Planning Applications Kit - In Memory Planning in Action. Dr. Gerd Schöffl / CSA Technology

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1 Planning Applications Kit - In Memory Planning in Action Dr. Gerd Schöffl / CSA Technology

2 Legal disclaimer 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 SAP AG. All rights reserved. 2

3 Agenda In Memory Planning in SAP NetWeaver BW Runtime Comparison Planning Applications Kit against BW-IP Demo Restrictions for Planning Applications Kit Implementation Recommendations 2012 SAP AG. All rights reserved. 3

4 SAP HANA SAP NW BW In-Memory Planning The Technological Change Classic Database In-Memory Database User Interface Layer Application Layer Database Layer xdb SAP NW BW Presentation Orchestration Calculation Data SAP NW BW SAP NW BW Presentation Orchestration Calculation Data Authorization Locking Hierarchies Input Enablement Planning Functions MultiProvider Handling Conversions Aggregation 2012 SAP AG. All rights reserved. 4

5 SAP NetWeaver BW 7.30 In-Memory Planning - Simple Disaggregation Example user changes a plan value Traditional Approach 1. Determine the delta Disaggregate (in appl. server) per week (52) per branch (500) combinations / values 3. Send values to DB to save HANA-Based Approach 1. Determine the delta Send 1 value to DB + instruction to disaggregate and how 3. Disaggregate (in DB engine) per week (52) per branch (500) create + save values 2012 SAP AG. All rights reserved. 5

6 SAP NetWeaver BW 7.30 In-Memory Planning - Simple Disaggregation Example user changes a plan value Traditional Approach 1. Determine the delta Disaggregate (in application server) per week (52) per branch (500) combinations / values 3. Send values to DB to save HANA-Based Approach 1. Determine the delta Send 1 value to HANA database + instruction to disaggregate and how 3. Disaggregate (in HANA engine) per week (52) per branch (500) create + save values 2012 SAP AG. All rights reserved. 6

7 Current Planning Architecture User Interface Layer Presentation BEx Query Metadata Application Layer Plan Session Aggregation Engine Constraints Logic Local Cache Delta Buffer Small data flow Large data flow Database Layer Data Save 2012 SAP AG. All rights reserved. 7

8 In-Memory Planning Architecture User interface Layer Presentation Application Layer BEx Query Plan Session Metadata Database Layer Aggregation Engine Metadata Constraints Logic Small data flow Large data flow Delta Buffer Data Index Save 2012 SAP AG. All rights reserved. 8

9 SAP BW powered by HANA Deployment options for planning (SAP BW 7.30 SP5) A B C Standard without in-memory Standard with in-memory Deep in-memory integration User interface User interface User interface $ $ $ BEX AO BEX AO BEX AO SAP BW SAP BW SAP BW Integr. planning Integr. planning ABAP planning applications kit $ Classic DB Database (SQL) HANA Planning engine Calculation engine Database (SQL) $ HANA Planning engine $ Calculation engine Database (SQL) Full compatibility + in-memory acceleration of reads + in-memory acceleration of core functions Database migration Toggle switch (see SAP Note ) 2012 SAP AG. All rights reserved. 9

10 In-Memory Planning Value Proposition runtime BW-IP (on any DB) Planning applications kit (on HANA) Improved plan quality (better response times allow more simulations cycles) Data volume Improved user experience (better response time) Improved plan accuracy (higher data volume can processed in same time) 2012 SAP AG. All rights reserved. 10

11 In-Memory Planning runtime High volume, simple calculations 5 10x Medium volume complex calculations 1 2x BW-IP (on any DB) Planning applications kit (on HANA) Data volume Benefit is scenario specific (fix costs might dominate low volume scenarios) 2012 SAP AG. All rights reserved. 11

12 Demo Web Demo 2012 SAP AG. All rights reserved. 12

13 ABAP Planning Applications Kit Features Executed In-Memory Features executed in In-Memory calculation engine Query execution for reading and writing the data Disaggregation in queries Characteristic Relationships type Attribute, DSO Planning functions: FOX (with some restrictions, see next slides) Distribution by reference data Copy Delete Revaluate Repost Set Value 2012 SAP AG. All rights reserved. 13

14 ABAP Planning Applications Kit Features using ABAP Calculations (see Note ) Features executed in ABAP calc. engine, data is transported to ABAP layer for this step only (!) Planning functions: Custom defined (ABAP Exit) Distribution with Keys Unit Conversion/Currency Conversion With more than one condition Disaggregation in Query in certain cases Query based on MultiProvider on top of aggregation level Reference key figure is a formula itself Key figure is restricted to multiple values (multiple single values, interval, hierarchy node) 2012 SAP AG. All rights reserved. 14

15 ABAP Planning Applications Kit Features NOT Supported in ABAP Planning Appl. Kit (see Note ) Entire planning model is executed in ABAP: Characteristic Relationships: Type Exit Type Hierarchy Key figures Type TIMS and DATS (time and date) With other aggregation than SUM Data Slices (all kinds) Logging BAdI Commands in Fox formulas: CURC (currency conversion) CP, CO, CA, CS (string operations) Call Function (calling ABAP function module) Transient master data; virtual key figures and characteristics 2012 SAP AG. All rights reserved. 15

16 Implementation Recommendations 1 Fox rules Use Fox formulas instead of ABAP exists = Think Big Think of new scenarios Use few large planning functions instead of several small ones Enlarge filters to reduce planning functions and complexity 2012 SAP AG. All rights reserved. 16

17 Implementation Recommendations 2 Avoid shifts between In-Memory and ABAP execution of planning functions If ABAP (custom-defined) planning functions are necessary, try to pool them Reduce complexity Disaggregation in query can make additional aggregation levels unnecessary Be up-to-date regarding which features are supported by PAK See SAP Note SAP AG. All rights reserved. 17

18 Thank you Contact information: Dr. Gerd Schöffl Expert, CSA Technology Dietmar Hopp Allee 16, Walldorf, Deutschland