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python-javaobj is a python library that provides functions for reading and writing (writing is WIP currently) Java objects serialized or will be deserialized by ObjectOutputStream. This form of object representation is a standard data interchange format in Java world.

The javaobj module exposes an API familiar to users of the standard library marshal, pickle and json modules.

About this repository

This project is a fork of python-javaobj by Volodymyr Buell, originally from Google Code and now hosted on GitHub.

This fork intends to work both on Python 2.7 and Python 3.4+.

Compatibility Warnings

New implementation of the parser

Implementations Version
v1, v2 0.4.0+

Since version 0.4.0, two implementations of the parser are available:

  • v1: the classic implementation of javaobj, with a work in progress implementation of a writer.
  • v2: the new implementation, which is a port of the Java project jdeserialize, with support of the object transformer (with a new API) and of the numpy arrays loading.

You can use the v1 parser to ensure that the behaviour of your scripts doesn't change and to keep the ability to write down files.

You can use the v2 parser for new developments which won't require marshalling and as a fallback if the v1 fails to parse a file.

Object transformers V1

Implementations Version
v1 0.2.0+

As of version 0.2.0, the notion of object transformer from the original project as been replaced by an object creator.

The object creator is called before the deserialization. This allows to store the reference of the converted object before deserializing it, and avoids a mismatch between the referenced object and the transformed one.

Object transformers V2

Implementations Version
v2 0.4.0+

The v2 implementation provides a new API for the object transformers. Please look at the Usage (V2) section in this file.

Bytes arrays

Implementations Version
v1 0.2.3+

As of version 0.2.3, bytes arrays are loaded as a bytes object instead of an array of integers.

Custom Transformer

Implementations Version
v2 0.4.2+

A new transformer API has been proposed to handle objects written with a custom Java writer. You can find a sample usage in the Custom Transformer section in this file.


  • Java object instance un-marshalling
  • Java classes un-marshalling
  • Primitive values un-marshalling
  • Automatic conversion of Java Collections to python ones (HashMap => dict, ArrayList => list, etc.)
  • Basic marshalling of simple Java objects (v1 implementation only)
  • Automatically uncompresses GZipped files


  • Python >= 2.7 or Python >= 3.4
  • enum34 and typing when using Python <= 3.4 (installable with pip)
  • Maven 2+ (for building test data of serialized objects. You can skip it if you do not plan to run

Usage (V1 implementation)

Un-marshalling of Java serialised object:

import javaobj

with open("obj5.ser", "rb") as fd:
    jobj =

pobj = javaobj.loads(jobj)

Or, you can use JavaObjectUnmarshaller object directly:

import javaobj

with open("objCollections.ser", "rb") as fd:
    marshaller = javaobj.JavaObjectUnmarshaller(fd)
    pobj = marshaller.readObject()

    print(pobj.value, "should be", 17)
    print(, "should be", True)

    pobj = marshaller.readObject()

Note: The objects and methods provided by javaobj module are shortcuts to the javaobj.v1 package, for Compatibility purpose. It is recommended to explicitly import methods and classes from the v1 (or v2) package when writing new code, in order to be sure that your code won't need import updates in the future.

Usage (V2 implementation)

The following methods are provided by the javaobj.v2 package:

  • load(fd, *transformers, use_numpy_arrays=False): Parses the content of the given file descriptor, opened in binary mode (rb). The method accepts a list of custom object transformers. The default object transformer is always added to the list.

    The use_numpy_arrays flag indicates that the arrays of primitive type elements must be loaded using numpy (if available) instead of using the standard parsing technic.

  • loads(bytes, *transformers, use_numpy_arrays=False): This the a shortcut to the load() method, providing it the binary data using a BytesIO object.

Note: The V2 parser doesn't have the marshalling capability.

Sample usage:

import javaobj.v2 as javaobj

with open("obj5.ser", "rb") as fd:
    pobj = javaobj.load(fd)


Object Transformer

An object transformer can be called during the parsing of a Java object instance or while loading an array.

The Java object instance parsing works in two main steps:

  1. The transformer is called to create an instance of a bean that inherits JavaInstance.

  2. The latter bean is then called:

    • When the object is written with a custom block data
    • After the fields and annotations have been parsed, to update the content of the Python bean.

Here is an example for a Java HashMap object. You can look at the code of the javaobj.v2.transformer module to see the whole implementation.

class JavaMap(dict, javaobj.v2.beans.JavaInstance):
    Inherits from dict for Python usage, JavaInstance for parsing purpose
    def __init__(self):
        # Don't forget to call both constructors

    def load_from_blockdata(self, parser, reader, indent=0):
    Reads content stored in a block data.

    This method is called only if the class description has both the
    `SC_EXTERNALIZABLE` and `SC_BLOCK_DATA` flags set.

    The stream parsing will stop and fail if this method returns False.

    :param parser: The JavaStreamParser in use
    :param reader: The underlying data stream reader
    :param indent: Indentation to use in logs
    :return: True on success, False on error
    # This kind of class is not supposed to have the SC_BLOCK_DATA flag set
    return False

    def load_from_instance(self, indent=0):
        # type: (int) -> bool
        Load content from the parsed instance object.

        This method is called after the block data (if any), the fields and
        the annotations have been loaded.

