This code is released under the Apache License Version 2.0 http://www.apache.org/licenses/.
It is a library to compress and uncompress arrays of integers very fast. The assumption is that most (but not all) values in your array use less than 32 bits. These sort of arrays often come up when using differential coding in databases and information retrieval (e.g., in inverted indexes or column stores).
This libary is used by ClueWeb Tools (https://github.com/lintool/clueweb). This library inspired a compression scheme used by Apache Lucene (e.g., see http://lucene.apache.org/core/4_6_1/core/org/apache/lucene/util/PForDeltaDocIdSet.html ).
It is a java port of the fastpfor C++ library (https://github.com/lemire/FastPFor). There is also a Go port (https://github.com/reducedb/encoding). The C++ library is used by the zsearch engine (http://victorparmar.github.com/zsearch/) as well as in GMAP and GSNAP (http://research-pub.gene.com/gmap/).
Some CODECs ("integrated codecs") assume that the integers are in sorted orders. Most others do not.
Using this code in your own project is easy with maven, just add the following code in your pom.xml file:
<dependencies> <dependency> <groupId>me.lemire.integercompression</groupId> <artifactId>JavaFastPFOR</artifactId> <version>0.0.11</version> </dependency> </dependencies>
Naturally, you should replace "version" by the version you desire.
You can also download JavaFastPFOR from the Maven central repository: http://repo1.maven.org/maven2/me/lemire/integercompression/JavaFastPFOR/
We found no library that implemented state-of-the-art integer coding techniques such as Binary Packing, NewPFD, OptPFD, Variable Byte, Simple 9 and so on in Java. We wrote one.
with contributions by
In our tests, Kamikaze PForDelta is slower than our implementations. See the benchmarkresults directory for some results.
A recent Java compiler. Java 7 or better is recommended.
Good instructions on installing Java 7 on Linux:
See example.java for a simple demonstration.
Compile the code and execute me.lemire.integercompression.benchmarktools.Benchmark.
I recommend running all the benchmarks with the "-server" flag on a desktop machine.
Speed is always reported in millions of integers per second.
If you use Apache ant, please try this:
$ ant Benchmark
$ ant Benchmark -Dbenchmark.target=BenchmarkBitPacking
This library was a key ingredient in the best paper at ECIR 2014 :
Matteo Catena, Craig Macdonald, Iadh Ounis, On Inverted Index Compression for Search Engine Efficiency, Lecture Notes in Computer Science 8416 (ECIR 2014), 2014. http://dx.doi.org/10.1007/978-3-319-06028-6_30
We wrote a research paper which documents many of the CODECs implemented here:
Daniel Lemire and Leonid Boytsov, Decoding billions of integers per second through vectorization, Software Pratice & Experience (to appear) http://arxiv.org/abs/1209.2137
Daniel Lemire, Leonid Boytsov, Nathan Kurz, SIMD Compression and the Intersection of Sorted Integers, arXiv:1401.6399, 2014 http://arxiv.org/abs/1401.6399
Ikhtear Sharif wrote his M.Sc. thesis on this library:
Ikhtear Sharif, Performance Evaluation of Fast Integer Compression Techniques Over Tables, M.Sc. thesis, UNB 2013. http://hdl.handle.net/1882/45703
He also posted his slides online: http://www.slideshare.net/ikhtearSharif/ikhtear-defense
This work was supported by NSERC grant number 26143.