Not so easy functional programming in JavaScript

Introduction

JavaScript allows for operating on arrays in a functional way, e.g. using filter or map functions. As an argument for these functions we can pass lambda expression or function reference. Is there a difference between them? The answer is yes.

What's the problem?

In our project we are builing a mapping using String.fromCharCode function. To simplify the usage of this function looked similar to:

[66, 67, 68].map(v => String.fromCharCode(v))

When we run this code with node we received [ 'B', 'C', 'D' ], but when we decided to refactor it to use function reference the result was different:

> [66, 67, 68].map(String.fromCharCode)
[ 'B\u0000\u0000', 'C\u0001\u0000', 'D\u0002\u0000' ]

What happened?

To find the reason of this behavior, let's first play with function String.fromCharCode alone:

> String.fromCharCode(66)
'B'
> String.fromCharCode([66, 67, 68])
'\u0000'
> String.fromCharCode(66, 67, 68)
'BCD'

String.fromCharCode deals with various types and numbers of arguments.

Now, let's examine the function map:

> [66, 67, 68].map(v => v)
[ 66, 67, 68 ]
> [66, 67, 68].map((v, u) => [v, u])
[ [ 66, 0 ], [ 67, 1 ], [ 68, 2 ] ]
> [66, 67, 68].map((v, u, w) => [v, u, w])
[ [ 66, 0, [ 66, 67, 68 ] ],
  [ 67, 1, [ 66, 67, 68 ] ],
  [ 68, 2, [ 66, 67, 68 ] ] ]

map, like many other array functions, passes always three arguments to function. First is current value, second is the index of current value and third is the whole array.

It means that passing String.fromCharCode to map function under the hood looks like this:

> [66, 67, 68].map((v, u, w) => String.fromCharCode(v, u, w))
[ 'B\u0000\u0000', 'C\u0001\u0000', 'D\u0002\u0000' ]

and it is equal to the initial example.

Conclusion

We have to be careful when we want to use a function which can take more than one argument, but we want to pass only the value. We have to pass the function as a lambda expression:

> [66, 67, 68].map(v => String.fromCharCode(v))
[ 'B', 'C', 'D' ]

or create another function which ensures that only the first argument will be passed to desired function:

> const useOnlyValue = f => v => f(v);
undefined
> [66, 67, 68].map(useOnlyValue(String.fromCharCode))
[ 'B', 'C', 'D' ]

Loops performance in Groovy

Introduction

In the 2018 Advent of Code challenged I solved all the puzzles in Groovy. It is pretty obvious, that choosing good data structure is the most important to obtain performant solution. However, the way we iterate over those structures is also very significant, at least when using Groovy.

Measuring performance

I want to measure how long it takes to sum some numbers. For testing performance of loops I prepared a small function that simply sums some numbers:

void printAddingTime(String message, long to, Closure<Long> adder) {
    LocalTime start = LocalTime.now()
    long sum = adder(to)
    println("$message: $sum calculated in ${Duration.between(start, LocalTime.now()).toMillis()} ms")
}

Pseudo code for summing functions is below:

for i = 1 to n
  for j = 1 to n
    sum += i * j
  end
end

Loops types

Let's implement the summing function in various ways.

collect and sum

First loop type is to use built-in (by Groovy) function collect and sum on collections (Range it this example):

(1..n).collect { long i ->
  (1..n).collect { long j ->
    i * j
  }.sum()
}.sum()

each

Next, let's write the same function using each built-in function on collections (Range it this example) and then add results to accumulator variable:

long sum = 0
(1..n).each { long i ->
    (1..n).each { long j ->
        sum += i * j
    }
}
return sum

times

Now instead of using each we could use the function times built-in on Number by Groovy:

long sum = 0
n.times { long i ->
  n.times { long j ->
    sum += (i + 1)*(j+1)
  }
}
return sum

We have to add 1 to i and j because times generates numbers from 0 to n exclusive.

LongStream with sum

Java 8 came with a new feature - streams. One example of streams is LongStream. Fortunately, it has sum built-in function, which we can use:

LongStream.range(0, n).map { i ->
    LongStream.range(0, n).map { j ->
        (i + 1) * (j + 1)
    }.sum()
}.sum()

LongStream generates numbers in the same way as times function, so we also have to add 1 to i and j here.

LongStream with manual sum

Instead of sum function on LongStream, we can add all numbers manually:

long sum = 0
LongStream.range(0, n).forEach { i ->
    LongStream.range(0, n).forEach { j ->
        sum += (i + 1) * (j + 1)
    }
}
return sum

while

Of course since Groovy inherits from Java a big part of its syntax, we can use the while loop:

long sum = 0
long i = 1
while(i <= n){
    long j = 1
    while(j <= n){
        sum+= i*j
        ++j
    }
    ++i
}
return sum

for

As we can use while, we can also use for loop in Groovy:

long sum = 0
for (long i = 1; i <= n; ++i) {
    for (long j = 1; j <= n; ++j) {
        sum += i * j
    }
}
return sum

Results

My tests I run on Java 1.8 and Groovy 2.5.5. Script loops.groovy was fired using bash script:

#!/bin/sh
for x in 10 100 1000 10000 100000; do
  echo $x
  groovy loops.groovy $x
  echo
done

Values are in milliseconds

Loop  n	10	100	1000	10000	100000
collect + sum	7	22	216	16244	1546822
each	12	17	118	7332	706781
times	2	10	109	8264	708684
LongStream + sum	7	17	127	7679	763341
LongStream + manual sum	18	35	149	6857	680804
while	8	20	103	3166	301967
for	7	10	25	359	27966

As you can spot, for small amount of iterations using built-in Groovy functions is good enough, but for much bigger amount of iterations we should use while or for loops like in plain, old Java.

Show me the code

Code for those examples are available here. You can run those examples on your machine and check performance on your own.