Apache Fory™ is a blazingly-fast multi-language serialization framework powered by JIT compilation, zero-copy techniques, and advanced code generation, achieving up to 170x performance improvement while maintaining simplicity and ease of use.

https://fory.apache.org

Important

Apache Fory™ was previously named as Apache Fury. For versions before 0.11, please use "fury" instead of "fory" in package names, imports, and dependencies, see Fury Docs for how to use Fury in older versions.

Key Features

🚀 High-Performance Serialization

Apache Fory™ delivers exceptional performance through advanced optimization techniques:

• JIT Compilation: Runtime code generation for Java eliminates virtual method calls and inlines hot paths
• Static Code Generation: Compile-time code generation for Rust, C++, and Go delivers peak performance without runtime overhead
• Zero-Copy Operations: Direct memory access without intermediate buffer copies; row format enables random access and partial serialization
• Intelligent Encoding: Variable-length compression for integers and strings; SIMD acceleration for arrays (Java 16+)
• Meta Sharing: Class metadata packing reduces redundant type information across serializations

🌍 Cross-Language Serialization

The xlang serialization format enables seamless data exchange across programming languages:

• Automatic Type Mapping: Intelligent conversion between language-specific types (type mapping)
• Reference Preservation: Shared and circular references work correctly across languages
• Polymorphism: Objects serialize/deserialize with their actual runtime types
• Schema Evolution: Optional forward/backward compatibility for evolving schemas
• Automatic Serialization: No IDL or schema definitions required; serialize any object directly without code generation

📊 Row Format

A cache-friendly row format optimized for analytics workloads:

• Zero-Copy Random Access: Read individual fields without deserializing entire objects
• Partial Operations: Selective field serialization and deserialization for efficiency
• Apache Arrow Integration: Seamless conversion to columnar format for analytics pipelines
• Multi-Language: Available in Java, Python, Rust and C++

🔒 Security & Production-Readiness

Enterprise-grade security and compatibility:

• Class Registration: Whitelist-based deserialization control (enabled by default)
• Depth Limiting: Protection against recursive object graph attacks
• Configurable Policies: Custom class checkers and deserialization policies
• Platform Support: Java 8-24, GraalVM native image, multiple OS platforms

Protocols

Apache Fory™ implements multiple binary protocols optimized for different scenarios:

Protocol	Use Case	Key Features
Xlang Serialization	Cross-language object exchange	Automatic serialization, references, polymorphism
Java Serialization	High-performance Java-only	Drop-in JDK serialization replacement, 100x faster
Row Format	Analytics and data processing	Zero-copy random access, Arrow compatibility
Python Native	Python-specific serialization	Pickle/cloudpickle replacement with better performance

All protocols share the same optimized codebase, allowing improvements in one protocol to benefit others.

Benchmarks

Note: Different serialization frameworks excel in different scenarios. Benchmark results are for reference only. For your specific use case, conduct benchmarks with appropriate configurations and workloads.

Java Serialization Performance

The following benchmarks compare Fory against popular Java serialization frameworks. Charts labeled "compatible" show schema evolution mode with forward/backward compatibility enabled, while others show schema consistent mode where class schemas must match.

Test Classes:

• Struct: Class with 100 primitive fields
• MediaContent: Class from jvm-serializers
• Sample: Class from Kryo benchmark

Serialization Throughput:

Deserialization Throughput:

Important: Fory's runtime code generation requires proper warm-up for performance measurement:

For additional benchmarks covering type forward/backward compatibility, off-heap support, and zero-copy serialization, see Java Benchmarks.

Rust Serialization Performance

Fory Rust demonstrates competitive performance compared to other Rust serialization frameworks.

For more detailed benchmarks and methodology, see Rust Benchmarks.

Installation

Java:

<dependency>
  <groupId>org.apache.fory</groupId>
  <artifactId>fory-core</artifactId>
  <version>0.13.0</version>
</dependency>
<!-- Optional row format support -->
<!--
<dependency>
  <groupId>org.apache.fory</groupId>
  <artifactId>fory-format</artifactId>
  <version>0.13.0</version>
</dependency>
-->
<!-- SIMD acceleration for array compression (Java 16+) -->
<!--
<dependency>
  <groupId>org.apache.fory</groupId>
  <artifactId>fory-simd</artifactId>
  <version>0.13.0</version>
</dependency>
-->

Snapshots are available from https://repository.apache.org/snapshots/ (version 0.14.0-SNAPSHOT).

