Tài liệu High-Performance Parallel Database Processing and Grid Databases- P1 pdf

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High-Performance
Parallel Database
Processing
and Grid Databases
David Taniar
Monash University, Australia
Clement H.C. Leung
Hong Kong Baptist University and Victoria University, Australia
Wenny Rahayu
La Trobe University, Australia
Sushant Goel
RMIT University, Australia
A John Wiley & Sons, Inc., Publication
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High-Performance
Parallel Database
Processing
and Grid Databases
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High-Performance
Parallel Database
Processing
and Grid Databases
David Taniar
Monash University, Australia
Clement H.C. Leung
Hong Kong Baptist University and Victoria University, Australia
Wenny Rahayu

La Trobe University, Australia
Sushant Goel
RMIT University, Australia
A John Wiley & Sons, Inc., Publication
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Copyright  2008 by John Wiley & Sons, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
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Library of Congress Cataloging-in-Publication Data:
Taniar, David.

High-performance parallel database processing and grid databases / by David
Taniar, Clement Leung, Wenny Rahayu.
p. cm.
Includes bibliographical references.
ISBN 978-0-470-10762-1 (cloth : alk. paper)
1. High performance computing. 2. Parallel processing (Electronic computers)
3. Computational grids (Computer systems) I. Leung, Clement H. C. II. Rahayu,
Johanna Wenny. III. Title.
QA76.88.T36 2008
004’ .35—dc22
2008011010
Printed in the United States of America
10987654321
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Contents
Preface xv
Part I Introduction
1. Introduction 3
1.1. A Brief Overview: Parallel Databases and Grid Databases 4
1.2. Parallel Query Processing: Motivations 5
1.3. Parallel Query Processing: Objectives 7
1.3.1. Speed Up 7
1.3.2. Scale Up 8
1.3.3. Parallel Obstacles 10
1.4. Forms of Parallelism 12
1.4.1. Interquery Parallelism 13
1.4.2. Intraquery Parallelism 14
1.4.3. Intraoperation Parallelism 15
1.4.4. Interoperation Parallelism 15
1.4.5. Mixed Parallelism—A More Practical Solution 18

1.5. Parallel Database Architectures 19
1.5.1. Shared-Memory and Shared-Disk Architectures 20
1.5.2. Shared-Nothing Architecture 22
1.5.3. Shared-Something Architecture 23
1.5.4. Interconnection Networks 24
1.6. Grid Database Architecture 26
1.7. Structure of this Book 29
1.8. Summary 30
1.9. Bibliographical Notes 30
1.10. Exercises 31
v
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vi CONTENTS
2. Analytical Models 33
2.1. Cost Models 33
2.2. Cost Notations 34
2.2.1. Data Parameters 34
2.2.2. Systems Parameters 36
2.2.3. Query Parameters 37
2.2.4. Time Unit Costs 37
2.2.5. Communication Costs 38
2.3. Skew Model 39
2.4. Basic Operations in Parallel Databases 43
2.4.1. Disk Operations 44
2.4.2. Main Memory Operations 45
2.4.3. Data Computation and Data Distribution 45
2.5. Summary 47
2.6. Bibliographical Notes 47
2.7. Exercises 47
Part II Basic Query Parallelism

3. Parallel Search 51
3.1. Search Queries 51
3.1.1. Exact-Match Search 52
3.1.2. Range Search Query 53
3.1.3. Multiattribute Search Query 54
3.2. Data Partitioning 54
3.2.1. Basic Data Partitioning 55
3.2.2. Complex Data Partitioning 60
3.3. Search Algorithms 69
3.3.1. Serial Search Algorithms 69
3.3.2. Parallel Search Algorithms 73
3.4. Summary 74
3.5. Bibliographical Notes 75
3.6. Exercises 75
4. Parallel Sort and GroupBy 77
4.1. Sorting, Duplicate Removal, and Aggregate Queries 78
4.1.1. Sorting and Duplicate Removal 78
4.1.2. Scalar Aggregate 79
4.1.3. GroupBy 80
4.2. Serial External Sorting Method 80
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CONTENTS vii
4.3. Algorithms for Parallel External Sort
83
4.3.1. Parallel Merge-All Sort 83
4.3.2. Parallel Binary-Merge Sort 85
4.3.3. Parallel Redistribution Binary-Merge Sort 86
4.3.4. Parallel Redistribution Merge-All Sort 88
4.3.5. Parallel Partitioned Sort 90
4.4. Parallel Algorithms for GroupBy Queries 92

