# Bloom filter

Reference: Bloom: "Space/time trade-offs in hash coding with allowable errors"

Note

## What it is

A bloom filter is the prototypical probabilistic data structure. Elements can be added to a bloom filter, and afterwards, the filter can be queried about whether or not an element is in the filter. A bloom filter exhibits false positives, but not false negatives. In other words, a bloom filter will sometimes report an object to be present when it in fact is not, but whenever the object is not found in the bloom filter, it is guaranteed to truly not be in the filter. Element cannot be extracted from a bloom filter.

A bloom filter is parameterized by two parameters, its length, m and the parameter k. Memory usage is m/8 bytes plus a few bytes of overhead.

Bloom filters have infinite capacity, but their false positive rates asymptotically approach 1 as more objects are added. The capacity given for a bloom filter by this package refers to the number of distinct elements at which the expected false positive rate is below a given threshold.

### Querying (in)

Querying time is constant. A filter with parameters m and k containing N distinct object has an expected false positive rate of (1-exp(-k*N/m))^k.

### Pushing (push!)

Pushing time is constant and does not change the memory usage of the bloom filter. All hashable object can be pushed to the filter.

### Deletion

Bloom filters do not support deletion.

## Usage example

Let's use the same example as for the Cuckoo filter: Again, I have a stream of kmers that I want to count. Of course I use BioJulia, so these kmers are represented by a DNAKmer{31} object. I suspect my stream has up to 2 billion different kmers, so keeping a counting Dict would use up all my memory. However, most kmers are measurement errors that only appear once and that I do not need spend memory on keeping count of. So I keep track of which kmers I've seen using a Cuckoo filter. If I see a kmer more than once, I add it to the Dict.

params = constrain(BloomFilter, fpr=0.02, capacity=2_000_000_000)
if params.memory > 4e9 # I'm only comfortable using 4 GB of memory for this
error("Too little memory :(")
end
filter = BloomFilter(params.m, params.k)
counts = Dict{Kmer, UInt8}() # Don't need to count higher than 255

for kmer in each(DNAKmer{31}, fastq_parser)
if kmer in filter
# Only add kmers we've seen before
count = min(0xfe, get(counts, kmer, 0x01)) # No integer overflow
counts[kmer] = count + 0x01
else
push!(filter, kmer)
end
end

## Interface

### Construction

Bloom filters can be constructed directly given m and k:

x = BloomFilter(100_000_000, k=10)

And this will work just fine. However, in typical cases, people want to construct bloom filters with a set of constrains like "I have 100 MB memory and I want to hold object with a false positive rate of at most 5%". For this purpose, use the constrain function.

This function takes a type and two of three keyword arguments:

• fpr: Maximal false positive rate
• memory: Maximal memory usage
• capacity: Minimum number of distinct elements it can contain

It returns a NamedTuple with the parameters for an object of the specified type which fits the criteria:

julia> constrain(BloomFilter, fpr=0.05, memory=100_000_000)
(m = 799999808, k = 4, fpr = 0.04999999240568489, memory = 100000000, capacity = 128061884)

This means the optimal bloom filter consuming less than 100 MB of RAM and having a FPR of less than 0.05 can be constructed by:

x = BloomFilter(799999808, 4)

### Central functions

Base.inMethod
in(item, filter::BloomFilter)

Determine if item is in bloom filter. This sometimes returns true when the correct answer is false, but never returns false when the correct answer is true.

source
Base.push!Method
push!(filter::BloomFilter, items...)

Add one or more hashable items to the bloom filter.

source

### Misc functions

Note

Bloom filters supports the following operations, which have no bloom-specific docstring because they behave as stated in the documentation in Base:

Base.copy!
Base.copy
Base.union!
Base.union
Base.sizeof # This one includes the underlying array
Base.lengthMethod
length(filter::BloomFilter) -> Float64

Provide an estimate of the number of distinct elements in the filter. This may return Inf if the filter is entirely full.

Examples

julia> a = BloomFilter(10000, 4); for i in 1:5000 push!(a, i) end; length(a)
4962.147247984721
source
Base.isemptyMethod
isempty(filter::BloomFilter)

Determine if bloom filter is empty, i.e. has no elements in it. This is guaranteed to be correct, but does not mean the fitler consumes no RAM.

source
Base.empty!Method
empty!(filter::BloomFilter)

Remove all elements from BloomFilter, resetting it to initial state.

source
Probably.constrainMethod
constrain(Type{BloomFilter}; fpr=nothing, mem=nothing, capacity=nothing)

Given BloomFilter and two of three keyword arguments, as constrains, optimize the elided keyword argument. Returns a NamedTuple with (m, k, fpr, memory, capacity), which applies to the optimized Bloom filter.

Examples

julia> # Bloom filter with FPR ≤ 0.05, and memory usage ≤ 50_000_000 bytes

julia> c = constrain(BloomFilter, fpr=0.05, memory=50_000_000)
(m = 399999808, k = 4, fpr = 0.049999979847949585, memory = 50000000, capacity = 6403092
1)

julia> x = BloomFilter(c.m, c.k); # capacity optimized
source