• 目前仅redis4.0以上版本支持

安装插件

git clone https://github.com/RedisBloom/RedisBloom.git
cd RedisBloom
make //编译 会生成一个rebloom.so文件
redis-server --loadmodule /path/to/rebloom.so
redis-cli -h 127.0.0.1 -p 6379

添加依赖，目前redis的jar包不支持布隆过滤器命令

<dependency>
    <groupId>com.redislabs</groupId>
    <artifactId>jrebloom</artifactId>
    <version>1.0.1</version>
</dependency>

代码示例

private void addTobloomFilter(String userid, String[] hashs) {
        Client client = null;
        try {
            // 布隆过滤器去重
            Calendar cal = Calendar.getInstance();
            int dayOfYear = cal.get(Calendar.DAY_OF_YEAR);
            client = new Client("192.168.17.49", 6379, 100000, 1000);
            String bloomKey = "rebloom:" + userid + "_" + dayOfYear;
            client.addMulti(bloomKey, hashs);
        } catch (Exception e) {
            e.printStackTrace();
        } finally {
            client.close();
        }
    }

    private boolean[] bloomFilter(String userid, String[] hashs) {
        boolean[] exists = new boolean[hashs.length];
        Client client = null;
        try {
            // 布隆过滤器去重
            Calendar cal = Calendar.getInstance();
            int dayOfYear = cal.get(Calendar.DAY_OF_YEAR);
            client = new Client("192.168.17.49", 6379, 100000, 1000);
            String bloomKey = "rebloom:" + userid;
            try {
                // 新建过滤器 没找到判断过滤器是否存在的接口
                // 参数分别为（过滤器名称，预期数据量，容错率）
                client.createFilter(bloomKey + "_" + dayOfYear, 1000000, 0.0001);
            } catch (Exception e) {
//                System.out.println(e.getMessage());
            }
            for (int k : getKeys()) {//7天内有任意一天存在则为存在
                boolean[] e = client.existsMulti(bloomKey + "_" + k, hashs);
                Stream.iterate(0, n -> n + 1)
                        .limit(hashs.length).forEach(index -> {
                    exists[index] = exists[index] || e[index];
                });
            }

        } catch (Exception e) {
            e.printStackTrace();
        } finally {
            client.close();
        }
        LOGGER.info("rebloom-hash是否被去重：");
        LOGGER.info(JSON.toJSONString(hashs));
        LOGGER.info(JSON.toJSONString(exists));
        return exists;
    }

布隆过滤器特点：

1. 预期数据量越大占用内存越大，容错率越低占用内存越大

2. 判断结果为不存在那肯定是不存在，判断结果为存在其实可能不存在

3. 不支持删除元素