- •Contents
- •Introduction
- •Who This Book Is For
- •What This Book Covers
- •How This Book Is Structured
- •What You Need to Use This Book
- •Conventions
- •Source Code
- •Errata
- •p2p.wrox.com
- •The Basics of C++
- •The Obligatory Hello, World
- •Namespaces
- •Variables
- •Operators
- •Types
- •Conditionals
- •Loops
- •Arrays
- •Functions
- •Those Are the Basics
- •Diving Deeper into C++
- •Pointers and Dynamic Memory
- •Strings in C++
- •References
- •Exceptions
- •The Many Uses of const
- •C++ as an Object-Oriented Language
- •Declaring a Class
- •Your First Useful C++ Program
- •An Employee Records System
- •The Employee Class
- •The Database Class
- •The User Interface
- •Evaluating the Program
- •What Is Programming Design?
- •The Importance of Programming Design
- •Two Rules for C++ Design
- •Abstraction
- •Reuse
- •Designing a Chess Program
- •Requirements
- •Design Steps
- •An Object-Oriented View of the World
- •Am I Thinking Procedurally?
- •The Object-Oriented Philosophy
- •Living in a World of Objects
- •Object Relationships
- •Abstraction
- •Reusing Code
- •A Note on Terminology
- •Deciding Whether or Not to Reuse Code
- •Strategies for Reusing Code
- •Bundling Third-Party Applications
- •Open-Source Libraries
- •The C++ Standard Library
- •Designing with Patterns and Techniques
- •Design Techniques
- •Design Patterns
- •The Reuse Philosophy
- •How to Design Reusable Code
- •Use Abstraction
- •Structure Your Code for Optimal Reuse
- •Design Usable Interfaces
- •Reconciling Generality and Ease of Use
- •The Need for Process
- •Software Life-Cycle Models
- •The Stagewise and Waterfall Models
- •The Spiral Method
- •The Rational Unified Process
- •Software-Engineering Methodologies
- •Extreme Programming (XP)
- •Software Triage
- •Be Open to New Ideas
- •Bring New Ideas to the Table
- •Thinking Ahead
- •Keeping It Clear
- •Elements of Good Style
- •Documenting Your Code
- •Reasons to Write Comments
- •Commenting Styles
- •Comments in This Book
- •Decomposition
- •Decomposition through Refactoring
- •Decomposition by Design
- •Decomposition in This Book
- •Naming
- •Choosing a Good Name
- •Naming Conventions
- •Using Language Features with Style
- •Use Constants
- •Take Advantage of const Variables
- •Use References Instead of Pointers
- •Use Custom Exceptions
- •Formatting
- •The Curly Brace Alignment Debate
- •Coming to Blows over Spaces and Parentheses
- •Spaces and Tabs
- •Stylistic Challenges
- •Introducing the Spreadsheet Example
- •Writing Classes
- •Class Definitions
- •Defining Methods
- •Using Objects
- •Object Life Cycles
- •Object Creation
- •Object Destruction
- •Assigning to Objects
- •Distinguishing Copying from Assignment
- •The Spreadsheet Class
- •Freeing Memory with Destructors
- •Handling Copying and Assignment
- •Different Kinds of Data Members
- •Static Data Members
- •Const Data Members
- •Reference Data Members
- •Const Reference Data Members
- •More about Methods
- •Static Methods
- •Const Methods
- •Method Overloading
- •Default Parameters
- •Inline Methods
- •Nested Classes
- •Friends
- •Operator Overloading
- •Implementing Addition
- •Overloading Arithmetic Operators
- •Overloading Comparison Operators
- •Building Types with Operator Overloading
- •Pointers to Methods and Members
- •Building Abstract Classes
- •Using Interface and Implementation Classes
- •Building Classes with Inheritance
- •Extending Classes
- •Overriding Methods
- •Inheritance for Reuse
- •The WeatherPrediction Class
- •Adding Functionality in a Subclass
- •Replacing Functionality in a Subclass
- •Respect Your Parents
- •Parent Constructors
- •Parent Destructors
- •Referring to Parent Data
- •Casting Up and Down
