232255230-SAP-HANA

SAP Hana

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© 2011 ALFATEC Group d.o.o.

SAP HANA


How did HANA evolve?


A Shift of Frontiers in Computer Science

Tape is Dead Disk is Tape Main Memory is Disk CPU Cache is Main Memory


What is SAP HANA?

SAP HANA is an in-memory data platform, at its core, it is an innovative in-memory relational database management system that makes full use of the capabilities of current hardware to increase application performance.

SAP HANA runs on multi-core CPUs with fast communication between processor cores, and containing terabytes of main memory.

With SAP HANA, all data is available in main memory, which avoids the performance penalty of disk I/O.

Either disk or solid-state drives are still required for permanent persistency in the event of a power failure or some other catastrophe.

This does not slow down performance, however, because the required backup operations to disk can take place asynchronously as a background task.


In-Memory Computing Engine and Surroundings


What is SAP HANA appliance?

SAP HANA appliance is a flexible, multi-purpose, data-source agonistic in-memory appliance that combines SAP software components optimized on hardware provided, and delivered, by SAP’s leading hardware partners such as Dell, Cisco, IBM, HP, Fujitsu, and Intel and

Alfatec Group .

It includes a number of integrated SAP software components including the SAP HANA database, real-time replication services, data services, data and lifecycle management, support for multiple interfaces based on industry standards and easy to use data modeling tool called SAP HANA studio.


HANA Combines Software and Hardware

In-Memory Computing Engine (Software)

+

Pre-Installed Systems (Hardware)


What is SAP HANA?


SAP In-Memory Appliance (SAP HANA™)

SAP HANA™

In-Memory software + hardware (HP, IBM, Fujitsu, Cisco, Dell)

Data Modeling and Data Management

Real-time Data Replication

SAP BusinessObjects Data Services for ETL capabilities from SAP Business Suite, SAP NetWeaver Business Warehouse (SAP NetWeaver BW), and 3rd Party Systems


HANA on BOBJ - Across entire BI SuiteBusiness Objects is the only supported BI tool certificated on HANA

4.0 fully supports HANA including SAP Explorer on HANA and SAP Analysis Clients

3.1 SP4 supports HANA as a Universe source and direct connections with Crystal Reports


SAP Analysis Office on HANA


SAP HANA System Landscape


SAP HANA Architecture


Business Intelligence Clients and SAP HANA


How do I acquire data into Hana?

SAP BusinessObjects Data Services and SAP HANA

LT Replication Concept: Trigger-Based ApproachLandscape Transformation - Replication Server for SAP HANA


SAP BusinessObjects Data Services and SAP HANA

Data Services is the engine to load all data into HANA

The HANA Modeler will generate initial loading jobs– Modeler will use Data Services to browse and ‘import’ external metadata– Modeler will generate initial flows to load data into HANA tables– Further modifications to flows done via Data Services Designer

SAP Business Objects Data Services (DS) is positioned for batch load-based data replication from non-SAP sources and SAP sources (with complex ETL requirements) -> Mainly recommended for batch type data replication business scenarios


SAP LT Replication Server for SAP HANA

The SAP Landscape Transformation (LT) Replication Server is the SAP technology that allows you to load and replicate data in real-time from SAP source systems and non-SAP source systems to an SAP HANA environment

The SAP LT Replication Server uses a trigger-based replication approach to pass data from the source system to the target system.

The SAP LT Replication Server can be installed either as a separate SAP system, or if the technical prerequisites permit, on an SAP source system.


LT Replication Concept: Trigger-Based Approach


In-Memory Relational Engines – Data StorageA database table is conceptually a two-dimensional data structure organized in rows and columns. Computer memory, in contrast, is organized as a linear structure.

A table can be represented in row-order or column-order.

A row-oriented organization stores a table as a sequence of records.

Conversely, in column storage the entries of a column are stored in contiguous memory locations.

SAP HANA supports both, but is particularly optimized for column-order storage.


In-Memory Relational Engines


Row Store

Row Store - One of the relational engines

Interfaced from calculation / execution layerPure in-memory storePersistence managed in persistence layer


Indexes for Row Store Tables

Each row-store table has a primary index Primary index maps ROW ID primary key of table ROW ID: a number specifying for each record its memory segment and page

Secondary indexes can be created if needed

Persistence of indexes in row storeIndexes in row store only exist in memory

No persistence of index data

Index definition stored with table metadata

Indexes filled on-the-fly when system loads tables into memory on system start-up


Column Store

Column Store - One of the relational engines Interfaced from calculation / execution layerPure in-memory storePersistence managed in persistence layer

Optimized for high performance of read operationGood performance of write operationsEfficient data compression


Column Store – Delta Management

Delta Merge Operation Purpose

To move changes in delta storage into the compressed and read optimized main storage

CharacteristicsHappens asynchronously

Even during merge operation the columnar table will be still available for read and write operations

To fulfil this requirement, a second delta and main storage are used internally


Persistence Layer

Why Does An In-memory Database Need A Persistence Layer?Main Memory is volatile. What happens upon…

Database restart?Power outage?...Data needs to be stored in a non-volatile way

Backup and restore

SAP in-memory computing engine offers one persistence layer which is used by row store and column store

Regular “savepoints”full persisted image of DB at time of savepoint

Logs capturing all DB transactions since last savepoint (redo logs and undo logs written)

restore DB from latest savepoint onwards

Ability to create "snapshots”used for backups


Row Store vs. Column Store - When to Use Which Store

Modeling Only Possible For Column TablesThis answers the frequently asked question: "Where should I put a table – row store or column store?”

