
finGrid.addColumn(colmbrs[0]*.essbaseMbrName as String[])
rfinGrid.addColumn(colmbrs[0]*.essbaseMbrName as String[])

dataGrid.dataCellIterator('Jan').each{ it ->

def sAcct = "${acctMap.get(it.getMemberName('Account'))}"
 def sValues = []
 List addcells = new ArrayList()
 colmbrs[0].each{cName -> 
 sValues.add(it.crossDimCell(cName.essbaseMbrName).data) 
 addcells << it.crossDimCell(cName.essbaseMbrName).data
 }

finGrid.addRow([acctMap.get(it.getMemberName('Account'))],addcells)
 rfinGrid.addRow([acctMap.get(it.getMemberName('Account'))],addcells)
}
DataGridBuilder.Status status = new DataGridBuilder.Status()
DataGridBuilder.Status rstatus = new DataGridBuilder.Status()
DataGrid grid = finGrid.build(status)
DataGrid rgrid = rfinGrid.build(rstatus)

finCube.saveGrid(grid)
rfinCube.saveGrid(rgrid)

Finishing Up

This is a relatively simple concept and not terribly difficult to implement.  It is also something most will benefit from when synchronizing data with the dataGridBuilder.  Have something to add?  Post a comment and I will get back to you promptly.

 

Groovy offers creative ways to force data into acceptable ranges.  But, what happens when there is an acceptable reason for abnormal trends, or drivers?  With Groovy, you can not only add limits to data cells, but you can also allow entry outside of those ranges if the user enters an explanation.  With Groovy, you get the best of both worlds! Read more

 

Say what? An application where no calculations are performed with the Essbase calculation engine?

Business logic, currency, and every other calculation can be performed within Groovy outside of Essbase. I don’t know how I feel about this yet, but the thought of building this in calculations on an ASO cube is intriguing. The elimination of consolidating BSO cubes and data movements with Smart Pushes is also interesting. Some of my coworkers have built entire applications this way with great success and speed. I wanted to replicate this on a small scale to identify the pros and cons of this methodology, and here is what I found. Read more

 

Almost every ePBCS application will require the need to run business logic and execute data movements on specific months based on a scenario selected.  Almost every Data Form will require action to be taken dynamically based on the selection of the Scenario.  Read more

 

One of the challenges working with grids is validating the results.  As with an Essbase calculation, Smart View reports are developed to validate results.  The same happens when calculations, or data movement, is executed in Groovy via grids.  When the results in Smart View aren’t accurate, where do you go? Read more

 

Gridbuilders are the link to moving data from BSO to ASO, and ASO to ASO.  Gridbuilders can also be used to save calculated results that are executed directly in Groovy.  If it jumped into your head, yes, you can completely bypass the Essbase calculation engine.  I have a coworker that has done exactly that – everything in Groovy and had all the forms connected directly to ASO!  There is an example of this in my presentation at KScope, Last Minute ODTUG Planning Sourvenirs.  Before we get too far ahead, back to the basics. Read more

 

Introduction

Yes, it is true that Groovy is available in on-premise and cloud (PBCS) versions of Hyperion Planning.  No, it is not true that the same flavor of Groovy exists in both.  Both have their advantages, and both have their drawbacks.  The likelihood that they will ever be the same is extremely low, and here is why.

The Difference Is

On-Premise gives developers the ability to write and use independent Groovy compiled applications.  These can be used in Business Rules as CDFs (custom defined functions).  Developers have complete functionality to make this do whatever they want.  It can return results to save to Essbase/Planning, it can interact with SQL, can run other programs, pretty much anything you can access that has a JAVA API.

PBCS doesn’t have the same flexibility.  Custom defined functions can’t be compiled and stored on the server.  PBCS, rather, has “Groovy Calculations.”  This gives developers the flexibility to interact with the Data Forms that on-premise doesn’t have.  Developers can iterate through the cells and act accordingly.  It can stop the form from saving, calculate and override data entered, color code cells, customize Data Maps, Smart Pushes, dynamically generate calculations, move data between databases, all with access to much of the Groovy functionality.

PBCS also supports the REST API, so Groovy can be used to access that and do everything, even more, that EPM Automate can do.

Why They Will Never Be The Same

This is just an opinion.  Technology changes so rapidly that this may change.  Corporate strategy changes almost as rapidly.

If PBCS had to ability to do what on-premise does, the ability for Oracle to support the instance would be a challenge.  CDFs can delete all the files on a server, for instance, and I don’t see a cloud provider giving developers this much control in a shared environment.

I also don’t see on-premise to have the same proactive interaction that PBCS has with Groovy Calculations purely because Oracle is pushing the cloud, and they want the most current functionality to exist in the platform they are pushing clients to use.

My Two Cents

I understand why there is a difference, and I don’t expect it to change in the near future.  3 years ago I didn’t expect that I would tell you that I would rather do a cloud implementation than on prem, either.  I do think as people get more comfortable with the cloud, and security improves, there will be advances.  I think there will be a future state where the cloud offerings will be closer to having the flexibility to the on-premise implementations.

 

 

Introduction

One of the challenges with Hyperion Planning is the ability to move data between applications in real time.  A classic example of this is a P&L application with other modules that have greater detail.  The following is an example. Read more

 

Introduction

I know you can argue this is a user issue and a training issue, but the fact is, sometimes people will save a form without editing any data.  There are at least three negative issues as a result.  One, the business rules and smart pushes execute, consuming unnecessary resources.  Two, users may think they made changes and expect changes in the results.  Three, if the processes are time consuming (like applying allocations or currency rates globally), the user will have to wait to correct the issue.  There is a very simple way to stop all the processes from executing and inform the user that they haven’t made any changes. Read more

 

Introduction

One of the huge benefits that available in Groovy Calculations is the ability to interact with a user, validate data, and act on the validation.  Now, we can interrupt form saves, stop Run Time Prompts from continuing, and communicate information back to the user.

This may sound repetitive if you have read part 13 and part 14, and you can skip to the code example to learn more about run time prompt validation.  If not, you must have an understanding of the validation functions and the components of the messageBundle. Read more