sap hana – a technical snapshot

~ Debajit BanerjeeConsultant

©Copyright – Debajit Banerjee, 2011 1

February 16th, 2011

San Jose, California

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SAP High-performance ANalytic Appliance(HANA)is a pre-configured Analytic Appliance.So, if you order SAP HANA, you will get In-Memory software bundled with hardware delivered directly from the hardware partner.SAP HANA certified H/W partners : HP, IBM, Fujitsu, CiscoSoftware : SAP In-Memory Computing Engine (IMCE) andTools for data modeling, data and life cycle management, security, operations, etc.HANA will work with Data replication, ETL, and BI.HANA supports multiple interfaces.

SAP HANA provides- Operational and Analytical Data mart scenarios-Access and Analyze Real-Time Information for high volume of data with the following key features:- High Performance- Flexible Analytic Models- Minimum Data Duplication- Ease of Use- Low TCO and Risk

HANA enables organizations to analyze their business operations, based on huge volumes of transactional information, as business happens. It is a move from ‘After-Event Analysis’ to ‘Real-Time Decision Making’.

©Copyright – Debajit Banerjee, 2011

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In-Memory Computing is a technology that allows the processing of massive quantities of real time data in the main memory of the server to provide immediate results from analyses and transactions.

Why the need for In-Memory Technology?

For Conventional Databases - READ Data Access Speed from DISK : 5 milliseconds

For In-Memory Databases - READ Data Access Speed : 5 nanoseconds; simply 1 Million times FASTER

IT Challenges Business Challenges

Data Explosion Inadequate access to real time operational information

High Costs to increase physical limit for handling data volumes

Need to react faster to events impacting operations for the dynamic business changes

High TCO for infrastructure management

Unable to blend analytics and operations as planning, forecasting, financial close processes, etc are based on not real time information

Dissatisfied business users- Processing & analysis results delayed- Data is not real time


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Distribution of data –Linear scalability enabled by massive parallel workload execution; minimize data movement

Calculation models –extreme performance and flexibility with calculations on the fly


Traditional concept is changing.

Presentation

Orchestration

Calculation

Data

Presentation

Calculation

Data

Orchestration

Classic Database In-Memory Database

User Interface Layer

Application Layer

Database Layer

In-Memory Computing Engine is the heart of SAP HANA that offers data acceleration and management software in a single integrated environment.It leverage SAP's latest in-memory technologies based on columnar databases, massively parallel processing (MPP), in-memory computing, and data compression techniques.

5©Copyright – Debajit Banerjee, 2011

Before SAP HANA After SAP HANA

Any Comparison between SAP BWA and SAP HANA 1.0?

Although, there are some similarities(Column store, Aggregation Engine) between these two but HANA is technically far more than BWA – i) standard interfaces (SQL, MDX), ii) real persistence layer, iii) Row Store(P*time), iv) transactions (MaxDB), v) SQL parser (P*time), ….

HANA 1.0 is intended as a Data Mart.

On the other side, one can only load InfoCubes into BWA and all the other associated things (complex logic, defining data model, analysis authorizations) can be done on BW as BWA has BW on top.

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Here it is the complete architectural view of SAP HANA 1.0 components.

simplistic view

©Copyright – Debajit Banerjee, 2011different use-case view

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Data Modeling involves :

In-Memory Computing Studio

•Modeling

In-Memory Computing Engine

•Metadata Manager

Business Objects Enterprise

•Data Services

•Data Services Designer

•SBO Information Design Tool

Reporting involves :

In-Memory Computing Engine

•Relational Engines

- Row Store

- Column Store

•Request Processing/Execution Control

- SQL Parser

- SQL Script

- Calculation Engine

- MDX

Business Objects Enterprise

•SBO BI4 Servers (prog for client)

Clients

•SAP BI4 Universes

•BI4 Explorer

•BI4 Analysis

•MS Excel

•Dashboard Design


Administration involves :

In-Memory Computing Studio

•Administration

Persistence Layer

•Page Management and Logger

Disk Storage

•Data Volumes and Log Volumes

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ROW Store is the relational engine to store data in row format.

