Java – Is ++i Really Faster Than i++ in For-Loops?

From Item 46 in Effective Java by Joshua Bloch :

The for-each loop, introduced in release 1.5, gets rid of the clutter and the opportunity for error by hiding the iterator or index variable completely. The resulting idiom applies equally to collections and arrays:

// The preferred idiom for iterating over collections and arrays
for (Element e : elements) {
    doSomething(e);
}

When you see the colon (:), read it as “in.” Thus, the loop above reads as “for each element e in elements.” Note that there is no performance penalty for using the for-each loop, even for arrays. In fact, it may offer a slight performance advantage over an ordinary for loop in some circumstances, as it computes the limit of the array index only once. While you can do this by hand (Item 45), programmers don’t always do so.

If you want to test something like this, you really must optimize your microbenchmark to measure what you care about.

First, make the loop inexpensive but impossible to skip. Computing a sum usually does the trick.

Second, compare the two timings.

Here's some code that does both:

import java.util.*;

public class Test {

public static long run1() {
  final List<String> myList = Arrays.asList("A", "B", "C", "D");
  final long start = System.nanoTime();
  int sum = 0;
  for (int i = 1000000000; i > myList.size(); i--) sum += i;
  final long stop = System.nanoTime();
  System.out.println("Finish: " + (stop - start)*1e-9 + " ns/op; sum = " + sum);
  return stop-start;
}

public static long run2() {
  final List<String> myList = Arrays.asList("A", "B", "C", "D");
  final long start = System.nanoTime();
  int sum = 0;
  int limit = myList.size();
  for (int i = 1000000000; i > limit; i--) sum += i;
  final long stop = System.nanoTime();
  System.out.println("Finish: " + (stop - start)*1e-9 + " ns/op; sum = " + sum);
  return stop-start;
}

public static void main(String[] args) {
  for (int i=0 ; i<5 ; i++) {
    long t1 = run1();
    long t2 = run2();
    System.out.println("  Speedup = " + (t1-t2)*1e-9 + " ns/op\n");
  }
}

}

And if we run it, on my system we get:

Finish: 0.481741256 ns/op; sum = -243309322
Finish: 0.40228402 ns/op; sum = -243309322
  Speedup = 0.079457236 ns/op

Finish: 0.450627151 ns/op; sum = -243309322
Finish: 0.43534661700000005 ns/op; sum = -243309322
  Speedup = 0.015280534 ns/op

Finish: 0.47738474700000005 ns/op; sum = -243309322
Finish: 0.403698331 ns/op; sum = -243309322
  Speedup = 0.073686416 ns/op

Finish: 0.47729349600000004 ns/op; sum = -243309322
Finish: 0.405540508 ns/op; sum = -243309322
  Speedup = 0.071752988 ns/op

Finish: 0.478979617 ns/op; sum = -243309322
Finish: 0.36067492700000003 ns/op; sum = -243309322
  Speedup = 0.11830469 ns/op

which means that the overhead of the method call is approximately 0.1 ns. If your loop does things that take no more than 1-2 ns, then you should care about this. Otherwise, don't.