The cbor-x package is an extremely fast and conformant CBOR NodeJS/JavaScript implementation. Currently, it is over 3-10x faster than any other CBOR JS implementation (including cbor-js and cborg) and faster than most MessagePack encoders, Avro, and generally faster than native V8 JSON.stringify/parse, on NodeJS. It implements the CBOR format as specificed in RFC-8949, RFC-8746, RFC-8742, Packed CBOR, numerous registered IANA tag extensions (the x in cbor-x), and proposed optional record extension, for defining record structures that makes CBOR even faster and more compact, often over twice as fast as even native JSON functions, and 15-50% more compact. See the performance section for more details. Structured cloning (with support for cyclical references) is supported through these tag extensions.
Install on NodeJS with:
npm i cbor-x
And import or require it for basic standard serialization/encoding (encode) and deserialization/decoding (decode) functions:
import { decode, encode } from 'cbor-x';
let serializedAsBuffer = encode(value);
let data = decode(serializedAsBuffer);This encode function will generate standard CBOR without any extensions that should be compatible with any standard CBOR parser/decoder. It will serialize JavaScript objects as CBOR maps by default. The decode function will deserialize CBOR maps as an Object with the properties from the map. The cbor-x package runs on any modern JS platform, but does have additional optimizations for NodeJS usage (and will use a node addon for performance boost as an optional dependency).
Cbor-x modules are standard ESM modules and can be loaded directly from the deno.land registry for cbor for use in Deno. The standard encode and decode functionality is available on Deno, like other platforms.
We can use the including streaming functionality (which further improves performance). The EncoderStream is a NodeJS transform stream that can be used to serialize objects to a binary stream (writing to network/socket, IPC, etc.), and the DecoderStream can be used to deserialize objects from a binary sream (reading from network/socket, etc.):
import { EncoderStream } from 'cbor-x';
let stream = new EncoderStream();
stream.write(myData);Or for a full example of sending and receiving data on a stream:
import { EncoderStream } from 'cbor-x';
let sendingStream = new EncoderStream();
let receivingStream = new DecoderStream();
// 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 EncoderStream and DecoderStream instances will have also the record structure extension enabled by default (see below).
Cbor-x modules are standard ESM modules and can be loaded directly from the deno.land registry for cbor for use in Deno. The standard pack/encode and unpack/decode functionality is available on Deno, like other platforms.
Cbor-x works as standalone JavaScript as well, and runs on modern browsers. It includes a bundled script, at dist/index.js for ease of direct loading:
<script src="node_modules/cbor-x/dist/index.js"></script>This is UMD based, and will register as a module if possible, or create a CBOR global with all the exported functions.
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 { decode } from 'cbor-x/decode' // if you only need to decodeYou can also use cbor-x 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, using registered CBOR tag extensions. For example:
let obj = {
set: new Set(['a', 'b']),
regular: /a\spattern/
};
obj.self = obj;
let encoder = new Encoder({ structuredClone: true });
let serialized = encoder.encode(obj);
let copy = encoder.decode(serialized);
copy.self === copy // true
copy.set.has('a') // trueThis option is disabled by default because 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.)
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 CBOR 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. Cbor-x automatically generates record definitions that are reused and referenced by objects with the same structure. Records use CBOR's tags to align well CBOR's tag/extension mechanism. 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 Encoder instance. By default a new Encoder instance will have the record extension enabled:
import { Encoder } from 'cbor-x';
let encoder = new Encoder();
encoder.encode(myBigData);Another way to further leverage the benefits of the cbor-x 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 Encoder, EncoderStream, or DecoderStream 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 encode and decode functions).
Streaming with record structures works by encoding a structure the first time it is seen in a stream and referencing the structure in later messages that are sent across that stream. When an encoder can expect a decoder to understand previous structure references, this can be configured using the sequential: true flag, which is auto-enabled by streams, but can also be used with Packr instances.
Another useful way of using cbor-x, 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. In the simplest form, provide a structures array, which is updated if any new object structure is encountered:
import { Encoder } from 'cbor-x';
let encoder = new Encoder({
structures: [... structures that were last generated ...]
