Actual doSomething function posts ele to a remote API to do some calculations.
My calc function supposed to get the summation of the remote API's calculation for each element, It should run for every element without affecting how nested they are located.
However, Currently, I can't get this to work. How do I fix this?
const doSomething = (ele) => new Promise(resolve => {
console.log(ele);
resolve(ele * 2);//for example
})
const calc = (arr) => new Promise(
async(resolve) => {
console.log(arr.filter(ele => !Array.isArray(ele)));
let sum = 0;
const out = await Promise.all(arr.filter(ele => !Array.isArray(ele))
.map(ele => doSomething(ele)));
sum += out.reduce((a, b) => a + b, 0);
const out2 = await Promise.all(arr.filter(ele => Array.isArray(ele))
.map(ele => calc(ele)));
sum += out2.reduce((a, b) => a + b, 0);
resolve(sum);
}
)
const process = async () => {
console.log('processing..');
const arr = [1, 2, 3, 4, 5, [6,7], 1, [8,[10,11]]];
const out = await calc(arr);
console.log(out);
}
process();
While it may look like I've addressed issues that are non-existent - the original code in the question had ALL the flaws I address in this answer, including Second and Third below
yes, the code in the question now works! But it clearly was flawed
First: no need for Promise constructor in calc function, since you use Promise.all which returns a promise, if you make calc async, just use await
Second: dosomething !== doSomething
Third: out2 is an array, so sum += out2 is going to mess you up
Fourth: .map(ele => doSomething(ele)) can be written .map(doSoemthing) - and the same for the calc(ele) map
So, working code becomes:
const doSomething = (ele) => new Promise(resolve => {
resolve(ele * 2); //for example
})
const calc = async(arr) => {
const out = await Promise.all(arr.filter(ele => !Array.isArray(ele)).map(doSomething));
let sum = out.reduce((a, b) => a + b, 0);
const out2 = await Promise.all(arr.filter(ele => Array.isArray(ele)).map(calc));
sum += out2.reduce((a, b) => a + b, 0);
return sum;
}
const process = async() => {
console.log('processing..');
const arr = [1, 2, 3, 4, 5, [6, 7], 1, [8, [10, 11]]];
const out = await calc(arr);
console.log(out);
}
process();
Can I suggest a slightly different breakdown of the problem?
We can write one function that recursively applies your function to all (nested) elements of your array, and another to recursively total the results.
Then we await the result of the first call and pass it to the second.
I think these functions are simpler, and they are also reusable.
const doSomething = async (ele) => new Promise(resolve => {
setTimeout(() => resolve(ele * 2), 1000);
})
const recursiveCall = async (proc, arr) =>
Promise .all (arr .map (ele =>
Array .isArray (ele) ? recursiveCall (proc, ele) : proc (ele)
))
const recursiveAdd = (ns) =>
ns .reduce ((total, n) => total + (Array .isArray (n) ? recursiveAdd (n) : n), 0)
const process = async() => {
console.log('processing..');
const arr = [1, 2, 3, 4, 5, [6, 7], 1, [8, [10, 11]]];
const processedArr = await recursiveCall (doSomething, arr);
const out = recursiveAdd (processedArr)
console.log(out);
}
process();
I think a generic deepReduce solves this problem well. Notice it's written in synchronous form -
const deepReduce = (f, init = null, xs = []) =>
xs.reduce
( (r, x) =>
Array.isArray(x)
? deepReduce(f, r, x)
: f(r, x)
, init
)
Still, we can use deepReduce asynchronously by initialising with a promise and reducing with an async function -
deepReduce
( async (r, x) =>
await r + await doSomething(x)
, Promise.resolve(0)
, input
)
.then(console.log, console.error)
See the code in action here -
const deepReduce = (f, init = null, xs = []) =>
xs.reduce
( (r, x) =>
Array.isArray(x)
? deepReduce(f, r, x)
: f(r, x)
, init
)
const doSomething = x =>
new Promise(r => setTimeout(r, 200, x * 2))
const input =
[1, 2, 3, 4, 5, [6,7], 1, [8,[10,11]]]
deepReduce
( async (r, x) =>
await r + await doSomething(x)
, Promise.resolve(0)
, input
)
.then(console.log, console.error)
// 2 + 4 + 6 + 8 + (10 + 14) + 2 + (16 + (20 + 22))
// => 116
console.log("doing something. please wait...")