Information Modeler only works with column tablesReplication server creates tables in column store per defaultData Services creates tables in column store per defaultSQL to create column table: "CREATE COLUMN TABLE ...”Store can be changed with "ALTER TABLE …“

System Tables Are Created Where They Fit BestAdministrative tables in row store:

Schema SYS - caches, administrative tables of engineTables from statistics server

Administrative tables in column store:Schema _SYS_BI - metadata of created views + master data for MDXSchema _SYS_BIC - some generated tables for MDXSchema _SYS_REPO - e.g. lists of active/modified versions of models


SAP HANA Studio


Information Modeler Terminology

DataAttributes – descriptive data (known as Characteristics SAP BW

terminology)Measures – data that can be quantified and calculated (known as key

figures in SAP BW)

ViewsAttribute Views – i.e. DimensionsAnalytic Views – i.e. CubesCalculation Views – similar to virtual provider with services concept in BW

HierarchiesLeveled – based on multiple attributesParent-child hierarchy

Analytic Privilege – security object


SAP HANA Modeling Fundementals


SAP HANA Modeling Best Practices


SAP HANA Studio

Navigator View - Default Catalog


SAP HANA Studio

Navigator View - Information Models


Attribute Views

Attribute ViewWhat is an Attribute View?

Attributes add context to data.Attributes are modeled using Attribute Views.Can be regarded as Master Data tablesCan be linked to fact tables in Analytical ViewsA measure e.g. weight can be defined as an attribute.

Table Joins and PropertiesJoin Types

leftOuter, rightOuter, fullOuter, textTable

Cardinality1:1N:11:NLanguage Column


Create Attribute view From Package: To Create attribute view, right click on the package and choose New and then attribute view


Create Attribute view From Quick Launch,choose Attribute view, and then click Create button( choose the correct Package)


Create Attribute view Choose the correct type of Attribute view required.STANDARD : Give you empty attribute viewCOPY FROM : One can easily copy from an existing attribute viewTIME will create both view and data for time attributes – data month etc.DERIVED :will create new attribute view with the existing one , but with same definition and an new name


Create Attribute view Attribute view created. But still there is no data


Create Attribute view Drag and Drop the fields in the DATA FOUNDATION in the Scenario TAB


Create Attribute view Creating table joins


Create Attribute view Saving and Activating View


Analytical Views

Analytical ViewAn Analytical View can be regarded as a “cube”.

Analytical Views does not store any data.

The data is stored in column store or table view based on the Analytical View Structure.

Star schema consist of one fact table containing the key figuresDimensions describe the key figures and enrich the dataSlicing and dicing is a feature whereby users can take out (slicing) a specific set of data and view (dicing) the slices from different viewpointsCardinality in star schemas is generally N:1 fact to dimensionJoins in star schemas are generally Left Outer Joins Analytical models are highly optimized for aggregating mass data


Analytical View: Data Preview

There are three main views one can select from when previewing data.Raw Data – table format of dataDistinct Values – graphical and text format identifying unique values Analysis – select fields (attributes and measures) to display in graphical format.


Create Analytical View

Directly click on 'Analytic View' in 'Quick Launch' screen or Right click on your package --> New ---> Analytic View.



Create an Information View



Add Tables



Add Joins



Add Output Columns



Semantics view



Dimensions, measures and aggregates



Save and validate, Save and Activate



Data preview – Raw Data



Generated SQL


Calculation View

Several options availableUse the Graphical ModelerWrite SQL Script and use CE FunctionsWrite SQL Script and using SQL

Suggested option = Graphical Modeler

No SQL or SQL Script knowledge requiredBuilt-in Union Constant support

Graphical & SQL Script + CE Functions Result in similar performance gains (e.g. Field pruning, Parallelization, join ommision)

Standard SQLDoes not provide field pruning and can be less optimized. Useful for POCs and rapid prototyping


Calculation View – SQL vs CE Functions

Calculation Engine (CE) Functions• Preferred over SQL• Improved performance, can be optimized by the engine (i.e. field pruning & parallelized)


Create Graphical Calculation View

New Calculation View



Select objects for Calculation



Tools



Tools - Union



Attributes, measures, hierarchies, etc ...


Application libraries

SAP HANA In-Memory Computing Engine offers various algorithms for in-memory computing.