});If you are working with persisted data, you will need to persist the structures data when it is updated. Cbor-x provides an API for loading and saving the structures on demand (which is robust and can be used in multiple-process situations where other processes may be updating this same structures array), 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 { Encoder } from 'cbor-x';
let encoder = new Encoder({
getStructures() {
// storing our data in file (but we could also store in a db or key-value store)
return decode(readFileSync('my-shared-structures.cbor')) || [];
},
saveStructures(structures) {
writeFileSync('my-shared-structures.cbor', encode(structures))
},
structures: []
});Cbor-x will automatically add and saves structures as it encounters any new object structures (up to a limit of 64). It will always add structures in incremental/compatible way: Any object encoded with an earlier structure can be decoded with a later version (as long as it is persisted).
If you have a buffer with multiple values sequentially encoded, you can choose to parse and read multiple values. This can be done using the decodeMultiple function/method, which can return an array of all the values it can sequentially parse within the provided buffer. For example:
let data = new Uint8Array([1, 2, 3]) // encodings of values 1, 2, and 3
let values = decodeMultiple(data) // [1, 2, 3]Alternately, you can provide a callback function that is called as the parsing occurs with each value, and can optionally terminate the parsing by returning false:
let data = new Uint8Array([1, 2, 3]) // encodings of values 1, 2, and 3
decodeMultiple(data, (value) => {
// called for each value
// return false if you wish to end the parsing
})KeyMaps can be used to remap properties of source Objects and Maps to numerical equivalents for more efficient encoding.
The principle driver for this feature is to support application/senml+cborcontent-encoding as defined in https://datatracker.ietf.org/doc/html/rfc8428#section-6 for use in LWM2M application (see http://www.openmobilealliance.org/release/LightweightM2M/V1_2-20201110-A/HTML-Version/OMA-TS-LightweightM2M_Core-V1_2-20201110-A.html#7-4-7-0-747-SenML-CBOR)
Records are also supported in conjunction with keyMaps, but these are disabled by default when keyMaps are specified as use of the two features does not introduce any additional compression efficiency unless that the data arrays are quite large (> 10 items).
import { Encoder } from 'cbor-x'
const data = [
{ bn: '/3303/0/5700', bt: 1278887, v: 35.5 },
{ t: 10, v: 34 },
{ t: 20, v: 33 },
{ t: 30, v: 32 },
{ t: 40, v: 31 },
{ t: 50, v: 30 }
]
let senmlKeys = { bs: -6, bv: -5, bu: -4, bt: -3, bn: -2, bver: -1, n: 0, u: 1, v: 2, vs: 3, vb: 4, s: 5, t: 6, ut: 7, vd: 8}}
let senmlCbor = new Encoder({ keyMap: senmlKeys })
let basicCbor = new Encoder()
let senmlBuff = senmlCbor.encode(data)
let basicBuff = basicCbor.encode(data)
console.log('Senml CBOR size:', senmlBuff.length) // 77
console.log('Basic CBOR size:', basicBuff.length) // 90
assert.deepEqual(senmlEncoder.decode(senmlBuff), data)Packed CBOR is additional specification for CBOR which allows for compact encoding of data that has repeated values. Cbor-x supports decoding packed CBOR, no or flags/options needed. Cbor-x can also optionally generate packed CBOR (with the pack option), which will cause the encoder to look for repeated strings in a data structure that is being encoded, and store the strings in a packed table that can be referenced, to reduce encoding size. This involves extra overhead and reduces encoding performance, and generally does not yield as much compaction as standard compression tools. However, this is can be much faster than encoding plus compression, while still providing some level of reduction in encoding size. In addition to size reduction, packed CBOR is also usually faster to decode (assuming that some repetitive values could be found/packed).
Cbor-x also has in-progress effort to support shared packed tables.
