External Hashing for Disk Files
17.8.2 External Hashing for Disk Files
Hashing for disk files is called external hashing. To suit the characteristics of disk storage, the target address space is made of buckets, each of which holds multiple
records. A bucket is either one disk block or a cluster of contiguous disk blocks. The hashing function maps a key into a relative bucket number, rather than assigning an absolute block address to the bucket. A table maintained in the file header converts the bucket number into the corresponding disk block address, as illustrated in Figure 17.9.
The collision problem is less severe with buckets, because as many records as will fit in a bucket can hash to the same bucket without causing problems. However, we must make provisions for the case where a bucket is filled to capacity and a new record being inserted hashes to that bucket. We can use a variation of chaining in which a pointer is maintained in each bucket to a linked list of overflow records for the bucket, as shown in Figure 17.10. The pointers in the linked list should be record pointers , which include both a block address and a relative record position within the block.
Hashing provides the fastest possible access for retrieving an arbitrary record given the value of its hash field. Although most good hash functions do not maintain records in order of hash field values, some functions—called order preserving— do. A simple example of an order preserving hash function is to take the leftmost three digits of an invoice number field that yields a bucket address as the hash
610 Chapter 17 Disk Storage, Basic File Structures, and Hashing
Bucket Number Block address on disk
M –2 M –1
Figure 17.9
Matching bucket numbers to disk block addresses.
example is to use an integer hash key directly as an index to a relative file, if the hash key values fill up a particular interval; for example, if employee numbers in a com- pany are assigned as 1, 2, 3, ... up to the total number of employees, we can use the identity hash function that maintains order. Unfortunately, this only works if keys are generated in order by some application.
The hashing scheme described so far is called static hashing because a fixed number of buckets M is allocated. This can be a serious drawback for dynamic files. Suppose that we allocate M buckets for the address space and let m be the maximum number of records that can fit in one bucket; then at most (m * M) records will fit in the allo- cated space. If the number of records turns out to be substantially fewer than (m * M), we are left with a lot of unused space. On the other hand, if the number of records increases to substantially more than (m * M), numerous collisions will result and retrieval will be slowed down because of the long lists of overflow records. In either case, we may have to change the number of blocks M allocated and then use a new hashing function (based on the new value of M) to redistribute the records. These reorganizations can be quite time-consuming for large files. Newer dynamic file organizations based on hashing allow the number of buckets to vary dynamically with only localized reorganization (see Section 17.8.3).
When using external hashing, searching for a record given a value of some field other than the hash field is as expensive as in the case of an unordered file. Record deletion can be implemented by removing the record from its bucket. If the bucket has an overflow chain, we can move one of the overflow records into the bucket to replace the deleted record. If the record to be deleted is already in overflow, we sim- ply remove it from the linked list. Notice that removing an overflow record implies that we should keep track of empty positions in overflow. This is done easily by maintaining a linked list of unused overflow locations.
17.8 Hashing Techniques 611
Main buckets Bucket 0
Record pointer NULL Overflow buckets
Bucket 1 321
Record pointer 761
Record pointer NULL
Record pointer
Record pointer
Record pointer 72
Record pointer NULL 522
Record pointer
Record pointer
(Pointers are to records within the overflow blocks)
Bucket 9 399 89
Figure 17.10
Record pointer
Handling overflow for buckets
NULL
by chaining.
Modifying a specific record’s field value depends on two factors: the search condi- tion to locate that specific record and the field to be modified. If the search condi- tion is an equality comparison on the hash field, we can locate the record efficiently by using the hashing function; otherwise, we must do a linear search. A nonhash field can be modified by changing the record and rewriting it in the same bucket. Modifying the hash field means that the record can move to another bucket, which requires deletion of the old record followed by insertion of the modified record.
