README
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Bloom Filter
A Bloom filter is a space-efficient data structure used to test whether an element is a member of a set. It can have false positives but no false negatives.
Pros: Can store additional information beyond membership, efficient space utilization. Cons: Higher false positive rate, limited support for deletion.
Use Cases:
Web caching: Web caching systems like Squid use Bloom filters to check if a requested URL is already cached, avoiding unnecessary disk lookups.
Database systems: Bloom filters can be used to speed up query processing in databases by quickly determining if a specific record exists in a large dataset without scanning the entire dataset.
Spell checking: Bloom filters can be used to store a dictionary of correctly spelled words, allowing for fast spell checking by querying the filter for a given word.
Documentation
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Index ¶
Constants ¶
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Variables ¶
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Functions ¶
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Types ¶
type BloomFilter ¶
type BloomFilter[T encoding.BinaryMarshaler] interface { Add(value T) Contains(value T) bool }
type BloomFilterHash ¶
type BloomFilterHash[T encoding.BinaryMarshaler] struct { // contains filtered or unexported fields }
func NewBloomFilter ¶
func NewBloomFilter[T encoding.BinaryMarshaler](size int, numHash int) *BloomFilterHash[T]
NewBloomFilter creates a new BloomFilter with the specified size and number of hash functions
func (*BloomFilterHash[T]) Add ¶
func (bf *BloomFilterHash[T]) Add(element T)
Add inserts a new element into the Bloom filter
func (*BloomFilterHash[T]) Contains ¶
func (bf *BloomFilterHash[T]) Contains(element T) bool
Contains checks if the Bloom filter possibly contains the given element
Source Files