The msgpackr package is an extremely fast MessagePack NodeJS/JavaScript implementation. Currently, it is significantly faster than any other known implementations, faster than Avro (for JS), and generally faster than native V8 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. See the performance section for more details. Structured cloning (with support for cyclical references) is also optional supported through extensions.
Install with:
npm i msgpackr
And import or require it for basic standard 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.
The msgpackr package runs on any modern JS platform, but is optimized for NodeJS usage (and will use a node addon for performance boost as an optional dependency).
We can use the including streaming functionality (which further improves performance). The PackrStream is a NodeJS 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 are 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).
Msgpackr works as standalone JavaScript as well, and runs on modern browsers. It includes a bundled script for ease of direct loading. For module-based development, it is recommended that you directly import the module of interest, to minimize dependencies that get pulled into your application:
import { unpack } from 'msgpackr/unpack' // if you only need to unpack
You can also use msgpackr for structured cloning. By enabling the structuredClone option, you can include references to other objects or cyclic references, and object identity will be preserved. Structured cloning also enables preserving certain typed objects like Error, Set, RegExp and TypedArray instances. For example:
let obj = {
set: new Set(['a', 'b']),
regular: /a\spattern/
};
obj.self = obj;
let packr = new Packr({ structuredClone: true });
let serialized = packr.pack(obj);
let copy = packr.unpack(serialized);
copy.self === copy // true
copy.set.has('a') // true
This option is disabled by default because it uses extensions and reference checking degrades performance (by about 25-30%). (Note this implementation doesn't serialize every class/type specified in the HTML specification since not all of them make sense for storing across platforms.)
If you prefer to use encoder/decode terminology, msgpackr exports aliases, so decode is equivalent to unpack, encode is pack, Encoder is Packr, Decoder is Unpackr, and EncoderStream and DecoderStream can be used as well.
There is a critical difference between maps (or dictionaries) that hold an arbitrary set of keys and values (JavaScript Map is designed for these), and records or object structures that have a well-defined set of fields. Typical JS objects/records may have many instances re(use) the same structure. 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 decoding 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 significant 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(bigDataWithLotsOfObjects);
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, Unpackr, PackrStream, or UnpackrStream instance, we can enable or disable the record structure extension with the useRecords property. When this is false, the record structure extension will be disabled (standard/compatibility mode), 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).
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: []
});
Msgpackr will automatically add and saves structures as it encounters any new object structures (up to a limit of 32). It will always add structures in an incremental/compatible way: Any object encoded with an earlier structure can be decoded with a later version (as long as it is persisted).
The following options properties can be provided to the Packr or Unpackr constructor:
useRecords- Setting this tofalsedisables the record extension and stores JavaScript objects as MessagePack maps, and unpacks maps as JavaScriptObjects, which ensures compatibilty with other decoders.structures- Provides the array of structures that is to be used for record extension, if you want the structures saved and used again. This array will be modified in place with new record structures that are serialized (if less than 32 structures are in the array).structuredClone- This enables the structured cloning extensions that will encode object/cyclic references and additional built-in types/classes.mapsAsObjects- Iftrue, this will decode MessagePack maps and JSObjects with the map entries decoded to object properties. Iffalse, maps are decoded as JavaScriptMaps. This is disabled by default ifuseRecordsis enabled (which allowsMaps to be preserved), and is enabled by default ifuseRecordsis disabled.useFloat32- This will enable msgpackr to encode non-integer numbers asfloat32. See next section for possible values.variableMapSize- This will use varying map size definition (fixmap, map16, map32) based on the number of keys when encoding objects, which yields slightly more compact encodings (for small objects), but is typically 5-10% slower during encoding. This is only relevant when record extension is disabled.copyBuffers- When decoding a MessagePack with binary data (Buffers are encoded as binary data), copy the buffer rather than providing a slice/view of the buffer. If you want your input data to be discarded or modified while the decoded embedded buffer continues to be used, you can use this option (there is extra overhead to copying).useTimestamp32- Encode JSDates in 32-bit format when possible by dropping the milliseconds. This is a more efficient encoding of dates. You can also cause dates to use 32-bit format by manually setting the milliseconds to zero (date.setMilliseconds(0)).
By default all non-integer numbers are serialized as 64-bit float (double). This is fast, and ensures maximum precision. However, often real-world data doesn't not need 64-bits of precision, and using 32-bit encoding can be much more space efficient. There are several options that provide more efficient encodings. Using the decimal rounding options for encoding and decoding provides lossless storage of common decimal representations like 7.99, in more efficient 32-bit format (rather than 64-bit). The useFloat32 property has several possible options, available from the module as constants:
import { ALWAYS, DECIMAL_ROUND, DECIMAL_FIT } from 'msgpackr'
ALWAYS(1) - Always will encode non-integers (absolute less than 2147483648) as 32-bit float.DECIMAL_ROUND(3) - Always will encode non-integers as 32-bit float, and when decoding 32-bit float, round to 7 significant decimal digits (or 6 or 8 digits for some ranges).DECIMAL_FIT(4) - Only encode non-integers as 32-bit float if all significant digits can be unamiguously encoded as a 32-bit float, and decode/unpack with decimal rounding (same as above). This will ensure round-trip encoding/decoding without loss in precision and use 32-bit when possible.