- •Inheritance for Polymorphism
- •Return of the Spreadsheet
- •Designing the Polymorphic Spreadsheet Cell
- •The Spreadsheet Cell Base Class
- •The Individual Subclasses
- •Leveraging Polymorphism
- •Future Considerations
- •Multiple Inheritance
- •Inheriting from Multiple Classes
- •Naming Collisions and Ambiguous Base Classes
- •Interesting and Obscure Inheritance Issues
- •Special Cases in Overriding Methods
- •Copy Constructors and the Equals Operator
- •The Truth about Virtual
- •Runtime Type Facilities
- •Non-Public Inheritance
- •Virtual Base Classes
- •Class Templates
- •Writing a Class Template
- •How the Compiler Processes Templates
- •Distributing Template Code between Files
- •Template Parameters
- •Method Templates
- •Template Class Specialization
- •Subclassing Template Classes
- •Inheritance versus Specialization
- •Function Templates
- •Function Template Specialization
- •Function Template Overloading
- •Friend Function Templates of Class Templates
- •Advanced Templates
- •More about Template Parameters
- •Template Class Partial Specialization
- •Emulating Function Partial Specialization with Overloading
- •Template Recursion
- •References
- •Reference Variables
- •Reference Data Members
- •Reference Parameters
- •Reference Return Values
- •Deciding between References and Pointers
- •Keyword Confusion
- •The const Keyword
- •The static Keyword
- •Order of Initialization of Nonlocal Variables
- •Types and Casts
- •typedefs
- •Casts
- •Scope Resolution
- •Header Files
- •C Utilities
- •Variable-Length Argument Lists
- •Preprocessor Macros
- •How to Picture Memory
- •Allocation and Deallocation
- •Arrays
- •Working with Pointers
- •Array-Pointer Duality
- •Arrays Are Pointers!
- •Not All Pointers Are Arrays!
- •Dynamic Strings
- •C-Style Strings
- •String Literals
- •The C++ string Class
- •Pointer Arithmetic
- •Custom Memory Management
- •Garbage Collection
- •Object Pools
- •Function Pointers
- •Underallocating Strings
- •Memory Leaks
- •Double-Deleting and Invalid Pointers
- •Accessing Out-of-Bounds Memory
- •Using Streams
- •What Is a Stream, Anyway?
- •Stream Sources and Destinations
- •Output with Streams
- •Input with Streams
- •Input and Output with Objects
- •String Streams
- •File Streams
- •Jumping around with seek() and tell()
- •Linking Streams Together
- •Bidirectional I/O
- •Internationalization
- •Wide Characters
- •Non-Western Character Sets
- •Locales and Facets
- •Errors and Exceptions
- •What Are Exceptions, Anyway?
- •Why Exceptions in C++ Are a Good Thing
- •Why Exceptions in C++ Are a Bad Thing
- •Our Recommendation
- •Exception Mechanics
- •Throwing and Catching Exceptions
- •Exception Types
- •Throwing and Catching Multiple Exceptions
- •Uncaught Exceptions
- •Throw Lists
- •Exceptions and Polymorphism
- •The Standard Exception Hierarchy
- •Catching Exceptions in a Class Hierarchy
- •Writing Your Own Exception Classes
- •Stack Unwinding and Cleanup
- •Catch, Cleanup, and Rethrow
- •Use Smart Pointers
- •Common Error-Handling Issues
- •Memory Allocation Errors
- •Errors in Constructors
- •Errors in Destructors
- •Putting It All Together
- •Why Overload Operators?
- •Limitations to Operator Overloading
- •Choices in Operator Overloading
- •Summary of Overloadable Operators
- •Overloading the Arithmetic Operators
- •Overloading Unary Minus and Unary Plus
- •Overloading Increment and Decrement
- •Overloading the Subscripting Operator
- •Providing Read-Only Access with operator[]
- •Non-Integral Array Indices
- •Overloading the Function Call Operator
- •Overloading the Dereferencing Operators
- •Implementing operator*
- •Implementing operator->
- •What in the World Is operator->* ?
