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最近看到两篇文章，一个是江南白衣的陌生但默默一统江湖的MurmurHash,另外一篇是张洋的一致性哈希算法及其在分布式系统中的应用。虽然我在项目中使用memcached的java客户端spymemcached好几年了，但是对它的一致性哈希算法的细节从来没有仔细研究过。趁此机会，特别的看了一下它的源代码。
我们知道，Memcached本身没有提供分布式的功能，一般客户端会实现一致性Hash算法，根据Key值计算出应该在哪个节点进行存取。
spymemcached实现了几种Hash算法：NATIVE_HASH,CRC_HASH,FNV1_64_HASH,FNV1A_64_HASH,FNV1_32_HASH,FNV1A_32_HASH,KETAMA_HASH。
相比较前几个hash算法，KETAMA HASH算法可以将服务器的虚拟节点相对均匀的分布到环上，它是一种基于MD5散列的Hash算法。
下面这个类是我精简的spymemcached的KetamaNodeLocator类，用来测试生成的虚拟节点的分布情况,它会打印出两个虚拟节点之间的间隔。 如果间隔比较均匀，我们相信使用同样的Hash算法计算的key值应该可以均匀的落在每个节点上。
spymemcached为每个节点计算虚拟节点时使用节点地址 + "-i"格式， i最大的每个节点的虚拟节点数，默认是160个。
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package com.colobu.consistenthashing;
import java.util.List;
import java.util.TreeMap;
public class Ketama {
public TreeMap<Long, Node> hashNodes;
public HashAlgorithm hashAlgorithm;
protected void setKetamaNodes(List<Node> nodes) {
TreeMap<Long, Node> newNodeMap = new TreeMap<Long, Node>();
int numReps = 160;
for (Node node : nodes) {
if (hashAlgorithm == HashAlgorithm.KETAMA_HASH) {
for (int i = 0; i < numReps / 4; i++) {
byte[] digest = HashAlgorithm.computeMd5(node.getName() + "-" + i);
for (int h = 0; h < 4; h++) {
Long k = ((long) (digest[3 + h * 4] & 0xFF) << 24)
| ((long) (digest[2 + h * 4] & 0xFF) << 16)
| ((long) (digest[1 + h * 4] & 0xFF) << 8)
| (digest[h * 4] & 0xFF);
newNodeMap.put(k, node);
}
}
} else {
for (int i = 0; i < numReps; i++) {
newNodeMap.put(hashAlgorithm.hash(node + "-" + i), node);
}
}
}
hashNodes = newNodeMap;
}
}
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写一个测试类，看看虚拟节点的分布情况：
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package com.colobu.consistenthashing;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.Map.Entry;
public class Main {
public static void main(String[] args) {
//System.out.println("测试 ketama hash");
//testKetama();
//System.out.println("\r\n\r\n测试 native hash");
//testHash(HashAlgorithm.NATIVE_HASH);
//System.out.println("\r\n\r\n测试 CRC hash"); //max=32767
//testHash(HashAlgorithm.CRC_HASH);
//System.out.println("\r\n\r\n测试 FNV1_64_HASH");
//testHash(HashAlgorithm.FNV1_64_HASH);
//System.out.println("\r\n\r\n测试 FNV1A_64_HASH");
//testHash(HashAlgorithm.FNV1A_64_HASH);
//System.out.println("\r\n\r\n测试 MurmurHash 32");
//testHash(HashAlgorithm.MurmurHash_32);
System.out.println("\r\n\r\n测试 MurmurHash 64");
testHash(HashAlgorithm.MurmurHash_64);
}
private static void testHash(HashAlgorithm hash) {
Ketama ketama = new Ketama();
ketama.hashAlgorithm = hash;
List<Node> nodes = new ArrayList<>();
for(int i=0; i< 10; i++) {
nodes.add(new Node("name-" + i));
}
ketama.setKetamaNodes(nodes);
Iterator<Entry<Long, Node>> it = ketama.hashNodes.entrySet().iterator();
Entry<Long, Node> prior = it.next();
while(it.hasNext()) {
Entry<Long, Node> current = it.next();
System.out.println("间隔：" + (current.getKey() - prior.getKey()) + "=" + current.getKey() + "-" + prior.getKey());
prior = current;
}
}
private static void testKetama() {
Ketama ketama = new Ketama();
ketama.hashAlgorithm = HashAlgorithm.KETAMA_HASH;
List<Node> nodes = new ArrayList<>();
for(int i=0; i< 10; i++) {
nodes.add(new Node("name-" + i));
}
ketama.setKetamaNodes(nodes);
Iterator<Entry<Long, Node>> it = ketama.hashNodes.entrySet().iterator();
Entry<Long, Node> prior = it.next();
while(it.hasNext()) {
Entry<Long, Node> current = it.next();
System.out.println("间隔：" + (current.getKey() - prior.getKey()));
prior = current;
}
}
}
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实际结果看到ketama算法还是不错的。
江南白衣的那篇文章介绍了MurmurHash算法，开源中国社区也翻译了一篇 Hash 函数概览的科普文章。
如果我们将MurmurHash算法加入到spymemcached会怎么样呢。我没有测试它的性能，但是从分布上来看还是不错的。
网上有几个MurmurHash的实现，如Guava, Cassandra等。我不想额外引入第三方的包，所以直接复制了Viliam Holub的实现。
在HashAlgorithm算法中加入MurmurHash枚举类型。
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package com.colobu.consistenthashing;
import java.security.MessageDigest;
import java.security.NoSuchAlgorithmException;
import java.util.zip.CRC32;
public enum HashAlgorithm {
/**
* Native hash (String.hashCode()).
