Three well-known computer scientists at Stanford University-Hector Garcia-Molina, Jeffrey D. Ullman, and Jennifer Widom-have written one of the most comprehensive books on database system implementation. Hector Garcia-Molina pioneered Database System Implementation at Stanford as a second database systems course for computer science majors and industry-based professionals. It focuses on the implementation of database systems, including storage structures, query processing, and transaction management. This book is valuable as an academic textbook or a professional reference. This text covers a broad spectrum of knowledge and technology. This carefully class-tested, highly readable presentation provides students or professionals with the next level of study. Written from the point of view of the database designer, user, and application programmer, this book provides practical advice from well-known experts on how to implement state-of-the-art database systems.

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1 Introduction to DBMS Implementation

1.1 Introducing: The Megatron 2000 Database System
1.1.1 Megatron 2000 Implementation Details
1.1.2 How Megatron 2000 Executes Queries
1.1.3 What's Wrong With Megatron 2000?
1.2 Overview of a Database Management System
1.2.1 DataDefinition Language Commands
1.2.2 Overview of Query Processing
1.2.3 Main--Memory Buffers and the Buffer Manager
1.2.4 Thansaction Processing
1.2.5 The Query Processor
1.3 Outline of This Book
1.3.1 Prerequisites
1.3.2 Storage-- M anagement Overview
1.3.3 Query-Proce8sing Overview
1.3.4 Thansaction- P rocessing Overview
1.3.5 Information Integration Overview
1.4 Review of Database Models and Languages
1.4.1 Relational Model Review
1.4.2 SQL Review
1.4.3 Re1ational and Object-Oriented Data
1.5 Summary of Chapter 1
1.6 References for Chapter 1

2 Data Storage

2.1 The Memory Hierarchy
2.1.1 Cache
2.1.2 Main Memory
2.1.3 Virtual Memory
2.1.4 Secondary Storage
2.1.5 Tertiary Storage
2.1.6 Volatile and Nonvolatile Storage
2.1.7 Exercises for Section 2.1
2.2 Disks
2.2.1 Mechanics Of Disks
2.2.2 The Disk Controller
2.2.3 Disk Storage Characteristics
2.2.4 Disk Access Characteristics
2.2.5 Writing Blocks
2.2.6 Modifying Blocks
2.2.7 Exercises for Section 2.2
2.3 Using Secondary Storage Effectively
2.3.1 The I/O Model of Computation
2.3.2 Sorting Data in SecondaJry Storage
2.3.3 Merge-Sort
2.3.4 Two-Phase, Multiway Merge--Sort
2.3.5 Extension of Multiway Merging to Larger Relatbos
2.3.6 Exercises for Section 2.3
2.4 Improving the Access Time of Secondary Storage
2.4.1 Organizing Data by Cylinders
2.4.2 Using Multiple Disks
2.4.3 Mirroring Disks
2.4.4 Disk Scheduling and the Elevator Algorithm
2.4.5 Prefetching and Large-Scale Buffering
2.4.6 SummaJry.of Strategies and nadeoffe
2.4.7 Exercises fOr Section 2.4
2.5 Disk Failures
2.5.1 1ntermittent Falures
2.5.2 Checksums
2.5.3 Stable Storage
2.5.4 Error-Handling Capabilities of Stable Storage
2.5.5 Exercises for Section 2.5
2.6 Recovery from Disk Crashes
2.6.1 The Failure Model for Disks
2.6.2 Mirroring as a Redundancy Technique
2.6.3 Paxity Blocks
2.6.4 An Improvment: RAID 5
2.6.5 Coping With Multiple Disk Cfashes
2.6.6 Exercises for Section 2.6
2.7 Summary.of ChaPter 2
2.8 References for ChaPter 2

3 Representing Datu Elements

3.1 Data Elements and Fields
3.1.1 Representing Relational Database Elements
3.1.2 Representing Objects
3.1.3 Representing Data Elements
3.2 Records
3.2.1 Building Fixed-Length Records
3.2.2 Record Headers
3.2.3 Packing Fixed-Length Records into Blocks
3.2.4 Exercises for Section 3.2
3.3 Represention Block and Record Addresses
3.3.1 Client--Server Systems
3.3.2 LogicaJ and Structured Addresses.
3.3.3 Pointer Swizzling
3.3.4 Returning Blocks to Disk
3.3.5 Pinned Records and Blocks
3.3.6 Exercises for Section 3.3
3.4 Variable-Length Data and Records
3.4.1 Records With Variable-Length Fields
3.4.2 Records With Repeating Fields
3.4.3 Variable-Format Records
3.4.4 Records That Do Not Fit in a Block
3.4.5 BLOBS
3.4.6 Exercises for Section 3.4
3.5 Record Modifications
3.5.1 Insertion
3.5.2 Deletion
3.5.3 Update
3.5.4 Exercises for Section 3.5
3.6 Summary of Chapter 3
3.7 References for Chapter 3

