Google Git
Sign in
apache / beam / d8d876144ea74335ebfd2b47030cc3c4f26e4416 / . / sdks / java / core / src / main / java / org / apache / beam / sdk / transforms / ApproximateQuantiles.java
blob: 1e5fd48ae371431f0544a63810a81bfd0392804e [file] [log] [blame]
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.beam.sdk.transforms;
import static org.apache.beam.vendor.guava.v20_0.com.google.common.base.Preconditions.checkArgument;
import java.io.DataInputStream;
import java.io.DataOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.Comparator;
import java.util.Iterator;
import java.util.List;
import java.util.Objects;
import java.util.PriorityQueue;
import javax.annotation.Nullable;
import org.apache.beam.sdk.coders.BigEndianIntegerCoder;
import org.apache.beam.sdk.coders.Coder;
import org.apache.beam.sdk.coders.CoderException;
import org.apache.beam.sdk.coders.CoderRegistry;
import org.apache.beam.sdk.coders.CustomCoder;
import org.apache.beam.sdk.coders.ListCoder;
import org.apache.beam.sdk.transforms.Combine.AccumulatingCombineFn;
import org.apache.beam.sdk.transforms.Combine.AccumulatingCombineFn.Accumulator;
import org.apache.beam.sdk.transforms.display.DisplayData;
import org.apache.beam.sdk.util.WeightedValue;
import org.apache.beam.sdk.util.common.ElementByteSizeObserver;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;
import org.apache.beam.vendor.guava.v20_0.com.google.common.collect.Iterators;
import org.apache.beam.vendor.guava.v20_0.com.google.common.collect.Lists;
import org.apache.beam.vendor.guava.v20_0.com.google.common.collect.UnmodifiableIterator;
/**
* {@code PTransform}s for getting an idea of a {@code PCollection}'s data distribution using
* approximate {@code N}-tiles (e.g. quartiles, percentiles, etc.), either globally or per-key.
*/
public class ApproximateQuantiles {
private ApproximateQuantiles() {
// do not instantiate
}
/**
* Returns a {@code PTransform} that takes a {@code PCollection<T>} and returns a {@code
* PCollection<List<T>>} whose single value is a {@code List} of the approximate {@code N}-tiles
* of the elements of the input {@code PCollection}. This gives an idea of the distribution of the
* input elements.
*
* <p>The computed {@code List} is of size {@code numQuantiles}, and contains the input elements'
* minimum value, {@code numQuantiles-2} intermediate values, and maximum value, in sorted order,
* using the given {@code Comparator} to order values. To compute traditional {@code N}-tiles, one
* should use {@code ApproximateQuantiles.globally(N+1, compareFn)}.
*
* <p>If there are fewer input elements than {@code numQuantiles}, then the result {@code List}
* will contain all the input elements, in sorted order.
*
* <p>The argument {@code Comparator} must be {@code Serializable}.
*
* <p>Example of use:
*
* <pre>{@code
* PCollection<String> pc = ...;
* PCollection<List<String>> quantiles =
* pc.apply(ApproximateQuantiles.globally(11, stringCompareFn));
* }</pre>
*
* @param <T> the type of the elements in the input {@code PCollection}
* @param numQuantiles the number of elements in the resulting quantile values {@code List}
* @param compareFn the function to use to order the elements
*/
public static <T, ComparatorT extends Comparator<T> & Serializable>
PTransform<PCollection<T>, PCollection<List<T>>> globally(
int numQuantiles, ComparatorT compareFn) {
return Combine.globally(ApproximateQuantilesCombineFn.create(numQuantiles, compareFn));
}
/**
* Like {@link #globally(int, Comparator)}, but sorts using the elements' natural ordering.
*
* @param <T> the type of the elements in the input {@code PCollection}
* @param numQuantiles the number of elements in the resulting quantile values {@code List}
*/
public static <T extends Comparable<T>> PTransform<PCollection<T>, PCollection<List<T>>> globally(
int numQuantiles) {
return Combine.globally(ApproximateQuantilesCombineFn.<T>create(numQuantiles));
}
/**
* Returns a {@code PTransform} that takes a {@code PCollection<KV<K, V>>} and returns a {@code
* PCollection<KV<K, List<V>>>} that contains an output element mapping each distinct key in the
* input {@code PCollection} to a {@code List} of the approximate {@code N}-tiles of the values
* associated with that key in the input {@code PCollection}. This gives an idea of the
* distribution of the input values for each key.
