BloomFilter (bloom_filter v1.1.0)
Bloom Filter implementation in Elixir. Bloom filters are probabilistic data structures designed to efficiently tell you whether an element is present in a set.
## Usage Example
iex> f = BloomFilter.new 100, 0.001
iex> f = BloomFilter.add(f, 42)
iex> BloomFilter.has?(f, 42)
trueLink to this section Summary
Functions
Adds a given item to the set.
Approximates the number of items in the filter.
Checks whether a given item is likely to exist in the set.
Creates a new bloom filter, given an estimated number of elements and a desired error rate (0.0..1).
Link to this section Types
Link to this type
bit()
Specs
bit() :: 0 | 1
Link to this type
hash_func()
Specs
hash_func() :: (any() -> pos_integer())
Specs
t() :: %BloomFilter{
bits: [bit(), ...],
capacity: pos_integer(),
error_rate: float(),
hash_functions: [hash_func(), ...],
num_bits: pos_integer()
}
Link to this section Functions
Link to this function
add(bloom, item)
Specs
Adds a given item to the set.
Link to this function
count(bloom_filter)
Specs
Approximates the number of items in the filter.
Link to this function
has?(bloom_filter, item)
Specs
Checks whether a given item is likely to exist in the set.
Link to this function
new(capacity, error_rate)
Specs
new(pos_integer(), float()) :: t()
Creates a new bloom filter, given an estimated number of elements and a desired error rate (0.0..1).