4 Index Structure8

4.1 Indexes on Sequential Files
4.1.1 Sequential Files
4.1.2 Dense Indexes
4.1.3 Sparse Indexes
4.1.4 Multiple Levels of Index
4.1.5 Indexes With Duplicate Search Keys
4.1.6 Managing Indexes During Data Modifications
4.1.7 Exercises fOr Section 4.1
4.2 Secondary Indexes
4.2.1 Design of Secondary Indexes
4.2.2 Applications of Secondary Indexes
4.2.3 Indirection in Secondaxy Indexes
4.2.4 Document Retrieval and Inverted Indexes
4.2.5 Exercises fOr Section 4.2
4.3 B-nees
4.3.1 The Structure of B--trees
4.3.2 Applications of B-trees
4.3.3 Lookup in B-Trees
4.3.4 Range Queries
4.3.5 Insertion Into B-nees
4.3.6 Deletion nom B-nees
4.3.7 Efficiency of B-Trees
4.3.8 Exercises fOr Section 4.3
4.4 Hash Tables
4.4.1 Secondary-Storage Hash Tables
4.4.2 Insertion Into a Hash Table
4.4.3 Hash-Table Deletion
4.4.4 Efficiency of Hash Table Indexes
4.4.5 Extensible Hash Tables
4.4.6 Insertion Into Extensible Hash Tables
4.4.7 Linear Hash Tables
4.4.8 Insertion 1nto Linear Hash Tables
4.4.9 Exercises fOr Section 4.4
4.5 Summary Of Chapter 4
4.6 References for Chapter 4

5 Multidimensional Indexes

5.1 Applications Needing Multiple Dimensions
5.1.1 GWaPhic Information System8
5.1.2 Data Cubes
5.1.3 Multidimensional Queries in SQL
5.1.4 Executing Range Queries Using Conventional 1ndexes
5.1.5 Executing Nearest--Neighbor Queries Using ConventionalIndexes
5.1.6 Other Limitations of Conventional Indexes
5.1.7 Overview of Multidimensional Index Strllctures
5.1.8 Exercises for Section 5.1
5.2 Hash-Like Structures for Multidimensional Data
5.2.1 Grid Files
5.2.2 Lookup in a Grid File
5.2.3 Insertion Into Grid Files
5.2.4 Performance Of Grid Files
5.2.5 Patitioned Hash minctions
5.2.6 Comparison of Grid Files and Partitioned Hashing
5.2.7 Exercises for Section 5.2
5.3 Thee-Like Structures fOr Multidimensional Data
5.3.1 Multiple-Key Indexes
5.3.2 Performance of MultiplesKey Indexes
5.3.3 kdnees
5.3.4 Operations on kdnees
5.3.5 AdaPting kdThees to Secondary Storage
5.3.6 Quad Thees
5.3.7 RTrees
5.3.8 Operations on Rtrees
5.3.9 Exercises for Section 5.3
5.4 Bitmap Indexes
5.4.1 Motivation for Bitmap Indexes
5.4.2 Compressed BitmaPS
5.4.3 Operating.on Run-Lengt h- Encoded Bit- Vectors
5.4.4 Managing BitmaP Indexes
5.4.5 Exercises for Section 5.4
5.5 Summary of Chapter 5
5.6 References for Chapter 5

