@thi.ng/transducers

@thi.ng/transducers

Lightweight transducer implementations for ES6 / TypeScript (19KB minified, full lib).

Currently, the library provides altogether 65+ transducers, reducers and generators for composing data transformation pipelines.

The overall concept and many of the functions offered here are directly inspired by the original Clojure implementation by Rich Hickey, though the implementation does differ and several generally highly useful operators have been added, with more are to come.

Please see the @thi.ng/iterators & @thi.ng/csp partner modules for related functionality, supplementing features of this library.

Having said this, since 0.8.0 this project largely supersedes the @thi.ng/iterators library for most use cases and offers are more powerful API and potentially faster execution of composed transformations (due to lack of ES generator overheads).

Installation

yarn add @thi.ng/transducers

Usage examples

Almost all functions can be imported selectively, but for development purposes full module re-exports are defined.

// full import
import * as tx from "@thi.ng/transducers";

// selective
import { transduce } from "@thi.ng/transducers/transduce";
import { push } from "@thi.ng/transducers/rfn/push";
import { map } from "@thi.ng/transducers/xforms/map";

xform = tx.comp(
    tx.filter(x => (x & 1) > 0), // odd numbers only
    tx.distinct(),               // distinct numbers only
    tx.map(x=> x * 3)            // times 3
);

// collect as array (tx.push)
tx.transduce(xform, tx.push(), [1, 2, 3, 4, 5, 4, 3, 2, 1]);
// [ 3, 9, 15 ]

// re-use xform, but collect as set (tx.conj)
tx.transduce(xform, tx.conj(), [1, 2, 3, 4, 5, 4, 3, 2, 1]);
// Set { 3, 9, 15 }

// apply as transforming iterator
for(let x of tx.iterator(xform, [1, 2, 3, 4, 5])) {
    console.log(x);
}
// 3
// 9
// 15

// moving average using sliding window
// use nested reduce to compute window averages
// this combined transducer is also directly
// available as: `tx.movingAverage(n)`
tx.transduce(
    tx.comp(
        tx.partition(5, 1),
        tx.map(x => tx.reduce(tx.mean(), x))
    ),
    tx.push(),
    [1, 2, 3, 3, 4, 5, 5, 6, 7, 8, 8, 9, 10]
);
// [ 2.6, 3.4, 4, 4.6, 5.4, 6.2, 6.8, 7.6, 8.4 ]

// apply inspectors to debug transducer pipeline
// alternatively, use tx.sideEffect() for any side fx
tx.transduce(
    tx.comp(
        tx.inspect("orig"),
        tx.map(x => x + 1),
        tx.inspect("mapped"),
        tx.filter(x => (x & 1) > 0)
    ),
    tx.push(),
    [1, 2, 3, 4]
);

// orig 1
// mapped 2
// orig 2
// mapped 3
// orig 3
// mapped 4
// orig 4
// mapped 5
// [ 3, 5 ]


// histogram generation
tx.transduce(tx.map(x => x.toUpperCase()), tx.frequencies(), "hello world")
// Map { 'H' => 1, 'E' => 1, 'L' => 3, 'O' => 2, ' ' => 1, 'W' => 1, 'R' => 1, 'D' => 1 }

// reduction only (no transform)
tx.reduce(tx.frequencies(), [1, 1, 1, 2, 3, 4, 4])
// Map { 1 => 3, 2 => 1, 3 => 1, 4 => 2 }


// CSV parsing
tx.transduce(
    tx.comp(
        // split into rows
        tx.mapcat(x => x.split("\n")),
        // split each row
        tx.map(x => x.split(",")),
        // convert each row into object, rename array indices
        tx.rename({ id: 0, name: 1, alias: 2, num: "length" })
    ),
    tx.push(),
    ["100,typescript\n101,clojure,clj\n110,rust,rs"]
);
// [ { num: 2, name: 'typescript', id: '100' },
//   { num: 3, alias: 'clj', name: 'clojure', id: '101' },
//   { num: 3, alias: 'rs', name: 'rust', id: '110' } ]


// early termination:
// result is realized after max. 7 values, irrespective of nesting
tx.transduce(
    tx.comp(tx.flatten(), tx.take(7)),
    tx.push(),
    [1, [2, [3, 4, [5, 6, [7, 8], 9, [10]]]]]
)
// [1, 2, 3, 4, 5, 6, 7]