- •Writing Conversion Operators
- •Ambiguity Problems with Conversion Operators
- •Conversions for Boolean Expressions
- •How new and delete Really Work
- •Overloading operator new and operator delete
- •Overloading operator new and operator delete with Extra Parameters
- •Two Approaches to Efficiency
- •Two Kinds of Programs
- •Is C++ an Inefficient Language?
- •Language-Level Efficiency
- •Handle Objects Efficiently
- •Use Inline Methods and Functions
- •Design-Level Efficiency
- •Cache as Much as Possible
- •Use Object Pools
- •Use Thread Pools
- •Profiling
- •Profiling Example with gprof
- •Cross-Platform Development
- •Architecture Issues
- •Implementation Issues
- •Platform-Specific Features
- •Cross-Language Development
- •Mixing C and C++
- •Shifting Paradigms
- •Linking with C Code
- •Mixing Java and C++ with JNI
- •Mixing C++ with Perl and Shell Scripts
- •Mixing C++ with Assembly Code
- •Quality Control
- •Whose Responsibility Is Testing?
- •The Life Cycle of a Bug
- •Bug-Tracking Tools
- •Unit Testing
- •Approaches to Unit Testing
- •The Unit Testing Process
- •Unit Testing in Action
- •Higher-Level Testing
- •Integration Tests
- •System Tests
- •Regression Tests
- •Tips for Successful Testing
- •The Fundamental Law of Debugging
- •Bug Taxonomies
- •Avoiding Bugs
- •Planning for Bugs
- •Error Logging
- •Debug Traces
- •Asserts
- •Debugging Techniques
- •Reproducing Bugs
- •Debugging Reproducible Bugs
- •Debugging Nonreproducible Bugs
- •Debugging Memory Problems
- •Debugging Multithreaded Programs
- •Debugging Example: Article Citations
- •Lessons from the ArticleCitations Example
- •Requirements on Elements
- •Exceptions and Error Checking
- •Iterators
- •Sequential Containers
- •Vector
- •The vector<bool> Specialization
- •deque
- •list
- •Container Adapters
- •queue
- •priority_queue
- •stack
- •Associative Containers
- •The pair Utility Class
- •multimap
- •multiset
- •Other Containers
- •Arrays as STL Containers
- •Strings as STL Containers
- •Streams as STL Containers
- •bitset
- •The find() and find_if() Algorithms
- •The accumulate() Algorithms
- •Function Objects
- •Arithmetic Function Objects
- •Comparison Function Objects
- •Logical Function Objects
- •Function Object Adapters
- •Writing Your Own Function Objects
- •Algorithm Details
- •Utility Algorithms
- •Nonmodifying Algorithms
- •Modifying Algorithms
- •Sorting Algorithms
- •Set Algorithms
- •The Voter Registration Audit Problem Statement
- •The auditVoterRolls() Function
- •The getDuplicates() Function
- •The RemoveNames Functor
- •The NameInList Functor
- •Testing the auditVoterRolls() Function
- •Allocators
- •Iterator Adapters
- •Reverse Iterators
- •Stream Iterators
- •Insert Iterators
- •Extending the STL
- •Why Extend the STL?
