org.apache.flink.api.java.sampling

Class ReservoirSamplerWithReplacement<T>

java.lang.Object

org.apache.flink.api.java.sampling.RandomSampler<T>

org.apache.flink.api.java.sampling.DistributedRandomSampler<T>

org.apache.flink.api.java.sampling.ReservoirSamplerWithReplacement<T>

Type Parameters:

T - The type of sample.

@Internal
public class ReservoirSamplerWithReplacement<T>
extends DistributedRandomSampler<T>

A simple in memory implementation of Reservoir Sampling with replacement and with only one pass through the input iteration whose size is unpredictable. The basic idea behind this sampler implementation is quite similar to ReservoirSamplerWithoutReplacement. The main difference is that, in the first phase, we generate weights for each element K times, so that each element can get selected multiple times.

This implementation refers to the algorithm described in "Optimal Random Sampling from Distributed Streams Revisited".

Field Summary

Fields inherited from class org.apache.flink.api.java.sampling.DistributedRandomSampler

emptyIntermediateIterable, numSamples

Fields inherited from class org.apache.flink.api.java.sampling.RandomSampler

emptyIterable, EPSILON

Constructor Summary

Constructors

Constructor and Description
ReservoirSamplerWithReplacement(int numSamples) Create a sampler with fixed sample size and default random number generator.
ReservoirSamplerWithReplacement(int numSamples, long seed) Create a sampler with fixed sample size and random number generator seed.
ReservoirSamplerWithReplacement(int numSamples, Random random) Create a sampler with fixed sample size and random number generator.

Method Summary

Modifier and Type	Method and Description
Iterator<IntermediateSampleData<T>>	sampleInPartition(Iterator<T> input) Sample algorithm for the first phase.

Methods inherited from class org.apache.flink.api.java.sampling.DistributedRandomSampler

sample, sampleInCoordinator

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ReservoirSamplerWithReplacement

public ReservoirSamplerWithReplacement(int numSamples)

Create a sampler with fixed sample size and default random number generator.

Parameters:

numSamples - Number of selected elements, must be non-negative.

ReservoirSamplerWithReplacement

public ReservoirSamplerWithReplacement(int numSamples,
                                       long seed)

Create a sampler with fixed sample size and random number generator seed.

Parameters:

numSamples - Number of selected elements, must be non-negative.

seed - Random number generator seed

ReservoirSamplerWithReplacement

public ReservoirSamplerWithReplacement(int numSamples,
                                       Random random)

Create a sampler with fixed sample size and random number generator.

Parameters:

numSamples - Number of selected elements, must be non-negative.

random - Random number generator

Method Detail

sampleInPartition

public Iterator<IntermediateSampleData<T>> sampleInPartition(Iterator<T> input)

Description copied from class: DistributedRandomSampler

Sample algorithm for the first phase. It operates on a single partition.

Specified by:

sampleInPartition in class DistributedRandomSampler<T>

Parameters:

input - The DataSet input of each partition.

Returns:

Intermediate sample output which will be used as the input of the second phase.