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Add histogram facet capabilities. #14204

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1 change: 1 addition & 0 deletions lucene/facet/src/java/module-info.java
Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@
requires org.apache.lucene.core;

exports org.apache.lucene.facet;
exports org.apache.lucene.facet.histogram;
exports org.apache.lucene.facet.range;
exports org.apache.lucene.facet.sortedset;
exports org.apache.lucene.facet.taxonomy;
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/*
* 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.lucene.facet.histogram;

import java.io.IOException;
import org.apache.lucene.index.DocValues;
import org.apache.lucene.index.DocValuesSkipper;
import org.apache.lucene.index.DocValuesType;
import org.apache.lucene.index.FieldInfo;
import org.apache.lucene.index.LeafReaderContext;
import org.apache.lucene.index.NumericDocValues;
import org.apache.lucene.index.SortedNumericDocValues;
import org.apache.lucene.internal.hppc.LongIntHashMap;
import org.apache.lucene.search.CollectionTerminatedException;
import org.apache.lucene.search.Collector;
import org.apache.lucene.search.LeafCollector;
import org.apache.lucene.search.Scorable;
import org.apache.lucene.search.ScoreMode;

final class HistogramCollector implements Collector {
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This feels like a bit of an odd fit for the facet module given that these are generally implementations of the Facets interface that compute aggregations over a set of documents that's already been collected. Did you look at the newer sandbox faceting module at all for this? I wonder if this would hook into that module better since it's meant to compute aggregations while collecting (which is exactly what this is doing). The downside is burying something like this in sandbox... so maybe it's not great?


private final String field;
private final long interval;
private final LongIntHashMap counts;

HistogramCollector(String field, long interval) {
this.field = field;
this.interval = interval;
this.counts = new LongIntHashMap();
}

@Override
public LeafCollector getLeafCollector(LeafReaderContext context) throws IOException {
FieldInfo fi = context.reader().getFieldInfos().fieldInfo(field);
if (fi == null) {
// The segment has no values, nothing to do.
throw new CollectionTerminatedException();
}
if (fi.getDocValuesType() != DocValuesType.NUMERIC
&& fi.getDocValuesType() != DocValuesType.SORTED_NUMERIC) {
throw new IllegalStateException(
"Expected numeric field, but got doc-value type: " + fi.getDocValuesType());
}
SortedNumericDocValues values = DocValues.getSortedNumeric(context.reader(), field);
NumericDocValues singleton = DocValues.unwrapSingleton(values);
if (singleton == null) {
return new HistogramNaiveLeafCollector(values, interval, counts);
} else {
DocValuesSkipper skipper = context.reader().getDocValuesSkipper(field);
if (skipper != null) {
long leafMinQuotient = Math.floorDiv(skipper.minValue(), interval);
long leafMaxQuotient = Math.floorDiv(skipper.maxValue(), interval);
if (leafMaxQuotient - leafMinQuotient <= 1024) {
// Only use the optimized implementation if there is a small number of unique quotients,
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👍 If they are many quotients, it is very unlikely that the skipper would help as probably there is no skipper block that belongs to just one quotient.

// so that we can count them using a dense array instead of a hash table.
return new HistogramLeafCollector(singleton, skipper, interval, counts);
}
}
return new HistogramNaiveSingleValuedLeafCollector(singleton, interval, counts);
}
}

@Override
public ScoreMode scoreMode() {
return ScoreMode.COMPLETE_NO_SCORES;
}

LongIntHashMap getCounts() {
return counts;
}

/**
* Naive implementation of a histogram {@link LeafCollector}, which iterates all maches and looks
* up the value to determine the corresponding bucket.
*/
private static class HistogramNaiveLeafCollector implements LeafCollector {

private final SortedNumericDocValues values;
private final long interval;
private final LongIntHashMap counts;

HistogramNaiveLeafCollector(
SortedNumericDocValues values, long interval, LongIntHashMap counts) {
this.values = values;
this.interval = interval;
this.counts = counts;
}

@Override
public void setScorer(Scorable scorer) throws IOException {}

@Override
public void collect(int doc) throws IOException {
if (values.advanceExact(doc)) {
int valueCount = values.docValueCount();
long prevQuotient = Long.MIN_VALUE;
for (int i = 0; i < valueCount; ++i) {
final long value = values.nextValue();
final long quotient = Math.floorDiv(value, interval);
// We must not double-count values that divide to the same quotient since this returns doc
// counts as opposed to value counts.
if (quotient != prevQuotient) {
counts.addTo(quotient, 1);
prevQuotient = quotient;
}
}
}
}
}

/**
* Naive implementation of a histogram {@link LeafCollector}, which iterates all maches and looks
* up the value to determine the corresponding bucket.
*/
private static class HistogramNaiveSingleValuedLeafCollector implements LeafCollector {

private final NumericDocValues values;
private final long interval;
private final LongIntHashMap counts;

HistogramNaiveSingleValuedLeafCollector(
NumericDocValues values, long interval, LongIntHashMap counts) {
this.values = values;
this.interval = interval;
this.counts = counts;
}

@Override
public void setScorer(Scorable scorer) throws IOException {}

@Override
public void collect(int doc) throws IOException {
if (values.advanceExact(doc)) {
final long value = values.longValue();
final long quotient = Math.floorDiv(value, interval);
counts.addTo(quotient, 1);
}
}
}

