Xlang Serialization

pyfory supports xlang object graph serialization, allowing you to serialize data in Python and deserialize it in Java, C++, Go, Rust, JavaScript/TypeScript, C#, Swift, Dart, Scala, Kotlin, or another supported language.

Create an Xlang Runtime

Python defaults to xlang mode with compatible schema evolution. Set the mode explicitly in xlang examples:

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

Xlang Example

Python (Serializer)

import pyfory
from dataclasses import dataclass

f = pyfory.Fory(xlang=True, ref=True)

# Register type for xlang 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()
    .withXlang(true)
    .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().xlang(true).build();

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

Type Annotations for Xlang

Use pyfory type annotations for explicit xlang type mapping:

from dataclasses import dataclass
from typing import Dict, List
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
    values: Dict[pyfory.Int32, List[pyfory.Int64]]

Nested collection annotations are part of the field schema. Compatible-mode reads consume bytes with the remote schema metadata, then assign only when the decoded value safely satisfies the local schema.

Reduced-Precision Types

pyfory.Float16 and pyfory.BFloat16 are reserved annotation markers for xlang reduced-precision fields. They are not runtime value classes; scalar values deserialize as native Python float.

Dense reduced-precision arrays use public dense wrappers with list-like sequence behavior. Construct them from Python numeric values with pyfory.Float16Array.from_values([...]) 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.

Type Mapping

Python marker/carrier	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

Lists and Dense Arrays

Python List[T] maps to Fory list<T>. Use pyfory.Array[T], pyfory.NDArray[T], or pyfory.PyArray[T] only when the schema is the dense one-dimensional array<T> kind.

Fory schema	Python annotation and default carrier
list<int32>	List[pyfory.Int32]
array<bool>	pyfory.Array[bool] -> BoolArray
array<int8>	pyfory.Array[pyfory.Int8] -> Int8Array
array<int16>	pyfory.Array[pyfory.Int16] -> Int16Array
array<int32>	pyfory.Array[pyfory.Int32] -> Int32Array
array<int64>	pyfory.Array[pyfory.Int64] -> Int64Array
array<uint8>	pyfory.Array[pyfory.UInt8] -> UInt8Array
array<uint16>	pyfory.Array[pyfory.UInt16] -> UInt16Array
array<uint32>	pyfory.Array[pyfory.UInt32] -> UInt32Array
array<uint64>	pyfory.Array[pyfory.UInt64] -> UInt64Array
array<float16>	pyfory.Array[pyfory.Float16] -> Float16Array
array<bfloat16>	pyfory.Array[pyfory.BFloat16] -> BFloat16Array
array<float32>	pyfory.Array[pyfory.Float32] -> Float32Array
array<float64>	pyfory.Array[pyfory.Float64] -> Float64Array

The pyfory.*Array wrappers accept iterable constructors such as pyfory.Float32Array([1, 2, 3]) and expose list-like sequence behavior over dense owned storage.

pyfory.Array[T], pyfory.NDArray[T], and pyfory.PyArray[T] all describe the same Fory array<T> schema. They differ only in the Python carrier contract:

Python field annotation	Value accepted for that field	Deserialized carrier
pyfory.Array[T]	pyfory.*Array, numpy.ndarray, array.array, list	pyfory.*Array
pyfory.NDArray[T]	numpy.ndarray	numpy.ndarray
pyfory.PyArray[T]	Python array.array	Python array.array

In compatible mode, a writer and reader can use different Python carriers for the same named field as long as both annotations lower to the same Fory array<T> schema. For example, a writer field declared as pyfory.Array[pyfory.Int32] can be read by a Python class whose matching field is declared as pyfory.NDArray[pyfory.Int32], and the reader receives a NumPy int32 ndarray. The reverse pattern also works for pyfory.PyArray[T]; that name always means Python array.array.

PyArrow is a separate row/columnar format surface, not a pyfory.PyArray carrier. Use pyfory.format.from_arrow_schema(...) and pyfory.format.to_arrow_schema(...) to convert between PyArrow schemas and Fory row-format schemas.

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

• Xlang Serialization Specification
• Type Mapping Reference
• Java Xlang Serialization Guide
• Rust Xlang Serialization Guide

Related Topics

• Configuration - xlang mode settings
• Schema Evolution - Compatible mode
• Type Registration - Registration patterns