Doc-1216 Sap Hana Modeling

SAP HANA Modeling Overview

SAP HANA Product Management November, 2013

SAP HANA Modeling Overview

© 2012 SAP AG. All rights reserved. 3 Public

SAP HANA Modeling Views - Overview

Data and Processing in SAP HANA

In-Memory Data Stores: Column- and Row Store

Optimized for Query and OLTP Workload

SQL & OLAP Processing and specific Calculation Operators

Application Function Library for specific Scenarios, e.g. Predictive Analytics

Modeling in SAP HANA

Information Models (SAP HANA Views) are optimized for SAP HANA Engines and Calculation Operators

Data/Columns are classified as Attributes or Measures in SAP HANA Views

Attributes – descriptive data (known as Characteristics SAP BW terminology)

Measures – data that can be quantified and calculated (known as key figures in SAP BW)

No materialized aggregates

Three levels of modeling: Attribute View > Analytic View > Calculation View


SAP HANA Modeling Views - Overview

Attribute Views

• Compose a dimensional view

with a series of attributes derived

from a collection of tables

e.g. Master Data Views

Highly re-used and shared in

Analytic- and Calculation Views

Used to build Hierarchies

Hierarchies are key elements in

use with Analytic View for multi-

dimensional reporting

Analytic Views

• Combines Fact-Tables with

Attribute-Views to Star-Schema-

or OLAP Cube-like objects for

multidimensional reporting.

• Stores no aggregates and mass-

aggregates on the fly

• Hierarchies are key for multi-

dimensional access (navigation,

filtering, slicing and aggregation)

Calculation View

• Great flexibility for advanced use

• Approach to model custom

scenarios like

Combined use of Multiple-Fact

Tables/Analytics Views

Build Models on Normalized Data

Re-Use and stack views

Make use of custom scripted views


SAP HANA Modeling Views - Multidimensional Model Scenario

Analytical View Attribute View Column Table

Calculation View

Reporting Tools can usually directly consume HANA Calculation Views or Analytic Views.

Multidimensional Tools support Hierarchies for Navigation, Filtering and Aggregation and HANA Prompts (Variables & Input Parameters) for efficient Pre-Filtering of Data.

Calculation Views are usually build upon Analytic-, Attribute- Views, and Column Tables


SAP HANA Modeling Views - Normalized Data Model Scenario

SAP HANA Calculation Views provide the means to model sophisticate views based on normalized data structures.

See SAP Note 1857202

Complex Calculation Views demand a more explicit intent and control of the modeled set-based data flow, i.e. slicing, aggregation and filtering of sets as input to joins, unions etc.

SAP HANA Calculation Views typically feed data to Business Applications, like SAP HANA XS build Applications


The SAP HANA Studio Model Editor

The Graphical Model Editor

Standardized graphical editing across

different SAP HANA view types

Build of different common panels

– Scenario provides Overview

– Semantic node provides better summary

of output structure of the model

+ editor view of output objects

+ general view properties

– Logical-Join- and Data Foundation-Nodes

are specific to Attribute- and Analytic Views

– Calculation-Views are modeled in the scenario panel

based on a palette of node-objects like join, union, etc.

to compose a custom data flow.

Model Scenario General View Properties, Semantic Information

Logical Join of Data Foundation & Dimensions

Data Foundation

SAP HANA Studio Modeling

Environment


SAP HANA Studio – Modeling Perspective

The SAP HANA Modeler within the SAP HANA studio

• Eclipse-perspective targeting Power Users for Content Design

• Graphical Information Model Design Environment

for HANA optimized Models

‒ Attribute-, Analytic- and Calculation Views

‒ without materialized aggregates

• Development of advanced

Calculation Models

‒ SQL & SQLScript-based,

Use of Application Function

Library- and SQLScript-based

Stored Procedures



The SAP HANA Modeler Eclipse-perspective

Perspectives are build upon different View-Windows in the Eclipse environment

Perspectives

Typical View-Windows



The SAP HANA Modeler within the SAP HANA studio

• Quick Launch access to common modeling tools

Wizards for creating / graphical designing HANA

Content objects as Information Models

Managing the work environment and system

connections

Actions or wizards for working with content

Actions or wizards for working with data or working with a SQL console


SAP HANA Studio – The HANA System Environment

The content contains the HANA Studio Models meta data, e.g. for Analytic Views, Attribute Views, Calculation Views. The content is organized in packages.

