General Python Documentation

Since Jython is an implementation of Python for the JVM, most of the standard Python documentation applies. Look in the following places for general information:

• The Python Tutorial (start here)
• The Python Library Reference. Most of these modules are available, although some coded in C for CPython must be re-implemented in Java for Jython. In general, those that are implemented follow the library reference as closely as possible.
• The Python Language Reference (for language lawyers).

Other Useful Links

• Jython and CPython are two different implementations of the same language. There are naturally some differences between the two implementations, sometimes reflecting undefined aspects of the Python language, sometimes reflecting quirks of the independent implementations. In general, Jython has no global interpreter lock and does not use reference counting.
• The Jython FAQ may already contain the answer to your question.
• If it doesn't, then check Jython-users mailing list archives.
• If you are still stuck you can post a question to the Jython-users mailing list

Making Jython Scripts Executable

To make a jython ".py" file executable on a Unix system:

• Make sure that jython is on your standard PATH.

• Make the ".py" file executable. Typically, this is done with the command chmod +x foo.py

• Add the following line to the top of the file:

  #!/usr/bin/env jython

Registry Properties

The following properties are recognized by Jython. There may be others that aren't documented here; consult the comments in registry file for details.

python.path

Equivalent to CPython's PYTHONPATH environment variable

python.cachedir

The directory to use for caches - currently just package information. This directory must be writable by the user. If the directory is an absolute path, it is used as given, otherwise it is interpreted as relative to sys.prefix.

python.verbose

Sets the verbosity level for varying degrees of informative messages. Valid values in order of increasing verbosity are "error", "warning", "message", "comment", "debug"

python.security.respectJavaAccessibility

Normally, Jython can only provide access to public members of classes. However if this property is set to false and you are using Java 1.2 then Jython can access non-public fields, methods, and constructors.

python.jythonc.compiler

The Java compiler to use with the jythonc tool, which now generates Java source code. This should be the absolute path to a Java compiler, or the name of a compiler on your standard PATH.

python.jythonc.classpath

Extensions to the standard java.class.path property for use with jythonc. This is useful if you use Jikes as your compiler.

python.jythonc.compileropts

Options to pass to the Java compiler when using jythonc.

python.console

The name of a console class. An alternative console class that supports GNU readline can be installed with this property. Jython already include such a console class and it can be enabled by setting this property to org.python.util.ReadlineConsole

python.console.readlinelib

Allow a choice of backing implementation for GNU readline support. Can be either GnuReadline or Editline. This property is only used when python.console is set to org.python.util.ReadlineConsole.

Finding the Registry File

To find the Jython registry file and set the Python values for sys.prefix, you must first locate a root directory.

• If a "python.home" exists, it is used as the root directory by default.
• If "python.home" does not exist, "install.root" is used.
• If neither of these exist, then Jython searches for the file "jython.jar" on the Java classpath, as defined in the system property java.class.path. (The actual file system isn't searched, only the paths defined on the classpath and one of them must literally include "jython.jar").

Once the root directory is found, sys.prefix and sys.exec_prefix are set to this, and sys.path has rootdir/Lib appended to it. The registry file used is then rootdir/registry.

Accessing Java from Jython

One of the goals of Jython is to make it as simple as possible to use existing Java libraries from Python. Example

The following example of an interactive session with Jython shows how a user could create an instance of the Java random number class (found in java.util.Random) and then interact with that instance.

$ jython2.5
Jython 2.5.0 (Release_2_5_0:6476, Jun 16 2009, 13:33:26)
[Java HotSpot(TM) 64-Bit Server VM (Apple Inc.)] on java1.6.0_13
Type "help", "copyright", "credits" or "license" for more information.
>>> from java.util import Random
>>> r = Random()
>>> r.nextInt()
501203849
>>> for i in xrange(5):
...     print r.nextDouble()
...
0.435789109087
0.0702903104743
0.962867215318
0.674547069552
0.434106849824
>>>

More Details

Hopefully, this example should make it clear that there are very few differences between using Java packages and using Python packages when working under Jython. There are a few things to keep in mind.

