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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/10/28 05:30:38 UTC
[GitHub] [flink-ml] lindong28 commented on a diff in pull request #162: [FLINK-29593] Add QuantileSummary to help calculate approximate quantiles
lindong28 commented on code in PR #162:
URL: https://github.com/apache/flink-ml/pull/162#discussion_r1007633262
##########
flink-ml-lib/src/main/java/org/apache/flink/ml/common/util/QuantileSummary.java:
##########
@@ -0,0 +1,400 @@
+/*
+ * 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.flink.ml.common.util;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.util.Preconditions;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+import java.util.stream.IntStream;
+
+/**
+ * Helper class to compute an approximate quantile summary. This implementation is based on the
+ * algorithm proposed in the paper: "Space-efficient Online Computation of Quantile Summaries" by
+ * Greenwald, Michael and Khanna, Sanjeev. (https://doi.org/10.1145/375663.375670)
+ */
+@Internal
+public class QuantileSummary implements Serializable {
+
+ /** The target relative error. */
+ private final double relativeError;
+
+ /**
+ * The compression threshold. After the internal buffer of statistics crosses this size, it
+ * attempts to compress the statistics together.
+ */
+ private final int compressThreshold;
+
+ /** The count of all the elements inserted to be calculated. */
+ private final long count;
+
+ /** A buffer of quantile statistics. */
+ private final List<StatsTuple> sampled;
+
+ /** The default size of head buffer. */
+ private static final int DEFAULT_HEAD_SIZE = 50000;
Review Comment:
`private static final` variables are typically put above `private final` variables.
##########
flink-ml-lib/src/main/java/org/apache/flink/ml/common/util/QuantileSummary.java:
##########
@@ -0,0 +1,400 @@
+/*
+ * 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.flink.ml.common.util;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.util.Preconditions;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+import java.util.stream.IntStream;
+
+/**
+ * Helper class to compute an approximate quantile summary. This implementation is based on the
+ * algorithm proposed in the paper: "Space-efficient Online Computation of Quantile Summaries" by
+ * Greenwald, Michael and Khanna, Sanjeev. (https://doi.org/10.1145/375663.375670)
+ */
+@Internal
Review Comment:
According to the Java doc of `@Internal`, it is an "Annotation to mark methods within stable, public APIs as an internal developer API".
Thus it seems unnecessary to add this annotation.
@zhipeng93 Could you confirm that we should remove this annotation?
##########
flink-ml-lib/src/main/java/org/apache/flink/ml/common/util/QuantileSummary.java:
##########
@@ -0,0 +1,400 @@
+/*
+ * 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.flink.ml.common.util;
+
+import org.apache.flink.annotation.Internal;
+import org.apache.flink.util.Preconditions;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+import java.util.stream.IntStream;
+
+/**
+ * Helper class to compute an approximate quantile summary. This implementation is based on the
+ * algorithm proposed in the paper: "Space-efficient Online Computation of Quantile Summaries" by
+ * Greenwald, Michael and Khanna, Sanjeev. (https://doi.org/10.1145/375663.375670)
+ */
+@Internal
+public class QuantileSummary implements Serializable {
+
+ /** The target relative error. */
+ private final double relativeError;
+
+ /**
+ * The compression threshold. After the internal buffer of statistics crosses this size, it
+ * attempts to compress the statistics together.
+ */
+ private final int compressThreshold;
+
+ /** The count of all the elements inserted to be calculated. */
+ private final long count;
+
+ /** A buffer of quantile statistics. */
+ private final List<StatsTuple> sampled;
+
+ /** The default size of head buffer. */
+ private static final int DEFAULT_HEAD_SIZE = 50000;
+
+ /** The default compression threshold. */
+ private static final int DEFAULT_COMPRESS_THRESHOLD = 10000;
+
+ /** A buffer of the latest samples seen so far. */
+ private List<Double> headBuffer = new ArrayList<>(DEFAULT_HEAD_SIZE);
+
+ /**
+ * QuantileSummary Constructor.
