“Design patterns help you learn from others' successes instead
of your own failures.”
#[BT_6]#Probably the most
important step forward in object-oriented design is the “design patterns”
movement, chronicled in Design Patterns (ibid).
That book shows 23 different solutions to particular classes of problems. In
this book, the basic concepts of design patterns will be introduced along with
examples. This should whet your appetite to read Design Patterns by
Gamma, et. al., a source of what has now become an essential, almost mandatory,
vocabulary for OOP programmers.
#[BT_7]#The latter part of
this book contains an example of the design evolution process, starting with an
initial solution and moving through the logic and process of evolving the
solution to more appropriate designs. The program shown (a trash sorting
simulation) has evolved over time, and you can look at that evolution as a
prototype for the way your own design can start as an adequate solution to a
particular problem and evolve into a flexible approach to a class of problems.
#[BT_8]#Initially, you can
think of a pattern as an especially clever and insightful way of solving a
particular class of problems. That is, it looks like a lot of people have
worked out all the angles of a problem and have come up with the most general,
flexible solution for it. The problem could be one you have seen and solved
before, but your solution probably didn’t have the kind of completeness you’ll
see embodied in a pattern.
#[BT_9]#Although they’re
called “design patterns,” they really aren’t tied to the realm of design. A
pattern seems to stand apart from the traditional way of thinking about
analysis, design, and implementation. Instead, a pattern embodies a complete
idea within a program, and thus it can sometimes appear at the analysis phase
or high-level design phase. This is interesting because a pattern has a direct
implementation in code and so you might not expect it to show up before
low-level design or implementation (and in fact you might not realize that you
need a particular pattern until you get to those phases).
#[BT_10]#The basic concept
of a pattern can also be seen as the basic concept of program design: adding a
layer of abstraction. Whenever you abstract something you’re isolating
particular details, and one of the most compelling motivations behind this is
to separate things that change from things that stay the same. Another
way to put this is that once you find some part of your program that’s likely
to change for one reason or another, you’ll want to keep those changes from
propagating other changes throughout your code. Not only does this make the
code much cheaper to maintain, but it also turns out that it is usually simpler
to understand (which results in lowered costs).
#[BT_11]#Often, the most
difficult part of developing an elegant and cheap-to-maintain design is in
discovering what I call “the vector of change.” (Here, “vector” refers to the
maximum gradient and not a container class.) This means finding the most
important thing that changes in your system, or put another way, discovering
where your greatest cost is. Once you discover the vector of change, you have
the focal point around which to structure your design.
#[BT_12]#So the goal of
design patterns is to isolate changes in your code. If you look at it this way,
you’ve been seeing some design patterns already in this book. For example, inheritance can be thought of as a design pattern (albeit one implemented by the
compiler). It allows you to express differences in behavior (that’s the thing
that changes) in objects that all have the same interface (that’s what stays
the same). Composition can also be considered a pattern, since it allows you to
change—dynamically or statically—the objects that implement your class, and
thus the way that class works.
#[BT_13]#You’ve also
already seen another pattern that appears in Design Patterns: the iterator (Java 1.0 and 1.1 capriciously calls it the Enumeration; Java
2 containers use “iterator”). This hides the particular implementation of the
container as you’re stepping through and selecting the elements one by one. The
iterator allows you to write generic code that performs an operation on all of
the elements in a sequence without regard to the way that sequence is built.
Thus your generic code can be used with any container that can produce an
iterator.
#[BT_14]#One of the events
that’s occurred with the rise of design patterns is what could be thought of as
the “pollution” of the term – people have begun to use the term to mean just
about anything synonymous with “good.” After some pondering, I’ve come up with
a sort of hierarchy describing a succession of different types of categories:
1. #[BT_15]#Idiom: how we write code in a
particular language to do this particular type of thing. This could be
something as common as the way that you code the process of stepping through an
array in C (and not running off the end).
2. #[BT_16]#Specific Design: the solution that we
came up with to solve this particular problem. This might be a clever design,
but it makes no attempt to be general.
3. #[BT_17]#Standard Design: a way to solve this kind
of problem. A design that has become more general, typically through reuse.
4. #[BT_18]#Design Pattern: how to solve an entire
class of similar problem. This usually only appears after applying a standard
design a number of times, and then seeing a common pattern throughout these
applications.
#[BT_19]#I feel this helps
put things in perspective, and to show where something might fit. However, it
doesn’t say that one is better than another. It doesn’t make sense to try to
take every problem solution and generalize it to a design pattern – it’s not a
good use of your time, and you can’t force the discovery of patterns that way;
they tend to be subtle and appear over time.
#[BT_20]#One could also
argue for the inclusion of Analysis Pattern and Architectural Pattern
in this taxonomy.
(Update from slides to here)
When I put out a call for ideas in my newsletter,
a number of suggestions came back which turned out to be very useful, but
different than the above classification, and I realized that a list of design
principles is at least as important as design structures, but for a different
reason: these allow you to ask questions about your proposed design, to apply
tests for quality.
·
Principle of least astonishment (don’t be astonishing).
·
Make common things easy, and rare things possible
·
Consistency. One thing has become very clear to me,
especially because of Python: the more random rules you pile onto the
programmer, rules that have nothing to do with solving the problem at hand, the
slower the programmer can produce. And this does not appear to be a linear
factor, but an exponential one.
·
Law of Demeter: a.k.a. “Don’t talk to strangers.” An
object should only reference itself, its attributes, and the arguments of its
methods.
·
Subtraction: a design is finished when you cannot take
anything else away.
·
Simplicity before generality.
(A variation of Occam’s Razor, which says “the simplest solution is the
best”). A common problem we find in frameworks is that they are designed to be
general purpose without reference to actual systems. This leads to a dizzying
array of options that are often unused, misused or just not useful. However,
most developers work on specific systems, and the quest for generality does not
always serve them well. The best route to generality is through understanding
well-defined specific examples. So, this principle acts as the tie breaker
between otherwise equally viable design alternatives. Of course, it is entirely
possible that the simpler solution is the more general one.
·
Reflexivity (my suggested term). One abstraction per
class, one class per abstraction. Might also be called Isomorphism.
·
Independence or Orthogonality. Express independent
ideas independently. This complements Separation, Encapsulation and Variation,
and is part of the Low-Coupling-High-Cohesion message.
·
Once and once only: Avoid duplication of logic and
structure where the duplication is not accidental, ie where both pieces of code
express the same intent for the same reason.
In the process of brainstorming this idea, I hope to come up
with a small handful of fundamental ideas that can be held in your head while
you analyze a problem. However, other ideas that come from this list may end up
being useful as a checklist while walking through and analyzing your design.
#[BT_27]#The Design
Patterns book discusses 23 different patterns, classified under three purposes
(all of which revolve around the particular aspect that can vary). The three
purposes are:
1.
Creational: how an object can be created. This often
involves isolating the details of object creation so your code isn’t dependent
on what types of objects there are and thus doesn’t have to be changed when you
add a new type of object. The aforementioned Singleton is classified as
a creational pattern, and later in this book you’ll see examples of Factory
Method and Prototype.
2.
Structural: designing objects to satisfy particular
project constraints. These work with the way objects are connected with other
objects to ensure that changes in the system don’t require changes to those
connections.
3.
Behavioral: objects that handle particular types of
actions within a program. These encapsulate processes that you want to perform,
such as interpreting a language, fulfilling a request, moving through a
sequence (as in an iterator), or implementing an algorithm. This book contains
examples of the Observer and the Visitor patterns.
#[BT_28]#The Design
Patterns book has a section on each of its 23 patterns along with one or
more examples for each, typically in C++ (rather restricted C++, at that) but
sometimes in Smalltalk. (You’ll find that this doesn’t matter too much since
you can easily translate the concepts from either language into Java.) This
book will revisit many of the patterns shown in Design Patterns but with
a Java orientation, since the language changes the expression and understanding
of the patterns. However, the GoF examples will not be repeated here, since I
believe that it’s possible to produce more illuminating examples given some
effort. The goal is to provide you with a decent feel for what patterns are
about and why they are so important.
#[BT_29]#After years of
looking at these things, it began to occur to me that the patterns themselves
use basic principles of organization, other than (and more fundamental than)
those described in Design Patterns. These principles are based on the
structure of the implementations, which is where I have seen great similarities
between patterns (more than those expressed in Design Patterns).
Although we generally try to avoid implementation in favor of interface, for
awhile I thought that it was easier to understand the patterns in terms of
these structural principles, and tried reorganizing the book around the patterns
based on their structure instead of the categories presented in Design
Patterns.
However, a later insight made me realize that it’s more
useful to organize the patterns in terms of the problems they solve. I believe
this is a subtle but important distinction from the way Metsker organizes the
patterns by intent in Design Patterns Java Workshop (Addison-Wesley
2002), because I hope that you will then be able to recognize your problem and
search for a solution, if the patterns are organized this way.
In the process of doing all this “book refactoring” I
realized that if I changed it once, I would probably change it again (there’s
definitely a design maxim in there), so I removed all references to chapter numbers
in order to facilitate this change (the little-known “numberless chapter”
pattern J).HowHH
#[BT_30]#Issues of
development, the UML process, Extreme Programming.
#[BT_31]#Is evaluation
valuable? The Capability Immaturity Model:
#[BT_32]#Wiki Page: http://c2.com/cgi-bin/wiki?CapabilityImMaturityModel
Article: http://www.embedded.com/98/9807br.htm
#[BT_33]#Pair
programming research:
#[BT_34]#http://collaboration.csc.ncsu.edu/laurie/
In an earlier version of this book I decided that unit
testing was essential (for all of my books) and that JUnit was too verbose and
clunky to consider. At that time I wrote my own unit testing framework using
Java reflection to simplify the syntax necessary to achieve unit testing. For
the third edition of Thinking in Java, we developed another unit testing
framework for that book which would test the output of examples.
In the meantime, JUnit has changed to add a syntax
remarkably similar to the one that I used in an earlier version of this book. I
don’t know how much influence I may have had on that change, but I’m simply
happy that it has happened, because I no longer feel the need to support my own
system (which you can still find <some URL here>) and can simply
recommend the defacto standard.
I have introduced and described the style of JUnit coding
that I consider a “best practice” (primarily because of simplicity), in Thinking
in Java, 3rd edition, chapter 15. That section provides an
adequate introduction to any of the unit testing you will see associated with
this book (however, the unit testing code will not normally be included in the
text of this book). When you download the code for this book, you will find (4/9/2003: Eventually, not yet) unit tests along with the code examples whenever possible.
(From Bill):
Public: in test subdirectory; different package
(don’t include in jar).
Package access: same package, subdirectory path underneath
library code (don’t include in jar)
Private access: (white box testing). Nested class, strip
out, or Junit addons.
Before getting into more complex techniques, it’s helpful to
look at some basic ways to keep code simple and straightforward.
The most trivial of these is the messenger, which
simply packages information into an object to be passed around, instead of
passing all the pieces around separately. Note that without the messenger, the
code for translate() would be much more confusing to read:
//: simplifying:MessengerDemo.java
package simplifying;
import junit.framework.*;
class Point { // A messenger
public int x, y, z; // Since it's just
a carrier
public Point(int x, int y, int z) {
this.x = x;
this.y = y;
this.z = z;
}
public Point(Point p) { //
Copy-constructor
this.x = p.x;
this.y = p.y;
this.z = p.z;
}
public String toString() {
return "x: " + x + "
y: " + y + " z: " + z;
}
}
class Vector {
public int magnitude, direction;
public Vector(int magnitude, int
direction) {
this.magnitude = magnitude;
this.direction = direction;
}
}
class Space {
public static Point translate(Point p,
Vector v) {
p = new Point(p); // Don't modify the
original
// Perform calculation using v. Dummy
calculation:
p.x = p.x + 1;
p.y = p.y + 1;
p.z = p.z + 1;
return p;
}
}
public class MessengerDemo extends
TestCase {
public void test() {
Point p1 = new Point(1, 2, 3);
Point p2 = Space.translate(p1, new
Vector(11, 47));
String result = "p1: " + p1
+ " p2: " + p2;
System.out.println(result);
assertEquals(result,
"p1: x: 1 y: 2 z: 3 p2: x: 2
y: 3 z: 4");
}
public static void main(String[] args)
{
junit.textui.TestRunner.run(MessengerDemo.class);
}
} ///:~
Since the goal of a messenger is only to carry data, that
data is made public for easy access. However, you may also have reasons
to make the fields private.
Messenger’s big brother is the collecting parameter,
whose job is to capture information from the method to which it is passed.
Generally, this is used when the collecting parameter is passed to multiple
methods, so it’s like a bee collecting pollen.
A container makes an especially useful collecting parameter,
since it is already set up to dynamically add objects:
//:
simplifying:CollectingParameterDemo.java
package simplifying;
import java.util.*;
import junit.framework.*;
class CollectingParameter extends
ArrayList {}
class Filler {
public void f(CollectingParameter cp) {
cp.add("accumulating");
}
public void g(CollectingParameter cp) {
cp.add("items");
}
public void h(CollectingParameter cp) {
cp.add("as we go");
}
}
public class CollectingParameterDemo
extends TestCase {
public void test() {
Filler filler = new Filler();
CollectingParameter cp = new
CollectingParameter();
filler.f(cp);
filler.g(cp);
filler.h(cp);
String result = "" + cp;
System.out.println(cp);
assertEquals(result,"[accumulating, items, as we go]");
}
public static void main(String[] args)
{
junit.textui.TestRunner.run(
CollectingParameterDemo.class);
}
} ///:~
The collecting parameter must have some way to set or insert
values. Note that by this definition, a messenger could be used as a collecting
parameter. The key is that a collecting parameter is passed about and modified
by the methods it is passed to.
The two patterns described here are solely used to control the
quantity of objects.
Singleton could actually be thought of as a special
case of Object Pool, but the applications of the Object Pool tend
to be uniqe enough from Singleton that it’s worth treating the two
separately.
#[BT_21]#Possibly the
simplest design pattern is the singleton, which is a way to provide one
and only one object of a particular type. An important aspect of Singleton is
that you provide a global access point, so singletons are often a solution for
what you would have used a global variable for in C. In addition, a singleton
often has the characteristics of a registry or lookup service – it’s a place
you go to find references to other objects.
Singletons can be found in the Java libraries, but here’s a
more direct example:
//: singleton:SingletonPattern.java
// The Singleton design pattern: you can
// never instantiate more than one.
package singleton;
import junit.framework.*;
// Since this isn't inherited from a
Cloneable
// base class and cloneability isn't
added,
// making it final prevents cloneability
from
// being added through inheritance:
final class Singleton {
private static Singleton s = new
Singleton(47);
private int i;
private Singleton(int x) { i = x; }
public static Singleton getReference()
{
return s;
}
public int getValue() { return i; }
public void setValue(int x) { i = x; }
}
public class SingletonPattern extends
TestCase {
public void test() {
Singleton s =
Singleton.getReference();
String result = "" +
s.getValue();
System.out.println(result);
assertEquals(result, "47");
Singleton s2 =
Singleton.getReference();
s2.setValue(9);
result = "" + s.getValue();
System.out.println(result);
assertEquals(result, "9");
try {
// Can't do this: compile-time
error.
// Singleton s3 =
(Singleton)s2.clone();
} catch(Exception e) {
throw new RuntimeException(e);
}
}
public static void main(String[] args)
{
junit.textui.TestRunner.run(SingletonPattern.class);
}
} ///:~
#[BT_22]#
#[BT_23]#The key to
creating a singleton is to prevent the client programmer from having any way to
create an object except the ways you provide. You must make all constructors private, and you must create at least one constructor to
prevent the compiler from synthesizing a default constructor for you (which it will create using package access).
#[BT_24]#At this point,
you decide how you’re going to create your object. Here, it’s created
statically, but you can also wait until the client programmer asks for one and
create it on demand. In any case, the object should be stored privately. You
provide access through public methods. Here, getReference( )
produces the reference to the Singleton object. The rest of the
interface (getValue( ) and setValue( )) is the regular
class interface.
#[BT_25]#Java also allows
the creation of objects through cloning. In this example, making the class final
prevents cloning. Since Singleton is inherited directly from Object,
the clone( ) method remains protected so it cannot be used
(doing so produces a compile-time error). However, if you’re inheriting from a
class hierarchy that has already overridden clone( ) as public
and implemented Cloneable, the way to prevent cloning is to override clone( )
and throw a CloneNotSupportedException as described in Appendix A of Thinking
in Java, 2nd edition. (You could also override clone( )
and simply return this, but that would be deceiving since the client
programmer would think they were cloning the object, but would instead still be
dealing with the original.) Actually, this isn’t precisely true, because even
in the above situation someone could still use reflection to invoke clone( )
[[is this true? clone( ) is still protected so I’m not so sure. If it is
true, you’d have to throw CloneNotSupportedException as the only way to
guarantee un-cloneability ]]
1.
SingletonPattern.java always creates an object, even if
it’s never used. Modify this program to use lazy initialization, so the
singleton object is only created the first time that it is needed.
2.
Create a registry/lookup service that accepts a Java interface
and produces a reference to an object that implements that interface.
