transducers-js
A high performanceTransducersimplementation for JavaScript.
Transducers are composable algorithmic transformations. They are independent from the context of their input and output sources and specify only the essence of the transformation in terms of an individual element. Because transducers are decoupled from input or output sources, they can be used in many different processes - collections, streams, channels, observables, etc. Transducers compose directly, without awareness of input or creation of intermediate aggregates.
For further details about Transducers see the following resources:
transducers-js is brought to you by Cognitect Labs.
Latest release: 0.4.180
You can include either the release (2K gzipped) or development build of transducers-js on your webpage. We also provide Require.js compatible release and dev builds.
transducers-js is released to npm. Add transducers-js to your package.json
dependencies:
{... "dependencies": { "transducers-js": "0.4.180" } ...}
You can also include transducers-js in your bower.json
dependencies:
{... "dependencies": { "transducers-js": "0.4.180" } ...}
To import the library under Node.js you can just use require
:
var t = require("transducers-js");
The browser release of the library simply exports a top leveltransducers
object:
var t = transducers;
With <=ES5:
var map = t.map, filter = t.filter, comp = t.comp, into = t.into;var inc = function(n) { return n + 1; };var isEven = function(n) { return n % 2 == 0; };var xf = comp(map(inc), filter(isEven));console.log(into([], xf, [0,1,2,3,4])); // [2,4]
With ES6:
let {map, filter, comp, into} = t;let inc = (n) => n + 1;let isEven = (n) => n % 2 == 0;let xf = comp(map(inc), filter(isEven));console.log(into([], xf, [0,1,2,3,4])); // [2,4]
Documentation can be found here
transducers-js can also easily be used in combination with existingreduce implementations, whether native or the shims provided byUnderscore andLodash. Doing so with native and Underscore can
deliver significant performance benefits. Transducers may be easily
converted from their object representation into the necessary
two-arity function via toFn
.
var arr = [0,1,2,3,4,5,6,7,8,9,10], apush = function(arr, x) { arr.push(x); return arr; }, xf = comp(map(inc), filter(isEven)), toFn = t.toFn;arr.reduce(toFn(xf, apush), []); // native_(arr).reduce(toFn(xf, apush), []); // underscore or lodash
transducers-js can work with custom collection types and still deliver the same performance benefits, for example with Immutable-js:
var Immutable = require("immutable"), t = require("transducers-js"), comp = t.comp, map = t.map, filter = t.filter, transduce = t.transduce;var inc = function(n) { return n + 1; };var isEven = function(n) { return n % 2 == 0; };var sum = function(a,b) { return a+b; };var largeVector = Immutable.List();for(var i = 0; i < 1000000; i++) { largeVector = largeVector.push(i); }// built in Immutable-js functionalitylargeVector.map(inc).filter(isEven).reduce(sum);// faster with transducersvar xf = comp(map(inc),filter(isEven));transduce(xf, sum, 0, largeVector);
ES6 collections return iterators and therefore can be reduced/transduced. For example withtransit-js collections which satisfy many of the proposed Map/Set methods:
var transit = require("transit-js"), t = require("transducers-js"), m = transit.map(["foo", "bar", "baz", "woz"]), vUC = function(kv) { return [kv[0], kv[1].toUpperCase()]; }, xf = t.map(vUC); madd = function(m, kv) { m.set(kv[0], kv[1]); return m; };transduce(xf, madd, transit.map(), m.entries()); // Map ["foo", "BAR", "baz", "WOZ"]
It is a goal that all JavaScript transducer implementations interoperate regardless of the surface level API. Towards this end the following outlines the protocol all transducers must follow.
Transducers are simply a function of one arity. The only argument
is another transducer transformer (labeled xf
in the code base).
Note the distinction between the transducer which is a function of
one argument and the transformer an object whose methods we'll
describe in the following section.
For example the following simplified definition of map
:
var map = function(f) { return function(xf) { return Map(f, xf); }; };
Since transducers are simply functions of one argument they can be composed easily via function composition to create transformer pipelines. Note that transducers return transformers when invoked.
Transformers are objects. They must implement 3 methods, @@transducer/init
,@@transducer/result
and @@transducer/step
. If a transformer is intended to
be composed with other transformers they should either close over the next
transformer or store it in a field.
For example the Map
transformer could look something like the
following:
var Map = function(f, xf) { return { "@@transducer/init": function() { return xf["@@transducer/init"](); }, "@@transducer/result": function(result) { return xf["@@transducer/result"](result); }, "@@transducer/step": function(result, input) { return xf["@@transducer/step"](result, f(input)); } }; };
Note how we take care to call the next transformer in the pipeline. We
could have of course created Map
as a proper JavaScript type with
prototype methods - this is in fact how it is done in transducers-js.
Detecting the reduced state is critical to short circuiting a
reduction/transduction. A reduced value is denoted by any JavaScript
object that has the property @@transducer/reduced
set to true
.
The reduced value should be stored in the @@transducer/value
property of this
object.
Anything which implements @@iterator
which returns an ES6 compliant
iterator is reducible/transducible. An ES6 iterator may also just be given directly to reduce
or transduce
.
Fetch the dependencies:
bin/deps
To build for Node.js
bin/build_release_node
To build for the browser
bin/build_release_browser
Make sure you've first fetched the dependencies, then:
bin/test
This library is open source, developed internally by Cognitect. Issues can be filed using GitHub Issues.
This project is provided without support or guarantee of continued development. Because transducers-js may be incorporated into products or client projects, we prefer to do development internally and do not accept pull requests or patches.
Copyright © 2014-2015 Cognitect
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
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