// this transducer uses 2 scans (a scan = inner reducer per item)
// 1) counts incoming values
// 2) forms an array of the current counter value `x` & repeated `x` times
// 3) emits results as series of reductions in the outer array produced
//    by the main reducer
// IMPORTANT: since arrays are mutable we use `pushCopy` as the inner reducer
// instead of `push` (the outer reducer)
xform = tx.comp(
    tx.scan(tx.count),
    tx.map(x => [...tx.iterator(tx.repeat(x), [x])]),
    tx.scan(tx.pushCopy)
);

tx.transduce(xform, tx.push(), [1, 1, 1, 1]);
// [ [ [ 1 ] ],
//   [ [ 1 ], [ 2, 2 ] ],
//   [ [ 1 ], [ 2, 2 ], [ 3, 3, 3 ] ],
//   [ [ 1 ], [ 2, 2 ], [ 3, 3, 3 ], [ 4, 4, 4, 4 ] ] ]

// more simple & similar to previous, but without the 2nd xform step
tx.transduce(tx.comp(tx.scan(tx.count), tx.scan(tx.pushCopy)), tx.push(), [1,1,1,1])
// [ [ 1 ], [ 1, 2 ], [ 1, 2, 3 ], [ 1, 2, 3, 4 ] ]

// single step execution (doesn't work w/ mapcat() or repeat())
f = tx.step(tx.dedupe());
f(1); // 1
f(2); // 2
f(2); // undefined -> skip repetiton
f(3); // 3
f(3); // undefined
f(3); // undefined
f(1); // 1

API

Types

Apart from type aliases, the only real types defined are:

Reducer

Reducers are the core building blocks of transducers. Unlike other implementations using OOP approaches, a Reducer in this lib is a simple 3-element array of functions, each addressing a separate processing step.

Since v0.6.0 the bundled reducers are all wrapped in functions to provide a uniform API (and some of them can be preconfigured). However, it's fine to define stateless reducers as constant arrays.

interface Reducer<A, B> extends Array<any> {
    /**
     * Initialization, e.g. to provide a suitable accumulator value,
     * only called when no initial accumulator has been provided by user.
     */
    [0]: () => A,
    /**
     * Completion. When called usually just returns `acc`, but stateful
     * transformers should flush/apply their outstanding results.
     */
    [1]: (acc: A) => A,
    /**
     * Reduction step. Combines new input with accumulator.
     * If reduction should terminate early, wrap result via `reduced()`
     */
    [2]: (acc: A, x: B) => A | Reduced<A>,
}

// A concrete example:
const push: Reducer<any[], any> = [
    // init
    () => [],
    // completion (nothing to do in this case)
    (acc) => acc,
    // step
    (acc, x) => (acc.push(x), acc),
];

Currently partition, partitionBy, streamSort, streamShuffle are the only operators making use of the 1-arity completing function of their reducer.

Reduced

class Reduced<T> implements IDeref<T> {
    protected value: T;
    constructor(val: T);
    deref(): T;
}

Simple type wrapper to identify early termination of a reducer. Does not modify wrapped value by injecting magic properties. Instances can be created via reduced(x) and handled via these helper functions:

reduced(x: any): any

isReduced(x: any): boolean

ensureReduced(x: any): Reduced<any>

unreduced(x: any): any

Transducer

From Rich Hickey's original definition:

A transducer is a transformation from one reducing function to another

As shown in the examples above, transducers can be dynamically composed (using comp()) to form arbitrary data transformation pipelines without causing large overheads for intermediate collections.

type Transducer<A, B> = (rfn: Reducer<any, B>) => Reducer<any, A>;

// concrete example of stateless transducer (expanded for clarity)
function map<A, B>(fn: (x: A) => B): Transducer<A, B> {
    return (rfn: Reducer<any, B>) => {
        return [
            () => rfn[0](),
            (acc) => rfn[1](acc),
            (acc, x: A) => rfn[2](acc, fn(x))
        ];
    };
}

// stateful transducer
// removes successive value repetitions
function dedupe<T>(): Transducer<T, T> {
    return (rfn: Reducer<any, T>) => {
        // state initialization
        let prev = {};
        return [
            () => rfn[0](),
            (acc) => rfn[1](acc),
            (acc, x) => {
                acc = prev === x ? acc : rfn[2](acc, x);
                prev = x;
                return acc;
            }
        ];
    };
}

Transformations

comp(f1, f2, ...)

Returns new transducer composed from given transducers. Data flow is from left to right. Offers fast paths for up to 10 args. If more are given, composition is done dynamically via for loop.

compR(rfn: Reducer<any, any>, fn: (acc, x) => any): Reducer<any, any>

Helper function to build reducers.

iterator<A, B>(tx: Transducer<A, B>, xs: Iterable<A>): IterableIterator<B>

Similar to transduce(), but emits results as ES6 iterator (and hence doesn't use a reduction function).

reduce<A, B>(rfn: Reducer<A, B>, acc: A, xs: Iterable<B>): A

Reduces iterable using given reducer and optional initial accumulator/result.

transduce<A, B, C>(tx: Transducer<A, B>, rfn: Reducer<C, B>, acc: C, xs: Iterable<A>): C

Transforms iterable using given transducer and combines results with given reducer and optional initial accumulator/result.