*
* <p>Each of the computed {@code List}s is of size {@code numQuantiles}, and contains the input
* values' minimum value, {@code numQuantiles-2} intermediate values, and maximum value, in sorted
* order, using the given {@code Comparator} to order values. To compute traditional {@code
* N}-tiles, one should use {@code ApproximateQuantiles.perKey(compareFn, N+1)}.
*
* <p>If a key has fewer than {@code numQuantiles} values associated with it, then that key's
* output {@code List} will contain all the key's input values, in sorted order.
*
* <p>The argument {@code Comparator} must be {@code Serializable}.
*
* <p>Example of use:
*
* <pre>{@code
* PCollection<KV<Integer, String>> pc = ...;
* PCollection<KV<Integer, List<String>>> quantilesPerKey =
* pc.apply(ApproximateQuantiles.<Integer, String>perKey(stringCompareFn, 11));
* }</pre>
*
* <p>See {@link Combine.PerKey} for how this affects timestamps and windowing.
*
* @param <K> the type of the keys in the input and output {@code PCollection}s
* @param <V> the type of the values in the input {@code PCollection}
* @param numQuantiles the number of elements in the resulting quantile values {@code List}
* @param compareFn the function to use to order the elements
*/
public static <K, V, ComparatorT extends Comparator<V> & Serializable>
PTransform<PCollection<KV<K, V>>, PCollection<KV<K, List<V>>>> perKey(
int numQuantiles, ComparatorT compareFn) {
return Combine.perKey(ApproximateQuantilesCombineFn.create(numQuantiles, compareFn));
}
/**
* Like {@link #perKey(int, Comparator)}, but sorts values using the their natural ordering.
*
* @param <K> the type of the keys in the input and output {@code PCollection}s
* @param <V> the type of the values in the input {@code PCollection}
* @param numQuantiles the number of elements in the resulting quantile values {@code List}
*/
public static <K, V extends Comparable<V>>
PTransform<PCollection<KV<K, V>>, PCollection<KV<K, List<V>>>> perKey(int numQuantiles) {
return Combine.perKey(ApproximateQuantilesCombineFn.<V>create(numQuantiles));
}
/////////////////////////////////////////////////////////////////////////////
/**
* The {@code ApproximateQuantilesCombineFn} combiner gives an idea of the distribution of a
* collection of values using approximate {@code N}-tiles. The output of this combiner is a {@code
* List} of size {@code numQuantiles}, containing the input values' minimum value, {@code
* numQuantiles-2} intermediate values, and maximum value, in sorted order, so for traditional
* {@code N}-tiles, one should use {@code ApproximateQuantilesCombineFn#create(N+1)}.
*
* <p>If there are fewer values to combine than {@code numQuantiles}, then the result {@code List}
* will contain all the values being combined, in sorted order.
*
* <p>Values are ordered using either a specified {@code Comparator} or the values' natural
* ordering.
*
* <p>To evaluate the quantiles we use the "New Algorithm" described here:
*
* <pre>
* [MRL98] Manku, Rajagopalan &amp; Lindsay, "Approximate Medians and other
* Quantiles in One Pass and with Limited Memory", Proc. 1998 ACM
* SIGMOD, Vol 27, No 2, p 426-435, June 1998.
* http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.6.6513&amp;rep=rep1&amp;type=pdf
* </pre>
*
* <p>The default error bound is {@code 1 / N}, though in practice the accuracy tends to be much
* better.
*
* <p>See {@link #create(int, Comparator, long, double)} for more information about the meaning of
* {@code epsilon}, and {@link #withEpsilon} for a convenient way to adjust it.
*
* @param <T> the type of the values being combined
*/
public static class ApproximateQuantilesCombineFn<
T, ComparatorT extends Comparator<T> & Serializable>
extends AccumulatingCombineFn<T, QuantileState<T, ComparatorT>, List<T>> {
/**
* The cost (in time and space) to compute quantiles to a given accuracy is a function of the
* total number of elements in the data set. If an estimate is not known or specified, we use
* this as an upper bound. If this is too low, errors may exceed the requested tolerance; if too
* high, efficiency may be non-optimal. The impact is logarithmic with respect to this value, so
* this default should be fine for most uses.
