SAP HANA 2.0 Modeling Guide

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1 SAP HANA Modeling Guide.....................................................7 2 Introduction to Modeling......................................................8 3 SAP HANA Architecture.......................................................9 3.1 SAP HANA In-Memory Database..................................................9 Columnar Data Storage......................................................9 Parallel Processing.........................................................10Simplifying Applications.....................................................11 3.2 SAP HANA Database Architecture.................................................
Time Range to generate Time data 7.2 Using Attribute vi 45 Create Attribute views Attribute View Type 50 3 Using Analytic Vi Create Analytic Views 52 7.4 Using Calculation Views Create Script-Based Calculation Vi Create Graphical Calculation Views Supported Data Categories for Information Views 72 7.5 Working With View Nodes Create Joins Create U Create Rank nodes 94 Filter Output of Data Foundation Node 95 Filter Output of aggregation or Projection view nodes b 7.6 Preview Information View Output 98 Data Preview editor 100 SQL Edit Working with Attributes and Measures 102 8. 1 Create Counters 102 Counter Properties 103 Example: Co 104 8.2 Create Calculated Columns 105 Calculated Column Properties 107 Example: Calculated measures 107 Example: Calculated Attributes .108 8. 3 Create Restricted Columns 110 Restricted Colurmn Properties Example: Restricted Columns 112 8.4 Assign Variables 112 Supported variable Types 115 Variable Properti 115 8.5 Assign Semanti 115 Extract and Copy semantics From Underlying Data Sources 116 Propagate Columns to Semantics 117 Supported Semantic Types for Measures 118 Supported Semantic Types for Attributes 118 8.6 Create Input Parameters 119 Map Input parameters or variables ..,122 put Parameters 124 SAP HANA Modeling Guide Content PUBLIC Input Parameter Properties 126 Using Hierarchies for Reporting 126 Create Level hierarchies 127 Create Parent-Child Hierarchies 130 Query Shared Hierarchies 134 Root Node visibility 135 Orphan Nodes 136 8.8 Using Currency and Unit of Measure Conversions Associate Measures with Currency 137 Associate measures with Unit of Measure 8.9 Enable Attributes for Drilldown in Reporting Tools 143 Supported Drilldown Types for Attributes 8.10 Trace Columns in Information Views With Data Lineage 144 8.11 Assign Valule Help for attributes 8.12 Add Descriptions to Attributes 146 8.13 Group Related Measures 8. 14 Convert Attribute values to Required Formats 9 Working With Information View Properties. n.149 9. 1 Deprecate Information Views 9.2 Filter Data for Specific Clients 150 Assign Default Client 151 Default client values 151 9.3 Enable Information Views for Time Travel Queries 152 9.4 Invalidate Cached Content 153 Enable Support for Cache Invalidation 154 9.5 Maintain Modeler Object Labels in Multiple Languages 154 9.6 Quick Reference: Information View Properties 155 10 Defining Data Access Privileges. ,,161 10.1 Create Classical XML-based Analytic Privileges ..163 Analytic Privilege 165 Structure of XML-Based Analytic Privileges 166 Dynamic value Filters in the Attribute Restriction of XMl- Based Analytic Privileges 171 Runtime Authorization Check of Analytic Privileges. 173 Example: Using Analytic Privileges 175 Example: Create an XML-Based Analytic Privilege with Dynamic Value Filter. Supported Restriction Types in Analytic Privileges 179 10.2 Create SQL Analytic Privileges 180 Static SQL Analytic Privileges 182 Dynamic sQL Analytic Privileges 82 Structure ot sQL-Based Analytic Privileges 182 SAP HANA Modeling Guide PUBLIC Content 11 Migrating an Object Type to a Different Object Type .185 11.1 Convert Attribute views and Analytic Views to Graphical Calculation Views 186 Migration Impact 11.2 Convert Script-based Calculation Views to Graphical Calculation Views .189 11.3 Convert Classical XML-based Analytic Privileges to sQL-based Analytic Privileges .192 11.4 Simulate a Migration Activity 194 11.5 Undo Migration Changes 11.6 Activate Migrated Objects 196 11.7 Migration Lo 197 11.8 Best Practice: Migrating an Object Type to a Different Object Type Additional Functionality for Information Views........,......,.. 