Noodle Printing Machine


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    A small library for generalized transformation of data. This provides a bunch of transformation functions that can be applied to any data structure. It is a direct port of Clojure's transducers in JavaScript. Read more in this post.

    The algorithm behind this, explained in the above post, not only allows for it to work with any data structure (arrays, objects, iterators, immutable data structures, you name it) but it also provides better performance than other alternatives such as underscore or lodash. This is because there are no intermediate collections. See this post for benchmarks.

    npm install fast-transducers --save

    For browsers, grab the file dist/transducers.js.

    When writing programs, we frequently write methods that take in collections, do something with them, and return a result. The problem is that we frequently only write these functions to work a specific data structure, so if we ever change our data type or wanted to reuse that functionality, you can't. We need to decouple these kinds of concerns.

    A transducer is a function that takes a reducing function and returns a new one. It can perform the necessary work and call the original reducing function to move on to the next "step". In this library, a transducer a little more than that (it's actually an object that also supports init and finalizer methods) but generally you don't have to worry about these internal details. Read my post if you want to learn more about the algorithm.

    const transform = compose(
      map(x => x * 3),
      filter(x => x % 2 === 0),
    seq([1, 2, 3, 4, 5], transform);
    // -> [ 6, 12 ]
    function* nums() {
      const i = 1;
      while(true) {
        yield i++;
    into([], transform, nums());
    // -> [ 6, 12 ]
    into([], transform, Immutable.List.of(1, 2, 3, 4, 5))
    // -> [ 6, 12 ]

    All of these work with arrays, objects, and any iterable data structure (like immutable-js) and you get all the high performance guarantees for free. The above code always only performs 2 transformations because of take(2), no matter how large the array. This is done without laziness or any overhead of intermediate structures.


    The following transformations are available, and there are more to come (like partition).

    • map(coll?, f, ctx?) — call f on each item
    • filter(coll?, f, ctx?) — only include the items where the result of calling f with the item is truthy
    • remove(coll?, f, ctx?) — only include the items where the result of calling f with the item is falsy
    • keep(coll?) — remove all items that are null or undefined
    • take(coll?, n) — grab only the first n items
    • takeWhile(coll?, f, ctx?) — grab only the first items where the result of calling f with the item is truthy
    • drop(coll?, n) — drop the first n items and only include the rest
    • dropWhile(coll?, f, ctx?) — drop the first items where the result of calling f with the item is truthy
    • dedupe(coll?) — remove consecutive duplicates (equality compared with ===)

    The above functions optionally take a collection to immediately perform the transformation on, and a context to bind this to when calling f. That means you can call them in four ways:

    • Immediately perform a map: map([1, 2, 3], x => x + 1)
    • Same as above but call the function with this as ctx: map([1, 2, 3], function(x) { return x + 1; }, ctx)
    • Make a map transducer: map(x => x + 1)
    • Same as above but with this as ctx: map(function(x) { return x + 1; }, ctx)

    (I will be using the ES6 fat arrow syntax, but if that's not available just function instead)

    The signature of running an immediate map is the same familiar one as seen in lodash and underscore, but now you can drop the collection to make a transducer and run multiple transformations with good performance:

    const transform = compose(
      map(x => x + 1),
      filter(x => x % 2 === 0),

    compose is a provided function that simply turns compose(f, g) into x => f(g(x)). You use it to build up transformations. The above transformation would always run the map and filter only twice becaue only two items are needed, and it short-circuits once it gets two items. Again, this is done without laziness, read more here.

    There are also 2 transducers available for taking collections and "catting" them into the transformation stream:

    • cat — take collections and forward each item individually, essentially flattening it
    • mapcat(f) — same as cat, but first apply f to each collection

    Just pass cat straight through like so: compose(filter(x => x.length < 10), cat). That would take all arrays with a length less than 10 and flatten them out into a single array.

    Applying Transformations

    Building data structure-agnostic transformations is cool, but how do you actually use them? transducers.js provides several integration points.

    To use a transformation, we need to know how to iterate over the source data structure and how to build up a new one. The former is easy; we can work with arrays, objects, and anything can uses the ES6 iterator protocol (Maps, Sets, generators, etc). All the the below functions works with them.

