There is a third option - using stream().toArray() - see comments under why didn't stream have a toList methodwhy didn't stream have a toList method. It turns out to be slower than forEach() or collect(), and less expressive. It might be optimised in later JDK builds, so adding it here just in case.
assuming List<String>
myFinalList = Arrays.asList(
myListToParse.stream()
.filter(Objects::nonNull)
.map(this::doSomething)
.toArray(String[]::new)
);
with a micro-micro benchmark, 1M entries, 20% nulls and simple transform in doSomething()
private LongSummaryStatistics benchmark(final String testName, final Runnable methodToTest, int samples) {
long[] timing = new long[samples];
for (int i = 0; i < samples; i++) {
long start = System.currentTimeMillis();
methodToTest.run();
timing[i] = System.currentTimeMillis() - start;
}
final LongSummaryStatistics stats = Arrays.stream(timing).summaryStatistics();
System.out.println(testName + ": " + stats);
return stats;
}
the results are
parallel:
toArray: LongSummaryStatistics{count=10, sum=3721, min=321, average=372,100000, max=535}
forEach: LongSummaryStatistics{count=10, sum=3502, min=249, average=350,200000, max=389}
collect: LongSummaryStatistics{count=10, sum=3325, min=265, average=332,500000, max=368}
sequential:
toArray: LongSummaryStatistics{count=10, sum=5493, min=517, average=549,300000, max=569}
forEach: LongSummaryStatistics{count=10, sum=5316, min=427, average=531,600000, max=571}
collect: LongSummaryStatistics{count=10, sum=5380, min=444, average=538,000000, max=557}
parallel without nulls and filter (so the stream is SIZED):
toArrays has the best performance in such case, and .forEach() fails with "indexOutOfBounds" on the recepient ArrayList, had to replace with .forEachOrdered()
toArray: LongSummaryStatistics{count=100, sum=75566, min=707, average=755,660000, max=1107}
forEach: LongSummaryStatistics{count=100, sum=115802, min=992, average=1158,020000, max=1254}
collect: LongSummaryStatistics{count=100, sum=88415, min=732, average=884,150000, max=1014}
