<dependency>
<groupId>com.google.guava</groupId>
<artifactId>guava</artifactId>
<version>23.0</version>
</dependency>
public class TestSomething {
private static final int capacity = 1000000;
private static final int key = 999998;
private static BloomFilter<Integer> bloomFilter = BloomFilter.create(Funnels.integerFunnel(), capacity);
static {
for (int i = 0; i < capacity; i++) {
bloomFilter.put(i);
}
}
public static void main(String[] args) {
/*返回计算机最精确的时间,单位微妙*/
long start = System.nanoTime();
if (bloomFilter.mightContain(key)) {
System.out.println("成功过滤到" + key);
}
long end = System.nanoTime();
System.out.println("布隆过滤器消耗时间:" + (end - start));
int sum = 0;
for (int i = capacity + 20000; i < capacity + 30000; i++) {
if (bloomFilter.mightContain(i)) {
sum = sum + 1;
}
}
System.out.println("错判率为:" + sum);
}
}
布隆过滤器实际上是一个很长的二进制向量和一系列随机映射函数。布隆过滤器可以用于检索一个元素是否在一个集合中。它的优点是空间效率和查询时间都远远超过一般的算法，缺点是有一定的误识别率和删除困难
当一个元素被加入集合时，通过K个散列函数将这个元素映射成一个位数组中的K个点，把它们置为1。检索时，我们只要看看这些点是不是都是1就（大约）知道集合中有没有它了：如果这些点有任何一个0，则被检元素一定不在；如果都是1，则被检元素很可能在。因为存在哈希冲突导致3%%左右的误判，即没有存在的判断存在，但是在的一定就是在的。
因此BloomFilter最理想的应用场景是在一些复杂的查询时，在DB上做一层BloomFilter判断，如果BloomFilter判断不存在，则没必要到DB去查了。顶多就是出现误判时，多到DB查询一下，而这个概率是很低的。利用redis的高性能以及通过pipeline将多条bit操作命令批量提交，实现了多机BloomFilter的bit数据共享，数据限制512M。
在java中实现存在2个问题：1.OOM 2.持久化
整合redis如下
@Component
@Scope("prototype")
public class BloomFilter<E> {
@Autowired
private RedisUtil redisUtil;
@Value("${bloomfilter.expireDays}")
private long expireDays;
private String redisKey = "DEFAULT";
/**
* 总长度
*/
private int sizeOfBloomFilter;
/**
* 预估过滤数
*/
private int expectedNumberOfFilterElements;
/**
* 哈希次数
*/
private int numberOfHashFunctions;
private final Charset charset = Charset.forName("UTF-8");
private static final String hashName = "MD5";
private static final MessageDigest digestFunction;
// The digest method is reused between instances
static {
MessageDigest tmp;
try {
tmp = java.security.MessageDigest.getInstance(hashName);
} catch (NoSuchAlgorithmException e) {
tmp = null;
}
digestFunction = tmp;
}
public BloomFilter() {
this(0.0001, 600000);
}
/**
* Constructs an empty Bloom filter.
*
* @param m is the total length of the Bloom filter.
* @param n is the expected number of elements the filter will contain.
* @param k is the number of hash functions used.
*/
public BloomFilter(int m, int n, int k) {
this.sizeOfBloomFilter = m;
this.expectedNumberOfFilterElements = n;
this.numberOfHashFunctions = k;
}
/**
* Constructs an empty Bloom filter with a given false positive probability.
* The size of bloom filter and the number of hash functions is estimated
* to match the false positive probability.
* 给定期望的错误率，过滤量
*
* @param falsePositiveProbability is the desired false positive probability.
* @param expectedNumberOfElements is the expected number of elements in the Bloom filter.
*/
public BloomFilter(double falsePositiveProbability, int expectedNumberOfElements) {
// m = ceil(kn/ln2) k = ceil(-ln(f)/ln2)
this((int) Math.ceil((int) Math.ceil(-(Math.log(falsePositiveProbability) / Math.log(2))) * expectedNumberOfElements / Math.log(2)),
expectedNumberOfElements,
(int) Math.ceil(-(Math.log(falsePositiveProbability) / Math.log(2))));
}
/**
* Adds all elements from a Collection to the Bloom filter.
*
* @param c Collection of elements.
*/
public void addAll(Collection<? extends E> c) {
for (E element : c) {
add(element);
}
}
/**
* Adds an object to the Bloom filter. The output from the object's
* toString() method is used as input to the hash functions.
* 添加元素
*
* @param element is an element to register in the Bloom filter.
