HANA Overview and Capabilities

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HANA Overview and Capabilities. Dr. Bjarne Berg. Why In-Memory Processing? . Focus. Technology. 1990. 2012. Improvement. 0.05 MIPS/$. 304.17 MIPS/$. 6083 x. CPU. 0.02 MB/$. 52.27 MB/$. 2614x. Memory. 2 16. 2 64. 2 48 x. Addressable Memory. 100 Mbps. 100 Gbps. 1000 x. - PowerPoint PPT Presentation

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HANA Overview and Capabilities

Dr. Bjarne Berg

Why In-Memory Processing?

2

FocusImprovement20121990

216 Addressable Memory

2614x52.27MB/$

0.02MB/$

Memory

6083x304.17MIPS/$

0.05MIPS/$

CPU

Technology

620MBPS

5MBPS

Disk Data Transfer

124x

1000 x100Gbps

100Mbps

Network Speed

264 248x

Source: 1990 numbers SAP AG, 2012 numbers, Dr. Berg

Disk speed is growing slower than all other hardware components, while the need for speed is increasing.

Source: BI Survey of 534 BI professionals, InformationWeek, 2010

In Memory Processing — General Highlights — BWA

SAP BW

InfoCubes

DSOs

1. Indexing and compression stored on a file system

2. Indexes copied into RAM on blades

BI Analytical Engine

3. Queries are routed to BWA by the Analytical engine

BWA = SAP BW Accelerator

SAP HANA — In Memory Options• SAP HANA is sold as an in-memory appliance. This means that both Software and Hardware are included from the vendors

• Currently you can buy SAP HANA solutions from Cisco, Dell, Fujitsu, IBM, and Hewlett-Packard

• SAP HANA currently indexes the data from a variety of sources, including ERP and BW and store the result on a dedicated server

• The future of SAP HANA is to replace the databases of ERP and BW and run these on the in-memory platform

Source SAP AG,2011

SAP HANA has the potential to radically change the way

databases operate and make systems dramatically faster.

The Different Editions of HANA

Software Component

Enterprise extended

editionEnterprise

EditionPlatform Edition

HANA Studio

HANA Information Composer

HANA Client

HANA Client for Excel

HANA User Interface for information Access -INA

HANA Database

HANA Host Agent

Diagnostics Agent

BusinessObjects Data Services

HANA Direct Extractor Connection (DXC)

Landscape Transformation Add-on (SLT)

Landscape Transformation Replication Server

HANA Load Controller (LC)

Sybase Replication Server and Agent

Sybase Adaptive Service Enterprise (ASE)

Persistence Layer

Looking Inside SAP HANA — In-Memory Computing Engine (IMCE)

Disk Storage

Data Volumes Page Mgmt.

BusinessObjects Data Services

Log

Volumes

Logger

AAAA

Metadata ManagerAuthorization

Manager

Transaction

Manager

Relational Engine

-Row Store-Column Store

Load Controller

SQL Script

Calculation

Engine

Replication Server

SQL Parser

MDX

Session Manager

Inside the Computing Engine of SAP HANA we have many different components that manage the access and storage of the data. This include MDX and SQL access, as

well as Load Controller (LC) and the Replication Server.

Row based index

Row- vs. Column-Based Indexing (cont.)

• As we can see, there are only 7 unique states and 3 unique customer classes in the data. This allows SAP HANA to compress this data set significantly

• By including the Row ID in the column-based index in SAP HANA, the “ownership” of the values in the index can still be mapped back to the record

Column-based indexes on fields with repeated values often leads to better compression ratios and thereby lower size of the indexes (as

we can see, there are few values repeated in the rows).

Row ID Name State Class Birth date Income1 Jane Hansen NC Gold 8/7/1959 71,927$ 2 Olav Petersen TX Silver 2/24/1963 35,633$ 3 Peter Johnsen FL Platinum 1/1/1959 144,077$ 4 Thomas Berg TX Gold 2/13/1981 85,087$ 5 John Beatty FL Platinum 12/26/1958 123,456$ 6 Jim O'Brian NC Silver 6/11/1977 76,506$ 7 Jeff Pinolli NY Platinum 5/9/1971 73,503$ 8 Carol VanZyck NY Platinum 3/13/1969 68,987$ 9 Fredrick Davidson FL Gold 9/8/1980 100,600$ 10 Tone Leffler CA Platinum 2/10/1955 105,943$ 11 Carol Hansen CA Silver 9/9/1980 112,096$ 12 Jim Petersen NY Gold 2/23/1974 41,080$ 13 Jeff Johnsen CA Platinum 3/10/1978 118,481$ 14 Peter Berg FL Platinum 12/14/1981 50,900$ 15 Thomas Beatty IN Silver 10/25/1954 78,304$ 16 John O'Brian IN Gold 11/27/1970 38,809$ 17 Olav Pinolli CA Gold 10/1/1955 157,105$ 18 Jane VanZyck FL Platinum 6/27/1960 151,067$ 19 Tone Davidson NC Silver 11/19/1958 63,169$ 20 Fredrick Leffler SC Gold 12/21/1973 65,628$