*/
public static final long DEFAULT_MAX_NUM_ELEMENTS = (long) 1e9;
/** The comparison function to use. */
private final ComparatorT compareFn;
/**
* Number of quantiles to produce. The size of the final output list, including the minimum and
* maximum, is numQuantiles.
*/
private final int numQuantiles;
/** The size of the buffers, corresponding to k in the referenced paper. */
private final int bufferSize;
/** The number of buffers, corresponding to b in the referenced paper. */
private final int numBuffers;
private final long maxNumElements;
private ApproximateQuantilesCombineFn(
int numQuantiles,
ComparatorT compareFn,
int bufferSize,
int numBuffers,
long maxNumElements) {
checkArgument(numQuantiles >= 2);
checkArgument(bufferSize >= 2);
checkArgument(numBuffers >= 2);
this.numQuantiles = numQuantiles;
this.compareFn = compareFn;
this.bufferSize = bufferSize;
this.numBuffers = numBuffers;
this.maxNumElements = maxNumElements;
}
/**
* Returns an approximate quantiles combiner with the given {@code compareFn} and desired number
* of quantiles. A total of {@code numQuantiles} elements will appear in the output list,
* including the minimum and maximum.
*
* <p>The {@code Comparator} must be {@code Serializable}.
*
* <p>The default error bound is {@code 1 / numQuantiles}, which holds as long as the number of
* elements is less than {@link #DEFAULT_MAX_NUM_ELEMENTS}.
*/
public static <T, ComparatorT extends Comparator<T> & Serializable>
ApproximateQuantilesCombineFn<T, ComparatorT> create(
int numQuantiles, ComparatorT compareFn) {
return create(numQuantiles, compareFn, DEFAULT_MAX_NUM_ELEMENTS, 1.0 / numQuantiles);
}
/** Like {@link #create(int, Comparator)}, but sorts values using their natural ordering. */
public static <T extends Comparable<T>> ApproximateQuantilesCombineFn<T, Top.Natural<T>> create(
int numQuantiles) {
return create(numQuantiles, new Top.Natural<T>());
}
/**
* Returns an {@code ApproximateQuantilesCombineFn} that's like this one except that it uses the
* specified {@code epsilon} value. Does not modify this combiner.
*
* <p>See {@link #create(int, Comparator, long, double)} for more information about the meaning
* of {@code epsilon}.
*/
public ApproximateQuantilesCombineFn<T, ComparatorT> withEpsilon(double epsilon) {
return create(numQuantiles, compareFn, maxNumElements, epsilon);
}
/**
* Returns an {@code ApproximateQuantilesCombineFn} that's like this one except that it uses the
* specified {@code maxNumElements} value. Does not modify this combiner.
*
* <p>See {@link #create(int, Comparator, long, double)} for more information about the meaning
* of {@code maxNumElements}.
*/
public ApproximateQuantilesCombineFn<T, ComparatorT> withMaxInputSize(long maxNumElements) {
return create(numQuantiles, compareFn, maxNumElements, maxNumElements);
}
/**
* Creates an approximate quantiles combiner with the given {@code compareFn} and desired number
* of quantiles. A total of {@code numQuantiles} elements will appear in the output list,
* including the minimum and maximum.
*
* <p>The {@code Comparator} must be {@code Serializable}.
*
* <p>The default error bound is {@code epsilon}, which holds as long as the number of elements
* is less than {@code maxNumElements}. Specifically, if one considers the input as a sorted
* list x_1, ..., x_N, then the distance between the each exact quantile x_c and its
* approximation x_c' is bounded by {@code |c - c'| < epsilon * N}. Note that these errors are
* worst-case scenarios; in practice the accuracy tends to be much better.
*/
public static <T, ComparatorT extends Comparator<T> & Serializable>
ApproximateQuantilesCombineFn<T, ComparatorT> create(
int numQuantiles, ComparatorT compareFn, long maxNumElements, double epsilon) {
// Compute optimal b and k.
