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In this article, the editor introduces in detail the "installation of Redis5 BloomFilter under mac and how to use it with python". The content is detailed, the steps are clear, and the details are handled properly. I hope that this article "installation of Redis5 BloomFilter under mac and how to use it with python" can help you solve your doubts.

Installation and use of Bloom filter

Install and use Bloom filter (BloomFilter) on Redis 5.x on Centos7

1 enter the redis installation directory: cd / usr/local/redis-5.0.42. Download plug-in: git clone https://github.com/RedisBloom/RedisBloom.git # https://github.com/RedisBloom/RedisBloom if slow, you can use the public network to access 3. Enter the plug-in directory: cd redisbloom/ (RedisBloom before renaming) 4. Execution: make5. Modify redis.conf and add configuration: loadmodule / usr/local/redis-5.0.4/redisbloom/redisbloom.so6. Start redis: src/redis-server. / redis.conf7. Connect the client: src/redis-cli-p 6379 8. Test, execute successively: bf.add users francis bf.exists users francis 9. For more information, please refer to https://oss.redislabs.com/redisbloom/

The use of python

1. The first method is to connect to redis using native statements using the

From redis import StrictRedisfrom django.conf import settingsclass 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 = fancibf{ 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 = fancibf{ task_id} 'res = self.bf_add (key) Value) return res def bf_local_exists (self, task_id, value): key = fancibf _ {task_id} 'res = self.bf_exists (key, value) return res def bf_local_del (self, task_id): key = fancibf{ task_id}' res = self.client.delete (key) return res# bf_redis = CrawlRedisClient (0)

Using the tool module of python

Python2 installation: pip install pybloompython3 installation: pip install pybloom-live

Demo

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

BloomFilter is a constant volume filter, error_rate means that the maximum false alarm rate is 0.1%, while ScalableBloomFilter is an indefinite capacity Bloom filter, which can constantly add elements. The add method adds an element, returns true if the element is already in the Bloom filter, and adds the element to the filter if it no longer returns fasle. To determine whether an element is in the filter, you only need to use the in operator.

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