4.4.1. Traditional Methods (Merge-All and Hierarchical
Merging) 92
4.4.2. Two-Phase Method 93
4.4.3. Redistribution Method 94
4.5. Cost Models for Parallel Sort 96
4.5.1. Cost Models for Serial External Merge-Sort 96
4.5.2. Cost Models for Parallel Merge-All Sort 98
4.5.3. Cost Models for Parallel Binary-Merge Sort 100
4.5.4. Cost Models for Parallel Redistribution Binary-Merge
Sort 101
4.5.5. Cost Models for Parallel Redistribution Merge-All Sort 102
4.5.6. Cost Models for Parallel Partitioned Sort 103
4.6. Cost Models for Parallel GroupBy 104
4.6.1. Cost Models for Parallel Two-Phase Method 104
4.6.2. Cost Models for Parallel Redistribution Method 107
4.7. Summary 109
4.8. Bibliographical Notes 110
4.9. Exercises 110
5. Parallel Join 112
5.1. Join Operations 112
5.2. Serial Join Algorithms 114
5.2.1. Nested-Loop Join Algorithm 114
5.2.2. Sort-Merge Join Algorithm 116
5.2.3. Hash-Based Join Algorithm 117
5.2.4. Comparison 120
5.3. Parallel Join Algorithms 120
5.3.1. Divide and Broadcast-Based Parallel Join Algorithms 121
5.3.2. Disjoint Partitioning-Based Parallel Join Algorithms 124
5.4. Cost Models 128
5.4.1. Cost Models for Divide and Broadcast 128

5.4.2. Cost Models for Disjoint Partitioning 129
5.4.3. Cost Models for Local Join 130
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viii CONTENTS
5.5. Parallel Join Optimization 132
5.5.1. Optimizing Main Memory 132
5.5.2. Load Balancing 133
5.6. Summary 134
5.7. Bibliographical Notes 135
5.8. Exercises 136
Part III Advanced Parallel Query Processing
6. Parallel GroupBy-Join 141
6.1. Groupby-Join Queries 141
6.1.1. Groupby Before Join 142
6.1.2. Groupby After Join 142
6.2. Parallel Algorithms for Groupby-Before-Join
Query Processing
143
6.2.1. Early Distribution Scheme 143
6.2.2. Early GroupBy with Partitioning Scheme 145
6.2.3. Early GroupBy with Replication Scheme 146
6.3. Parallel Algorithms for Groupby-After-Join
Query Processing
148
6.3.1. Join Partitioning Scheme 148
6.3.2. GroupBy Partitioning Scheme 150
6.4. Cost Model Notations 151
6.5. Cost Model for Groupby-Before-Join Query Processing 153
6.5.1. Cost Models for the Early Distribution Scheme 153
6.5.2. Cost Models for the Early GroupBy with Partitioning