The catalog contains the HANA database schema and the stored objects, like column tables

From content tree and the package structure the Editor to build new HANA Views can be called


SAP HANA Studio – The Unified Model Editor

Unified Graphical Editor Standardized graphical editing across

different HANA Database view types

Build of different common panels

– Scenario provides Overview

– Semantic node provides better summary

of output structure of the model

+ editor view of output objects

+ general view properties

– Logical-Join- and Data Foundation-Nodes

are specific to Attribute- and Analytic Views

– Calculation-View* supports different nodes

Model Scenario General View Properties, Semantic Information


Data Foundation

*New in SAP HANA SPS6

Getting Started with Modeling Modeling Attribute Views

Modeling Analytic Views

Modeling Calculation Views


Modeling Attribute Information – HANA Attribute Views

© SAP AG 2011

What is an Attribute View?

• ... are the reusable Dimensions Used for Analysis

which add context to data.

• Can be regarded as Master Data Views

Build a semantic attribute information collection

from various source tables (e.g. join ‘Plant’ to ‘Material’)

Can be re-used as dimensions in Analytical Views

Specific time-based Attribute Views are supported

Semantic Attribute Information

Attribute Data Foundation


Modeling Attribute Information – HANA Attribute Views

© SAP AG 2011

What are the capabilities of Attribute Views?

• Attribute Views supports

Description Mapping & Text-Join Lookup master data tables

e.g. used for handling of multi-language master data

Various joins types between data foundation tables

Time Dimension Attribute Views

Hierarchies (Level, Parent-Child)

Calculated Attributes (static or dynamic calculations)

Define filter values on column

…


Attribute View – Data Foundation

Building the Attribute Data Foundation

• An Standard Attribute View is a join of several tables

Referential, Inner, Left Outer, Right Outer and

Text Join

Text Join are used to join a text table to a master data

table to lookup language dependent texts like product

name

Filter (static or dynamic) on source object columns

The output structure of the view is explicitly defined

Calculated attributes added


Attribute View – Define View Semantics

Define Semantic Output Representation of Attributes

• Classify the Columns and Calculated Columns as attributes and key attributes

• Maintain General View Properties

Default Client, Enable Analytic Privileges

• Attributes can be specified as hidden,

if only used in calculated attributes

• For each output column a ‘label column’

can be specified, which points to another column

containing a description for the original column

• Define Attribute Hierarchies

Level Based and Parent-Child Hierarchies


Attribute View – Save, Activate and Preview

Create executable version of the view

• Save the view: Creates a Repository/Design-Time Object

This information model itself is not visible to reporting tools

• Activate the view: Creates a Catalog/Run-Time Object

This creates a database view in schema ‘_SYS_BIC’

(a so-called ‘column view’)

This column view can be accessed from reporting tools

• Data Preview





Modeling Facts and Dimensions – OLAP Star Schemas

What is an OLAP Star Schema?

• Star schema usually has one fact table containing

the key figures

• Key figures are measureable

numbers

• Dimensions describe the key figures

and enrich them with additional information (usually

master data)

• Cardinality in star schemas is generally

N:1 fact to dimension

• Joins in star schemas are generally

Left Outer Joins

• Slicing and dicing is a feature whereby users can

take out (slicing) a specific set of data of the cube

and view (dicing) the slices from different

viewpoints.

HANA Analytic Views build and extend this concept of a

OLAP Star Schema Model


Modeling Facts and Dimensions – HANA Analytic Views

© SAP AG 2011

What is an Analytic View?

• Can be regarded as Cube-/OLAP Star Schema-like, but without storing aggregated data

Data is read from the joined database tables

Joins and calculated measures are evaluated at run time

• Fact Data from the ‘Data Foundation’ is joined against modeled Dimensions (Attribute Views)

fact table contain the key figures

‘Measures’

Dimensions describe the

key figures and enrich the data

HANA Analytical Views are

highly optimized

for aggregating mass data

Semantic Information



Modeling Facts and Dimensions – HANA Analytic Views

© SAP AG 2011

What are the capabilities of Analytic Views?

