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A modular, composable, strongly typed and lightweight implementation of the [SCALE Codec](https://docs.substrate.io/v3/advanced/scale-codec/)
A modular, composable, strongly typed and lightweight implementation of the SCALE Codec
npm install --save scale-ts
import { bool, _void, str, u32, Enum, Struct, Vector } from "scale-ts"
const myCodec = Struct({
id: u32,
name: str,
friendIds: Vector(u32),
event: Enum({
_void,
one: str,
many: Vector(str),
allOrNothing: bool,
}),
})
/*
Something really cool about this library is that by having composable
codecs and higher order codecs with really good typings, then the
inferred types of the custom codecs are also really good. For instance,
the inferred types of the `myCodec` defined above are:
*/
type MyCodec = Codec<{
id: number;
name: string;
friendIds: number[];
event:
| { tag: _void; value?: undefined };
| { tag: one; value: string; }
| { tag: many; value: string[]; }
| { tag: allOrNothing; value: boolean; };
}>
/*
That's very useful, because on the one hand we will get a TS error if
we try to pass an invalid input to the encoder, for instance TS will
complain that the `value` of the property `event` is not valid, in the
following example:
*/
myCodec.enc({
event: { tag: 'one', value: 5 },
name: "Some name",
id: 100,
friendIds: [1, 2, 3],
})
/*
On the other hand, the result of the decoded value, also has that
same interface, which is extremely useful.
An example on how to encoded/decode a valid value:
*/
myCodec.enc({
id: 100,
name: "Some name",
friendIds: [1, 2, 3],
event: { tag: "allOrNothing" as const, value: true },
})
// => 0x6400000024536f6d65206e616d650c0100000002000000030000000301
const decodedData = myCodec.dec(
'0x6400000024536f6d65206e616d650c0100000002000000030000000301'
)
console.log(JSON.stringify(decodedData, null, 2))
// =>
//{
// "id": 100,
// "name": "Some name",
// "friendIds": [
// 1,
// 2,
// 3
// ],
// "event": {
// "tag": "allOrNothing",
// "value": true
// }
//}
In this library you won't find common definitions like AccountId
.
However, since the definitions of this library are enhanceable and
composable, it is very easy to create new custom definitions. For
instance, the implementation of the bool
Codec looks like this:
import { enhanceCodec, u8, Codec } from "../"
const booleanToNumber = (value: boolean) => (value ? 1 : 0)
const numberToBoolean = Boolean
export const bool: Codec<boolean> = enhanceCodec(
u8,
booleanToNumber,
numberToBoolean,
)
Similarly, you could implement any other definitions are that based
on other definitions. For instance, a possible implementation of an
AccountId
definition could be:
import { enhanceCodec, Bytes } from "scale-ts"
import { decodeAddress, encodeAddress } from "@polkadot/util-crypto"
export const AccountId = enhanceCodec(Bytes(32), decodeAddress, encodeAddress)
Supported codecs are: u8
, u16
, u32
, u64
, u128
,
i8
, i16
, i32
, i64
, i128
i128.enc(-18676936063680574795862633153229949450n)
// => 0xf6f5f4f3f2f1f0f9f8f7f6f5f4f3f2f1
i128.dec("0xf6f5f4f3f2f1f0f9f8f7f6f5f4f3f2f1")
// => -18676936063680574795862633153229949450n
compact.enc(65535)
// => 0xfeff0300
compact.dec("0xfeff0300")
// => 65535
bool.enc(false)
// => 0x00
bool.dec("0x01")
// => true
Normal cases:
cosnt optionalCompact = Option(compact)
optionalCompact.enc()
// => 0x00
optionalCompact.enc(undefined)
// => 0x00
optionalCompact.enc(1)
// => 0x0104
Exceptionally, if the input is bool
, then it always returns one byte:
cosnt optionalBool = Option(bool)
optionalBool.enc()
// => 0x00
optionalBool.enc(true)
// => 0x01
optionalBool.enc(false)
// => 0x02
const resultCodec = Result(u8, bool)
resultCodec.enc({ success: true, value: 42 })
// => 0x002a
resultCodec.enc({ success: false, value: false })
// => 0x0100
Dynamic, for when the size is known at run time:
const numbers = Vector(u16)
numbers.enc([4, 8, 15, 16, 23, 42])
// => 0x18040008000f00100017002a00
Fixed, for when the size is known at compile time:
const fiveNumbers = Vector(u16, 5)
numbers.enc([4, 8, 15, 16, 23])
// => 0x040008000f0010001700
str.enc("a$¢ह€한𐍈😃")
// => 0x546124c2a2e0a4b9e282aced959cf0908d88f09f9883
const compactAndBool = Tuple(compact, bool)
compactAndBool.enc([3, false])
// => 0x0c00
const myCodec = Struct({
id: u32,
name: str,
friendIds: Vector(u32),
event: Enum({
_void,
one: str,
many: Vector(str),
allOrNothing: bool,
}),
})
myCodec.enc({
id: 100,
name: "Some name",
friendIds: [1, 2, 3],
event: { tag: "allOrNothing" as const, value: true },
})
// => 0x6400000024536f6d65206e616d650c0100000002000000030000000301
const { enc, dec } = Enum({
nothingHere: _void,
someNumber: u8,
trueOrFalse: bool,
optionalBool: Option(bool),
optVoid: Option(_void),
})
enc({ tag: "nothingHere" })
// => 0x00
dec("0x012a")
// => { tag: "someNumber", value: 42 }
Sometimes, mainly when creating your custom codecs, it's usefull
to have a codec that simply reads/writes a certain amount of bytes.