- Pure in-memory store (Future versions will also have an option of disk based store)

- In memory object store (in future) for live cache functionality

- Transactions Version Memory is the heart of row store


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Column Store- improves read functionality significantly- also improves write functionality using asynchronous merge for delta storage- highly compressed data- no real files, virtual files- compression by create dictionary and applying further compression methods- merge operation can also be triggered manually with an SQL command


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As of today, OS and Processor is fixed for SAP HANA; OS:SUSE Linux Enterprise Server 11 SP1, Processor: Intel Xeon 7560, code name Nehalem-EX. *SQ = Stable Queue i.e., Storage used by Sybase Replication server when target is slow or down; For <5GB, 5GB-20GB, >20GB Data Loads/hr. using 4-8GB Memory with 1Gbps Network, Storage reqd. 100GB, 100-500GB, 500GB+ respectively.


Appliance Sizes OS Processor CPU Cores Memory Log Storage Vol. Data Storage Vol.

Small SLES11 SP1 Nehalem-EX 2 CPU x 8 256 GB 320 GB 1 TB + SQ*

Small+ (Scale up) SLES11 SP1 Nehalem-EX Up to 4 CPU x 8 256 GB - 512 GB 320 GB – 640 GB (1 TB – 2 TB) + SQ

Medium SLES11 SP1 Nehalem-EX 4 CPU x 8 512 GB 640 GB 2 TB + SQ

Medium+ (Scale up) SLES11 SP1 Nehalem-EX Up to 8 CPU x 8 512 GB - 1 TB 640 GB – 1.2 TB (2 TB – 4 TB) + SQ

Large SLES11 SP1 Nehalem-EX 8 CPU x 8 1 TB 1.2 TB 4 TB + SQ

X-Large / Scale out SLES11 SP1 Nehalem-EX N-times Large Configuration

Network Requirements Storage Requirements

Internal Disk External Storage

1 Gbps dedicated network between ERP & HANA (10 Gbps optional) 3 x 1 Gbps dedicated networks for BI client access, data & appliance managementOR1 x 10 Gbps network with VLAN setup for all of the above including the network between ERP & HANA

For Scale out configuration only:- 10 Gbps dedicated network between the clustered servers / blades- 10 Gbps dedicated network between HANA nodes & external storage

Redundant infrastructure for high availability & failover (IP paths, NICs etc.)

Rack servers with internal disks: PCIe-Flash/ SSD for LOG volume (100k IOPS)

Scale out configurations with external storage: 100k IOPS for LOG volume (e.g. via external SSD RAID)

SAS / SSD for DATA volume (800MB-1GB/s sequential read, min. 10k rotational speed for SAS drives)

800MB-1GB/s sequential read for DATA volume (e.g. via SAS or FC drives 10k rotational speed min.)

SAP Landscape with SAP BusinessObjects

Portfolio (SBOP)

SAP HANA Data Providers


SAP ERP SystemSAP HANA System

Admin Client End User Client

SAP IMCE Server, Client, Studio 1.0 SAP HANA Load Controller 1.0 SAP Host Agent 7.20 SYBASE Replication Server 15.5 and Enterprise Connect Data Access (ECDA)

SAP Host Agent 7.20 SYBASE Replication Agent

SAP IMCE Studio 1.0 SAP IMCE Client 1.0

SAP IMCE Client 1.0MS Excel

SAP BW System

Non-SAP System

SAP BusinessObjects Portfolio (SBOP) Data Services

DS Designer DS Job Server

SBOP Enterprise

SBOP BI Platform SBOP BI Clients

(Explorer, Xcelsius, Dashboard)

Access End User Clients via Browser


Agile Scenarios- Operational Data Mart- Agile Data Mart

can be used in Industries• Banking• CPG• Retail• Utilities• Telco• Hi-Tech

Lines of Business CO-PA ERP HCM CRM Finance

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Today’s SAP Landscape;

It is relatively a complex one with traditional SAP ERP, BI and Data Warehousing.


Corporate BI

DB

Enterprise Data Warehouse (BW)

DB

SAP ERP 1(or CRM, SRM, SCM)

DB

SAP ERP n(or CRM, SRM, SCM)

DB

Non-SAP Business

Appl.

