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title Cross-Language Serialization
sidebar_position 10
id cross_language
license Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

pyfory supports cross-language object graph serialization, allowing you to serialize data in Python and deserialize it in Java, Go, Rust, or other supported languages.

Enable Cross-Language Mode

To use xlang mode, create Fory with xlang=True:

import pyfory
fory = pyfory.Fory(xlang=True, ref=False, strict=True)

Cross-Language Example

Python (Serializer)

import pyfory
from dataclasses import dataclass

# Cross-language mode for interoperability
f = pyfory.Fory(xlang=True, ref=True)

# Register type for cross-language compatibility
@dataclass
class Person:
    name: str
    age: pyfory.int32

f.register(Person, typename="example.Person")

person = Person("Charlie", 35)
binary_data = f.serialize(person)
# binary_data can now be sent to Java, Go, etc.

Java (Deserializer)

import org.apache.fory.*;

public class Person {
    public String name;
    public int age;
}

Fory fory = Fory.builder()
    .withLanguage(Language.XLANG)
    .withRefTracking(true)
    .build();

fory.register(Person.class, "example.Person");
Person person = (Person) fory.deserialize(binaryData);

Rust (Deserializer)

use fory::Fory;
use fory::ForyObject;

#[derive(ForyObject)]
struct Person {
    name: String,
    age: i32,
}

let mut fory = Fory::builder()
    .compatible(true)
    .xlang(true).build();

fory.register_by_namespace::<Person>("example", "Person");
let person: Person = fory.deserialize(&binary_data)?;

Type Annotations for Cross-Language

Use pyfory type annotations for explicit cross-language type mapping:

from dataclasses import dataclass
import pyfory

@dataclass
class TypedData:
    int_value: pyfory.int32      # 32-bit integer
    long_value: pyfory.int64     # 64-bit integer
    float_value: pyfory.float32  # 32-bit float
    double_value: pyfory.float64 # 64-bit float

Reduced-Precision Types

pyfory.serialization exports Cython-only carrier types for xlang reduced-precision values:

  • float16 and float16array
  • bfloat16 and bfloat16array

These names are compiled into the pyfory.serialization extension and re-exported from pyfory. There is no pure-Python fallback module for them.

The scalar wrappers behave like reduced-precision numeric value types. They support arithmetic and ordering with Python numeric operands, and each operation quantizes the result back to the wrapper's own format (pyfory.float16 or pyfory.bfloat16).

The array wrappers are value-oriented public APIs. Construct them from Python numeric values with pyfory.float16array([...]), pyfory.float16array.from_values([...]), pyfory.bfloat16array([...]), or pyfory.bfloat16array.from_values([...]). Use from_buffer(...) and to_buffer() only when you already need packed little-endian uint16 storage and want the raw-buffer fast path. Both array carriers also implement the CPython buffer protocol, so memoryview(pyfory.float16array(...)) and memoryview(pyfory.bfloat16array(...)) expose the packed uint16 storage directly.

Type Mapping

Python Java Rust Go
str String String string
int long i64 int64
pyfory.int32 int i32 int32
pyfory.int64 long i64 int64
float double f64 float64
pyfory.float32 float f32 float32
pyfory.float16 Float16 Float16 float16.Float16
pyfory.bfloat16 BFloat16 BFloat16 bfloat16.BFloat16
pyfory.float16array Float16List Vec<Float16> []float16.Float16
pyfory.bfloat16array BFloat16List Vec<BFloat16> []bfloat16.BFloat16
list List Vec []T
dict Map HashMap map[K]V

Differences from Python Native Mode

The binary protocol and API are similar to pyfory's python-native mode, but Python-native mode can serialize any Python object—including global functions, local functions, lambdas, local classes, and types with customized serialization using __getstate__/__reduce__/__reduce_ex__, which are not allowed in xlang mode.

See Also

Related Topics