6 Query Execution

6.1 An Algebra for Queries
6.1.1 Union, Intersection, and Difference
6.1.2 The Selection Operator
6.1.3 The Projection Operator
6.1.4 The Product of Relations
6.1.5 Joins
6.1.6 Duplicate Elimination
6.1.7 Grouping and Aggregaion
6.1.8 The Sorting Operator
6.1.9 Expression nees
6.1.10 Exercises for Section 6.1
6.2 Introduction to Physical-Query-Plan Operators
6.2.1 Scanning Tables
6.2.2 Sorting While Scanning Tables
6.2.3 The Model of Computation for Physical Operators
6.2.4 Parameters for Measuring Costs
6.2.5 I/O Cost for Scan Operators
6.2.6 Iterators for Implementation of Physical Operators
6.3 One-Pass Algorithms for Database Operations
6.3.1 One--Pass Algorithms for TUplesat-aTime Operations
6.3.2 One-Pass Algorithms for Unary, FulLRelation Operai
6.3.3 One-Pass Algorithms for Binary Operations
6.3.4 Exercises for Section 6.3
6.4 Nested-Loop Joins
6.4.1 Tuple-Based Nested-Loop Join
6.4.2 An Iterator for Thple--Based Nested--Loop Join
6.4.3 A Block-Based Nested--Loop Join Algorithm
6.4.4 Analysis of Nested-Loop Join
6.4.5 Summary of AlgOrithms so Far
6.4.6 Exercises fOr Section 6.4
6.5 TwcaPass Algorithms Based on Sorting
6.5.1 Duplicate Elimination Using Sorting
6.5.2 Grouping and Aggregation Using Sorting
6.5.3 A Sort-Based Union Algorithm
6.5.4 Sort-Based Algorithms for Intersection and Difference
6.5.5 A Simple Sort--Based Join Algorithm
6.5.6 Analysis of Simple Sort-Join
6.5.7 A More Efficient Sort-Based Join
6.5.8 Summary Of Sort-Based Algorithms
6.5.9 Exercises for Section 6.5
6.6 Two-Pass AlgOrithms Based on Hashing
6.6.1 Partitioning Relations by Hashing
6.6.2 A Hash-Based Algorithm for Duplicate Elimination
6.6.3 A Hash--Based Algorithm for Grouping and Aggrgation
6.6.4 Hash-Based Algorithms for Union, Intersection, and Dif ference
6.6.5 The Hash-Join Algorithm
6.6.6 Saving Some Disk I/O's
6.6.7 Summary of Hash-Based Algorithms
6.6.8 Exercises for Section 6.6
6.7 Index-Based Algorithms
6.7.1 Clustering and Nonclustering Indexes
6.7.2 Index--Based Selection
6.7.3 Joining by Using an Index
6.7.4 Joins Using a Sorted Index
6.7.5 Exercises for Section 6.7
6.8 Buffer Management
6.8.1 Buffer Management Architecture
6.8.2 Buffer Manapement Strategies
6.8.3 The Relationship Between Physical Operator Selection and Buffer Management
6.8.4 Exercises for Section 6.8
6.9 Algorithms Using More Than Two Passes
6.9.1 Multipass Sort-Based Algorithms
6.9.2 Performance of Multipass, Sort--Based Algorithms
6.9.3 Multipass Hash-Based Algorithms
6.9.4 Performance of Multipass Hash-Based Algorithms
6.9.5 Exercises fOr Section 6.9
6.10 PaxaJlel Algorithms fOr Relational Operations.
6.10.1 Models of Paxallelism
6.10.2 Tuple-at-aTime Operations in Parallel
6.10.3 Parallel Algorithms for Full--Relation Operations
6.10.4 Performance of Parallel Algorithms
6.10.5 Exercises for Section 6.10
6.11 SummaJry of Chapter 6
6.12 References for ChaPter 6