Scala:

// Scala 2.13
libraryDependencies += "org.apache.fory" % "fory-scala_2.13" % "0.13.0"

// Scala 3
libraryDependencies += "org.apache.fory" % "fory-scala_3" % "0.13.0"

Kotlin:

<dependency>
  <groupId>org.apache.fory</groupId>
  <artifactId>fory-kotlin</artifactId>
  <version>0.13.0</version>
</dependency>

Python:

pip install pyfory

# With row format support
pip install pyfory[format]

Rust:

[dependencies]
fory = "0.13"

Golang:

go get github.com/apache/fory/go/fory

Quick Start

This section provides quick examples for getting started with Apache Fory™. For comprehensive guides, see the Documentation.

Native Serialization

Always use native mode when working with a single language. Native mode delivers optimal performance by avoiding the type metadata overhead required for cross-language compatibility. Xlang mode introduces additional metadata encoding costs and restricts serialization to types that are common across all supported languages. Language-specific types will be rejected during serialization in xlang-mode.

Java Serialization

When you don't need cross-language support, use Java mode for optimal performance.

import org.apache.fory.*;
import org.apache.fory.config.*;

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

  public static void main(String[] args) {
    // Create Fory instance - should be reused across serializations
    BaseFory fory = Fory.builder()
      .withLanguage(Language.JAVA)
      .requireClassRegistration(true)
      // replace `build` with `buildThreadSafeFory` for Thread-Safe Usage
      .build();
    // Register your classes (required when class registration is enabled)
    fory.register(Person.class);
    // Serialize
    Person person = new Person();
    person.name = "chaokunyang";
    person.age = 28;
    byte[] bytes = fory.serialize(person);
    Person result = (Person) fory.deserialize(bytes);
    System.out.println(result.name + " " + result.age);  // Output: chaokunyang 28
  }
}

For detailed Java usage including compatibility modes, compression, and advanced features, see Java Serialization Guide and java/README.md.

Python Serialization

Python native mode provides a high-performance drop-in replacement for pickle/cloudpickle with better speed and compatibility.

from dataclasses import dataclass
import pyfory

@dataclass
class Person:
    name: str
    age: pyfory.int32

# Create Fory instance - should be reused across serializations
fory = pyfory.Fory()
# Register your classes (required when class registration is enabled)
fory.register_type(Person)
person = Person(name="chaokunyang", age=28)
data = fory.serialize(person)
result = fory.deserialize(data)
print(result.name, result.age)  # Output: chaokunyang 28

For detailed Python usage including type hints, compatibility modes, and advanced features, see Python Guide.

Scala Serialization

Scala native mode provides optimized serialization for Scala-specific types including case classes, collections, and Option types.

import org.apache.fory.Fory
import org.apache.fory.config.Language
import org.apache.fory.serializer.scala.ScalaSerializers

case class Person(name: String, age: Int)

object Example {
  def main(args: Array[String]): Unit = {
    // Create Fory instance - should be reused across serializations
    val fory = Fory.builder()
      .withLanguage(Language.JAVA)
      .requireClassRegistration(true)
      .build()
    // Register Scala serializers for Scala-specific types
    ScalaSerializers.registerSerializers(fory)
    // Register your case classes
    fory.register(classOf[Person])
    val bytes = fory.serialize(Person("chaokunyang", 28))
    val result = fory.deserialize(bytes).asInstanceOf[Person]
    println(s"${result.name} ${result.age}")  // Output: chaokunyang 28
  }
}

For detailed Scala usage including collection serialization and integration patterns, see Scala Guide.

Kotlin Serialization

Kotlin native mode provides optimized serialization for Kotlin-specific types including data classes, nullable types, and Kotlin collections.

import org.apache.fory.Fory
import org.apache.fory.config.Language
import org.apache.fory.serializer.kotlin.KotlinSerializers

data class Person(val name: String, val age: Int)

fun main() {
    // Create Fory instance - should be reused across serializations
    val fory = Fory.builder()
        .withLanguage(Language.JAVA)
        .requireClassRegistration(true)
        .build()
    // Register Kotlin serializers for Kotlin-specific types
    KotlinSerializers.registerSerializers(fory)
    // Register your data classes
    fory.register(Person::class.java)
    val bytes = fory.serialize(Person("chaokunyang", 28))
    val result = fory.deserialize(bytes) as Person
    println("${result.name} ${result.age}")  // Output: chaokunyang 28
}

For detailed Kotlin usage including null safety and default value support, see kotlin/README.md.

