SAP NetWeaver Business Warehouse powered by SAP HANA Deep Dive. Rainer Uhle Brian Wood

Transcription

1 [ SAP NetWeaver Business Warehouse powered by SAP HANA Deep Dive Rainer Uhle Brian Wood

2 RUN COOLER

3 [ Agenda / Take away The evolution of in-memory technology at SAP moves along We did it our way Latest stage: SAP HANA Database as a full fledged in-memory database Ja wir können. SAP BW as one of the first applications fully enabled to leverage the key strength of the new HANA In-memory database Accelerated performance Simplified administration and infrastructure Dedicated optimizations available for specific BW modeling objects First Results from Customer Projects POCs and Ramp-Up next steps LSA in times of HANA together with System Demos Look&Feel.. Non-disruptive BWA like HANA Optimizations..

4 Take away 1 The evolution of in-memory technology at SAP moves along We did it our way In-memory technology has been offered for caching strategies in many ways so far at SAP (BW: DB -, OLAP-, Query-Caching; BW Accelerator; APO Live Cache; ) Caching guarantees better performance but also stands for replication, buffering, snapshooting, read-only, invalidation after updates, reload, rebuild etc. Realizing full support for an in-memory data base means much more

5 [ In-Memory Computing Product - Vision SAP High Performance Analytic Appliance BI Clients Mobile SAP BusinessSuite SAP BusinessSuite SAP BusinessWarehouse New SAP Applications Further Applications In-Memory Computing Platform This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation is not subject to your license agreement or any other agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to develop or release any functionality mentioned in this presentation. This presentation and SAP's strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent.

6 [ The Evolution of SAP HANA Landscape Options SAP HANA 1.0 is an appliance to the application (e.g. SAP ERP). Its major benefit is increasing performance of transactional reporting for one system. SAP HANA 1.0 replicates /loads data using replication/ ETL tools Option SAP HANA 1.0 SAP ERP SAP ERP RDBMS RDBMS SAP HANA 1.0 SAP HANA 1.0, SPS3 is the primary persistence for SAP NetWeaver BW 7.3, SP5. All functionality of SAP HANA 1.0 will still be available in Release 1.0, SPS3. All features of SAP NetWeaver BW are supported by SAP HANA 1.0, SPS3 Option SAP HANA 1.0, SPS3 SAP NW BW RDBMS BWA SAP NW BW SAP HANA SAP HANA Vision is the next evolution step and long term will also replace the data base of the ERP system. Option SAP HANA Vision SAP ERP SAP ERP SAP NW BW (*) Migrating to further SAP HANA releases is optional RDBMS HANA Vision

7 [ SAP NetWeaver BW7.3 Powered by SAP HANA Added Value Accelerated performance BWA Excellent query performance as proven with BWA Accelerated In-Memory planning capabilities Performance boost for ETL processes Simplified administration and infrastructure SAP NW BW RDBMS BW Upgrade DB Migrate DB and BWA merging in one instance for lower TCO Simplified administration via one set of admin tools e.g. for Data Recovery and High Availability Column based storage with highly compression rates and significantly less data to be materialized No special efforts to guarantee fast reporting on any DB object Simplified data modeling and reduced materialized layers Integrated and embedded flexibility for Data Marts SAP NW BW SAP HANA This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation is not subject to your license agreement or any other agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to develop or release any functionality mentioned in this presentation. This presentation and SAP's strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent.

8 Take away 2 Latest stage: SAP HANA Database as a full fledged in-memory database Ja wir können. DBSL available for SAP HDB DBA Cockpit for Administration BW Accelerator obsolete

9 [ Database Shared Library (DBSL) Purpose: Open Platform Concept supporting several Standard RDBMS The database-dependent part of the SAP database interface can be found in its own library that is dynamically linked to the SAP kernel. This database library contains the Database Shared Library (DBSL), as well as libraries belonging to the corresponding database manufacturer. These are either statically or dynamically linked to the database library.