further generalisation
Above we are hand-encoding a summing function, (+), with the empty sum 0. In reality, this function could be more complex and maybe we want a more general pattern so we can construct our program piecewise. Below we take synchronous add and convert it to an asynchronous function using liftAsync2(add) -
const add = (x = 0, y = 0) =>
x + y // <-- synchronous
const main =
pipe
( deepMap(doSomething) // <-- first do something for every item
, deepReduce(liftAsync2(add), Promise.resolve(0)) // <-- then reduce
)
main([1, 2, 3, 4, 5, [6,7], 1, [8,[10,11]]])
.then(console.log, console.error)
// 2 + 4 + 6 + 8 + (10 + 14) + 2 + (16 + (20 + 22))
// => 116
deepMap and deepReduce generics. These are in curried form so they can plug directly into pipe, but that is only a matter of style -
const deepReduce = (f = identity, init = null) => (xs = []) =>
xs.reduce
( (r, x) =>
Array.isArray(x)
? deepReduce(f, r)(x)
: f(r, x)
, init
)
const deepMap = (f = identity) => (xs = []) =>
xs.map
( x =>
Array.isArray(x)
? deepMap(f)(x)
: f(x)
)
liftAsync2 takes a common binary (has two parameters) function and "lifts" it into the asynchronous context. pipe and identity are commonly available in most functional libs or easy to write yourself -
const identity = x =>
x
const pipe = (...fs) =>
x => fs.reduce((r, f) => f(r), x)
const liftAsync2 = f =>
async (x, y) => f (await x, await y)
Here's all of the code in a demo you can run yourself. Notice because deepMap synchronously applies doSomething to all nested elements, all promises are run in parallel. This is in direct contrast to the serial behaviour in the first program. This may or may not be desirable so it's important to understand the difference in how these run -
const identity = x =>
x
const pipe = (...fs) =>
x => fs.reduce((r, f) => f(r), x)
const liftAsync2 = f =>
async (x, y) => f (await x, await y)
const deepReduce = (f = identity, init = null) => (xs = []) =>
xs.reduce
( (r, x) =>
Array.isArray(x)
? deepReduce(f, r)(x)
: f(r, x)
, init
)
const deepMap = (f = identity) => (xs = []) =>
xs.map
( x =>
Array.isArray(x)
? deepMap(f)(x)
: f(x)
)
const doSomething = x =>
new Promise(r => setTimeout(r, 200, x * 2))
const add =
(x, y) => x + y
const main =
pipe
( deepMap(doSomething)
, deepReduce(liftAsync2(add), Promise.resolve(0))
)
main([1, 2, 3, 4, 5, [6,7], 1, [8,[10,11]]])
.then(console.log, console.error)
// 2 + 4 + 6 + 8 + (10 + 14) + 2 + (16 + (20 + 22))
// => 116
console.log("doing something. please wait...")
Related
So consider you have a one line auto return arrow function:
const test = (val) => val;
How would you check the val without doing:
const test = (val) => {
console.log(val);
return val;
};
You can actually use || between the arrow function and the returned value like so:
const test = (val) => console.log(val) || val;
This will not interfere with the process and will also log val without the hassle of adding {} and return and undoing it once you're done
echo is a great function for this -
const echo = (x) =>
( console.log(x)
, x
)
const test = x => echo(x) + 1
const arr = [ 1, 2, 3 ]
const result = arr.map(test)
console.log(result)
Arrow functions are a functional style. Utilising functional techniques will make it more enjoyable to work with arrow expressions -
const effect = f => x =>
(f(x), x)
const echo =
effect(console.log)
const test = x => echo(x + 1) // <-- wrap echo around anything
const arr = [ 1, 2, 3 ]
const result = arr.map(test)
console.log(result)
// 2
// 3
// 4
// [ 2, 3, 4 ]
You can use echof to wrap an entire function, such as echof(test) -
const effect = f => x =>
(f(x), x)
const comp = (f, g) =>
x => f(g(x))
const echo =
effect(console.log)
const echof = f =>
comp(echo, f)
const test = x => x * x
const arr = [ 1, 2, 3 ]
const result = arr.map(echof(test)) // <-- echof
console.log(result)
// 1
// 4
// 9
// [ 1, 4, 9 ]
I have the following recursive compose function:
const compose = (f, n = 1) => n > 1 ?
compose(compose(f), n - 1) :
g => x => f(g(x));
const length = a => a.length;
const filter = p => a => a.filter(p);
const countWhere = compose(length, 2)(filter);
const odd = n => n % 2 === 1;
console.log(countWhere(odd)([1,2,3,4,5,6,7,8,9])); // 5
Now, what I'd like to do is flip the arguments of compose so that the default argument is first:
const compose = (n = 1, f) => n > 1 ? // wishful thinking
compose(n - 1, compose(f)) : // compose(f) is the same as compose(1, f)
g => x => f(g(x));
const length = a => a.length;
const filter = p => a => a.filter(p);
const countWhere = compose(2, length)(filter); // I want to call it like this
const odd = n => n % 2 === 1;
console.log(countWhere(odd)([1,2,3,4,5,6,7,8,9])); // 5
What's the most elegant way to write such functions where the default arguments come first?