Importing

$ jython2.5
Jython 2.5.0 (Release_2_5_0:6476, Jun 16 2009, 13:33:26)
[Java HotSpot(TM) 64-Bit Server VM (Apple Inc.)] on java1.6.0_13
Type "help", "copyright", "credits" or "license" for more information.
>>> from java.util import *
>>> Random
<type 'java.util.Random'>
>>> HashMap
<type 'java.util.HashMap'>
>>>

Creating Class Instances

You can create an instance of a Java class exactly the way you would create an instance of a Python class. You must "call" the class with a set of arguments that is appropriate for one of the Java class's constructors. See the section below for more details on what constitutes appropriate arguments.

Calling Java Methods and Functions

Java classes have both static and instance methods this makes them behave much like a cross between a Python module and class. As a user, you should rarely need to be concerned with this difference.

Java methods and functions are called just exactly like their Python counterparts. There is some automatic type coercion that goes on both for the types being passed in and for the value returned by the method. The following table shows how Python objects are coerced to Java objects when passed as arguments in a function call. The Java Types show the expected Java type for the argument, and the Allowed Python Types shows what Python objects can be converted to the given Java type. Notice the special behavior of String's when a java.lang.Object is expected. This behavior might change if it is shown to cause problems.

Returned values from a Java method are also possibly coerced back to an object that is more readily usable in Python. The following table shows those coercions.

Overloaded Java Method Signatures

Java methods are allowed to be overloaded for different signatures (types and number of arguments). When different versions of the method differ in the number of arguments that they expect, the appropriate method can be easily determined from the number of arguments passed to the method.

When the difference is instead in the types of the arguments, more work is required. The possible signatures are sorted in a consistent order that should ensure the appropriate method is chosen first. TBD: document this order!

If you need to call a Java method with a particular signature and this is not happening in the easy way, you can use the following workaround:

Assume that foo has two methods, "void foo(int x); void foo(byte x);". To call the second method you could write the following:

from java.lang import Byte
foo(Byte(10))

Naming Conflicts with Python Keywords

Because Java has a different set of keywords than Python, there are many Java classes that have method and function names that conflict with Python's keyword set. Where the intent can be unambiguously determined, no identifier mangling is necessary, such as when keywords are used as attributes on objects. Thus you can naturally write:

java.lang.System.out.print("hi")

or

java.lang.Runtime.getRuntime().exec(cmd)

In the rare case where the conflict can't be resolved due to Python's grammar, you should modify the reserved word by appended an underscore to the end of it, e.g. print_

Properties

Jython uses JavaBean properties to make it easier to interact with most Java classes. These properties can be used as normal object attributes, and can also be specified to the class constructor as keyword arguments (this idea is stolen from TkInter where it seems to work extremely well).

These properties are generated automatically using the JavaBean Introspector which identifies properties either from common design patterns, or from explicitly specified BeanInfo.

As a first example, consider the case where you wish to create a button that is disabled.

The first example shows how you would do this in the typical Java fashion:

b = awt.Button()
b.setEnabled(0)

The second example shows how enabled can be set as a property:

b = awt.Button()
b.enabled = 0

The final example sets this property at instantiation time using a keyword argument:

b = awt.Button(enabled=0)

Tuples

If the value of a property is specified as a tuple, then the property will be created by applying the constructor for the type of the property to the tuple. This is particularly handy for specifying sizes:

frame = awt.Frame(size=(500,100))

It can also be handy for specifying color as an RGB triple:

frame.background = 255,255,0

will set the background color of the frame to yellow.

Event Properties

In standard Java, the event handlers for a widget are specified by passing in an instance of a class that implements the appropriate interface. This is the only reasonable approach to take in a language that doesn't have first-class functions. In Jython, for every event listener supported by a class, there will be a property added to the class for each method supplied by the event listener class. These properties can be set to give a function to be called when the appropriate event occurs.

The standard Java style for setting an event listener is shown below:

class action(awt.event.ActionListener):
    def actionPerformed(self,event):
        java.lang.System.exit(0)

button = awt.Button("Close Me!")
button.addActionListener(action())

This can be written in a more Pythonesque (and compact) style by using event properties as follows:

def exit(event):
  java.lang.System.exit(0)

button = awt.Button("Close Me!", actionPerformed=exit)

Methods, Properties and Event Properties

Jython have only one namespace for these three class attributes. Java can be seen as having a unique namespace for each of the three types. As a consequense, there can be conflicts between methods, properties and event properties. These conflicts are resolved so that:

properties < event-properties < fields < methods

This means that a method will override a field with the same name. Some carefull handling of properties and static fields allow for the existence of, and access to, both an instance property and a static field with the same name.

