Apache Commons logo Apache Commons RNG

1. Purpose

Commons RNG provides generators of "pseudo-randomness", i.e. the generators produce deterministic sequences of bytes, currently in chunks of 32 (a.k.a. int) or 64 bits (a.k.a. long), depending on the implementation.

The goal was to provide an API that is simple and unencumbered with old design decisions.

The design is clean and its rationale is explained in the code and Javadoc (as well as in the extensive discussions on the "Apache Commons" project's mailing list).

The code evolved during several months in order to accommodate the requirements gathered from the design issues identified in the org.apache.commons.math3.random package and the explicit design goal of severing ties to java.util.Random.

The library is divided into modules:

2. Usage overview

Please refer to the generated documentation (of the appropriate module) for details on the API illustrated by the following examples.

  • Random number generator objects are instantiated through factory methods defined in RandomSource, an enum that declares all the available implementations.
    import org.apache.commons.rng.UniformRandomProvider;
    import org.apache.commons.rng.simple.RandomSource;
    UniformRandomProvider rng = RandomSource.create(RandomSource.MT);
  • A generator will return a randomly selected element from a range of possible values of some Java (primitive) type.
    boolean isOn = rng.nextBoolean(); // "true" or "false".
    int n = rng.nextInt(); // Integer.MIN_VALUE <= n <= Integer.MAX_VALUE.
    int m = rng.nextInt(max); // 0 <= m < max.
    long n = rng.nextLong(); // Long.MIN_VALUE <= n <= Long.MAX_VALUE.
    long m = rng.nextLong(max); // 0 <= m < max.
    float x = rng.nextFloat(); // 0 <= x < 1.
    double x = rng.nextDouble(); // 0 <= x < 1.
  • A generator will fill a given byte array with random values.
    bytes[] a = new bytes[47];
    // The elements of "a" are replaced with random values from the interval [-128, 127].
    bytes[] a = new bytes[47];
    // Replace 3 elements of the array (at indices 15, 16 and 17) with random values.
    rng.nextBytes(a, 15, 3);
  • In order to generate reproducible sequences, generators must be instantiated with a user-defined seed.
    UniformRandomProvider rng = RandomSource.create(RandomSource.SPLIT_MIX_64, 5776);

    If no seed is passed, a random seed is generated implicitly.

    Convenience methods are provided for explicitly generating random seeds of the various types.

    int seed = RandomSource.createInt();
    long seed = RandomSource.createLong();
    int[] seed = RandomSource.createIntArray(128); // Length of returned array is 128.
    long[] seed = RandomSource.createLongArray(128); // Length of returned array is 128.
  • Any of the following types can be passed to the create method as the "seed" argument:
    • int or Integer
    • long or Long
    • int[]
    • long[]
    • byte[]
    UniformRandomProvider rng = RandomSource.create(RandomSource.ISAAC, 5776);
    UniformRandomProvider rng = RandomSource.create(RandomSource.ISAAC, new int[] { 6, 7, 7, 5, 6, 1, 0, 2 });
    UniformRandomProvider rng = RandomSource.create(RandomSource.ISAAC, new long[] { 0x638a3fd83bc0e851L, 0x9730fd12c75ae247L });

    Note however that, upon initialization, the underlying generation algorithm

    • may not use all the information contents of the seed,
    • may use a procedure (using the given seed as input) for further filling its internal state (in order to avoid a too uniform initial state).

    In both cases, the behaviour is not standard but should not change between releases of the library (bugs notwithstanding).

    Each RNG implementation has a single "native" seed; when the seed argument passed to the create method is not of the native type, it is automatically converted. The conversion preserves the information contents but is otherwise not specified (i.e. different releases of the library may use different conversion procedures).

    Hence, if reproducibility of the generated sequences across successive releases of the library is necessary, users should ensure that they use native seeds.

    long seed = 9246234616L;
    if (!RandomSource.TWO_CMRES.isNativeSeed(seed)) {
        throw new IllegalArgumentException("Seed is not native");

    For each available implementation, the native seed type is specified in the Javadoc.

  • Whenever a random source implementation is parameterized, the custom arguments are passed after the seed.
    int seed = 96912062;
    int first = 7; // Subcycle identifier.
    int second = 4; // Subcycle identifier.
    UniformRandomProvider rng = RandomSource.create(RandomSource.TWO_CMRES_SELECT, seed, first, second);

    In the above example, valid "subcycle identifiers" are in the interval [0, 13].

  • The current state of a generator can be saved and restored later on.
    import org.apache.commons.rng.RestorableUniformRandomProvider;
    import org.apache.commons.rng.RandomProviderState;
    RestorableUniformRandomProvider rng = RandomSource.create(RandomSource.WELL_512_A);
    RandomProviderState state = rng.saveState();
    double x = rng.nextDouble();
    double y = rng.nextDouble(); // x == y.
  • The UniformRandomProvider objects returned from the create methods do not implement the java.io.Serializable interface.

