資料：
wikipedia--bloom filter
使用場景，原理簡介之中文資料：
數學之美系列二十一 － 布隆過濾器（Bloom Filter）
核心內容（摘自Google黑板報文章內容）：
BloomFilter簡易實現：
public class SimpleBloomFilter {
private static final int DEFAULT_SIZE = 2 << 24;
private static final int[] seeds = new int[] { 7, 11, 13, 31, 37, 61, };
private BitSet bits = new BitSet(DEFAULT_SIZE);
private SimpleHash[] func = new SimpleHash[seeds.length];
public static void main(String[] args) {
String value = "stone2083@yahoo.cn";
SimpleBloomFilter filter = new SimpleBloomFilter();
System.out.println(filter.contains(value));
filter.add(value);
System.out.println(filter.contains(value));
}
public SimpleBloomFilter() {
for (int i = 0; i < seeds.length; i++) {
func[i] = new SimpleHash(DEFAULT_SIZE, seeds[i]);
}
}
public void add(String value) {
for (SimpleHash f : func) {
bits.set(f.hash(value), true);
}
}
public boolean contains(String value) {
if (value == null) {
return false;
}
boolean ret = true;
for (SimpleHash f : func) {
ret = ret && bits.get(f.hash(value));
}
return ret;
}
public static class SimpleHash {
private int cap;
private int seed;
public SimpleHash(int cap, int seed) {
this.cap = cap;
this.seed = seed;
}
public int hash(String value) {
int result = 0;
int len = value.length();
for (int i = 0; i < len; i++) {
result = seed * result + value.charAt(i);
}
return (cap - 1) & result;
}
}
}