For example, see the example above for creating AccountId
.
const [encode, decode] = Bytes(3)
encode(new Uint8Array([0, 15, 255]))
// => 0x000fff
decode("0x000fff00")
// => 0x000fff
_void
This is a special codec that it's mostly useful in combination with
Enum
, its type is Codec<void>
, and as you can imagine calling
_void.enc()
returns an empty Uint8Array
, while calling
_void.dec()
returns undefined
.
TODO: document them
A very impotant remark is that in this library you will only find the basic primitives that can be used for building more complex codecs. That being said, this library provides a set of utils to facilitate that.
Probably the easiest way to explain this is by solving a couple of examples, so let's get to it.
MapCodec
:Let's say that you want to have a MapCodec
function that works like this:
const myMap: Codec<Map<number, string>> = MapCodec(u8, str)
How could we create that MapCodec
with this scale-ts
? Basically, what we
want to do is to transform the result of a Vector(Tuple(keyCodec, valueCodec))
to a Map, and viceversa.
So, let's first create the encoder function, using enahnceEncoder
:
const MapEncoder = <K, V>(key: Encoder<K>, value: Encoder<V>) =>
enhanceEncoder(Vector.enc(Tuple.enc(key, value)), (input: Map<K, V>) =>
Array.from(input.entries()),
)
Now, let's create its decoder counterpart, using enhanceDecoder
:
const MapDecoder = <K, V>(key: Decoder<K>, value: Decoder<V>) =>
enhanceDecoder(
Vector.dec(Tuple.dec(key, value)),
(entries) => new Map(entries),
)
Finally, lets create the MapCodec
function:
export const MapCodec = <K, V>(
key: Codec<K>,
value: Codec<V>,
): Codec<Map<K, V>> =>
createCodec(MapEncoder(key.enc, value.enc), MapDecoder(key.dec, value.dec))
MapCodec.enc = MapEncoder
MapCodec.dec = MapDecoder
And that's it 🎉!
ClassCodec
:Now, let's see how we can create a more complex function, like something
for encoding and decoding the instances of our classes, even if those instances
are more than mere setters/getters. Let's say that we want to create a
ClassCodec
function that can be used like this:
class RepeatedString {
constructor(item: string, nTimes: number) {
this.repetition = Array(nTimes).fill(item)
}
}
// It's not necessary to add the `: Codec<RepeatedString> notation
// because it's going to be inferred.
const repeatedStrCodec: Codec<RepeatedString> = ClassCodec(
RepeatedString,
[str, compact],
(value: RepeatedString) => [value.repetition[0], value.repetition.length],
)
How can we implement this ClassCodec
with this scale-ts
?
Basically, what we want to do is to instantiate our class using the result of a
Tuple
and then, using a function that takes the instance of our class and
returns the values that must be encoded, return the Codec for the class.
It goes without saying that this function could have more overloads, of course, but this is just an example.
What's very difficult about creating a function like this is to get the types right, but let's not shy away from it.
First, let's write the function for encoding:
const ClassEncoder =
<
A extends Array<Encoder<any>>,
OT extends { [K in keyof A]: A[K] extends Encoder<infer D> ? D : unknown },
Constructor extends new (...args: OT) => any,
>(
mapper: (instance: InstanceType<Constructor>) => OT,
): Encoder<InstanceType<Constructor>> =>
(instance) => {
return Tuple.enc(...mapper(instance)) as any
}
Again, leaving aside the complex types for inferring the arguments, the actual JS code is fairly straight-forward.
Then, let's create the function for creating the Decoder:
const ClassDecoder = <
A extends Array<Decoder<any>>,
OT extends { [K in keyof A]: A[K] extends Decoder<infer D> ? D : unknown },
Constructor extends new (...args: OT) => any,
>(
classType: Constructor,
...decoders: A
): Decoder<InstanceType<Constructor>> =>
enhanceDecoder(
Tuple.dec(...decoders),
(args) => new classType(...(args as any)),
)
Same deal, complex types b/c we care about our users, but aside from that, the actual JS code is pretty straight-forward.
And now we are ready to put everything together:
const ClassCodec = <
A extends Array<Codec<any>>,
OT extends { [K in keyof A]: A[K] extends Codec<infer D> ? D : unknown },
Constructor extends new (...args: OT) => any,
>(
classType: Constructor,
codecs: A,
mapper: (instance: InstanceType<Constructor>) => OT,
) =>
createCodec(
ClassEncoder(mapper),
ClassDecoder(classType, ...codecs.map((c) => c.dec)),
)
ClassCodec.enc = ClassEncoder
ClassCodec.dec = ClassDecoder
Hopefully, this 2 examples showcase the main goal of the library: to provide good and lean building blocks, so that we can build complex things with them.
Also, it's worth pointing out that in the past this library used to have some
"sugar" codecs (Hex
, MapCodec
, SetCodec
, date32
, etc). However, they
have all been removed because since all these codecs can be implemented in
userland, if we start adding sugar, then this library could easily become a
chaotic directory with all sorts of Codecs.
That's why it's very important that its building blocks are as minimalist and ergonomic as possible.
FAQs
A modular, composable, strongly typed and lightweight implementation of the [SCALE Codec](https://docs.substrate.io/v3/advanced/scale-codec/)
The npm package scale-ts receives a total of 33,849 weekly downloads. As such, scale-ts popularity was classified as popular.
We found that scale-ts demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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