7 The Query Compiler

7.1 Parsing
7.1.1 Syntax Analysis and Parse nees
7.1.2 A Grammar for a Simple Subset of SQL
7.1.3 The Preprocessor
7.1.4 Exercises for Section 7.1
7.2 Algebraic Laws for Improving Query Plans
7.2.1 Commutative and Associative Laws
7.2.2 Laws Involving Selection
7.2.3 Pushing Selections
7.2.4 Laws Involving Projection
7.2.5 Laws About Joins and Products
7.2.6 Laws Involving Duplicate Elimination
7.2.7 Laws lnvolving Grouping and Aggregation
7.2.8 Exercises for Section 7.2
7.3 From PaJrse Thees to Logical Query Plans
7.3.1 Conversion to Relational Algebra
7.3.2 Removing Subqueries nom Conditions
7.3.3 Improving the Logical Query Plan
7.3.4 Grouping Associative/Commutat ive O perators
7.3.5 Exercises for Section 7.3
7.4 Estimating the Cost of Operations
7.4.1 Estimating Sizes of Illtermediate ffelations
7.4.2 Estimating the Size of a PrOjectiOn
7.4.3 Estimating the Size of a Selectbo
7.4.4 Estimating the Size of a Join
7.4.5 Natural Joins With Multiple Join Attributes
7.4.6 Joins of Many Relations
7.4.7 Estim8ting Sizes fOr Other Operations
7.4.8 Exercises for Section 7.4
7.5 Introduction to Cost-Based Plan Selection
7.5.1 Obtaining Estimates for Size Parameters
7.5.2 Incremental Computation of Statistics
7.5.3 Heuristics for Reducing the Cost of Logical Query P
7.5.4 Approaches to Enumerating Physical Plans
7.5.5 Exercises for Section 7.5
7.6 Choosing an Order for Joins
7.6.1 Significance of Left and mght Join ArgUments
7.6.2 Join nees
7.6.3 Left-Deep Join nees
7.6.4 Dynarnic Programming to Select a Join Order and Gr
7.6.5 Dynamic Programming With More Detailed Cost fu
7.6.6 A Greedy Algorithm for Selecting a Join Order
7.6.7 Exercises for Section 7.6
7.7 Completing the Physical-Query--Plan Selection
7.7.1 Choosing a Selection Method
7.7.2 Choosing a Join Method
7.7.3 Pipelining Versus Materialization
7.7.4 Pipelining Unary Operations
7.7.5 Pipelining Binary Operations
7.7.6 Notation for Physical Query PlaJns
7.7.7 Ordering Of Physical Operations
7.7.8 Exercises for Section 7.7
7.8 Summary of Chapter 7
7.9 References for ChaPter 7

8 Coping With System Failures

8.1 Issues and Models fOr Resilient Operation
8.1.1 Failure Modes
8.1.2 More About nansactions
8.1.3 Correct Execution of nansactions
8.1.4 The Primitive Operations of Transactions
8.1.5 Exercises for Section 8.1
8.2 Undo Logging
8.2.1 Log Records
8.2.2 The UndthLogging Rules
8.2.3 Recovery Using Undo Logging
8.2.4 Checkpointing
8.2.5 Nonquiescent Checkpointing
8.2.6 Exercises for Section 8.2
8.3 Redo Logging
8.3.1 The Redo--Logging Rule
8.3.2 RetiOvery With Redo Logging
8.3.3 Checkpointing a Redo Log
8.3.4 Recovery With a Checkpointed Redo Log
8.3.5 Exercises for Section 8.3
8.4 Undo/Redo Logging
8.4.1 The Undo/Redo Rules
8.4.2 Recovery With Undo/Redo Logging
8.4.3 Checkpointing aJn Undo/Redo Log
8.4.4 Exercises for Section 8.4
8.5 Protecting Against Media Failures
8.5.1 The Archive
8.5.2 Nonquiescent Archiving
8.5.3 Recovery Using an Archive and Log
8.5.4 Exercises for Section 8.5
8.6 Summaxy of Chapter 8
8.7 References for ChaPter 8

9 Concurrency Control

9.1 Serial and Serializable Schedules
9.1.1 Schedules
9.1.2 Serial Schedules
9.1.3 Serializable Schedules
9.1.4 The Effect of Transaction Semantics
9.1.5 A Notation for nansactions and Schedules
9.1.6 Exercises for Section 9.1
9.2 Conflict - Serializability
9.2.1 Confiicts
9.2.2 Precedence Graphs and a Test for Conflict-Serializability
9.2.3 Why the Precedence--Graph Test Works
9.2.4 Exercises for Section 9.2
9.3 Enforcing Serializability by Locks
9.3.1 Locks
9.3.2 The Locking Scheduler
9.3.3 Two--Phase Locking
9.3.4 Why Two-Phase Locking Works
9.3.5 Exercises for Section 9.3
9.4 Locking Systems With Several Lock Modes
9.4.1 Shared and Exclusive Locks
9.4.2 Compatibility Matrices
9.4.3 Upgrading Locks
9.4.4 Update Locks
9.4.5 Increment Locks
9.4.6 Exercises for Section 9.4
9.5 An Architecture for a Locking Scheduler
9.5.1 A Scheduler That Inserts Lock Actions
9.5.2 The Lock Table
9.5.3 Exercises for Section 9.5
9.6 Managing Hierarchies of DatabaJse Elements
9.6.1 Locks With Multiple Granularity
9.6.2 Warning Locks
9.6.3 Phantoms and Handling Insertions Correctly
9.6.4 Exercises fOr Section 9-6
9.7 The Tree Protocol
9.7.1 Motivation for nee-Based Locking
9.7.2 Rules for Access to Tree-Structured Data
9.7.3 Why the nee Protocol Works
9.7.4 Exercises for Section 9.7
9.8 Concurrency COntrol by TimeStamps
9.8.1 Timestamps
9.8.2 Physically Unrealizable Behaviors
9.8.3 Problems With Dirty Data
9.8.4 The Rules fOr Timestamp-Based Scheduling
9.8.5 Multiversion Timestamps
9.8.6 Timestaznps and Locking
9.8.7 Exercises for Section 9.8
9.9 Concurrency Control by Validation
9.9.1 Architecture of a Validation-Based Scheduler
9.9.2 The Validation Rules
9.9.3 Comparison Of Three Concurrency-Control Mechanisms
9.9.4 Exercises for Section 9.9
9.10 Summary Of ChaPter 9
9.11 References for ChaPter 9

