LB中使用到的一致性Hash算法的简单实现

关于一致性hash算法，可以参考这篇文章： https://zhuanlan.zhihu.com/p/34985026

1、类的Diagram

 2、代码实现

2.1、Node类，每个Node代表集群里面的一个节点或者具体说是某一台物理机器；

package consistencyhash;

import lombok.Getter;
import lombok.RequiredArgsConstructor;

import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
import lombok.ToString;

/**
 * @author xfyou
 * @date 2019/9/2
 */
@Getter
@RequiredArgsConstructor
@ToString(exclude = "data")
public class Node {

  private final String domain;

  private final String ip;

  private final Map<String, Object> data = new ConcurrentHashMap<>();

  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);
  }

}

2.2、 AbstractCluster，cluster抽象类，集群抽象类；

package consistencyhash;

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

/**
 * @author xfyou
 * @date 2019/9/2
 */
public abstract class AbstractCluster {

  protected final List<Node> nodes;

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

  public abstract void addNode(Node node);

  public abstract void removeNode(Node node);

  public abstract Node get(String key);

}

 2.3、Cluster类，集群类，一致性hash算法的具体实现类

package consistencyhash;

import com.google.common.hash.Hashing;
import java.nio.charset.StandardCharsets;
import java.util.SortedMap;
import java.util.TreeMap;
import java.util.stream.IntStream;

/**
 * @author xfyou
 * @date 2019/9/2
 */
public class ConsistencyHashCluster extends AbstractCluster {

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

  private static final int VIR_NODE_COUNT = 160;

  @Override
  public void addNode(Node node) {
    this.nodes.add(node);
    IntStream.range(0, VIR_NODE_COUNT / 4).forEach(i -> {
      byte[] digest = Hashing.md5().hashBytes((node.toString() + i).getBytes(StandardCharsets.UTF_8)).asBytes();
      for (int h = 0; h < 4; h++) {
        virNodes.put(hash(digest, h), node);
      }
    });
  }

  /**
   * 物理节点被删除的话，这个物理节点所对应的所有的虚拟节点也同时被删
   */
  @Override
  public void removeNode(Node node) {
    nodes.removeIf(o -> node.getIp().equals(o.getIp()));
    IntStream.range(0, VIR_NODE_COUNT / 4).forEach(i -> {
      byte[] digest = Hashing.md5().hashBytes((node.toString() + i).getBytes(StandardCharsets.UTF_8)).asBytes();
      for (int h = 0; h < 4; h++) {
        virNodes.remove(hash(digest, h));
      }
    });
  }

  @Override
  public Node get(String key) {
    long hash = calHash(key);
    SortedMap<Long, Node> subMap = hash >= virNodes.lastKey() ? virNodes.tailMap(0L) : virNodes.tailMap(hash);
    if (subMap.isEmpty()) {
      return virNodes.get(virNodes.firstKey());
    }
    System.out.println("hash=" + hash + ",subMap.firstKey=" + subMap.firstKey());
    return subMap.get(subMap.firstKey());
  }

  private long calHash(String key) {
    byte[] keyBytes = Hashing.md5().hashBytes(key.getBytes(StandardCharsets.UTF_8)).asBytes();
    return hash(keyBytes, 0);
  }

  /**
   * 取MD5后16个字节中的连续的4个字节并通过移位操作来转换为 long 类型的 hash 值
   */
  private long hash(byte[] digest, int number) {
    return (((long) (digest[3 + number * 4] & 0xFF) << 24)
            | ((long) (digest[2 + number * 4] & 0xFF) << 16)
            | ((long) (digest[1 + number * 4] & 0xFF) << 8)
            | (digest[number * 4] & 0xFF))
            & 0xFFFFFFFFL;
  }

}

 2.4、Test类，测试类

package consistencyhash;

import java.util.stream.IntStream;

/**
 * @author xfyou
 * @date 2019/9/2
 */
public class Test {

  private static final int DATA_CONT = 20;

  private static final String PRE_KEY = "PRE_KEY";

  public static void main(String[] args) {

    AbstractCluster cluster = new ConsistencyHashCluster();
    cluster.addNode(new Node("c1.yywang.info", "192.168.0.1"));
    cluster.addNode(new Node("c2.yywang.info", "192.168.0.2"));
    cluster.addNode(new Node("c3.yywang.info", "192.168.0.3"));

    IntStream.range(0, DATA_CONT).forEach(index -> {
      Node node = cluster.get(PRE_KEY + index);
      node.put(PRE_KEY + index, "cached_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());
    });

    cluster.removeNode(new Node("c1.yywang.info", "192.168.0.1"));

    // 查询命中率，如果没有命中则需要从后端 DB 中查询
    long hitCount = IntStream.range(0, DATA_CONT).filter(index -> cluster.get(PRE_KEY + index).get(PRE_KEY + index) != null).count();
    System.out.println("hitCount=" + hitCount);
    System.out.println("缓存命中率：" + hitCount * 1f / DATA_CONT);
  }

}