Parts
» Fundamentals_of_Database_Systems,_6th_Edition
» Characteristics of the Database Approach
» Advantages of Using the DBMS Approach
» A Brief History of Database Applications
» Schemas, Instances, and Database State
» The Three-Schema Architecture
» The Database System Environment
» Centralized and Client/Server Architectures for DBMSs
» Classification of Database Management Systems
» Domains, Attributes, Tuples, and Relations
» Key Constraints and Constraints on NULL Values
» Relational Databases and Relational Database Schemas
» Integrity, Referential Integrity, and Foreign Keys
» Update Operations, Transactions, and Dealing with Constraint Violations
» SQL Data Definition and Data Types
» Specifying Constraints in SQL
» The SELECT-FROM-WHERE Structure of Basic SQL Queries
» Ambiguous Attribute Names, Aliasing, Renaming, and Tuple Variables
» Substring Pattern Matching and Arithmetic Operators
» INSERT, DELETE, and UPDATE Statements in SQL
» Comparisons Involving NULL and Three-Valued Logic
» Nested Queries, Tuples, and Set/Multiset Comparisons
» The EXISTS and UNIQUE Functions in SQL
» Joined Tables in SQL and Outer Joins
» Grouping: The GROUP BY and HAVING Clauses
» Discussion and Summary of SQL Queries
» Specifying General Constraints as Assertions in SQL
» Introduction to Triggers in SQL
» Specification of Views in SQL
» View Implementation, View Update, and Inline Views
» Schema Change Statements in SQL
» Sequences of Operations and the RENAME Operation
» The UNION, INTERSECTION, and MINUS Operations
» The CARTESIAN PRODUCT (CROSS PRODUCT) Operation
» Variations of JOIN: The EQUIJOIN and NATURAL JOIN
» Additional Relational Operations
» Examples of Queries in Relational Algebra
» The Tuple Relational Calculus
» The Domain Relational Calculus
» Using High-Level Conceptual Data Models
» Entity Types, Entity Sets, Keys, and Value Sets
» Relationship Types, Relationship Sets, Roles, and Structural Constraints
» ER Diagrams, Naming Conventions, and Design Issues
» Example of Other Notation: UML Class Diagrams
» Relationship Types of Degree Higher than Two
» Subclasses, Superclasses, and Inheritance
» Constraints on Specialization and Generalization
» Specialization and Generalization Hierarchies
» Modeling of UNION Types Using Categories
» A Sample UNIVERSITY EER Schema, Design Choices, and Formal Definitions
» Data Abstraction, Knowledge Representation, and Ontology Concepts
» ER-to-Relational Mapping Algorithm
» Discussion and Summary of Mapping for ER Model Constructs
» Mapping EER Model Constructs
» The Role of Information Systems
» The Database Design and Implementation Process
» Use of UML Diagrams as an Aid to Database Design Specification 6
» Rational Rose: A UML-Based Design Tool
» Automated Database Design Tools
» Introduction to Object-Oriented Concepts and Features
» Object Identity, and Objects versus Literals
» Complex Type Structures for Objects and Literals
» Encapsulation of Operations and Persistence of Objects
» Type Hierarchies and Inheritance
» Other Object-Oriented Concepts
» Object-Relational Features: Object Database Extensions to SQL
» Overview of the Object Model of ODMG
» Built-in Interfaces and Classes in the Object Model
» Atomic (User-Defined) Objects
» Extents, Keys, and Factory Objects
» The Object Definition Language ODL
» Differences between Conceptual Design of ODB and RDB
» Mapping an EER Schema to an ODB Schema
» Query Results and Path Expressions
» Overview of the C++ Language Binding in the ODMG Standard
» Structured, Semistructured, and Unstructured Data
» XML Hierarchical (Tree) Data Model
» Well-Formed and Valid XML Documents and XML DTD
» XPath: Specifying Path Expressions in XML
» XQuery: Specifying Queries in XML
» Extracting XML Documents from
» Database Programming: Techniques
» Retrieving Single Tuples with Embedded SQL
» Retrieving Multiple Tuples with Embedded SQL Using Cursors
» Specifying Queries at Runtime Using Dynamic SQL
» SQLJ: Embedding SQL Commands in Java
» Retrieving Multiple Tuples in SQLJ Using Iterators
» Database Programming with SQL/CLI Using C
» JDBC: SQL Function Calls for Java Programming
» Database Stored Procedures and SQL/PSM
» PHP Variables, Data Types, and Programming Constructs
» Overview of PHP Database Programming
» Imparting Clear Semantics to Attributes in Relations
» Redundant Information in Tuples and Update Anomalies
» Normal Forms Based on Primary Keys
» General Definitions of Second and Third Normal Forms
» Multivalued Dependency and Fourth Normal Form
» Join Dependencies and Fifth Normal Form
» Inference Rules for Functional Dependencies
» Minimal Sets of Functional Dependencies
» Properties of Relational Decompositions
» Dependency-Preserving Decomposition
