Groovy

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  n10100100010000100000
collect + sum722216162441546822
each12171187332706781
times2101098264708684
LongStream + sum7171277679763341
LongStream + manual sum18351496857680804
while8201033166301967
for7102535927966

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.

Published on Java Code Geeks with permission by Dominik Przybysz, partner at our JCG program. See the original article here: Loops performance in Groovy

Opinions expressed by Java Code Geeks contributors are their own.

Dominik Przybysz

Dominik is a software developer in TouK, committer in Apache Aries and contributor in some open source projects. He writes code using generally the JVM languages, occasionally also makes some scripts in python or shell. Dominik loves testing (especially written in Spock) and any automation in the software development process. He takes care of the clean code (his or someone's else) through frequent code review
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