10 More About nansaction Managemeot

10.1 Thansactions that Read Uncommitted Data
10.1.1 The Dirty-Data Problem
10.1.2 Cascading Rollback
10.1.3 Managing Rollbacks
10.1.4 Group Commit
10.1.5 Logical Logging
10.1.6 Exercises for Section 10.1
10.2 View Serializability
10.2.1 View Equivalence
10.2.2 PolygraPhs and the Test for View-Serializability
10.2.3 Testing for View-Serializability
10.2.4 Exercises for Section 10.2
10.3 Resolving Deadlocks
10.3.1 Deedlock Detection by Timeout
10.3.2 The Waits-For GraPh
10.3.3 Deadlock Prevention by Ordering Elements
10.3.4 Detecting Deadlocks by Timestamps
10.3.5 Comparison of Deadloch Management Methods
10.3.6 Exercises for Section 10.3
10.4 Distributed Databases
10.4.1 Distribution of Data
10.4.2 Distributed nansactions
10.4.3 Data Replication
10.4.4 Distributed Query Optimization
10.4.5 Exercises for SeCtion 10.4
10.5 Distributed Commit
10.5.1 Supporting Distributed Atomicity
10.5.2 TwcrPhase Commit
10.5.3 Recovery of Distributed' Thansactions
10.5.4 Exercises for Section 10.5
10.6 Distributed Locking
10.6.1 Centralized Lock Systems
10.6.2 A Cost Model for Distributed Locking Algorithms
10.6.3 Locking Replicated Elements
10.6.4 Primary-CoPy Locking
10.6.5 Global Locks Wom Local Locks
10.6.6 Exercises for Section 10.6
10.7 Long--Duration nansactions
10.7.1 Problems of Long nansactions
10.7.2 sasas
10.7.3 Compensating nansactions
10.7.4 Why Compensating nansactions Work
10.7.5 Exercises for Section 10.7
10.8 Summary of ChaPter 10
10.9 References for ChaPter 10

11 Information Integration

11.1 Modes of Information Illtegration
11.1.1 Problems of Information Integration
11.1.2 Federated Database Systems
11.1.3 Data Waehouses
11.1.4 Mediators
11.1.5 Exercises for Section 11.1
11.2 WraPpers in Mediator-Based Systems
11.2.1 Templates for Query Patterns
11.2.2 WraPper Generators
11.2.3 Filters
11.2.4 Other Operations at the Wrapper
11.2.5 Exercises for Section 11.2
11.3 On--Line AnaJytic Processing
11.3.1 OLAP Applications
11.3.2 A Multidimensional View of OLAP Data
11.3.3 StaJr Schemas
11.3.4 Slicing and Dicing
11.3.5 Exercises for Section 11.3
11.4 Data Cubes
11.4.1 The Cube Operator
11.4.2 Cube Implementation by Materialized Views
11.4.3 The Lattice of Views
11.4.4 Exercises for Section 11.4
11.5 Data Mining
11.5.1 Data-Mining Applications
11.5.2 Association-Rule Mining
11.5.3 The A-Priori Algorithm
11.6 Summary of Chapter 11
11.7 References for Chapter 11

Index