56

作为我对在流中使用复杂过滤器或多个过滤器之间的区别进行调查的一部分,我注意到 Java 12 的性能比 Java 8 慢得多。

这些奇怪的结果有什么解释吗?我在这里错过了什么吗?

配置:

  • 爪哇 8

    • OpenJDK 运行时环境 (build 1.8.0_181-8u181-b13-2~deb9u1-b13)
    • OpenJDK 64 位服务器 VM(内部版本 25.181-b13,混合模式)
  • 爪哇 12

    • OpenJDK 运行时环境 (build 12+33)
    • OpenJDK 64-Bit Server VM(build 12+33,混合模式,共享)
  • 虚拟机选项:-XX:+UseG1GC -server -Xmx1024m -Xms1024m

  • CPU:8核

JMH 吞吐量结果

  • 预热:10 次迭代,每次 1 秒
  • 测量:10 次迭代,每次 1 秒
  • 线程:1个线程,将同步迭代
  • 单位:操作/秒

比较表

代码

流 + 复杂过滤器

public void complexFilter(ExecutionPlan plan, Blackhole blackhole) {
        long count = plan.getDoubles()
                .stream()
                .filter(d -> d < Math.PI
                        && d > Math.E
                        && d != 3
                        && d != 2)
                .count();

        blackhole.consume(count);
    }

流 + 多个过滤器

public void multipleFilters(ExecutionPlan plan, Blackhole blackhole) {
        long count = plan.getDoubles()
                .stream()
                .filter(d -> d > Math.PI)
                .filter(d -> d < Math.E)
                .filter(d -> d != 3)
                .filter(d -> d != 2)
                .count();

        blackhole.consume(count);
    }

并行流+复杂过滤器

public void complexFilterParallel(ExecutionPlan plan, Blackhole blackhole) {
        long count = plan.getDoubles()
                .stream()
                .parallel()
                .filter(d -> d < Math.PI
                        && d > Math.E
                        && d != 3
                        && d != 2)
                .count();

        blackhole.consume(count);
    }

并行流+多个过滤器

public void multipleFiltersParallel(ExecutionPlan plan, Blackhole blackhole) {
        long count = plan.getDoubles()
                .stream()
                .parallel()
                .filter(d -> d > Math.PI)
                .filter(d -> d < Math.E)
                .filter(d -> d != 3)
                .filter(d -> d != 2)
                .count();

        blackhole.consume(count);
    }

旧时尚 java 迭代

public void oldFashionFilters(ExecutionPlan plan, Blackhole blackhole) {
        long count = 0;
        for (int i = 0; i < plan.getDoubles().size(); i++) {
            if (plan.getDoubles().get(i) > Math.PI
                    && plan.getDoubles().get(i) > Math.E
                    && plan.getDoubles().get(i) != 3
                    && plan.getDoubles().get(i) != 2) {
                count = count + 1;
            }
        }

        blackhole.consume(count);
    }

您可以通过运行 docker 命令自行尝试:

对于 Java 8:

docker run -it volkodav/java-filter-benchmark:java8

对于 Java 12:

docker run -it volkodav/java-filter-benchmark:java12

源代码:

https://github.com/volkodavs/javafilters-benchmarks

4

1 回答 1

24

谢谢大家的帮助,尤其是@Aleksey Shipilev!

在对 JMH 基准进行更改后,结果看起来更真实(?)

变化:

  1. 更改要在每次基准测试迭代之前/之后执行的设置方法。

    @Setup(Level.Invocation)->@Setup(Level.Iteration)

