https://github.com/zaghaghi/pdstl

Probabilistic Data Structure Template Library
https://github.com/zaghaghi/pdstl

big-data bloom-filter cpp14 datastructures probabilistic-data-structures quotient-filter template-library

Last synced: about 1 year ago
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Probabilistic Data Structure Template Library

• Host: GitHub
• URL: https://github.com/zaghaghi/pdstl
• Owner: zaghaghi
• License: apache-2.0
• Created: 2020-05-24T20:13:01.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2020-06-18T20:32:32.000Z (almost 6 years ago)
• Last Synced: 2024-04-17T16:12:52.339Z (about 2 years ago)
• Topics: big-data, bloom-filter, cpp14, datastructures, probabilistic-data-structures, quotient-filter, template-library
• Language: C++
• Size: 168 KB
• Stars: 28
• Watchers: 3
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# PDSTL

[![Documentation Status](https://readthedocs.org/projects/pdstl/badge/?version=latest)](https://pdstl.readthedocs.io/en/latest/?badge=latest)

[![Build Status](https://travis-ci.org/zaghaghi/pdstl.svg?branch=master)](https://travis-ci.org/zaghaghi/pdstl)

PDSTL, is a header only template library for probabilistic data structures.

# Build Example

To Build examples do as follows.

```bash

# Setup project with meson

meson build

# Build with ninja

ninja -C build -j 4

# Build docs

cd docs

make html

```

# Documentation

Read the reference documentations on [pdstl.readthedocs.io](https://pdstl.readthedocs.io/)

# Implemented Data Structures

## Membership

| Data Structure          | Insert     | Delete          |

|-------------------------|------------|-----------------|

| Bloom Filter            | Supported  | Not Supported   |

| Counting Bloom Filter   | Supported  | Supported       |

| Quotient Filter         | Supported  | Not Implemented |

| Quotient Hash Table     | Supported  | Not Implemented |

| Cuckoo Filter           | Supported  | Supported       |

## Cardinality

| Data Structure           | Insert     | Delete          |

|--------------------------|------------|-----------------|

| Linear Counting          | Supported  | Not Supported   |

| Flajolet–Martin Counting | Supported  | Not Supported   |

# References

* [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)

* Fan, L., et al. (2000) “Summary cache: a scalable wide-area web cache sharing protocol”, Journal IEEE/ACM Transactions on Networking, Vol. 8 (3), pp. 281–293.

* Bender, M., et al. (2012) “Don’t Thrash: How to Cache your Hash on Flash”, Proceedings of the VLDB Endowment, Vol. 5 (11), pp. 1627–1637.

* Fan, B., et al. (2014) “Cuckoo Filter: Practically Better Than Bloom”, Proceedings of the 10th ACM International on Conference on emerging Networking Experiments and Technologies, Sydney, Australia — December 02–05, 2014, pp. 75–88, ACM New York, NY.

* Whang, K.-Y., Vander-Zanden, B.T., Taylor H.M. (1990) “A Linear-Time Probabilistic Counting Algorithm for Database Applications”, Journal ACM Transactions on Database Systems,

Vol. 15 (2), pp. 208–229.

* Flajolet, P., Martin, G.N. (1985) “Probabilistic Counting Algorithms for Data Base Applications”, Journal of Computer and System Sciences, Vol. 31 (2), pp. 182–209.