SAP HANA – A Technical Snapshot
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Transcript of 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
©Copyright – Debajit Banerjee, 2011
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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
©Copyright – Debajit Banerjee, 2011
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
©Copyright – Debajit Banerjee, 2011
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
©Copyright – Debajit Banerjee, 2011
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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
©Copyright – Debajit Banerjee, 2011
10©Copyright – Debajit Banerjee, 2011
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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.
©Copyright – Debajit Banerjee, 2011
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
12©Copyright – Debajit Banerjee, 2011
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
13©Copyright – Debajit Banerjee, 2011
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
14©Copyright – Debajit Banerjee, 2011
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Today’s SAP Landscape;
It is relatively a complex one with traditional SAP ERP, BI and Data Warehousing.
©Copyright – Debajit Banerjee, 2011
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
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.
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.
©Copyright – Debajit Banerjee, 2011
SAP HANA – VISION :
OLTP and OLAP Convergence
Corporate BI
HANA X.X
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.
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
17©Copyright – Debajit Banerjee, 2011
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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.
©Copyright – Debajit Banerjee, 2011
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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
©Copyright – Debajit Banerjee, 2011
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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
©Copyright – Debajit Banerjee, 2011
21©Copyright – Debajit Banerjee, 2011
Row Store Architecture
Currently not present in HANA 1.0
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Column Store Architecture
Currently not present in HANA 1.0
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Importance of Persistence Layer
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SAP In-Memory Computing Studio and Modeling
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© Copyright – Debajit Banerjee, 2011.
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