java 8 streamと伝統的な書き方の性能比較簡単測定

Streamで繰り返して、リストから属性を抽出する機能と伝統的な書き方を比較してみました。 性能によって違いがあります。
   二千万の記録は重くなり、百万は属性を取る：

static class Class1 {
    public String staffId;

    public String getStaffId() {
      return staffId;
    }

    public String getStaffName() {
      return staffName;
    }

    public String staffName;
    public Class1(){}
    public Class1(String staffId,String staffName){
      this.staffId=staffId;
      this.staffName=staffName;
    }
    public String toString(){
      return "{id:"+staffId+",name:"+staffName+"}";
    }
    /*@Override
    public int hashCode(){
      return staffId.hashCode()+staffName.hashCode();
    }*/
    /*
    @Override
    public boolean equals(Object v){
      Class1 a=(Class1) v;
      return a.staffId.equals(this.staffId)&&a.staffName.equals(this.staffName);
    }
    @Override
    public int hashCode(){
      return staffId.hashCode()*staffName.hashCode();
    }*/
  }
  static class ResultBean{
    public List result;
    public long consume;
  }
  public ResultBean getUnique(List pvData,Function,List> pvFun){
    ResultBean lvRet=new ResultBean();
    long lvTmStart=System.currentTimeMillis();
    lvRet.result= pvFun.apply(pvData);
    lvRet.consume= System.currentTimeMillis()-lvTmStart;
    return lvRet;
  }
  static class ResultBean2{
    public List result;
    public long consume;
  }
  public ResultBean2 getUnique2(List pvData,Function,List> pvFun){
    ResultBean2 lvRet=new ResultBean2();
    long lvTmStart=System.currentTimeMillis();
    lvRet.result= pvFun.apply(pvData);
    lvRet.consume= System.currentTimeMillis()-lvTmStart;
    return lvRet;
  }
  @Test
  public void testRemoveDuplicate() {
    List lvList1 = new ArrayList<>();
    lvList1.add("A");
    lvList1.add("B");
    lvList1.add("B");
    lvList1.add("A");
    lvList1.add("C");
    lvList1.add("C");
    lvList1 = lvList1.stream().distinct().collect(Collectors.toList());
    System.out.println(lvList1);

    List lvList2 = new ArrayList<>();
    for (int i = 0; i < 1000000; i++) {
      lvList2.add(new Class1("001", "Name001"));
      lvList2.add(new Class1("002", "Name002"));
      lvList2.add(new Class1("003", "Name003"));
      lvList2.add(new Class1("001", "Name001"));
      lvList2.add(new Class1("002", "Name002"));
      lvList2.add(new Class1("003", "Name003"));
      lvList2.add(new Class1("001", "Name001"));
      lvList2.add(new Class1("002", "Name002"));
      lvList2.add(new Class1("003", "Name003"));
      lvList2.add(new Class1("003", "Name003"));
      lvList2.add(new Class1("003", "Name003"));
      lvList2.add(new Class1("004", "Name003"));
      lvList2.add(new Class1("005", "Name003"));
      lvList2.add(new Class1("004", "Name003"));
      lvList2.add(new Class1("005", "Name003"));
      lvList2.add(new Class1("004", "Name003"));
      lvList2.add(new Class1("005", "Name003"));
      lvList2.add(new Class1("003", "Name003"));
      lvList2.add(new Class1("002", "Name003"));
      lvList2.add(new Class1("002", "Name003"));
    }
    System.out.println(lvList2.size());

    //lvList2=lvList2.stream().distinct().collect(Collectors.toList());    
    ResultBean lvLambdaResult = getUnique(lvList2, p -> {
      return lvList2.stream().collect(Collectors.collectingAndThen(
              Collectors.toCollection(() -> new TreeSet<>(Comparator.comparing(Class1::getStaffId)
                      .thenComparing(Class1::getStaffName))), ArrayList::new
              )
      );
      //return lvList2.stream().distinct().collect(Collectors.toList());
    });
    System.out.println("Consume:" + lvLambdaResult.consume);
    System.out.println("result:" + lvLambdaResult.result);
    ResultBean lvNormalResult = getUnique(lvList2, p -> {
      TreeSet lvTree = new TreeSet(new Comparator() {
        @Override
        public int compare(Class1 class1, Class1 t1) {
          int lvRet = class1.staffId.compareTo(t1.staffId) * 100;
          return lvRet + class1.staffName.compareTo(t1.staffName);
        }
      });
      lvTree.addAll(p);
      return Arrays.asList(lvTree.toArray(new Class1[lvTree.size()]));
    });
    System.out.println("Consume:" + lvNormalResult.consume);
    System.out.println("result:" + lvNormalResult.result);


    lvList2.clear();
    for (int i = 0; i < 1000000; i++) {
      lvList2.add(new Class1(i+"", "Name00"+i));
    }
    ResultBean2 lvLambdaResult2=getUnique2(lvList2,p->{
      return p.stream().map(i->i.staffId).collect(Collectors.toList());
    });
    System.out.println("Consume:"+lvLambdaResult2.consume);
    ResultBean2 lvNormalResult2=getUnique2(lvList2,p->{
      List lvRet=new ArrayList<>();
      for (Class1 rec:p){
        lvRet.add(rec.staffId);
      }
      return lvRet;
    });
    System.out.println("Consume:"+lvNormalResult2.consume);

  }

その結果、stream関数式の書き方と伝統的な書き方の性能は低いです。 重いものは13%遅く、 抽出が42%遅いです。性能の要求が高いところでは避けられます。
 

20000000
Consume:806
result:[{id:001,name:Name001}, {id:002,name:Name002}, {id:002,name:Name003}, {id:003,name:Name003}, {id:004,name:Name003}, {id:005,name:Name003}]
Consume:713
result:[{id:001,name:Name001}, {id:002,name:Name002}, {id:002,name:Name003}, {id:003,name:Name003}, {id:004,name:Name003}, {id:005,name:Name003}]
Consume:30
Consume:21