/**
* Optimized histogram {@link LeafCollector}, that takes advantage of the doc-values index to
* speed up collection.
*/
private static class HistogramLeafCollector implements LeafCollector {

private final NumericDocValues values;
private final DocValuesSkipper skipper;
private final long interval;
private final int[] counts;
private final long leafMinQuotient;
private final LongIntHashMap collectorCounts;

/**
* Max doc ID (inclusive) up to which all docs may map to values that have the same quotient.
*/
private int upToInclusive = -1;

/** Whether all docs up to {@link #upToInclusive} map to values that have the same quotient. */
private boolean upToSameQuotient;

/** Index in {@link #counts} for docs up to {@link #upToInclusive}. */
private int upToQuotientIndex;

HistogramLeafCollector(
NumericDocValues values,
DocValuesSkipper skipper,
long interval,
LongIntHashMap collectorCounts) {
this.values = values;
this.skipper = skipper;
this.interval = interval;
this.collectorCounts = collectorCounts;

leafMinQuotient = Math.floorDiv(skipper.minValue(), interval);
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minor: Since you're already computing these min/max values from the calling code, is it worth passing them along instead of recomputing?

long leafMaxQuotient = Math.floorDiv(skipper.maxValue(), interval);
counts = new int[Math.toIntExact(leafMaxQuotient - leafMinQuotient + 1)];
}

@Override
public void setScorer(Scorable scorer) throws IOException {}

private void advanceSkipper(int doc) throws IOException {
if (doc > skipper.maxDocID(0)) {
skipper.advance(doc);
}
upToSameQuotient = false;

if (skipper.minDocID(0) > doc) {
// Corner case which happens if `doc` doesn't have a value and is between two intervals of
// the doc-value skip index.
upToInclusive = skipper.minDocID(0) - 1;
return;
}

upToInclusive = skipper.maxDocID(0);

// Now find the highest level where all docs have the same quotient.
for (int level = 0; level < skipper.numLevels(); ++level) {
int totalDocsAtLevel = skipper.maxDocID(level) - skipper.minDocID(level) + 1;
long minQuotient = Math.floorDiv(skipper.minValue(level), interval);
long maxQuotient = Math.floorDiv(skipper.maxValue(level), interval);

if (skipper.docCount(level) == totalDocsAtLevel && minQuotient == maxQuotient) {
// All docs at this level have a value, and all values map to the same quotient.
upToInclusive = skipper.maxDocID(level);
upToSameQuotient = true;
upToQuotientIndex = (int) (minQuotient - this.leafMinQuotient);
} else {
break;
}
}
}

@Override
public void collect(int doc) throws IOException {
if (doc > upToInclusive) {
advanceSkipper(doc);
}

if (upToSameQuotient) {
counts[upToQuotientIndex]++;
} else if (values.advanceExact(doc)) {
final long value = values.longValue();
final long quotient = Math.floorDiv(value, interval);
counts[(int) (quotient - leafMinQuotient)]++;
}
}

@Override
public void finish() throws IOException {
// Put counts that we computed in the int[] back into the hash map.
for (int i = 0; i < counts.length; ++i) {
collectorCounts.addTo(leafMinQuotient + i, counts[i]);
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OK, lack of understanding on my part, but could you help explain why you're accumulating into an array and then transferring into the map instead of accumulating directly into the map?

}
}
}
}
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/*
* 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.lucene.facet.histogram;

import java.io.IOException;
import java.util.Collection;
import java.util.Objects;
import org.apache.lucene.document.FieldType;
import org.apache.lucene.internal.hppc.LongIntHashMap;
import org.apache.lucene.internal.hppc.LongIntHashMap.LongIntCursor;
import org.apache.lucene.search.CollectorManager;

/**
* {@link CollectorManager} that computes a histogram of the distribution of the values of a field.
*
* <p>The returned {@link LongIntHashMap} maps quotients to the number of documents whose value
* returns this number when divided by the given {@code interval}.
*
* <p>This implementation is optimized for the case when {@code field} is part of the index sort and
* has a {@link FieldType#setDocValuesSkipIndexType skip index}.
*
* <p>Note: this collector is inspired from "YU, Muzhi, LIN, Zhaoxiang, SUN, Jinan, et al.
* TencentCLS: the cloud log service with high query performances. Proceedings of the VLDB
* Endowment, 2022, vol. 15, no 12, p. 3472-3482.", where the authors describe how they run
* "histogram queries" by sorting the index by timestamp and pre-computing ranges of doc IDs for
* every possible bucket.
*/
public final class HistogramCollectorManager
implements CollectorManager<HistogramCollector, LongIntHashMap> {

private final String field;
private final long interval;

/** Sole constructor. */
public HistogramCollectorManager(String field, long interval) {
this.field = Objects.requireNonNull(field);
this.interval = interval;
if (interval < 2) {
throw new IllegalArgumentException("interval must be at least 2, got: " + interval);
}
}

@Override
public HistogramCollector newCollector() throws IOException {
return new HistogramCollector(field, interval);
}

@Override
public LongIntHashMap reduce(Collection<HistogramCollector> collectors) throws IOException {
LongIntHashMap reduced = new LongIntHashMap();
for (HistogramCollector collector : collectors) {
for (LongIntCursor cursor : collector.getCounts()) {
reduced.addTo(cursor.key, cursor.value);
}
}
return reduced;
}
}
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/*
* 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.
*/

/** Provides histotgram faceting capabilities. */
package org.apache.lucene.facet.histogram;
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