Cross-Language Serialization

Only use xlang mode when you need cross-language data exchange. Xlang mode adds type metadata overhead for cross-language compatibility and only supports types that can be mapped across all languages. For single-language use cases, always prefer native mode for better performance.

The following examples demonstrate serializing a Person object across Java and Rust. For other languages (Python, Go, JavaScript, etc.), simply set the language mode to XLANG and follow the same pattern.

Java

import org.apache.fory.*;
import org.apache.fory.config.*;

public class XlangExample {
  public record Person(String name, int age) {}

  public static void main(String[] args) {
    // Create Fory instance with XLANG mode
    Fory fory = Fory.builder()
      .withLanguage(Language.XLANG)
      .build();

    // Register with cross-language type id/name
    fory.register(Person.class, 1);
    // fory.register(Person.class, "example.Person");
    Person person = new Person("chaokunyang", 28);
    byte[] bytes = fory.serialize(person);
    // bytes can be deserialized by Rust, Python, Go, or other languages
    Person result = (Person) fory.deserialize(bytes);
    System.out.println(result.name + " " + result.age);  // Output: chaokunyang 28
  }
}

Rust

use fory::{Fory, ForyObject};

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

fn main() -> Result<(), Error> {
    let mut fory = Fory::default();
    fory.register::<Person>(1)?;
    // fory.register_by_name::<Person>("example.Person")?;
    let person = Person {
        name: "chaokunyang".to_string(),
        age: 28,
    };
    let bytes = fory.serialize(&person);
    // bytes can be deserialized by Java, Python, Go, or other languages
    let result: Person = fory.deserialize(&bytes)?;
    println!("{} {}", result.name, result.age);  // Output: chaokunyang 28
}

Key Points for Cross-Language Serialization:

• Use Language.XLANG mode in all languages
• Register types with consistent IDs or names across all languages:
  • By ID (fory.register(Person.class, 1)): Faster serialization, more compact encoding, but requires coordination to avoid ID conflicts
  • By name (fory.register(Person.class, "example.Person")): More flexible, less prone to conflicts, easier to manage across teams, but slightly larger encoding
• Type IDs/names must match across all languages for successful deserialization
• Only use types that have cross-language mappings (see Type Mapping)

For examples with circular references, shared references, and polymorphism across languages, see:

• Cross-Language Serialization Guide
• Java Serialization Guide - Cross Language
• Python Guide - Cross Language

Row Format Encoding

Row format provides zero-copy random access to serialized data, making it ideal for analytics workloads and data processing pipelines.

Java

import org.apache.fory.format.*;
import java.util.*;
import java.util.stream.*;

public class Bar {
  String f1;
  List<Long> f2;
}

public class Foo {
  int f1;
  List<Integer> f2;
  Map<String, Integer> f3;
  List<Bar> f4;
}

RowEncoder<Foo> encoder = Encoders.bean(Foo.class);
Foo foo = new Foo();
foo.f1 = 10;
foo.f2 = IntStream.range(0, 1000000).boxed().collect(Collectors.toList());
foo.f3 = IntStream.range(0, 1000000).boxed().collect(Collectors.toMap(i -> "k"+i, i -> i));

List<Bar> bars = new ArrayList<>(1000000);
for (int i = 0; i < 1000000; i++) {
  Bar bar = new Bar();
  bar.f1 = "s" + i;
  bar.f2 = LongStream.range(0, 10).boxed().collect(Collectors.toList());
  bars.add(bar);
}
foo.f4 = bars;

// Serialize to row format (can be zero-copy read by Python)
BinaryRow binaryRow = encoder.toRow(foo);

// Deserialize entire object
Foo newFoo = encoder.fromRow(binaryRow);

// Zero-copy access to nested fields without full deserialization
BinaryArray binaryArray2 = binaryRow.getArray(1);  // Access f2 field
BinaryArray binaryArray4 = binaryRow.getArray(3);  // Access f4 field
BinaryRow barStruct = binaryArray4.getStruct(10);   // Access 11th Bar element
long value = barStruct.getArray(1).getInt64(5);     // Access nested value

// Partial deserialization
RowEncoder<Bar> barEncoder = Encoders.bean(Bar.class);
Bar newBar = barEncoder.fromRow(barStruct);
Bar newBar2 = barEncoder.fromRow(binaryArray4.getStruct(20));

Python

from dataclasses import dataclass
from typing import List, Dict
import pyarrow as pa
import pyfory

@dataclass
class Bar:
    f1: str
    f2: List[pa.int64]