*/
NATIVE_HASH,
/**
* CRC_HASH as used by the perl API. This will be more consistent both
* across multiple API users as well as java versions, but is mostly likely
* significantly slower.
*/
CRC_HASH,
/**
* FNV hashes are designed to be fast while maintaining a low collision rate.
* The FNV speed allows one to quickly hash lots of data while maintaining a
* reasonable collision rate.
*
* @see <a href="http://www.isthe.com/chongo/tech/comp/fnv/">fnv
* comparisons</a>
* @see <a href="http://en.wikipedia.org/wiki/Fowler_Noll_Vo_hash">fnv at
* wikipedia</a>
*/
FNV1_64_HASH,
/**
* Variation of FNV.
*/
FNV1A_64_HASH,
/**
* 32-bit FNV1.
*/
FNV1_32_HASH,
/**
* 32-bit FNV1a.
*/
FNV1A_32_HASH,
MurmurHash_32,
MurmurHash_64,
/**
* MD5-based hash algorithm used by ketama.
*/
KETAMA_HASH;
private static final long FNV_64_INIT = 0xcbf29ce484222325L;
private static final long FNV_64_PRIME = 0x100000001b3L;
private static final long FNV_32_INIT = 2166136261L;
private static final long FNV_32_PRIME = 16777619;
private static MessageDigest md5Digest = null;
static {
try {
md5Digest = MessageDigest.getInstance("MD5");
} catch (NoSuchAlgorithmException e) {
throw new RuntimeException("MD5 not supported", e);
}
}
/**
* Compute the hash for the given key.
*
* @return a positive integer hash
*/
public long hash(final String k) {
long rv = 0;
int len = k.length();
switch (this) {
case NATIVE_HASH:
rv = k.hashCode();
break;
case CRC_HASH:
// return (crc32(shift) >> 16) & 0x7fff;
CRC32 crc32 = new CRC32();
crc32.update(k.getBytes());
rv = (crc32.getValue() >> 16) & 0x7fff;
break;
case FNV1_64_HASH:
// Thanks to pierre@demartines.com for the pointer
rv = FNV_64_INIT;
for (int i = 0; i < len; i++) {
rv *= FNV_64_PRIME;
rv ^= k.charAt(i);
}
break;
case FNV1A_64_HASH:
rv = FNV_64_INIT;
for (int i = 0; i < len; i++) {
rv ^= k.charAt(i);
rv *= FNV_64_PRIME;
}
break;
case FNV1_32_HASH:
rv = FNV_32_INIT;
for (int i = 0; i < len; i++) {
rv *= FNV_32_PRIME;
rv ^= k.charAt(i);
}
break;
case FNV1A_32_HASH:
rv = FNV_32_INIT;
for (int i = 0; i < len; i++) {
rv ^= k.charAt(i);
rv *= FNV_32_PRIME;
}
break;
case MurmurHash_32:
rv = MurmurHash.hash32(k);
break;
case MurmurHash_64:
rv = MurmurHash.hash64(k);
break;
case KETAMA_HASH:
byte[] bKey = computeMd5(k);
rv = ((long) (bKey[3] & 0xFF) << 24)
| ((long) (bKey[2] & 0xFF) << 16)
| ((long) (bKey[1] & 0xFF) << 8)
| (bKey[0] & 0xFF);
break;
default:
assert false;
}
return rv & 0xffffffffL; /* Truncate to 32-bits */
}
/**
* Get the md5 of the given key.
*/
public static byte[] computeMd5(String k) {
MessageDigest md5;
try {
md5 = (MessageDigest) md5Digest.clone();
} catch (CloneNotSupportedException e) {
throw new RuntimeException("clone of MD5 not supported", e);
}
md5.update(k.getBytes());
return md5.digest();
}
}
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实际结果看MurmurHash也是相当的均匀。
xmemcached是另外一个memcached java客户端，它实现了类似spymemcached的hash算法。只不过增加了几种新的hash算法：MYSQL_HASH,ELF_HASH,RS_HASH,LUA_HASH,ONE_AT_A_TIME。
Twemproxy是一个Memcahced的网关程序。 它实现了下面几种Hash算法。
转： memcached Java客户端spymemcached的一致性Hash算法
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原文地址：http://www.cnblogs.com/jhj117/p/5687825.html