+ *
+ * @param relativeError The target relative error.
+ */
+ public QuantileSummary(double relativeError) {
+ this(relativeError, DEFAULT_COMPRESS_THRESHOLD);
+ }
+
+ /**
+ * QuantileSummary Constructor.
+ *
+ * @param relativeError The target relative error.
+ * @param compressThreshold the compression threshold. After the internal buffer of statistics
+ * crosses this size, it attempts to compress the statistics together.
+ */
+ public QuantileSummary(double relativeError, int compressThreshold) {
+ this(relativeError, compressThreshold, Collections.EMPTY_LIST, 0);
+ }
+
+ /**
+ * QuantileSummary Constructor.
+ *
+ * @param relativeError The target relative error.
+ * @param compressThreshold the compression threshold.
+ * @param sampled A buffer of quantile statistics. See the G-K article for more details.
+ * @param count The count of all the elements inserted in the sampled buffer.
+ */
+ private QuantileSummary(
+ double relativeError, int compressThreshold, List<StatsTuple> sampled, long count) {
+ Preconditions.checkArgument(
+ relativeError > 0 && relativeError < 1,
+ "An appropriate relative error must lay between 0 and 1.");
+ Preconditions.checkArgument(
+ compressThreshold > 0, "An compress threshold must greater than 0.");
+ this.relativeError = relativeError;
+ this.compressThreshold = compressThreshold;
+ this.sampled = sampled;
+ this.count = count;
+ }
+
+ /**
+ * Insert a new observation to the summary.
+ *
+ * @param item The new observation to insert into the summary.
+ * @return A summary with the given observation inserted into the summary.
+ */
+ public QuantileSummary insert(Double item) {
+ headBuffer.add(item);
+ if (headBuffer.size() >= DEFAULT_HEAD_SIZE) {
+ QuantileSummary result = insertHeadBuffer();
+ if (result.sampled.size() >= compressThreshold) {
+ return result.compress();
+ } else {
+ return result;
+ }
+ } else {
+ return this;
+ }
+ }
+
+ /**
+ * Returns a new summary that compresses the summary statistics and the head buffer.
+ *
+ * <p>This implements the COMPRESS function of the GK algorithm.
+ *
+ * @return The compressed summary.
+ */
+ public QuantileSummary compress() {
+ QuantileSummary inserted = insertHeadBuffer();
+ Preconditions.checkState(inserted.headBuffer.isEmpty());
+ Preconditions.checkState(inserted.count == count + headBuffer.size());
+
+ List<StatsTuple> compressed =
+ compressInternal(inserted.sampled, 2 * relativeError * inserted.count);
+ return new QuantileSummary(relativeError, compressThreshold, compressed, inserted.count);
+ }
+
+ /**
+ * Merges two summaries together.
+ *
+ * @param other The summary to be merged.
+ * @return The merged summary.