It provides several application libraries for developers, partners, and customers who develop applications that run on SAP HANA.

The libraries are linked dynamically to the SAP HANA database kernel.


Business Function Library (BFL)

The Business Function Library (BFL) is one of these application libraries.

It contains pre-built parameter-driven functions in the financial area.

The functions are implemented by C++.

This library helps you develop compound business algorithms that are fully compliant with the SAP HANA calculation engine.



The following lists some functions in the Business Function Library.

Function Description

Discounted Cash Flow Converts a future stream of cash flow to constant prices. It calculates the inflated value of today's money.

Forecast Combines actual and forecast data to produce a rolling forecast. Eliminates scripting of feeds.

Future Calculates the closing balance of an account given the start balance and the conditions under which the account runs.

Inflated Cash Flow Calculates the amount of cash you must receive in a future period to compensate for inflation.

... ...

... ...



Inflated Cash Flow

In order to compensate inflation, this function calculates the cash amount in a specific future period that you need to receive.

FormulaWhere, r = discount rate expressed as a decimal fraction, n = number of periods into the future

Input tables

Output tables

Name Direction Number of Columns

Column Type Column Name Description

Prime Input Table 1 Double PRIME Prime/base value

Time Input Table 1 String TIME The periods to be calculated

Rate Input Table 1 Double RATE Discount rate

APR Input Table 1 Double APR =annual % by default = annual rate (rate=%/100) = Periodic % = Periodic rate

Switchover Input Table 1 Double SWITCHOVER The switchover date defines the last historic period

Switchover date Input Table 1 String SWITCOVERDATE Specify the switchover date

Name Direction Number of Columns Column Type Column Name Description

Result Output Table 1 DOUBLE RESULT Constant value



Inflated Cash Flow

Example

SET SCHEMA BFL_TEST;

DROP TABLE BFL_ICF_PRIME_TBL; CREATE COLUMN TABLE BFL_ICF_PRIME_TBL( "PRIME" DOUBLE ) ; -- CREATE COLUMN TABLE !!!INSERT INTO BFL_ICF_PRIME_TBL VALUES (1000) ;

DROP TABLE BFL_ICF_TIME_TBL;CREATE COLUMN TABLE BFL_ICF_TIME_TBL( "TIME" VARCHAR(255)) ; -- CREATE COLUMN TABLE !!!INSERT INTO BFL_ICF_TIME_TBL VALUES ('20120101') ; INSERT INTO BFL_ICF_TIME_TBL VALUES ('20130101') ;INSERT INTO BFL_ICF_TIME_TBL VALUES ('20140101') ;INSERT INTO BFL_ICF_TIME_TBL VALUES ('20150101') ;

DROP TABLE BFL_ICF_RATE_TBL;CREATE COLUMN TABLE BFL_ICF_RATE_TBL( "RATE" DOUBLE ) ;INSERT INTO BFL_ICF_RATE_TBL VALUES (0.1) ;

DROP TABLE BFL_ICF_RESULTS_TBL ;CREATE COLUMN TABLE BFL_ICF_RESULTS_TBL ( "RESULT" DOUBLE);

CALL _SYS_AFL.AFLBFL_INFLATEDCASHFLOW_PROC(BFL_ICF_PRIME_TBL, BFL_ICF_TIME_TBL, BFL_ICF_RATE_TBL, BFL_ICF_APR_TBL, BFL_ICF_SWITCHOVER_TBL, BFL_ICF_SWITCHOVERDATE_TBL, BFL_ICF_RESULTS_TBL) WITH OVERVIEW;

SELECT * FROM BFL_ICF_RESULTS_TBL;


Predictive Analysis Library

The Predictive Analysis Library (PAL) defines functions that can be called from within SQLScript procedures to perform analytic algorithms

This release of PAL includes classic and universal predictive analysis algorithms in seven data-mining categories:

● Clustering● Classification● Association● Time Series● Preprocessing● Social Network Analysis● Miscellaneous

The algorithms in PAL were carefully selected based on the following criteria:

● The algorithms are needed for SAP HANA applications.● The algorithms are the most commonly used based on market surveys (e.g. Rexer Analytics and KDnuggets polls).● The algorithms are generally available in other database products.

Application functions are like database procedures written in C++ and called from outside to perform data intensive and complex operations


R - statistical computing and graphics

R is an open source programming language and software environment for statistical computing and graphics.

The R language has become very popular among statisticians and data miners for developing statistical software and is widely used for advanced data analysis.

The goal of the integration of the SAP HANA database with R is to enable the embedding of R code in the SAP HANA database context.

That is, the SAP HANA database allows R code to be processed in-line as part of the overall query execution plan.

SAP does not ship the R environment with the SAP HANA database, as R is open source and is available under the General Public License.

SAP does not provide support for R. In order to use the SAP HANA integration with R, you need to download R from the open-source community and configure it.

232255230-SAP-HANA

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