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blog/2019-05-03-making-bread/index.mdx
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---
slug: 2019/05/making-bread
title: "Making Bread"
title: Making bread
date: 2019-05-03 12:00:00
authors: [bspeice]
tags: []

2
blog/2019-06-31-high-performance-systems/index.mdx
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---
slug: 2019/06/high-performance-systems
title: "On Building High Performance Systems"
title: On building high performance systems
date: 2019-06-31 12:00:00
last_updated:
date: 2019-09-21 12:00:00

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---
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 ~~procrastinating~~ researching indefinitely.
So let's say you're in need of a binary serialization format. 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, so formats that support zero-copy de/serialization are given priority. And the more
languages supported, the better; I use Rust, but can't predict what other languages this could
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 unmaintained
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/speice-io/marketdata-shootout) for this post.
We'll discuss more in detail, but a quick preview of the results:
- Cap'n Proto: Theoretically performs incredibly well, the implementation had issues
- Flatbuffers: Has some quirks, but largely lived up to its "zero-copy" promises
- SBE: Best median and worst-case performance, but the message structure has a limited feature set
# Prologue: Binary Parsing with Nom
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, read it back, and calculate total size of stock traded and the lowest/highest quoted prices.
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.
If you don't already know about `nom`, it's a "parser generator". By combining different smaller
parsers, you can assemble a parser to handle complex structures without writing 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/speice-io/marketdata-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/speice-io/marketdata-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 tons of 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/speice-io/marketdata-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 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 launched](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, and unlike Cap'n Proto, parsing messages was actually zero-copy and zero-allocation. However,
there were still some issues.
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/speice-io/marketdata-shootout/blob/e9d07d148bf36a211a6f86802b313c4918377d1b/src/flatbuffers_runner.rs#L83)
in a `SmallVec` before building the final `MultiMessage`, but it was a painful process that I
believe contributed to poor serialization performance.
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](https://users.rust-lang.org/t/zero-cost-abstraction-frontier-no-copy-low-allocation-ordered-decoding/11515/9).
However, if you don't need union types, and can accept that schemas are XML documents, it's still
worth using. SBE's implementation had the best streaming support of all formats I tested, and
doesn't trigger allocation during de/serialization.
# Results
After building a test harness
[for](https://github.com/speice-io/marketdata-shootout/blob/master/src/capnp_runner.rs)
[each](https://github.com/speice-io/marketdata-shootout/blob/master/src/flatbuffers_runner.rs)
[format](https://github.com/speice-io/marketdata-shootout/blob/master/src/sbe_runner.rs), it was
time to actually take them for a spin. I used
[this script](https://github.com/speice-io/marketdata-shootout/blob/master/run_shootout.sh) to run
the benchmarks, and the raw results are
[here](https://github.com/speice-io/marketdata-shootout/blob/master/shootout.csv). All data reported
below is the average of 10 runs on 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/speice-io/marketdata-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/speice-io/marketdata-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.

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View File

@ -0,0 +1,265 @@
---
slug: 2019/09/binary-format-shootout
title: "Binary format shootout"
date: 2019-09-28 12:00:00
authors: [bspeice]
tags: []
---
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 ~~procrastinating~~ researching indefinitely.
So let's say you're in need of a binary serialization format. 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, so formats that support zero-copy de/serialization are given priority. And the more
languages supported, the better; I use Rust, but can't predict what other languages this could
interact with.
Given these requirements, the candidates I could find were:
<!-- truncate -->
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 unmaintained
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/speice-io/marketdata-shootout) for this post.
We'll discuss more in detail, but a quick preview of the results:
- Cap'n Proto: Theoretically performs incredibly well, the implementation had issues
- Flatbuffers: Has some quirks, but largely lived up to its "zero-copy" promises
- SBE: Best median and worst-case performance, but the message structure has a limited feature set
# Prologue: Binary Parsing with Nom
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, read it back, and calculate total size of stock traded and the lowest/highest quoted prices.
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.
If you don't already know about `nom`, it's a "parser generator". By combining different smaller
parsers, you can assemble a parser to handle complex structures without writing 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/speice-io/marketdata-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/speice-io/marketdata-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 tons of 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/speice-io/marketdata-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 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 launched](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, and unlike Cap'n Proto, parsing messages was actually zero-copy and zero-allocation. However,
there were still some issues.
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/speice-io/marketdata-shootout/blob/e9d07d148bf36a211a6f86802b313c4918377d1b/src/flatbuffers_runner.rs#L83)
in a `SmallVec` before building the final `MultiMessage`, but it was a painful process that I
believe contributed to poor serialization performance.
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](/2019/06/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](https://users.rust-lang.org/t/zero-cost-abstraction-frontier-no-copy-low-allocation-ordered-decoding/11515/9).
However, if you don't need union types, and can accept that schemas are XML documents, it's still
worth using. SBE's implementation had the best streaming support of all formats I tested, and
doesn't trigger allocation during de/serialization.
# Results
After building a test harness
[for](https://github.com/speice-io/marketdata-shootout/blob/master/src/capnp_runner.rs)
[each](https://github.com/speice-io/marketdata-shootout/blob/master/src/flatbuffers_runner.rs)
[format](https://github.com/speice-io/marketdata-shootout/blob/master/src/sbe_runner.rs), it was
time to actually take them for a spin. I used
[this script](https://github.com/speice-io/marketdata-shootout/blob/master/run_shootout.sh) to run
the benchmarks, and the raw results are
[here](https://github.com/speice-io/marketdata-shootout/blob/master/shootout.csv). All data reported
below is the average of 10 runs on 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/speice-io/marketdata-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/speice-io/marketdata-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.