转： memcached Java客户端spymemcached的一致性Hash算法

转自：http://colobu.com/2015/04/13/consistent-hash-algorithm-in-java-memcached-client/

memcached Java客户端spymemcached的一致性Hash算法

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。

Ketama Hash的实现

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; } }

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; } }   }

实际结果看到ketama算法还是不错的。

加入MurmurHash算法

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(); } }

实际结果看MurmurHash也是相当的均匀。

xmemcached的实现

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

Twemproxy是一个Memcahced的网关程序。 它实现了下面几种Hash算法。

• one_at_a_time
• md5
• crc16
• crc32 (crc32 implementation compatible with libmemcached)
• crc32a (correct crc32 implementation as per the spec)
• fnv1_64
• fnv1a_64
• fnv1_32
• fnv1a_32
• hsieh
• murmur
• jenkins