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237 lines
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Markdown
237 lines
14 KiB
Markdown
---
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layout: post
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title: "Binary Format Shootout"
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description: "Cap'n Proto vs. Flatbuffers vs. SBE"
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category:
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tags: [rust]
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---
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I've found that in many personal projects, [analysis paralysis](https://en.wikipedia.org/wiki/Analysis_paralysis)
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is particularly deadly. Making good decisions in the beginning avoids pain and suffering later;
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if extra research prevents future problems, I'm happy to continue ~~procrastinating~~ researching indefinitely.
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So let's say you're in need of a binary serialization format. Data will be going over the network, not just in memory,
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so having a schema document and code generation is a must. Performance is crucial, so formats that support zero-copy
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de/serialization are given priority. And the more languages supported, the better; I use Rust,
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but can't predict what other languages this could interact with.
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Given these requirements, the candidates I could find were:
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1. [Cap'n Proto](https://capnproto.org/) has been around the longest, and is the most established
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2. [Flatbuffers](https://google.github.io/flatbuffers/) is the newest, and claims to have a simpler encoding
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3. [Simple Binary Encoding](https://github.com/real-logic/simple-binary-encoding) has the simplest encoding,
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but the Rust implementation is unmaintained
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Any one of these will satisfy the project requirements: easy to transmit over a network, reasonably fast,
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and polyglot support. But how do you actually pick one? It's impossible to know what issues will follow that choice,
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so I tend to avoid commitment until the last possible moment.
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Still, a choice must be made. Instead of worrying about which is "the best," I decided to build a small
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proof-of-concept system in each format and pit them against each other. All code can be found in the
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[repository](https://github.com/speice-io/marketdata-shootout) for this post.
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We'll discuss more in detail, but a quick preview of the results:
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- Cap'n Proto: Theoretically performs incredibly well, the implementation had issues
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- Flatbuffers: Has some quirks, but largely lived up to its "zero-copy" promises
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- SBE: Best median and worst-case performance, but the message structure has a limited feature set
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# Prologue: Binary Parsing with Nom
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Our benchmark system will be a simple data processor; given depth-of-book market data from
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[IEX](https://iextrading.com/trading/market-data/#deep), serialize each message into the schema format,
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read it back, and calculate total size of stock traded and the lowest/highest quoted prices. This test
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isn't complex, but is representative of the project I need a binary format for.
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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
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called [`nom`](https://github.com/Geal/nom). Version 5.0 was recently released and brought some big changes,
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so this was an opportunity to build a non-trivial program and get familiar.
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If you don't already know about `nom`, it's a "parser generator". By combining different smaller parsers,
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you can assemble a parser to handle complex structures without writing tedious code by hand.
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For example, when parsing [PCAP files](https://www.winpcap.org/ntar/draft/PCAP-DumpFileFormat.html#rfc.section.3.3):
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```
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0 1 2 3
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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
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+---------------------------------------------------------------+
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0 | Block Type = 0x00000006 |
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+---------------------------------------------------------------+
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4 | Block Total Length |
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+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
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8 | Interface ID |
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+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
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12 | Timestamp (High) |
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+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
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16 | Timestamp (Low) |
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+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
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20 | Captured Len |
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+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
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24 | Packet Len |
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+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
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| Packet Data |
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| ... |
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```
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...you can build a parser in `nom` that looks like
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[this](https://github.com/speice-io/marketdata-shootout/blob/369613843d39cfdc728e1003123bf87f79422497/src/parsers.rs#L59-L93):
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```rust
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const ENHANCED_PACKET: [u8; 4] = [0x06, 0x00, 0x00, 0x00];
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pub fn enhanced_packet_block(input: &[u8]) -> IResult<&[u8], &[u8]> {
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let (
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remaining,
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(
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block_type,
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block_len,
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interface_id,
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timestamp_high,
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timestamp_low,
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captured_len,
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packet_len,
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),
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) = tuple((
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tag(ENHANCED_PACKET),
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le_u32,
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le_u32,
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le_u32,
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le_u32,
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le_u32,
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le_u32,
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))(input)?;
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let (remaining, packet_data) = take(captured_len)(remaining)?;
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Ok((remaining, packet_data))
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}
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```
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While this example isn't too interesting, more complex formats (like IEX market data) are where
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[`nom` really shines](https://github.com/speice-io/marketdata-shootout/blob/369613843d39cfdc728e1003123bf87f79422497/src/iex.rs).