The following options properties can be provided to the Encoder or Decoder constructor:
keyMap- This can be set to an object which will be used to map keys in the source Object or Map to other keys including integers. This allows for more efficient encoding, and enables support for numeric cbar tag encodings such as used byapplication/senml+cbor(https://datatracker.ietf.org/doc/html/rfc8428#section-6)useRecords- Setting this tofalsedisables the record extension and stores JavaScript objects as CBOR maps (with tag 259), and decodes 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 64 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 CBOR maps and JSObjects with the map entries decoded to object properties. Iffalse, maps are decoded as JavaScriptMaps. This is disabled by default ifuseRecordsis enabled (Maps are preserved since they are distinct from records), and is enabled by default ifuseRecordsis disabled.useFloat32- This will enable cbor-x to encode non-integer numbers as 32-bit (4 byte) floating point numbers. See next section for possible values.pack- This will enable CBOR packing for encoding, as described above.variableMapSize- This will use varying map size definition (from single-byte to full 32-bit representation) 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 CBOR message 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 collected or modified while the decoded embedded buffer continues to live on, you can use this option (there is extra overhead to copying).bundleStrings- Iftruethis uses a custom extension that bundles strings together, so that they can be decoded more quickly on browsers and Deno that do not have access to the NodeJS addon. This a custom extension, so both encoder and decoder need to support this. This can yield significant decoding performance increases on browsers (30%-50%).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)).sequential- Encode structures in serialized data, and reference previously encoded structures with expectation that decoder will read the encoded structures in the same order as encoded, withunpackMultiple.largeBigIntToFloat- If a bigint needs to be encoded that is larger than will fit in 64-bit integers, it will be encoded as a float-64 (otherwise will throw a RangeError).useTag259ForMaps- This flag indicates if tag 259 (explicit maps) should be used to encode JSMaps. When using records is enabled, this is disabled by default, since plain objects are encoded with record structures and unambigiously differentiated fromMaps, which are encoded as CBOR maps. Without using records, this enabled by default and is necessary to distinguish plain objects fromMaps (but can be disabled by setting this tofalse).int64AsNumber- This will decode uint64 and int64 numbers as standard JS numbers rather than as bigint numbers.
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 'cbor-x'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 the significant decimal digits (usually 7, but 6 or 8 digits for some ranges).DECIMAL_FIT(4) - Only encode non-integers as 32-bit float if all significant digits (usually up to 7) can be unamiguously encoded as a 32-bit float, and decode with decimal rounding (same as above). This will ensure round-trip encoding/decoding without loss in precision and uses 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.
In addition, msgpackr exports a roundFloat32(number) function that can be used to round floating point numbers to the maximum significant decimal digits that can be stored in 32-bit float, just as DECIMAL_ROUND does when decoding. This can be useful for determining how a number will be decoded prior to encoding it.
Cbor-x 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):
Cbor-x employs an optional native node-addon to accelerate the parsing of strings. This should be automatically installed and utilized on NodeJS. However, you can verify this by checking the isNativeAccelerationEnabled property that is exported from cbor-x. If this is false, the cbor-extract package may not have been properly installed, and you may want to verify that it is installed correctly:
import { isNativeAccelerationEnabled } from 'cbor-x'
if (!isNativeAccelerationEnabled)
console.warn('Native acceleration not enabled, verify that install finished properly')| operation | op | ms | op/s |
|---|---|---|---|
| buf = Buffer(JSON.stringify(obj)); | 78200 | 5004 | 15627 |
| obj = JSON.parse(buf); | 89600 | 5003 | 17909 |
| require("cbor-x").encode(obj); | 163100 | 5001 | 32613 |
| require("cbor-x").decode(buf); | 100200 | 5004 | 20023 |
| cbor-x w/ shared structures: packr.encode(obj); | 178300 | 5002 | 35645 |
| cbor-x w/ shared structures: packr.decode(buf); | 414000 | 5000 | 82800 |
| buf = require("cbor").encode(obj); | 7800 | 5016 | 1555 |
| obj = require("cbor").decode(buf); | 3200 | 5087 | 629 |
| buf = require("cbor-sync").encode(obj); | 18600 | 5012 | 3711 |
| obj = require("cbor-sync").decode(buf); | 20000 | 5020 | 3984 |
| buf = require("msgpack-lite").encode(obj); | 30900 | 5013 | 6163 |
| obj = require("msgpack-lite").decode(buf); | 15800 | 5012 | 3152 |
| buf = require("notepack").encode(obj); | 62600 | 5006 | 12504 |
| obj = require("notepack").decode(buf); | 33700 | 5007 | 6730 |
| 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 cbor-x with shared structures).