10 [ Final Result - SAP HANA Database Overview (DBACOCKPIT)

11 [ After system migration: BW Accelerator obsolete In BW on a relational DataBase we had speedy query execution using the BWA In the BW system on HANA DB we do not need the BWA anymore as data is stored in memory anyway Check transaction RSDDBIAMON: SAP NetWeaver BW Accelerator Monitor

12 [ SAP SMART Meter Analytics BW on HANA based example Dashboard Instantly aggregate time of use blocks and total consumption profiles Segment customers based on energy consumption patterns Provide energy efficiency benchmarking This analysis is based on a query accessing a Smart Metering BW 7.3 MultiProvider with data access in BW on HANA BW on HANA - Look&Feel: BWA like Query performance out of the box

13 [ SAP NetWeaver BW7.3 Powered by SAP HANA How Does BW 7.3 Running on HANA Differ from BW Running on xdb? SAP NetWeaver BW 7.x on xdb Standard DataStore Objects Data Base server and SAP NetWeaver BWA Standard InfoCubes BW Integrated Planning HANA Data Marts running side-by-side BW SAP NetWeaver BW 7.3 on HANA SAP HANA-optimized DataStore Objects SAP HANA In-Memory platform SAP HANA-optimized InfoCubes In-Memory planning engine Consumption of HANA artifacts created via HANA studio BW staging from HANA BWA SAP NW BW BW Upgrade SAP NW BW RDBMS DB Migrate SAP HANA Migration without reimplementation - no disruption of existing scenarios

14 Take away 3 SAP BW as one of the first applications fully enabled to leverage the key strength of the new HANA In-memory database Accelerated performance No special efforts to guarantee fast BWA like reporting on any DB object Accelerated In-Memory planning capabilities Performance boost for ETL processes (DSO Activation 5-10 times faster, InfoCube load 5 times faster ) Simplified administration and infrastructure DB and BWA merging in one instance for lower TCO Column based storage with highly compression rates and significantly less data to be materialized and managed Simplified data modeling and reduced materialized layers Dedicated optimizations available for different BW modeling objects

15 [ In-Memory Optimized DataStore Objects Mapping Between Application Server and HANA DB Activation Queue Active Data Change Log (BW / ABAP) DataStore Request Activation (HANA DB / C++) Column based table Former Load 5455 I 30 Actual Load 5455 I 20 History Index Valid from to I dt1...dt2 Main Index I 20...valid from dt2 Calculation View Before Image 5455 I -30 After Image 5455 I 20 Standard column based table no primary key, performance advantage 20% Temporal table Delta Index Table replaced by calc view (uses history index to create a change log view of the data) View calculates technical key on the fly Multiple updates for a particular key are consolidated into one

16 [ Delta Management and Consistent View in Column Store

17 [ DSO Load&Activation Performance Overview Example DSO Type Classic DSO on RDBMS Classic DSO on HANA DB Loadtime 22 min 18 min 14 min ActivationTime 5650 secs 1923 secs 394 secs HANA optimized DSO

18 [ SAP HANA-optimized InfoCubes Faster Data Loads and Easier Modeling Traditional InfoCubes tailored to a relational DB consist of 2 Fact Tables and the according Dimension tables SAP HANA-optimized InfoCubes represent flat structures without Dimension tables and E tables*: Up to 5 times faster data loads (Lab Results) Creation of DIM Ids no longer required Simplified Data modeling Faster remodeling of structural changes After the upgrade to BW7.3, SP5 all InfoCubes remain unchanged Tool support for converting standard InfoCubes Preliminary lab result: 250 Million records in 4 minutes No changes of processes, MultiProvider, Queries required *Tables for compressed data MD MD MD MD together with BWA like MD D D MD MD MD Facts Conversion/New System Demos Look&Feel.. Non-disruptive This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation is not subject to your license agreement or any other agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to develop or release any functionality mentioned in this presentation. This presentation and SAP's strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent. Facts HANA Optimizations.. F E F

19 [ Query performance Proven Query Performance as Known from BWA Query acceleration on BW InfoCubes No replication fast query access directly on primary data persistence Indexes on InfoCubes and InfoObjects no longer required -> No Roll-ups, Change runs In-memory Calculation Engine TopN, BottomN, Exception aggregation Currency conversion... Snapshot Indexes for Virtual- and QueryProvider Query acceleration on BW DataStore Objects(DSO) Acceleration via In-Memory column storage Query on DSO, BW InfoSet Additional acceleration via Logical Views to SIDs on top of DSO No changes of processes, MultiProvider, Queries required SQL Engine SAP NW BW SAP HANA Query on InfoCube, Masterdata AnalyticIndex, CompositeProvider Calc Engine Aggregation Engine on In-Memory data This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation is not subject to your license agreement or any other agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to develop or release any functionality mentioned in this presentation. This presentation and SAP's strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent.