Note, that the performance is decreased with decimal rounding by about 20-25%, although if only 5% of your values are floating point, that will only have about a 1% impact overall.
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):
| operation | op | ms | op/s |
|---|---|---|---|
| buf = Buffer(JSON.stringify(obj)); | 75900 | 5003 | 15170 |
| obj = JSON.parse(buf); | 90800 | 5002 | 18152 |
| require("msgpackr").pack(obj); | 158400 | 5000 | 31680 |
| require("msgpackr").unpack(buf); | 99200 | 5003 | 19828 |
| msgpackr w/ shared structures: packr.pack(obj); | 183400 | 5002 | 36665 |
| msgpackr w/ shared structures: packr.unpack(buf); | 415000 | 5000 | 83000 |
| buf = require("msgpack-lite").encode(obj); | 30600 | 5005 | 6113 |
| obj = require("msgpack-lite").decode(buf); | 15900 | 5030 | 3161 |
| buf = require("@msgpack/msgpack").encode(obj); | 101200 | 5001 | 20235 |
| obj = require("@msgpack/msgpack").decode(buf); | 71200 | 5004 | 14228 |
| buf = require("notepack").encode(obj); | 65300 | 5006 | 13044 |
| obj = require("notepack").decode(buf); | 32300 | 5001 | 6458 |
| require("avsc")...make schema/type...type.toBuffer(obj); | 86900 | 5002 | 17373 |
| require("avsc")...make schema/type...type.fromBuffer(obj); | 106100 | 5000 | 21220 |
All benchmarks were performed on Node 14.8.0 (Windows i7-4770 3.4Ghz).
(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:
| operation (1000000 x 2) | op | ms | op/s |
|---|---|---|---|
| new PackrStream().write(obj); | 1000000 | 372 | 2688172 |
| new UnpackrStream().write(buf); | 1000000 | 247 | 4048582 |
| stream.write(msgpack.encode(obj)); | 1000000 | 2898 | 345065 |
| stream.write(msgpack.decode(buf)); | 1000000 | 1969 | 507872 |
| stream.write(notepack.encode(obj)); | 1000000 | 901 | 1109877 |
| stream.write(notepack.decode(buf)); | 1000000 | 1012 | 988142 |
| msgpack.Encoder().on("data",ondata).encode(obj); | 1000000 | 1763 | 567214 |
| msgpack.createDecodeStream().write(buf); | 1000000 | 2222 | 450045 |
| msgpack.createEncodeStream().write(obj); | 1000000 | 1577 | 634115 |
| msgpack.Decoder().on("data",ondata).decode(buf); | 1000000 | 2246 | 445235 |
See the benchmark.md for more benchmarks and information about benchmarking.
You can add your own custom extensions, which can be used to encode specific classes in certain ways. This is done by using the addExtension function, and specifying the class, extension type code (should be a number from 1-100, reserving negatives for MessagePack, 101-127 for msgpackr), and your pack and unpack functions (or just the one you need). You can use msgpackr encoding and decoding within your extensions, but if you do so, you must create a separate Packr instance, otherwise you could do override data in the same encoding buffer:
import { addExtension, Packr } from 'msgpackr';
class MyCustomClass {...}
let extPackr = new Packr();
addExtension({
Class: MyCustomClass,
type: 11, // register our own extension code (a type code from 1-100)
pack(instance) {
// define how your custom class should be encoded
return extPackr.pack(instance.myData); // return a buffer
}
unpack(buffer) {
// define how your custom class should be decoded
let instance = new MyCustomClass();
instance.myData = extPackr.unpack(buffer);
return instance; // decoded value from buffer
}
});
Msgpackr is already fast, but here are some tips for making it faster:
Msgpackr is designed to work well with reusable buffers. Allocating new buffers can be relatively expensive, so if you have Node addons, it can be much faster to reuse buffers and use memcpy to copy data into existing buffers. Then msgpackr unpack can be executed on the same buffer, with new data, and optionally take a second paramter indicating the effective size of the available data in the buffer.
During the serialization process, data is written to buffers. Again, 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.
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}
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).
msgpackr saves all JavaScript Dates using the standard MessagePack date extension (type -1), using the smallest of 32-bit, 64-bit or 96-bit format needed to store the date without data loss (or using 32-bit if useTimestamp32 options is specified).
With structured cloning enabled, msgpackr will also use extensions to store Set, Map, Error, RegExp, ArrayBufferView objects and preserve their types.
MIT
It is worth noting that while msgpackr works well in modern browsers, the MessagePack format itself is often not an ideal format for web use. If you want compact data, brotli or gzip are most effective in compressing, and MessagePack's character frequency tends to defeat Huffman encoding used by these standard compression algorithms, resulting in less compact data than compressed JSON. The modern browser architecture is heavily optimized for parsing JSON from HTTP traffic, and it is difficult to achieve the same level of overall efficiency and ease with MessagePack.
Various projects have been inspirations for this, and code has been borrowed from https://github.com/msgpack/msgpack-javascript and https://github.com/mtth/avsc.