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    Utilities for functional programming with asynchronous JavaScript data (using async iterators). Use functional programming operations on async data (for example, streams)!

    Of course it's possible to just build up the raw data from an async generator or stream and then apply functional operations synchronously. However, for large sets of data this can require significant amounts of memory. This library operates on the data stream as it flows, applying the reduce operations to one piece of data at a time over time -- so only the final transformed data is stored as a single blob in memory.

    You can have your (functional) cake and still use memory-saving async techniques!


    import { compose, filter } from 'async-functional-utils';
    // Get some "async" data using an async generator
    async function* count() {
      for await (const a of [8, 9, 10, 11, 12]) {
        yield a;
    // Use `compose` to combine functional operations
    const result = await compose(
      // `filter` takes a filter function and only passes through
      // values that match the filter criteria
      filter((value) => value > 10),
      // `map` passes through each value, transformed by the map operation
      map((value) => value + 1)
    )(count() /* call generator function here */);
    // result === [12, 13]


    compose(...reducers)(iterator): Promise<Array|Object>

    The compose function is the core of the library. Use compose to specify the iterator (usually created by a function* generator) whose data will be transformed, and the functional operations to apply to that data.

    As pieces of data are pushed out by the iterator, compose calls the reducers one-by-one with each new piece of data. Reducers are chained, so the output of the first reducer (applied to the value) is passed to the next reducer as the input value. The accumulated result of the final reducer is eventually returned (once all iterations are complete).

    compose is a higher-order function -- it creates a composed function of functional operations. You call that function, passing it a generator function (the "data source"), and it returns a Promise that is resolved when the generator returns an iterator with done: true:

    async function* myGenerator() { ... };
    const resultPromise = compose(
    resultPromise.then((result) => {
      console.log('result', result); // the final result of generator -> transformations

    context Arguments:

    • ...reducers: One or more reducer operations to use to transform the data. These can be the operations from this library (reduce, map, filter) or you can write your own higher-level operations. (Tip: all the funcitonal operations are built on top of reduce).

    context Return value:

    • A composed function composedFn(iterator):Promise<Array|Object>: Call this function with an iterator (usually returned by an async generator); the reducer operations will be asynchronously applied to the iterator values as each new value is yielded.

    composedFn Argument:

    • iterator: An object that conforms to the iterator protocol. In practice this will be an async iterator returned from calling an async generator (async function*) function.

    composedFn Return value:

    • Promise<Array|Object>: A Promise that resolves when the iterator has yielded its final value.

    The resolved value is an Object if the final reducer passed to compose is a reduce reducer; otherwise (e.g. if the final reducer is a map or filter reducer) the resolved value is an Array with one element per iteration value.


    Use the filter reducer to only pass a subset of data to subsequent reducers (or the result). Like Array.filter(), it takes a filter function as an argument to specify the filter criteria.

    filter Argument:

    • filterFn: (value: any) => boolean: A function you provide to test each value to determine whether to keep or discard it. Return true to pass the value through to the next reducer (or result); return false to exclude it.


    Use the map reducer to apply a transformation to each value. This behaves like -- you pass it a function to define the transform operation.

    map Argument:

    • mapFn: (value: any) => any: A function you provide to define the transformation to map input values to output values. Given value as the first parameter, return the output value that will be passed to the next reducer (or result).

    reduce(reducerFn, initialValue)

    Use the reduce reducer to combine the set of values into a single result value. This can be some type of collection, an object (for example with properties derived from values), the result of an aggregate computation (e.g. the sum of all values), or anything else you desire. This behaves like Array.reduce() in that you give it two arguments: a reducer function to define the transformation, and an initial value that is passed to the reducer function as the accumulator on the first iteration.

