Maciej A. Czyzewski Blog Talks About

Bloom filters, fast and simple

Posted on

Everyone is always raving about bloom filters. But what exactly are they, and what are they useful for?

The Bloom filter is a space-efficient, probabilistic data structure – used to test whether an item does not belong to a collection.


The basic bloom filter supports two operations: add and query.

Query is used to check whether a given element is in the set or not. It can only return a boolean value:

  • true, if the element is probably in the set.
  • false, if the element is definitely not in the set.

Add simply adds an element to the set.

Removal is impossible without introducing false negatives, but there are versions where is possible to remove the element e.g. counting filters.



Internally Bloom filters use a bit array, and multiple different hash functions.


Let’s say for instance we have a bit array of a 100 elements and 3 hash functions.

Add, when we want to insert the word “Maciej” into the filter:

  1. We pass it through hash functions:
    • hash 1, returns 33
    • hash 2, returns 7
    • hash 3, returns 22
  2. Next, we go to each of those elements in the array and set them to 1.

Query, now to test whether the word might be in the collection:

  1. We pass it through hash functions, and check those elements in the bit array:
    • true, if all 3 elements are set to 1.
    • false, if any one of the elements are set to zero.


#!/usr/bin/env python

from hashlib import sha256

class Filter(object):
  """A simple bloom filter for lots of int()"""

  def __init__(self, array_size=(1 * 1024), hashes=13):
    """Initializes a Filter() object
      array_size (in bytes): 4 * 1024 for a 4KB filter
      hashes (int): for the number of hashes to perform
    self.filter = bytearray(array_size)     # The filter itself
    self.bitcount = array_size * 8          # Bits in the filter
    self.hashes = hashes                    # The number of hashes to use

  def _hash(self, value):
    """Creates a hash of an int and yields a generator of hash functions
      value: int()
      generator of ints()
    # Build an int() around the sha256 digest of int() -> value
    digest = int(sha256(value.__str__()).hexdigest(), 16)
    for _ in range(self.hashes):
      # bitwise AND of the digest and all of the available bit positions
      # in the filter
      yield digest & (self.bitcount - 1)
      # Shift bits in digest to the right, based on 256 (in sha256)
      # divided by the number of hashes needed be produced.
      # Rounding the result by using int().
      # So: digest >>= (256 / 13) would shift 19 bits to the right.
      digest >>= (256 / self.hashes)

  def add(self, value):
    """Bitwise OR to add value(s) into the self.filter
      value: generator of digest ints()
    for digest in self._hash(value):
      # In-place bitwise OR of the filter, position is determined
      # by the (digest / 8) digest is described above in self._hash()
      # Bitwise OR is undertaken on the value at the location and
      # 2 to the power of digest modulo 8. Ex: 2 ** (30034 % 8)
      # to grantee the value is <= 128, the bytearray not being able
      # to store a value >= 256. Q: Why not use ((modulo 9) -1) then?
      self.filter[(digest / 8)] |= (2 ** (digest % 8))
      # The purpose here is to spread out the hashes to create a unique
      # "fingerprint" with unique locations in the filter array,
      # rather than just a big long hash blob.

  def query(self, value):
    """Bitwise AND to query values in self.filter
      value: value to check filter against (assumed int())
    # If all() hashes return True from a bitwise AND (the opposite
    # described above in self.add()) for each digest returned from
    # self._hash return True, else False
    return all(self.filter[(digest / 8)] & (2 ** (digest % 8))
      for digest in self._hash(value))

if __name__ == "__main__":
  bf = Filter()


  print("Filter size {0} bytes").format(bf.filter.__sizeof__())

  print bf.query(1)            # True
  print bf.query(40005)        # True
  print bf.query(123)          # False