javaobj-py3

python-javaobj is a python library that provides functions for reading and writing Java objects serialized or to 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

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

• v1: the classic implementation of javaobj, with a work in progress implementation of a writer.
• v2: a rewritten 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.
• v3: a new implementation, written from scratch to benefit from Python 3.12+ features, with full read and write support.

You can use the v1 parser to ensure that the behaviour of your scripts doesn't change. It also provides a basic marshalling capability.

You can use the v2 parser for developments in Python versions lower than 3.12 and which won't require marshalling, or as a fallback if the v1 parser fails to parse a file.

For new development, you should use the v3 parser, which supports both reading and writing Java object streams.

Object transformers V1

As of version 0.2.0, the notion of object transformer from the original project as been replaced by an object creator.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

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

Object transformers V3

The v3 implementation is a full rewrite targeting Python 3.12+. It uses dataclasses, structural pattern matching (match/case) and PEP 604 union types. Its API is intentionally similar to v2 but fixes several correctness issues and adds stricter safety limits. Please look at the Usage (V3) and Migration to V3 sections in this file.

Bytes arrays

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

Custom Transformer

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.

Features

• 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)
• Full un-marshalling and marshalling of Java object streams, with typed errors and configurable safety limits (v3 implementation)
• Automatically uncompresses GZipped files

Requirements

• Python >= 2.7 or Python >= 3.4 for v1 and v2
• Python >= 3.12 for v3
• enum34 and typing when using Python <= 3.4 (installable with pip)
• Maven 2+ (for building test data of serialized objects. The fixtures are already committed under tests/, so you only need Maven to regenerate them; skip it otherwise, or set JAVAOBJ_NO_MAVEN=1 to disable the regeneration when running the test suite)

Usage (V1 implementation)

Un-marshalling of Java serialised object:

import javaobj

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

pobj = javaobj.loads(jobj)
print(pobj)

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(pobj.next, "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, v2, or v3 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)

print(pobj.dump())

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
        dict.__init__(self)
        JavaInstance.__init__(self)

    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 cd.name == "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 classdesc.name == "java.util.HashMap":
            # We can handle this class description
            return JavaMap()
        else:
            # 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 {
        this.writeObject(out);
    }

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

class RandomChild extends Random {

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

    RandomChild(int seed) {
        super(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 {
        out.writeInt(CURRENT_SERIAL_VERSION);
        out.writeObject(custom_obj);
    }
}

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

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

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 classdesc.name in self.handled_classes:
            self.instance = self.handled_classes[classdesc.name]()
            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__()
        self.name = "java.util.Random"
        self.field_names = ["haveNextNextGaussian", "nextNextGaussian", "seed"]
        self.field_types = [
            javaobj.v2.beans.FieldType.BOOLEAN,
            javaobj.v2.beans.FieldType.DOUBLE,
            javaobj.v2.beans.FieldType.LONG,
        ]

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

        fields = []
        values = []
        for f_name, f_type in zip(self.field_names, self.field_types):
            values.append(parser._read_field_value(f_type))
            fields.append(javaobj.v2.beans.JavaField(f_type, f_name))

        class_desc = javaobj.v2.beans.JavaClassDesc(
            javaobj.v2.beans.ClassDescType.NORMALCLASS
        )
        class_desc.name = self.name
        class_desc.desc_flags = javaobj.v2.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):
        javaobj.v2.beans.JavaInstance.__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)
            )[0]
            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 = [
                self.field_data[self.classdesc][self.classdesc.fields[i]]
                for i in range(len(fields))
            ]
            self.field_data = dict(zip(fields, values))
            if (
                self.classdesc.super_class
                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 = [
    CustomWriterTransformer(),
    RandomChildTransformer(),
    JavaRandomTransformer()
]
with open("custom_objects.ser", "rb") as fd:
    pobj = javaobj.load(fd, *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: https://stackoverflow.com/a/16477421/12621168
print(pobj.field_data["int_not_in_fields"])

Usage (V3 implementation)

Requires Python 3.12+.