• Defining Calculated & Restricted Measures

Aggregation (sum, max, ..)

Expressions / Operators / Functions

• Currency and Unit Conversion

• Variables and Input Parameters

Variables and Parameters are usually entered

manually by the users using UI Prompts

Note: Variables are bound to attributes and used to

filter using WHERE clauses (Single, Interval, Range).


Analytical View – Structural Overview

Semantics Information

• Classify the Columns and Calculated

Columns as attributes and measures.

You can also create variables/input

parameters, and assign variables to the

columns in the Semantics node.

Logical Join with Attribute View

• Join attribute views to these tables in the

Logical Join node.

Data Foundation of Fact Data

• Create a fact table by adding and joining

the tables

Semantic Information


Data Foundation


Analytical View – Data Foundation

© SAP AG 2011

Building the Analytic View Data Foundation

• Fact Data is modeled in the ‘Data Foundation’ pane

Add source fact table(s), which contain the key figures ‘Measures’

Note: multiple tables can be joined, however measures may only be selected or derived from one of the table

Select attributes and measures from table(s) this defines the data foundation

Input Parameter can be defined

Filter on source object columns

• Can be based on any table column

• Column does not need to be selected

for output ([key] attribute)

• Input Parameters* are supported.

Data Foundation

*Since SPS6 Input Parameter references can be used (typed in) for filters on data foundation in Analytic and Attribute Views


Analytical View – Logical View

© SAP AG 2011

Building the Logical Analytic View

• Define joins between Attribute Views and Data Foundation

Typically Left Outer-, Inner-, Referential- or Temporal Joins


Analytical View – Logical View

© SAP AG 2011

Building the Logical Analytic View

• Creating Calculated Columns in the Logical View Output

Aggregation (sum, min, max, count), Counter (DistinctCounts)

Data Types (decimals, numbers etc.)

Expressions / Operators

Functions (String, Date Math, Conversion…)

• Advanced Semantic Calculations

Unit of Measure and Currency Conversion

• Creating Restricted Columns

Support constraints to apply for data used for

calculating the measures

• Creating Input Parameters

Prompted Parameter use e.g. in Calculation

and/or Filters


Analytical View – Define View Semantics

Define Semantic Output Representation of the Analytical View

Classify columns as attributes or measures Assign Variables to columns Create Measures Display Folders Order Columns, specify as hidden, select label columns, …

Create Variables and Input Parameters

Inspect Hierarchies from underlying Attribute Views

Set View Properties


Analytical View – Define View Semantics

Define Semantic Output Representation of the Analytical View


Default Client, Enable Analytic Privileges

and further advance properties.

• Semantic Column Area

Filter Variables can be directly specified and

Variable Names specified with Filter-Information

For Multidimensional Reporting Clients,

Measure Display Folder can be defined to better

structure measures

Column Semantic-type can be specified and

full unit/currency conversion specifications

Calculated column definition reviewed

Where-used analysis of columns


Analytical View – Excursus Variable and Input Parameters

© SAP AG 2011

What are Variables and Input Parameters?


Variables and Parameters are usually entered

manually by the users using UI Prompts

Variables are bound to attributes and used to

filter using WHERE clauses

(Single, Interval, Range)

Input Parameters are passed by

Placeholders and used in Formulas (Attribute,

Currency, Date, Static List) & Filters

Variables can accelerate the joining of views

by reducing the data set before join occurs

SELECT

SUM(ORDER_QTY), COUNTRY

FROM

"_SYS_BIC"."eim260/SALES_DELIVERY_CV"

('PLACEHOLDER' = ('$$IP_COUNTRY$$', 'US'))

WHERE

("CREATED_ON" BETWEEN ('20120101') and ('20120501')) AND

("DELIVERY_DATE" BETWEEN ('20120101') and ('20120501'))

GROUP BY "COUNTRY"

Variables

Input Parameter

as Filter

if("COUNTRY"= '$$IP_COUNTRY$$',"GROSS_AMOUNT",0)

Input Parameter in Calculations



© SAP AG 2011

Creating Variables


Revisited create variable dialog texts to be more self-explaining

Selected Attributes in Variables are automatically defaulted as applied

for the variable filter

Variables can be directly created from the details area of the semantic

node where the variable name is show along with the filter icon

External views or tables for value help dialogs can be referred to.

