msgpackr

The msgpackr package is an extremely fast MessagePack NodeJS/JavaScript implementation. At the time of writing, it is significantly faster than any other known implementations, faster than Avro (for JS), and generally even faster than native JSON.stringify/parse. It also includes an optional record extension (the r in msgpackr), for defining record structures that makes MessagePack even faster and more compact, often over twice as fast as even native JSON functions and several times faster than other JS implementations.

Basic Usage

Install with:

npm install msgpackr

And import or require it for basic serialization/encoding (pack) and deserialization/decoding (unpack) functions:

import { unpack, pack } from 'msgpackr';
let serializedAsBuffer = pack(value);
let data = unpack(serializedAsBuffer);

This pack function will generate standard MessagePack without any extensions that should be compatible with any standard MessagePack parser/decoder. It will serialize JavaScript objects as MessagePack maps by default. The unpack function will deserialize MessagePack maps as an Object with the properties from the map.

Streams

We can use the including streaming functionality (which further improves performance). The PackrStream is a transform stream that can be used to serialize objects to a binary stream (writing to network/socket, IPC, etc.), and the UnpackrStream can be used to deserialize objects from a binary sream (reading from network/socket, etc.):

import { PackrStream } from 'msgpackr';
let stream = PackrStream();
stream.write(myData);

Or for a full example of sending and receiving data on a stream:

import { PackrStream } from 'msgpackr';
let sendingStream = PackrStream();
let receivingStream = UnpackrStream();
// we just piping to our own stream, but normally you would send and
// receive over some type of inter-process or network connection.
sendingStream.pipe(receivingStream);
sendingStream.write(myData);
receivingStream.on('data', (data) => {
    // received data
});

The PackrStream and UnpackrStream instances will have also the record structure extension enabled by default (see below).

Record / Object Structures

There is a critical difference between maps (or dictionaries) that hold an arbitrary set of keys and values (JavaScript Maps are best for these), and records or object structures that have a well-defined set of fields which may have many instances using that same structure (most objects in JS). By using the record extension, this distinction is preserved in MessagePack and the encoding can reuse structures and not only provides better type preservation, but yield much more compact encodings and increase parsing/deserialization performance by 2-3x. Msgpackr automatically generates record definitions that are reused and referenced by objects with the same structure. There are a number of ways to use this to our advantage. For large object structures with repeating nested objects with similar structures, simply serializing with the record extension can yield benefits. To use the record structures extension, we create a new Packr instance. By default a new Packr instance will have the record extension enabled:

import { Packr } from 'msgpackr';
let packr = Packr();
packr.pack(myBigData);

Another way to further leverage the benefits of the msgpackr record structures is to use streams that naturally allow for data to reuse based on previous record structures. The stream classes have the record structure extension enabled by default and provide excellent out-of-the-box performance.

When creating a new Packr, PackrStream, or UnpackrStream instance, we can enable or disable the record structure extension with the objectsAsMaps property. When this is true, the record structure extension will be disabled, and all objects will revert to being serialized using MessageMap maps, and all maps will be deserialized to JS Objects as properties (like the standalone pack and unpack functions).

Shared Record Structures

Another useful way of using msgpackr, and the record extension, is for storing data in a databases, files, or other storage systems. If a number of objects with common data structures are being stored, a shared structure can be used to greatly improve data storage and deserialization efficiency. We just need to provide a way to store the generated shared structure so it is available to deserialize stored data in the future:

import { Packr } from 'msgpackr';
let packr = Packr({
    getStructures() {
        // storing our data in file (but we could also store in a db or key-value store)
        return unpack(readFileSync('my-shared-structures.mp')) || [];
    },
    saveStructures(structures) {
        writeFileSync('my-shared-structures.mp', pack(structures))
    },
    structures: []
});

resetMemory

During the serialization process, data is written to buffers. Allocating new buffers is a relatively expensive process, and the resetMemory method can help allow reuse of buffers that will further improve performance. The resetMemory method can be called when previously created buffer(s) are no longer needed. For example, if we serialized an object, and wrote it to a database, we could indicate that we are done:

let buffer = packr.pack(data);
writeToStorageSync(buffer);
// finished with buffer, we can reset the memory on our packr now:
packr.resetMemory()
// future serialization can now reuse memory for better performance

The use of resetMemory is never required, buffers will still be handled and cleaned up through GC if not used, it just provides a small performance boost.

Performance

Msgpackr is fast. Really fast. Here is comparison with the next fastest JS projects using the benchmark tool from msgpack-lite (and the sample data is from some clinical research data we use that has a good mix of different value types and structures). It also includes comparison to V8 native JSON functionality, and JavaScript Avro (avsc, a very optimized Avro implementation):

(avsc is schema-based and more comparable in style to msgpackr with shared structures).

Here is a benchmark of streaming data (again borrowed from msgpack-lite's benchmarking), where msgpackr is able to take advantage of the structured record extension and really demonstrate its performance capabilities:

Record Structure Extension Definition

The record struction extension uses extension id 0x72 ("r") to declare the use of this functionality. The extension "data" byte (or bytes) identifies the byte or bytes used to identify the start of a record in the subsequent MessagePack block or stream. The identifier byte (or the first byte in a sequence) must be from 0x40 - 0x7f (and therefore replaces one byte representations of positive integers 64 - 127, which can alternately be represented with int or uint types). The extension declaration must be immediately follow by an MessagePack array that defines the field names of the record structure.

Once a record identifier and record field names have been defined, the parser/decoder should proceed to read the next value. Any subsequent use of the record identifier as a value in the block or stream should parsed as a record instance, and the next n values, where is n is the number of fields (as defined in the array of field names), should be read as the values of the fields. For example, here we have defined a structure with fields "foo" and "bar", with the record identifier 0x40, and then read a record instance that defines the field values of 4 and 2, respectively:

+--------+--------+--------+~~~~~~~~~~~~~~~~~~~~~~~~~+--------+--------+--------+
|  0xd4  |  0x72  |  0x40  | array: [ "foo", "bar" ] |  0x40  |  0x04  |  0x02  |
+--------+--------+--------+~~~~~~~~~~~~~~~~~~~~~~~~~+--------+--------+--------+

Which should generate an object that would correspond to JSON:

{ "name" : 4, "bar": 2}

Additional value types

msgpackr supports undefined (using fixext1 + type: 0 + data: 0 to match other JS implementations), NaN, Infinity, and -Infinity (using standard IEEE 754 representations with doubles/floats).

License

MIT