Scheme 156
6.5.3. Cost Models for the Early GroupBy with Replication
Scheme 158
6.6. Cost Model for “Groupby-After-Join” Query Processing 159
6.6.1. Cost Models for the Join Partitioning Scheme 159
6.6.2. Cost Models for the GroupBy Partitioning Scheme 161
6.7. Summary 163
6.8. Bibliographical Notes 164
6.9. Exercises 164
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CONTENTS ix
7. Parallel Indexing
167
7.1. Parallel Indexing–an Internal Perspective on Parallel Indexing
Structures
168
7.2. Parallel Indexing Structures 169
7.2.1. Nonreplicated Indexing (NRI) Structures 169
7.2.2. Partially Replicated Indexing (PRI) Structures 171
7.2.3. Fully Replicated Indexing (FRI) Structures 178
7.3. Index Maintenance 180
7.3.1. Maintaining a Parallel Nonreplicated Index 182
7.3.2. Maintaining a Parallel Partially Replicated Index 182
7.3.3. Maintaining a Parallel Fully Replicated Index 188
7.3.4. Complexity Degree of Index Maintenance 188
7.4. Index Storage Analysis 188
7.4.1. Storage Cost Models for Uniprocessors 189
7.4.2. Storage Cost Models for Parallel Processors 191
7.5. Parallel Processing of Search Queries using Index 192
7.5.1. Parallel One-Index Search Query Processing 192

7.5.2. Parallel Multi-Index Search Query Processing 195
7.6. Parallel Index Join Algorithms 200
7.6.1. Parallel One-Index Join 200
7.6.2. Parallel Two-Index Join 203
7.7. Comparative Analysis 207
7.7.1. Comparative Analysis of Parallel Search Index 207
7.7.2. Comparative Analysis of Parallel Index Join 213
7.8. Summary 216
7.9. Bibliographical Notes 217
7.10. Exercises 217
8. Parallel Universal Qualification—Collection Join Queries 219
8.1. Universal Quantification and Collection Join 220
8.2. Collection Types and Collection Join Queries 222
8.2.1. Collection-Equi Join Queries 222
8.2.2. Collection–Intersect Join Queries 223
8.2.3. Subcollection Join Queries 224
8.3. Parallel Algorithms for Collection Join Queries 225
8.4. Parallel Collection-Equi Join Algorithms 225
8.4.1. Disjoint Data Partitioning 226
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x CONTENTS
8.4.2. Parallel Double Sort-Merge Collection-Equi
Join Algorithm 227
8.4.3. Parallel Sort-Hash Collection-Equi Join Algorithm 228
8.4.4. Parallel Hash Collection-Equi Join Algorithm 232
8.5. Parallel Collection-Intersect Join Algorithms 233
8.5.1. Non-Disjoint Data Partitioning 234
8.5.2. Parallel Sort-Merge Nested-Loop Collection-Intersect Join
Algorithm 244
8.5.3. Parallel Sort-Hash Collection-Intersect Join Algorithm 245

8.5.4. Parallel Hash Collection-Intersect Join Algorithm 246
8.6. Parallel Subcollection Join Algorithms 246
8.6.1. Data Partitioning 247
8.6.2. Parallel Sort-Merge Nested-Loop Subcollection Join
Algorithm 248
8.6.3. Parallel Sort-Hash Subcollection Join Algorithm 249
8.6.4. Parallel Hash Subcollection Join Algorithm 251
8.7. Summary 252
8.8. Bibliographical Notes 252
8.9. Exercises 254
9. Parallel Query Scheduling and Optimization 256
9.1. Query Execution Plan 257
9.2. Subqueries Execution Scheduling Strategies 259
9.2.1. Serial Execution Among Subqueries 259
9.2.2. Parallel Execution Among Subqueries 261
9.3. Serial vs. Parallel Execution Scheduling 264
9.3.1. Nonskewed Subqueries 264
9.3.2. Skewed Subqueries 265
9.3.3. Skewed and Nonskewed Subqueries 267
9.4. Scheduling Rules 269
9.5. Cluster Query Processing Model 270
9.5.1. Overview of Dynamic Query Processing 271
9.5.2. A Cluster Query Processing Architecture 272
9.5.3. Load Information Exchange 273
9.6. Dynamic Cluster Query Optimization 275
9.6.1. Correction 276
9.6.2. Migration 280
9.6.3. Partition 281
9.7. Other Approaches to Dynamic Query Optimization 284
9.8. Summary 285

