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: 00:02:44 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。