#[BT_26]#Note that you
aren’t restricted to creating only one object. This is also a technique to
create a limited pool of objects. In that situation, however, you can be
confronted with the problem of sharing objects in the pool. If this is an issue,
you can create a solution involving a check-out and check-in of the shared
objects.AsAAAAA
As an example, consider a database. Commercial databases
often restrict the number of connections that you can use at any one time. Here
is an implementation that uses an object pool to manage the connections. First,
the basic concept of managing a pool of objects is implemented as a separate
class:
//: singleton:PoolManager.java
package singleton;
import java.util.*;
public class PoolManager {
private static class PoolItem {
boolean inUse = false;
Object item;
PoolItem(Object item) { this.item =
item; }
}
private ArrayList items = new
ArrayList();
public void add(Object item) {
items.add(new PoolItem(item));
}
static class EmptyPoolException extends
Exception {}
public Object get() throws
EmptyPoolException {
for(int i = 0; i < items.size();
i++) {
PoolItem pitem =
(PoolItem)items.get(i);
if(pitem.inUse == false) {
pitem.inUse = true;
return pitem.item;
}
}
// Fail early:
throw new EmptyPoolException();
// return null; // Delayed failure
}
public void release(Object item) {
for(int i = 0; i < items.size();
i++) {
PoolItem pitem =
(PoolItem)items.get(i);
if(item == pitem.item) {
pitem.inUse = false;
return;
}
}
throw new RuntimeException(item +
" not found");
}
} ///:~
//: singleton:ConnectionPoolDemo.java
package singleton;
import junit.framework.*;
interface Connection {
Object get();
void set(Object x);
}
class ConnectionImplementation implements
Connection {
public Object get() { return null; }
public void set(Object s) {}
}
class ConnectionPool { // A singleton
private static PoolManager pool = new
PoolManager();
public static void addConnections(int
number) {
for(int i = 0; i < number; i++)
pool.add(new
ConnectionImplementation());
}
public static Connection
getConnection()
throws PoolManager.EmptyPoolException {
return (Connection)pool.get();
}
public static void releaseConnection(Connection
c) {
pool.release(c);
}
}
public class ConnectionPoolDemo extends
TestCase {
static {
ConnectionPool.addConnections(5);
}
public void test() {
Connection c = null;
try {
c = ConnectionPool.getConnection();
} catch
(PoolManager.EmptyPoolException e) {
throw new RuntimeException(e);
}
c.set(new Object());
c.get();
ConnectionPool.releaseConnection(c);
}
public void test2() {
Connection c = null;
try {
c = ConnectionPool.getConnection();
} catch
(PoolManager.EmptyPoolException e) {
throw new RuntimeException(e);
}
c.set(new Object());
c.get();
ConnectionPool.releaseConnection(c);
}
public static void main(String args[])
{
junit.textui.TestRunner.run(ConnectionPoolDemo.class);
}
} ///:~
1.
Add unit tests to ConnectionPoolDemo.java to demonstrate the
problem that the client may release the connection but still continue to use
it.
#[BT_35]#
#[BT_36]#
#[BT_92]#Both Proxy
and State provide a surrogate class that you use in your code; the real
class that does the work is hidden behind this surrogate class. When you call a
method in the surrogate, it simply turns around and calls the method in the
implementing class. These two patterns are so similar that the Proxy is
simply a special case of State. One is tempted to just lump the two
together into a pattern called Surrogate, but the term “proxy” has a
long-standing and specialized meaning, which probably explains the reason for
the two different patterns.
#[BT_93]#The basic idea is
simple: from a base class, the surrogate is derived along with the class or
classes that provide the actual implementation:
#[BT_94]#
#[BT_95]#When a surrogate
object is created, it is given an implementation to which to send all of the
method calls.
#[BT_96]#Structurally, the
difference between Proxy and State is simple: a Proxy has
only one implementation, while State has more than one. The application
of the patterns is considered (in Design Patterns) to be distinct: Proxy
is used to control access to its implementation, while State allows you
to change the implementation dynamically. However, if you expand your notion of
“controlling access to implementation” then the two fit neatly together.
Proxy: fronting for another object
#[BT_97]#If we implement Proxy
by following the above diagram, it looks like this:
//: proxy:ProxyDemo.java
// Simple demonstration of the Proxy
pattern.
package proxy;
import junit.framework.*;
interface ProxyBase {
void f();
void g();
void h();
}
class Proxy implements ProxyBase {
private ProxyBase implementation;
public Proxy() {
implementation = new
Implementation();
}
// Pass method calls to the
implementation:
public void f() { implementation.f(); }
public void g() { implementation.g(); }
public void h() { implementation.h(); }
}
class Implementation implements ProxyBase
{
public void f() {
System.out.println("Implementation.f()");
}
public void g() {
System.out.println("Implementation.g()");
}
public void h() {
System.out.println("Implementation.h()");
}
}
public class ProxyDemo extends TestCase {
Proxy p = new Proxy();
public void test() {
// This just makes sure it will
complete
// without throwing an exception.
p.f();
p.g();
p.h();
}
public static void main(String args[])
{
junit.textui.TestRunner.run(ProxyDemo.class);
}
} ///:~
#[BT_98]#
#[BT_99]#Of course, it
isn’t necessary that Implementation have the same interface as Proxy;
as long as Proxy is somehow “speaking for” the class that it is
referring method calls to then the basic idea is satisfied (note that this
statement is at odds with the definition for Proxy in GoF). However, it is
convenient to have a common interface so that Implementation is forced
to fulfill all the methods that Proxy needs to call.
//: proxy:PoolManager.java
package proxy;
import java.util.*;
public class PoolManager {
private static class PoolItem {
boolean inUse = false;
Object item;
PoolItem(Object item) { this.item =
item; }
}
public class ReleasableReference { //
Used to build the proxy
private PoolItem reference;
private boolean released = false;
public ReleasableReference(PoolItem
reference) {
this.reference = reference;
}
public Object getReference() {
if(released)
throw new RuntimeException(
"Tried to use reference
after it was released");
return reference.item;
}
public void release() {
released = true;
reference.inUse = false;
}
}
private ArrayList items = new
ArrayList();
public void add(Object item) {
items.add(new PoolItem(item));
}
// Different (better?) approach to
running out of items:
public static class EmptyPoolItem {}
public ReleasableReference get() {
for(int i = 0; i < items.size();
i++) {
PoolItem pitem =
(PoolItem)items.get(i);
if(pitem.inUse == false) {
pitem.inUse = true;
return new ReleasableReference(pitem);
}
}
// Fail as soon as you try to cast
it:
// return new EmptyPoolItem();
return null; // temporary
}
} ///:~
//: proxy:ConnectionPoolProxyDemo.java
package proxy;
import junit.framework.*;
interface Connection {
Object get();
void set(Object x);
void release();
}
class ConnectionImplementation implements
Connection {
public Object get() { return null; }
public void set(Object s) {}
public void release() {} // Never
called directly
}
class ConnectionPool { // A singleton
private static PoolManager pool = new
PoolManager();
private ConnectionPool() {} // Prevent
synthesized constructor
public static void addConnections(int
number) {
for(int i = 0; i < number; i++)
pool.add(new ConnectionImplementation());
}
public static Connection
getConnection() {
PoolManager.ReleasableReference rr =
(PoolManager.ReleasableReference)pool.get();
if(rr == null) return null;
return new ConnectionProxy(rr);
}
// The proxy as a nested class:
private static
class ConnectionProxy implements
Connection {
private
PoolManager.ReleasableReference implementation;
public
ConnectionProxy(PoolManager.ReleasableReference rr) {
implementation = rr;
}
public Object get() {
return
((Connection)implementation.getReference()).get();
}
public void set(Object x) {
((Connection)implementation.getReference()).set(x);
}
public void release() {
implementation.release(); }
}
}
public class ConnectionPoolProxyDemo
extends TestCase {
static {
ConnectionPool.addConnections(5);
}
public void test() {
Connection c =
ConnectionPool.getConnection();
c.set(new Object());
c.get();
c.release();
}
public void testDisable() {
Connection c =
ConnectionPool.getConnection();
String s = null;
c.set(new Object());
c.get();
c.release();
try {
c.get();
} catch(Exception e) {
s = e.getMessage();
System.out.println(s);
}
assertEquals(s,
"Tried to use reference after
it was released");
}
public static void main(String args[])
{
junit.textui.TestRunner.run(
ConnectionPoolProxyDemo.class);
}
} ///:~
In JDK 1.3, the Dynamic Proxy was introduced.
Although a little complex at first, this is an intruiging tool.
Here's an interesting little starting example, which works
and proves that yes, indeed, the invocation handler is being called so the
proxying etc. is actually working. So it's pretty cool, and it's in my head
now, but I still have to figure out something reasonable to do with the
invocation handler to come up with a useful example...
// proxy:DynamicProxyDemo.java
// Broken in JDK 1.4.1_01
package proxy;
import java.lang.reflect.*;
interface Foo {
void f(String s);
void g(int i);
String h(int i, String s);
}
public class DynamicProxyDemo {
public static void main(String[]
clargs) {
Foo prox =
(Foo)Proxy.newProxyInstance(
Foo.class.getClassLoader(),
new Class[]{ Foo.class },
new InvocationHandler() {
public Object invoke(
Object proxy, Method method,
Object[] args) {
System.out.println(
"InvocationHandler
called:" +
"\n\tMethod = " +
method);
if (args != null) {
System.out.println("\targs = ");
for (int i = 0; i <
args.length; i++)
System.out.println("\t\t" + args[i]);
}
return null;
}
});
prox.f("hello");
prox.g(47);
prox.h(47, "hello");
}
} ///:~
Exercise: Use the Java dynamic proxy to create an
object that acts as a front end for a simple configuration file. For example,
in good_stuff.txt you can have entries like this:
A client programmer of this NeatPropertyBundle could
then write:
NeatPropertyBundle p =
new
NeatPropertyBundle("good_stuff");
System.out.println(p.a);
System.out.println(p.b);
System.out.println(p.c);
The contents of the configuration file can contain anything,
with any variable names. The dynamic proxy will either map to the name or tell
you it doesn’t exist somehow (probably by returning null). If you set a
property and it doesn’t already exist, the dynamic proxy will create the new
entry. The toString( ) method should display all the current entries.
Exercise: similar to the previous exercise, use the
Java dynamic proxy to make a connection to the DOS Autoexec.bat file.
Exercise: Accept an SQL query which returns data,
then read the DB metadata. Now, for each record, provide an object which has
attributes corresponding to the column names and of appropriate data types.
Exercise: Create a simple server and client that uses
XML-RPC. Each object the client returns should use the dynamic proxy concept to
exercise the remote methods.
Andrea writes:
I'm not sure about the exercises
you suggest, except for the last one. The thing is that I like to think at
invocation handler as somthing providing features that are orthogonal to the
ones provided by the object being "proxied".
In other words: the implementation
of the invocation handler is completely independent from the interface(s) of
the object that the dynamically-generated proxy represent. Which means that
once you have implemented an invocation handler, you can use for any class that
exposes interfaces, even for classes and interfaces that were not present when
the handler was implemented.
That's why I say the the handler
provides services that are orthogonal to the ones provided by the proxied
object. Rickard has a few handlers in his SmartWorld example, and they one I
like the best is a call-retry handler. It basically makes a call into the
actual object, and if the call generates an exception if waits for a while,
then makes the same call again for a total of three times. If all three calls
fails, it returns an exception. And you can use such a handler on _any_ class.
The implementation is way too
complex for what you are trying to demonstrate. I'm using this example just to
explain what I mean by orthogonal services.
In your list of exercises, the only
one that, in my opinion, makes sense to implement using dynamic proxies is the
last one, the one using XML-RPC to communicate with an object. And that's
because the mechanism you use to dispatch the message (XML-RPC) is orthogonal
to the services provided by the object you want to reach.
State: changing object behavior
An object that appears to change its class.
Indications: conditional code in most or all methods.
#[BT_100]#The State
pattern switches from one implementation to another during the lifetime of the
surrogate, in order to produce different behavior from the same method call(s).
It’s a way to improve the implementation of your code when you seem to be doing
a lot of testing inside each of your methods before deciding what to do for
that method. For example, the fairy tale of the frog-prince contains an object
(the creature) that behaves differently depending on what state it’s in. You
could implement this using a boolean that you test:
//: state:KissingPrincess.java
package state;
import junit.framework.*;
class Creature {
private boolean isFrog = true;
public void greet() {
if(isFrog)
System.out.println("Ribbet!");
else
System.out.println("Darling!");
}
public void kiss() {
isFrog = false; }
}
public class KissingPrincess extends
TestCase {
Creature creature = new Creature();
public void test() {
creature.greet();
creature.kiss();
creature.greet();
}
public static void main(String args[])
{
junit.textui.TestRunner.run(KissingPrincess.class);
}
} ///:~
However, the greet() method, and any other methods
that must test isFrog before they perform their operations, ends up with
awkward code. By delegating the operations to a State object that can be
changed, this code is simplified.
//: state:KissingPrincess2.java
package state;
import junit.framework.*;
class Creature {
private interface State {
String response();
}
private class Frog implements State {
public String response() { return
"Ribbet!"; }
}
private class Prince implements State {
public String response() { return
"Darling!"; }
}
private State state = new Frog();
public void greet() {
System.out.println(state.response());
}
public void kiss() { state = new
Prince(); }
}
public class KissingPrincess2 extends
TestCase {
Creature creature = new Creature();
public void test() {
creature.greet();
creature.kiss();
creature.greet();
}
public static void main(String args[])
{
junit.textui.TestRunner.run(KissingPrincess2.class);
}
} ///:~
In addition, changes to the State are automatically
propagated throughout, rather than requiring an edit across the class methods
in order to effect changes.
Here’s the basic structure of State:[[ [[
//: state:StateDemo.java
// Simple demonstration of the State
pattern.
package state;
import junit.framework.*;
interface State {
void operation1();
void operation2();
void operation3();
}
class ServiceProvider {
private State state;
public ServiceProvider(State state) {
this.state = state;
}
public void changeState(State newState)
{
state = newState;
}
// Pass method calls to the
implementation:
public void service1() {
// ...
state.operation1();
// ...
state.operation3();
}
public void service2() {
// ...
state.operation1();
// ...
state.operation2();
}
public void service3() {
// ...
state.operation3();
// ...
state.operation2();
}
}
class Implementation1 implements State {
public void operation1() {
System.out.println("Implementation1.operation1()");
}
public void operation2() {
System.out.println("Implementation1.operation2()");
}
public void operation3() {
System.out.println("Implementation1.operation3()");
}
}
class Implementation2 implements State {
public void operation1() {
System.out.println("Implementation2.operation1()");
}
public void operation2() {
System.out.println("Implementation2.operation2()");
}
public void operation3() {
System.out.println("Implementation2.operation3()");
}
}
public class StateDemo extends TestCase
{
static void run(ServiceProvider sp) {
sp.service1();
sp.service2();
sp.service3();
}
ServiceProvider sp =
new ServiceProvider(new
Implementation1());
public void test() {
run(sp);
sp.changeState(new
Implementation2());
run(sp);
}
public static void main(String args[])
{
junit.textui.TestRunner.run(StateDemo.class);
}
} ///:~
#[BT_101]#
#[BT_102]#In main( ),
you can see that the first implementation is used for a bit, then the second
implementation is swapped in and that is used.
There are a number of details that are choices that you must
make according to the needs of your own implementation, such as whether the
fact that you are using State is exposed to the client, and how the changes to
State are made. Sometimes (as in the Swing LayoutManager) the client may pass
in the object directly, but in KissingPrincess2.java the fact that State is
used is invisible to the client. In addition, the mechanism for changing state
may be simple or complex – in State Machine, described later in this book,
larger sets of states and different mechanisms for changing are explored.
The Swing LayoutManager example mentioned above is an
interesting example because it show behavior of both Strategy and State.
#[BT_103]#The difference
between Proxy and State is in the problems that are solved. The
common uses for Proxy as described in Design Patterns are:
1.
Remote proxy. This proxies for an object in a different
address space. A remote proxy is created for you automatically by the RMI
compiler rmic as it creates stubs and skeletons.
2.
Virtual proxy. This provides “lazy initialization” to
create expensive objects on demand.
3.
Protection proxy. Used when you don’t want the client
programmer to have full access to the proxied object.
4.
Smart reference. To add additional actions when the
proxied object is accessed. For example, or to keep track of the number of
references that are held for a particular object, in order to implement the copy-on-write
idiom and prevent object aliasing. A simpler example is keeping track of the
number of calls to a particular method.
#[BT_104]#You could look
at a Java reference as a kind of protection proxy, since it controls access to
the actual object on the heap (and ensures, for example, that you don’t use a null
reference).
#[BT_105]#[[ Rewrite this:
In Design Patterns, Proxy and State are not seen as
related to each other because the two are given (what I consider arbitrarily)
different structures. State, in particular, uses a separate
implementation hierarchy but this seems to me to be unnecessary unless you have
decided that the implementation is not under your control (certainly a
possibility, but if you own all the code there seems to be no reason not to
benefit from the elegance and helpfulness of the single base class). In
addition, Proxy need not use the same base class for its implementation,
as long as the proxy object is controlling access to the object it “fronting”
for. Regardless of the specifics, in both Proxy and State a
surrogate is passing method calls through to an implementation object.]]]
State can be found everywhere because it’s such a
fundamental idea. For example, in Builder, the “Director” uses a backend
Builder object to produce different behaviors.Stat
Alexander Stepanov thought for years about the problem of
generic programming techniques before creating the STL (along with Dave
Musser). He came to the conclusion that all algorithms are defined on algebraic
structures – what we would call containers.
#[BT_193]#In the process,
he realized that iterators are central to the use of algorithms, because they decouple
the algorithms from the specific type of container that the algorithm might
currently be working with. This means that you can describe the algorithm
without worrying about the particular sequence it is operating on. More
generally, any code that you write using iterators is decoupled from the
data structure that the code is manipulating, and thus your code is more
general and reusable.
#[BT_194]#The use of
iterators also extends your code into the realm of functional programming,
whose objective is to describe what a program is doing at every step
rather than how it is doing it. That is, you say “sort” rather than
describing the sort. The objective of the C++ STL was to provide this generic
programming approach for C++ (how successful this approach will actually be
remains to be seen).
