Doc-1216 Sap Hana Modeling

SAP HANA Modeling Overview SAP HANA Product Management

November, 2013

SAP HANA Modeling Overview

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

© 2012 SAP AG. All rights reserved.

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SAP HANA Modeling Views - Overview

Attribute Views

Analytic Views

Calculation View

• Compose a dimensional view • Combines Fact-Tables with • Great flexibility for advanced use with a series of attributes derived Attribute-Views to Star-Schema- • Approach to model custom from a collection of tables or OLAP Cube-like objects for scenarios like e.g. Master Data Views multidimensional reporting.  Combined use of Multiple-Fact  Highly re-used and shared in • Stores no aggregates and massTables/Analytics Views Analytic- and Calculation Views aggregates on the fly  Build Models on Normalized Data  Used to build Hierarchies • Hierarchies are key for multi Re-Use and stack views  Hierarchies are key elements in dimensional access (navigation,  Make use of custom scripted views use with Analytic View for multifiltering, slicing and aggregation) dimensional reporting


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SAP HANA Modeling Views - Multidimensional Model Scenario Reporting Tools can usually directly consume HANA Calculation Views or Analytic Views.

Calculation View

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-, AttributeViews, and Column Tables


Analytical View

Attribute View

Column Table

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SAP HANA Modeling Views - Normalized Data Model Scenario

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

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

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.

See SAP Note 1857202 © 2012 SAP AG. All rights reserved.

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The SAP HANA Studio Model Editor Model Scenario

General View Properties, Semantic Information

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.


Logical Join of Data Foundation & Dimensions

Data Foundation

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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 © 2012 SAP AG. All rights reserved.

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SAP HANA Studio – Modeling Perspective Typical View-Windows

The SAP HANA Modeler Eclipse-perspective

Perspectives

Perspectives are build upon different ViewWindows in the Eclipse environment


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SAP HANA Studio – Modeling Perspective 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

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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 © 2012 SAP AG. All rights reserved.

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SAP HANA Studio – The Unified Model Editor Model Scenario

General View Properties, Semantic Information

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


Data Foundation

*New in SAP HANA SPS6 © 2012 SAP AG. All rights reserved.

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Getting Started with Modeling Modeling Attribute Views Modeling Analytic Views Modeling Calculation Views

Modeling Attribute Information – HANA Attribute Views What is an Attribute View? • ... are the reusable Dimensions Used for Analysis which add context to data.

Semantic Attribute Information

• 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


© SAP AG 2011

Attribute Data Foundation

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Modeling Attribute Information – HANA Attribute Views 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 …


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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


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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


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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


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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)

HANA Analytic Views build and extend this concept of a OLAP Star Schema Model

• 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. © 2012 SAP AG. All rights reserved.

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Modeling Facts and Dimensions – HANA Analytic Views 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’

Semantic Information

 Dimensions describe the key figures and enrich the data Logical Join of Data Foundation & Dimensions



HANA Analytical Views are highly optimized for aggregating mass data


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Modeling Facts and Dimensions – HANA Analytic Views 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).


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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.

Semantic Information


Logical Join with Attribute View • Join attribute views to these tables in the Logical Join node. Data Foundation

Data Foundation of Fact Data • Create a fact table by adding and joining the tables


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Analytical View – Data Foundation 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

Data Foundation

• Can be based on any table column • Column does not need to be selected for output ([key] attribute) • Input Parameters* are supported.

*Since SPS6 Input Parameter references can be used (typed in) for filters on data foundation in Analytic and Attribute Views © 2012 SAP AG. All rights reserved.

© SAP AG 2011

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Analytical View – Logical View Building the Logical Analytic View • Define joins between Attribute Views and Data Foundation  Typically Left Outer-, Inner-, Referential- or Temporal Joins


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Analytical View – Logical View 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 © 2012 SAP AG. All rights reserved.