    However, users can easily set up a custom serialization scheme if the random source is known at both ends of the communication channel. This would be useful namely to save the state to persistent storage, and restore it such that the sequence will continue from where it left off.

    import org.apache.commons.rng.RestorableUniformRandomProvider;
    import org.apache.commons.rng.simple.RandomSource;
    import org.apache.commons.rng.core.RandomProviderDefaultState;
    RandomSource source = RandomSource.MT_64; // Known source identifier.
    RestorableUniformRandomProvider rngOrig = RandomSource.create(source); // Original RNG instance.
    // Save and serialize state.
    RandomProviderState stateOrig = rngOrig.saveState(rngOrig);
    ByteArrayOutputStream bos = new ByteArrayOutputStream();
    ObjectOutputStream oos = new ObjectOutputStream(bos);
    oos.writeObject(((RandomProviderDefaultState) stateOrig).getState());
    // Deserialize state.
    ByteArrayInputStream bis = new ByteArrayInputStream(bos.toByteArray());
    ObjectInputStream ois = new ObjectInputStream(bis);
    RandomProviderState stateNew = new RandomProviderDefaultState((byte[]) ois.readObject());
    RestorableUniformRandomProvider rngNew = RandomSource.create(source); // New RNG instance from the same "source".
    // Restore original state on the new instance.
  • Generation of random deviates for various distributions.
    import org.apache.commons.rng.sampling.distribution.ContinuousSampler;
    import org.apache.commons.rng.sampling.distribution.BoxMullerGaussianSampler;
    ContinuousSampler sampler = new BoxMullerGaussianSampler(RandomSource.create(RandomSource.MT_64),
                                                             45.6, 2.3);
    double random = sampler.sample();
    import org.apache.commons.rng.sampling.distribution.DiscreteSampler;
    import org.apache.commons.rng.sampling.distribution.RejectionInversionZipfSampler;
    DiscreteSampler sampler = new RejectionInversionZipfSampler(RandomSource.create(RandomSource.ISAAC),
                                                                5, 1.2);
    int random = sampler.sample();
  • Permutation, sampling from a Collection and shuffling utilities.
    import org.apache.commons.rng.sampling.PermutationSampler;
    PermutationSampler sampler = new PermutationSampler(RandomSource.create(RandomSource.KISS),
                                                        6, 3);
    // 3 elements from a shuffling of the (0, 1, 2, 3, 4, 5) tuplet.
    int[] random = sampler.sample();
    import java.util.ArrayList;
    import org.apache.commons.rng.sampling.CollectionSampler;
    ArrayList<String> list = new ArrayList<String>();
    CollectionSampler<String> sampler = new CollectionSampler<String>(RandomSource.create(RandomSource.MWC_256),
                                                                      list, 1);
    String word = sampler.sample().get(0);

3. Library layout

The API for client code consists of classes and interfaces defined in package org.apache.commons.rng.

  • Interface UniformRandomProvider provides access to a sequence of random values uniformly distributed within some range.
  • Interfaces RestorableUniformRandomProvider and RandomProviderState provide the "save/restore" API.

The API for instantiating generators is defined in package org.apache.commons.rng.simple.

  • Enum RandomSource determines which algorithm to use for generating the sequence of random values.

The org.apache.commons.rng.simple.internal package contain classes for supporting initialization (a.k.a. "seeding") of the generators. They must not be used directly in applications, as all the necessary utilities are accessible through methods defined in RandomSource.

  • ProviderBuilder: contains methods for instantiating the concrete RNG implementations based on the source identifier; it also takes care of calling the appropriate classes for seed type conversion.
  • SeedFactory: contains factory methods for generating random seeds.
  • SeedConverter: interface for classes that transform between supported seed types.
  • Various classes that implement SeedConverter in order to transform from caller's seed to "native" seed.

The org.apache.commons.rng.core. package contain the implementation of the algorithms for the generation of pseudo-random sequences. Applications should not directly import or use classes defined in this package: all generators can be instantiated through the RandomSource factory.

