Hash 一致性算法的 Java 实现

import java.util.HashMap;
import java.util.Map;

/**
 * Created by weiyuan on 2020/07/06
 */
public class Node {
    
    private String ip;
    /**
     * 节点存储数据
     */
    private Map<String,Object> data = new HashMap<String,Object>();

    public Node(String ip) {
        this.ip = ip;
    }

    public <T> void put(String key, T value) {
        data.put(key, value);
    }

    public void remove(String key){
        data.remove(key);
    }

    public <T> T get(String key) {
        return (T) data.get(key);
    }

    public String getIp() {
        return ip;
    }

    public void setIp(String ip) {
        this.ip = ip;
    }

    public Map<String, Object> getData() {
        return data;
    }

    public void setData(Map<String, Object> data) {
        this.data = data;
    }
}

import java.util.ArrayList;
import java.util.List;

/**
 * Created by weiyuan on 2020/07/06
 */
public abstract class Cluster {

    protected List<Node> nodes;

    public Cluster() {
        this.nodes = new ArrayList<>();
    }

    public abstract void addNode(Node node);

    public abstract void removeNode(Node node);

    public abstract Node get(String key);

    /**
     * 这里选用FNV1_32_HASH算法，因String重写的hash算法会产生负数，
     * 而且算出的hashCode值分布比较集中，数据分布不均匀
     * @param str
     * @return
     */
    public static int getHash(String str) {
        final int p = 16777619;
        int hash = (int) 2166136261L;
        for (int i = 0; i < str.length(); i++)
            hash = (hash ^ str.charAt(i)) * p;
        hash += hash << 13;
        hash ^= hash >> 7;
        hash += hash << 3;
        hash ^= hash >> 17;
        hash += hash << 5;

        // 如果算出来的值为负数则取其绝对值
        if (hash < 0)
            hash = Math.abs(hash);
        return hash;
    }

}

/**
 * Created by weiyuan on 2020/07/06
 */
public class NormalHashCluster extends Cluster {

    public NormalHashCluster() {
        super();
    }

    @Override
    public void addNode(Node node) {
        this.nodes.add(node);
    }

    @Override
    public void removeNode(Node node) {
        this.nodes.removeIf(o -> o.getIp().equals(node.getIp()));
    }

    @Override
    /**
     * 取模算法，求请求资源分发到哪个集群节点
     */
    public Node get(String key) {
        long hash = getHash(key);
        long index =  hash % nodes.size();
        return nodes.get((int)index);
    }
}

import java.util.SortedMap;
import java.util.TreeMap;
import java.util.stream.IntStream;

/**
 * Created by weiyuan on 2020/07/06
 */
public class ConsistencyHashCluster extends Cluster {

    private SortedMap<Long, Node> virNodes = new TreeMap<Long, Node>();

    private int virNodeCount;

    private static final String SPLIT = "#";

    public ConsistencyHashCluster(int virNodeCount) {
        super();
        this.virNodeCount = virNodeCount;
    }

    @Override
    public void addNode(Node node) {
        this.nodes.add(node);
        IntStream.range(0, virNodeCount)
                .forEach(index -> {
                    long hash = getHash(node.getIp() + SPLIT + index);
                    virNodes.put(hash, node);
                });
    }

    @Override
    public void removeNode(Node node) {
        nodes.removeIf(o -> node.getIp().equals(o.getIp()));
        IntStream.range(0, virNodeCount)
                .forEach(index -> {
                    long hash = getHash(node.getIp() + SPLIT + index);
                    virNodes.remove(hash);
                });
    }

    @Override
    /**
     * 虚拟节点算法，求请求资源分发到哪个集群节点
     */
    public Node get(String key) {
        long hash = getHash(key);
        SortedMap<Long, Node> subMap = hash >= virNodes.lastKey() ? virNodes.tailMap(0L) : virNodes.tailMap(hash);
        if (subMap.isEmpty()) {
            return null;
        }
        return subMap.get(subMap.firstKey());
    }
}

import org.junit.Test;

import java.util.stream.IntStream;

/**
 * Created by weiyuan on 2020/07/06
 */
public class HashTest {
    @Test
    public void hashTest(){
//        Cluster cluster = new NormalHashCluster();
        Cluster cluster=new ConsistencyHashCluster(150);
        cluster.addNode(new Node("192.168.0.1"));
        cluster.addNode(new Node("192.168.0.2"));
        cluster.addNode(new Node("192.168.0.3"));
        cluster.addNode(new Node("192.168.0.4"));
        IntStream.range(0, (int)(Math.pow(2,23) - 1))
                .forEach(index -> {
                    Node node = cluster.get("Test Data" + index);
                    node.put("Test Data" + index, "Test Data");
                });

        System.out.println("数据分布情况：");
        cluster.nodes.forEach(node -> {
            System.out.println("IP:" + node.getIp() + ",数据量:" + node.getData().size());
        });
        //缓存命中率
        long hitCount = IntStream.range(0, (int)(Math.pow(2,23) - 1))
                .filter(index -> cluster.get("Test Data" + index).get("Test Data" + index) != null)
                .count();
        System.out.println("缓存命中率：" + hitCount * 1f / (int)(Math.pow(2,23) - 1));
        cluster.addNode(new Node("192.168.0.5"));
        long hitCount1 = IntStream.range(0, (int)(Math.pow(2,23) - 1))
                .filter(index -> cluster.get("Test Data" + index).get("Test Data" + index) != null)
                .count();
        System.out.println("增加节点后缓存命中率：" + hitCount1 * 1f / (int)(Math.pow(2,23) - 1));
    }
}