int b = 2;
while ((b - 2) * (1 << (b - 2)) < epsilon * maxNumElements) {
b++;
}
b--;
int k = Math.max(2, (int) Math.ceil(maxNumElements / (float) (1 << (b - 1))));
return new ApproximateQuantilesCombineFn<>(numQuantiles, compareFn, k, b, maxNumElements);
}
@Override
public QuantileState<T, ComparatorT> createAccumulator() {
return QuantileState.empty(compareFn, numQuantiles, numBuffers, bufferSize);
}
@Override
public Coder<QuantileState<T, ComparatorT>> getAccumulatorCoder(
CoderRegistry registry, Coder<T> elementCoder) {
return new QuantileStateCoder<>(compareFn, elementCoder);
}
@Override
public void populateDisplayData(DisplayData.Builder builder) {
super.populateDisplayData(builder);
builder
.add(DisplayData.item("numQuantiles", numQuantiles).withLabel("Quantile Count"))
.add(DisplayData.item("comparer", compareFn.getClass()).withLabel("Record Comparer"));
}
int getNumBuffers() {
return numBuffers;
}
int getBufferSize() {
return bufferSize;
}
}
/** Compact summarization of a collection on which quantiles can be estimated. */
static class QuantileState<T, ComparatorT extends Comparator<T> & Serializable>
implements Accumulator<T, QuantileState<T, ComparatorT>, List<T>> {
private ComparatorT compareFn;
private int numQuantiles;
private int numBuffers;
private int bufferSize;
@Nullable private T min;
@Nullable private T max;
/** The set of buffers, ordered by level from smallest to largest. */
private PriorityQueue<QuantileBuffer<T>> buffers;
/**
* The algorithm requires that the manipulated buffers always be filled to capacity to perform
* the collapse operation. This operation can be extended to buffers of varying sizes by
* introducing the notion of fractional weights, but it's easier to simply combine the
* remainders from all shards into new, full buffers and then take them into account when
* computing the final output.
*/
private List<T> unbufferedElements = Lists.newArrayList();
private QuantileState(
ComparatorT compareFn,
int numQuantiles,
@Nullable T min,
@Nullable T max,
int numBuffers,
int bufferSize,
Collection<T> unbufferedElements,
Collection<QuantileBuffer<T>> buffers) {
this.compareFn = compareFn;
this.numQuantiles = numQuantiles;
this.numBuffers = numBuffers;
this.bufferSize = bufferSize;
this.buffers =
new PriorityQueue<>(numBuffers + 1, (q1, q2) -> Integer.compare(q1.level, q2.level));
this.min = min;
this.max = max;
this.unbufferedElements.addAll(unbufferedElements);
this.buffers.addAll(buffers);
}
public static <T, ComparatorT extends Comparator<T> & Serializable>
QuantileState<T, ComparatorT> empty(
ComparatorT compareFn, int numQuantiles, int numBuffers, int bufferSize) {
return new QuantileState<>(
compareFn,
numQuantiles,
null, /* min */
null, /* max */
numBuffers,
bufferSize,
Collections.emptyList(),
Collections.emptyList());
}
public static <T, ComparatorT extends Comparator<T> & Serializable>
QuantileState<T, ComparatorT> singleton(
ComparatorT compareFn, int numQuantiles, T elem, int numBuffers, int bufferSize) {
return new QuantileState<>(
compareFn,
numQuantiles,
elem, /* min */
elem, /* max */
numBuffers,
bufferSize,
Collections.singletonList(elem),
Collections.emptyList());
}
/** Add a new element to the collection being summarized by this state. */
@Override
public void addInput(T elem) {
if (isEmpty()) {
min = max = elem;
} else if (compareFn.compare(elem, min) < 0) {
min = elem;
} else if (compareFn.compare(elem, max) > 0) {
max = elem;
}
addUnbuffered(elem);
}
/** Add a new buffer to the unbuffered list, creating a new buffer and collapsing if needed. */
private void addUnbuffered(T elem) {
unbufferedElements.add(elem);
if (unbufferedElements.size() == bufferSize) {
unbufferedElements.sort(compareFn);
buffers.add(new QuantileBuffer<>(unbufferedElements));
unbufferedElements = Lists.newArrayListWithCapacity(bufferSize);
collapseIfNeeded();
}
}
/**
* Updates this as if adding all elements seen by other.
*
* <p>Note that this ignores the {@code Comparator} of the other {@link QuantileState}. In
* practice, they should generally be equal, but this method tolerates a mismatch.