20 Take away 4 Next Steps LSA in times of HANA Simplification through Layer Reduction HANA optimized SID Handling during Activation Query Performance on DSOs Near-line Storage (NLS) in times of HANA BW based NLS Interface Example: NLS with Sybase IQ

21 [ SAP HANA-optimized DataStore object Simplification through Layer Reduction PoC Example Classical Architecture SAP HANAoptimized Reporting Aspects Reporting directly on DataStore objects No replication to InfoCubes / BWA Significant reduction of TCO/TCD and load time improvements

22 [ SAP HANA-optimized DataStore object SAP HANA-optimized SID Handling (Anti-Join) 0COMPCODE 0DEBITOR 0SALESORG 0AMOUNT SELECT * FROM Activation Queue SELECT DISTINCT InfoObject FROM Activation Queue WHERE InfoObject NOT IN SELECT * FROM SID-Table 0DEBITOR SALESORG SID COMPCODE SID COMPCODE 0DEBITOR 0SALESORG 0AMOUNT Activation Queue 0DEBITOR SID Classical Architecture SAP HANA-optimized

23 [ DSO SID Handling during Activation Overview Example SID Condition SIDs exist SIDs missing ActivationTime 375 secs 485 secs

24 [ SID Handling with Anti-Join Lab Results Scenario Setup Standard DataStore Key Fields: 11 Data Fields: Mio rec in Activation Queue 3000 Cardinality (distinct values): R u n t i m e x4 x Worst-Case (empty SID tables) 30 characteristics < 100 rec 3 characteristics < 1000 rec 2 characteristics < rec 1 characteristics < 1 Mio rec 1 characteristics = 10.3 Mio i n s e c Best-Case (SIDs exist) 0 Anti-Join 70 Classical

25 [ Runtime Estimation Process View Based on Lab Results 10 Mio rec Delta, 100 Mio rec Active DataStore on RDBMS SIDs available HANA opt. DataStore SIDs not available HANA opt. DataStore SIDs available DataStore on RDBMS HANA opt. DataStore

26 [ SAP HANA-optimized DataStore object Reporting Performance DSO against InfoCube Classical Architecture HANA InfoCube SAP HANAoptimized optimized Reporting DSO Reporting

27 [ Bill Inmon s Corporate Information Factory & Near-Line Storage DSS Applications Departmental Data Marts ETL Marketing Acctg Finance Sales ERP ERP ERP CRM Changed Data Staging Area EDW ecomm. Bus. Int. Internet ERP Corporate Applications local ODS Global ODS Dialogue Manager Cookie Cognition Preformatted dialogues Oper. Mart Session Analysis Web Logs Granularity Manager Archives Exploration warehouse/ data mining Cross media Storage Near line Management Storage Source:Bill Inmon

28 [ The Nearline Storage Solution for SAP NetWeaver BW Based on the NearLine Storage Interface Development Partners can implement their Solutions for Archiving and NLS into the SAP BW 3rd Party NLS Solutions are implemented within the SAP BW ABAP Stack in partner specific namespaces have to pass a certification process can offer specific Application Area in the SAP Support Portal have to be licensed in addition to SAP licenses can have a different release cycle compared to SAP NetWeaver BW NLS Partner Solution Present development partners Certified since SAP BW 7.0 (in alphabetical order of their products) CBW PBS Software Dynamic NearLine Access - SAND Technology DB2 Viper IBM DataVard OutBoard 1.0 yes yes 7.01 SP6 yes (see also )

29 [ SAP NetWeaver BW Nearline Storage on PBS CBW With CBW PBS provides an add-on solution for the data retrieval of archived/nearlined BW data. On the basis of the SAP ADK files, the CBW solution creates aggregates on the archive data and (optionally, if needed) index structures to enable a fast retrieval option for the archived data directly from the end-user's original query. PBS CBW NLS IQ for Sybase Analytics Server IQ utilizes the Sybase Analytics Server IQ as a nearline database and offers extremely fast query response times.

30 [ SAP BW powered by HANA and Sybase IQ NLS BW InfoProvider Near-line Storage Archiving HANA DB Acquisition Next generation near-line SMART STORE solution for BW powered by HANA. Combining real-time analysis with cost-efficient data storage Cost- + Performance-Optimized EDW Data lifecycle management Data Aging strategy Access - very frequently frequently not frequently rarely

31 [ SAP BW powered by HANA NLS Example with PBS CBW and Sybase IQ

32 Vielen Dank! Many Thanks!

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