- •Writing an STL Algorithm
- •Writing an STL Container
- •The Appeal of Distributed Computing
- •Distribution for Scalability
- •Distribution for Reliability
- •Distribution for Centrality
- •Distributed Content
- •Distributed versus Networked
- •Distributed Objects
- •Serialization and Marshalling
- •Remote Procedure Calls
- •CORBA
- •Interface Definition Language
- •Implementing the Class
- •Using the Objects
- •A Crash Course in XML
- •XML as a Distributed Object Technology
- •Generating and Parsing XML in C++
- •XML Validation
- •Building a Distributed Object with XML
- •SOAP (Simple Object Access Protocol)
- •. . . Write a Class
- •. . . Subclass an Existing Class
- •. . . Throw and Catch Exceptions
- •. . . Read from a File
- •. . . Write to a File
- •. . . Write a Template Class
- •There Must Be a Better Way
- •Smart Pointers with Reference Counting
- •Double Dispatch
- •Mix-In Classes
- •Object-Oriented Frameworks
- •Working with Frameworks
- •The Model-View-Controller Paradigm
- •The Singleton Pattern
- •Example: A Logging Mechanism
- •Implementation of a Singleton
- •Using a Singleton
- •Example: A Car Factory Simulation
- •Implementation of a Factory
- •Using a Factory
- •Other Uses of Factories
- •The Proxy Pattern
- •Example: Hiding Network Connectivity Issues
- •Implementation of a Proxy
- •Using a Proxy
- •The Adapter Pattern
- •Example: Adapting an XML Library
- •Implementation of an Adapter
- •Using an Adapter
- •The Decorator Pattern
- •Example: Defining Styles in Web Pages
- •Implementation of a Decorator
- •Using a Decorator
- •The Chain of Responsibility Pattern
- •Example: Event Handling
- •Implementation of a Chain of Responsibility
- •Using a Chain of Responsibility
- •Example: Event Handling
- •Implementation of an Observer
- •Using an Observer
- •Chapter 1: A Crash Course in C++
- •Chapter 3: Designing with Objects
- •Chapter 4: Designing with Libraries and Patterns
- •Chapter 5: Designing for Reuse
- •Chapter 7: Coding with Style
- •Chapters 8 and 9: Classes and Objects
- •Chapter 11: Writing Generic Code with Templates
- •Chapter 14: Demystifying C++ I/O
- •Chapter 15: Handling Errors
- •Chapter 16: Overloading C++ Operators
- •Chapter 17: Writing Efficient C++
- •Chapter 19: Becoming Adept at Testing
- •Chapter 20: Conquering Debugging
- •Chapter 24: Exploring Distributed Objects
- •Chapter 26: Applying Design Patterns
- •Beginning C++
- •General C++
- •I/O Streams
- •The C++ Standard Library
- •C++ Templates
- •Integrating C++ and Other Languages
- •Algorithms and Data Structures
- •Open-Source Software
- •Software-Engineering Methodology
- •Programming Style
- •Computer Architecture
- •Efficiency
- •Testing
- •Debugging
- •Distributed Objects
- •CORBA
- •XML and SOAP
- •Design Patterns
- •Index
Exploring Distributed
Objects
Distributed computing is simply the idea that a program’s operation can be spread across multiple computers on a network. As networks grow faster and more common, an increasing number of applications leverage other computers on the network during their processing.
In this chapter, you will learn about distributed objects: the application of object-oriented technologies to distributed computing. This chapter begins by defining distributed computing and distributed objects in more detail, including sample use cases. Next, you will be introduced to CORBA, a powerful architecture for programming distributed objects. Finally, you’ll read about XML technologies and their role in distributed computing.
The Appeal of Distributed Computing
Distributed computing has received much attention in the last decade as the Internet has risen to prominence. Yet it is more than a buzzword — a distributed program is an ideal fit for certain types of applications. For example, try to imagine writing a program that contains all of the information available on the Web. It would be nearly impossible. The Web is only able to contain its massive amount of data and dynamic content because it is distributed across many different machines.
Distribution for Scalability
In an average day, your desktop computer probably spends most of its time doing nothing. Even when you are actively using your computer, modern processors are so fast that they are idle much of the time, waiting for us human beings to catch up. The reality is that the world contains many computers, most of which aren’t exactly overworked. They are, however, ready to be put to use.
