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‎.gitignore

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target/

‎CHANGES.txt

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2.0.0:
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* Initial Release.

‎LICENSE.txt

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Apache License
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THIRD-PARTY DEPENDENCIES
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‎README.mdown

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## Description
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A Java library for summarizing data in streams for which it is
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infeasible to store all events. More specifically, there are classes
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for estimating: cardinality (i.e. counting things); set membership;
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and the top-k elements. One particularly useful feature is that
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cardinality estimators with compatible configurations may be safely
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merged.
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These classes may be used directly in a JVM project or with the
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provided shell scripts and good old Unix IO redirection.
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The ideas here are not original to us. We have endeavored to create
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useful implementations from iterating over the existing academic
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literature. As such this library relies heavily on the work of
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others. Please read the [Sources](#Sources) and
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[Reference](#References) sections.
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## Examples
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$ echo -e "foo\nfoo\nbar" | ./bin/topk
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item count error
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---- ----- -----
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foo 2 0
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bar 1 0
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Item count: 3
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$ echo -e "foo\nfoo\nbar" | ./bin/cardinality
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Item Count Cardinality Estimate
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---------- --------------------
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3 2
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## Building
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Assuming you have [Apache Maven](http://maven.apache.org/) installed
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and configured:
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mvn package
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And you should be all set.
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## Sources
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The set membership code is the Bloom Filter implementation from Apache
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Cassandra circa December 2009. The changes here are minimal and were
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for the purpose of testing and independent use. Apache Software
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Foundation headers have been retained on these files. By extension we
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also include [murmurhash](http://murmurhash.googlepages.com/).
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We were inspired to use this code by Jonathan Ellis' post
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[All you ever wanted to know about writing bloom filters](http://spyced.blogspot.com/2009/01/all-you-ever-wanted-to-know-about.html).
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## References
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There are javadoc references to specific papers. These were the ones
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we found most relevant during out research.
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#### Cardinality
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* Min Cai, Jianping Pan, Yu K. Kwok, and Kai Hwang. Fast and accurate
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traffic matrix measurement using adaptive cardinality counting. In
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MineNet ’05: Proceedings of the 2005 ACM SIGCOMM workshop on
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Mining network data, pages 205–206, New York, NY, USA, 2005. ACM.
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* Ahmed Metwally, Divyakant Agrawal, and Amr E. Abbadi. Why go
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logarithmic if we can go linear?: Towards effective distinct counting of
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search traffic. In EDBT ’08: Proceedings of the 11th international
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conference on Extending database technology, pages 618–629, New York,
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NY, USA, 2008. ACM.
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* Nikos Ntarmos, Peter Triantafillou, and Gerhard Weikum. Counting at
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large: Efficient cardinality estimation in Internet-Scale data networks.
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In ICDE ’06: Proceedings of the 22nd International Conference on Data
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Engineering, pages 40+, Washington, DC, USA, 2006. IEEE Computer
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Society.
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* Marianne Durand and Philippe Flajolet. LogLog counting of large
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cardinalities. In ESA03, volume 2832 of LNCS, pages 605–617, 2003.
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* Kyu Y. Whang, Brad T. Vander Zanden, and Howard M. Taylor. A
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linear-time probabilistic counting algorithm for database applications.
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ACM Trans. Database Syst., 15(2):208–229, 1990.
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* Moses Charikar, Kevin Chen, and Martin F. Colton. Finding frequent
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items in data streams. In ICALP ’02: Proceedings of the 29th
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International Colloquium on Automata, Languages and Programming,
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pages 693–703, London, UK, 2002. Springer-Verlag.
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#### Top-K
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* Graham Cormode and S. Muthukrishnan. An improved data stream
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summary: The Count-Min sketch and its applications. pages 29–38.
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2004.
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* Cheqing Jin, Weining Qian, Chaofeng Sha, Jeffrey X. Yu, and Aoying
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Zhou. Dynamically maintaining frequent items over a data stream. In
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CIKM ’03: Proceedings of the twelfth international conference on
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Information and knowledge management, pages 287–294, New York,
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NY, USA, 2003. ACM.
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* Ahmed Metwally, Divyakant Agrawal, and Amr Abbadi. Efficient
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computation of frequent and top-k elements in data streams. pages
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398–412. 2005.
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‎bin/card-test-and-graph.sh

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#!/bin/bash -x
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# Wrap the maven boilerplate to run the cardinality tests and graph results.
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mvn -e exec:java -Dexec.classpathScope="test" -Dexec.mainClass="com.clearspring.analytics.stream.cardinality.TestAndGraphResults" -Dexec.args="$*"

‎bin/cardinality

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#!/bin/bash
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STREAM_LIB_DIR=${STREAM_LIB_DIR:-./target}
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HELP=0
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function help () {
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cat <<-HELP
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cardinality -- Cardinality estimation command line utility.
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Usage: $( basename $0 ) [options] [RATE]
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Calculates the cardinality of a stream, reporting a summary at the end.
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cardinality looks for the Stream Summary analytics library at the location
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of the environment variable STREAM_LIB_DIR (default is ./target).
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Arguments:
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RATE Report interim summary every RATE elements.
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Options:
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-h Displays this help.
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Stream Lib Dir: ${STREAM_LIB_DIR}
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HELP
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}
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function fail () {
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echo "PREDICTABLE FAILURE. $1"
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if [ "$2" ]; then
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help
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fi
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exit 1
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}
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SHIFT=0
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function incshift () {
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SHIFT=$(( $SHIFT + ${1:-1} ))
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}
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for opt in $*; do
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case "$opt" in
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-h | -he | -hel | -help | --h | --he | --hel | --help )
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HELP=1 ;;
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esac
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done
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while getopts "h" opt; do
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case $opt in
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h ) HELP=1; incshift ;;
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# $opt ) B=$OPTARG; incshift 2 ;;
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esac
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done
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shift $SHIFT
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if test $HELP == 1; then
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help
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exit 0
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fi
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java -cp "${STREAM_LIB_DIR}"/stream-*-exec.jar com.clearspring.analytics.util.ObyCount $*
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