Special default values are now supported and can be authored via

an expression editor

– E.g. formulas as default like “date(Now())“ for today



© SAP AG 2011

Creating Input Parameters

• Input Parameters

Special default values are now supported and can be authored via

an expression editor – E.g. formulas as default like “date(Now())“ for today

– Evaluated expression will be shown, e.g. for now-function the day will be displays

External views or tables for value help dialogs can be referred to

Semantic type for input parameter specifications


Analytical View - Save, Activate and Preview

Create an executable version of the analytical view and preview



• Call the preview , for which there are 3 modes available

Raw data (table display)

Number of distinct values per column

Interactive graphical analysis

Can create filters

Number of records is limited

(as specified in the

SAP HANA Studio preferences)


Analytical View - Save, Activate and Preview

Create an executable version of the analytical view and preview



• Call the preview , for which there are 3 modes available

Raw data (table display)

Number of distinct values per column

Interactive graphical analysis

Can create filters

Number of records is limited

(as specified in the

SAP HANA Studio preferences)


Activation creates a column view can be accessed from reports

Exposed like regular views via SQL/MDX interfaces

Analytical View - Run-time object: column view

© SAP AG 2011

Column Store

Activate

Reports





Modeling Custom Scenarios – HANA Calculation Views

What is a Calculation Views?

• Calculation Views are composite views and can be used to combine other views

Can consume other Analytical-, Attribute-, other Calculation Views & tables

• Approach to model custom scenarios like

Combined use of Multiple-Fact Tables/Analytics Views

Build Models on Normalized Data

Re-Use and stack views

Make use of custom scripted views

• Great flexibility for advanced use

• Modeling Approaches: Graphical Modeler or

SQLScript-based Editor

Calculation View

Application UI


Modeling Custom Scenarios – HANA Calculation Views

What are the capabilities of Calculation Views?

• Graphical Calculation Views

Can consume other Analytical-, Attribute-, other Calculation Views & tables

Built-in Union, Join, Projection & Aggregation nodes

Besides flexibility provides additional features like

DistinctCount calcualtion, dynamic joins, ….

No SQL or SQL Script knowledge required

• SQLScript-based Calculation Views

Can be regarded as a function, defined in the HANA-specific

language “SQLScript”, which is build upon SQL commands

or special HANA pre-defined functions.

Must be read-only.

Analytical View

Calculation View

Attribute View

Column Table

Calculation View


Graphical Calculation View – Building the Scenario

Designing the Calculation View Scenario

Select View Type: Select Graphical or SQL Script

Select Output Type: should the CV’s final output set aggregate measures or rather produce a list-like output

without aggregating the measures.

– Enable Data Category “CUBE” aggregates measures, then the default (top output)-node is pre-selected as an

aggregation node (can be switched later) otherwise as an projection node

If Data Category is left ”<empty” • The output node does not offer any measures

but just attributes

• The Calculation view is not consumable via

Multidimensional Reporting Clients

• The Calculation view is only consumable via SQL

if Data Category is set to ”DIMENSION” • The output node does not offer any measures

but just attributes

• The Calculation view is to be used with CUBE-

StarJoin enabled calculation views.


Graphical Calculation View – Building the Scenario

Designing the Calculation View Scenario

Composing a calculation flow with nodes

– Projection, Aggregation, Join and Union-Nodes

can be drag’n dropped from tools palette into the scenario

and connected appropriate to demands

– Contributing objects to a node, are now

collapsed into the node

Adding table or view objects

– Select via drag’n drop from catalog or content

– search from repository & local project workspace

– Catalog Tables, Calc Views (Graphical, Scripted),

Attribute and Analytic views

– Drop elements into node placeholders directly or

connect later to nodes


Graphical Calculation View – Working with Nodes

Working with the Projection-Node in the Scenario

• Projection nodes can be added to add extra fields.

For example, setting the Actual versus Planned indicator

Drag a connection line between all the nodes

The output node will represent the data flow graph end

• Projection output set can be customized with

Renaming, Calculated Columns, Input Parameters,

Filters, ….