199 12.1 Performance analysis Open Information views in Performance Analysis Mode 200 Debug Calculation Views 202 Validate Performance of calculation views .204 12.2 Maintain Comments for Modeler Objects 205 12.3 Replacing Nodes and Data Sources 207 Replace a view Node in calculation Views 207 Remove and replace a view node in calculation views 209 Replace a data Source in Calculation Views ·.210 12.4 Renaming Information views and columns 210 Rename Information views 210 Rename Columns in Information views 211 12.5 Using Functions in Expressions 212 Conversion functions 213 String Functions 214 Mathematical functions Date Functions 218 Miscellaneous fuinctions 219 Spatial Functions Spatial Predicates 223 12.6 Trace Performance issues 224 12.7 Maintain Search Attributes 224 12.8 Configure Tracing 225 12.9 View the Job Log 12.10 Validate Models .226 12.11 Manage Editor Layout 226 12.12 Search for tables, models and column views 13 Managing Objects in SAP HANA Systems.................... 229 13.1 Activate objects 229 SAP HANA Modeling Guide Content PUBLIC 13.2 Copy an Object 232 3 Manage Information Views with Missing Objects 233 13.4 Check Object References 13.5 Generate Object Documentation. 13.6 Refactoring Objects 236 Refactor Modeler Objects in SAP HANA Modeler Perspective 236 Refactor Modeler Objects in SAP HANA Development Perspective 237 14 Working with SAP BW Models 239 14.1 Import BW Objects 239 14.2 BW Info Providers as sAP HAna models 241 14.3 BW Analysis Authorizations as Analytic Privileges 242 Working with Decision Tables n,,.,245 15.1 Migrate Decision Tables 15.2 Create Decision Tables .246 Add Tables, a Table Type, or Information Views 248 Create Joi 250 Add Attributes 250 Add conditions and actior Add Conditions and Action values 251 Optional Step: Validate Decision Table .....253 Activate Decision Table Execute Decision Table Procedure 15.3 Changing the Layout of a Decision Table 256 154 Use Parameters in a decision table 257 Supported Parameter Types 257 15.5 Use Calculated Attributes in Decision Tables 258 16 Managing Object Versions 260 16.1 Switch Ownership of Inactive Objects 260 16.2 Toggle Versions of Content Objects 261 16.3 View Version History of Content Objects 1 SAP HANA Modeling Guide This guide explains how to create information models based on data that can be used for analytical purposes using the SAP HANa modeler. It includes graphical data modeling tools that allow you to create and edit data models and stored procedures SAP HANA Modeling guide SAP HANA Modeling Guide PUBLIC 2 Introduction to Modeling Modeling refers to an activity of refining or slicing data in database tables by creating views to depict a business scenario. The views can be used for reporting and decision making The modeling process involves the simulation of entities, such as customer, product, and sales, and the relationships between them. These related entities can be used in analytics applications such as SAP Businessobjects Explorer and Microsoft Oftice In SAP HANA, these views are known as information views nformation views use various combinations of content data(that is, non-metadata) to model a business use case. Content data can be classified as follows Attribute: Descriptive data, such as customer ID, city, and country Measure: Quantifiable data, such as revenue, quantity sold and counters. You can model entities in SAP HANA using the Modeler perspective, which includes graphical data modeling tools that allow you to create and edit data models(content models)and stored procedures. With these tools, you can also create analytic privileges that govern the access to the models, and decision table related business rules in a tabular format for decision automation You can create the following types of information views Attribute vi Analytic Views Calculation views Who should read this guide This guide is intended for a modeler, who is also known as a business analyst. data analyst or database expert. concerned with the definition of the model and schemas that will be used in SAP HANA, the specitication and definition of tables, views, primary keys, indexes, partitions, and other aspects of the layout and interrelationship ot the data in SAP HANA The data modeler is also concerned with designing and defining authorization and access control, through the specification of privileges, roles, and users The modeler uses the Administration Console and Modeler perspectives and tools of the saP hana studio SAP HANA Modeling Guide PUBLIC Introduction to Modeling 3 SAP HANA Architecture SAP HANA is an in-memory data platform that can be deployed on premise or on demand. At its core, it is an innovative in-memory relational database management system SAP HANA can make full use of the capabilities of current hardware to increase application performance. reduce cost of ownership, and enable new scenarios and applications that were not previously possible. With SAP HANA, you can build applications that integrate the business control logic and the database layer with unprecedented performance. As a developer, one of the key questions is how you can minimize data movements. The more you can do directly on the data in memory next to the CPus, the better the application will perform. This is the key to development on the SAP hANa data platform 3.1 SAP HANA In-Memory Database SAP HANA runs on multi-core CPUs with fast communication between processor cores, and containing terabytes of main memory With SAP HANA, all data is available in main memory, which avoids the performance penalty of disk 1/0. Either disk or solid-state drives are still required for permanent persistency in the event of a power failure or some other catastrophe. This does not slow down performance, however. because the required backup operations to disk can take place asynchronously as a background task 3.1.1 Columnar Data Storage A database table is conceptually a two-dimensional data structure organized in rows and columns. Computer memory, in contrast, is organized as a linear structure. a table can be represented in row-order or column- order. A row-oriented organization stores a table as a sequence of records. Conversely, in column storage the entries of a colurmn are stored in contiguous mernory locations. SAP HANA supports both but is particularly optimized for column-order storage Table Row Store Column store Country Product sales RON 1 A thi 7 US UK Row 2 Agha Product Row 3 Alpha Alpha TOD 3.ODD 450 450 Columnar data storage allows highly efficient compression if a column is sorted, often there are repeated adjacent values. SAP HANA employs highly efficient compression methods, such as run-length encoding SAP HANA MOdeling Guide SAP HANA Architecture PUBLIC cluster coding and dictionary coding With dictionary encoding, columns are stored as sequences of bit-coded integers. That means that a check for equality can be executed on the integers: for example, during scans or join operations. This is much faster than comparing, for example, string values Columnar storage, in many cases, eliminates the need for additional index structures Storing data in columns is functionally similar to having a built-in index for each column the column scanning speed of the in-memory column store and the compression mechanisms -especially dictionary compression -allow read operations with very high performance. In many cases, it is not required to have additional indexes Eliminating additional indexes reduces complexity and eliminates the effort of detining and maintaining metadata 3.1.2 Parallel Processing SAP HANA was designed to perform its basic calculations, such as analytic joins, scans and aggregations in parallel. Often it uses hundreds of cores at the same time, fully utilizing the available computing resources of distributed systems With columnar data, operations on single columns, such as searching or aggregations, can be implemented as ops over an array stored in contiguous memory locations. Such an operation has high spatial locality and can efficiently be executed in the CPU cache With row-oriented storage the same operation would be much slower because data of the same column is distributed across memory and the cpu is slowed down by cache misses Compressed data can be loaded into the CPU cache faster. This is because the limiting factor is the data transport between memory and CPU cache, and so the performance gain exceeds the additional computing time needed for decompression Column-based storage also allows execution of operations in parallel using multiple processor cores In a column store, data is already vertically partitioned. This means that operations on different columns can easily be processed in parallel. If multiple columns need to be searched or aggregated, each of these operations can be assigned to a different processor core. In addition, operations on one column can be parallelized by partitioning the column into multiple sections that can be processed by different processor cores Core 2 ColA ColB Cole 45451 761 63725 ee757 435 3632423 2341231 3434 Core 3 G256 943 342455 33331 121 l317777 4523523 56743 2423 6767312 342564 123123123 7986 4523523 81E9c9 Core 4 44711 TATE FFTa AP HANA Model ing Guide PUBLIC SAP HANA Architecture

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