0200 - modeling - overview

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© 2011 SAP AG. All rights reserved. 1

SAP HANA 1.0Modeling fundamentals




Agenda

Modeling Fundamentals & Best Practices

Analytical View & Calculation View Fundamentals

Design time vs. Runtime objects

General Modeling Principles

Modeling new functionality




Attribute Views (Dimensions, Time, Hierarchy, Derived)

Analytical Views (Facts/Star Schemas)

Calculation Views (Composite views, Modeled or Script)

Transportable design time artifacts stored in the repository

Database objects (Column store views) are generated from these

development artifacts

L a n g

a u g e

Main procedural language of the SAP HANA database

Push data intensive operations into the database

Utilized in Calculation Views and Procedures

Read-Only procedures (Calculation views, highly optimized,

potential of parallelism)

Read-Write procedures (cursors, loops, conditions, transactions)

SQL

Script

SAP HANA Modeling Fundementals

M o d e l i n g

Views




SAP HANA Modeling Best Practices

Analytical View Attribute View Column Table

Calculation View




Agenda









SAP HANA Analytical View

Star schema consist of one fact table

containing the key figures

Dimensions describe the key figures

and enrich the data

Slicing and dicing is a feature whereby

users can take out (slicing) a specificset of data and view (dicing) the slices

from different viewpoints

Cardinality in star schemas is generally

N:1 fact to dimension

Joins in star schemas are generally Left

Outer Joins

Analytical models are highly optimized

for aggregating mass data

Analyt ical View

Attr ibute View

Dimension

Left Outer

Fact Table

N N

1

11




SAP HANA Calculation View

Several options available

Modeling using the Graphical Modeler

Write SQL Script and use CE Functions

Write SQL Script and using SQL

Suggested option = Graphical Modeler

No SQL or SQL Script knowledge required

Built-in Union Constant support

Graphical & SQL Script + CE Functions

Result in similar performance gains (e.g.

Field pruning, Parallelization, join

ommision)

Standard SQL

Does not provide field pruning and can be

less optimized. Useful for POCs and rapid

prototyping

Graphical

CE Functions

SQL




Agenda









SAP HANA Design Time vs. Runtime Time Objects

© SAP AG 2011

Column View

Activate

Activation of models creates an

executable, optimized Column

(database) view of the model

Front end’s queries column views and

as a result a specific execution plan is

instantiated based on fields requested

Un-used fields are pruned; un-usedtables joins are omitted; filters are

pushed down

Attributes are retrieved and Measures

are calculated in parallel

Design time Model

SELECT PERIO, VKORG, SUM(CM1)




Agenda









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 usingGROUP BY, reducing Coulmns)

Avoid transfer data of large resultsets between the HANADB and client application

- Do calculation after aggregation.

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

Join on Key Columns or IndexedColumns

Avoid calculations beforeaggregation on line item level

Reduce data transfer between views




SPART MATNR WERKS KWMENG

ABC DPC1017 100 60

FILTERS pushed

down

SELECT SPART, MATNR, WERKS,

SUM(KWMENG) FROM VIEW WHEREWERKS = ? AND MATNR = ?

SELECT

SPART

SUM(KWMENGA)

SUM(KWMENGB)

SUM(KWMENGC)

SUM(TOTAL)

FROM CALCVIEWWHERE MATNR =

DPC1017

GROUP BY SPART

OLAP AOLAP AOLAP A

SPART PLANT A PLANT B PLANT C TOTAL

ABC 60 60 0 120

SPART MATNR KWMENGA KWMENGB KWMENGC

ABC DPC1017 60 0 0

ABC DOC1017 0 60 0


ABC DPC1017 1000 60


- - - -

SAP HANA Basic Modeling Principle (Aggregate, filter, pushdown)




Agenda









SAP HANA – Variables & input parameters

Variables and Parameters are usually entered

manually by the user 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) It is the task of the client to convert the user input

into either WHERE clause or Placeholders when

accessing the data

Devine Variable

Use Variable as a Prompt




SAP HANA - Aggregation Node

A new Aggregated Columns folder is visible in the output of a Graphical Calculation View

The definition of the specific aggregation type is maintained in the property view

Only columns of numerical & date data types are allowed (like for measures)

The calculation and filtering occurs after the aggregation

Aggregation




SAP HANA - Exceptional Aggregation

A new Counters folder is visible in the output view of a Graphical Calculation view

Ability to define Count Distinct functions based on specific Dimensions (1..N)

Count




SAP HANA - Optional Measures

Within Calculation views measures can now be made optional

The output node instead will be a projection node

Models should be simple SQL views that do not automatically aggregate the measures

Multidimensional

Reporting Flag

The output node does not offer any measures

(or hierarches) but just attributes

The Calculation view is not consumable via

MDX (Explorer / AAO)

The Calculation view is only consumable via

SQL

To disable measures set the Multidimensional

Reporting flag accordingly within the

properties view




SAP HANA - Join Suggestions

The goal is to simplify modeling by proposing tables and suggesting joins

This features is available in both Attribute and Analytical Views

Prerequisite: table DD08L needs to exist in HANA

Select Propose Tables, then choose the required tables

Highlight all the tables and select Suggest Joins

Propose Tables

Suggest Joins




SAP HANA - Analytical Privi leges

In addition to equal and between SP4 provides GE (greater equal), LE (less equal) and

CP (contains pattern), GT (Greater Than) and LT (Less Than)




SAP HANA - Intellisense

Intellisence assistance provided in SQL editor and function editors

Provides both the function Syntax details and code Examples




SAP HANA - Multi User Support

The same object will not exist in the inactive workspace of several users

Only one inactive version of any object is allowed

In addition there is now functionality to discard an inactive version and to return it to the

active status

System Privileges - REPO.WORK_IN_FOREIGN_WORKSPACE required




SAP HANA - Union

Constant Union enhancement

Allow to add unmapped columns that

only have a constant mapping

Additionally it is required to specify a

data type

Example. The Planned vs. Actual

indicator can now be set in the Unioninstead of defining the flag within a

Projection node

Constant Union

Manage Mapping




SAP HANA – Intermediate Data Preview

Calculation View enhancement

Allows easier design and prototyping

Requires that the View has been

activated

Example: Data Preview before and

after filtering a Join




SAP HANA – Decision Tables

SQLScript Enhancement

Graphical user interface to simplify

changing data

Supports Tables and Analytical Views

Example: Updating prices based on

attribute and measure conditions




SAP HANA – Attribute views

Table Al iases

Add the same table to an Attribute View multiple times with the assignment of an alias.

Upon selecting a table twice the system will prompt for an alias.

Hide attributes

Functionality to hide attributes has

been extended to attribute views

from within the context of an

analytic view.




SAP HANA - Auto Documentation

Generate the metadata documentation for information models and packages created on

SAP HANA systems




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