A Short Example

The example below should both demonstrate how this subclassing is performed and why it is useful. At first glance, the code looks exactly like subclassing any other Python class. The key difference in this example is that awt.event.ActionListener is a Java class, not a Python one. In the 4th line from the end, "b.addListener(SpamListener())", a Java method is being called that requires an instance of the Java class ActionListener. By providing a Python subclass of this Java class, everybody is happy.

from java import awt

class SpamListener(awt.event.ActionListener):
    def actionPerformed(self,event):
        if event.getActionCommand() == "Spam":
            print 'Spam and eggs!'

f = awt.Frame("Subclassing Example")
b = awt.Button("Spam")
b.addActionListener(SpamListener())
f.add(b, "Center")
f.pack()
f.setVisible(1)

Note: This example can be accomplished much more elegantly by using JavaBeans properties (and event properties).

Calling Methods in Your Superclass

In Python, if I want to call the foo method in my superclass, I use the form:

SuperClass.foo(self)

This works with the majority of methods, but protected methods cannot be called from subclasses in this way. Instead you have to use the "self.super__foo()" call style.

Example

The following example shows how the java.io.InputStream class can be effectively subclassed. What makes this class difficult is that the read method is overloaded for three different method signatures:

1. abstract int read()
2. int read(byte[])
3. int read(byte[], int, int)

The first one of these methods must be overridden in a subclass. The other two versions can be ignored. Unfortunately, Python has no notion of method overloading based on type signatures (this might be related to the fact that Python doesn't have type signatures ;-) In order to implement a subclass of java.io.InputStream that overrides the "read" method, a Python method must be implemented that handles all three possible cases. The example below shows the easiest way to acheive this:

from java.io import InputStream

class InfiniteOnes(InputStream):
    def read(self, *args):
        if len(args) > 0:
        # int read(byte[])
        # int read(byte[], int, int)
            return InputStream.read(self, *args)
        return 1

io = InfiniteOnes()

for i in range(10):
    print io.read(),
print

Example Continued

To continue the example above, this new instance of java.io.InputStream can be passed to any Java method that expects an InputStream as shown below:

from java.io import DataInputStream

dp = DataInputStream(io)
dp.skipBytes(1000)
print dp.readByte()
print dp.readShort()
print dp.readInt()

Invoking Your Superclass's Constructor

You can explictly invoke your superclass's constructor using the standard Python syntax of explictly calling the "__init__" method on the superclass and passing in "self" as the first argument. If you wish to call your superclass's constructor, you must do so within your own "__init__" method. When your "__init__" method finishes, if your Java superclasses have not yet been explicitly initialized, their empty constructors will be called at this point.

It's important to realize that your superclass is not initialized until you either explictly call it's "__init__" method, or your own "__init__" method terminates. You must do one of these two things before accessing any methods in your superclass.

Example

from java.util import Random

class rand(Random):
    def __init__(self, multiplier=1.0, seed=None):
        self.multiplier = multiplier
        if seed is None:
            Random.__init__(self)
        else:
            Random.__init__(self, seed)

    def nextDouble(self):
        return Random.nextDouble(self) * self.multiplier

r = rand(100, 23)

for i in range(10):
    print r.nextDouble()

This example shows how the superclass's constructor can be effectively called in order to explictly choose a non-empty version.

Using JSR 223

JSR 223, Scripting for the Java language, added the javax.script package to Java 6. It allows multiple scripting languages to be used through the same API as long as the language provides a script engine. It can be used to embed Jython in your application alongside many other languages that have script engines such as JRuby or Groovy.

The usage of PythonInterpreter above translates to the following using JSR 223:

import javax.script.ScriptEngine;
import javax.script.ScriptEngineManager;
import javax.script.ScriptException;

public class JSR223 {

    public static void main(String[] args) throws ScriptException {
        ScriptEngine engine = new ScriptEngineManager().getEngineByName("python");
        engine.eval("import sys");
        engine.eval("print sys");
        engine.put("a", 42);
        engine.eval("print a");
        engine.eval("x = 2 + 2");
        Object x = engine.get("x");
        System.out.println("x: " + x);
    }
}

As of Jython 2.5.1 an implementation of JSR 223 is bundled in jython.jar. Simply add jython to your CLASSPATH and ask for the python script engine.