    For the latter, you need to specify what you want back. The following functions allow you to make a new data structure and possibly apply a transformation:

    • toArray(coll, xform?) — Turn coll into an array, applying the transformation xform to each item if provided. The transform is optional in case you want to do something like turn an iterator into an array.
    • toObj(coll, xform?) — Turn coll into an object if possible, applying the transformation xform if provided. When an object is iterated it produces two-element arrays [key, value], and obj will turn these back into an object.
    • toIter(coll, xform?) — Make an iterator over coll, and apply the transformation xform to each value if specified. Note that coll can just be another iterator. Transformations will be applied lazily.
    • seq(coll, xform) — A generalized method that will return the same data type that was passed in as coll, with xform applied. You will usually use this unless you know you want an array, object, or iterator. If coll is an iterator, another iterator will be returned and transformations will be applied lazily.
    • into(to, xform, from) — Apply xform to each item in from and append it to to. This has the effect of "pouring" elements into to. You will commonly use this when converting one type of object to another.
    • transduce(coll, xform, reducer, init?) — Like reduce, but apply xform to each value before passing to reducer. If init is not specify it will attempt to get it from reducer.

    The possibilities are endless:

    // Map an object
    seq({ foo: 1, bar: 2 }, map(kv => [kv[0], kv[1] + 1]));
    // -> { foo: 2, bar: 3 }
    // Make an array from an object
    toArray({ foo: 1, bar: 2 });
    // -> [ [ 'foo', 1 ], [ 'bar', 2 ] ]
    // Make an array from an iterable
    function* nums() {
      const i = 1;
      while(true) {
        yield i++;
    into([], take(3), nums());
    // -> [ 1, 2, 3 ]
    // Lazily transform an iterable
    const iter = seq(nums(), compose(map(x => x * 2),
                                   filter(x => x > 4));; // -> 6; // -> 8; // -> 10


    Transducers remove the requirement of being lazy to optimize for things like take(10). However, it can still be useful to "bind" a collection to a set of transformations and pass it around, without actually evaluating the transformations.

    As noted above, whenever you apply transformations to an iterator it does so lazily. It's easy convert array transformations into a lazy operation, just use the utility function iterator to grab an iterator of the array instead:

    seq(iterator([1, 2, 3]),
          map(x => x + 1),
          filter(x => x % 2 === 0)))
    // -> <Iterator>

    Our transformations are completely blind to the fact that our transformations may or may not be lazy.

    Utility Functions

    This library provides a few small utility functions:

    • iterator(coll) — Get an iterator for coll, which can be any type like array, object, iterator, or custom data type
    • push(arr, value) — Push value onto arr and return arr
    • merge(obj, value) — Merge value into obj. value can be another object or a two-element array of [key, value]
    • range(n) — Make an array of size n filled with numbers from 0..n.


    We've talked about how this can be applied to any data structure — let's see that in action. Here's how you could use this with immutable-js.

      seq(Immutable.Vector(1, 2, 3, 4, 5),
            map(function(x) { return x + 10; }),
            map(function(x) { return x * 2; }),
            filter(function(x) { return x % 5 === 0; }),
            filter(function(x) { return x % 2 === 0; })))

    We can use our familiar seq function because Immutable.Vector implements the iterator protocol, so we can iterator over it. Because seq is working with an iterator, it returns a new iterator that will lazily transform each value. We can simply pass this iterator into Immutable.Vector.from to construct a new one, and we have a new transformed immutable vector with no intermediate collections except for one lazy transformer!

    The builtin transformations perform well because they minimize allocations, but since we don't have any intermediate structures or laziness machinery, this performs slightly better. The point is not to beat it, but to show that both are high-performance but we can apply our performance to any data structure.

    CSP Channels

    This not only works with all the JavaScript data structures you can think of, but it even works for things like streams. Soon channels from js-csp will be able to take a transformation and you get all of this for channels for free:

    const ch = chan(1, compose(
      map(x => x + 1),

    The transducer protocol

    While it's great that you can apply transducers to custom data structures, it's a bit annoying to always have to use constructor functions like Immutable.fromJS. One option is to define a new protocol complementary to iterator.

    This conforms to the official transducer spec so if you implement this, you can use it with all transducer libraries that conform to it.

    To implement the transducer protocol, you add methods to the prototype of your data structure. A transformer is an object with three methods: init, result, and step. init returns a new empty object, result, can perform any finalization steps on the resulting collection, and step performs a reduce.

    These methods are namespaced and in the future could be symbols. Here's what it looks like for Immutable.List:

    Immutable.List.prototype['@@transducer/init'] = function() {
      return Immutable.List().asMutable();
    Immutable.List.prototype['@@transducer/result'] = function(lst) {
      return lst.asImmutable();
    Immutable.List.prototype['@@transducer/step'] = function(lst, x) {
      return lst.push(x);

    If you implement the transducer protocol, now your data structure will work with all of the builtin functions. You can just use seq like normal and you get back an immutable vector!

    t.seq(Immutable.List.of(1, 2, 3, 4, 5),
   { return x + 10; }),
   { return x * 2; }),
            t.filter(function(x) { return x % 5 === 0; }),
            t.filter(function(x) { return x % 2 === 0; })));
    // -> List [ 30 ]

    Running Tests

    npm install
    mocha build/tests



    npm i fast-transducers

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