Verfügbarkeit: In-memory data management | Technische Universität München

In-memory data management: an inflection point for enterprise applications

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Bibliographische Detailangaben
Beteiligte Personen:	Plattner, Hasso 1944- (VerfasserIn), Zeier, Alexander 1969- (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Berlin [u.a.] Springer 2011
Schlagwörter:	In-Memory-Datenbank Data-Warehouse-Konzept Datenmanagement Betriebliches Informationssystem Data Mining Business Intelligence
Links:	http://deposit.dnb.de/cgi-bin/dokserv?id=3658624&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022467436&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	XVIII, 236 S. graph. Darst. 235 mm x 155 mm
ISBN:	9783642193620

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Datensatz im Suchindex

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adam_text	IMAGE 1 CONTENTS FOREWORD XI BY PROF. JOHN L. HENNESSY (STANFORD UNIVERSITY, CALIFORNIA, USA) AND PROF. DAVID A. PATTERSON (UNIVERSITY OF CALIFORNIA AT BERKELEY, USA) PREFACE XIII INTRODUCTION 1 PART I - AN INFLECTION POINT FOR ENTERPRISE APPLICATIONS 5 1 DESIRABILITY, FEASIBILITY, VIABILITY-THE IMPACT OF IN-MEMORY 7 1.1 INFORMATION IN REAL TIME - ANYTHING, ANYTIME, ANYWHERE 7 1.1.1 RESPONSE TIME AT THE SPEED OF THOUGHT 9 1.1.2 REAL-TIME ANALYTICS AND COMPUTATION ON THE FLY 10 1.2 THE IMPACT OF RECENT HARDWARE TRENDS 11 1.2.1 DATABASE MANAGEMENT SYSTEMS FOR ENTERPRISE APPLICATIONS... 11 1.2.2 MAIN MEMORY IS THE NEW DISK 14 1.2.3 FROM MAXIMIZING CPU SPEED TO MULTI-CORE PROCESSORS 15 1.2.4 INCREASED BANDWIDTH BETWEEN CPU AND MAIN MEMORY 17 1.3 REDUCING COST THROUGH IN-MEMORY DATA MANAGEMENT 20 1.3.1 TOTAL COST OF OWNERSHIP 20 1.3.2 COST FACTORS IN ENTERPRISE SYSTEMS 21 1.3.3 IN-MEMORY PERFORMANCE BOOSTS COST REDUCTION 22 1.4 CONCLUSION 23 2 WHY ARE ENTERPRISE APPLICATIONS SO DIVERSE? 25 2.1 CURRENT ENTERPRISE APPLICATIONS 25 2.2 EXAMPLES OF ENTERPRISE APPLICATIONS 27 2.3 ENTERPRISE APPLICATION ARCHITECTURE 29 2.4 DATA PROCESSING IN ENTERPRISE APPLICATIONS 30 2.5 DATA ACCESS PATTERNS IN ENTERPRISE APPLICATIONS 31 2.6 CONCLUSION 31 BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1009744283 DIGITALISIERT DURCH IMAGE 2 CONTENTS 3 SANSSOUCIDB - BLUEPRINT FOR AN IN-MEMORY ENTERPRISE DATABASE SYSTEM 33 3.1 TARGETING MULTI-CORE AND MAIN MEMORY 34 3.2 DESIGNING AN IN-MEMORY DATABASE SYSTEM 36 3.3 ORGANIZING AND ACCESSING DATA IN MAIN MEMORY 37 3.4 CONCLUSION 40 PART II - SANSSOUCIDB -A SINGLE SOURCE OF TRUTH THROUGH IN-MEMORY. 41 4 THE TECHNICAL FOUNDATIONS OF SANSSOUCIDB 43 4.1 UNDERSTANDING MEMORY HIERARCHIES 43 4.1.1 INTRODUCTION TO MAIN MEMORY 44 4.1.2 ORGANIZATION OF THE MEMORY HIERARCHY 47 4.1.3 TRENDS IN MEMORY HIERARCHIES 49 4.1.4 MEMORY FROM A PROGRAMMER S POINT OF VIEW 50 4.2 PARALLEL DATA PROCESSING USING MULTI-CORE AND ACROSS SERVERS 57 4.2.1 INCREASING CAPACITY BY ADDING RESOURCES 57 4.2.2 PARALLEL SYSTEM ARCHITECTURES 59 4.2.3 PARALLELIZATION IN DATABASES FOR ENTERPRISE APPLICATIONS 61 4.2.4 PARALLEL DATA PROCESSING IN SANSSOUCIDB 64 4.3 COMPRESSION FOR SPEED AND MEMORY CONSUMPTION 68 4.3.1 LIGHT-WEIGHT COMPRESSION 69 4.3.2 HEAVY-WEIGHT COMPRESSION 73 4.3.3 DATA-DEPENDENT OPTIMIZATION 73 