grep之字符串搜索算法Boyer-Moore由浅入深（比KMP快3-5倍）

这篇长文历时近两天终于完成了，前两天帮网站翻译一篇文章“为什么GNU grep如此之快？”，里面提及到grep速度快的一个重要原因是使用了Boyer-Moore算法作为字符串搜索算法，兴趣之下就想了解这个算法，发现这个算法一开始还挺难理解的，也许是我理解能力不是很好吧，花了小半天才看懂，看懂了过后就想分享下，因为觉得这个算法真的挺不错的，以前一直以为字符串搜索算法中KMP算很不错的了，没想到还有更好的，Boyer-Moore算法平均要比KMP快3-5倍。

下面是我对该算法的理解，参考了一些关于该算法的介绍，里面每一张图都画的很认真，希望能讲清楚问题，有什么错误、疑问或不懂的地方麻烦大家一定要提出来，共同学习进步！下面正文开始。

1. 简单介绍

在用于查找子字符串的算法当中，BM（Boyer-Moore）算法是目前被认为最高效的字符串搜索算法，它由Bob Boyer和J Strother Moore设计于1977年。 一般情况下，比KMP算法快3-5倍。该算法常用于文本编辑器中的搜索匹配功能，比如大家所熟知的GNU grep命令使用的就是该算法，这也是GNU grep比BSD grep快的一个重要原因，具体推荐看下我最近的一篇译文“为什么GNU grep如此之快？”作者是GNU grep的编写者Mike Haertel。

2. 主要特征

假设文本串text长度为n，模式串pattern长度为m，BM算法的主要特征为：

- 从右往左进行比较匹配（一般的字符串搜索算法如KMP都是从从左往右进行匹配）；

- 算法分为两个阶段：预处理阶段和搜索阶段；

- 预处理阶段时间和空间复杂度都是是O(m+)，是字符集大小，一般为256；

- 搜索阶段时间复杂度是O(mn)；

- 当模式串是非周期性的，在最坏的情况下算法需要进行3n次字符比较操作；

- 算法在最好的情况下达到O(n / m)，比如在文本串bⁿ中搜索模式串a^m-1b ，只需要n/m次比较。

这些特征先让大家对该算法有个基本的了解，等看懂了算法再来看这些特征又会有些额外的收获。

3.算法基本思想

常规的匹配算法移动模式串的时候是从左到右，而进行比较的时候也是从左到右的，基本框架是：

j = 0；

while（j <= strlen(text) - strlen(pattern)）{
    for (i = 0; i < strlen(pattern) && pattern[i] == text[i + j]; ++i);

    if (i == strlen(pattern)) {
        Match;
        break;
    }
    else
        ++j；
}

而BM算法在移动模式串的时候是从左到右，而进行比较的时候是从右到左的，基本框架是：

j = 0；

while（j <= strlen(text) - strlen(pattern)）{
    for (i = strlen(pattern); i >= 0 && pattern[i] == text[i + j]; --i);

    if (i < 0)) {
        Match;
        break;
    }
    else
        j += BM()；
}

BM算法的精华就在于BM(text, pattern),也就是BM算法当不匹配的时候一次性可以跳过不止一个字符。即它不需要对被搜索的字符串中的字符进行逐一比较，而会跳过其中某些部分。通常搜索关键字越长，算法速度越快。它的效率来自于这样的事实：对于每一次失败的匹配尝试，算法都能够使用这些信息来排除尽可能多的无法匹配的位置。即它充分利用待搜索字符串的一些特征，加快了搜索的步骤。

BM算法实际上包含两个并行的算法（也就是两个启发策略）：坏字符算法（bad-character shift）和好后缀算法（good-suffix shift）。这两种算法的目的就是让模式串每次向右移动尽可能大的距离（即上面的BM()尽可能大）。

下面不直接书面解释这两个算法，为了更加通俗易懂，先用实例说明吧，这是最容易接受的方式。

4. 字符串搜索头脑风暴

大家来头脑风暴下：如何加快字符串搜索？举个很简单的例子，如下图所示，navie表示一般做法，逐个进行比对，从右向左，最后一个字符c与text中的d不匹配，pattern右移一位。但大家看一下这个d有什么特征？pattern中没有d，因此你不管右移1、2、3、4位肯定还是不匹配，何必花这个功夫呢？直接右移5（strlen(pattern)）位再进行比对不是更好吗？好，就这样做，右移5位后，text中的b与pattern中的c比较，发现还是不同，这时咋办？b在pattern中有所以不能一下右移5位了，难道直接右移一位吗？No，可以直接将pattern中的b右移到text中b的位置进行比对，但是pattern中有两个b，右移哪个b呢？保险的办法是用最右边的b与text进行比对，为啥？下图说的很清楚了，用最左边的b太激进了，容易漏掉真正的匹配，图中用最右边的b后发现正好所有的都匹配成功了，如果用最左边的不就错过了这个匹配项吗？这个启发式搜索就是BM算法做的。