The javaobj.v3 package is a full rewrite of the Java object stream parser. It provides the same two entry-points as v2:

• load(fd, *transformers, use_numpy_arrays=False, max_array_size=…, max_depth=500): Parses a binary file descriptor opened in rb mode and returns the top-level object if the stream contains exactly one, a list of objects if there are several, or None for an empty stream. Pass additional ObjectTransformer instances as positional arguments.

• loads(data, *transformers, …): Convenience wrapper around load() that accepts bytes.

Sample usage:

import javaobj.v3 as javaobj

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

# Access fields by name (preferred)
value = pobj.get_field("myField")

# Or use attribute-style access (issues a warning on ambiguity)
value = pobj.myField

Automatic type conversion (V3)

javaobj.v3's default transformer converts the most common Java standard library classes into their natural Python equivalent:

Any other class falls back to a generic JavaInstance, accessed through get_field() as shown above. Provide your own ObjectTransformer (see "Object Transformer V3" below) to handle additional classes.

New features in V3

Security limits

v3 adds two optional safety limits that prevent resource exhaustion when parsing untrusted streams:

import javaobj.v3 as javaobj

with open("untrusted.ser", "rb") as fd:
    pobj = javaobj.load(
        fd,
        max_array_size=10 * 1024 * 1024,  # 10 MiB max per array
        max_depth=100,                      # max object-graph depth
    )

Error handling (V3)

javaobj.v3.exceptions defines a typed hierarchy so callers can catch exactly what they expect instead of a bare Exception:

import javaobj.v3 as javaobj
from javaobj.v3.exceptions import JavaObjError, ParseError, SecurityError

with open("obj5.ser", "rb") as fd:
    try:
        pobj = javaobj.load(fd)
    except SecurityError:
        # A max_depth or max_array_size limit was exceeded
        ...
    except ParseError as e:
        # The stream does not follow the protocol; e.offset is the byte
        # offset in the stream where the error occurred, or -1 if unknown
        print(e, "at offset", e.offset)
    except JavaObjError:
        # Catch-all base class for everything else javaobj.v3 raises
        ...

• JavaObjError -- base class for every exception javaobj.v3 raises.
• ParseError -- the stream cannot be decoded according to the protocol; carries an .offset attribute.
  • UnexpectedOpcodeError -- a ParseError subclass raised when an opcode byte is not among the values expected at that point; carries .expected (a tuple of acceptable values) and .got.
• UnsupportedFeatureError -- the stream uses a protocol feature v3 does not implement yet (for example Externalizable objects on read).
• SecurityError -- a configured max_depth/max_array_size limit was exceeded (see "Security limits" above).

Object Transformer V3

The ObjectTransformer base class in v3 has the same three override points as in v2:

• create_instance(classdesc) — return a JavaInstance subclass (or None to fall back to the next transformer).
• load_array(reader, type_code, size) — called for TC_ARRAY records; return the array data (bytes or list) or None to use the default logic.
• load_custom_writeObject(parser, reader, class_name) — called when a class written with writeObject() requires fully custom parsing.

The DefaultObjectTransformer additionally exposes a public handles(name) method that returns True when the transformer knows how to load the given Java class name.

Using NumPy arrays (V3)

import javaobj.v3 as javaobj

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

When use_numpy_arrays=True, a NumpyArrayTransformer is appended to the transformer list and primitive arrays are returned as numpy.ndarray.

Marshalling / Writing (V3)

The javaobj.v3 package exposes two additional entry-points for serializing beans back to the Java Object Serialization binary format:

• dump(fd, *objects): Writes one or more parsed objects to a binary file descriptor opened in wb mode.
• dumps(*objects) -> bytes: Returns the serialized stream as a bytes object.

Both functions accept any combination of JavaInstance, JavaArray, JavaString, JavaEnum, JavaClass, BlockData, and None (written as TC_NULL) as positional arguments.