To customize the path and other variables in sys for a ScriptEngine instance, you need to create a PySystemState and make it active before creating the engine:

PySystemState engineSys = new PySystemState();
engineSys.path.append(Py.newString("my/lib/directory"));
Py.setSystemState(engineSys);
ScriptEngine engine = new ScriptEngineManager().getEngineByName("python");

After that, any calls engine will use the sys from engineSys. This can be used to make separate system states for individual engines.

Getting a Connection

First, make sure a valid JDBC driver is in your classpath. Then start Jython and import the zxJDBC connection factory. Using a Driver

The most common way to establish a connection is through a Driver. Simply supply the database, username, password and JDBC driver classname to the connect method. If your driver requires special arguments, pass them into the connect method as standard Python keyword arguments. You will be returned a connection object.

Jython 2.1b1 on java1.4.0-beta3 (JIT: null)
Type "copyright", "credits" or "license" for more information.
>>> from com.ziclix.python.sql import zxJDBC
>>> d, u, p, v = "jdbc:mysql://localhost/test", None, None,
"org.gjt.mm.mysql.Driver"
>>> db = zxJDBC.connect(d, u, p, v)
 optionally
>>> db = zxJDBC.connect(d, u, p, v, CHARSET='iso_1')
>>>

Using a DataSource (or ConnectionPooledDataSource)

The only required argument is the fully-qualified classname of the DataSource, all keywords will use JavaBeans reflection to set properties on the DataSource.

Jython 2.1b1 on java1.4.0-beta3 (JIT: null)
Type "copyright", "credits" or "license" for more information.
>>> from com.ziclix.python.sql import zxJDBC
>>> params = {}
>>> params['serverName'] = 'localhost'
>>> params['databaseName'] = 'ziclix'
>>> params['user'] = None
>>> params['password'] = None
>>> params['port'] = 3306
>>> db = zxJDBC.connectx("org.gjt.mm.mysql.MysqlDataSource", **params)
>>>

Using a JNDI lookup

It is possible for zxJDBC to use a Connection found through a JNDI lookup. This is particularly useful in an application server (such as when using PyServlet). The bound object can be either a String, Connection, DataSource or ConnectionPooledDataSource. The lookup will figure out the instance type and access the Connection accordingly,

The only required argument is the JNDI lookup name. All keyword arguments will be converted to their proper Context field value if the keyword matches one of the constants. If a field name does not exist for the keyword, it will passed as declared. The resulting environment will be used to build the InitialContext.

This example uses the simple Sun FileSystem JNDI reference implementation. Please consult the JNDI implementation you intend to use for the InitialContextFactory classname as well as the connection URL.

Jython 2.1b1 on java1.4.0-beta3 (JIT: null)
Type "copyright", "credits" or "license" for more information.
>>> from com.ziclix.python.sql import zxJDBC
>>> jndiName = "/temp/jdbc/mysqldb"
>>> factory = "com.sun.jndi.fscontext.RefFSContextFactory"
>>> db = zxJDBC.lookup(jndiName, INITIAL_CONTEXT_FACTORY=factory)
>>>

Getting a Cursor

In order execute any operation, a cursor is required from the connection. There are two different kinds of cursors: static and dynamic.

The primary difference between the two is the way they manage the underlying ResultSet. In the static version, the entire ResultSet is iterated immediately, the data converted and stored with the cursor and the ResultSet closed. This allows the cursor to know the rowcount (not available otherwise within JDBC) and set the .rowcount attribute properly. The major disadvantage to this approach is the space/time constraints might be extraordinary.

The solution to the problem are dynamic cursors which keep a handle to the open ResultSet and iterate as required. This drastically decreases memory consumption and increases perceived response time because no work is done until asked. The drawback is the .rowcount attribute can never be accurately set.

To execute a query simply provide the SQL expression and call execute. The cursor now has a description attribute detailing the column information. To navigate the result set, call one of the fetch methods and a list of tuples will be returned.

