zaghaghi / pdstl

Probabilistic Data Structure Template Library

PDSTL

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

Build Example

To Build examples do as follows.

# 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

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 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.