Java实现简单的Bloom Filter

布隆过滤器在信息去重，比如网页的去重、邮件去重以及URL去重上很常用，其原理也相对简单，主要是通过多个哈希函数来设置bit位作为某条记录的指纹，查询和空间效率都非常不错。

一、基本原理

这篇博客讲的非常好，我就不再狗尾续貂了：

http://www.cnblogs.com/haippy/archive/2012/07/13/2590351.html

简单来说，就是对数据库中的记录$X，Y，Z$分别使用多个（图中为3个）哈希函数，映射到对应的比特位，将比特位置1，如果下一个数据使用相同三个哈希后对应的比特位全为1，则该数据已存在数据库中。

推导结论为(m为bit位个数，n为待添加的记录数，p为冲突概率)

如果已经限定了bitset容量和待添加的记录数，则最优的哈希函数的个数k为

此时冲突率为：

如果已经限定了冲突概率，则bit数组的大小最优为：

二、构造函数

public class Bloomfilter {      private BitSet bitSet;      private int bitSetSize;      private int addedElements;      private int hashFunctionNumber;      /**       * 构造一个布隆过滤器，过滤器的容量为c * n 个bit.       * @param c 当前过滤器预先开辟的最大包含记录,通常要比预计存入的记录多一倍.       * @param n 当前过滤器预计所要包含的记录.       * @param k 哈希函数的个数，等同每条记录要占用的bit数.       */      public Bloomfilter(int c, int n, int k) {          this.hashFunctionNumber = k;          this.bitSetSize = (int) Math.ceil(c * k);          this.addedElements = n;          this.bitSet = new BitSet(this.bitSetSize);      }

