Pemasangan Redis5 BloomFilter di bawah mac dan cara menggunakannya dengan python

1 进入redis安装目录：cd /usr/local/redis-5.0.4
2. 下载插件： git clone https://github.com/RedisBloom/RedisBloom.git  
	# https://github.com/RedisBloom/RedisBloom 如果慢 可以使用外网访问
3. 进入插件目录： cd redisbloom/  （重命名之前为RedisBloom）
4. 执行： make
5. 修改 redis.conf，增加配置： loadmodule /usr/local/redis-5.0.4/redisbloom/redisbloom.so
6. 启动redis:  src/redis-server ./redis.conf
7. 连接客户端： src/redis-cli -p 6379 
8. 测试，先后执行： bf.add users francis     bf.exists users francis  
9. 更多内容可参考： https://oss.redislabs.com/redisbloom/

from redis import StrictRedis
from django.conf import settings


class BfRedis:
    def __init__(self, db, host=settings.BF_REDIS_HOST, port=settings.BF_REDIS_PORT, password=settings.BF_REDIS_PASSWORD):
        self.client = StrictRedis(db=db, host=host, port=port, password=password)

    def bf_init(self, key: str, error_rate: float(), size: int):
        res = self.client.execute_command('BF.RESERVE', key, error_rate, size)
        return res

    def bf_exists(self, key, value):
        res = self.client.execute_command('BF.exists', key, value)
        return res

    def bf_add(self, key, value):
        return self.client.execute_command('BF.add', key, value)

    def bf_local_init(self, task_id, error_rate=0.0001, size=10000):
        """
        """
        key = f'bf_{task_id}'
        if self.client.exists(key):
            return True
        res = self.bf_init(key, error_rate, size)
        return res

    def bf_local_add(self, task_id, value):
        key = f'bf_{task_id}'
        res = self.bf_add(key, value)
        return res

    def bf_local_exists(self, task_id, value):
        key = f'bf_{task_id}'
        res = self.bf_exists(key, value)
        return res

    def bf_local_del(self, task_id):
        key = f'bf_{task_id}'
        res = self.client.delete(key)
        return res
# bf_redis = CrawlRedisClient(0)

python2安装：pip install pybloom
python3安装：pip install pybloom-live

from pybloom import BloomFilter, ScalableBloomFilter
bf = BloomFilter(capacity=10000, error_rate=0.001)
bf.add('test')
print 'test' in bf
sbf = ScalableBloomFilter(mode=ScalableBloomFilter.SMALL_SET_GROWTH)
sbf.add('dddd')
print 'ddd' in sbf