Memcache-Java-Client-Release源码阅读（之七）

一、主要内容
本章节的主要内容是介绍Memcache Client的Native，Old_Compat，New_Compat三个Hash算法的应用及实现。

二、准备工作
1、服务器启动192.168.0.106:11211，192.168.0.106:11212两个服务端实例。
2、示例代码：

String[] servers = { "192.168.0.106:11211", "192.168.0.106:11212" };
SockIOPool pool = SockIOPool.getInstance();
pool.setServers(servers);
pool.setInitConn(10);
pool.setMinConn(5);
pool.setMaxConn(250);
pool.setSocketTO(3000);
// 设置Hash算法
pool.setHashingAlg(SockIOPool.NATIVE_HASH);
pool.initialize();
// 省略......

Memcache客户端默认的Hash算法是NATIVE_HASH。

三、概述
四个Hash算法的调用都集中在SchoonerSockIOPool类的getHash()方法里，源码如下：

/**
 * Returns a bucket to check for a given key.
 *
 * @param key
 *            String key cache is stored under
 * @return int bucket
 */
private final long getHash(String key, Integer hashCode) {

    if (hashCode != null) {
        if (hashingAlg == CONSISTENT_HASH)
            return hashCode.longValue() & 0xffffffffL;
        else
            return hashCode.longValue();
    } else {
        switch (hashingAlg) {
        case NATIVE_HASH:
            return (long) key.hashCode();
        case OLD_COMPAT_HASH:
            return origCompatHashingAlg(key);
        case NEW_COMPAT_HASH:
            return newCompatHashingAlg(key);
        case CONSISTENT_HASH:
            return md5HashingAlg(key);
        default:
            // use the native hash as a default
            hashingAlg = NATIVE_HASH;
            return (long) key.hashCode();
        }
    }
}

1、Native Hash算法实现
实际上是调用String类的hashCode()方法，本质上是乘法Hash，Hash公式为：s[0]*31^(n-1) + s[1]*31^(n -2) + … + s[n -1]，n为String对象字符长度。
源码如下：

/**
 * Returns a hash code for this string. The hash code for a
 * <code>String </code> object is computed as
 * <blockquote><pre>
 * s[0]*31^(n-1) + s[1]*31^(n -2) + ... + s[n -1]
 * </pre></blockquote>
 * using <code>int </code> arithmetic, where <code> s[i]</code> is the
 * <i>i </i>th character of the string, <code> n</code> is the length of
 * the string, and <code>^ </code> indicates exponentiation.
 * (The hash value of the empty string is zero.)
 *
 * @return  a hash code value for this object.
 */
public int hashCode() {
int h = hash;
    int len = count;
if (h == 0 && len > 0) {
    int off = offset;
    char val[] = value;

        for (int i = 0; i < len; i++) {
            h = 31*h + val[off++];
        }
        hash = h;
    }
    return h;
}

很精巧的代码，一个循环内完成赋值和累加，选用31作为相乘因数的原因是31是素数并且只占5bit。

2、OLD_COMPAT Hash算法
内部实现跟String的hashCode()方法类似，使用相同的算法结构，只是相乘因数为33。源码如下：

/**
 * Internal private hashing method.
 *
 * This is the original hashing algorithm from other clients. Found to be
 * slow and have poor distribution.
 *
 * @param key
 *            String to hash
 * @return hashCode for this string using our own hashing algorithm
 */
private static long origCompatHashingAlg(String key) {
    long hash = 0;
    char[] cArr = key.toCharArray();

    for (int i = 0; i < cArr.length; ++i) {
        hash = (hash * 33) + cArr[i];
    }

    return hash;
}

选了一个非质数，并且占用6bit，这个算法实际上要比String的hashCode()方法低效，而且更占空间，所以基本上不用了，还不如使用Native Hash。