#[BT_195]#If you’ve used
containers in Java (and it’s hard to write code without using them), you’ve
used iterators – in the form of the Enumeration in Java 1.0/1.1 and the Iterator
in Java 2. So you should already be familiar with their general use. If not,
see Chapter 9, Holding Your Objects, under Iterators in Thinking
in Java, 2nd edition (freely downloadable from www.BruceEckel.com).
#[BT_196]#Because the Java
2 containers rely heavily on iterators they become excellent candidates for
generic/functional programming techniques. This chapter will explore these
techniques by converting the STL algorithms to Java, for use with the Java 2
container library.
#[BT_197]#In Thinking
in Java, 2nd edition, I show the creation of a type-safe
container that will only accept a particular type of object. A reader, Linda
Pazzaglia, asked for the other obvious type-safe component, an iterator that
would work with the basic java.util containers, but impose the constraint
that the type of objects that it iterates over be of a particular type.
#[BT_198]#If Java ever
includes a template mechanism, this kind of iterator will have the added
advantage of being able to return a specific type of object, but without
templates you are forced to return generic Objects, or to require a bit
of hand-coding for every type that you want to iterate through. I will take the
former approach.
#[BT_199]#A second design
decision involves the time that the type of object is determined. One approach
is to take the type of the first object that the iterator encounters, but this
is problematic because the containers may rearrange the objects according to an
internal ordering mechanism (such as a hash table) and thus you may get
different results from one iteration to the next. The safe approach is to
require the user to establish the type during construction of the iterator.
#[BT_200]#Lastly, how do
we build the iterator? We cannot rewrite the existing Java library classes that
already produce Enumerations and Iterators. However, we can use
the Decorator design pattern, and create a class that simply wraps the Enumeration
or Iterator that is produced, generating a new object that has the
iteration behavior that we want (which is, in this case, to throw a RuntimeException
if an incorrect type is encountered) but with the same interface as the
original Enumeration or Iterator, so that it can be used in the
same places (you may argue that this is actually a Proxy pattern, but
it’s more likely Decorator because of its intent). Here is the code:
//:
com:bruceeckel:util:TypedIterator.java
package com.bruceeckel.util;
import java.util.*;
public class TypedIterator implements
Iterator {
private Iterator imp;
private Class type;
public TypedIterator(Iterator it, Class
type) {
imp = it;
this.type = type;
}
public boolean hasNext() {
return imp.hasNext();
}
public void remove() { imp.remove(); }
public Object next() {
Object obj = imp.next();
if(!type.isInstance(obj))
throw new ClassCastException(
"TypedIterator for type
" + type +
" encountered type: " +
obj.getClass());
return obj;
}
} ///:~
1.
Create an example of the “virtual proxy.”
2.
Create an example of the “Smart reference” proxy where you keep
count of the number of method calls to a particular object.
3.
Create a program similar to certain DBMS systems that only allow
a certain number of connections at any time. To implement this, use a
singleton-like system that controls the number of “connection” objects that it
creates. When a user is finished with a connection, the system must be informed
so that it can check that connection back in to be reused. To guarantee this,
provide a proxy object instead of a reference to the actual connection, and
design the proxy so that it will cause the connection to be released back to
the system.
4.
Using the State pattern, make a class called UnpredictablePerson
which changes the kind of response to its hello( ) method depending
on what kind of Mood it’s in. Add an additional kind of Mood
called Prozac.
5.
Create a simple copy-on write implementation.
6.
The java.util.Map has no way to automatically load a
two-dimensional array of objects into a Map as key-value pairs. Create
an adapter class that does this.
7.
Create an Adapter Factory that dynamically finds and
produces the adapter that you need to connect a given object to a desired
interface.
8.
Solve the above exercise using the dynamic proxy that’s
part of the Java standard library.
9.
Modify the Object Pool solution so that the objects are returned
to the pool automatically after a certain amount of time.
10.
Modify the above solution to use “leasing” so that the client can
renew the lease on the object to prevent it from being automatically released
by the timer.
11.
Modify the Object Pool system to take threading issues into
account.
Applying the “once and only once” principle produces the most
basic pattern of putting code that changes into a method.
This can be expressed two ways:
Strategy: choosing the
algorithm at run-time
#[BT_231]#Strategy
also adds a “Context” which can be a surrogate class that controls the
selection and use of the particular strategy object—just like State!
Here’s what it looks like:
//: strategy:StrategyPattern.java
package strategy;
import com.bruceeckel.util.*; // Arrays2.toString()
import junit.framework.*;
// The strategy interface:
interface FindMinima {
// Line is a sequence of points:
double[] algorithm(double[] line);
}
// The various strategies:
class LeastSquares implements FindMinima
{
public double[] algorithm(double[]
line) {
return new double[] { 1.1, 2.2 }; //
Dummy
}
}
class NewtonsMethod implements FindMinima
{
public double[] algorithm(double[]
line) {
return new double[] { 3.3, 4.4 }; //
Dummy
}
}
class Bisection implements FindMinima {
public double[] algorithm(double[]
line) {
return new double[] { 5.5, 6.6 }; //
Dummy
}
}
class ConjugateGradient implements
FindMinima {
public double[] algorithm(double[]
line) {
return new double[] { 3.3, 4.4 }; //
Dummy
}
}
// The "Context" controls the
strategy:
class MinimaSolver {
private FindMinima strategy;
public MinimaSolver(FindMinima strat) {
strategy = strat;
}
double[] minima(double[] line) {
return strategy.algorithm(line);
}
void changeAlgorithm(FindMinima
newAlgorithm) {
strategy = newAlgorithm;
}
}
public class StrategyPattern extends
TestCase {
MinimaSolver solver =
new MinimaSolver(new LeastSquares());
double[] line = {
1.0, 2.0, 1.0, 2.0, -1.0,
3.0, 4.0, 5.0, 4.0 };
public void test() {
System.out.println(
Arrays2.toString(solver.minima(line)));
solver.changeAlgorithm(new
Bisection());
System.out.println(
Arrays2.toString(solver.minima(line)));
}
public static void main(String args[])
{
junit.textui.TestRunner.run(StrategyPattern.class);
}
} ///:~
Note similarity with template method – TM claims distinction
that it has more than one method to call, does things piecewise. However, it’s
not unlikely that strategy object would have more than one method call;
consider Shalloway’s order fulfullment system with country information in each
strategy.
Strategy example from JDK: comparator objects.
Although GoF says that Policy is just another name for
strategy, their use of Strategy implicitly assumes a single method in the
strategy object – that you’ve broken out your changing algorithm as a single
piece of code.
Others use Policy to mean
an object that has multiple methods that may vary independently from class to
class. This gives more flexibility than being restricted to a single method.
For example, a shipping policy for a product can be used to
describe shipping issues for sending a package to various different countries.
This may include the available methods of shipping, how to calculate postage or
shipping cost, customs requirements and fees, and special handling costs. All
these things may vary independently of each other, and more importantly you may
need the information from each at different points in the shipping process.
It also seems generally useful to distinguish Strategies
with single methods from Policies with multiple methods.
#[BT_232]#
#[BT_233]#
#[BT_86]#An application
framework allows you to inherit from a class or set of classes and create a new
application, reusing most of the code in the existing classes and overriding
one or more methods in order to customize the application to your needs. A
fundamental concept in the application framework is the Template Method
which is typically hidden beneath the covers and drives the application by
calling the various methods in the base class (some of which you have
overridden in order to create the application).
#[BT_87]#For example, whenever
you create an applet you’re using an application framework: you inherit from JApplet
and then override init( ). The applet mechanism (which is a Template
Method) does the rest by drawing the screen, handling the event
loop, resizing, etc.
#[BT_88]#An important
characteristic of the Template Method is that it is defined in the base
class and cannot be changed. It’s sometimes a private method but it’s
virtually always final. It calls other base-class methods (the ones you
override) in order to do its job, but it is usually called only as part of an
initialization process (and thus the client programmer isn’t necessarily able
to call it directly).
//: templatemethod:TemplateMethod.java
// Simple demonstration of Template
Method.
package templatemethod;
import junit.framework.*;
abstract class ApplicationFramework {
public ApplicationFramework() {
templateMethod(); // Dangerous!
}
abstract void customize1();
abstract void customize2();
final void templateMethod() {
for(int i = 0; i < 5; i++) {
customize1();
customize2();
}
}
}
// Create a new "application":
class MyApp extends ApplicationFramework
{
void customize1() {
System.out.print("Hello ");
}
void customize2() {
System.out.println("World!");
}
}
public class TemplateMethod extends
TestCase {
MyApp app = new MyApp();
public void test() {
// The MyApp constructor does all the
work.
// This just makes sure it will
complete
// without throwing an exception.
}
public static void main(String args[])
{
junit.textui.TestRunner.run(TemplateMethod.class);
}
} ///:~
#[BT_89]#
#[BT_90]#The base-class
constructor is responsible for performing the necessary initialization and then
starting the “engine” (the template method) that runs the application (in a GUI
application, this “engine” would be the main event loop). The client programmer
simply provides definitions for customize1( ) and customize2( )
and the “application” is ready to run.
1.
Create a framework that takes a list of file names on the command
line. It opens each file except the last for reading, and the last for writing.
The framework will process each input file using an undetermined policy and
write the output to the last file. Inherit to customize this framework to
create two separate applications:
1) Converts all the letters in each file to uppercase.
2) Searches the files for words given in the first file.
#[BT_91]#
Objects are created by cloning a prototypical instance. An
example of this appears in the “Pattern Refactoring” chapter.
The goal of builder is to separate the construction from the
“representation,” to allow multiple different representations. The construction
process stays the same, but the resulting object has different possible
representations. GoF points out that the main difference with Abstract Factory
is that a Builder creates the object step-by-step, so the fact that the
creation process is spread out in time seems to be important. In addition, it
seems that the “director” gets a stream of pieces that it passes to the
Builder, and each piece is used to perform one of the steps in the build
process.
One example given in GoF is that of a text format converter.
The incoming format is RTF, and once it is parsed the directives are passed to
the text converter, which may be implemented in different ways depending on
whether the resulting format is ASCII, TeX, or a “GUI Text Widget.” Although
the resulting “object” (the entire converted text file) is created over time,
if you consider the conversion of each RTF directive to be an object, this
feels to me a little more like Bridge, because the specific types of converters
extend the interface of the base class. Also, the general solution to the problem
would allow multiple readers on the “front end” and multiple converters on the
“back end,” which is a primary characteristic of Bridge.
To me, the fact that Builder has multiple steps in creating
an object, and those steps are accessed externally to the Builder object, is
the essence of what distinguishes it (structurally, anyway) from a regular
factory. However, GoF emphasizes that you’re able to create different
representations using the same process. They never define exactly what they
mean by representation. (Does the “representation” involve an object that is
too large? Would the need for Builder vanish if the representation was broken
into smaller objects?)
The other example in GoF creates a maze
object and adds rooms within the maze and doors within the rooms. Thus it is a
multistep process, but alas, the different “representations” are the “Standard”
and “Complex” mazes – not really different kinds of mazes, but instead
different complexity. I think I would have tried to create one maze builder
that could handle arbitrarily complex mazes. The final variation of the maze
builder is something that doesn’t create mazes at all, but instead counts the
rooms in an existing maze.
Neither the RTF converter nor the Mazebuilder example makes
an overwhelmingly compelling case for Builder. Readers have suggested that the
output of the Sax XML parser, and standard compiler parsers, might naturally be
fed into a Builder.
Here’s an example that may be a little more compelling, or
at least give more of an idea of what Builder is trying to do. Media may be
constructed into different representations, in this case books, magazines and
web sites. The example argues that the steps involved are the same, and so can
be abstracted into the director class.
//: builder:BuildMedia.java
// Example of the Builder pattern
package builder;
import java.util.*;
import junit.framework.*;
// Different "representations"
of media:
class Media extends ArrayList {}
class Book extends Media {}
class Magazine extends Media {}
class WebSite extends Media {}
// ... contain different kinds of media
items:
class MediaItem {
private String s;
public MediaItem(String s) { this.s =
s; }
public String toString() { return s; }
}
class Chapter extends MediaItem {
public Chapter(String s) { super(s); }
}
class Article extends MediaItem {
public Article(String s) { super(s); }
}
class WebItem extends MediaItem {
public WebItem(String s) { super(s); }
}
// ... but use the same basic
construction steps:
class MediaBuilder {
public void buildBase() {}
public void addMediaItem(MediaItem
item) {}
public Media getFinishedMedia() {
return null; }
}
class BookBuilder extends MediaBuilder {
private Book b;
public void buildBase() {
System.out.println("Building
book framework");
b = new Book();
}
public void addMediaItem(MediaItem
chapter) {
System.out.println("Adding
chapter " + chapter);
b.add(chapter);
}
public Media getFinishedMedia() {
return b; }
}
class MagazineBuilder extends
MediaBuilder {
private Magazine m;
public void buildBase() {
System.out.println("Building
magazine framework");
m = new Magazine();
}
public void addMediaItem(MediaItem
article) {
System.out.println("Adding
article " + article);
m.add(article);
}
public Media getFinishedMedia() {
return m; }
}
class WebSiteBuilder extends MediaBuilder
{
private WebSite w;
public void buildBase() {
System.out.println("Building web
site framework");
w = new WebSite();
}
public void addMediaItem(MediaItem
webItem) {
System.out.println("Adding web
item " + webItem);
w.add(webItem);
}
public Media getFinishedMedia() {
return w; }
}
class MediaDirector { // a.k.a.
"Context"
private MediaBuilder mb;
public MediaDirector(MediaBuilder mb) {
this.mb = mb; // Strategy-ish
}
public Media produceMedia(List input) {
mb.buildBase();
for(Iterator it = input.iterator();
it.hasNext();)
mb.addMediaItem((MediaItem)it.next());
return mb.getFinishedMedia();
}
};
public class BuildMedia extends TestCase
{
private List input = Arrays.asList(new
MediaItem[] {
new MediaItem("item1"), new
MediaItem("item2"),
new MediaItem("item3"), new
MediaItem("item4"),
});
public void testBook() {
MediaDirector buildBook =
new MediaDirector(new
BookBuilder());
Media book =
buildBook.produceMedia(input);
String result = "book: " +
book;
System.out.println(result);
assertEquals(result,
"book: [item1, item2, item3,
item4]");
}
public void testMagazine() {
MediaDirector buildMagazine =
new MediaDirector(new
MagazineBuilder());
Media magazine =
buildMagazine.produceMedia(input);
String result = "magazine:
" + magazine;
System.out.println(result);
assertEquals(result,
"magazine: [item1, item2,
item3, item4]");
}
public void testWebSite() {
MediaDirector buildWebSite =
new MediaDirector(new
WebSiteBuilder());
Media webSite =
buildWebSite.produceMedia(input);
String result = "web site:
" + webSite;
System.out.println(result);
assertEquals(result,
"web site: [item1, item2,
item3, item4]");
}
public static void main(String[] args)
{
junit.textui.TestRunner.run(BuildMedia.class);
}
} ///:~
Note that in some ways this could be seen as a more
complicated State pattern, since the behavior of the director depends on what
type of builder you use. Instead of simply forwarding the requests through to
the underlying State object, however, the director has a sequence of operations
to perform, and it uses the State object as a Policy to fulfill its job. Thus,
Builder could be described as using a Policy to create objects.
1.
Break a text file up into an input stream of words (consider
using regular expressions for this). Create one Builder that puts the words
into a java.util.TreeSet, and another that produces a java.util.HashMap
containing words and occurrences of those words (that is, it does a word
count).
The odd thing about flyweight, in the company of the other
design patterns, is that it’s a performance hack. It’s generally ideal to
simply make an object for every item in your system, but some problems generate
a prohibitive number of objects, which may result in excessive slowness or
running out of memory.
Flyweight solves this problem by reducing the number of
objects. To do this, you externalize some of the data in an object, so that you
can pretend that you have more objects than you really do. However, this adds
complexity to the interface for using such objects, because you must pass in
additional information to method calls in order to tell the method how to find
the externalized information.
As a very simple example, consider a DataPoint object
that holds an int, a float, and an id that carries the
object number. Suppose you need to create a million of these objects, and then
manipulate them, like so:
//: flyweight:ManyObjects.java
class DataPoint {
private static int count = 0;
private int id = count++;
private int i;
private float f;
public int getI() { return i; }
public void setI(int i) { this.i = i; }
public float getF() { return f; }
public void setF(float f) { this.f = f;
}
public String toString() {
return "id: " + id +
", i = " + i + ", f = " + f;
}
}
public class ManyObjects {
static final int size = 1000000;
public static void main(String[] args)
{
DataPoint[] array = new
DataPoint[size];
for(int i = 0; i < array.length;
i++)
array[i] = new DataPoint();
for(int i = 0; i < array.length;
i++) {
DataPoint dp = array[i];
dp.setI(dp.getI() + 1);
dp.setF(47.0f);
}
System.out.println(array[size -1]);
}
} ///:~
Depending on your computer, this program may take several
seconds to run. More complex objects and more involved operations may cause the
overhead to become untenable. To solve the problem the DataPoint can be
reduced from a million objects to one object by externalizing the data held in
the DataPoint:
//: flyweight:FlyWeightObjects.java
class ExternalizedData {
static final int size = 5000000;
static int[] id = new int[size];
static int[] i = new int[size];
static float[] f = new float[size];
static {
for(int i = 0; i < size; i++)
id[i] = i;
}
}
class FlyPoint {
private FlyPoint() {}
public static int getI(int obnum) {
return ExternalizedData.i[obnum];
}
public static void setI(int obnum, int
i) {
ExternalizedData.i[obnum] = i;
}
public static float getF(int obnum) {
return ExternalizedData.f[obnum];
}
public static void setF(int obnum,
float f) {
ExternalizedData.f[obnum] = f;
}
public static String str(int obnum) {
return "id: " +
ExternalizedData.id[obnum] +
", i = " +
ExternalizedData.i[obnum] +
", f = " +
ExternalizedData.f[obnum];
}
}
public class FlyWeightObjects {
public static void main(String[] args)
{
for(int i = 0; i <
ExternalizedData.size; i++) {
FlyPoint.setI(i, FlyPoint.getI(i) +
1);
FlyPoint.setF(i, 47.0f);
}
System.out.println(
FlyPoint.str(ExternalizedData.size
-1));
}
} ///:~
Since all the data is now in ExternalizedData, each
call to a FlyPoint method must include the index into ExternalizedData.