    Note: unlike other reducers, reduce must be the final reducer in the composed reducer chain. The output of reduce can be any object type, so it is not iterable.

    reduce Arguments:

    • reducerFn: (accumulator: <T>, value: any) => <T>: A function you provide to define the transformation that creates the eventual result. Your reducer function should accept two arguments and return the accumulator to be passed to the next iteration (or to be returned as the final result).

      reducerFn Arguments:
      • accumulator: <T>: the current state of the result after all previous iterations; on the first iteration this will be the initialValue passed as the second argument to reduce(), and it will usually be a modified version of that value.
      • value: any: the value passed into this reducer for the current iteration
      reducerFn Return value:
      • <T>: The state of the in-progress result (i.e. the accumulator) at the end of the current iteration. This value is passed to the next call of reducerFn as the accumulator argument, and (after the final iteration) returned as the result of the functional operation(s).
    • initialValue: <T>: The starting point of the value that is being computed by the reducer. This value is passed to the reducerFn on the first iteration, and will usually be an "empty" version of the eventual result.

    Making a custom reducer

    One foundation of functional programming is that all functional operations are simply specialized implementations built on top of reduce. That is true for this library as well (map and filter are both implemented as reduce operations, because internally compose treats all reducers as reduce operations). This makes writing a reducer for this library very much like writing a reducer function for Array.reduce().

    In fact, with map and reduce already available, chances are you don't even need to make a custom reducer -- almost any transformation you want to apply to the data can be implemented using the built-in reducers:

    • map: for one-to-one operations, where each input value is transformed in some way and then added to the output as an individual value
    • reduce: for many-to-one (or many-to-N) operations, where each input value is used to modify the final output in some way
    • filter: for when you want to exclude certain values immediately, before applying map or reduce operations.

    Nevertheless, if you find yourself wanting to make a custom reducer, you certainly can (also, please submit a pull request to add the functionality to this library 😊)

    To make your own custom reducer to use with this library, you must specify two things:

    1. The reducer function to be called (like the first argument of Array.reduce())
    2. The initial value of the reducer (like the second argument of Array.reduce())

    In this case the two things are bundled together in a single object: the reducer function itself, and a property named initial added to the reducer Function object.

    The reducer function

    A function that implements the reducer signature (a modified version of the signature for functions passed to Array.reduce()):

    function reducer(accumulator: <T>, value: any): { accumulator: [], valueOut: any }

    reducer Arguments:

    • accumulator: The "result" of the reducer, collected and modified over each iteration
    • value: The value passed through for the current iteration.

    Note that unlike Array.reduce, no third and fourth arguments (index and collection) are passed to the reducer function. This is because of the asynchronous nature of the operations (so "index" and "collection" don't really exist) as well as the fact that maintaining the entire collection in memory would go against the point of this library, to be able to operate on the data one piece at a time over time.

    reducer Return value:

    • An object with two properties:
      • accumulator: The new "result" of the reducer, usually the previous accumulator argument (potentially) modified in some way based on the value
      • valueOut: The output value that is passed to the next reducer. This is usually the value argument with some transformation applied. Set valueOut to undefined to short-circuit the current iteration and not pass anything to any subsequent reducer(s).

    The initial value:

    This is the intial state of the accumulator result of this reducer operation. This is conceptually identical to the second argument passed to Array.reduce(). If the operation is a map-like operation that treats each iteration value as an element in an Array, set initial to []. Otherwise set it to a reasonable initial value for your accumulator.

    function myCustomReducerOperation(accumulator, value) { /* ... */ }
    myCustomReducerOperation.initial = []; // or {} or whatever you want

    Putting it together

    Your final reducer should be a function that takes any necessary arguments and returns a function (the reducer function augmented with initial property). For example here is a reducer that is like lodash's map operation with the "property name" shorthand (i.e. it "picks" the property with the specified name from each value and returns an array of those properties' values):

    function pickMap(propName) {
      // Define the reducer
      function pickMapReducer(accumulator, value) {
        const picked = value[propName];
        return {
          accumulator: [...accumulator, picked],
          valueOut: picked,
      // Add the initial value
      mapByPropertyReducer.initial = [];
      return mapByPropertyReducer
    // Assume a generator `aAndB` that returns values in the form {a: 'foo', b: 'bar' }
    const result = await compose(pickMap('a'))(aAndB());
    // result === ['foo', ...]


    npm i async-generator-functional-utils

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    • probertson