speice.io/_posts/2019-09-01-binary-format-shootout.md

---
layout: post
title: "Binary Format Shootout"
description: "Cap'n Proto vs. Flatbuffers vs. SBE"
category: 
tags: [rust]
---

I've found that in many personal projects, [analysis paralysis](https://en.wikipedia.org/wiki/Analysis_paralysis)
is particularly deadly. Making good decisions in the beginning avoids pain and suffering later;
if extra research prevents future problems, I'm happy to continue researching indefinitely.

So let's say you're in need of a binary serialization schema. Data will be going over the network, not just in memory,
so having a schema document and code generation is a must. Performance is crucial; there's no reason to use Protocol Buffers
when other formats support similar features at faster speeds. And the more languages supported, the better; I use Rust,
but can't predict what other languages this will interact with.

Given these requirements, the candidates I could find were:

1. [Cap'n Proto](https://capnproto.org/) has been around the longest, and is the most established
2. [Flatbuffers](https://google.github.io/flatbuffers/) is the newest, and claims to have a simpler encoding
3. [Simple Binary Encoding](https://github.com/real-logic/simple-binary-encoding) has the simplest encoding,
   but the Rust implementation is [essentially unmaintained](https://users.rust-lang.org/t/zero-cost-abstraction-frontier-no-copy-low-allocation-ordered-decoding/11515/9)

Any one of these will satisfy the project requirements: easy to transmit over a network, reasonably fast,
and polyglot support. But how do you actually pick one? It's impossible to know what issues will follow that choice,
so I tend to avoid commitment until the last possible moment.

Still, a choice must be made. Instead of worrying about which is "the best," I decided to build a small 
proof-of-concept system in each format and pit them against each other. All code can be found in the
[repository](https://github.com/bspeice/speice.io-md_shootout) for this post.

We'll discuss more in detail, but a quick preview of the results:

- Cap'n Proto can theoretically perform incredibly well, but the implementation had performance issues
- Flatbuffers had some quirks, but largely lived up to its "zero-copy" promises
- SBE has the best median and worst-case performance, but the message structure has a limited feature set
  relative to Cap'n Proto and Flatbuffers

# Prologue: Reading the Data

Our benchmark system will be a simple data processor; given depth-of-book market data from
[IEX](https://iextrading.com/trading/market-data/#deep), serialize each message into the schema format,
then read back the message for some basic aggregation. This test isn't complex, but is representative
of the project I need a binary format for.

But before we make it to that point, we have to actually read in the market data. To do so, I'm using a library
called [`nom`](https://github.com/Geal/nom). Version 5.0 was recently released and brought some big changes,
so this was an opportunity to build a non-trivial program and get familiar again.

If you don't already know about `nom`, it's a "parser generator". By combining different smaller parsers,
you can build a parser to handle more complex structures without writing all the tedious code by hand.
For example, when parsing [PCAP files](https://www.winpcap.org/ntar/draft/PCAP-DumpFileFormat.html#rfc.section.3.3):

```
   0                   1                   2                   3
   0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1
   +---------------------------------------------------------------+
 0 |                    Block Type = 0x00000006                    |
   +---------------------------------------------------------------+
 4 |                      Block Total Length                       |
   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
 8 |                         Interface ID                          |
   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
12 |                        Timestamp (High)                       |
   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
16 |                        Timestamp (Low)                        |
   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
20 |                         Captured Len                          |
   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
24 |                          Packet Len                           |
   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
   |                          Packet Data                          |
   |                              ...                              |
```

...you can build a parser in `nom` that looks like
[this](https://github.com/bspeice/speice.io-md_shootout/blob/369613843d39cfdc728e1003123bf87f79422497/src/parsers.rs#L59-L93):

```rust
const ENHANCED_PACKET: [u8; 4] = [0x06, 0x00, 0x00, 0x00];
pub fn enhanced_packet_block(input: &[u8]) -> IResult<&[u8], &[u8]> {
    let (
        remaining,
        (
            block_type,
            block_len,
            interface_id,
            timestamp_high,
            timestamp_low,
            captured_len,
            packet_len,
        ),
    ) = tuple((
        tag(ENHANCED_PACKET),
        le_u32,
        le_u32,
        le_u32,
        le_u32,
        le_u32,
        le_u32,
    ))(input)?;

    let (remaining, packet_data) = take(captured_len)(remaining)?;
    Ok((remaining, packet_data))
}
```

While this example isn't too interesting, more complex formats (like IEX market data) are where
[`nom` really shines](https://github.com/bspeice/speice.io-md_shootout/blob/369613843d39cfdc728e1003123bf87f79422497/src/iex.rs).