For consistency, and to remind the reader of the similarity with the implicit this
pointer in method calls, the “this index” is passed in as the first argument.
Naturally, it’s worth repeating admonishments against
premature optimization. “First make it work, then make it fast – if you have
to.” Also, a profiler is the tool to use for discovering performance
bottlenecks, not guesswork.
The use of layered objects to dynamically and transparently add
responsibilities to individual objects is referred to as the decorator
pattern.
#[BT_155]#Used when
subclassing creates too many (& inflexible) classes
#[BT_156]#All decorators
that wrap around the original object must have the same basic interface
#[BT_157]#Dynamic
proxy/surrogate?
#[BT_158]#This accounts
for the odd inheritance structure
#[BT_159]#Tradeoff: coding
is more complicated when using decorators
#[BT_160]#
#[BT_161]#Consider going
down to the local coffee shop, BeanMeUp, for a coffee. There are
typically many different drinks on offer -- espressos, lattes, teas, iced
coffees, hot chocolate to name a few, as well as a number of extras (which cost
extra too) such as whipped cream or an extra shot of espresso. You can also
make certain changes to your drink at no extra cost, such as asking for decaf coffee
instead of regular coffee.
#[BT_162]#Quite clearly if
we are going to model all these drinks and combinations, there will be sizeable
class diagrams. So for clarity we will only consider a subset of the coffees:
Espresso, Espresso Con Panna, Café Late, Cappuccino and Café Mocha. We'll
include 2 extras - whipped cream ("whipped") and an extra shot of
espresso; and three changes - decaf, steamed milk ("wet") and foamed
milk ("dry").
#[BT_163]#One solution is
to create an individual class for every combination. Each class describes the
drink and is responsible for the cost etc. The resulting menu is huge, and a
part of the class diagram would look something like this:
#[BT_164]# 
#[BT_165]#Here is one of
the combinations, a simple implementation of a Cappuccino:
class Cappuccino {
private float cost = 1;
private String description =
"Cappucino";
public float getCost() {
return cost;
}
public String getDescription() {
return description;
}
}
#[BT_166]#
#[BT_167]#The key to using
this method is to find the particular combination you want. So, once you've
found the drink you would like, here is how you would use it, as shown in the
CoffeeShop class in the following code:
//: decorator:nodecorators:CoffeeShop.java
// Coffee example with no decorators
package decorator.nodecorators;
import junit.framework.*;
class Espresso {}
class DoubleEspresso {}
class EspressoConPanna {}
class Cappuccino {
private float cost = 1;
private String description =
"Cappucino";
public float getCost() {
return cost;
}
public String getDescription() {
return description;
}
}
class CappuccinoDecaf {}
class CappuccinoDecafWhipped {}
class CappuccinoDry {}
class CappuccinoDryWhipped {}
class CappuccinoExtraEspresso {}
class CappuccinoExtraEspressoWhipped {}
class CappuccinoWhipped {}
class CafeMocha {}
class CafeMochaDecaf {}
class CafeMochaDecafWhipped {
private float cost = 1.25f;
private String description =
"Cafe Mocha decaf whipped
cream";
public float getCost() {
return cost;
}
public String getDescription() {
return description;
}
}
class CafeMochaExtraEspresso {}
class CafeMochaExtraEspressoWhipped {}
class CafeMochaWet {}
class CafeMochaWetWhipped {}
class CafeMochaWhipped {}
class CafeLatte {}
class CafeLatteDecaf {}
class CafeLatteDecafWhipped {}
class CafeLatteExtraEspresso {}
class CafeLatteExtraEspressoWhipped {}
class CafeLatteWet {}
class CafeLatteWetWhipped {}
class CafeLatteWhipped {}
public class CoffeeShop extends TestCase {
public void testCappuccino() {
// This just makes sure it will
complete
// without throwing an exception.
// Create a plain cappuccino
Cappuccino cappuccino = new
Cappuccino();
System.out.println(cappuccino.getDescription()
+ ": $" +
cappuccino.getCost());
}
public void testCafeMocha() {
// This just makes sure it will
complete
// without throwing an exception.
// Create a decaf cafe mocha with
whipped
// cream
CafeMochaDecafWhipped cafeMocha =
new CafeMochaDecafWhipped();
System.out.println(cafeMocha.getDescription()
+ ": $" +
cafeMocha.getCost());
}
public static void main(String[] args)
{
junit.textui.TestRunner.run(CoffeeShop.class);
}
} ///:~
#[BT_168]#
#[BT_169]#And here is the
corresponding output:
Cafe Mocha decaf whipped cream: $1.25
#[BT_170]#
#[BT_171]#You can see that
creating the particular combination you want is easy, since you are just
creating an instance of a class. However, there are a number of problems with
this approach. Firstly, the combinations are fixed statically so that any
combination a customer may wish to order needs to be created up front.
Secondly, the resulting menu is so huge that finding your particular
combination is difficult and time consuming.
#[BT_172]#Another approach
would be to break the drinks down into the various components such as espresso
and foamed milk, and then let the customer combine the components to describe a
particular coffee.
#[BT_173]#In order to do
this programmatically, we use the Decorator pattern. A Decorator adds
responsibility to a component by wrapping it, but the Decorator conforms to the
interface of the component it encloses, so the wrapping is transparent.
Decorators can also be nested without the loss of this transparency.
#[BT_174]#
#[BT_175]#Methods invoked
on the Decorator can in turn invoke methods in the component, and can of course
perform processing before or after the invocation.
#[BT_176]#So if we added getTotalCost()
and getDescription() methods to the DrinkComponent interface, an
Espresso looks like this:
class Espresso extends Decorator {
private float cost = 0.75f;
private String description = "
espresso";
public Espresso(DrinkComponent
component) {
super(component);
}
public float getTotalCost() {
return component.getTotalCost() +
cost;
}
public String getDescription() {
return component.getDescription() +
description;
}
}
#[BT_177]#
#[BT_178]#You combine the
components to create a drink as follows, as shown in the code below:
//: decorator:alldecorators:CoffeeShop2.java
// Coffee example using decorators
package decorator.alldecorators;
import junit.framework.*;
interface DrinkComponent {
String getDescription();
float getTotalCost();
}
class Mug implements DrinkComponent {
public String getDescription() {
return "mug";
}
public float getTotalCost() {
return 0;
}
}
abstract class Decorator implements
DrinkComponent
{
protected DrinkComponent component;
Decorator(DrinkComponent component) {
this.component = component;
}
public float getTotalCost() {
return component.getTotalCost();
}
public abstract String
getDescription();
}
class Espresso extends Decorator {
private float cost = 0.75f;
private String description = "
espresso";
public Espresso(DrinkComponent
component) {
super(component);
}
public float getTotalCost() {
return component.getTotalCost() +
cost;
}
public String getDescription() {
return component.getDescription() +
description;
}
}
class Decaf extends Decorator {
private String description = "
decaf";
public Decaf(DrinkComponent component)
{
super(component);
}
public String getDescription() {
return component.getDescription() +
description;
}
}
class FoamedMilk extends Decorator {
private float cost = 0.25f;
private String description = "
foamed milk";
public FoamedMilk(DrinkComponent
component) {
super(component);
}
public float getTotalCost() {
return component.getTotalCost() +
cost;
}
public String getDescription() {
return component.getDescription() +
description;
}
}
class SteamedMilk extends Decorator {
private float cost = 0.25f;
private String description = "
steamed milk";
public SteamedMilk(DrinkComponent
component) {
super(component);
}
public float getTotalCost() {
return component.getTotalCost() +
cost;
}
public String getDescription() {
return component.getDescription() +
description;
}
}
class Whipped extends Decorator {
private float cost = 0.25f;
private String description = "
whipped cream";
public Whipped(DrinkComponent
component) {
super(component);
}
public float getTotalCost() {
return component.getTotalCost() +
cost;
}
public String getDescription() {
return component.getDescription() +
description;
}
}
class Chocolate extends Decorator {
private float cost = 0.25f;
private String description = "
chocolate";
public Chocolate(DrinkComponent
component) {
super(component);
}
public float getTotalCost() {
return component.getTotalCost() +
cost;
}
public String getDescription() {
return component.getDescription() +
description;
}
}
public class CoffeeShop2 extends TestCase
{
public void testCappuccino() {
// This just makes sure it will
complete
// without throwing an exception.
// Create a plain cappucino
DrinkComponent cappuccino = new
Espresso(
new FoamedMilk(new Mug()));
System.out.println(cappuccino.
getDescription().trim() + ":
$" +
cappuccino.getTotalCost());
}
public void testCafeMocha() {
// This just makes sure it will
complete
// without throwing an exception.
// Create a decaf cafe mocha with
whipped
// cream
DrinkComponent cafeMocha = new
Espresso(
new SteamedMilk(new Chocolate(new
Whipped(
new Decaf(new Mug())))));
System.out.println(cafeMocha.getDescription().
trim() + ": $" +
cafeMocha.getTotalCost());
}
public static void main(String[] args)
{
junit.textui.TestRunner.run(CoffeeShop2.class);
}
} ///:~
#[BT_179]#
#[BT_180]#This approach
would certainly provide the most flexibility and the smallest menu. You have a
small number of components to choose from, but assembling the description of
the coffee then becomes rather arduous.
#[BT_181]#If you want to
describe a plain cappuccino, you create it with
new Espresso(new FoamedMilk(new Mug()))
#[BT_182]#Creating a decaf
Café Mocha with whipped cream requires an even longer description.
#[BT_183]#The previous
approach takes too long to describe a coffee. There will also be certain
combinations that you will describe regularly, and it would be convenient to
have a quick way of describing them.
#[BT_184]#The 3rd approach
is a mixture of the first 2 approaches, and combines flexibility with ease of
use. This compromise is achieved by creating a reasonably sized menu of basic
selections, which would often work exactly as they are, but if you wanted to
decorate them (whipped cream, decaf etc.) then you would use decorators to make
the modifications. This is the type of menu you are presented with in most
coffee shops.
#[BT_185]#
#[BT_186]#Here is how to
create a basic selection, as well as a decorated selection:
//: decorator:compromise:CoffeeShop3.java
// Coffee example with a compromise of
basic
// combinations and decorators
package decorator.compromise;
import junit.framework.*;
interface DrinkComponent {
float getTotalCost();
String getDescription();
}
class Espresso implements DrinkComponent
{
private String description =
"Espresso";
private float cost = 0.75f;
public float getTotalCost() {
return cost;
}
public String getDescription() {
return description;
}
}
class EspressoConPanna implements
DrinkComponent {
private String description =
"EspressoConPare";
private float cost = 1;
public float getTotalCost() {
return cost;
}
public String getDescription() {
return description;
}
}
class Cappuccino implements
DrinkComponent {
private float cost = 1;
private String description =
"Cappuccino";
public float getTotalCost() {
return cost;
}
public String getDescription() {
return description;
}
}
class CafeLatte implements DrinkComponent
{
private float cost = 1;
private String description = "Cafe
Late";
public float getTotalCost() {
return cost;
}
public String getDescription() {
return description;
}
}
class CafeMocha implements DrinkComponent
{
private float cost = 1.25f;
private String description = "Cafe
Mocha";
public float getTotalCost() {
return cost;
}
public String getDescription() {
return description;
}
}
abstract class Decorator implements
DrinkComponent {
protected DrinkComponent component;
public Decorator(DrinkComponent
component) {
this.component = component;
}
public float getTotalCost() {
return component.getTotalCost();
}
public String getDescription() {
return component.getDescription();
}
}
class ExtraEspresso extends Decorator {
private float cost = 0.75f;
public ExtraEspresso(DrinkComponent
component) {
super(component);
}
public String getDescription() {
return component.getDescription() +
" extra espresso";
}
public float getTotalCost() {
return cost +
component.getTotalCost();
}
}
class Whipped extends Decorator {
private float cost = 0.50f;
public Whipped(DrinkComponent
component) {
super(component);
}
public float getTotalCost() {
return cost +
component.getTotalCost();
}
public String getDescription() {
return component.getDescription() +
" whipped cream";
}
}
class Decaf extends Decorator{
public Decaf(DrinkComponent component)
{
super(component);
}
public String getDescription() {
return component.getDescription() +
" decaf";
}
}
class Dry extends Decorator {
public Dry(DrinkComponent component) {
super(component);
}
public String getDescription() {
return component.getDescription() +
" extra foamed milk";
}
}
class Wet extends Decorator {
public Wet(DrinkComponent component) {
super(component);
}
public String getDescription() {
return component.getDescription() +
" extra steamed milk";
}
}
public class CoffeeShop3 extends TestCase
{
public void testCappuccino() {
// This just makes sure it will
complete
// without throwing an exception.
// Create a plain cappucino
DrinkComponent cappuccino = new Cappuccino();
System.out.println(cappuccino.getDescription()
+ ": $" +
cappuccino.getTotalCost());
}
public void testCafeMocha() {
// This just makes sure it will
complete
// without throwing an exception.
// Create a decaf cafe mocha with
whipped
// cream
DrinkComponent cafeMocha = new
Whipped(
new Decaf(new CafeMocha()));
System.out.println(cafeMocha.getDescription()
+ ": $" +
cafeMocha.getTotalCost());
}
public static void main(String[] args)
{
junit.textui.TestRunner.run(CoffeeShop3.class);
}
} ///:~
#[BT_187]#
#[BT_188]#You can see that
creating a basic selection is quick and easy, which makes sense since they will
be described regularly. Describing a decorated drink is more work than when
using a class per combination, but clearly less work than when only using
decorators.
#[BT_189]#The final result
is not too many classes, but not too many decorators either. Most of the time
it's possible to get away without using any decorators at all, so we have the
benefits of both approaches.
#[BT_190]#What happens if
we decide to change the menu at a later stage, such as by adding a new type of
drink? If we had used the class per combination approach, the effect of adding
an extra such as syrup would be an exponential growth in the number of classes.
However, the implications to the all decorator or compromise approaches are the
same - one extra class is created.
#[BT_191]#How about the
effect of changing the cost of steamed milk and foamed milk, when the price of
milk goes up? Having a class for each combination means that you need to change
a method in each class, and thus maintain many classes. By using decorators,
maintenance is reduced by defining the logic in one place.
1.
Add a Syrup class to the decorator approach described above. Then
create a Café Latte (you'll need to use steamed milk with an espresso) with
syrup.
2.
Repeat Exercise 1 for the compromise approach.
3.
Create a simple decorator system that models the fact that some
birds fly and some don’t, some swim and some don’t, and some do both.
4.
Implement the decorator pattern to create a Pizza restaurant,
which has a set menu of choices as well as the option to design your own
pizza. Follow the compromise approach to create a menu consisting of a
Margherita, Hawaiian, Regina, and Vegetarian pizzas, with toppings (decorators)
of Garlic, Olives, Spinach, Avocado, Feta and Pepperdews. Create a Hawaiian
pizza, as well as a Margherita decorated with Spinach, Feta, Pepperdews and
Olives.
5.
About Decorator, the Design Patterns book states:
“With decorators, responsibilities can be added and removed at run-time simply
by attaching and detaching them.” Implement the coffee decoration system to
allow this “simple” detaching of a responsibility from the middle of the list
of decorators of a complex coffee beverage.
#[BT_192]#
The important thing here is that all elements in the
part-whole have operations, and that performing an operation on a
node/composite also performs that operation on any children of that
node/composite. GoF includes implementation details of containment and
visitation of children in the interface of the base class, but this doesn’t
seem necessary. In the following example, the Composite class simply
inherits ArrayList in order to gain its containment abilities.
//: composite:CompositeStructure.java
package composite;
import java.util.*;
import junit.framework.*;
interface Component {
void operation();
}
class Leaf implements Component {
private String name;
public Leaf(String name) { this.name =
name; }
public String toString() { return name;
}
public void operation() {
System.out.println(this);
}
}
class Node extends ArrayList implements
Component {
private String name;
public Node(String name) { this.name =
name; }
public String toString() { return name;
}
public void operation() {
System.out.println(this);
for(Iterator it = iterator();
it.hasNext(); )
((Component)it.next()).operation();
}
}
public class CompositeStructure extends
TestCase {
public void test() {
Node root = new Node("root");
root.add(new
Leaf("Leaf1"));
Node c2 = new
Node("Node1");
c2.add(new Leaf("Leaf2"));
c2.add(new Leaf("Leaf3"));
root.add(c2);
c2 = new Node("Node2");
c2.add(new Leaf("Leaf4"));
c2.add(new Leaf("Leaf5"));
root.add(c2);
root.operation();
}
public static void main(String args[])
{
junit.textui.TestRunner.run(CompositeStructure.class);
}
} ///:~
While this approach seems to be “the simplest thing that
could possibly work,” it’s possible that in a larger system problems could
arise. However, it’s probably best to start with the simplest approach and
change it only if the situation demands.
#[BT_383]#Like the other
forms of callback, this contains a hook point where you can change code. The
difference is in the observer’s completely dynamic nature. It is often used for
the specific case of changes based on other object’s change of state, but is
also the basis of event management. Anytime you want to decouple the source of
the call from the called code in a completely dynamic way.
