[数据结构与算法]布隆过滤器的简单使用

创建一个简单的布隆过滤器.

添加依赖:

<!--  布隆过滤器   -->
        <dependency>
            <groupId>com.google.guava</groupId>
            <artifactId>guava</artifactId>
            <version>23.0</version>
        </dependency>

java代码的实现:

import com.google.common.hash.BloomFilter;
import com.google.common.hash.Funnels;

import java.nio.charset.Charset;


/**
 * @create: 2021-10-19 21:00
 * @author: by 房东的猫
 * @program: flink_doit
 */
public class BloomFilterDemo {
    public static void main(String[] args) {

        /*
            参数一 : 字符编码集
            参数二 : 添加的key的数量
            参数三 : 预期的误判率
         */
        BloomFilter<Integer> bloomFilter = BloomFilter.create(Funnels.integerFunnel(), 100000, 0.000000001);

        // 添加数据
        boolean b1 = bloomFilter.put("阿超".hashCode());
        boolean b2 = bloomFilter.put("黑糖".hashCode());

        //判断是否存在
        System.out.println(bloomFilter.mightContain("阿超".hashCode()));//true
        System.out.println(bloomFilter.mightContain("老付".hashCode()));//false

        BloomFilter<String> bloomFilter2 = BloomFilter.create(Funnels.stringFunnel(Charset.forName("UTF8")), 100000, 0.001);

        // 添加数据
        boolean b3 = bloomFilter2.put("阿超");//true
        boolean b4 = bloomFilter2.put("黑糖");//false

        //判断是否存在
        System.out.println(bloomFilter.mightContain("阿超".hashCode()));
        System.out.println(bloomFilter.mightContain("小超".hashCode()));

        //查看存储多少条数据
        long l = bloomFilter.approximateElementCount();
        long l1 = bloomFilter2.approximateElementCount();

        System.out.println(l+l1);//4

    }

}