Edit: I actually want to create the map and ap methods of functions of various arities, so that I can write:
const length = a => a.length;
const filter = p => a => a.filter(p);
const countWhere = length.map(2, filter); // length <$> filter
const pair = x => y => [x, y];
const add = x => y => x + y;
const mul = x => y => x * y;
const addAndMul = pair.map(2, add).ap(2, mul); // (,) <$> (+) <*> (*)
Hence, I'd rather not curry the methods as Bergi suggested in his answer.
For more information, read: Is implicit wrapping and unwrapping of newtypes in Haskell a sound idea?
I would recommend to just not overload your functions or use default parameters:
const compose = n => f => n > 1
? compose(n - 1)(composeOne(f))
: g => x => f(g(x));
const composeOne = compose(1);
In this case you could probably also just inline it, as it seems composeOne wouldn't be called anywhere else:
const compose = n => f => n > 1
? compose(n - 1)(compose(1)(f))
: g => x => f(g(x));
Or even not do a recursive call at all, but always create the g => x => … lambda and transform it conditionally:
const compose = n => f => {
const l = g => x => f(g(x));
return n > 1 ? compose(n - 1)(l) : l;
};
// same without temporary variables:
const id = x => x;
const compose = n => f => (n > 1 ? compose(n-1) : id)(g => x => f(g(x)))
What's the most elegant way to write such functions where the default arguments come first?
Using only default initialisers requires some arcane hackery:
function demo(n, f = [n, n = 1][0]) {
console.log(n, f);
}
demo(2, "f"); // 2 f
demo("g"); // 1 g
console.log(demo.length) // 1
The most straightforward way would be destructuring with a conditional operator:
function demo(...args) {
const [n, f] = args.length < 2 ? [1, ...args] : args;
console.log(n, f);
}
demo(2, "f"); // 2 f
demo("g"); // 1 g
console.log(demo.length) // 0
More in the spirit of "reversing the order of arguments" might be doing that literally:
function demo(...args) {
const [f, n = 1] = args.reverse();
console.log(n, f);
}
demo(2, "f"); // 2 f
demo("g"); // 1 g
console.log(demo.length) // 0
The latter two attempts have the drawback of requiring an extra declaration (preventing us from using concise arrow functions) and also don't reflect the actual number or required parameters in the .length.
I have this recursive function sum which computes sum of all numbers that were passed to it.
function sum(num1, num2, ...nums) {
if (nums.length === 0) { return num1 + num2; }
return sum(num1 + num2, ...nums);
}
let xs = [];
for (let i = 0; i < 100; i++) { xs.push(i); }
console.log(sum(...xs));
xs = [];
for (let i = 0; i < 10000; i++) { xs.push(i); }
console.log(sum(...xs));
It works fine if only 'few' numbers are passed to it but overflows call stack otherwise. So I have tried to modify it a bit and use trampoline so that it can accept more arguments.
function _sum(num1, num2, ...nums) {
if (nums.length === 0) { return num1 + num2; }
return () => _sum(num1 + num2, ...nums);
}
const trampoline = fn => (...args) => {
let res = fn(...args);
while (typeof res === 'function') { res = res(); }
return res;
}
const sum = trampoline(_sum);
let xs = [];
for (let i = 0; i < 10000; i++) { xs.push(i); }
console.log(sum(...xs));
xs = [];
for (let i = 0; i < 100000; i++) { xs.push(i); }
console.log(sum(...xs));
While the first version isn't able to handle 10000 numbers, the second is. But if I pass 100000 numbers to the second version I'm getting call stack overflow error again.
I would say that 100000 is not really that big of a number (might be wrong here) and don't see any runaway closures that might have caused the memory leak.
Does anyone know what is wrong with it?
The other answer points out the limitation on number of function arguments, but I wanted to remark on your trampoline implementation. The long computation we're running may want to return a function. If you use typeof res === 'function', it's not longer possible to compute a function as a return value!
Instead, encode your trampoline variants with some sort of unique identifiers
const bounce = (f, ...args) =>
({ tag: bounce, f: f, args: args })
const done = (value) =>
({ tag: done, value: value })
const trampoline = t =>
{ while (t && t.tag === bounce)
t = t.f (...t.args)
if (t && t.tag === done)
return t.value
else
throw Error (`unsupported trampoline type: ${t.tag}`)
}
Before we hop on, let's first get an example function to fix
const none =
Symbol ()
const badsum = ([ n1, n2 = none, ...rest ]) =>
n2 === none
? n1
: badsum ([ n1 + n2, ...rest ])
We'll throw a range of numbers at it to see it work
const range = n =>
Array.from
( Array (n + 1)
, (_, n) => n
)
console.log (range (10))
// [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ]
console.log (badsum (range (10)))
// 55
But can it handle the big leagues?
