Query Results and Path Expressions
11.5.2 Query Results and Path Expressions
In general, the result of a query can be of any type that can be expressed in the ODMG object model. A query does not have to follow the select ... from ... where ... structure; in the simplest case, any persistent name on its own is a query, whose result is a reference to that persistent object. For example, the query
Q1:
DEPARTMENTS ;
returns a reference to the collection of all persistent DEPARTMENT objects, whose type is set < DEPARTMENT >. Similarly, suppose we had given (via the database bind operation, see Figure 11.8) a persistent name CS_DEPARTMENT to a single DEPARTMENT object (the Computer Science department); then, the query
Q1A:
CS_DEPARTMENT ;
returns a reference to that individual object of type DEPARTMENT . Once an entry point is specified, the concept of a path expression can be used to specify a path to related attributes and objects. A path expression typically starts at a persistent object name, or at the iterator variable that ranges over individual objects in a collection. This name will be followed by zero or more relationship names or attribute names connected using the dot notation. For example, referring to the UNIVERSITY data- base in Figure 11.10, the following are examples of path expressions, which are also valid queries in OQL:
Q2:
CS_DEPARTMENT.Chair ;
Q2A:
CS_DEPARTMENT.Chair.Rank ;
Q2B :
CS_DEPARTMENT.Has_faculty ;
The first expression Q2 returns an object of type FACULTY , because that is the type of the attribute Chair of the DEPARTMENT class. This will be a reference to the FACULTY object that is related to the DEPARTMENT object whose persistent name is CS_DEPARTMENT via the attribute Chair ; that is, a reference to the FACULTY object who is chairperson of the Computer Science department. The second expression Q2A is similar, except that it returns the Rank of this FACULTY object (the Computer Science chair) rather than the object reference; hence, the type returned by Q2A is string , which is the data type for the Rank attribute of the FACULTY class.
Path expressions Q2 and Q2A return single values, because the attributes Chair (of DEPARTMENT ) and Rank (of FACULTY ) are both single-valued and they are applied to a single object. The third expression, Q2B , is different; it returns an object of type set < FACULTY > even when applied to a single object, because that is the type of the
11.5 The Object Query Language OQL 401
relationship Has_faculty of the DEPARTMENT class. The collection returned will include references to all FACULTY objects that are related to the DEPARTMENT object
whose persistent name is CS_DEPARTMENT via the relationship Has_faculty ; that is, references to all FACULTY objects who are working in the Computer Science depart- ment. Now, to return the ranks of Computer Science faculty, we cannot write
Q3 : CS_DEPARTMENT.Has_faculty.Rank ; because it is not clear whether the object returned would be of type set < string > or
bag < string > (the latter being more likely, since multiple faculty may share the same rank). Because of this type of ambiguity problem, OQL does not allow expressions such as Q3
Q3A or Q3B below:
Q3A: select F. Rank from
F in CS_DEPARTMENT.Has_faculty ; Q3B:
select distinct F .Rank
F in CS_DEPARTMENT.Has_faculty ; Here, Q3A returns bag < string > (duplicate rank values appear in the result), whereas
from
Q3B returns set < string > (duplicates are eliminated via the distinct keyword). Both Q3A and Q3B illustrate how an iterator variable can be defined in the from clause to range over a restricted collection specified in the query. The variable F in Q3A and Q3B ranges over the elements of the collection CS_DEPARTMENT.Has_faculty , which is of type set < FACULTY >, and includes only those faculty who are members of the Computer Science department.
In general, an OQL query can return a result with a complex structure specified in the query itself by utilizing the struct keyword. Consider the following examples:
Q4: CS_DEPARTMENT.Chair.Advises ; Q4A:
select struct ( name: struct ( last_name: S. name.Lname , first_name:
S. name.Fname ), degrees: ( select struct ( deg: D. Degree , yr: D. Year , college: D. College ) from D in S. Degrees ))
from S in CS_DEPARTMENT.Chair.Advises ; Here, Q4 is straightforward, returning an object of type set < GRAD_STUDENT > as
its result; this is the collection of graduate students who are advised by the chair of the Computer Science department. Now, suppose that a query is needed to retrieve the last and first names of these graduate students, plus the list of previous degrees of each. This can be written as in Q4A , where the variable S ranges over the collec- tion of graduate students advised by the chairperson, and the variable D ranges over the degrees of each such student S. The type of the result of Q4A is a collection of (first-level) struct s where each struct has two components: name and degrees . 44
402 Chapter 11 Object and Object-Relational Databases
The name component is a further struct made up of last_name and first_name , each being a single string. The degrees component is defined by an embedded query and is itself a collection of further (second level) structs, each with three string compo- nents: deg , yr , and college .
Note that OQL is orthogonal with respect to specifying path expressions. That is, attributes, relationships, and operation names (methods) can be used interchange- ably within the path expressions, as long as the type system of OQL is not compro- mised. For example, one can write the following queries to retrieve the grade point average of all senior students majoring in Computer Science, with the result ordered by GPA, and within that by last and first name:
Q5A:
select struct ( last_name: S .name.Lname , first_name: S .name.Fname ,
gpa: S. gpa )
from
S in CS_DEPARTMENT.Has_majors
where
S. Class = ‘senior’
order by
gpa desc , last_name asc , first_name asc ;
Q5B:
select struct ( last_name: S. name.Lname , first_name: S. name.Fname ,
gpa: S. gpa )
from
S in STUDENTS
where
S. Majors_in.Dname = ‘Computer Science’ and S. Class = ‘senior’
gpa desc , last_name asc , first_name asc ; Q5A used the named entry point CS_DEPARTMENT to directly locate the reference
order by
to the Computer Science department and then locate the students via the relation- ship Has_majors , whereas Q5B searches the STUDENTS extent to locate all students majoring in that department. Notice how attribute names, relationship names, and operation (method) names are all used interchangeably (in an orthogonal manner) in the path expressions: gpa is an operation; Majors_in and Has_majors are relation- ships; and Class , Name , Dname , Lname , and Fname are attributes. The implementa- tion of the gpa operation computes the grade point average and returns its value as
a float type for each selected STUDENT . The order by clause is similar to the corresponding SQL construct, and specifies in
which order the query result is to be displayed. Hence, the collection returned by a query with an order by clause is of type list.
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» 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
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» Classification of Database Management Systems
» Domains, Attributes, Tuples, and Relations
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» 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
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» 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
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» The UNION, INTERSECTION, and MINUS Operations
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» Variations of JOIN: The EQUIJOIN and NATURAL JOIN
» Additional Relational Operations
» Examples of Queries in Relational Algebra
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» Entity Types, Entity Sets, Keys, and Value Sets
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» ER Diagrams, Naming Conventions, and Design Issues
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» Subclasses, Superclasses, and Inheritance
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» 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
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» Overview of the Object Model of ODMG
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» 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
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» Overview of the C++ Language Binding in the ODMG Standard
» Structured, Semistructured, and Unstructured Data
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» Well-Formed and Valid XML Documents and XML DTD
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» 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
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» 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
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» Files of Unordered Records (Heap Files)
» Files of Ordered Records (Sorted Files)
» External Hashing for Disk Files
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» 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
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» 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
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» 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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