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CONTENTS xi
9.9. Bibliographical Notes
286
9.10. Exercises 286
Part IV Grid Databases
10. Transactions in Distributed and Grid Databases 291
10.1. Grid Database Challenges 292
10.2. Distributed Database Systems and Multidatabase Systems 293
10.2.1. Distributed Database Systems 293
10.2.2. Multidatabase Systems 297
10.3. Basic Definitions on Transaction Management 299
10.4. Acid Properties of Transactions 301
10.5. Transaction Management in Various Database Systems 303
10.5.1. Transaction Management in Centralized and Homogeneous
Distributed Database Systems 303
10.5.2. Transaction Management in Heterogeneous Distributed Database
Systems 305
10.6. Requirements in Grid Database Systems 307
10.7. Concurrency Control Protocols 309
10.8. Atomic Commit Protocols 310
10.8.1. Homogeneous Distributed Database Systems 310
10.8.2. Heterogeneous Distributed Database Systems 313
10.9. Replica Synchronization Protocols 314
10.9.1. Network Partitioning 315
10.9.2. Replica Synchronization Protocols 316
10.10. Summary 318
10.11. Bibliographical Notes 318
10.12. Exercises 319
11. Grid Concurrency Control 321

11.1. A Grid Database Environment 321
11.2. An Example 322
11.3. Grid Concurrency Control 324
11.3.1. Basic Functions Required by GCC 324
11.3.2. Grid Serializability Theorem 325
11.3.3. Grid Concurrency Control Protocol 329
11.3.4. Revisiting the Earlier Example 333
11.3.5. Comparison with Traditional Concurrency Control
Protocols 334
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xii CONTENTS
11.4. Correctness of GCC Protocol 336
11.5. Features of GCC Protocol 338
11.6. Summary 339
11.7. Bibliographical Notes 339
11.8. Exercises 339
12. Grid Transaction Atomicity and Durability 341
12.1. Motivation 342
12.2. Grid Atomic Commit Protocol (Grid-ACP) 343
12.2.1. State Diagram of Grid-ACP 343
12.2.2. Grid-ACP Algorithm 344
12.2.3. Early-Abort Grid-ACP 346
12.2.4. Discussion 348
12.2.5. Message and Time Complexity Comparison Analysis 349
12.2.6. Correctness of Grid-ACP 350
12.3. Handling Failure of Sites with Grid-ACP 351
12.3.1. Model for Storing Log Files at the Originator and
Participating Sites 351
12.3.2. Logs Required at the Originator Site 352
12.3.3. Logs Required at the Participant Site 353

12.3.4. Failure Recovery Algorithm for Grid-ACP 353
12.3.5. Comparison of Recovery Protocols 359
12.3.6. Correctness of Recovery Algorithm 361
12.4. Summary 365
12.5. Bibliographical Notes 366
12.6. Exercises 366
13. Replica Management in Grids 367
13.1. Motivation 367
13.2. Replica Architecture 368
13.2.1. High-Level Replica Management Architecture 368
13.2.2. Some Problems 369
13.3. Grid Replica Access Protocol (GRAP) 371
13.3.1. Read Transaction Operation for GRAP 371
13.3.2. Write Transaction Operation for GRAP 372
13.3.3. Revisiting the Example Problem 375
13.3.4. Correctness of GRAP 377
13.4. Handling Multiple Partitioning 378
13.4.1. Contingency GRAP 378
13.4.2. Comparison of Replica Management Protocols 381
13.4.3. Correctness of Contingency GRAP 383
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CONTENTS xiii
13.5. Summary
384
13.6. Bibliographical Notes 385
13.7. Exercises 385
14. Grid Atomic Commitment in Replicated Data 387
14.1. Motivation 388
14.1.1. Architectural Reasons 388
14.1.2. Motivating Example 388

