---
title: Basic Serialization
sidebar_position: 1
id: basic-serialization
license: |
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  this work for additional information regarding copyright ownership.
  The ASF licenses this file to You under the Apache License, Version 2.0
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---

This page covers the Python xlang quickstart. `pyfory.Fory()` defaults to xlang mode with
compatible schema evolution; examples set `xlang=True` explicitly so the mode choice is visible.

## Basic Object Serialization

Serialize and deserialize Python objects with a simple API:

```python
import pyfory

fory = pyfory.Fory(xlang=True)

# Serialize xlang-compatible values
data = fory.dumps({"name": "Alice", "age": 30, "scores": [95, 87, 92]})

# Deserialize back to Python object
obj = fory.loads(data)
print(obj)  # {'name': 'Alice', 'age': 30, 'scores': [95, 87, 92]}
```

**Note**: `dumps()`/`loads()` are aliases for `serialize()`/`deserialize()`. Both APIs are identical, use whichever feels more intuitive.

## Custom Class Serialization

Use dataclasses and type annotations for stable xlang payloads:

```python
import pyfory
from dataclasses import dataclass
from typing import List, Dict

@dataclass
class Person:
    name: str
    age: pyfory.Int32
    scores: List[pyfory.Int32]
    metadata: Dict[str, str]

fory = pyfory.Fory(xlang=True, ref=True)
fory.register(Person, name="example.Person")
person = Person("Bob", 25, [88, 92, 85], {"team": "engineering"})
data = fory.serialize(person)
result = fory.deserialize(data)
print(result)  # Person(name='Bob', age=25, ...)
```

## Reference Tracking & Circular References

Handle repeated references safely when the payload uses xlang-compatible types:

```python
import pyfory

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

shared = ["shared"]
value = [shared, shared]

data = f.serialize(value)
result = f.deserialize(data)
assert result[0] is result[1]
```

For arbitrary Python object graphs, local classes, functions, and methods, use
[Native Serialization](native.md).

## Performance Tips

1. **Disable `ref=True` if not needed**: Reference tracking has overhead
2. **Use type_id instead of name**: Integer IDs are faster than string names
3. **Reuse Fory instances**: Create once, use many times
4. **Enable Cython**: Make sure `ENABLE_FORY_CYTHON_SERIALIZATION=1`

```python
# Good: Reuse instance
fory = pyfory.Fory(xlang=True)
for obj in objects:
    data = fory.dumps(obj)

# Bad: Create new instance each time
for obj in objects:
    fory = pyfory.Fory(xlang=True)  # Wasteful!
    data = fory.dumps(obj)
```

## Cross-Language Interoperability

The default xlang format is shared by all Fory runtimes. The following sections cover its cross-language type mapping, type identity, and interoperability requirements.

`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.

### Xlang Configuration

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

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

### Xlang Example

#### Python (Serializer)

```python
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, name="example.Person")

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

#### Java (Deserializer)

```java
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)

```rust
use fory::Fory;
use fory::ForyStruct;

#[derive(ForyStruct)]
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:

Use these markers directly in Python type annotations. Field values remain
ordinary Python `int` or `float` values, and Fory serializes them with the
requested xlang numeric width and encoding.

```python
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 value wrapper 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 custom serialization using `__getstate__/__reduce__/__reduce_ex__`, which are **not allowed** in xlang mode.

### Specifications and References

- [Xlang Serialization Specification](../../specification/xlang_serialization_spec.md)
- [Type Mapping Reference](../../specification/xlang_type_mapping.md)
- [Java Interoperability Guide](../java/basic-serialization.md#cross-language-interoperability)
- [Rust Interoperability Guide](../rust/basic-serialization.md#cross-language-interoperability)

### Related Guides

- [Configuration](configuration.md) - xlang mode settings
- [Schema Evolution](schema-evolution.md) - Compatible mode
- [Type Registration](type-registration.md) - Registration patterns

### Read the Java file example

```python
import pyfory
from dataclasses import dataclass

@dataclass
class Person:
    name: str
    age: pyfory.Int32

fory = pyfory.Fory(xlang=True)
fory.register_type(Person, name="example.Person")

with open("person.bin", "rb") as f:
    data = f.read()

person = fory.deserialize(data)
print(f"Name: {person.name}, Age: {person.age}")
# Output: Name: Alice, Age: 30
```

### Built-in values

```python
import pyfory
import numpy as np

fory = pyfory.Fory(xlang=True)
object_list = [True, False, "str", -1.1, 1,
               np.full(100, 0, dtype=np.int32), np.full(20, 0.0, dtype=np.double)]
data = fory.serialize(object_list)
# bytes can be deserialized by other languages
new_list = fory.deserialize(data)
object_map = {"k1": "v1", "k2": object_list, "k3": -1}
data = fory.serialize(object_map)
# bytes can be deserialized by other languages
new_map = fory.deserialize(data)
print(new_map)
```

### Custom values

```python
from dataclasses import dataclass
from typing import List, Dict, Any
import pyfory, array


@dataclass
class SomeClass1:
    f1: Any
    f2: Dict[pyfory.Int8, pyfory.Int32]


@dataclass
class SomeClass2:
    f1: Any = None
    f2: str = None
    f3: List[str] = None
    f4: Dict[pyfory.Int8, pyfory.Int32] = None
    f5: pyfory.Int8 = None
    f6: pyfory.Int16 = None
    f7: pyfory.Int32 = None
    # int type will be taken as `pyfory.Int64`.
    # use `pyfory.Int32` for type hint if peer uses more narrow type.
    f8: int = None
    f9: pyfory.Float32 = None
    # float type will be taken as `pyfory.Float64`
    f10: float = None
    f11: pyfory.Array[pyfory.Int16] = None
    f12: List[pyfory.Int16] = None


if __name__ == "__main__":
    f = pyfory.Fory(xlang=True)
    f.register_type(SomeClass1, name="example.SomeClass1")
    f.register_type(SomeClass2, name="example.SomeClass2")
    obj1 = SomeClass1(f1=True, f2={-1: 2})
    obj = SomeClass2(
        f1=obj1,
        f2="abc",
        f3=["abc", "abc"],
        f4={1: 2},
        f5=2 ** 7 - 1,
        f6=2 ** 15 - 1,
        f7=2 ** 31 - 1,
        f8=2 ** 63 - 1,
        f9=1.0 / 2,
        f10=1 / 3.0,
        f11=array.array("h", [1, 2]),
        f12=[-1, 4],
    )
    data = f.serialize(obj)
    # bytes can be deserialized by other languages
    print(f.deserialize(data))
```

### Shared and circular references

```python
from typing import Dict
import pyfory

class SomeClass:
    f1: "SomeClass"
    f2: Dict[str, str]
    f3: Dict[str, str]

fory = pyfory.Fory(xlang=True, ref=True)
fory.register_type(SomeClass, name="example.SomeClass")
obj = SomeClass()
obj.f2 = {"k1": "v1", "k2": "v2"}
obj.f1, obj.f3 = obj, obj.f2
data = fory.serialize(obj)
# bytes can be deserialized by other languages
print(fory.deserialize(data))
```

## Related Topics

- [Configuration](configuration.md) - Fory parameters
- [Type Registration](type-registration.md) - Registration patterns
- [Native Serialization](native.md) - Functions and lambdas
- [Out-of-Band Serialization](out-of-band.md) - Buffer callback APIs