A Bloom Filter is a data structure designed to tell you, rapidly and memory-efficiently, whether an element is present in a set. It is based on a probabilistic mechanism where false positive retrieval results are possible, but false negatives are not. In this post we will see a pure python implementation of the Bloom Filter and the end we will see how to tune the parameters in order to minimize the number of false positive results.
Let's begin with a little bit of theory. The idea behind the filter is to allocate a bit vector of length
m, initially all set to 0, and then choose
k independent hash functions,
h1, h2, ..., hk, each with range [
1 m].
When an element
a is added to the set then the bits at positions
h(a)1, h(a)2, ..., h(a)k in the bit vector are set to 1.
Given a query element
q we can test whether it is in the set using the bits at positions
h(q)1, h(q)2, ..., h(q)k in the vector. If any of these bits is 0 we report that
q is not in the set otherwise we report that
q is. The thing we have to care about is that in the first case there remains some probability that
q is not in the set which could lead us to a false positive response.
The following class is a naive implementation of a Bloom Filter (pay attention: this implementation is not supposed to be suitable for production. It is made just to show how a Bloom Filter works and to study its behavior):
class Bloom:
""" Bloom Filter """
def __init__(self,m,k,hash_fun):
"""
m, size of the vector
k, number of hash fnctions to compute
hash_fun, hash function to use
"""
self.m = m
# initialize the vector
# (attention a real implementation
# should use an actual bit-array)
self.vector = [0]*m
self.k = k
self.hash_fun = hash_fun
self.data = {} # data structure to store the data
self.false_positive = 0
def insert(self,key,value):
""" insert the pair (key,value) in the database """
self.data[key] = value
for i in range(self.k):
self.vector[self.hash_fun(key+str(i)) % self.m] = 1
def contains(self,key):
""" check if key is cointained in the database
using the filter mechanism """
for i in range(self.k):
if self.vector[self.hash_fun(key+str(i)) % self.m] == 0:
return False # the key doesn't exist
return True # the key can be in the data set
def get(self,key):
""" return the value associated with key """
if self.contains(key):
try:
return self.data[key] # actual lookup
except KeyError:
self.false_positive += 1
The usage of this filter is pretty easy, we have to initialize the data structure with a hash function, a value for
k and the size of the bit vector then we can start adding items as in this example:
import hashlib
def hash_f(x):
h = hashlib.sha256(x) # we'll use sha256 just for this example
return int(h.hexdigest(),base=16)
b = Bloom(100,10,hash_f)
b.insert('this is a key','this is a value')
print b.get('this is a key')
Now, the problem is to choose the parameters of the filter in order to minimize the number of false positive results. We have that after inserting
n elements into a table of size
m, the probability that a particular bit is still 0 is exactly
Hence, afer
n insertions, the probability that a certain bit is 1 is
So, for fixed parameters
m and
n, the optimal value
k that minimizes this probability is
With this in mind we can test our filter. The first thing we need is a function which tests the Bloom Filter for fixed values of
m,
n and
k countinig the percentage of false positive:
import random
def rand_data(n, chars):
""" generate random strings using the characters in chars """
return ''.join(random.choice(chars) for i in range(n))
def bloomTest(m,n,k):
""" return the percentage of false positive """
bloom = Bloom(m,k,hash_f)
# generating a random data
rand_keys = [rand_data(10,'abcde') for i in range(n)]
# pushing the items into the data structure
for rk in rand_keys:
bloom.insert(rk,'data')
# adding other elements to the dataset
rand_keys = rand_keys + [rand_data(10,'fghil') for i in range(n)]
# performing a query for each element of the dataset
for rk in rand_keys:
bloom.get(rk)
return float(bloom.false_positive)/n*100.0
If we fix
m = 10000 and
n = 1000, according to the equations above, we have that the value of
k which minimizes the false positive number is around 6.9314. We can confirm that experimentally with the following test:
# testing the filter
m = 10000
n = 1000
k = range(1,64)
perc = [bloomTest(m,n,kk) for kk in k] # k is varying
# plotting the result of the test
from pylab import plot,show,xlabel,ylabel
plot(k,perc,'--ob',alpha=.7)
ylabel('false positive %')
xlabel('k')
show()
The result of the test should be as follows
Looking at the graph we can confirm that for k around 7 we have the lowest false positive percentage.