console.log (badsum (range (1000)))
// 500500
console.log (badsum (range (20000)))
// RangeError: Maximum call stack size exceeded
See the results in your browser so far
const none =
Symbol ()
const badsum = ([ n1, n2 = none, ...rest ]) =>
n2 === none
? n1
: badsum ([ n1 + n2, ...rest ])
const range = n =>
Array.from
( Array (n + 1)
, (_, n) => n
)
console.log (range (10))
// [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ]
console.log (badsum (range (1000)))
// 500500
console.log (badsum (range (20000)))
// RangeError: Maximum call stack size exceeded
Somewhere between 10000 and 20000 our badsum function unsurprisingly causes a stack overflow.
Besides renaming the function to goodsum we only have to encode the return types using our trampoline's variants
const goodsum = ([ n1, n2 = none, ...rest ]) =>
n2 === none
? n1
? done (n1)
: goodsum ([ n1 + n2, ...rest ])
: bounce (goodsum, [ n1 + n2, ...rest ])
console.log (trampoline (goodsum (range (1000))))
// 500500
console.log (trampoline (goodsum (range (20000))))
// 200010000
// No more stack overflow!
You can see the results of this program in your browser here. Now we can see that neither recursion nor the trampoline are at fault for this program being slow. Don't worry though, we'll fix that later.
const bounce = (f, ...args) =>
({ tag: bounce, f: f, args: args })
const done = (value) =>
({ tag: done, value: value })
const trampoline = t =>
{ while (t && t.tag === bounce)
t = t.f (...t.args)
if (t && t.tag === done)
return t.value
else
throw Error (`unsupported trampoline type: ${t.tag}`)
}
const none =
Symbol ()
const range = n =>
Array.from
( Array (n + 1)
, (_, n) => n
)
const goodsum = ([ n1, n2 = none, ...rest ]) =>
n2 === none
? done (n1)
: bounce (goodsum, [ n1 + n2, ...rest ])
console.log (trampoline (goodsum (range (1000))))
// 500500
console.log (trampoline (goodsum (range (20000))))
// 200010000
// No more stack overflow!
The extra call to trampoline can get annoying, and when you look at goodsum alone, it's not immediately apparent what done and bounce are doing there, unless maybe this was a very common convention in many of your programs.
We can better encode our looping intentions with a generic loop function. A loop is given a function that is recalled whenever the function calls recur. It looks like a recursive call, but really recur is constructing a value that loop handles in a stack-safe way.
The function we give to loop can have any number of parameters, and with default values. This is also convenient because we can now avoid the expensive ... destructuring and spreading by simply using an index parameter i initialized to 0. The caller of the function does not have the ability to access these variables outside of the loop call
The last advantage here is that the reader of goodsum can clearly see the loop encoding and the explicit done tag is no longer necessary. The user of the function does not need to worry about calling trampoline either as it's already taken care of for us in loop
const goodsum = (ns = []) =>
loop ((sum = 0, i = 0) =>
i >= ns.length
? sum
: recur (sum + ns[i], i + 1))
console.log (goodsum (range (1000)))
// 500500
console.log (goodsum (range (20000)))
// 200010000
console.log (goodsum (range (999999)))
// 499999500000
Here's our loop and recur pair now. This time we expand upon our { tag: ... } convention using a tagging module
const recur = (...values) =>
tag (recur, { values })
const loop = f =>
{ let acc = f ()
while (is (recur, acc))
acc = f (...acc.values)
return acc
}
const T =
Symbol ()
const tag = (t, x) =>
Object.assign (x, { [T]: t })
const is = (t, x) =>
t && x[T] === t
Run it in your browser to verify the results
const T =
Symbol ()
const tag = (t, x) =>
Object.assign (x, { [T]: t })
const is = (t, x) =>
t && x[T] === t
const recur = (...values) =>
tag (recur, { values })
const loop = f =>
{ let acc = f ()
while (is (recur, acc))
acc = f (...acc.values)
return acc
}
const range = n =>
Array.from
( Array (n + 1)
, (_, n) => n
)
const goodsum = (ns = []) =>
loop ((sum = 0, i = 0) =>
i >= ns.length
? sum
: recur (sum + ns[i], i + 1))
console.log (goodsum (range (1000)))
// 500500
console.log (goodsum (range (20000)))
// 200010000
console.log (goodsum (range (999999)))
// 499999500000
extra
My brain has been stuck in anamorphism gear for a few months and I was curious if it was possible to implement a stack-safe unfold using the loop function introduced above
Below, we look at an example program which generates the entire sequence of sums up to n. Think of it as showing the work to arrive at the answer for the goodsum program above. The total sum up to n is the last element in the array.