Transducers

benchmark(): Transducer<any, number>

cat<T>(): Transducer<T[], T>

dedupe<T>(equiv?: (a: T, b: T) => boolean): Transducer<T, T>

delayed<T>(t: number): Transducer<T, Promise<T>>

distinct<T>(mapfn?: (x: T) => any): Transducer<T, T>

drop<T>(n: number): Transducer<T, T>

dropNth<T>(n: number): Transducer<T, T>

dropWhile<T>(pred: Predicate<T>): Transducer<T, T>

duplicate<T>(n?: number): Transducer<T, T>

filter<T>(pred: Predicate<T>): Transducer<T, T>

flatten<T>(): Transducer<T | Iterable<T>, T>

flattenWith<T>(fn: (x: T) => Iterable<T>): Transducer<T | Iterable<T>, T>

indexed<T>(): Transducer<T, [number, T]>

inspect<T>(prefix?: string): Transducer<T, T>

interleave<A, B>(sep: B | (() => B)): Transducer<A, A | B>

interpose<A, B>(sep: B | (() => B)): Transducer<A, A | B>

keep<T>(f?: ((x: T) => any)): Transducer<T, T>

map<A, B>(fn: (x: A) => B): Transducer<A, B>

mapcat<A, B>(fn: (x: A) => Iterable<B>): Transducer<A, B>

mapIndexed<A, B>(fn: (i: number, x: A) => B): Transducer<A, B>

mapNth<A, B>(n: number, offset: number, fn: (x: A) => B): Transducer<A, B>

movingAverage(n: number): Transducer<number, number>

partition<T>(size: number, step?: number, all?: boolean): Transducer<T, T[]>

partitionBy<T>(fn: (x: T) => any): Transducer<T, T[]>

partitionSort<A, B>(n: number, key?: ((x: A) => B), cmp?: Comparator<B>): Transducer<A, A>

pluck<A, B>(key: PropertyKey): Transducer<A, B>

sample<T>(prob: number): Transducer<T, T>

scan<A, B>(rfn: Reducer<B, A>, acc?: B): Transducer<A, B>

selectKeys(...keys: PropertyKey[]): Transducer<any, any>

sideEffect<T>(fn: (x: T) => void): Transducer<T, T>

streamShuffle<T>(n: number, maxSwaps?: number): Transducer<T, T>

streamSort<A, B>(n: number, key?: ((x: A) => B), cmp?: Comparator<B>): Transducer<A, A>

swizzle<T>(order: PropertyKey[]): Transducer<T, any>

take<T>(n: number): Transducer<T, T>

takeLast<T>(n: number): Transducer<T, T>

takeNth<T>(n: number): Transducer<T, T>

takeWhile<T>(pred: Predicate<T>): Transducer<T, T>

throttle<T>(delay: number): Transducer<T, T>

Reducers

add(): Reducer<number, number>

assocMap<A, B>(): Reducer<Map<A, B>, [A, B]>

assocObj<T>(): Reducer<IObjectOf<T>, [PropertyKey, T]>

conj<T>(): Reducer<Set<T>, T>

count(): Reducer<number, number>

frequencies<T>(): Reducer<Map<T, number>, T>

groupBinary<T>(bits: number, key: (x: T) => number, branch?: () => IObjectOf<T[]>, leaf?: Reducer<any, T>, left?: PropertyKey, right?: PropertyKey): Reducer<any, T>

groupByMap<A, B, C>(key: (x: A) => B, rfn?: Reducer<C, A>): Reducer<Map<B, C>, A>

groupByObj<A, C>(key: (x: A) => PropertyKey, rfn?: Reducer<C, A>, init?: () => IObjectOf<C>): Reducer<IObjectOf<C>, A>

last(): last<T>(): Reducer<T, T>

max(): Reducer<number, number>

mean(): Reducer<number, number>

min(): Reducer<number, number>

mul(): Reducer<number, number>

push<T>(): Reducer<T[], T>

pushCopy<T>(): Reducer<T[], T>

Generators / Iterators

constantly<T>(x: T): IterableIterator<T>

cycle<T>(input: Iterable<T>): IterableIterator<T>

iterate<T>(fn: (x: T) => T, seed: T): IterableIterator<T>

pairs(x: any): IterableIterator<[string, any]>

range(from?: number, to?: number, step?: number): IterableIterator<number>

repeat<T>(x: T, n?: number): IterableIterator<T>

repeatedly<T>(fn: () => T, n?: number): IterableIterator<T>

reverse<T>(input: Iterable<T>): IterableIterator<any>

tuples(...src: Iterable<any>[]): IterableIterator<any[]>

Authors

• Karsten Schmidt

License

© 2016-2018 Karsten Schmidt // Apache Software License 2.0