gakhov / Pdsa

PDSA: Probabilistic Data Structures and Algorithms in Python

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.. contents ::

The Book

Everybody interested in learning more about probabilistic data structures and algorithms could be referred to our recently published book:

Probabilistic Data Structures and Algorithms for Big Data Applications <https://pdsa.gakhov.com>_ by Andrii Gakhov

2019, ISBN: 978-3748190486 (paperback) ASIN: B07MYKTY8W (e-book)

Introduction

Probabilistic data structures is a common name of data structures based on different hashing techniques.

Unlike regular (or deterministic) data structures, they always provide approximated answers, but usually with reliable ways to estimate the error probability.

The potential losses or errors are fully compensated by extremely low memory requirements, constant query time and scaling.

GitHub repository: <https://github.com/gakhov/pdsa>_

Dependencies

• Python 3.5+ (http://python.org/download/)
• Cython 0.28+ (http://cython.org/#download)

Documentation

The latest documentation can be found at <http://pdsa.readthedocs.io/en/latest/>_

Membership problem

• Bloom Filter <http://pdsa.readthedocs.io/en/latest/membership/bloom_filter.html>_
• Counting Bloom Filter <http://pdsa.readthedocs.io/en/latest/membership/counting_bloom_filter.html>_

Cardinality problem

• Linear counter <http://pdsa.readthedocs.io/en/latest/cardinality/linear_counter.html>_
• Probabilistic counter (Flajolet–Martin algorithm) <http://pdsa.readthedocs.io/en/latest/cardinality/probabilistic_counter.html>_
• HyperLogLog <http://pdsa.readthedocs.io/en/latest/cardinality/hyperloglog.html>_

Frequency problem

• Count Sketch <http://pdsa.readthedocs.io/en/latest/frequency/count_sketch.html>_
• Count-Min Sketch <http://pdsa.readthedocs.io/en/latest/frequency/count_min_sketch.html>_

Rank problem

• Random Sampling <http://pdsa.readthedocs.io/en/latest/rank/random_sampling.html>_
• q-digest <http://pdsa.readthedocs.io/en/latest/rank/qdigest.html>_

License

MIT License

Source code

• https://github.com/gakhov/pdsa/

Authors

• Maintainer: Andrii Gakhov <[email protected]>

Install with pip

Installation requires a working build environment.

Using pip, PDSA releases are currently only available as source packages.

.. code:: bash

$ pip3 install -U pdsa

When using pip it is generally recommended to install packages in a virtualenv to avoid modifying system state:

.. code:: bash

$ virtualenv .env -p python3 --no-site-packages
$ source .env/bin/activate
$ pip3 install -U cython
$ pip3 install -U pdsa

Compile from source

The other way to install PDSA is to clone its GitHub repository <https://github.com/gakhov/pdsa>_ and build it from source.

.. code:: bash

$ git clone https://github.com/gakhov/pdsa.git
$ cd pdsa

$ make install

$ bin/pip3 install -r requirements-dev.txt
$ make test