        :param indent: Indentation to use while logging
        :return: True on success (currently ignored)
        # Maps have their content in their annotations
        for cd, annotations in self.annotations.items():
            # Annotations are associated to their definition class
            if == "java.util.HashMap":
                # We are in the annotation created by the handled class
                # Group annotation elements 2 by 2
                # (storage is: key, value, key, value, ...)
                args = [iter(annotations[1:])] * 2
                for key, value in zip(*args):
                    self[key] = value

                # Job done
                return True

        # Couldn't load the data
        return False

class MapObjectTransformer(javaobj.v2.api.ObjectTransformer):
    Creates a JavaInstance object with custom loading methods for the
    classes it can handle
    def create_instance(self, classdesc):
        # type: (JavaClassDesc) -> Optional[JavaInstance]
        Transforms a parsed Java object into a Python object

        :param classdesc: The description of a Java class
        :return: The Python form of the object, or the original JavaObject
        if == "java.util.HashMap":
            # We can handle this class description
            return JavaMap()
            # Return None if the class is not handled
            return None

Custom Object Transformer

The custom transformer is called when the class is not handled by the default object transformer. A custom object transformer still inherits from the ObjectTransformer class, but it also implements the load_custom_writeObject method.

The sample given here is used in the unit tests.

Java sample

On the Java side, we create various classes and write them as we wish:

class CustomClass implements Serializable {

    private static final long serialVersionUID = 1;

    public void start(ObjectOutputStream out) throws Exception {

    private void writeObject(ObjectOutputStream out) throws IOException {
        CustomWriter custom = new CustomWriter(42);

class RandomChild extends Random {

    private static final long serialVersionUID = 1;
    private int num = 1;
    private double doub = 4.5;

    RandomChild(int seed) {

class CustomWriter implements Serializable {
    protected RandomChild custom_obj;

    CustomWriter(int seed) {
        custom_obj = new RandomChild(seed);

    private static final long serialVersionUID = 1;
    private static final int CURRENT_SERIAL_VERSION = 0;

    private void writeObject(ObjectOutputStream out) throws IOException {

An here is a sample writing of that kind of object:

ObjectOutputStream oos = new ObjectOutputStream(
    new FileOutputStream("custom_objects.ser"));
CustomClass writer = new CustomClass();

Python sample

On the Python side, the first step is to define the custom transformers. They are children of the javaobj.v2.transformers.ObjectTransformer class.

class BaseTransformer(javaobj.v2.transformers.ObjectTransformer):
    Creates a JavaInstance object with custom loading methods for the
    classes it can handle

    def __init__(self, handled_classes=None):
        self.instance = None
        self.handled_classes = handled_classes or {}

    def create_instance(self, classdesc):
        Transforms a parsed Java object into a Python object

        :param classdesc: The description of a Java class
        :return: The Python form of the object, or the original JavaObject
        if in self.handled_classes:
            self.instance = self.handled_classes[]()
            return self.instance

        return None

class RandomChildTransformer(BaseTransformer):
    def __init__(self):
        super(RandomChildTransformer, self).__init__(
            {"RandomChild": RandomChildInstance}

class CustomWriterTransformer(BaseTransformer):
    def __init__(self):
        super(CustomWriterTransformer, self).__init__(
            {"CustomWriter": CustomWriterInstance}

class JavaRandomTransformer(BaseTransformer):
    def __init__(self):
        super(JavaRandomTransformer, self).__init__() = "java.util.Random"
        self.field_names = ["haveNextNextGaussian", "nextNextGaussian", "seed"]
        self.field_types = [

    def load_custom_writeObject(self, parser, reader, name):
        if name !=
            return None

        fields = []
        values = []
        for f_name, f_type in zip(self.field_names, self.field_types):
            fields.append(javaobj.beans.JavaField(f_type, f_name))

        class_desc = javaobj.beans.JavaClassDesc(
        ) =
        class_desc.desc_flags = javaobj.beans.ClassDataType.EXTERNAL_CONTENTS
        class_desc.fields = fields
        class_desc.field_data = values
        return class_desc

Second step is defining the representation of the instances, where the real object loading occurs. Those classes inherit from javaobj.v2.beans.JavaInstance.

class CustomWriterInstance(javaobj.v2.beans.JavaInstance):
    def __init__(self):

    def load_from_instance(self):
        Updates the content of this instance
        from its parsed fields and annotations
        :return: True on success, False on error
        if self.classdesc and self.classdesc in self.annotations:
            # Here, we known there is something written before the fields,
            # even if it's not declared in the class description
            fields = ["int_not_in_fields"] + self.classdesc.fields_names
            raw_data = self.annotations[self.classdesc]
            int_not_in_fields = struct.unpack(
                ">i", BytesIO(raw_data[0].data).read(4)
            custom_obj = raw_data[1]
            values = [int_not_in_fields, custom_obj]
            self.field_data = dict(zip(fields, values))
            return True

        return False

class RandomChildInstance(javaobj.v2.beans.JavaInstance):
    def load_from_instance(self):
        Updates the content of this instance
        from its parsed fields and annotations
        :return: True on success, False on error
        if self.classdesc and self.classdesc in self.field_data:
            fields = self.classdesc.fields_names
            values = [
                for i in range(len(fields))
            self.field_data = dict(zip(fields, values))
            if (
                and self.classdesc.super_class in self.annotations
                super_class = self.annotations[self.classdesc.super_class][0]
                self.annotations = dict(
                    zip(super_class.fields_names, super_class.field_data)
            return True

        return False

Finally we can use the transformers in the loading process. Note that even if it is not explicitly given, the DefaultObjectTransformer will be also be used, as it is added automatically by javaobj if it is missing from the given list.

# Load the object using those transformers
transformers = [
pobj = javaobj.loads("custom_objects.ser", *transformers)

# Here we show a field that isn't visible from the class description
# The field belongs to the class but it's not serialized by default because
# it's static. See: