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-gc true를 사용하는 Java 12 대 Java 8의 스트림 API에 대한 신비한 마이크로 벤치 마크 결과

nicepro 2021. 1. 8. 22:53
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-gc true를 사용하는 Java 12 대 Java 8의 스트림 API에 대한 신비한 마이크로 벤치 마크 결과


복잡한 필터를 사용하거나 스트림에서 여러 필터를 사용하는 것의 차이점에 대한 조사의 일환으로 Java 12의 성능이 Java 8보다 훨씬 느립니다.

그 이상한 결과에 대한 설명이 있습니까? 내가 여기서 뭔가 놓친 건가요?

구성 :

  • 자바 8

    • OpenJDK 런타임 환경 (빌드 1.8.0_181-8u181-b13-2 ~ deb9u1-b13)
    • OpenJDK 64 비트 서버 VM (빌드 25.181-b13, 혼합 모드)
  • 자바 12

    • OpenJDK 런타임 환경 (빌드 12 + 33)
    • OpenJDK 64 비트 서버 VM (빌드 12 + 33, 혼합 모드, 공유)
  • VM 옵션 : -XX:+UseG1GC -server -Xmx1024m -Xms1024m

  • CPU : 8 코어

JMH 처리량 결과 :

  • 워밍업 : 10 회 반복, 각각 1 초
  • 측정 : 10 회 반복, 각각 1 초
  • 스레드 : 1 개의 스레드, 반복 동기화
  • 단위 : ops / s

비교표

암호

스트림 + 복합 필터

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);
    }

스트림 + 여러 필터

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);
    }

병렬 스트림 + 복합 필터

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);
    }

병렬 스트림 + 다중 필터

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);
    }

구식 자바 반복

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);
    }

docker 명령을 실행하여 직접 시도 할 수 있습니다.

Java 8의 경우 :

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

Java 12의 경우 :

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

소스 코드:

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


도움을 주신 모든 분들, 특히 @Aleksey Shipilev에게 감사드립니다!

JMH 벤치 마크에 변경 사항을 적용하면 결과가 더욱 현실적으로 보입니다 (?)

변경 사항 :

  1. 벤치 마크 반복 전 / 후에 실행할 설정 방법을 변경합니다.

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

  2. 반복 사이에 JMH 강제 GC를 중지합니다. 각 반복 전에 Full GC를 강제로 실행하면 GC 휴리스틱을 포기할 가능성이 높습니다. (c) Aleksey Shipilev

    -gc true -> -gc false

참고 : 기본적으로 gc false입니다.

비교표

새로운 성능 벤치 마크에 따르면 Java 8에 비해 Java 12에서 성능 저하가 없습니다.

참고 : 이러한 변경 후 작은 어레이 크기에 대한 처리량 오류는 100 % 이상 크게 증가하여 큰 데이터 세트는 동일하게 유지됩니다.

결과 테이블

원시 결과

자바 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

자바 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

참조 URL : https://stackoverflow.com/questions/55375803/mystifying-microbenchmark-result-for-stream-api-on-java-12-vs-java-8-with-gc-t

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