*/
@Override
public void mergeAccumulator(QuantileState<T, ComparatorT> other) {
if (other.isEmpty()) {
return;
}
if (min == null || compareFn.compare(other.min, min) < 0) {
min = other.min;
}
if (max == null || compareFn.compare(other.max, max) > 0) {
max = other.max;
}
for (T elem : other.unbufferedElements) {
addUnbuffered(elem);
}
buffers.addAll(other.buffers);
collapseIfNeeded();
}
public boolean isEmpty() {
return unbufferedElements.isEmpty() && buffers.isEmpty();
}
private void collapseIfNeeded() {
while (buffers.size() > numBuffers) {
List<QuantileBuffer<T>> toCollapse = Lists.newArrayList();
toCollapse.add(buffers.poll());
toCollapse.add(buffers.poll());
int minLevel = toCollapse.get(1).level;
while (!buffers.isEmpty() && buffers.peek().level == minLevel) {
toCollapse.add(buffers.poll());
}
buffers.add(collapse(toCollapse));
}
}
private QuantileBuffer<T> collapse(Iterable<QuantileBuffer<T>> buffers) {
int newLevel = 0;
long newWeight = 0;
for (QuantileBuffer<T> buffer : buffers) {
// As presented in the paper, there should always be at least two
// buffers of the same (minimal) level to collapse, but it is possible
// to violate this condition when combining buffers from independently
// computed shards. If they differ we take the max.
newLevel = Math.max(newLevel, buffer.level + 1);
newWeight += buffer.weight;
}
List<T> newElements = interpolate(buffers, bufferSize, newWeight, offset(newWeight));
return new QuantileBuffer<>(newLevel, newWeight, newElements);
}
/**
* If the weight is even, we must round up or down. Alternate between these two options to avoid
* a bias.
*/
private long offset(long newWeight) {
if (newWeight % 2 == 1) {
return (newWeight + 1) / 2;
} else {
offsetJitter = 2 - offsetJitter;
return (newWeight + offsetJitter) / 2;
}
}
/** For alternating between biasing up and down in the above even weight collapse operation. */
private int offsetJitter = 0;
/**
* Emulates taking the ordered union of all elements in buffers, repeated according to their
* weight, and picking out the (k * step + offset)-th elements of this list for {@code 0 &lt;= k
* &lt; count}.
*/
private List<T> interpolate(
Iterable<QuantileBuffer<T>> buffers, int count, double step, double offset) {
List<Iterator<WeightedValue<T>>> iterators = Lists.newArrayList();
for (QuantileBuffer<T> buffer : buffers) {
iterators.add(buffer.sizedIterator());
}
// Each of the buffers is already sorted by element.
Iterator<WeightedValue<T>> sorted =
Iterators.mergeSorted(iterators, (a, b) -> compareFn.compare(a.getValue(), b.getValue()));
List<T> newElements = Lists.newArrayListWithCapacity(count);
WeightedValue<T> weightedElement = sorted.next();
double current = weightedElement.getWeight();
for (int j = 0; j < count; j++) {
double target = j * step + offset;
while (current <= target && sorted.hasNext()) {
weightedElement = sorted.next();
current += weightedElement.getWeight();
}
newElements.add(weightedElement.getValue());
}
return newElements;
}
/**
* Outputs numQuantiles elements consisting of the minimum, maximum, and numQuantiles - 2 evenly
* spaced intermediate elements.
*
* <p>Returns the empty list if no elements have been added.
*/
@Override
public List<T> extractOutput() {
if (isEmpty()) {
return Lists.newArrayList();
}
long totalCount = unbufferedElements.size();
for (QuantileBuffer<T> buffer : buffers) {
totalCount += bufferSize * buffer.weight;
}
List<QuantileBuffer<T>> all = Lists.newArrayList(buffers);
if (!unbufferedElements.isEmpty()) {
unbufferedElements.sort(compareFn);
all.add(new QuantileBuffer<>(unbufferedElements));
}
double step = 1.0 * totalCount / (numQuantiles - 1);
double offset = (1.0 * totalCount - 1) / (numQuantiles - 1);
List<T> quantiles = interpolate(all, numQuantiles - 2, step, offset);
quantiles.add(0, min);
quantiles.add(max);
return quantiles;
}
}
/** A single buffer in the sense of the referenced algorithm. */
private static class QuantileBuffer<T> {
private int level;
private long weight;
private List<T> elements;
public QuantileBuffer(List<T> elements) {
this(0, 1, elements);
}
public QuantileBuffer(int level, long weight, List<T> elements) {
this.level = level;