Projection node as an intermediate node in the calculation flow



Working with the Aggregation-Node in the Scenario

• In order to have further control of how the aggregation is done,

it is beneficial to use the aggregation node

in Graphical Calculation Views.

When using the aggregation node you can specify

which columns should be aggregated and

also the aggregation type (sum, min or max).

You can also add Calculated Columns to the node.

These calculations will be performed after aggregation.

Note: The definition of the specific aggregation type for calculated

columns is maintained in the property view.

Selecting Measures as aggregated output of the node



Working with the Join-Node in the Scenario

• Used to query data from two data sources,

which are joined by a specific criteria

Inner-, Left/Right-Outer, Text- and Dynamic Join

If more than two tables have to be joined, join-nodes

have to be used in a stacked-flow

Join properties can also be maintained in the properties view of the join details.

Join dialog window



Working with the Union-Node in the Scenario

• Union Operations

When multiple result sets should be combined by using

placing them on top of each other use the union-node

• Column Mapping in Unions

In order for the columns from the different sources

to go into the correct target, a mapping will need

to be provided.

This can be done via a drag and drop in the union

editor area or by using Auto Map by Name

in order to auto map columns by name between sources

and target

Define the UNION by mapping both Projection Nodes to the target



Working with the Union-Node in the Scenario

• Using constants in unions

When sources have a different number of columns,

a constant value can be set for the source columns that

do not have the target column and map the constant to the

target column

Allow to add unmapped columns that only have a

constant mapping (without a match to any source column)

• Setting constant values

The Constant Value can be set by right-clicking on the

Target column, selecting “Manage Mappings”.

Additionally it is required to specify a data type

If targets column don’t yet exist, e.g. for constant mapping, they can just be added in the union

Example: The Planned vs. Actual indicator can now be set in

the Union instead of defining the flag within a Projection node.



Best Practice for Joins and Unions

• Be careful and do not JOIN Analytical Views, this could lead to performance implications as Analytical

Views often expose mass data, where joins can be challenging

• Instead Unions may be used alternativels, as union do not compare records between right and left table.

Unions are simple set-combinings between these tables if the structure or the field mapping is identical.

Unions are used to combine the result-set of two or more SELECT statements


Graphical Calculation View – Define View Result Set

Define the structure of the Result Set of the Calculation View

• Calculation View Result Set

In the default node, a projection or

aggregation node, below the semantic

node, the output columns can be selected

from its input node

Add Attributes and Measures to the Output

Define Calculated Columns or Counters

Define Input Parameters




• Creating Calculated Columns

Calculated columns in CV can combine data from multiple input

source and thus measures across different subject areas

Calculated Column Editor can be used to create Calc. Measure

and Attribute




• Creating Calculated Columns and counters

• In the default (last/top node (projection or aggregation) special

calculated column can be defined

Special semantic measures: Amount with Currency and

Quantity with Unit of Measure

Counters: A counter is a distinct count of values from one or

multiple attribute columns Note: counters can only be defined in calculation views


Graphical Calculation View – Define View Semantics

Define Semantic Output Representation of the Calculation View


Default Client, Enable Analytic Privileges and further advanced properties.

• Columns, Hierarchies, Variables/Input Parameters

Classify columns as Attributes and Measures

specify measure aggregation, if hidden, label

columns, filter variables, measures folder, …

Define hierarchies, variables, Input parameters

Column Semantic-type can be specified and

full unit/currency conversion specifications

Calculated column definition reviewed

Where-used analysis of columns


Graphical Calculation View – Save, Activate and Preview

Create an executable version of the calculation view and preview



Activation will create the Column view that can be accessed by the front end

tools or call the preview .

In calculation views, data preview is also supported

for intermediate nodes

Column Store

Activate

Extending Models with advanced capabilities


HANA Hierarchies

Authoring Hierarchies in Attribute- and Calculation Views

• Hierarchy is a description of responsibilities /relationship of the different

units/member that run the unit.

• This relationship help to visualize the data for analytical purpose by organizing the

same based on the relationship of various level of units / members.

• In SAP HANA, we have a choice of creating 2 types of hierarchies,

Level Hierarchy

Parent Child Hierarchy

• HANA Hierarchies can only be used by Multidimensional Reporting Clients.