This is a good use case for unfold. We could write this using loop directly, but the point of this was to stretch the limits of unfold so here goes
const sumseq = (n = 0) =>
unfold
( (loop, done, [ m, sum ]) =>
m > n
? done ()
: loop (sum, [ m + 1, sum + m ])
, [ 1, 0 ]
)
console.log (sumseq (10))
// [ 0, 1, 3, 6, 10, 15, 21, 28, 36, 45 ]
// +1 ↗ +2 ↗ +3 ↗ +4 ↗ +5 ↗ +6 ↗ +7 ↗ +8 ↗ +9 ↗ ...
If we used an unsafe unfold implementation, we could blow the stack
// direct recursion, stack-unsafe!
const unfold = (f, initState) =>
f ( (x, nextState) => [ x, ...unfold (f, nextState) ]
, () => []
, initState
)
console.log (sumseq (20000))
// RangeError: Maximum call stack size exceeded
After playing with it a little bit, it is indeed possible to encode unfold using our stack-safe loop. Cleaning up the ... spread syntax using a push effect makes things a lot quicker too
const push = (xs, x) =>
(xs .push (x), xs)
const unfold = (f, init) =>
loop ((acc = [], state = init) =>
f ( (x, nextState) => recur (push (acc, x), nextState)
, () => acc
, state
))
With a stack-safe unfold, our sumseq function works a treat now
console.time ('sumseq')
const result = sumseq (20000)
console.timeEnd ('sumseq')
console.log (result)
// sumseq: 23 ms
// [ 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ..., 199990000 ]
Verify the result in your browser below
const recur = (...values) =>
tag (recur, { values })
const loop = f =>
{ let acc = f ()
while (is (recur, acc))
acc = f (...acc.values)
return acc
}
const T =
Symbol ()
const tag = (t, x) =>
Object.assign (x, { [T]: t })
const is = (t, x) =>
t && x[T] === t
const push = (xs, x) =>
(xs .push (x), xs)
const unfold = (f, init) =>
loop ((acc = [], state = init) =>
f ( (x, nextState) => recur (push (acc, x), nextState)
, () => acc
, state
))
const sumseq = (n = 0) =>
unfold
( (loop, done, [ m, sum ]) =>
m > n
? done ()
: loop (sum, [ m + 1, sum + m ])
, [ 1, 0 ]
)
console.time ('sumseq')
const result = sumseq (20000)
console.timeEnd ('sumseq')
console.log (result)
// sumseq: 23 ms
// [ 0, 1, 3, 6, 10, 15, 21, 28, 36, 45, ..., 199990000 ]
Browsers have practical limits on the number of arguments a function can take
You can change the sum signature to accept an array rather than a varying number of arguments, and use destructuring to keep the syntax/readability similar to what you have. This "fixes" the stackoverflow error, but is increadibly slow :D
function _sum([num1, num2, ...nums]) { /* ... */ }
I.e.:, if you're running in to problems with maximum argument counts, your recursive/trampoline approach is probably going to be too slow to work with...
The other answer already explained the issue with your code. This answer demonstrates that trampolines are sufficiently fast for most array based computations and offer a higher level of abstraction:
// trampoline
const loop = f => {
let acc = f();
while (acc && acc.type === recur)
acc = f(...acc.args);
return acc;
};
const recur = (...args) =>
({type: recur, args});
// sum
const sum = xs => {
const len = xs.length;
return loop(
(acc = 0, i = 0) =>
i === len
? acc
: recur(acc + xs[i], i + 1));
};
// and run...
const xs = Array(1e5)
.fill(0)
.map((x, i) => i);
console.log(sum(xs));
If a trampoline based computation causes performance problems, then you can still replace it with a bare loop.
I really like chaining Array.prototype.map, filter and reduce to define a data transformation. Unfortunately, in a recent project that involved large log files, I could no longer get away with looping through my data multiple times...
My goal:
I want to create a function that chains .filter and .map methods by, instead of mapping over an array immediately, composing a function that loops over the data once. I.e.:
const DataTransformation = () => ({
map: fn => (/* ... */),
filter: fn => (/* ... */),
run: arr => (/* ... */)
});
const someTransformation = DataTransformation()
.map(x => x + 1)
.filter(x => x > 3)
.map(x => x / 2);
// returns [ 2, 2.5 ] without creating [ 2, 3, 4, 5] and [4, 5] in between
const myData = someTransformation.run([ 1, 2, 3, 4]);
My attempt:
Inspired by this answer and this blogpost I started writing a Transduce function.