Here is a benchmark of streaming data (again borrowed from msgpack-lite's benchmarking), where cbor-x 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 EncoderStream().write(obj); | 1000000 | 372 | 2688172 |
| new DecoderStream().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 types/classes in certain ways. This is done by using the addExtension function, and specifying the class, extension type code (custom extensions should be a number greater than 40500, all others are reserved for CBOR or cbor-x), and your encode and decode functions (or just the one you need). You can use cbor-x encoding and decoding within your extensions:
import { addExtension, Encoder } from 'cbor-x';
class MyCustomClass {...}
let extEncoder = new Encoder();
addExtension({
Class: MyCustomClass,
tag: 43311, // register our own extension code (a tag code)
encode(instance, encode) {
// define how your custom class should be encoded
encode(instance.myData); // return a buffer
}
decode(data) {
// define how your custom class should be decoded
let instance = new MyCustomClass();
instance.myData = data
return instance; // decoded value from buffer
}
});If no extension is registered for a tag, the decoder will return an instance of the Tag class, where the value provided for the tag will be available in the value property of the Tag instance. The Tag class is an export of the package and decode module.
The cbor-x package is designed to encode and decode to the CBOR extended generic data model, implementing extensions to support the extended model, and will generally attempt to use preferred serializations where feasible. When duplicate keys are encountered in maps, previous entries will be lost, and the final entry is preserved.
Cbor-x is already fast, but here are some tips for making it faster.
During the serialization process, data is written to buffers. Again, allocating new buffers is a relatively expensive process, and the useBuffer method can help allow reuse of buffers that will further improve performance. With useBuffer method, you can provide a buffer, serialize data into it, and when it is known that you are done using that buffer, you can call useBuffer again to reuse it. The use of useBuffer is never required, buffers will still be handled and cleaned up through GC if not used, it just provides a small performance boost.
Cbor-x currently uses tag id 105 and 26880-27135 for its proposed extension for records.
Cbor-x saves all JavaScript Dates using the standard CBOR date extension (tag 1).
With structured cloning enabled, cbor-x will also use tags/extensions to store Set, Map, Error, RegExp, ArrayBufferView objects and preserve their types.
Here is a list of CBOR tags that are supported for decoding:
- 0 - String date
- 1 - Numeric Date
- 2 - BigInt
- 3 - Negative BigInt
- 6 - Packed string reference
- 27 - Generic named objects (used for Error, RegExp)
- 28, 29 - Value sharing/object referencing
- 51 - Packed table
- 64 - Uint8Array
- 68 - Uint8ClampedArray
- 69 - Uint16Array
- 70 - Uint32Array
- 71 - BigUint64Array
- 72 - Int8Array
- 77 - Int16Array
- 78 - Int32Array
- 79 - BigInt64Array
- 81 - Float32Array
- 82 - Float64Array
- 105 - Records
- 258 - Set
- 259 - Map
- 57344 - 57599 - Records
The high-performance serialization and deserialization algorithms in this package are also available in the msgpackr for the MessagePack format, with the same API and design. A quick summary of the pros and cons of using MessagePack vs CBOR are:
- MessagePack has wider adoption and msgpackr has broader usage.
- CBOR has an official IETF standardization track, and the record extensions is conceptually/philosophically a better fit for CBOR tags.
MIT
CBOR can be a great choice for high-performance data delivery to browsers, as reasonable data size is possible without compression. And CBOR works very well in modern browsers. However, it is worth noting that if you want highly compact data, brotli or gzip are most effective in compressing, and CBOR's character frequency tends to defeat Huffman encoding used by these standard compression algorithms, often resulting in less compact data than compressed JSON.
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.