In-Memory Data Management : An Inflection Point for Enterprise Applications
by Plattner, Hasso; Zeier, AlexanderFormat: Hardcover
Pub. Date: 2011-06-23
Publisher(s): Springer-Verlag New York Inc
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Summary
In the last fifty years the world has been completely transformed through the use of IT. We have now reached a new inflection point. Here we present, for the first time, how in-memory computing is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Analytical data resides in warehouses, synchronized periodically with transactional systems. This separation makes flexible, real-time reporting on current data impossible. Multi-core CPUs, large main memories, cloud computing and powerful mobile devices are serving as the foundation for the transition of enterprises away from this restrictive model. We describe techniques that allow analytical and transactional processing at the speed of thought and enable new ways of doing business. The book is intended for university students, IT-professionals and IT-managers, but also for senior management who wish to create new business processes by leveraging in-memory computing.
Table of Contents
| Foreword | p. XI |
| Preface | p. XIII |
| Introduction | p. 1 |
| An Inflection Point for Enterprise Applications | p. 5 |
| Desirability, Feasibility, Viability - The Impact of In-Memory | p. 7 |
| Information in Real Time - Anything, Anytime, Anywhere | p. 7 |
| Response Time at the Speed of Thought | p. 9 |
| Real-Time Analytics and Computation on the Fly | p. 10 |
| The Impact of Recent Hardware Trends | p. 11 |
| Database Management Systems for Enterprise Applications | p. 11 |
| Main Memory Is the New Disk | p. 14 |
| From Maximizing CPU Speed to Multi-Core Processors | p. 15 |
| Increased Bandwidth between CPU and Main Memory | p. 17 |
| Reducing Cost through In-Memory Data Management | p. 20 |
| Total Cost of Ownership | p. 20 |
| Cost Factors in Enterprise Systems | p. 21 |
| In-Memory Performance Boosts Cost Reduction | p. 22 |
| Conclusion | p. 23 |
| Why Are Enterprise Applications So Diverse? | p. 25 |
| Current Enterprise Applications | p. 25 |
| Examples of Enterprise Applications | p. 27 |
| Enterprise Application Architecture | p. 29 |
| Data Processing in Enterprise Applications | p. 30 |
| Data Access Patterns in Enterprise Applications | p. 31 |
| Conclusion | p. 31 |
| SanssouciDB - Blueprint for an In-Memory Enterprise Database System | p. 33 |
| Targeting Multi-Core and Main Memory | p. 34 |
| Designing an In-Memory Database System | p. 36 |
| Organizing and Accessing Data in Main Memory | p. 37 |
| Conclusion | p. 40 |
| SanssouciDB - A Single Source of Truth through In-Memory | p. 41 |
| The Technical Foundations of SanssouciDB | p. 43 |
| Understanding Memory Hierarchies | p. 43 |
| Introduction to Main Memory | p. 44 |
| Organization of the Memory Hierarchy | p. 47 |
| Trends in Memory Hierarchies | p. 49 |
| Memory from a Programmer's Point of View | p. 50 |
| Parallel Data Processing Using Multi-Core and Across Servers | p. 57 |
| Increasing Capacity by Adding Resources | p. 57 |
| Parallel System Architectures | p. 59 |
| Parallelization in Databases for Enterprise Applications | p. 61 |
| Parallel Data Processing in SanssouciDB | p. 64 |
| Compression for Speed and Memory Consumption | p. 68 |
| Light-Weight Compression | p. 69 |
| Heavy-Weight Compression | p. 73 |
| Data-Dependent Optimization | p. 73 |
| Compression-Aware Query Execution | p. 73 |
| Compression Analysis on Real Data | p. 74 |
| Column, Row, Hybrid - Optimizing the Data Layout | p. 75 |
| Vertical Partitioning | p. 75 |
| Finding the Best Layout | p. 78 |
| Challenges for Hybrid Databases | p. 81 |
| The Impact of Virtualization | p. 81 |
| Virtualizing Analytical Workloads | p. 82 |
| Data Model and Benchmarking Environment | p. 82 |
| Virtual versus Native Execution | p. 83 |
| Response Time Degradation with Concurrent VMs | p. 84 |