通常我们要预设一个相对大一点的bit空间，这样冲突才会比较小，哈希函数的数量K也是越多越好，但是通常8个以上就行了，要看能接受的冲突阈值。

代码中也提供了获得当前冲突率的函数。

三、完整代码

完整代码包括两个类，一个哈希函数库（从网上搜的8个hash函数），和过滤器类：

package crawler.analysis;  import java.io.BufferedReader;  import java.io.FileReader;  import java.io.IOException;  import java.util.ArrayList;  import java.util.BitSet;  import java.util.List;  public class Bloomfilter {      private BitSet bitSet;      private int bitSetSize;      private int addedElements;      private int hashFunctionNumber;      /**       * 构造一个布隆过滤器，过滤器的容量为c * n 个bit.       * @param c 当前过滤器预先开辟的最大包含记录,通常要比预计存入的记录多一倍.       * @param n 当前过滤器预计所要包含的记录.       * @param k 哈希函数的个数，等同每条记录要占用的bit数.       */      public Bloomfilter(int c, int n, int k) {          this.hashFunctionNumber = k;          this.bitSetSize = (int) Math.ceil(c * k);          this.addedElements = n;          this.bitSet = new BitSet(this.bitSetSize);      }      /**       * 通过文件初始化过滤器.       * @param file       */      public void init(String file) {          BufferedReader reader = null;          try {              reader = new BufferedReader(new FileReader(file));              String line = reader.readLine();              while (line != null && line.length() > 0) {                  this.put(line);                  line = reader.readLine();              }          } catch (Exception e) {              e.printStackTrace();          } finally {              try {                  if (reader != null) reader.close();              } catch (IOException e) {                  e.printStackTrace();              }          }      }      public void put(String str) {          int[] positions = createHashes(str.getBytes(), hashFunctionNumber);          for (int i = 0; i < positions.length; i++) {              int position = Math.abs(positions[i] % bitSetSize);              bitSet.set(position, true);          }      }      public boolean contains(String str) {          byte[] bytes = str.getBytes();          int[] positions = createHashes(bytes, hashFunctionNumber);          for (int i : positions) {              int position = Math.abs(i % bitSetSize);              if (!bitSet.get(position)) {                  return false;              }          }          return true;      }      /**       * 得到当前过滤器的错误率.       * @return       */      public double getFalsePositiveProbability() {          // (1 - e^(-k * n / m)) ^ k          return Math.pow((1 - Math.exp(-hashFunctionNumber * (double) addedElements / bitSetSize)),                  hashFunctionNumber);      }      /**       * 将字符串的字节表示进行多哈希编码.       * @param bytes 待添加进过滤器的字符串字节表示.       * @param hashNumber 要经过的哈希个数.       * @return 各个哈希的结果数组.       */      public static int[] createHashes(byte[] bytes, int hashNumber) {          int[] result = new int[hashNumber];          int k = 0;          while (k < hashNumber) {              result[k] = HashFunctions.hash(bytes, k);              k++;          }          return result;      }      public static void main(String[] args) throws Exception {          Bloomfilter bloomfilter = new Bloomfilter(30000000, 10000000, 8);          System.out.println("Bloom Filter Initialize ... ");          bloomfilter.init("data/base.txt");          System.out.println("Bloom Filter Ready");          System.out.println("False Positive Probability : "                  + bloomfilter.getFalsePositiveProbability());          // 查找新数据          List<String> result = new ArrayList<String>();          long t1 = System.currentTimeMillis();          BufferedReader reader = new BufferedReader(new FileReader("data/input.txt"));          String line = reader.readLine();          while (line != null && line.length() > 0) {              if (!bloomfilter.contains(line)) {                  result.add(line);              }              line = reader.readLine();          }          reader.close();          long t2 = System.currentTimeMillis();          System.out.println("Parse 9900000 items, Time : " + (t2 - t1) + "ms , find "                  + result.size() + " new items.");          System.out.println("Average : " + 9900000 / ((t2 - t1) / 1000) + " items/second");      }  }  class HashFunctions {      public static int hash(byte[] bytes, int k) {          switch (k) {              case 0:                  return RSHash(bytes);              case 1:                  return JSHash(bytes);              case 2:                  return ELFHash(bytes);              case 3:                  return BKDRHash(bytes);              case 4:                  return APHash(bytes);              case 5:                  return DJBHash(bytes);              case 6:                  return SDBMHash(bytes);              case 7:                  return PJWHash(bytes);          }          return 0;      }      public static int RSHash(byte[] bytes) {          int hash = 0;          int magic = 63689;          int len = bytes.length;          for (int i = 0; i < len; i++) {              hash = hash * magic + bytes[i];              magic = magic * 378551;          }          return hash;      }      public static int JSHash(byte[] bytes) {          int hash = 1315423911;          for (int i = 0; i < bytes.length; i++) {              hash ^= ((hash << 5) + bytes[i] + (hash >> 2));          }          return hash;      }      public static int ELFHash(byte[] bytes) {          int hash = 0;          int x = 0;          int len = bytes.length;          for (int i = 0; i < len; i++) {              hash = (hash << 4) + bytes[i];              if ((x = hash & 0xF0000000) != 0) {                  hash ^= (x >> 24);                  hash &= ~x;              }          }          return hash;      }      public static int BKDRHash(byte[] bytes) {          int seed = 131;          int hash = 0;          int len = bytes.length;          for (int i = 0; i < len; i++) {              hash = (hash * seed) + bytes[i];          }          return hash;      }      public static int APHash(byte[] bytes) {          int hash = 0;          int len = bytes.length;          for (int i = 0; i < len; i++) {              if ((i & 1) == 0) {                  hash ^= ((hash << 7) ^ bytes[i] ^ (hash >> 3));              } else {                  hash ^= (~((hash << 11) ^ bytes[i] ^ (hash >> 5)));              }          }          return hash;      }      public static int DJBHash(byte[] bytes) {          int hash = 5381;          int len = bytes.length;          for (int i = 0; i < len; i++) {              hash = ((hash << 5) + hash) + bytes[i];          }          return hash;      }      public static int SDBMHash(byte[] bytes) {          int hash = 0;          int len = bytes.length;          for (int i = 0; i < len; i++) {              hash = bytes[i] + (hash << 6) + (hash << 16) - hash;          }          return hash;      }      public static int PJWHash(byte[] bytes) {          long BitsInUnsignedInt = (4 * 8);          long ThreeQuarters = ((BitsInUnsignedInt * 3) / 4);          long OneEighth = (BitsInUnsignedInt / 8);          long HighBits = (long) (0xFFFFFFFF) << (BitsInUnsignedInt - OneEighth);          int hash = 0;          long test = 0;          for (int i = 0; i < bytes.length; i++) {              hash = (hash << OneEighth) + bytes[i];              if ((test = hash & HighBits) != 0) {                  hash = (int) ((hash ^ (test >> ThreeQuarters)) & (~HighBits));              }          }          return hash;      }  }

Bloom Filter Initialize ...  Bloom Filter Ready  False Positive Probability : 4.169085162009671E-5  Parse 9900000 items, Time : 17288ms , find 100 new items.  Average : 582352 items/second