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 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: 00:02:44

Benchmark Mode Cnt Score Error Units

StreamBenchTest.benchParallelStream avgt 20 155868805.437 ± 1509175.840 ns/op

StreamBenchTest.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 Units

StreamBenchTest.benchParallelStream avgt 20 414230854.631 ± 725294.455 ns/op

StreamBenchTest.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，感谢你对脚本之家的支持。