const filterer = pred => reducer => (acc, x) =>
pred(x) ? reducer(acc, x) : acc;
const mapper = map => reducer => (acc, x) =>
reducer(acc, map(x));
const Transduce = (reducer = (acc, x) => (acc.push(x), acc)) => ({
map: map => Transduce(mapper(map)(reducer)),
filter: pred => Transduce(filterer(pred)(reducer)),
run: arr => arr.reduce(reducer, [])
});
The problem:
The problem with the Transduce snippet above, is that it runs “backwards”... The last method I chain is the first to be executed:
const someTransformation = Transduce()
.map(x => x + 1)
.filter(x => x > 3)
.map(x => x / 2);
// Instead of [ 2, 2.5 ] this returns []
// starts with (x / 2) -> [0.5, 1, 1.5, 2]
// then filters (x < 3) -> []
const myData = someTransformation.run([ 1, 2, 3, 4]);
Or, in more abstract terms:
Go from:
Transducer(concat).map(f).map(g) == (acc, x) => concat(acc, f(g(x)))
To:
Transducer(concat).map(f).map(g) == (acc, x) => concat(acc, g(f(x)))
Which is similar to:
mapper(f) (mapper(g) (concat))
I think I understand why it happens, but I can't figure out how to fix it without changing the “interface” of my function.
The question:
How can I make my Transduce method chain filter and map operations in the correct order?
Notes:
I'm only just learning about the naming of some of the things I'm trying to do. Please let me know if I've incorrectly used the Transduce term or if there are better ways to describe the problem.
I'm aware I can do the same using a nested for loop:
const push = (acc, x) => (acc.push(x), acc);
const ActionChain = (actions = []) => {
const run = arr =>
arr.reduce((acc, x) => {
for (let i = 0, action; i < actions.length; i += 1) {
action = actions[i];
if (action.type === "FILTER") {
if (action.fn(x)) {
continue;
}
return acc;
} else if (action.type === "MAP") {
x = action.fn(x);
}
}
acc.push(x);
return acc;
}, []);
const addAction = type => fn =>
ActionChain(push(actions, { type, fn }));
return {
map: addAction("MAP"),
filter: addAction("FILTER"),
run
};
};
// Compare to regular chain to check if
// there's a performance gain
// Admittedly, in this example, it's quite small...
const naiveApproach = {
run: arr =>
arr
.map(x => x + 3)
.filter(x => x % 3 === 0)
.map(x => x / 3)
.filter(x => x < 40)
};
const actionChain = ActionChain()
.map(x => x + 3)
.filter(x => x % 3 === 0)
.map(x => x / 3)
.filter(x => x < 40)
const testData = Array.from(Array(100000), (x, i) => i);
console.time("naive");
const result1 = naiveApproach.run(testData);
console.timeEnd("naive");
console.time("chain");
const result2 = actionChain.run(testData);
console.timeEnd("chain");
console.log("equal:", JSON.stringify(result1) === JSON.stringify(result2));
Here's my attempt in a stack snippet:
const filterer = pred => reducer => (acc, x) =>
pred(x) ? reducer(acc, x) : acc;
const mapper = map => reducer => (acc, x) => reducer(acc, map(x));
const Transduce = (reducer = (acc, x) => (acc.push(x), acc)) => ({
map: map => Transduce(mapper(map)(reducer)),
filter: pred => Transduce(filterer(pred)(reducer)),
run: arr => arr.reduce(reducer, [])
});
const sameDataTransformation = Transduce()
.map(x => x + 5)
.filter(x => x % 2 === 0)
.map(x => x / 2)
.filter(x => x < 4);
// It's backwards:
// [-1, 0, 1, 2, 3]
// [-0.5, 0, 0.5, 1, 1.5]
// [0]
// [5]
console.log(sameDataTransformation.run([-1, 0, 1, 2, 3, 4, 5]));
before we know better
I really like chaining ...
I see that, and I'll appease you, but you'll come to understand that forcing your program through a chaining API is unnatural, and more trouble than it's worth in most cases.
const Transduce = (reducer = (acc, x) => (acc.push(x), acc)) => ({
map: map => Transduce(mapper(map)(reducer)),
filter: pred => Transduce(filterer(pred)(reducer)),
run: arr => arr.reduce(reducer, [])
});
I think I understand why it happens, but I can't figure out how to fix it without changing the “interface” of my function.
The problem is indeed with your Transduce constructor. Your map and filter methods are stacking map and pred on the outside of the transducer chain, instead of nesting them inside.
Below, I've implemented your Transduce API that evaluates the maps and filters in correct order. I've also added a log method so that we can see how Transduce is behaving
const Transduce = (f = k => k) => ({
map: g =>
Transduce(k =>
f ((acc, x) => k(acc, g(x)))),
filter: g =>
Transduce(k =>
f ((acc, x) => g(x) ? k(acc, x) : acc)),
log: s =>
Transduce(k =>
f ((acc, x) => (console.log(s, x), k(acc, x)))),
run: xs =>
xs.reduce(f((acc, x) => acc.concat(x)), [])
})
const foo = nums => {
return Transduce()
.log('greater than 2?')