HANA Hierarchies - Level Hierarchies

Level Hierarchy

Each level represents a position in the hierarchy

It can be something related to time (Year, Quarter, Month, Day) or geography (Country, Region, State).

Level Hierarchies are relatively easy as the various levels are either stored in different columns of the table or even

in different tables (snow flake).


HANA Hierarchies - Parent Child Hierarchies

Parent Child Hierarchy

Represents the relationship which is recursive in nature.

Defined as 2 table columns as pairs of predecessor and successor node IDs.

Modeled in HANA by taking input as a FLAT table where the rows corresponds to the Hierarchy nodes and contains

a node Id and a parent node id.

Examples :

Company organizational structure

Object levels: President – Vice President – Director

Object type: Person

Inventory BOM (Bill of Materials)

Object levels: Product – Assembly – Sub-Assembly – Component

Object type: Inventory item


HANA Hierarchies - Example of Parent Child Hierarchy

EMP

EMP_ID

FIRST_NAME

LAST_NAME

DEPT_NO

MGR_ID

For example, a company organization chart is made up of people. Some people are at

different levels, and there are therefore relationships from one person to another. In order to

show all of the relationships from the top of the company to the bottom (or the bottom to the

top) We have to keep going back to the same table. That is recursion

MGR

EMP_ID

FIRST_NAME

LAST_NAME

DEPT_NO

MGR_ID

Alias EMP_ID LAST_NAME MGR_ID

101 Frazier ?

102 Arthur 101

103 Greene 101

104 Rob 103

105 Bob 103


SAP HANA Currency Conversion

HANA Currency Conversion logic follows the identical logic of the SAP ERP Currency

Conversion. There is no refactoring!

Referencing ABAP function modules implementation: CONVERT_TO_LOCAL_CURRENCY,

CONVERT_TO_FOREIGN_CURRENCY

Inherits SAP ERP decimal and rounding inaccuracy

Inherits SAP ERP decimal shift problematic (e.g. Japanese Yen, Bahrain Dollar, …)

Pre-requisites SAP ERP currency tables and configuration (TCUR* tables)

Conversion is SAP Client dependent


SAP HANA Currency Conversion – Using CC with Measures

1. Define a calculated measure

3. Set the source amount column

4. Set the target currency:

• fixed value

• target currency column

• input parameter

5. Fill in all other conversion parameters: • source currency column • exchange type • conversion date column • Behavior on conversion failure

2. Set the default schema, where the SAP ERP TCUR*-tables are stored


SAP HANA Analytical Privileges

Analysis authorizations for HANA Information Models

• Analysis authorizations for row-level security

Can be based on attributes in analytic views

• Analytic privilege is always a concrete implementation

I.e. Specific authorization for specified values of given attribute

you have to create privileges for each group of users

or define dynamic analytic priviledges

Building SQL-Script-based

Calculation Views


Modeling Custom Scenarios – SQL Script-based Calculation Views

Scripts-based Calculation Views

• Calculation Views are side affect free / READ-ONLY functions

• Use of custom SQLScript to build Calculation Views

Write SQLScript CE-functions or standard SQL-statements with raw tables or

modeled views (preferred)

Define output structure,

activation creates column

store based on

Script Output

Semantic Node




• Use of custom SQLScript to build Calculation Views

Calculation Engine functions should not be mixed with standard SQL statements

Client queries can be well optimized and parallelized by the CE engine

Usually much better performance results than calculation view via standard SQL

Preferred

• Parallel query execution

• Only selected fields will be fetched


Excursus SQLScript – Standard SQL and CE-functions

SQL CE function

SELECT on column table out = SELECT A, B, C from "COLUMN_TABLE" out = CE_COLUMN_TABLE("COLUMN_TABLE", [A, B, C])

SELECT on attribute view out = SELECT A, B, C from "ATTRIBUTE_VIEW" out = CE_JOIN_VIEW("ATTRIBUTE_VIEW", [A, B, C])

SELECT on analytic view out = SELECT A, B, C, SUM(D) from "ANALYTIC_VIEW" GROUP BY

A, B, C

out = CE_OLAP_VIEW("ANALYTIC_VIEW", [A, B, C]);

