测试1

@BenchmarkMode(Mode.AverageTime)@OutputTimeUnit(TimeUnit.NANOSECONDS)@Warmup(iterations = 5, time = 3, timeUnit = TimeUnit.SECONDS)@Measurement(iterations = 20, time = 3, timeUnit = TimeUnit.SECONDS)@Fork(1)@State(Scope.Benchmark)public class StreamBenchTest { List<String> data = new ArrayList<>(); @Setup public void init() { // prepare for(int i=0;i<100;i++){ data.add(UUID.randomUUID().toString()); } } @TearDown public void destory() { // destory } @Benchmark public void benchStream(){ data.stream().forEach(e -> { e.getBytes(); try { Thread.sleep(10); } catch (InterruptedException e1) { e1.printStackTrace(); } }); } @Benchmark public void benchParallelStream(){ data.parallelStream().forEach(e -> { e.getBytes(); try { Thread.sleep(10); } catch (InterruptedException e1) { e1.printStackTrace(); } }); } public static void main(String[] args) throws RunnerException { Options opt = new OptionsBuilder() .include(".*" +StreamBenchTest.class.getSimpleName()+ ".*") .forks(1) .build(); new Runner(opt).run(); }}

parallelStream线程数

默认是Runtime.getRuntime().availableProcessors() - 1，这里为7

运行结果

# Run complete. Total time: 00:02:44Benchmark Mode Cnt Score Error UnitsStreamBenchTest.benchParallelStream avgt 20 155868805.437 ± 1509175.840 ns/opStreamBenchTest.benchStream avgt 20 1147570372.950 ± 6138494.414 ns/op

测试2

将数据data改为30，同时sleep改为100

Benchmark Mode Cnt Score Error UnitsStreamBenchTest.benchParallelStream avgt 20 414230854.631 ± 725294.455 ns/opStreamBenchTest.benchStream avgt 20 3107250608.500 ± 4805037.628 ns/op

可以发现sleep越长，parallelStream优势越明显。

小结

parallelStream在阻塞场景下优势更明显，其线程池个数默认为
Runtime.getRuntime().availableProcessors() - 1，如果需修改则需设置-Djava.util.concurrent.ForkJoinPool.common.parallelism=8

以上就是本次讲述知识点的全部内容，感谢你对的支持。