  • Class RandomProviderDefaultState implements the RandomProviderState interface to enable "save/restore" for all RestorableUniformRandomProvider instances created through the RandomSource factory methods.
  • BaseProvider: base class for all concrete RNG implementations; it contains higher-level algorithms nextInt(int n) and nextLong(long n) common to all implementations.
  • org.apache.commons.rng.core.util
    • NumberFactory: contains utilities for interpreting and combining the output (int or long) of the underlying source of randomness into the requested output, i.e. one of the Java primitive types supported by UniformRandomProvider.
  • org.apache.commons.rng.core.source32
    • RandomIntSource: describes an algorithm that generates randomness in 32-bits chunks (a.k.a Java int).
    • IntProvider: base class for concrete classes that implement RandomIntSource.
    • Concrete RNG algorithms that are subclasses of IntProvider.
  • org.apache.commons.rng.core.source64
    • RandomLongSource: describes an algorithm that generates randomness in 64-bits chunks (a.k.a Java long).
    • LongProvider: base class for concrete classes that implement RandomLongSource.
    • Concrete RNG algorithms that are subclasses of LongProvider.

4. Performance

This section reports performance benchmarks of the RNG implementations.

All runs were performed on a platform with the following characteristics:

  • CPU: Intel(R) Core(TM) i7-3770 CPU @ 3.40GHz
  • Java runtime: 1.7.0_95-b00
  • JVM: OpenJDK 64-Bit Server VM 24.95-b01

The following tables indicate the performance (as measured by JMH) for generating

  • a sequence of 32-bits integers (a.k.a. Java type int)
  • a sequence of 64-bits integers (a.k.a. Java type long)
  • a sequence of 64-bits floating point numbers (a.k.a. Java type double)

The first column is the RNG identifier (see RandomSource); the performance value is the ratio of the (JMH) score with respect to the score of RandomSource.JDK.

In these tables, lower is better.

  • Generating int values
    RNG identifier Score ratio
    MWC_256 0.42168
    SPLIT_MIX_64 0.42845
    TWO_CMRES 0.46360
    XOR_SHIFT_1024_S 0.48818
    ISAAC 0.56263
    KISS 0.56340
    MT_64 0.62591
    MT 0.65198
    WELL_512_A 0.83364
    WELL_1024_A 0.88204
    WELL_44497_A 0.99618
    JDK 1.00000
    WELL_44497_B 1.00641
    WELL_19937_A 1.09770
    WELL_19937_C 1.13420
  • Generating long values
    RNG identifier Score ratio
    SPLIT_MIX_64 0.23505
    XOR_SHIFT_1024_S 0.26918
    TWO_CMRES 0.28069
    MT_64 0.34193
    MWC_256 0.40359
    KISS 0.55043
    MT 0.63092
    ISAAC 0.63944
    WELL_512_A 0.65085
    WELL_1024_A 0.71561
    JDK 1.00000
    WELL_19937_A 1.03761
    WELL_44497_A 1.06495
    WELL_44497_B 1.14565
    WELL_19937_C 1.23338
  • Generating double values
    RNG identifier Score ratio
    SPLIT_MIX_64 0.28609
    XOR_SHIFT_1024_S 0.32866
    TWO_CMRES 0.34069
    MWC_256 0.39083
    MT_64 0.39368
    KISS 0.60581
    ISAAC 0.64429
    MT 0.67086
    WELL_1024_A 0.73629
    WELL_512_A 0.78037
    JDK 1.00000
    WELL_19937_A 1.11497
    WELL_44497_A 1.13362
    WELL_19937_C 1.15334
    WELL_44497_B 1.22613

5. Quality

This section reports results of performing "stress tests" that aim at detecting failures of an implementation to produce sequences of numbers that follow a uniform distribution.

Two different test suites were used:

The first column is the RNG identifier (see RandomSource). The second and third columns contain the number of tests which Dieharder and TestU01 respectively reported as below the accepted threshold for considering the sequence as uniformly random; hence, in this table, lower is better.

For each the two test suites, three runs were performed (using random seeds): Click on one of the numbers of the comma-separated list in order to see the text report of the corresponding run. Note: For Dieharder, a failure on the "Diehard Sums Test" can be ignored.

RNG identifier Dieharder TestU01 (BigCrush)
JDK 14, 16, 15 78, 75, 76
MT 0, 0, 0 2, 2, 2
WELL_512_A 0, 0, 0 6, 6, 6
WELL_1024_A 0, 0, 1 4, 6, 5
WELL_19937_A 0, 0, 0 2, 3, 4
WELL_19937_C 0, 0, 0 2, 2, 2
WELL_44497_A 0, 0, 0 3, 3, 2
WELL_44497_B 0, 1, 0 2, 2, 3
ISAAC 0, 0, 0 1, 0, 0
MT_64 0, 0, 0 2, 2, 3
SPLIT_MIX_64 0, 0, 0 0, 0, 0
XOR_SHIFT_1024_S 0, 0, 0 0, 0, 0
TWO_CMRES 0, 1, 0 0, 0, 0
MWC_256 0, 0, 0 0, 0, 0
KISS 0, 0, 0 0, 1, 0

6. Dependencies

Apache Commons RNG requires JDK 1.6+ and has no runtime dependencies.