图1

But, 如果遇到下面这样的情况，开始pattern中的c和text中的b不匹配，Ok，按上面的规则将pattern右移直至最右边的b与text的b对齐进行比对。再将pattern中的c与text中的c进行比对，匹配继续往左比对，直到位置3处pattern中的a与text中的b不匹配了，按上面讲的启发式规则应该将pattern中最右边的b与text的b对齐，可这时发现啥了？pattern走了回头路，干吗？当然不干，才不要那么傻，针对这种情况，只需要将pattern简单的右移一步即可，坚持不走回头路！

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" alt="" />

图2

好了，这就是所谓的“坏字符算法”，简单吧，通俗易懂吧，上面用红色粗体字标注出来的b就是“坏字符”，即不匹配的字符，坏字符是针对text的。

BM难道就这么简单？就一个启发式规则就搞定了？当然不是了，大家再次头脑风暴一下，有没有其他加快字符串搜索的方法呢？比如下面的例子

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" alt="" />

图3

一开始利用了坏字符算法一下移了4位，不错，接下来遇到了回头路，没办法只能保守移一位，但真的就只能移一位吗？No，因为pattern中前面其他位置也有刚刚匹配成功的后缀ab，那么将pattern前面的ab右移到text刚匹配成功的ab对齐继续往前匹配不是更好吗？这样就可以一次性右移两位了，很好的有一个启发式搜索规则啊。有人可能想：要是前面没已经匹配成功的后缀咋办？是不是就无效了？不完全是，这要看情况了，比如下面这个例子。

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" alt="" />

图4

cbab这个后缀已经成功匹配，然后b没成功，而pattern前面也没发现cbab这样的串，这样就直接保守移一位？No，前面有ab啊，这是cbab后缀的一部分，也可以好好利用，直接将pattern前面的ab右移到text已经匹配成功的ab位置处继续往前匹配，这样一下子就右移了四位，很好。当然，如果前面完全没已经匹配成功的后缀或部分后缀，比如最前面的babac，那就真的不能利用了。

好了，这就是所谓的“好后缀算法”，简单吧，通俗易懂吧，上面用红色字标注出来的ab（前面例子）和cbab（上面例子）就是“好后缀”，好后缀是针对pattern的。

下面，最后再举个例子说明啥是坏字符，啥是好后缀。

主串  :  mahtavaatalomaisema omalomailuun

模式串: maisemaomaloma

坏字符：主串中的“t”为坏字符。

好后缀：模式串中的aloma为“好后缀”。

BM就这么简单？是的，容易理解但并不是每个人都能想到的两个启发式搜索规则就造就了BM这样一个优秀的算法。那么又有个问题？这两个算法怎么运用，一下坏字符的，一下好后缀的，什么时候该用坏字符？什么时候该用好后缀呢？很好的问题，这就要看哪个右移的位数多了，比如上面的例子，一开始如果用好后缀的话只能移一位而用坏字符就能右移三位，此时当然选择坏字符算法了。接下来如果继续用坏字符则只能右移一位而用好后缀就能一下右移四位，这时候你说用啥呢？So，这两个算法是“并行”的，哪个大用哪个。

光用例子说明当然不够，太浅了，而且还不一定能完全覆盖所有情况，不精确。下面就开始真正的理论探讨了。

5. BM算法理论探讨

（1）坏字符算法

当出现一个坏字符时, BM算法向右移动模式串, 让模式串中最靠右的对应字符与坏字符相对，然后继续匹配。坏字符算法有两种情况。

Case1：模式串中有对应的坏字符时，让模式串中最靠右的对应字符与坏字符相对（PS：BM不可能走回头路，因为若是回头路，则移动距离就是负数了，肯定不是最大移动步数了），如下图。

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Case2：模式串中不存在坏字符，很好，直接右移整个模式串长度这么大步数，如下图。

