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, Closureadder) { 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.