14.2. Modified Grid Atomic Commitment Protocol 390
14.2.1. Modified Grid-ACP 390
14.2.2. Correctness of Modified Grid-ACP 393
14.3. Transaction Properties in Replicated Environment 395
14.4. Summary 397
14.5. Bibliographical Notes 397
14.6. Exercises 398
Part V Other Data-Intensive Applications
15. Parallel Online Analytic Processing (OLAP) and Business
Intelligence
401
15.1. Parallel Multidimensional Analysis 402
15.2. Parallelization of ROLLUP Queries 405
15.2.1. Analysis of Basic Single ROLLUP Queries 405
15.2.2. Analysis of Multiple ROLLUP Queries 409
15.2.3. Analysis of Partial ROLLUP Queries 411
15.2.4. Parallelization Without Using ROLLUP 412
15.3. Parallelization of CUBE Queries 412
15.3.1. Analysis of Basic CUBE Queries 413
15.3.2. Analysis of Partial CUBE Queries 416
15.3.3. Parallelization Without Using CUBE 417
15.4. Parallelization of Top-N and Ranking Queries 418
15.5. Parallelization of Cume Dist Queries 419
15.6. Parallelization of NTILE and Histogram Queries 420
15.7. Parallelization of Moving Average and Windowing Queries 422
15.8. Summary 424
15.9. Bibliographical Notes 424
15.10. Exercises 425
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xiv CONTENTS

16. Parallel Data Mining—Association Rules and Sequential Patterns 427
16.1. From Databases To Data Warehousing To Data Mining:
A Journey
428
16.2. Data Mining: A Brief Overview 431
16.2.1. Data Mining Tasks 431
16.2.2. Querying vs. Mining 433
16.2.3. Parallelism in Data Mining 436
16.3. Parallel Association Rules 440
16.3.1. Association Rules: Concepts 441
16.3.2. Association Rules: Processes 444
16.3.3. Association Rules: Parallel Processing 448
16.4. Parallel Sequential Patterns 450
16.4.1. Sequential Patterns: Concepts 452
16.4.2. Sequential Patterns: Processes 456
16.4.3. Sequential Patterns: Parallel Processing 459
16.5. Summary 461
16.6. Bibliographical Notes 461
16.7. Exercises 462
17. Parallel Clustering and Classification 464
17.1. Clustering and Classification 464
17.1.1. Clustering 464
17.1.2. Classification 465
17.2. Parallel Clustering 467
17.2.1. Clustering: Concepts 467
17.2.2. k-Means Algorithm 468
17.2.3. Parallel k-Means Clustering 471
17.3. Parallel Classification 477
17.3.1. Decision Tree Classification: Structures 477
17.3.2. Decision Tree Classification: Processes 480

17.3.3. Decision Tree Classification: Parallel Processing 488
17.4. Summary 495
17.5. Bibliographical Notes 498
17.6. Exercises 498
Permissions 501
List of Conferences and Journals 507
Bibliography 511
Index 541
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Preface
The sizes of databases have seen exponential growth in the past, and such growth
is expected to accelerate in the future, with the steady drop in storage cost accom-
panied by a rapid increase in storage capacity. Many years ago, a terabyte database
was considered to be large, but nowadays they are sometimes regarded as small,
and the daily volumes of data being added to some databases are measured in
terabytes. In the future, petabyte and exabyte databases will be common.
With such volumes of data, it is evident that the sequential processing paradigm
will be unable to cope; for example, even assuming a data rate of 1 terabyte per
second, reading through a petabyte database will take over 10 days. To effectively
manage such volumes of data, it is necessary to allocate multiple resources to it,
very often massively so. The processing of databases of such astronomical propor-
tions requires an understanding of how high-performance systems and parallelism
work. Besides the massive volume of data in the database to be processed, some
data has been distributed across the globe in a Grid environment. These massive
data centers are also a part of the emergence of Cloud computing, where data
access has shifted from local machines to powerful servers hosting web appli-
cations and services, making data access across the Internet using standard web
browsers pervasive. This adds another dimension to such systems.
Parallelism in databases has been around since the early 1980s, when
many researchers in this area aspired to build large special-purpose database