>>> c = db.cursor()   # this gets a static cursor
 or
>>> c = db.cursor(1)  # this gets a dynamic cursor
>>> c.execute("select count(*) c from player")
>>> c.description
[('c', 3, 17, None, 15, 0, 1)]
>>> for a in c.fetchall():
...  print a
...
(13569,)
>>>

When finished, close the connections.

>>> c.close()
>>> db.close()
>>>

To call a stored procedure or function provide the name and any params to callproc. The database engine must support stored procedures. The examples below have been tested with Oracle, SQLServer and Informix. Refer to the Python DP API spec for how OUT and INOUT parameters work.

>>> c = db.cursor() # open the database as in the examples above
>>> c.execute("use northwind")
>>> c.callproc(("northwind", "dbo", "SalesByCategory"), ["Seafood",
"1998"], maxrows=2)
>>> for a in c.description:
...  print a
...
('ProductName', -9, 40, None, None, None, 0)
('TotalPurchase', 3, 17, None, 38, 2, 1)
>>> for a in c.fetchall():
...  print a
...
('Boston Crab Meat', 5318.0)
('Carnarvon Tigers', 8497.0)
>>> c.nextset()
1
>>> print c.fetchall()
[(0,)]
>>> print c.description
[('@RETURN_VALUE', 4, -1, 4, 10, 0, 0)]
>>>

>>> c = db.cursor() # open the database as in the examples above
>>> c.execute("create or replace function funcout (y out varchar2)
return varchar2 is begin y := 'tested'; return 'returned'; end;")
>>> params = [None]
>>> c.callproc("funcout", params)
>>> print params
['tested']
>>> print c.description
[(None, 12.0, -1, None, None, None, 1)]
>>> print c.fetchall()
[('returned',)]
>>>

>>> c.close()
>>> db.close()
>>>

Datatype mapping callbacks through DataHandler

The DataHandler interface has three methods for handling type mappings. They are called at two different times, one when fetching and the other when binding objects for use in a prepared statement. I have chosen this architecture for type binding because I noticed a number of discrepancies in how different JDBC drivers handled database types, in particular the additional types available in later JDBC versions.

public PyObject getPyObject(CallableStatement stmt, int col, int type)
throws SQLException;
   This method is called upon fetching data from the database after
   calling a stored procedure or function. Given the JDBC type, return
   the appropriate PyObject subclass from the Java object at column
   col in the CallableStatement.

public void registerOut(CallableStatement statement, int index, int
colType, int dataType, String dataTypeName) throws SQLException;
   This method is called to register an OUT or INOUT parameter on the
   stored procedure. The dataType comes from java.sql.Types while the
   dataTypeName is a vendor specific string.

public String getProcedureName(PyObject catalog, PyObject schema,
PyObject name);
   This method is called to build a stored procedure's name.

Jython 2.1b1 on java1.4.0-beta3 (JIT: null)
Type "copyright", "credits" or "license" for more information.
>>> from com.ziclix.python.sql import zxJDBC
>>> zxJDBC.autocommit = 0
>>> d, u, p, v = "database", "user", "password",
"com.informix.jdbc.IfxDriver"
>>> db = zxJDBC.connect(d, u, p, v)
>>> c = db.cursor()
>>> c.execute("create table g (a boolean)")
>>> c.execute("insert into g values (?)", [1])
Traceback (innermost last):
File "<console>", line 1, in ?
Error: No cast from integer to boolean. [SQLCode: -9634]
>>> from com.ziclix.python.sql.informix import InformixDataHandler
>>> c.datahandler = InformixDataHandler(c.datahandler)
>>> c.execute("insert into g values (?)", [1], {0:zxJDBC.OTHER})
>>>

>>> class PyHandler(DataHandler):
>>>  def __init__(self, handler):
>>>   self.handler = handler
>>>  def getPyObject(self, set, col, datatype):
>>>   return self.handler.getPyObject(set, col, datatype)
>>>  def getJDBCObject(self, object, datatype):
>>>   print "handling prepared statement"
>>>   return self.handler.getJDBCObject(object, datatype)
>>>
>>> c.datahandler = PyHandler(c.datahandler)
>>> c.execute("insert into g values (?)", [1])
handling prepared statement
>>>

dbexts

dbexts is a wrapper around DB API 2.0 compliant database modules. It currently supports zxJDBC and mxODBC but could easily be modified to support others. It allows developers to write scripts without knowledge of the implementation language of Python (either C or Java). It also greatly eases the burden of database coding as much of the functionality of the Python API is exposed through easier to use methods.

