I’ve found that in many personal projects, 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:

Cap’n Proto has been around the longest, and is the most established Flatbuffers is the newest, and claims to have a simpler encoding 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 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, 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 . 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:

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:

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.

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 for maintaining the Rust port since 2014!). 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, we have to allocate a new buffer for every single message. I was able to work around this with a special builder that could re-use the buffer, but it required reading through Cap’n Proto’s benchmarks to find an example, and used std::mem::transmute 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” 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. However, accomplishing zero-copy deserialization required code in the private API (since fixed), 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 didn’t pan out, official support was recently launched. 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.

On the whole, I enjoyed using Flatbuffers; the tooling 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 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. 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. I’ve talked previously about how important variance is in high-performance systems, so it was encouraging to read about a format that directly addressed 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 to handle variable-length data, unions, and various other features. In contrast, messages in SBE are essentially just structs; 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. 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 each format, it was time to actually take them for a spin. I used this script to run the benchmarks, and the raw results are here. 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, 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, 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.