Bloom filter in Redis and how to use PHP

Redis is an open source in-memory database that is widely used in scenarios such as caching, message queues, and distributed locks. Among them, Bloom filter is an efficient data structure that can be used to determine whether an element exists in a set, and has been widely used in Redis. This article will introduce the implementation principle and usage of Bloom filters in Redis as well as the support for Bloom filters in PHP.

1. Implementation principle of Bloom filter

Bloom filter is a very simple and efficient data structure, which is implemented by using multiple hash functions and a binary vector Judgment of sets. When an element is added to a set, it passes the element into multiple hash functions, each of which generates a unique hash value (usually a number), which is then converted into a binary The corresponding position in the vector is set to 1. When it is necessary to determine whether an element exists in the set, it will pass the element into these hash functions, generate multiple hash values, and query whether the binary vector positions corresponding to these hash values ​​are all 1, and if they are all 1, it is considered that this element may be in the set, otherwise it can be determined that this element is definitely not in the set. It should be noted that due to the characteristics of the hash function, the Bloom filter will have a certain false positive rate, but this false positive rate can be adjusted by setting the number of hash functions and the size of the binary vector.

2. How to use Bloom filters in Redis

The commands to implement Bloom filters in Redis are BF.ADD, BF.EXISTS, BF.MADD and BF.MEXISTS, respectively. Used to add elements to the Bloom filter, determine whether a single element exists in the Bloom filter, add multiple elements to the Bloom filter, and determine whether multiple elements exist in the Bloom filter. When using it, you need to create a Bloom filter first, which can be created through the BF.RESERVE command. You need to specify the name of the Bloom filter, the number of hash functions, the size of the binary vector, and the false positive rate. For example, to create a Bloom filter named test, the number of hash functions is 10, the binary vector size is 10000, and the false positive rate is 1%, you can use the following command:

BF.RESERVE test 10 10000 0.01

Then you can Add elements to this Bloom filter or determine whether the element exists in the collection. The specific usage is as follows:

1. Add the element to the Bloom filter

BF.ADD test element1

This command will add the element element1 to the Bloom filter named test.

2. Determine whether a single element exists in the Bloom filter

BF.EXISTS test element1

This command will determine whether the element element1 exists in the Bloom filter named test. If it returns 1 It means it may exist. If it returns 0, it means it definitely does not exist.

3. Add multiple elements to the Bloom filter

BF.MADD test element1 element2 element3

This command will add elements element1, element2 and element3 to the Bloom filter named test.

4. Determine whether multiple elements exist in the Bloom filter

BF.MEXISTS test element1 element2 element3

This command will determine whether the elements element1, element2 and element3 exist in the Bloom filter named test , if one of the returned results is 0, it means that at least one of the elements must not exist in the Bloom filter.

3. Support for Bloom filters in PHP

The support for Bloom filters in PHP is implemented through the redis extension. You need to ensure that the redis extension has been installed before use. . For specific usage, please refer to the following code examples:

$redis = new Redis();
$redis->connect('127.0.0.1', 6379);
// 创建布隆过滤器，哈希函数个数为10，二进制向量大小为10000，误判率为1%
$redis->rawCommand('BF.RESERVE', 'test', 10, 10000, 0.01);
// 将元素element1加入到布隆过滤器中
$redis->rawCommand('BF.ADD', 'test', 'element1');
// 判断元素element1是否存在于布隆过滤器中
$result = $redis->rawCommand('BF.EXISTS', 'test', 'element1');
if ($result) {
    echo 'element1可能存在于布隆过滤器中';
} else {
    echo 'element1一定不存在于布隆过滤器中';
}
// 将元素element2和element3加入到布隆过滤器中
$redis->rawCommand('BF.MADD', 'test', 'element2', 'element3');
// 判断元素element1、element2和element3是否存在于布隆过滤器中
$result = $redis->rawCommand('BF.MEXISTS', 'test', 'element1', 'element2', 'element3');
if (in_array(0, $result)) {
    echo '其中至少一个元素一定不存在于布隆过滤器中';
} else {
    echo '所有元素可能存在于布隆过滤器中';
}

Through the above PHP code examples, you can use the Bloom filter in Redis.

Summary:

In actual application scenarios, because Bloom filters have the characteristics of fast search and efficient storage, they are widely used in fields such as preventing cache penetration and anti-spam. . In Redis, Bloom filter related operations can be easily implemented through the support of BF.ADD, BF.EXISTS, BF.MADD and BF.MEXISTS commands. Of course, we can also easily implement the use of bloom filters by installing the redis extension in PHP.

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