*/
public void add(E element) {
add(element.toString().getBytes(charset));
}
/**
* Adds an array of bytes to the Bloom filter.
*
* @param bytes array of bytes to add to the Bloom filter.
*/
public void add(byte[] bytes) {
if (redisUtil.get(redisKey) == null) {
redisUtil.setBit(redisKey, 0, false);
redisUtil.expire(redisKey, expireDays);
}
int[] hashes = createHashes(bytes, numberOfHashFunctions);
for (int hash : hashes) {
redisUtil.setBit(redisKey, Math.abs(hash % sizeOfBloomFilter), true);
}
}
/**
* Returns true if the element could have been inserted into the Bloom filter.
* Use getFalsePositiveProbability() to calculate the probability of this
* being correct.
*
* @param element element to check.
* @return true if the element could have been inserted into the Bloom filter.
*/
public boolean contains(E element) {
return contains(element.toString().getBytes(charset));
}
/**
* Returns true if the array of bytes could have been inserted into the Bloom filter.
* Use getFalsePositiveProbability() to calculate the probability of this
* being correct.
*
* @param bytes array of bytes to check.
* @return true if the array could have been inserted into the Bloom filter.
*/
public boolean contains(byte[] bytes) {
int[] hashes = createHashes(bytes, numberOfHashFunctions);
for (int hash : hashes) {
if (!redisUtil.getBit(redisKey, Math.abs(hash % sizeOfBloomFilter))) {
return false;
}
}
return true;
}
/**
* Returns true if all the elements of a Collection could have been inserted
* into the Bloom filter. Use getFalsePositiveProbability() to calculate the
* probability of this being correct.
*
* @param c elements to check.
* @return true if all the elements in c could have been inserted into the Bloom filter.
*/
public boolean containsAll(Collection<? extends E> c) {
for (E element : c) {
if (!contains(element)) {
return false;
}
}
return true;
}
/**
* Generates digests based on the contents of an array of bytes and splits the result into 4-byte int's and store them in an array. The
* digest function is called until the required number of int's are produced. For each call to digest a salt
* is prepended to the data. The salt is increased by 1 for each call.
*
* @param data specifies input data.
* @param hashes number of hashes/int's to produce.
* @return array of int-sized hashes
*/
public static int[] createHashes(byte[] data, int hashes) {
int[] result = new int[hashes];
int k = 0;
byte salt = 0;
while (k < hashes) {
byte[] digest;
synchronized (digestFunction) {
digestFunction.update(salt);
salt++;
digest = digestFunction.digest(data);
}
for (int i = 0; i < digest.length / 4 && k < hashes; i++) {
int h = 0;
for (int j = (i * 4); j < (i * 4) + 4; j++) {
h <<= 8;
h |= ((int) digest[j]) & 0xFF;
}
result[k] = h;
k++;
}
}
return result;
}
public int getSizeOfBloomFilter() {
return this.sizeOfBloomFilter;
}
public int getExpectedNumberOfElements() {
return this.expectedNumberOfFilterElements;
}
public int getNumberOfHashFunctions() {
return this.numberOfHashFunctions;
}
/**
* Compares the contents of two instances to see if they are equal.
*
* @param obj is the object to compare to.
* @return True if the contents of the objects are equal.
*/
@Override
public boolean equals(Object obj) {
if (obj == null) {
return false;
}
if (getClass() != obj.getClass()) {
return false;
}
final BloomFilter<E> other = (BloomFilter<E>) obj;
if (this.sizeOfBloomFilter != other.sizeOfBloomFilter) {
return false;
}
if (this.expectedNumberOfFilterElements != other.expectedNumberOfFilterElements) {
return false;
}
if (this.numberOfHashFunctions != other.numberOfHashFunctions) {
return false;
}
return true;
}
public String getRedisKey() {
return redisKey;
}
public void setRedisKey(String redisKey) {
this.redisKey = redisKey;
}
/**
* Calculates a hash code for this class.
*
* @return hash code representing the contents of an instance of this class.
*/
@Override
public int hashCode() {
int hash = 7;
hash = 61 * hash + this.sizeOfBloomFilter;
hash = 61 * hash + this.expectedNumberOfFilterElements;
hash = 61 * hash + this.numberOfHashFunctions;
return hash;
}
}
使用场景：黑名单，URL重复检查，字典纠错，垃圾邮件，缓存穿透： 将数据库中所有的查询条件，放到布隆过滤器中。当一个查询请求来临的时候，先经过布隆过滤器进行检查，如果请求存在这个条件中，那么继续执行，如果不在，直接丢弃。