3、NEW_COMPAT Hash算法
使用CRC32循环冗余校验算法，本质上是查表Hash，Memcache客户端相关的源码如下：

/**
 * Internal private hashing method.
 *
 * This is the new hashing algorithm from other clients. Found to be fast
 * and have very good distribution.
 *
 * UPDATE: This is dog slow under java
 *
 * @param key
 * @return
 */
private static long newCompatHashingAlg(String key) {
    CRC32 checksum = new CRC32();
    checksum.update(key.getBytes());
    long crc = checksum.getValue();
    return (crc >> 16) & 0x7fff;
}

实际的查表Hash实现源码可以参照如下Java版本：

public static String getCRC32(String str){
        int[] table = {
        0x00000000, 0x77073096, 0xee0e612c, 0x990951ba, 0x076dc419, 0x706af48f, 0xe963a535, 0x9e6495a3,
        0x0edb8832, 0x79dcb8a4, 0xe0d5e91e, 0x97d2d988, 0x09b64c2b, 0x7eb17cbd, 0xe7b82d07, 0x90bf1d91,
        0x1db71064, 0x6ab020f2, 0xf3b97148, 0x84be41de, 0x1adad47d, 0x6ddde4eb, 0xf4d4b551, 0x83d385c7,
        0x136c9856, 0x646ba8c0, 0xfd62f97a, 0x8a65c9ec, 0x14015c4f, 0x63066cd9, 0xfa0f3d63, 0x8d080df5,
        0x3b6e20c8, 0x4c69105e, 0xd56041e4, 0xa2677172, 0x3c03e4d1, 0x4b04d447, 0xd20d85fd, 0xa50ab56b,
        0x35b5a8fa, 0x42b2986c, 0xdbbbc9d6, 0xacbcf940, 0x32d86ce3, 0x45df5c75, 0xdcd60dcf, 0xabd13d59,
        0x26d930ac, 0x51de003a, 0xc8d75180, 0xbfd06116, 0x21b4f4b5, 0x56b3c423, 0xcfba9599, 0xb8bda50f,
        0x2802b89e, 0x5f058808, 0xc60cd9b2, 0xb10be924, 0x2f6f7c87, 0x58684c11, 0xc1611dab, 0xb6662d3d,
        0x76dc4190, 0x01db7106, 0x98d220bc, 0xefd5102a, 0x71b18589, 0x06b6b51f, 0x9fbfe4a5, 0xe8b8d433,
        0x7807c9a2, 0x0f00f934, 0x9609a88e, 0xe10e9818, 0x7f6a0dbb, 0x086d3d2d, 0x91646c97, 0xe6635c01,
        0x6b6b51f4, 0x1c6c6162, 0x856530d8, 0xf262004e, 0x6c0695ed, 0x1b01a57b, 0x8208f4c1, 0xf50fc457,
        0x65b0d9c6, 0x12b7e950, 0x8bbeb8ea, 0xfcb9887c, 0x62dd1ddf, 0x15da2d49, 0x8cd37cf3, 0xfbd44c65,
        0x4db26158, 0x3ab551ce, 0xa3bc0074, 0xd4bb30e2, 0x4adfa541, 0x3dd895d7, 0xa4d1c46d, 0xd3d6f4fb,
        0x4369e96a, 0x346ed9fc, 0xad678846, 0xda60b8d0, 0x44042d73, 0x33031de5, 0xaa0a4c5f, 0xdd0d7cc9,
        0x5005713c, 0x270241aa, 0xbe0b1010, 0xc90c2086, 0x5768b525, 0x206f85b3, 0xb966d409, 0xce61e49f,