Ultimately, because the `nom` code in this shootout was the same for all formats, we're not too interested in its performance.
Still, it's worth mentioning that building the market data parser was actually fun; I didn't have to write all the boring code by hand.

# Part 1: Cap'n Proto

Now it's time to get into the meaty part of the story. Cap'n Proto was the first format I tried because of how long
it has supported Rust (thanks to [dwrensha](https://github.com/dwrensha) for maintaining the Rust port since
[2014!](https://github.com/capnproto/capnproto-rust/releases/tag/rustc-0.10)). However, I had a ton of performance concerns
once I started using it.

To serialize new messages, Cap'n Proto uses a "builder" object. This builder allocates memory on the heap to hold the message
content, but because builders [can't be re-used](https://github.com/capnproto/capnproto-rust/issues/111), we have to allocate
a new buffer for every single message. I was able to work around this with a
[special builder](https://github.com/bspeice/speice.io-md_shootout/blob/369613843d39cfdc728e1003123bf87f79422497/src/capnp_runner.rs#L17-L51)
that could re-use the buffer, but it required reading through Cap'n Proto's
[benchmarks](https://github.com/capnproto/capnproto-rust/blob/master/benchmark/benchmark.rs#L124-L156)
to find an example, and used [`std::mem::transmute`](https://doc.rust-lang.org/std/mem/fn.transmute.html) to bypass Rust's borrow checker.

The process of reading messages was better, but still had issues. Cap'n Proto has two message encodings: a ["packed"](https://capnproto.org/encoding.html#packing)
representation, and an "unpacked" version. When reading "packed" messages, we need a buffer to unpack the message into before we can use it;
Cap'n Proto allocates a new buffer for each message we unpack, and I wasn't able to figure out a way around that.
In contrast, the unpacked message format should be where Cap'n Proto shines; its main selling point is that there's [no decoding step](https://capnproto.org/).
However, accomplishing zero-copy deserialization required code in the private API ([since fixed](https://github.com/capnproto/capnproto-rust/issues/148)),
and we still allocate a vector on every read for the segment table.

In the end, I put in significant work to make Cap'n Proto as fast as possible, but there were too many issues for me to feel comfortable
using it long-term.

# Part 2: Flatbuffers

This is the new kid on the block. After a [first attempt](https://github.com/google/flatbuffers/pull/3894) didn't pan out,
official support was [recently added](https://github.com/google/flatbuffers/pull/4898). Flatbuffers intends to address
the same problems as Cap'n Proto: high-performance, polyglot, binary messaging. The difference is that Flatbuffers claims
to have a simpler wire format and [more flexibility](https://google.github.io/flatbuffers/flatbuffers_benchmarks.html).

On the whole, I enjoyed using Flatbuffers; the [tooling](https://crates.io/crates/flatc-rust) is nice enough, and unlike
Cap'n Proto, parsing messages was actually zero-copy and zero-allocation. There were some issues though.

First, Flatbuffers (at least in Rust) can't handle nested vectors. This is a problem for formats like the following:

```
table Message {
  symbol: string;
}
table MultiMessage {
  messages:[Message];
}
```

We want to create a `MultiMessage` which contains a vector of `Message`, and each `Message` itself contains a vector (the `string` type).
I was able to work around this by [caching `Message` elements](https://github.com/bspeice/speice.io-md_shootout/blob/e9d07d148bf36a211a6f86802b313c4918377d1b/src/flatbuffers_runner.rs#L83)
in a `SmallVec` before building the final `MultiMessage`, but it was a painful process.

Second, streaming support in Flatbuffers seems to be something of an [afterthought](https://github.com/google/flatbuffers/issues/3898).
Where Cap'n Proto in Rust handles reading messages from a stream as part of the API, Flatbuffers just sticks a `u32` at the front of each
message to indicate the size. Not specifically a problem, but calculating message size without that tag is nigh on impossible.

