reducers

Library for higher-order manipulation of collections, based upon reduce.

Rationale

Most functional languages (including beloved JS) typically come with some collection transformation functions like filter and map that take a logical collections and return transformed version of it. Unfortunately they tend to complect, by implying mechanism, order, laziness and representation. This library is an attempt to provide simple solution for some of the hard problems by decomplecting and building upon simple premise - minimum definition of collection is something that is reducible.

More specifically library defines super-generalized and minimal abstraction for collections - a collection is some set of things that, when given a function to apply to its contents, can do so and give you the result, i.e. a collection is (at minimum) reducible. In other words, you can call reduce on it.

A very minimal abstraction for collection is more powerful than it may seem at first!

Basics

Demonstration of features of this library requires some basic understanding of the abstraction above. So let's take a more practical look at the idea. Let's say we have a reduce function with (very familiar) API:

reduce(source, f, initial) // => accumulated result

It takes reducing function, a reducible source and initial value to accumulate reductions upon. In return it outputs an accumulated result. Reducing functions performing accumulation have a following shape:

f(result, value) // => new result

A reducing function is simply a binary function, akin to the one you might pass to reduce. While the two arguments might be treated symmetrically by the function, there is an implied semantic that distinguishes the arguments: the first argument is a result or accumulator that is being built up by the reduction, while the second is some new input value from the source being reduced.

Transformations

All of the collection operations can be expressed in terms of transformations. By the definition all transformations will produce reducible collections that can be reduced via reduce function defined above:

map(source, JSON.parse) // => reducible collection
filter(numbers, isEven) // => reducible collection

In order to explain transformations we'll need a primitive API for producing reducible collections. Let's define one in form of reducible function that takes accumulator function and returns something that can be reduced via reduce function:

reducible(accumulator) // => reducible

Argument it takes, accumulator is a function that performs has following shape:

accumulate(next, initial) // => accumulated result

And when invoked it performs reductions via next reducing function starting from initial result.

Now consider following implementation of map & filter transformation functions:

function map(f, source) {
  return reducible(function accumulator(next, initial) {
    return reduce(source, function reducer(result, input) {
      return next(result, f(input))
    }, initial)
  })
}

function filter(predicate, source) {
  return reducible(function accumulator(next, initial) {
    return reduce(source, function reducer(result, input) {
      return predicate(input) ? next(result, input) : result
    }, initial)
  })
}

There are a few things to note here:

Type of the source is irrelevant as long as it is reducible and there for can be reduced via reduce function.
Transformations do not traverse collections, instead they compose results that can be reduced by a receiver of the result later.
Transformations do not imply timing in which reducer in invoked with an each input of the source, there for source can be asynchronous.
Filtering can skip inputs by simply returning the incoming result.

Features

Laziness

Library consists of transformation functions which, as seen above, when called do nothing except the creation of a recipe for a new collection, a recipe that is itself reducible. No work is done yet to the contained elements and no concrete collection is produced. All the transformations defer actual work to a point where result of transformations pipeline is being reduced.

The beautiful thing is that this mechanism also works for all other traditional transformations take, drop, merge etc. Note the fact that filter is (potentially) contractive, and flatten is (potentially) expansive per step - the mechanism is general and not limited to 1:1 transformations.

Uniformity

Transformation functions are absolutely agnostic of the actual type of the source, as they just describe transformations and leave it up to source to do a reduction when result is consumed.

Library takes a advantage of this feature and takes it even step further by treating every possible value as a reducible collection. Non collection values like numbers, booleans, objects etc. are treated as collection of single item, item being a value. Also null and undefined are considered as empty collections.

This means that library can be used on any data type and more importantly transformations between different data types & compose naturally, which is great, let's you define logic in terms of abstractions instead of specific types.

Composability

All the transformations are fully composable as a matter of fact transformation pipelines produce compositions equivalent of a function compositions created by a compose. Also type agnostic nature of the transformation functions enables compositions between different types of data.

Performance

Since transformations doesn't do the work, but merely create a recipe, there is no per-step allocation overhead, so it's faster. Also note that transformations are composed by curring transformation functions and all the actual work happens in a pipe line at the end when result is consumed, which means that no intermediate collections are produced, unlike it's a case with arrays etc..

Think monad & category theory if you fancy that.

It can even outperform arrays when used wisely, although it's not the point & arrays are not the primary use case.

Asynchronicity

As it was already pointed out transformation functions do not imply any timing of individual value delivery, which means they can be used on asynchronous data structures like node streams or FRP events & signals.

This feature is extremely powerful as it allows structuring complex asynchronous programs in simple intuitive code without a callback hell and manual error propagation. See lstree for examples.

Even better actually exact same code can be used with both synchronous and asynchronous data structures. For example exact same code in fs-reduce can be forced to do blocking IO by via options.sync option.

Extensibility

Since transformations are source type agnostic it's highly extensible. In fact implementation is based of polymorphic method dispatch library and enables one to add support for new data types without any changes to this library or data types / classes them self. This feature is used by stream-reduce library to add support for node streams. There are more examples of this feature in callback-reduce, dom-reduce, http-reduce...

Very likely all data types like signal provided by this library will be move out into own libraries too.

Automatic disposal

Reducible data structures feature auto cleanup of the resources at the end of consumption. For example dom-reduce and fs-reduce use this feature to remove event listeners / close file descriptors once input is consumed and to set you free from clean up constraints. This means you spend more time on actual problems rather and less on plumbing.

Infinity

Infinite data structures can be trivially represented via reducibles since nothing implies the end. In fact dom-reduce uses this feature to represent user events in form of reducibles that pretty much can be infinite.

That being said reducibles are not the best abstraction for the some types of infinite data structures specially ones that rather better be polled instead.

F.A.Q.

1. Q: Can this handle "back pressure" ?

A: Short answer is Yes.

See IO Coordination for more detailed answer

Install

npm install reducers

Gozala/reducers