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（2）好后缀算法

如果程序匹配了一个好后缀, 并且在模式中还有另外一个相同的后缀或后缀的部分, 那把下一个后缀或部分移动到当前后缀位置。假如说，pattern的后u个字符和text都已经匹配了，但是接下来的一个字符不匹配，我需要移动才能匹配。如果说后u个字符在pattern其他位置也出现过或部分出现，我们将pattern右移到前面的u个字符或部分和最后的u个字符或部分相同，如果说后u个字符在pattern其他位置完全没有出现，很好，直接右移整个pattern。这样，好后缀算法有三种情况，如下图所示：

Case1：模式串中有子串和好后缀完全匹配，则将最靠右的那个子串移动到好后缀的位置继续进行匹配。

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Case2：如果不存在和好后缀完全匹配的子串，则在好后缀中找到具有如下特征的最长子串,使得P[m-s…m]=P[0…s]。

aaarticlea/png;base64,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" alt="" />

Case3：如果完全不存在和好后缀匹配的子串，则右移整个模式串。

（3）移动规则

BM算法的移动规则是：

将3中算法基本框架中的j += BM()，换成j += MAX（shift（好后缀），shift（坏字符）），即

BM算法是每次向右移动模式串的距离是，按照好后缀算法和坏字符算法计算得到的最大值。

shift（好后缀）和shift（坏字符）通过模式串的预处理数组的简单计算得到。坏字符算法的预处理数组是bmBc[]，好后缀算法的预处理数组是bmGs[]。

6. BM算法具体执行

BM算法子串比较失配时，按坏字符算法计算pattern需要右移的距离，要借助bmBc数组，而按好后缀算法计算pattern右移的距离则要借助bmGs数组。下面讲下怎么计算bmBc[]和bmGs[]这两个预处理数组。

（1）计算坏字符数组bmBc[]

这个计算应该很容易，似乎只需要bmBc[i] = m - 1 - i就行了，但这样是不对的，因为i位置处的字符可能在pattern中多处出现（如下图所示），而我们需要的是最右边的位置，这样就需要每次循环判断了，非常麻烦，性能差。这里有个小技巧，就是使用字符作为下标而不是位置数字作为下标。这样只需要遍历一遍即可，这貌似是空间换时间的做法，但如果是纯8位字符也只需要256个空间大小，而且对于大模式，可能本身长度就超过了256，所以这样做是值得的（这也是为什么数据越大，BM算法越高效的原因之一）。

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" 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如前所述，bmBc[]的计算分两种情况，与前一一对应。

Case1：字符在模式串中有出现，bmBc['v']表示字符v在模式串中最后一次出现的位置，距离模式串串尾的长度，如上图所示。

Case2：字符在模式串中没有出现，如模式串中没有字符v，则BmBc['v'] = strlen(pattern)。

写成代码也非常简单：

void PreBmBc(char *pattern, int m, int bmBc[])
{
	int i;

	for(i = 0; i < 256; i++)
	{
		bmBc[i] = m;
	}

	for(i = 0; i < m - 1; i++)
	{
		bmBc[pattern[i]] = m - 1 - i;
	}
}

计算pattern需要右移的距离，要借助bmBc数组，那么bmBc的值是不是就是pattern实际要右移的距离呢？No，想想也不是，比如前面举例说到利用bmBc算法还可能走回头路，也就是右移的距离是负数，而bmBc的值绝对不可能是负数，所以两者不相等。那么pattern实际右移的距离怎么算呢？这个就要看text中坏字符的位置了，前面说过坏字符算法是针对text的，还是看图吧，一目了然。图中v是text中的坏字符（对应位置i+j）,在pattern中对应不匹配的位置为i，那么pattern实际要右移的距离就是：bmBc['v'] - m + 1 + i。

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" 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（2）计算好后缀数组bmGs[]

这里bmGs[]的下标是数字而不是字符了，表示字符在pattern中位置。

如前所述，bmGs数组的计算分三种情况，与前一一对应。假设图中好后缀长度用数组suff[]表示。

Case1：对应好后缀算法case1，如下图，j是好后缀之前的那个位置。

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" 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Case2：对应好后缀算法case2：如下图所示：

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" alt="" />

Case3：对应与好后缀算法case3，bmGs[i] = strlen（pattern）= m

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" alt="" />

这样就清晰了，代码编写也比较简单：

void PreBmGs(char *pattern, int m, int bmGs[])
{
	int i, j;
	int suff[SIZE];  

	// 计算后缀数组
	suffix(pattern, m, suff);