Chapter 24
Most computer users have more than enough power on their desktop PC’s. If you use a monitoring tool to examine the utilization of your processor, you’ll see that it rarely hits 100 percent for any length of time. Some applications, however, are extremely processor-intensive. It might take hours to run a program full of complex calculations on your desktop computer. If you were able to harness the power of the unused processors on your network, that time could be greatly reduced. This technique is referred to as grid computing.
One of the classic real-world examples of an application that uses distribution for speed is image rendering. The generation of just one still frame of movie-quality computer animation requires an enormous amount of computation. 3-D rendering applications have taken advantage of distributed computing for many years. So-called rendering farms can be assembled with high-speed network connections. One machine might serve as the central “brain” and hand out small chunks of computation to each machine in the farm. The brain machine would gather the results to assemble the final image or movie.
The SETI@home project is another example of using distributed computing to increase computational performance. The program aids in the Search for Extra-Terrestrial Intelligence (SETI) by spreading the analysis of signals from space among all participating computers. It would be impractical to attempt to analyze such an enormous amount of data on a single computer. The SETI@home project gives users a program they can run on their home PC’s that processes chunks of data when the computer isn’t being used. Of course, we can’t call it a success quite yet (no little green men have been found), but as an early widespread distributed Internet application, it helped popularize the technology.
Distribution for Reliability
Distributed computing can be seen as a solution to Murphy’s Law — whatever can go wrong, will go wrong. Certain applications, such as Web sites and databases, need to be available and working at all times. These applications can be written so that they are running on multiple computers on the network. If one machine goes down, another can immediately take over. In cases like this, distribution becomes mainly an issue of synchronization. All instances of the application need to communicate enough so that when a failover occurs, the user doesn’t experience any change.
When you access a large-scale Web site, you are really connecting to one of many servers, all of which contain mirror images of the same data. A device known as a load balancer is often used to route incoming requests to an available machine. If one machine fails, the load balancer ceases to send it any requests until it is repaired.
Distribution for Centrality
It is often useful to have one system on a network that controls or monitors the behavior of other applications. For example, Sassafras Software’s KeyServer is an application that allows network administrators to ensure that software being used on their network doesn’t violate licensing terms. When a user on the network launches an application, the application contacts the KeyServer to request permission to run. The KeyServer keeps track of how many copies are currently running. If additional licenses are available, the requested application starts up normally. Incidentally, KeyServer also makes use of distribution for reliability — administrators can install multiple “shadow” KeyServers in case one goes down.
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Exploring Distributed Objects
Distributed Content
Peer-to-peer applications have exploded in popularity recently. The basic idea is that users on a network all run an application that lets them communicate on a one-to-one basis. In a file-sharing application, each user makes files stored on his or her local computers available on the network. Users seeking that file can connect to the user who has it and begin a transfer. By distributing the file-sharing application across all of its users, the application is able to offer a larger quantity of content than could be housed on a single server. It also distributes the communications load, so there is no central server that becomes a bottleneck for all requests.
Distributed versus Networked
You should keep in mind that not all applications that take advantage of networking are necessarily distributed applications. A networked application communicates with other machines to request or transfer data. The term distributed implies a much richer interaction.
The distinction is sometimes difficult to see. For example, consider a video game. If the game communicates with a central server to check for updates, it would be a networked application because the game itself isn’t running on multiple applications — it simply talks to another machine in the course of its operation. However, if the game allowed multiple players to participate on different machines, it could be implemented as a distributed application.
The best approach to labeling an application as distributed or networked (other than deciding it’s an entirely academic distinction and doesn’t really matter) is to take the position of one machine and determine whether computation is happening on another machine. In the networked game case, the individual copies of the game are most likely operating on their own machines. They use the network only for status updates, as shown in Figure 24-1. As the figure shows, when the player on Machine A shoots his or her gun, the information about the shot is transmitted to Machine B. Both machines take appropriate action based on the same event. This style of networked game isn’t generally considered a distributed application.
Machine A |
Machine B |
Player A shoots gun |
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|
Machine A sends event |
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Machine B receives event |
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||
Machine A calculates result |
Machine B calculates result |
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Figure 24-1 |
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