#[BT_384]#The observer pattern solves a fairly common problem: What if a group of objects needs to
update themselves when some object changes state? This can be seen in the
“model-view” aspect of Smalltalk’s MVC (model-view-controller), or the
almost-equivalent “Document-View Architecture.” Suppose that you have some data
(the “document”) and more than one view, say a plot and a textual view. When
you change the data, the two views must know to update themselves, and that’s
what the observer facilitates. It’s a common enough problem that its solution
has been made a part of the standard java.util library.
#[BT_385]#There are two
types of objects used to implement the observer pattern in Java. The Observable class keeps track of everybody who wants to be informed when a
change happens, whether the “state” has changed or not. When someone says “OK,
everybody should check and potentially update themselves,” the Observable
class performs this task by calling the notifyObservers( ) method for each one on the list. The notifyObservers( ) method is part of the
base class Observable.
#[BT_386]#There are actually
two “things that change” in the observer pattern: the quantity of observing
objects and the way an update occurs. That is, the observer pattern allows you
to modify both of these without affecting the surrounding code.
#[BT_387]#-------------
#[BT_388]#Observer
is an “interface” class that only has one member function, update( ).
This function is called by the object that’s being observed, when that object
decides its time to update all its observers. The arguments are optional; you
could have an update( ) with no arguments and that would still fit
the observer pattern; however this is more general—it allows the observed
object to pass the object that caused the update (since an Observer may
be registered with more than one observed object) and any extra information if
that’s helpful, rather than forcing the Observer object to hunt around
to see who is updating and to fetch any other information it needs.
#[BT_389]#The “observed
object” that decides when and how to do the updating will be called the Observable.
#[BT_390]#Observable
has a flag to indicate whether it’s been changed. In a simpler design, there
would be no flag; if something happened, everyone would be notified. The flag
allows you to wait, and only notify the Observers when you decide the time
is right. Notice, however, that the control of the flag’s state is protected,
so that only an inheritor can decide what constitutes a change, and not the end
user of the resulting derived Observer class.
#[BT_391]#Most of the work
is done in notifyObservers( ). If the changed flag has not
been set, this does nothing. Otherwise, it first clears the changed flag
so repeated calls to notifyObservers( ) won’t waste time. This is
done before notifying the observers in case the calls to update( )
do anything that causes a change back to this Observable object. Then it
moves through the set and calls back to the update( ) member
function of each Observer.
#[BT_392]#At first it may
appear that you can use an ordinary Observable object to manage the
updates. But this doesn’t work; to get an effect, you must inherit from Observable
and somewhere in your derived-class code call setChanged( ). This is the member function that sets the “changed” flag,
which means that when you call notifyObservers( ) all of the
observers will, in fact, get notified. Where you call setChanged( )
depends on the logic of your program.
#[BT_393]#Here is an
example of the observer pattern:
//: observer:ObservedFlower.java
// Demonstration of "observer"
pattern.
package observer;
import java.util.*;
import junit.framework.*;
class Flower {
private boolean isOpen;
private OpenNotifier oNotify =
new OpenNotifier();
private CloseNotifier cNotify =
new CloseNotifier();
public Flower() { isOpen = false; }
public void open() { // Opens its
petals
isOpen = true;
oNotify.notifyObservers();
cNotify.open();
}
public void close() { // Closes its
petals
isOpen = false;
cNotify.notifyObservers();
oNotify.close();
}
public Observable opening() { return
oNotify; }
public Observable closing() { return
cNotify; }
private class OpenNotifier extends
Observable {
private boolean alreadyOpen = false;
public void notifyObservers() {
if(isOpen && !alreadyOpen)
{
setChanged();
super.notifyObservers();
alreadyOpen = true;
}
}
public void close() { alreadyOpen =
false; }
}
private class CloseNotifier extends
Observable{
private boolean alreadyClosed =
false;
public void notifyObservers() {
if(!isOpen &&
!alreadyClosed) {
setChanged();
super.notifyObservers();
alreadyClosed = true;
}
}
public void open() { alreadyClosed =
false; }
}
}
class Bee {
private String name;
private OpenObserver openObsrv =
new OpenObserver();
private CloseObserver closeObsrv =
new CloseObserver();
public Bee(String nm) { name = nm; }
// An inner class for observing
openings:
private class OpenObserver implements
Observer{
public void update(Observable ob,
Object a) {
System.out.println("Bee "
+ name
+ "'s breakfast
time!");
}
}
// Another inner class for closings:
private class CloseObserver implements
Observer{
public void update(Observable ob,
Object a) {
System.out.println("Bee "
+ name
+ "'s bed time!");
}
}
public Observer openObserver() {
return openObsrv;
}
public Observer closeObserver() {
return closeObsrv;
}
}
class Hummingbird {
private String name;
private OpenObserver openObsrv =
new OpenObserver();
private CloseObserver closeObsrv =
new CloseObserver();
public Hummingbird(String nm) { name =
nm; }
private class OpenObserver implements
Observer{
public void update(Observable ob,
Object a) {
System.out.println("Hummingbird " + name
+ "'s breakfast
time!");
}
}
private class CloseObserver implements
Observer{
public void update(Observable ob,
Object a) {
System.out.println("Hummingbird
" + name
+ "'s bed time!");
}
}
public Observer openObserver() {
return openObsrv;
}
public Observer closeObserver() {
return closeObsrv;
}
}
public class ObservedFlower extends
TestCase {
Flower f = new Flower();
Bee
ba = new Bee("A"),
bb = new Bee("B");
Hummingbird
ha = new Hummingbird("A"),
hb = new Hummingbird("B");
public void test() {
f.opening().addObserver(ha.openObserver());
f.opening().addObserver(hb.openObserver());
f.opening().addObserver(ba.openObserver());
f.opening().addObserver(bb.openObserver());
f.closing().addObserver(ha.closeObserver());
f.closing().addObserver(hb.closeObserver());
f.closing().addObserver(ba.closeObserver());
f.closing().addObserver(bb.closeObserver());
// Hummingbird B decides to sleep in:
f.opening().deleteObserver(
hb.openObserver());
// A change that interests observers:
f.open();
f.open(); // It's already open, no
change.
// Bee A doesn't want to go to bed:
f.closing().deleteObserver(
ba.closeObserver());
f.close();
f.close(); // It's already closed; no
change
f.opening().deleteObservers();
f.open();
f.close();
}
public static void main(String args[])
{
junit.textui.TestRunner.run(ObservedFlower.class);
}
} ///:~
#[BT_394]#
#[BT_395]#The events of
interest are that a Flower can open or close. Because of the use of the
inner class idiom, both these events can be separately observable phenomena. OpenNotifier
and CloseNotifier both inherit Observable, so they have access to
setChanged( ) and can be handed to anything that needs an Observable.
#[BT_396]#The inner class
idiom also comes in handy to define more than one kind of Observer, in Bee
and Hummingbird, since both those classes may want to independently
observe Flower openings and closings. Notice how the inner class idiom
provides something that has most of the benefits of inheritance (the ability to
access the private data in the outer class, for example) without the
same restrictions.
#[BT_397]#In main( ),
you can see one of the prime benefits of the observer pattern: the ability to
change behavior at run time by dynamically registering and un-registering Observers
with Observables.
#[BT_398]#If you study the
code above you’ll see that OpenNotifier and CloseNotifier use the
basic Observable interface. This means that you could inherit other
completely different Observer classes; the only connection the Observers
have with Flowers is the Observer interface.
#[BT_399]#The following
example is similar to the ColorBoxes example from Chapter 14 in Thinking
in Java, 2nd Edition. Boxes are placed in a grid on the screen
and each one is initialized to a random color. In addition, each box implements
the Observer interface and is registered with an Observable
object. When you click on a box, all of the other boxes are notified that a
change has been made because the Observable object automatically calls
each Observer object’s update( ) method. Inside this method,
the box checks to see if it’s adjacent to the one that was clicked, and if so
it changes its color to match the clicked box.
//: observer:BoxObserver.java
// Demonstration of Observer pattern
using
// Java's built-in observer classes.
package observer;
import javax.swing.*;
import java.awt.*;
import java.awt.event.*;
import java.util.*;
// You must inherit a new type of
Observable:
class BoxObservable extends Observable {
public void notifyObservers(Object b) {
// Otherwise it won't propagate
changes:
setChanged();
super.notifyObservers(b);
}
}
public class BoxObserver extends JFrame {
Observable notifier = new
BoxObservable();
public BoxObserver(int grid) {
setTitle("Demonstrates Observer
pattern");
Container cp = getContentPane();
cp.setLayout(new GridLayout(grid,
grid));
for(int x = 0; x < grid; x++)
for(int y = 0; y < grid; y++)
cp.add(new OCBox(x, y,
notifier));
}
public static void main(String[] args)
{
int grid = 8;
if(args.length > 0)
grid = Integer.parseInt(args[0]);
JFrame f = new BoxObserver(grid);
f.setSize(500, 400);
f.setVisible(true);
f.setDefaultCloseOperation(EXIT_ON_CLOSE);
}
}
class OCBox extends JPanel implements
Observer {
Observable notifier;
int x, y; // Locations in grid
Color cColor = newColor();
static final Color[] colors = {
Color.BLACK, Color.BLUE, Color.CYAN,
Color.DARK_GRAY, Color.GRAY,
Color.GREEN,
Color.LIGHT_GRAY, Color.MAGENTA,
Color.ORANGE, Color.PINK, Color.RED,
Color.WHITE, Color.YELLOW
};
static Random rand = new Random();
static final Color newColor() {
return
colors[rand.nextInt(colors.length)];
}
OCBox(int x, int y, Observable
notifier) {
this.x = x;
this.y = y;
notifier.addObserver(this);
this.notifier = notifier;
addMouseListener(new ML());
}
public void paintComponent(Graphics g)
{
super.paintComponent(g);
g.setColor(cColor);
Dimension s = getSize();
g.fillRect(0, 0, s.width, s.height);
}
class ML extends MouseAdapter {
public void mousePressed(MouseEvent
e) {
notifier.notifyObservers(OCBox.this);
}
}
public void update(Observable o, Object
arg) {
OCBox clicked = (OCBox)arg;
if(nextTo(clicked)) {
cColor = clicked.cColor;
repaint();
}
}
private final boolean nextTo(OCBox b) {
return Math.abs(x - b.x) <= 1
&&
Math.abs(y - b.y) <= 1;
}
} ///:~
#[BT_400]#When you first
look at the online documentation for Observable, it’s a bit confusing
because it appears that you can use an ordinary Observable object to
manage the updates. But this doesn’t work; try it—inside BoxObserver,
create an Observable object instead of a BoxObservable object and
see what happens: nothing. To get an effect, you must inherit from Observable
and somewhere in your derived-class code call setChanged( ). This is the method that sets the “changed” flag, which
means that when you call notifyObservers( ) all of the observers
will, in fact, get notified. In the example above setChanged( ) is
simply called within notifyObservers( ), but you could use any
criterion you want to decide when to call setChanged( ).
#[BT_401]#BoxObserver
contains a single Observable object called notifier, and every
time an OCBox object is created, it is tied to notifier. In OCBox,
whenever you click the mouse the notifyObservers( ) method is
called, passing the clicked object in as an argument so that all the boxes
receiving the message (in their update( ) method) know who was
clicked and can decide whether to change themselves or not. Using a combination
of code in notifyObservers( ) and update( ) you can
work out some fairly complex schemes.
#[BT_402]#It might appear
that the way the observers are notified must be frozen at compile time in the notifyObservers( )
method. However, if you look more closely at the code above you’ll see that the
only place in BoxObserver or OCBox where you're aware that you’re
working with a BoxObservable is at the point of creation of the Observable
object—from then on everything uses the basic Observable interface.
This means that you could inherit other Observable classes and swap them
at run time if you want to change notification behavior then.
Sweep coupling under the rug, how is this different from
MVC?
MVC has distinct model and view; mediator could be anything.
MVC a flavor of mediator
1.
Create a minimal Observer-Observable design in two classes. Just
create the bare minimum in the two classes, then demonstrate your design by
creating one Observable and many Observers, and cause the Observable
to update the Observers.
2.
Create a minimal Observer system using java.util.Timer
inside your Observable, to generate events that are reported to the Observers.
Create several different Observers using anonymous inner classes,
register these with the Observable, and show that they are called when
the Timer events occur.
3.
Modify BoxObserver.java to turn it into a simple game. If
any of the squares surrounding the one you clicked is part of a contiguous
patch of the same color, then all the squares in that patch are changed to the
color you clicked on. You can configure the game for competition between
players or to keep track of the number of clicks that a single player uses to
turn the field into a single color. You may also want to restrict a player's
color to the first one that was chosen.
#[BT_403]#
#[BT_404]#
#[BT_256]#This chapter
looks at the value of crossing language boundaries. It is often very
advantageous to solve a problem using more than one programming language,
rather than being arbitrarily stuck using a single language. As you’ll see in
this chapter, a problem that is very difficult or tedious to solve in one
language can often be solved quickly and easily in another. If you can combine
the use of languages, you can create your product much more quickly and
cheaply.
#[BT_257]#The most
straightforward use of this idea is the Interpreter design pattern,
which adds an interpreted language to your program to allow the end user to
easily customize a solution. In Java, the easiest and most powerful way to do
this is with Jython, an implementation
of the Python language in pure Java byte codes.
#[BT_258]#Interpreter
solves a particular problem – that of creating a scripting language for the
user. But sometimes it’s just easier and faster to temporarily step into
another language to solve a particular aspect of your problem. You’re not
creating an interpreter, you’re just writing some code in another language.
Again, Jython is a good example of this, but CORBA also allows you to cross
language boundaries.
#[BT_259]#If the
application user needs greater run time flexibility, for example to create
scripts describing the desired behavior of the system, you can use the Interpreter
design pattern. Here, you create and embed a language interpreter into your
program.
#[BT_260]#Remember that
each design pattern allows one or more factors to change, so it’s important to
first be aware of which factor is changing. Sometimes the end users of your
application (rather than the programmers of that application) need complete
flexibility in the way that they configure some aspect of the program. That is,
they need to do some kind of simple programming. The interpreter pattern
provides this flexibility by adding a language interpreter.
#[BT_261]#The problem is
that developing your own language and building an interpreter is a
time-consuming distraction from the process of developing your application. You
must ask whether you want to finish writing your application or create a new
language. The best solution is to reuse code: embed an interpreter that’s
already been built and debugged for you. The Python language can be freely
embedded into your for-profit application without signing any license
agreement, paying royalties, or dealing with strings of any kind. There are
basically no restrictions at all when you're using Python.
#[BT_262]#Python is a
language that is very easy to learn, very logical to read and write, supports
functions and objects, has a large set of available libraries, and runs on
virtually every platform. You can download Python and learn more about it by
going to www.Python.org.
#[BT_263]#For solving Java
problems, we will look at a special version of Python called Jython. This is
generated entirely in Java byte codes, so incorporating it into your
application is quite simple, and it’s as portable as Java is. It has an
extremely clean interface with Java: Java can call Python classes, and Python
can call Java classes.
#[BT_264]#Python is
designed with classes from the ground up and is a truly pure object oriented language
(both C++ and Java violate purity in various ways). Python scales up so that
you can create very big programs without losing control of the code.
#[BT_265]#To install
Python, go to www.Python.org and
follow the links and instructions. To install Jython, go to http://jython.sourceforge.net.
The download is a .class file, which will run an installer when you
execute it with Java. You also need to add jython.jar to your
CLASSPATH. You can find further installation instructions at http://www.bruceeckel.com/TIPatterns/Building-Code.html.
#[BT_266]#To get you
started, here is a brief introduction for the experienced programmer (which is
what you should be if you’re reading this book). You can refer to the full
documentation at www.Python.org (especially the incredibly useful HTML
page A Python Quick Reference), and also numerous books such as Learning
Python by Mark Lutz and David Ascher (O’Reilly, 1999).
#[BT_267]#Python is often
referred to as a scripting language, but scripting languages tend to be
limiting, especially in the scope of the problems that they solve. Python, on
the other hand, is a programming language that also supports scripting. It is
marvelous for scripting, and you may find yourself replacing all your batch
files, shell scripts, and simple programs with Python scripts. But it is far
more than a scripting language.
#[BT_268]#Python is
designed to be very clean to write and especially to read. You will find that
it’s quite easy to read your own code long after you’ve written it, and also to
read other people’s code. This is accomplished partially through clean, to-the-point
syntax, but a major factor in code readability is indentation – scoping in
Python is determined by indentation. For example:
##interpreter:if.py
response = "yes"
if response == "yes":
print "affirmative"
val = 1
print "continuing..."
##~
#[BT_269]#The ‘#’
denotes a comment that goes until the end of the line, just like C++ and Java ‘//’
comments.
#[BT_270]#First notice
that the basic syntax of Python is C-ish; notice the if statement. But
in a C if, you would be required to use parentheses around the
conditional, whereas they are not necessary in Python (but it won’t complain if
you use them anyway).
#[BT_271]#The conditional
clause ends with a colon, and this indicates that what follows will be a group
of indented statements, which are the “then” part of the if statement.
In this case, there is a “print” statement which sends the result to standard
output, followed by an assignment to a variable named val. The
subsequent statement is not indented so it is no longer part of the if.
Indenting can nest to any level, just like curly braces in C++ or Java, but
unlike those languages there is no option (and no argument) about where the
braces are placed – the compiler forces everyone’s code to be formatted the
same way, which is one of the main reasons for Python’s consistent readability.
#[BT_272]#Python normally
has only one statement per line (you can put more by separating them with
semicolons), thus no terminating semicolon is necessary. Even from the brief
example above you can see that the language is designed to be as simple as
possible, and yet still very readable.
