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title Rust Serialization Guide
sidebar_position 0
id serialization_index
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.

Apache Fory™ is a blazing fast multi-language serialization framework powered by JIT compilation and zero-copy techniques, providing up to ultra-fast performance while maintaining ease of use and safety.

The Rust implementation provides versatile and high-performance serialization with automatic memory management and compile-time type safety. It supports both xlang mode for cross-language payloads and native mode for Rust-only payloads.

Why Apache Fory™ Rust?

  • Fast binary encoding: Zero-copy deserialization and optimized binary protocols
  • Xlang: Seamlessly serialize/deserialize data across Java, Python, C++, Go, JavaScript, and Rust
  • Type-safe: Compile-time type checking with derive macros
  • Circular references: Automatic tracking of shared and circular references with Rc/Arc and weak pointers
  • Polymorphic: Serialize trait objects with Box<dyn Trait>, Rc<dyn Trait>, and Arc<dyn Trait>
  • Schema evolution: Compatible mode for independent schema changes
  • Two formats: Object graph serialization and zero-copy row-based format

Crates

Crate Description Version
fory High-level API with derive macros crates.io
fory-core Core serialization engine crates.io
fory-derive Procedural macros crates.io

Quick Start

Add Apache Fory™ to your Cargo.toml:

[dependencies]
fory = "1.0.0"

Basic Example

use fory::{Fory, Error, Reader};
use fory::ForyStruct;

#[derive(ForyStruct, Debug, PartialEq)]
struct User {
    name: String,
    age: i32,
    email: String,
}

fn main() -> Result<(), Error> {
    let mut fory = Fory::builder().xlang(true).build();
    fory.register::<User>(1)?;

    let user = User {
        name: "Alice".to_string(),
        age: 30,
        email: "alice@example.com".to_string(),
    };

    // Serialize
    let bytes = fory.serialize(&user)?;
    // Deserialize
    let decoded: User = fory.deserialize(&bytes)?;
    assert_eq!(user, decoded);

    // Serialize to specified buffer
    let mut buf: Vec<u8> = vec![];
    fory.serialize_to(&mut buf, &user)?;
    // Deserialize from specified buffer
    let mut reader = Reader::new(&buf);
    let decoded: User = fory.deserialize_from(&mut reader)?;
    assert_eq!(user, decoded);
    Ok(())
}

Xlang Mode And Native Mode

Use xlang mode for cross-language payloads and schemas shared with other Fory runtimes. Xlang mode is the default Rust wire mode, and Rust examples that use it set .xlang(true) explicitly so the mode choice is visible.

Use native mode for Rust-only traffic. Native mode is selected with .xlang(false), uses schema-consistent payloads unless compatible mode is enabled, and keeps Rust object serialization on the Rust runtime path. It is optimized for Rust's type system and covers Rust-specific object features such as trait objects and shared-reference patterns that are not portable xlang payloads.

See Xlang Serialization for Rust xlang registration and interoperability rules, and Native Serialization for Rust-only payloads.

Thread Safety

Apache Fory™ Rust is fully thread-safe: Fory implements both Send and Sync, so one configured instance can be shared across threads for concurrent work. The internal read/write context pools are lazily initialized with thread-safe primitives, letting worker threads reuse buffers without coordination.

use fory::{Fory, Error};
use fory::ForyStruct;
use std::sync::Arc;
use std::thread;

#[derive(ForyStruct, Clone, Copy, Debug, PartialEq)]
struct Item {
    value: i32,
}

fn main() -> Result<(), Error> {
    let mut fory = Fory::builder().xlang(true).build();
    fory.register::<Item>(1000)?;

    let fory = Arc::new(fory);
    let handles: Vec<_> = (0..8)
        .map(|i| {
            let shared = Arc::clone(&fory);
            thread::spawn(move || {
                let item = Item { value: i };
                shared.serialize(&item)
            })
        })
        .collect();

    for handle in handles {
        let bytes = handle.join().unwrap()?;
        let item: Item = fory.deserialize(&bytes)?;
        assert!(item.value >= 0);
    }

    Ok(())
}

Tip: Perform registrations (such as fory.register::<T>(id)) before spawning threads so every worker sees the same metadata. Once configured, wrapping the instance in Arc is enough to fan out serialization and deserialization tasks safely.

Architecture

The Rust implementation consists of three main crates:

fory/                   # High-level API
├── src/lib.rs         # Public API exports

fory-core/             # Core serialization engine
├── src/
│   ├── fory.rs       # Main serialization entry point
│   ├── buffer.rs     # Binary buffer management
│   ├── serializer/   # Type-specific serializers
│   ├── resolver/     # Type resolution and metadata
│   ├── meta/         # Meta string compression
│   ├── row/          # Row format implementation
│   └── types.rs      # Type definitions

fory-derive/           # Procedural macros
├── src/
│   ├── object/       # ForyStruct macro
│   └── fory_row.rs  # ForyRow macro

Use Cases

Object Serialization

  • Complex data structures with nested objects and references
  • Cross-language communication in microservices
  • General-purpose serialization with full type safety
  • Schema evolution with compatible mode
  • Graph-like data structures with circular references

Row-Based Serialization

  • High-throughput data processing
  • Analytics workloads requiring fast field access
  • Memory-constrained environments
  • Real-time data streaming applications
  • Zero-copy scenarios

Next Steps