On the clever use of stream and map

The title is like this
// hypothesis db There are two such tables in The data are 100w The final thing to return is Provice Tabular List aggregate If status by fasle Then don't go back How to be in java Level solution ？

public class Mydemo {
       
    static ArrayList<Province> provinces = new ArrayList<>();
    static ArrayList<ProvinceStatus> provinceStatusList = new ArrayList<>();

    static {
    
        for (int i = 0; i < 10000; i++) {
    
            Province province = new Province();
            ProvinceStatus provinceStatus = new ProvinceStatus();
            province.setName(UUID.randomUUID().toString());
            province.setProvinceId(i);
            provinceStatus.setProvinceId(i);
            provinceStatus.setStatus(new Random().nextBoolean());
            // add to 
            provinces.add(province);
            provinceStatusList.add(provinceStatus);
        }
        System.out.println(" The size of the displayed list is :" + provinceStatusList.stream().filter(ProvinceStatus::getStatus).count());
    }

    public static void main(String[] args) {
    
        ArrayList<Province> resList = new ArrayList<>();


        System.out.println(" The number of final displays ：" + resList.size());

    }
}

@Data
class Province {
    
    private Integer provinceId;
    private String name;
}

@Data
class ProvinceStatus {
    
    private Integer provinceId;
    private Boolean status;
}

The answer is as follows , If you have better ideas, please leave a message ！

public static void main(String[] args) {
    
        // double for loop ？  No   use hash It's a good idea 
        long start1 = System.currentTimeMillis();
        System.out.println();
        Map<Integer, Boolean> map = provinceStatusList.stream()
                .collect(Collectors.toMap(ProvinceStatus::getProvinceId, ProvinceStatus::getStatus));
        ArrayList<Province> resList = new ArrayList<>();
        for (Province province : Mydemo.provinces) {
    
            if (map.get(province.getProvinceId())) {
    
                // Show these 
                resList.add(province);
            }
        }
        long start2 = System.currentTimeMillis();
        System.out.println(" when :" + (start2 - start1));
        System.out.println(" The number of final displays ：" + resList.size());

    }
}

When there is a large amount of data , Parallel streams are slower , It should be because there is no calculation , After all, parallel flow is suitable for CPU Intensive tasks , That is, when a large number of calculations are involved .