  2. 停止 JMH 在迭代之间强制 GC。在每次迭代之前强制进行 Full GC 很可能会抛弃 GC 启发式。(c) 阿列克谢·希皮列夫

    -gc true->-gc false

注意:gc 默认为 false。

比较表

基于新的性能基准,Java 12 与 Java 8 相比没有性能下降。

注意:在这些更改之后,小数组大小的吞吐量误差显着增加了 100% 以上,而大数据集保持不变。

结果表

原始结果

爪哇 8

# Run complete. Total time: 04:36:29

Benchmark                                (arraySize)   Mode  Cnt         Score         Error  Units
FilterBenchmark.complexFilter                     10  thrpt   50   5947577.648 ±  257535.736  ops/s
FilterBenchmark.complexFilter                    100  thrpt   50   3131081.555 ±   72868.963  ops/s
FilterBenchmark.complexFilter                   1000  thrpt   50    489666.688 ±    6539.466  ops/s
FilterBenchmark.complexFilter                  10000  thrpt   50     17297.424 ±      93.890  ops/s
FilterBenchmark.complexFilter                 100000  thrpt   50      1398.702 ±      72.820  ops/s
FilterBenchmark.complexFilter                1000000  thrpt   50        81.309 ±       0.547  ops/s
FilterBenchmark.complexFilterParallel             10  thrpt   50     24515.743 ±     450.363  ops/s
FilterBenchmark.complexFilterParallel            100  thrpt   50     25584.773 ±     290.249  ops/s
FilterBenchmark.complexFilterParallel           1000  thrpt   50     24313.066 ±     425.817  ops/s
FilterBenchmark.complexFilterParallel          10000  thrpt   50     11909.085 ±      51.534  ops/s
FilterBenchmark.complexFilterParallel         100000  thrpt   50      3260.864 ±     522.565  ops/s
FilterBenchmark.complexFilterParallel        1000000  thrpt   50       406.297 ±      96.590  ops/s
FilterBenchmark.multipleFilters                   10  thrpt   50   3785766.911 ±   27971.998  ops/s
FilterBenchmark.multipleFilters                  100  thrpt   50   1806210.041 ±   11578.529  ops/s
FilterBenchmark.multipleFilters                 1000  thrpt   50    211435.445 ±   28585.969  ops/s
FilterBenchmark.multipleFilters                10000  thrpt   50     12614.670 ±     370.086  ops/s
FilterBenchmark.multipleFilters               100000  thrpt   50      1228.127 ±      21.208  ops/s
FilterBenchmark.multipleFilters              1000000  thrpt   50        99.149 ±       1.370  ops/s
FilterBenchmark.multipleFiltersParallel           10  thrpt   50     23896.812 ±     255.117  ops/s
FilterBenchmark.multipleFiltersParallel          100  thrpt   50     25314.613 ±     169.724  ops/s
FilterBenchmark.multipleFiltersParallel         1000  thrpt   50     23113.388 ±     305.605  ops/s
FilterBenchmark.multipleFiltersParallel        10000  thrpt   50     12676.057 ±     119.555  ops/s
FilterBenchmark.multipleFiltersParallel       100000  thrpt   50      3373.367 ±     211.108  ops/s
FilterBenchmark.multipleFiltersParallel      1000000  thrpt   50       477.870 ±      70.878  ops/s
FilterBenchmark.oldFashionFilters                 10  thrpt   50  45874144.758 ± 2210325.177  ops/s
FilterBenchmark.oldFashionFilters                100  thrpt   50   4902625.828 ±   60397.844  ops/s
FilterBenchmark.oldFashionFilters               1000  thrpt   50    662102.438 ±    5038.465  ops/s
FilterBenchmark.oldFashionFilters              10000  thrpt   50     29390.911 ±     257.311  ops/s
FilterBenchmark.oldFashionFilters             100000  thrpt   50      1999.032 ±       6.829  ops/s
FilterBenchmark.oldFashionFilters            1000000  thrpt   50       200.564 ±       1.695  ops/s

爪哇 12

# Run complete. Total time: 04:36:20
    
Benchmark                                (arraySize)   Mode  Cnt         Score         Error  Units
FilterBenchmark.complexFilter                     10  thrpt   50  10338525.553 ? 1677693.433  ops/s
FilterBenchmark.complexFilter                    100  thrpt   50   4381301.188 ?  287299.598  ops/s
FilterBenchmark.complexFilter                   1000  thrpt   50    607572.430 ?    9367.026  ops/s
FilterBenchmark.complexFilter                  10000  thrpt   50     30643.286 ?     472.033  ops/s
FilterBenchmark.complexFilter                 100000  thrpt   50      1450.341 ?       3.730  ops/s
FilterBenchmark.complexFilter                1000000  thrpt   50       138.996 ?       2.052  ops/s
FilterBenchmark.complexFilterParallel             10  thrpt   50     21289.444 ?     183.245  ops/s
FilterBenchmark.complexFilterParallel            100  thrpt   50     20105.239 ?     124.759  ops/s
FilterBenchmark.complexFilterParallel           1000  thrpt   50     19418.830 ?     141.664  ops/s
FilterBenchmark.complexFilterParallel          10000  thrpt   50     13874.585 ?     104.418  ops/s
FilterBenchmark.complexFilterParallel         100000  thrpt   50      5334.947 ?      25.452  ops/s
FilterBenchmark.complexFilterParallel        1000000  thrpt   50       781.046 ?       9.687  ops/s
FilterBenchmark.multipleFilters                   10  thrpt   50   5460308.048 ?  478157.935  ops/s
FilterBenchmark.multipleFilters                  100  thrpt   50   2227583.836 ?  113078.932  ops/s
FilterBenchmark.multipleFilters                 1000  thrpt   50    287157.190 ?    1114.346  ops/s
FilterBenchmark.multipleFilters                10000  thrpt   50     16268.016 ?     704.735  ops/s
FilterBenchmark.multipleFilters               100000  thrpt   50      1531.516 ?       2.729  ops/s
FilterBenchmark.multipleFilters              1000000  thrpt   50       123.881 ?       1.525  ops/s
FilterBenchmark.multipleFiltersParallel           10  thrpt   50     20403.993 ?     147.247  ops/s
FilterBenchmark.multipleFiltersParallel          100  thrpt   50     19426.222 ?      96.979  ops/s
FilterBenchmark.multipleFiltersParallel         1000  thrpt   50     17692.433 ?      67.606  ops/s
FilterBenchmark.multipleFiltersParallel        10000  thrpt   50     12108.482 ?      34.500  ops/s
FilterBenchmark.multipleFiltersParallel       100000  thrpt   50      3782.756 ?      22.044  ops/s
FilterBenchmark.multipleFiltersParallel      1000000  thrpt   50       589.972 ?      71.448  ops/s
FilterBenchmark.oldFashionFilters                 10  thrpt   50  41024334.062 ? 1374663.440  ops/s
FilterBenchmark.oldFashionFilters                100  thrpt   50   6011852.027 ?  246202.642  ops/s
FilterBenchmark.oldFashionFilters               1000  thrpt   50    553243.594 ?    2217.912  ops/s
FilterBenchmark.oldFashionFilters              10000  thrpt   50     29188.753 ?     580.958  ops/s
FilterBenchmark.oldFashionFilters             100000  thrpt   50      2061.738 ?       8.456  ops/s
FilterBenchmark.oldFashionFilters            1000000  thrpt   50       196.105 ?       3.203  ops/s
于 2019-03-28T11:15:42.657 回答