java8 stream与传统写法的性能对比简测

    以下对比了用Stream完成去重复和从list中提取其中一个属性的功能与传统的写法作对比, 看性能有否差异:

   两千万记录去重,一百万取属性:

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());不起作用
    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);

  }

结果如下, stream函数式的写法与传统写法性能要低些, 去重慢13%, 提取慢42%. 如果是在性能要求比较高的地方就要避免了.

 

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