.filter(x => x > 2)
.log('\tsquare:')
.map(x => x * x)
.log('\t\tless than 30?')
.filter(x => x < 30)
.log('\t\t\tpass')
.run(nums)
}
// keep square(n), forall n of nums
// where n > 2
// where square(n) < 30
console.log(foo([1,2,3,4,5,6,7]))
// => [ 9, 16, 25 ]
untapped potential
Inspired by this answer ...
In reading that answer I wrote, you overlook the generic quality of Trans as it was written there. Here, our Transduce only attempts to work with Arrays, but really it can work with any type that has an empty value ([]) and a concat method. These two properties make up a category called Monoids and we'd be doing ourselves a disservice if we didn't take advantage of transducer's ability to work with any type in this category.
Above, we hard-coded the initial accumulator [] in the run method, but this should probably be supplied as an argument – much like we do with iterable.reduce(reducer, initialAcc)
Aside from that, both implementations are essentially equivalent. The biggest difference is that the Trans implementation provided in the linked answer is Trans itself is a monoid, but Transduce here is not. Trans neatly implements composition of transducers in the concat method whereas Transduce (above) has composition mixed within each method. Making it a monoid allows us to rationalize Trans the same way do all other monoids, instead of having to understand it as some specialized chaining interface with unique map, filter, and run methods.
I would advise building from Trans instead of making your own custom API
have your cake and eat it too
So we learned the valuable lesson of uniform interfaces and we understand that Trans is inherently simple. But, you still want that sweet chaining API. OK, ok...
We're going to implement Transduce one more time, but this time we'll do so using the Trans monoid. Here, Transduce holds a Trans value instead of a continuation (Function).
Everything else stays the same – foo takes 1 tiny change and produces an identical output.
// generic transducers
const mapper = f =>
Trans(k => (acc, x) => k(acc, f(x)))
const filterer = f =>
Trans(k => (acc, x) => f(x) ? k(acc, x) : acc)
const logger = label =>
Trans(k => (acc, x) => (console.log(label, x), k(acc, x)))
// magic chaining api made with Trans monoid
const Transduce = (t = Trans.empty()) => ({
map: f =>
Transduce(t.concat(mapper(f))),
filter: f =>
Transduce(t.concat(filterer(f))),
log: s =>
Transduce(t.concat(logger(s))),
run: (m, xs) =>
transduce(t, m, xs)
})
// when we run, we must specify the type to transduce
// .run(Array, nums)
// instead of
// .run(nums)
Expand this code snippet to see the final implementation – of course you could skip defining a separate mapper, filterer, and logger, and instead define those directly on Transduce. I think this reads nicer tho.
// Trans monoid
const Trans = f => ({
runTrans: f,
concat: ({runTrans: g}) =>
Trans(k => f(g(k)))
})
Trans.empty = () =>
Trans(k => k)
const transduce = (t, m, xs) =>
xs.reduce(t.runTrans((acc, x) => acc.concat(x)), m.empty())
// complete Array monoid implementation
Array.empty = () => []
// generic transducers
const mapper = f =>
Trans(k => (acc, x) => k(acc, f(x)))
const filterer = f =>
Trans(k => (acc, x) => f(x) ? k(acc, x) : acc)
const logger = label =>
Trans(k => (acc, x) => (console.log(label, x), k(acc, x)))
// now implemented with Trans monoid
const Transduce = (t = Trans.empty()) => ({
map: f =>
Transduce(t.concat(mapper(f))),
filter: f =>
Transduce(t.concat(filterer(f))),
log: s =>
Transduce(t.concat(logger(s))),
run: (m, xs) =>
transduce(t, m, xs)
})
// this stays exactly the same
const foo = nums => {
return Transduce()
.log('greater than 2?')
.filter(x => x > 2)
.log('\tsquare:')
.map(x => x * x)
.log('\t\tless than 30?')
.filter(x => x < 30)
.log('\t\t\tpass')
.run(Array, nums)
}
// output is exactly the same
console.log(foo([1,2,3,4,5,6,7]))
// => [ 9, 16, 25 ]
wrap up
So we started with a mess of lambdas and then made things simpler using a monoid. The Trans monoid provides distinct advantages in that the monoid interface is known and the generic implementation is extremely simple. But we're stubborn or maybe we have goals to fulfill that are not set by us – we decide to build the magic Transduce chaining API, but we do so using our rock-solid Trans monoid which gives us all the power of Trans but also keeps complexity nicely compartmentalised.
dot chaining fetishists anonymous
Here's a couple other recent answers I wrote about method chaining
Is there any way to make a functions return accessible via a property?