#[BT_273]#With languages
like C++ and Java, containers are add-on libraries and not integral to the
language. In Python, the essential nature of containers for programming is
acknowledged by building them into the core of the language: both lists and
associative arrays (a.k.a. maps, dictionaries, hash tables) are fundamental
data types. This adds much to the elegance of the language.
#[BT_274]#In addition, the
for statement automatically iterates through lists rather than just
counting through a sequence of numbers. This makes a lot of sense when you
think about it, since you’re almost always using a for loop to step
through an array or a container. Python formalizes this by automatically making
for use an iterator that works through a sequence. Here’s an example:
## interpreter:list.py
list = [ 1, 3, 5, 7, 9, 11 ]
print list
list.append(13)
for x in list:
print x
##~
#[BT_275]#The first line
creates a list. You can print the list and it will look exactly as you put it
in (in contrast, remember that I had to create a special Arrays2 class
in Thinking in Java, 2nd Edition in order to print arrays in
Java). Lists are like Java containers – you can add new elements to them (here,
append( ) is used) and they will automatically resize themselves.
The for statement creates an iterator x which takes on each value
in the list.
#[BT_276]#You can create a
list of numbers with the range( ) function, so if you really need
to imitate C’s for, you can.
#[BT_277]#Notice that
there aren’t any type declarations – the object names simply appear, and Python
infers their type by the way that you use them. It’s as if Python is designed
so that you only need to press the keys that absolutely must. You’ll find after
you’ve worked with Python for a short while that you’ve been using up a lot of
brain cycles parsing semicolons, curly braces, and all sorts of other extra
verbiage that was demanded by your non-Python programming language but didn’t
actually describe what your program was supposed to do.
#[BT_278]#To create a
function in Python, you use the def keyword, followed by the function
name and argument list, and a colon to begin the function body. Here is the
first example turned into a function:
## interpreter:myFunction.py
def myFunction(response):
val = 0
if response == "yes":
print "affirmative"
val = 1
print "continuing..."
return val
print myFunction("no")
print myFunction("yes")
##~
#[BT_279]#Notice there is
no type information in the function signature – all it specifies is the name of
the function and the argument identifiers, but no argument types or return
types. Python is a weakly-typed language, which means it puts the
minimum possible requirements on typing. For example, you could pass and return
different types from the same function:
## interpreter:differentReturns.py
def differentReturns(arg):
if arg == 1:
return "one"
if arg == "one":
return 1
print differentReturns(1)
print differentReturns("one")
##~
#[BT_280]#The only
constraints on an object that is passed into the function are that the function
can apply its operations to that object, but other than that, it doesn’t care.
Here, the same function applies the ‘+’ operator to integers and
strings:
## interpreter:sum.py
def sum(arg1, arg2):
return arg1 + arg2
print sum(42, 47)
print sum('spam ', "eggs")
##~
#[BT_281]#When the
operator ‘+’ is used with strings, it means concatenation (yes, Python
supports operator overloading, and it does a nice job of it).
#[BT_282]#The above
example also shows a little bit about Python string handling, which is the
best of any language I’ve seen. You can use single or double quotes to
represent strings, which is very nice because if you surround a string with
double quotes, you can embed single quotes and vice versa:
## interpreter:strings.py
print "That isn't a horse"
print 'You are not a "Viking"'
print """You're just
pounding two
coconut halves
together."""
print '''"Oh no!" He exclaimed.
"It's the blemange!"'''
print r'c:\python\lib\utils'
##~
#[BT_283]#Note that Python
was not named after the snake, but rather the Monty Python comedy troupe, and
so examples are virtually required to include Python-esque references.
#[BT_284]#The triple-quote
syntax quotes everything, including newlines. This makes it particularly useful
for doing things like generating web pages (Python is an especially good CGI
language), since you can just triple-quote the entire page that you want
without any other editing.
#[BT_285]#The ‘r’
right before a string means “raw,” which takes the backslashes literally so you
don’t have to put in an extra backslash.
#[BT_286]#Substitution in
strings is exceptionally easy, since Python uses C’s printf( )
substitution syntax, but for any string at all. You simply follow the string
with a ‘%’ and the values to substitute:
## interpreter:stringFormatting.py
val = 47
print "The number is %d" % val
val2 = 63.4
s = "val: %d, val2: %f" % (val,
val2)
print s
##~
#[BT_287]#As you can see
in the second case, if you have more than one argument you surround them in
parentheses (this forms a tuple, which is a list that cannot be
modified).
#[BT_288]#All the
formatting from printf( ) is available, including control over the
number of decimal places and alignment. Python also has very sophisticated
regular expressions.
#[BT_289]#Like everything
else in Python, the definition of a class uses a minimum of additional syntax.
You use the class keyword, and inside the body you use def to create
methods. Here’s a simple class:
## interpreter:SimpleClass.py
class Simple:
def __init__(self, str):
print "Inside the Simple
constructor"
self.s = str
# Two methods:
def show(self):
print self.s
def showMsg(self, msg):
print msg + ':',
self.show() # Calling another method
if __name__ == "__main__":
# Create an object:
x = Simple("constructor
argument")
x.show()
x.showMsg("A message")
##~
#[BT_290]#Both methods
have “self” as their first argument. C++ and Java both have a
hidden first argument in their class methods, which points to the object that
the method was called for and can be accessed using the keyword this.
Python methods also use a reference to the current object, but when you are defining
a method you must explicitly specify the reference as the first argument.
Traditionally, the reference is called self but you could use any
identifier you want (if you do not use self you will probably confuse a
lot of people, however). If you need to refer to fields in the object or other
methods in the object, you must use self in the expression. However,
when you call a method for an object as in x.show( ), you do not
hand it the reference to the object – that is done for you.
#[BT_291]#Here, the first
method is special, as is any identifier that begins and ends with double
underscores. In this case, it defines the constructor, which is automatically
called when the object is created, just like in C++ and Java. However, at the
bottom of the example you can see that the creation of an object looks just
like a function call using the class name. Python’s spare syntax makes you
realize that the new keyword isn’t really necessary in C++ or Java,
either.
#[BT_292]#All the code at
the bottom is set off by an if clause, which checks to see if something
called __name__ is equivalent to __main__. Again, the double
underscores indicate special names. The reason for the if is that any
file can also be used as a library module within another program (modules are
described shortly). In that case, you just want the classes defined, but you
don’t want the code at the bottom of the file to be executed. This particular if
statement is only true when you are running this file directly; that is, if you
say on the command line:
#[BT_293]#However, if this
file is imported as a module into another program, the __main__ code is
not executed.
#[BT_294]#Something that’s
a little surprising at first is that you define fields inside methods, and not
outside of the methods like C++ or Java (if you create fields using the
C++/Java style, they implicitly become static fields). To create an object
field, you just name it – using self – inside of one of the methods
(usually in the constructor, but not always), and space is created when that
method is run. This seems a little strange coming from C++ or Java where you
must decide ahead of time how much space your object is going to occupy, but it
turns out to be a very flexible way to program.
Inheritance
#[BT_295]#Because Python
is weakly typed, it doesn’t really care about interfaces – all it cares about
is applying operations to objects (in fact, Java’s interface keyword
would be wasted in Python). This means that inheritance in Python is different
from inheritance in C++ or Java, where you often inherit simply to establish a
common interface. In Python, the only reason you inherit is to inherit an
implementation – to re-use the code in the base class.
#[BT_296]#If you’re going
to inherit from a class, you must tell Python to bring that class into your new
file. Python controls its name spaces as aggressively as Java does, and in a
similar fashion (albeit with Python’s penchant for simplicity). Every time you
create a file, you implicitly create a module (which is like a package in Java)
with the same name as that file. Thus, no package keyword is needed in
Python. When you want to use a module, you just say import and give the
name of the module. Python searches the PYTHONPATH in the same way that Java
searches the CLASSPATH (but for some reason, Python doesn’t have the same kinds
of pitfalls as Java does) and reads in the file. To refer to any of the
functions or classes within a module, you give the module name, a period, and
the function or class name. If you don’t want the trouble of qualifying the
name, you can say
#[BT_297]#from module
import name(s)
#[BT_298]#Where “name(s)”
can be a list of names separated by commas.
#[BT_299]#You inherit a
class (or classes – Python supports multiple inheritance) by listing the
name(s) of the class inside parentheses after the name of the inheriting class.
Note that the Simple class, which resides in the file (and thus, module)
named SimpleClass is brought into this new name space using an import
statement:
## interpreter:Simple2.py
from SimpleClass import Simple
class Simple2(Simple):
def __init__(self, str):
print "Inside Simple2
constructor"
# You must explicitly call
# the base-class constructor:
Simple.__init__(self, str)
def display(self):
self.showMsg("Called from display()")
# Overriding a base-class method
def show(self):
print "Overridden show()
method"
# Calling a base-class method from
inside
# the overridden method:
Simple.show(self)
class Different:
def show(self):
print "Not derived from
Simple"
if __name__ == "__main__":
x = Simple2("Simple2 constructor
argument")
x.display()
x.show()
x.showMsg("Inside main")
def f(obj): obj.show() # One-line
definition
f(x)
f(Different())
##~
#[BT_300]#Simple2
is inherited from Simple, and in the constructor, the base-class
constructor is called. In display( ), showMsg( ) can be
called as a method of self, but when calling the base-class version of
the method you are overriding, you must fully qualify the name and pass self
in as the first argument, as shown in the base-class constructor call. This can
also be seen in the overridden version of show( ).
#[BT_301]#In __main__,
you will see (when you run the program) that the base-class constructor is
called. You can also see that the showMsg( ) method is available in
the derived class, just as you would expect with inheritance.
#[BT_302]#The class Different
also has a method named show( ), but this class is not derived from
Simple. The f( ) method defined in __main__
demonstrates weak typing: all it cares about is that show( ) can be
applied to obj, and it doesn’t have any other type requirements. You can
see that f( ) can be applied equally to an object of a class
derived from Simple and one that isn’t, without discrimination. If
you’re a C++ programmer, you should see that the objective of the C++ template
feature is exactly this: to provide weak typing in a strongly-typed language.
Thus, in Python you automatically get the equivalent of templates – without
having to learn that particularly difficult syntax and semantics.
#[BT_303]#It turns out to
be remarkably simple to use Jython to create an interpreted language inside
your application. Consider the greenhouse controller example from Chapter 8 of Thinking
in Java, 2nd edition. This is a situation where you want the end
user – the person managing the greenhouse – to have configuration control over
the system, and so a simple scripting language is the ideal solution.
#[BT_304]#To create the
language, we’ll simply write a set of Python classes, and the constructor of
each will add itself to a (static) master list. The common data and behavior
will be factored into the base class Event. Each Event object
will contain an action string (for simplicity – in reality, you’d have
some sort of functionality) and a time when the event is supposed to run. The
constructor initializes these fields, and then adds the new Event object
to a static list called events (defining it in the class, but outside of
any methods, is what makes it static):
#:interpreter:GreenHouseLanguage.py
class Event:
events = [] # static
def __init__(self, action, time):
self.action = action
self.time = time
Event.events.append(self)
# Used by sort(). This will cause
# comparisons to be based only on time:
def __cmp__ (self, other):
if self.time < other.time: return
-1
if self.time > other.time: return
1
return 0
def run(self):
print "%.2f: %s" %
(self.time, self.action)
class LightOn(Event):
def __init__(self, time):
Event.__init__(self, "Light
on", time)
class LightOff(Event):
def __init__(self, time):
Event.__init__(self, "Light
off", time)
class WaterOn(Event):
def __init__(self, time):
Event.__init__(self, "Water
on", time)
class WaterOff(Event):
def __init__(self, time):
Event.__init__(self, "Water
off", time)
class ThermostatNight(Event):
def __init__(self, time):
Event.__init__(self,"Thermostat
night", time)
class ThermostatDay(Event):
def __init__(self, time):
Event.__init__(self, "Thermostat
day", time)
class Bell(Event):
def __init__(self, time):
Event.__init__(self, "Ring
bell", time)
def run():
Event.events.sort();
for e in Event.events:
e.run()
# To test, this will be run when you say:
# python GreenHouseLanguage.py
if __name__ == "__main__":
ThermostatNight(5.00)
LightOff(2.00)
WaterOn(3.30)
WaterOff(4.45)
LightOn(1.00)
ThermostatDay(6.00)
Bell(7.00)
run()
##~
#[BT_305]#The constructor
of each derived class calls the base-class constructor, which adds the new
object to the list. The run( ) function sorts the list, which
automatically uses the __cmp__( ) method that was defined in Event
to base comparisons on time only. In this example, it only prints out the list,
but in the real system it would wait for the time of each event to come up and
then run the event.
#[BT_306]#The __main__
section performs a simple test on the classes.
#[BT_307]#The above file
is now a module that can be included in another Python program to define all
the classes it contains. But instead of an ordinary Python program, let’s use
Jython, inside of Java. This turns out to be remarkably simple: you import some
Jython classes, create a PythonInterpreter object, and cause the Python
files to be loaded:
//- interpreter:GreenHouseController.java
package interpreter;
import org.python.util.PythonInterpreter;
import org.python.core.*;
import junit.framework.*;
public class
GreenHouseController extends TestCase {
PythonInterpreter interp =
new PythonInterpreter();
public void test() throws PyException
{
System.out.println(
"Loading GreenHouse
Language");
interp.execfile("GreenHouseLanguage.py");
System.out.println(
"Loading GreenHouse
Script");
interp.execfile("Schedule.ghs");
System.out.println(
"Executing GreenHouse
Script");
interp.exec("run()");
}
public static void
main(String[] args) throws PyException
{
junit.textui.TestRunner.run(GreenHouseController.class);
}
} ///:~
#[BT_308]#
#[BT_309]#The PythonInterpreter
object is a complete Python interpreter that accepts commands from the Java
program. One of these commands is execfile( ), which tells it to
execute all the statements it finds in a particular file. By executing GreenHouseLanguage.py,
all the classes from that file are loaded into our PythonInterpreter
object, and so it now “holds” the greenhouse controller language. The Schedule.ghs
file is the one created by the end user to control the greenhouse. Here’s an
example:
//:! interpreter:Schedule.ghs
Bell(7.00)
ThermostatDay(6.00)
WaterOn(3.30)
LightOn(1.00)
ThermostatNight(5.00)
LightOff(2.00)
WaterOff(4.45)
///:~
#[BT_310]#
#[BT_311]#This is the goal
of the interpreter design pattern: to make the configuration of your program as
simple as possible for the end user. With Jython you can achieve this with
almost no effort at all.
#[BT_312]#One of the other
methods available to the PythonInterpreter is exec( ), which
allows you to send a command to the interpreter. Here, the run( )
function is called using exec( ).
#[BT_313]#Remember, to run
this program you must go to http://jython.sourceforge.net
and download and install Jython (actually, you only need jython.jar
in your CLASSPATH). Once that’s in place, it’s just like running any other Java
program.
#[BT_314]#The prior
example only creates and runs the interpreter using external scripts. In the
rest of this chapter, we shall look at more sophisticated ways to interact with
Jython. The simplest way to exercise more control over the PythonInterpreter
object from within Java is to send data to the interpreter, and pull data back
out.
#[BT_315]#To inject data
into your Python program, the PythonInterpreter class has a deceptively
simple method: set( ). However, set( ) takes many
different data types and performs conversions upon them. The following example
is a reasonably thorough exercise of the various set( )
possibilities, along with comments that should give a fairly complete
explanation:
//- interpreter:PythonInterpreterSetting.java
// Passing data from Java to python when
using
// the PythonInterpreter object.
package interpreter;
import org.python.util.PythonInterpreter;
import org.python.core.*;
import java.util.*;
import com.bruceeckel.python.*;
import junit.framework.*;
public class
PythonInterpreterSetting extends TestCase
{
PythonInterpreter interp =
new PythonInterpreter();
public void test() throws PyException
{
// It automatically converts Strings
// into native Python strings:
interp.set("a", "This
is a test");
interp.exec("print a");
interp.exec("print a[5:]");
// A slice
// It also knows what to do with
arrays:
String[] s = { "How",
"Do", "You", "Do?" };
interp.set("b", s);
interp.exec("for x in b: print
x[0], x");
// set() only takes Objects, so it
can't
// figure out primitives. Instead,
// you have to use wrappers:
interp.set("c", new
PyInteger(1));
interp.set("d", new
PyFloat(2.2));
interp.exec("print c + d");
// You can also use Java's object
wrappers:
interp.set("c", new
Integer(9));
interp.set("d", new
Float(3.14));
interp.exec("print c + d");
// Define a Python function to print
arrays:
interp.exec(
"def prt(x): \n" +
" print x \n" +
" for i in x: \n" +
" print i, \n" +
" print x.__class__\n");
// Arrays are Objects, so it has no
trouble
// figuring out the types contained
in arrays:
Object[] types = {
new boolean[]{ true, false, false,
true },
new char[]{ 'a', 'b', 'c', 'd' },
new byte[]{ 1, 2, 3, 4 },
new int[]{ 10, 20, 30, 40 },
new long[]{ 100, 200, 300, 400 },
new float[]{ 1.1f, 2.2f, 3.3f, 4.4f
},
new double[]{ 1.1, 2.2, 3.3, 4.4 },
};
for(int i = 0; i < types.length;
i++) {
interp.set("e",
types[i]);
interp.exec("prt(e)");
}
// It uses toString() to print Java
objects:
interp.set("f", new
Date());
interp.exec("print f");
// You can pass it a List
// and index into it...