        0x5edef90e, 0x29d9c998, 0xb0d09822, 0xc7d7a8b4, 0x59b33d17, 0x2eb40d81, 0xb7bd5c3b, 0xc0ba6cad,
        0xedb88320, 0x9abfb3b6, 0x03b6e20c, 0x74b1d29a, 0xead54739, 0x9dd277af, 0x04db2615, 0x73dc1683,
        0xe3630b12, 0x94643b84, 0x0d6d6a3e, 0x7a6a5aa8, 0xe40ecf0b, 0x9309ff9d, 0x0a00ae27, 0x7d079eb1,
        0xf00f9344, 0x8708a3d2, 0x1e01f268, 0x6906c2fe, 0xf762575d, 0x806567cb, 0x196c3671, 0x6e6b06e7,
        0xfed41b76, 0x89d32be0, 0x10da7a5a, 0x67dd4acc, 0xf9b9df6f, 0x8ebeeff9, 0x17b7be43, 0x60b08ed5,
        0xd6d6a3e8, 0xa1d1937e, 0x38d8c2c4, 0x4fdff252, 0xd1bb67f1, 0xa6bc5767, 0x3fb506dd, 0x48b2364b,
        0xd80d2bda, 0xaf0a1b4c, 0x36034af6, 0x41047a60, 0xdf60efc3, 0xa867df55, 0x316e8eef, 0x4669be79,
        0xcb61b38c, 0xbc66831a, 0x256fd2a0, 0x5268e236, 0xcc0c7795, 0xbb0b4703, 0x220216b9, 0x5505262f,
        0xc5ba3bbe, 0xb2bd0b28, 0x2bb45a92, 0x5cb36a04, 0xc2d7ffa7, 0xb5d0cf31, 0x2cd99e8b, 0x5bdeae1d,
        0x9b64c2b0, 0xec63f226, 0x756aa39c, 0x026d930a, 0x9c0906a9, 0xeb0e363f, 0x72076785, 0x05005713,
        0x95bf4a82, 0xe2b87a14, 0x7bb12bae, 0x0cb61b38, 0x92d28e9b, 0xe5d5be0d, 0x7cdcefb7, 0x0bdbdf21,
        0x86d3d2d4, 0xf1d4e242, 0x68ddb3f8, 0x1fda836e, 0x81be16cd, 0xf6b9265b, 0x6fb077e1, 0x18b74777,
        0x88085ae6, 0xff0f6a70, 0x66063bca, 0x11010b5c, 0x8f659eff, 0xf862ae69, 0x616bffd3, 0x166ccf45,
        0xa00ae278, 0xd70dd2ee, 0x4e048354, 0x3903b3c2, 0xa7672661, 0xd06016f7, 0x4969474d, 0x3e6e77db,
        0xaed16a4a, 0xd9d65adc, 0x40df0b66, 0x37d83bf0, 0xa9bcae53, 0xdebb9ec5, 0x47b2cf7f, 0x30b5ffe9,
        0xbdbdf21c, 0xcabac28a, 0x53b39330, 0x24b4a3a6, 0xbad03605, 0xcdd70693, 0x54de5729, 0x23d967bf,
        0xb3667a2e, 0xc4614ab8, 0x5d681b02, 0x2a6f2b94, 0xb40bbe37, 0xc30c8ea1, 0x5a05df1b, 0x2d02ef8d,
        };
        byte[] bytes = str.getBytes();
        int crc = 0xffffffff;
        for (byte b : bytes) {
        crc = (crc >>>8 ^ table[(crc ^ b) & 0xff]);
        }
        crc = crc ^ 0xffffffff;
        return Integer.toHexString(crc);
    }

这个算法要比乘法Hash更高效，并且分布性更好。

4、一致性Hash算法
前一篇文章已经讲解过，可以前往查阅。

四、总结
以上几种Hash算法，这里只是简单的介绍一下，若对算法非常感兴趣，可以进一步查阅更详细全面的资料。
Memcache-Java-Client-Release源码阅读暂时就告一段落了，相信整理的心得只是冰山一角，更多更精彩的内容仍然是源码本身，期待各位去挖掘，目前已整理的内容就当抛砖引玉了，还望各位多多指点。