The HyperLogLog algorithm [1] is a space efficient method to estimate the cardinality of extraordinarily large data sets. This module provides an implementation, written in C using a Murmur3 hash, for python 2.7.x or python 3.x.

[] (https://travis-ci.org/ascv/HyperLogLog)

v0.98

Setup

You will need the python development package. On Ubuntu/Mint you can install this package using:

sudo apt-get install python-dev

Now install using pip:

sudo pip install HLL

Alternatively, install using setup.py:

sudo python setup.py install

Quick start

from HLL import HyperLogLog

hll = HyperLogLog(5) # use 2^5 registers
hll.add('some data')
estimate = hll.cardinality()

Documentation

add(data)

Adds data to the estimator where data is a string, buffer, or bytes type.

HyperLogLog(k, seed=314)

Create a new HyperLogLog using 2^k registers, k must be in the range [2, 16]. Set seed to determine the seed value for the Murmur3 hash. The default value was chosen arbitrarily.

merge(hll)

Merges another HyperLogLog into the current one. Merging compares individual registers and takes the maximum value for each one. The registers of the other HyperLogLog are unaffected.

murmur3_hash(data, seed=314)

Gets a signed integer from a Murmur3 hash of data where data is a string, buffer, or bytes (python 3.x). Set seed to determine the seed value for the Murmur3 hash. The default value was chosen arbitrarily.

registers()

Gets a bytearray of the registers.

seed()

Gets the seed value used in the Murmur3 hash.

set_register(index, value)

Sets the register at index to value. Indexing is zero-based.

size()

Gets the number of registers.

License

This software is released under the MIT License.

References

[1] http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf