Show HN: Apache Fory Rust – 10-20x faster serialization than JSON/Protobuf

Binary XML encoding (like W3C EXI) is useful in some contexts, but it’s generally not as efficient as modern binary serialization formats. It also can’t naturally express shared or circular reference semantics, which are important for complex object graphs.

Fory’s format was designed from the ground up to handle those cases efficiently, while still enabling cross‑language compatibility and schema evolution.

I am not sure if W3C EXI, or ASN.1 BER or something else is better, but agree that using DOP (rather than OOP) design principles is the right answer -- which means focusing on the encoding first, and working backwards towards the languages / clients.

That’s a fair point — with more languages in the mix, having a formal schema can definitely help prevent drift.

The way I think about it is: • Single‑language projects often work best without an IDL — it keeps things simple and avoids extra steps. • Two languages – both IDL and no‑IDL approaches can work, depending on the team’s habits. • Three or more – an IDL can be really useful as a single source of truth and to avoid manually writing struct definitions in every language.

For Apache Fory, my plan is to add optional IDL support, so teams who want that “single truth” can generate definitions automatically, and others can continue with language‑first development. My hope is to give people flexibility to choose what fits their situation best.

Looks like there's a type mapping chart for supported types: https://fory.apache.org/docs/docs/guide/xlang_type_mapping

Otherwise, the schema seems to be derived from the class being serialized for typed languages, or otherwise annotated in code. The serializer and deserializer code must be manually written to be compatible instead of both sides being codegen'd to match from a schema file. He's the example I found for python: https://fory.apache.org/docs/docs/guide/python_serialization...

I am confused on this as well, they list polyglot teams[0] as their top use case and consider not needing schema files a feature

[0] https://fory.apache.org/blog/2025/10/29/fory_rust_versatile_...

I am skeptical that it's possible to make this work in the long run.

Not explaining this case makes me wonder how much this lib is actually used in production. This was also the first question I asked myself.

IMO, not a fair benchmark.

I can see the source of an 10x improvement on an Intel(R) Xeon(R) Gold 6136 CPU @ 3.00GHz, but it drops to 3x improvement when I remove the to/from that clones or collects Vecs, and always allocate an 8K Vec instead of a ::Default for the writable buffer.

If anything, the benches should be updated in a tower service / codec generics style where other formats like protobuf do not use any Fory-related code at all.

Note also that Fory has some writer pool that is utilized during the tests:

https://github.com/apache/fory/blob/fd1d53bd0fbbc5e0ce6d53ef...

Original bench selection for Fory:

    Benchmarking ecommerce_data/fory_serialize/medium: Collecting 100 samples in estimated 5.0494 s (197k it
    ecommerce_data/fory_serialize/medium
                            time:   [25.373 µs 25.605 µs 25.916 µs]
                            change: [-2.0973% -0.9263% +0.2852%] (p = 0.15 > 0.05)
                            No change in performance detected.
    Found 4 outliers among 100 measurements (4.00%)
      2 (2.00%) high mild
      2 (2.00%) high severe

    Benchmarking ecommerce_data/protobuf_serialize/medium: Collecting 100 samples in estimated 5.0419 s (20k
    ecommerce_data/protobuf_serialize/medium
                            time:   [248.85 µs 251.04 µs 253.86 µs]
    Found 18 outliers among 100 measurements (18.00%)
      8 (8.00%) high mild
      10 (10.00%) high severe

    fair_ecommerce_data/protobuf_serialize/medium
                            time:   [73.114 µs 73.885 µs 74.911 µs]
                            change: [-1.8410% -0.6702% +0.5190%] (p = 0.30 > 0.05)
                            No change in performance detected.
    Found 14 outliers among 100 measurements (14.00%)
      2 (2.00%) high mild
      12 (12.00%) high severe

No, it's not needed. We plan to remove Google Guava from the Fory Java dependency. Our philosophy is that the core should have as few dependencies as possible for maintainability and minimal footprint.