+ */
+ public QuantileSummary merge(QuantileSummary other) {
+ Preconditions.checkState(
+ headBuffer.isEmpty(), "Current buffer needs to be compressed before merge.");
+ Preconditions.checkState(
+ other.headBuffer.isEmpty(), "Other buffer needs to be compressed before merge.");
+
+ if (other.count == 0) {
+ return shallowCopy();
+ } else if (count == 0) {
+ return other.shallowCopy();
+ } else {
+ List<StatsTuple> mergedSampled = new ArrayList<>();
+ double mergedRelativeError = Math.max(relativeError, other.relativeError);
+ long mergedCount = count + other.count;
+ long additionalSelfDelta =
+ new Double(Math.floor(2 * other.relativeError * other.count)).longValue();
+ long additionalOtherDelta =
+ new Double(Math.floor(2 * relativeError * count)).longValue();
+
+ int selfIdx = 0;
+ int otherIdx = 0;
+ while (selfIdx < sampled.size() && otherIdx < other.sampled.size()) {
+ StatsTuple selfSample = sampled.get(selfIdx);
+ StatsTuple otherSample = other.sampled.get(otherIdx);
+ StatsTuple nextSample;
+ long additionalDelta = 0;
+ if (selfSample.value < otherSample.value) {
+ nextSample = selfSample;
+ if (otherIdx > 0) {
+ additionalDelta = additionalSelfDelta;
+ }
+ selfIdx++;
+ } else {
+ nextSample = otherSample;
+ if (selfIdx > 0) {
+ additionalDelta = additionalOtherDelta;
+ }
+ otherIdx++;
+ }
+ nextSample = nextSample.shallowCopy();
+ nextSample.delta = nextSample.delta + additionalDelta;
+ mergedSampled.add(nextSample);
+ }
+ IntStream.range(selfIdx, sampled.size())
+ .forEach(i -> mergedSampled.add(sampled.get(i)));
+ IntStream.range(otherIdx, other.sampled.size())
+ .forEach(i -> mergedSampled.add(other.sampled.get(i)));
+
+ List<StatsTuple> comp =
+ compressInternal(mergedSampled, 2 * mergedRelativeError * mergedCount);
+ return new QuantileSummary(mergedRelativeError, compressThreshold, comp, mergedCount);
+ }
+ }
+
+ /**
+ * Runs a query for a given percentile. The query can only be run on a compressed summary, you
+ * need to call compress() before using it.
+ *
+ * @param percentile The target percentile.
+ * @return The corresponding approximate quantile.
+ */
+ public double query(double percentile) {
+ return query(new double[] {percentile})[0];
+ }
+
+ /**
+ * Runs a query for a given sequence of percentiles. The query can only be run on a compressed
+ * summary, you need to call compress() before using it.
+ *
+ * @param percentiles A list of the target percentiles.
+ * @return A list of the corresponding approximate quantiles, in the same order as the input.
+ */
+ public double[] query(double[] percentiles) {
+ Arrays.stream(percentiles)
+ .forEach(
+ x ->
+ Preconditions.checkState(
+ x >= 0 && x <= 1.0,
+ "percentile should be in the range [0.0, 1.0]."));
+ Preconditions.checkState(
+ headBuffer.isEmpty(),
+ "Cannot operate on an uncompressed summary, call compress() first.");
+ Preconditions.checkState(
+ sampled != null && !sampled.isEmpty(),
+ "Cannot query percentiles without any records inserted.");
+ double targetError = Long.MIN_VALUE;
+ for (StatsTuple tuple : sampled) {
+ targetError = Math.max(targetError, (tuple.delta + tuple.g));
+ }
+ targetError = targetError / 2;
+ Map<Double, Integer> zipWithIndex = new HashMap<>(percentiles.length);
+ IntStream.range(0, percentiles.length).forEach(i -> zipWithIndex.put(percentiles[i], i));
+
+ int index = 0;
+ long minRank = sampled.get(0).g;
+ double[] sorted = Arrays.stream(percentiles).sorted().toArray();
+ double[] result = new double[percentiles.length];
+
+ for (int i = 0; i < sorted.length; i++) {
+ int percentileIndex = zipWithIndex.get(sorted[i]);
+ if (sorted[i] <= relativeError) {
+ result[percentileIndex] = sampled.get(0).value;
+ } else if (sorted[i] >= 1 - relativeError) {
+ result[percentileIndex] = sampled.get(sampled.size() - 1).value;
+ } else {
+ QueryResult queryResult =
+ findApproximateQuantile(index, minRank, targetError, sorted[i]);
+ index = queryResult.index;
+ minRank = queryResult.minRankAtIndex;
+ result[percentileIndex] = queryResult.percentile;
+ }
+ }
+ return result;
+ }
+
+ /**
+ * Check whether the QuantileSummary has inserted rows. Running query on an empty
+ * QuantileSummary would cause {@link java.lang.IllegalStateException}.
+ *
+ * @return True if the QuantileSummary is empty, otherwise false.
+ */
+ public boolean isEmpty() {
Review Comment:
Should it be `private`?
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