@dataclass
class Foo:
    f1: pa.int32
    f2: List[pa.int32]
    f3: Dict[str, pa.int32]
    f4: List[Bar]

encoder = pyfory.encoder(Foo)
foo = Foo(
    f1=10,
    f2=list(range(1000_000)),
    f3={f"k{i}": i for i in range(1000_000)},
    f4=[Bar(f1=f"s{i}", f2=list(range(10))) for i in range(1000_000)]
)

# Serialize to row format
binary: bytes = encoder.to_row(foo).to_bytes()

# Zero-copy random access without full deserialization
foo_row = pyfory.RowData(encoder.schema, binary)
print(foo_row.f2[100000])           # Access element directly
print(foo_row.f4[100000].f1)        # Access nested field
print(foo_row.f4[200000].f2[5])     # Access deeply nested field

For more details on row format, see Row Format Guide.

Documentation

User Guides

Guide	Description	Source	Website
Java Serialization	Comprehensive guide for Java serialization	java_serialization_guide.md	📖 View
Cross-Language Serialization	Multi-language object exchange	xlang_serialization_guide.md	📖 View
Row Format	Zero-copy random access format	row_format_guide.md	📖 View
Python	Python-specific features and usage	python_guide.md	📖 View
Rust	Rust implementation and patterns	rust_guide.md	📖 View
Scala	Scala integration and best practices	scala_guide.md	📖 View
GraalVM	Native image support and AOT compilation	graalvm_guide.md	📖 View
Development	Building and contributing to Fory	DEVELOPMENT.md	📖 View

Protocol Specifications

Specification	Description	Source	Website
Xlang Serialization	Cross-language binary protocol	xlang_serialization_spec.md	📖 View
Java Serialization	Java-optimized protocol	java_serialization_spec.md	📖 View
Row Format	Row-based binary format	row_format_spec.md	📖 View
Type Mapping	Cross-language type conversion	xlang_type_mapping.md	📖 View

Compatibility

Schema Compatibility

Apache Fory™ supports class schema forward/backward compatibility across Java, Python, Rust, and Golang, enabling seamless schema evolution in production systems without requiring coordinated upgrades across all services. Fory provides two schema compatibility modes:

1. Schema Consistent Mode (Default): Assumes identical class schemas between serialization and deserialization peers. This mode offers minimal serialization overhead, smallest data size, and fastest performance: ideal for stable schemas or controlled environments.

2. Compatible Mode: Supports independent schema evolution with forward and backward compatibility. This mode enables field addition/deletion, limited type evolution, and graceful handling of schema mismatches. Enable using withCompatibleMode(CompatibleMode.COMPATIBLE) in Java, compatible=True in Python, compatible_mode(true) in Rust, or NewFory(true) in Go.

Binary Compatibility

Current Status: Binary compatibility is not guaranteed between Fory major releases as the protocol continues to evolve. However, compatibility is guaranteed between minor versions (e.g., 0.13.x).

Recommendations:

• Version your serialized data by Fory major version
• Plan migration strategies when upgrading major versions
• See upgrade guide for details

Future: Binary compatibility will be guaranteed starting from Fory 1.0 release.

Security

Overview

Serialization security varies by protocol:

• Row Format: Secure with predefined schemas
• Object Graph Serialization (Java/Python native): More flexible but requires careful security configuration

Dynamic serialization can deserialize arbitrary types, which may introduces risks. For example, the deserialization may invoke init constructor or equals/hashCode method, if the method body contains malicious code, the system will be at risk.

Fory enables class registration by default for dynamic protocols, allowing only trusted registered types. Do not disable class registration unless you can ensure your environment is secure.

If this option is disabled, you are responsible for serialization security. You should implement and configure a customized ClassChecker or DeserializationPolicy for fine-grained security control

To report security vulnerabilities in Apache Fory™, please follow the ASF vulnerability reporting process.

Community and Support

Getting Help

• Slack: Join our Slack workspace for community discussions
• Twitter/X: Follow @ApacheFory for updates and announcements
• GitHub Issues: Report bugs and request features at apache/fory
• Mailing Lists: Subscribe to Apache Fory mailing lists for development discussions

Contributing

We welcome contributions! Please read our Contributing Guide to get started.

Ways to Contribute:

• 🐛 Report bugs and issues
• 💡 Propose new features
• 📝 Improve documentation
• 🔧 Submit pull requests
• 🧪 Add test cases
• 📊 Share benchmarks

See Development Guide for build instructions and development workflow.

License

Apache Fory™ is licensed under the Apache License 2.0.