原文：https://blog.csdn.net/qq_18495465/article/details/78500472
BloomFilter（大数据去重）+Redis（持久化）策略
背景
之前在重构一套文章爬虫系统时，其中有块逻辑是根据文章标题去重，原先去重的方式是，插入文章之前检查待插入文章的标题是否在ElasticSearch中存在，这无疑加重了ElasticSearch的负担也势必会影响程序的性能！
BloomFilter算法
简介：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。
原理：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。
优点：相比于其它的数据结构，布隆过滤器在空间和时间方面都有巨大的优势。布隆过滤器存储空间和插入/查询时间都是常数（O(k)）。而且它不存储元素本身，在某些对保密要求非常严格的场合有优势。
缺点：一定的误识别率和删除困难。
结合以上几点及去重需求（容忍误判，会误判在，在则丢，无妨），决定使用BlomFilter。
思想
位数组和k个散列函数
位数组
初始状态时，BloomFilter是一个长度为m的位数组，每一位都置为0。
添加元素（k个独立的hash函数）
添加元素时，对x使用k个哈希函数得到k个哈希值，对m取余，对应的bit位设置为1。
判断元素是否存在
判断y是否属于这个集合，对y使用k个哈希函数得到k个哈希值，对m取余，所有对应的位置都是1，则认为y属于该集合（哈希冲突，可能存在误判），否则就认为y不属于该集合。
图中y1不是集合中的元素，y2属于这个集合或者是一个false positive。
BloomFilter有以下参数:
m 位数组的长度
n 加入其中元素的数量
k 哈希函数的个数
f False Positive
问题：如何根据输入元素个数n，确定位数组的大小m和哈希函数的个数k?
BloomFilter的f满足下列公式：
在给定m和n时，能够使f最小化的k值为：
此时给出的f为：
根据以上公式，对于任意给定的f，我们有：
同时，我们需要k个hash来达成这个目标：
由于k必须取整数，我们在Bloom Filter的程序实现中，还应该使用上面的公式来求得实际的f：
以上3个公式是程序实现Bloom Filter的关键公式。
故可以通过调节参数，使用Hash函数的个数，位数组的大小来降低失误率。
实现
可以使用JDK自带的BitSet来实现，但存在两个问题：OOM和持久化问题。
结合Redis的BitMap能够解决，唯一需要注意的是Redis的BitMap只支持2^32大小，对应到内存也就是512MB,数组的下标最大只能是2^32-1。不过这个限制可以通过构建多个Redis的Bitmap通过hash取模的方式分散一下即可。万分之一的误判率，512MB可以放下2亿条数据。
好了，扯了这么多，贴代码！(注：在MagnusS/Java-BloomFilter的基础上加上了Redis持久化的实现)
@Component
public class BloomFilter<E> {
@Autowired
private RedisTemplate<String, E> redisTemplate;
@Value("${bloomfilter.expireDays}")
private long expireDays;
// total length of the Bloom filter
private int sizeOfBloomFilter;
// expected (maximum) number of elements to be added
private int expectedNumberOfFilterElements;
// number of hash functions
private int numberOfHashFunctions;
// encoding used for storing hash values as strings
private final Charset charset = Charset.forName("UTF-8");
// MD5 gives good enough accuracy in most circumstances. Change to SHA1 if it's needed
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.
*
* @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) {
this((int) Math.ceil((int) Math.ceil(-(Math.log(falsePositiveProbability) / Math.log(2))) * expectedNumberOfElements / Math.log(2)), // m = ceil(kn/ln2)
expectedNumberOfElements,
(int) Math.ceil(-(Math.log(falsePositiveProbability) / Math.log(2)))); // k = ceil(-ln(f)/ln2)
}
/**
* Adds an object to the Bloom filter. The output from the object's
* toString() method is used as input to the hash functions.
*
* @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 (redisTemplate.opsForValue().get(RedisConsts.CRAWLER_BLOOMFILTER) == null) {
redisTemplate.opsForValue().setBit(RedisConsts.CRAWLER_BLOOMFILTER, 0, false);
redisTemplate.expire(RedisConsts.CRAWLER_BLOOMFILTER, expireDays, TimeUnit.DAYS);
}
int[] hashes = createHashes(bytes, numberOfHashFunctions);
for (int hash : hashes) {
redisTemplate.opsForValue().setBit(RedisConsts.CRAWLER_BLOOMFILTER, Math.abs(hash % sizeOfBloomFilter), true);
}
}
/**
* 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);
}
}
/**
* 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 (!redisTemplate.opsForValue().getBit(RedisConsts.CRAWLER_BLOOMFILTER, 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;
}
/**
* 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;
}
public static void main(String[] args) {
BloomFilter<String> bloomFilter = new BloomFilter<>(0.0001, 600000);
System.out.println(bloomFilter.getSizeOfBloomFilter());
System.out.println(bloomFilter.getNumberOfHashFunctions());
}
}
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作者：Joker_Coding
来源：CSDN
原文：https://blog.csdn.net/qq_18495465/article/details/78500472
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