Shrinking Data with Serialization

Most of the time when we send data around, we use text formats, like JSON. JSON.stringify and JSON.parse are really fast, but serializing anything to a string is going to make the package larger compared to its binary counterpart.

For most situations, this doesn’t matter. But when you need to keep bandwidth low, perhaps for a real-time multiplayer game or web worker communication, every bit of data counts.

Take 1 for example - as a number, it can literally be stored with a single binary bit. The string "1" on the other hand required a full byte, 8 times as much memory as the number.

What is this for?

I’m in the process of building a real-time spaceship game called Thorium Nova that I want to run over the internet, so I need to be careful about how I allocate bandwidth. When the player is looking at a starmap, the server sends them a message 10 times a second that includes the position and rotation of each ship. The message also includes metadata that the client can use for linear interpolation between network messages to keep animations at 60fps.

{
  "id": "abc123",
  "time": 1654789171491,
  "state": [
    {
      "id": 1,
      "x": 57832,
      "y": -1692,
      "z": 105858235,
      "r": {
        "x": 0.3535533845424652,
        "y": 0.3535533845424652,
        "z": 0.1464466154575348,
        "w": 0.8535534143447876
      }
    }
  ]
}

Serializing this data with a single ship to JSON creates a string 187 bytes. That’s our baseline - let’s see if we can make it smaller.

There are dozens of serialization formats. So, where do we start?

MessagePack

JSON is obviously the easiest serialization format for JavaScript - it’s built right in! One that’s almost as easy is MessagePack. Just install @msgpack/msgpack from NPM, and you can encode and decode with ease! The object is encoded into a Uint8Array which is more compact than JSON.

import { encode, decode } from "@msgpack/msgpack";

// ...

const encodedMessage = encode(data);

When we encode our message, the resulting Uint8Array has a length of 101 bytes, or 45% less than the JSON string!

The cool thing about MessagePack is we didn’t have to tell it anything about our data - we just throw an object at it and it serializes it into something really small.

But what if the serializer did know something about our data, like what shape it had and how big each of the values could be? Maybe then we could make it even smaller.

Schema serialization

Schema serialization lets you shrink data even more by not including any of the structure in the message, just the data itself. The schema is shared between the sender and receiver and is used to put the structure back into the data once it’s arrived at its destination.

This only works if the messages all have the same shape and each value has the same byte length. That limits us a bit - we can’t have excessively large strings - but our message fits into those constraints.

In essence, the serialized data will look something like this, where each chunk of data is tacked on to the end:

abc1231654789171491157832-1692105858235...
|----||-----------|||---||---||-------|

Of course, we won’t just cram the data together. Instead, we’ll convert it to its binary representation, making sure that each chunk is always the same length. Then, to deserialize it, we just pull off each chunk by its size and convert it back to the appropriate type.

The main struggle with this plan is that we’re mixing different types of data in the same ArrayBuffer, so we can’t just use a TypedArray. And manually reading and converting the binary using bitwise operators is a huge hassle. Instead, we can rely on another built-in JavaScript object: DataView.

Mixing binary data with DataView

DataView’s purpose is to combine multiple types of binary data together in one ArrayBuffer. You do that by creating a new ArrayBuffer, building a DataView with it, and then calling view.setUint8() (or its other setter methods) with a byte index and the data you want to put at that index. The DataView will convert the data to the correct binary type and put it in the ArrayBuffer for you. By keeping track of the byte index, you can create a predictable sequence of data.

So, lets create an encoder for our message, starting with the string ID. Each byte of the string can be stored as a Uint8.

const buffer = new ArrayBuffer(8192);
const dataView = new DataView(buffer);
let byteIndex = 0;

for (let i = 0; i < message.id.length; i++) {
  dataView.setUint8(byteIndex, message.id.charCodeAt(i));
  byteIndex++;
}

We do run into a slight problem with our time value - it’s too long for a Uint32, which means we need to represent it as a BigUInt64, which only accepts a BigInt value. Notice how we have to increment our byte index by the number of bytes that the type takes up.

dataView.setBigUint64(byteIndex, message.time);
byteIndex += 8;

Next, we’ll loop over our state array and append each value with the appropriate type. We’re assuming the ID value doesn’t go above 65535, so we can use Uint16 for the first value. The positions all need to be signed, so we’ll use Int32. And the rotations must have decimal points, but they don’t need to be as precise, so we’ll use Float32.

message.state.forEach((entity) => {
  dataView.setUint32(bytes, entity.id);
  bytes += 4;
  dataView.setInt32(bytes, entity.x);
  bytes += 4;
  dataView.setInt32(bytes, entity.y);
  bytes += 4;
  dataView.setInt32(bytes, entity.z);
  bytes += 4;
  dataView.setFloat32(bytes, entity.r.x);
  bytes += 4;
  dataView.setFloat32(bytes, entity.r.y);
  bytes += 4;
  dataView.setFloat32(bytes, entity.r.z);
  bytes += 4;
  dataView.setFloat32(bytes, entity.r.w);
  bytes += 4;
});

And our ArrayBuffer is done! But when we initialized it, we had to give it a starting size. The rest of those bytes are just empty space, so we’ll want to create a new array ArrayBuffer and copy over the data from the old one.

const newBuffer = new ArrayBuffer(bytes);
const view = new DataView(newBuffer);

// copy all data to a new (resized) ArrayBuffer
for (let i = 0; i < bytes; i++) {
  view.setUint8(i, dataView.getUint8(i));
}

The great thing about this is we can easily know the size of this data - just look at the bytes value! Our data now fits in a staggering 46 bytes, which is 75% smaller than the initial 186 bytes. Plus, each additional ship only adds 32 more bytes.

What about deserializing at the end? Well, since we know exactly what shape the data is in, we can easily use the opposite DataView methods to get the appropriate data out.

function deserialize(buffer) {
  const view = new DataView(buffer);
  let byteIndex = 0;
  let id = "";
  for (let i = 0; i < 6; i++) {
    const char = String.fromCharCode(view.getUint8(byteIndex));
    id += char;
    byteIndex++;
  }
  const time = Number(view.getBigUint64(byteIndex));
  byteIndex += 8;

  const state = [];

  while (byteIndex < view.byteLength) {
    let entity = { r: {} };
    entity.id = view.getUint32(byteIndex);
    byteIndex += 4;
    entity.x = view.getInt32(byteIndex);
    byteIndex += 4;
    entity.y = view.getInt32(byteIndex);
    byteIndex += 4;
    entity.z = view.getInt32(byteIndex);
    byteIndex += 4;
    entity.r.x = view.getFloat32(byteIndex);
    byteIndex += 4;
    entity.r.y = view.getFloat32(byteIndex);
    byteIndex += 4;
    entity.r.z = view.getFloat32(byteIndex);
    byteIndex += 4;
    entity.r.w = view.getFloat32(byteIndex);
    byteIndex += 4;
    state.push(entity);
  }

  const message = {
    id,
    time,
    state,
  };
  return message;
}

And this method is fast too. Even with 10,000 ships, the serialization process only takes 5 milliseconds. Here’s the stats for the others when serializing 10,000 items.

Format	Size (bytes)	Time
JSON	1,540,042	13ms
MsgPack	850,030	35ms
DataView	440,010	4.8ms

Obviously this only works in a narrow set of cases - your data has to have a predictable format and your values are constrained to the binary data types. Even still, if you need every bit of bandwidth you can muster, this is a great tool to keep on hand.

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Do you have a use for ArrayBuffers and DataView? Send me a tweet . I’m curious to see what others are doing with this.