» Dependency-Preserving and Nonadditive (Lossless) Join Decomposition into 3NF Schemas
» Problems with NULL Values and Dangling Tuples
» Discussion of Normalization Algorithms and Alternative Relational Designs
» Further Discussion of Multivalued Dependencies and 4NF
» Other Dependencies and Normal Forms
» Memory Hierarchies and Storage Devices
» Hardware Description of Disk Devices
» Magnetic Tape Storage Devices
» Placing File Records on Disk
» Files of Unordered Records (Heap Files)
» Files of Ordered Records (Sorted Files)
» External Hashing for Disk Files
» Hashing Techniques That Allow Dynamic File Expansion
» Other Primary File Organizations
» Parallelizing Disk Access Using RAID Technology
» Types of Single-Level Ordered Indexes
» Some General Issues Concerning Indexing
» Algorithms for External Sorting
» Implementing the SELECT Operation
» Implementing the JOIN Operation
» Algorithms for PROJECT and Set
» Notation for Query Trees and Query Graphs
» Heuristic Optimization of Query Trees
» Catalog Information Used in Cost Functions
» Examples of Cost Functions for SELECT
» Examples of Cost Functions for JOIN
» Example to Illustrate Cost-Based Query Optimization
» Factors That Influence Physical Database Design
» Physical Database Design Decisions
» An Overview of Database Tuning in Relational Systems
» Transactions, Database Items, Read and Write Operations, and DBMS Buffers
» Why Concurrency Control Is Needed
» Transaction and System Concepts
» Desirable Properties of Transactions
» Serial, Nonserial, and Conflict-Serializable Schedules
» Testing for Conflict Serializability of a Schedule
» How Serializability Is Used for Concurrency Control
» View Equivalence and View Serializability
» Types of Locks and System Lock Tables
» Guaranteeing Serializability by Two-Phase Locking
» Dealing with Deadlock and Starvation
» Concurrency Control Based on Timestamp Ordering
» Multiversion Concurrency Control Techniques
» Validation (Optimistic) Concurrency
» Granularity of Data Items and Multiple Granularity Locking
» Using Locks for Concurrency Control in Indexes
» Other Concurrency Control Issues
» Recovery Outline and Categorization of Recovery Algorithms
» Caching (Buffering) of Disk Blocks
» Write-Ahead Logging, Steal/No-Steal, and Force/No-Force
» Transaction Rollback and Cascading Rollback
» NO-UNDO/REDO Recovery Based on Deferred Update
» Recovery Techniques Based on Immediate Update
» The ARIES Recovery Algorithm
» Recovery in Multidatabase Systems
» Introduction to Database Security Issues 1
» Discretionary Access Control Based on Granting and Revoking Privileges
» Mandatory Access Control and Role-Based Access Control for Multilevel Security
» Introduction to Statistical Database Security
» Introduction to Flow Control
» Encryption and Public Key Infrastructures
» Challenges of Database Security
» Distributed Database Concepts 1
» Types of Distributed Database Systems
» Distributed Database Architectures
» Data Replication and Allocation
» Example of Fragmentation, Allocation, and Replication
» Query Processing and Optimization in Distributed Databases
» Overview of Transaction Management in Distributed Databases
» Overview of Concurrency Control and Recovery in Distributed Databases
» Current Trends in Distributed Databases
» Distributed Databases in Oracle 13
» Generalized Model for Active Databases and Oracle Triggers
» Design and Implementation Issues for Active Databases
» Examples of Statement-Level Active Rules
» Time Representation, Calendars, and Time Dimensions
» Incorporating Time in Relational Databases Using Tuple Versioning
» Incorporating Time in Object-Oriented Databases Using Attribute Versioning
» Temporal Querying Constructs and the TSQL2 Language
» Spatial Database Concepts 24
» Multimedia Database Concepts
» Clausal Form and Horn Clauses
» Datalog Programs and Their Safety
» Evaluation of Nonrecursive Datalog Queries
» Introduction to Information Retrieval
» Types of Queries in IR Systems
» Evaluation Measures of Search Relevance
» Web Analysis and Its Relationship to Information Retrieval
» Analyzing the Link Structure of Web Pages
» Approaches to Web Content Analysis
» Trends in Information Retrieval
» Data Mining as a Part of the Knowledge
» Goals of Data Mining and Knowledge Discovery
» Types of Knowledge Discovered during Data Mining
» Market-Basket Model, Support, and Confidence
» Frequent-Pattern (FP) Tree and FP-Growth Algorithm
» Other Types of Association Rules
» Approaches to Other Data Mining Problems
» Commercial Data Mining Tools
» Data Modeling for Data Warehouses
» Difficulties of Implementing Data Warehouses
» Grouping, Aggregation, and Database Modification in QBE
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