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Ultimately, because the `nom` code in this shootout was the same for all formats, we're not too interested in its performance.
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Still, it's worth mentioning that building the market data parser was actually fun; I didn't have to write tons of boring code by hand.
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# Part 1: Cap'n Proto
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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
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it has supported Rust (thanks to [dwrensha](https://github.com/dwrensha) for maintaining the Rust port since
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[2014!](https://github.com/capnproto/capnproto-rust/releases/tag/rustc-0.10)). However, I had a ton of performance concerns
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once I started using it.
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To serialize new messages, Cap'n Proto uses a "builder" object. This builder allocates memory on the heap to hold the message
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content, but because builders [can't be re-used](https://github.com/capnproto/capnproto-rust/issues/111), we have to allocate
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a new buffer for every single message. I was able to work around this with a
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[special builder](https://github.com/speice-io/marketdata-shootout/blob/369613843d39cfdc728e1003123bf87f79422497/src/capnp_runner.rs#L17-L51)
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that could re-use the buffer, but it required reading through Cap'n Proto's
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[benchmarks](https://github.com/capnproto/capnproto-rust/blob/master/benchmark/benchmark.rs#L124-L156)
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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.
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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)
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representation, and an "unpacked" version. When reading "packed" messages, we need a buffer to unpack the message into before we can use it;
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Cap'n Proto allocates a new buffer for each message we unpack, and I wasn't able to figure out a way around that.
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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/).
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However, accomplishing zero-copy deserialization required code in the private API ([since fixed](https://github.com/capnproto/capnproto-rust/issues/148)),
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and we allocate a vector on every read for the segment table.
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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
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using it long-term.
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# Part 2: Flatbuffers
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This is the new kid on the block. After a [first attempt](https://github.com/google/flatbuffers/pull/3894) didn't pan out,
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official support was [recently launched](https://github.com/google/flatbuffers/pull/4898). Flatbuffers intends to address
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the same problems as Cap'n Proto: high-performance, polyglot, binary messaging. The difference is that Flatbuffers claims
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to have a simpler wire format and [more flexibility](https://google.github.io/flatbuffers/flatbuffers_benchmarks.html).
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On the whole, I enjoyed using Flatbuffers; the [tooling](https://crates.io/crates/flatc-rust) is nice, and unlike
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Cap'n Proto, parsing messages was actually zero-copy and zero-allocation. However, there were still some issues.
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First, Flatbuffers (at least in Rust) can't handle nested vectors. This is a problem for formats like the following:
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```
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table Message {
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symbol: string;
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}
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table MultiMessage {
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messages:[Message];
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}
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```
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We want to create a `MultiMessage` which contains a vector of `Message`, and each `Message` itself contains a vector (the `string` type).
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I was able to work around this by [caching `Message` elements](https://github.com/speice-io/marketdata-shootout/blob/e9d07d148bf36a211a6f86802b313c4918377d1b/src/flatbuffers_runner.rs#L83)
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in a `SmallVec` before building the final `MultiMessage`, but it was a painful process that I believe contributed to poor serialization performance.
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Second, streaming support in Flatbuffers seems to be something of an [afterthought](https://github.com/google/flatbuffers/issues/3898).
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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
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message to indicate the size. Not specifically a problem, but calculating message size without that tag is nigh on impossible.
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Ultimately, I enjoyed using Flatbuffers, and had to do significantly less work to make it perform well.