Simple round-trip

import javaobj.v3 as javaobj

# Parse an existing file
with open("obj5.ser", "rb") as fd:
    pobj = javaobj.load(fd)

# Serialize back to bytes
data = javaobj.dumps(pobj)

# Or write directly to a file
with open("obj5_copy.ser", "wb") as fd:
    javaobj.dump(fd, pobj)

Writing multiple objects

import javaobj.v3 as javaobj
from javaobj.v3.beans import JavaString

with open("a.ser", "rb") as fd:
    obj_a = javaobj.load(fd)

# Write two objects into one stream
data = javaobj.dumps(obj_a, JavaString("hello"))

# Re-parse: returns a list when the stream holds more than one object
result = javaobj.loads(data)   # -> [obj_a, JavaString("hello")]

Supported constructs

Note: Back-references are tracked automatically by identity: if the same object appears more than once in the graph, subsequent occurrences are written as TC_REFERENCE — exactly as Java's ObjectOutputStream does.

Building a Java object from scratch

You can construct the v3 beans manually to serialize a Python object as if it were a Java one. The key types are:

• JavaClassDesc — the class descriptor (name, serialVersionUID, flags, fields)
• JavaField — one field entry (type code + name, and optionally the binary class name for object/array fields)
• JavaInstance — the object instance (field_data maps each class descriptor to a {JavaField: value} dict)
• JavaString — a Java String value

All beans accept handle=0 when created from scratch; the writer assigns real handles automatically during serialization.

import javaobj.v3 as javaobj
from javaobj.constants import ClassDescFlags
from javaobj.v3.beans import (
    FieldType,
    JavaClassDesc,
    ClassDescType,
    JavaField,
    JavaInstance,
    JavaString,
)

# ── 1. Describe the Java class ────────────────────────────────────────────────
#
#  Java equivalent:
#
#    package com.example;
#    public class Point implements java.io.Serializable {
#        private static final long serialVersionUID = 1L;
#        public int x;
#        public int y;
#    }

field_x = JavaField(type=FieldType.INTEGER, name="x")
field_y = JavaField(type=FieldType.INTEGER, name="y")

point_cd = JavaClassDesc(
    handle=0,                              # assigned by the writer
    name="com.example.Point",
    serial_version_uid=1,
    desc_flags=ClassDescFlags.SC_SERIALIZABLE,
    fields=[field_x, field_y],
)

# ── 2. Create an instance ─────────────────────────────────────────────────────

point = JavaInstance(
    handle=0,
    classdesc=point_cd,
    field_data={
        point_cd: {
            field_x: 42,
            field_y: -7,
        }
    },
)

# ── 3. Serialize ──────────────────────────────────────────────────────────────

data = javaobj.dumps(point)

# ── 4. Round-trip check ───────────────────────────────────────────────────────

restored = javaobj.loads(data)
print(restored.get_field("x"))   # 42
print(restored.get_field("y"))   # -7

For object-type fields (e.g. a String attribute), use FieldType.OBJECT, set class_name to the binary class name, and pass a JavaString as the value:

field_name = JavaField(
    type=FieldType.OBJECT,
    name="name",
    class_name="Ljava/lang/String;",   # binary name for java.lang.String
)

person_cd = JavaClassDesc(
    handle=0,
    name="com.example.Person",
    serial_version_uid=1,
    desc_flags=ClassDescFlags.SC_SERIALIZABLE,
    fields=[field_name, field_x],      # reuse field_x from above
)

alice = JavaInstance(
    handle=0,
    classdesc=person_cd,
    field_data={
        person_cd: {
            field_name: JavaString(handle=0, value="Alice"),
            field_x: 30,
        }
    },
)

data = javaobj.dumps(alice)

Logging (V3)

The parser and the writer log through the standard logging module, under javaobj.v3.parser and javaobj.v3.writer respectively:

import logging

logging.getLogger("javaobj.v3").setLevel(logging.DEBUG)

Migration to V3

From V1 to V3

Shallow conversion helper (best-effort, for gradual migration):

from javaobj.v3._compat import v1_to_v3
v3_obj = v1_to_v3(v1_obj)

From V2 to V3

Shallow conversion helper (best-effort, for gradual migration):

from javaobj.v3._compat import v2_to_v3
v3_obj = v2_to_v3(v2_obj)

Note: v3 requires Python 3.12+. For writing Java object streams on older Python versions, use v1.

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