© SAP AG 2011

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Analytical View – Define View Semantics Define Semantic Output Representation of the Analytical View

Set View Properties

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


Inspect Hierarchies from underlying Attribute Views

Create Variables and Input Parameters

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Analytical View – Define View Semantics Define Semantic Output Representation of the Analytical View • Maintain General View Properties  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


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Analytical View – Excursus Variable and Input Parameters What are Variables and Input Parameters? • 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

Input Parameter SELECT as Filter 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 in Calculations if("COUNTRY"= '$$IP_COUNTRY$$',"GROSS_AMOUNT",0)


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Analytical View – Excursus Variable and Input Parameters Creating Variables • Variables and Input Parameters  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


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Analytical View – Excursus Variable and Input Parameters 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


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Analytical View - Save, Activate and Preview Create an executable version of the analytical view and preview • Save the view: Creates a Repository/Design-Time Object

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


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Analytical View - Save, Activate and Preview Create an executable version of the analytical view and preview • Save the view: Creates a Repository/Design-Time Object

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


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Analytical View - Run-time object: column view Activation creates a column view can be accessed from reports 

Reports

Exposed like regular views via SQL/MDX interfaces

Column Store

Activate © 2012 SAP AG. All rights reserved.

© SAP AG 2011

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Modeling Custom Scenarios – HANA Calculation Views What is a Calculation Views?

Application UI

• 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

Calculation View

• Great flexibility for advanced use • Modeling Approaches: Graphical Modeler or SQLScript-based Editor


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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 Calculation View  Besides flexibility provides additional features like DistinctCount calcualtion, dynamic joins, ….  No SQL or SQL Script knowledge required • SQLScript-based Calculation Views

Attribute View

 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.

Analytical View

 Must be read-only. Calculation View © 2012 SAP AG. All rights reserved.

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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 ”
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 CUBEStarJoin enabled calculation views.


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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


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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

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Graphical Calculation View – Working with Nodes 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


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Graphical Calculation View – Working with Nodes 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 dialog window

Join properties can also be maintained in the properties view of the join details. © 2012 SAP AG. All rights reserved.

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Graphical Calculation View – Working with Nodes 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

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Graphical Calculation View – Working with Nodes 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)

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

• 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 Example: The Planned vs. Actual indicator can now be set in the Union instead of defining the flag within a Projection node. © 2012 SAP AG. All rights reserved.

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Graphical Calculation View – Working with Nodes 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


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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


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Graphical Calculation View – Define View Result Set Define the structure of the Result Set of the Calculation View • 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


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Graphical Calculation View – Define View Result Set Define the structure of the Result Set of the Calculation View • 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


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Graphical Calculation View – Define View Semantics Define Semantic Output Representation of the Calculation View • Maintain General View Properties  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


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Graphical Calculation View – Save, Activate and Preview Create an executable version of the calculation view and preview • Save the view: Creates a Repository/Design-Time Object

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


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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.


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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).


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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


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HANA Hierarchies - Example of Parent Child Hierarchy  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 EMP

MGR

EMP_ID

LAST_NAME

MGR_ID

EMP_ID

EMP_ID

101

Frazier

?

FIRST_NAME

FIRST_NAME

102

Arthur

101

LAST_NAME

LAST_NAME

103

Greene

101

DEPT_NO

DEPT_NO

104

Rob

103

MGR_ID

MGR_ID

105

Bob

103


Alias

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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


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SAP HANA Currency Conversion – Using CC with Measures 1. Define a calculated measure 4. Set the target currency: •

fixed value

•

target currency column

•

input parameter

3. Set the source amount column

5. Fill in all other conversion parameters: • source currency column • exchange type • conversion date column • Behavior on conversion failure © 2012 SAP AG. All rights reserved.

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

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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


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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


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Modeling Custom Scenarios – SQL Script-based Calculation Views Scripts-based Calculation Views • 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


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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);


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Modeling Custom Scenarios – SQL Script-based Calculation Views Scripts-based Calculation Views • 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

Stored Procedure

 Invoke via Procedure CALL


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General Modeling Principles

SAP HANA General Modeling Principles Client / Application

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

G

Y

- Do calculation after aggregation.

Z

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

Calculation Views

Attribute Views

Analytical Views

Reduce data transfer between views A

G

Y

A

B

C

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

D

G

Y

Join on Key Columns or Indexed Columns Avoid calculations before aggregation on line item level

A

Column Store © 2012 SAP AG. All rights reserved.

B

C

D

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

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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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