List x = new ArrayList();
for(int i = 0; i < 10; i++)
x.add(new Integer(i * 10));
interp.set("g", x);
interp.exec("print g");
interp.exec("print g[1]");
// ... But it's not quite smart
enough
// to treat it as a Python array:
interp.exec("print
g.__class__");
// interp.exec("print g[5:]); //
Fails
// If you want it to be a python
array, you
// must extract the Java array:
System.out.println("ArrayList to
array:");
interp.set("h",
x.toArray());
interp.exec("print
h.__class__");
interp.exec("print h[5:]");
// Passing in a Map:
Map m = new HashMap();
m.put(new Integer(1), new
Character('a'));
m.put(new Integer(3), new
Character('b'));
m.put(new Integer(5), new
Character('c'));
m.put(new Integer(7), new
Character('d'));
m.put(new Integer(11), new
Character('e'));
System.out.println("m:
" + m);
interp.set("m",
m);
interp.exec("print m,
m.__class__, " +
"m[1], m[1].__class__");
// Not a Python dictionary, so this
fails:
//! interp.exec("for x in
m.keys():" +
//! "print x, m[x]");
// To convert a Map to a Python dictionary,
// use com.bruceeckel.python.PyUtil:
interp.set("m",
PyUtil.toPyDictionary(m));
interp.exec("print m,
m.__class__, " +
"m[1], m[1].__class__");
interp.exec("for x in
m.keys():print x,m[x]");
}
public static void
main(String[] args) throws PyException
{
junit.textui.TestRunner.run(
PythonInterpreterSetting.class);
}
} ///:~
#[BT_316]#
#[BT_317]#As usual with
Java, the distinction between real objects and primitive types causes trouble.
In general, if you pass a regular object to set( ), it knows what
to do with it, but if you want to pass in a primitive you must perform a
conversion. One way to do this is to create a “Py” type, such as PyInteger
or PyFloat. but it turns out you can also use Java’s own object wrappers
like Integer and Float, which is probably going to be a lot
easier to remember.
#[BT_318]#Early in the
program you’ll see an exec( ) containing the Python statement:
#[BT_319]#The colon inside
the indexing statement indicates a Python slice, which produces a range
of elements from the original array. In this case, it produces an array
containing the elements from number 5 until the end of the array. You could
also say ‘a[3:5]’ to produce elements 3 through 5, or ‘a[:5]’ to
produce the elements zero through 5. The reason a slice is used in this
statement is to make sure that the Java String has really been converted
to a Python string, which can also be treated as an array of characters.
#[BT_320]#You can see that
it’s possible, using exec( ), to create a Python function (although
it’s a bit awkward). The prt( ) function prints the whole array,
and then (to make sure it’s a real Python array), iterates through each element
of the array and prints it. Finally, it prints the class of the array, so we
can see what conversion has taken place (Python not only has run-time type
information, it also has the equivalent of Java reflection). The prt( )
function is used to print arrays that come from each of the Java primitive
types.
#[BT_321]#Although a Java ArrayList
does pass into the interpreter using set( ), and you can index into
it as if it were an array, trying to create a slice fails. To completely
convert it into an array, one approach is to simply extract a Java array using toArray( ),
and pass that in. The set( ) method converts it to a PyArray
– one of the classes provided with Jython – which can be treated as a Python
array (you can also explicitly create a PyArray, but this seems
unnecessary).
#[BT_322]#Finally, a Map
is created and passed directly into the interpreter. While it is possible to do
simple things like index into the resulting object, it’s not a real Python
dictionary so you can’t (for example) call the keys( ) method.
There is no straightforward way to convert a Java Map into a Python
dictionary, and so I wrote a utility called toPyDictionary( ) and
made it a static method of com.bruceeckel.python.PyUtil. This
also includes utilities to extract a Python array into a Java List, and
a Python dictionary into a Java Map:
//- com:bruceeckel:python:PyUtil.java
// PythonInterpreter utilities
package com.bruceeckel.python;
import org.python.util.PythonInterpreter;
import org.python.core.*;
import java.util.*;
public class PyUtil {
/** Extract a Python tuple or array
into a Java
List (which can be converted into other
kinds
of lists and sets inside Java).
@param interp The Python interpreter
object
@param pyName The id of the python list
object
*/
public static List
toList(PythonInterpreter interp, String
pyName){
return new ArrayList(Arrays.asList(
(Object[])interp.get(
pyName, Object[].class)));
}
/** Extract a Python dictionary into a
Java Map
@param interp The Python interpreter
object
@param pyName The id of the python
dictionary
*/
public static Map
toMap(PythonInterpreter interp, String
pyName){
PyList pa =
((PyDictionary)interp.get(
pyName)).items();
Map map = new HashMap();
while(pa.__len__() != 0) {
PyTuple po = (PyTuple)pa.pop();
Object first = po.__finditem__(0)
.__tojava__(Object.class);
Object second = po.__finditem__(1)
.__tojava__(Object.class);
map.put(first, second);
}
return map;
}
/** Turn a Java Map into a
PyDictionary,
suitable for placing into a
PythonInterpreter
@param map The Java Map object
*/
public static PyDictionary
toPyDictionary(Map map) {
Map m = new HashMap();
Iterator it =
map.entrySet().iterator();
while(it.hasNext()) {
Map.Entry e = (Map.Entry)it.next();
m.put(Py.java2py(e.getKey()),
Py.java2py(e.getValue()));
}
// PyDictionary constructor wants a
Hashtable:
return new PyDictionary(new
Hashtable(m));
}
} ///:~
#[BT_323]#
#[BT_324]#Here is the
(black-box) unit testing code:
//- com:bruceeckel:python:Test.java
package com.bruceeckel.python;
import org.python.util.PythonInterpreter;
import java.util.*;
import junit.framework.*;
public class Test extends TestCase {
PythonInterpreter pi =
new PythonInterpreter();
public void test1() {
pi.exec("tup=('fee','fi','fo','fum','fi')");
List lst = PyUtil.toList(pi,
"tup");
System.out.println(lst);
System.out.println(new HashSet(lst));
}
public void test2() {
pi.exec("ints=[1,3,5,7,9,11,13,17,19]");
List lst =
PyUtil.toList(pi, "ints");
System.out.println(lst);
}
public void test3() {
pi.exec("dict = { 1 : 'a', 3 :
'b', " +
"5 : 'c', 9 : 'd', 11 :
'e'}");
Map mp = PyUtil.toMap(pi,
"dict");
System.out.println(mp);
}
public void test4() {
Map m = new HashMap();
m.put("twas", new
Integer(11));
m.put("brillig", new
Integer(27));
m.put("and", new
Integer(47));
m.put("the", new
Integer(42));
m.put("slithy", new
Integer(33));
m.put("toves", new Integer(55));
System.out.println(m);
pi.set("m",
PyUtil.toPyDictionary(m));
pi.exec("print m");
pi.exec("print
m['slithy']");
}
public static void main(String args[])
{
junit.textui.TestRunner.run(Test.class);
}
} ///:~
#[BT_325]#
#[BT_326]#We’ll see the
use of the extraction tools in the next section.
#[BT_327]#There are a
number of different ways to extract data from the PythonInterpreter. If
you simply call the get( ) method, passing it the object identifier
as a string, it returns a PyObject (part of the org.python.core
support classes). It’s possible to “cast” it using the __tojava__( )
method, but there are better alternatives:
1.
The convenience methods in the Py class, such as py2int( ),
take a PyObject and convert it to a number of different types.
2.
An overloaded version of get( ) takes the desired
Java Class object as a second argument, and produces an object that has
that run-time type (so you still need to perform a cast on the result in your
Java code).
#[BT_328]#Using the second
approach, getting an array from the PythonInterpreter is quite easy.
This is especially useful because Python is exceptionally good at manipulating
strings and files, and so you will commonly want to extract the results as an
array of strings. For example, you can do a wildcard expansion of file names
using Python’s glob( ), as shown further down in the following
code:
//- interpreter:PythonInterpreterGetting.java
// Getting data from the
PythonInterpreter object.
package interpreter;
import org.python.util.PythonInterpreter;
import org.python.core.*;
import java.util.*;
import com.bruceeckel.python.*;
import junit.framework.*;
public class
PythonInterpreterGetting extends TestCase
{
PythonInterpreter interp =
new PythonInterpreter();
public void test() throws PyException
{
interp.exec("a = 100");
// If you just use the ordinary
get(),
// it returns a PyObject:
PyObject a =
interp.get("a");
// There's not much you can do with a
generic
// PyObject, but you can print it
out:
System.out.println("a = " +
a);
// If you know the type it's supposed
to be,
// you can "cast" it using
__tojava__() to
// that Java type and manipulate it
in Java.
// To use 'a' as an int, you must use
// the Integer wrapper class:
int ai=
((Integer)a.__tojava__(Integer.class))
.intValue();
// There are also convenience
functions:
ai = Py.py2int(a);
System.out.println("ai + 47 = " + (ai + 47));
// You can
convert it to different types:
float af = Py.py2float(a);
System.out.println("af + 47 =
" + (af + 47));
// If you try to cast it to an
inappropriate
// type you'll get a runtime
exception:
//! String as = (String)a.__tojava__(
//! String.class);
// If you know the type, a more
useful method
// is the overloaded get() that takes
the
// desired class as the 2nd argument:
interp.exec("x = 1
+ 2");
int x =
((Integer)interp
.get("x",
Integer.class)).intValue();
System.out.println("x
= " + x);
// Since Python
is so good at manipulating
// strings and files, you will often
need to
// extract an array of Strings. Here,
a file
// is read as a Python array:
interp.exec("lines = " +
"open('PythonInterpreterGetting.java')"
+
".readlines()");
// Pull it in as a Java array of
String:
String[] lines = (String[])
interp.get("lines",
String[].class);
for(int i = 0; i < 10; i++)
System.out.print(lines[i]);
// As an example of useful string
tools,
// global expansion of ambiguous file
names
// using glob is very useful, but
it's not
// part of the standard Jython
package, so
// you'll have to make sure that your
// Python path is set to include
these, or
// that you deliver the necessary
Python
// files with your application.
interp.exec("from glob import
glob");
interp.exec("files =
glob('*.java')");
String[] files = (String[])
interp.get("files",
String[].class);
for(int i = 0; i < files.length;
i++)
System.out.println(files[i]);
// You can extract tuples and arrays
into
// Java Lists with
com.bruceeckel.PyUtil:
interp.exec(
"tup = ('fee', 'fi', 'fo',
'fum', 'fi')");
List tup = PyUtil.toList(interp,
"tup");
System.out.println(tup);
// It really is a list of String
objects:
System.out.println(tup.get(0).getClass());
// You can easily convert it to a
Set:
Set tups = new HashSet(tup);
System.out.println(tups);
interp.exec("ints=[1,3,5,7,9,11,13,17,19]");
List ints = PyUtil.toList(interp,
"ints");
System.out.println(ints);
// It really is a List of Integer
objects:
System.out.println((ints.get(1)).getClass());
// If you have a Python dictionary,
it can
// be extracted into a Java Map,
again with
// com.bruceeckel.PyUtil:
interp.exec("dict = { 1 : 'a', 3
: 'b'," +
"5 : 'c', 9 : 'd', 11 : 'e'
}");
Map map = PyUtil.toMap(interp,
"dict");
System.out.println("map:
" + map);
// It really is
Java objects, not PyObjects:
Iterator it =
map.entrySet().iterator();
Map.Entry e = (Map.Entry)it.next();
System.out.println(e.getKey().getClass());
System.out.println(e.getValue().getClass());
}
public static void
main(String[] args) throws PyException
{
junit.textui.TestRunner.run(
PythonInterpreterGetting.class);
}
} ///:~
#[BT_329]#
#[BT_330]#The last two
examples show the extraction of Python tuples and lists into Java Lists,
and Python dictionaries into Java Maps. Both of these cases require more
processing than is provided in the standard Jython library, so I have again
created utilities in com.bruceeckel.pyton.PyUtil: toList( )
to produce a List from a Python sequence, and toMap( ) to
produce a Map from a Python dictionary. The PyUtil methods make
it easier to take important data structures back and forth between Java and
Python.
#[BT_331]#It’s also worth
noting that you can have multiple PythonInterpreter objects in a
program, and each one has its own name space:
//- interpreter:MultipleJythons.java
// You can run multiple interpreters,
each
// with its own name space.
package interpreter;
import org.python.util.PythonInterpreter;
import org.python.core.*;
import junit.framework.*;
public class MultipleJythons extends
TestCase {
PythonInterpreter
interp1 = new PythonInterpreter(),
interp2 = new PythonInterpreter();
public void test() throws PyException {
interp1.set("a", new
PyInteger(42));
interp2.set("a", new
PyInteger(47));
interp1.exec("print a");
interp2.exec("print a");
PyObject x1 =
interp1.get("a");
PyObject x2 =
interp2.get("a");
System.out.println("a from
interp1: " + x1);
System.out.println("a from
interp2: " + x2);
}
public static void
main(String[] args) throws PyException
{
junit.textui.TestRunner.run(MultipleJythons.class);
}
} ///:~
#[BT_332]#
#[BT_333]#When you run the
program you’ll see that the value of a is distinct within each PythonInterpreter.
#[BT_334]#Since you have
the Java language at your disposal, and you can set and retrieve values in the
interpreter, there’s a tremendous amount that you can accomplish with the above
approach (controlling Python from Java). But one of the amazing things about
Jython is that it makes Java classes almost transparently available from within
Jython. Basically, a Java class looks like a Python class. This is true for
standard Java library classes as well as classes that you create yourself, as
you can see here:
## interpreter:JavaClassInPython.py
#=M jython.bat JavaClassInPython.py
# Using Java classes within Jython
from java.util import Date, HashSet,
HashMap
from interpreter.javaclass import
JavaClass
from math import sin
d = Date() # Creating a Java Date object
print d # Calls toString()
# A "generator" to easily
create data:
class ValGen:
def __init__(self, maxVal):
self.val = range(maxVal)
# Called during 'for' iteration:
def __getitem__(self, i):
# Returns a tuple of two elements:
return self.val[i], sin(self.val[i])
# Java standard containers:
map = HashMap()
set = HashSet()
for x, y in ValGen(10):
map.put(x, y)
set.add(y)
set.add(y)
print map
print set
# Iterating through a set:
for z in set:
print z, z.__class__
print map[3] # Uses Python dictionary
indexing
for x in map.keySet(): # keySet() is a
Map method
print x, map[x]
# Using a Java class that you create
yourself is
# just as easy:
jc = JavaClass()
jc2 = JavaClass("Created within
Jython")
print jc2.getVal()
jc.setVal("Using a Java class is
trivial")
print jc.getVal()
print jc.getChars()
jc.val = "Using bean
properties"
print jc.val
##~
#[BT_335]#The “=M”
comment is recognized by the makefile generator tool (that I created for this
book) as a replacement makefile command. This will be used instead of the
commands that the extraction tool would normally place in the makefile.
#[BT_336]#Note that the import
statements map to the Java package structure exactly as you would expect. In
the first example, a Date( ) object is created as if it were a
native Python class, and printing this object just calls toString( ).
#[BT_337]#ValGen
implements the concept of a “generator” which is used a great deal in the C++
STL (Standard Template Library, part of the Standard C++ Library). A
generator is an object that produces a new object every time its “generation
method” is called, and it is quite convenient for filling containers. Here, I
wanted to use it in a for iteration, and so I needed the generation
method to be the one that is called by the iteration process. This is a special
method called __getitem__( ), which is actually the overloaded
operator for indexing, ‘[ ]’. A for loop calls this method every
time it wants to move the iteration forward, and when the elements run out, __getitem__( )
throws an out-of-bounds exception and that signals the end of the for
loop (in other languages, you would never use an exception for ordinary control
flow, but in Python it seems to work quite well). This exception happens
automatically when self.val[i] runs out of elements, so the __getitem__( )
code turns out to be simple. The only complexity is that __getitem__( )
appears to return two objects instead of just one. What Python does is
automatically package multiple return values into a tuple, so you still only
end up returning a single object (in C++ or Java you would have to create your
own data structure to accomplish this). In addition, in the for loop
where ValGen is used, Python automatically “unpacks” the tuple so that
you can have multiple iterators in the for. These are the kinds of
syntax simplifications that make Python so endearing.
#[BT_338]#The map
and set objects are instances of Java’s HashMap and HashSet,
again created as if those classes were just native Python components. In the for
loop, the put( ) and add( ) methods work just like they
do in Java. Also, indexing into a Java Map uses the same notation as for
dictionaries, but note that to iterate through the keys in a Map you
must use the Map method keySet( ) rather than the Python
dictionary method keys( ).
#[BT_339]#The final part
of the example shows the use of a Java class that I created from scratch, to
demonstrate how trivial it is. Notice also that Jython intuitively understands
JavaBeans properties, since you can either use the getVal( ) and setVal( )
methods, or assign to and read from the equivalent val property. Also, getChars( )
returns a Character[] in Java, and this becomes an array in Python.
#[BT_340]#The easiest way
to use Java classes that you create for use inside a Python program is to put
them inside a package. Although Jython can also import unpackaged java classes
(import JavaClass), all such unpackaged java classes will be treated as
if they were defined in different packages so they can only see each other’s
public methods.
#[BT_341]#Java packages
translate into Python modules, and Python must import a module in order to be
able to use the Java class. Here is the Java code for JavaClass:
//- interpreter:javaclass:JavaClass.java
package interpreter.javaclass;
import junit.framework.*;
import com.bruceeckel.util.*;
public class JavaClass {
private String s = "";
public JavaClass() {
System.out.println("JavaClass()");
}
public JavaClass(String a) {
s = a;
System.out.println("JavaClass(String)");
}
public String getVal() {
System.out.println("getVal()");
return s;
}
public void setVal(String a) {
System.out.println("setVal()");
s = a;
}
public Character[] getChars() {
System.out.println("getChars()");
Character[] r = new
Character[s.length()];
for(int i = 0; i < s.length();
i++)
r[i] = new Character(s.charAt(i));
return r;
}
public static class Test extends
TestCase {
JavaClass
x1 = new JavaClass(),
x2 = new
JavaClass("UnitTest");
public void test1() {
System.out.println(x2.getVal());
x1.setVal("SpamEggsSausageAndSpam");
System.out.println(
Arrays2.toString(x1.getChars()));
}
}
public static void main(String[] args)
{
junit.textui.TestRunner.run(Test.class);
}
} ///:~
#[BT_342]#
#[BT_343]#You can see that
this is just an ordinary Java class, without any awareness that it will be used
in a Jython program. For this reason, one of the important uses of Jython is in
testing Java code. Because Python
is such a powerful, flexible, dynamic language it is an ideal tool for
automated test frameworks, without making any changes to the Java code that’s
being tested.