4.3.4 COMPRESSION-AWARE QUERY EXECUTION 73 4.3.5 COMPRESSION ANALYSIS ON REAL DATA 74 4.4 COLUMN, ROW, HYBRID - OPTIMIZING THE DATA LAYOUT 75 4.4.1 VERTICAL PARTITIONING 75 4.4.2 FINDING THE BEST LAYOUT 78 4.4.3 CHALLENGES FOR HYBRID DATABASES 81 4.5 THE IMPACT OF VIRTUALIZATION 81 4.5.1 VIRTUALIZING ANALYTICAL WORKLOADS 82 4.5.2 DATA MODEL AND BENCHMARKING ENVIRONMENT 82 4.5.3 VIRTUAL VERSUS NATIVE EXECUTION 83 4.5.4 RESPONSE TIME DEGRADATION WITH CONCURRENT VMS 84 4.6 CONCLUSION 86 5 ORGANIZING AND ACCESSING DATA IN SANSSOUCIDB 89 5.1 SQL FOR ACCESSING IN-MEMORY DATA 90 5.1.1 THEROLEOFSQL 90 5.1.2 THE LIFECYCLE OF A QUERY 91 5.1.3 STORED PROCEDURES 91 5.1.4 DATA ORGANIZATION AND INDICES 91 5.2 INCREASING PERFORMANCE WITH DATA AGING 92 5.2.1 ACTIVE AND PASSIVE DATA 93 IMAGE 3 CONTENTS XVII 5.2.2 IMPLEMENTATION CONSIDERATIONS FOR AN AGING PROCESS 95 5.2.3 THE USE CASE FOR HORIZONTAL PARTITIONING OF LEADS 95 5.3 EFFICIENT RETRIEVAL OF BUSINESS OBJECTS 98 5.3.1 RETRIEVING BUSINESS DATA FROM A DATABASE 98 5.3.2 OBJECT DATA GUIDE 99 5.4 HANDLING DATA CHANGES IN READ-OPTIMIZED DATABASES 100 5.4.1 THE IMPACT ON SANSSOUCIDB 101 5.4.2 THE MERGE PROCESS 103 5.4.3 IMPROVING PERFORMANCE WITH SINGLE COLUMN MERGE 107 5.5 APPEND, NEVER DELETE, TO KEEP THE HISTORY COMPLETE 109 5.5.1 INSERT-ONLY IMPLEMENTATION STRATEGIES 110 5.5.2 MINIMIZING LOCKING THROUGH INSERT-ONLY ILL 5.5.3 THE IMPACT ON ENTERPRISE APPLICATIONS 114 5.5.4 FEASIBILITY OF THE INSERT-ONLY APPROACH 117 5.6 ENABLING ANALYTICS ON TRANSACTIONAL DATA 118 5.6.1 AGGREGATION ON THE FLY 119 5.6.2 ANALYTICAL QUERIES WITHOUT A STAR SCHEMA 128 5.7 EXTENDING DATA LAYOUT WITHOUT DOWNTIME 135 5.7.1 REORGANIZATION IN A ROW STORE 135 5.7.2 ON-THE-FLY ADDITION IN A COLUMN STORE 136 5.8 BUSINESS RESILIENCE THROUGH ADVANCED LOGGING TECHNIQUES 137 5.8.1 RECOVERY IN COLUMN STORES 138 5.8.2 DIFFERENTIAL LOGGING FOR ROW-ORIENTED DATABASES 140 5.8.3 PROVIDING HIGH AVAILABILITY 141 5.9 THE IMPORTANCE OF OPTIMAL SCHEDULING FOR MIXED WORKLOADS 142 5.9.1 INTRODUCTION TO SCHEDULING 142 5.9.2 CHARACTERISTICS OF A MIXED WORKLOAD 145 5.9.3 SCHEDULING SHORT AND LONG RUNNING TASKS 146 5.10 CONCLUSION 148 PART III-HOW IN-MEMORY CHANGES THE GAME 151 6 APPLICATION DEVELOPMENT 153 6.1 OPTIMIZING APPLICATION DEVELOPMENT FOR SANSSOUCIDB 153 6.1.1 APPLICATION ARCHITECTURE 154 6.1.2 MOVING BUSINESS LOGIC INTO THE DATABASE 155 6.1.3 BEST PRACTICES 157 6.2 INNOVATIVE ENTERPRISE APPLICATIONS 158 6.2.1 NEW ANALYTICAL APPLICATIONS 158 6.2.2 OPERATIONAL PROCESSING TO SIMPLIFY DAILY BUSINESS 162 6.2.3 INFORMATION AT YOUR FINGERTIPS WITH INNOVATIVE USER-INTERFACES . 164 6.3 CONCLUSION 169 IMAGE 4 XVIII CONTENTS 7 FINALLY, A REAL BUSINESS INTELLIGENCE SYSTEM IS AT HAND 171 7.1 ANALYTICS ON OPERATIONAL DATA 171 7.1.1 YESTERDAY S BUSINESS INTELLIGENCE 171 7.1.2 TODAY S BUSINESS INTELLIGENCE 174 7.1.3 DRAWBACKS OF SEPARATING ANALYTICS FROM DAILY OPERATIONS . .. 176 7.1.4 DEDICATED DATABASE DESIGNS FOR ANALYTICAL SYSTEMS 178 7.1.5 ANALYTICS AND QUERY LANGUAGES 180 7.1.6 ENABLERS FOR CHANGING BUSINESS INTELLIGENCE 182 7.1.7 TOMORROW S BUSINESS INTELLIGENCE 183 7.2 HOW TO EVALUATE DATABASES AFTER THE GAME HAS CHANGED 185 7.2.1 BENCHMARKS IN ENTERPRISE COMPUTING 185 7.2.2 CHANGED BENCHMARK REQUIREMENTS FOR A MIXED WORKLOAD.... 