Ultimately, I enjoyed using Flatbuffers, and had to do significantly less work to make it perform well.

# Part 3: Simple Binary Encoding

Support for SBE was added by the author of one of my favorite
[Rust blog posts](https://web.archive.org/web/20190427124806/https://polysync.io/blog/session-types-for-hearty-codecs/).
I've [talked previously]({% post_url 2019-06-31-high-performance-systems %}) about how important variance is in
high-performance systems, so it was encouraging to read about a format that
[directly addressed](https://github.com/real-logic/simple-binary-encoding/wiki/Why-Low-Latency) my concerns. SBE has by far
the simplest binary format, but it does make some tradeoffs.

Both Cap'n Proto and Flatbuffers use [message offsets](https://capnproto.org/encoding.html#structs) to handle
variable-length data, [unions](https://capnproto.org/language.html#unions), and various other features. In contrast,
messages in SBE are essentially [just structs](https://github.com/real-logic/simple-binary-encoding/blob/master/sbe-samples/src/main/resources/example-schema.xml);
variable-length data is supported, but there's no union type.

As mentioned in the beginning, the Rust port of SBE works well, but is essentially unmaintained. However, if you
don't need union types, and can accept that schemas are XML documents, it's still worth using. The implementation
had the best streaming support of all formats being tested, and doesn't trigger allocation during de/serialization.

# Results

After building a test harness [for](https://github.com/bspeice/speice.io-md_shootout/blob/master/src/capnp_runner.rs)
[each](https://github.com/bspeice/speice.io-md_shootout/blob/master/src/flatbuffers_runner.rs)
[format](https://github.com/bspeice/speice.io-md_shootout/blob/master/src/sbe_runner.rs),
it was time to actually take them for a spin. I used
[this script](https://github.com/bspeice/speice.io-md_shootout/blob/master/run_shootout.sh) to manage the benchmarking,
and the raw results are [here](https://github.com/bspeice/speice.io-md_shootout/blob/master/shootout.csv). All data
reported below is the average of 10 runs over a single day of IEX data. Results were validated to make sure
that each format parsed the data correctly.

## Serialization

This test measures, on a
[per-message basis](https://github.com/bspeice/speice.io-md_shootout/blob/master/src/main.rs#L268-L272),
how long it takes to serialize the IEX message into the desired format and write to a pre-allocated buffer.

| Schema               | Median | 99th Pctl | 99.9th Pctl | Total  |
|:---------------------|:-------|:----------|:------------|:-------|
| Cap'n Proto Packed   | 413ns  | 1751ns    | 2943ns      | 14.80s |
| Cap'n Proto Unpacked | 273ns  | 1828ns    | 2836ns      | 10.65s |
| Flatbuffers          | 355ns  | 2185ns    | 3497ns      | 14.31s |
| SBE                  | 91ns   | 1535ns    | 2423ns      | 3.91s  |

## Deserialization

This test measures, on a
[per-message basis](https://github.com/bspeice/speice.io-md_shootout/blob/master/src/main.rs#L294-L298),
how long it takes to read the previously-serialized message and
perform some basic aggregation. The aggregation code is the same for each format,
so any performance differences are due solely to the format implementation.

| Schema               | Median | 99th Pctl | 99.9th Pctl | Total  |
|:---------------------|:-------|:----------|:------------|:-------|
| Cap'n Proto Packed   | 539ns  | 1216ns    | 2599ns      | 18.92s |
| Cap'n Proto Unpacked | 366ns  | 737ns     | 1583ns      | 12.32s |
| Flatbuffers          | 173ns  | 421ns     | 1007ns      | 6.00s  |
| SBE                  | 116ns  | 286ns     | 659ns       | 4.05s  |

# Conclusion

Building a benchmark turned out to be incredibly helpful in making a decision; because a
"union" type isn't important to me, I can be confident that SBE best addresses my needs.

While SBE was the fastest in terms of both median and worst-case performance, its worst case
performance was proportionately far higher than any other format. It seems to be that de/serialization
time scales with message size, but I'll need to do some more research to understand what exactly
is going on.