#[BT_344]#Inner classes
becomes attributes on the class object. Instances of static inner
classes can be create by the usual call:
com.foo.JavaClass.StaticInnerClass()
#[BT_345]#Non-static
inner classes must have an outer class instance supplied explicitly as the
first argument:
com.foo.JavaClass.InnerClass(com.foo.JavaClass())
#[BT_346]#Jython wraps the
Java libraries so that any of them can be used directly or via inheritance. In
addition, Python shorthand simplifies coding.
#[BT_347]#As an example,
consider the HTMLButton.java example from Chapter 9 of Thinking in
Java, 2nd edition (you presumably have already downloaded and
installed the source code for that book from www.BruceEckel.com, since a
number of examples in this book use libraries from that book). Here is its
conversion to Jython:
## interpreter:PythonSwing.py
# The HTMLButton.java example from
# "Thinking in Java, 2nd
edition," Chapter 13,
# converted into Jython.
# Don’t run this as part of the automatic
make:
#=M @echo skipping PythonSwing.py
from javax.swing import JFrame, JButton,
JLabel
from java.awt import FlowLayout
frame = JFrame("HTMLButton",
visible=1,
defaultCloseOperation=JFrame.EXIT_ON_CLOSE)
def kapow(e):
frame.contentPane.add(JLabel("<html>"+
"<i><font
size=+4>Kapow!"))
# Force a re-layout to
# include the new label:
frame.validate()
button =
JButton("<html><b><font size=+2>" +
"<center>Hello!<br><i>Press me now!",
actionPerformed=kapow)
frame.contentPane.layout = FlowLayout()
frame.contentPane.add(button)
frame.pack()
frame.size=200, 500
##~
#[BT_348]#If you compare
the Java version of the program to the above Jython implementation, you’ll see
that Jython is shorter and generally easier to understand. For example, in the
Java version to set up the frame you had to make several calls: the constructor
for JFrame( ), the setVisible( ) method and the setDefaultCloseOperation( )
method, whereas in the above code all three of these operations are performed
with a single constructor call.
#[BT_349]#Also notice that
the JButton is configured with an actionListener( ) method
inside the constructor, with the assignment to kapow. In addition,
Jython’s JavaBean awareness means that a call to any method with a name that
begins with “set” can be replaced with an assignment, as you can see
above.
#[BT_350]#The only method
that did not come over from Java is the pack( ) method, which seems
to be essential in order to force the layout to happen properly. It’s also
important that the call to pack( ) appear before the size
setting.
#[BT_351]#You can easily
inherit from standard Java library classes in Jython. Here’s the Dialogs.java
example from Chapter 13 of Thinking in Java, 2nd edition,
converted into Jython:
## interpreter:PythonDialogs.py
# Dialogs.java from "Thinking in
Java, 2nd
# edition," Chapter 13, converted
into Jython.
# Don’t run this as part of the automatic
make:
#=M @echo skipping PythonDialogs.py
from java.awt import FlowLayout
from javax.swing import JFrame, JDialog,
JLabel
from javax.swing import JButton
class MyDialog(JDialog):
def __init__(self, parent=None):
JDialog.__init__(self,
title="My dialog",
modal=1)
self.contentPane.layout =
FlowLayout()
self.contentPane.add(JLabel("A
dialog!"))
self.contentPane.add(JButton("OK",
actionPerformed =
lambda e, t=self: t.dispose()))
self.pack()
frame = JFrame("Dialogs",
visible=1,
defaultCloseOperation=JFrame.EXIT_ON_CLOSE)
dlg = MyDialog()
frame.contentPane.add(
JButton("Press
here to get a Dialog Box",
actionPerformed = lambda e:
dlg.show()))
frame.pack()
##~
#[BT_352]#MyDialog
is inherited from JDialog, and you can see named arguments being used in
the call to the base-class constructor.
#[BT_353]#In the creation
of the “OK” JButton, note that the actionPerformed method is set
right inside the constructor, and that the function is created using the Python
lambda keyword. This creates a nameless function with the arguments
appearing before the colon and the expression that generates the returned value
after the colon. As you should know, the Java prototype for the actionPerformed( )
method only contains a single argument, but the lambda expression indicates
two. However, the second argument is provided with a default value, so the
function can be called with only one argument. The reason for the second
argument is seen in the default value, because this is a way to pass self
into the lambda expression, so that it can be used to dispose of the dialog.
#[BT_354]#Compare this
code with the version that’s published in Thinking in Java, 2nd
edition. You’ll find that Python language features allow a much more
succinct and direct implementation.
#[BT_355]#Although it does
not directly relate to the original problem of this chapter (creating an
interpreter), Jython has the additional ability to create Java classes directly
from your Jython code. This can produce very useful results, as you are then
able to treat the results as if they are native Java classes, albeit with
Python power under the hood.
#[BT_356]#To produce Java
classes from Python code, Jython comes with a compiler called jythonc.
#[BT_357]#The process of
creating Python classes that will produce Java classes is a bit more complex
than when calling Java classes from Python, because the methods in Java classes
are strongly typed, while Python functions and methods are weakly typed. Thus,
you must somehow tell jythonc that a Python method is intended to have a
particular set of argument types and that its return value is a particular
type. You accomplish this with the “@sig” string, which is placed right after
the beginning of the Python method definition (this is the standard location
for the Python documentation string). For example:
def returnArray(self):
"@sig public java.lang.String[]
returnArray()"
#[BT_358]#The Python
definition doesn’t specify any return type, but the @sig string gives the full
type information about what is being passed and returned. The jythonc
compiler uses this information to generate the correct Java code.
#[BT_359]#There’s one
other set of rules you must follow in order to get a successful compilation:
you must inherit from a Java class or interface in your Python class (you do
not need to specify the @sig signature for methods defined in the
superclass/interface). If you do not do this, you won’t get your desired
methods – unfortunately, jythonc gives you no warnings or errors in this
case, but you won’t get what you want. If you don’t see what’s missing, it can
be very frustrating.
#[BT_360]#In addition, you
must import the appropriate java class and give the correct package
specification. In the example below, java is imported so you must
inherit from java.lang.Object, but you could also say from
java.lang import Object and then you’d just inherit from Object
without the package specification. Unfortunately, you don’t get any warnings or
errors if you get this wrong, so you must be patient and keep trying.
#[BT_361]#Here is an
example of a Python class created to produce a Java class. This also introduces
the ‘=T’ directive for the makefile builder tool, which specifies a
different target than the one that is normally used by the tool. In this case,
the Python file is used to build a Java .class file, so the class file
is the desired makefile target. To accomplish this, the default makefile
command is replaced using the ‘=M’ directive (notice how you can break
across lines using ‘\’):
## interpreter:PythonToJavaClass.py
#=T
python\java\test\PythonToJavaClass.class
#=M jythonc.bat --package
python.java.test \
#=M PythonToJavaClass.py
# A Python class created to produce a
Java class
from jarray import array
import java
class
PythonToJavaClass(java.lang.Object):
# The '@sig' signature string is used
to create
# the proper signature in the resulting
# Java code:
def __init__(self):
"@sig public
PythonToJavaClass()"
print "Constructor for
PythonToJavaClass"
def simple(self):
"@sig public void simple()"
print "simple()"
# Returning values to Java:
def returnString(self):
"@sig public java.lang.String
returnString()"
return "howdy"
# You must construct arrays to return
along
# with the type of the array:
def returnArray(self):
"@sig public java.lang.String[]
returnArray()"
test = [ "fee",
"fi", "fo", "fum" ]
return array(test, java.lang.String)
def ints(self):
"@sig public java.lang.Integer[]
ints()"
test = [ 1, 3, 5, 7, 11, 13, 17, 19,
23 ]
return array(test, java.lang.Integer)
def doubles(self):
"@sig public java.lang.Double[]
doubles()"
test = [ 1, 3, 5, 7, 11, 13, 17, 19,
23 ]
return array(test, java.lang.Double)
# Passing arguments in from Java:
def argIn1(self, a):
"@sig public void
argIn1(java.lang.String a)"
print "a: %s" % a
print "a.__class__",
a.__class__
def argIn2(self, a):
"@sig public void
argIn1(java.lang.Integer a)"
print "a + 100: %d" % (a +
100)
print "a.__class__",
a.__class__
def argIn3(self, a):
"@sig public void
argIn3(java.util.List a)"
print "received List:", a,
a.__class__
print "element type:",
a[0].__class__
print "a[3] + a[5]:", a[5]
+ a[7]
#! print "a[2:5]:", a[2:5]
# Doesn't work
def argIn4(self, a):
"@sig public void \
argIn4(org.python.core.PyArray
a)"
print "received type:",
a.__class__
print "a: ", a
print "element type:",
a[0].__class__
print "a[3] + a[5]:", a[5]
+ a[7]
print "a[2:5]:", a[2:5] # A
real Python array
# A map must be passed in as a
PyDictionary:
def argIn5(self, m):
"@sig public void \
argIn5(org.python.core.PyDictionary m)"
print "received Map: ", m,
m.__class__
print "m['3']:", m['3']
for x in m.keys():
print x, m[x]
##~
#[BT_362]#First note that PythonToJavaClass
is inherited from java.lang.Object; if you don’t do this you will
quietly get a Java class without the right signatures. You are not required to
inherit from Object; any other Java class will do.
#[BT_363]#This class is
designed to demonstrate different arguments and return values, to provide you
with enough examples that you’ll be able to easily create your own signature
strings. The first three of these are fairly self-explanatory, but note the
full qualification of the Java name in the signature string.
#[BT_364]#In returnArray( ),
a Python array must be returned as a Java array. To do this, the Jython array( )
function (from the jarray module) must be used, along with the type of
the class for the resulting array. Any time you need to return an array to Java,
you must use array( ), as seen in the methods ints( )
and doubles( ).
#[BT_365]#The last methods
show how to pass arguments in from Java. Basic types happen automatically as
long as you specify them in the @sig string, but you must use objects
and you cannot pass in primitives (that is, primitives must be ensconced in
wrapper objects, such as Integer).
#[BT_366]#In argIn3( ),
you can see that a Java List is transparently converted to something
that behaves just like a Python array, but is not a true array because you
cannot take a slice from it. If you want a true Python array, then you must
create and pass a PyArray as in argIn4( ), where the slice
is successful. Similarly, a Java Map must come in as a PyDictionary
in order to be treated as a Python dictionary.
#[BT_367]#Here is the Java
program to exercise the Java classes produced by the above Python code. This
also introduces the ‘=D’ directive for the makefile builder tool, which
specifies a dependency in addition to those detected by the tool. Here, you
can’t compile TestPythonToJavaClass.java until PythonToJavaClass.class
is available:
//- interpreter:TestPythonToJavaClass.java
//+D
python\java\test\PythonToJavaClass.class
package interpreter;
import java.lang.reflect.*;
import java.util.*;
import org.python.core.*;
import junit.framework.*;
import com.bruceeckel.util.*;
import com.bruceeckel.python.*;
// The package with the Python-generated
classes:
import python.java.test.*;
public class
TestPythonToJavaClass extends TestCase {
PythonToJavaClass p2j = new
PythonToJavaClass();
public void testDumpClassInfo() {
System.out.println(
Arrays2.toString(
p2j.getClass().getConstructors()));
Method[] methods =
p2j.getClass().getMethods();
for(int i = 0; i < methods.length;
i++) {
String nm = methods[i].toString();
if(nm.indexOf("PythonToJavaClass") != -1)
System.out.println(nm);
}
}
public void test1() {
p2j.simple();
System.out.println(p2j.returnString());
System.out.println(
Arrays2.toString(p2j.returnArray()));
System.out.println(
Arrays2.toString(p2j.ints());
System.out.println(
Arrays2.toString(p2j.doubles()));
p2j.argIn1("Testing
argIn1()");
p2j.argIn2(new Integer(47));
ArrayList a = new ArrayList();
for(int i = 0; i < 10; i++)
a.add(new Integer(i));
p2j.argIn3(a);
p2j.argIn4(
new PyArray(Integer.class,
a.toArray()));
Map m = new HashMap();
for(int i = 0; i < 10; i++)
m.put("" + i, new
Float(i));
p2j.argIn5(PyUtil.toPyDictionary(m));
}
public static void main(String[] args)
{
junit.textui.TestRunner.run(
TestPythonToJavaClass.class);
}
} ///:~
#[BT_368]#
#[BT_369]#For Python
support, you’ll usually only need to import the classes in org.python.core.
Everything else in the above example is fairly straightforward, as PythonToJavaClass
appears, from the Java side, to be just another Java class. dumpClassInfo( )
uses reflection to verify that the method signatures specified in PythonToJavaClass.py
have come through properly.
#[BT_370]#Part of the
trick of creating Java classes from Python code is the @sig information in the
method documentation strings. But there’s a second problem which stems from the
fact that Python has no “package” keyword – the Python equivalent of packages
(modules) are implicitly created based on the file name. However, to bring the
resulting class files into the Java program, jythonc must be given
information about how to create the Java package for the Python code. This is
done on the jythonc command line using the --package flag,
followed by the package name you wish to produce (including the separation
dots, just as you would give the package name using the package keyword
in a Java program). This will put the resulting .class files in the
appropriate subdirectory off of the current directory. Then you only need to
import the package in your Java program, as shown above (you’ll need ‘.’
in your CLASSPATH in order to run it from the code directory).
#[BT_371]#Here are the make
dependency rules that I used to build the above example (the backslashes at
the ends of the lines are understood by make to be line continuations).
These rules are encoded into the above Java and Python files using the comment
syntax that’s understood by my makefile builder tool:
TestPythonToJavaClass.class: \
TestPythonToJavaClass.java \
python\java\test\PythonToJavaClass.class
javac TestPythonToJavaClass.java
python\java\test\PythonToJavaClass.class:
\
PythonToJavaClass.py
jythonc.bat --package
python.java.test \
PythonToJavaClass.py
#[BT_372]#The first
target, TestPythonToJavaClass.class, depends on both TestPythonToJavaClass.java
and the PythonToJavaClass.class, which is the Python code that’s
converted to a class file. This latter, in turn, depends on the Python source
code. Note that it’s important that the directory where the target lives be
specified, so that the makefile will create the Java program with the minimum
necessary amount of rebuilding.
#[BT_373]#An alternative
to Jython is the Java-Python Extension (JPE), which directly connects
with your native C-Python implementation.
#[BT_374]#Jython runs
entirely within the JavaVM, which produces two fundamental limitations: Jython
cannot be called from CPython, and native Python extensions are not accessible
from JPython. JPE is linked with the Python C libraries, so JPE can be called
from C-Python, and native Python extensions can be called from Java through
JPE.
#[BT_375]#If you need to
access the features on your native platform, JPE might be the easiest solution.
You can find JPE at http://www.arakne.com/jpe.htm.
#[BT_376]#This chapter has
arguably gone much deeper into Jython than required to use the interpreter
design pattern. Indeed, once you decide that you need to use interpreter and
that you’re not going to get lost inventing your own language, the solution of
installing Jython is quite simple, and you can at least get started by
following the GreenHouseController example.
#[BT_377]#Of course, that
example is often too simple and you may need something more sophisticated,
often requiring more interesting data to be passed back and forth. When I
encountered the limited documentation, I felt it necessary to come up with a
more thorough examination of Jython.
#[BT_378]#In the process,
note that there could be another equally powerful design pattern lurking in
here, which could perhaps be called multiple languages. This is based on
the experience of having each language solve a certain class of problems better
than the other; by combining languages you can solve problems much faster than
with either language by itself. CORBA is another way to bridge across languages,
and at the same time bridging between computers and operating systems.
#[BT_379]#To me, Python
and Java present a very potent combination for program development because of
Java’s architecture and tool set, and Python’s extremely rapid development
(generally considered to be 5-10 times faster than C++ or Java). Python is
usually slower, however, but even if you end up re-coding parts of your program
for speed, the initial fast development will allow you to more quickly flesh
out the system and uncover and solve the critical sections. And often, the
execution speed of Python is not a problem – in those cases it’s an even bigger
win. A number of commercial products already use Java and Jython, and because
of the terrific productivity leverage I expect to see this happen more in the
future.
1.
Modify GreenHouseLanguage.py so that it checks the times
for the events and runs those events at the appropriate times.
2.
Modify GreenHouseLanguage.py so that it calls a function
for action instead of just printing a string.
3.
Create a Swing application with a JTextField (where the
user will enter commands) and a JTextArea (where the command results
will be displayed). Connect to a PythonInterpreter object so that the
output will be sent to the JTextArea (which should scroll). You’ll need
to locate the PythonInterpreter command that redirects the output to a
Java stream.
4.
Modify GreenHouseLanguage.py to add a master controller
class (instead of the static array inside Event) and provide a run( )
method for each of the subclasses. Each run( ) should create and
use an object from the standard Java library during its execution. Modify GreenHouseController.java
to use this new class.
5.
Modify the resulting GreenHouseLanguage.py from exercise
two to produce Java classes (add the @sig documentation strings to produce the
correct Java signatures, and create a makefile to build the Java .class
files). Write a Java program that uses these classes.
#[BT_380]#