machines—databases employing dedicated specialized parallel hardware.
Some projects were born, including Bubba, Gamma, etc. These came and
went. However, commercial DBMS vendors quickly realized the importance
of supporting high performance for large databases, and many of them have
incorporated parallelism and grid features into their products. Their commitment
to high-performance systems and parallelism, as well as grid configurations,
shows the importance and inevitability of parallelism.
In addition, while traditional transactional data is still common, we see
an increasing growth of new application domains, broadly categorized as
data-intensive applications. These include data warehousing and online analytic
processing (OLAP) applications, data mining, genome databases, and multiple
media databases manipulating unstructured and semistructured data. Therefore,
it is critical to understand the underlying principle of data parallelism, before
specialized and new application domains can be properly addressed.
xv
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xvi PREFACE
This book is written to provide a fundamental understanding of parallelism in
data-intensive applications. It features not only the algorithms for database opera-
tions but also quantitative analytical models, so that performance can be analyzed
and evaluated more effectively.
The present book brings into a single volume the latest techniques and principles
of parallel and grid database processing. It provides a much-needed, self-contained
advanced text for database courses at the postgraduate or final year undergraduate
levels. In addition, for researchers with a particular interest in parallel databases
and related areas, it will serve as an indispensable and up-to-date reference. Prac-
titioners contemplating building high-performance databases or seeking to gain a
good understanding of parallel database technology too will find this book valuable
for the wealth of techniques and models it contains.
STRUCTURE OF THE BOOK

This book is divided into five parts. Part I gives an introduction to the topic, includ-
ing the rationale behind the need for high-performance database processing, as well
as basic analytical models that will be used throughout the book.
Part II, consisting of three chapters, describes parallelism for basic query opera-
tions. These include parallel searching, parallel aggregate and sorting, and parallel
join. These are the foundation of query processing, whereby complex queries can
be decomposed into any of these atomic operations.
Part III, consisting of the next four chapters, focuses on more advanced query
operations. This part covers groupby-join operations, parallel indexing, parallel
object-oriented query processing, in particular, collection join, and query schedul-
ing and optimization.
Just as the previous two parts deal with parallelism of read-only queries, the next
part, Part IV, concentrates on transactions, also known as write queries. We use
the grid environment to study transaction management. In grid transaction man-
agement, the focus is mainly on grid concurrency control, atomic commitment,
durability, as well as replication.
Finally, Part V introduces other data-intensive applications, including data
warehousing, OLAP, business intelligence, and parallel data mining.
ACKNOWLEDGMENTS
The authors would like to thank the publisher, John Wiley & Sons, for agreeing
to embark on this exciting journey. In particular, we would like to thank Paul
Petralia, Senior Editor, for supporting this project. We would also like to thank
Whitney Lesch and Anastasia Wasko, Assistants to the Editor, for their endless
efforts to ensure that we remained on track from start to completion. Without their
encouragement and reminders, we would not have been able to finish this book.
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PREFACE xvii
We also thank Bruna Pomella, who proofread the entire manuscript, for com-
menting on ambiguous sentences and correcting grammatical mistakes.
Finally, we would like to express our sincere thanks to our respective univer-