WHERE HAVING out = SELECT A, B, C, SUM(D) from "ANALYTIC_VIEW" WHERE B =

'value' AND C = 'value'

col_tab= CE_COLUMN_TABLE("COLUMN_TABLE"); out = CE_PROJECTION(col_tab,

[A, B, C], ' "B" = ''value'' AND "C" = ''value'' ');

GROUP BY out = SELECT A, B, C, SUM(D) FROM"COLUMN_TABLE" GROUP

BY A, B, C

col_tab= CE_COLUMN_TABLE("COLUMN_TABLE"); out = CE_AGGREGATION(

(col_tab, SUM(D), [A, B, C]);

INNER JOIN out = SELECT A, B, Y, SUM(D) from "COLTAB1" INNER JOIN

"COLTAB2" WHERE "COLTAB1"."KEY1" = "COLTAB2"."KEY1" AND

"COLTAB1"."KEY2" = "COLTAB2"."KEY2"

out = CE_JOIN("COLTAB1","COLTAB2", [KEY1, KEY2], [A, B, Y, D])

LEFT OUTER JOIN out = SELECT A, B, Y, SUM(D) from "COLTAB1" LEFT OUTER JOIN

"COLTAB2" WHERE "COLTAB1"."KEY1" = "COLTAB2"."KEY1" AND

"COLTAB1"."KEY2" = "COLTAB2"."KEY2"

out = CE_LEFT_OUTER_JOIN("COLTAB1","COLTAB2", [KEY1, KEY2], [A, B, Y, D])

SQL Expressions out = SELECT A, B, C, SUBSTRING(D,2,5) FROM

"COLUMN_TABLE"

proj_tab = CE_COLUMN_TABLE("COLUMN_TABLE"); out = CE_PROJECTION(

:proj_tab, ["A", "B", "C", CE_CALC('midstr("D",2,5)', string) ]);

UNION ALL col_tab1 = SELECT A, B, C, D FROM "COLUMN_TABLE1"; col_tab2

= SELECT A, B, C, D FROM "COLUMN_TABLE2"; out = SELECT *

FROM :col_tab1 UNION ALL SELECT * FROM :col_tab2;

col_tab1 = CE_COLUMN_TABLE("COLUMN_TABLE1",[A,B,C,D]); col_tab2 =

CE_COLUMN_TABLE("COLUMN_TABLE2",[A,B,C,D]); out =

CE_UNION_ALL(:col_tab1,:col_tab2);




• Invoke and consume other functions from within Script-based calculation

Stored Procedures built in CE or generic SQL

Read-only / Read-write procedures can be created

IN (Input) parameters can be of scalar or table type

OUT (Output) parameters must be of table type

Tables types required are generated automatically

Invoke via Procedure CALL

Stored Procedure

General Modeling Principles


SAP HANA General Modeling Principles

Column

Store

Analytical

Views

Attribute

Views

Calculation Views

Client / Application

A B C D

A B C D G Y

A G Y

A G Y Z

Filter data amount as early as possible in the lower layers (CONSTRAINTS, WHERE Clause, Analytical Privileges..)

Aggregate data records (e.g using GROUP BY, reducing Coulmns)

Avoid transfer data of large resultsets between the HANA DB and client application

- Do calculation after aggregation.

- Avoid Complex expressions (IF, CASE, ... )

Join on Key Columns or Indexed Columns

Avoid calculations before aggregation on line item level

Reduce data transfer between views


Disclaimer

This presentation outlines our general product direction and should not be relied on in making

a purchase decision. This presentation is not subject to your license agreement or any other

agreement with SAP.

SAP has no obligation to pursue any course of business outlined in this presentation or to

develop or release any functionality mentioned in this presentation. This presentation and

SAP’s strategy and possible future developments are subject to change and may be changed

by SAP at any time for any reason without notice.

This document is provided without a warranty of any kind, either express or implied, including

but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or

non-infringement. SAP assumes no responsibility for errors or omissions in this document,

except if such damages were caused by SAP intentionally or grossly negligent.

Thank you

Contact information

Christoph Morgen

SAP HANA Product Management

[email protected]

To get the best overview of what’s new in SAP HANA SPS 07, read this blog.

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http://www.saphana.com/community/blogs/blog/2013/11/20/whats-new-in-sap-hana-sps07


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Doc-1216 Sap Hana Modeling