DB

Data Mart

DB

Data Mart

DB

Data Mart

Local BI

BI BI

ETL ETL

Corporate BI

DB


DB

SAP ERP 1(or CRM, SRM, SCM)

DB

SAP ERP n(or CRM, SRM, SCM)

DB

Non-SAP Business

Appl.

Local BI

Sync Sync

HANA 1.0

HANA 1.0

HANA 1.0

BWA

SAP HANA 1.0 enables zero latency reporting and analytics against side-by-side with SAP ERP. It is the first movement towards ‘Real-Time Decision Making’.

BWA provides general performance improvement for SAP BW.

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SAP HANA – FUTURE :

BWA evolves into SAP HANA X.Xas SAP in-memory computing becomes the primary persistence mechanism for BW.


SAP HANA – VISION :

OLTP and OLAP Convergence

Corporate BI

HANA X.X


DB

SAP ERP 1(or CRM, SRM,

SCM)

DB

SAP ERP n(or CRM, SRM,

SCM)

DB

Non-SAP Business

Appl.

Local BIVirtualData Mart

New Application

VirtualData Mart

VirtualData Mart

Corporate BI

DB

Non-SAP Business

Appl.

ERP 1 ERP n EDW (BW)

HANA

Local BIVirtualData Mart

VirtualData Mart

VirtualData Mart

Now Future Trend

SAP HANA 1.0Data Mart next to

ERPNo BW or BWA

BWABI on top of a

corporate EDW(BW)

SAP HANA 1.5

SAP HANA 2.0

New Application

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(1) ERP and BW can be connected to HANA using Data Services or Sybase Replication Server

(2) No name space concept in HANA 1.0 at this moment, current release does not support connecting to several ERP instances. However this issue can be avoided using data sources.

(3) CRM also can use HANA

(4) Data from BW into HANA can be loaded into using Data Services and Infospokes

(5) BWA Hardware can be upgraded to HANA given that hardware is relatively new

(6) BWA Licenses can be transferred to HANA

(7) SAP BOBJ tools can directly report HANA

(8) HANA supports BICS (Business Intelligence Consumer Services) interface

(9) External tools can connect to HANA using JDBC and ODBC drivers

(10)HANA currently doesn't support complete MDX set, it supports EXCEL 2010 standard MDX

(11)SQL Script, MDX statements are passed to optiomizer which is included in calculation engine; it generally breaks up a model into sub processes for optimized performance on cost based.

(12)Planning Engine Will be included in next release. It will include planning functions like distribute and copy functions.


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Key Benefits delivered by SAP HANA as follows :

(1) Real-Time Decision Making

Fast and easy creation of ad-hoc views on business; enabling a 360 degree view of the business

Access of Real time analysis from “What happened yesterday”

Close to zero latency

(2) Accelerate Business Performance

Increase speed of transactional information flow in areas such as planning, forecasting, pricing, offers…

(3) Unlock New Insights

Remove constraints for analyzing large data volumes – trends, data mining, predictive analytics, etc.

Structured and unstructured data

(4) Improve Business Productivity

Business designed and owned analytical models

Business self service which in turn reduces reliance on IT

Use data from anywhere

(5) Improve IT efficiency

Manage growing data volume and complexity efficiently

Lower landscape costs

Non-Disruptive to the existing EDW strategy


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References :

SAP website

http://www.sap.com

http://www.sap.com/platform/in-memory-computing/index.epx

SAP SDN website

http://www.sdn.sap.com/irj/sdn/index?language=en

SAP ASUG website

http://www.asug.com


http://www.sap.com/






http://www.sdn.sap.com/irj/sdn/index?language=en

http://www.asug.com/


Row Store Architecture

Currently not present in HANA 1.0


Column Store Architecture

Currently not present in HANA 1.0


Importance of Persistence Layer


SAP In-Memory Computing Studio and Modeling


© Copyright – Debajit Banerjee, 2011.

All rights reserved.

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sap hana – a technical snapshot

Technology

data modeling

data acceleration

data replication

data access speed

memory databases

high volume of data

data compression techniques

handling data volumesneed