pdsa 0.5.0

Description:

pdsa 0.5.0

Contents

The Book
Introduction
Dependencies
Documentation
License
Source code
Authors
Install with pip
Compile from source



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 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
Counting Bloom Filter

Cardinality problem

Linear counter
Probabilistic counter (Flajolet–Martin algorithm)
HyperLogLog

Frequency problem

Count Sketch
Count-Min Sketch

Rank problem

q-digest



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.
$ pip3 install -U pdsa
When using pip it is generally recommended to install packages in a virtualenv
to avoid modifying system state:
$ 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 and build it from
source.
$ git clone https://github.com/gakhov/pdsa.git
$ cd pdsa

$ make install

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