187 7.2.3 A NEW BENCHMARK FOR DAILY OPERATIONS AND ANALYTICS 188 7.3 CONCLUSION 192 8 SCALING SANSSOUCIDB IN THE CLOUD 193 8.1 WHAT IS CLOUD COMPUTING? 194 8.2 TYPES OF CLOUD APPLICATIONS 195 8.3 CLOUD COMPUTING FROM THE PROVIDER PERSPECTIVE 197 8.3.1 MULTI-TENANCY 197 8.3.2 LOW-END VERSUS HIGH-END HARDWARE 201 8.3.3 REPLICATION 201 8.3.4 ENERGY EFFICIENCY BY EMPLOYING IN-MEMORY TECHNOLOGY . . .. 202 8.4 CONCLUSION 204 9 THE IN-MEMORY REVOLUTION HAS BEGUN 205 9.1 RISK-FREE TRANSITION TO IN-MEMORY DATA MANAGEMENT 205 9.1.1 OPERATING IN-MEMORY AND TRADITIONAL SYSTEMS SIDE BY SIDE .. 206 9.1.2 SYSTEM CONSOLIDATION AND EXTENSIBILITY 207 9.2 CUSTOMER PROOF POINTS 208 9.3 CONCLUSION 209 REFERENCES 211 ABOUT THE AUTHORS 221 GLOSSARY 223 ABBREVIATIONS 231 INDEX 233
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author	Plattner, Hasso 1944- Zeier, Alexander 1969-
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spellingShingle	Plattner, Hasso 1944- Zeier, Alexander 1969- In-memory data management an inflection point for enterprise applications In-Memory-Datenbank (DE-588)7744803-0 gnd Data-Warehouse-Konzept (DE-588)4406462-7 gnd Datenmanagement (DE-588)4213132-7 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd Data Mining (DE-588)4428654-5 gnd Business Intelligence (DE-588)4588307-5 gnd
subject_GND	(DE-588)7744803-0 (DE-588)4406462-7 (DE-588)4213132-7 (DE-588)4069386-7 (DE-588)4428654-5 (DE-588)4588307-5
title	In-memory data management an inflection point for enterprise applications
title_auth	In-memory data management an inflection point for enterprise applications
title_exact_search	In-memory data management an inflection point for enterprise applications
title_full	In-memory data management an inflection point for enterprise applications Hasso Plattner ; Alexander Zeier
title_fullStr	In-memory data management an inflection point for enterprise applications Hasso Plattner ; Alexander Zeier
title_full_unstemmed	In-memory data management an inflection point for enterprise applications Hasso Plattner ; Alexander Zeier
title_short	In-memory data management
title_sort	in memory data management an inflection point for enterprise applications
title_sub	an inflection point for enterprise applications
topic	In-Memory-Datenbank (DE-588)7744803-0 gnd Data-Warehouse-Konzept (DE-588)4406462-7 gnd Datenmanagement (DE-588)4213132-7 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd Data Mining (DE-588)4428654-5 gnd Business Intelligence (DE-588)4588307-5 gnd
topic_facet	In-Memory-Datenbank Data-Warehouse-Konzept Datenmanagement Betriebliches Informationssystem Data Mining Business Intelligence
url	http://deposit.dnb.de/cgi-bin/dokserv?id=3658624&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022467436&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT plattnerhasso inmemorydatamanagementaninflectionpointforenterpriseapplications AT zeieralexander inmemorydatamanagementaninflectionpointforenterpriseapplications

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Teilbibliothek Mathematik & Informatik

Bestandsangaben von Teilbibliothek Mathematik & Informatik
Signatur:	0102 WIR 523f 2012 A 6249 Lageplan
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