sities, Monash University, Victoria University, Hong Kong Baptist University, La
Trobe University, and RMIT, where the research presented in this book was con-
ducted. We are grateful for the facilities and time that we received during the
writing of this book. Without these, the book would not have been written in the
first place.
David Taniar
Clement H.C. Leung
Wenny Rahayu
Sushant Goel
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Part I
Introduction
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Chapter 1
Introduction
Parallel databases are database systems that are implemented on parallel com-
puting platforms. Therefore, high-performance query processing focuses on query
processing, including database queries and transactions, that makes use of paral-
lelism techniques applied to an underlying parallel computing platform in order to
achieve high performance.
In a Grid environment, applications need to create, access, manage, and distribute
data on a very large scale and across multiple organizations. The main challenges
arise due to the volume of data, distribution of data, autonomy of sites, and hetero-
geneity of data resources. Hence, Grid databases can be defined loosely as being
data access in a Grid environment.
This chapter gives an introduction to parallel databases, parallel query processing,
and Grid databases. Section 1.1 gives a brief overview. In Section 1.2, the motivations
for using parallelism in database processing are explained. Understanding the moti-

vations is a critical starting point in exploring parallel database processing in depth.
This will answer the question of why parallelism is necessary in modern database
processing.
Once we understand the motivations, we need to know the objectives or the goals
of parallel database processing. These are explained in Section 1.3. The objectives
will become the main aim of any parallel algorithms in parallel database systems,
and this will answer the question of what it is that parallelism aims to achieve in
parallel database processing.
Once we understand the objectives, we also need to know the various kinds of par-
allelism forms that are available for parallel database processing. These are described
in Section 1.4. The forms of parallelism are the techniques used to achieve the objec-
tives described in the previous section. Therefore, this section answers the questions
of how parallelism can be performed in parallel database processing.
High-Performance Parallel Database Processing and Grid Databases,
by David Taniar, Clement Leung, Wenny Rahayu, and Sushant Goel
Copyright  2008 John Wiley & Sons, Inc.
3
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4 Chapter 1 Introduction
Without an understanding of the kinds of parallel technology and parallel
machines that are available for parallel database processing, our introductory
discussion on parallel databases will not be complete. Therefore, in Section 1.5, we
introduce various parallel architectures available for database processing.
Section 1.6 introduces Grid databases. This includes the basic Grid architecture
for data-intensive applications, and its current technological status is also outlined.
Section 1.7 outlines the components of this book, including parallel query pro-
cessing, and Grid transaction management.
1.1 A BRIEF OVERVIEW: PARALLEL DATABASES
AND GRID DATABASES
In 1965, Intel cofounder Gordon Moore predicted that the number of transistors

on a chip would double every 24 months, a prediction that became known pop-
ularly as Moore’s law. With further technological development, some researchers
claimed the number would double every 18 months instead of 24 months. Thus it
is expected that the CPU’s performance would increase roughly by 50–60% per
year. On the other hand, mechanical delays restrict the advancement of disk access
time or disk throughput, which reaches only 8–10%. There has been some debate
regarding the accuracy of these figures. Disk capacity is also increasing at a much
higher rate than that of disk throughput. Although researchers do not agree com-
pletely with these values, they show the difference in the rate of advancement of
each of these two areas.
In the above scenario, it becomes increasingly difficult to use the available disk
capacity effectively. Disk input/output (I/O) becomes the bottleneck as a result
of such skewed processing speed and disk throughput. This inevitable I/O bottle-
neck was one of the major forces that motivated parallel database research. The
necessity of storing high volumes of data, producing faster response times, scal-
ability, reliability, load balancing, and data availability were among the factors
that led to the development of parallel database systems research. Nowadays, most
commercial database management systems (DBMS) vendors include some parallel
processing capabilities in their products.
Typically, a parallel database system assumes only a single administrative
domain, a homogeneous working environment, and close proximity of data
storage (i.e., data is stored in different machines in the same room or building).
Below in this chapter, we will discuss various forms of parallelism, motivations,
and architectures.
With the increasing diversity of scientific disciplines, the amount of data col-
lected is increasing. In domains as diverse as global climate change, high-energy
physics, and computational genomics, the volume of data being measured and
stored is already scaling terabytes and will soon increase to petabytes. Data can
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