18.4. Jython: Python for Java
Jython (formerly known as JPython) is an entirely distinct implementation of the Python programming language that allows programmers to use Python as an easy-to-use scripting component in Java-based applications. The effect is much the same as deploying standard Python as a scripting language for C and C++ libraries.
In short, Jython makes Python code look like Java and consequently offers a variety of technology options inherited from the Java world. With Jython, Python code may be run as client-side applets in web browsers, as server-side scripts, and in a variety of other roles. Jython is distinct from other systems mentioned in this section in terms of its scope: while it is based on the core Python language we've seen in this book, it actually replaces the underlying implementation of that language rather than augmenting it.[*]
This section briefly explores Jython and highlights some of the reasons you may or may not want to use it instead of the standard Python implementation. Although Jython is primarily of interest to programmers writing Java-based applications, it underscores integration possibilities and language definition issues that merit the attention of Python users. Because Jython is Java-centric, you need to know something about Java development to make the most sense of Jython, and this book doesn't pretend to teach that in the next few pages. For more details, interested readers should consult other materials, including Jython documentation at http://www.jython.org.
18.4.1. A Quick Introduction to Jython
Functionally speaking, Jython is a collection of Java classes that run Python code. It consists of a Python compiler, written in Java, that translates Python scripts to Java bytecode so they can be executed by a Java Virtual Machinethe runtime component that executes Java programs and is used by major web browsers. Moreover, Jython automatically exposes all Java class libraries for use in Python scripts. In a nutshell, here's what comes with the Jython system:
The net effect of all this is that Jython allows us to write Python programs that can run on any Java-aware machinein particular, in the context of most web browsers. More important, because Python programs are translated into Java bytecode, Jython provides an incredibly seamless and natural integration between the two languages. Both walk and talk in terms of the Java model, so calls across language boundaries are trivial. With Jython's approach, it's even possible to subclass a Java class in Python, and vice versa.
18.4.2. Why Jython?
So why go to all this trouble to mix Python into Java environments? The most obvious answer is that Jython makes Java components easier to use: Jython scripts are typically a fraction of the size of their Java equivalents, and are much less complex. More generally, the answer is really the same as it is for C and C++ environments. Python, as an easy-to-use, object-oriented scripting language, naturally complements the Java programming language.
By now, it is clear to most people that Java is too complex to serve as a scripting or rapid-development tool. But this is exactly where Python excels; by adding Python to the mix with Jython, we add a scripting component to Java systems, exactly as we do when integrating Python with C or C++. For instance, we can use Jython to quickly prototype Java systems, test Java classes interactively, and open up Java systems for end-user customization. In general, adding Python to Java development can significantly boost programmer productivity, just as it does for C and C++ systems.
18.4.3. A Simple Jython Example
Once a Python program is compiled with Jython, it is all Java: the program is translated to Java bytecode, it uses Java classes to do its work, and there is no Python left except for the original source code. Because the compiler tool itself is also written in Java, Jython is sometimes called "100 percent pure Java." That label may be more profound to marketers than programmers, though, because Jython scripts are still written using standard Python syntax, use Python datatypes, and import many of Python's standard library modules. For instance, Example 18-5 is a legal Jython program, derived from an example originally written by Guido van Rossum.
Example 18-5. PP3E\Internet\Other\Jython\jycalc.py
The first thing you should notice is that this is genuine Python codeJython scripts use the same core language that we've been using all along in this book. That's good news, both because Python is such an easy language to use and because you don't need to learn a new, proprietary scripting language to use Jython. It also means that all of Python's high-level language syntax and tools are available. For example, in this script, the Python eval built-in function is used to parse and evaluate constructed expressions all at once, saving us from having to write an expression evaluator from scratch.
18.4.4. Interface Automation Tricks
The previous calculator example also illustrates two interface automations performed by Jython: function callback and attribute mappings. Java programmers may have already noticed that this example doesn't use classes. Like standard Python and unlike Java, Jython supports but does not impose object-oriented programming (OOP). Simple Python functions work fine as callback handlers. In Example 18-5, assigning key.actionPerformed to a Python function object has the same effect as registering an instance of a class that defines a callback handler method:
def push(event): ... key = swing.JButton(label) key.actionPerformed = push
This is noticeably simpler than the more Java-like:
class handler(awt.event.ActionListener): def actionPerformed(self, event): ... key = swing.JButton(label) key.addActionListener(handler( ))
Jython automatically maps Python functions to the Java class method callback model. Java programmers may now be wondering why we can assign to something named key.actionPerformed in the first place. Jython's second magic feat is to make Java data members look like simple object attributes in Python code. In abstract terms, Jython code of the form:
X = Object(argument) X.property = value + X.property
is equivalent to the more traditional and complex Java style:
X = Object(argument) X.setProperty(value + X.getProperty( ))
That is, Jython automatically maps attribute assignments and references to Java accessor method calls by inspecting Java class signatures (and possibly Java BeanInfo files if used). Moreover, properties can be assigned with keyword arguments in object constructor calls, such that:
X = Object(argument, property=value)
is equivalent to both this more traditional form:
X = Object(argument) X.setProperty(value)
as well as the following, which relies on attribute name mapping:
X = Object(argument) X.property = value
We can combine both callback and property automation for an even simpler version of the callback code snippet:
def push(event): ... key = swing.JButton(label, actionPerformed=push)
You don't need to use these automation tricks, but again, they make Jython scripts simpler, which is most of the point behind mixing Python with Java.
18.4.5. Writing Java Applets in Jython
I would be remiss if I didn't include a brief example of Jython code that more directly masquerades as a Java applet: code that lives on a server machine but is downloaded to and run on the client machine when its Internet address is referenced. Most of the magic behind this is subclassing the appropriate Java class in a Jython script, demonstrated in Example 18-6.
Example 18-6. PP3E\Internet\Other\Jython\jyapplet.py
The Python class in this code inherits all the necessary applet protocol from the standard Java Applet superclass, so there is not much new to see here. Under Jython, Python classes can always subclass Java classes, because Python objects really are Java objects when compiled and run. The Python-coded paint method in this script will be automatically run from the Java AWT event loop as needed; it simply uses the passed-in gc user-interface handle object to draw a text message.
If we use Jython's jythonc command-line tool to compile this into a Java .class file and properly store that file on a web server, it can then be used exactly like applets written in Java. Because most web browsers include a JVM, this means that such Python scripts may be used as client-side programs that create sophisticated user-interface devices within the browser, and so on.
18.4.6. Jython Trade-Offs
Depending on your background, the potentially less good news about Jython is that even though the calculator and applet scripts discussed here are straight Python code, the libraries they use are different from what we've seen so far. In fact, the library calls employed are radically different. The calculator, for example, relies primarily on imported Java class libraries, not on standard Python libraries. You need to understand Java's awt and swing libraries to make sense of its code, and this library skew between language implementations becomes more acute as programs grow larger. The applet example is even more Java bound: it depends both on Java user-interface libraries and on Java applet protocols.
If you are already familiar with Java libraries, this isn't an issue at all, of course. But because most of the work performed by realistic programs is done by using libraries, the fact that most Jython code relies on very different libraries makes compatibility with standard Python less potent than it may seem at first glance. To put that more strongly, apart from simple core language examples, many Jython programs won't run on the standard Python interpreter, and many standard Python programs won't work under Jython.
Generally, Jython presents a number of trade-offs. I want to point out upfront that Jython is indeed an excellent Java scripting toolarguably the best one available, and most of its trade-offs are probably of little or no concern to Java developers. For instance, if you are coming to Jython from the Java world, the fact that Java libraries are at the heart of Jython scripts may be more asset than downside. But if you are presented with a choice between the standard and Java-based Python language implementations, some of Jython's implications are worth knowing about:
Some incompatibilities between Jython and standard Python can be very subtle. For instance, Jython inherits all of the Java runtime engine's behavior, including Java security constraints and garbage collection. Java garbage collection is not based on standard Python's reference count scheme, and therefore can automatically collect cyclic objects.[*] It also means that some common Python programming idioms won't work. For example, it's typical in Python to code file-processing loops in this form:
for filename in bigfilenamelist: text = open(filename).read( ) dostuffwith(text)
That works because files are automatically closed when garbage collected in standard Python, and we can be sure that the file object returned by the open call will be immediately garbage collected (it's temporary, so there are no more references as soon as we call read). This won't work in Jython, though, because we can't be sure when the temporary file object will be reclaimed. To avoid running out of file descriptors, we usually need to code this differently for Jython:
for filename in bigfilenamelist: file = open(filename) text = file.read( ) dostuffwith(text) file.close( )
You may face a similar implementation mismatch if you assume that output files are immediately closed: open(name,'w').write(bytes) collects and closes the temporary file object and hence flushes the bytes out to the file under the standard C implementation of Python only, and Jython instead collects the file object at some arbitrary time in the future.
18.4.7. Picking Your Python
Because of concerns such as those just mentioned, the Jython implementation of the Python language is probably best used only in contexts where Java integration or web-browser interoperability is an important design goal. You should always be the judge, of course, but the standard C implementation seems better suited to most other Python applications. Still, that leaves a very substantial domain to Jythonalmost all Java systems and programmers can benefit from adding Jython to their tool sets.
Jython allows programmers to write programs that use Java class libraries in a fraction of the code and complexity required by Java-coded equivalents. Hence, Jython excels as an extension language for Java-based systems, especially those that will run in the context of web browsers. Because Java is a standard component of most web browsers, Jython scripts will often run automatically without extra install steps on client machines. Furthermore, even Java-coded applications that have nothing to do with the Web can benefit from Jython's ease of use; its seamless integration with Java class libraries makes Jython arguably the best Java scripting and testing tool available today.
For most other applications, though, the standard Python implementation, possibly integrated with C and C++ components, is probably a better design choice. The resulting system will likely run faster, cost less to ship, have access to all Python extension modules, be more robust and portable, and be more easily maintained by people familiar with standard Python.
On the other hand, I want to point out again that the trade-offs listed here are mostly written from the Python perspective; if you are a Java developer looking for a scripting tool for Java-based systems, many of these detriments may be of minor concern. And to be fair, some of Jython's problems may be addressed in future releases; for instance, its speed will probably improve over time. Yet even as it exists today, Jython clearly makes an ideal extension-language solution for Java-based applications, and it offers a much more complete Java scripting solution than those currently available for other scripting languages.[*]
For more details, consult the Jython home page, currently maintained at http://www.jython.org. You will also find commercially published books about Jython as well; search the Web for pointers. See also the sidebar "The IronPython C# Python Compiler," later in this chapter, about the new Python implementation for the C#/.NET environment on Windows (and its Mono open source equivalent). Though still emerging, it seems likely that there will be three Pythons to choose from very soon and perhaps more in the future. All will likely implement the same core Python language we've used in this text, but they may emphasize alternative integration schemes, application domains, development environments, and so on.