架构师训练营第五周作业

import java.util.Arrays;
import java.util.Random;
/**
 * @author ray.zhangr
 * @date 2020/7/8 9:57 下午
 */
public class ConsistentHash {
    private int serverNum;
    private int[] svrLocation;
    public ConsistentHash(int serverNum) {
        this.serverNum = serverNum;
        this.svrLocation = serverLocate(serverNum);
    }
    private int[] serverLocate(int serverNum) {
        int[] svrLocation = new int[serverNum];
        for (int i = 0; i < serverNum; i++) {
            svrLocation[i] = new Random().nextInt(); // 用当前日期的哈希值模拟服务器的哈希值
        }
        Arrays.sort(svrLocation);
        return svrLocation;
    }
    public int[] dataBelong(int[] keysHash) {
        int[] belongs = new int[serverNum];
        for (int keyHash : keysHash) {
            for (int i = 0; i < serverNum; i++) {
                if (keyHash <= svrLocation[i]) {
                    belongs[i]++;
                    break;
                }
            }
            if (keyHash > svrLocation[serverNum - 1])
                belongs[0]++;
        }
        return belongs;
    }
    public static void main(String args[]) {
        ConsistentHash ch = new ConsistentHash(10);
        int[] dataset = new int[1000000];
        for (int i = 0; i < 1000000; i++) {
            dataset[i] = new Random().nextInt();
        }
        int[] belongs = ch.dataBelong(dataset);
        for (int i = 0; i < belongs.length; i++) {
            System.out.println(String.format("Server %s: %s", i, belongs[i]));
        }
        // 评估标准差
        System.out.println("标准差" + StandardDiviation(belongs));
    }
    public static double StandardDiviation(int[] x) {
        int m=x.length;
        double sum=0;
        for(int i=0;i<m;i++){//求和
            sum+=x[i];
        }
        double dAve=sum/m;//求平均值
        double dVar=0;
        for(int i=0;i<m;i++){//求方差
            dVar+=(x[i]-dAve)*(x[i]-dAve);
        }
        //reture Math.sqrt(dVar/(m-1));
        return Math.sqrt(dVar/m);
     }
}

import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Random;
/**
 * @author ray.zhangr
 * @date 2020/7/9 10:07 上午
 */
public class ConsistentHashWithVirtualNode {
    private int serverNum;
    private int multiple;
    private List<VirtualNode> nodes = new ArrayList<>();
    public ConsistentHashWithVirtualNode(int serverNum, int multiple) {
        this.serverNum = serverNum;
        this.multiple = multiple;
        for (int i = 0; i < serverNum; i++) {
            for (int j = 0; j < multiple; j++) {
                VirtualNode node = new VirtualNode(new Random().nextInt(), i);
                nodes.add(node);
            }
        }
        Collections.sort(nodes);
    }
    public int[] dataBelong(int[] keysHash) {
        int[] belongs = new int[serverNum];
        int virtualNodeNum = nodes.size();
        for (int keyHash : keysHash) {
            for (int i = 0; i < virtualNodeNum; i++) {
                if (keyHash <= nodes.get(i).hashCode) {
                    belongs[nodes.get(i).belongServer]++;
                    break;
                }
            }
            if (keyHash > nodes.get(virtualNodeNum - 1).hashCode)
                belongs[nodes.get(virtualNodeNum - 1).belongServer]++;
        }
        return belongs;
    }
    class VirtualNode implements Comparable<VirtualNode> {
        private int hashCode;
        private int belongServer;
        VirtualNode(int hashCode, int belongServer) {
            this.hashCode = hashCode;
            this.belongServer = belongServer;
        }
        @Override
        public int compareTo(VirtualNode o) {
            return this.hashCode > o.hashCode ? 1 : -1;
        }
    }
    public static void main(String args[]) {
        ConsistentHashWithVirtualNode ch = new ConsistentHashWithVirtualNode(10, 20);
        int[] dataset = new int[1000000];
        for (int i = 0; i < 1000000; i++) {
            dataset[i] = new Random().nextInt();
        }
        int[] belongs = ch.dataBelong(dataset);
        for (int i = 0; i < belongs.length; i++) {
            System.out.println(String.format("Server %s: %s", i, belongs[i]));
        }
        // 评估标准差
        System.out.println("标准差" + StandardDiviation(belongs));
    }
    public static double StandardDiviation(int[] x) {
        int m=x.length;
        double sum=0;
        for(int i=0;i<m;i++){//求和
            sum+=x[i];
        }
        double dAve=sum/m;//求平均值
        double dVar=0;
        for(int i=0;i<m;i++){//求方差
            dVar+=(x[i]-dAve)*(x[i]-dAve);
        }
        //reture Math.sqrt(dVar/(m-1));
        return Math.sqrt(dVar/m);
    }
}