	// 先全部赋值为m，包含Case3
	for(i = 0; i < m; i++)
	{
		bmGs[i] = m;
	}

	// Case2
	j = 0;
	for(i = m - 1; i >= 0; i--)
	{
		if(suff[i] == i + 1)
		{
			for(; j < m - 1 - i; j++)
			{
				if(bmGs[j] == m)
					bmGs[j] = m - 1 - i;
			}
		}
	}

	// Case1
	for(i = 0; i <= m - 2; i++)
	{
		bmGs[m - 1 - suff[i]] = m - 1 - i;
	}
}

So easy? 结束了吗？还差一步呢，这里的suff[]咋求呢？

在计算bmGc数组时，为提高效率，先计算辅助数组suff[]表示好后缀的长度。

suff数组的定义：m是pattern的长度

a. suffix[m-1] = m;

b. suffix[i] = k  

    for [ pattern[i-k+1] ....,pattern[i]] == [pattern[m-1-k+1]，pattern[m-1]]

看上去有些晦涩难懂，实际上suff[i]就是求pattern中以i位置字符为后缀和以最后一个字符为后缀的公共后缀串的长度。不知道这样说清楚了没有，还是举个例子吧：

i     : 0 1 2 3 4 5 6 7 
pattern: b c  a b a b a b

当i=7时，按定义suff[7] = strlen(pattern) = 8

当i=6时，以pattern[6]为后缀的后缀串为bcababa，以最后一个字符b为后缀的后缀串为bcababab，两者没有公共后缀串，所以suff[6] = 0

当i=5时，以pattern[5]为后缀的后缀串为bcabab，以最后一个字符b为后缀的后缀串为bcababab，两者的公共后缀串为abab，所以suff[5] = 4

以此类推……

当i=0时，以pattern[0]为后缀的后缀串为b，以最后一个字符b为后缀的后缀串为bcababab，两者的公共后缀串为b，所以suff[0] = 1

这样看来代码也很好写：

void suffix(char *pattern, int m, int suff[])
{
	int i, j;
	int k;

	suff[m - 1] = m;

	for(i = m - 2; i >= 0; i--)
	{
		j = i;
		while(j >= 0 && pattern[j] == pattern[m - 1 - i + j]) j--;

		suff[i] = i - j;
	}
}

这样可能就万事大吉了，可是总有人对这个算法不满意，感觉太暴力了，于是有聪明人想出一种方法，对上述常规方法进行改进。基本的扫描都是从右向左，改进的地方就是利用了已经计算得到的suff[]值，计算现在正在计算的suff[]值。具体怎么利用，看下图：

i是当前正准备计算suff[]值的那个位置。

f是上一个成功进行匹配的起始位置（不是每个位置都能进行成功匹配的，  实际上能够进行成功匹配的位置并不多）。

g是上一次进行成功匹配的失配位置。

如果i在g和f之间，那么一定有P[i]=P[m-1-f+i]；并且如果suff[m-1-f+i] < i-g, 则suff[i] = suff[m-1-f+i]，这不就利用了前面的suff了吗。

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" alt="" />

PS：这里有些人可能觉得应该是suff[m-1-f+i] <= i - g，因为若suff[m-1-f+i] = i - g，还是没超过suff[f]的范围，依然可以利用前面的suff[]，但这是错误的，比如一个极端的例子：

i      ：0 1 2 3 4 5 6 7 8 9
pattern：a  a a a a b a a a  a

suff[4] = 4，这里f=4，g=0，当i=3是，这时suff[m-1=f+i]=suff[8]=3，而suff[3]=4，两者不相等，因为上一次的失配位置g可能会在这次得到匹配。

好了，这样解释过后，代码也比较简单：

void suffix(char *pattern, int m, int suff[]) {
   int f, g, i;

   suff[m - 1] = m;
   g = m - 1;
   for (i = m - 2; i >= 0; --i) {
      if (i > g && suff[i + m - 1 - f] < i - g)
         suff[i] = suff[i + m - 1 - f];
      else {
         if (i < g)
            g = i;
         f = i;
         while (g >= 0 && pattern[g] == pattern[g + m - 1 - f])
            --g;
         suff[i] = f - g;
      }
   }
}

结束了？OK，可以说重要的算法都完成了，希望大家能够看懂，为了验证大家到底有没有完全看明白，下面出个简单的例子，大家算一下bmBc[]、suff[]和bmGs[]吧。

举例如下：

aaarticlea/png;base64,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" alt="" />

