測試1
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@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線程數
默認是Runtime.getRuntime().availableProcessors() - 1，這里為7
運行結果
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# 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 |
測試2
將數據data改為30，同時sleep改為100
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Benchmark Mode Cnt Score Error UnitsStreamBenchTest.benchParallelStream avgt 20 414230854.631 ± 725294.455 ns/opStreamBenchTest.benchStream avgt 20 3107250608.500 ± 4805037.628 ns/op |
可以發現sleep越長，parallelStream優勢越明顯。
小結
parallelStream在阻塞場景下優勢更明顯，其線程池個數默認為
Runtime.getRuntime().availableProcessors() - 1，如果需修改則需設置-Djava.util.concurrent.ForkJoinPool.common.parallelism=8
以上就是本次講述知識點的全部內容，感謝你對服務器之家的支持。
原文鏈接：https://segmentfault.com/a/1190000012755594
