The implementation principle of high-efficiency Bloom filter in Golang is based on CACH technology.

The implementation principle of high-efficiency Bloom filter in Golang based on CACH technology

The Bloom filter is a very space-efficient data structure based on the hash function, which is used to determine whether an element exists. in a collection. Because of its low space complexity, it is widely used in large-scale data processing, web crawlers, information filtering and other fields. In Golang, the implementation principle of high-efficiency Bloom filter is mainly based on CACH technology.

CACH (Concurrency-Aware Cuckoo Hashing) is an efficient concurrency algorithm based on hashing. It supports concurrent increase and query operations, and uses CAS (Compare And Swap)-based non-blocking during insertion and query. Algorithm to avoid lock competition issues. The CACH algorithm is based on the Cuckoo hash algorithm and Bloom filter, and achieves efficient hash table operations through clever algorithm design and optimization.

In Golang, the implementation of Bloom filter is mainly divided into three parts: hash function, bit array and CACH algorithm.

Hash functions usually use a combination of multiple independent hash functions, which can better reduce the misjudgment rate. In implementation, hash functions such as MurmurHash3 can be used to perform hash operations to ensure the uniformity and sufficient randomness of the hash.

The bit array is the core data structure of the Bloom filter, which is used to store the bits corresponding to the hash values ​​generated by multiple hash functions. Bit arrays are generally represented by an array of unsigned integers, each integer represents a binary bit. In Golang, you can use the uint64 type to represent a bit, and read and write bits through bit operations.

As a representative of efficient concurrent hashing algorithms, the CACH algorithm can support high-speed insertion and query operations, and achieve fast search through hash tables and Bloom filters. The core idea of ​​the CACH algorithm is to map all elements to two positions in the hash table and resolve conflicts through alternate replacement. Specifically, for an element, two positions are first calculated through the hash function, and it is inserted into one of the empty positions in turn. If the insertion of a new element causes a conflict, the original element is moved to its other hash position until there is enough empty space. In this way, only one replacement will occur for each insertion operation, so the operation is very efficient.

When implementing a Bloom filter, you can use the CACH algorithm as the storage and query engine for bit arrays. For a new element, first map it to multiple positions in the bit array through multiple hash functions, and set the corresponding bits of these positions to 1. For each query operation, the hash value of the query element is also mapped to multiple bits, and it is determined whether these bits are all 1. If any bit is not 1, it indicates that the query element is not in the set. Since the bit array is a fixed-length array, and the hash function and CACH algorithm are both calculated for a single element, the space complexity of the Bloom filter will not increase linearly with the increase in the number of elements.

To sum up, the implementation of efficient Bloom filter in Golang is based on CACH technology, which combines hash function and bit array to achieve efficient Bloom filter operation. Compared with traditional methods, Bloom filters based on the CACH algorithm not only have better performance, but also support high-concurrency operations and are suitable for large-scale high-concurrency scenarios.

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