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# Part 3: Simple Binary Encoding
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Support for SBE was added by the author of one of my favorite
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[Rust blog posts](https://web.archive.org/web/20190427124806/https://polysync.io/blog/session-types-for-hearty-codecs/).
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I've [talked previously]({% post_url 2019-06-31-high-performance-systems %}) about how important variance is in
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high-performance systems, so it was encouraging to read about a format that
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[directly addressed](https://github.com/real-logic/simple-binary-encoding/wiki/Why-Low-Latency) my concerns. SBE has by far
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the simplest binary format, but it does make some tradeoffs.
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Both Cap'n Proto and Flatbuffers use [message offsets](https://capnproto.org/encoding.html#structs) to handle
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variable-length data, [unions](https://capnproto.org/language.html#unions), and various other features. In contrast,
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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);
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variable-length data is supported, but there's no union type.
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As mentioned in the beginning, the Rust port of SBE works well, but is
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[essentially unmaintained](https://users.rust-lang.org/t/zero-cost-abstraction-frontier-no-copy-low-allocation-ordered-decoding/11515/9).
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However, if you don't need union types, and can accept that schemas are XML documents, it's still worth using. SBE's implementation
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had the best streaming support of all formats I tested, and doesn't trigger allocation during de/serialization.
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# Results
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After building a test harness [for](https://github.com/speice-io/marketdata-shootout/blob/master/src/capnp_runner.rs)
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[each](https://github.com/speice-io/marketdata-shootout/blob/master/src/flatbuffers_runner.rs)
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[format](https://github.com/speice-io/marketdata-shootout/blob/master/src/sbe_runner.rs),
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it was time to actually take them for a spin. I used
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[this script](https://github.com/speice-io/marketdata-shootout/blob/master/run_shootout.sh) to run the benchmarks,
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and the raw results are [here](https://github.com/speice-io/marketdata-shootout/blob/master/shootout.csv). All data
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reported below is the average of 10 runs on a single day of IEX data. Results were validated to make sure
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that each format parsed the data correctly.
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## Serialization
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This test measures, on a
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[per-message basis](https://github.com/speice-io/marketdata-shootout/blob/master/src/main.rs#L268-L272),
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how long it takes to serialize the IEX message into the desired format and write to a pre-allocated buffer.
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| Schema | Median | 99th Pctl | 99.9th Pctl | Total |
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|:---------------------|:-------|:----------|:------------|:-------|
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| Cap'n Proto Packed | 413ns | 1751ns | 2943ns | 14.80s |
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| Cap'n Proto Unpacked | 273ns | 1828ns | 2836ns | 10.65s |
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| Flatbuffers | 355ns | 2185ns | 3497ns | 14.31s |
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| SBE | 91ns | 1535ns | 2423ns | 3.91s |
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## Deserialization
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This test measures, on a
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[per-message basis](https://github.com/speice-io/marketdata-shootout/blob/master/src/main.rs#L294-L298),
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how long it takes to read the previously-serialized message and
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perform some basic aggregation. The aggregation code is the same for each format,
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so any performance differences are due solely to the format implementation.
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| Schema | Median | 99th Pctl | 99.9th Pctl | Total |
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|:---------------------|:-------|:----------|:------------|:-------|
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| Cap'n Proto Packed | 539ns | 1216ns | 2599ns | 18.92s |
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| Cap'n Proto Unpacked | 366ns | 737ns | 1583ns | 12.32s |
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| Flatbuffers | 173ns | 421ns | 1007ns | 6.00s |
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| SBE | 116ns | 286ns | 659ns | 4.05s |
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# Conclusion
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Building a benchmark turned out to be incredibly helpful in making a decision; because a
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"union" type isn't important to me, I can be confident that SBE best addresses my needs.
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While SBE was the fastest in terms of both median and worst-case performance, its worst case
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performance was proportionately far higher than any other format. It seems to be that de/serialization
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time scales with message size, but I'll need to do some more research to understand what exactly
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is going on.
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