Fory also compress integers using varint or zigzag encoding. The size are basically same:

| data type | data size | fory | protobuf |

| --------------- | --------- | ------- | -------- |

| simple-struct | small | 21 | 19 |

| simple-struct | medium | 70 | 66 |

| simple-struct | large | 220 | 216 |

| simple-list | small | 36 | 16 |

| simple-list | medium | 802 | 543 |

| simple-list | large | 14512 | 12876 |

| simple-map | small | 33 | 36 |

| simple-map | medium | 795 | 1182 |

| simple-map | large | 17893 | 21746 |

| person | small | 122 | 118 |

| person | medium | 873 | 948 |

| person | large | 7531 | 7865 |

| company | small | 191 | 182 |

| company | medium | 9118 | 9950 |

| company | large | 748105 | 782485 |

| e-commerce-data | small | 750 | 737 |

| e-commerce-data | medium | 53275 | 58025 |

| e-commerce-data | large | 1079358 | 1166878 |

| system-data | small | 311 | 315 |

| system-data | medium | 24301 | 26161 |

| system-data | large | 450031 | 479988 |

It appears there are two schema compatibility modes and no guarantee of minor version binary compatibility.

Probably not for everyone. The current limit of 4096 types could be expanded if there’s a real need — it’s not a hard technical barrier.

I’m curious though: what’s an example scenario you’ve seen that requires so many distinct types? I haven’t personally come across a case with 4,096+ protocol messages defined.

Guessing one of these

https://fory.apache.org/docs/docs/introduction/benchmark

https://fory.apache.org/docs/docs/guide/rust_serialization

Apache Arrow is more of a memory format than a general‑purpose data serialization system. It’s great for in‑memory analytics and GPU‑friendly columnar storage.

Apache Fory, on the other hand, has its own wire‑stream format designed for sending data across processes or networks. Most of the code is focused on efficiently converting in‑memory objects into that stream format (and back) — with features like cross‑language support, circular reference handling, and schema evolution.

Fory also has a row format, which is a memory format, and can complement or compete with Arrow’s columnar format depending on the use case.

Maybe I'm missing it, but they mention Flatbuffers a lot here, then don't show benchmarks:

https://fory.apache.org/blog/fury_blazing_fast_multiple_lang...

But flatbuffers is _much_ faster than protobuf/json:

https://flatbuffers.dev/benchmarks/

JS support is still experimental, I have not publish it to npm

    use fory::{Fory, ForyObject};

    #[derive(ForyObject, Debug, PartialEq)]
    struct Struct {
        nan: f32,
        inf: f32,
    }

    fn main() {
        let mut fory = Fory::default();
        fory.register::<Struct>(1).unwrap();

        let original = Struct {
            nan: f32::NAN,
            inf: f32::INFINITY,
        };
        dbg!(&original);

        let serialized = fory.serialize(&original).unwrap();

        let back: Struct = fory.deserialize(&serialized).unwrap();
        dbg!(&back);
    }

     cargo run
       Compiling rust-seed v0.0.0-development (/home/random-code/fory-nan-inf)
        Finished `dev` profile [unoptimized + debuginfo] target(s) in 0.28s
         Running `target/debug/fory-nan-inf`
    [src/main.rs:17:9] &original = Struct {
        nan: NaN,
        inf: inf,
    }
    [src/main.rs:22:9] &back = Struct {
        nan: NaN,
        inf: inf,
    }

Apache Fory is also a drop-in replacement for pickle/cloudpickle, you can use it to serialize code object such as local function/Classes too.

https://github.com/apache/fory/tree/main/python#serialize-lo...

When serializing code objects, pyfory is 3× higher compression ratio compared cloudpickle

And pyfory also provide extra security audit capability to avoid maliciously deserialization data attack.

The https://github.com/apache/fory/blob/main/AGENTS.md is a very detailed document only for AI coding, but an excelent reference for development. But you are right, it may introduce concerns, let me remove it from landing pages

should be ok now