Configuration file

dbexts needs a configuration file in order to create a connection. The configuration file has the following format:

[default]
name=mysql

[jdbc]
name=mysql
url=jdbc:mysql://localhost/ziclix
user=
pwd=
driver=org.gjt.mm.mysql.Driver
datahandler=com.ziclix.python.sql.handler.MySQLDataHandler

[jdbc]
name=ora
url=jdbc:oracle:thin:@localhost:1521:ziclix
user=ziclix
pwd=ziclix
driver=oracle.jdbc.driver.OracleDriver
datahandler=com.ziclix.python.sql.handler.OracleDataHandler

API

dbexts will default to looking for a file named 'dbexts.ini' in the same directory as dbexts.py but can optionally be passed a filename to the cfg attribute.

__init__(self, dbname=None, cfg=None, resultformatter=format_resultset,
autocommit=1)
   The initialization method for the dbexts class. If dbname is None,
   the default connection, as specified in the cfg file will be used.

isql(self, sql, params=None, bindings=None, maxrows=None)
   Interactively execute sql statement. If self.verbose is true, then
   the results (if any) are displayed using the result formatting
   method. If maxrows is specified, only maxrows are displayed.

raw(self, sql, params=None, bindings=None, delim=None, comments=comments)
   Executes the sql statement with params and bindings as
   necessary. Returns a tuple consisting of (headers, results).

schema(table, full=0, sort=1)
   Displays the schema (indicies, foreign keys, primary keys and
   columns) for the table parameter. If full is true, also compute
   the exported (or referenced) keys. If sort is true (the default),
   sort the column names.

   >>> d.schema("store")
   Table
     store

   Primary Keys
     store_id {store_3}

   Imported (Foreign) Keys
     location (city.city_id) {store_7}

   Exported (Referenced) Keys
     store_id (site_store.store_id) {site_store_8}

   Columns
     location           int(4), non-nullable
     store_id           serial(4), non-nullable
     store_name         varchar(32), non-nullable

   Indices
     unique index {523_8115} on (store_id)
     unique index {store_ix_1} on (store_name)
   >>>

table(table=None, types=("TABLE",), owner=None, schema=None)
   If no table argument, displays a list of all tables. If a table
   argument, displays the columns of the given table.

proc(self, proc=None, owner=None, schema=None)
   If no proc argument, displays a list of all procedures. If a proc
   argument, displays the parameters of the given procedure.

bcp(src, table, where='(1=1)', parameters=[], selective=[], ignorelist=[],
   autobatch=0)
   Bulk Copy from one database/table to another. The current instance
   of dbexts is the source database to which the results of the query
   on the foreign database will be inserted. An optional where clause
   can narrow the number of rows to be copied.

The following are generally not called since isql and raw can handle almost all cases.

begin(self)

Creates a new cursor.

rollback(self)

Rollback all the statements since the creation of the cursor.

commit(self, cursor=None, maxrows=None)

Commit all the statements since the creation of the cursor.

display(self)

Display the results using the formatter.

Example session

Jython 2.1b1 on java1.4.0-beta3 (JIT: null)
Type "copyright", "credits" or "license" for more information.
>>> from dbexts import dbexts
>>> d = dbexts()
>>> d.isql("create table store (store_id int, store_name varchar(32),
location int)")
>>> d.isql("insert into store values (?, ?, ?)", [(1, "amazon.com", 3),
(2, "egghead.com", 4)])
>>> d.isql("insert into store values (?, ?, ?)", [(15, "800.com", 1),
(19, "fogdog.com", 3)])
>>> d.isql("insert into store values (?, ?, ?)", [(5, "nike.com", 4)])
>>> d.isql("select * from store order by store_name")

STORE_ID | STORE_NAME  | LOCATION
---------------------------------
15       | 800.com     | 1
1        | amazon.com  | 3
2        | egghead.com | 4
19       | fogdog.com  | 3
5        | nike.com    | 4

5 rows affected

>>>