PS：这里也许有人会问：bmBc['b']怎么等于2，它不是最后出现在pattern最后一个位置吗？按定义应该是0啊。请大家仔细看下bmBc的算法：

for(i = 0; i < m - 1; i++)
{
	bmBc[pattern[i]] = m - 1 - i;
}

这里是i < m - 1不是i < m，也就是最后一个字符如果没有在前面出现过，那么它的bmBc值为m。为什么最后一位不计算在bmBc中呢？很容易想啊，如果记在内该字符的bmBc就是0，按前所述，pattern需要右移的距离bmBc['v']-m+1+i=-m+1+i <= 0，也就是原地不动或走回头路，当然不干了，前面这种情况已经说的很清楚了，所以这里是m-1。

好了，所有的终于都讲完了，下面整合一下这些算法吧。

#include <stdio.h>
#include <string.h>

#define MAX_CHAR 256
#define SIZE 256
#define MAX(x, y) (x) > (y) ? (x) : (y)

void BoyerMoore(char *pattern, int m, char *text, int n);

int main()
{
	char text[256], pattern[256];

	while(1)
	{
		scanf("%s%s", text, pattern);
		if(text == 0 || pattern == 0) break;

		BoyerMoore(pattern, strlen(pattern), text, strlen(text));
		printf("\n");
	}

	return 0;
}

void print(int *array, int n, char *arrayName)
{
	int i;
	printf("%s: ", arrayName);
	for(i = 0; i < n; i++)
	{
		printf("%d ", array[i]);
	}
	printf("\n");
}

void PreBmBc(char *pattern, int m, int bmBc[])
{
	int i;

	for(i = 0; i < MAX_CHAR; i++)
	{
		bmBc[i] = m;
	}

	for(i = 0; i < m - 1; i++)
	{
		bmBc[pattern[i]] = m - 1 - i;
	}

/*	printf("bmBc[]: ");
	for(i = 0; i < m; i++)
	{
		printf("%d ", bmBc[pattern[i]]);
	}
	printf("\n"); */
}

void suffix_old(char *pattern, int m, int suff[])
{
	int i, j;

	suff[m - 1] = m;

	for(i = m - 2; i >= 0; i--)
	{
		j = i;
		while(j >= 0 && pattern[j] == pattern[m - 1 - i + j]) j--;

		suff[i] = i - j;
	}
}

void suffix(char *pattern, int m, int suff[]) {
   int f, g, i;

   suff[m - 1] = m;
   g = m - 1;
   for (i = m - 2; i >= 0; --i) {
      if (i > g && suff[i + m - 1 - f] < i - g)
         suff[i] = suff[i + m - 1 - f];
      else {
         if (i < g)
            g = i;
         f = i;
         while (g >= 0 && pattern[g] == pattern[g + m - 1 - f])
            --g;
         suff[i] = f - g;
      }
   }

//   print(suff, m, "suff[]");
}

void PreBmGs(char *pattern, int m, int bmGs[])
{
	int i, j;
	int suff[SIZE];  

	// 计算后缀数组
	suffix(pattern, m, suff);

	// 先全部赋值为m，包含Case3
	for(i = 0; i < m; i++)
	{
		bmGs[i] = m;
	}

	// Case2
	j = 0;
	for(i = m - 1; i >= 0; i--)
	{
		if(suff[i] == i + 1)
		{
			for(; j < m - 1 - i; j++)
			{
				if(bmGs[j] == m)
					bmGs[j] = m - 1 - i;
			}
		}
	}

	// Case1
	for(i = 0; i <= m - 2; i++)
	{
		bmGs[m - 1 - suff[i]] = m - 1 - i;
	}

//	print(bmGs, m, "bmGs[]");
}

void BoyerMoore(char *pattern, int m, char *text, int n)
{
	int i, j, bmBc[MAX_CHAR], bmGs[SIZE];

	// Preprocessing
	PreBmBc(pattern, m, bmBc);
	PreBmGs(pattern, m, bmGs);

	// Searching
	j = 0;
	while(j <= n - m)
	{
		for(i = m - 1; i >= 0 && pattern[i] == text[i + j]; i--);
		if(i < 0)
		{
			printf("Find it, the position is %d\n", j);
			j += bmGs[0];
			return;
		}
		else
		{
			j += MAX(bmBc[text[i + j]] - m + 1 + i, bmGs[i]);
		}
	}

	printf("No find.\n");
}

运行效果如下：

aaarticlea/png;base64,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" alt="" />

参考资料

1. Boyer-Moore algorithm

2. wiki: Boyer–Moore string search algorithm

3. Boyer-Moore算法学习