JavaFastPFOR: A simple integer compression library in Java

What does this do?

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 much less than 32 bits, or that the gaps between the integers use much 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).

Please note that random integers are not compressible, by this library or by any other means. If you ever had the means of systematically compressing random integers, you could compress any data source to nothing, by recursive application of your technique.

This library can decompress integers at a rate of over 1.2 billions per second (4.5 GB/s). It is significantly faster than generic codecs (such as Snappy, LZ4 and so on) when compressing arrays of integers.

The library is used in LinkedIn Pinot, a realtime distributed OLAP datastore. Part of this library has been integrated in Parquet (http://parquet.io/). A modified version of the library is included in the search engine Terrier (http://terrier.org/). This libary is used by ClueWeb Tools (https://github.com/lintool/clueweb). It is also used by Apache NiFi.

This library inspired a compression scheme used by Apache Lucene and Apache Lucene.NET (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/).

Usage

package org.example;

import me.lemire.integercompression.FastPFOR128;
import me.lemire.integercompression.IntWrapper;

import java.util.Arrays;

public class Main {
    public static void main(String[] args) {
        FastPFOR128 fastpfor = new FastPFOR128();

        int N = 9984;
        int[] data = new int[N];
        for (var i = 0; i < N; i += 150) {
            data[i] = i;
        }

        int[] compressedoutput1 = new int[N + 1024];

        IntWrapper inputoffset1 = new IntWrapper(0);
        IntWrapper outputoffset1 = new IntWrapper(0);

        fastpfor.compress(data, inputoffset1, N, compressedoutput1, outputoffset1);
        int compressedsize1 = outputoffset1.get();

        int[] recovered1 = new int[N];
        inputoffset1 = new IntWrapper(0);
        outputoffset1 = new IntWrapper(0);
        fastpfor.uncompress(compressedoutput1, outputoffset1, compressedsize1, recovered1, inputoffset1);

        // quick verification: count mismatches
        int mismatches = 0;
        for (int i = 0; i < N; i++) {
            if (data[i] != recovered1[i]) mismatches++;
        }

        System.out.println("N=" + N + " compressedSizeWords=" + compressedsize1 + " mismatches=" + mismatches);
        System.out.println("first 20 original: " + Arrays.toString(Arrays.copyOf(data, 20)));
        System.out.println("first 20 recovered: " + Arrays.toString(Arrays.copyOf(recovered1, 20)));
    }
}

For more examples, see example.java or the examples folder.

JavaFastPFOR supports compressing and uncompressing data in chunks (e.g., see advancedExample in https://github.com/lemire/JavaFastPFOR/blob/master/example.java).

Some CODECs ("integrated codecs") assume that the integers are in sorted orders and use differential coding (they compress deltas). They can be found in the package me.lemire.integercompression.differential. Most others do not.

The Java Team at Intel (R) introduced the vector implementation for FastPFOR based on the Java Vector API that showed significant gains over the non-vectorized implementation. For an example usage, see examples/vector/Example.java. On aarch64 (e.g. Graviton3), use JDK 24 or later: earlier releases lack the Vector API SVE intrinsics and run a fallback that is slower than the scalar codec.

JavaFastPFOR as a dependency

JavaFastPFOR is available both on Maven Central and JitPack, so you can easily include it in your project using either source.

We have a demo project using JavaFastPFOR as a dependency (both Maven and Gradle). See...

https://github.com/fast-pack/JavaFastPFORDemo

Maven Central

You can add JavaFastPFOR directly from Maven Central — no extra repository configuration needed:

Maven

<dependency>
    <groupId>me.lemire.integercompression</groupId>
    <artifactId>JavaFastPFOR</artifactId>
    <version>0.3.8</version>
</dependency>

Gradle (Groovy)

dependencies {
    implementation 'me.lemire.integercompression:JavaFastPFOR:0.3.8'
}

JitPack

If you prefer or need to use JitPack, you can include the dependency like this:

Maven

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

<dependency>
    <groupId>com.github.fast-pack</groupId>
    <artifactId>JavaFastPFOR</artifactId>
    <version>JavaFastPFOR-0.3.8</version>
</dependency>

Gradle (groovy)

repositories {
    mavenCentral()
    maven {
        url 'https://jitpack.io'
    }
}

dependencies {
    implementation 'com.github.fast-pack:JavaFastPFOR:JavaFastPFOR-0.3.8'
}

Naturally, you should replace "version" by the version you desire.