this.weight = weight;
this.elements = elements;
}
@Override
public String toString() {
return "QuantileBuffer["
+ "level="
+ level
+ ", weight="
+ weight
+ ", elements="
+ elements
+ "]";
}
public Iterator<WeightedValue<T>> sizedIterator() {
return new UnmodifiableIterator<WeightedValue<T>>() {
Iterator<T> iter = elements.iterator();
@Override
public boolean hasNext() {
return iter.hasNext();
}
@Override
public WeightedValue<T> next() {
return WeightedValue.of(iter.next(), weight);
}
};
}
}
/** Coder for QuantileState. */
private static class QuantileStateCoder<T, ComparatorT extends Comparator<T> & Serializable>
extends CustomCoder<QuantileState<T, ComparatorT>> {
private final ComparatorT compareFn;
private final Coder<T> elementCoder;
private final Coder<List<T>> elementListCoder;
private final Coder<Integer> intCoder = BigEndianIntegerCoder.of();
public QuantileStateCoder(ComparatorT compareFn, Coder<T> elementCoder) {
this.compareFn = compareFn;
this.elementCoder = elementCoder;
this.elementListCoder = ListCoder.of(elementCoder);
}
@Override
public void encode(QuantileState<T, ComparatorT> state, OutputStream outStream)
throws CoderException, IOException {
intCoder.encode(state.numQuantiles, outStream);
intCoder.encode(state.bufferSize, outStream);
elementCoder.encode(state.min, outStream);
elementCoder.encode(state.max, outStream);
elementListCoder.encode(state.unbufferedElements, outStream);
BigEndianIntegerCoder.of().encode(state.buffers.size(), outStream);
for (QuantileBuffer<T> buffer : state.buffers) {
encodeBuffer(buffer, outStream);
}
}
@Override
public QuantileState<T, ComparatorT> decode(InputStream inStream)
throws CoderException, IOException {
int numQuantiles = intCoder.decode(inStream);
int bufferSize = intCoder.decode(inStream);
T min = elementCoder.decode(inStream);
T max = elementCoder.decode(inStream);
List<T> unbufferedElements = elementListCoder.decode(inStream);
int numBuffers = BigEndianIntegerCoder.of().decode(inStream);
List<QuantileBuffer<T>> buffers = new ArrayList<>(numBuffers);
for (int i = 0; i < numBuffers; i++) {
buffers.add(decodeBuffer(inStream));
}
return new QuantileState<>(
compareFn, numQuantiles, min, max, numBuffers, bufferSize, unbufferedElements, buffers);
}
private void encodeBuffer(QuantileBuffer<T> buffer, OutputStream outStream)
throws CoderException, IOException {
DataOutputStream outData = new DataOutputStream(outStream);
outData.writeInt(buffer.level);
outData.writeLong(buffer.weight);
elementListCoder.encode(buffer.elements, outStream);
}
private QuantileBuffer<T> decodeBuffer(InputStream inStream)
throws IOException, CoderException {
DataInputStream inData = new DataInputStream(inStream);
return new QuantileBuffer<>(
inData.readInt(), inData.readLong(), elementListCoder.decode(inStream));
}
/**
* Notifies ElementByteSizeObserver about the byte size of the encoded value using this coder.
*/
@Override
public void registerByteSizeObserver(
QuantileState<T, ComparatorT> state, ElementByteSizeObserver observer) throws Exception {
elementCoder.registerByteSizeObserver(state.min, observer);
elementCoder.registerByteSizeObserver(state.max, observer);
elementListCoder.registerByteSizeObserver(state.unbufferedElements, observer);
BigEndianIntegerCoder.of().registerByteSizeObserver(state.buffers.size(), observer);
for (QuantileBuffer<T> buffer : state.buffers) {
observer.update(4L + 8);
elementListCoder.registerByteSizeObserver(buffer.elements, observer);
}
}
@Override
public boolean equals(Object other) {
if (other == this) {
return true;
}
if (!(other instanceof QuantileStateCoder)) {
return false;
}
QuantileStateCoder<?, ?> that = (QuantileStateCoder<?, ?>) other;
return Objects.equals(this.elementCoder, that.elementCoder)
&& Objects.equals(this.compareFn, that.compareFn);
}
@Override
public int hashCode() {
return Objects.hash(elementCoder, compareFn);
}
@Override
public void verifyDeterministic() throws NonDeterministicException {
verifyDeterministic(this, "QuantileState.ElementCoder must be deterministic", elementCoder);
verifyDeterministic(
this, "QuantileState.ElementListCoder must be deterministic", elementListCoder);
}
}
}