Enterprise Data Warehouse – SAP BW

SAP HANA — Virtual Marts and Applications• Virtual data marts and new applications were built that run on SAP

NetWeaver BW, which is again enabled by SAP HANA in-memory processing

ERP

Database

HANA (in-the works)

Virtual Data Marts

Applications

Databases

Virtual Data Marts

Virtual Data Marts

Virtual Data Marts

BI Solutions

Files

This provides much tighter integration with the source system (less data latency) and much faster query response time for high-volume analysis

Applications developed by SAP

1. Planning & consolidation

2. Customer revenue performance mgmt

3. Predictive segmentation & targeting

4. Trade promotion management

5. Merchandise & assortment planning

6. Sales & operations planning (SOP)

7. Demand signal repository

8. Profitability analysis

9. Dynamic cash management

10.Strategic workforce planning

11.Smart meter analytics (power companies)

The Hardware – IBM Example

Client Demo

11

SAP HANA — Loading the Application and Performance

You can load the application based on the logs in the source system, ETL-based (Extract Transform and Load) loads, and SAP trigger-based replication

Tool PurposeBusinessObjects Data Services 4.0 – ETL-based replicationSybase replication server & Load Controller – Log-based replicationSAP Landscape Transformation (LT) – Trigger-based replication

Log based replication is possible on IBM DB 2 LUW/UDB, MSFT SQL Server Enterprise Edition, Oracle Enterprise Edition, and Sybase ASE

Max Min AverageBank 1 : 6.3 521.6 258.8 369.5HANA POC (SAP) 1 : 5.2 484.3 301.4 350.3

Query speed improvementsCompression (data)

Project

Some Reported SAP HANA Performance Achivements

Opening HANA Admin

Adding New System in HANA

Adding New System in HANA

Creating HANA system

connection

Setup HANA Security

Authentication

Changing a HANA password

Creating HANA Security questions

Creating HANA Security questions

Your HANA System in the

Navigator

Searching for a table in HANA

The table definition

inside HANA

All HANA tables

Open a HANA table

Browsing data in a HANA table

Opening HANA Admin

HANA memory usage

Creating a New HANA Table

Creating a New HANA Table

Creating a New Products

HANA Table

Defining a new Sales

HANA table

Accessing Data Services to Load

data to HANA

Accessing Data Services to Load

data to HANA

Linking Data Services to HANA

datastores

Linking Data Services to HANA

datastores

Linking Data Services to HANA

datastores

Our new Data Services HANA

repository

Data Services HANA repository

objects

Importing HANA table definitions

Importing HANA Customer table

definition

Importing HANA Sales table definition

Linking data files to load to HANA

Linking data files to load to HANA

Defining file format for loading

data to HANA

Defining data file format for HANA

data load

Saving file formats

Customer file for HANA data load

ReplicatingProduct file for HANA data load

Replicated Product file for HANA dataload

Replicating Sales file for

HANA data load

Replicated Sales file format loading

data to HANA

Create a Project for Data Services

Create a Project for Data Services

Create a Batch job for HANA data loads

The new batch job for a HANA

data load

A new dataflow for HANA data load

Making HANA tables the data

target

Creating data mapping to load

data to HANA

Creating data mapping to load

data to HANA

Detailed data mapping to load

data to HANA

Detailed data mapping to load

data to HANA

Execute a HANA data load

Execute a HANA data load

Execute a HANA data load

HANA data load log

Opening HANA Studio

Opening HANA Studio

Opening ourCustomer table in

HANA

Our Customer table in HANA

What can you do with HANA and BO Explorer?

The system looks at the data and formats it based on implied hierarchies (i.e., time, geography, customer) as well as measures. Users may navigate and change measures, graphs, and tables.

New Calculations

Any data panel can be sorted in many ways

Measures used on any graph can be calculated “on-the-fly.”

We can also add our own measures

In our example we are adding the measure “Margin Per Unit” as total margin divided by “quantity sold”

72

HANA Optimized InfoCubes

74

Data Store Objects (DSO) In HANA

Main IndexDelta Index

History Index

Insert Only Index

Read delta, between snapshot 1 and 2 Index Read

Data Load

Activation

The DSO in HANA is a ‘closed’ object where you can do:

• Index reads (snapshots)• Delta reads for updates• Activate data• Querying

PS! a table, an analytic or calculation view in a HANA schema can be accessed via a

BW DataSource. This is based on ‘DB connect’ using a second DB connection to

the underlying HANA DBMS. Source: T. Zurek, SAP AG

SAP HANA — Test Drive

• You can see demos and do a test drive at: https://www.experiencesaphana.com

This site contains a lot of great information and you can also try the Information Composer and see recorded demos.

Register and Take a Free BI Test Drive with SAP HANA• You can register for a free test drive at: https://bi.ondemand.com/session/new

You can also upload your own data and try the tool to see if it is something for your organization. There is even quick guides, videos, and wizards to get you started. 76

Questions and AnswersDr. Berg

Bberg@Comerit.com