A Java library for summarizing data in streams for which it is infeasible to store all events. More specifically, there are classes for estimating: cardinality (i.e. counting things); set membership; top-k elements and frequency. One particularly useful feature is that cardinality estimators with compatible configurations may be safely merged.
These classes may be used directly in a JVM project or with the provided shell scripts and good old Unix IO redirection.
The ideas here are not original to us. We have endeavored to create useful implementations from iterating over the existing academic literature. As such this library relies heavily on the work of others. Please read the Sources and Reference sections.
$ echo -e "foo\nfoo\nbar" | ./bin/topk item count error ---- ----- ----- foo 2 0 bar 1 0 Item count: 3 $ echo -e "foo\nfoo\nbar" | ./bin/cardinality Item Count Cardinality Estimate ---------- -------------------- 3 2
Assuming you have Apache Maven installed and configured:
And you should be all set.
Where People Hang Out
Mailing list: http://groups.google.com/group/stream-lib-user
The set membership code is the Bloom Filter implementation from Apache Cassandra circa December 2009. The changes here are minimal and were for the purpose of testing and independent use. Apache Software Foundation headers have been retained on these files. By extension we also include murmurhash.
We were inspired to use this code by Jonathan Ellis' post All you ever wanted to know about writing bloom filters.
There are javadoc references to specific papers. These were the ones we found most relevant during out research.
Min Cai, Jianping Pan, Yu K. Kwok, and Kai Hwang. Fast and accurate traffic matrix measurement using adaptive cardinality counting. In MineNet ’05: Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data, pages 205–206, New York, NY, USA, 2005. ACM.
Ahmed Metwally, Divyakant Agrawal, and Amr E. Abbadi. Why go logarithmic if we can go linear?: Towards effective distinct counting of search traffic. In EDBT ’08: Proceedings of the 11th international conference on Extending database technology, pages 618–629, New York, NY, USA, 2008. ACM.
Nikos Ntarmos, Peter Triantafillou, and Gerhard Weikum. Counting at large: Efficient cardinality estimation in Internet-Scale data networks. In ICDE ’06: Proceedings of the 22nd International Conference on Data Engineering, pages 40+, Washington, DC, USA, 2006. IEEE Computer Society.
Marianne Durand and Philippe Flajolet. LogLog counting of large cardinalities. In ESA03, volume 2832 of LNCS, pages 605–617, 2003.
Kyu Y. Whang, Brad T. Vander Zanden, and Howard M. Taylor. A linear-time probabilistic counting algorithm for database applications. ACM Trans. Database Syst., 15(2):208–229, 1990.
Moses Charikar, Kevin Chen, and Martin F. Colton. Finding frequent items in data streams. In ICALP ’02: Proceedings of the 29th International Colloquium on Automata, Languages and Programming, pages 693–703, London, UK, 2002. Springer-Verlag.
Stefan Heule, Marc Nunkesser, Alex Hall. HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm. Proceedings of the EDBT 2013 Conference, ACM, Genoa, Italy
- Graham Cormode and S. Muthukrishnan. An improved data stream summary: The Count-Min sketch and its applications. pages 29–38.
Cheqing Jin, Weining Qian, Chaofeng Sha, Jeffrey X. Yu, and Aoying Zhou. Dynamically maintaining frequent items over a data stream. In CIKM ’03: Proceedings of the twelfth international conference on Information and knowledge management, pages 287–294, New York, NY, USA, 2003. ACM.
Ahmed Metwally, Divyakant Agrawal, and Amr Abbadi. Efficient computation of frequent and top-k elements in data streams. pages 398–412. 2005.
- Graham Cormode and S. Muthukrishnan. An improved data stream summary: The Count-Min sketch and its applications. 2004.