Chaining functions and using an anonymous function
Pass result of functional chain to function
I think you need to change the order of your implementations:
const filterer = pred => reducer => (x) =>pred((a=reducer(x) )?x: undefined;
const mapper = map => reducer => (x) => map(reducer(x));
Then you need to change the run command to:
run: arr => arr.reduce((a,b)=>a.concat([reducer(b)]), []);
And the default reducer must be
x=>x
However, this way the filter wont work. You may throw undefined in the filter function and catch in the run function:
run: arr => arr.reduce((a,b)=>{
try{
a.push(reducer(b));
}catch(e){}
return a;
}, []);
const filterer = pred => reducer => (x) =>{
if(!pred((a=reducer(x))){
throw undefined;
}
return x;
};
However, all in all i think the for loop is much more elegant in this case...
I'm following an article about Transducers in JavaScript, and in particular I have defined the following functions
const reducer = (acc, val) => acc.concat([val]);
const reduceWith = (reducer, seed, iterable) => {
let accumulation = seed;
for (const value of iterable) {
accumulation = reducer(accumulation, value);
}
return accumulation;
}
const map =
fn =>
reducer =>
(acc, val) => reducer(acc, fn(val));
const sumOf = (acc, val) => acc + val;
const power =
(base, exponent) => Math.pow(base, exponent);
const squares = map(x => power(x, 2));
const one2ten = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
res1 = reduceWith(squares(sumOf), 0, one2ten);
const divtwo = map(x => x / 2);
Now I want to define a composition operator
const more = (f, g) => (...args) => f(g(...args));
and I see that it is working in the following cases
res2 = reduceWith(more(squares,divtwo)(sumOf), 0, one2ten);
res3 = reduceWith(more(divtwo,squares)(sumOf), 0, one2ten);
which are equivalent to
res2 = reduceWith(squares(divtwo(sumOf)), 0, one2ten);
res3 = reduceWith(divtwo(squares(sumOf)), 0, one2ten);
The whole script is online.
I don't understand why I can't concatenate also the last function (sumOf) with the composition operator (more). Ideally I'd like to write
res2 = reduceWith(more(squares,divtwo,sumOf), 0, one2ten);
res3 = reduceWith(more(divtwo,squares,sumOf), 0, one2ten);
but it doesn't work.
Edit
It is clear that my initial attempt was wrong, but even if I define the composition as
const compose = (...fns) => x => fns.reduceRight((v, fn) => fn(v), x);
I still can't replace compose(divtwo,squares)(sumOf) with compose(divtwo,squares,sumOf)
Finally I've found a way to implement the composition that seems to work fine
const more = (f, ...g) => {
if (g.length === 0) return f;
if (g.length === 1) return f(g[0]);
return f(more(...g));
}
Better solution
Here it is another solution with a reducer and no recursion
const compose = (...fns) => (...x) => fns.reduceRight((v, fn) => fn(v), ...x);
const more = (...args) => compose(...args)();
usage:
res2 = reduceWith(more(squares,divtwo,sumOf), 0, one2ten);
res3 = reduceWith(more(divtwo,squares,sumOf), 0, one2ten);
full script online
Your more operates with only 2 functions. And the problem is here more(squares,divtwo)(sumOf) you execute a function, and here more(squares,divtwo, sumOf) you return a function which expects another call (fo example const f = more(squares,divtwo, sumOf); f(args)).
In order to have a variable number of composable functions you can define a different more for functions composition. Regular way of composing any number of functions is compose or pipe functions (the difference is arguments order: pipe takes functions left-to-right in execution order, compose - the opposite).
Regular way of defining pipe or compose:
const pipe = (...fns) => x => fns.reduce((v, fn) => fn(v), x);
const compose = (...fns) => x => fns.reduceRight((v, fn) => fn(v), x);
You can change x to (...args) to match your more definition.
Now you can execute any number of functions one by one:
const pipe = (...fns) => x => fns.reduce((v, fn) => fn(v), x);
const compose = (...fns) => x => fns.reduceRight((v, fn) => fn(v), x);
const inc = x => x + 1;
const triple = x => x * 3;
const log = x => { console.log(x); return x; } // log x, then return x for further processing
// left to right application
const pipe_ex = pipe(inc, log, triple, log)(10);
// right to left application
const compose_ex = compose(log, inc, log, triple)(10);
I still can't replace compose(divtwo,squares)(sumOf) with compose(divtwo,squares,sumOf)
Yes, they are not equivalent. And you shouldn't try anyway! Notice that divtwo and squares are transducers, while sumOf is a reducer. They have different types. Don't build a more function that mixes them up.
If you insist on using a dynamic number of transducers, put them in an array:
[divtwo, squares].reduceRight((t, r) => t(r), sumOf)