| Conclusion | p. 86 |
| Organizing and Accessing Data in SanssouciDB | p. 89 |
| SQL for Accessing In-Memory Data | p. 90 |
| The Role of SQL | p. 90 |
| The Lifecycle of a Query | p. 91 |
| Stored Procedures | p. 91 |
| Data Organization and Indices | p. 91 |
| Increasing Performance with Data Aging | p. 92 |
| Active and Passive Data | p. 93 |
| Implementation Considerations for an Aging Process | p. 95 |
| The Use Case for Horizontal Partitioning of Leads | p. 95 |
| Efficient Retrieval of Business Objects | p. 98 |
| Retrieving Business Data from a Database | p. 98 |
| Object Data Guide | p. 99 |
| Handling Data Changes in Read-Optimized Databases | p. 100 |
| The Impact on SanssouciDB | p. 101 |
| The Merge Process | p. 103 |
| Improving Performance with Single Column Merge | p. 107 |
| Append, Never Delete, to Keep the History Complete | p. 109 |
| Insert-Only Implementation Strategies | p. 110 |
| Minimizing Locking through Insert-Only | p. 111 |
| The Impact on Enterprise Applications | p. 114 |
| Feasibility of the Insert-Only Approach | p. 117 |
| Enabling Analytics on Transactional Data | p. 118 |
| Aggregation on the Fly | p. 119 |
| Analytical Queries without a Star Schema | p. 128 |
| Extending Data Layout without Downtime | p. 135 |
| Reorganization in a Row Store | p. 135 |
| On-The-Fly Addition in a Column Store | p. 136 |
| Business Resilience through Advanced Logging Techniques | p. 137 |
| Recovery in Column Stores | p. 138 |
| Differential Logging for Row-Oriented Databases | p. 140 |
| Providing High Availability | p. 141 |
| The Importance of Optimal Scheduling for Mixed Workloads | p. 142 |
| Introduction to Scheduling | p. 142 |
| Characteristics of a Mixed Workload | p. 145 |
| Scheduling Short and Long Running Tasks | p. 146 |
| Conclusion | p. 148 |
| How In-Memory Changes the Game | p. 151 |
| Application Development | p. 153 |
| Optimizing Application Development for SanssouciDB | p. 153 |
| Application Architecture | p. 154 |
| Moving Business Logic into the Database | p. 155 |
| Best Practices | p. 157 |
| Innovative Enterprise Applications | p. 158 |
| New Analytical Applications | p. 158 |
| Operational Processing to Simplify Daily Business | p. 162 |
| Information at Your Fingertips with Innovative User-Interfaces | p. 164 |
| Conclusion | p. 169 |
| Finally, a Real Business Intelligence System Is at Hand | p. 171 |
| Analytics on Operational Data | p. 171 |
| Yesterday's Business Intelligence | p. 171 |
| Today's Business Intelligence | p. 174 |
| Drawbacks of Separating Analytics from Daily Operations | p. 176 |
| Dedicated Database Designs for Analytical Systems | p. 178 |
| Analytics and Query Languages | p. 180 |
| Enablers for Changing Business Intelligence | p. 182 |
| Tomorrow's Business Intelligence | p. 183 |
| How to Evaluate Databases after the Game Has Changed | p. 185 |
| Benchmarks in Enterprise Computing | p. 185 |
| Changed Benchmark Requirements for a Mixed Workload | p. 187 |
| A New Benchmark for Daily Operations and Analytics | p. 188 |
| Conclusion | p. 192 |
| Scaling SanssouciDB in the Cloud | p. 193 |
| What Is Cloud Computing? | p. 194 |
| Types of Cloud Applications | p. 195 |
| Cloud Computing from the Provider Perspective | p. 197 |
| Multi-Tenancy | p. 197 |
| Low-End versus High-End Hardware | p. 201 |
| Replication | p. 201 |
| Energy Efficiency by Employing In-Memory Technology | p. 202 |
| Conclusion | p. 204 |
| The In-Memory Revolution Has Begun | p. 205 |
| Risk-Free Transition to In-Memory Data Management | p. 205 |
| Operating In-Memory and Traditional Systems Side by Side | p. 206 |
| System Consolidation and Extensibility | p. 207 |
| Customer Proof Points | p. 208 |
| Conclusion | p. 209 |
| References | p. 211 |
| About the Authors | p. 221 |
| Glossary | p. 223 |
| Abbreviations | p. 231 |
| Index | p. 233 |
| Table of Contents provided by Ingram. All Rights Reserved. |
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