package com.jd.demo.test;

import java.util.Arrays;
import java.util.BitSet;
import java.util.concurrent.atomic.AtomicBoolean;

public class MyBloomFilter {
  //你的布隆過(guò)濾器容量
  private static final int DEFAULT_SIZE = 2 << 28;
  //bit數(shù)組，用來(lái)存放結(jié)果
  private static BitSet bitSet = new BitSet(DEFAULT_SIZE);
  //后面hash函數(shù)會(huì)用到，用來(lái)生成不同的hash值，可隨意設(shè)置，別問(wèn)我為什么這么多8，圖個(gè)吉利
  private static final int[] ints = {1, 6, 16, 38, 58, 68};

  //add方法，計(jì)算出key的hash值，并將對(duì)應(yīng)下標(biāo)置為true
  public void add(Object key) {
    Arrays.stream(ints).forEach(i -> bitSet.set(hash(key, i)));
  }

  //判斷key是否存在，true不一定說(shuō)明key存在，但是false一定說(shuō)明不存在
  public boolean isContain(Object key) {
     boolean result = true;
    for (int i : ints) {
    	//短路與，只要有一個(gè)bit位為false，則返回false
      result = result && bitSet.get(hash(key, i));
    }
    return result;
  }

  //hash函數(shù)，借鑒了hashmap的擾動(dòng)算法
  private int hash(Object key, int i) {
    int h;
    return key == null ? 0 : (i * (DEFAULT_SIZE - 1) & ((h = key.hashCode()) ^ (h >>> 16)));
  }
}

public static void main(String[] args) {
  MyNewBloomFilter myNewBloomFilter = new MyNewBloomFilter();
  myNewBloomFilter.add("張學(xué)友");
  myNewBloomFilter.add("郭德綱");
  myNewBloomFilter.add(666);
  System.out.println(myNewBloomFilter.isContain("張學(xué)友"));//true
  System.out.println(myNewBloomFilter.isContain("張學(xué)友 "));//false
  System.out.println(myNewBloomFilter.isContain("張學(xué)友1"));//false
  System.out.println(myNewBloomFilter.isContain("郭德綱"));//true
  System.out.println(myNewBloomFilter.isContain(666));//true
  System.out.println(myNewBloomFilter.isContain(888));//false
}

<dependency>
  <groupId>com.google.guava</groupId>
  <artifactId>guava</artifactId>
  <version>25.1-jre</version>
</dependency>12345

public class Test1 {

  private static int size = 1000000;

  private static BloomFilter<Integer> bloomFilter = BloomFilter.create(Funnels.integerFunnel(), size);

  public static void main(String[] args) {
    for (int i = 0; i < size; i++) {
      bloomFilter.put(i);
    }

    long startTime = System.nanoTime(); // 獲取開(kāi)始時(shí)間
    //判斷這一百萬(wàn)個(gè)數(shù)中是否包含29999這個(gè)數(shù)
    if (bloomFilter.mightContain(29999)) {
      System.out.println("命中了");
    }
    long endTime = System.nanoTime();  // 獲取結(jié)束時(shí)間
    System.out.println("程序運(yùn)行時(shí)間： " + (endTime - startTime) + "納秒");
  }

}

命中了
程序運(yùn)行時(shí)間： 441616納秒

public class Test3 {

  private static int size = 1000000;

  private static BloomFilter<Integer> bloomFilter = BloomFilter.create(Funnels.integerFunnel(), size, 0.01);

  public static void main(String[] args) {
    for (int i = 0; i < size; i++) {
      bloomFilter.put(i);
    }
    List<Integer> list = new ArrayList<Integer>(1000);
    // 故意取10000個(gè)不在過(guò)濾器里的值，看看有多少個(gè)會(huì)被認(rèn)為在過(guò)濾器里
    for (int i = size + 10000; i < size + 20000; i++) {
      if (bloomFilter.mightContain(i)) {
        list.add(i);
      }
    }
    System.out.println("誤判的數(shù)量：" + list.size());
  }

}

誤判的數(shù)量：941