Thread safety

Some codecs are thread-safe while others are not. For this reason, it is best to use one codec per thread. The memory usage of a codec instance is small in any case.

Nevertheless, if you want to reuse codec instances, note that by convention, unless the documentation of a codec specify that it is not thread-safe, then it can be assumed to be thread-safe.

How does it compare to the Kamikaze PForDelta library?

In our tests, Kamikaze PForDelta is slower than our implementations. See the benchmarkresults directory for some results.

https://github.com/lemire/JavaFastPFOR/blob/master/benchmarkresults/benchmarkresults_icore7_10may2013.txt

Reference: http://sna-projects.com/kamikaze/

Requirements

Releases up to 0.1.12 require Java 7 or better.

The current development versions assume JDK 21 or better.

How fast is it?

Compile the code and execute me.lemire.integercompression.benchmarktools.Benchmark.

Speed is always reported in millions of integers per second.

For Maven users

mvn compile
mvn exec:java

You may run our examples as follows:

mvn package
javac -cp target/classes/:. example.java
java -cp target/classes/:. example

For ant users (legacy, currently untested)

If you use Apache ant, please try this:

$ ant Benchmark

or:

$ ant Benchmark -Dbenchmark.target=BenchmarkBitPacking

API Documentation

http://www.javadoc.io/doc/me.lemire.integercompression/JavaFastPFOR/

Citing this work

If you use JavaFastPFOR in your work, please consider citing the project. A recommended BibTeX entry is:

@misc{lemire2025_javafastpfor,
  author = {Daniel Lemire},
  title = {{JavaFastPFOR: A simple integer compression library in Java}},
  year = {2025},
  howpublished = {\url{https://github.com/fast-pack/JavaFastPFOR}},
}

Want to read more?

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 several research papers documenting many of the CODECs implemented here:

• Daniel Lemire, Nathan Kurz, Christoph Rupp, Stream VByte: Faster Byte-Oriented Integer Compression, Information Processing Letters (to appear) https://arxiv.org/abs/1709.08990
• Daniel Lemire, Leonid Boytsov, Nathan Kurz, SIMD Compression and the Intersection of Sorted Integers, Software Practice & Experience Volume 46, Issue 6, pages 723-749, June 2016 http://arxiv.org/abs/1401.6399
• Daniel Lemire and Leonid Boytsov, Decoding billions of integers per second through vectorization, Software Practice & Experience 45 (1), 2015. http://arxiv.org/abs/1209.2137 http://onlinelibrary.wiley.com/doi/10.1002/spe.2203/abstract
• Jeff Plaisance, Nathan Kurz, Daniel Lemire, Vectorized VByte Decoding, International Symposium on Web Algorithms 2015, 2015. http://arxiv.org/abs/1503.07387
• Wayne Xin Zhao, Xudong Zhang, Daniel Lemire, Dongdong Shan, Jian-Yun Nie, Hongfei Yan, Ji-Rong Wen, A General SIMD-based Approach to Accelerating Compression Algorithms, ACM Transactions on Information Systems 33 (3), 2015. http://arxiv.org/abs/1502.01916

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. https://unbscholar.lib.unb.ca/islandora/object/unbscholar%3A9399/datastream/PDF/view

He also posted his slides online: http://www.slideshare.net/ikhtearSharif/ikhtear-defense

Other recommended libraries

• Fast integer compression in Go: https://github.com/ronanh/intcomp
• Encoding: Integer Compression Libraries for Go https://github.com/zhenjl/encoding
• CSharpFastPFOR: A C# integer compression library https://github.com/Genbox/CSharpFastPFOR
• TurboPFor is a C library that offers lots of interesting optimizations and Java wrappers. Well worth checking! (Uses a GPL license.) https://github.com/powturbo/TurboPFor

Funding

This work was supported by NSERC grant number 26143.